Assessment of Soil Erosion Rates Using Revised
Universal Soil Loss Equation (RUSLE)
and GIS in Bačka (Serbia)

Milentijević, Nikola; Ostojić, Miloš; Fekete, Renata; Kalkan, Kristina; Ristić, Dušan; Bačević, Nikola; Stevanović, Vladica; Pantelić, Milana

doi:10.15244/pjoes/135617

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…The lowest erosivity values mainly occur in NE Serbia and the far south of Albania. RUSLE model calculations are consistent with the results from local studies, e.g., the territory of the Bačka region [35] in NW Serbia, which ranges from 180 MJ mm ha −1 h −1 yr −1 to 270 MJ mm ha −1 h −1 yr −1 . However, the study of Panagos et al [102] produced a map of long-term erosivity patterns where the R-factor values vary between 400 MJ mm ha −1 h −1 yr −1 and 3100 MJ mm ha −1 h −1 yr −1 .…”

Section: R-factor Variabilitysupporting

confidence: 84%

“…However, data about water erosion rates in the WB countries were not included. Instead, contemporary research regarding the WB region was conducted only in sub-regional studies based on the precipitation data for smaller territories covering [12,[24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] or partial regional studies based on gridded rainfall datasets [42].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Rainfall Erosivity in the Western Balkans by Mapping and Clustering ERA5 Reanalysis Data

et al. 2023

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The Western Balkans (WB) region is highly prone to water erosion processes, and therefore, the estimation of rainfall erosivity (R-factor) is essential for understanding the complex relationships between hydro-meteorological factors and soil erosion processes. The main objectives of this study are to (1) estimate the spatial-temporal distribution R-factor across the WB region by applying the RUSLE and RUSLE2 methodology with data for the period between 1991 and 2020 and (2) apply cluster analysis to identify places of high erosion risk, and thereby offer a means of targeting suitable mitigation measures. To assess R-factor variability, the ERA5 reanalysis hourly data (0.25° × 0.25° spatial resolution) comprised 390 grid points were used. The calculations were made on a decadal resolution (i.e., for the 1990s, the 2000s, and the 2010s), as well as for the whole study period (1991–2020). In order to reveal spatial patterns of rainfall erosivity, a k-means clustering algorithm was applied. Visualization and mapping were performed in python using the Matplotlib, Seaborn, and Cartopy libraries. Hourly precipitation intensity and monthly precipitation totals exhibited pronounced variability over the study area. High precipitation values were observed in the SW with a >0.3 mm h−1 average, while the least precipitation was seen in the Pannonian Basin and far south (Albanian coast), where the mean intensity was less than an average of 0.1 mm h−1. R-factor variability was very high for both the RUSLE and RUSLE2 methods. The mean R-factor calculated by RUSLE2 was 790 MJ mm ha−1·h−1·yr−1, which is 58% higher than the mean R-factor obtained from RUSLE (330 MJ mm ha−1·h−1·yr−1). The analysis of the R-factor at decadal timescales suggested a rise of 14% in the 2010s. The k-means algorithm for both the RUSLE and RUSLE2 methods implies better spatial distribution in the case of five clusters (K = 5) regarding the R-factor values. The rainfall erosivity maps presented in this research can be seen as useful tools for the assessment of soil erosion intensity and erosion control works, especially for agriculture and land use planning. Since the R-factor is an important part of soil erosion models (RUSLE and RUSLE2), the results of this study can be used as a guide for soil control works, landscape modeling, and suitable mitigation measures on a regional scale.

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Section: R-factor Variabilitysupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Evaluation of Rainfall Erosivity in the Western Balkans by Mapping and Clustering ERA5 Reanalysis Data

et al. 2023

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“…Similar studies have also been conducted in surrounding countries (Diodato, 2006;Mikoš et al, 2010;Simic Belnovic et al, 2013;Valjarević et al, 2014;Blinkov, 2015;Zdruli et al, 2016;Traykova-Pavlova et al, 2017;Bon Gregorič et al, 2018;Micić Ponjiger et al, 2021;Milentijević et al, 2021;Pandey et al, 2021). The RUSLE method in combination with GIS has been used in numerous modern works and research studies (Kouli et al, 2008;Prasannakumar et al, 2012;Tošić et al, 2013;Blackey et al, 2015;Efthimiou, 2016;Nasir and Selvakumar, 2018;Lanorte et al, 2019;Nolė et al, 2020;Polykretis et al, 2020;Milentijević et al, 2021;Sánchez Sánchez et al, 2021), which led to very satisfactory results.…”

Section: Introductionmentioning

confidence: 79%

Estimation of potential soil erosion reduction using GIS-based RUSLE under different land cover management models: A case study of Pale Municipality, B&H

Golijanin

Nikolić

Valjarević

et al. 2022

Front. Environ. Sci.

