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

Juris Soms

Abstract


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-1 y-1 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.

Keywords


GIS; headwater gully catchments; RUSLE model; soil erosion

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References


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DOI: https://doi.org/10.17770/etr2015vol2.233

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