UNCERTAINTY OF THE WATER RESOURCES ASSESSMENT
IN THE YELLOW RIVER BASIN
DAWEN YANG1, 3, CHONG LI2, GUANGHENG NI3 AND HEPING HU3
Department of Civil Engineering, University of Tokyo, Tokyo 113-8656, Japan
2
China Institute of Water Resources and Hydropower research, Beijing, China
3
Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
1
For assessing the water resources variability under long-term changes of climate and land
cover, a distributed, physically based hydrological model is necessary since it can
represent the spatial distribution of related river basin properties and can examine the
impact of local land cover change on the basin hydrological cycle. Before the quantitative
assessment of water resources, it is important to understand the possible uncertainties in
the hydrological simulation. The present study applies a distributed model to the Yellow
River basin and focuses on exploring the uncertainty from the meteorological input, land
use and groundwater initial conditions.
INTRODUCTION
In a consequence of human activity and climate fluctuation the water resources shortage
and eco-environment degradation in the Yellow River basin of China became essential
problems since the end of 20th century. In particular, the serious drying-up of the main
river along the lower reaches during the 1990s has drawn a lot of attention from all over
the world. The Yellow River Conservancy Commission (YRCC, is the Yellow River
authority under the Ministry of Water Resources) is promoting an integrated enhanced
management of water resources in this basin. Key issues related to the water resources
management include the availability and variability of natural water resources, impact of
water soil conservation on the basin hydrological cycle, sustainability of eco-environment
and potential risk of floods.
The atmosphere, land cover and human activity are the driving force and important
variable respectively for the changes in hydrological cycle. The records of river runoff
usually are the integrated results of all changes in atmosphere and catchment surface and
the artificial direct uses of river water. For quantitative evaluation of these changes
individually, a distributed physically-based hydrological model is needed since it can
represent the spatial distribution of related basin properties and can examine the impacts
of local changes on the basin hydrological cycle. Precipitation usually shows a
considerable spatial variation over a large river basin. In practice, use of the traditional
gauge measurement of precipitation as the input to hydrological models, it needs a spatial
interpolation. There are different methods for interpolating the point data into a gridded
data. It may introduce additional uncertainty during the process of interpolation. Bias of
precipitation can introduce significant errors in model predictions [1].
1
The use of hydrological model for evaluating the effect of changes in land cover is
not an easy work, because of the lack of continuous data and difficulty in determining
'correct' parameter values for both pre-change and post-change catchment conditions [1].
Uses of the hydrological modeling approach to simulating the impact of land-use changes
on stream flow, it usually alters the proportions of existing vegetation types in the
catchment, rather than introducing new species which may have given problems in
obtaining the ‘correct’ parameters for the hydrologic models [1].
Initial conditions, in particular the groundwater water initial condition can change
the result of hydrological simulation. In the natural condition, groundwater water level
keeps dynamically stable, but it decreases under the over-pumping condition. Therefore it
is also necessary to examine the effects of groundwater changes on the river runoff.
The present study applies a distributed physically-based model to the Yellow River basin
and focuses on exploring the uncertainty from the meteorological input, land use and
groundwater initial condition.
STUDY AREA AND DATA AVAILABILITY
Target Catchment
The Yellow River, the second longest river basin in China, is the target basin in the
present study. It originates from the Tibetan plateau, wanders through the northern
semiarid region, crosses the loess plateau, passes through the eastern plain, and finally
discharges into the Bohai Gulf (see Fig. 1). The Yellow River flows about 5,500 km in
distance in the main course and accumulates 753,000 km2 of drainage area. About 100
million people live within the catchment, and it consists of 1200 million ha of farmland
of which nearly half is irrigated by the Yellow River.
Figure 1. The Yellow River basin
2
From the origins to the river mouth, the Yellow River experiences three typical
landforms, the Qingzhang (Tibet) high plateau with elevations from 2,000 to 5,000
meters, the loess plateau and midstream tributaries with elevations from 500 to 2,000
meters, and the alluvial plain in the eastern part. The climate conditions vary from cold to
temperate zones, and change from arid and semi-arid to semi-humid regions. The main
irrigation areas are located in the northern part, in the tributaries of the midstream and on
both sides along the lower reaches (see Fig. 1).
Data Availability
The geographical information concerning the Yellow River basin used in this research is
obtained from a number of global data sets. The digital elevation data of 1-km resolution
is obtained from the USGS HYDRO1k data set which is available. Land cover is
obtained from the USGS Global Land Cover Characteristics Data Base Version 2.0. This
dataset has a spatial resolution of 1-km. Besides the global dataset, another land use map
of 1:250000 scale is obtained from Chinese Academy of Sciences. For estimating the
leaf-area-index (LAI), a monthly NDVI with 8-km resolution is obtained from the DAAC
of GSFC/NASA. This dataset is available from 1982 onwards. The soil type and the
texture data are obtained from the Digital Soil Map of the World and Derived Soil
Properties [2]. It is developed at 5-minute resolution using the FAO-UNESCO soil
classification. The soil properties used for the hydrological simulation including the
porosity, the saturated hydraulic conductivity, and the other soil water parameters
corresponding to each soil type in this map are obtained from the Global Soil Data Task
[3]. The water-retention relationship and unsaturated hydraulic conductivity are
represented by Van Genuchten’s formula, and the parameters are available in this data set.
