Assessment of the impact of LULC changes on peak discharge and runoff volume in Kebir river catchment Northeastern of Algeria

The focus of this study is to examine and analysis the impacts of land use land cover (LULC) change for a period between 1985 and 2040 on the peak discharge and runoff volume in a Kebir river catchment using HEC-HMS model and remote sensing-GIS techniques. Therefore, this research started by analyzing changes in LULC by classifying Landsat 5 and Landsat 7 satellite images from 1985 to 2020, whereas the LULC change map of 2040 was obtained by prediction. Data analysis and projection were performed using an integrated Cellular Automata Artificial Neural Network (CA-ANN) methodology within the Modules of Land Use Change Evaluation (MOLUSCE) plugin in QGIS. The accuracy assessment of the classified images was performed by error matrix, where the overall accuracy and Kappa values were found to be 99.27% and 0.98 respectively. The classification results obtained are quite satisfactory, and thus, the classified image is utilized to evaluate changes in LULC throughout the study period. The findings reveal an increase of 0.403%, 0.584%, and 1.020% in agricultural lands, water bodies, and built-up lands respectively. Also, a decrease of 0.722% and 1.285% in Barren lands, and forests, respectively, between 1985 and 2040, was shown. To simulate the changes in the peak discharge and runoff volume, the classified LULC maps of 1985, 2003, 2020, and the predicted LULC map of 2040 are used in the HEC-HMS model during the calibration period from 18/12/1984 to 31/07/1985 and validation period from 01/01/2003 to 31/07/2003. The simulated results show a 1.93% rise in peak discharge during the calibration period and a 2.20% increase during the validation period. In addition, the runoff volume saw a 1.15% increase in the calibration period and a 1.53% increase in the validation period. Further, the performance results of the model were good for both calibration (RMSE = 0.50, NSE = 0.701 to 0.720, KGE = 0.36 to 0.56, and R2 = 0.92) and validation (RMSE = 0.60, NSE = 0.598 to 0.608, KGE = 0.31 to 0.34, and R2 = 0.86 to 0.87).