General circulation models (GCMs) allow the analysis of potential changes in the climate system under different emissions scenarios. However, their spatial resolution is too coarse to produce useful climate information for impact/adaptation assessments. This is especially relevant for regions with complex orography and coastlines, such as in Chile. Downscaling techniques attempt to reduce the gap between global and regional/local scales; for instance, statistical downscaling methods establish empirical relationships between large-scale predictors and local predictands. Here, statistical downscaling was employed to generate climate change projections of daily maximum/minimum temperatures and precipitation in more than 400 locations in Chile using the analog method, which identifies the most similar or analog day based on similarities of largescale patterns from a pool of historical records. A cross-validation framework was applied using different sets of potential predictors from the NCEP/NCAR reanalysis following the perfect prognosis approach. The best-performing set was used to downscale six different CMIP5 GCMs (forced by three representative concentration pathways, RCPs). As a result, minimum and maximum temperatures are projected to increase in the entire Chilean territory throughout all seasons. Specifically, the minimum (maximum) temperature is projected to increase by more than 2 °C (6 °C) under the RCP8.5 scenario in the austral winter by the end of the twenty-first century. Precipitation changes exhibit a larger spatial variability. By the end of the twenty-first century, a winter precipitation decrease exceeding 40% is projected under RCP8.5 in the central-southern zone, while an increase of over 60% is projected in the northern Andes.

No abstract

No abstract

The natural salt meadows of Tilopozo in the hyperarid, Atacama Desert of northern Chile, which are located at approximately 2800 m above sea level, are under pressure from industrial activity, and cultivation and grazing by local communities. In this research, the land surface covered by salt meadow vegetation was estimated from normalized difference vegetation indices (NDVI) derived from Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager (OLI) data from 1985 to 2016. The vegetated area of the Tilopozo salt meadows decreased by 34 ha over the 32-year period studied. Multiple regression models of the area covered by vegetation and climate data and groundwater depths were derived on an annual basis, as well as for both the dry and wet seasons and had R2 values of 83.0%, 72.8% and 92.4% respectively between the vegetated areas modeled and those estimated from remotely sensed data. These models are potentially useful tools for studies into the conservation of the Tilopozo salt meadows, as they provide relevant information on the state of vegetation and enable changes in vegetation in response to fluctuations in climate parameters and groundwater depths to be predicted.