Linking the past, present and future scenarios of soil erosion modeling in a river basin

BACKGROUND AND OBJECTIVE: Soil erosion is considered one of the major indicators of soil degradation in our environment. Extensive soil erosion process leads to erosion of nutrients in the topsoil and decreases in fertility and hence productivity. Moreover, creeping erosion leads to landslides in the hilly regions of the study area that affects the socio-economics of the inhabitants. The current study focuses on the estimation of soil erosion rate for the year ۲۰۱۱ to ۲۰۱۹ and projection for the years ۲۰۲۱, ۲۰۲۳ and ۲۰۲۵. METHODS: In this study, the Revised Universal Soil Loss Equation is used for estimation of soil erosion in the study area for the year ۲۰۱۱ to ۲۰۱۹. Using Artificial Neural Network-based Cellular Automata simulation, the Land Use Land Cover is projected for the future years ۲۰۲۱, ۲۰۲۳ and ۲۰۲۵. Using the projected layer as one of the spatial variables and applying the same model, Soil Erosion based on Revised Universal soil loss equation is projected for a corresponding years. FINDINGS: For both cases of projection, simulated layers of ۲۰۱۹ (land use land cover and soil erosion) are correlated with the estimated layer of ۲۰۱۹ using actual variables and validated. The agreement and accuracy of the model used in the case land use are ۰.۹۲ and ۹۶.۲۱% for the year ۲۰۱۹. The coefficient of determination of the model for both simulations is also observed to be ۰.۸۷۵ and ۰.۸۳۸. The simulated future soil erosion rate ranges from minimum of ۰ t/ha/y to maximum of ۵۲۴.۲۷۱ t/ha/y, ۱۱۶۰.۲۱۲ t/ha/y and ۷۸۳.۱۳۵ t/ha/y in the year ۲۰۲۱, ۲۰۲۳ and ۲۰۲۵, respectively. CONCLUSION: The study has emphasized the use of artificial neural network-based Cellular automata model for simulation of land use and land cover and subsequently estimation of soil erosion rate. With the simulation of future soil erosion rate, the study describes the trend in the erosion rate from past to future, passing through present scenario. With the scarcity of data, the methodology is found to be accurate and reliable for the region under study. ==========================================================================================COPYRIGHTS©۲۰۲۱ The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY ۴.۰), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.========================================================================================== 

Keywords:

artificial neural network (ANN) , cellular automata (CA) , RUSLE , Soil erosion