Soil Erosion Risk Assessment in the Sincanlı Sub-Watershed of the Akarçay Basin (Afyonkarahisar, Turkey) Using the Universal Soil Loss Equation (USLE)

Soil erosion is crucial problem in India where more than 70% of land in degraded. This study is to establish conservation priorities of the sub watersheds across the entire terrain, and suggest suitable conservation measures. Soil conservation practices are not only from erosion data both qualitative SES (Soil Erosion Status) model and quantitative MMF (Morgan, Morgan and Finney) model erosion, but we have to consider LCC (Land Capability Classification) and LULC (Land Use Land Cover). Study demonstrated the use of RS (Remote Sensing) and GIS (Geographic Information System) in soil erosion risk assessment by deriving soil and vegetation parameters in the erosion models. Sub-watersheds were prioritized based on average soil loss and the area falls under various erosion risk classes for conservation planning. The annual rate of soil loss based on MMF model was classified into five soil erosion risk classes for soil conservation measures. From 11 sub watersheds, for the first priority of the watershed is catchment with the small area and the steep slope. Recommendation for steep areas (classes VI, VII, and VIII) land use allocation should be made to maintain forest functions.

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Estimation of Soil Loss Using Remote Sensing And GIS-Based Universal Soil Loss Equation In Northern Catchment of Lake Tana Sub-Basin, Upper Blue Nile Basin, Northwest Ethiopia

10.21203/rs.3.rs-78824/v1 ◽

2020 ◽

Author(s):

Veera Narayana Balabathina ◽

Raju RP ◽

Wuletaw Mulualem ◽

Gedefaw Tadele

Keyword(s):

Remote Sensing ◽

Soil Erosion ◽

Soil Loss ◽

Universal Soil Loss Equation ◽

Lake Tana ◽

Erosion Risk ◽

Priority Areas ◽

Erosion Rates ◽

The Mean ◽

Soil Loss Equation

Abstract Background: Soil erosion, one of the major environmental challenges, is influenced by topography, climate, soil characteristics, and human activities and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. The present study attempts to estimate soil erosion risk in the Northern catchment of Lake Tana basin, situated in northwest part of Ethiopia, with available data through the application of the Universal Soil Loss Equation model integrated with Geographic Information System and remote sensing technologies to identify priority areas for controlling soil erosion. In addition, it analyzes the effect of land use and land cover, topography, erodibility, and drainage density on soil erosion potential of the catchment, and the possible relationships among them. Results: The results show that the mean annual soil loss of catchment is estimated at 37.89 ± 59.2 t ha−1yr−1 with a total annual soil loss of 1,705,370 tons. The topography (LS-factor), followed by the support practice (P-factor) and the soil erodibility (K-factor) were the most sensitive factors affecting soil erosion in the catchment. To identify high priority areas for management, the study area was subdivided into five major sub-basins and further categorized into five erosion classes based on erosion severity. The mean soil erosion rates of the Derma, Megech, Gumara, Garno, and Gabi Kura River sub-basins are 46.8, 40.98, 30.95, 30.04, and 29.66 t ha−1yr−1, respectively. About 58.9% of the area was found in very low erosion risk which extends from 0-1 t ha−1yr−1 and accounted only 1.1% of total soil loss, while 12.4% of the area was found to be under high and extreme erosion risk with erosion rates of 10 t ha−1yr−1 or more that contributes about 82.1% of total soil loss warrant high priority for reducing the risk of soil erosion. Conclusions: This study permits the understanding of the soil erosion process and the various factors that lead to the spatial variability of the risk in the catchment, and thus enhances the effectiveness of proposed conservation strategies for sustainable land management.

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Soil erosion risk assessment at small catchments scale: comparison of GIS-based modelling and field survey data and its implication for environmental maintenance of rivers

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2015vol2.233 ◽

2015 ◽

Vol 2 ◽

pp. 289

Author(s):

Juris Soms

Keyword(s):

Risk Assessment ◽

Soil Erosion ◽

Soil Loss ◽

Field Survey ◽

Spatial Scales ◽

Theoretical Data ◽

Economic Effects ◽

Erosion Risk ◽

Soil Erosion Risk ◽

Small Catchments

One of the limitations to implementation of effective measures to mitigate negative environmental and economic effects associated with soil erosion is the lack of data on the geographic distribution of erosion risk and potential erosion hotspots. Hence, experts and policy makers in many cases have no spatially referenced information on which to base their decisions. There is a trend approved by EU institutions and agencies to use soil erosion models which can be integrated into geographic information systems (GIS) environment in order to obtain data at different spatial scales and to assist such decision-making. Despite that, until now in Latvia only some studies on the GIS-based modelling of potential soil losses have been conducted. Considering that, in the study presented in this paper soil erosion risk assessment has been performed by the widely used Revised Universal Soil Loss Equation (RUSLE) model over five selected small catchments of the river Daugava valley. In order to validate the results of modelling and to assess if theory accords with a real situation, the theoretical data were compared with information gained from the field survey of the same catchments. Modelled potential soil loss from each of five catchments under study totals 0.25; 0.26; 0.42; 0.51 and 0.58 t ha<sup>-1</sup> y<sup>-1</sup> in average. However, results of the comparison indicate the discrepancies between modelled and measured values, i.e. the used empirical model underestimates the soil erosion risk. The recognition of this fact raises implication for appropriate environmental maintenance of rivers, due to possible underestimation of eroded material delivery to receiving streams and, subsequently, under-prediction of water pollution.

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