High Andean wetlands of the elevated plateaus of the Andes Mountains of Chile, Argentina, Perú and Bolivia are true oases that sustain life in this arid region. Despite their ecological value, they have been rarely studied and are vulnerable to climate change and human activities that require groundwater resources. One such activity that may be intensified in the near future is mining for nonmetallic minerals such as lithium, whose worldwide demand is expected to increase with the rise of electric vehicles that need batteries. To determine a baseline of the natural dynamics of these systems, which allows sustainable management, it is essential to understand the spatiotemporal dynamics of these wetlands. In this article, we studied the temporal and spatial dynamics of high Andean wetlands of Chile, with the aim of identifying the key processes that govern their dynamics. To do this, we used time series of Landsat data from 1984 to 2019 to study 10 high Andean wetlands. Furthermore, to characterize the climate variability in these systems, we studied the long-term relation between the changes in water and vegetation areas with rainfall and evaporation variability. It was found that the groundwater reservoir plays a key role in sustaining the high Andean wetlands. Wet years with a period of occurrence of 20-30 years are the years in which the groundwater reservoirs are actually recharged, and in between wet years, the groundwater reservoirs gradually release the water that sustains the aquatic ecosystems. Hence, groundwater exploitation should be carefully designed from a long-term perspective, as groundwater levels could take decades to recover.

The intensification of the hydrological cycle because of global warming raises concerns about future floods and their impact on large cities where exposure to these events has also increased. The development of adequate adaptation solutions such as early warning systems is crucial. Here, we used deep learning (DL) for weather-runoff forecasting in región Metropolitana of Chile, a large urban area in a valley at the foot of the Andes Mountains, with more than 7 million inhabitants. The final goal of this research is to develop an effective forecasting system to provide timely information and support in real-time decision making. For this purpose, we implemented a coupled model of a near-future global meteorological forecast with a short-range runoff forecasting system. Starting from a traditional hydrological conceptual model, we defined the hydro-meteorological and geomorphological variables that were used in the data-driven weather-runoff forecast models. The meteorological variables were obtained through statistical scaling of the Global Forecast System (GFS), thus enabling near-future prediction, and two data-driven approaches were implemented for predicting the entire hourly flow time-series in the near future (3 days), a simple Artificial Neural Networks (ANN) and a Deep Learning (DL) approach based on Long-Short Term Memory (LSTM) cells. We show that the coupling between meteorological forecasts and data-driven weather-runoff forecast models are able to satisfy two basic requirements that any early warning system should have: The forecast should be given in advance, and it should be accurate and reliable. In this context, DL significantly improves runoff forecast when compared with a traditional data-driven approach such as ANN, being accurate in predicting time-evolution of output variables, with an error of 5% for DL, measured in terms of the root mean square error (RMSE) for predicting the peak flow, compared to 15.5% error for ANN, which is adequate to warn communities at risk and initiate disaster response operations.