Inland water is fundamental for the conservation of flora and fauna and is a source of drinking water for humans; therefore, monitoring its quality and ascertaining its status is essential for making decisions in water resources management. As traditional measuring methods present limitations in monitoring with high spatial and temporal coverage, using satellite images to have greater control over lake observation can be a handy tool and have satisfactory results. The study of chlorophyll-a (Chl-a) has been widely used to ascertain the quality of the inland aquatic environment using remote sensing, but in general, it depends on the local conditions of the water body. In this study, the suitability of the Sentinel-2 MSI sensor for Chl-a estimation in a lake in south-central Chile is tested. An empirical approach is proposed, applying multiple linear regressions, comparing the efficiency and performance with L1C and L2A products, separating the equations constructed with spring-summer and fall-winter data, and restricting Chl-a ranges to those measured in the field to generate these regressions. The algorithms combining spectral bans proved to have a good correlation with Chl-a measured in the field, generally resulting in R² greater than 0.87 and RMSE and MAE with errors less than 6 μg L⁻¹. The spatial distribution of Chl-a concentrations at the study site was obtained based on the proposed equations.

Remobilization and deformation of surficial subaqueous slope sediments create turbidites and soft sediment deformation structures, which are common features in many depositional records. Palaeoseismic studies have used seismically‐induced turbidites and soft sediment deformation structures preserved in sedimentary sequences to reconstruct recurrence patterns and – in some cases – allow quantifying rupture location and magnitude of past earthquakes. However, current understanding of earthquake‐triggered remobilization and deformation lacks studies targeting where these processes take place, the subaqueous slope and involving direct comparison of sedimentary fingerprint with well‐documented historical earthquakes. This study investigates the sedimentary imprint of six megathrust earthquakes with varying rupture characteristics in 17 slope sediment cores from two Chilean lakes, Riñihue and Calafquén, and evaluates how it links to seismic intensity, peak ground acceleration, bracketed duration and slope angle. Centimetre‐scale stratigraphic gaps ranging from ca 1 to 20 cm – caused by remobilization of surficial slope sediment – were identified using high‐resolution multi‐proxy core correlation of slope to basin cores, and six types of soft sediment deformation structures ranging from ca 1 to 25 cm thickness using high‐resolution three‐dimensional X‐ray computed tomography data. Stratigraphic gaps occur on slope angles of ≥2.3°, whereas deformation already occurs from slope angle 0.2°. The thickness of both stratigraphic gaps and soft sediment deformation structures increases with slope angle, suggesting that increased gravitational shear stress promotes both surficial remobilization and deformation. Seismic shaking is the dominant trigger for surficial remobilization and deformation at the studied lakes. Total remobilization depth correlates best with bracketed duration and is highest in both lakes for the strongest earthquakes (M_w ca 9.5). In lake Riñihue, soft sediment deformation structure thickness and type correlate best with peak ground acceleration providing the first field‐based evidence of progressive soft sediment deformation structure development with increasing peak ground acceleration for soft sediment deformation structures caused by Kelvin‐Helmholtz instability. The authors propose that long duration and low frequency content of seismic shaking favours surficial remobilization, whereas ground motion amplitude controls Kelvin‐Helmholtz instability‐related soft sediment deformation structure development.

