Effect of multiple climate change scenarios and predicted land-cover on soil erosion: a way forward for the better land management

Aslam, Bilal; Khalil, Umer; Saleem, Maryam; Maqsoom, Ahsen; Khan, Emadullah

doi:10.1007/s10661-021-09559-0

Cited by 4 publications

(2 citation statements)

References 63 publications

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“…The authors predict the rate of soil erosion to be worse in the future with changes in land use and climatic conditions. Under RCP (Representative Concentration Pathway) 8.5, it has been predicted that the rate of soil erosion in the same region may reach up to 550 tons ha −1 yr −1 during 2030–40 [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

Himalayan watersheds in Nepal record high soil erosion rates estimated using the RUSLE model and experimental erosion plots

Joshi

Adhikari

Bhandari

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Himalayan watersheds in Nepal record high soil erosion rates estimated using the RUSLE model and experimental erosion plots

Joshi

Adhikari

Bhandari

et al. 2023

Heliyon

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“…The primary distinction between TerrSet and other software is that the actual calculation is performed on raster data, but all analyses are conducted on homogeneous surfaces whose parameters are selected when the RUSLE model is initiated and are viewed as isolated patches [6,32]. Many studies show that TerrSet is a good tool for determining the impact of climate change and predicted land use and land cover on soil erosion [6,33,34].…”

Section: Introductionmentioning

confidence: 99%

Soil Erosion Modelling and Accumulation Using RUSLE and Remote Sensing Techniques: Case Study Wadi Baysh, Kingdom of Saudi Arabia

et al. 2023

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This study examines the sediment retention in Wadi Baysh using the Revised Universal Soil Loss Equation (RUSLE) and TerrSet models, accompanied by integrated remote sensing and Geographic Information System (GIS) techniques. The contribution of this study is mainly associated with the application of TerrSet integrated with high resolution datasets to precisely estimate sediments load, which provide useful information to operate dams and improve the operational efficiency of dams. The Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data are utilized to delineate the basin and have been used as an input to the TerrSet model. The rainfall erosivity (R factor) was calculated using the Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) in the research area during 2015–2020. The soil erodibility (K factor) and LULC categorization are calculated using the digital soil map of the world (DSMW) and Sentinel-2 datasets, respectively. The R factor calculated for Wadi Baysh ranges between 91.35 and 115.95 MJ mm/ha/h/year, while the estimated K factor ranges from 0.139 to 0.151 t ha h/ha M. The Support Vector Machine (SVM) method categorized LULC of the study area into four major classes including barren land (81% of the total area), built-up area (11%), vegetation (8%), and water bodies (1%). Results from the sediment retention module (TerrSet) indicated that each year, 57.91 million tons of soil loss occurred in the basin. The data show that soil loss is greater in the northeast and south, whereas it is typical in the middle of Wadi Baysh. It is concluded from the current analyses that the dam lake of Wadi Baysh, located downstream, will be filled soon in the coming few years if sediment loads are carried to the lake at the same rate. Surface dam operators can obtain a full understanding of sedimentation and take proactive measures to reduce its influence on dam operations by leveraging TerrSet’s sophisticated capabilities.

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Spatial variability of water-induced soil erosion under climate change and land use/cover dynamics: From assessing the past to foreseeing the future in the Mediterranean island of Crete

Polykretis,

Grillakis,

Manoudakis

et al. 2023

Geomorphology

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Effect of multiple climate change scenarios and predicted land-cover on soil erosion: a way forward for the better land management

Cited by 4 publications

References 63 publications

Himalayan watersheds in Nepal record high soil erosion rates estimated using the RUSLE model and experimental erosion plots

Himalayan watersheds in Nepal record high soil erosion rates estimated using the RUSLE model and experimental erosion plots

Soil Erosion Modelling and Accumulation Using RUSLE and Remote Sensing Techniques: Case Study Wadi Baysh, Kingdom of Saudi Arabia

Spatial variability of water-induced soil erosion under climate change and land use/cover dynamics: From assessing the past to foreseeing the future in the Mediterranean island of Crete

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