Discharge Simulation in the Huaihe River Using Fine Resolution Downscaled
Experimental Data Derived from Game Data Product* R. K. Shrestha 1, Y. Tachikawa 2, K. Takara 2(1: Graduate School of Urban and Environmental Engineering, Kyoto University, Japan,
2: Disaster Prevention Research Institute, Kyoto University, Japan)* DPRI, Kyoto University, Japan, 611-0011, Gokasho, Uji, Kyoto, Japane-mail: roshan@rdp.dpri.kyoto-u.ac.jp
Abstract
There is a large scale-gap between the atmospheric model’s output and the need of hydrological simulation tools. The
simulation of spatial rainfall field based on non-homogenous random cascade method can be a powerful tool to fill thisgap by disaggregating the large-scale data into a scale smaller enough to apply in hydrological simulations. We
developed multiplicative random cascade HSA method and applied it to disaggregate the GAME Reanalysis 1.25
degree precipitation data into 10 arc-minute spatial resolutions. And thereafter applied the disaggregated data into the
MaScOD model to simulate the runoff discharge. The disaggregated data has produced satisfactory results for threedifferently sized study catchments inside the Huaihe River basin. Further we aggregated the fine resolution data into acoarser resolution to investigate the suitable scale of input data for the distributed hydrological model. This part of study
has reinforced the concept of IC-Ratio (the ratio between grid cell area and the catchment’s area) (Shrestha et al., 2002)
to be higher than 1:10 in order to obtain a satisfactory result of discharge simulation using the distributed input. Thisfinding suggests that the needed data resolution depends upon the catchment area. For a smaller catchment area, theresolution should be finer, but a coarser resolution data can well serve the simulation need for a larger catchment. Thisfinding may help atmospheric modeler to understand the extent of data need for hydrological simulation.
Keyword: spatial disaggregation, MaScOD model, input resolution, IC-Ratio.

1. Introduction
Most of the current generation meteorological models
and hydrological models are grid based models. Thesemodels are often dependent to each other. It is a normal
practice to conduct a hydrological simulation to test,
calibrate and validate the atmospheric model.
Hydrological simulations use rainfall and evaporation
data as the important forcing input. These data areusually supplied from the works conducted inmeteorological discipline. The outcomes of both models
not only provide feedback to improve themselves (e.g.
Chiew et al., 1996; Polcher et al., 1996) but also make itpossible to analyze the past present and future scenario
of changes in meteorological regime, hydrologicalregime and their impacts on environment.
Linking the meteorological models and hydrologicalmodels are not as simple as a data-transferring
mechanism in between the two different systems. But it
has to think of the large scale-gap that exists in between
the two modeling fashion. Hydrological models can notjustify themselves to choose a coarse spatial resolution
equal to the one chosen in meteorological practice.
Hydrological models need a finer resolution toincorporate the effect of spatial heterogeneity of therunoff generation mechanism through surface
sub-surface hydrological processes. On the other hand,meteorological models can not operate in a resolution as
fine as the one required by the hydrological models
because of the risk of numerical instability, need of hugeparameter sets and lack of precise meteorologicalunderstanding in a finer resolution. To bridge thescale-gap, there are some scaling methods to transfer thedata across the scale. However, it is quite uncertain thatthe meteorological data obtained from such a scaling
method does really help the hydrological simulation ornot.

In this study, we test the rainfall data produced by adisaggregation method, so-called random cascade HSA
method, using a macro scale distributed hydrologicalmodel, the MaScOD (Macro Scale OHyMoS assisted
Distributed) model. The source of the data before
disaggregation is the GAME Re-analysis data
(1.25-degree resolution, version 1.1).
In addition, there is a question, often asked by ameteorological scientist to a hydrologist, “Whatresolution of the data a hydrologist needs?” An exact
answer of the question helps the meteorological scientist
to devise the meteorological model and its
parameterization more efficiently, at least for the scalethat a hydrologist needs. But to answer the question is adifficult task because the hydrological analysis itself ishighly scale dependent. We present here a criterion for
selection of an appropriate input data resolution which isexpressed in terms of the IC-Ratio (the ratio between themodel input spatial resolution and the area of thecatchment; Shrestha et al. 2002), based on the sensitivityof the MaScOD model’s performance to the scale of anexperimental hydro-meteorological input dataset basedon GAME reanalysis data. The study is conducted inside
the Huaihe River basin, taking the cases at Bengbu,
Wangjiaba and Suiping having catchments areas of
132,350 km2, 29,844 km2, 2,093 km2 respectively(Figure 1).