Table 1. Available data sets for the Yellow River
Data set
Scale
Source
Content/quality
DEM
1000-m
USGS
Global
1000-m
USGS
Global, 24 types, 1990
1:250000
CAS
China, 6 classes, 25 types, 1990
5-km
FAO
Global, texture
10-km
Global Soil Task
Meteorological
data
Point
China
Meteorological
Administration
Global,
Daily, precipitation, maximum,
minimum and mean air
temperature, wind speed, relative
humidity, sunshine hours and pan
evaporation
River discharge
Point
Ministry of Water
Resources, China
Land cover
Soil
Daily or monthly
3
The meteorological data are obtained from the China Meteorological Administration,
which is available at a daily temporal resolution at 108 gauges in the Yellow River basin.
The discharge data collected is from the “Hydrological Year Book” published by the
Hydrological Bureau of the Ministry of Water Resources of China. The data availability
in the Yellow River is summarized in Table 1.
METHODOLOGY
The present study employs a distributed model for estimating the natural runoff in the
Yellow River basin, in which only natural hydrological processes are included (there is
no consideration of irrigation or reservoir control). The model uses a grid system with a
10-km spatial resolution, and runs in hourly time steps. The methodology used for
constructing this model includes a basin subdivision scheme, a sub-grid parameterization
scheme, a physically-based hydrological simulation on hillslope and a kinematic wave
flow routing method.
The catchment is the minimum unit for implementing water resources management.
For a large river basin, it needs to be subdivided into sub-basins and to simulate the
variability of water resources in each sub-basin. For subdividing the Yellow River basin,
the Pfafstetter scheme [4] is applied in the present study. In the present application, a total
of 137 sub-basins have been identified in the upstream of the Huayuankou gauge. Since
the model uses a 10-km grid, the heterogeneity inside a grid affects the hydrological
processes, and therefore, a sub-grid parameterization is necessary. The sub-grid
parameterization used in this research includes representations of the sub-grid
variabilities in topography and land-cover. The topographical parameterization uses the
catchment geomorphologic properties, which represents a grid by a number of hillslopes.
The hillslopes located in a 10-km grid are grouped according to the land cover types. The
hydrological simulation is carried out for each land cover group. The hillslope is a
fundamental computational unit for hydrological simulation. A physically-based model is
used for simulating the hillslope hydrology. The hydrological processes included in this
model are the snowmelt, the canopy interception, evapotranspiration, infiltration, surface
flow, subsurface flow and the exchange between the groundwater and the river [5]. The
runoff generated from grid is the lateral inflow into the river at the same flow interval.
Flow routing in the river network is solved using the kinematic wave approach.
The topography and soil are treated as being constant over time. The model uses the
land cover of the 1990 as the base map and considers annual and seasonal changes in
vegetation using remotely-sensed NDVI data. The 10-km gridded atmospherical forcing
data used in the hydrological simulation is interpolated from the point dataset. For the
precipitation data, both angular-distance weighting method [6] and nearest distance
method [12] are used. For specifying an appropriate initial groundwater level for the
natural condition, the present study carries out a 20-years test run from 1981 to 2000 for
achieving a stable groundwater level. Then, the groundwater level and soil moisture
contents at the end of the test run are used as the initial conditions for simulating the
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natural hydrological cycle. Besides the natural groundwater conditions, the present study
tests also the decline in groundwater level during the 1980s and 1990s. The groundwater
level at the beginning of each year in the natural condition is saved as the base. Then the
groundwater level is declined from the base by 4 meters in 1980s and by 8 meters in
1990s for the urban areas and the agricultural areas with no surface water irrigation, and
is used as the initial condition for testing change of the river runoff. In a summary, the
hydrological simulations carried out in the present study include a base simulation, a test
for land cover, a test for precipitation and a test for groundwater (see Table 2).
Table 2. Hydrological simulations designed in the present study
Case
Case1: Base
simulation
Case 2: Test
for land
cover
Case3: Test
for
precipitation
Case4: Test
for
groundwater
Simulation conditions
1. The 10-km gridded precipitation data is generated using the
angular-distance weighting method;
2. The land cover data is from USGS and is grouped into 9
categories including water bodies (0.7%), urban areas (0.1%),
bare land (1.17%), forest (4.55%), irrigated cropland (6.48%),
non-irrigated cropland (16.73%), grassland (46.96%), shrub
(23.3%), and wetland (0.01%);
3. Uses the natural groundwater conditions. The groundwater level
after a 20-yrs test run is used as the initial condition and no
artificial decline is made during the simulation.
