气候变化领域集成服务门户(CCPortal): Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

CCPortal

DOI	10.5194/hess-23-1505-2019
	Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model
	Li J.; Yuan D.; Liu J.; Jiang Y.; Chen Y.; Hsu K.L.; Sorooshian S.
发表日期	2019
ISSN	1027-5606
起始页码	1505
结束页码	1532
卷号	23 期号:3
英文摘要	In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km-2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash-Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of page1506 the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins. © 2019 Author(s).
语种	英语
scopus关键词	Classification (of information); Climate models; Errors; Floods; Forecasting; Hydrogeology; Neural networks; Rain; Rain gages; Rivers; Runoff; Watersheds; Weather satellites; Application prospect; Cloud classification systems; Correlation coefficient; Distributed hydrological model; Hydrological models; Nash-Sutcliffe coefficient; Postprocessing methods; Underground runoffs; Landforms; flood; hydrological modeling; karst hydrology; peak flow; precipitation (climatology); prediction; rainfall; river basin; runoff; water budget; watershed; China
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159727
作者单位	Li, J., School of Geographical Sciences of Southwest University, Chongqing Key Laboratory of Karst Environment, Chongqing, 400715, China; Yuan, D., School of Geographical Sciences of Southwest University, Chongqing Key Laboratory of Karst Environment, Chongqing, 400715, China, Karst Dynamic Laboratory, Ministry of Land and Resources, Guilin, 541004, China; Liu, J., Chongqing Hydrology and Water Resources Bureau, Chongqing, 401120, China; Jiang, Y., School of Geographical Sciences of Southwest University, Chongqing Key Laboratory of Karst Environment, Chongqing, 400715, China; Chen, Y., Department of Water Resources and Environment, Sun Yat-Sen University, Guangzhou, 510275, China; Hsu, K.L., Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA, United States; Sorooshian, S., Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CA...
推荐引用方式 GB/T 7714	Li J.,Yuan D.,Liu J.,et al. Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model[J],2019,23(3).
APA	Li J..,Yuan D..,Liu J..,Jiang Y..,Chen Y..,...&Sorooshian S..(2019).Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model.Hydrology and Earth System Sciences,23(3).
MLA	Li J.,et al."Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model".Hydrology and Earth System Sciences 23.3(2019).