M. A. Mahmoodi; S. P. Naghshbandi
Abstract
Introduction: Soil erosion is a serious environmental threat leading to loss of nutrient from surface soil, increased runoff, lake and reservoir sedimentation, and water pollution. Thus, estimation of soil loss and identification of critical area for implementation of best management practice is central ...
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Introduction: Soil erosion is a serious environmental threat leading to loss of nutrient from surface soil, increased runoff, lake and reservoir sedimentation, and water pollution. Thus, estimation of soil loss and identification of critical area for implementation of best management practice is central to success of soil conservation programs. Soil erosion modeling is an efficient method to simulate soil erosion, to identify sediment source areas, and to evaluate soil conservation measures. One of the most widely applied empirical models for assessing the sheet and rill erosion is the Universal Soil Loss Equation (USLE). Originally, USLE was developed mainly for soil erosion estimation in croplands or gently sloping topography. The RUSLE is an extension of the original USLE with improvements in determining the factors controlling erosion. It is an empirical model commonly used to estimate soil loss potential by water from hillslopes across large areas of land. RUSLE is a linear equation that estimates the annual soil loss as the product of environmental factors include rainfall, soil erodibility, slope length, slope steepness, cover management and conservation practices as inputs. To implement RUSLE over large areas, detailed sets of spatially explicit data are needed for precipitation, soil type, topographic slope, land cover and land use type. Conventionally, the collection of all these data from field studies is time-consuming and expensive. The integration of field data and data provided by remote sensing technologies through the use of geographic information systems (GIS) offers potential to estimate spatially input data for RUSLE over large and relatively sparsely sampled areas. Keeping in view of the above aspects, the objectives of the present study were 1) to integrate the field data and data provided by Landsat Enhanced Thematic Mapper (ETM) imagery with RUSLE through the use of GIS to estimate spatial distribution of soil erosion at Gawshan dam basin in west of Iran and 2) to delineate soil erosion probability zones by reclassifying of the prepared soil erosion map.
Materials and Methods: The annual rainfall erosivity factor (R) was determined from monthly rainfall data of 11 years (2005-2015) for 7 rain gauge stations in the the study area. Spatial distribution of R was estimated using ordinary kriging method of interpolation. The soil erodibility factor (K) was estimated on the basis of soil map prepared from land survey and Landsat ETM remote sensing data. The physical and chemical parameters required to calculate K were measured in the different soil units, and its spatial distribution was coincident with the soil unit boundaries. The topographic factor (LS) was derived from digital elevation model (DEM) of 30 m resolution. The annual crop management factor (C) was calculated from normalized difference vegetation index (NDVI) derived from Landsat ETM imagery for different seasons. Since there is a lack of field data regarding the conservation practices that have been taken place in the study area, the conservation support practice factor (P) value was taken as 1. Finally, average annual soil loss was estimated as the product of the mentioned factors, and categorized into four classes viz., low, moderate, high and very high erosion.
Results and Discussion: The estimated R, K, LS and C range from 564 to 1311 MJ mm ha-1 h-1 y-1, 0.02 to 0.04 t h MJ-1 mm-1, 0 to 2436 and 0 to 1, respectively. The results indicate the estimated mean annual potential soil loss of about 2.35 t ha-1, however in the 50% of the basin area annual soil loss is lower than 0.92 t ha-1. Based on categorized soil erosion map about nearly 52.5% of the basin area produces low erosion of 0.43 t ha-1 annually, whereas very high probability zone covers about 4% of the basin area, located dominantly in the southwestern part of the basin. Our results showed that slope steepness factor is the most important factor that controls soil erosion rate in the basin.
Conclusion: This study demonstrates the integration of field data and Landsat ETM imagery data with RUSLE through the use of GIS to estimate spatial distribution of soil erosion in Gawshan dam basin. The results of this study can be helpful for identifying critical areas for implementation of conservation practice and provide options to policy makers for prioritization of different regions of the basin for treatment.
