Abstract
Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha−1 h−1y−1 in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha−1 y−1, which will generate 5.52 t ha−1 y−1 sediment. The difference between estimated and observed sediment was 1.42 t ha−1 year−1 at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.
Highlights
Climate change and land use change relate to one another
Given that the purpose of this study was to predict the future of land use modifications, the use of the normalized difference vegetation index (NDVI) was not adequate for the Revised Universal Soil Loss Equation (RUSLE) model and the C factor was derived from Land Use/Land Cover (LULC) map of the watershed and the values were determined based on previous studies [51–54]
Transition potential modeling from one land use to another was done using an multi-layer perceptron (MLP) artificial neural network and Table 2 present the results of the factors accuracy rate, training error and testing error that were determined for evaluation of transition potential modeling
Summary
Climate change and land use change relate to one another. Land use change is a driver of climate change, and a changing climate can lead to land cover changes [1]. Farmers may convert to crops of higher efficiency due to conditions of climate change and adjust land use to the new climate [2]. The increase of temperature leads to drought and to the degradation of vegetation cover since it is dependent on water. Land use change affects climate due to the global levels of greenhouse gases [3]. Soil erosion has become a major challenge for environment and natural resources in the present century. The arable land annual soil loss in the world is 75 billion tons [4] and is more than
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