Geomorphology
Mousa Abedini; AmirHesam Pasban
Abstract
Soil erosion is one of the serious environmental threats that can affect the political, social and economic aspects of countries. One of the widely used experimental models for estimating the amount of soil erosion is the modified global soil erosion equation known as the RUSLE model. The purpose of ...
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Soil erosion is one of the serious environmental threats that can affect the political, social and economic aspects of countries. One of the widely used experimental models for estimating the amount of soil erosion is the modified global soil erosion equation known as the RUSLE model. The purpose of this research is to analyze and zonate the amount of soil erosion and its relationship with hydrogeomorphic indicators and vegetation cover of Khiavchai Meshkinshahr watershed in Ardabil province. RUSLE model factors include rain erosion (R), soil erodibility (K), topography (LS), vegetation (C) and protection operations (P). respectively, by using rainfall data, soil texture layer, digital model of height and land use were prepared in the environment of geographic information system (GIS) and after overlapping the layers, the amount of annual soil erosion between 0 and 150.54 tons per hectare per year in The area level was estimated. In the next step, the hydrogeomorphic and vegetation indices that are effective in soil erosion include topographic moisture index (TWI), waterway capacity index (SPI), domain curvature index (Curvatore), section curvature index (Profil Curvatore), surface curvature index (Plan) Curvatore) and Normalized Vegetation Index (NDVI) were created in ArcMap environment and zoning maps were prepared. The results of this research also showed that the topography factor with a correlation coefficient of 0.92% had the greatest impact on the estimation of annual soil erosion by the RUSLE model. In another study, the relationship between hydrogeomorphic indices and vegetation cover with annual soil erosion rate was conducted, and the results showed that normal vegetation cover indices and cross-sectional curvature were the most and least effective with correlation coefficients of 0.57 and 0.05, respectively, compared to other indices.The results of this research confirm the possibility of combining the effective indicators of hydrogeomorphic and vegetation on erosion, as well as the possibility of using other effective indicators and the capabilities of RS and GIS to quantitatively estimate the amounts of soil erosion.
yaser hoseini
Abstract
1-Introduction Flood is a natural phenomenon, which threatens the life and properties of a large number of people all over the world, yearly. Flood discharge, regarding water resource exploitation, flood control, construction of dams, basin management, and hydrologic studies, is of high importance in ...
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1-Introduction Flood is a natural phenomenon, which threatens the life and properties of a large number of people all over the world, yearly. Flood discharge, regarding water resource exploitation, flood control, construction of dams, basin management, and hydrologic studies, is of high importance in studies. Therefore, the accuracy of these studies and the safety of waterworks and water structures depend on the methods of studies to a large extent. It is impossible to manage water resources in basins without the accurate determination of the peak flood discharge. The advances in flood estimation techniques have made it possible to use rainfall-runoff models to assess the hydrographic properties of the flood in watersheds and decrease the risks of the flood. In studies on water resources, it is of high importance to determine the flood discharge of different basins. Studies of Dile and Srinivasan (2014) and Hoseini et al. (2017) showed that basin level and rainfall can be the most important factor in runoff flow. Consequently, proper simulation and modeling of flood runoff are the important parameters in flood management in the region. However, it is necessary to use new models to determine flood hydrograph parameters. So, this study aimed to determine the peak flood discharge of the Darrehrood basin using regression mode for return periods of 10, 25, 50, and 100 years. 2-Methodology Darrehrood basin is located in Northwest Iran. The basin is surrounded by mountains and is considered the main basin of Ardabil Province. It lies within 47°30' to 48°55' longitude and 37°45' to 39°42' latitude. Its area is approximately 12900 km2. Discharge data were collected from 16 hydrometric stations with a statistical period of 15 years during 2001-2015. Incomplete data related to stations were completed using statistical methods and considering the best statistical distribution of floods in the studied sub-basins, floods with different return periods were calculated then the physiographic characteristics of sub-basins that affect flood rate include: area, slope, shape factor, height average, concentration-time, and curve number achieved using ArcGIS and WMS (watershed modeling system). To evaluate the model, maximum error (ME), root mean square error (RMSE), relative percentage error (ε), mean absolute error (MAE), coefficient of determination (R2), Coefficient of residual mass (CRM), and model efficiency (EF) were used. 3-Results and Discussion The model calibration results showed that the simulated peak discharge and flow volume are in good correspondence with the observed values, so that, the lowest goodness of fit (R2) value in the return periods of 10, 25, 50, and 100 years were estimated to be 97, 96.6, 95.8 and 94.7 %, respectively. The results showed that the linear regression model with very good accuracy can predict the peak discharge in the sub-basins in Darrehrood using the physiographic parameters of the basin and with increasing the return period, the accuracy of the model is slightly reduced. Model evaluation indicators for the return period of 100 years include root mean square error (RMSE), relative percentage error (ε), mean absolute error (MAE), Coefficient of residual mass (CRM), and model efficiency (EF) were calculated 40.75, 52.12, 0.52, 0.92 and 0.62 respectively. Cross-validation diagrams showed that all models were partially underestimated and the scatter of points around the one by one axis was very suitable for the whole return periods. According to the paired t-test of the difference between predicted and actual values in different return periods in the level confidence of 1% are not significant. 4-Conclusions The results of this study show that the model has good accuracy for estimating floods in sub-basins of Ardabil province.
