Applications of a distributed hydrological model to theAWCI demonstration river basins
OLIVER CRISTIAN SAAVEDRA VALERIANO & TOSHIO KOIKE
Department of Civil Engineering, University of Tokyo, Bunkyo-ku, Tokyo, 113-8656, Japanoliver@hydra.t.u-tokyo.ac.jp

Abstract In order to tackle extreme events such as floods, reliable weather forecast isrequired to input into land surface models to make sound decisions. In Asia thecountries affected by these events recognized the need to establish the Asian Water
Cycle Initiative (AWCI). The objective is to promote integrated water resourcesmanagement by making usable information from Global Earth Observation System ofSystems (GEOSS) to address the common water-related issues in Asia. Within thisframework early successful results are achieved. For example, real-time flood
management using quantitative precipitation forecast at meso-scale in Japan. Optimal
dam operation was achieved maximizing the hydroelectricity production in The
Philippines. Flood forecasting forced by meso-scale High Resolution Model in
Vietnam. Flood simulation was obtained using corrected satellite rainfall data inBangladesh. Optimal dam operation was achieved at Pampanga basin in The
Philippines. The results might be useful as reference tools to the decision makers
contributing to societal benefits.
Keywords Floods; AWCI; distributed hydrological model; Tone River; climate change impacts

INTRODUCTION
Extreme events usually occur due to large water cycle fluctuations at global andregional scales, while disasters and damages take place at local scale. The observations
and predictions of the water-related hazards and their damages can be enhanced by
combining global earth observation and prediction systems and local information.
There is a growing concern about impacts of the global warming in flood risks andthe need to address them in a more coordinated way. The Fourth Assessment Report of
the Intergovernmental Panel on Climate Change (IPCC) summarized and projected thetrends of precipitation in past and future in the following ways: "the frequency of heavy
precipitation events has increased over most land areas, consistent with warming andobserved increases of atmospheric water vapor."; and "It is very likely that heavy
precipitation events will continue to become more frequent.", respectively (IPCC,2007). The flood hazards will be intensified due to the global warming. We needtimely, quality, long-term, global water cycle information as a basis for sound andeffective flood risk management.
In Asia the cooperation among 18 countries has being established as Asian Water
Cycle Initiative (AWCI). The objective is to promote integrated water resourcesmanagement by making usable information from Global Earth Observation System ofSystems (GEOSS) to address the common water-related problems in Asia. Four main
water issues are targeted: floods, droughts, water pollution and climate change impacts
on water cycle. The key points of AWCI are: convergence of satellite observations,data integration, open data and source policies, and capacity building. Under thisinitiative each member country proposed one demonstration River basin with observedhydro-meteorological data. According to the needs of each River basin management, adistributed hydrological model is set-up to take advantage of spatially distributed
forcing data. Additionally, an optimization algorithm might be coupled to improve theefficiency of water resources management such as flood reduction, water allocation atdams, hydroelectricity, flood alarming and evacuation programs. Some of the early
achievements are: optimal flood management of Upper Tone River in Japan using adam network and Quantitative Precipitation Forecast (QPF) at meso-scale. Similarly,
the optimal dam operation was achieved at Pampanga basin in The Philippines
reducing the flood peaks downstream and also maximizing the hydroelectricity
production. In Vietnam, the flood inundation at Hue City was predicted for flood
alarming and preparedness using a High Resolution Model (HRM) with 6 hours leadtime. In Bangladesh, the discharge of the Meghna River basin was simulated using
corrected satellite rainfall measurements from the Tropical Rainfall Measurement
Mission (TRMM). All these results might be useful as reference tools to the decision
makers contributing to societal benefits.
The objective of this paper is to introduce the flood management system applied in
the AWCI demonstration basin in Japan in detail among all applications. Section two
introduces the features of the distributed hydrological model as a component of thesystem. Section three summarizes the coupled system and results obtained in the Upper
Tone River application.
METHODOLOGY
In order to meet the increasing societal needs for improving flood prediction, including
the climate change impacts on the global to local water cycle variations, there arestrong research efforts to apply hydrological models throughout the world for getting
better understanding of water and energy cycle variations. Much of this work has
involved coupling Distributed Hydrological Models (DHMs) with Land Surface
Models (LSMs), since this coupling potentially improves the land surface
representation, benefiting both the stream flow prediction capabilities of thehydrological models as well as providing improved estimates of water and energy
fluxes into the atmosphere (Pietroniro and Soulis, 2003). This study employed the socalled Water and Energy Budget based-Distributed Hydrological Model (WEB-DHM),
which couples the widely used SiB2 by Sellers et al. (1996), with a Geomorphology
Based Hydrological Model (GBHM) developed at the University of Tokyo by Yang etal. (2002). The GBHM is a physically based model which was conceived to simulate
large River basins but at low computational expenses. So far, the entire Yellow River,Yangtze River and Chao Phraya River were simulated successfully. One of the GBHM
versions has embedded dam network operation within the river network using a massbalance function with a linear release function as introduced by Saavedra et al. (2006).
APPLICATION IN TONE RIVER
There is growing concerns on decision support tools which can effectively introduce
the flood prediction with improved accuracy to decision making on flood control. In
South-east Asia region, dam operation during tropical cyclones is crucial to reduce
flood peaks down stream and maximize the water level at the reservoirs for different
future demands. To overcome the control problem of dam operation, Labadie (2004)
reported the benefit of heuristic programming models to find the global optima. Ngo etal. (2007) reported successful coupling of MIKE 11 and Shuffled Complex Evolution(SCE) by optimizing historical operation rule curves of Hoa Binh dam, Vietnam. The

