Sub-Daily Simulation of Mountain Flood Processes Based on the Modified Soil Water Assessment Tool (SWAT) Model

Duan, Yongchao; Meng, Fanhao; Li, Shipeng; Huang, Yanyan; Luo, Min; Xing, Wei; Maeyer, Philippe De

doi:10.3390/ijerph16173118

Cited by 18 publications

(10 citation statements)

References 82 publications

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“…The sequential uncertainty fitting (SUFI-2) program version 2 embedded in the SWATCUP software [47] was used for sensitivity analysis and model calibration. The T-states and p-values of the parameters were used before and after improving the statistical model to obtain parameter sensitivity [48,49]. The period from 1996 to1997 was chosen as the spin-up period, while calibration and validation periods were from 1998 to 2007 and 2008 to 2010, respectively.…”

Section: Hydrological Modelingmentioning

confidence: 99%

Climate Change Impacts on Extreme Flows Under IPCC RCP Scenarios in the Mountainous Kaidu Watershed, Tarim River Basin

Huang

et al. 2020

Sustainability

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In the 21st century, heavier rainfall events and warmer temperatures in mountainous regions have significant impacts on hydrological processes and the occurrence of flood/drought extremes. Long-term modeling and peak flow detection of streamflow series are crucial in understanding the behavior of flood and drought. This study was conducted to analyze the impacts of future climate change on extreme flows in the Kaidu River Basin, northwestern China. The soil water assessment tool (SWAT) was used for hydrological modeling. The projected future precipitation and temperature under Intergovernmental Panel on Climate Change (IPCC) representative concentration pathway (RCP) scenarios were downscaled and used to drive the validated SWAT model. A generalized extreme value (GEV) distribution was employed to assess the probability distribution of flood events. The modeling results showed that the simulated discharge well matched the observed ones both in the calibration and validation periods. Comparing with the historical period, the ensemble with 15 general circulation models (GCMs) showed that the annual precipitation will increase by 7.9-16.1% in the future, and extreme precipitation events will increase in winter months. Future temperature will increase from 0.42 • C/10 a to 0.70 • C/10 a. However, with respect to the hydrological response to climate change, annual mean runoff will decrease by 21.5-40.0% under the mean conditions of the four RCP scenarios. A reduction in streamflow will occur in winter, while significantly increased discharge will occur from April to May. In addition, designed floods for return periods of five, 10 and 20 years in the future, as predicted by the GEV distribution, will decrease by 3-20% over the entire Kaidu watershed compared to those in the historical period. The results will be used to help local water resource management with hazard warning and flood control.

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Section: Hydrological Modelingmentioning

confidence: 99%

Climate Change Impacts on Extreme Flows Under IPCC RCP Scenarios in the Mountainous Kaidu Watershed, Tarim River Basin

Huang

et al. 2020

Sustainability

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“…Some physics-based hydrological models, such as SWAT [7][8][9], TOPMODEL/BTOP-MC [10,11], and MIKE SHE [12,13], have already been widely used to simulate the rainfall-runoff relationship for runoff prediction because of the definite and explainable mechanisms and the theoretically high accuracy of the modelling results, in addition to the user-friendly interfaces of the modelling software. However, the above-mentioned physics-based models usually require the observed rainfall as the input and the observed runoff data for calibration/validation and some additional data, like the digital elevation model (DEM), soil types, and land use information.…”

