SATELLITE WATER MONITORING
AND FLOW FORECASTING SYSTEM
FOR THE YELLOW RIVER BASIN
Sino-Dutch Cooperation Project
ORET 02/09-CN00069
Scientific Final Report
December 2008
Title page
SATELLITE WATER MONITORING
AND FLOW FORECASTING SYSTEM
FOR THE YELLOW RIVER BASIN
Sino-Dutch Cooperation Project
ORET 02/09-CN00069
Scientific Final Report
December 2008
Authors
Andries Rosema, Marjolein de Weirdt and
Steven Foppes
EARS, Kanaalweg 1, 2628 EB Delft,
Netherlands. Email: ears@ears.nl
Yuanze Gu, Weimin Zhao, Chunqing
Wang, Xiaowei Liu, Suqiu Rao, Dong
Dai, Yong Zhang, Liye Wen, Dongling
Chen, Yanyan Di, Shuhui Qiu, Qingzhai
Wang, Liuzhu Zhang, Jifeng Liu,
Longqing Liu, Li Xie, Ronggang Zhang,
Jian Yang, Yawei Zhang, Meng Luo, Bo
Hou, Lai Zhao, Lihua Zhu, Xiaodong
Chen and Tequn Yang.
Hydrology Bureau, Yellow River
Conservancy Commission, Ministry of
Water Resources, P.R. China, No.12
Chengbe East Road, Zhengzhou 450004,
China.
Raymond Venneker and Shreedar Maskey
UNESCO-IHE Institute for Water Education,
Westvest 7, 2611 AX, Delft, Netherlands.
Hongqi Shang, Songchang Ren, Feng Sun,
Yangbo Sun, Falu Zheng, Yunpeng Xue,
Zhongqun Yuan and Hui Pang.
Bureau of Science, Technology and Foreign
Affairs, Yellow River Conservancy
Commission, Ministry of Water Resources,
PR. China, No.11, Jinshui Road, Zhengzhou
450003, China
Chengyang Lu, Gensheng Liu, Xijun Guo
and Deyan Du.
Upper Hydrology Bureau of YRCC, No.
157 Wudu Road, Lanzhou 730030, China.
Bastiaan Bink and Xiaobo Wu
Hofung Limited, The Hague, Netherlands
and Beijing, China. Email:
info.hofung@gmail.com
Xiaoying He, Xinwu Tu and Wenjuan Sun.
Sanmenxia Hydrology Bureau of YRCC,
No.7 Hepingxiduan, Sanmenxia 472000,
China
Scientific final report of the project Establishment of a Satellite Based Water Monitoring and Flow
Forecasting System in the Yellow River Basin, commissioned by the Yellow River Conservancy Commission
to a consortium consisting of EARS Earth Environment Monitoring BV, UNESCO-IHE Institute for Water
Education and Hofung Ltd. The project was co-funded by the Yellow River Conservancy Commission and the
Government of the Kingdom of the Netherlands through Grant Agreement CN200400105 related to ORET
project 02/09 – CN00069.
This report may be referred to as: Rosema, A; De Weirdt, M; Foppes, S; Venneker, R; Maskey, S; Gu, Y;
Zhao, W; Wang, C; Liu, X; Rao, S; Dai, D; Zhang, Y; Wen, L; Chen, D; Di, Y; Qiu, S; Wang, Q; Zhang, L;
Liu, J; Liu, L; Xie, L; Zhang, R; Yang, J; Zhang, Y; Luo, M; Hou, B; Zhao, L; Zhu, L; Chen, X; Yang, T;
Shang, H; Ren, S; Sun, F; Sun, Y; Zheng, F; Xue, Y; Yuan, Z; Pang, H; Lu, C; Liu, G; Guo,X; Du, D; He, X;
Tu , X; Sun, W; Bink, B; Wu, X. (2008) “Satellite Monitoring and Flow Forecasting System for the Yellow
River Basin”, Scientific final report of ORET project 02/09-CN00069, EARS, Delft, the Netherlands, 144 pg,
December 2008.
Cover: Yellow River at Tangke
3
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Acknowledgement
This project has been approved and supported by Chinese Ministry of Water
Resources, Chinese Ministry of Finance and the Yellow River Conservancy
Commission. We especially thank Yongfu Zheng of the Ministry of Finance,
Jianming Liu, Zhiguang Liu, Xingjun Yu, Ge Li, Hai Jin, Qingping Zhu, Mengzhuo
Guo and Yubo Shi of the Ministry of Water Resources, Zijiang Huang, Xiaoyan Liu,
Yuguo Niu, Hanxia Yang, Hongyue Zhang, Shuili Tian, Shiqing Huo and Long Wang
of the Yellow River Organisation. We are grateful for their interest and support
during project initiation, development and implementation.
The authors also wish to thank the many people from the hydrology stations at
Jingchuan, Tangneihai, Jungong Jun and Tangke for their work on the establishment
and maintenance of the Large Aperture Scintillometer Systems, as well as for reading
and forwarding the measuring data. We thank Wouter Meijninger of Kipp & Zonen in
Delft for his support in questions related to the LAS data processing.
We are also grateful to Rongzhang Wu of the China National Satellite Meteorological
Centre, Yun Bai of the Shinetek Company, Guang Zhu and Qian Qian of CITC CMC
International Tendering Corporation for their support in different phases of the project.
We thank Dave van den Nieuwenhof, Huub Lavooij, Albert de Haas and Liqun Li of
Royal Dutch Embassy in Beijing for their interest and support. The project would not
have been possible without the grant received from the Government of the
Netherlands, through the ORET organization. We are grateful for the fair and proper
settlement of all administrative, financial and contractual matters related to this
project.
4
Contents
CONTENTS
Section
Title
Page
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.8.1
1.8.2
1.8.3
1.9
1.10
2
2.1
2.1.1
2.1.2
2.1.3
2.2
2.2.1
2.2.2
2.2.3
3
3.1
3.1.1
3.1.2
3.1.3
3.1.4
3.1.5
3.2
3.2.1
3.2.2
3.2.3
3.2.4.
3.2.5
3.2.6
3.3
3.3.1
3.3.2
3.3.3
3.3.4
3.3.5
3.3.6
3.3.7
3.3.8
3.4
3.4.1
3.4.2
3.4.3
3.5
INTRODUCTION
China’s water resources problems
Flooding
Water shortages
Need for basin wide water resources monitoring and management
Sino-Dutch water monitoring and flow forecasting project
Project objectives
Project deliverables
Project approach
Development phase
Implementation and testing phase
Demonstration phase
Project impact
References
THE YELLOW RIIVER TARGET AREAS
The source area of the Yellow River
Hydrological observations in the source area
Information acquisition and transmission
Hydrological forecasting
The lower Weihe River
Hydrological observations
Information acquisition and transmission
Flood forecasting
ENERGY AND WATER BALANCE MONITORING SYSTEM
System components
Pre-processing
Precipitation mapping
Energy balance monitoring
Snow and snowmelt
Drought monitoring
LAS measurements
LAS theory
LAS equipment and installation
LAS measuring sites
Data collection
Data processing
LAS results
EWBMS software system
Satellite data reception and pre-processing
Rain gauge data reception and pre-processing
Precipitation module
Energy balance module
Freeze/Thaw module
Drought monitoring system
Processing information data base
Imageshow-2 analysis tool
Catchment drought monitoring system
Climatic drought
Hydrological drought
Agricultural drought
Validation of EWBMS products
5
7
7
8
8
9
10
10
11
11
12
13
14
15
16
17
17
18
22
22
23
24
25
25
29
31
31
32
33
42
43
45
45
47
46
46
47
48
52
53
54
55
55
58
58
62
62
64
64
65
67
69
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
3.5.1
3.5.2
3.5.3
3.5.4
3.5.5.
3.6
4
4.1
4.1.1
4.1.2
4.1.3
4.1.4
4.2
4.2.1
4.2.2
4.3
4.3.1
4.3.2
4.4
4.4.1
4.4.2
4.5
4.5.1
4.5.2
4.5.3
4.5.4
4.6
5
5.1
5.1.1
5.1.2
5.1.3
5.1.4
5.1.5
5.2
5.2.1
5.2.2
5.3
5.3.1
5.3.2
6
Validation of precipitation
Validation of air temperature
Validation of net radiation
Validation of sensible heat flux
Validation of catchment water budget
References
LARGE SCALE HYDROLOGICAL MODEL
Technical reference
Land component transport
River routing
Land-river coupling
Forecasting of river flows
System implementation
Software components
User interface
Upper Yellow River Water Resources Forecasting System
Description of the data requirements
Description of the terrain data
Weihe basin High Water Forecasting System
Description of the data requirements
Description of the terrain data
Evaluation of the simulation results
Validation data
WFRS validation results
HWFS validation results
Discussion
References
SYSTEM IMPLEMENTATION AT YRCC
System set-up
Satellite receiving and processing system
Computer network
Data base
Organization and operation
LAS station, data collection and processing
Catchment monitoring bulletin
Reporting flood and drought information
Bulletin contents
Catchment monitoring website
Target users
Website design and structure
CONCLUSIONS, OUTLOOK AND RECOMMENDATIONS
ANNEX 1: LAS STATIONS INFORMATION
ANNEX 2: CATCHMENT MONITORING BULLETIN
6
69
74
77
79
81
85
87
87
88
90
91
92
93
93
94
96
96
96
98
98
98
99
99
101
103
105
108
111
111
111
113
115
117
118
118
118
119
119
119
120
123
127
131
Chapter 1 - Introduction
1
INTRODUCTION
This document is the final report of the project “Satellite Based Water Monitoring
and Flow Forecasting System in the Yellow River Basin”. This Sino-Dutch project
was funded by the Chinese and Dutch Government. The Dutch funding contribution
was provided through the ORET program, a program that supports export transactions
that are relevant for social economic development and for the environment, but are
not feasible in a commercial sense. After signature of the contract in November 2003
and the Grant agreement in May 2004, the project started in June 2004. The project
was completed in the last month of 2008.
1.1
China’s water resources problems.
Water is one of the most important issues in relation to China's development. With a
growing population and a booming economy the water demand is increasing steadily.
At the same time water availability – especially in the north of the country - is limited
and characterized by a highly uneven distribution geographically and seasonally.
The Yellow river (Huanghe) is, after the Yangtze, the second largest river in China.
The river basin is situated in the arid, semi-arid and sub-humid zones, which zones
are characterized by relatively low but highly variable rainfall. The average annual
run-of is about 58 billion m3. The lower reach of the Yellow river runs through a
relatively narrow corridor towards the sea. By consequence the Yellow river basin
has only a short coast line.
While water availability in the Yellow river catchment is highly irregular, water
demand is steadily increasing. China’s impressive economic growth and improving
living standards - alongside a still increasing population - put pressure on water
resources. Urban areas, industry, agriculture and nature are all competing for a share
of the precious natural resource. Also some large cities, which are situated outside of
the catchment (e.g. Tianjin), depend on water from the river. Due to water intake
from the Yellow river for industry, agriculture and residential use, the flow tends to
dry up in the lower reach during the summer period. However, in case of high
precipitation the risk of flooding looms in the lower reach, where the riverbed runs
elevated high above the land. Growth and development are held back or become ‘non
sustainable’ in water shortage areas, resulting in damage to local economy and nature.
This puts more and more pressure on decision making concerning the allocation and
development of water resources.
A detailed assessment of water resources in the Yellow river is currently carried out
every 10 years. But in view of the climatic variability, more frequent assessments are
highly desirable. Monitoring (measuring time series) is currently restricted to
precipitation and river flow at a limited number of locations. These measurements
suffer from a lack of overview. A new satellite based water resources monitoring and
flow forecasting technology has been implemented to help addressing this problem.
There are two neighbouring basins, that of the Hai and Huai river respectively. The
Yellow (Huanghe), the Hai and Huai river basins are together referred to as the 3-H
basins. 40% of the Chinese population lives here. The 3-H basins are the breadbaskets
of China and produce 67% of its wheat, 44% of its corn and 72% of its millet. In
addition they produce 65% of its peanuts, 64% of its sunflower and 42% of its cotton.
At the same time these basins have only 10% of China's water resources.
7
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Water resources issues in the 3-H basins are related. Excess water, if available, may
be transported from the Yellow river to the densely populated areas along the coast in
particularly the Hai River basin (Beijing, Tianjin). The whole region is regularly hit
by disasters of both water scarcity and excess.
1.2
Flooding
The Yellow river carries a huge amount of sediments, originating from the Loess
Plateau in the upper and middle reaches. 75 % of the sediments is deposited in the
lower reaches and the river estuary. As a result the river floor is rising 5-10
centimetre per year, and the levees in the floodplain had to be rebuilt 4 times during
the second half of the 20th century: in 1950, 1955, 1964 and 1977. Costs are
approximately 2 Billion US$ each time. The river water level is now up to 10 meter
above the surrounding land. The combination of this phenomenon, with the
sometimes-high precipitation in the middle reach, creates a high risk of flooding.
Since 600 BC, dike bursts occurred 1590 times and the river changed its course 26
times. Very serious floods occurred in the 1930's. A major flood in 1933 caused more
than 50 dike bursts. 18000 people were killed and 3.6 million ha of farmland was
damaged. In 1938 another flood inundated 27 counties and caused 3.4 million
victims.
According to the report Agenda for Water Sector Strategy for North China
(Worldbank 2001) flood losses in the Yellow river basin increased from 1500
RMB/ha in the 1950's to 9000 RMB/ha in the 1990's. In 1998 losses were in the order
of 1% of the GDP. In the Yellow river basin the provinces most affected were Henan
and Shandong, followed by Shanxi and Shaanxi.
The total flood prone area of the Yellow river is 118000 km2. 71 million people are
living in this area. An estimation of losses in case of complete flooding of this area,
based on the fore mentioned figure of 9000 RMB/ha, would then be 100 billion RMB
or 13 billion US$.
According to the document "An overview of Chinese water issues" (Unknown 1997)
the losses due to flooding in China were 20 billion US$ in 1990 and 10 billion $ in
1996. Very severe flooding occurred in 1998. These floods killed 3500 people,
damaged 7 million houses and submerged 250.000 km2 of farmland. Total damage
amounted to 30 billion US$ nation wide. In these events 250,000 km2 of land was
inundated.
1.3
Water shortages
Since the 1980's water shortages in the 3-H basins have been growing in magnitude
and frequency of occurrence. This has created severe economic losses. Water demand
in 2000 was 169 billion m3 and exceeds the total supply, which is 132 billion m3 per
year (table 1.1). Shortages are expected to grow from 37 billion m3 to 56 billion m3
in 2050 if no measures are taken (Worldbank 2001). Current demands and shortages
for these basins are presented in following table.
8
Chapter 1 - Introduction
Table 1.1: Water demand, supply and shortages in the 3-H basins (billion m3/yr)
Huai
Hai
Total
Huanghe
Demand
47
72
50
169
Supply total
37
55
32
124
Shortage
10
17
18
37
•
domestic + industry
2
4
4
5
•
agriculture
8
13
14
32
Roughly 80% of all water is used in agriculture, and within this sector the use for
irrigation is far dominant. The water demand structure in the Yellow river basin is as
follows.
Table 1.2: Water demand in the Yellow river basin
Category
Share (%)
Urban life
3
Urban industry
12
Rural life
2
Rural industry
2
Irrigation
76
Livestock
1
Fisheries / Pasture
3
As a result of surface water shortages, there is an increasing reliance on groundwater.
Groundwater extraction in the 3H basins is about 50 billion m3 per year and about 13
billion m3 in the Yellow river basin alone.
Table 1.3: Utilization of ground water in the 3H basins (billion m3/yr)
Huai
Hai
Huanghe
Groundwater extraction
13
16
22
Total
51
In many areas ground water resources are over exploited and ground water tables are
falling as much as several meter per year. As a result the cost of ground water
extraction is growing. Other effects are: saline water intrusion in the coastal areas
(now covering an area of 142 km2) and ground subsidence. The urban areas of Tianjin
and Beijing suffer from subsidence, causing settlement of structures, bridge collapse,
storm water drainage problems and reduction in flood protection. Ground subsidence
has caused 1.4 billion RMB damages to structures during the 1990's
The earlier mentioned Worldbank study also estimates the economic value of water
for different sectors. In agriculture, predominantly irrigation, the value is 0.8-1.6
RMB/m2, or 1.2 RMB/m3 on average. For domestic and industrial use the value varies
between 3 and 6 RMB/m3.
1.4
Need for basin wide water resources monitoring and management
Provinces in the upper reach of the Yellow river have been overusing the available
water, which has lead to shortages and even drying up of the river in downstream
areas. In 1997 the irrigation rate in the upper reach was 12000 m3/ha on average,
while 3700 m3 was used in the middle reach and 4500 m3 in the lower reach of the
river. According to Changming Liu (2000) average gross irrigation water use in the
north-east of China was 8400 m3/ha. This is about 4 times the water required for a
single crop. The problem of water shortage versus overuse has caused intense
9
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
conflicts between political entities. Therefore a basin wide control of the limited
water resources has become essential.
China's Agenda for Water Sector Strategy for North China recommends that: the
River Basin Councils are to be charged with, and given the necessary legislative
support for: (a) Determining water resources allocations (surface and groundwater)
for the provinces, (b) Developing policies and programs to promote sustainable water
resources management, particularly with respect to flood control and drought relief,
ground water management, water resources protection and pollution control,
promotion of increased water use (especially irrigation) efficiency, and
comprehensive basin development planning.
The Yellow River Conservancy Commission is the first river commission that has
been charged with such far reaching tasks. It is clear however, that proper decision
making in relation to these tasks, requires a lot of information on river flow and water
resources on the one side, and water needs on the other side. On the demand side
water needs in agriculture is by far the largest and most important part. It is also the
most difficult demand to assess, because of the variability of “natural” water supply
by precipitation. In dry years, water needs for irrigation will be much larger than in
more wet years.
1.5
Sino-Dutch water monitoring and flow forecasting project
The Sino-Dutch project “Satellite Based Water Monitoring and Flow Forecasting
System in the Yellow River Basin” has developed and implemented an operational
water balance monitoring and flow forecasting system for the Yellow river basin. The
main components of this system are the “Energy and Water Balance Monitoring
System” (EWBMS) developed by the Dutch remote sensing company EARS and the
Large Scale Hydrological Model (LSHM) developed by UNESCO-IHE, both in
Delft, Netherlands. Based on these technological components the following dedicated
subsystems have been developed and implemented for the Yellow River basin:
• Flow Forecasting system in the upper reach of the Yellow river
• Flow and high water forecasting system for the Weihe tributary.
• Drought monitoring system for the entire Yellow river basin.
The system is to become a major tool in the hands of the Yellow River Conservancy
Commission. It will help to carry out tasks with respect to (1) the management of
water resources in the Yellow river basin, (2) flood forecasting and early warning,
and (3) the monitoring and early warning of drought.
1.6
Project objectives
The satellite based water monitoring and flow forecasting project has been carried out
with the following objectives:
• To develop a system for energy and water balance monitoring.
• To develop a system for drought monitoring and early warning.
• To develop a prototype system for flow forecasting in the Upper Reach.
• To develop a prototype system flow and flood forecasting in the Weihe.
• To calibrate, test and improve these systems.
• To implement these systems at the YRCC premises.
• To train the partners in understanding and effectively using these systems.
• To assist the YRCC in monitoring of water resources and drought.
10
Chapter 1 - Introduction
1.7
Project deliverables
In course of project the partners have provided the following deliverables to YRCC:
•
•
•
•
•
•
•
•
•
•
•
•
•
1.8
Two FY2c geostationary satellite receiving systems.
EARS Energy and Water Balance Monitoring System (EWBMS), providing daily
data fields of: surface temperature, 1.5 m air temperature, global and net
radiation, actual and potential evapotranspiration, rainfall, snow height and
effective precipitation.
4 Surface flux measuring systems consisting of a Large Aperture Scintillometer
(LAS), a CNR1 net radiometer and a data logger.
Drought Monitoring and early warning System (DMS) for the entire basin.
Water Resources Forecasting System for the Upper Reach (WRFS).
Flood Forecasting System for the Weihe (HWFS).
EWBMS, DMS, WRFS and HWFS methodology description reports.
EWBMS, DMS, WRFS and HWFS user manuals.
A project final report (this document).
Half yearly project progress reports.
17 man-years of technical assistance.
30 man-months of training in understanding, use and application of the
monitoring system and its technological components.
36 man-months of research fellowships, to carry out joint research and
development.
Project approach
The project has been carried out in 3 phases. The 1st phase, the system development
phase has been used to design the various systems, to resolve a number of technical
and methodological questions, to develop the proto-types and to train the Chinese
partners in understanding the backgrounds and potential of the technology. The first
phase took about 2 years.
The 2nd phase is for implementation and testing. The prototype monitoring systems
were installed at the YRCC premises. Sites for the surface flux measuring systems
have been selected and the LAS, radiation, temperature and wind sensors were
installed; three on the Qinghai plateau in the upper reach, and one in the Loess
plateau area. The flux data measured with these systems have been used to validate
the satellite based systems and to optimize their performance. For validation also data
from regular weather and flow measuring stations have been used. This phase has
also taken at least two years with considerable time overlap with the 1st and 3rd phase.
The 3rd phase is the demonstration phase. With a total duration of also 2 years, the
system has been run by YRCC in a semi-operational way. Water resources, water
level and drought information have been generated operationally. Monthly river flow
bulletins have been developed and published. A website has been developed too
inform a larger public. The activities carried out in the different phases are briefly
described hereafter.
11
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
1.8.1
Development phase
Satellite data receiving system
At the beginning of the project two PC based receiving systems for the geostationary
satellite data have been selected and were purchased from the Beijing based company
Shinetek. One system has been implemented in Zhengzhou and the second one in
Lanzhou. They are both receiving the Chinese geostationary meteorological satellite
FengYun-2c, and serve as mutual back-up. FY2 is one of the most operational
satellite systems. Its follow-up has already been launched and in case of unexpected
failure can fast replace the current satellite. With the two receiving systems visual and
thermal infrared images are received every hour. The data are stored on hard disk
until being further processing once every day.
EWBMS adaptation
The EWBMS has been adapted to the needs of YRCC. User requirements have been
discussed early in the project phase on the basis of a report describing the current
methodology. On the basis of this report discussions were held and modifications
were agreed on in the interest of the present application of the EWBMS system. The
most important user requirements that were agreed for implementation are the
following two:
1) Generation of all basic data fields on a daily basis in stead of 10 daily. 10 daily
data products are also generated, but they are shifting averages, i.e. each day a 10
daily average is produced on the basis of the last 10 days.
2) Extension of the EWBMS software so as to take care of precipitation at below zero
temperatures, the storage of snow during winter, and melting of snow in spring. This
in view of the climatic conditions in the upper reaches of the river.
The second modification was a considerable effort and required the development of
the theoretical framework and a special algorithm.
Drought monitoring system
For the drought monitoring system (DMS) it is proposed to use the Climatic Moisture
Index as proposed in the framework of the UN Convention to Combat Desertification
(1.1)
CMI = P / EP
Where P is the precipitation, EP the potential evapotranspiration and CMI the Climatic
Moisture Index, a parameter which indicates climatic drought. More directly related
to the drought conditions at the ground surface is the "Soil Moisture Index" (SMI),
which may be defined as
SMI = E / EP
(1.2)
Where E denotes the actual evapotranspiration. In both indices the potential
evapotranspiration is not one of the basic products. The potential evapotranspiration
can be derived in several ways. The "EARS method" estimates the potential
evapotranspiration as 0.8 times the net radiation. The factor 0.8 is derived by
approximation from the Penman-Monteith equation. A second approach is to estimate
the potential evapotranspiration from the satellite observed air temperatures using the
Thornthwaite formula. Other drought products may be developed according to the
YRCC needs, for example Number of Dry Days, etc.
12
Chapter 1 - Introduction
Water Resources Forecasting System (WRFS) for the upper reach
The WRFS has been developed as a grid-based modelling system, which can
assimilate the precipitation, snow melt and evaporation data from the EWBMS at the
time-scale of 1 day, without the need to aggregate on the spatial scale. In this phase,
the distributed water balance model has been developed and the components have
been fit into the structure arising from the geometrical terrain arrangement. The initial
model parameterisation of other hydrological characteristics was carried out. The
data transfer from the EWBMS to the water resources forecasting model component
was developed as separate module, which enhances the capability of independent
incremental upgrading during later stages of the project. During definition of the
required key model parameters to be incorporated, and during construction of the
model, extensive use of information and experience from YRCC has been
incorporated through intense collaboration. Similarly, data requirements and
procedures for calibration and validation of the model components were jointly
defined. The possibilities and limitations for calibration and validation, however,
depend to a large degree on existing historical data records. Part of the procedure
consisted of a sensitivity assessment for the individual parameters.
Flood Forecasting System (HWFS) for the lower Weihe River
A flood forecasting systems has been developed, initially by extending the presently
available flow routing tools used by the YRCC. This requires establishing a coupling
with a grid-based model structure, to be built as a separate module, which is capable
to the EWBMS generated data in a similar fashion as for the WRFS component.
Starting from pilot sub-catchment, progressive improvements have been introduced
and tested, finally covering the entire Weihe catchment. Similarly to the development
of the WRFS, procedures, data requirements and selection of data records for the
calibration and validation phase have been established and carried out. YRCC
participated actively in the development of the flood forecasting system and
contributed essential information and knowledge of the area and its hydrological
behaviour.
Training
A detailed training program including satellite reception, energy and water balance
processing, drought monitoring, water resources forecasting and high water level
forecasting has been prepared during the development phase of the project.
1.8.2
Implementation and testing phase
EWBMS and drought monitoring system
At the beginning of the second year the Energy and Water Balance Monitoring
system and the Drought Monitoring System were implemented at the YRCC premises
in Zhengzhou, in addition to the satellite data receiving system. Also rain gauge data
reception ran smoothly by that time. The EWBMS system was made operational in
such a way that it could produce the basic products, such as precipitation, melt water,
evapotranspiration, radiation and air temperature on a daily basis. Dekadal or monthly
drought monitoring products (CMS, DMS) could be generated as well. YRCC
operators were trained in using the system and in generating the products.
13
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The first part of this phase was used to test and validate the quality of the distributed
data products. For testing of the data products the following methods were used.
1) Precipitation data will were tested by means of the Jack-knifing method.
2) Radiation data were tested by comparison with radiometer data; mainly for the net
radiometers installed together with the LAS systems (see point 3)
3) Sensible heat flux data were tested by means of Large Aperture Scintillometer
(LAS) systems. 4 systems have been installed, 3 in the Upper Reach and 1 in the
Loess plateau area, two different climatic regions of the Yellow river basin.
4) Actual evapotranspiration can be considered validated if the radiation and sensible
heat flux are validated and calibrated (see 2 and 3).
5) 1.5-meter air temperatures derived from satellite will be tested with measured air
temperatures available from existing weather stations.
6) In addition the water balance (rainfall minus actual evapotranspiration) has been
tested by comparing this quantity for a period of one or more years with the discharge
measured at the basin outlet.
By comparison of the data derived from the satellite and those measured on the
ground the EWBMS have been tested. Deviations were studied and the system was
improved wherever deficiencies in the models could be identified.
Having calibrated the basic satellite data products, also the drought monitoring
indices (CMI and SMI), which are ratio's of the previous fluxes, can be considered
reliable. At the end of this phase a Validation and Calibration Report has been
generated.
Water Resources and High Water Level Forecasting Systems (WRFS and HWFS)
During this phase, the WRFS and HWFS have been implemented, tested and assessed
at YRCC. A first appraisal of their functioning was done on the basis of comparing
test results against hydrological response observations and measurements from field
studies and measuring sites, i.e. validation. Where necessary, improvements were
made to the individual modules and alternative solutions to certain were carried
through. The HWFS component was extended to comprise the full area of the subbasin. Training was conducted in order to familiarise YRCC staff with the systems
operation.
1.8.3
Demonstration phase
During the demonstration phase the EWBMS, the drought monitoring system (DMS),
the Water Resources Forecasting System (WRFS) for the upper river, and the Flood
Forecasting System (HWFS), for the Weihe River, have been run in an operational
way. Products were generated and provided to end-users. A satellite monitoring
bulletin and a website were developed for this purpose. At the end of the
demonstration phase the project has been assessed by means of a validation
workshop.
14
Chapter 1 - Introduction
EWBMS and Drought Monitoring System (DMS)
The EWBMS and drought monitoring system were demonstrated operationally during
the last two years of the project. A drought monitoring and early warning bulletin for
the Yellow River basin was developed and will be published regularly. Drought
related products will be generated and published in this bulletin, for example: rainfall
and the soil moisture index (= relative evapotranspiration), sub-catchment water
balances, etc. Other drought products, such as the cumulative number of dry days,
may be added.
Water Resources and High Water Level Forecasting Systems (WRFS and HWFS)
Based on monitoring of the results and evaluation of the performance of both systems
since the implementation and testing phase, the systems implementation was finalized
and the documentation completed. Particular attention will be paid to ensure that the
improvements arising from the systems performance assessment, in combination with
those from initial calibration and validation are implemented. Training of system
operators at the YRCC Hydrological Bureau continued, so as to assure that they
master the technology well and the products become established tools in support of
operations duly carried out by the YRCC.
1.9
Project impact
Water budget monitoring in the upper reach and forecasting of the rivers base flow
leads to earlier knowledge of the amount of water that is available for use in the
middle reach of the river and will enable a more rational and sustainable water
allocation to users. Lack of information has so far prohibited this. The main water
intake along the river is for irrigation and it has been noted that water quota for
irrigation have been far too high. This has adversely affected users in the middle and
lower reaches of the river. For an equitable water distribution the system provides
early information on river run-off and drought, i.e. actual water needs. This will allow
water managers sufficient time to prepare rational water distribution plans based on
actual water supply and needs.
Water resources forecasting
The system is expected to bring an overall increase of water use efficiency, which is
conservatively estimated at 1%. Given a total water use for irrigation in the Yellow
river basin of 27 billion m3 per year, and a related average economic value of water of
1.2 RMB/m3, the estimated yearly social economic value that can be attributed to this
part of the system may then be estimated at 0.01*1.2*27.109 RMB/year or 324
million RMB/year.
Flood forecasting
The flood forecasting system implemented for the Weihe tributary provides run-off
and related river level forecasts on a daily basis. These forecasts are based on satellite
based daily distributed data fields of precipitation and evapotranspiration which cover
the whole sub-catchment. The system will help to provide improved predictions of
high water levels and to timely alert authorities and the population to take flood
protection measures.
15
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
According to the Worldbank (2001), flood losses in the Yellow River basin increased
from 1500 RMB/ha in the 1950’s to 9000 RMB in the 1990’s. The total flood prone
area is about 118000 km2. Based on these figures the potential costs of a complete
flooding could nowadays be estimated at 120 billion RMB. If such a flood occurs
once every century and by improved high water forecasting 10% of the damage could
be prevented, than the social economic value of the system would be 120 million
RMB per year.
Drought monitoring
Another important functionality of the EWBMS is drought and desertification
monitoring. Such information has significant meaning in relation to food security and
food trade, ass well as in relation to land degradation. The drought monitoring system
may be used to monitor and forecast the production of crop and grass lands. Crop
yield forecasts help to optimize food market performance and reduce price
fluctuations. According to Hayami and Peterson (1972) improved market monitoring
and reduced price fluctuations increase population welfare. They also allow realizing
better prices in food trade. Finally the system can help to reduce desertification
damage. The total benefits of drought monitoring and early warning for China would
be about 3 billion RMB per year, or about 300 million RMD in the Yellow river area.
Summarizing it may be concluded that the impact and social economic returns of the
project can be very high, in the order of 700 million RMB per year.
1.10
References
Hayami, Y and Peterson, W. (1972) “Social Returns to Public Information Services:
Statistical Reporting of US Farm Commodities”, The American Economic Review,
Vol. 62, 1972, pp 119-130.
Worldbank (2001) “Agenda for the Water Sector Strategy for North China”,
Worldbank report 22040-CHA, April 2, 2001.
Unknown (1997) “An Overview of Chinese Water Issues”, China Environment
Series, 10 September 1997, pp 46-48.
Changming Liu (2000) “Water Resources Development in the First Half of the 21th
Century in China”, 2nd World Water Forum, China Water Session, pp 1-16), March
2000.
