year 13, Issue 1 (Spring 2023 2023)
E.E.R. 2023, 13(1): 70-95 |
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Noraei Sefat E, Bakhtyari Kia M, Akbarian M. Investigation of Soil Loss Changes with an Emphasis on Runoff Erosion in the Kol River Catchment. E.E.R. 2023; 13 (1) :70-95
URL: http://magazine.hormozgan.ac.ir/article-1-720-en.html
URL: http://magazine.hormozgan.ac.ir/article-1-720-en.html
Geography Department, Faculty of Humanities, University of Hormozgan, BandarAbbas , bakhtyari@hormozgan.ac.ir
Abstract: (2007 Views)
1- Introduction
Soil erosion has been considered as the primary cause of soil degradation because soil erosion leads to the loss of topsoil and soil organic matter, which are essential for the growing of plants. Quantification of soil loss is a significant issue for soil and water conservation practitioners and policy makers. At the watershed level, the two most important hydrological phenomena that can occur from rainfall procedures are surface runoff and soil erosion.
There are many models to study the soil erosion. One of the most widely used models for estimating this phenomenon is the Revised Universal Soil Loss Equation (RUSLE). Six factors are included in the RUSLE model: rainfall erosivity, soil erodibility, slope length and steepness, vegetation cover and management, and supporting practices. In this study, multi-source data are used to generate the necessary parameters of the RUSLE model. Since surface runoff and soil erosion are the two significant hydrologic reactions, the Soil Conservation Service Curve Number (SCS-CN) model is used to estimate runoff. This study has shown a strong relationship between the surface runoff estimation and the maximum erosion potential in the study area. The advantage offered by this model is its simplicity and the diversity of the parameters used reflecting the functions of runoff under the hydrological system.
2- Methodology
To generate soil erosion significant factors, annual average precipitation, land use land cover (LULC) derived from LANDSAT 8 OLI/TIRS, soil hydrological group, and Digital Elevation Model (DEM) were used as input. In this study, RUSLE model was used to generate the spatially varied soil erosion severity map. The model is representing soil erosion risk by considering six factors, where each of the functions is expressed numerically, forming an equation to predict soil loss. All these parameters were mapped in GIS raster format. It is represented by the following equation:
𝐴 = 𝑅 ∗ 𝐾 ∗ 𝐿𝑆 ∗ 𝐶 ∗ 𝑃
Where:
A = estimated average annual soil loss (ton/ha-1/yr-1); R = rainfall erosivity factor (MJ mm ha-1 yr-1); K = soil erodibility factor (tons/ha-1/mm-1); L = slope length factor; S = slope gradient factor; C = vegetation cover-management factor; P = support practices.
In this study, the aim was to use the amount of runoff in the RUSLE model. To achieve this goal, the SCS-CN method was used. The preparation of a curved number is one of the prerequisites of this method, which is prepared by using the land use map and the map of the hydrological group of the soil of the region. In order to calculate the amount of precipitation erosion factor, the amount of runoff height is considered. After calculating the precipitation values, a precipitation erosion factor map was estimated and by placing the precipitation erosion zoning map in Equation 6, the potential values of runoff production were prepared and by multiplying the six factors, the erosion maps of the study area were prepared.
3- Results
The annual soil loss ranges from 5.5 to 29.04 t ha−1 yr−1 for study duration and the average annual soil loss was estimated at 17.3 t ha−1 yr−1. Based on the results, the watershed erosion has been categorized into four classes. About 61.7 % of the watershed area was characterized by slight erosion rate (<10 t ha−1 yr−1), 28.5 % of the area was found to be moderate erosion rate (10-40 t ha−1 yr−1), 5.3 % of the area is under high erosion rate (40–70 t ha−1 yr−1), while around 4.5 % of the area was under very high erosion rate (>70 t ha−1 yr−1). In order to evaluate the model and also to calculate the sediment output of the basin, the sediment rating curve obtained from the observation data of the Qalat Paein station was used. The calculated coefficient of determination (R2) equal to 0.73 indicates a suitable accuracy. After using the results of the model and by comparing the five sediment delivery ratio methods and then choosing the optimal method, a sediment zoning map of different years was produced for the study basin.
4- Discussion & Conclusions
The results have approved the link between rainfall and erosion rates. This study has confirmed the results of the previous investigations (Vaezi & Haqqani, 2020; Telak & Bogunovic, 2021; Assouline & Ben-Hur, 2006), and pointed out the feasibility to jointly apply GIS technology, SCS-CN and RUSLE models for the estimation of spatially distributed soil erosion potential at large scales in Kol watershed and especially in the condition of limited data availability. In general, the presented methodology can be a viable solution to previous conventional methodology, especially when lack of time, data or funds make the detailed on-ground measurements and the application and development of more accurate erosion models difficult.
Kol River Basin is quite large and is characterized by high spatial heterogeneity of erosion factors. In these situations, the application of SCS-CN and RUSLE models together with GIS is of substantial importance for a preliminary mapping of the soil erosion rate. However, a more accurate on-ground acquisition of data could be required in detailed studies aiming at the comparison of different mitigation measures and evaluation of different management scenarios under actual and future land use and expected climate changes.
Received: 2022/03/12 | Published: 2023/04/28
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