DISTRIBUTED HYDROLOGICAL MODELING AND RIVER
FLOW FORECAST FOR WATER ALLOCATION IN A LARGESCALED INLAND BASIN OF NORTHWEST CHINA
YANGWEN JIA, HAO WANG, JIANHUA WANG AND DAYONG QIN
Department of Water Resources, Institute of Water Resources & Hydropower Research
20 Che-Gong-Zhuang West Road
Beijing, 100044, China
This paper describes an application of WEP model to a large-scaled inland basin of
Northwest China, namely the Heihe Basin (141363km2) for rational allocation of water
Resource. With the aid of GIS software, the input data were prepared and the model
parameters were estimated at first. The WEP model, a grid-based distributed
hydrological model, was then modified to satisfy the conditions in the Heihe basin by
adding a snowmelt module. The basin was discretized into 141363 grid cells each having
a size of 1km by 1km. A mosaic method was adopted to calculate the water and heat
fluxes in every grid cell by averaging those in 8 types of land use patches inside the cell.
A simulation of 21 years from 1982 to 2002 was carried out in a time step of 1 day. The
model was calibrated from 1996 to 2000 and verified in remaining years using the
observed discharges at several gauge stations. The simulated results have higher values
of the Nash-Sutcliffe efficiency and lower relative errors. The validated model was
applied to estimate the annually averaged water resources in the basin from 1992 to 2002
and to forecast monthly runoff in low flow periods and five-day runoff in high flow
periods to assist the water allocation in the basin.
INTRODUCTION
Study on hydrological cycles in watersheds is to solve water flow fields and to quantify
water fluxes that are precondition for studying water resources evolvement, sediment
movement, pollutant transport and ecological processes, and thus it can provide scientific
and technological basis for sustainable development. With the development of PC, GIS
and RS techniques, distributed watershed hydrological modelling received high attentions
and got a great development since 1980s. A lot of models (Singh and Woolhiser [1]) have
been developed since the occurrences of SHE model (Abbott et al. [2]). Distributed
hydrological modelling can play important roles in the following aspects: (1) water
resources allocation and flood forecast for hazard prevention; (2) analysis of human
activities’ impact on water resources and environment; (3) predictions of possible
changes of water resources and ecology with the global change in future; (4) hydrological
predictions in un-gauged or badly gauged basins (PUBs); (5) support for studies of
sediment movement, no-point source pollutant transport and ecological problems etc.
The WEP (Water and Energy transfer Processes) model is a grid-based distributed
hydrological model. Its original version was based on the studies of Jia and Tamai [3] on
1
2
water and heat budgets in Tokyo metropolis. It was greatly improved at the Public Works
Research Institute (Jia et al. [4]). The model was verified in several urbanized or partially
urbanized watersheds in Japan, and ever applied to evaluate the effects of
countermeasures like infiltration facilities and retarding ponds on hydrological cycle
improvement. In 2003, after adding a snowmelt module and considering the
hydrodynamic mechanism of frozen soils in high mountain areas, the WEP model was
successfully applied to a large-scaled inland basin, the Heihe Basin for water resources
evaluation and river flow forecast.
The Heihe Basin is an inland basin located in arid areas of Northwest China, with an
area of 141363km2 (see Figure 1). Almost all of water resources are generated in the
upstream mountain area (annual precipitation is 350mm) and consumed in the middlestream area (annual precipitation is 140mm) and in the downstream area (annual
precipitation is 47mm). With the development of local economy in the Heihe basin, water
shortage and irrational water utilization in the middle-stream area caused a serious
ecological and environmental crisis to the downstream area. In addition to administration
countermeasures, a national key study project was carried out in the past 3 years to
provide a decision-support information system for rational allocation of water resources
in the basin. Hydrological modeling and river flow forecast are the bases of water
allocation and thus are one of study topics of the project.
Figure 1. The Heihe basin and land use distribution in 2000.
