Applicability assessment of the CASCade Two Dimensional SEDiment (CASC2D‐SED) distributed hydrological model for flood forecasting across four typical medium and small watersheds in China

Lei, Chao; Zhang, Ke; Li, Zhijia; Wang, Jingfeng; Yao, Cheng; Li, Qiaoling

doi:10.1111/jfr3.12518

Cited by 37 publications

(15 citation statements)

References 77 publications

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“…In comparison, our LSTM model showed better model prediction ability in Chenhe with an NSE of 0.93 for the testing stage, which suggests the superiority of the LSTM model. In another study, Chao [39] established the CASCade Two Dimensional SEDiment (CASC2D-SED) model in Xianbeigou Catchment; the coefficient of determination (i.e., a similar metric with NSE) of eight flood events was greater than 0.7 with an average of 0.85, and the accuracy of simulation results was higher than that of LSTM found in this study. This is because the CASC2D-SED model utilizes the Greet-Ampt formula to simulate surface flow, which is suitable for this area with steep hydrographs that show the infiltration-excess mechanism.…”

Section: Discussioncontrasting

confidence: 55%

“…The hydrograph is featured by steep rising and falling limbs. It should be emphasized that small watersheds in the northern and the northwestern mountainous areas of China have inadequate capacity for flood control and water retention and thus have high surface runoff and rapid hydrological response to rainfall events [39]. Flooding occurs between June and September as a result of uneven distribution and inter-annual variation in precipitation [40].…”

Section: Data Preprocessingmentioning

confidence: 99%

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The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China

Liu

Huang

et al. 2020

Water

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Flow forecasting is an essential topic for flood prevention and mitigation. This study utilizes a data-driven approach, the Long Short-Term Memory neural network (LSTM), to simulate rainfall–runoff relationships for catchments with different climate conditions. The LSTM method presented was tested in three catchments with distinct climate zones in China. The recurrent neural network (RNN) was adopted for comparison to verify the superiority of the LSTM model in terms of time series prediction problems. The results of LSTM were also compared with a widely used process-based model, the Xinanjiang model (XAJ), as a benchmark to test the applicability of this novel method. The results suggest that LSTM could provide comparable quality predictions as the XAJ model and can be considered an efficient hydrology modeling approach. A real-time forecasting approach coupled with the k-nearest neighbor (KNN) algorithm as an updating method was proposed in this study to generalize the plausibility of the LSTM method for flood forecasting in a decision support system. We compared the simulation results of the LSTM and the LSTM-KNN model, which demonstrated the effectiveness of the LSTM-KNN model in the study areas and underscored the potential of the proposed model for real-time flood forecasting.

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Section: Discussioncontrasting

confidence: 55%

Section: Data Preprocessingmentioning

confidence: 99%

The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China

Liu

Huang

et al. 2020

Water

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“…However, flash flood forecasting and prevention of the small and medium catchments (SMCs) remains an urgent problem [4,5]. The flash floods taking place in the SMCs are characterized by short routing time, which makes flood prediction difficult [6]. Additionally, flood simulation and forecasting for SMCs face more challenges due to sparse observation and inadequate information of field data.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

Sun

Yao

et al. 2020

Water

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The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h).

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“…The effect of climate change on streamflow is analyzed in previous researches. Hydrologic modelling is a valuable tool for flood forecasting and decision making in water resources organization [11]. Previously, various hydrologic models with a snow factor were applied to model the daily stream flows in snow-and glacier-fed catchments [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Climate Change Scenarios and Effects on Snow-Melt Runoff

2020

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Climate change is an important environmental issue, as progression of melting glaciers and snow cover is sensitive to climate alteration. The aim of this research was to model climate alterations forecasts, and to assess potential changes in snow cover and snow-melt runoff under the different climate change scenarios in the case study of the Zayandeh-rud River Basin. Three cluster models for climate change (NorESM1-M, IPSL-CM5A-LR and CSIRO-MK3.6.0) were applied under RCP 8.5, 4.5 and 2.6 scenarios, to examine climate influences on precipitation and temperature in the basin. Temperature and precipitation were determined for all three scenarios for four periods of 2021-2030, 2031-2040, 2041-2050 and 2051-2060. MODIS (MOD10A1) was also applied to examine snow cover using temperature and precipitation data. The relationship between snow-covered area, temperature and precipitation was used to forecast future snow cover. For modeling future snow melt runoff, a hydrologic model of SRM was used including input data of precipitation, temperature and snow cover. The results indicated that all three RCP scenarios lead to an increase in temperature, and reduction in precipitation and snow cover. Investigation in snowmelt runoff throughout the observation period (November 1970 to May 2006) showed that most of annual runoff is derived from snow melting. Maximum snowmelt runoff is generated in winter. The share of melt water in the autumn and spring runoff is estimated at 35 and 53%, respectively. The results of this study can assist water manager in making better decisions for future water supply.

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Applicability assessment of the CASCade Two Dimensional SEDiment (CASC2D‐SED) distributed hydrological model for flood forecasting across four typical medium and small watersheds in China

Cited by 37 publications

References 77 publications

The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China

The Applicability of LSTM-KNN Model for Real-Time Flood Forecasting in Different Climate Zones in China

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

Climate Change Scenarios and Effects on Snow-Melt Runoff

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