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

2018 ◽  
Author(s):  
Ji Li ◽  
Daoxian Yuan ◽  
Jiao Liu ◽  
Yongjun Jiang ◽  
Yangbo Chen ◽  
...  
Keyword(s):  
River Basin ◽  
Relative Error ◽  
Hydrological Model ◽  
Peak Flow ◽  
Flood Forecasting ◽  
Distributed Hydrological Model ◽  
Practical Application ◽  
Time Error ◽  
Flood Simulation ◽  
Karst River

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 58 270 km2. 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 25 hours 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.

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

2019 ◽  
Vol 23 (3) ◽  
pp. 1505-1532 ◽  
Author(s):  
Ji Li ◽  
Daoxian Yuan ◽  
Jiao Liu ◽  
Yongjun Jiang ◽  
Yangbo Chen ◽  
...  
Keyword(s):  
River Basin ◽  
Relative Error ◽  
Hydrological Model ◽  
Peak Flow ◽  
Coupled Model ◽  
Distributed Hydrological Model ◽  
Flood Simulation ◽  
Flood Prediction ◽  
Physically Based ◽  
Karst River

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.

scholarly journals Large-watershed flood forecasting with high-resolution distributed hydrological model

2017 ◽  
Vol 21 (2) ◽  
pp. 735-749 ◽  
Author(s):  
Yangbo Chen ◽  
Ji Li ◽  
Huanyu Wang ◽  
Jianming Qin ◽  
Liming Dong
Keyword(s):  
High Resolution ◽  
River Basin ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Threshold Model ◽  
Grid Cell ◽  
Flood Forecasting ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Computation Efficiency

Abstract. A distributed hydrological model has been successfully used in small-watershed flood forecasting, but there are still challenges for the application in a large watershed, one of them being the model's spatial resolution effect. To cope with this challenge, two efforts could be made; one is to improve the model's computation efficiency in a large watershed, the other is implementing the model on a high-performance supercomputer. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil and land use are downloaded from the website freely, and the model structure with a high resolution of 200 m  ×  200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the Particle Swarm Optimization (PSO) algorithm; the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recommended. Computation time needed for running a distributed hydrological model increases exponentially at a power of 2, not linearly with the increasing of model spatial resolution, and the 200 m  ×  200 m model resolution is proposed for modeling the Liujiang River basin flood with the Liuxihe model in this study. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling the Liujiang River basin flood is a 500 m  ×  500 m grid cell, but the model spatial resolution with a 200 m  ×  200 m grid cell is recommended in this study to keep the model at a better performance.

scholarly journals Large watershed flood forecasting with high resolution distributed hydrological model

2016 ◽  
Author(s):  
Yangbo Chen ◽  
Ji Li ◽  
Huanyu Wang ◽  
Jianming Qin ◽  
Liming Dong
Keyword(s):  
High Resolution ◽  
River Basin ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Threshold Model ◽  
Grid Cell ◽  
Flood Forecasting ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Computation Efficiency

Abstract. Flooding is one of the most devastating natural disasters in the world with huge damages, and flood forecasting is one of the flood mitigation measurements. Watershed hydrological model is the major tool for flood forecasting, although the lumped watershed hydrological model is still the most widely used model, the distributed hydrological model has the potential to improve watershed flood forecasting capability. Distributed hydrological model has been successfully used in small watershed flood forecasting, but there are still challenges for the application in large watershed, one of them is the model’s spatial resolution effect. To cope with this challenge, two efforts could be made, one is to improve the model's computation efficiency in large watershed, another is implementing the model on high performance supercomputer. By employing Liuxihe Model, a physically based distributed hydrological model, this study sets up a distributed hydrological model for the flood forecasting of Liujiang River Basin in southern China that is a large watershed. Terrain data including DEM, soil type and land use type are downloaded from the website freely, and the model structure with a high resolution of 200 m * 200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the PSO algorithm, the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe Model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recommended. Computation time needed for running a distributed hydrological model increases exponentially at a power of 2, not linearly with the increasing of model spatial resolution, and the 200 m * 200 m model resolution is proposed for modeling Liujiang River Basin flood with Liuxihe Model in this study. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling Liujiang River Basin flood is 500 m * 500 m grid cell, but the model spatial resolution at 200 m * 200 m grid cell is recommended in this study to keep the model a better performance.

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