research focused on the trade-off between flood control and hydropower generationachieving optimized rule curves. However, it would also be interesting to include
forecast data in the real-time operation. The present system focuses on effective flood
control operation of a multi-purpose and multi-reservoir including the features below:(a) quantitative rainfall prediction with the estimated prediction error to an
optimization scheme;(b) introduction of a physically based distributed hydrological model to address multireservoir by making maximum use of spatially distributed prediction rainfall; and(c) introduction of multi-purpose optimization functions for getting a quantitativesolution.
In order to reduce flood peaks downstream and store water at dams for future
use a desirable discharge Qopt_gauge is introduced as a constant value taking as reference
the average discharge during flood period. Firstly, WEB-DHM is run to simulatedischarge downstream without the dam effect, Qwithout_dam. If this value does not exceed
the desirable discharge Qdesirable, the system is not activated. If opposite, the simulated
discharge downstream with the dam effect Qwith_dam needs to be calculated. The
potential flood volume flood_voldam to be reduced is then quantified as the integrative
volume between the simulated discharge Qwithout_dam and Qwith_dam, but exceeding thedesirable discharge Qdesirable as seen in Fig. 1. Then, the total flood volume is obtainedby using all dams. The intersection of Qwithout_dam with the desirable flow Qdesirable
downstream defines stages I and II denoted by t. In this way, the system is expected torelease an amount less than or equal to the total predicted inflow to dams during stage I
to leave enough flood control volume. Once stage II is reached, the gates are closed
expecting to store water as replenishment. The end of the latter procedure will bedefined by time step τ, which could match with the lead-time or till simulated
discharge is lower than Qopt_gauge. Therefore, the objective function is defined as tominimize the absolute difference between the flood volume and the total released
volume from dams by using the heuristic optimization algorithm SCE developed byDuan et al. (1992).
Run WEB-DHM neglecting dams

No need special flood operation

Qwithout_dam>Qdesir.
NoYes

Situation downstream

Run WEB-DHM including dams
flood_voldam
QStage I

Quantify max. potential flood
volume to be cut, flood_voldam

Qwithout_damQdesirable

Identify stages I and II by setting tTime
Qwith_damt

τIt’s expected to release water during
stage I according to cost function
Gates are closed during stage II up
to time step τFig. 1 Objective function for flood control and water use

The meso-scale weather prediction model coupled with the GCM provides
meteorological variables namely Grid Point Value (GPV) at 0.125 degree spatial
resolution issued every 6 hours and records up to 18 hours lead-time. The GPV datawith the coverage of all Japan is originally provided by Japan Meteorological Agency(JMA) and is archived from first of July 2002 up to current days on GEOSS/DIAS at
the University of Tokyo. A weight, which is inversely proportional to the standard
deviation of the error forecast from the contributing areas to the dams, is introduced
into the decision variables which are the dam releases. The error is calculated as thesimple bias, absolute bias, Root Mean Square Error (RMSE). In this way, it is expectedthat lower values of standard deviation (better accuracy of forecast) affect morepositively the iteration process of the optimization.
The developed system was applied to an actual dam network in the upper Tone
River in Japan. Not only the flood peak and volume was reduced downstream, but also
the water volume in the dams was replenished after the flood event. After the iteration
process completed by the SCE algorithm, the optimized water release values forSonohara and Fujiwara dams were obtained. The dam effect at Iwamoto gauge using
GPV 7-12 rainfall forecast series can be seen in Fig. 2a. The flood peak and recession
curve were reduced due to effective storage of water at the dams. Then, Fujiwara andSonohara dams' status can be seen in Fig. 2b and 2c. Sonohara dam started operating as
free flow from 10 pm on 10 of July 2002 when the maximum safety capacity wasreached. On the other hand, simulated water level in Fujiwara dam has reached as highas 646.24 m. These results mean higher potential of hydroelectricity generation.

(a)

(b)

(c)

Fig. 2 Optimized dam operation using 7-12 series of rainfall prediction at Iwamoto gauge (a), Fujiwara
dam (b), and Sonohara dam (b).

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