Spatial Analysis and Extent of Soil Erosion Risk Using the Rusle Approach in the Swat River Basin, Eastern Hindukush DOI Creative Commons

Abdullah Khan,

Atta-ur Rahman

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 6, 2023

Abstract Soil erosion is a severe problem posing number of adverse effects on the environment. It prominent hazard damaging fertile agricultural land. Therefore, in this study, spatio-temporal assessment soil was carried out Swat River Basin, Pakistan by employing Revised Universal Loss Equation (RUSLE). The parameters RUSLE model are rainfall erosivity, erodibility, slope length and steepness, land management support practice. These factors were developed from monthly mean data obtained Regional Metrology Department Peshawar, FAO database, use prepared Landsat-5 8 satellite imageries, topographic ALOS PALSAR Digital Elevation Model (DEM). analysis discovered that 13% study area experienced erosion. Results spatial distribution vulnerability to within Basin have been categorized into different zones such as very low (59.7%), (19.5%), moderate (5.37%), high (6.86%), (5.96%). results accentuate necessity for mitigation measures mitigate loss valuable topsoil. This research possesses potential offer insights decision-making planning reduce risk

Language: Английский

Impact of the Grain-for-Green Programme and climate change on the soil erosion decline in the Yangtze River, China DOI
Boyan Li,

Yunchen Wang

Journal of Geographical Sciences, Journal Year: 2024, Volume and Issue: 34(3), P. 527 - 542

Published: March 1, 2024

Language: Английский

Citations

8
Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia DOI Creative Commons
Alemu Eshetu Fentaw, Assefa Abegaz

Heliyon, Journal Year: 2024, Volume and Issue: 10(12), P. e32880 - e32880

Published: June 1, 2024

Soil erosion is a major environmental problem in Ethiopia, reducing topsoil and agricultural land productivity. loss estimation critical component of sustainable management practices because it provides important information about soil hotspot areas prioritizes that require immediate interventions. This study integrates the Revised Universal Loss Equation (RUSLE) with Google Earth Engine (GEE) to estimate rates map Upper Tekeze Basin, Northern Ethiopia. SoilGrids250 m, CHIRPS-V2, SRTM-V3, MERIT Hydrograph, NDVI from sentinel collections use cover (LULC) data were accessed processed GEE Platform. LULC was classified using Random forest (RF) classification algorithm platform. Landsat surface reflectance images 8 Operational imager (OLI) sensors (2021) used for classification. Besides, different auxiliary utilized improve accuracy. Using RUSLE-GEE framework, we analyzed rate agroecologies types upper basin Waghimra zone. The results showed average 25.5 t ha

Language: Английский

Citations

6
Assessment of soil erosion and prioritization of conservation and restoration measures using RUSLE and Geospatial techniques: the case of upper Bilate watershed DOI Creative Commons

Eliyas Arega,

Kiros Tsegay Deribew, Mitiku Badasa Moisa

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: April 4, 2024

Soil erosion is still a vector of environmental and economic concern affecting most parts the world, especially in Sub-Saharan African countries. Nevertheless, recent human activities hills, coupled with poor conservation measures practices, could have amplified rate at which soil lost southwestern highlands Ethiopia. This study focuses on quantifying prioritizing micro-watersheds that require actions by piloting spatial modeling loss upper Bilate watershed. Sentinel image, soil, DEM, rainfall, support practice data were used. A Revised Universal Loss Equation (RUSLE) using GIS satellite images was applied. The estimated average annual demonstrated to be 24.1 t ha−1yr−1 varied between 0.05 498.24 ha−1yr−1. About 51.2% total revealed has high truncation trait, 40% cropland exceeded tolerances Ethiopia tropical regions. affected are MWS 16, 8, 6, 3, contributed 39.4% rate, indicating hotspots problems region. will far-reaching off-site impacts food security, productivity, lives, infrastructures, ecosystem service provisions.

Language: Английский

Citations

4
Integrated water management and agroforestry planning in the Kulsi river basin: a data-driven decision-making approach DOI
Ananya Kalita, Ankur Pan Saikia, Pranveer Singh

et al.

Agroforestry Systems, Journal Year: 2025, Volume and Issue: 99(5)

Published: April 28, 2025

Language: Английский

Citations

0
Spatial estimation of soil erosion risk using RUSLE model in District Swat, Eastern Hindu Kush, Pakistan DOI Creative Commons

Abdullah Khan,

Atta-ur Rahman, Shakeel Mahmood

et al.