3
MODEL DEVELOPMENT
The WEP model is a physically based spatial distributed (PBSD) model. In addition to
hydrological modelling, it also has functions of heat transfer simulation, flow component
separation and pollutant transport simulation. Its hydrological component is only
introduced here.
The vertical structure of the WEP model within a grid cell is shown in Figure 2(a).
The state variables include depression storage on land surfaces and canopies, soil
moisture content, land surface temperature, groundwater level, and water stage in rivers,
etc. A brief description of modelling approaches of hydrological processes follows with
equations and modelling approaches of energy transfer processes referred to in Jia and
Tamai [3] and Jia et al. [4]. The horizontal structure of the WEP model is shown in
Figure 2(b). Treated as overland flow, runoff from a grid cell is routed along the steepest
of eight directions to its adjacent cells using the kinematic wave method in the 1-D
scheme and the downhill Newton method. The computation sequence is determined from
the lowest flow accumulation number (headwater area) to the largest flow accumulation
number (river outlet). River flow is routed for every tributary and a main channel using
the kinematic wave method or the dynamic wave method in the 1-D scheme and the
Newton-Raphson method.
Evaporation is calculated with the Penman equation and transpiration is calculated
by using the Penman-Monteith equation. The average evapotranspiration in a grid cell is
obtained by areally averaging those from each land use.
Infiltration during heavy rains is calculated utilizing the generalized Green-Ampt
model for infiltration into multi-layered soil profiles suggested by Jia and Tamai [3]
whereas soil moisture movement in unsaturated soils during other periods is solved using
the Richards model. A heavy rainfall period is defined as a period during which the
rainfall intensity is greater than the saturated soil hydraulic conductivity.
Surface runoff from the soil-vegetation group consists of two parts, namely the
infiltration excess during heavy rainfall periods and the saturation excess during the other
periods. Infiltration excess occurs when the depression storage on the land surface
surpasses its maximum value. The depression storage is balanced with rainfall as inflow
and infiltration, evaporation and infiltration excess as outflows. Saturation excess during
the remaining periods may occur if the groundwater level in the unconfined aquifer rises
and the topsoil layer becomes nearly saturated. It is deduced by applying the Richards
model. Subsurface runoff is calculated according to land slopes and unsaturated soil
hydraulic conductivities in those grid cells adjacent to rivers.
Groundwater flow in multi-layered aquifers is simulated using the Boussinesq
equation and the interactions between surface water and groundwater of the unconfined
aquifer are considered through a source term. The source term includes the recharge from
unsaturated soil layers, the groundwater outflow to rivers, the water use leakage, the
pumped groundwater, the percolation to the lower aquifer, and the evapotranspiration
from groundwater. Groundwater outflow is calculated according to the hydraulic
4
conductivity of riverbed material and the difference between river water stage and
groundwater head in the unconfined aquifer.
The temperature-index approach is adopted to simulate snow storage and snowmelt
processes in mountain areas and the hydraulic conductivity of frozen soil is assumed to
be exponentially decreased when the air temperature is below the critical value.
Precipitation
Water Body
Group
Soil-Vegetation
Group
Interception
Layer
Impervious
Area Group
Transpiration
Depression
Layer
Subsurfac
e
Runoff
Surface
Runoff
Evaporation
Longwave
Radiation
Shortwave
Radiation
Top Soil Layer
Suction
Diffusion
Infiltration 2nd Soil Layer
3rd Soil Layer
Recharge
Flow in
Ae
heat
fluxes
Confined Aquifer 1
GWP
Transition Layer
Exchange by
seepage or Unconfined Aquifer
outflow
Aquitard 1
Flow in
WUL
Flow out
Percolation 1
Flow out
Aquitard 2
Flow in
Confined Aquifer 2
Percolation 2
Flow out
(a)
1D Overland Flow
1D River Flow
Tributary
Main River
(b)
Figure 2. (a) Vertical structure in a grid cell of WEP model and (b) horizontal structure of
WEP model.
