Estimation of soil erosion rate in the Democratic People's Republic of Korea using the RUSLE model

Lee, Eun Jai; Ahn, Sujung; Im, Sangjun

doi:10.1080/21580103.2017.1341435

Cited by 16 publications

(14 citation statements)

References 20 publications

(23 reference statements)

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“…Larger differences (decrease) in the forest area result from to the LS of the steep slopes as well as the presence of more landslides and bank undercuts. Lee, Ahn, and Im () noted the overestimated values of soil erosion in the RUSLE model, especially in the forest areas, but also noted that forests on steep hills generally have relatively lower soil erosion than agricultural lands because of surface protection provided by litter cover and natural barriers. Hammad, Lundekvam, and Børresen () indicated an overestimation of soil erosion by an average of 66%.…”

Section: Discussionmentioning

confidence: 99%

Long‐term impact of land use changes on soil erosion in an agricultural catchment (in the Western Polish Carpathians)

2018

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The change in land use and land cover (LULC) from natural vegetation to agricultural in mountain areas usually dramatically accelerates soil erosion rates if the land is used for crop production. The aim of research was to calculate soil erosion magnitude basing on the Revised Universal Soil Loss Equation (RUSLE) in response to long‐term changes in LULC in an agricultural Homerka catchment (19.3 km2) of the Polish Carpathians. The changes in LULC were derived from cadastral maps for 1846 and orthophotomaps for 2009. Three variants with different factors, rainfall–run‐off erosivity (R), practice support (P), and LULC, were analysed to investigate which has had the greatest impact on soil erosion changes over the last 160 years. An increase in forest area (by 67.81%) and decrease in cultivated land (by 91.94%) were observed, primarily due to the collapse of the communist system. The estimated soil erosion using the RUSLE model in the analysed catchment decreased by 77% (from 18.13 t ha−1 yr−1 in 1846 to 4.11 t ha−1 yr−1 in 2009). The long‐term changes in soil erosion rates and their spatial distribution are mainly associated with transformation from cultivated land to forest or grassland. The P factor was responsible for reduction of soil erosion rates by 8%, a minor impact. Over the last 160 years, the average and maximum annual precipitation changes were also statistically insignificant. Results reveal a dominant role of human impact, particularly with respect to LULC on soil erosion changes in mid‐mountain areas.

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Section: Discussionmentioning

confidence: 99%

Long‐term impact of land use changes on soil erosion in an agricultural catchment (in the Western Polish Carpathians)

2018

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“…Soil (K) factor indicates how susceptible the soil is to erosion by rain (Lee et al, 2017;Pradhan et al, 2018). When it comes to erosion resistance, different soil types have varying degrees of resistance.…”

Section: Soil (K) Factormentioning

confidence: 99%

Remote sensing, GIS, and RUSLE in soil loss estimation in the Kulfo river catchment, Rift valley, Southern Ethiopia

Jothimani

Getahun

Abebe

2022

J.Degrade.Min.Land Manage.

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Quantification of soil is crucial for maximizing the advantages of land resources while minimizing the negative consequences of land degradation in the long term. It will also make it possible to identify locations that need immediate soil erosion management. The present study was carried out in the Kulfo river catchment, Rift valley, Southern Ethiopia. The Revised Universal Soil Loss Equation (RUSLE) method was utilized to estimate the mean yearly soil loss in the research region using remote sensing, other collateral data. The RUSLE model inputs were mapped and integrated into the ArcGIS software, and the results show that 0 and 1211 t ha−1year−1 are the minima and maximum soil loss in the present study area. Soil erosion-prone regions were divided into three categories: 0-42 t ha−1year−1 (low), 43-128 t ha−1 year−1 (medium), and > 128 t ha−1 year−1 (high). And the average rate of soil erosion is 68.47 t ha−1year−1. Low, medium, and high soil erosion areal extent and area percentages in the current research area is 270 km2 (77 %), 61 km2 (17 %), and 19 km2 (6%), respectively. A high rate of soil erosion was found where high steep slope, barren land, and high precipitation occurred in the present study area. The current study's outcomes were confirmed by comparing soil loss estimates in the same geo-environmental conditions found in Ethiopia's highlands. The outcome of this study is important for decision-makers and policymakers.

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“…A major and long-standing area of concern for soil scientists, geomorphologists, and others is the estimation of soil loss rates from agricultural land over the longer term (periods of years). Predictive models are routinely used for this, based in part on empirical data from experimental plots, including the universal soil loss equation (USLE), revised universal soil loss equation (RUSLE), water erosion prediction project (WEPP), and many other models and classes of models (Lee et al, 2017; Zerihun et al, 2018). One important influencing factor in RUSLE-type models is the rainfall factor ( R ), which reflects the mean rainfall intensity and kinetic energy delivered to the soil surface.…”

Section: The Significance Of Rainfall Intensity In Geomorphologymentioning

confidence: 99%

Rainfall intensity in geomorphology: Challenges and opportunities

Dunkerley

2020

Progress in Physical Geography: Earth and Environment

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Rainfall arrival at the land surface drives or influences many geomorphic processes. These range from the mechanisms through which vegetation transforms rain into erosive gravity drops or stemflow, infiltration and water partitioning at the soil surface, the drop-impact sealing of soil surfaces, splash, sheet, and gully erosion, and triggering of the various forms of mass movement including landslides and debris flows. Rainfall intensity is a key influence on many of these mechanisms but is not a straightforward parameter to quantify, partly owing to the customary aggregation of rainfall data to hourly or other clock-time totals. This aggregation conceals intensity fluctuations including erosive ‘intensity bursts’ as well as the intermittency of rainfall. Nevertheless, much research shows that rainfall intensity over short time periods – 10–30 minutes – has great explanatory power. Much of our understanding of the influence of rainfall intensity is based on rainfall simulation experiments, but the value of the findings is limited because simulation is normally carried out using a high and constant rainfall intensity, quite unlike natural rainfall. This has limited our ability to build an understanding of the other important aspects of rainfall intensity, including, critically, its time variation and changed character among different environments – arid, temperate, or tropical. Thus, significant challenges and opportunities remain in the exploration of rainfall intensity in relation to geomorphology and geomorphic processes.

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Estimation of soil erosion rate in the Democratic People's Republic of Korea using the RUSLE model

Cited by 16 publications

References 20 publications

Long‐term impact of land use changes on soil erosion in an agricultural catchment (in the Western Polish Carpathians)

Long‐term impact of land use changes on soil erosion in an agricultural catchment (in the Western Polish Carpathians)

Remote sensing, GIS, and RUSLE in soil loss estimation in the Kulfo river catchment, Rift valley, Southern Ethiopia

Rainfall intensity in geomorphology: Challenges and opportunities

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