Simulation of runoff process based on the 3-D river network

Xue, Yuan; Qin, Chao; Wu, Baosheng; Zhang, Ga; Fu, Xudong; Ma, Hongbo; Li, Dan; Wang, Bingjie

doi:10.1016/j.jhydrol.2023.130192

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…Xue et al suggested a method to obtain three-dimensional river networks with a relatively novel effect. However, this workflow is mainly designed for larger river systems and may not be able to obtain information from small erosion gully networks [13].…”

Section: Introductionmentioning

confidence: 99%

Erosion Gully Networks Extraction Based on InSAR Refined Digital Elevation Model and Relative Elevation Algorithm—A Case Study in Huangfuchuan Basin, Northern Loess Plateau, China

Lu,

Zhang,

Wang

et al. 2024

Remote Sensing

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The time-effective mapping of erosion gullies is crucial for monitoring and early detection of developing erosional progression. However, current methods face challenges in obtaining large-scale erosion gully networks rapidly due to limitations in data availability and computational complexity. This study developed a rapid method for extracting erosion gully networks by integrating interferometric synthetic aperture radar (InSAR) and the relative elevation algorithm (REA) within the Huangfuchuan Basin, a case basin in the northern Loess Plateau, China. Validation in the study area demonstrated that the proposed method achieved an F1 score of 81.94%, representing a 9.77% improvement over that of the reference ASTER GDEM. The method successfully detected small reliefs of erosion gullies using the InSAR-refined DEM. The accuracy of extraction varied depending on the characteristics of the gullies in different locations. The F1 score showed a positive correlation with gully depth (R2 = 0.62), while the fragmented gully heads presented a higher potential of being missed due to the resolution effect. The extraction results provided insights into the erosion gully networks in the case study area. A total of approximately 28,000 gullies were identified, exhibiting pinnate and trellis patterns. Most of the gullies had notable intersecting angles exceeding 60°. The basin’s average depth was 64 m, with the deepest gully being 140 m deep. Surface fragmentation indicated moderate erosive activity, with the southeastern loess region showing more severe erosion than the Pisha sandstone-dominated central and northwestern regions. The method described in this study offers a rapid approach to map gullies, streamlining the workflow of erosion gully extraction and enabling efficiently targeted interventions for erosion control efforts. Its practical applicability and potential to leverage open-source data make it accessible for broader application in similar regions facing erosion challenges.

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Section: Introductionmentioning

confidence: 99%

Erosion Gully Networks Extraction Based on InSAR Refined Digital Elevation Model and Relative Elevation Algorithm—A Case Study in Huangfuchuan Basin, Northern Loess Plateau, China

Lu,

Zhang,

Wang

et al. 2024

Remote Sensing

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Undervalued dry riverbeds: A key factor in equating intermittent river CO2 emissions to perennial rivers

Qin,

Xue,

et al. 2024

Water Research

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Exploring Spatio-Temporal Dynamics of Future Extreme Precipitation, Runoff, and Flood Risk in the Hanjiang River Basin, China

Wang,

Shao,

Liu

et al. 2024

Remote Sensing

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The hydrological cycle is altered by climate change and human activities, amplifying extreme precipitation and heightening the flood risk regionally and globally. It is imperative to explore the future possible alterations in flood risk at the regional scale. Focusing on the Hanjiang river basin (HRB), this study develops a framework for establishing a scientific assessment of spatio-temporal dynamics of future flood risks under multiple future scenarios. In this framework, a GCMs statistical downscaling method based on machine learning is used to project future precipitation, the PLUS model is used to project future land use, the digitwining watershed model (DWM) is used to project future runoff, and the entropy weight method is used to calculate risk. Six extreme precipitation indices are calculated to project the spatio-temporal patterns of future precipitation extremes in the HRB. The results of this study show that the intensity (Rx1day, Rx5day, PRCPTOT, SDII), frequency (R20m), and duration (CWD) of future precipitation extremes will be consistently increasing over the HRB during the 21st century. The high values of extreme precipitation indices in the HRB are primarily located in the southeast and southwest. The future annual average runoff in the upper HRB during the near-term (2023–2042) and mid-term (2043–2062) is projected to decrease in comparison to the baseline period (1995–2014), with the exception of that during the mid-term under the SSP5-8.5 scenario. The high flood risk center in the future will be distributed in the southwestern region of the upper HRB. The proportions of areas with high and medium–high flood risk in the upper HRB will increase significantly. Under the SSP5-8.5 scenario, the area percentage with high flood risk during the future mid-term will reach 24.02%. The findings of this study will facilitate local governments in formulating effective strategic plans for future flood control management.

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Simulation of runoff process based on the 3-D river network

Cited by 4 publications

References 52 publications

Erosion Gully Networks Extraction Based on InSAR Refined Digital Elevation Model and Relative Elevation Algorithm—A Case Study in Huangfuchuan Basin, Northern Loess Plateau, China

Erosion Gully Networks Extraction Based on InSAR Refined Digital Elevation Model and Relative Elevation Algorithm—A Case Study in Huangfuchuan Basin, Northern Loess Plateau, China

Undervalued dry riverbeds: A key factor in equating intermittent river CO2 emissions to perennial rivers

Exploring Spatio-Temporal Dynamics of Future Extreme Precipitation, Runoff, and Flood Risk in the Hanjiang River Basin, China

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