Implementation of Real-Time Flood Prediction and its Application to Dam Operations by Data Integration Analysis System

Yoshihiro Shibuo; Eiji Ikoma; Oliver Saavedra Valeriano; Lei Wang; Peter Lawford; Masaru Kitsuregawa; Toshio Koike

doi:10.20965/jdr.2016.p1052

single-dr.php

« previous

JDR Vol.11 No.6 pp. 1052-1061

(2016)

doi: 10.20965/jdr.2016.p1052

Paper:

Views over last 60 days: 1,167

Implementation of Real-Time Flood Prediction and its Application to Dam Operations by Data Integration Analysis System

Yoshihiro Shibuo^1,†, Eiji Ikoma^2, Oliver Saavedra Valeriano^3, Lei Wang^4, Peter Lawford^5, Masaru Kitsuregawa^2,6, and Toshio Koike^1,*5

^*1International Centre for Hydrological and Flood Risk Management, Public Work Research Institute
1-6 Minamihara, Tsukuba, Ibaraki 305-8516, Japan

^†Corresponding author,

^*2Institute of Industrial Science, the University of Tokyo, Tokyo, Japan

^*3Civil and Environmental Engineering Research Center, Bolivian Private University, Cochabamba, Bolivia

^*4Chinese Academy of Sciences, Inst. Tibetan Plateau Research, Beijing, China

^*5Department of Civil Engineering, the University of Tokyo, Tokyo, Japan

^*6National Institute of Informatics, Tokyo, Japan

Received:

June 24, 2016

Accepted:

October 25, 2016

Published:

December 1, 2016

Keywords:

data-archive and model integrated system, flood forecast, ensemble streamflow prediction, virtual reservoir simulator

Abstract

Despite recent advances in hydrological models and observation technology, the prediction of floods using advanced models and data has not yet been fully implemented for practical use. The major issues in prediction originate from the underlying uncertainty of the initial conditions of the basin and the accuracy of the precipitation forecast. Effective transmission of flood information to corresponding authorities is also necessary when considering countermeasures against an oncoming flood. We present in this article a data archive and model integrated system to overcome these issues. The system realizes flood forecasting by employing a land surface model coupled with hydrological model and an ensemble precipitation forecast model to address the accuracy of initial conditions and precipitation. While the Water and Energy Budget Based Distributed Hydrological Model (WEB-DHM) rigorously estimates the physical state of the basin, the ensemble precipitation forecast model analyzes historical errors in forecasts and returns precipitation ensembles reflecting the uncertainty in the forecast specifically regarding the target basin. A combination of these models yields an ensemble of streamflow forecasts. We further develop a virtual reservoir simulator to enhance the proactive use of forecast information to support decision-making by reservoir managers. These models are integrated into the Data Integration Analysis System (DIAS). The feasibility of the system for practical use is tested against data from recent typhoon events.

Cite this article as:

Y. Shibuo, E. Ikoma, O. Valeriano, L. Wang, P. Lawford, M. Kitsuregawa, and T. Koike, “Implementation of Real-Time Flood Prediction and its Application to Dam Operations by Data Integration Analysis System,” J. Disaster Res., Vol.11 No.6, pp. 1052-1061, 2016.

Data files:

References

[1] H. Kardhana and A. Mano, “Uncertainty Evaluation in a Flood Forecasting Model Using JMA Numerical Weather Prediction,” J. of Disaster Research, Vol.4, No.4, pp. 272-277, 2009.
[2] Y. Sugihara, S. Imagama, N. Matsunaga, and Y. Hisada, “Numerical Experiments on Spatially Averaged Precipitation in Heavy Rainfall Event Using the WRF Model,” J. of Disaster Research, Vol.10, No.3, pp. 436-447, 2015.
[3] Y. Shibuo, E. Ikoma, O. Saavedra, L. Wang, P. Koudelova, M. Kitsuregawa, and T. Koike, “Development of operational realtime ensemble flood forecast system,” Annual J. of Hydraulic Engineering, JSCE, B1 (Hydraulic Engineering), Vol.70, No.4, pp. I_397-I_402, 2014.
[4] T. Sayama, Y. Tachikawa, and K. Taara, “ Data assimilation of a distributed rainfall-runoff prediction system by Kalman filter with bias correction,” Doboku Gakkai Ronbunshuu B, Vol.64, No.4, pp. 226-239, 2008.
[5] Y. Tachikawa, J. Sudo, M. Shiba, K. Yorozu, and S. Kim, “ Development of a real-time river stage forecasting method using a particle filter,” JSCE, B1 (Hydraulic Engineering), Vol.67, No.4, pp. I_511-I_516, 2011.
[6] Q. Xiao and J. Sun, “Multiple radar data assimilation and short-range QPF of a squall line observed during IHOP_2002,” Mon. Wea. Rev., 135, pp. 3318-3404, 2007.
[7] E. M. Sukovich, F. Martin Ralph, F. E. Barthold, D. W. Reynolds, and D. R. Novak, “Extreme quantitative precipitation forecast performance at the weather prediction center from 2001 to 2011,” Wea. Forecasting, Vol.29, pp. 894-911, 2014.
[8] L. Wang, T. Koike, K. Yang, T. K. Jackson, R. Bindlish, and D. Yang, “Development of a distributed biosphere hydrological model and its evaluation with the Southern Great Plains Experiments (SGP97 and SGP99),” J. Geophys. Res., Vol.114, D08107, doi:10.1029/2008JD010800, 2009.
[9] D. Yang, T. Koike, and H. Tanizawa, “Application of a distributed hydrological model and weather radar observations for flood management in the upper Tone River of Japan,” Hydrol. Process., Vol.18, pp. 3119-3132, 2004.
[10] P. J. Sellers, D. A. Randall, G. J. Collatz, J. A. Berry, C. B. Field, D. A. Dazlich, C. Zhang, G.D. Collelo, and L. Bounoua, “A revised land surface parameterization (SiB2) for atmospheric GCMs. 1. Model formulation,” J. Climate, Vol.9, pp. 676-705, 1996.
[11] L. Wang, T. Koike, K. Yang, and P. J.-F. Yeh, “Assessment of a distributed biosphere hydrological model against streamflow and MODIS land surface temperature in the upper Tone River Basin,” J. Hydrol., Vol.377, pp. 21-34, 2009.
[12] M. R. J. Turner, J. P. Walker, and P. R. Oke, “Ensemble member generation for sequential data assimilation,” Remote Sens. Environ., Vol.112, pp. 1421-1433, 2008.
[13] O. Saavedra Valeriano, T. Koike, K. Yang, T. Graf, X. Li, L. Wang, and X. Han, “Decision support for dam release during floods using a distributed biosphere hydrological model driven by quantitative precipitation forecasts,” Water Resour. Res., Vol.46, W10544, 2010.
[14] E. E. Ebert, and J. L. McBride, “Verification of precipitation in weather systems: Determination of systematic errors,” J. Hydrol., Vol.239, pp. 179-202, 2000.
[15] P. Goovaerts,“ Geostatistics for Natural Resources Evaluation,” p. 483, Oxford Univ. Press, New York, 1997.
[16] R. Seto, T. Koike, and M. Rasmy, “Development of a Satellite Land and Cloud Data Assimilation System Coupled with Wrf, and its Application to Kanto Area,” JSCE, B1 (Hydraulic Engineering), Vol.70, No.4, pp. I_ 535-I_540, 2014.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] H. Kardhana and A. Mano, “Uncertainty Evaluation in a Flood Forecasting Model Using JMA Numerical Weather Prediction,” J. of Disaster Research, Vol.4, No.4, pp. 272-277, 2009.

[2] [2] Y. Sugihara, S. Imagama, N. Matsunaga, and Y. Hisada, “Numerical Experiments on Spatially Averaged Precipitation in Heavy Rainfall Event Using the WRF Model,” J. of Disaster Research, Vol.10, No.3, pp. 436-447, 2015.

[3] [3] Y. Shibuo, E. Ikoma, O. Saavedra, L. Wang, P. Koudelova, M. Kitsuregawa, and T. Koike, “Development of operational realtime ensemble flood forecast system,” Annual J. of Hydraulic Engineering, JSCE, B1 (Hydraulic Engineering), Vol.70, No.4, pp. I_397-I_402, 2014.