16
Chapter 2 – The Yellow River Target Areas
2
THE YELLOW RIVER TARGET AREAS
2.1
The source area of the Yellow River
The upper reach of the Yellow River covers the area above Tangnaihai hydrological
station on the main stream. The drainage area of the upper reach covers 121972 km2,
located between 95°00′ and 103°30′ E and from 32°19 to 36°08′ N. The river length
to the upstream source is 1552.4 km. With an altitude over 3000 m, this area has a
low air density, with oxygen content between 0.166 and 0.186 g/m3. There are many
mountains in the source area, such as Bayankala, Animaqin and Min Mountain, and
there are scattered plains, rivers, basins and hills. The mountain summits over 4000 m
are bare, while the lower slopes are covered with grassland. The glacier of the
Animaqin covers an area of 191.95 km2. Its melting water makes up 1.0% of the
runoff at Tangnaihai.
Temperature and Ice
The annual temperature is below zero and only July and August are frost-free. The
difference between yearly minimum and maximum temperature amounts to 75 oC.
The recorded lowest temperature is –53 oC. The warmest period generally falls in
August, the coldest in January. The following table provide an overview of the ice
situation in the upper reach
Table 2.1: Characteristics of river ice in the YR source area
Reach
Ice flooded from
Fully frozen
Above Jimai
October D3
January
Mengtang-Maqu
November D3
December
Jungong-Tangnaihai November D1-2
Melting from
March D3
March D3
March D1-2
D=dekad, period of 10 days
Figure 2.1: The project focal areas: the Yellow River Upper Reach and the lower
reach of the Weihe River
17
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Rainfall and Evaporation
The average annual rainfall in the river source area is 474.6 mm. The rainfall in the
area of Hongyuan, Ruoergai，Maqu and Jiuzhi is in the range of 500-800 mm, while
rainfall, while rainfall in the reaches from Maqu to Jungong is much lower: 250-400
mm. 91% of the rain falls from May to October. Incidental rain falls between October
and May. Above Maduo rain is scattered throughout the year. Pan evaporation is
between 1200 and 2300 mm/yr and declines from north to south.
River runoff
The average annual rainfall on the area amounts to 69.9 billion m3. The average
runoff is 20.5 billion m3, corresponding to 168 mm water depth, which on average is
35% of the rainfall. There are usually two peaks in the run-off: in July and
September. Most runoff occurs between May and October (78.5%).
2.1.1
Hydrological observations in the source area
Figure 2.2 and table 2.2 and 2.3 provide an overview of the hydrological observations
network in the source area of the Yellow River.
The Development of Automatic Observation and Reporting System
The project Automatic Observation and Reporting System for the River Source Areas
of the Yellow River (here in after referred to as the River Source Project) is
composed of four sub-systems: data collection system, data transmission system,
monitoring and information centre, and water resources monitoring system. In
consideration of the hydrological situation in the upper reaches of the Yellow River,
the development of the system is mainly based on the construction of the data
Figure 2.2: Hydrological station network in the source area
18
Chapter 2 – The Yellow River Target Areas
collection platform. The system will function only with people in charge, but without
requiring their permanent presence. The data will be collected and processed in an
automatic, digital, distance-observed and remote-controlled way, supported by
regular inspection visits. The River Source Project has completed the rainfall
collection sub-project and the construction of the sub-centre. The other projects are
scheduled for construction. Considerable progress is expected to be made in the
observation and reporting techniques and in modernization of the data collection.
Distribution of the stations
There are 10 hydrological stations or water level stations in the river source areas, of
which 14 are all administered by the Xining Hydrological Reconnaissance Bureau of
the Yellow River Conservancy Commission (YRCC). The stations are located at:
Huangheyan, Jimai, Mengtang, Maqu, Jungong, and Tangnaihai station, on the
mainstream, while the following stations are situated on the branches: Hanghe on the
Requ, Jiuzhi on the Shakequ, Tangke on the Baihe river and Dashui on Heihe river.
In addition, there are 5 entrusted rainfall stations in: Awancang, Longriba, Waqie,
Maiwa and Dongqinggou. See also table 2.2.
Items of Hydrological Observation
The stations are responsible for the observation and reporting of the following items:
- Rainfall and Evaporation: The rainfall observation instruments include the manual
rainfall device and the siphon rainfall device; the evaporation observation instruments
include E601 Evaporator and 20 cm Diameter Evaporator. Recording and processing
of the data is manual.
- Water level: Three instruments are used: vertical rule, floater stage, and ultrasonic
no-touching stage. Vertical rules are used in all stations. Some stations are equipped
with HW-1000 ultrasonic devices, and only a few use the floater device. Water level
recording is automatic in 4 stations: Tangnaihai, Xunhua, Guide and Xiangtang. In
the other 10 stations data are recorded manually. Computation and processing is also
manually.
- Discharge: The major way of discharge observation is by continuous measurement.
Only a few stations are measured discontinuously or at regular intervals. The tools
used include observation boat, running speed cableway, hanging box cableway and
buoy projector. Boat observation is used at Huangheyan, Jimai, Mengtang, Maqu,
Jungong, and Tangnaihai. In addition at Tangnaihai, the half-automatic running speed
cableway is used to measure low and high flows. Bridge observation is adopted in
Huanghe station. Rubber boats are used in Jiuzhi,Tangke and Dashui. The stations at
Maqu and Tangnaihai are equipped with a buoy projector to measure flood. However,
most of them were built already in the 1960s and 1970s, and the trestles and cables
have expired. The same data can be collected by buoy projectors. The collection,
analysis, computation, and processing of the data involved is mainly done manually.
- Sediment. Sampling of silt is done manually with the help of a horizontal sampling
device. The processing and analysis of the sample sediment, and the related data
processing is done by hand.
19
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Table 2.2 Information of the hydrological station network in the source area
No.
River
Station
Station type
water level
water level
hydrology
Established
(yr.m)
planned
planned
1955.6
1
2
3
Zhaling Lake
Eling Lake
Yellow River
Zhaling Lake
Eling Lake
Huangheyan
4
Yellow River
5
34°51′
35°05′
34°53′
Drainage
area
（km2）
17728
18428
20930
97°30′
97°45′
98°10′
4215
99°39′
33°46′
45019
3948
1987.8
101°03′
33°46′
59655
3636
hydrology
1959.1
102°05′
33°58′
86048
3400
Jungong
hydrology
1979.8
100°39′
34°42′
98414
3079
Yellow River
Tang-naihai
hydrology
1955.8
100°39′
35°30′
121972
2665
9
Yellow River
Duide
hydrology
1954.1
101°24′
36°02′
133650
2201
10
Yellow River
Xunhua
hydrology
1945.10
102°30′
35°50′
145459
1850
11
Requ
Huanghe
hydrology
1978.8
98°16′
34°36′
6446
4200
12
Shakequ
Jiuzhi
hydrology
1978.9
101°30′
33°26′
1248
3560
13
Baihe River
Tangke
hydrology
1978.9
102°28′
33°25′
5374
3410
14
Heihe River
Dashui
hydrology
1984.6
102°16′
33°59′
7421
3400
15
Datong River
Xiangtang
hydrology
1940.1
102°50′
36°21′
15126
1776
16
Huang-shui
River
Minhe
hydrology
1940.1
102°48′
36°20′
15432
1752
17
Saierqu
Awangcang
rainfall
1977
101°42′
33°47′
water level
Water level
Water level,
volume of flow,
silt, evaporation
Water level,
volume of flow,
silt, evaporation
Water level,
volume of flow,
rainfall
Water level,
volume of flow,
silt, rainfall,
evaporation
Water level,
volume of flow,
silt, rainfall,
evaporation
Water level,
volume of flow,
silt, rainfall
Water level,
volume of flow,
silt, rainfall,
evaporation
Water level,
volume of flow,
silt, rainfall
Water level,
volume of flow,
rainfall
Water level,
volume of flow,
rainfall
Water level,
volume of flow,
silt, rainfall,
evaporation
Water level,
volume of flow,
rainfall
Water level,
volume of flow,
silt
Water level,
volume of flow,
silt, rainfall,
evaporation
Rainfall
Jimai
hydrology
1958.6
Yellow River
Mengtang
hydrology
6
Yellow River
Maqu
7
Yellow River
8
18
Baihe River
Longriba
rainfall
1976
102°22′
32°27′
Rainfall
19
Baihe River
Waqie
rainfall
1976
102°37
33°08′
Rainfall
20
Black River
Maiwa
rainfall
1977
102°54′
32°03′
Rainfall
21
Qiemuqu
rainfall
1977
99°58′
34°32′
Rainfall
22
Gequ
Dongqinggo
u
Maqin
rainfall
planned
100°15′
34°29′
Rainfall
23
Shakequ
Jiuzhi
rainfall
planned
101°29′
33°25′
Rainfall
24
Baihe River
Hongyuan
rainfall
planned
102°34′
32°48′
Rainfall
25
Heihe River
Ruoergai
rainfall
planned
102°58′
33°35′
Rainfall
26
Zequ
Zeku
rainfall
planned
101°28′
35°02′
Rainfall
27
Gande
Xikequ
rainfall
planned
99°54′
33°58′
Rainfall
28
Henan
Zequ
rainfall
planned
101°35′
34°45′
Rainfall
Rem
Coordinates
Long.
Lat.
Altitude
(m)
Observation
Item
3 additional tour surveying sections are established in Ruoergai on the mainstream, Jimai and Jiaqu on the branches.
20
Chapter 2 – The Yellow River Target Areas
Table 2.3: Data collection in the river source area
No River
Station
Instruments and manner of observation
Water level
Discharge
1
Yellow
River
Huangheyan
Manual vertical
rule
2
Yellow
River
Jimai
Manual vertical
rule
3
Yellow
River
Mengtang
Manual vertical
rule
4
Yellow
River
Maqu
5
Yellow
River
6
Sediment
concentration
Manual , horiz.
sampling device
Sediment load
Rainfall
1year every 4
years
Purchase from local Purchase from local
met.eo station
meteo station
Manual , horiz.
sampling device
1year every 4
years
Manual., horiz.
sampling device
Manual
20cm evaporator
Manual , rainfall
device
N/A
N/A
N/A
Manual vertical
rule
Conttin. in flood ,
discont. in non-flood
season , hanging boat
Conttin. in flood ,
discont. in non-flood
season , hanging boat
Conttin. in flood ,
discont. in non-flood
season , hanging boat
Continuous., hanging
boat, buoy projector
Manual , horiz.
sampling device
1 year every 4
years
Jungong
Manual vertical
rule
1 year discont. every 4
years, hanging boat
Manual , horiz.
sampling device
Yellow
River
Tangnaihai
Yellow
River
Guide
8
Yellow
River
Xunhua
9
Requ
Yellow
River
Jiuzhi
Contin. , cableway
with streaml. weight,
hanging boat, buoy
projector
Contin. , cableway
with streaml. weight,
hanging boat, buoy
projector
Contin. , cableway
with streaml. weight,
hanging boat, buoy
projector
Disc. bridge observ.
Manual , horiz.
sampling device
7
Record. water
level, floater
fluviograph,
vertical rule
Record. water
level, floater
fluviograph,
vertical rule
Record. water
level, floater
fluviograph,
vertical rule
Manual, vertical
rule
Manual, vertical
rule
Manual, vertical
rule
10 Shakequ
11 Baihe
River
Tangke
12 Heihe
River
13 Datong
River
Dashui
Xiangtang
15 Saierqu
Awancang
16 Baihe
River
17 Baihe
River
18 Heihe
River
19 Qiermuqu
Longriba
Manual, vertical
rule
Record. water
level, floater
fluviograph,
vertical rule
1 year discont.. every 4
years, rubber boat
Regular in flood,
irregular in non-flood
season, rubber boat
1year observ, every
4years, rubber boat
Regular, cableway
with streamlined
weight, hanging boat,
buoy projector
Man. & recording
siphon rainfall and
rainfall device
1 year every 4
Man. & recording
years
siphon rainfall and
rainfall device
Boat., horizontal Man. & recording
sampling device siphon rainfall and
rainfall device
Maiwa
Dongqinggou
21
Manual
20cm evaporator
Manual
20cm evaporator
N/A
Manual , horiz.
sampling device
Boat., horizontal Man. & recording
sampling device siphon rainfall and
rainfall device
Manual,
E601 evaporator,
20CM evaporator
Manual , horiz.
sampling device
Boat., horizontal Man. & recording
sampling device siphon rainfall and
rainfall device
N/A
N/A
N/A
N/A
N/A
N/A
Manual，
horizontal
sampling device
N/A
Manual, horiz.
sampling device
Boat , horiz.
sampling device
Manual,
rainfall device
N/A
N/A
N/A
Manual,
rainfall device
Manual,
20CM evaporator
N/A
Manual,
rainfall device
N/A
N/A
Manual observ.
rainfall device
Manual observ.
rainfall device
Manual observ.
rainfall device
Manual observ.
rainfall device
Manual observ.
rainfall device
Waqie
Evaporation
N/A
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
2.1.2
Information acquisition and transmission
At present, there are 3 levels of real time water regime data collection: the
hydrological stations, the data collection sub-centres and the data collection centre.
The sub-centre of the upper Yellow River is located in the Upper Yellow River
Hydrology and Water Resources Bureau of YRCC (Lanzhou, Gansu province). The
data collection centre is in Hydrology Bureau of YRCC (Zhengzhou, Henan
province).
All reporting stations are communicating by PSTN (Public Switched Telephone
Network), GSM (Global System for Mobile Communications) or satellite. Most
stations use PSTN and GSM, and part of them use GSM and satellite.
Rain gauges have been realized that automatically collect and transmit the rainfall
information, while hydrological stations automatically transmit the discharge
information after putting them manually into the computer. More than 90% of data
can be transmitted to Zhengzhou within 20 minutes, and more than 95% of the data
within 30 minutes. The sub-centre of the Upper Yellow River is in charge of the real
time water information transmission. The sub-centre communicates with the centre in
Zhengzhou through SDH (Synchronous Digital Hierarchy) with 2Mbaud rate.
The centre in Zhengzhou is in charge of the real time water information reception,
transmission and decoding, and the storage of these data into the real time water
information database.
2.1.3
Hydrological forecasting
So far, the hydrological forecasting tasks in the source area of the Yellow River
mainly concern the middle and long-term runoff forecasting and flood forecasting,
and which consists particularly of the following:
- Before the end of April, the long term monthly runoff forecast at Tangnaihai for
the period May-October should be made.
- During the flood season (May-October), every first dekad the updated forecast of
runoff for the next month should be made.
- During the flood season, short-term flood forecasts at Tangnaihai should be made
when heavy and large-scale rainfall may cause flood in the source area.
- At the end of the flood season, a long-term runoff forecast for the non-flood
season (November-June) should be made.
22
Chapter 2 – The Yellow River Target Areas
2.2
The lower Weihe River
The Weihe is the largest tributary of the Yellow River. It originates from the Niaoshu
Mountain in Weiyuan county in Gansu province, flows through Gansu, Ningxia and
Shannxi provinces and flows into the Yellow River in Tongguan county in Shanxi
Province. The total length of the main flow is 818 km and the basin area is 134800
km2. The reach from Xianyang to the outlet is the lower Weihe River. The length of
this part is 211 km. The river bed slope is around 0.68-0.15‰. Down of Lintong the
river is most winding. The Weihe River water system is dissymmetrically developed.
On its left bank, the tributaries are long, with larger catchments, and carry more
sediment. But on the right bank, the tributaries are short and steep, and carry more
water and less sediment.
According to physical and geographic conditions, the lower Weihe basin can be
divided in four types: soil-tor, loess hill, loess terrace and plain region. The soil-tor
region is found in the upper and middle reaches of the south tributary. It has steep
slopes, abundant precipitation, dense vegetation, little water and soil loss, high runoff
coefficient and easily generates runoff. The loess hill region is mostly situated on the
north side of the upper and middle Shichuan River. There is little vegetation, serious
water and soil loss and not easily generates runoff .The loess terrace is mainly found
on the south side of the lower Shichuan river and middle reaches. Finally the plain
region is situated in the vicinity of Lower Weihe River. The area is flat with fertile
soil and has a lower runoff coefficient.
There are many tributaries in lower Weihe. The tributaries on the north side mostly
originate from the loess hill and plateau, such as the Shichuan, Jinghe and Beiluohe
rivers. They have a large catchment, slow fall, high sediment load and are main
sources of sediment. The Jinghe is the largest tributary. With a length of 455.1 km
and a basin area of 45400 km2, it makes up 33.7% of the Weihe river basin. The
Beiluohe is the second largest tributary. Its length is 680 km2 and its basin area 26900
km2, thus covering 20% of the Weihe basin. There are a number of smaller rivers on
the south side: Fenghe, Zaohe, Chanbahe, Dayuhe, Xihe, Linghe, Youhe, Chishuihe,
Yuxianhe, Shidihe, which originate from the Qinling Mountain. They are short, have
rapid flow, great runoff and low sediment concentration. They represent major storm
flood sources in lower Weihe River.
The lower Weihe River belongs to the warm temperate zone and semi-arid and humid
climate. The annual mean temperature is 6-13 oC, the annual mean precipitation 500800 mm and the annual mean pan evaporation is 700-1000 mm. The rainfall in
southern mountainous region is more than that in the valley and northern part of the
basin. Rain storms occur from July to October and bring 50-60% of the yearly
precipitation. Maximum precipitation mostly occurs in July and August, as a result of
strong and short-duration storms. Autumn rainfall occurs from September to October,
and may last 5-10 days or longer.
Hydrological Characteristics
Runoff comes mostly from the main stream and south tributaries. The mean yearly
runoff is 7.96 Bm3, 7.26 Bm3 at Huaxian, 1.37 Bm3 at Zhangjiashan of the Jinghe
River and 0.696 Bm3 at Zhuangtou on the Beiluohe River, respectively. The interannual variation amounts to a factor 10. The maximum runoff of 20.6 Bm3 occurred
in 1964, while the minimum runoff, only 2.1 Bm3, took place in 1995. The yearly
runoff is also not equally distributed: 60% occurs during the flood season.
23
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The multi-annual average sediment runoff is 444 Mton in the lower Weihe River,
359Mton at Huaxian, 246 Mton Zhangjiashan and 85 Mton in Zhuangtou. The
sediment is mainly from north tributaries, especially the Jinghe and Beiluohe rivers.
They contribute 55.4% and 19.1%, respectively, to the total of the Weihe River. Most
sediment transport takes place during the flood season.
The flood in the lower Weihe basin comes mainly from upper Xianyang, Jinghe River
and the south tributaries. Discharge and sediment concentration are high, and rise and
drop steeply. The flood is of the flat type, with high runoff volume in autumn.
`2.2.1 Hydrological observations
The observation stations belong to Hydrology bureau of YRCC, Shannxi Hydrology
Bureau (SHB) and Shannxi Sanmenxia Bureau (SSB). There are 12 reporting
hydrological stations, 3 water level stations, 6 reporting rain gauge stations. Except
the reporting rain gauging stations, there are 55 basic rain gauge stations, of which 33
in south of the Weihe River, and 1 basic hydrological station at Liulin in the
Shishanchuan River. They are not reporting. They belong to the Shannxi Hydrology
Bureau. See figure 2.3 and table 2.4 and 2.5.
A tele-metering system of water level and rainfall is built since 1998 by the Shannxi
Sanmenxia Bureau. The system includes 3 water level stations (Gengzhen, Jiaokou,
Weinan), and 4 rain gauge stations (Quancao, Dianzi, Wulongshan, Shanghuichi).
Furthermore 44 silting cross-sections from Xianyang to the outlet of the Weihe and
22 from Zhuangtou to the outlet of the Beiluohe, have been set up by the Shannxi
Sanmenxia Reservoir Administrative Bureau for measuring the channel
sedimentation changes caused by the Sanmenxia Reservoir.
Figure 2.3: Reporting station network in the lower Weihe river.
24
Chapter 2 – The Yellow River Target Areas
The hydrological observations in the lower Weihe include 9 items including:
precipitation, evaporation, water level, discharge, sediment concentration, sediment
delivery rate, particles size analysis, water temperature and water quality. The same
items except water temperature but including ice regime are observed at Xianyang
and Huaxian.
Precipitation is observed by using solid-storage and tipping-bucket rainfall recording
every 2 hours, both at Xianyang and Huaxian station. At Xianyang evaporation data
are obtained manually from the local meteorological station. The station at Huaxian
has a vertical gauge, an HW-1000 ultrasonic recorder to measure the water level.
Huayin station has the vertical gauge. Xianyang station has not only the vertical
gauge and HW-1000 ultrasonic recorder, but also the wire weight gauge to observe
water level. The measurement facilities at Xianyang station consist of a double
permanent cable with ferroconcrete brackets, which serve as electrical current
measuring cables. The measuring facilities at Huaxian station consist of a single cable
with free-standing steel bracket lifting ship, one suspending ship, one hydrological
capstan, two electric boats. Xianyang and Huaxian stations measure the sediment
concentration using a horizontal-bottle sediment sampler. The sediment particle size
is analysed at both stations.
2.2.2
Information acquisition and transmission
The information sub-centre for the Weihe River is located at the Sanmenxia Reservoir
Hydrology and Water Resources Bureau of YRCC (Sanmenxia City, Heman
Provence). The centre is again the Hydrology Bureau of YRCC in Zhengzhou. All
reporting stations are communicating by PSTN (Public Switched Telephone
Network), GSM (Global System for Mobile Communications) or satellite. Most of
them use PSTN and GSM, and part of them use GSM and satellite.
Rain gauges have been realized that automatically collect and transmit rainfall
information, while hydrological stations automatically transmit discharge after
manually putting the information into a computer. More than 90% of data can be
transmitted to Zhengzhon within 20 minutes, and more than 95% within half an hour.
The sub-centre of Sanmenxia is in charge of real time informatiom transmission. The
sub-centre is communicating with the centre in Zhengzhou by SDH (Synchronous
Digital Hierarchy) at 2Mbaud rate. The centre in Zhengzhou is in charge of real time
water informatiom reception, transmission and decoding, and storage of the
information into the real time water information database.
2.2.3
Flood forecasting
Huaxian is the forecasting station in the lower Weihe. The forecasting items include
the discharge hydrograph, in particular the flood peak and its time of occurrence. The
peak is usually over 2000 m3/s. The forecast is based on the correlation between the
peaks at Lintong and Huaxian station. The parameters in the scheme are the
momentaneous discharge at Huaxian and the coefficient of excess in Lintong. In
addition the correlations of the flood peak at Xianyang and Zhangjiashan with that at
Huaxian are developed. The routing times of each are based on the discharges at
Xianyang and Zhangjiashan separately.
25
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Table 2.4: Reporting stations in the lower Weihe River
River name
Weihe
Weihe
Weihe
Weihe
Jinghe
Beiluohe
Beiluohe
Beiluohe
Chanhe
Juhe
Dayuhe
Bahe
Bahe
Qishuihe
Yeyuhe
Wangchuanhe
Linghe
Shichuanhe
Shichuanhe
Shichuanhe
Weihe
Station name
Xianyang
Lintong
Huaxian
Huayin
Zhangjiashan
Zhuangtou
Nanronghua
Chaoyi
Qinduzhen
Gaoqiao
Dayu
Luolicun
Maduwang
Yaoxian
Chunhua
Gepaizhen
Tielu
Fuping
Meiyuan
Yaoqu
Weinan
Station type
Hydrology
Hydrology
Hydrology
Water level
Hydrology
Hydrology
Hydrology
Water level
Hydrology
Hydrology
Hydrology
Hydrology
Hydrology
Hydrology
Hydrology
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Meteorology
East Long.
108 42
109 12
109 46
North Lat.
34 19
34 26
34 35
108 08
109 50
109 53
109 52
108 46
108 49
109 07
109 22
109 09
108 59
108 35
109 30
109 27
109 10
109 21
108 53
109 30
34 38
35 03
34 46
34 46
34 06
34 06
34 00
34 09
34 14
34 55
34 47
33 55
34 24
34 45
34 54
35 12
34 30
Owner
YRCC
SSB
YRCC
YRCC
SHB
SHB
SSB
SSB
SHB
SHB
SHB
SHB
SHB
SHB
SHB
SHB
SHB
SHB
SHB
SHB
SHB
Rem.
South
South
South
South
South
North
North
South
South
North
North
North
The flood peak correlation forecasting is based on the antecedent rainfall, local
rainfall and the coefficient of excess. The hydraulic characteristics can be included in
the scheme, which is easy in use. But the channel siltation and different hydraulic
characteristics of the main flow and overflow floods are not taken into account.
The river channel is wide and shallow in the lower Weihe. The cross section is
compound. Normal flow is through the main channel, while the high flood is
overflowing. Considering the different flood characteristics and the routing rules
through the main channel and overbank, a Muskingum layered routing scheme has
been developed. The layered outputs are calculated with the different parameters of
the main channel and the overbank, and subsequently added. The scheme parameters
can be optimized when overflow occurs in order to forecast the flood peak, the time it
occurs and its progression. Local inflow, however, is not considered. In fact, the flood
peak correlation and the Muskingum layered routing scheme are combined. The
forecast is also optimized on the basis of real-time rain and flow information so as to
improve the forecasting accuracy.
Currently also the national flood forecasting system (NFFS) is being studied and
tested for flood forecasting in the lower Weihe River, and will become operational
soon.
26
Chapter 2 – The Yellow River Target Areas
Table 2.5: Basic stations in the lower Weihe River
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
River name
Fenghe
Fenghe
Jianhe
Taipinghe
Taipinghe
Gaoguanyu
Shibianyu
Shibianyu
Xiangzihe
Dayuhe
Dayuhe
Fenghe
Weihe
Weihe
Weihe
Weihe
Wukonghe
Qinghe
Wangchuanhe
Wangchuanhe
Wangchuanhe
Wangchuanhe
Bahe
Bahe
Tangyuhe
Chanhe
Weihe
Yuchuanhe
Donggou
Juhe
Qishuihe
Wujiahe
Qishuihe
Qishuihe
Qishuihe
Zhaoshihe
Shichuanhe
Shichuanhe
Yeyuhe
Yeyuhe
Dongyuhe
Yeyuhe
Yeyuhe
Yeyuhe
Qingyuhe
Qingyuhe
Qingyuhe
Qingyuhe
Qingyuhe
Linghe
Qiuhe
Qiuhe
Chishuihe
Weihe
Shidihe
Juhe
Station name
Jiwozi
Qinggangshu
Bianzigou
Meichang
Taipingyu
Xingjialing
Xianrencha
Shibianyu
Wangqu
Banmiaozi
Xinguansi
Doumen
Mazhuang
Yaodian
Bayuan
Mujiayan
Muhuguan
Lanqiao
Yuchuan
Longwangmiao
Wangchuan
Gepaizhen
Pantaowan
Xiqu
Gaobaozhen
Mingdu
Tongyuanfang
Yuchuan
Qingcaoping
Miaowan
Jinsuoguan
Yunmeng
Chenlu
Huangbao
Shizhu
Potou
Caocunzhen
Guanshan
Anziwa
Qinhe
Nancun
Bujiacun
Kouzhen
yunyang
Xiaoqiu
Fangli
Fanjiahe
Sanyuan
Duli
Jinshanzhen
Houzizhen
Chongning
Longjiawan
Gushi
Huichi
Liulin
Station type
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
Rain gauging
hydrological
27
East long.
108 50
108 51
108 54
108 39
108 43
108 44
108 56
108 57
108 58
109 08
109 07
108 45
108 39
108 51
109 41
109 32
109 30
109 27
109 23
109 20
109 22
109 30
109 14
109 05
109 12
109 06
109 03
109 19
108 48
108 46
109 03
109 11
109 09
109 02
108 58
108 53
109 12
109 23
108 35
108 36
108 38
108 31
108 42
108 48
108 47
108 44
108 54
108 56
109 04
109 23
109 31
109 35
109 41
109 35
109 48
108 49
North lat.
33 52
33 55
34 04
33 56
34 00
33 53
33 56
33 59
34 05
33 57
33 59
34 14
34 26
34 24
34 09
34 11
34 03
34 06
33 58
33 54
34 05
33 55
34 13
34 18
34 02
34 08
34 33
34 21
35 17
35 10
35 13
35 13
35 02
35 01
35 04
34 52
34 54
34 42
35 01
34 55
34 53
34 56
34 42
34 38
34 55
34 50
34 44
34 37
34 38
34 17
34 16
34 23
34 25
34 38
34 25
35 03
Remark
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
south
north
north
north
north
north
north
north
north
north
north
north
north
north
north
north
north
north
north
north
north
north
south
south
south
south
south
south
north
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
28
Chapter 3 – Energy and Water Balance Monitoring System
3
ENERGY AND WATER BALANCE MONITORING SYSTEM
Meteorological satellites have been used mainly for weather analysis and forecasting.
Since the 1980’s new applications, related to the energy and water balance of the
earth surface, have emerged. Surface reflectance, measured in the visible wavelength
band (VIS) enables the estimation of the amount of solar energy that is absorbed by the
ground. Surface temperatures, measured in the thermal infrared band (TIR) enable the
assessment of the partitioning of this absorbed energy between sensible and latent heat,
the latter representing the evapotranspiration of water. Geostationary meteorological
satellites provide thermal infrared and visible data at 3 or 5 km resolution. Polar
orbiting meteorological satellites may also be used to measure planetary
temperatures. But, the lower repeat coverage makes them less suitable for cloud and
rainfall monitoring. The time of data capture, the large scan angles and the variable
imaging geometry makes them also less valuable for energy balance monitoring
In figure 3.1 the overview of the Energy and Water Balance System (EWBMS), as
used in this project, is shown. Images from the geostationary meteorological FY2c
and GMS satellites are received hourly. Cloud top level frequencies or "cloud
durations" are determined. From the hourly full image data, composites are prepared
which represent local noon and local midnight VIS and TIR values. The extracted
data are then processed to quantitative, spatially continuous image maps of rainfall,
radiation, sensible heat flux, temperatures and evapotranspiration. Besides the
satellite images, hardly any additional input is needed. Only ground point
precipitation data, used for generating the rainfall maps, are required. The actual
evapotranspiration, rainfall and temperature are the inputs for the drought monitoring
model, the freeze/thaw model and the Large Scale Hydrological Model (LSHM). The
latter is discussed in detail in chapter 4.
Theoretical backgrounds of the EWBMS and the generation of products is discussed
in section 3.1: System Components. To collect validation data, use is made of four
Large Aperture Scintillometers (LAS) and some additional instrumentation, which
has been established for this purpose at 4 sites in the Yellow River basin. The theory
and set-up of these measurements are presented in section 3.2: LAS measurements. In
section 3.3 the EWBMS software system is described. All modules: pre-processing,
basic product modules, application modules, analysis tools and the EWBMS
processing information metadata base are discussed briefly.
FY2c
GMS
Precipitation
ground
point data
Cloud
durations
Hourly VIS, TIR
Local noon
and midnight
composites
Pre-processing
Rainfall
Hydrological
model
Flow & Flood
forecasts
Evaporation
Drought
monitoring
model
Drought/
Desert.
indices
Temperature
Freeze/Thaw
model
Snow and
Snowmelt
Rainfall
mapping
Energy
balance
processing
Basic products
Applications
Figure 3.1: Energy and Water Balance System (EWBMS) overview.
29
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Section 3.4 discusses some results of the Catchment drought monitoring system.
Climatologic, hydrologic and agricultural drought indices are calculated and
presented in spatially continuous maps, and an analysis of the drought situation in the
Yellow River basin is made.
Section 3.5, Evaluation of EWBMS results is dedicated to the validation of the
EWBMS basic products. The satellite derived data, precipitation, air temperature,
sensible heat flux, and global and net radiation, are compared with data from the LAS
systems and other sources. Evapotranspiration is evaluated indirectly, since no such
data are measured regularly on the ground. It is assumed that if the validation results
of two components of the energy balance: sensible heat flux and net radiation, are
satisfactory, also the remaining component, obtained by subtracting the previous two,
can be trusted. A final approach to validation is by comparing the net precipitation,
i.e. rainfall minus evapotranspiration, with the river discharge at the outlet of the
catchment.
