Characteristics of watershed dynamic sediment delivery based on improved RUSLE model

Xu, Zan; Zhang, Shanghong; Zhou, Yang; Hou, Xiaoning; Yang, Xuesong

doi:10.1016/j.catena.2022.106602

Cited by 15 publications

(11 citation statements)

References 35 publications

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“…The predictive model established in this study is able to explain 86% of the SDR variation and suggests fairly good performance compared to models in literature (Ali & De Boer, 2010;Li et al, 2019;Rajbanshi & Bhattacharya, 2020;Sedighi et al, 2021;Xu et al, 2022).…”

Section: The Importance Of the Proposed Modelsupporting

confidence: 52%

“…In wet years, more and larger particles on the riverbed can be entrained downstream, whereas larger particles tend to deposit during drier years. The uncertainty of sediment transport process is reflected in Figure 6b; therefore, SDR is of more significance on a multi‐year‐average scale (Jing, 2002; Xu et al, 2022). These SDR averages varied from 0.05 (Catchment #12) to 0.42 (Catchment #9), which characterised the degree of discrepancy in catchments' sediment delivery capacity.…”

Section: Resultsmentioning

confidence: 99%

“…Previous criteria combining geomorphic, morphologic and hydrological factors are consistent with our findings (Diodato & Grauso, 2009; Park et al, 2010), except that we indicate the run‐off potential with an easy‐to‐find H max rather than mean elevation, rainfall or curve number, as well as using SCA to describe the steepness rather basin slope. Given that hydrological parameters usually involve uncertainties and measurement challenges throughout timeframes (Xu et al, 2022), whereas H max and SCA can be determined by DEM, the factor set obtained in this study not only provides comprehensive criteria but also avoids the aforementioned problems.…”

Section: Discussionmentioning

confidence: 99%

“…Regionalised SDR can be determined using observed sediment yields (or sediment loads) and simulated erosion estimates (Xu et al, 2022; Zabaleta et al, 2016). However, observation in the LJRB is scarce, for example, the gauged area of tributary stations covers less than 40% of the basin area.…”

Section: Introductionmentioning

confidence: 99%

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Predicting soil erosion and sediment delivery in large, data‐sparse, mountainous basins

Tan,

Liu,

2024

Earth Surf Processes Landf

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Intense hydroelectric exploitation requires information on sediment flux to prevent the potential siltation of cascade reservoirs. As the main sediment source for the Yangtze River, the lower Jinsha River Basin (LJRB) lacks data on its sediment budget, which urges a thorough investigation to provide reliable estimates. This paper proposed a modelling framework to study soil erosion, sediment delivery, and sediment yield in this critical area with an emphasis on analyzing the underlying controls. Over the past 50 years, basin average erosion rates decreased from 26.5 to 23.7 t ha−1 a−1, with erosion hotspots (>50 t ha−1 a−1) comprising only 21.1% of the basin area but contributing 69.5% of the gross erosion. Local steepness and sparse landcover conditions were found to be directly responsible for high erosions, rather than precipitation or soils. With a dimensionality reduction strategy to remove redundant factors, the optimal variables for modelling sediment delivery ratio (SDR) were identified as Specific Catchment Area, Maximum Elevation, and Drainage Area. These three variables all had a positive correlation with SDR, and the established model can explain 86% of the SDR variation. Further modelling revealed regionalised SDRs ranging from 0.07 to 0.38 for 39 subbasins, about 30% of which were no less than 0.35. Regionalised specific sediment yield (SSY) ranged from 1.3 to 16.9 t ha−1 a−1 with 38% of the high SSYs (above 8 t ha−1 a−1) concentrating in the reservoir area from Wudongde to Xiluodu that poses siltation risks. This study addressed the scarcity of sediment budget knowledge in the LJRB, and the coupled model of erosion and sediment delivery has been validated with satisfactory predictions. Therefore, the modelling framework proposed may be adopted to diverse river basins for its low parameter requirements and solid modelling performance.

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Section: The Importance Of the Proposed Modelsupporting

confidence: 52%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting soil erosion and sediment delivery in large, data‐sparse, mountainous basins

Tan,

Liu,

2024

Earth Surf Processes Landf

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“…Gully erosion accounts for 50% of the total water erosion in some small watersheds of loess hilly and gully regions (Li et al, 2003; Zhang, 2020). The Yanhe Watershed has a complex geomorphology with numerous gullies, which may be an important factor affecting the simulation results, and large soil erosion or debris flows caused by extreme storms can also affect the sediment budget (Xu et al, 2022). In addition, the sediment loading calculated using RUSLE, IC and SDR was mainly related to the soil erosion from the slope to the stream network.…”

Section: Discussionmentioning

confidence: 99%

An integrated watershed modelling framework to explore the covariation between sediment connectivity and soil erosion

Guo,

Wu,

Liu

et al. 2023

European J Soil Science

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Quantitative identification of the covariation between sediment connectivity and soil erosion can contribute to provide the key information for watershed sediment management. However, this covariation and its spatiotemporal response mechanisms are still unclear, especially whether this covariation can be used as a basis for identifying critical source areas of sediment in large‐scale ecological restored watersheds. In this study, an integrated methodology framework by the RUSLE, index of connectivity (IC), and sediment delivery ratio (SDR) was proposed to visually assess the spatiotemporal characteristics of erosion and sediment yield processes in the Yanhe Watershed with large‐scale ecological restoration from 1985 to 2020, and to identify the covariation between sediment connectivity and erosion in subbasins. The soil erosion estimated by RUSLE has decreased by over 80% since 1985 owing to increased vegetation cover and the effective implementation of soil conservation measures, but the upper reaches still have high erosion intensity due to differences in specific controlling factors such as topographic conditions and land cover, requiring focused soil conservation practice. The IC results showed that as the vegetation restoration and soil conservation measures in the Yanhe Watershed varied from year to year, their spatial and temporal patterns had a strong influence on the distribution of sediment connectivity, some local areas in the middle reaches showed local minima of IC in 1995, 1998 and 2010 mainly due to the implementation of long‐term ecological restoration project. The developed IC‐Erosion maps indicated that areas with high‐connectivity but low‐erosion accounted for over 60% of the total watershed area from 1985 to 1999, demonstrating a reverse correlation between sediment connectivity and erosion. Meanwhile, over 40% of the erosion occurred in a few areas (approximately 20%) with high‐connectivity and high‐erosion from 2000 to 2004, which can be characterized as the critical areas of erosion. The methods and results of this study provide ideas for separately defining both erosion and connectivity, and quantifying bi‐variable erosion‐connectivity classification that can be easily viewed on a scatterplot.This article is protected by copyright. All rights reserved.

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Influence of LS Factor Overestimation Soil Loss on RUSLE Model for Complex Topographies

Mejía-Parada,

Mora-Ruiz,

Vallejo-Borda

et al. 2024

J Indian Soc Remote Sens

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Characteristics of watershed dynamic sediment delivery based on improved RUSLE model

Cited by 15 publications

References 35 publications

Predicting soil erosion and sediment delivery in large, data‐sparse, mountainous basins

Predicting soil erosion and sediment delivery in large, data‐sparse, mountainous basins

An integrated watershed modelling framework to explore the covariation between sediment connectivity and soil erosion

Influence of LS Factor Overestimation Soil Loss on RUSLE Model for Complex Topographies

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