2. The MaScOD Hydrological Model
The MaScOD (Macro Scale OHyMoS- ‘Object
oriented Hydrological Modeling System’ assisted
Distributed) hydrological model has a fixed 10-minutespatial resolution in this study. The girded frame of
10-minute resolution, which is overlaid on the river
network, breaks the network into smaller river segments
defining connection relationships. Each grid cell contains

a MaScOD Element Model (MEM). Each MEM contains
a Runoff Process Model (RPM) for a river segment, anda Flow Routing Model (FRM) for a connected set ofriver segments within the grid cell. The RPM is based on
the Xinanjiang Model (Zhao, 1992), and the FRM isbased on the lumped stream kinematic wave model(Shiiba et al., 1996). The OHyMoS (Takasao et al., 1996;
Ichikawa et al., 2000) assists all those units of the model
to link each other and develop a total simulation system.
Details of the model are given by Tachikawa et al. (2002)
and Shrestha et al. (2004b).

(a)Fig. 1: Major rivers and discharge observation stations.

3. Discharge Simulation results using thedisaggregated data
The GAME Re-analysis data having 1.25 degree
resolution was successfully applied in the Huaihe River
Basin by Shrestha et al. (2002). They reported that thedata was successful to simulate discharge at Bengbu
having catchment of 132,350 km2, but failed in othersmaller catchments to produce good results. To
investigate whether the failure in smaller catchments isdue to a very coarse forcing data that could haveinsufficient description of the spatial variability of therainfall, a disaggregated rainfall data of 10-minuteresolution was felt necessary to test as the input data to
the MaScOD model. The model parameters andmodeling conditions are the same for both the GAME
1.25-degree data and downscaled 10-minute data. Figure
2 shows a typical spatial pattern of both data
We used the random cascade HSA method to obtain
the disaggregated data. An important point to note is theoutcomes of the MaScOD model using a disaggregatedinput data would be highly affected by a particularmethod adopted for the purpose of disaggregation. In thispaper, we omit discussing about the methods ofdisaggregation. Interested readers are referred to
Shrestha et al. (2004a). The results of discharge
simulation obtained at Bengbu, Wangjiaba and Suiping
having catchments areas 132,350 km2, 29,844 km2, and
2,093 km2 respectively. Figure 3 show the results
obtained from both the GAME 1.25-degree reanalyzed
data, and the disaggregated 10-min Experimental data.

(b)
Fig. 2: Spatial patterns of the rainfall data, a) GAME
1.25-degree resolution; and b) Downscaled 10-minuteresolution data.

Comparing between the results obtained in differently
sized catchments from the GAME 1.25-degree resolution
data, and the Experimental 10-min resolution data, we
can understand some important features related to theresolution and scale issues. For example, in a large
catchment like the Bengbu (Figure 3a), there is a little
difference between the simulated discharge by thecoarser resolution GAME data and by the finer
resolution disaggregated data. But, in a smallercatchment, like the Wangjiaba (Figure 3b) and theSuiping (Figure 3c), the disaggregated data produced
significantly improved discharge than the coarse
resolution GAME data, while comparing with theobserved discharge. The disaggregation method might
have included the needed sub-grid scale variability of therainfall in the finer resolution data. Otherwise, there is noother possible reason to cause the differences indischarge simulation, provided the constant simulation
conditions. This means that the GAME data have had
described the rainfall appropriately for the region but thatmerit was disappeared due to its coarser resolution –causing failure to model smaller catchments properly.
This also means that a smaller catchment may need tohave finer resolution data in order to include the needed
degree of sub-grid scale variability in their data
description.

