This research studies the effect of climate change on the hydrological behavior of two semi-arid basins. For this purpose, the Soil and Water Assessment Tool (SWAT) model was used with the simulation of two future climate change scenarios, one Representative Concentration Pathway moderate (RCP 4.5) and the other extreme (RCP 8.5). Three future periods were considered: close (2019–2040), medium (2041–2070), and distant (2071–2100). In addition, several climatic projections of the EURO-CORDEX model were selected, to which different bias correction methods were applied before incorporation into the SWAT model. The statistical indices for the monthly flow simulations showed a very good fit in the calibration and validation phases in the Upper Mula stream (NS = 0.79–0.87; PBIAS = −4.00–0.70%; RSR = 0.44–0.46) and the ephemeral Algeciras stream (NS = 0.78–0.82; PBIAS = −8.10–−8.20%; RSR = 0.4–0.42). Subsequently, the impact of climate change in both basins was evaluated by comparing future flows with those of the historical period. In the RCP 4.5 and RCP 8.5 scenarios, by the end of the 2071–2100 period, the flows of the Upper Mula stream and the ephemeral Algeciras stream will have decreased by between 46.3% and 52.4% and between 46.6% and 55.8%, respectively.

The use of check dams is a common strategy to reduce soil erosion in the Mediterranean headwaters. However, the effects of these control works on water flow rates and sediment yields have been scarcely investigated under possible scenarios of climate and land-use changes. On this regard, the use of hydrological models, such as SWAT, provide reliable hydrological predictions under variable environmental conditions. To fill this gap, this study has evaluated the effectiveness of check dams on the hydrological response of a forest headwater in Calabria (Southern Italy) in comparison with an unregulated subcatchment with very similar environmental conditions. In this regard, the effects of different combined scenarios of climate change (through three GCMs and two RCPs applied to a time period of the next 80 years) and land use (forest, pasture, and cropland) on water flow rates and sediment yields in the two headwaters were analysed using the SWAT model. The SWAT model was first calibrated in a third headwater with very similar climatic, soil, and land-use conditions, and this verification showed a satisfactory prediction capacity of water flow rate. The water flow rate prediction capacity of the model was satisfactory (coefficients of determination and efficiency of Nash and Sutcliffe equal to 0.71 and 0.67, respectively, and percent bias of 14.9%). No significant differences were detected for the water flow rates and sediment yields between the two subcatchments (with or without check dams) among the different land-use and climate change scenarios. This was linked to the low hydrological response of both headwaters to the forcing actions, which influenced the low effectiveness of the control works. SWAT estimated higher values of both mean and maximum values of water flow rates and sediment yields under RCP2.6 compared with RCP8.5. Both water flow rates and sediment yields were predicted to be very low under all climate and land-use scenarios. The regulated headwater with check dams was predicted to always produce more runoff and erosion compared with the subcatchment without check dams. The increases were predicted to be up to 60% for the maximum flow rate and 30–35% for the sediment yield in forest land use under RCP2.6. Although there was a limitation in this study due to the lack of validation of the erosion data (due to unavailable records of sediment yield), this study demonstrated how the use of check dams in headwater catchments may be not effective for soil conservation purposes several decades after their installation in Mediterranean semiarid areas, where the water flow and erosion rate are limited.

This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time-series prediction across diverse catchments and compares its results against SWAT predictions. We found strong evidence of RF’s better performance by adding historical flows and time-lags for meteorological values over using only actual meteorological values. On a daily scale, RF demonstrated robust performance (Nash–Sutcliffe efficiency [NSE] > 0.5), whereas SWAT generally yielded unsatisfactory results (NSE < 0.5) and tended to overestimate daily streamflow by up to 27% (PBIAS). However, SWAT provided better monthly predictions, particularly in catchments with irregular flow patterns. Although both models faced challenges in predicting peak flows in snow-influenced catchments, RF outperformed SWAT in an arid catchment. RF also exhibited a notable advantage over SWAT in terms of computational efficiency. Overall, RF is a good choice for daily predictions with limited data, whereas SWAT is preferable for monthly predictions and understanding hydrological processes in depth.