1. The land cover data is from CAS and is grouped into 9 categories
including water bodies (0.55%), urban areas (0.63%), bare land
(8.86%), forest (6.59%), irrigated cropland (6.13%), non-irrigated
cropland (20.11%), grassland (51.29%), shrub 5.5%), and wetland
(0.34%);
2. The other conditions are the same as the base simulation.
1. The 10-km gridded precipitation data is generated using the
nearest distance method;
2. The other conditions are the same as the base simulation.
1. Uses the declined groundwater level (by 4 m in 1980s and by 8 m
in 1990s for the urban areas and the agricultural areas with no
surface water irrigation) as the initial condition at the beginning
of each year;
2. The other conditions are the same as the base simulation.
RESULTS AND DISCUSSIONS
Model Calibration and Validation
A 5-year test run from 1981 to 1985 is carried out for calibrating the model parameters.
One of the calibrated parameters is the snowmelt factor in the temperature-based
snowmelt equation. Another calibrated parameter, the hydraulic conductivity of the
groundwater, is calibrated by checking the base flow in different sub-basins. Model
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validation is carried out from 1986 to 1990 in the upstream of the Tangnaihai gauge (see
Fig. 1), where the direct human activity is negligible, and the snowmelt runoff and
groundwater flow are the main sources of river discharge.
Figure 2. Comparison of simulated and observed river discharge at the Tannaihai gauge
Figure 2 shows a comparison between the simulated and observed daily discharges at
the Tangnaihai gauge for both the calibration and validation periods. Based on the daily
discharges, the ratio of the absolute error to the mean and the Nash coefficient are
calculated to be 19% and 0.88 respectively for the calibration period and to be 17% and
0.89 respectively for the validation period. A good agreement between the simulated
daily hydrograph with the observed and a consistency of the simulations in both the
calibration and validation periods are achieved. The water balance error is -2% in the
calibration period and 0.2% in the validation period.
Model Sensitivity Analysis
Using the calibrated model parameters, the hydrological simulations are carried out from
1981 to 2000 for these four cases listed in Table 2. The annual water balance of twenty
years average is checked and summarized in Table 3. Figure 3 shows the monthly
hydrographs of twenty-year means for the four cases. Regarding the annual runoff, in
general, the precipitation had the largest effect, the groundwater had the smallest effect.
The changes in land cover and groundwater level made decreases in annual runoff, but
the precipitation made an increase in annual runoff. The effect in the upstream of
Lanzhou gauge is smaller than that in the downstream of Lanzhou gauge.
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Case 1
Case 2
Case 3
Case4
(Base)
(Land cover)
(Precipitation)
(Groundwater)
Upstream of the
Lanzhou
P (mm/yr)
434.3
434.3
E (mm/yr)
310.6
318.4
299.2
Q (mm/yr)
119.6
111.1 (-7.1%)
140.3 (17.3%)
S (mm/yr)
4.1
4.8
1.0
310.6
119.6
(0.0%)
4.1
Downstream of
the Lanzhou
Table 3. Annual water balance for twenty years average
P (mm/yr)
423.9
423.9
422.9 (-0.2%)
423.9
E (mm/yr)
378.8
369.3
Q (mm/yr)
41.8
52.6
S (mm/yr)
3.3
394.1
30.3
(-27.5%)
-0.5
367.7
34.9
(-16.5%)
21.3
P (mm/yr)
427.2
427.2
428.6
E (mm/yr)
355.7
368.7
Q (mm/yr)
67.7
57.3 (-15.4%)
S (mm/yr)
3.8
1.2
Whole basin
Case
440.5
(1.4%)
434.3
*
(25.8%)
1
(0.3%)
345.8
81.9
(21.0%)
0.9
427.2
349.3
61.7
(-8.9%)
16.2
* The number in bracket is the relative difference from the base simulation (case 1); P: the annual
precipitation, E: the annual actual evapotranspiration, Q: the annual runoff, and S: the annual
change of water storage in the catchment.
Figure 3. The 20-year mean monthly hydrographs for the four cases
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Figure 4. Comparison of the spatial distribution of precipitation interpolated using (a) the
angular-distance weighting method and (b) using the nearest distance method
The change in land cover from the case 1 to case 2 made an increase in
evapotranspiration. This is mainly because of the increases in forest, cropland (both
irrigated and non-irrigated) and grassland. The area fraction of shrub from the land cover
map of the USGS is much higher than that from the CAS data. But it was treated as very
sparse shrub in the hydrological simulation. The nearest distance interpolation method
made a slight increase in annual precipitation in the upstream of the Lanzhou gauge.
However, it made a large increase in annual runoff in both upstream and downstream of
the Lanzhou gauge. This is due to the spatially high concentration of rainfall (see Fig. 4).
8
Decrease in groundwater level made an increase in the recharge into groundwater, and
therefore, the annual runoff was decreased.
CONCLUSION
The annual runoff in the Yellow River basin is less than 20% of the annual precipitation.
Therefore the accuracy of estimating the actual evapotranspiration is critical for the
confidence of water resources assessment. It was found that the land cover and spatial
concentration of precipitation had significant effects on the actual evapotranspiration.
Impact by the groundwater level on the river runoff was found to be mainly from the
change of recharge. With deeper groundwater level, higher recharge was required. This
caused the decrease in river runoff.
ACKNOWLEDGEMENT
This research was partially supported by the Core Research for Evolutional Science and
Technology (CREST) program of Japan Science and Technology Agency (JST). The
authors would like to appreciate their grant in aid on this research.
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