Run
off
( mm/ hr)

1.6

Downscaled10‑min

1.4

Ob
se
rved

1.2

GAME 1.25‑degree

5. Suitable input
IC-Ratio

1.0

0.6

5.1.
Aug 11

Aug 71

Aug23

Aug29

Aug 71

Aug23

Aug29

Aug29

Aug 71

Aug23

Aug5

Aug 11

Ju
l30

Aug5
Aug 11

Ju
l24

Ju
l30

Ju
l 81

Ju
l24

Ju
l 21

Ju
l 81

Ju
l6

Ju
l 21

Jun
30

Jun
24

Jun21

Jun81

May
31

Jun
6

May
25

May9 1

May3 1

May 1

0.0

May
7

0.2

(a)
3.5

Dow
nsc
aled01‑mi
n
Ob
sr
ev
ed

3.0

GAME 1.25‑de
gree

2.5

Run
off( mm/ hr)

in

terms

of

In this section, we briefly describe the results obtained
from the hydrological model using various resolutions of
input data.

0.8

0.4

2.0
1.5
1.0

Ju
l6

Jun
30

Jun
24

Jun21

Jun81

Jun
6

May
31

May
25

May9 1

May3 1

May 1

0.0

May
7

0.5

(b)
8

Run
off
( mm/ hr)resolution

Downscaled01‑min

7

Ob
sr
eved

6

GAME .125‑de
gree

5
4
3
2

What is the IC-Ratio?

The IC-Ratio is a ratio between the area of input gridcell and the size of a catchment (Shrestha et al., 2002). It
is difficult to compare the constitutive relations fordifferent flow processes that are function of the detailed
geometry of flow pathways in different catchments(Beven, 2002). In many cases, the simulation result
characteristics are found to be dependent on sizes ofcatchments. Size of catchments is the only absolute
figure to provide consistent information that provides asound base to investigate on the suitable resolution forhydrologic modeling. The IC-Ratio is the index that
relates these two components. This is a useful index forinvestigating the effects of various input resolution in
multiple catchments having different size, particularly incase when other information are not consistent. The
convention of the index is fixed to keep the numerator
always one such that a higher denominator value
corresponds to finer resolution of input data and is called
as the higher IC-Ratio. Similarly a lower denominatorvalue corresponds to coarser resolution of input data andis called as the lower IC-Ratio, because the size remains
constant for a catchment while examining the data ofvarious resolutions. We analyze here the performance ofhydrological model in terms of the IC-Ratio index.

Aug5

Ju
l30

Ju
l24

Ju
l 81

Ju
l 21

Ju
l6

Jun
30

Jun
24

Jun21

Jun81

Jun
6

May
31

May
25

May9 1

May3 1

May 1

0

May
7

1

(c)
Fig. 3: Comparison of discharge simulation results using
GAME 1.25-degree data and the 10-minute disaggregateddata with observed discharge at a) Bengbu (132,350 km2);b) Wangjiaba (29,844 km2); and c) Suiping (2,093 km2).

4. Simulation
resolutions

resultsusing

various

input

Use of the disaggregated data produced good results indifferent catchments of different size and characteristics
using the 10-minute Experimental data as the input data
for the hydrological modeling, the 10-minute resolution
may not be a suitable resolution for modeling thecatchments. This is to investigate with input data ofvarious resolutions. In this study, because of the
MaScOD model’s limitation, we could not test the input
data resolution finer than 10-mintues although it ispossible to obtain them. However, we can have coarser
data e.g. 20-minutes, 30-minutes and so on to use as the
input data of various resolution. The input data having
various resolutions are fed into the MaScOD model tosimulate the discharge and compare the performance.

Fig. 4: Effect of input data resolution in model
performance in terms of the IC-Ratio

5.2.

The IC-Ratio for optimal performance

The goodness of fit of the simulated discharge andobserved discharge is evaluated by performancemeasuring criteria. Figure 4 show the differences in
performance those obtained using the various input
resolutions and expressed in terms of the IC-Ratio. In
this figure the performance measuring index is thePearson’s Moment Correlation coefficient. It is clear thatthe performance increases with a higher IC-Ratio
irrespective of the size of catchments. Sensitivity of the
input data resolution is higher in smaller catchment.
However, the improvement of performance tends to level
off while the IC-Ratio is very high.
Figure 5 is showing the model performance versus the
IC-Ratio using some additional performance measuring
indices. The results obtained in this experiment has
displayed that a range of IC-Ratio values produce
satisfactory results. Poorer performance below the
IC-Ratio 1:10 is likely to mean that if there is less than
10 units of distributization of the input data in thecatchment, the distributed model does not yield niceresult, which is more critical in smaller basins. Looking
toward higher degree of distributization has tendency toyield better result always, but too much distributizationof the input data is also unable to improve theperformance much. For example the model performs
almost similar at 1:20 and 1:200, but the later one needs
much more data and efforts in modeling. Thus the rangeof IC-Ratio in between (1:10 ~ 1:20) can be considered
as optimal range keeping the efforts needed to model inmind. This idea is likely to be very useful to set up a
criterion of suitable resolution of hydro meteorological
data product.