30
Chapter 3 – Energy and Water Balance Monitoring System
3.1
System Components
3.1.1
Pre-processing
The pre-processing calculates cloud durations and composes local noon and local
midnight images from the hourly VIS and TIR images, obtained with the satellite
receiving system (section 3.3.1). The cloud duration mapping uses only the TIR
images. The radiance of an observed object in the infrared spectrum, measured in
counts, is directly related to the temperature of that object. The object observed from
the satellite is the earth’s surface or the top of the highest clouds present. The cloud
temperature is proportional to the height above the ground: a typical lapse rate is –6.5
°C per 1000 m. Based on analysis of image histograms, four cloud level classes are
discriminated. The thresholds in TIR counts are converted to planetary temperatures.
The corresponding temperatures and heights are shown in table 3.1.
Table 3.1: Definition of cloud levels and corresponding temperatures and heights.
CLOUD LEVEL
TEMPERATURE RANGE
HEIGHT RANGE
Cold
< 226 K
> 10.8 km
High
226 – 240 K
8.5 – 10.8 km
Medium high
240 – 260 K
5.2 – 8.5 km
Medium low
260 – 279 K
2.2 – 5.2 km
For every hour a new TIR image is received, for each pixel is determined if there is a
cloud present and to which cloud class the cloud belongs. The results are stored in 4
files (one for every cloud class). These files are updated every day, so daily
multilevel cloud class frequencies are produced.
The main input data for the energy balance are the local noon and local midnight
composites, representing the situation at local noon or midnight in one image. In the
pre-processing these images are composed from the hourly VIS and TIR images. For
each pixel in the image, the time of local noon or midnight is calculated based on its
longitude position, and expressed in GMT (Greenwich Mean Time). Then, the two
hourly images closest in GMT-time to the local noon or midnight are selected. Pixel
values for the composite images are calculated by interpolating the pixel values of the
two selected hourly images. Both VIS and TIR local noon composites are produced,
but local midnight composites are only calculated with TIR images. When one of the
Figure 3.2: Example of a thermal infrared (left) and a visual (right) satellite
image with sea mask overlay.
31
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
two hourly images, closest in time to the local noon or midnight of a pixel, is not
present, no interpolation is carried out. Therefore, if many hourly images are missing,
the composite images will show distinct lines. When 3 or more subsequent hourly
images, necessary for the creation of the composite images, are missing, no
composite image for that noon or midnight day is created.
3.1.2
Precipitation mapping
The estimation of the spatially distributed precipitation is based on two sources of
information: (1) point precipitation data from meteorological stations and (2) cloud
frequency data from derived from the FY2c meteorological geostationary satellite.
Point data are obtained from the WMO Global Telecommunication System (GTS)
and additional meteorological station data from the China Meteorological
Administration (CMA). The GTS data consist of meteorological measurements from
approximately eleven thousand meteorological stations spread over the globe. 95% of
these measurements are available within six hours through the GTS. In China, there
are about 400 meteorological stations reporting precipitation through the WMO-GTS
network. CMA provides data from another 800 national stations.
In the past several methods have been developed to create rainfall fields from
meteorological satellite data. Well known is the so-called Cold Cloud Duration
(CCD) technique, which relates the presence of very high and “cold” clouds to rain
gauge measurements. Calibration is done on historical data sets. The CCD technique
is only suitable to estimate convective rainfall. The method of EARS differs in two
ways. First it uses four cloud levels and the so-called “temperature threshold excess”
in contrast to only one cloud level. So also lower cloud levels, associated with frontal
precipitation, are accounted for. Secondly, the method uses no calibration on the basis
of historic data, but combines rain gauge data and cloud durations in near real time.
EWBMS rainfall processing starts with the derivation of a multiple ‘local’ regression
between the satellite derived cloud data and the precipitation data for each pixel that
contains a rain gauge. This ‘local’ regression is based on the station under
consideration and its 12 nearest neighbours. The resulting equation for station j is:
Pj,est = Σ(aj,n · CDj,n) + bj
(3.1)
where CDn is the cloud duration (frequency) at cloud level n. The regression
equation, however, is an imperfect estimator of precipitation P. Therefore at each
station the residual Dj between the estimated and the observed precipitation is
determined:
Dj = Pj,obs – Pj,est
(3.2)
Subsequently, the regression coefficients aj,n, bj from (3.1) and the residual Dj from
(3.2) are interpolated between 6 precipitation stations, using a weighed inverse
distance method, so as to obtain the corresponding values for pixel i. The spatially
distributed precipitation is finally calculated pixel by pixel with:
Pi,est = Σ(ai,n · CDi,n) + bi + Di
(3.3)
Note that the estimated precipitation at the location of a station is always equal to the
reported precipitation. In the current project, which includes considerable parts of the
Tibetan plateau area, the previous technique has been extended so as to include
effects of altitude. Such effects are insufficiently present in the point rainfall data,
32
Chapter 3 – Energy and Water Balance Monitoring System
because the measuring stations are usually at relatively low altitude. It is known that
precipitation depends on the amount of precipitable water between the surface and the
tropopause at ∼11km. Therefore precipitation can be expected to be proportional with
the height or mass of the atmosphere column between the surface and the tropopause.
We have investigated these options. A correction based on height gave the best result
in the overall water balance of the upper Yellow river:
Pi,cor = Pi,est · (Htrp – Hpix) / (Htrp – Hstat,avg)
(3.4)
Where Pi,cor is the corrected rainfall, Htrp the height of the tropopauze, Hpix the
altitude of the pixel and Hstat,avg the average altitude of the 6 rain gauge stations
involved. In this way the precipitation at high altitudes, where no meteorological
stations are located, will be lower than at lower altitudes.
Figure 3.3: The energy balance of the earth surface
3.1.3
Energy balance monitoring
The purpose of the energy balance monitoring component of the EWBMS is to
determine the components of the surface energy balance, which reads:
LE = In – H – E – G
(3.5)
where: LE = latent heat flux (W/m2)
In = net radiation (W/m2)
H = sensible heat flux (W/m2)
E = photosynthetic electron transport (W/m2)
G = soil heat flux (W/m2)
Surface albedo and surface temperature are the main input data. The energy used for
the evaporation of surface water (LE) equals the net radiation energy provided to the
ground surface (In) minus the energy used for heating the air (H), the energy used by
vegetation for photosynthetic electron transport and the energy for heating the soil
(G). On a daily basis the soil heat flux can be considered zero (G≈0). Consequently,
the surface energy balance can be rewritten as:
33
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
LE ≈ In – H – E
(3.6)
Only noon and midnight satellite images are used for the processing of energy fluxes.
Fourier analysis of the daily solar cycle, a chopped cosine function, is used to relate
the noon value of radiation and sensible heat flux to daily averages. As additional
information the geographic coordinates and day number is required. This approach
assumes that atmospheric transmission remains unchanged during the day. A
correction to the daily radiation is applied based on cloud presence in the hours
around noon.
Atmospheric correction
By calibration of the VIS and TIR infrared digital values the planetary albedo
(reflectivity) and temperature are obtained, i.e. as observed through the atmosphere.
However, to calculate the different components of the surface energy balance, the
surface albedo and temperature are needed. To make this conversion, atmospheric
corrections procedures are carried out. Absorption and scattering of solar radiation in
the atmosphere cause the planetary albedo to differ from the surface albedo.
Absorption in the atmosphere is mainly due to water vapour. Scattering occurs as a
result of the presence of air molecules (e.g. N2, O2) and aerosols. Planetary
temperatures are lower than actual surface temperatures because of the absorption and
re-emission of infrared radiation by particularly water vapour. Scattering plays only a
minor role. The atmospheric correction procedures have been designed such that they
do not require information on atmospheric composition and stratification, but make
use of reference information in the image, for example visual contrast. Image contrast
will decrease with increasing atmospheric turbidity.
For the visible band we use the global radiation transmission model of Kondratyev
(1969). The model is applied to direct and diffuse solar radiation. Simultaneous
differential equations are formulated for downward and upward global radiation
fluxes separately. The down welling flux is direct; the up welling flux is diffuse
(figure 3.4). The radiation transmission model of Kondratyev only accounts for
backscatter and ignores absorption of radiation in the atmosphere. Atmospheric
absorption, however, is in the order of 10%. An absorption factor (k) was introduced
in the model. In the slab δτ in figure 3.4 the global flux is modified due to backscatter
and absorption. The approximate differential equations are:
Figure 3.4: Visualisation of downward and upward radiation fluxes.
34
Chapter 3 – Energy and Water Balance Monitoring System
δI/δτ = + a.I - b.J
δJ/δτ = - c.J + d.I
(3.7)
(3.8)
where:
a = (α+k) / cos(is)
b = 2α
c = 2(α+k)
d = α/cos(is)
α = Backscatter coefficient of light (≈ 0.1)
k = absorption coefficient of light (≈ 0.03)
is = solar zenith angle
The differential equations are solved analytically. As a result two functions may be
derived. One relates the surface albedo to the planetary albedo and the optical depth
(τ). The other relates the solar radiation transmission through a cloud free atmosphere
(t) to the surface albedo and the optical depth. The optical depth is an indication of
the amount of optical active matter in the atmosphere.
A
t
with:
= f(A’, τ)
= f(A, τ)
(3.9)
(3.10)
A’ = observed planetary albedo (-)
A = surface albedo (-)
τ = optical depth of the atmosphere (-)
Figure 3.5 shows the surface albedo and absorbed solar radiation as a function of
planetary albedo and optical depth. Solar radiation absorbed by the earth’s surface is
defined as 1 minus the surface albedo (1-A) times the transmission through the
atmosphere (t). Once the optical depth is known, equation 3.9 converts the planetary
albedo to a surface albedo for each pixel. The influence of the optical depth is highest
at minimum surface albedo, which is found in densely vegetated areas. To determine
the daily optical depth, the first step is to determine for every pixel in the image the
minimum 10-daily planetary albedo. Subsequently the “darkest” pixels with the
lowest planetary albedo are obtained. These are related to a minimum surface albedo.
When sufficient dense forest is present in the image, this value is typically 7%. From
the minimum planetary albedo and the minimum surface albedo, the optical depth can
be calculated. Having determined the optical depth, which is applied for the whole
image, the planetary albedo of each pixel can be converted to a surface albedo.
1.00
Absorbed solar radiation (1-A)*t
0.50
Surface albedo (A)
0.40
0.30
0.20
tau=0
0.10
tau=2.5
0.00
0.80
tau=0
0.60
tau=2.5
0.40
0.20
0.00
0
0.1
0.2
0.3
0.4
0.5
0
Plane tary albe do (A')
0.1
0.2
0.3
0.4
Plane tary albe do (A')
0.5
Figure 3.5: Surface albedo and absorbed solar radiation as a function of planetary
albedo and optical depth (τ). α=0.12, is=0, k=0.03
35
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The transmission model has been compared with global radiation measurements.
Some tuning of the EBMS global radiation output is required to achieve a better
match. For this purpose, two calibration coefficients have been introduced in (3.10):
t = C1 * f(A, C2 * τa)
(3.11)
There are two reasons to do so. First, the transmission through the atmosphere is
determined at noon. On a daily basis the effective transmission may be somewhat
lower because of lower solar inclinations during most of the day. Second, the
transmission of solar radiation through the atmosphere is on average less than in the
visible window. A best match with observed global radiation values has been
obtained with the following values C1 = 0.77 and C2 = 0.65.
For the thermal infrared band a different method of atmospheric correction is used.
The relation between the planetary temperature (T0') and the surface temperature (T0)
is described as:
(T0 − Ta ) =
wth:
(
k
T0' − Ta
cos(im )
)
(3.12)
k = atmospheric correction coefficient
im = satellite zenith angle
Ta = air temperature at the top of the atmospheric boundary layer (K)
The air temperature at the top of the boundary layer (Ta), is obtained on the basis of a
linear regression between the noon and midnight pixel temperatures, as illustrated in
figure 3.6. An estimate of the air temperature is found for the case of perfect heat
transfer so that T0,noon = T0,midnight = Ta. The top of the atmospheric boundary layer
varies at daytime usually between one and two kilometres. A map of the air
temperature at the top of the boundary layer covering the whole region is obtained by
applying this method to a shifting window of 200*200 km. In order to calculate the
correction coefficient, the driest pixels in the image are selected and are assumed to
correspond with the condition of no evapotranspiration. For each pixel a dryness
index (DI) is calculated, which is defined as follows:
noon planetary temperature (Kelvin)
360
350
340
330
To max
To' max
LE = 0
320
310
300
290
Ta
280
270
260
260
270
280
290
300
310
320
midnight planetary temperature (Kelvin)
Figure 3.6: Derivation of reference temperatures from the scatter gram of planetary
noon and midnight temperatures
36
Chapter 3 – Energy and Water Balance Monitoring System
DI =
T 0'−Ta
In
(3.13)
Where In is the net radiation. For the driest pixels in the image it is assumed that the
latent heat flux (LE) is zero and therefore the net radiation equals the sensible heat
flux (H). Once the sensible heat flux for the driest pixels is known, the corresponding
surface temperature can be calculated from the net radiation and air temperature with
T0 = Ta + In/α. Because in equation 3.8 the correction coefficient (k) is then the only
unknown variable, its value can be determined. The correction coefficient is applied
to the whole image. After the correction coefficient and the air temperature are
known, it is possible to calculate the surface temperature for each pixel. These surface
temperatures may then be used for calculating the sensible heat flux.
Cloud detection
The EBMS system calculates evapotranspiration for both cloudy and cloud free
conditions. A cloud detection algorithm is used which separates cloudy pixels from
cloud free pixels. When cloudy or partly cloudy pixels are erroneously classified as
cloud free, evapotranspiration will be overestimated. This has to be prevented as
much as possible.
For the purpose of cloud detection, threshold tests on visible and infrared images are
used. Four cloud detection criteria have been defined. If one of these four criteria
returns true, a pixel is flagged cloudy. The four tests are:
1)
2)
3)
4)
A’ ≥ A’min + threshold1
T0’noon ≤ T0’noon, max – threshold2
T0’noon ≤ T0’midnight – threshold3
T0’noon ≤ Ta
= minimum planetary albedo as observed in a dekad (-)
where: A’min
T0’noon, max = cloud free planetary temperature at noon (K)
The first and second cloud detection tests are based on a comparison between
observed pixel values and pixel values associated with a cloud free situation. To
obtain a cloud free minimum planetary albedo value, used in test 1, a minimum
albedo map is composed from a sequence of 10 daily noon VIS channel composite
images. It is assumed that during a 10-day period (a dekad) each pixel was at least
once cloud free. Test 1 can only be applied during daylight hours.
Test 2 compares planetary pixel temperatures with cloud free planetary temperatures.
A special methodology is applied to estimate the cloud free planetary temperature.
This method uses TIR channel data from a square window of 1000*1000 km. Within
this window the pixels with the highest planetary temperature are localized. It is
assumed that these pixels represent cloud free spots. The temperature for the centre
pixels is then calculated with a weighting distance averaging method. The application
of this method was demonstrated for Europe by De Valk et al (1998). Test 2 is
applied to detect cloudy pixels in noon and midnight TIR images. The first and
second test, separately detect both about 70% of cloudy pixels. In combination the
detection result increases to 80%. Test 3 and 4 add only few extra cloudy pixels to the
ones already detected by test 1 and 2. The threshold values used in test 1 and 2 have
to be determined empirically. De Valk et al (1998) determined threshold values for
test 1 and test 2. They found that a fixed threshold could be used both during winter
and summer.
37
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
After applying these tests relative evapotranspiration values at the edge of cloud
systems often appeared to be very high compared to the relative evapotranspiration
values a bit further from the cloud’s edge. This high relative evapotranspiration value
results from a low planetary temperature, suggesting that clouds still contaminate the
pixels. Therefore after processing of the energy balance products, an additional
procedure is used to detect such cloud contaminated pixels which were not flagged
cloudy by the four cloud tests. This procedure consists in finding pixels, which border
cloudy pixels and which have a significant higher relative evapotranspiration value
then the non-cloudy pixels in their surrounding. These pixels are flagged cloudy and
their energy balance is recalculated.
Global radiation
Global radiation transmission through the atmosphere (t) may be calculated with the
Kondratyev model. The global radiation at the Earth surface at noon (Ignoon) is then
obtained with:
I gnoon = t × cos(i ) × S
(3.14)
where: i = solar zenith angle at noon
S = intensity of solar radiation at the edge of the atmosphere (1355 W.m-2)
The next step is to convert the global radiation at noon (Ignoon) to the daily average
value of the global radiation (Ig). The conversion factor ν=Ig/ Ignoon is determined by
integration of the daily solar cycle and is a function of latitude and day number.
When a pixel is flagged cloudy, the solar radiation transmission through clouds (tc) is
calculated using a relation derived from Kubelka-Munk theory.
A' = ( 1 - t c ) × ACb + t c × A
2
2
(3.15)
Where ACb is the albedo of cumulus nimbus clouds (= 0.92). For the determination of
the surface albedo (A) on a pixel-by-pixel basis, a minimum dekadal ground albedo
map is composed from daily VIS images. It is assumed that during 10 days each pixel
was cloud free at least once. Figure 3.7 shows the relation between the planetary
albedo and tc for three different values of the surface albedo. Global solar radiation on
cloudy days at noon at the Earth surface (Ignoon) is then estimated by:
cloud transmission
1
0.9
0.8
0.7
surface albedo
0.6
0.08
0.5
0.2
0.4
0.32
0.3
0.2
0.1
0
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
cloud transmission (ratio)
Figure 3.7: Transmission of solar radiation through clouds
38
1.00
Chapter 3 – Energy and Water Balance Monitoring System
I gnoon = t c × cos(i ) × S
(3.16)
The conversion of global solar radiation at noon to the daily average value is the same
as in cloud free conditions.
Cloud duration
If the EBMS system only uses noon composite images, the radiation will be
overestimated if noon conditions are clear, while the morning and afternoon the
conditions are cloudy and reverse. In order to generate more accurate daily radiation
values, also hourly TIR images, depicting the amount of cloud cover between 9AM
and 3PM local time, are taken into account. Daily cloud cover information files are
generated, giving information on the degree of cloudiness between 9h and 15h. These
data fields are used as additional input and enable the calculated daily radiation
values to be corrected and improved significantly.
Detection of clouds in the TIR channel is done using threshold values, which are a
function of local time, day of the year and latitude. This, because in the morning
temperatures are lower and a pixels could be classified as cloudy if the threshold
value for noon was not adapted to the colder conditions in de morning. The same
applies for day of the year and latitude. Also taken into account is difference in
incoming solar radiation between morning/afternoon and noon. For adaptation the
radiation values that are solely based on the noon composites, the cloud cover
conditions at 11 hour and 13 hour local time are for instance more important than the
cloud cover conditions at 9 hour and 15 hour local time.
Net radiation
Net radiation (In) at the earth’s surface is calculated as the net result of the short wave
(solar) and long wave (terrestrial, thermal, infrared) radiative fluxes. Expressed in
terms of daily averages:
In = (1 – A)*Ig + Ln
Ln = ε0La – L0 = ε0 εa σ Ta4– ε0 σ To4
(3.17)
(3.18)
where: Ig = daily average global solar radiation at the earths surface (W.m2)
ε0La = absorbed downward long wave (thermal) radiation (W.m2)
L0 = long wave radiation emitted by the surface (W.m2)
ε0 = surface emissivity (-)
σ = Stefan-Boltzmann constant (W.m-2.K-4)
εa = atmosphere emissivity (-)
The net long wave radiation (Ln) is calculated from the surface and atmospheric
temperatures and emissivities. Land surface emissivities (ε0) generally vary between
0.85 (desert) and 0.95 (vegetation). We assume an average value of 0.9. The
atmospheric emissivity (εa) is derived with the empiric Brunt equation, using the
specific air humidity (Sa ) as input parameter:
εa = 0.58 + 2.73 * Sa0.5
(3.19)
Equation 3.18 may be transformed into:
39
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Ln = ε 0 σT04 − ε 0 σTa4 + ε 0 σTa4 − ε 0 ε a σTa4
≈ 4ε 0 σ T (T0 − Ta ) + ε 0 (1 − ε a )σTa4
3
(3.20)
= Hr + Lnc
Where T = (To + Ta )/2 is the mean temperature. On average the climatic net long
wave radiation (Lnc) is in the order of 80 W.m-2. The first term on the right hand of
(3.20) may be called the radiative heat flux (Hr). It depends on the temperature
difference between the surface and the top of the boundary layer. Hr may for practical
reasons be combined with the sensible heat flux (H) in the energy balance equation.
For the calculation of net radiation when a pixel is cloud covered, it is assumed that
the long wave radiation fluxes below clouds cancel (Ln = 0). Net radiation is then
estimated by:
In = (1 – A)*Ig
(3.21)
Sensible heat flux
At noon and at clear conditions, the heat exchange (H) with the atmosphere may be
calculated with:
H = Hc + Hr
H= αc (To - Ta ) + αr (To - Ta )
H = α (To - Ta )
(3.22)
αc = C * va
(3.23)
where:
αr = 4ε 0 σT
3
(3.24)
and
C = drag coefficient (W.m-3.s.K-1)
va = average wind speed (m.s-1)
ε0 = earth surface emissivity (-)
σ = Stefan-Bolzman constant (W.m-2.K-4)
T = Mean temperature (K) = (To + Ta ) / 2
The atmospheric sensible heat transfer coefficient (α) is the sum of the convective
sensible heat transfer coefficient (αc) and the radiative sensible heat transfer
coefficient (αr). Fixed values of the average wind speed and the earth surface
emissivity are used. Therefore, the difference between the surface temperature and
the air temperature determines the magnitude of the sensible heat flux (H).
Rewriting (3.22) results in:
H = (αc + αr ) (T0 – Ta )=Cva(T0 – Ta ) + 4ε0σT 3(T0 – Ta )
(3.25)
To relate the noon sensible heat flux value to the average daily sensible heat flux, it is
assumed that the Bowen ratio, i.e. the energy distribution, remains constant during the
day. Consequently the daily average sensible heat flux (H) is calculated from:
H = Hnoon * (In / Innoon)
(3.26)
40
Chapter 3 – Energy and Water Balance Monitoring System
The drag coefficient C, formerly taken constant, has been reformulated so as to take
account for the specific conditions in the Yellow river source area on the Tibetan
plateau:
C = (0.37 10-3 h+0.92) exp(-h/H)
(3.27)
where h the elevation of the surface and H the scaling height. The first term on the
right in (3.27) quantifies the effect of elevation on aerodynamic roughness of the area.
The second term represents the influence of decreasing air density with elevation.
Roughness is assumed to increase slightly with altitude when the terrain becomes
more irregular and the surface more complex at higher elevations. The effect of air
density is quantified by the scaling height H which was determined from the
relationship between air pressure and elevation:
H = - h /( log P – log Po)
(3.28)
where P the atmospheric pressure and Po the sea level atmospheric pressure. With Eq.
3.28 and using pressure measurements at four locations in the Yellow River basin, the
scaling height H was determined to be 8439 m.
Photosynthetic energy consumption
When vegetation is present, a part of the solar radiation is used for photosynthetic
electron transport (E) and the fixation of CO2. The amount of energy used may be
estimated with:
E = ε*(1-A) * Ig * Cv
with:
(3.29)
ε = photosynthetic light use efficiency on a daily basis (-)
Cv = fraction of the surface covered by vegetation (-)
The photosynthetic light use efficiency is estimated on the basis of the Photosystem
Deactivation Model (Rosema et al. 1998). The vegetation cover Cv is not known
independently. It is clear however that presence of vegetation is usually characterised
by high evapotranspiration values. We therefore use the relative evapotranspiration
(LE/LEp) as a proxy of crop coverage.
Cv = LE/LEp ≈ LE / (0.8*In)
(3.30)
Actual evapotranspiration
Having determined the net radiation (In), the sensible heat flux (H) and the
photosynthetic electron transport (E), the latent heat flux (i.e. the actual
evapotranspiration in energy units) is obtained as:
LE = In – H – E
(3.31)
The soil heat flux, on a daily time scale, may be neglected. If a pixel is flagged
cloudy, the surface temperature is not known. As a consequence the sensible heat flux
(H) and the latent heat flux (LE) cannot be calculated. In that case it is assumed that
the Bowen ratio (β = H/LE) is the same as on the last cloud free day. The Bowen ratio
is determined by moisture availability, which is assumed to remain constant. The
actual evapotranspiration is then estimated by:
41
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
LE =
In
(1 + β )
(3.32)
1.5 m Air temperature
The daily average air temperature at 1.5 m above the surface (T1.5m) is determined
with the air temperature at the top of the atmospheric boundary layer (Ta) and the
surface temperature (To). The daily average surface temperature To is defined as the
average of the surface temperature at noon (To,n) and the surface temperature at
midnight (To,m). It is calculated by means of a weighing function, which has been
derived by comparing the satellite derived with observed 1.5 m air temperature data
from 25 WMO-GTS stations in the Yellow River basin.
T1.5m = 5.73+0.58To+0.26Ta
(3.33)
T1.5m temperatures, for which the absolute difference with the decadal temperature is
larger than 10°C, are replaced with the 10-daily value to correct for erroneous
satellite temperature measurements.
3.1.4
Snow and snowmelt
Outputs of the precipitation mapping, discussed in section 3.1.2 and the energy
balance monitoring, discussed in section 3.1.3, are used to calculate snow storage and
snowmelt. In order to determine whether precipitation is rain or snow, the 1.5m air
temperature is used. Precipitation falls as snow when the air temperature is at or
below 0 °C. If precipitation is snow, it is added to the snow storage (SS). If the
temperature is higher than zero precipitation is rain. The decrease of snow storage
depends on the available latent energy LEa and the temperature.
When the temperature is at or below 0°C, the available latent energy LEa is used to
sublimate snow. The snow storage changes as a result of precipitation and
sublimation:
∆SS = P - LE/Ls
(3.34)
where the latent heat of sublimation Ls = 2833 J/mm. When the snow storage is
depleted the sublimated water is assumed to be withdrawn from the soil.
When the temperature is above 0°C, it is assumed that, as long as there is snow
available, potential sublimation occurs. If there is, after potential sublimation, any
latent energy and snow left, the remaining latent energy is used to calculate the snow
melt M:
M = (LE - LEP) / Lf
(3.35)
where the latent heat of fusion Lf = 333 J/mm. The energy used for potential
sublimation in this equation has been studied and is taken equivalent to 1mm water
per day. The change in snow storage is given by:
∆SS = -LEP/Ls -M
(3.36)
If at a certain day the snow storage is completely sublimated or melted, the remaining
energy is used for evaporating soil water.
42
Chapter 3 – Energy and Water Balance Monitoring System
3.1.5
Drought monitoring
The drought monitoring system uses the information from the energy balance model
and the rainfall mapping module to generate three different drought indicators: the
Climatic Moisture Index (CMI), the Soil Moisture Index (SMI) and the
Evapotranspiration Drought Index (EDI). These indices have different definition and
time scale and should be evaluated together to provide a comprehensive and complete
evaluation of the drought conditions. Drought originates from a rainfall deficiency.
Therefore rainfall and the CMI are important to quantify drought. Another, perhaps
even more useful parameter to quantify the severity of drought is the relative
evapotranspiration. Relative evapotranspiration (LE/LEP) is an output of the energy
balance module and is defined as the ratio of actual to potential evapotranspiration.
The SMI and EDI are based on relative evapotranspiration data. They indicate water
availability at the surface, in particular water availability to plants.
Climatic Moisture Index
The Climatic Moisture Index (CMI) is a numerical indicator of the degree of dryness
of a climate. The CMI is an aridity index that was proposed by UNEP in 1992,
serving to classify climatic regions that suffer from water shortage and
desertification. The CMI is defined as:
CMI = P / EP
(3.37)
where: P: annual precipitation in mm
EP: annual potential evapotranspiration in mm.
The index is multiplied by 100 and expressed in %. Five climatic zones are classified
on the basis of the following thresholds:
Climatic zone
hyper arid area
arid area
semi-arid area
dry sub-humid area
humid area
CMI
<5
5-20
20-50
50-65
>65
The CMI is a numerical indicator of the degree of dryness of the climate and provides
a scientific and practical indicator for desertification monitoring and combating. The
United Nations Convention to Combat desertification (UNCCD) has defined
desertification as “land degradation in arid, semi-arid and sub-humid” areas.
Therefore countries that have subscribed this convention must prepare a CMI map so
as to identify their national areas where the combat of desertification should be
focussed on, and which areas by consequence would be entitled to related funds.
Soil Moisture Index
In analogy with the UNEP Climatic Moisture Index, we have introduced the Soil
Moisture Index (SMI), in which the rainfall is replaced by the actual
evapotranspiration.
SMI = LE / LEp
(3.39)
43
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
where: LE : annual actual evapotranspiration;
LEP: annual potential evapotranspiration.
The index is multiplied by 100 and expressed in %. While the CMI is an indicator of
wetness of climate, the SMI reflects the dryness at the soil surface. The SMI is well
related to soil water content when vegetation is present. While the CMI only indicates
a climate type, the SMI also includes the effect of vegetation cover, soil type, terrain
slope, land use and other factors that influence the drought situation.
Evapotranspiration Drought Index
The Evapotranspiration Drought Index (EDI) is an agricultural drought indicator.
Agricultural drought occurs when there is not enough moisture available to meet the
needs of crops. The EDI is more than an indicator of soil water content. It also
integrates effects of soil properties, plant physiology and weather. In fact the relative
evapotranspiration and the EDI are directly related to photosynthesis and dry matter
production. It is therefore an excellent indicator of agricultural drought. The
agricultural drought is evaluated for two monthly periods, a suitable time scale to
represent the conditions during critical phases of the growing season.
EDI = LE2m / LEP2m
(3.40)
where: LE2m : 2 monthly actual evapotranspiration;
LEP2m : 2 monthly potential evapotranspiration.
The index is multiplied by 100 and expressed in %. Like the SMI, the agricultural
drought indicator is strongly related to the soil moisture content in the root zone, but
more important: it is a direct measure of crop dry matter production. Five different
agricultural drought classes have been defined according to the EDI value:
Agricultural Drought
Extreme drought
Severe drought
Moderate drought
Light drought
No drought
EDI
<30
30-50
50-60
60-80
>80
44
Chapter 3 – Energy and Water Balance Monitoring System
3.2
LAS measurements
Four Large Aperture Scintillometer (LAS) systems have been installed to measure the
sensible heat flux and the net radiation. The scintillometer is an optical device used to
monitor the fluctuations in the refractive index Cn2 of the turbulent atmosphere over a
relatively large area (Meijninger 2003). Cn2 is derived from the relative intensity
fluctuations of a received signal. With additional data from a meteorological weather
station installed with each LAS system, heat fluxes from the surface layer into the
atmosphere can be calculated. The advantage of the LAS is that it measures the
sensible heat flux along a path up to a few kilometers length. The scale of the
measurement is thus fairly similar to that of the satellite pixel. Combined with
measurements of net radiation, the actual evapotranspiration can be derived as the
remaining component in the energy balance (see section 3.1.3).
3.2.1
LAS theory
When an electromagnetic (EM) beam of radiation propagates through the atmosphere,
it is distorted by small fluctuations in the refractive index of air (n). The refractive
index fluctuations lead to intensity fluctuations of the beam, known as scintillations.
The scintillations measured by the LAS instrument are expressed in terms of the
structure parameter of the refractive index of air Cn2. Temperature (T), humidity (Q)
and to a lesser extend pressure (P) fluctuations in the atmosphere cause air density
fluctuations and, as a result, fluctuations in the refractive index of air. The structure
function parameter of the refractive index Cn2 can be decomposed into the structure
parameters of temperature CT2, humidity CQ2 and the covariance term CTQ in the
following way (Hill et al. 1980):
C 2n = A T2
C T2
T
2
+ A Q2
C Q2
Q
2
+ 2A T A Q
C TQ
TQ
(3.41)
AT and AQ are a function of the wavelength λ and the mean values of temperature,
specific humidity and atmospheric pressure. In the visible and near infrared
wavelength region of the EM spectrum the coefficients AT and AQ are defined by
(Andreas 1989):
P
A T = −0.78 ⋅ 10 −6 + 0.126.10 −6 R v Q
T
(3.42)
A Q = −0.126 ⋅ 10 −6 R v Q
(3.43)
Rv is the specific gas constant for water vapour (461.5 J K-1 kg-1). Because under
normal atmospheric conditions AT is much larger than AQ the contribution of
humidity related scintillations is much smaller than temperature related scintillations.
