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

2018 ◽  
Author(s):  
Anonymous
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Distributed Hydrological Model ◽  
Physically Based ◽  
Karst River

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 Identifying Key Hydrological Processes in Highly Urbanized Watersheds for Flood Forecasting with a Distributed Hydrological Model

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1641 ◽  
Author(s):  
Huanyu Wang ◽  
Yangbo Chen
Keyword(s):  
Water Content ◽  
Parameter Uncertainty ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Physically Based ◽  
Distributed Hydrological Models

The world has experienced large-scale urbanization in the past century, and this trend is ongoing. Urbanization not only causes land use/cover (LUC) changes but also changes the flood responses of watersheds. Lumped conceptual hydrological models cannot be effectively used for flood forecasting in watersheds that lack long time series of hydrological data to calibrate model parameters. Thus, physically based distributed hydrological models are used instead in these areas, but considerable uncertainty is associated with model parameter derivation. To reduce model parameter uncertainty in physically based distributed hydrological models for flood forecasting in highly urbanized watersheds, a procedure is proposed to control parameter uncertainty. The core concept of this procedure is to identify the key hydrological and flood processes in the highly urbanized watersheds and the sensitive model parameters related to these processes. Then, the sensitive model parameters are adjusted based on local runoff coefficients to reduce the parameter uncertainty. This procedure includes these steps: collecting the latest LUC information or estimating this information using satellite remote sensing images, analyzing LUC spatial patterns and identifying dominant LUC types and their spatial structures, choosing and establishing a distributed hydrological model as the forecasting tool, and determining the initial model parameters and identifying the key hydrological processes and sensitive model parameters based on a parameter sensitivity analysis. A highly urbanized watershed called Shahe Creek in the Pearl River Delta area was selected as a case study. This study finds that the runoff production processes associated with both the ferric luvisol and acric ferralsol soil types and the runoff routing process on urban land are key hydrological processes. Additionally, the soil water content under saturated conditions, the soil water content under field conditions and the roughness of urban land are sensitive parameters.

scholarly journals Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

2016 ◽  
Vol 20 (1) ◽  
pp. 375-392 ◽  
Author(s):  
Y. Chen ◽  
J. Li ◽  
H. Xu
Keyword(s):  
Parameter Optimization ◽  
Hydrological Model ◽  
Pso Algorithm ◽  
Flood Forecasting ◽  
Model Parameters ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Inertia Weight ◽  
Physically Based ◽  
Distributed Hydrological Models

Abstract. Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

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 Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

2015 ◽  
Vol 12 (10) ◽  
pp. 10603-10649 ◽  
Author(s):  
Y. Chen ◽  
J. Li ◽  
H. Xu
Keyword(s):  
Parameter Optimization ◽  
Hydrological Model ◽  
Pso Algorithm ◽  
Flood Forecasting ◽  
Model Parameters ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Inertia Weight ◽  
Physically Based ◽  
Distributed Hydrological Models

Abstract. Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be used for Liuxihe model parameter optimization effectively, and could improve the model capability largely in catchment flood forecasting, thus proven that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively.

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.

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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