5
MODEL APPLICATION
The basin was discretized into 141363 grid cells each having a size of 1km by 1km. With
the aid of GIS software, the input data like land use, topography, vegetation, soil, aquifer
and water use etc were prepared. The overland flow in the upstream mountain area
(28168 grid cells) was simulated using the kinematic wave equation whereas that in the
downstream plain area was neglected because surface runoff is rarely generated in the
plain area. The river flow routing was carried out for 10 main river channels, which were
divided into 178 river links.
Parameter estimation
Parameters of the WEP model can be roughly classified into 3 categories. The first one
includes the parameters related to land surface and river networks like the Manning
roughness of overland and rivers, the thickness and hydraulic conductivity of the riverbed
material, the impervious ratio of urban land covers, the maximum storage of land
depression etc. The second category is the vegetation parameters including the vegetation
fraction, the maximum interception storage, the leaf area index (LAI), the aerodynamic
resistance, the stomal resistance and the root distribution parameters etc. The third
category is soil and aquifer parameters including the soil layer thickness, the soil
porosity, the soil residual moisture content, the suction at the wet front, the saturated
hydraulic conductivity, the moisture-suction curve parameters, the moisture-conductivity
parameters, the conductivity and thickness of aquifers and aquitards, and thickness,
storage coefficient or specific field of aquifers etc. It is found that 7 parameters, i.e., the
soil saturated hydraulic conductivity, the impervious ratio of urban area, the maximum
storage of land surface depression, the Manning roughness, the aquifer conductivity, the
aquifer specific yield or storage coefficient and the conductivity of riverbed material are
key ones. Among these key parameters, the soil saturated hydraulic conductivity, the
impervious ratio of urban area and the maximum storage of land surface depression are
sensitive to infiltration excess, the maximum storage of land surface depression and the
Manning roughness have big effects on the shape of flood hydrograph and the remaining
ones effect saturation excess and groundwater outflow or river seepage. Except the key
parameters, others are usually directly referred to deduced values without tuning.
Results
A simulation of 21 years from 1982 to 2002 was carried out in a time step of 1 day. The
model was calibrated from 1996 to 2000 and verified in remaining years using the
observed discharges at several gauge stations, one example is shown in Figure 3. The
simulated results have higher values of the Nash-Sutcliffe efficiency and lower relative
errors (see Table1).
The validated model was applied to estimate the annually averaged water resources
in the Heihe basin from 1992 to 2002 (see Table2). Because there is almost no runoff
generated in the middle and downstream areas, the amount of water resources from
6
0
50
100
150
200
250
300
350
400
450
500
400
300
Simulated
Observed
200
100
0
Monthly Rainfall (mm)
500
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
Monthly Discharge (m3/s)
upstream mountainous area is approximately the total amount of water resources in the
whole Heihe basin. Though the runoff from main rivers can be obtained from observation,
the runoff from small rivers and lateral groundwater inflow are usually difficult to obtain
and the WEP model plays a role in this case.
Figure 3. Comparison of simulated and observed monthly discharges at the Yingluoxia
gauge station.
Table 1. Evaluation of simulated river flow results.
