Improving the flood forecasting capability of the Xinanjiang model for small- and medium-sized ungauged catchments in South China

Gong, Junfu; Yao, Cheng; Li, Zhijia; Chen, Yuanfang; Huang, Yin; Tong, Bingxing

doi:10.1007/s11069-021-04531-0

Cited by 24 publications

(7 citation statements)

References 57 publications

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“…The model is widely applied to daily-scale streamflow prediction due to its adaptability and generalizability (Ahirwar et al, 2018;Rahman and Lu, 2015). Gong et al applied the Xinanjiang model to simulate hourly flood timing of small-to-medium watersheds in China's southern mountains (Gong et al, 2021). The Xinanjiang model integrated with regionalization methods has achieved favorable results in simulating hourly flood events.…”

Section: Introductionmentioning

confidence: 99%

Xinanjiang-based interval forecasting model for daily streamflow considering climate change impacts

Ke,

Wang,

Dong

et al. 2024

Preprint

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One indication of the impacts of climate change on the water cycle is growing streamflow uncertainty, which is especially evident in high and cold regions. An interval forecasting model is established, which couples a snowmelt module and an uncertainty module, based on the Xinanjiang model. The model can consider the climate change impacts by quantifying the streamflow variations in the form of interval forecasts. The model’s performance was assessed by applying it in the headwater region of the Yellow River Basin. Interval forecasts and uncertainty analyses were conducted. Results show that the model can accurately describe the daily streamflow process in the study area. Unlike the deterministic forecasting model, the interval forecasting model effectively addresses shortcomings in forecasting high-flow scenarios. Furthermore, outcomes from the uncertainty analysis indicate that the model parameter K (the ratio of potential evapotranspiration to pan evaporation) plays a crucial role in water balance computations; the model parameter B (exponent of distribution of soil tension water capacity curve) exhibits sensitivity, suggesting challenges in attaining complete soil saturation across the entire basin. In addition, the insensitivity of the snowmelt module parameters implies that the proportion of snowmelt streamflow is relatively low in the annual streamflow and remains stable. The study results can provide theoretical references for water resource planning and reservoir regulation in the Yellow River Basin.

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Section: Introductionmentioning

confidence: 99%

Xinanjiang-based interval forecasting model for daily streamflow considering climate change impacts

Ke,

Wang,

Dong

et al. 2024

Preprint

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show abstract

“…The primary economic and demographic focal points of China are primarily concentrated in humid and semi-humid regions, thus emphasizing the paramount significance of studying flood forecasting in these areas [3]. In recent years, the risk of extreme floods in the large basins of China has been substantially reduced due to improvements in flood forecasting [4]. However, the floods that occur in small and medium catchments (SMCs) are characterized by rapidity and suddenness, and usually have a short flood duration and cause a rapid rise in the flow peak, which makes flood prediction difficult [5].…”

Section: Introductionmentioning

confidence: 99%

The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China

Gong,

Hu,

Yao

et al. 2023

Water

Self Cite

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The distributed Grid-Xin’anjiang (Grid-XAJ) model is very sensitive to the spatial and temporal distribution of data when used in humid and semi-humid small and medium catchments. We used the successive correction method to merge the gauged rainfall with rainfall forecasted by the Weather Research and Forecasting (WRF) model to enhance the spatiotemporal accuracy of rainfall distribution. And we used the Penman–Monteith equation to calculate the potential evapotranspiration (PEPM). Then, we designed two forcing scenarios (WRF-driven rainfall (Wr) + PEPM, WRF-merged rainfall (Wm) + PEPM) to drive the Grid-XAJ model for flood forecasting. We found the WRF-driven Grid-XAJ model held significant potential in flood forecasting. The Grid-XAJ model provided only an approximation of flood hygrographs when driven by scenario Wr + PEPM. The results in scenario Wm + PEPM showed a high degree-of-fit with observed floods with mean Nash–Sutcliffe efficiency coefficient (NSE) values of 0.94 and 0.68 in two catchments. Additionally, scenario Wm + PEPM performed better flood hygrographs than scenario Wr + PEPM. The flood volumes and flow peaks in scenario Wm + PEPM had an obvious improvement compare to scenario Wr + PEPM. Finally, we observed that the model exhibited superior performance in forecasting flood hydrographs, flow peaks, and flood volumes in humid catchments compared with semi-humid catchments.

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“…Water storage capacity curve (WSCC) is the key to the calculation of saturation-excess runoff and is applied to describe the distribution of basin water storage capacity for runoff simulation [14]. WSCC is represented by empirical Generalized Pareto Distribution, which has been extensively applied worldwide for a wide range of watershed scales, hydrology environmental conditions, flood simulation, and provides a scientific way to analyze the process of rainfall-runoff [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Land-Use and Land Cover Is Driving Factor of Runoff Yield: Evidence from A Remote Sensing-Based Runoff Generation Simulation

Yang

et al. 2022

Water

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The spatial distribution of water storage capacity has always been the critical content of the study of saturation-excess runoff. Xin′anjiang model uses the water storage capacity curve (WSCC) to characterize the distribution of water storage capacity for runoff yield calculation. However, the mathematical and physical foundations of WSCC are unclear, which is impossible to simulate runoff generation with complex basins accurately. To fill this gap, we considered the dominant role of basin physical characteristics in water storage capacity and developed a new integrated approach to solve the spatial distribution of water storage capacity (L-WSCC) to account for the spatiotemporal dynamics of their impact on runoff generation. The main contribution of L-WSCC was to confer WSCC more physical meaning and the spatial distribution of water storage capacity was explicitly represented more accurately, so as to better express the runoff generation and provide a new approach for runoff yield calculation in non-data basin. L-WSCC was applied to Misai basin in China and promising results had been achieved, which verified the rationality of the method (the mean Nash–Sutcliffe efficiency (NSE):0.86 and 0.82 in daily and hourly scale, respectively). Compared with WSCC, the performance of L-WSCC was improved (mean NSE: 0.82 > 0.78, mean absolute value of flood peak error (PE): 12.74% < 21.66%). Moreover, the results of local sensitivity analyses demonstrated that land-use and land cover was the major driving factor of runoff yield (the change of mean absolute error (ΔMAE): 131.38%). This work was significant for understanding the mechanisms of runoff generation, which can be used for hydrological environmental management and land-use planning.

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Improving the flood forecasting capability of the Xinanjiang model for small- and medium-sized ungauged catchments in South China

Cited by 24 publications

References 57 publications

Xinanjiang-based interval forecasting model for daily streamflow considering climate change impacts

Xinanjiang-based interval forecasting model for daily streamflow considering climate change impacts

The WRF-Driven Grid-Xin’anjiang Model and Its Application in Small and Medium Catchments of China

Land-Use and Land Cover Is Driving Factor of Runoff Yield: Evidence from A Remote Sensing-Based Runoff Generation Simulation

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