Abstract
Abstract. 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 km2, 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 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.
Highlights
The highly anisotropic karst water-bearing media and intricate hydraulic conditions cause karst flood processes to exhibit significant differences in time and space, which leads to laminar flow and turbulent flow transmutation in karst areas; flood events in karst river basins are more complicated than those in non-karst areas (Ford and Williams, 1989; Goldscheider and Drew, 2007)
The respective Quantitative precipitation estimates (QPEs) products of the PERSIANN-CCS in 2008, 2009, 2011, 2012 and 2013 were produced, and the results indicated that five rainfall events corresponded to the five karst flood processes
How to obtain reasonable rainfall data for the development of a hydrological model that can be used for flood prediction is especially important
Summary
The highly anisotropic karst water-bearing media and intricate hydraulic conditions cause karst flood processes to exhibit significant differences in time and space, which leads to laminar flow and turbulent flow transmutation in karst areas; flood events in karst river basins are more complicated than those in non-karst areas (Ford and Williams, 1989; Goldscheider and Drew, 2007). This difference makes it difficult to precisely simulate and forecast the karst flood process using a hydrological model. Karst flood simulation results usually have some errors that cannot be ignored, and these errors represent the main problem in flood prediction in karst river basins (Kovacs and Perrochet, 2011)
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