Therefore a simplified expression can be derived in which CT is expressed as
(Kohsiek 1982b):
CT = Cn
2
2
T 2  0.03 
1 +

2 
β 
AT 
−2
(3.44)
where β is the Bowen ratio, included in a correction term for humidity related
scintillations. The Bowen ratio is the ratio between sensible heat (H) and latent heat
flux (LE) and is large (>3) over dry areas. This means that the correction term in Eq.
2.4 is small. When surface conditions are very dry, CT2 is directly proportional to CN2:
45
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
CT = Cn
2
2
T2
(−0.78.10 −6 P) 2
(3.45)
Once CT2 is known, the sensible heat flux can be derived from similarity relationships
that have been derived for CT2 which are based on the Monin-Obukhov similarity
theory
C T (z LAS − d) 2 / 3
2
θ 2*
z −d

= c T1 1 − c T 2 s

L 

−2 / 3
(3.46)
d is the zero-displacement height, zLAS is the height of the scintillometer beam above
the ground, cT1 and cT2 are empirical constants, θ* is a temperature scale defined as:
θ* =
−H
ρc p u *
(3.47)
and L is the Obukhov length:
L=
Tu *
k v gθ *
(3.48)
ρ is the density of air, cp the specific heat of air at constant, u* the friction velocity, kv
the von Karman constant (~0.40) and g the gravitational acceleration (9.81 m.s-2). The
set of equations is completed by the expression for the friction velocity u*:
u* =
k u (z u )
z −d
z −d
z 
 − Ψm  u
ln u
 + Ψm  0 
L
 L 
 z0 
(3.49)
Where, u is the mean wind speed, zu is the height above the ground where the wind
speed is measured and Ψm is the integrated stability function for momentum
(Panofsky and Dutton 1984). With data on CT2, mean wind speed u at one height,
mean absolute temperature T and an estimate of the roughness length z0, the sensible
heat flux can be determined iteratively from the combination of (3.46) to (3.49).
However, under conditions of free convection u* is no longer relevant and the
sensible heat flux can be derived from scintillometer data only in combination with
the mean absolute temperature (T):
1/ 2
g
H = ρc p b( z s − d ) 
T
(C )
2 3/ 4
T
In this equation b is an empirical constant which equals 0.48 (Kohsiek 1982b).
46
(3.50)
Chapter 3 – Energy and Water Balance Monitoring System
3.2.2
LAS equipment and installation
In August 2005, four LAS systems were installed in the Yellow River basin. Three
locations on the Qinghai-Tibetan Plateau in the upper reaches were chosen: Maqin,
Tangke and Xinghai, located at altitudes above 3000 m. A fourth LAS system was
installed in the WeiHe catchment, in the Sanmenxia region of the Loess Plateau, at
Jingchuan (see figure 3.8). The coordinates and altitudes of the four stations are given
in Table 3.2. Each LAS-ET system consists of two parts: a scintillometer and a
weather station. The structure parameter CN2 is measured by the scintillometer and
additional meteorological data from the weather stations are used to determine the
sensible heat flux H. A data logger (Combilog 1020) collects the signals from the
meteorological sensors and from the scintillometer.
Table 3.2: Coordinates and altitude of the four LAS stations
LAS site
Latitude
Longitude
Jingchuan
35˚ 20’ N
107˚ 21’ W
Maqin
34°28’ N
100°14′ W
Xinghai
35°36’ N
99°59′ W
Tangke
33°24’ N
102°28’ W
Altitude (m)
1061
3725
3327
3445
The weather station consists of two resistance thermometers, a wind speed sensor
mounted at the top of the meteorological mast, a net radiation (NR-Lite) sensor and a
barotransmitter. Two air temperature sensors (shelter platinum resistance
thermometers) are mounted on the meteorological mast at 1m and 3 m above the
surface. The difference between upper and lower temperature indicates the stability of
the atmosphere; the calculation method of the sensible heat flux is different for stable
and unstable conditions. Each LAS station is also equipped with a wind speed sensor,
mounted on top of the meteorological mast at about 4 m above the surface. Wind
speed data are needed to determine the friction velocity u* in (3.47). The wind speed
is measured electronically with a 3-D cup assembly reflecting light barrier frequency
output which has an accuracy of ± 0.3 m/s.
Figure 3.8: Location of the four LAS-ET systems: Xinghai, Maqin, Tangke and
Jingchuan
47
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The NR-Lite net radiometer has an upward and downward facing conical shaped
sensor surface of black coated Teflon that measures the difference between the
downward and upward short-wave and long-wave radiation fluxes. The down facing
sensor reading is automatically subtracted from the up facing sensor value and
converted to a single output signal. A pressure sensor is also installed in the data
logger protection box. The sensor is a barotransmitter with a piezo-resistive sensing
element and has an accuracy of ± 1hPa. Measurements of atmospheric pressure are
needed to derive the structure parameter of the temperature (CT2) from the structure
parameter of refractive index of air (Cn2), as given by equation (3.45).
In 2006, power supply problems occurred at the LAS stations on the plateau, which
worked on solar panels. This happened during the night and in the early morning on
days with high cloud cover. Solar power supply from the panels appeared insufficient
to keep the system operational during the entire day. To prevent fall out of the
systems it was decided to reduce daily power. An overview of the daily power
consumption of the LAS system is given in table 3.3. The table shows that the power
consumption of the heater of the LAS window is very high (36 W) compared to the
other components. A programmable timer was connected to the heater of the LAS
instrument window reducing its functioning to 4 hours (12 Ah) per day. This
intervention reduced the daily power consumption of the LAS-ET system from 97.2
Ah to 37.2 Ah. The heater is needed to keep the window free from condensation
which is important for an optical instrument like the LAS. At the plateau, rainfall is
not so high and relative humidity is quite low so the heater is switched on only in the
morning from 5h00 until 9h00 local time to prevent dew and condensation on the
window.
Table 3.3: Daily power consumption of LAS-ET system instrumentation
Power (A)
Time (h)
Combilog Data logger
0.004
24
Heater of the wind speed sensor
0.5
24
Heater of the air pressure sensor
0.01
24
LAS
0.5
24
Heater of the LAS window
18
24
3.2.3
(Ah)
0.096
12
0.24
12
72
LAS measuring sites
The LAS stations measure the scintillation as well as wind speed, air pressure, net
radiation and temperature. 10 Minute averages are stored on a Combilog 1020 data
logger. In Maqin, Xinghai and Tangke, on the Qinghai plateau, the LAS systems are
measuring from April to October. The measuring equipment is brought indoor during
the winter months to protect it against the extreme low temperatures (< -20°C) and
high wind speeds. The system on the Loess plateau in Jingchuan reports the entire
year and is measuring uninterrupted since August 2005. The Upper Reach Branch
Hydrology Bureau of YRCC in Lanzhou is responsible for the three LAS stations on
the Qinghai-Tibetan Plateau. The Hydrology and Water Resources Bureau of
Sanmenxia is taking care of the LAS station in Jingchuan, Henan province. A detailed
specification of the LAS stations is presented in Annex 1.
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Chapter 3 – Energy and Water Balance Monitoring System
3.2.4
Data collection
The head of the local hydrological bureau is in charge of maintaining the LAS-ET
and keeping the system operational. Every two weeks the data are downloaded and at
the same time the system is inspected. The LAS data are downloaded by replacing the
PCMCIA memory card or connecting a laptop to the RS232 interface of the
Combilog data logger. The physical conditions of all sensors are checked for
malfunctioning. If necessary, the sensors are cleaned and any dirt is removed. The
condition of all cables and wires is checked. Weather conditions, changes in
configuration of the system, changes in the landscape at the site, as well as possible
malfunctioning are reported in a LAS operations form. After downloading the data all
information is send to the respective hydrological bureau and then to the Yellow
River Hydrology Bureau in Zhengzhou and EARS, as shown in figure 3.9.
Jingchuan
Xinghai
Maqin
Tangke
Upper Reach
Hydrology Bureau
Sanmenxia
Hydrology Bureau
Zhengzhou
Hydrology Bureau
EARS
Delft
Figure 3.9: LAS data collection and data transfer
3.2.5
Data processing
With the EVATION software provided with the LAS by Kipp&Zonen in Delft,
Netherlands, the collected scintillometer and meteorological data are processed to
fluxes of sensible heat and actual evapotranspiration. The software interface is shown
in figure 3.10. Before running the software, the user selects a working directory for
each of the four LAS systems. EVATION automatically creates the subdirectories
“input”, “output”, “config” and “auxiliary”. The configuration parameters of each
LAS system are set in the configuration tab sheet (figure 3.10 right side). These
include the surface profile, the height above the surface of the receiver and the
transmitter, the wind speed and temperature sensor data. Also information on path
length and terrain characteristics (roughness length, etc) is added.
The data files from the Combilog contain 10 minute averages of the scintillometer
signals and the meteorological sensors. To be able to compare with the daily
EWBMS products, daily averaged values are calculated with EVATION.
49
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 3.10: Interface of the EVATION software
Some stations on the Tibetan plateau however were not logging continuously during
the night due to power failures in 2005 and 2006. These input data need some
additional manual pre-processing to fill up the gaps in the data sets. For each month
hourly averages were calculated from all available data in 2007, i.e. a matrix of 12 by
24 average values was created. These values were then used to replace the missing
data. However, for the specific purpose of validation (section 3.5.3), only days with
more than 80% of the actual measurements available were used.
Additional pre-processing was necessary in relation to the temperature measurements.
The EVATION algorithm uses the temperature difference to determine whether the
atmosphere is stable or unstable. However in some cases the temperature difference
did erroneously not change sign and could not be used as such. Therefore an
alternative method was used based on the sign of the measured net radiation In. The
raw data input into the EVATION software was modified as follows:
If In > 0
If In < 0
then T- = T+ + 0.5 °C
then T- = T+ - 0.5 °C
Where T- is the value of the lower temperature sensor and T+ that of the upper
temperature sensor. As a result the sign of temperature difference is determined by
the change in sign of the net radiation and the EVATION software will apply the
correct formula for the calculation of sensible heat flux H.
3.2.6
LAS results
Some examples of ten minutes average data of temperature, wind speed and pressure
signals, which were collected at Jingchuan in April 2007 are shown in figures 3.11 to
3.14. Figure 3.13 shows the structure parameter of the refractive index Cn2 as
measured by the scintillometer, and the demodulated carrier signal UDEMOD. The latter
is not used in the calculations, but it is an important parameter for monitoring the
signal transmission along the scintillometer path and the related quality of the Cn2
measurements.
50
Chapter 3 – Energy and Water Balance Monitoring System
30
Temperature (°C)
25
20
15
10
5
0
-5
T diff (Tlow - Tup)
T upper
T lower
-10
05.04.2007
01:30:00
06.04.2007
01:30:00
07.04.2007
01:30:00
08.04.2007
01:30:00
09.04.2007
01:30:00
Figure 3.11: Temperature daily variation upper and lower sensor in Jingchuan
14
950
940
12
Air Pressure
920
910
8
900
6
890
880
4
Air Pressure [hPa]
Wind Speed [m/s]
930
Wind Speed
10
870
2
860
0
850
02.04.2007
00:00:00
02.04.2007
00:00:00
03.04.2007
00:00:00
04.04.2007
00:00:00
05.04.2007
00:00:00
Figure 3.12: Ten minute averages of wind speed and air pressure in Jingchuan
0
Cn2
Signal Strength
-100
75
-200
50
-300
25
-400
0
02.04.2007
00:00:00
-500
02.04.2007
00:00:00
03.04.2007
00:00:00
04.04.2007
00:00:00
05.04.2007
00:00:00
Figure 3.13: Structure parameter of the refractive index of air Cn2 and the
demodulated carrier signal in Jingchuan
51
Signal Strength (mV)
Refractive Index Cn2 (m-2/3)
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
3.3
EWBMS software system
The EWBMS software needs two types of input data: specially prepared satellite
images and WMO-GTS rain gauge data. To obtain effective precipitation and
freeze/thaw products for the large scale hydrological model, a sequence of three
processing steps has to be followed as shown in figure 3.14. The EWBMS software is
supplemented with utility software tools for quick evaluation of the results, display of
output products and conversion of outputs into the ASCII format of the Large Scale
Hydrological Model. All software modules are called from the main EWBMS menu
bar (figure 3.15). A module is started with a single click on an icon. The format of the
output data fields is generic 8 or 16 bit raw binary data. This format can easily be
imported in most GIS software systems like ArcMap or Idrisi. The EWBMS is
accompanied with the Imageshow 2 GIS tool, specially developed to view in an easy
and convenient way the generic binary output data sets and to do some basic
calculations and analysis with the data.
Figure 3.14: EWBMS processing flow chart
Figure 3.15: EWBMS main menu bar
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Chapter 3 – Energy and Water Balance Monitoring System
All processing times the processing information and metadata from the various
software modules are stored in the EWBMS processing data base. Changes in
the user interface settings of the modules are saved on each pc in files with the
extension *.ini. EWBMS core software modules like the Energy Balance and
Rainfall module need a licensed hardware key to run. Application processing
tools and analysis tools are not protected and will also run without the
hardware key.
3.3.1
Satellite data reception and pre-processing
The Cloud Durations and Image Composition module of step 1, has been developed
to create the input data that are needed to run the EWBMS precipitation and energy
balance monitoring modules. It produces noon and midnight composites from visible
(VIS) and thermal infrared (TIR) satellite images. These composites are used as input
for the energy balance monitoring software. The module also produces daily and six
hourly cloud duration maps, which are used as input for the rainfall mapping
software, as well as daily cloud cover information maps, used in the energy balance
monitoring software.
Cloud duration maps
The cloud duration maps are used as input for the precipitation mapping module
(section 3.1.2). They contain the number of hours that clouds of a certain cloud height
class have been detected on a location. Cloud duration maps are based on
classification of hourly TIR images, and stored as daily and six hourly files. All
available hourly TIR images are used for cloud duration mapping. The more hourly
TIR images are available, the more reliable the output of the precipitation mapping
module is.
Figure 3.16: Output tab sheet of the cloud durations and image composition
module.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 3.17: Cloud duration image for the medium low cloud class
(June 12, 2008).
Daily cloud cover information maps
The daily cloud cover information maps are used as secondary input files by the
energy balance monitoring module. They represent the amount of cloud cover from
9AM to 3PM solar time. The maps are used to improve the radiation values (and thus
all derived energy fluxes), that are calculated by the energy balance module.
Noon and midnight composites
The inputs for the noon and midnight composite images are the hourly VIS and TIR
images received. Each day one VIS composite file (noon) and two composite TIR
files (noon and midnight) are produced. In these composite images, the local noon (or
midnight) values are extracted for each pixel by interpolation from the hourly images.
See also section 3.1.1.
3.3.2
Rain gauge data reception and pre-processing
The rainfall mapping software uses cloud duration files and rainfall data from
meteorological stations (WMO-GTS and CMA stations). The rainfall mapping
software runs either with a time step of 6 hours or 24 hours. The CMA sends coded
files with data covering 1 to 24 hours (so called Synops files). These files are
decoded, error filtered and aggregated to the proper time scale. The GTS Synops
Decode software produces a database with daily and six hourly meteorological data.
Apart from the GTS and CMA stations, YRCC also has its own station network. The
GTS Synops Decode module enables the user to import these data to the database
used for precipitation mapping.
54
Chapter 3 – Energy and Water Balance Monitoring System
Figure 3.18: The settings tab sheet of the GTS synops decode module enables
erroneous data filtering.
3.3.3
Precipitation Module
The Precipitation module calculates spatially continuous precipitation maps based on
rain gauge measurements and satellite derived cloud durations. The input tab sheet is
shown in figure 3.19. The GTS Synops Decode module (section 3.3.2) provides a
database with precipitation ground point measurements that is used as input for the
Precipitation module. The cloud duration files are generated by the Cloud Duration
and Image Composition module (section 3.3.1). The precipitation map contains daily
or six hourly spatially continuous precipitation values in mm. Apart from the
precipitation map, a comma-separated quality check file is generated. The quality
check file gives information to evaluate the accuracy of the rainfall map. For instance,
reported rainfall and predicted rainfall figures are given. The predicted rainfall was
calculated through a jack-knifing procedure: rainfall at the location of a rainfall
station is calculated without using data from the rainfall station itself. For analysis,
the jack-knifing file can be loaded into MS Excel. To extract certain stations for a
longer period, the jack-knifing analysis tool may be used.
3.3.4
Energy Balance Module
The Energy Balance Module (EBM) has been developed to derive energy balance
products from geostationary satellite imagery, collected in the visible and thermal
infrared. The module can produce daily and 10 daily-averaged maps of
evapotranspiration, radiation, sensible heat flux, temperature, albedo, photosynthetic
light use and cloud cover. The methodology is described in detail in section 3.1.3.
Input
The main inputs required to run the Energy Balance Mapping module are the noon
and midnight visible and infrared composites. The daily cloud cover information
files, generated with the Cloud Duration and Image Composition pre-processing
55
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 3.19: Input tab sheet of the Precipitation Module.
module, are used in addition to improve the results. Next to the composite files and
the cloud duration maps, additional information like sea mask map, satellite settings,
relative humidity file, digital elevation model and calibration data is needed. The sea
mask map shows the location of land and water (sea, rivers, lakes). Only land pixels
are processed. The digital elevation map is used to correct the optical depth and the
air density for altitude. The daily relative humidity values used are average climatic
averages for the whole region.
Both VIS and TIR images have attached a footer containing a calibration table
determined pre-launch. The VIS calibration in the footer of the FY2C images is not
realistic in space and an after launch alternative vicarious calibration table for the VIS
channel is used instead.
Output
The Energy Balance module can generate daily and ten-daily averaged output data
fields of latent heat flux, boundary layer temperature, cloud mask, surface albedo,
potential evapotranspiration, actual evapotranspiration, global radiation, net radiation,
photosynthetic light use, relative evapotranspiration, noon surface temperature and
1.5 m air temperature. The required outputs are chosen on the daily and dekadely
output product tab sheets shown in figure 3.21 and 3.22.
56
Chapter 3 – Energy and Water Balance Monitoring System
Figure 3.20: Main window of the Energy Balance module
Figure 3.21: Daily output products tab sheet..
57
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 3.22: Dekadely output products tab sheet.
3.3.5
Freeze/Thaw module
The Freeze/Thaw module calculates spatially continuous winter products. Based on
inputs from the precipitation module (section 3.3.3) and the energy balance module
(section 3.3.4) snow height, snowmelt and snow cover are calculated. Figure 3.23
shows the input tab sheet of the module. To define freezing/melting and snow/rainfall
conditions, the 10 daily-averaged temperatures from the energy balance module is
used. To calculate the snow height, snowmelt, sublimation/evaporation, precipitation
from the precipitation module, and daily actual evapotranspiration from the energy
balance is used.
Figure 3.24 shows the output tab sheet of the freeze/thaw module. The snow cover
output maps show where the earth’s surface is covered by snow and for how many
days already. The snow height maps show how much snow there is, and the snowmelt
maps how much snow has melted on a certain day (both in tenths of mm water).
Since snow is not available for run-off, the input for the hydrological model (chapter
4), also called effective precipitation, is redefined. Melt water is included, while
snowfall is excluded from the ‘effective’ precipitation. Sublimation from the snow
pack is excluded for the same reason. The total water balance will be slightly
different from the situation without the freeze/thaw module, since sublimation uses
more energy than evapotranspiration, and thus less energy is left for evaporation
when the freeze/thaw module is used.
58
Chapter 3 – Energy and Water Balance Monitoring System
Figure 3.23: The input tab sheet of the freeze/thaw module.
Figure 3.24: The output tab sheet of the freeze/thaw module.
59
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 3.25: Settings tab sheet of the Drought Monitoring System
3.3.6
Drought Monitoring System
The Drought Monitoring system generates drought information on a pixel-by-pixel
basis. The module can generate three different drought indices: CMI, SMI and EDI.
More explanation on the drought indices is given in section 3.1.5.
Input
The Drought Monitoring module needs actual evapotranspiration and potential
evapotranspiration information from the Energy Balance Module. To calculate the
Climatic Moisture Index (CMI), also rainfall products are needed as an input. The
user has to specify the processing period and in which directory the energy balance
and the rainfall products are stored.
Output
The user can choose the desired drought indices and the desired output directory in
which the drought products are stored. The processing period of the module is set to
one year for generating CMI or SMI. For EDI, the processing period is two months at
least. The output products are expressed in %.
60
Chapter 3 – Energy and Water Balance Monitoring System
Figure 3.26: Selection of output products in Drought Monitoring tab sheet.
Figure 3.27: Structure of the Access EWBMS processing information database
61
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
3.3.7
Processing information database
Every time the user runs a EWBMS software module on his computer, the processing
information is automatically stored in a Microsoft Access metadata base called
‘EWBMS processing database’. The processing information database holds
information on execution time, input data specifications and the generated output
data, quality of the input and output data, values of daily changing variables in the
energy balance mapping module, etc. The database does not hold information on
analysis work done with ImageShow2.
Figure 3.27 gives an overview of the various tables in the EWBMS processing
Access database. The main table ProcessingJobs stores the basic processing
information. Here the settings and time of execution of all EWBMS processing
modules are stored. Every time the user runs an EWBMS module, one line of
processing information is added to this table. The main table is linked to seven other
tables that contain specific processing information on each of the EWBMS processing
modules. More information on this metadata base may be found in the EWBMS user
manual (EARS 2008).
3.3.8
Imageshow-2 analysis tool
The processing modules are the core of the EWBMS software. Next to the processing
modules an additional analysis tool, ImageShow2 is provided. ImageShow2 is a GIS
tool, intended for quick evaluation, analysis and post processing of the EWBMS
products. Figure 3.28 shows the interface of ImageShow2, which has the following
main functions.
1. Display: quick and easy display of the generic raw binary format EWBMS
products. Several additional functionalities are available: scaling, zoom,
histogram, display of boundary overlays, display of legend, creating a scatter plot
with a second input map, etc.
Figure 3.28: Interface of the Analysis software tool ImageShow2
62
Chapter 3 – Energy and Water Balance Monitoring System
2. Calculation: calculations on a series of maps or on a single map. It is
possible to calculate an average map or a sum map for a specified time period.
It is also possible to perform calculations (subtract, sum, multiply, divide) on
two different maps or on a single map.
3. Point Value: find a value for a location with given latitude and longitude.
Additionally a time graph of pixel values for a given location can be extracted from a
series of maps. The time series may then be saved as a comma separated file for
further analysis in e.g. Excel.
4. Polygon Averaging: calculation of country, provincial or county averages of
EWBMS products. Several secondary maps with the location of countries, zones,
provinces or agricultural areas can be used.
5. Classification: changes the digital numbers in EWBMS output products into single
values or classes. A spatially continuous map can be transformed into a categorical
map or a zoning map.
6. Export: exports the EWBMS products from generic raw binary file to a png, jpg,
bmp or gif format and to geographic referenced tif or ASCII grid files. The various
formats permit to import EWBMS products easily into other general purpose GIS
software like ArcMap, Ilwis, ERDAS Imagine or Idrisi.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
3.4
Catchment drought monitoring system
To provide for a comprehensive and complete evaluation of the drought
conditions and water use in the Yellow River basin, three different drought
maps may be produced: the CMI map, showing climatic drought, a
hydrological water balance map and the agricultural drought index EDI map.
Although the different types of drought originate from the same precipitation and
water resources deficiency, climatic, hydrological and agricultural drought events
may come to expression at different times. For example, rainfall shortages may be
immediately noted in the agricultural sector, but the impact on reservoir levels may
not affect hydroelectric power production for many months.
A drought monitoring bulletin for the Yellow River has been developed (section 5.2),
which may be updated and distributed regularly, thus keeping up with the
development of the agricultural, hydrological and climatic drought situation.
Information on agricultural drought and a detailed spatial assessment of the water
balance in the entire Yellow River basin is to be provided on a monthly basis.
Climatic drought is reported yearly.
Figure 3.29: Climatic zones and aridity in the Yellow River basin in 2006 (left) and
2007 (right)
3.4.1
Climatic drought
The climatic moisture index or the aridity index can be used to monitor yearly
changes and long term desertification related climatic trends for the entire Yellow
River basin. The CMI map for 2006 and 2007 with the classification of climate types
of is shown in figure 3.29. It is clear that there are slight differences in aridity
between the two years. Note the difference in classification of the large irrigated area
in the basin at Yinchuan, which received less rainfall in 2006. Differences are also
seen in the southern part of the basin, which was more humid in 2007 than in 2006.
The delineation of the climatic zones will usually be based on more than one year.
Programs to combat or prevent desertification, like planting of trees, respond to long
term developments. For the design of such programs longer term CMI maps of the
Yellow River basin should be created, while in the implementation phase the most
recent CMI maps may be useful to adapt or fine tune the activities.
64
Chapter 3 – Energy and Water Balance Monitoring System
Figure 3.30 shows the average CMI map for 2006 and 2007. When more satellite data
are available in the future, the long term average climatic moisture map or
desertification map may be calculated from five or ten years of data. The map defines
the arid, semi-arid and dry sub-humid zones, where desertification may occur, and
which are indicated for prevention of desertification and land degradation. Every five
or ten years, the climatic zone map should be updated with the most recent EWBMS
rainfall and evapotranspiration data. Regularly updated CMI maps allow focusing
attention and action to those regions that need prevention of desertification the most.
Figure 3.30: Climatic zones of the Yellow River basin (average of 2006 and 2007)
3.4.2
Hydrological drought
Hydrological drought refers to deficiencies in surface and subsurface water supplies
and is noted in stream flow amounts, reservoirs and ground water levels. The shortage
in water affects reservoir levels for hydropower, water use for irrigation, stream flows
and water levels for navigation, recreational water use and ground water levels. Water
availability in the Yellow River basin is highly irregular in time and geographically
unevenly distributed. Timely and regular assessments of the resources in the entire
basin are needed to prevent or reduce impacts from hydrological droughts as much as
possible.
The frequency and severity of hydrological drought in the Yellow River basin is
quantified by the water balance in the basin and its sub-catchments. The water
balance of a catchment is calculated by subtracting the total EWBMS actual
evapotranspiration from the total EWBMS precipitation during the hydrological year
at sub-basin level. An example of a detailed inventory of water resources in the
Yellow River basin is given in figure 3.31. The map shows the water balance for
three recent ‘hydrological’ years. The map informs on the amount of water each
component of the river network receives. At the same time it indicates the water
resources that are available for irrigation, hydropower, urban and industrial use at sub
basin level. Note that this hydrological drought indicator is not based on rainfall only,
like most classical indicators (SPI, PAI, etc) but also on measured actual
evapotranspiration. This information is essential because in the northern part of the
basin the evaporation losses can be twice the precipitation, while in the whole basin
about 70% of the rainfall is lost by evapotranspiration.
65
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
(a) July 2005 – June 2006
(b) July 2006 – June 2007
(c) July 2007 – June 2008
Figure 3.31: Hydrological drought in Yellow River sub basins: water balance in mm
for the last three hydrological years.
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Chapter 3 – Energy and Water Balance Monitoring System
The water balance maps for the three years are clearly different and thus water
resources for industry, irrigation, etc. vary considerably from year to year. In the
south-east of the basin there was a considerable surplus in 2007/2008 and 2005/2006.
But in 2006/2007 evapotranspiration offsets the rainfall. In the northern part of the
basin the simultaneous changes are opposite. Also in the eastern part of the basin
there are clear differences between the three years. In 2007/2008 the water resources
are much higher than during the previous years. However, in the western source area
of the river, the changes are opposite and the water budget has decreased during the
past three years.
3.4.3
Agricultural drought
Agricultural drought occurs when there is not enough water available to meet the
needs of the crops. Agricultural drought leads to reduced crop yields. An analysis of
agricultural drought in the Yellow River basin during 2008 has been made using EDI
maps, as discussed in section 3.1.5. EDI classified maps have been created for the
first and second part of the growing season, i.e. for May-June and July-August of
2008. See figures 3.32 and 3.33.
There is a clear difference between the two maps: water availability for the crops is
much higher in July-August than in May-June. This is not unusual as the rainy period
starts in May and by July-August more water will be available to the vegetation. Also
the crops are more developed than at the beginning of the growing season. The
patterns of both maps are similar: drier areas are located in the northern and northcentral areas and wetter areas towards the east and the south.
The impact of agricultural drought on crop yield does not only depend on the degree
of drought but also on the duration of dry conditions during the entire growing
season. It is important to consider the duration of drought because prolonged droughts
have a larger impacts on final yields than short dry periods from which some crops
can partially recover. Therefore a drought duration map for 2008 was created: the
number of dekades with an EDI lower than 45% from March to October. The result is
shown in figure 3.32.
The information in the drought duration map is complementary to the EDI maps in
figures 3.32 and 3.33 because it informs on the agricultural drought during the entire
growing season and not only on a two months period. The drought duration map
confirms that the driest regions are located in the northern parts, in and around
Ningxia province and north of Lanzhou. The patterns are very similar to the ones in
the EDI map of July-August above. The added value of the drought duration map is
seen for example in the regions around Zhengzhou and Kaifeng where the drought
duration map shows drier conditions than one would expect from the July-August
EDI map.
67
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 3.32: Agricultural drought (EDI) in May-June 2008
Figure 3.33: Agricultural drought(EDI) in July-August 2008
Figure 3.34: Agricultural drought duration from March to October 200, i.e. the
number of dekads with EDI < 0.45.
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Chapter 3 – Energy and Water Balance Monitoring System
3.5
Validation of EWBMS products
The purpose of the validation is to provide information on the performance of the
EWBMS products in the rainfall-runoff forecasting context. The accuracy of a
number of basic products is assessed against independent measurements. Such
measurements are usually point data, while the EWBMS product data refer to areas of
one satellite pixel of 5x5km sub-satellite. Both data types have their sources of error
and do not accurately represent the reality. It is noted that the ground data are samples
and not areal averages. In this respect they have considerable sampling errors.
Therefore perfect correspondence between field data and EWBMS pixel values
cannot be expected.
The precipitation maps are validated by means of the ‘jack-knifing’ method. Results
for twenty stations in and around the Weihe and the Upper Yellow River basin are
presented in section 3.5.1. In section 3.5.2, the EWBMS 1.5 m air temperature energy
is validated against an extensive dataset of readily available GTS data with a dense
geographical distribution covering the entire Yellow River basin. Lacking ground
measurements, the actual evapotranspiration is validated through the net radiation and
sensible heat flux, from which it is derived by subtraction. The EWBMS net radiation
is validated by comparison with similar measurements on the ground, the sensible
heat flux by comparison with LAS measurements of the same, as earlier discussed in
section 3.2. Measuring the sensible heat flux with the LAS is considered the most
appropriate approach, since the LAS footprint is of the same order of magnitude as
the satellite pixel. But, because of costs, the number of LAS stations is limited to 4.
Results of the validation are presented in section 3.5.3 (net radiation) and 3.5.4
(sensible heat flux).
In addition the overall water balance is validated by comparing the yearly net or
‘effective’ precipitation, i.e precipitation minus evapotranspiration, of a basin with
the river runoff measured at the outlet of that basin. Results are presented in section
3.5.5.
3.5.1
Validation of precipitation
Validation of EWBMS precipitation maps has been quite difficult because an
independent data set is not always available. Therefore the so called jack-knifing
method has been used. With this method one precipitation station is left out of the
data set used in the rainfall field calculations. The EWBMS rainfall value and the
rainfall measured at that location on the ground, provides one validation data pair.
The rainfall mapping procedure is then repeated every time taking out another station
and putting back the previous one. In this way as many independent rainfall
validation data pairs are obtained as there are rainfall stations. This validation data
set is then analyzed by means of regression. The results of the jack-knifing validation
give a good indication of the quality of the rainfall mapping method. However, the
actual mapping results will be better, as the nearest and thus most influential
precipitation data point is not used as input in the Jack Knifing run.