[4] [4] T. Sayama, Y. Tachikawa, and K. Taara, “ Data assimilation of a distributed rainfall-runoff prediction system by Kalman filter with bias correction,” Doboku Gakkai Ronbunshuu B, Vol.64, No.4, pp. 226-239, 2008.

[5] [5] Y. Tachikawa, J. Sudo, M. Shiba, K. Yorozu, and S. Kim, “ Development of a real-time river stage forecasting method using a particle filter,” JSCE, B1 (Hydraulic Engineering), Vol.67, No.4, pp. I_511-I_516, 2011.

[6] [6] Q. Xiao and J. Sun, “Multiple radar data assimilation and short-range QPF of a squall line observed during IHOP_2002,” Mon. Wea. Rev., 135, pp. 3318-3404, 2007.

[7] [7] E. M. Sukovich, F. Martin Ralph, F. E. Barthold, D. W. Reynolds, and D. R. Novak, “Extreme quantitative precipitation forecast performance at the weather prediction center from 2001 to 2011,” Wea. Forecasting, Vol.29, pp. 894-911, 2014.

[8] [8] L. Wang, T. Koike, K. Yang, T. K. Jackson, R. Bindlish, and D. Yang, “Development of a distributed biosphere hydrological model and its evaluation with the Southern Great Plains Experiments (SGP97 and SGP99),” J. Geophys. Res., Vol.114, D08107, doi:10.1029/2008JD010800, 2009.

[9] [9] D. Yang, T. Koike, and H. Tanizawa, “Application of a distributed hydrological model and weather radar observations for flood management in the upper Tone River of Japan,” Hydrol. Process., Vol.18, pp. 3119-3132, 2004.

[10] [10] P. J. Sellers, D. A. Randall, G. J. Collatz, J. A. Berry, C. B. Field, D. A. Dazlich, C. Zhang, G.D. Collelo, and L. Bounoua, “A revised land surface parameterization (SiB2) for atmospheric GCMs. 1. Model formulation,” J. Climate, Vol.9, pp. 676-705, 1996.

[11] [11] L. Wang, T. Koike, K. Yang, and P. J.-F. Yeh, “Assessment of a distributed biosphere hydrological model against streamflow and MODIS land surface temperature in the upper Tone River Basin,” J. Hydrol., Vol.377, pp. 21-34, 2009.

[12] [12] M. R. J. Turner, J. P. Walker, and P. R. Oke, “Ensemble member generation for sequential data assimilation,” Remote Sens. Environ., Vol.112, pp. 1421-1433, 2008.

[13] [13] O. Saavedra Valeriano, T. Koike, K. Yang, T. Graf, X. Li, L. Wang, and X. Han, “Decision support for dam release during floods using a distributed biosphere hydrological model driven by quantitative precipitation forecasts,” Water Resour. Res., Vol.46, W10544, 2010.

[14] [14] E. E. Ebert, and J. L. McBride, “Verification of precipitation in weather systems: Determination of systematic errors,” J. Hydrol., Vol.239, pp. 179-202, 2000.

[15] [15] P. Goovaerts,“ Geostatistics for Natural Resources Evaluation,” p. 483, Oxford Univ. Press, New York, 1997.

[16] [16] R. Seto, T. Koike, and M. Rasmy, “Development of a Satellite Land and Cloud Data Assimilation System Coupled with Wrf, and its Application to Kanto Area,” JSCE, B1 (Hydraulic Engineering), Vol.70, No.4, pp. I_ 535-I_540, 2014.

Implementation of Real-Time Flood Prediction and its Application to Dam Operations by Data Integration Analysis System

Yoshihiro Shibuo*1,†, Eiji Ikoma*2, Oliver Saavedra Valeriano*3, Lei Wang*4, Peter Lawford*5, Masaru Kitsuregawa*2,*6, and Toshio Koike*1,*5

Yoshihiro Shibuo^1,†, Eiji Ikoma^2, Oliver Saavedra Valeriano^3, Lei Wang^4, Peter Lawford^5, Masaru Kitsuregawa^2,6, and Toshio Koike^1,*5