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Spatial assessment of soil erosion is an important indicator of ecological soil change and global environmental changes. This is especially true for countries with rich forest cover such as Bosnia and Herzegovina. In this study, the risk of soil erosion was assessed using the Revised Universal Soil Loss Equation (RUSLE) model and the impact of changes in the forest ecosystem, current conditions were compared with possible future forest management scenarios, and measures and solutions were proposed to reduce soil erodibility in vulnerable areas of the Pale Municipality in Bosnia and Herzegovina. The studied area is at increased risk of soil erosion due to natural conditions (mountain relief, climate change, and the frequency of extreme climatic events—drought and heavy rains, which occur more and more frequently in a short period of time) and due to anthropogenic factors, such as large-scale deforestation and conversion of mountain areas for tourism purposes, tracing and construction of ski slopes and ski resorts in general, and expansion of settlements. All this leads to threats to water conservation areas, landslides, floods, forest fires, and additional reduction of forest areas due to drying of forests and expansion of settlements. GIS as a tool provides us with a quick and accurate way to find possible solutions to problems resulting from the intensive use and inadequate monitoring. In this study, we have tried to offer possible solutions and show the benefits that can be obtained by varying the factors that affect soil erodibility and depend on vegetation cover, that is, land use (C-factor). This study presents the application of RUSLE methods in combination with GIS for the purpose of planning economic activities, such as winter tourism development in the community of Pale. An increase in soil loss due to inappropriate land use was found, with the average annual soil loss due to deforestation in the ski area increasing to 909.43 t ha−1 year−1.

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“…distinct streams [29][30][31][32][33]. The concentration of water is determined by the length of the slope.…”

Section: Author Copy • Author Copy • Author Copy • Author Copy • Auth...mentioning

confidence: 99%

Forecasting Soil Erosion Risk Using GIS and Remote Sensing for the Nam Un Basin, Sakon Nakhon Province, Thailand

Ruksajai¹,

Konyai²,

Sriboonlue³

2023

Pol. J. Environ. Stud.

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Geohazard mapping using remote sensing and GIS is effective. Nam UN classic terrain with soil erosion and other geohazards. The Nam UN Basin's yearly soil loss and high erosion potential are estimated using RUSLE, remote sensing, and GIS. 14.26 t/ha/year of soil erosion is seen on the map. Soil erosion zones are also shown on the map. According to the study, 33.40 percent of the whole area (457.07 kilometers) is prone to severe soil erosion, while 7.72 percent (105.76 kilometers) is prone to high erosion. To decrease soil erosion, decision-makers use soil erosion prognosis analysis. The Analytical Hierarchy Process (AHP) was utilized to identify key soil erosion prone locations by incorporating geo-environmental variables such land use/land cover, geomorphology, Dem, drainage density, slope, elevation, LS factor, rainfall, soil texture, and soil depth. 33.40% of the region is highly prone to soil erosion.

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Assessment of Soil Erosion Rates Using Revised Universal Soil Loss Equation (RUSLE) and GIS in Bačka (Serbia)

Cited by 9 publications

References 26 publications

Evaluation of Rainfall Erosivity in the Western Balkans by Mapping and Clustering ERA5 Reanalysis Data

Evaluation of Rainfall Erosivity in the Western Balkans by Mapping and Clustering ERA5 Reanalysis Data

Estimation of potential soil erosion reduction using GIS-based RUSLE under different land cover management models: A case study of Pale Municipality, B&H

Forecasting Soil Erosion Risk Using GIS and Remote Sensing for the Nam Un Basin, Sakon Nakhon Province, Thailand

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