As shown in figure 3.37 precipitation measurements are scarce in the Upper Yellow
River basin (UYRB), especially in the western areas. Due to the decreasing influence
of the summer monsoon in north-western direction, yearly precipitation is decreasing
in the same direction. Ten stations in or close to the UYRB are selected for the jackknifing analysis. These 10 stations are divided into two areas: 5 stations in the wet
69
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 3.37: Precipitation in the Upper Yellow River Basin 2006, and available
precipitation measurement stations (dots).
south-eastern area (red dots) and 5 stations in the dryer northern and western areas
(blue dots). Because of the limited number of stations in the upper Yellow river basin,
2 stations, just outside the basin, are also used for the analysis. These stations are
considered to be close enough to the basin, to be valuable for the estimation of the
EWBMS precipitation performance. Also, since sub-sets of 5 stations are available
now, results can be compared with similar sub-sets of the Weihe basin.
In table 3.4 the specifications of the selected stations in the northern and western
areas of the upper Yellow river basin are given. The altitude and remoteness of the
stations cause difficulties for station maintenance and offer a challenge for satellite
precipitation mapping. As shown in table 3.5 the results are very good. Except for
2006, Pearson’s R2 is well over 0.7 and the relative differences between the yearly
reported and estimated rain are well under 10%. In 2006 however the results are a bit
worse. For the whole period, from June 2005 to August 2008, the average of 5
stations differs only 4%. The left part of figure 3.38 shows the 5 station averages of
reported and estimated values for the whole period. Ideally all dots would be on the
1:1 line. However, errors in both field measurements and satellite derived data result
in scatter.
Figure 3.37 shows that in the south-east of the upper Yellow river basin, the station
network is a bit denser than in the central and western part, but is still low. Yearly
precipitation is higher than in the northern and western areas. Table 3.6 shows the
specifications of the 5 stations in the south-east. Table 3.7 shows that the results are
excellent. Pearson’s R2 is close to 0.9 and the relative differences between the yearly
reported and estimated rain are very small. For the whole period, from June 2005 to
July 2008, the average of 5 stations differs only by 1%. The right scatter plot of figure
3.38 shows the related scatter plot. Compared to the same plot of the northern and
western stations (figure 3.40, left) the points are closer to the 1:1 line, resulting in a
higher R2, and indicating better performance of the EWBMS precipitation module in
the south-eastern area. From a rainfall-runoff modeling point of view, this is
preferable, since precipitation in the southeastern area is higher.
70
Chapter 3 – Energy and Water Balance Monitoring System
Table 3.4: Stations used for jack-knifing in the north and west part of the UYRB
WMO nr.
Name
Latitude
Longitude
Altitude (m)
56029
Yushu
33.02
97.02
3682
56033
Maduo
34.92
98.22
4273
56043
Guolouo
34.80
100.30
3720
56046
Dari
33.75
99.65
3968
56065
Henan
34.73
101.60
3501
Table 3.5: Jack-knifing results for the average of the northern and western stations
Year
Pearson’s
Reported
Estimated
Difference
R2
rain (mm)
rain (mm)
(%)
2005 (from 6/20) 0.76
402
397
-1
2006
0.66
444
528
19
2007
0.85
587
552
-6
2008 (until 7/31)
0.75
311
330
6
Total
0.76
1744
1807
4
Table 3.6: Stations used for the jack-knifing analysis in the SE area of the UYRB
WMO nr.
Name
Latitude
Longitude
Altitude (m)
56067
Jiuzhi
33.40
101.50
3630
56074
Maqu
34.00
102.10
3473
56079
Ruoergai
33.58
102.97
3441
56151
Banma
32.90
100.80
3530
56173
Hongyuan
32.80
102.60
3493
Table 3.7: Jack-knifing results for the average of the SE stations of the UYRB
Reported
Etimated
Difference
Year
Pearson’s
rain (mm)
rain (mm)
(%)
R2
2005 (from 6/20)
0.87
546
503
-8
2006
0.86
661
685
4
2007
0.89
649
650
0
2008 (until 7/31)
0.90
338
335
-1
Total
0.88
2194
2174
-1
Daily Rainfall Scatter Plot, Average of 5 southeastern
UYRB stations
Daily Rainfall Scatter Plot, Average of 5 northern and
western UYRB stations
16
16
1:1, R2=0.88
1:1, R2=0.76
14
Estim ated rainfall (m m )
Estimated rainfall (mm)
14
12
10
8
6
4
12
10
8
6
4
2
2
0
0
0
2
4
6
8
10
12
14
16
0
2
4
6
8
10
12
14
16
Reported rainfall (mm)
Reported rainfall (mm)
Figure 3.38: Daily rainfall scatter plots of observed versus estimated rainfall average
of 5 stations in the north and west UYRB (left) and south-east (right).
71
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 3.39: Precipitation in the Weihe Basin 2006, and available precipitation
measuring stations (dots).
Figure 3.39 shows that the station network in the Weihe basin is much denser than in
the upper Yellow river basin. But also here, stations are scarce in the northern part.
But this is less serious whereas, due to the decreasing influence of the summer
monsoon, yearly precipitation is decreasing to the north. Ten stations in the Weihe
basin are selected for the jack-knifing analysis. These are divided in two sets: 5
stations in the wet south-eastern and downstream area (white dots) and 5 stations in
the dryer northern and western upstream areas (blue dots).
In table 3.8 the specifications of the selected stations in the upstream areas of the
Weihe basin are given. The jack-knifing analysis is done in the same way as in the
upper Yellow river basin, discussed earlier in this section. Table 3.9 shows that the
results are very good, except for 2008. Pearson’s R2 is well over 0.7 and the relative
differences between the yearly reported and estimated rain are less than 5%. For the
year 2008 however, the results are a bit worse. But, since in 2008 the time period was
shorter and the precipitation lower, this outcome is less representative. For the whole
period, from June 2005 to August 2008, the average between the measured and
satellite derived rainfall values of the 5 stations differs only 4%. The left scatter plot
of figure 3.40 shows the 5 stations averages of all daily reported and daily estimated
values for the whole period.
Figure 3.39 shows that in the south-eastern downstream areas of the Weihe basin, the
station network is well distributed and relatively dense. Yearly precipitation is higher
than in the northern and western areas. Table 3.10 shows the specifications of the 5
stations of the south-eastern downstream areas that are used in the jack-knifing
analysis. Table 3.11 shows that the jack-knifing results are good, though differences
between the years are considerable. Pearson’s R2 varies between 0.77 and 0.92 and
the difference between the yearly reported and estimated rain between -16% and 6%.
For the whole period, from June 2005 to August 2008, the average between the
measured and satellite derived rainfall differ -6%. The right part of figure 3.40 shows
the corresponding scatter plot. Compared to the scatter plot of the upstream areas
(figure 3.40, left) the values are much closer to the ideal line, resulting in a higher R2
and indicating a better performance of the EWBMS precipitation module in the
south-eastern high precipitation area, as desirable for rainfall-runoff forecasting.
72
Chapter 3 – Energy and Water Balance Monitoring System
Table 3.8: Stations used for jack-knifing in the upstream area of the Weihe basin.
WMO nr.
Name
Latitude
Longitude
Altitude (m)
53738
Wuqi
36.90
108.20
1331
53915
Pingliang
35.55
106.67
1348
53923
Xifengzhen
35.73
107.63
1423
56092
Longxi
35.00
104.70
1729
57014
Beidao
34.50
105.90
1085
Table 3.9: Jack-knifing results for the average of upstream stations of the Weihe.
Year
Pearson’s
Reported rain Estimated rain Difference
(mm)
(mm)
(%)
R2
2005 (from 6/20) 0.72
410
397
-3
2006
0.83
501
511
2
2007
0.74
518
541
4
2008 (until 7/31)
0.68
202
242
20
total
0.71
1631
1690
4
Table 3.10: Stations used for jack-knifing in the downstream area of the Weihe basin.
WMO nr.
Name
Latitude
Longitude
53929
Changwu
35.20
107.80
53942
Luochuan
35.80
109.50
57025
Fengxiang
34.50
107.40
57037
Yiaxian
34.90
109.00
57134
Foping
33.90
108.00
Table 3.11: Jack-knifing results for the average of downstreamstations of the Weihe.
Year
Pearson’s
Reported rain Estimated rain Difference
R2
(mm)
(mm)
(%)
2005 (from 6/20) 0.89
484
515
6
2006
0.92
674
652
- 3
2007
0.77
736
658
-11
2008 (until 7/31)
0.78
353
297
-16
total
0.82
2248
2121
- 6
Daily Rainfall Scatter Plot, Average of 5 Weihe upstream
stations
Daily Rainfall Scatter Plot, Average of 5 Weihe
downstream stations
16
16
1:1, R2=0.71
1:1, R2=0.82
14
12
Estimated rainfall (mm)
Estimated rainfall (mm)
14
10
8
6
4
12
10
8
6
4
2
2
0
0
2
4
6
8
10
12
14
16
Reported rainfall (mm)
0
0
2
4
6
8
10
12
14
16
Reported rainfall (mm)
Figure 3.40: Daily rainfall scatter plots of observed versus estimated rainfall average
of 5 stations in upstream (left) and downstream parts (right) of the Weihe basin.
73
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Considering all the areas of the Upper Yellow River basin and the Weihe basin,
during the whole validation period (June 20, 2005 – July 31, 2008), the jack-knifing
validation results can be considered good. Since over- and underestimations are well
balanced between the different areas and years, systematic errors in the EWBMS
precipitation are not expected. Moreover Pearson’s R2 is higher in the areas with
more precipitation, which reduces the effects of random errors on the rainfall-runoff
simulation.
3.5.2
Validation of air temperature
Daily averaged 1.5 m air temperature (T1.5m) data from the WMO-GTS are compared
to corresponding EWBMS values with a grid size of 0.05° by 0.05° (about 5.6 km by
4.5 km in the Yellow River basin). Figure 3.41 shows the location of the GTS stations
involved.
Figure 3.41: Location of the GTS 1.5 m air temperature stations used for validation
The WMO-GTS temperatures are measured with a sensor that is placed at 1.5 m
above the surface and is protected from precipitation and sunlight by a white painted
louvered box. The EWBMS daily average 1.5m temperatures are determined with a
linear regression between the daily average surface temperature To and the boundary
layer temperature Ta, as discussed in section 3.1.3. These are compared with the
measured air temperatures at the 25 GTS stations. In 2006, the resulting average
difference on a daily basis is 0.49 °C, the RMSE is 5.23°C and the correlation
coefficient is 0.89. For ten daily averaged temperatures, the difference of 2006
reduces to 0.00 °C, the RMSE is 3.25 and the correlation coefficient increases to 0.96.
Similar results are found for the data of 2007. Table 3.5.1 presents the summary
statistics of the comparison.
The results show that the 10-daily GTS and EWBMS temperatures are closer than the
daily data. For this reason it was decided to use the ten daily moving averages of the
EWBMS temperature, instead of the daily values, as input for the freeze/thaw module
(see section 3.1.4). The results in the table also indicates that EWBMS temperatures
seem to perform evenly well in all seasons. The RMSE is smallest in winter and the
correlation coefficient slightly higher in spring and autumn than in summer, but there
is no large difference between the seasons. Examples of these time series are
presented in figure 3.42. The agreement between GTS and EWBMS measured 1.5m
air temperature is good. The presence of clouds could affect the accuracy of the
74
Chapter 3 – Energy and Water Balance Monitoring System
1.5 m Air Temperature (°C)
40
53614 - 2006
GTS
EWBMS
30
20
10
0
-10
-20
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
1.5 m Air Temperature (°C)
40
54823 - 2007
GTS
EWBMS
30
20
10
0
-10
-20
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Figure 3.42: Yearly course of ten daily average 1.5 m air temperatures in
2006 and 2007 for two stations in the Yellow River basin.
EWBMS measured temperature. To investigate this influence, days with high cloud
cover were omitted, retaining consecutively only those days in the data set where
cloud cover from 9am until 3pm is less than 50%. Figure 3.43 shows the scatter plots
of 10 daily average EWBMS versus GTS temperatures for 2007 and the influence of
cloud cover on the results. When the cloudy days are eliminated minor improvement
in temperature is seen in 2007: the correlation coefficient increases to 0.97 and the
RMSE reduces to 2.85°C. But for the data of 2006, no improvement is obtained: the
RMSE increases to 3.25°C and the correlation decreases to 0.94. So there is no clear
influence of cloud cover on the accuracy of the EWBMS 1.5 m air temperature data.
Table 3.12: Average difference, RMSE and correlation of T1.5m (2006-2007)
2006
Year
Winter
Spring
Summer
Autumn
Year
Winter
Spring
Summer
Autumn
∆
0.46
2.07
-0.45
-0.99
1.47
-1.00
-2.51
-0.80
-0.01
-0.55
Daily temperature
RMSE
R
5.22
0.89
5.18
0.69
5.57
0.74
5.10
0.65
5.07
0.84
5.77
0.89
5.30
0.66
6.20
0.78
6.31
0.65
5.19
0.70
∆ = average difference TEWBMS-TGTS.
75
10 Daily average temperature
∆
RMSE
R
-0.03
3.20
0.96
0.17
2.66
0.86
-0.21
3.29
0.89
-0.34
3.61
0.78
0.39
3.34
0.92
-0.20
3.11
0.96
-0.43
2.61
0.88
-0.72
3.09
0.92
-0.49
3.00
0.86
0.92
3.45
0.88
40
40
30
30
T 1.5 m EWBMS (°C)
T 1.5 m EWBMS (°C)
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
20
10
0
20
10
0
-10
-10
-20
-20
Cloud Cover < 50%
-20
-10
0
10
20
30
40
T 1.5 m GTS (°C)
-20
-10
0
10
20
30
40
T 1.5 m GTS (°C)
Figure 3.43: Influence of cloud cover on 1.5m air temperature. The left side shows
the entire dataset, the right side shows data with cloud cover lower than 50% .
The geographical distribution of errors is shown in figure 3.44. The deviations
between ground and EWBMS temperatures are smaller in the northern and western
part of the Yellow River basin. Higher annual rainfall in the south and the east can
partially explain the slightly larger deviations. The overall agreement between the
ground data and EWBMS temperatures is good. The difference with ground data is
smaller for 10 daily average temperatures and cloud cover has only a moderate effect
on the results. EWBMS temperatures are more precise in the northern and eastern
parts of the Yellow River basin.
Figure 3.44: Geographical distribution of differences between the GTS and
EWBMS 1.5m temperature in 2007. Red areas indicate larger, green areas
indicate smaller deviations.
76
Chapter 3 – Energy and Water Balance Monitoring System
-2
EWBMS Net Radiation (W.m )
200
150
100
50
0
-50
-50
0
50
100
150
200
-2
NR-Lite Net Radiation (W.m )
Figure 3.45: Daily averaged net radiation determined by the NR-Lite versus EWMBS
net radiation. Days on which less than 80% of data was logged are excluded.
3.5.3
Validation of net radiation
Daily averaged net radiation data from all LAS sites were compared to corresponding
EWBMS values with a grid size of 0.1° by 0.1° or 11 km by 9 km. Details on the net
radiation measurements and the location of the LAS sites was already presented in
section 3.2.3. Figure 3.45 shows net radiation measured with the NR-Lite net
radiometers in the field plotted against EWBMS net radiation. In the analysis the data
measured at the four LAS stations for the period August 2005 until December 2007
are used. Data were quality checked and only days where with more than 80% of the
10 minutes values available, were included. The final dataset consists of 1066 data
points.
The comparison EWBMS and NR-Lite net radiation shows that the satellite derived
values and the values measured in the field are quite consistent. The relationship
exhibits a Pearson’s correlation coefficient of 0.80, an average difference of 2.5 Wm-2
and a standard error of 27 Wm-2. The discrepancies between the observed and
modeled values are minor with absolute differences in magnitude larger than 50 Wm-2
in only 4% of the cases.
Table 3.13: Average difference, RMSE and correlation of daily and 10 daily average
net radiation (2005-2007)
daily
10 daily average
Nr
∆
RMSE
R
∆
RMSE
R
(-)
(W.m-2) (W.m-2)
(-)
(W.m-2) (W.m-2)
(-)
Jingchuan 597
10
24
0.88
10
15
0.93
Xinghai
192
-2
23
0.81
-2
8
0.87
Maqin
208
-11
29
0.76
-11
16
0.84
Tangke
69
-12
29
0.84
-13
14
0.86
∆: average difference RN EWBMS - RN NR-Lite
Nr : number of data points used (quality controlled data with more than 80% of 10minutes logging)
77
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
-2
Net Radiation (W.m )
200
Jingchuan- 2006
GTS
EWBMS
150
100
50
0
-50
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
-2
Net Radiation (W.m )
200
Xinghai - 2007
150
100
50
0
GTS
EWBMS
-50
Apr
May
Jun
Jul
Aug
Sep
Figure 3.46: EWBMS (red) and ground measured (blue) net radiation time series at
Jingchuan and Xinghai
200
Xinghai - 2007
Jingchuan - 2006
LAS
EWBMS
Difference
-2
Sensible Heat Flux (W. m )
200
150
150
100
100
50
50
0
0
-50
Mar
May
Jul
-50
May
Sep
Jun
Jul
Aug
Sep
Figure 3.47: EWBMS (red) and ground measured (blue) 10-daily net
radiation time series at Jingchuan and Xinghai. The difference is shown in
orange.
Table 3.13 shows the yearly average differences, correlations and RMSE for daily
and ten daily data at each of the four LAS sites. The relationship for the entire dataset
exhibits an overall correlation coefficient of 0.85, an average difference of 2 W.m-2
and a RMSE of 25 W.m-2 for daily data. The errors are random and observed
differences on a daily basis are balanced equally with 56% of the differences being
positive and 44% of the differences being negative. On a ten daily basis the RSME
decreases to 14 Wm-2 and the correlation coefficient increases to 0.90. The average
difference remains the same.
78
Chapter 3 – Energy and Water Balance Monitoring System
-2
EWBMS Net Radiation (W.m )
200
150
100
50
0
Precitation < 1mm
-50
-50
0
50
100
150
200
-2
NR-Lite Net Radiation (W.m )
Figure 3.48: Daily averaged net radiation determined by the NR-Lite versus EWMBS
net radiation. Data on rainy days and on days where less than 80% of the daily data
were logged are excluded.
In figures 3.46 and 3.47 examples are given of a time series comparison of daily and
10-daily net radiation at Jingchuan 2006 and Xinghai 2007. The peak values of
EWBMS data are less extreme than those observed with the NR-Lite. This may be
due to the difference in scale of the measurements. Another potential cause for
differences between the two measurements is precipitation, which may leave residual
moisture on the NR-Lite instrument. Therefore all data collected during days or
nights when precipitation was recorded were excluded from the analysis. Figure 3.48
shows the resulting scatter plot (656 data points). There is no notable change in the
correlation coefficient or RMSE. The agreement between EWBMS and ground
measured net radiation is not influenced by rainfall.
Net radiation remains among the most difficult atmospheric parameters to measure
accurately on the ground. The NR-Lite is not perfect. The instrument is less sensitive
to long-wave radiation than to solar radiation (Cobos and Baker, 2003). Errors can be
caused by wind and precipitation. In this evaluation, only the precipitation effect has
been considered. The reference cited suggests that the effect of wind is minor
compared to the precipitation effects. It is likely that the differences between the
EWBMS and the ground data are caused the difference in scale. The NR-Lite is
measuring a small surface area of a few square meters, while the EWBMS net
radiation pertains to and area of about 99 km2.
In conclusion the net radiation values from the EWBMS correspond well with the
NR-Lite measurements on the ground. The differences are small compared to the
absolute values and are evenly distributed. Best results are obtained for the ten daily
time scale. The data show no influence of rainfall.
3.5.4
Validation of sensible heat flux
The LAS sensible heat fluxes were averaged by day and compared to corresponding
daily EWBMS values. Grid size of the EWBMS data is 0.1° by 0.1° or 11 km by 9
km. Figure 3.49 shows two examples of time series of the daily sensible heat flux
79
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
derived from the EWBMS and measured by LAS. In the detailed analysis, data of all
four LAS stations where used for the period august 2005 until august 2008. The LAS
data were quality checked and only days where more than 80% of the 10-minute
readings were available, have been evaluated. The final dataset consists of 755 data
points. The evaluation has shown EWBMS and LAS derived sensible heat fluxes are
consistent. Fore the entire dataset the relationship exhibits a correlation coefficient of
0.64, an average difference of -1 W.m-2 and a RMSE of 16 W.m-2. The errors are
random and observed differences on a daily basis are balanced well with 47% of the
differences being positive and 53% of the differences being negative. On a ten daily
average basis the RSME decreases to 10 W.m-2 and the correlation increases to 0.75.
The average difference remains the same. Table 3.14 shows the results for each LAS
site separately. The 10 daily correlation coefficients range from 0.63 to 0.83 and the
RMSE’s from 8 to 13 W.m-2. The agreement is a bit better in Xinghai than at the
other stations, but all stations show fair results.
A potential source for discrepancy between the EWBMS and LAS derived sensible
heat fluxes is precipitation; the scintillometer beam may be interrupted by rainfall.
Another potential influence is cloud cover. On cloudy days, the EBWMS results
make use of the Bowen ratio of the previous day, which may not always be
appropriate. Hence, the EWBMS sensible heat flux may deviate from the real
sensible heat flux when cloud cover is high. For this reason, rainy days and days with
high cloud cover were omitted from the dataset, retaining only those days where
precipitation is zero (385 data points) and those days where cloud cover is less than
40% (325 data points). However, precipitation does not seem to affect the results. The
RMSE is the same (16 W.m-2) and the correlation even decreases to 0.60. Also cloud
cover does not influence the results, RMSE stays the same and correlation decreases
to 0.57 on days with low cloud cover.
Although the scale of the LAS sensible heat flux measurements is of similar order of
magnitude as the EWMBS results, the surface area over which the LAS is measuring
is still much smaller than the EWBMS pixel. This explains why considerable
differences may occur between both measurements. However, the analysis that the
EWBMS sensible heat flux data are consistent with and similar to those measured
with the LAS. The differences are small compared to the absolute values and
distributed evenly. Better results are obtained for ten daily average values than for
daily values and the presence of rainfall or cloud cover seems not to influence the
results.
Table 3.14: Average difference, RMSE and correlation of daily and 10 daily average
sensible heat flux (2005-2008)
daily
10 daily average
Nr
∆
RMSE
R
∆
RMSE
R
(-)
(W.m-2) (W.m-2)
(-)
(W.m-2) (W.m-2)
(-)
Jingchuan 337
1
16
0.615
1
9
0.796
Xinghai
207
-3
14
0.717
-3
8
0.824
Maqin
126
0
15
0.606
-3
13
0.633
Tangke
69
-2
18
0.693
-1
12
0.826
∆ : average difference H EWBMS – HLAS
Nr : number of data points used (quality controlled data with more than 80% of 10minutes logging)
80
Chapter 3 – Energy and Water Balance Monitoring System
Jingchuan - 2006
-2
Sensible Heat Flux (W. m )
200
LAS
EWBMS
150
100
50
0
-50
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Xinghai - 2007
-2
Sensible Heat Flux (W. m )
200
LAS
EWBMS
150
100
50
0
-50
May
Jun
Jul
Aug
Sep
Oct
Figure 3.49: Daily EWBMS (red) sensible heat flux versus LAS data (blue) at
Jingchuan in 2006 and Xinghai in 2007.
200
Xinghai - 2007
Jingchuan - 2006
-2
Sensible Heat Flux (W. m )
200
150
150
100
100
50
50
0
0
LAS
EWBMS
Difference
-50
Feb Mar Apr May Jun Jul Aug Sep Oct
-50
May
Jun
Jul
Aug
Sep
Oct
Figure 3.50: Comparison of 10 daily averaged LAS and EWBMS sensible heat flux at
Jingchuan in 2006 and Xinghai in 2007.
3.5.5
Validation of catchment water budget
Assuming that there is no other loss of water than evapotranspiration and that changes
in catchment water storage can be neglected, the yearly net precipitation
(precipitation minus evapotranspiration) in a catchment should be equal to the river
discharge at the outlet of that catchment. In this section, the EWBMS net
precipitation for both the Upper Yellow River basin and the Weihe basin is compared
with the measured discharge. Of course, the comparison is not completely
‘waterproof’, since changes in water storage, both in the ground and as snow on the
surface, may occur. Nevertheless the exercise will give a valuable indication of the
suitability of the EWBMS products to be used as input for the dedicated rainfall-river
runoff model, discussed in chapter 4.
81
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Upper Yellow River basin
The outlet of the Upper Yellow River basin (UYRB) is situated at the Tangnaihai
hydraulic station. River discharges are derived from water levels and a historical
discharge-water level relation. In figure 3.51, daily net precipitation values averaged
for the entire basin are plotted with the daily averaged river discharge at Tangnaihai
for the period July 2005 to June 2008. As expected, the discharge values follow the
net precipitation trend with a little delay. Clearly shown are the monsoon periods
from June to October, with a lot of (net) precipitation and river discharge and the dry
winters in between, with small negative net precipitation values and hardly any river
discharge. In between, and just before the rainy periods in summer,
evapotranspiration can be quite high, because incoming radiation in these months is
quite high.
In figure 3.52 the river discharge and net precipitation are plotted in a cumulative
way. Also from this figure it is clear that there is hardly effective precipitation during
winter, where the graphs proceed almost horizontally. However, evapotranspiration is
already increasing in spring, while precipitation only starts in early summer. This
means that water is stored during the winter. The cumulative river discharge follows
the cumulative net precipitation very well, indicating that the water balance of the
basin is derived correctly from the satellite data.
Comparing yearly discharges with yearly net precipitation is a suitable evaluation
method provided that year to year changes in water storage are small. From this point
of view the calendar year may not be the most suitable evaluation period. Ideally an
evaluation year would starts just before the rainy season. Since most rain falls from
July on, and since we have satellite data available from July 2005, we also evaluate
the water balance of the catchment for yearly periods starting on July 1st. Table 3.14
shows the yearly discharge, net precipitation, precipitation and evapotranspiration for
both the calendar and the evaluation years. The results are very good; differences
between yearly discharge and yearly net precipitation are always below 30% and for
the entire period, the difference is less than 10%.
Weihe basin
The outlet of the Weihe basin is situated at Huaying, but the last hydrological station
before the outlet is at Huaxian. The area upstream of Huaxian covers about 80% of
the entire Weihe basin. River discharges measured here are derived from water levels,
using a historical discharge-water level relation. Figure 3.53 shows the daily net
precipitation values averaged for the Weihe basin upstream of Huaxian and the daily
river discharge at Huaxian, for the period July 2005 to June 2008. Also here the
discharge follows the net precipitation with some delay. However, compared to the
net precipitation, the river discharge seems to be very low. Clearly shown are the
monsoon periods from June to October, with a lot of (net) precipitation and relatively
high river discharge and the dryer winters in between, with small negative net
precipitation values and hardly any river discharge. In between and just before the
rainy season, evapotranspiration is very high (negative net precipitation), because
incoming radiation in these months is high.
82
Chapter 3 – Energy and Water Balance Monitoring System
Net precipitation 5 days-floating average
10
5
Jul-08
May-08
Mar-08
Jan-08
Nov-07
Sep-07
Jul-07
-4
Jan-07
-2
May-07
0
Mar-07
0
Nov-06
1
Sep-06
2
Jul-06
2
May-06
4
Mar-06
3
Jan-06
6
Nov-05
4
Sep-05
8
River Discharge (mm)
6
River dis charge at Tangnaihai
Jul-05
Net Precipitation (mm)
12
-1
-2
1800
1600
1400
1200
1000
800
600
Cum . evapotranspiration
Cum . net precipitation
Cum . precipitation
Cum . river dis charge
Jul-08
May-08
Mar-08
Jan-08
Nov-07
Sep-07
Jul-07
May-07
Mar-07
Jan-07
Nov-06
Sep-06
Jul-06
May-06
Mar-06
Jan-06
Nov-05
Sep-05
400
200
0
Jul-05
Water (mm)
Figure 3.51: Daily net precipitation and discharge in the upper Yellow River basin.
Figure 3.52: Cumulative net precipitation and discharge in upper Yellow River basin.
Table 3.15: Yearly net precipitation and river discharge in Upper Yellow River basin.
Period
Tangnaihai
Net
Precipitation Evapodischarge
Precipitation (mm)
transpiration
(mm)
(mm)
(mm)
July '05- June '06
193
165
551
385
2006
110
80
496
416
July '06- June '07
117
151
555
404
2007
145
146
549
403
July '07- June '08
135
99
500
401
2006-2007
256
226
1045
819
Total period
444
416
1606
1191
Figure 3.54 presents the cumulative components of the EWBMS water budget and the
river discharge at Huaxian. In the winter period the lines are almost horizontal
whereas there is almost no effective precipitation. But, evapotranspiration is already
increasing in early spring, causing a decrease in cumulative net precipitation in
spring. This implies that water is withdrawn from the soil. The cumulative river
discharge is smaller than the cumulative net precipitation, indicating that water
balances does not completely fit: there is more net precipitation than river discharge.
This is also clear from table 3.16. Here several remarks could me made.
83
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Net precipitation 5 days-floating average
10
5
Jul-08
May-08
Mar-08
Jan-08
Nov-07
Sep-07
Jul-07
-4
Sep-06
-2
May-07
0
Mar-07
0
Jan-07
1
Nov-06
2
Jul-06
2
May-06
4
Mar-06
3
Jan-06
6
Nov-05
4
Sep-05
8
River Discharge (mm)
6
River dis charge at Huaxian
Jul-05
Net Precipitation (mm)
12
-1
-2
Figure 3.53: Daily net precipitation and river discharge in the Weihe basin.
Cum. evapotrans piration
Cum. net precipitation
Cum. precipitation
Cum. river dis charge
800
Jul-08
May-08
Mar-08
Jan-08
Nov-07
Sep-07
Jul-07
May-07
Mar-07
Jan-07
Nov-06
Sep-06
Jul-06
May-06
Mar-06
Jan-06
Sep-05
200
0
Nov-05
600
400
Jul-05
Water (mm)
1800
1600
1400
1200
1000
Figure 3.54: Cumulative net precipitation and river discharge in the Weihe basin.
Table 3.16: Yearly net precipitation and river discharge in the Weihe basin.
Period
Huaxian
Net
Precipitation Evapodischarge
Precipitation (mm)
transpiration
(mm)
(mm)
(mm)
July '05- June '06
66
108
562
454
2006
35
58
541
483
July '06- June '07
29
13
511
498
2007
38
69
568
499
July '07- June '08
37
93
584
491
2006-2007
73
127
1109
982
Total period
133
214
1657
1443
First the assumption that no changes in water storage take place, may not me right. In
the Weihe basin there are a many reservoirs, where water is stored in wet and
released in dry periods. In addition we may not be able to exclude the possibility that
actual discharge may be somewhat different due to unknown leakage from the basin.
Finally it should ne noted that the water balance in the Weihe is quite delicate, as
runoff is only 10% of the precipitation. Therefore small errors in the precipitation and
evaporation lead to much larger errors in the run-off. From this point of view the
satellite based results can be considered good and certainly above our original
expectations.
84
Chapter 3 – Energy and Water Balance Monitoring System
3.6
References
Andreas, E.L. (1990) "Two-Wavelength Method of Measuring Path-Averaged
Turbulent Surface Heat Fluxes", J. Atmos. Ocean. Tech. 6, 280-292.
Cobos D.R. and Baker J.M. (2003) ‘Evaluation and Modification of a
Domeless Net Radiometer’, Agronomy Journal, 95, p. 177–183.
Hill, R.J., S.F. Clifford and R.S. Lawrence (1980) "Refractive Index and Absorption
Fluctuations in the Infrared Caused by Temperature, Humidity and Pressure
Fluctuations", J. Opt. Soc. Am 70, 1192-1205.
Kohsiek, W. (1982b) "Optical and In Situ Measuring of Structure Parameters
Relevant to Temperature and Humidity and Their Application to the Measuring of
Sensible and latent Heat Flux" NOAA Tech. Memor. ERL WPL-96, NOAA
Environmental Research Laboratories, Boulder, CO, USA, 64 pp.
Meijninger, W.M.L. (2003). Surface fluxes over natural landscapes using
scintillometry, Wageningen University, 164 p.
Panofsky, H.A. and J.A. Dutton (1984) "Atmospheric Turbulence: Models and
Methods for Engineering Applications", John Wiley and Sons, New York, 397 p.
Kondratyev, K.Y.(1969) ‘Radiation in the Atmosphere’, Academic Press, New York,
London.
