Several impacts over ecosystem services have been produced by land use/cover changes, placing it as one of the main factors driving global environmental change. In the present study, the SWAT model was used to assess the effect of land use/cover changes on the hydrology response in the Andalien river basin from the south-central zone of Chile. Three land use/cover scenarios (LU_1986, LU_2001, and LU_2011) were compared over a period of 30 years (1984–2013) to remove the effect of climate variability on hydrology. The results show a significant decrease in total annual flows among the three LU scenarios. The greater differences in the annual flows of 25.05 m³/s were observed between LU_1986 and LU_2011 scenarios. The hydrological cycle dynamics in the basin show an increasing trend of evapotranspiration and surface flows with a significant decrease in percolation and lateral flow on a monthly and seasonal scale. This behavior can be explained by the increasing percentage of the basin area covered by exotic plantations, from 35.22% to 63.93% during the period. The evidence of these changes and the evaluation of their effects are particularly relevant for the long-term sustainable management of water resources.

In this study, the SWAT (Soil Water Assessment Tool) hydrological model is implemented to determine the effect of climate change on various hydrological components in two basins located in the foothills of the Andes: the Quino and Muco river basins. The water cycle is analyzed by comparing the model results to climatic data observed in the past (1982–2016) to understand its trend behaviors. Then, the variations and geographical distribution of the components of the hydrological cycle were analyzed using the Representative Concentration Pathway (RCP)8.5 climate scenario to model two periods considering the immediate future (2020–2049) and intermediate future (2050–2079). In this way, in the study area, it is predicted that yearly average temperatures will increase up to 1.7 °C and that annual average precipitation will decrease up to 210 mm for the intermediate future. Obtained results show that the analyzed parameters presented the same trend behavior for both periods of time; however, a greater impact can be expected in the intermediate future. According to the spatial distribution, the impact worsens for all the parameters as the elevation increases in both basins. The model depicted that yearly average evapotranspiration would increase around 5.26% and 5.81% for Quino and Muco basins, respectively, due to the large increase in temperature. This may cause, when combined with the precipitation lessening, a decrease around 9.52% and 9.73% of percolation, 2.38% and 1.76% of surface flow, and 7.44% and 8.14% of groundwater for Quino and Muco basins, respectively, with a consequent decrease of the water yield in 5.25% and 4.98% in the aforementioned watersheds, respectively.

The world’s water ecosystems have been affected by various human activities. Artificial intelligence techniques, especially machine learning, have become an important tool for predicting the water quality of inland aquatic ecosystems. As an excellent biological indicator, chlorophyll-a was studied to determine the state of water quality in Lake Llanquihue, located in southern Chile. A 31-year time series (1989 to 2020) of data collected in situ was used to determine the evolution of limnological parameters at eight spaced stations covering all of the main points of the lake, and the year, month, day, and hour time intervals were selected. Using machine learning techniques, out of eight estimation algorithms that were applied with real data to estimate chlorophyll-a, three models showed better performance (XGBoost, LightGBM, and AdaBoost). The results for the best models show excellent performance, with a coefficient of determination between 0.81 and 0.99, a root-mean-square error of between 0.03 ug/L and 0.46 ug/L, and a mean bias error of between 0.01 and 0.27 ug/L. These models are scalable and applicable to other lake systems of interest that present similar conditions and can support decision making related to water resources.

Land use/cover change (LUCC) and climate change (CC) affect water resource availability as they alter important hydrological processes. Mentioned factors modify the magnitude of surface runoff, groundwater recharge, and river flow among other parameters. In the present work, changes that occurred in the recent decades at the Quino and Muco river watersheds in the south-central zone of Chile were evaluated to predict future cover/use changes considering a forest expansion scenario according to Chilean regulations. In this way an expansion by 42.3 km² and 52.7 km² at Quino and Muco watersheds, respectively, was predicted, reaching a watersheds’ occupation of 35.4% and 22.3% in 2051. Additionally, the local climatic model RegCM4-MPI-ESM-MR was used considering periods from 2020–2049 and 2050–2079, under the RCP 8.5 scenario. Finally, the SWAT model was applied to assess the hydrological response of both watersheds facing the considered forcing factors. Five scenarios were determined to evaluate the LUCC and CC individual and combined effects. The results depict a higher sensitivity of the watersheds to CC impacts, where an increase of evapotranspiration, with a lessening of percolation, surface flow, lateral flow, and groundwater flow, triggered a water yield (WYLD) decrease in all predicted scenarios. However, when both global changes act synergistically, the WYLD decreases considerably with reductions of 109.8 mm and 123.3 mm at the Quino and Muco watersheds, respectively, in the most extreme simulated scenario. This water scarcity context highlights the necessity to promote land use management strategies to counteract the imminent effects of CC in the watersheds.