TanDEM-X digital elevation model (DEM) is a global DEM released by the German Aerospace Center (DLR) at outstanding resolution of 12 m. However, the procedure for its creation involves the combination of several DEMs from acquisitions spread between 2011 and 2014, which casts doubt on its value for precise glaciological change detection studies. In this work we present TanDEM-X DEM as a high-quality product ready for use in glaciological studies. We compare it to Aerial Laser Scanning (ALS)-based dataset from April 2013 (1 m), used as the ground-truth reference, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) V003 DEM and SRTM v3 DEM (both 30 m), serving as representations of past glacier states. We use a method of sub-pixel coregistration of DEMs by Nuth and Kääb (2011) to determine the geometric accuracy of the products. In addition, we propose a slope-aspect heatmap-based workflow to remove the errors resulting from radar shadowing over steep terrain. Elevation difference maps obtained by subtraction of DEMs are analyzed to obtain accuracy assessments and glacier mass balance reconstructions. The vertical accuracy (± standard deviation) of TanDEM-X DEM over non-glacierized area is very good at 0.02 ± 3.48 m. Nevertheless, steep areas introduce large errors and their filtering is required for reliable results. The 30 m version of TanDEM-X DEM performs worse than the finer product, but its accuracy, −0.08 ± 7.57 m, is better than that of SRTM and ASTER. The ASTER DEM contains errors, possibly resulting from imperfect DEM creation from stereopairs over uniform ice surface. Universidad Glacier has been losing mass at a rate of −0.44 ± 0.08 m of water equivalent per year between 2000 and 2013. This value is in general agreement with previously reported mass balance estimated with the glaciological method for 2012–2014.

Excluding Antarctica and Greenland, 3.8% of the world’s glacier area is concentrated in Chile. The country has been strongly affected by the mega drought, which affects the south-central area and has produced an increase in dependence on water resources from snow and glacier melting in dry periods. Recent climate change has led to an elevation of the zero-degree isotherm, a decrease in solid-state precipitation amounts and an accelerated loss of glacier and snow storage in the Chilean Andes. This situation calls for a better understanding of future water discharge in Andean headwater catchments in order to improve water resources management in glacier-fed populated areas. The present study uses hydrological modeling to characterize the hydrological processes occurring in a glacio-nival watershed of the central Andes and to examine the impact of different climate change scenarios on discharge. The study site is the upper sub-watershed of the Tinguiririca River (area: 141 km²), of which nearly 20% is covered by Universidad Glacier. The semi-distributed Snowmelt Runoff Model + Glacier (SRM+G) was forced with local meteorological data to simulate catchment runoff. The model was calibrated on even years and validated on odd years during the 2008–2014 period and found to correctly reproduce daily runoff. The model was then forced with downscaled ensemble projected precipitation and temperature series under the RCP 4.5 and RCP 8.5 scenarios, and the glacier adjusted using a volume-area scaling relationship. The results obtained for 2050 indicate a decrease in mean annual discharge (MAD) of 18.1% for the lowest emission scenario and 43.3% for the most pessimistic emission scenario, while for 2100 the MAD decreases by 31.4 and 54.2%, respectively, for each emission scenario. Results show that decreasing precipitation lead to reduced rainfall and snowmelt contributions to discharge. Glacier melt thus partly buffers the drying climate trend, but our results show that the peak water occurs near 2040, after which glacier depletion leads to reducing discharge, threatening the long-term water resource availability in this region.

In this study, we combined machine learning and remote sensing techniques to estimate the value of chlorophyll-a concentration in a freshwater ecosystem in the South American continent (lake in Southern Chile). In a previous study, nine artificial intelligence (AI) algorithms were tested to predict water quality data from measurements during monitoring campaigns. In this study, in addition to field data (Case A), meteorological variables (Case B) and satellite data (Case C) were used to predict chlorophyll-a in Lake Llanquihue. The models used were SARIMAX, LSTM, and RNN, all of which showed generally good statistics for the prediction of the chlorophyll-a variable. Model validation metrics showed that all three models effectively predicted chlorophyll as an indicator of the presence of algae in water bodies. Coefficient of determination values ranging from 0.64 to 0.93 were obtained, with the LSTM model showing the best statistics in any of the cases tested. The LSTM model generally performed well across most stations, with lower values for MSE (<0.260 (μg/L)²), RMSE (<0.510 ug/L), MaxError (<0.730 μg/L), and MAE (<0.442 μg/L). This model, which combines machine learning and remote sensing techniques, is applicable to other Chilean and world lakes that have similar characteristics. In addition, it is a starting point for decision-makers in the protection and conservation of water resource quality.