Section: Introductionmentioning

confidence: 99%

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Wang

Liu

Yue

et al. 2020

Water

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A gated recurrent unit (GRU) network, which is a kind of artificial neural network (ANN), has been increasingly applied to runoff forecasting. However, knowledge about the impact of different input data filtering strategies and the implications of different architectures on the GRU runoff forecasting model’s performance is still insufficient. This study has selected the daily rainfall and runoff data from 2007 to 2014 in the Wei River basin in Shaanxi, China, and assessed six different scenarios to explore the patterns of that impact. In the scenarios, four manually-selected rainfall or runoff data combinations and principal component analysis (PCA) denoised input have been considered along with single directional and bi-directional GRU network architectures. The performance has been evaluated from the aspect of robustness to 48 various hypermeter combinations, also, optimized accuracy in one-day-ahead (T + 1) and two-day-ahead (T + 2) forecasting for the overall forecasting process and the flood peak forecasts. The results suggest that the rainfall data can enhance the robustness of the model, especially in T + 2 forecasting. Additionally, it slightly introduces noise and affects the optimized prediction accuracy in T + 1 forecasting, but significantly improves the accuracy in T + 2 forecasting. Though with relevance (R = 0.409~0.763, Grey correlation grade >0.99), the runoff data at the adjacent tributary has an adverse effect on the robustness, but can enhance the accuracy of the flood peak forecasts with a short lead time. The models with PCA denoised input has an equivalent, even better performance on the robustness and accuracy compared with the models with the well manually filtered data; though slightly reduces the time-step robustness, the bi-directional architecture can enhance the prediction accuracy. All the scenarios provide acceptable forecasting results (NSE of 0.927~0.951 for T + 1 forecasting and 0.745~0.836 for T + 2 forecasting) when the hyperparameters have already been optimized. Based on the results, recommendations have been provided for the construction of the GRU runoff forecasting model.

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“…Thereby, the five most sensitive parameters of the donor catchment model are further localized in the target catchment model according to the new transferring rules (Section 3.3.5). A large number of studies have also shown that TLAPS, PLAPS, and SMFMX are highly correlated to catchment elevation [8,45,54]. CH_N2 is a coefficient that comprehensively reflects the influence of a rough river on the runoff.…”

Section: The Difference Of Model Parameter Transferability Methodsmentioning

confidence: 99%

“…The hydrological model is an essential tool to understand the hydrological process of catchments under the various climate input and land management [1], which includes a lumped conceptual model, and semi-distributed or distributed hydrological models, e.g., the Xin'anjiang model [2,3], the TOPMODEL model [4,5], and the SWAT model [6][7][8]. These models have to satisfy the accuracy requirements of the hydrological process's simulation with measurement flow data by parameter calibration and validation.…”

Section: Introductionmentioning

confidence: 99%

Improved Model Parameter Transferability Method for Hydrological Simulation with SWAT in Ungauged Mountainous Catchments

Meng

et al. 2020

Sustainability

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The sustainability of water resources in mountainous areas has a significant contribution to the stabilization and persistence of the ecological and agriculture systems in arid and semi-arid areas. However, the insufficient understanding of hydrological processes in ungauged mountainous catchments (UMCs) is not able to scientifically support the sustainable management of water resources. The conventional parameter transferability method (transplanting the parameters of the donor catchment model with similar distances or attributes to the target catchment model) still has great potential for improving the accuracy of the hydrological simulation in UMC. In this study, 46 river catchments, with discharge survey stations and multi-type catchment characteristics in Xinjiang, are separated into the target catchments and donor catchments to promote an improved model parameter transferability method (IMPTM). This method synthetically processes the SWAT model parameters based on the distance approximation principle (DAP) and the attribute similarity principle (ASP). The performance of this method is tested in a random gauged catchment and compared with other traditional methods (DAP and ASP). The daily runoff simulation results in the target catchment have relatively low accuracy by both the DAP method ( N S = 0.27, R 2 = 0.55) and ASP method ( N S = 0.36, R 2 = 0.65), which implies the conventional approach is not capable of processing the parameters in the target regions. However, the simulation result by IMPTM is a significant improvement ( N S = 0.69, R 2 = 0.85). Moreover, the IMPTM can accurately catch the flow peak, appearance time, and recession curve. The current study provides a compatible method to overcome the difficulties of hydrological simulation in UMCs in the world and can benefit hydrological forecasting and water resource estimation in mountainous areas.

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Sub-Daily Simulation of Mountain Flood Processes Based on the Modified Soil Water Assessment Tool (SWAT) Model

Cited by 18 publications

References 82 publications

Climate Change Impacts on Extreme Flows Under IPCC RCP Scenarios in the Mountainous Kaidu Watershed, Tarim River Basin

Climate Change Impacts on Extreme Flows Under IPCC RCP Scenarios in the Mountainous Kaidu Watershed, Tarim River Basin

Impact of Input Filtering and Architecture Selection Strategies on GRU Runoff Forecasting: A Case Study in the Wei River Basin, Shaanxi, China

Improved Model Parameter Transferability Method for Hydrological Simulation with SWAT in Ungauged Mountainous Catchments

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