Efficiency Evaluation of RUSLE and ‌ICONA models in erosion zoning of Baladeh watershed, Mazandaran province

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:

Evaluation and zoning of soil erosion using models that are more accurate, helps to implement conservation activities and control soil erosion within the watershed and reduce the amount of sediment outside it. The aim of this study was to zoning the soil erosion intensity of Baladeh watershed using RUSLE and ICONA models and toa investigate their accuracy with observations and terrestrial reality values. RUSLE model factors including rainfall erosion (R), soil erodibility (K), topography (LS), vegetation (C) and conservation operations (P) were calculated from rainfall data, soil map, digital elevation model and remote sensing techniques, respectively. In ICONA model, soil erodibility map was obtained by combining two layers of slope and rock surfaces. Then, in order to prepare soil conservation layer, vegetation index and land use layers of the area were overlapped and by combining erosion and soil conservation layers, erosion zoning map was prepared with this model. The accuracy of these models was evaluated using statistical indices and BLM method, all of which were obtained during field observations. For this purpose, a map with 2500 points was prepared as regular networking for sampling the maps obtained from the models and accordingly, RMSE statistics (root mean squares error), MAE (mean absolute error), MSE (mean squares error) and NSEC (Nash-Sutcliffe Model Efficiency Coefficient) were calculated. The results of the mentioned statistical indices and the errors of the models shows that the mentioned models are not sufficient in baladeh watershed, but the efficiency and degree of adaptation of the erosion classes of ICONA model with BLM output as reference map is higher, because the amount of RMSE, MAE and MSE is lower and NSEC is closer to number one.

Language:
Persian
Published:
Journal of Natural environment hazards, Volume:11 Issue: 34, 2023
Pages:
159 to 178
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