Rosema A., Snel J.F.H, Zahn H., Buurmeijer W.F., Hove van L.W.A.(1998) ‘The
relation between Laser-Induced chlorophyll fluorescence and photosynthesis’,
Remote Sensing of Environment, 65, p. 143-153.
Valk, P.de, Feijt A., Roozekrans H., Roebeling R., Rosema A. (1998)
"Operationalisation of an algorithm for the automatic detection and characterisation
of clouds in Meteosat imagery”
UNEP (1992). World Atlas of Desertification. Edward Arnold. London.
85
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
86
Chapter 4 – Large Scale Hydrological Model
4
LARGE SCALE HYDROLOGICAL MODEL
Within the Hydrological Bureau (HB) of the Yellow River Conservancy Commission
(YRCC), the LSHM has been implemented for two selected areas. The
implementation for the Upper Yellow River is referred to as the Water Resources
Forecasting System (WRFS), which simulates the rainfall-runoff processes up to
Tangnaihai station in the upper reach of the Yellow River. A second implementation
has been set up for the Weihe sub-basin and is referred to as the Flood Forecasting
System (HWFS), which specifically allows monitoring and short-range forecasting of
high discharges during the flood season in the lower reaches of the Weihe up to the
confluence with the main branch of the Yellow River.
Both implementations are based on the same model code, but are slightly adapted to
specific details of the target areas. The operation and data requirements are also
mostly identical.
A technical description is presented in the next two sections. Details about the
temporal and spatial data for the models are described in sections 3 and 4. Evaluation
of the validation results and the model performance is detailed in the last section of
this chapter, which followed by a list of references cited in the text.
4.1
Technical reference
The terrain is represented on a regular two-dimensional grid for which the spacing
between the grid nodes, or cell centres, may differ between the x and y directions. In
the vertical direction, each grid node is characterized by a surface elevation and an
elevation to an imperious base level at some depth below the surface. The main river
and major tributaries form a schematic stream network that is coupled to the land
cells in the topographic valleys or river pathways. Figure 4.1 shows a cross-sectional
representation of the model domain geometry.
precipitation
evapotranspiration
infiltration
surface runoff
river flow
subsurface flow
z
TS
IB
zb
grid node spacing
datum
Figure 4.1: Schematic cross-section through the terrain illustrating the model
processes and the model grid discretization. TS: terrain surface; IB: impervious base.
87
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The model is forced by precipitation and actual evapotranspiration, which are input as
gridded fields in the spatial resolution of the model grid for each time interval. When
applicable snowmelt may be added to the precipitation can be replaced by snowmelt.
Since the evapotranspiration is computed offline, there is no explicit representation of
soil hydrological processes. Water infiltrating the surface (that is from precipitation
minus evaporation, or snowmelt) is assumed to directly recharge a subsurface lateral
flow storage that eventually drains into the stream network through which the water is
transported towards the outlet as river flow. The part of the net input that does not
infiltrate is routed down slope as surface runoff. The latter process is implemented to
allow for sub grid parameterization of flow in the stream network that is too fine to be
incorporated by an explicit river channel specification. This typically comprises the
small streams and creeks in headwater areas and slopes where the topographic relief
cannot be represented at the resolvable grid scale. Figure 4.1 also shows the overall
connection between the processes.
The theoretical background and implementation of the processes are separately
described in the subsequent sections. For a full account of the numerical solutions, the
reader is referred to the user manual (Maskey and Venneker, 2008).
4.1.1
Land component transport
The surface runoff routing is carried out by draining part of the ponded water from a
cell to its steepest decent neighbour cell, based on eight possible flow directions. A
similar approach was applied by Arora and Boer (1999) to a variable velocity land
surface water routing scheme in a general circulation model. The Manning equation
for uniform flow with the wide-rectangular channel assumption is applied to
parameterize the surface water discharge, i.e.:
q=
1 5 / 3 1/ 2
H S
N
(4.1)
where, q is the surface discharge per unit width (L2T−1) from the cell, H is the grid
cell average depth of ponded water (L), S is the topographic slope (L1L−1) to the
steepest decent neighbour cell and N is the effective Manning roughness coefficient
(L−1/3T) of the surface. A range of roughness coefficient values for various flood plain
conditions are given in Arcement and Schneider (1989).
The surface storage water balance for a unit surface area is described by a non-linear
ordinary differential equation, i.e.
dH
1 1 5 / 3 1/ 2
=−
H S + qI + r − I
dt
∆L N
(4.2)
where ∆L is the distance between the centres of the cells in the flow direction (L), qI
is the cumulative inflow from the contributing upslope cells (LT−1), r is the net
rainfall or snowmelt rate (LT−1), and I is the net rate of infiltration (LT−1) into the soil
surface recharging the subsurface storage.
In the absence of a soil water accounting scheme, because evaporation is obtained
from the EWBMS, a subgrid parameterization for the infiltration is formulated as:
88
Chapter 4 – Large Scale Hydrological Model
 H*

I = min
, I max 
 ∆t

(4.3)
in which H* is the amount of ponded water remaining after updating for surface
runoff and Imax is a prescribed maximum infiltration rate. The latter is generally
depending on soil type, and is generally smaller for clayey soils than for sandy soils.
The computations are carried out from upstream to downstream to accumulate the
surface runoff over all cells on the basis of pre-defined drainage direction map, which
is derived from the basin topography (Jenson and Domingue, 1988). An explicit finite
difference formulation of the water balance, as given in (4.2), is used to first update
the head due to surface runoff, after which the infiltration is accounted for.
For subsurface flow each cell in the terrain is regarded as a conceptual storage
reservoir that can be characterized by the properties and generalized flow behaviour
of porous media. In order to apply a general flow formulation in view of the sparse or
lacking subsurface data, it is necessary to make rigorous simplifications with respect
to the geometric structure and hydraulic parameters of the storage. For each cell, the
reservoir extends to a certain average depth below surface, which acts as an
impervious base. Parameters for the reservoir are considered effective parameters
representing average values that are valid for the full cell extents. As such, the
reservoir can be considered as an idealized homogeneous and isotropic unconfined
aquifer, which is being forced by the net infiltration flux at the surface and drains
laterally towards its neighbouring cells. A conceptual representation of the subsurface
storage geometry, and the associated hydraulic parameters and fluxes is shown in
figure 4.2. The subsurface storage water balance for a unit surface area is expressed
by the continuity equation:
∂ 
∂h  ∂ 
∂h 
∂h
 = −n e
−I
Dx
 +  D y
∂x 
∂x  ∂y 
∂y 
∂t
(4.4)
where h is the hydraulic head (L), D is the saturated hydraulic diffusivity (L2T−1), ne
is the effective porosity or specific yield (L3L−3), and I is the infiltration rate (LT−1) as
defined above that is recharging the reservoir. The hydraulic head is the sum of the
impervious base elevation zb and the (phreatic) storage head above that base elevation
η (L) i.e.
h = zb + η
(4.5)
The base elevation is parameterized by a simple linear relationship with the surface
elevation z, viz.
z b = z − (10000 − z ) s b
(4.6)
in which sb is a scaling parameter that can be fitted from comparison with runoff
records on a (sub)catchment basis or, if available, field observations. The lateral flow
per unit cross-sectional area (LT−1) is proportional to the hydraulic gradient as
described by Darcy's law, that is:
∂h
∂x
∂h
q y = −k e
∂y
q x = −k e
(4.7)
89
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
∆x
I
qx
η
z
h
qx
zb
datum
Figure 4.2:. Schematic cross-section of the grid cell subsurface storage geometry.
for both directions, and in which ke is the effective hydraulic conductivity (LT−1). The
saturated hydraulic diffusivity, or transmissivity, is dependent on saturated thickness
η (L) of the storage aquifer (e.g. Brutsaert, 2005), i.e.
D = k eη
(4.8)
The flow formulation of (4.4) is discretized in time and space using an explicit finite
difference scheme on a two-dimensional computational grid (e.g. Press et al., 1992).
It is possible that during a time step, water may have exfiltrated at the surface if
saturation excess has occurred. In such situations, the ponded water head on the
surface and the subsurface hydraulic head need to be adjusted accordingly to their
final values at t + ∆t.
4.1.2
River routing
The one-dimensional river flow component is based on the Muskingum-Cunge
routing method (Cunge 1969) with lateral inflow. This model routes the flow through
a discrete channel network from upstream to downstream points over specified time
intervals ∆t. The flow propagation from time step n to n+1 between points j to j+1 in
a segment of the channel network is given by:
Q nj++11 = C 0 Q nj +1 + C1Q nj + C 2 Q nj+1 + C 3 Q l
(4.9)
where, Q(j, n) is the discharge (L3T−1) at a point j along the channel reach and time
step n and Ql is the lateral inflow contribution of the land component to the river
network as described in the next section. Following Ponce (1986), the coefficients in
Eqn (4.8) are given by:
90
Chapter 4 – Large Scale Hydrological Model
− 1 + Co + Re
1 + Co + Re
1 + Co − Re
C1 =
1 + Co + Re
1 − Co + Re
C2 =
1 + Co + Re
2Co
C3 =
1 + Co + Re
C0 =
(4.10)
Here, C0 is the Courant number and Re is the Reynolds number, which are obtained
from
c∆t
∆s
Q
Re =
BS0 c∆s
Co =
(4.11)
(4.12)
where c is the wave celerity (LT−1), B is the channel top width (L), S0 is the channel
bed slope and ∆s is the channel segment length (L). The wave celerity is defined as
(Lighthill and Whitham, 1955)
 ∂Q 
c=

 ∂A  s
(4.13)
in which A is the cross-section flow area (L2), which is also a function of the
discharge Q. At any time step, the values of B, A, and the water depth h (L) are
dependent on the discharge, which is to be computed. Therefore, the variable
parameters Co and Re are commonly evaluated from a reference discharge Qref,
which is estimated using a three-point average (Tang et al., 1999), plus the
contribution by lateral inflow. With the estimated Qref, the cross-section parameters
such as water depth, top width and wet cross-section area are determined iteratively
using the normal depth condition, i.e. Manning’s flow equation (e.g. Chow, 1959):
Q=
1
AR 2 / 3S10/ 2
n
(4.14)
where n is the Manning roughness coefficient and R is the hydraulic radius (L).
4.1.3
Land-river coupling
Following Prudic et al. (2004), the volumetric flow from the subsurface storage into
the stream channel is described using Darcy’s equation
Q l = BLk b
h − hs
b
(4.15)
where B is a representative width (i.e., the channel top width) of the stream (L), L is
the effective length of the stream segment inside the grid cell (L), kb is the hydraulic
91
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
B
hr
h
η
zr
hs
datum
Figure 4.3: River cell geometry and illustration of the land-river flow exchange.
conductivity of the stream bed (LT−1), hs is the total head (L) in the stream channel,
and b is the effective thickness of the river bed material (L). If Ql is negative, the flux
is removed from the river flow and adds to the head increase of the subsurface
storage. Figure 4.3 shows the geometry and flow relations of a land cell that is
connected to the river network. The total stream head is defined as
hs = zr + hr
(4.16)
in which zr is the elevation of the stream bed (L). This is taken as
zr =
1
(z + z b )
2
(4.17)
The effective thickness of the river bed material is estimated as
b=
1
(z r − z b )
2
(4.18)
The above relations ensure the geometrical consistency of the three-dimensional landchannel model structure (see figure 4.3).
4.1.4
Forecasting of river flows
The large scale hydrological model, described in the previous sections, is used to
setup a flow forecasting scheme. In the forecasting mode the hydrological model uses
observed flows at a number of upstream hydrological stations, which are referred to
as forced boundary flows, and estimated rainfall for the forecast period. The estimates
of the forced boundary flows for the forecast period are based on a statistical model,
whereas the estimate of the future rainfall is either based on a number of rainfall
scenarios or is obtained from other rainfall forecasts. The model predicted flows at
the downstream forecast locations are updated using a simple assimilation scheme of
observed flows for the estimation of forecast errors. The forecasting algorithm can be
described by a set of equations as following. The flow at the upstream boundary
points is obtained from
q̂ t = α +
3
∑β q
i
i =1
(4.19)
t −i
92
Chapter 4 – Large Scale Hydrological Model
where q̂ t is the estimated forced boundary flow at one time step ahead and q t −i are
the observed flows at the previous time steps. The model parameters α and βi are
determined using regression analysis on daily time series of observed discharges (e.g.
Alt et al., 1989). At the downstream forecast points, both a forecast value of the flow
and an estimate of the prediction error are obtained through
Q ft = Q M
t + ê t
ê t = θ +
(4.20)
2
∑ φ (Q
i
i =1
t −i
− QM
t −i )
(4.21)
where Q ft is the forecast flow at one time step ahead, Q M
t is the model predicted flow
at one time step ahead, êt is the estimated error on the model predicted flow ( Q M
t )
and Q t −i and Q M
t −i are observed and model simulated flows, respectively, at previous
time steps. The parameters of the error model θ and φi are determined using
regression analysis on historic forecasts at that point (e.g. Alt et al., 1989).
4.2
System implementation
4.2.1
Software components
A schematic overview of the major system components is shown in figure 4.4. The
structural core of the LSHM is a distributed hydrological process model that requires
time-varying input of rainfall and actual evapotranspiration. The parameters for the
model are related to the hydraulic properties and configuration of the topography,
soils, land cover and stream channels. These data are typically obtained from global
data sets available through internet and published maps, such as those found in
YRCC (1987). The model parameters generally require calibration adjustment
through evaluation of simulation output against observation data, in order to produce
favourable results for the resolvable grid scale (see figure 4.4). For further details on
hydrological models and hydrological modelling practice, the reader is referred to the
general literature (e.g. Beven, 2001).
Figure 4.4. Schematic representation of the major components of the river monitoring
system.
93
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
The rainfall and evaporation inputs for the model are produced by the EWBMS from
satellite remote sensing and ground observation data (see figure 4.4). The EWBMS
produces near-real-time grid fields that are used as input for the LSHM, which is
structured on a two-dimensional grid with a spatial resolution matching that of the
input (approximately 5 by 5 km). From the storages on and below the land surface,
the water is transported towards a one-dimensional stream network representation,
which subsequently routes the water downstream along the river channels towards the
outlet of the model basin. At the end of a simulation run, the internal storage can be
saved in a file that can be used to specify the initial conditions at the start of
continuation run. Alternatively, the initial storage can be specified for example as a
constant value for the entire model grid.
Simulations can also be carried in forced boundary mode. When running in this mode
the observed discharge at some upstream station in the river network is used as
specified boundary condition for the period of simulation. The part of the basin that is
upstream of the boundary inflow point is then effectively cut off from the model area.
Figure 4.5 shows an overview of the system when running in forecast mode. In this
case the flow data of the station for which the forecast is to be made are assimilated
into the model from the real time observation data base. Then, the model is run in
combination with a user-specified rainfall scenario to produce a forecast of the
discharge for the next time interval. While running in operational forecast mode, the
model must be optimally calibrated and initialized.
Figure 4.5. Schematic representation of the forecast mode extension components.
4.2.2
User interface
The model is specifically designed to be used within an operational environment
consisting of a data base infrastructure connected with client machines through a
local area network. Details of the systems integration in the Bureau of Hydrology
data base and flood forecasting environment are outlined in chapter 5 of this report. A
full account of the user interface is presented in the User Manual (Maskey and
Venneker, 2008). Here, the description is restricted to an overview of the general
characteristics. The user interface is created in such a way that the model can also be
run as a stand-alone implementation, provided that the input data is available locally.
From a user point of view, this makes no difference. Besides the start-up screen, there
is no difference between the menu structure of the WRFS and the HWFS.
At the top level, a distinction is made between general file utilities, simulation and
graphical visualization, each with its own pull down menu. Simulation and graphic
94
Chapter 4 – Large Scale Hydrological Model
view facilities have a hierarchical structure that precludes actions to be performed if
the previous step has not been made first. Submenus within the simulation tree allow
to:
• specify the simulation control, such as timing and initial state
• prepare the simulation, i.e. read and preprocess the input data
• run the simulation, i.e. execute the model
• save the simulation result output for analysis
The latter action, in particular, will also save a large number of internal state data that
can be analyzed separately if required, but generally not as part of the operational
model running. The preparation and simulation itself will notify the user when ready,
and during model execution the progress is shown in the status bar section of the
main screen. Optionally, precipitation input can be processed separately for subbasins for later water balance comparison.
The graphic view menu has options to view the results itself or to compare the results
against observed discharge data. Further selections allow to choose a station from an
internally stored list or to choose independent points along the river channel network
and will show the hydrograph for the whole simulation period in a separate graphics
window. When output is displayed for a station at which an observational record is
available, a result summary is printed below the hydrograph to enable the operator to
carry out an objective analysis of the simulation results. The summary includes the
mean observed and simulated discharges, the mean error (model bias), the root mean
squared error and the coefficient of efficiency, as well as the simulated and observed
volumes and their percentage difference.
The forecast mode options are presented in a separate popup form in which the
operator selects the timing parameters and chooses from one or more likely rainfall
scenarios (see also figure 4.5). As with running in simulation mode, the forecast
results can be displayed graphically in a result screen.
It is noted that the graphics facilities are only intended for an initial assessment by the
operator, and when running as stand-alone model. Since the results are also
transferred to the data base, a much more detailed selection for visualization is
generally made from within the Yellow River Flood Forecasting System that is
directly connected with the data base infrastructure. This allows for a uniform
presentation of all data, both observed and simulated and is the preferred platform to
select and compile results for further assessment.
A full account of the user-changeable parameters and their values as set upon delivery
is given in the User Manual (Maskey and Venneker, 2008). These parameters can be
adjusted through a carefully designed and executed re-calibration process, when more
data have become available, or alternatively, when conditions have significantly
changed to justify the attempt to optimize the settings in order to warrant an improved
performance.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
4.3
Upper Yellow River Water Resources Forecasting System
4.3.1
Description of the data requirements
Atmospheric input data for WRFS are required on a daily time interval:
1. EWBMS daily precipitation fields
2. EWBMS daily actual evaporation fields
The projection of the satellite-derived input forcing data to the model grid
specification for the upper Yellow River model is carried out by the EWBMS. It is
noted that only liquid precipitation is to be provided as input. In cases where there is a
melting snow cover present in the area, the snowmelt is aggregated into the
precipitation fields by the EWBMS. For the WRFS, this is simply treated as a water
mass influx to the system.
In order to produce the output required for visualization and to evaluate performance
statistics, the WRFS needs daily average river discharges from one or more of the
following stations (see figure 4.6), with latitude and longitude in decimal degrees and
elevation in metres:
Tangnaihai
Jungong
Maqu
Dashui
Tanke
Jimai
Huangheyan
100.15 E, 35.50 N, 2665
100.65 E, 34.70 N, 3079
102.08 E, 33.97 N, 3400
102.27 E, 33.98 N, 3400
102.47 E, 33.42 N, 3410
99.65 E, 33.77 N, 3948
98.17 E, 34.88 N, 4215
Users can add or remove stations as required. It is not problematic if discharge data
for a station are not available. The model will run as usual and output results, but it is
not able produce the statistical comparison with measured data. If part of a station
time-series record is missing, it will not be used to evaluate the comparison statistics.
4.3.2
Description of the terrain data
The model grid uses a conformal conic projection with the following specifications:
Projection:
Ellipsoid:
Central meridian:
Reference latitude:
Standard parallel 1
Standard parallel 2
False easting:
False northing: 0 m
Lambert conformal conic (Beijing 1954 parameters)
Krasovsky 1940
108 E
28 N
34 N
39 N
500,000 m
Upper Y boundary:
Lower Y boundary:
Right X boundary:
Left X boundary:
Grid rows:
Grid columns:
898,770 m
473,770 m
76,000 m
−609,000 m
85; node spacing: 5000 m
137; node spacing: 5000 m
96
Chapter 4 – Large Scale Hydrological Model
Note that grid boundaries refer to the cell edges. Grid rows are counted along the Y
dimension, grid columns are counted along the X dimension. The X and Y directions
for the projected grid are not parallel to the E-W and N-S directions, respectively. See
Snyder (1987) for further details on the applied map projection.
The elevation data were obtained from the Shuttle Radar Topography Mission data
set with a resolution of 30 arc seconds (SRTM30), see Farr and Kobrick (2000). The
data were obtained from URL ftp://e0srp01u.ecs.nasa.gov, using tiles E60N40 and
E100N40 from the version 2 distribution. Re-projection and aggregation of the raw
data into the target grid specification above was carried out using standard GIS
software.
The stream network (see Fig. 4.6) and (sub)basin boundaries extracted from the DEM
using proprietary software that enables to specify the network start and end points as
required, and follows largely the overall procedure described by Jenson and
Domingue (1998), which entails the following steps:
1. Removal of spurious pits and other artefacts that would otherwise disrupt the
flow pattern.
2. Creation of a flow direction grid, which is used for subsequent analysis and
processing steps.
3. Delineation of drainage accumulation area (upslope catchment area) and
(sub)basin boundaries, using the method by Marks et al. (1984).
4. Extraction of the drainage channel network by automatically following the
flow direction grid down slope from the assigned stream start points to the
outlet or higher-order stream (see also O’Callaghan and Mark, 1984).
The pit removal was carried out manually in an iterative fashion to ensure that the
resulting drainage pattern and delineated (sub)basin boundaries (figure 4.6) are
consistent with those found in the printed maps (YRCC, 1987).
Tangnaihai
850000
Huangheyan
800000
Jungong
750000
700000
Jimai
Maqu Dashui
650000
Tanke
600000
550000
500000
-600000
-550000
-500000
-450000
-400000
-350000
-300000
-250000
-200000
-150000
-100000
-50000
Figure 4.6: Layout of the WRFS catchment and stream network geometry.
97
0
50000
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Auxiliary terrain data used to obtain first-guess estimates of model parameters are
soil maps (FAO-UNESCO, 2003) and land cover data from the Global Land Cover
Characteristics data set, obtained from URL http://eros.usgs.gov (see Loveland et al.
2000, for details). For river cross-section geometry, use was made of survey data
from YRCC where available.
4.4
Weihe basin High Water Forecasting System
4.4.1
Description of the data requirements
Atmospheric input data for HWFS are required on a daily time interval:
1. EWBMS daily precipitation fields
2. EWBMS daily actual evaporation fields
The EWBMS outputs for the Wei River are matched to the model grid by the HWFS
LSHM using bilinear interpolation. The EWBMS fields for the Weihe model are
different depending on the satellite (GMS or FY2C) resolution and geo-reference, and
are slightly larger than the model grid extents. Snow accumulation and ablation are
dealt with in a similar fashion as is done for the WRFS (see above).
Daily average river discharge data can be provided for the following stations (see Fig.
4.7), with latitude and longitude in decimal degrees and elevation in meters:
Huaxian
Zhuangtou
Lintong
Maduawang
Zhangjiashan
Xianyang
109.77 E, 34.58 N, 346.2
109.83 E, 35.05 N, 375.0
109.20 E, 34.43 N, 357.3
109.15 E, 34.23 N, 438.9
108.60 E, 34.63 N, 476.3
108.70 E, 34.32 N, 390.4
Users can add or remove stations as required. The treatment of missing river flow
observation data is similar to that of the WRFS as described above.
4.4.2
Description of the terrain data
The model grid is specified for a latitude-longitude coordinate system as follows:
Projection:
North boundary:
South boundary:
East boundary:
West boundary:
Grid rows:
Grid columns:
Geographic latitude-longitude
37.55 N
33.45 N
110.55 E
103.45 E
82; node spacing: 0.05 deg (5547.5 m, av.)
142; node spacing: 0.05 deg (4535.3 m, av.)
Note that grid boundaries refer to the cell edges. Grid rows are counted along the N-S
dimension, grid columns are counted along the W-E dimension. Metric node
distances for the Wei River grid are latitudinal range averages for the WGS 84
ellipsoid. In any case, node spacing is measured with respect to the reference ellipsoid
and changes with altitude of the terrain. See Snyder (1987) for further details on map
projections.
98
Chapter 4 – Large Scale Hydrological Model
The elevation data were obtained from the Shuttle Radar Topography Mission data
set with a resolution of 30 arc seconds (SRTM30), see Farr and Kobrick (2000). The
data were obtained from URL ftp://e0srp01u.ecs.nasa.gov, using tile E100N40 in the
version 2 distribution. Since the raw data are in a latitude-longitude grid, the only
processing step carried was to aggregate the elevations to the target resolution by
averaging. The extraction of the stream network and the basin delineation were
carried out as described for the upper Yellow River above. The stream network layout
is depicted in figure 4.7. Auxiliary terrain data used to obtain first-guess estimates of
model parameters are soil maps (FAO-UNESCO, 2003) and land cover data from the
Global Land Cover Characteristics data set, obtained from URL http://eros.usgs.gov
(see Loveland et al. 2000, for details). For river cross-section geometry, use was
made of survey data from YRCC where available.
Weihe LSHM Stream Network
Grid G55
Rev 2006-05-24
37.5
25
24
7
8
37
23
23
6
7
24
22
20
22
6
36.5
15
21
21
13
36
5
20
14
40
39
14
5
13
12
39
4
4
19
19
18
3
3
2
18
35.5
38
46
35
37
45
41
10
34
47
44
43
44
43
34.5
46
42
11
17
16
11
33
Liulin
16
32
1
Zhuangtou
32
Yaoxian
Chunhua
40
42
1
17
34
36
41
2
15
10
37
38
45
35
12
36
9
8 47
Zhangjiashan
9
Huaxian
Luofubu
33
Taoyuan
2531 Lintong
35
29
Xianyang
26
27
28
29
Qinduzhen
Goaqiao
26
Maduwang
30
Luolicun30
Dayu
34
27
31
28
33.5
103.5
104
104.5
105
105.5
106
106.5
107
107.5
108
108.5
109
109.5
110
110.5
Figure 4.7: Representation the Weihe basin HWRF stream network layout.
4.5
Evaluation of simulation results
4.5.1
Validation data
Input data for validation were produced by the EWBMS, delivering daily values of
precipitation and evaporation. The period of time is from 2006-01-01 to 2008-07-31,
covering a little more than 2.5 years (equivalent to 943 days), and almost three
rainfall seasons. All input data used for validation are derived by the EWBMS from
FY2C satellite imagery obtained during the demonstration phase of the project. This
choice to use exclusively FY2C data for validation is prompted by (i) the fact that
both the models and the EWBMS have been progressively adapted and optimized for
this input type during the latter parts of the testing phase, and (ii) that this particular
satellite will be used for further operation. Although FY2C data are available since
approximately mid-2005, the starting point for validation is deliberately chosen at the
beginning of the next calendar year, i.e. during the low flow period, in order to
minimize the uncertainty related to establishing realistic initial storage conditions of
the model. Given the relatively short period for which data are available, sensitivity to
initial conditions could adversely affect the validation outcome.
99
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Validation data used for the Upper Yellow River WRFS model consisted of mean
daily discharge, measured over 24 h intervals starting from 08:00 local time, obtained
at four hydrological stations. The discharge stations are located in the main branch of
the Upper Yellow river as indicated in Table 4.1 (see also Fig. 4.6). The station of
Jungong has a relatively short period of missing observation data, approximately 100
days in the first four months of 2006. Otherwise, the station records are complete for
all four stations during the entire validation period.
Validation data used for the Weihe Sub-basin HWFS consisted of mean daily
discharge, measured over 24 h intervals starting from 08:00 local time, obtained at the
hydrological stations Huaxian and Lintong. These stations are located relatively close
to each other. Note that the Huaxian hydrological station does not measure the flow
contributed by the Beiluohe tributary (the last major tributary in the in eastern part of
the basin, see figure 4.7). Data from this and the other major northern tributaries are
only sparsely available or are not representing daily mean values. Except for the
lower Weihe valley, stations in most of the Weihe basin fall outside the responsibility
of the YRCC. Due to the unstable bed conditions in the outlet reach of the Weihe
River, there is no discharge measurement station capturing the flow of the entire
Weihe sub-basin at the confluence with the Yellow River.
The performance of the models is assessed by several objective criteria. The squared
correlation coefficient (in the statistical sense: the coefficient of determination), is
indicated by R2, which is determined from a non-weighted linear regression between
the observed and the simulated discharge series. The value ranges from [−1;+1] with
the positive limit indicating perfect fit. A similar measure of fit is the coefficient of
(model) efficiency (COE) as defined by Nash and Sutcliffe (1970), viz.
∑ (Q
COE = 1 −
∑ (Q
N
i =1
N
i =1
i ,obs
− Qi ,sim )
2
i ,obs − Qavg )
(4.22)
2
Table 4.1: Location characteristics of discharge stations used for model validation
Upper Yellow River
Weihe Sub-basin
Station location Drainage area (km2) Station location Drainage area (km2)
Tangnaihai
118,725
Huaxian
106,300
Jungong
97,825
Lintong
93,700
Maqu
86,725
Jimai
45,800
Table 4.2: Spatial variation ranges of model parameters used for validation runs.
Parameters
Upper Yellow River Weihe River
Maximum infiltration rate (mm/day)
4-50
50-200
Hydraulic conductivity (mm/day)
1000
50-200
Effective porosity
0.25
0.25
Scaling parameter for base elevation
200
500
River bed thickness factor
1
2
Limit on land grid cell to river bank slope
Maximum
1:500
1:100
Minimum
1:2000
1:2500
Manning’s roughness coefficient
River flow
0.05
0.04
Surface flow
1
0.5
100
Chapter 4 – Large Scale Hydrological Model
where the subscripts denote observed, simulated and average of the discharge, and
the index i refers to the individual data points from a series of length N data. The
COE value ranges from minus infinity to +1 (exact fit). As a special case, a value of
zero indicates that the model behaves no better than assuming that the average
discharge is the best predictor. The model bias is given by:
BIAS = Qsim,avg − Qobs,avg
(4.23)
which indicates whether there is a general tendency towards over- or underestimation
of the model. The bias is also reported as a percentage error of the cumulative
discharge volume during the simulation period. Furthermore, the root mean squared
error (RMSE) and its normalized version, the relative root mean squared error
(RRMSE) are computed by
∑ (Q
N
RMSE =
i =1
− Qi ,sim )
2
i ,obs
N
(4.24)
RMSE
RRMSE =
Qavg
respectively, where the symbols are as defined above. The RMSE and RRMSE are
expressed in the same units as the discharge. Table 4.2 lists the spatial range of
variation in the model parameters for both implementations as determined by
successive calibration runs. The values are probably not optimal and may be revised
when more data are available.
4.5.2
WRFS validation results
The initial state of the Upper Yellow River WRFS has been setup by using an
independent spin-up series for 11 years of input data derived from interpolated GTS
observations for the period 1991-2001 (obtained from NOAA-NCDC), in
combination with evaporation computed by a land surface model processed on a halfdegree grid. On average some ten GTS gauges are reporting on a daily basis inside or
in the proximity of the Upper Yellow River drainage basin. Although the reliability of
the input and the accuracy of the evaporation computations have not been verified
except roughly for annual water balance checks, the spin-up procedure is assumed to
result in reasonably well-behaved initial storage conditions for low-flow situations,
compared to a no-knowledge scenario. The observed and simulated hydrographs
resulting from the validation run for each of the four stations are shown in figure 4.8.
Table 4.3: Model performance for the Upper Yellow River WRFS.
Criterion
Hydrological station
Jimai
Maqu
Jungong
Tangnaihai
forecast *)
2
R
0.80
0.82
0.80
0.80
0.93
COE
0.77
0.82
0.80
0.80
0.84
RMSE (m3/s)
55.5
128.2
162.3
189.3
161
RRMSE
0.45
0.38
0.37
0.39
0.17
BIAS (m3/s)
21.9
−2.1
2.6
−3.24
−78
% volume error
17.9
−0.61
0.6
−0.67
Drainage area (km2)
45800
86725
97825
118725
*) 24 h forecast results.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 4.8: Simulation results for the Upper Yellow River WRFS during the
validation period.
Figure 4.9: Results of daily 24 h forecast runs for Tangnaihai from June 1st to
November 27th, 2007.
102
Chapter 4 – Large Scale Hydrological Model
Figure 4.10: Scatter plots of the validation run for the Upper Yellow River WRFS.