Fig. 5: Model performance versus IC-Ratio for the four
indices.

6. Conclusion
The grid based description of meteorological modeland hydrological model are sensitive to resolution setting.
They need a different resolution setting that causes a
scale-gap while linking their products. Since the studies
in both disciplines are somehow mutually dependent, it isnecessary to investigate the issue well.
GAME 1.25-degree data were reported successful indischarge simulation at Bengbu (132.350 km2) but failed
in smaller catchments like the Suiping (2,093 km2). This
shortcoming was largely removed while the data isdisaggregated using a downscaling method so-called therandom cascade HSA method. This shows the need offiner resolution data for a smaller catchment.
Further investigation on sensitivity of the input data

resolution in the hydrological simulation using the
MaScOD model revealed the importance of catchment
size and the input data resolution to be considered. The
IC-Ratio successfully related these facts by showing thechange in model performance with respect to the
IC-Ratio. The obtained results showed that the IC-Ratio
within the range of 1:10 ~ 1:20 performs optimally,
which could guide to select an appropriate input data
resolution for a catchment in terms of the size of thecatchment. The appropriate resolution of input data to ahydrological model may provide a criterion of selecting
the resolution of a meteorological model.

References
Beven, K., Towards an alternative blueprint for a physically
based digitally simulated hydrologic response modelingsystem, Hydrol. Process. 16, 189–206, 2002.
Chiew, F. H. S., . Pitman, A. J and McMahon, T. A., Conceptual
catchment scale rainfall-runoff models and AGCM
land-surface parameterisation schemes, Journal of
Hydrology, 179, 137-157 1996.
Ichikawa, Y., Shiiba, M., Tachikawa, Y. and Takara K., Object
oriented hydrological modeling system, The FourthInternational
Conferenceon
HydroInformatics
CD-ROM, Cedar Rapids, Iowa, 2000.
Polcher, J., Laval, K., L. Dümenil, J. Lean and P. R. Rowntree,
Comparing three land surface schemes used in generalcirculation models, Journal of Hydrology, 180,
373-394, 1996.
Shiiba, M., Ichikawa, Y., Ikebuchi, S., Tachikawa, Y. andTakasao, T., Scale-up of a runoff model using GIS andan object-oriented hydrological modeling system.
Proceedings of HydroGIS 96: Application of
Geographic Information System in Hydrology andWater Resources Management, (eds. K. Kovar & H. P.
Nachtnebel), IAHS, 235, 63-70, 1996.
Shrestha, R., Tachikawa, Y. and Takara. K., Effect of forcing
data resolution in river discharge simulation, Annual
Journal of Hydraulic Engineering, JSCE, 46, 139-144,
2002.
Shrestha, R., Tachikawa, Y. and Takara. K., Downscaling
spatial rainfall field from global scale to local scaleusing improved multiplicative random cascade method,
Annuals of Disaster Prevention Research Institute,
Kyoto University, No. 47 B, 2004a.
Shrestha, R., Tachikawa, Y. and Takara. K., Performance
analysis of different meteorological data and resolutions
using MaScOD hydrological model, Hydrological
Processes, (in Press), 2004b.
Tachikawa, Y., Takara, K., Tanaka, K., Suishu, T., Ichikawa, Y.,
Shiiba, M., Simulation of River Discharge in the HuaiheRiver Basin in China, Journal of Japan Soc. Hydrol. &Water Resource, 15.(2), 139-151, 2002.
Takasao, T., Shiiba, M. and Ichikawa, Y., A runoff simulation
with structural hydrological modeling system. Journalof Hydroscience and Hydraulic Engineering, JSCE,
14(2), 47-55, 1996.
Zhao, R. J., The Xinanjiang model applied in China, Journal of
Hydrology, 135, 371-381, 1992.