ABSTRACT
The ecosystem, biodiversity, and anthropological existence in the Chitral district are in danger due to the sediments and soil erosion stemming from the changes in the land-cover and climate. This research aims to practice the RUSLE model with the changes in the land-cover and climate in upcoming situations for 2030 and 2040 to evaluate soil erosion annually as per the spatial dissemination and the tendency of sediment yield. The multilayer perceptron (MLP), an artificial neural network (ANN), besides the Markov chain analysis was used to model upcoming land-cover. The Max Planck Institute model, which demonstrated a revised bias as well as downscaled grid size under the Representative Concentration Pathways (RCPs), was used for examining the future changes in the climate. The modeled land-cover showed that the areas that are primarily comprised of natural trees and shrubs were transformed largely to agriculture and build-up areas. The average rainfall in the future under different RCP situations was elevated compared to the rainfall through historical time. The continuous variability in the R and C factors affects the probable soil erosion rate and sediment yield. Under RCP8.5 for both future years of 2030 and 2040, the extreme erosion rate was assessed at around 500 and 550 t/ha/year. Additionally, under the different RCP scenarios in 2030 and 2040, the outcomes of sediment yield were more significant than the sediment yield through historical time. The results showed that lower regions of the Chitral district are at risk of amplified soil erosion and sediment yield presently, as shown by the historical data and in the future. The produced soil erosion maps using ArcGIS 10.2 can play a valuable role in managing sustainable development, conservation of the watershed of the Chitral River, and reducing soil loss. Effective measures to overcome these concerns and mitigate the possible effects need to be planned and practiced, particularly the decrease in the storage volume of the reservoirs situated on the river.
