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Discussion papers | Copyright
https://doi.org/10.5194/hess-2018-438
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Sep 2018

Research article | 05 Sep 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Flood forecasting in large karst river basin by coupling PERSIANN CCS QPEs with a physically based distributed hydrological model

Ji Li1, Daoxian Yuan1,2, Jiao Liu3, Yongjun Jiang1, Yangbo Chen4, Kuo Lin Hsu5, and Soroosh Sorooshian5 Ji Li et al.
  • 1School of Geographical Sciences of Southwest University, Chongqing Key Laboratory of Karst Environment, Chongqing, 400715, China
  • 2Karst Dynamic Laboratory, Ministry of Land and Resources, Guilin 541004, China
  • 3Chongqing Hydrology and Water Resources Bureau, Chongqing, 401120, China
  • 4Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China
  • 5Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California

Abstract. There is no long-term meteorological or hydrological data in karst river basins to a large extent. Especially lack of typical rainfall data is a great challenge to build a hydrological model. Quantitative precipitation estimates (QPEs) based on the weather satellites could offer a good attempt to obtain the rainfall data in karst area. What's more, coupling QPEs with a distributed hydrological model has the potential to improve the precision for flood forecasting in large karst watershed. Precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PERSIANN-CCS) as a technology of QPEs based on satellites has been achieved a wide research results in the world. However, only few studies on PERSIANN-CCS QPEs are in large karst basins and the accuracy is always poor in practical application. In this study, the PERSIANN-CCS QPEs is employed to estimate the hourly precipitation in such a large river basin-Liujiang karst river basin with an area of 58270km2. The result shows that, compared with the observed precipitation by rain gauge, the distribution of precipitation by PERSIANN-CCS QPEs has a great similarity. But the quantity values of precipitation by PERSIANN-CCS QPEs are smaller. A post-processed method is proposed to revise the PERSIANN-CCS QPEs products. The result shows that coupling the post-processed PERSIANN-CCS QPEs with a distributed hydrological model-Liuxihe model has a better performance than the result with the initial PERSIANN-CCS QPEs in karst flood simulation. What's more, the coupling model’s performance improves largely with parameter re-optimized with the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices including Nash–Sutcliffe coefficient has a 14% increase, the correlation coefficient has a 14% increase, process relative error has a 8% decrease, peak flow relative error has a 18% decrease, the water balance coefficient has a 7% increase, and peak flow time error has 25hours decrease, respectively. Among them, the peak flow relative error and peak flow time error have the biggest improvement, which are the greatest concerned factors in flood forecasting.The rational flood simulation results by the coupling model provide a great practical application prospect for flood forecasting in large karst river basins.

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There is no long-term reasonable rainfall data to build a hydrological model in karst river basins to a large extent. In this paper, the PERSIANN-CCS QPEs is employed to estimate the precipitation data as an attempt in Liujiang karst river basin/58 270 km2, China. And an improved method is proposed to revise the results of the PERSIANN-CCS QPEs. The post-processed PERSIANN-CCS QPEs with a distributed hydrological model-Liuxihe model has a better performance in karst flood forecasting.
There is no long-term reasonable rainfall data to build a hydrological model in karst river...
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