Observed
annual
runoff
volume
(108m3)
Simulated
annual
runoff
volume
(108m3)
16.85
Calibration
period
(1996-2000)
Verification
period 1
(1982-1995)
Verification
period 2
(2001-2002)
Whole period
(1982-2002)
Relative
error
NashSutcliffe
efficiency
of monthly
discharge
Correlation
coefficient
of monthly
discharge
16.82
-0.2%
0.85
0.96
16.19
16.21
0.1%
0.89
0.96
14.61
14.10
-3.5%
0.91
0.95
16.25
16.30
-0.3%
0.88
0.96
Table 2. Annually averaged water resources in the Heihe basin from 1992 to 2002
estimated by using the WEP model
Amount
(108m3)
Runoff
from
main
rivers
Runoff from
small rivers and
groundwater
into Zhang-Ye
basin
Lateral
groundwater
inflow into
Central
basin
Lateral
groundwater
inflow into
Jiu-Quan
basin
Total
26.0
7.8
3.3
1.1
38.2
7
The validated model was also utilized to give monthly runoff forecast in low flow
periods (from October to April) and 5days’ runoff forecast in high flow periods to assist
the water allocation in the basin, as shown in Figure 3 and Figure 4. In the upstream area
of the Heihe basin, about 90% of annual precipitation concentrates in wet seasons (from
May to September), which provides good conditions for monthly runoff forecast in low
flow periods. The 3 steps’ forecast approach is adopted: (1) predicting the distributions of
soil moisture, groundwater level and accumulated snow at the end of September using the
WEP model and recorded climate data; (2) assuming the climate daily series in coming
low flow periods as the averaged ones from 1982 to 2002 and (3) using the model to
forecast river flow according to the assumed climate data and predicted initial conditions.
Fig.11 shows that the relative forecast errors of monthly discharges range from –14% to
15% whereas it is 5% in the whole dry period of 2002, thus the low flow forecast is quite
successful. As for 5days’ runoff forecast in high flow periods, because the averaged
concentration time of runoff in the upstream Heihe basin is about 2 to 3 days, it can be
realized by using a forecast of 3days’ rainfall. Fig.12 shows that the relative forecast
errors of 5days’ runoff volume in May 1999 have a range from –26% to 33% with the
minimum of -3％.
90
Observed
80
Predicted
70
60
50
40
30
20
10
4
3
2
1
12
11
0
10
Monthly averaged discharge (m3/s)
Oct.2000 - April 2001
100
2000
1500
Observed
Predicted
1000
500
26
-3
1
5.
21
-2
5
5.
16
-2
0
5.
10
6-
11
-1
5
5.
5.
5.
5.
5
0
1-
3
Water volume in 5 days（10000m ）
Figure 3. One example of monthly discharge forecast in dry seasons at the Yingluoxia
gauge station.
Figure 4. One example of 5days’ river flow forecast at the Yingluoxia gauge station.
8
CONCLUSIONS
Study results described above shows that this study provides a successful example for a
distributed hydrological model applied to a large-scaled inland basin to aid in water
resources estimation and regulation. The distributed hydrological models like the WEP
model are expected to play roles in practices of water resources management, flood
forecast, environmental evaluation and biology protection. Ongoing efforts include (1)
establishing a modelling module library to make model applications get easier, especially
for the case of inadequate data; (2) adopting changeable spatial and time steps for
different processes both to grasp physical mechanism and to save computation time; (3)
enhancing its parameter estimation and (4) coupling it with sediment movement and
pollutant transport modelling.
ACKNOWLEDGEMENTS
This study got financial support from Ministry of Science of China in a national key
study project of the Tenth Five-year Plan entitled Decision-support Information System
Development for Rational Allocation of Water Resources in the Heihe Basin.
REFERENCES
[1] Singh V.P. and D.A Woolhiser. Mathematical modeling of watershed hydrology,
Journal of Hydrologic Engineering, Vol.7, No.4, (2002), pp 270-292.
[2] Abbott M.B., Bathurst J.C., Cunge J.A., O’Connell P.E. and Rasmussen, J. “An
Introduction to the European Hydrological System - Systeme Hydrologique
Europeen, SHE, 2: Structure of a physically-based distributed modelling system”, J.
Hydrol., Vol.87, (1986), pp 61-77.
[3] Jia, Y. and Tamai N. “Integrated Analysis of Water and Heat Balances in Tokyo
Metropolis with a Distributed Model”, J. Japan Soc. Hydrol. & Water Resour. ,
Vol.11, No.2, (1998), pp 150-163.
[4] Jia Y., Ni G., Kawahara Y. and Suetsugi T. Development of WEP Model and Its
Application to an Urban Watershed, Hydrological Processes, Vol.15, (2001), pp
2175-2194.