Figure 4.11: Histograms of the simulation error distributions from the Upper Yellow
River WRFS validation run. There are 20 bins over the full scale on the x-axis.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Regression lines for the validation run at the same stations are presented in figure
4.10. Furthermore, figure 4.11 shows station histograms of the simulation error at the
four stations. Figure 4.9 shows the hydrograph for daily 24 h forecasts at Tangnaihai
during the 2007 rainfall season. The overall model performance results using the
criteria described in section 4.5.1 are presented in table 4.3 below.
4.5.3
HWFS validation results
Due to lack of long-term auxiliary rainfall and runoff data for the Weihe sub-basin,
the initial state of the HWFS could only be established by using the available data for
2006 and 2007. Starting from an arbitrary state, the two-year input has been recycled
through sequential model runs whereby the resulting state at the end of each run was
used as initial conditions for the subsequent run. This procedure has been repeated
four times to cover a total spin-up period of eight years. A procedure such as this
should be considered as a last resort, only to be used if nothing else is available and
does not permit to make statements about the reliability of the initial conditions.
Because YRCC does not have access to stream information from stations outside the
lower Weihe valley, and because of the numerous hydraulic operations in the Weihe
basin at large, it was decided to carry out the validation by running the model in
forced boundary mode, driven at Xianjiang in the main river branch, at Zhangjiashan
upstream of the outlet of the Jinghe, and for the smaller southern tributaries at
Qinduzhan and Maduwan. Note however, that for the last three stations, large parts of
the annual record data are not available. Particularly for the Zhangjiashan station this
means that part of the flow was driven from normal simulations in the Jinghe basin,
which has more than 80 reservoirs in its 42,000 km2 catchment area.
The observed and simulated hydrographs resulting from the validation run for the
stations are shown in figure 4.12. Note that the station at Lintong, as many other
stations in the lower Weihe region, is only operated during the flood season, generally
from June to September. As a result, a simulated-observed flow comparison for the
base flow periods cannot be made. Regression lines for the validation run at the same
stations are presented in figure 4.14. Furthermore, figure 4.15 shows station
histograms of the simulation error at the validation stations. Figure 4.13 shows the
hydrograph for daily 24 h forecasts at Huaxian during the 2007 rainfall season. The
overall model performance results using the criteria described in section 4.5.1 are
presented in table 4.4 below.
Table 4.4: Model performance for the Weihe sub-basin HWFS
Criterion
Hydrological station
Lintong
Huaxian
R2
COE
RMSE (m3/s)
RRMSE
BIAS (m3/s)
% volume error
Drainage area (km2)
0.75
0.71
97.0
0.46
−9.1
−4.4
93700
0.80
0.79
63.5
0.50
−14.1
−11.1
106300
*) 24 h forecast results.
104
forecast *)
0.79
0.75
110
0.37
-47
Chapter 4 – Large Scale Hydrological Model
4.5.4
Discussion
The hydrograph results for the Upper Yellow River WRFS show a consistent result
for all four stations (figure 4.8). The peak discharges appear to be over-estimated in
all stations except at the beginning of the simulation period. This may to some extent
be explained by a slight over-estimation in the area upstream of Jimai, covering about
one-third of the basin, although the effect is somewhat further enhanced between
Jimai and Maqu. Precipitation in the upper part of the basin is sparsely monitored,
Figure 4.12. Simulation results for the Weihe sub-basin HWFS during the validation
period.
Figure 4.13: Results of daily 24 h forecast runs for Huaxian from June 1st to
November 27th, 2007.
105
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
and is characterized by very high altitudes, which makes a proper water balance
analysis impossible. Minor deviations between observed and simulated discharges
appear in the beginning of the runoff seasons at the onset of snowmelt, which may
differ for altitudinal ranges.
The performance and error indicators for the upper Yellow River (table 4.4) show
values that can be considered generally good, considering the extent of the area. The
average error between observed and simulated discharge is small compared to the
range of flow, always less than 10% of the maximum peak flow during the simulation
period. With the exception of Jimai, simulations for the stations Tangnaihai, Jungong
and Maqu show a negligible bias and water balance error. The slightly larger values
for Jimai, with a positive bias indicating over-estimation of discharge are probably
related to the explanation above. It is noted that the base flow simulations for Jimai
are not deviating from the observations (figures 4.8 and 4.10). Moreover, the scatter
plots of figure 4.10 show little spread in the higher discharge regime, which is not
commonly seen in hydrological modeling studies. This is also apparent from the error
distributions of figure 4.11. The vast majority of the simulation errors are located in
the centre. Both figure 4.10 and 4.11 also show the over-estimation of peak flows,
which results in slightly skewed error distributions.
Figure 4.14: Scatter plots of the validation run for the Weihe sub-basin HWFS.
Figure 4.15: Histograms of the simulation error distributions from the Weihe subbasin HWFS validation run. There are 20 bins depicted over the full scale on the xaxis.
106
Chapter 4 – Large Scale Hydrological Model
The daily 24 h forecasts for Tangnaihai during the 2007 rainfall season compare well
with the observed discharges, as is shown in figure 4.9. Moreover, from table 4.3 it is
seen that the fits have improved with respect to the simulation runs. The bias error,
however, has reduced but is still relatively small compared to the magnitude of the
discharge. This may possibly be improved by a more elaborate data assimilation
scheme as well as using alternative rainfall forecasts instead of simple scenarios.
The situation for the Weihe HWFS is more difficult to assess. As can be seen from
the hydrographs (Fig. 4.12), the response to rainfall is very fast, in reality often rising
to peak discharge in much less time than the 24 hour observation and simulation
intervals. Moreover, the area is characterized by a very large number of hydraulic
operations. There are hundreds of reservoirs plus a large number of diversions present
in the area, mostly for, but not limited to, irrigation purposes. In general, details of the
operations are unknown, but from comparing mean daily and instantaneous discharge
data, and rainfall data for a small number of situations, it appears that at least some
reservoirs are releasing water very quickly, resulting in flow characteristics that
cannot be explained from the rainfall data.
Nevertheless, the performance indicators for the Weihe sub-basin HWFS summarized
in table 4.4 show reasonable model behavior, with only the R2 and COE for Lintong
being slightly less than those for the stations in the upper Yellow River. The overall
root mean squared error is less than 10% of the higher peak flows during the
simulation period (see figure 4.12). The bias and volume error are negligible
compared to the observed range of variation. Contrary to the simulations for the
upper Yellow River, the scatter plots for the Weihe simulations do show a larger
variation in the higher flow regimes (figure 4.14). This can probably be explained by
the reservoir operations taking place during high water periods. The simulation error
distributions presented in figure 4.15 show that the errors are located in a narrow
band around the zero point and in terms of frequency fall off rapidly for the larger
error ranges. This is in line with the rapid response of the area resulting in narrow
rainfall-runoff response peaks with a considerable peak flow-base flow range.
The daily 24 h forecasts for Huaxian during the 2007 rainfall season compare
favorably to the observed discharges, as is shown in figure 4.13. Contrary to the
upper Yellow River, the fits are slightly less with respect to the simulation runs (table
4.4). The major problem for forecasting flows in the Weihe area are probably related
to the reservoir operations. Although difficult to achieve, inclusion of reservoir
outflow data in the model scheme will be required to obtain improved forecast
results.
It is obvious that a period of only two-and-half years is short for a comprehensive
model performance assessment. The reasons for this are largely related to the
availability of data, with the FY2C imagery becoming available only in mid 2005. It
is therefore recommended to keep monitoring the model output during the coming
years in order to collect a more complete set of data that can be used for further
calibration and validation at a later stage. Furthermore, it would be useful for the
Weihe in particular to increase the monitoring frequency to four or six synoptic times
per day, and to investigate the possibility of acquiring (real-time) information of the
major hydraulic operations. This would also enable to identify and improve
shortcomings with the outlook on further optimizing the performance, which would
in turn improve the quality of the services that can be delivered from the results.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
4.6
References
Alt, F.B., Hung, K., and Wun, L.-M. (1989) Time Series Analysis, In: Wadsworth,
Jr., H.M. (ed.) Handbook of Statistical Methods for Engineers and Scientists, Chapter
18, McGraw-Hill.
Arcement, G.J., Jr., and Schneider, V.R. (1989) Guide for Selecting Manning’s
Roughness Coefficients for Natural Channels and Flood Plains, USGS Wat. Supply
Pap. 2339, U.S. Geological Survey, Denver, CO.
Arora, V.K. and Boer, G.J. (1999). A variable velocity flow routing algorithm for
GCMs, J. Geophys. Res., 104(D24):30965-39979.
Beven, K.J. (2001), Rainfall-Runoff Modelling, The Primer, John Wiley and Sons.
Brutsaert, W. (2005) Hydrology, an Introduction, Cambridge Univ. Press.
Chow, V.T. (1959), Open-Channel Hydraulics, McGraw-Hill Book Company, Inc.
Cunge, J.A. (1969), On the subject of a flood propagation method (Muskingum
method), J. Hydraul. Res., 7:205-230.
EARS (2005) Manual for the EARS Energy and Water Balance Monitoring System
(EWBMS), EARS, Delft, the Netherlands.
FAO-UNESCO (2003) The Digital Soil Map of the World, Version 3.6, Food and
Agriculture Organization of the United Nations, Rome, Italy.
Farr, T.G., and Kobrick, M. (2000) Shuttle Radar Topography Mission produces a
wealth of data. Eos, Trans. Am. Geophys. Union, 81:583-585.
Goode, D.J., and Appel, C.A. (1992) Finite-difference Interblock Transmissivity for
Unconfined Acquifers and for Acquifers Having a Smoothly Varying Transmissivity,
USGS Wat. Resour. Investigations Report 92-4124. U.S. Geological Survey, Denver,
CO.
Harbaugh, A.W. (2005) MODFLOW-2005, the U.S. Geological Survey Modular
Ground-water Model, the Ground-water Flow Process, U.S. Geological Survey
Techniques and Methods 6-A 16. U.S. Geological Survey, Denver, CO.
Jenson, S.K., and Domingue, J.O. (1988) Extracting topographic structure from
digital elevation data for geographic information system analysis, Photogramm. Eng.
Remote Sens., 54:1593-1600.
Lighthill, M.J., and Whitham, G.B. (1955) On kinematic waves, I. flood movement in
long rivers, Proc. Roy. Soc. London, 229A:281-316.
Loveland, T.R., Reed, B.C., Brown, J.F., Ohlen, D.O., Zhu, J, Yang, L., and
Merchant, J.W. (2000) Development of a Global Land Cover Characteristics database
and IGBP DISCover from 1-km AVHRR data: Int. J. Remote Sensing, 21:1303-1330.
Marks, D., Dozier, J., and Frew, J. (1984) Automated basin delineation from digital
elevation data, GeoProcess., 2:299-311.
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Chapter 4 – Large Scale Hydrological Model
Maskey, S., and Venneker, R. (2008) Large-Scale Hydrological Model for the
Satellite-based Water Monitoring and Flow Forecasting System in the Yellow River
Basin, User manual, Version 3, UNESCO-IHE Institute for Water Education, Delft,
Delft, the Netherlands.
Nash, J.E., and Sutcliffe, J.V. (1970) River flow forecasting through conceptual
models. Part I – A discussion of principles, J. Hydrol., 10:282-290.
O’Callaghan, J.F. and Mark, D.O. (1984) The extraction of drainage networks from
digital elevation data, Comput. Vision Graphics Image Process., 28:323-344.
Ponce, V.M. (1986), Diffusion wave modeling of catchment dynamics, J. Hydr.
Engrg., 112(8), 716–727.
Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery, B.P. (1992),
Numerical Recipes in C. The Art of Scientific Computing, Cambridge Univ. Press.
Prudic, D.E., Konikow, L.F., and Banta, E.R. (2004) A New Streamflow-routing
(SFR1) Package to Simulate Stream-Aquifer Interaction with MODFLOW-2000,
USGS OFR 2004-1042, U.S. Geological Survey, Denver, CO.
Snyder, J.P. (1987) Map Projections – A Working Manual, USGS Professional Paper
1395.
Tang, X.-N., Knight, D.W., and Samuels, P.G. (1999), Volume conservation in
variable parameter Muskingum-Cunge method, J. Hydr. Engrg., 125:610-620.
YRCC (1987), Huanghe River Valley Atlas. Yellow River Conservancy Commission
Publishing, Zhengzhou, China.
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110
Chapter 5 – System Implementation at YRCC
5
SYSTEM IMPLEMENTATION AT YRCC
5.1
System set-up
5.1.1
Satellite data receiving and processing system
The meteorological satellite data reception and processing system, is a PC-based
system, developed by Shinetek Satellite Application System Engineering Co. Ltd in
Beijing to receive, process and display FY-2c (or MTSAT) S-VISSR data. FY-2c is a
Chinese geostationary meteorological satellite, located at 105o E. The system has
been installed at the YRCC office in Zhengzhou and Lanzhou in April 2005. Since
then the YRCC Hydrology Bureau receives and processes hourly meteorological
satellite images for the use with the EWBMS. This includes, after processing and
projection, the creation of the VIS and IR band image formats that serve as input to
the EWBMS. All the files are put on the project FTP server.
Signal processing procedure
The high-frequency satellite signal (FY-2c: 1687.5 MHz, MTSAT: 1687.1 MHz) is
received by a parabolic antenna with a diameter of 3m. The satellite signal is
amplified and converted in the high-frequency unit to the first intermediate-frequency
of 137.5 MHz. This signal is sent to the receiver by cable, where it is converted by
filtering and amplifying to the second intermediate-frequency (10.7 MHz). After
demodulation, the fundamental signal of 660 kbps is generated. This signal is send to
a bit synchronizer for clock extraction and code conversion, and then to a frame
synchronizer for frame synchronous signal detection, channel separation and data
format conversion. Finally the data are transmitted to a computer for storage and later
processing by the EWBMS system to various products.
Figure 5.1: Satellite receiving antenna in Zhengzhou (middle).
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 5.2: The satellite receiving computer and receiver in Zhengzhou.
System configuration
The Meteorological satellite data reception and processing system is a PC-based
system (see figure 5.2) that consists of the following components:
• Parabolic antenna of 3 m diameter
• Feed horn
• Low noise amplifier with LNA down converter (high-frequency unit)
• High-frequency cable
• PCI ingestor card
• 2 PC’s
• 2 monitors
• Software
Receiving computer
The Geostationary meteorological satellite receiving software (GeoReceive) is an
important component of the front-end of the entire system. The software is
permanently running and managing the data reception according to a timetable set by
the user. The software interface will hide during idle time and pop-up and enter in
receiving mode on time of data reception. The software automatically adds year,
month, day, hours, minutes, seconds and milliseconds, scanning line number, and
automatically superimposes a latitude-longitude grid, as well as provincial
boundaries, rivers and lakes. Figure 5.5 shows the interface of the GeoReceive
software. Automatic projection software (AutoProject) allows automatic projection of
the received data according to parameters set by the user. The software also transmits
the projected files to the back-end and external servers. Figure 5.6 show the software
interface. The data product is automatically shown after reception.
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Chapter 5 – System Implementation at YRCC
Figure 5.3: Satellite data receiver hardware configuration.
Processing computer
The main processing software is on the processing computer and consists of software
for image processing, product generation, product storage in a database and the
catchment monitoring website. The image processing and product generation
software processes the satellite data to cloud products, which includes geographic
overlays, moving clouds animation, and automatic storage in the MYSQL database.
5.1.2
Computer network
The LAN at the YRCC Hydrology Bureau has a double star topology. There are two
Cisco 6500 switches in the network core, and many Cisco 3500 switches, which
together with the 1000 Mb web backbone make up the LAN. The server is placed in
the Hydrology Bureau network room, which realizes the network mutual connections
in the fastest and the most reliable way. Figure 5.4 shows the structure of the
Hydrology Bureau LAN.
Figure 5.4: Structure of the Hydrology Bureau LAN.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 5.5: The interface of the GeoReceive software.
Figure 5.6: The interface of AutoProject software.
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Chapter 5 – System Implementation at YRCC
FY-2c
Hydro-info
Server
Energy and Water Balance
monitoring PC
Server / Database
WMO-GTS
Satellite Receiving PC
WEB site
Present
Hydro
models
Runoff and flood
forecasting PC
Figure 5.7: System component integration structure
For the Sino-Dutch Project, all the systems are running and connecting to the LAN, in
particular the satellite receiving and processing system, the EWBMS, the runoff
forecasting system for the Yellow river upper reach, and the flood forecasting system
for the lower Weihe, as well as the catchment monitoring website system that
publishes the data on the internet. The detailed integration of the system hardware
components with the LAN is shown in figure 5.7.
5.1.3
Data base
The real-time water information of both the source area of the Yellow River and the
Weihe River are available through the Real-time Water Information Database
(RWDB) of the Yellow River. These data are loaded into the Sino-Dutch Database by
a special program. The RWDB is built as an individual system. There are three main
steps in the data reporting system: the reporting of the hydrological stations, the
transmission by the sub-centres, and the reception, translation and storage by the
centre in Zhengzhou. The RWDB is based upon the Sybase DB management system.
There 13 types of real-time data in the data base, including precipitation, water level,
discharge and evaporation.
According to the requirements of Sino-Dutch project, a special program was
developed, which can automatically read the real-time rainfall data (hourly, daily and
dekadly) and hydrological data (water level, discharge, etc.) from RWDB and write
these data into the rainfall table and hydrological table in Sino-Dutch DB for use with
the WRFS, HWFS and Sino-Dutch website. The Sino-Dutch DB (in Chinese and
English) is based upon the SQL Server, and there are 15 tables including the
SD_SPRPR, a table for rainfall duration at the hydrological stations, SD_SDPR, a
table for the daily rainfall at the hydrological stations, and SD_SHYDR, a table
containing the basic hydrological data. A flow chart of the data exchange is shown in
figure 5.8
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Begin
Open RWDB
Retrieving related today’ water information
Data format conversion
Open Sino-Dutch DB
Writing above data into Sino-Dutch DB
Close all DB
End
Figure 5.8 Sino-Dutch DB data flow chart
The data base server is currently a DELL 6600 PC, with PIII900 CPU, 1G memory
and 250G SCSI HD, running under a Windows 2000 operating system. The data base
management system is SQL Server 2000. Tables have been designed and created,
basic data have been loaded, and programs have been developed for loading the realtime flow data, the runoff and flood forecasting results, as well as for compiling and
loading the LAS station observations. At present, a Chinese and an English version of
the data base are operative on the SQL server. All basic station data are loaded in the
relevant tables. Real-time rainfall and flow data are loaded every day automatically.
Runoff and flood forecasting results and also LAS results are loaded manually. These
data can be inquired through the Chinese and English website. The Sino-Dutch data
base is composed of 15 tables in 3 sub-databases. They are:
• the basic information database (4 tables),
• the input information database (7 tables) and
• the processing results data base (4 tables).
The data base structure in presented in table 5.1.
There are 4 tables in the basic information database. The table for the basin code
provides district information. It includes 19 records, one for each district. The table
for the basic meteorological stations provides the basic information of the relevant
meteorological stations, but there is no record in this table. The table for the basic
hydrological station contains the basic information of the relevant hydrological
stations. In this table there are 11 records. The table for the LAS stations provides the
basic information of the LAS stations in Tangke, Maqin, Xinghai and Jingchuan.
Program for loading the real-time regime into database automatically
The main function of this program is to automatically read the real-time rainfall data
(period, day and dekad) and hydrological data (water level and discharge) from the
real-time hydrological regime database and write these data into the rainfall table and
hydrological table in the database.
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Chapter 5 – System Implementation at YRCC
Table 5.1 Sino-Dutch data base table structure
SubTable name
database
Basic
Table for basin code
informatio Table for basic meteorological station
n database Table for basic hydrological station
Table for basic LAS station
Input
Table for period rainfall of meteorology station
InformaTable for daily rainfall of weather station
Table for decadal rainfall of weather station
tion
database
Table for period rainfall of hydrologic station
Table for daily rainfall of hydrologic station
Table for decadal rainfall and monthly rainfall of
hydrologic station
Table for Basic Hydrology data
Processing Table for runoff forecasting result
results
Table for flood forecasting result
database
Table for LAS operation result (10-minute)
Table for LAS operation result (daily)
Table identifier
SD_BASINCD
SD_QSTATION
SD_SSTATION
SD_LASSTATION
SD_QPRPR
SD_QDPR
SD_QTPR
SD_SPRPR
SD_SDPR
SD_STPR
SD_SHYDR
SD_RUNOFF_FORE
SD_FLOOD_FORE
SD_LASOUTPUT1
SD_LASOUTPUT2
Program for loading the runoff and flood forecasting results into databases
This program’s main function is to convert the data format of the runoff and flood
forecasting results from the LSHM, and load these data into the database. As the
creation date of forecasting results is uncertain, this model is run manually.
Compiling and loading LAS station final results into database
There are two kinds of LAS final data, one is at 10-minute interval, and the other is
daily data. Because the data quality needs to be checked and analyzed, these data are
loaded into the database manually.
5.1.4
Organization and operation
For the Sino-Dutch Cooperative Project a steering group has been set up in April
2004 by the Hydrology Bureau of YRCC, lead by Gu Yuanze, deputy director of the
Hydrology Bureau and group leader, and Zhao Weimin, Chief engineer of Hydrology
Bureau as deputy group leader. Secretaries are: Dai Dong and Qiu Shuhui.
The system implemented in the framework of this project includes 6 subsystems,
which are:
• the LAS and automatic meteorological station observation system,
• the satellite images and GTS data receiving and processing system,
• the Energy and Water Balance Monitoring System,
• the upper Yellow river runoff forecasting system,
• the lower Weihe flood forecasting system and
• the achievement publishing web site system.
In order to guarantee the system’s operation, the Hydrology and Water Resources
Information Centre of the Hydrology Bureau has set up a subsystem operational
management regulation, and a group for management and operation. The regulation
contains the rules for running the mentioned subsystems and includes information on
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
(a) how to run, (b) when to run, (c) who is responsible, (d) result analysis, and (e)
reporting of problems. For the monitoring of satellite data reception and processing
operations a form is used that is to be completed by the operator every day and week.
Similar forms are used for the LSHM and LAS processing.
5.1.5
LAS station, data collection and processing
As part of the project four Large Aperture Scintillometers (LAS) have been installed
in Yellow River basin: one in Jingchuan on the Loess Plateau in the Weihe
catchment, and three on the Qinghai-Tibeta plateau at Xinghai, Maqin and Tangke.
The sites were selected during a field trip from April 10 to 24 2005, during which
5000 km was covered. More information on the LAS measurements is given in
section 3.2.3. Detailed information on each LAS site is presented in Annex 1.
The Upper Hydrology Bureau is responsible for the three LAS stations in the
Qinghai-Tibet Plateau area. The Sanmenxia Hydrology Bureau is responsible for the
Jingchuan LAS station. Local workers at the sites are charged with the LAS operation
and maintenance. The data are collected every month on the 1st and 16th. The local
workers take out the PCI card and download the data to a laptop and report any
malfunctioning. Finally they fill out the LAS log form. The LAS data is transmitted
to EARS as a part of the data transfer process. The LAS data and other data measured
at the site are processed with EVATION software to derive the sensible heat flux and
actual evapotranspiration. The most import outputs of the system and algorithm are:
air temperature, wind speed, net radiation, sensible heat flux and actual
evapotranspiration at 10 minute intervals. Detail on the LAS system and processing
are given in section 3.2.
5.2
Catchment monitoring bulletin
5.2.1
Reporting flood and drought information
A catchment monitoring bulletin has been developed in which flood or drought
situations in the Yellow River basin are summarized and reported. In the bulletin, the
energy and water balance situation is evaluated, a degree of deviation from the
normal situation is given and when appropriate and special advices or warnings for
certain authorities are made.
To determine the needed contents of the bulletin, a group of experts worked on two
case studies. The first case is a flood situation in the Weihe basin at the beginning of
October 2005 and the second case a period of mild drought beginning of April 2006.
In each case study, 10 days of data from the EWBMS were analyzed, information
was extracted and evaluated. During the decision making process of the group the
format and contents were determined and two template bulletins were produced.
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Chapter 5 – System Implementation at YRCC
5.2.2
Bulletin contents
Effective rainfall map: shows the distribution of effective rainfall, i.e. rainfall minus
actual evapotranspiration for a 10 day period in the Yellow River basin.
Local information
Tabulated detailed information and time series on rainfall and actual
evapotranspiration are presented for six important locations in Yellow River basin,
Zhengzhou, Xi’an, Yinchuan, Lanzhou, Jinan and Darlag.
Water balances
For each sub-catchment of the basin, the water balance is determined and tabulated.
The balance indicates the net amount of water added by rainfall or removed by
evapotranspiration from each component of the river network and informs on the
water resources that are available for irrigation, hydropower, urban and industrial use
at sub basin level.
Agricultural drought
Agricultural drought is evaluated by analysis of the EDI map and its development in
time. The severity of the drought and possible reduced crop yields are tabulated by
sub-catchment.
An example of the Yellow River Satellite Monitoring Bulletin is presented in Annex
2.
5.3
Catchment monitoring website
A catchment monitoring website has been developed to present the monitoring
results to YRCC and to other end users of the system. Furthermore, the
website also serves as a general information platform for the Sino-Dutch
Project.
5.3.1
Target users
The users of the catchment monitoring website belong to the following
categories:
•
First-level users. These users can browse all the system information, and are the
technical personnel who operate and apply this system in the Sino-Dutch project
at the Hydrology Bureau of YRCC.
• Second-level user. This user level can browse the majority of the information
through a personal user name and password. These users are the technical
personnel who operate the system.
Other users may browse the information through a default user name and password.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
5.3.2
Website design and structure
From a technical point of view, the web server generates both static and dynamic
content. Static content is composed of general descriptive pages, most often providing
background information. Dynamic content is generated upon user request. For
example: measurements or processing results at a specific location and for a specific
period of time. In this case, the web server connects to the data base, retrieves the
data, and converts the results into HTML code for a suitable presentation form, such
as for example a graph or map. Fig. 5.9 shows the connection between user and
system platforms. Attention is also paid to security and integrity issues, carried out
from within a user account and permissions management sub-system.
From a functional point of view the web site is divided into several sections, each
starting from the home page. These sections are:
• Project description including achievements and products;
• FTP access;
• Catchment monitoring bulletin archive section.
The first section constitutes the largest part of the website and is subdivided in
sections for the upper Yellow River reach and Weihe sub-basin. The material in the
section includes satellite images, LAS data, processed rainfall and actual evaporation,
hydrological and agricultural drought information, and runoff simulation and
forecasting results.
From a user point of view, the web site presents a uniform page structure to all users
in a layout that is self-explanatory. Furthermore, it aims at presenting the requested
information rapidly in a clear and understandable format. Users can request
information from a selection interface that has a similar appearance, for different
types of data. The selection involves specifying a data sub-type, a location or region,
and a fixed time or period of time, depending on the kind of data requested. In order
to optimize the information retrieval process for the user, it is possible to use shortcut
key strokes for frequently used functions. Figures 5.10 and 5.11 present the browser
homepage as seen by the users. The website is available in both Chinese and English
language.
.
Figure 5.9: System structure architecture
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Chapter 5 – System Implementation at YRCC
Figure 5.10: Layout of the catchment monitoring website homepage in Chinese
Figure 5.11: Layout of the catchment monitoring website homepage in English
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Figure 5.12: Example of an upper Yellow river flow forecast as presented on the
project website
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Chapter 6 – Conclusion, Outlook and Recommendations
6
CONCLUSIONS, OUTLOOK AND RECOMMENDATIONS
The Sino-Dutch project partners have successfully developed the first satellite based
water monitoring and river flow forecasting system in the world. This system has
been operationally implemented at the Hydrology Bureau of Yellow River
Conservancy Commission in Zhengzhou. The system is a combination of an
innovative satellite based climatic monitoring system, known as the Energy and
Water Balance Monitoring System (EWBMS), developed by EARS, and a dedicated
Large Scale Hydrological Model (LSHM), developed by UNESCO-IHE.
A most significant innovation is that the EWBMS does not only produce rainfall data
fields, the type of input that is traditionally used in rainfall-river runoff modeling, but
also generates data fields of the actual evapotranspiration. Such information was
never available from routine surface observations before. The latter data are crucial
for an accurate determination of the catchment’s water balance, particularly because
evapotranspiration amounts to 70 or 80 % of the precipitation. These data, which in
the past could only be estimated, can now be determined from space. The whole
EWBMS-LSHM system is almost independent of real time data, except that rainfall
point data, already available through the WMO-GTS system, are used to calibrate the
rainfall data fields in real time, and that the LSHM assimilates station discharge
observations when run in forecast mode.
Given the distributed data fields of temperature, radiation, evapotranspiration and
rainfall, generated for the entire basin on a daily basis, the system is very useful for
drought monitoring. The catchment drought monitoring system, that has been
implemented, produces climatic, hydrological and agricultural drought maps for the
whole basin, and aggregates tabulated data for each sub-catchment within the basin.
These data are an important part of the catchment monitoring bulletin and website
that have been developed. In principle the drought monitoring system is not restricted
to the Yellow River basin but may be used to monitor the entire territory. The system
would be very suitable and could immediately be used to monitor and assess the
drought currently occurring in the northern part of China.
The river runoff forecasting system has been implemented in two major sub-basins:
the upper reach of the Yellow river, upstream of Tangnaihai, and the Weihe tributary.
For both basins the LSHM can be run in a simulation and a forecasting mode. For
forecasting the user may introduce fixed boundaries for which measured discharge
data can be entered, and he can also extend the EWBMS effective precipitation with
simple scenario’s for the coming days. In this way the LSHM may be used to
anticipate high water in the lower Weihe.
In the upper Yellow river, the EWBMS has done better than expected in calculating
the water budget and simulating river runoff, particularly when considering the fact
that rainfall stations are scarce, and that the physical conditions on the plateau are
very different from these in the low land. To reach this result we have succeeded in
adapting the EWBMS so as to work well at high altitude. It was necessary to adapt
the heat exchange calculations for decreasing air density and increasing aerodynamic
roughness with height. For a proper estimation of rainfall it appeared essential to
account for the reduced height of the precipitable water column at higher altitude.
Another innovative element is that we have adapted the system to take account of
snow fall in winter and snow melt in spring.
Extensive work has been done on the validation of the data generated by the
EWBMS-LSHM system. EWBMS air temperature data fields have been validated
with temperature data from meteorological stations. Rain gauge data from such
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
stations were used to evaluate the performance of the EWBMS rainfall module. To
serve the validation of the EWBMS energy balance components, generated by the
EWBMS, four Large Aperture Scintillometer (LAS) systems have been implemented
in the basin, of which three on the Qinghai plateau and one on the Loess plateau, The
LAS is an innovative instrument that is capable of measuring the sensible heat flux
along a horizontal path of several kilometers. The LAS stations were also equipped
with net radiometers and air temperature sensors. With the measurements from these
advanced measuring sites, operated and maintained by the YRCC Bureau for the
Upper Yellow River in Lanzhou, and by the Sanmenxia Hydrological Bureau, it has
been possible to successfully validate the energy balance products of the system. In
addition the overall water budget, generated by the EWBMS, has been validated by
comparing the yearly effective precipitation (precipitation - actual evapotranspiration)
of the sub-basin with the corresponding river discharge. The results are very good.
Finally the EWBMS data have been used as input into the LSHM to generate river
flow. LSHM performance has been validated by comparing simulated with observed
flow. All these validation activities have produced very good results, certainly if it is
recognized that the validation data are usually measured at different scale, are
imperfect, and cannot be considered the truth when large scale areal estimates are
considered.
In the framework of the project a dedicated catchment monitoring bulletin has been
developed, which may contain drought, water budget at pixel, sub-catchment and
catchment level as well as river flow information. Also a website has been developed
that is instrumental in presenting the information to the YRCC departments and
services involved, to related government organizations as well as to a larger public.
The website is directly connected to the data base in which all information and
system processing results are stored, and which serves simultaneously as an
operational visualization platform. At the YRCC Hydrological Bureau, a team of
experts has been formed that is operating the system on a daily basis and will diffuse
the information through regular issues of the bulletin and updates of the website.
The EWBMS, after its development, is a cost-effective system. It provides a lot of
information for the whole river basin at very moderate costs. The satellite data are
free. The PC technology used is low cost. Highest operating costs are in the
manpower, but compared to the investments and number of people that would be
required to collect such data in the traditional way, the overall operating costs are also
very low.
