Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

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 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 postprocessed 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 reoptimization 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 Published by Copernicus Publications on behal...

Real-Time Flood Forecasting via Parameter Regionalization and Blending Nowcasts with NWP Forecasts over the Jiao River, China

“…However, IMERG-ER is more recommended for real-time flood detection with shorter latency and a tendency of higher precipitation extremes (Zhi Li et al 2021). Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), which provides near-real-time precipitation estimates at 0.048 spatial resolution and a 30-min temporal resolution with 1-h latency (Hong et al 2004), is widely used for short-duration extreme precipitation analysis (Sadeghi et al 2021) and flood prediction (Li et al 2019). It is found that the PERSIANN-CCS QPEs suffer serious underestimations (Li et al 2019;H.…”

A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations

“…(2) Empirical equations developed for similar basins were used to obtain the rainfall infiltration coefficients for different karst landforms and the rock permeability coefficient. For example, the rock permeability coefficient was calculated based on an empirical equation from a pumping test in a coal mine in the study area (Li et al, 2019). (3) A tracer experiment was conducted in the study area (Gou et al, 2010) to obtain information on the underground river direction and flow velocity; for instance, underground karst conduits are well developed in the area and form an underground river approximately 5 m wide.…”

“…For example, the distributed groundwater model MODFLOW-CFPM1 requires detailed data regarding the distribution of karst conduits in the study area (Reimann and Hill, 2009). Another example is the Karst-Liuxihe model (Li et al, 2019); there are 15 parameters and 5 underground vertical structures in this model. Such a complex structure results in large modelling-data demands, and modelling in karst areas is extremely difficult.…”

Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model

“…In practical applications, only a representative measured flood is needed to optimize the model parameters, which can greatly improve the performance of the model. The use of refined confluence calculation methods and highefficiency parameter optimization technology has enabled the Liuxihe model to achieve good results in forecasting floods in small and medium-sized rivers and in reservoir inflow forecasting in China [43][44][45][46][47][48][49]. technology has enabled the Liuxihe model to achieve good results in forecasting floods in small and medium-sized rivers and in reservoir inflow forecasting in China [43][44][45][46][47][48][49].…”