Urban flood simulation based on distributed hydrological model: Tongzhou District, Beijing

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.

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Applicability of Hydrological Models for Flash Flood Simulation in Small Catchments of Hilly Area in China

Open Geosciences ◽

10.1515/geo-2019-0089 ◽

2019 ◽

Vol 11 (1) ◽

pp. 1168-1181 ◽

Cited By ~ 1

Author(s):

Zhuohang Xin ◽

Ke Shi ◽

Chenchen Wu ◽

Lu Wang ◽

Lei Ye

Keyword(s):

Hydrological Model ◽

Flash Flood ◽

Flash Floods ◽

Vegetation Coverage ◽

Hydrological Models ◽

Distributed Hydrological Model ◽

Stream Network ◽

Flood Simulation ◽

Hilly Area ◽

Small Catchments

Abstract Flash flood in small catchments of hilly area is an extremely complicated nonlinear process affected by catchment properties and rainfall spatio-temporal variation characteristics including many physical-geographical factors, and thus accurate simulation of flash flood is very difficult. Given the fact that hundreds of hydrological models are available in the literature, how to choose a suitable hydrological model remains an unsolved task. In this paper, we selected five widely used hydrological models including three lumped hydrologic models, a semi-distributed hydrological model and a distributed hydrological model for flash flood simulation, and studied their applicability in fourteen typical catchments in hilly areas across China. The results show that the HEC-HMS distributed hydrological model outperforms the other models and is suitable to simulate the flash floods caused by highly intense rainfall. The Dahuofang model (lumped) has higher precision in peak runoff time simulation. However, its performance is quite poor on the flood volume simulation in the small catchments characterized by intense vegetation coverage and highly developed stream network. The Antecedent precipitation index and Xinanjiang models (lumped) can obtain good simulation results in small humid catchments as long as long-term historical precipitation and runoff data are provided. The TOPMODEL also shows good performance in small humid catchments, but it is unable to simulate the flash floods characterized by the rapid rise and recession. Our results could be very beneficial in practice, since these provide a solid foundation in the selection of hydrological model for flash flood simulation in small catchments in hilly area.

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

10.5194/hess-2018-438 ◽

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.

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Rainfall interpolation based on flood simulation in northren China

E3S Web of Conferences ◽

10.1051/e3sconf/202124803043 ◽

2021 ◽

Vol 248 ◽

pp. 03043

Author(s):

Zhao Ran-hang ◽

Zhou Lu ◽

Li Hua-xing ◽

Li Hong-tao ◽

Qi Zhen ◽

...

Keyword(s):

Short Duration ◽

Spatial Interpolation ◽

Hydrological Model ◽

Research Area ◽

Kriging Interpolation ◽

Distributed Hydrological Model ◽

Flood Simulation ◽

Hilly Area ◽

Interpolation Accuracy ◽

Important Input

Due to the short duration, high intensity and sudden intensity of torrential rain, mountain torrents are easily formed in the northern hilly area.The distributed hydrological model is used as the main means of rain-flood forecasting. Rainfall as an important input,its spatial interpolation accuracy and time scale directly affect the forecast results.Therefore, in this paper, the spatial interpolation calculation and analysis of rainstorm process with hourly scale is carried out in the northern hilly area. Taking Licheng district in Jinan as the research area, the spatial interpolation methods, such as IDW,OK and OCK, are used to calculate the spatial interpolation of 16 time-by-time rainstorm processes for 11 typical rainfall with different duration, and the interpolation results are cross-validated and error analysis is carried out.The results show that the accuracy of the collaborative Kriging interpolation considering the elevation is higher in the short duration rainstorm process of the hilly area.

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Modeling of Urban Flood in Xiamen Island, China

10.29007/dgf5 ◽

2018 ◽

Author(s):

Jiahong Liu ◽

Weiwei Shao ◽

Chenyao Xiang ◽

Chao Mei ◽

Zejin Li

Keyword(s):

Hydrological Model ◽

Flood Mitigation ◽

The Other ◽

City Management ◽

Urban Flooding ◽

Rapid Urbanization ◽

Flood Simulation ◽

Flood Prediction ◽

Urban Flood ◽

Water Administration

Rapid urbanization has greatly increased the impermeable surface in urban area, which led to serious urban flooding and waterlogging in China. There are more than 100 cities that suffered from urban flood every year since 2006, and more than 100 million citizens are involved in China. Urban flood mitigation is one of the most important issues for both water administration and city management agency. This paper simulated the urban flooding in Xiamen Island based on a hydrodynamic model coupled with hydrological model. The datasets of underlying surfaces were input to the model, including the terrain data, building plan, land use, etc. A typical rain pattern of 50 years return event were used for flood simulation. The results show that the main inundated areas (flooded depth more than 40cm) are located in three groups: south east to the Yundang Lake, around the Hubian Reservoir, along the Exhibition Road. The other inundated areas that less than 40 cm deep are scattered in the flat regions of Xiamen Island. The main inundated areas simulated are consistent with the point survey of urban flooding, which verifies that the suggest model is reasonable and useful for urban flood prediction.

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