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7 - 12 September 2009 University of Tokyo, Tokyo, Japan

Hydrological Process down-scaling for reduction of damages by Water-relatedhazards and Climate Change Adaptation

Toshio Koike

Professor, Department of Civil Engineering, the University of Tokyo

Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan, e-mail: tkoike@hydra.t.u-tokyo.ac.jp

Water-related hazards usually occur as causes and consequences of large water cycle fluctuations

on global and regional scales, while disasters and damages due to the hazards happen through

strong linkage with human activities on a 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. Global warming is changing the water-related

hazards. IPCC reported the increase of the frequency of heavy precipitation events, the area affected

by droughts and intense tropical cyclone activity, from observations and projections in its 4th

assessment report. Vulnerability due to water-related hazards will increase associated with the

global warming. By making maximum use of the opportunities of global observations and

predictions, this paper develops a downscaling system that converts global Earth observation data

and prediction outputs to usable information for sound decision making for reducing damages by

water related-hazards and adapting climate change impacts.

Dynamical downscaling can effectively combine general circulation model outputs, satelliteobservation data, and in-situ observation data and socio-economic needs on a river basin or smaller

scale. The system developed in this paper consists of three sub-systems: a satellite-based

atmosphereland coupled data assimilation system, a water and energy budget-based distributed

hydrological model, and decision making support tools for flood control including dam operation

and evacuation instructions. This system is now working on the data integration and analysis system

(DIAS).

The satellite-based atmosphereland coupled data assimilation system combines a land data

assimilation system (Boussetta et al, 2007) and a satellite-based cloud microphysics data

assimilation system (Mirza et al, 2008) by refining and coupling them with a physically based

land-atmosphere coupled radiative transfer model, which can represent microwave radiative transfer

in soil by considering surface roughness effects, volume scattering and emission in the soil volume,and atmospheric emission and scattering. Both data assimilation schemes use the advanced regional

prediction system (ARPS), developed at the Center for Analysis and Prediction of Storms at the

University of Oklahoma, which is coupled with the simple biosphere model 2 (SiB2) developed by

Sellers et al. (1996). To account for precipitation estimation, this assimilation sub-system includes

modifications allowing direct estimation of snow and rain rates as additional assimilation variables.

It was applied to the National Centers for Environmental Prediction global forecast system

reanalysis data and AMSR-E archived in DIAS, for downscaling to a mesoscale area of the Tibetan

Plateau. Using the assimilated atmosphere products as an initial condition of ARPS, the 24 hour

rainfall was predicted.

The water and energy budget-based distributed hydrological model couples SiB2 with a

geomorphology-based hydrological model (Wang et al, 2009). The results of the model application

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to the upper Tone river basin of Japan, using the digital elevation model, land use/cover, and soiltype datasets archived in DIAS, shows good performance in simulating floods, including those after

periods of low water flow. This means the model can provide reasonable initial conditions,

especially for soil moisture, by itself for the flood prediction after long-term low water flow.

There is a growing need for decision support tools that can effectively introduce the flood

prediction with improved accuracy to the decision making process in flood control. To meet these

needs, this downscaling system develops an optimization scheme focusing on effective flood

control operation of a multi-purpose and multi-reservoir system. The developed system was applied

to an actual dam network in the upper Tone river in Japan. Not only were the flood peak and

volume reduced downstream, but also the water volumes in the dam reservoirs were replenished

after the flood event.

The University of Tokyo has just developed a core system for data integration and analysis thatincludes the supporting functions of life cycle data management, data search, information

exploration, scientific analysis, and partial data downloading. The system integrates data from Earth

observation satellites and in-situ networks with other types of data, including numerical weather

prediction model outputs, geographical information, and socio-economic data.

AcknowledgementThis research was implemented as a part of the Data Integration and Analysis System (DIAS)

project founded by the Ministry of Education, Culture, Sports, Science and Technology of Japan.

ReferencesBoussetta, S., T. Koike, T. Graf, K. Yang, M. Pathmathevan (2007), Development of a coupled

land-atmosphere satellite data assimilation system for Improved local atmospheric simulations,

Remote Sensing of Environment, DOI 10.1016/j.rse.2007.06.002.

Mirza, C. R., T. Koike, K. Yang, and T. Graf (2008), The Development of 1-D Ice Cloud

Microphysics Data Assimilation System (IMDAS) for Cloud Parameter Retrievals by Integrating

Satellite Data, IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No.1, pp.119-129.

Sellers, P. J., S. O. Los, C. J. Tucker, C. O. Justice, D. A. Dazlich, G. J. Collatz and D. A. Randall

(1996), A revised land surface parameterization (SiB2) for atmospheric GCMs, part ii: the

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Wang, L., T. Koike, K. Yang, T. J. Jackson, R. Bindlish, and D. Yang (2009), Development of adistributed biosphere hydrological model and its evaluation with the Southern Great Plains

Experiments (SGP97 and SGP99), J. Geophys. Res., 114, D08107, doi:10.1029/2008JD010800.