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Simulation of Karst Floods with a Hydrological Model Improved by Meteorological Model Coupling

Ji LiaChongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Chongqing, China

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Daoxian YuanaChongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Chongqing, China
bKey Laboratory of Karst Dynamics, MNR & Guangxi, Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin, China

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Mingguo MaaChongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Chongqing, China

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Jiao LiucChongqing Municipal Hydrological Monitoring Station, Chongqing, China

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Abstract

Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst areas in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting (WRF) Model and quantitative precipitation estimation (QPE) from the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst–Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 h, respectively. Coupling the WRF Model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 h, which is important for flood warning and control.

Significance Statement

The WRF Model and PERSIANN-CCS can provide precipitation data for mountainous karst areas lacking rainfall gauges, and their rainfall results are forecasted effectively to reduce the uncertainty of input precipitation data. Then, the PERSIANN-CCS QPEs and WRF QPF are coupled with the improved KL model for karst flood simulation and prediction. This coupled model worked well in karst basins.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ji Li, 445776649@qq.com

Abstract

Karst basins are prone to rapid flooding because of their geomorphic complexity and exposed karst landforms with low infiltration rates. Accordingly, simulating and forecasting floods in karst regions can provide important technical support for local flood control. The study area, the Liujiang karst river basin, is the most well-developed karst areas in South China, and its many mountainous areas lack rainfall gauges, limiting the availability of precipitation information. Quantitative precipitation forecast (QPF) from the Weather Research and Forecasting (WRF) Model and quantitative precipitation estimation (QPE) from the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) can offer reliable precipitation estimates. Here, the distributed Karst–Liuxihe (KL) model was successfully developed from the terrestrial Liuxihe model, as reflected in improvements to its underground structure and confluence algorithm. Compared with other karst distributed models, the KL model has a relatively simple structure and small modeling data requirements, which are advantageous for flood prediction in karst areas lacking hydrogeological data. Our flood process simulation results suggested that the KL model agrees well with observations and outperforms the Liuxihe model. The average Nash coefficient, correlation coefficient, and water balance coefficient increased by 0.24, 0.19, and 0.20, respectively, and the average flood process error, flood peak error, and peak time error decreased by 13%, 11%, and 2 h, respectively. Coupling the WRF Model and PERSIANN-CCS with the KL model yielded a good performance in karst flood simulation and prediction. Notably, coupling the WRF and KL models effectively predicted the karst flood processes and provided flood prediction results with a lead time of 96 h, which is important for flood warning and control.

Significance Statement

The WRF Model and PERSIANN-CCS can provide precipitation data for mountainous karst areas lacking rainfall gauges, and their rainfall results are forecasted effectively to reduce the uncertainty of input precipitation data. Then, the PERSIANN-CCS QPEs and WRF QPF are coupled with the improved KL model for karst flood simulation and prediction. This coupled model worked well in karst basins.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ji Li, 445776649@qq.com
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