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Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system

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Abstract

The recent improvement of numerical weather prediction (NWP) models has a strong potential for extending the lead time of precipitation and subsequent flooding. However, uncertainties inherent in precipitation outputs from NWP models are propagated into hydrological forecasts and can also be magnified by the scaling process, contributing considerable uncertainties to flood forecasts. In order to address uncertainties in flood forecasting based on single-model precipitation forecasting, a coupled atmospheric-hydrological modeling system based on multi-model ensemble precipitation forecasting is implemented in a configuration for two episodes of intense precipitation affecting the Wangjiaba sub-region in Huaihe River Basin, China. The present study aimed at comparing high-resolution limited-area meteorological model Canadian regional mesoscale compressible community model (MC2) with the multiple linear regression integrated forecast (MLRF), covering short and medium range. The former is a single-model approach; while the latter one is based on NWP models [(MC2, global environmental multiscale model (GEM), T213L31 global spectral model (T213)] integrating by a multiple linear regression method. Both MC2 and MLRF are coupled with Chinese National Flood Forecasting System (NFFS), MC2-NFFS and MLRF-NFFS, to simulate the discharge of the Wangjiaba sub-basin. The evaluation of the flood forecasts is performed both from a meteorological perspective and in terms of discharge prediction. The encouraging results obtained in this study demonstrate that the coupled system based on multi-model ensemble precipitation forecasting has a promising potential of increasing discharge accuracy and modeling stability in terms of precipitation amount and timing, along with reducing uncertainties in flood forecasts and models. Moreover, the precipitation distribution of MC2 is more problematic in finer temporal and spatial scales, even for the high resolution simulation, which requests further research on storm-scale data assimilation, sub-grid-scale parameterization of clouds and other small-scale atmospheric dynamics.

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Acknowledgments

This work is supported by the Major Science and Technology Program for Water Pollution Control and Treatment (Grant No. 2012ZX07101-010), National Basic Research Program of China (973 Program) (Grant No. 2010CB428405), Special Public Sector Research Program of Ministry of Water Resources (Grant No. 201301040 and 201301070), Foundation for the Author of National Excellent Doctoral Dissertation of PR China (Grant No. 201161), the Qing Lan Project and Program for New Century Excellent Talents in University (Grant No. NCET-12-0842).

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Authors and Affiliations

  1. Institute of Water Problems, College of Hydrology and Water Resources, Hohai University, Nanjing, China

    Juan Wu, Guihua Lu & Zhiyong Wu

  2. Bureau of Hydrology Information Center of Taihu Basin Authority, Shanghai, China

    Juan Wu

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  1. Juan Wu
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  2. Guihua Lu
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  3. Zhiyong Wu
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Correspondence to Zhiyong Wu.

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Wu, J., Lu, G. & Wu, Z. Flood forecasts based on multi-model ensemble precipitation forecasting using a coupled atmospheric-hydrological modeling system. Nat Hazards 74, 325–340 (2014). https://doi.org/10.1007/s11069-014-1204-6

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