Journal of Water and Climate Change, Journal Year: 2023, Volume and Issue: 14(6), P. 1881 - 1899

Published: June 1, 2023

Abstract Soil erosion is a natural geomorphic process with the potential to damage fertile land. In this study, soil risk spatially estimated in District Swat by applying Revised Universal Loss Equation (RUSLE). The RUSLE parameters that trigger including rainfall erosivity, erodibility, topography, cover management, and support practices were derived from monthly data obtained Pakistan Metrology Department, texture map Survey of Digital Map World database, land use extracted SPOT 5 satellite image, whereas slope digital terrain Shuttle Radar Topographic Mission (SRTM) Elevation Model (DEM). It was found analysis out total reported area, 34.5% falls area affected very high erosion. results spatial pattern proneness study region have been further classified into low (45%), (8.5%), moderate (7%), (5.2%), zones (34.5%). show requires effective mitigation strategies curtail precious soil. This has assist decision makers planners for loss reduction.

Language: Английский

Citations

8
Prioritization of Sub-Watershed Based on Soil Loss Estimation Using RUSLE Model DOI Creative Commons
Dhanjit Deka, Jyoti Prasad Das,

Madine Hazarika

et al.

International Journal of Applied Geospatial Research, Journal Year: 2024, Volume and Issue: 15(1), P. 1 - 25

Published: March 12, 2024

Soil erosion is one of the most crucial land degradation problems and considered critical environmental hazard worldwide. The present study uses remote sensing data integrated with geographical information system (GIS) technique revised universal soil loss equation (RUSLE) model for assessing annual average Digaru watershed India 1999 2020. estimated mean gross yearly from entire was 102716 t yr-1 in 178931.6 overall rate increased significantly between 2020, rising 4.73 t—ha-1yr-1 to 8.43 t—ha-1yr-1. sub-watersheds are prioritized as high (≥ 40 ha−1yr−1), moderate (20–40 low (<20 ha−1yr−1) based on spatial distribution erosion. Seven have been grouped under priority, followed by seven priority priority. This demands instant attention water conservation efforts highly eroded areas.

Language: Английский

Citations

2
Prediction of the topo-hydrologic effects of soil loss using morphometric analysis in the upper Bilate watershed DOI
Kiros Tsegay Deribew,

Eliyas Arega,

Mitiku Badasa Moisa

et al.

Bulletin of Engineering Geology and the Environment, Journal Year: 2024, Volume and Issue: 83(5)

Published: April 8, 2024

Language: Английский

Citations

2
Prioritization of sub-watersheds in Tuirial river basin through geo-environment integration and morphometric parameters DOI
Imanuel Lawmchullova,

Ch. Udaya Bhaskara Rao,

Lal Rinkimi

et al.

Arabian Journal of Geosciences, Journal Year: 2024, Volume and Issue: 17(7)

Published: July 1, 2024

Language: Английский

Citations

2
Spatial analysis and extent of soil erosion risk using the RUSLE approach in the Swat River Basin, Eastern Hindukush DOI

Abdullah Khan,

Atta-ur Rahman

Applied Geomatics, Journal Year: 2024, Volume and Issue: 16(3), P. 545 - 560

Published: July 5, 2024

Language: Английский

Citations

2
Soil Erosion Prediction in Western Kazakhstan Through Deep Learning with a Neural Network Approach to LS-Factor Analysis DOI Creative Commons
Moldir Seitkazy, Nail Beisekenov, Moldir Rakhimova

et al.

Journal of the Indian Society of Remote Sensing, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 19, 2024

Abstract With the rapid shifts in environmental conditions, accurately predicting soil erosion has become crucial for sustainable management of land resources. This study introduces a deep learning-based approach to forecast risks Western Kazakhstan up 2030, focusing on LS factor defined by Universal Soil Loss Equation (USLE). High-resolution digital elevation models (DEMs) from ASTER GDEM and historical data climate use were utilized train convolutional neural network (CNN), enabling projections future LS-factor changes corresponding risks. To further improve accuracy calculations, System Automated Geoscientific Analyses (SAGA) was applied using multiple-flow algorithm. The results significant rise risk with areas having values between 8 24 expected increase 10%, those above 0.05%, potentially affecting an additional 24,000 km 2 . model achieved 92% rate, underscoring effectiveness learning analysis. integration SAGA provides more detailed understanding processes, enhancing precision predictions.

Language: Английский

Citations

1