عنوان مقاله [English]
In recent decades, the potential of soil erosion as serious threat against sustainable land management (SLM) and soil sustainability has been recognized. Furthermore, human-induced soil erosion lead to harmful environmental effects and transportation of sediment to water bodies is accompanied by loss of nutrients and eutrophication. Therefore, there is a need to focus on soil erosion outcomes to prevent its environmental impacts and mitigate the negative feedbacks of soil erosion. One of the most important factors that affecting the amount of erosion and sediment yield is soil erodibility (K-factor in USLE), which is an index of soil susceptibility. Moreover, K-factor is one of the 6 effective factors on soil erosion in universal soil loss equation and mainly is representative of soil properties. Regarding the difficulty of soil erodibility ´s measurement, therefore we need to an easily measurable parameter. In this study soil aggregate is used as an index of soil erodibility factor. Generally, topography alters from one slope to another one, which conduces to dramatically changes of soil erosion intensity, therefore topography in terms of geomorphometric parameters is effective on soil erosion processes; morphometric analysis and soil erosion modeling are robustly interconnected. Also, digital elevation model (DEM) is a base to extract geomorphometric parameters and the quality of DEM is effective on geomorphometric characteristics.
Methods and material
This study conducted with the aim of modelling the aggregate stability using multivariate linear regression and K-means clustering algorithm as an index of soil erodibility factor. For this purpose a study area with an approximate area of 20000 hectares selected. This area is located in Dehdez which is in the north east of Khuzestan province. Based on the climatologic data this area has xeric and thermic regimes. The main land uses in this area including rangeland and forest and in the rangeland the crop cover is mostly grasses. According to the purpose of this study and degree of homogeneity the soil sampling accomplished using completely randomized method. From each land use 25 soil samples collected (the depth of soil sampling was 0-30 cm). Then the soil samples prepared and the mean weight diameter (MWD) as an index of soil aggregate stability using wet sieving, soil texture with hydrometer method and organic carbon content measured in at least 3 replications. In another part of this project the digital elevation model (DEM) of study area extracted from SRTM (Shuttle radar topographic mission) with the spatial resolution of
30 m. After DEM preparation all preprocessing operations performed on DEM using Arc Hydro extension in ArcGIS 10.3 software in order to remove sinks and enhance the quality of DEM. In the next step the primary and secondary geomorphometric parameters extracted from this DEM using ArcGIS 10.3 and SAGA 6.3. software. Finally, multivariate linear regression and K-means clustering models developed between geomorphometric parameters and soil properties as dependent variables and aggregate stability as independent variable to achieve statistical indices in order to evaluate developed models.
Results and discussion
Our results showed that there was a significant correlation between some geomorphometric parameters and soil aggregate stability; therefore there is a possibility to apply these parameters in order to parameterize aggregate stability. Moreover, the results of modelling indicate that regression models using geomorphometric parameters and soil properties was able to cover approximately 75 and 70 percentage of spatial variation of aggregate stability in the rangeland and forest land use of the study area, respectively. While clustering-regression models were able to explain 77 and 82 percentage of the spatial variation of the aggregates stability in the first and second cluster respectively. Also, the results of validation of developed models in this study showed that the root mean square error (RMSE) of regression models for rangeland and forest land use was 0.33 and 0.26 respectively and RMSE of clustering-regression models for first and second clusters was 0.93 and 0.62 respectively. According to importance of soil aggregate stability on soil erodibility factor (K-factor) and the difficulty of soil erodibility ´s measurement therefore these developed models are useful tools in order to predict soil erosion and based on the distribution of soil erosion and deposition using STI (Sediment transport index) and TWI (Topographical wetness index) could be able to select and apply the best management practices in the critical areas. Indeed STI map and TWI map are indices of spatial distribution of soil erosion and deposition in the studied area therefore using these geomorphometric indices we able to control soil erosion and its negative feedbacks.
Regarding the difficulty of soil erodibility ´s measurement therefore we used some indices to simplify this process and our results illustrated that it is possible to develop some regression models in order to estimate aggregate stability as an index of soil erodibility. In summary our results confirm that geomorphometric parameters are easily available parameters based on the DEM to predict soil erodibility.
Keywords: Digital elevation model (DEM), geomorphometry, modelling, soil erosion, topographical indices