Given the successful operational implementation of the EWBMS-LSHM system it
may be wise to look forward and consider how the system could further improve the
cost-effectiveness of YRCC operations. A follow-up project could be considered in
which the run-off forecasting is implemented in the whole basin. Also its potential
functionality in operating the various dams could be addressed. In addition a further
extension of monitoring scale could be considered in relation to the S-N diversion,
when large amounts of water will be transported across the watershed, which will
have a considerable impact on both the source and destination areas.
Another possibility for further extending the utility of the system includes its
adaptation to use rainfall forecasts generated by meteorological models so as to
extend its forecasting capability from 1 to 3 days and possibly longer, up to a medium
range forecast of 10 days.
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Chapter 6 – Conclusion, Outlook and Recommendations
Recommendations
Based on the project results obtained and the considerations in this chapter, the
project partners come to the following recommendations:
(1) Given the good results of the EWBMS-LSHM and its successful operational
implementation in the Yellow River Upper Reach and in the Wei River, it is
proposed to extend the implementation of the system to the entire Yellow River
basin.
(2) It is also proposed to extend the forecasting range of the current system by
integrating future rainfall and evapotranspiration scenario’s using available
numerical weather forecasts.
(3) It is proposed to investigate the utility of the system for dam operations and to
develop the corresponding water management tools.
(4) It is proposed to develop a permanent hydrological, agricultural and
climatological drought monitoring facility for China, so as to document and learn
from the current drought episode and to prepare for similar events in the future.
(5) Given the available water resources on the one, and the agricultural water needs
on the other side, for which the information is both provided by the system, it is
proposed to develop a water allocation decision support system. Such
methodology will be beneficial to decision making in relation to the S-N water
diversion and for water allocation to alleviate and overcome drought.
(6) It is recommended that Dutch and Chinese government establish a lasting
cooperation in the field of satellite hydrology with the objective to draw the full
benefits of this new technology and to add a new and challenging perspective to
their existing relationship in the water domain.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
126
Annex 1 – LAS Stations Information
ANNEX 1: LAS STATIONS INFORMATION
Jingchuan LAS station
Location
Transmitter
Latitude
Longitude
Receiver
Latitude
107o 21’
Altitude
1m
2m
4m
1343 m
0.1 m
0.7 m
all year
1/1/2006
Longitude
35o 20’
1039 m
Altitude
1061 m
Height lower temperature measurement
Height upper temperature measurement
Height wind speed measurement
Path length
Roughness length
Zero displacement height
Measuring period
Start of measurements
Description:
The system is installed over a rough, but open cultivated landscape with maize
fields, grasslands and a single isolated tree in the measurement area. The LAS
receiver is mounted at the local hydrological station near the riverside. The
transmitter is installed on the top of a hill in a village. The LAS path direction is 30°
NE. The instruments are working on current grid both at the receiver and the
transmitter site. Receiver and transmitter are mounted on a steel construction of
supporting piles. The LAS is measuring at an effective height of 23.7 m over a
heterogeneous but representative area. With low crops and small dispersed obstacles
present, the roughness length z0 of the terrain is estimated at 0.1m.
Transmitter site at Jingchuan station
Path of Jingchuan LAS
127
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Xinghai LAS station
Location
Transmitter
Receiver
Latitude
Longitude
Latitude
Longitude
35゜35′34.7″
99゜59′03.7″
35゜36′36.0″
99゜58′40.7″
Altitude
3302 m
Altitude
3328 m
Height lower temperature measurement
1m
Height upper temperature measurement
3m
Height wind speed measurement
4.2 m
Path length
1871 m
Roughness length
0.1 m
Zero displacement height
0.7 m
Measuring period
15/3 – 15/11
Start of measurements
15/3/2006
Description:
The instruments are installed in Xinghai county, Qinghai province. The site is an
open landscape of hilly grasslands with weak slopes (<5%). No mountains peaks
were visible around the area. Yaks and sheep are regularly grazing on the site and in
the surrounding areas. One road is crossing the LAS measurement path. The LAS
transmitter is installed on a local government building. The receiver and the
meteorological station are mounted on a steel platform mounted by two concrete
pillars, near the house of a Tibetan family. The instruments are working on solar
power at the receiver site and on current grid at the transmitter site. The LAS is
measuring over a relatively homogeneous area of grassland and very low vegetation.
Roughness elements are not densely packed and therefore the roughness length z0 of
the terrain is taken to bee very small. The effect of z0 on H is minimized by installing
the LAS relatively high; the scintillometer beam is measuring at an effective height
of 24.4 m above the surface.
Transmitter at Xinghai LAS station
Path of Xinghai LAS
128
Annex 1 – LAS Stations Information
Maqin LAS station
Location
Transmitter
Receiver
Latitude
Longitude
Latitude
Longitude
34゜28′14.7″
100゜14′05.4″
34゜27′44.2″
100゜14′28.8″
Altitude
3721 m
Altitude
3727 m
Height lower temperature measurement
1m
Height upper temperature measurement
3.9 m
Height wind speed measurement
5.2 m
Path length
110 m
Roughness length
0.03 m
Zero displacement height
0.21 m
Measuring period
15/3 – 15/11
Start of measurements
15/3/2006
Description:
The system is operating in Maqin county in Qinghai province. The site is an open flat
grassland with a very weak slope (<0.6%). The valley is surrounded by mountain peaks
that are oriented in a north-south direction. Yaks are regularly grazing on the site and in
the surrounding areas. The LAS transmitter is installed near a housing block. The
receiver and the meteorological station are mounted near the house of a living Buddha.
The instruments are working on solar power at the receiver site and on current grid at the
transmitter site. Receiver and transmitter are mounted on a steel platform supported by
two robust concrete piles. The LAS is measuring at an effective height of 5.8m over a
homogeneous area of grassland. With no or very little roughness elements present, the
roughness length z0 of the terrain is estimated at 0.03m.
Working at Maqin LAS station
Path of Maqin LAS
129
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Tangke LAS station
Location
Transmitter
Receiver
Latitude
Longitude
Latitude
Longitude
33゜23′51.4″
102゜27′47.6″
33゜24′10.3″
102゜27′47.7″
Altitude
3438 m
Altitude
3430 m
Height lower temperature measurement
1m
Height upper temperature measurement
3.7 m
Height wind speed measurement
5.1 m
Path length
586 m
Roughness length
0.03 m
Zero displacement height
0.21 m
Measuring period
15/3 – 15/11
Start of measurements
15/3/2006
Description:
The fourth LAS system is operating near Tangke in Ruoergai county of Sichuan
province. The site is located in the more humid part of the Qinghai-Tibetan plateau, with
wet grasslands and peat lands. The site consists of a flat open landscape with grassland
and a very weak slope (<1%). Mountains peaks are visible on the horizon. Yaks are
regularly grazing on the measuring site and in the surrounding areas. Two small loam
houses near the transmitter site are located below the path of the LAS beam. The
instruments are working on solar power at the transmitter site and on current grid at the
receiver site. Receiver and transmitter are mounted on a steel platform supported by two
robust concrete piles at about 7 m above the surface. The LAS is measuring at an
effective height of 23.7 m over a heterogeneous but representative area. With uniform
grassland and very low vegetation, the roughness length z0 of the terrain is estimated at
0.03m
Receiver site at Tangke station
Path of Tangke LAS
130
Annex 2: Catchment Monitoring Bulletin
ANNEX 2: CATCHMENT MONITORING BULLETIN
E-mail: Swjwcq@126.com
Internet: http://218.28.41.1.4/zhweb
Issuing date: 30 September 2005
INTRODUCTION
The present document provides the following information:
• An overview of water availability in the Yellow River basin during the
current dekad
• An overview of agricultural drought conditions during the past four months
In Annex 3 the most important terms are explained.
DATA AND METHOD
EWBMS is the acronym of Energy and Water Balance Monitoring System, a
FY-2C based water resources monitoring system operated by the Hydrology
Bureau of YRCC. The assessment of water availability and drought in the
basin is based on visible and thermal infrared hourly data from the
geostationary meteorological satellite FY-2C.
(1) Hourly satellite data are processed to daily average values of surface
temperature, air temperature, net radiation, potential and actual
evapotranspiration.
2) Rainfall data are processed based on cloud frequency data derived from
hourly satellite data and rainfall measurements from WMO-GTS stations.
(3) Using GIS, evapotranspiration and rainfall results are integrated for subcatchment areas and water balance and drought monitoring information are
generated.
The information is available on daily basis and is spatially continuous with a
pixel resolution of 5 km.
131
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Summary and highlights
Like the previous days, the amounts of rainfall were very high between 21
and 30 of September, especially in the WeiHe basin, the middle and the lower
reaches of the Yellow River. The south-eastern part of the Yellow River basin
suffers from continued heavy rains that are much higher than normal. During
only ten days, at some locations in the WeiHe basin up to 200 mm of rain has
fallen.
Figure 1: Total effective rainfall (mm) during the period between 21 and 30
September 2005
During the entire period of ten days, the WeiHe basin, the middle and the
lower reaches of the Yellow River basin suffered form unusual high rainfalls.
Decadal precipitation topped even 200 mm at some locations in the WeiHe
basin. Also parts of the lower Yellow River basin and of Shandong province
suffered from heavy rainfalls. At some locations in this area precipitation
amounts exceeded 150 mm water. Daily rainfall at Xi’an for 5 consecutive
days was more than 15 mm, with a maximum of 23 mm at 29 September
2005.
Actual evapotranspiration was lowest in the plateau areas and towards the
north. During the last ten days on average 10 mm of water has evaporated,
with relatively more water evaporating in the southern and eastern parts of
the basin. The ten-daily sum of actual evapotranspiration is ranging from 5 to
20 mm in the basin.
Some sub-catchments have been receiving rainfall amounts that were 15
times higher than the amount of water evaporating from its surface. Effective
rainfall ranged from 50 mm to 150 mm in the WeiHe basin and from 25 mm to
90 mm in the Upper Yellow River basin.
132
133
30
29
28
27
26
25
/2
5
00
5
05
5
05
00
9/
20
/0
9
/0
/2
5
05
00
9/
20
/0
9
/0
/2
9/
20
/0
9
/0
05
1
00
10
30
29
28
27
26
25
24
23
5
05
05
05
05
5
05
00
20
/2
9/
9/
20
9/
20
9/
20
/0
9
/0
/0
/0
/0
05
05
00
20
/2
9/
9/
20
/0
9
/0
/0
30
29
28
27
26
25
24
23
22
21
/0
9/
20
9/
20
20
9/
20
5
5
5
5
5
05
05
05
05
05
00
00
00
00
00
9/
20
9/
/0
/0
/0
/0
/2
/2
9 /2
/0
9
/0
9
/0
/2
9 /2
9
/0
/0
05
2
1.5
20
1
5
0
35
Yinchuan
2.5
2
20
2
1.5
15
1
10
0.5
5
0.5
0
0
45
Zhengzhou
2. 5
25
35
2. 5
20
30
25
2
1. 5
20
1. 5
15
10
1
0. 5
5
0. 5
0
0
0
Figure 2: Daily rainfall (bar) and actual evapotranspiration (line), both in mm,
for selected locations from 21 to 30 September 2005.
[ mm ]
15
/2
2
/0
9
40
24
n
30
5
o
3
00
i
Xi’an
/2
0
0
/0
9
10
30
23
15
/2
0
20
/2
25
/0
9
2.5
/0
9
05
Lanzhou
22
/2
0
3
22
9
9/
20
05
05
05
2
a t
/0
/2
0
9/
20
9
5
Jinan
r
21
/0
/0
/0
00
/2
0
/2
0
i
05
30
29
28
9
/0
9
0
p
35
/0
0.5
s
0
05
5
05
00
/2
0
/2
5
10
/2
0
35
27
9
/0
9
/0
/2
0
15
30
n
5
26
25
24
/0
9
05
00
20
3
r a
0
/2
/2
0
25
/0
9
t
5
/0
9
/0
9
2.5
21
o
30
23
22
21
Darlag
p
[ mm ]
5
35
a
l
v
l
e
a
l
f
30
a
n
3
u
i
t
a
35
A c
R
Annex 2: Catchment Monitoring Bulletin
2.5
25
15
1.5
10
1
0.5
0
3
25
1.5
1
3
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Total since 01
July 2005*
Total since
Sep01
606
710
416
215
881
759
Highest in 10
days
Rainfall
11
131
32
199
14
145
10
86
23
196
44
197
Total
10 days [mm]
Total since 01
July 2005*
51
118
46
19
143
150
Total since
Sep01
Darlag
Jinan
Lanzhou
Yinchuan
Xi’an
Zhengzhou
Highest in 10
days
Station
Total
10 days [mm]
Table 1: Rainfall and actual evapotranspiration summary information for 6
selected locations.
Actual Evapotranspiration
8
1,1
31
143
16
2,5
62
274
5
1,4
18
124
12
1,8
27
193
13
2,3
62
233
16
2,8
66
268
Runoff and Water Balance
The water balance is fundamental for the hydrological cycle. It gives
information on the amount of water the river network will receive. At the same
time it indicates the water resources available for agriculture, industry or
residential use. A detailed inventory of the water resources in the Yellow
River basin is given below. The map shows the water balance in the Yellow
River for each of the sub-catchments. Water balance is defined by subtracting
evapotranspiration from precipitation and averaging the value for each subcatchment. The water balance is given in mm for each sub-catchment.
Effective rainfall increases from west to east and from north to south. Water
balance is positive in nearly every sub-catchment and extremely high in most
parts of the Weihe basin.
Figure 3: Water balance at sub-catchment level, i.e. the total amount of water
(mm) added to or leaving the sub-catchment during the dekad 21-30
September 2005
134
Annex 2: Catchment Monitoring Bulletin
In the sub-catchments of the Jinghe River, Dawenhe River, Manghe Basin,
Qinhe River, Dongpinghu Lake, Jishui and Tingshuihe the Weihe basin, the
situation is critical. The latter sub-catchments all saw their amount of water
increased by more than 120 mm in the period of only 10 days. The Dawenhe
Basin suffered most from the heavy rainfall, and average effective rainfall
over the basin is 139 mm. In all these areas, there is a high risk for flooding.
The sub-catchments with the smallest amounts of water are found in the north
of the Yellow River basin. In Maobulakongdui basin in the north, actual
evapotranspiration is a little higher than rainfall over the 10-day period, and
consequently this basin has a negative water balance.
The effective precipitation values (mm) are multiplied by the surface area for
each of the sub-catchments. The corresponding total amount of water in
millions of m3 water added or removed during the 10-day period is given in
the table below. This is the amount of water the river network will receive and
it gives an indication on the water resources availability.
The effective precipitation values (mm) are multiplied by the surface area for
each of the sub-catchments. The corresponding total amount of water in
millions of m3 water added or removed during the 10-day period is given in
the table below. This is the amount of water the river network will receive and
it gives an indication on the water resources availability.
135
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Table 2: Effective precipitation, area and total water volume by sub-catchment
Nr
Sub-catchment
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
Jiaqu Basin
Baihe Basin
Shaqu Basin
Zhanganhe Basin
Jimaihe Basin
Darilequ Basin
Heihe Basin
Kequ Basin
Xikehe Basin
HongnongjianheBasin
Dongkequ Basin
Xikequ Basin
Luohe Basin
Youerqu Basin
Requ Basin
Dongqu Basin
Lenaqu Basin
Qiemuqu Basin
Manghe Basin
Duoqu Basin
Zequ Basin
Haoqinghe Basin
DuoqinankelangBasin
Wenyanqu Basin
Kariqu Basin
Sushuihe Basin
Baqu Basin
Jishui Basin
Jinghe Basin
Daixahe Basin
Longwuhe Basin
Manglahe Basin
Qushianhe Basin
Taohe Basin
Wanchuanhe Basin
Xiaqu Basin
Gaohongyahe Basin
Shiwangchuan Basin
Jingdihe Basin
Daheba Basin
Dongpinghu Basin
Puhe Basin
Eff Precip.
(mm)
42
37
33
31
33
27
41
25
61
97
55
49
78
65
48
64
51
69
122
49
74
111
63
115
42
120
78
126
128
65
75
76
62
53
39
67
64
111
125
61
137
105
136
Area
(km2)
2195
5482
1602
1040
1856
3400
7931
2439
1002
2037
3437
2658
18864
1903
6599
1301
1536
5552
1907
5823
4745
572
1183
2396
3106
5595
4247
1068
18670
7160
4955
2918
5874
25438
1875
1411
1127
2351
4941
3955
761
7491
Volume
(106 m3)
93
202
52
32
61
93
327
61
62
197
187
131
1463
124
315
83
79
381
232
283
349
64
74
276
130
673
331
135
2388
463
373
221
365
1351
73
94
73
262
616
242
105
784
Annex 2: Catchment Monitoring Bulletin
Nr
Sub-catchment
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
Yunyanhe Basin
Zulihe Basin
Dawenhe Basin
Xinshuihe Basin
Qinhe Basin
Chuchanhe Basin
Yanhe Basin
Qingjianhe Basin
Malianhe Basin
Zhuanglanghe Basin
Qingshuihe Basin
Hongliugou Basin
Wulonghe Basin
Kushuihe Basin
Qingliangshigou Basin
Sanchuanhe Basin
Qiushuihe Basin
Jialuhe Basin
Huangshui Basin
Weifenhe Basin
Lanyihe Basin
Tuweihe Basin
Fenhe Basin
Wudinghe Basin
Zhujiachuan Basin
Gushanchuan Basin
Xianchuanhe Basin
Dusitu Basin
Qingshuichuan Basin
Pianguanhe Basin
Kuyehe Basin
Yangjiachuan Basin
Huangpuchuan Basin
Xiliugou Basin
Hashilachuan Basin
Erdous Basin
Mabulakongdui Basin
Hunhe Basin
Main stream
Kundulun Basin
Daheihe Basin
Wuliangsuhai Basin
Beiluohe Basin
Eff Precip.
(mm)
119
51
139
124
135
86
95
75
97
38
46
36
35
40
46
55
43
29
36
21
19
17
82
35
19
10
28
1
15
27
7
26
12
2
3
10
-2
24
43
9
17
5
109
137
Area
(km2)
1779
10752
8557
4293
12935
1220
7671
4068
19061
4083
14472
1186
375
7515
286
4082
1985
1137
32453
1478
2178
3199
39624
30232
2900
1280
1488
8840
884
2085
8708
996
3253
1298
1194
42415
1324
5540
167578
2800
17726
29558
27025
Volume
(106 m3)
212
548
1190
532
1748
105
726
306
1852
156
660
43
13
297
13
226
86
33
1152
31
40
56
3260
1051
56
13
42
10
13
57
58
26
40
2
4
438
-3
134
7226
26
295
149
2945
Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Agricultural Drought Monitoring
The agricultural drought indicator gives information on water availability for
crops and vegetation. The agricultural drought indicator is strongly related to
soil moisture and the actual drought conditions of the ground. Moisture
availability is the most important factor influencing the conditions of crops and
plants. The agricultural drought is evaluated over a two months period, a
suitable time period to evaluate the growth conditions of the crops and to
estimate possible crop yield losses during the growing season.
The map below shows the agricultural drought indicator EDI in the Yellow
River for each sub-catchment. The map gives the spatial distribution of
drought that was experienced by the vegetation over the past two months.
Note that EDI informs on conditions of the crop at the present dekad, but that
these conditions are mainly determined by the water availability during the
past two months. More information on agricultural drought and EDI is given in
Annex 1.
In the lower reaches and in the Weihe basin, crops and vegetation water
availability conditions are optimal to near optimal. Only light droughts, from
which crops can quickly recover are found in some sub-catchments of this
area. South of Shaanxi province and in the surroundings of Zhengzhou and
Xi’an, the sub-catchments all have an EDI that is higher than 78%.
Continued severe droughts are found in the North of the Yellow River basin.
Droughts are most pronounced in the provinces Inner Mongolia, Gansu,
Ningxia Huizu, the northern part of Shannxi and some parts in the source
area of the Yellow River. The EDI values for each sub-catchment are given in
the table below. EDI values of the previous dekad are also given, to indicate
the change of agricultural drought in time. Dekad Dn is the current dekad, Dn-1
the previous dekad, etc.
Figure 4: Agricultural drought at sub-catchment level in EDI (%)
138
Annex 2: Catchment Monitoring Bulletin
Table 3: Agricultural drought development in current and past five dekads in
the sub-catchments.
Evapotranspiration Drought Index
(EDI)
Sub
Drought class
D
D
n-5 Dn-4 Dn-3 Dn-2 Dn-1
n
catchment
1 Jiaqu Basin
0.64 0.65 0.66 0.69 0.68 0.69 light drought
2 Baihe Basin
0.69 0.70 0.73 0.76 0.76 0.76 light drought
3 Shaqu Basin
0.55 0.55 0.56 0.57 0.54 0.55 moderate drought
4 Zhanganhe Basin
0.61 0.62 0.62 0.64 0.62 0.60 light drought
5 Jimaihe Basin
0.60 0.58 0.56 0.57 0.56 0.58 moderate drought
6 Darilequ Basin
0.60 0.58 0.55 0.56 0.56 0.58 moderate drought
7 Heihe Basin
0.64 0.65 0.67 0.70 0.69 0.69 light drought
8 Kequ Basin
0.62 0.59 0.55 0.55 0.55 0.54 moderate drought
9 Xikehe Basin
0.61 0.60 0.59 0.59 0.55 0.54 moderate drought
10 Hongnongj. Basin
0.96 0.97 0.97 0.98 0.97 0.96 optimal WA*)
11 Dongkequ Basin
0.63 0.62 0.60 0.59 0.56 0.56 moderate drought
12 Xikequ Basin
0.60 0.58 0.56 0.55 0.53 0.54 moderate drought
13 Luohe Basin
0.88 0.91 0.92 0.93 0.93 0.92 optimal WA
14 Youerqu Basin
0.60 0.59 0.57 0.56 0.52 0.50 severe drought
15 Requ Basin
0.59 0.57 0.53 0.52 0.51 0.50 severe drought
16 Dongqu Basin
0.64 0.62 0.60 0.57 0.54 0.51 moderate drought
17 Lenaqu Basin
0.63 0.61 0.60 0.59 0.58 0.54 moderate drought
18 Qiemuqu Basin
0.64 0.62 0.60 0.58 0.54 0.53 moderate drought
19 Manghe Basin
0.86 0.86 0.86 0.86 0.86 0.86 near optimal WA
20 Duoqu Basin
0.61 0.60 0.60 0.60 0.60 0.58 moderate drought
21 Zequ Basin
0.64 0.63 0.62 0.62 0.60 0.59 moderate drought
22 Haoqinghe Basin
0.83 0.84 0.85 0.85 0.85 0.85 near optimal WA
23 Duoqinank. Basin
0.62 0.62 0.60 0.56 0.54 0.49 severe drought
24 Wenyanqu Basin
0.87 0.87 0.89 0.90 0.90 0.87 near optimal WA
25 Kariqu Basin
0.58 0.56 0.57 0.59 0.55 0.53 moderate drought
26 Sushuihe Basin
0.81 0.81 0.83 0.84 0.84 0.84 near optimal WA
27 Baqu Basin
0.67 0.66 0.65 0.65 0.62 0.60 light drought
28 Jishui Basin
0.72 0.73 0.76 0.78 0.77 0.77 light drought
29 Jinghe Basin
0.66 0.69 0.74 0.79 0.79 0.79 light drought
30 Daixahe Basin
0.73 0.74 0.73 0.71 0.69 0.65 light drought
31 Longwuhe Basin
0.71 0.71 0.70 0.70 0.68 0.63 light drought
32 Manglahe Basin
0.66 0.66 0.67 0.67 0.65 0.60 light drought
33 Qushianhe Basin
0.56 0.55 0.54 0.55 0.53 0.49 severe drought
34 Taohe Basin
0.70 0.71 0.70 0.69 0.67 0.67 light drought
35 Wanchuanhe Basin
0.57 0.59 0.58 0.56 0.54 0.52 moderate drought
36 Xiaqu Basin
0.57 0.57 0.56 0.56 0.55 0.53 moderate drought
37 Gaohongyahe Basin 0.61 0.61 0.58 0.58 0.57 0.53 moderate drought
38 Shiwangchuan Basin 0.75 0.77 0.78 0.80 0.80 0.81 near optimal WA
39 Jingdihe Basin
0.83 0.83 0.84 0.86 0.86 0.83 near optimal WA
40 Daheba Basin
0.47 0.46 0.46 0.50 0.51 0.49 severe drought
41 Dongpinghu Basin
0.80 0.84 0.86 0.89 0.89 0.84 near optimal WA
0.62 0.66 0.70 0.71 0.69 0.67 light drought
42 Puhe Basin
0.70 0.73 0.76 0.79 0.79 0.80 light drought
43 Yunyanhe Basin
0.58 0.60 0.61 0.62 0.59 0.54 moderate drought
44 Zulihe Basin
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
Dawenhe Basin
Xinshuihe Basin
Qinhe Basin
Chuchanhe Basin
Yanhe Basin
Qingjianhe Basin
Malianhe Basin
Zhuanglanghe Basin
Qingshuihe Basin
Hongliugou Basin
Wulonghe Basin
Kushuihe Basin
Qingliangshigou Basin
Sanchuanhe Basin
Qiushuihe Basin
Jialuhe Basin
Huangshui Basin
Weifenhe Basin
Lanyihe Basin
Tuweihe Basin
Fenhe Basin
Wudinghe Basin
Zhujiachuan Basin
Gushanchuan Basin
Xianchuanhe Basin
Dusitu Basin
Qingshuichuan Basin
Pianguanhe Basin
Kuyehe Basin
Yangjiachuan Basin
Huangpuchuan Basin
Xiliugou Basin
Hashilachuan Basin
Erdous Basin
Mabulakongdui Basin
Hunhe Basin
Main stream
Kundulun Basin
Daheihe Basin
Wuliangsuhai Basin
Beiluohe Basin
Weihe Basin
0.81
0.73
0.83
0.76
0.66
0.60
0.65
0.47
0.52
0.44
0.62
0.50
0.65
0.77
0.72
0.54
0.65
0.70
0.65
0.54
0.73
0.52
0.63
0.52
0.62
0.39
0.51
0.58
0.54
0.56
0.58
0.48
0.57
0.47
0.52
0.73
0.55
0.43
0.47
0.39
0.73
0.80
0.83
0.75
0.85
0.79
0.68
0.63
0.66
0.49
0.55
0.47
0.67
0.53
0.69
0.79
0.74
0.58
0.66
0.73
0.67
0.57
0.75
0.55
0.66
0.54
0.65
0.42
0.53
0.61
0.56
0.59
0.59
0.49
0.58
0.50
0.54
0.75
0.57
0.42
0.49
0.40
0.75
0.81
*) WA= water availability
140
0.84
0.78
0.86
0.81
0.72
0.67
0.69
0.52
0.55
0.46
0.67
0.52
0.73
0.81
0.77
0.63
0.67
0.76
0.70
0.61
0.77
0.59
0.68
0.59
0.69
0.45
0.57
0.65
0.61
0.64
0.62
0.55
0.63
0.53
0.57
0.77
0.57
0.42
0.52
0.39
0.78
0.83
0.87
0.80
0.87
0.88
0.74
0.69
0.70
0.51
0.54
0.43
0.71
0.50
0.77
0.86
0.83
0.67
0.67
0.80
0.75
0.66
0.81
0.62
0.71
0.64
0.73
0.48
0.61
0.69
0.66
0.69
0.65
0.58
0.67
0.56
0.58
0.81
0.58
0.42
0.49
0.39
0.80
0.84
0.87
0.80
0.86
0.86
0.71
0.67
0.69
0.49
0.52
0.39
0.73
0.47
0.78
0.85
0.83
0.66
0.64
0.78
0.74
0.66
0.80
0.62
0.71
0.63
0.73
0.47
0.60
0.68
0.63
0.69
0.63
0.56
0.66
0.54
0.55
0.80
0.56
0.40
0.49
0.37
0.80
0.82
0.86
0.80
0.86
0.84
0.70
0.67
0.66
0.45
0.48
0.37
0.74
0.43
0.78
0.82
0.82
0.66
0.60
0.76
0.73
0.65
0.79
0.62
0.69
0.58
0.70
0.46
0.56
0.65
0.61
0.66
0.59
0.55
0.64
0.51
0.51
0.79
0.54
0.39
0.49
0.36
0.79
0.80
near optimal WA
near optimal WA
near optimal WA
near optimal WA
light drought
light drought
light drought
severe drought
severe drought
severe drought
light drought
severe drought
light drought
near optimal WA
near optimal WA
light drought
light drought
light drought
light drought
light drought
light drought
light drought
light drought
moderate drought
light drought
severe drought
moderate drought
light drought
light drought
light drought
moderate drought
moderate drought
light drought
moderate drought
moderate drought
light drought
moderate drought
severe drought
severe drought
severe drought
light drought
light drought
Annex 2: Catchment Monitoring Bulletin
Summary and conclusions
The south-eastern area of the Yellow River basin suffers from continued
heavy rains that are much higher than normal. It is very likely that the river
network can not receive the increased amounts of water in time, enhancing
the risk of flooding in these areas.
Even in the areas with very high rainfall during this dekad, light agricultural
drought is present and may influence crop yields. In the source area, drought
conditions are more pronounced, effective rainfall is lower than in the east,
and droughts are light to severe. The northern part of the Yellow River basin
suffers from continued severe drought with only small effective rainfall
amounts, making it very difficult or impossible to grow crops.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Annex A: Agricultural Drought
Agricultural drought is defined as drought that occurs when there is not
enough moisture available to meet the needs of the vegetation. EDI, the
Evapotranspiration Drought Index indicates the availability of moisture for
crop/vegetation growth. The EDI is an agricultural drought indicator. This
means that EDI is more than an indicator of the actual drought state of the
ground surface. Not only it gives information on the amount of soil water
present. It also gives information on the physical and biological properties of
the soil and on crop conditions. Crop/vegetation conditions and
photosynthesis are directly related to the amount of water that is available for
the plants. EDI also includes influences from stage of growth, biological
characteristics of the plant, cattle grazing and weather conditions.
The EDI value is defined as the average of relative evapotranspiration for a
two month period:
EDI = Σ (RE ) / n = Σ (E / EP ) / n
where
EDI
E
EP
RE
n
: Evapotranspiration Drought Index
: Actual Evapotranspiration
: Potential Evapotranspiration
: Relative Evapotranspiration
: Number of days in 2 months
The agricultural drought classification according to the EDI value is as follows:
EDI value
Agricultural Drought Classification
0.9 - 1
0.8 – 0.9
0.6 – 0.8
0.5 – 0.6
0.3 – 0.5
0.0 – 0.3
Optimal water availability
Near optimal water availability
Light agricultural drought
Moderate agricultural drought
Severe agricultural drought
Extreme agricultural drought
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Annex 2: Catchment Monitoring Bulletin
Annex B: Sub-catchments of the Yellow River basin
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
Annex C: Retrieval methodology and explanation of terms
Term
Explanation
Rainfall (R)
The rainfall mapping is based on rainfall Satellite and
data gathered by WMO-GTS rainfall
GTS
stations and cloud durations data for 5
cloud levels generated from FY-2C
satellite data. A multiple regression
between satellite derived cloud data and
the GTS rain data gives pixel by pixel
rainfall estimations. The procedure has a
build-in quality control by means of the
jack knifing method.
Actual
evapotranspiration (E)
Actual evapotranspiration represents the Satellite
latent heat flux exchanged between the
land surface and the atmospheric
boundary layer. The latent heat flux is
obtained as the difference between net
radiation and sensible heat flux. It is
given as the amount of water in mm/day
that actually evaporates from the surface
(soil and plants).
Potential
evapotranspiration (EP)
The amount of water in mm/day that
would evaporate from the ground
surface (soil and plants) in case of
unrestricted water availability.
The ratio of actual over potential
evapotranspiration: RE = E / EP
Relative
evapotranspiration (RE)
Origin of data
Satellite
Satellite
Effective Rainfall
(ER)
Rainfall minus actual evapotranspiration. Satellite and
ER = R – E
GTS
Evapotranspiration Drought
Index (EDI)
Explained in Appendix A.
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Satellite Water Monitoring and Flow Forecasting System for the Yellow River
146