A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

Climate change (CC) is already affecting Canada’s hydrologic cycle, posing challenges for water management in mining operations and increasing associated environmental and social risks. However, there is limited research that quantifies the extent of anticipated CC impacts across Canadian watersheds with active mining. This paper aims to fill that gap by assessing CC impacts on key hydroclimatic variables important for Canada’s mine water management. Baseline conditions were established for six key variables: annual precipitation, 24 h intensity–duration–frequency (IDF) precipitation, 10 d extreme precipitation, annual mean temperature, hydrologic drought index like Standardized Precipitation and Evaporation Index (SPEI), and annual snow depth. Date sources included Environment and Climate Change Canada (ECCC) and Coupled Model Intercomparison Project Phase 5 (CMIP5). Future CC projections were generated using ECCC’s transformation equation for 24 h IDF precipitation, the quantile delta mapping (QDM) method for 10 d extreme precipitation, and downscaled, bias corrected CMIP5 ensemble projections for the remaining variables. The assessment considered two greenhouse emission scenarios (RCP4.5 and RCP8.5), and three future timeframes (2020s, 2050s, 2080s). The study reveals projected temperature increases within the case study watersheds of 2.4 °C–3.5 °C by the 2050s and 3 °C–7 °C by the 2080s under median or 50th percentile (p50) conditions. Annual precipitation is expected to rise by 11%–16% (2050s) and 15%–28% (2080s), with more intense shorter-duration events under p50 conditions. For example, the current 100 year 24 h IDF storm is expected to occur more frequently, decreasing to a 27–49 year return period by the 2050s and a 10–40 year return period by the 2080s. Annual average snow depth is projected to decline by 21%–73% (2050s) and 24%–89% (2080s) under p50 conditions. These findings highlights that water management in Canada’s mining regions is set to face escalating hydrological changes under a changing climate. Effective management strategies are therefore essential to prevent intensified environmental and social risks.