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 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 Comparing the performances of WRF QPF and PERSIANN-CCS QPEs in karst flood simulations and forecasting with a new Karst-Liuxihe model

2019 ◽  
Author(s):  
Ji Li ◽  
Daoxian Yuan ◽  
Aihua Hong ◽  
Yongjun Jiang ◽  
Jiao Liu ◽  
...  
Keyword(s):  
Lead Time ◽  
Peak Flow ◽  
Coupled Model ◽  
River Basins ◽  
Flood Simulation ◽  
Flood Peak ◽  
Karst Areas ◽  
Karst River ◽  
Flood Simulations ◽  
Weather Models

Abstract. Long-term, available rainfall data are very important for karst flood simulations and forecasting. However, in karst areas, there is often a lack of effective precipitation available to build distributed hydrological models. Forecasting karst floods is highly challenging. Quantitative precipitation forecasts (QPF) and estimates (QPEs) could provide rational methods to acquire the available precipitation results for karst areas. Furthermore, coupling a physically-based hydrological model with the QPF and QPEs felicitously could largely enhance the performance and extend the lead time of floods forecasting in karst areas, the performance of coupling the Weather Research and Forecasting Quantitative Precipitation Forecast (WRF QPF) and Precipitation Estimations through Remotely Sensed Information based on the Artificial Neural Network-Cloud Classification System (PERSIANN-CCS QPEs) with a new fully distributed and physical hydrological model, the Karst-Liuxihe model in flood simulations and forecasting in karst area. This study served 2 main purposes: one purpose is to compare the performances of WRF QPF and PERSIANN-CCS QPEs for rainfall forecasting in karst river basins. The other purpose is to test the effective feasibility and application of the karst flood simulation and forecasting by coupling the 2 weather models with a new Karst-Liuxihe model. The new Karst-Liuxihe model improved the structure of the model by adding the karst mechanism based on the Liuxihe model as follows: (1) Refine the model structure and put forward the concept of karst hydrological response units (KHRUs) in the model. The KHRU, as the smallest unit of the Karst-Liuxihe model, is defined in this paper to be suitable for karst basins; (2) Increase the calculations of water movement rules in the epikarst zone and underground river, such as the division of slow flow and rapid flow in the epikarst zone and the exchange of water flow between the karst fissures and conduit systems; thus, the convergence of the underground runoff calculation method is improved to be suitable for karst water-bearing media; and (3) Add some necessary hydrogeological parameters in the coupled model to reflect the true conditions of rainfall-runoff in the karst underlying surface. Moreover, the flood detention and peak clipping effects due to the upstream karst depressions during flooding were considered and reasonably calculated in the coupled model. The flood detention effect can affect the peak flow time error simulated in the model and make the true peak flow appear later; the flood peak clipping effect can affect the flood peak flow relative errors and the simulation errors of floods volume. The consideration of these 2 factors in the model makes the flood simulations and forecasting effects more credible. The rainfall forecasting result show that the precipitation distribution of the 2 weather models was very similar compared with the observed rainfall result. However, the precipitation amounts forecasted by WRF QPF were larger than that measured by the rain gauges, while the quantities were smaller by the PERSIANN-CCS QPEs. A postprocessing algorithm was adopted in this paper to correct the rainfall results by the 2 weather models. The karst flood simulation and forecasting results showed that the flood peak flow simulations were better by coupling the Karst-Liuxihe model with the PERSIANN-CCS QPEs, and coupling the Karst-Liuxihe model with WRF QPF could extend the lead time of flood forecasting largely, as a maximum lead time of 96 hours can provide an adequate amount of time for flood warnings and emergency responses. The satisfying and rational karst flood simulation evaluation indices proved that coupling the 2 weather models with the new Karst-Liuxihe model could be effectively used for karst river basins, which provides great practical application prospects for karst flood simulations and forecasting. In addition, the postprocessing method used to revise the 2 weather models in this paper is feasible and effective, and this method can largely improve the coupled model application effectiveness and prospect in karst river basins.

scholarly journals Impact of urbanization on flood of Shigu creek in Dongguan city

2018 ◽  
Vol 379 ◽  
pp. 55-60
Author(s):  
Luying Pan ◽  
Yangbo Chen ◽  
Tao Zhang
Keyword(s):  
Hydrological Model ◽  
Peak Flow ◽  
Flood Mitigation ◽  
Distributed Hydrological Model ◽  
Small Watershed ◽  
Rapid Urbanization ◽  
Dongguan City ◽  
Physically Based ◽  
Impact Of Urbanization ◽  
The Impact

Abstract. Shigu creek is a highly urbanized small watershed in Dongguan City. Due to rapid urbanization, quick flood response has been observed, which posted great threat to the flood security of Dongguan City. To evaluate the impact of urbanization on the flood changes of Shigu creek is very important for the flood mitigation of Shigu creek, which will provide insight for flood planners and managers for if to build a larger flood mitigation system. In this paper, the Land cover/use changes of Shigu creek from 1987–2015 induced by urbanization was first extracted from a local database, then, the Liuxihe model, a physically based distributed hydrological model, is employed to simulate the flood processes impacted by urbanization. Precipitation of 3 storms was used for flood processes simulation. The results show that the runoff coefficient and peak flow have increased sharply.

Evaluation of a Distributed Hydrological Model for the Guishui River Basin

2013 ◽  
Vol 353-356 ◽  
pp. 2511-2514
Author(s):  
Jing Zhang ◽  
Zhen Zheng ◽  
Hui Li Gong
Keyword(s):  
River Basin ◽  
Surface Runoff ◽  
Hydrological Model ◽  
Scientific Basis ◽  
Scale Model ◽  
Distributed Hydrological Model ◽  
Mike She ◽  
Structural Principle ◽  
Physically Based ◽  
The Impact

At present, the application of various hydrological model provides scientific basis for the realization of the water resources management. MIKE SHE model is a classic physically-based watershed-scale model with great advantages in the coupling of surface water and ground water. In the paper, the development history and the present study situation of the distributed hydrological model MIKE SHE and the structural principle of the model is summarized. Furthermore, the ability and applicability of MIKE SHE model was preliminary evaluated for simulating surface runoff in the Guishui river basin in Beijing, China. The impact of different land use practices (the year of 1980 and 2005) on the hydrological response of the selected basin was initially assessed. Overall, the model was able to simulate surface runoff reasonably on annual intervals, representing all the hydrological components adequately.

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