A time-varying distributed unit hydrograph method considering soil moisture

Yi, Bin; Chen, Lu; Zhang, Hansong; Singh, Vijay P.; Jiang, Ping; Liu, Yizhuo; Guo, Hexiang; Qiu, Hongya

doi:10.5194/hess-26-5269-2022

Cited by 15 publications

(7 citation statements)

References 68 publications

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“…To optimize the parameters of the flexible hybrid runoff generation model, we used the Shuffled Complex Evolution Algorithm (SCE-UA) [28,37]. For parameter calibration, a composite objective function composed of three measurement indexes was used [2,38,39]. The composite objective function and three metrics are given as…”

Section: Model Calibration and Evaluationmentioning

confidence: 99%

Influences of the Runoff Partition Method on the Flexible Hybrid Runoff Generation Model for Flood Prediction

Yi,

Chen,

Yang

et al. 2023

Water

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The partition of surface runoff and infiltration is crucial in hydrologic modeling. To improve the flood prediction, we designed four strategies to explore the influences of the runoff partition method on the flexible hybrid runoff generation model. The runoff partition strategies consist of a hydrological model without the runoff partition module, a two-source runoff partition method, an improved two-source runoff partition method considering the heterogeneity of the subsurface topography and land cover, and a three-source runoff partition method. The Xin’anjiang hydrological model was used as the modeling framework to simulate a six-hourly stream flow for the Xun River watershed in Shaanxi Province, China. And the saturation-excess runoff generation and infiltration-excess runoff generation mechanisms were combined to construct the flexible hybrid runoff generation model. The performances of the four strategies were compared and analyzed based on the continuous flow discharge as well as the flood events. The runoff components analysis method was used to test the model’s conformity with the reality of the watershed. The results showed that the three-source runoff partition method was not applicable to the flexible hybrid runoff generation model because it overestimated the surface runoff and almost ignored the subsurface stormflow runoff. The improved two-source runoff partition method outperformed the others as it considered the heterogeneity of the watershed.

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Section: Model Calibration and Evaluationmentioning

confidence: 99%

Influences of the Runoff Partition Method on the Flexible Hybrid Runoff Generation Model for Flood Prediction

Yi,

Chen,

Yang

et al. 2023

Water

Self Cite

View full text Add to dashboard Cite

show abstract

“…To improve the applicability of the distributed model in semi-humid and semi-arid watersheds, the effects of rainfall intensity and reservoir regulation on runoff generation and routing processes inevitably need to be addressed. Dramatic variations in rainfall intensity over time in semi-humid and semi-arid watersheds lead to flooding processes exhibiting strong non-linearity effects (Yi et al, 2022). The commonly used routing methods, including the diffusion wave, the kinematic wave method, and the horizontal routing (IRF-UH) method, only consider the spatial variability of rainfall while ignoring the effects of time-varying rainfall intensity on river routing (Lohmann et al, 1996;Zang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Developing a distributed modeling framework considering the spatiotemporally varying hydrological processes for sub-daily flood forecasting in semi-humid and semi-arid watersheds

Li,

Ye,

et al. 2024

Preprint

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The complex and varied climate, short duration and high intensity of rainfall, and complicated subsurface properties of semi-humid and semi-arid watersheds pose challenges for sub-daily flood forecasting. Previous studies revealed that lumped models are insufficient because they do not effectively account for the spatial variability of hydrological processes. Extending the lumped model to a distributed modeling framework is a reliable approach for runoff simulation. However, existing distributed models do not adequately characterize the strong spatiotemporal variability of the sub-daily hydrological processes in semi-humid and semi-arid watersheds. To address the above concerns, a distributed modeling framework was proposed that is extended by lumped models and accounts for the effects of time-varying rainfall intensity and reservoir regulation on hydrological processes. Moreover, the Fourier Amplitude Sensitivity Test (FAST) method is performed to identify the sensitive parameters for efficient calibration. To evaluate the performance of the proposed distributed model, it was tested in eight watersheds. The results indicate that the proposed distributed model simulates sub-daily flood events with mean evaluation metrics of 0.80, 9.2%, 13.0%, and 1.05 for NSE, BIAS, RPE, and PTE, respectively, superior to the lumped model. Furthermore, to further evaluate the difference between the proposed distributed model and the existing distributed models, it was compared with the Variable Infiltration Capacity (VIC) model at various time steps, including 3h, 6h, 12h, and24 h. The proposed distributed model was able to better capture the flooding processes at shorter time steps, especially 3 h. Therefore, it can be considered a practical tool for sub-daily flood forecasting in semi-humid and semi-arid watersheds.

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“…Forecasting EP, therefore, is one of the most effective methods for the reduction of disaster losses, flood prevention, reduction of economic losses, and avoiding casualties [5]. Therefore, an investigation into EP prediction and its influencing factors is of great importance for the quantitative assessment of global or regional disaster and environmental risk [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations

Yang

Chen

Singh

et al. 2023

Water

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Monthly extreme precipitation (EP) forecasts are of vital importance in water resources management and storage behind dams. Machine learning (ML) is extensively used for forecasting monthly EP, and improvements in model performance have been a popular issue. The innovation of this study is summarized as follows. First, a distance correlation-Pearson correlation (DC-PC) method was proposed to identify the complex nonlinear relationship between global sea surface temperature (SST) and EP and select key input factors from SST. Second, a random forest (RF) model was used for forecasting monthly EP, and the physical mechanism of EP was obtained based on the feature importance (FI) of RF and DC–PC relationship. The middle and lower reaches of the Yangtze River (MLYR) were selected as a case study, and monthly EP in summer (June, July and August) was forecasted. Furthermore, the physical mechanism between key predictors with a large proportion of FI and EP was investigated. Results showed that the proposed model had high accuracy and robustness, in which R2 in the test period was above 0.81, and RMSE as well as MAE were below 10 mm. Meanwhile, the key predictors in the high SST years could cause eastward extension of the South Asian High, westward extension of the Western Pacific Subtropical High, water vapor rising motion and an increase in the duration of atmospheric rivers exceeding 66 h, which lead to increasing EP in the MLYR. The results indicated that the DC–PC method could replace Pearson correlation for investigating the nonlinear relationship between SST and EP, as well as for selecting the factors. Further, the key predictors that account for a large proportion of FI can be used for explaining the physical mechanism of EP and directing forecasts.

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A time-varying distributed unit hydrograph method considering soil moisture

Cited by 15 publications

References 68 publications

Influences of the Runoff Partition Method on the Flexible Hybrid Runoff Generation Model for Flood Prediction

Influences of the Runoff Partition Method on the Flexible Hybrid Runoff Generation Model for Flood Prediction

Developing a distributed modeling framework considering the spatiotemporally varying hydrological processes for sub-daily flood forecasting in semi-humid and semi-arid watersheds

A Method for Monthly Extreme Precipitation Forecasting with Physical Explanations

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