Adaptation of the RUSLE2 Model to Assess Soil Losses Due To Water Erosion in the Northern Part of the Great Chinese Plain (Using the Example of Beijing)

Water erosion is one of the most extensive processes of soil and land degradation. This process significantly deteriorates the quality of ecosystem services provided. Studying the mechanism and consequences of this phenomenon will help develop strategies to mitigate its impact on the environment. One of the challenges in studying water erosion is the high cost of conducting field research over large areas. Another challenge is the limitation of existing models, which are often developed for specific regions. Our research aims to address these problems by adapting the widely used RUSLE2 model to local conditions. Beijing was chosen as the study area due to its rich information resources and extensive results from field measurements of soil water erosion. Calculations were performed using a raster data model, which included a slope angle model, slope length, soil erodibility, rainfall erosion potential factors from precipitation and snowmelt, land use types, and vegetation cover management factors. All data were taken from open sources. The average soil erosion in the studied area was 25 t · ha–1 per year. The discrepancy with other studies was less than 1%. The modified RUSLE2 model showed good results, correlating with other studies in this area.

Authors

Institutions

Publication Details

Journal
Moscow University Soil Science Bulletin
Published
2024-09-01
DOI
https://doi.org/10.3103/s0147687424700261
Primary Topic
Soil erosion and sediment transport
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Adaptation of the RUSLE2 Model to Assess Soil Losses Due To Water Erosion in the Northern Part of the Great Chinese Plain (Using the Example of Beijing)

N. R. Kriuchkov, Xin Li, Jianen Gao
Moscow University Soil Science Bulletin
Soil erosion and sediment transport
article

Adaptation of the RUSLE2 Model to Assess Soil Losses Due To Water Erosion in the Northern Part of the Great Chinese Plain (Using the Example of Beijing)

N. R. Kriuchkov, Xin Li, Jianen Gao
article en

Abstract

Water erosion is one of the most extensive processes of soil and land degradation. This process significantly deteriorates the quality of ecosystem services provided. Studying the mechanism and consequences of this phenomenon will help develop strategies to mitigate its impact on the environment. One of the challenges in studying water erosion is the high cost of conducting field research over large areas. Another challenge is the limitation of existing models, which are often developed for specific regions. Our research aims to address these problems by adapting the widely used RUSLE2 model to local conditions. Beijing was chosen as the study area due to its rich information resources and extensive results from field measurements of soil water erosion. Calculations were performed using a raster data model, which included a slope angle model, slope length, soil erodibility, rainfall erosion potential factors from precipitation and snowmelt, land use types, and vegetation cover management factors. All data were taken from open sources. The average soil erosion in the studied area was 25 t · ha–1 per year. The discrepancy with other studies was less than 1%. The modified RUSLE2 model showed good results, correlating with other studies in this area.

Moscow University Soil Science BulletinVol. 79(3)
Lomonosov Moscow State University (RU), Shenzhen Technology University (CN)
Clean water and sanitation
Openalex Percentile: Top 80%
Soil erosion and sediment transport
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.