Flood mapping requires the combination and integration of geomorphological and hydrological-hydraulic methods; however, despite this, there is very little scientific literature that compares and validates both methods. Two types of analysis are addressed in the present article. On the one hand, maps of flood plains have been elaborated using geomorphological evidence and historical flood data in the mountainous area of northwestern Spain, covering an area of more then 232 km 2 of floodplains. On the other hand, a hydrometeorological model has been developed (Clark semidistributed unit hydrograph) in the Sarria River basin (155 km 2 , NW Spain). This basin is not gauged, hence the model was subjected to a goodness-of-fit test of its parameter (curve number) by means of Monte Carlo simulation. The peak flows obtained by means of the hydrological model were used for hydraulic modeling (one-phase, one-dimensional and steady flow) in a 4 km 2 urban stretch of the river bed. The delineation of surface areas affected by floods since 1918, as well as those analyzed subsequent to the geomorphological study, reveals a high degree of reliability in the delineation of the flooded areas with frequent recurrence intervals (\50 years). If we compare these flooded surface areas with the estimate obtained by the hydrological-hydraulic method we can see that the latter method overestimates the extent of the surface water by 144% for very frequent recurrence intervals ([10 years) and underestimates it as the recurrence interval increases, by up to 80% less floodplain for

Since 2010, Chile has experienced one of the most severe droughts over the last century, the so-called mega-drought (MD). The MD conditions, combined with intensive agricultural activities and the current water management system, have led to water scarcity problems in Mediterranean and Semi-arid regions of Chile. An emblematic case is the Petorca basin, where a water crisis is undergone. To characterize this crisis, we analyzed water provision by using tree-ring records, remote sensing, instrumental data, and allocated water rights within the basin. Results indicate that the MD is the most severe dry period over the last 700-years of streamflow reconstruction. During the MD, streamflow and water bodies of the upper parts of the basin have been less affected than mid and low areas of this valley, where consumptive withdrawals reach up to 18% of the mean annual precipitation. This extracted volume is similar to the MD mean annual precipitation deficits. The impacts of the current drought, along with the drier climate projections for Central Chile, emphasize the urgency for faster policy changes related to water provision. Climate change adaptation plans and policies should enhance the current monitoring network and the public control of water use to secure the water access for inhabitants and productive activities.

No abstract

An assessment of climate change impacts on the habitat suitability of fish species is an important tool to improve the understanding and decision‐making needed to reduce potential climate change effects based on the observed relationships of biological responses and environmental conditions. In this study, we use historical (2010–2015) environmental sea surface temperature (SST), upwelling index (UI), chlorophyll‐a (Chl‐a) and biological (i.e., anchovy adults acoustic presence) data (i.e., Maxent) to determine anchovy habitat suitability in the coastal areas off central‐northern (25°S–32°S) Chile. Using geographic information systems (GIS), the model was forced by changes in regionalized SST, UI and Chl‐a as projected by IPCC models under the RPC (i.e., RCP2.6, RCP4.5, RCP6.0 and RCP8.5) emissions scenarios for the simulation period 2015–2050. The model simulates, for all RCP scenarios, negative responses in anchovy presence, reflecting the predicted changes in environmental variables, dominated by a future positive (warming) change in SST and UI, and a decrease in chlorophyll‐a (i.e., phytoplankton biomass). The model predicts negative changes in habitat suitability in coastal areas from north of Taltal (25°S) to south of Caldera (27°45′S) and in Coquimbo littoral zone (29°–30°12′S). The habitat suitability models and climate change predictions identified in this study may provide a scientific basis for the development of management measures for anchovy fisheries in the coastal areas of the South American coast and other parts of the world.

No abstract

Mountain regions have experienced above-average warming in the 20th century and this trend is likely to continue. These accelerated temperature changes in alpine areas are causing reduced snowfall and changes in the timing of snowfall and melt. Snow is a critical component of alpine areas - it drives hibernation of animals, determines the length of the growing season for plants and the soil microbial composition. Thus, changes in snow patterns in mountain areas can have serious ecological consequences. Here we use 35 years of Landsat satellite images to study snow changes in the Mocho-Choshuenco Volcano in the Southern Andes of Chile. Landsat images have 30 m pixel resolution and a revisit period of 16 days. We calculated the total snow area in cloud-free Landsat scenes and the snow frequency per pixel, here called “snow persistence” for different periods and seasons. Permanent snow cover in summer was stable over a period of 30 years and decreased below 20 km2 from 2014 onward at middle elevations (1,530–2,000 m a.s.l.). This is confirmed by negative changes in snow persistence detected at the pixel level, concentrated in this altitudinal belt in summer and also in autumn. In winter and spring, negative changes in snow persistence are concentrated at lower elevations (1,200–1,530 m a.s.l.). Considering the snow persistence of the 1984–1990 period as a reference, the last period (2015–2019) is experiencing a −5.75 km2 reduction of permanent snow area (snow persistence > 95%) in summer, −8.75 km2 in autumn, −42.40 km2 in winter, and −18.23 km2 in spring. While permanent snow at the high elevational belt (>2,000 m a.s.l.) has not changed through the years, snow that used to be permanent in the middle elevational belt has become seasonal. In this study, we use a probabilistic snow persistence approach for identifying areas of snow reduction and potential changes in alpine vegetation. This approach permits a more efficient use of remote sensing data, increasing by three times the amount of usable scenes by including images with spatial gaps. Furthermore, we explore some ecological questions regarding alpine ecosystems that this method may help address in a global warming scenario.

Forest fires are a major issue worldwide, and especially in Mediterranean ecosystems where the frequency, extension and severity of wildfire events have increased related to longer and more intense droughts. Open access remote sensing and climate datasets make it possible to describe in detail the precursory environmental conditions triggering major fire events under drought conditions. In this study, a probabilistic methodological approach is proposed and tested to evaluate extreme drought conditions prior to the occurrence of a wildfire in Central Chile, an area suffering an unprecedented prolonged drought. Using 21 years of monthly records of gridded climate and remotely sensed vegetation water status data, we detected that vegetation at the ground level, by means of fine and dead fuel moisture (FDFM), and canopy level, by means of the enhanced vegetation index (EVI) were extremely dry for a period of about 8 months prior to the fire event, showing records that fall into the 2.5% of the lowest values recorded in 21 years. These extremely dry conditions of the vegetation, consequence of low air humidity and precipitation, favored the ignition and horizontal and vertical propagation of this major wildfire. Post fire, we found high severity values for the native vegetation affected by the fire, with dNBR values >0.44 3 days after the fire and significant damage to the Mediterranean sclerophyllous and deciduous forest present in the burned area. The proposed probabilistic model is presented as an innovation and an alternative to evaluate not only anomalies of the meteorological and vegetation indices that promote the generation of extreme events, but also how unusual or extreme these conditions are. This is achieved by placing the abnormal values in the context of the reference historical frequency distribution of all available records, in this case, more than 20 years of remote sensing and climate data. This methodology can be widely applied by fire researchers to identify critical precursory fire conditions in different ecosystems and define environmental indicators of fire risk.

Abstract. The Chilean SNASPE is a complex network of 104 protected areas covering 18.5 million hectares of continental and insular Chile in South America. The geographical complexity and high biodiversity of the SNASPE make difficult to develop a unified monitoring system for conservation and management. In this contribution, we introduce a novel and remote-sensing web-platform for monitoring SNASPE units based completely in open acces data and software. The platform was designed in close cooperation with the Chilean forest service CONAF in order to make it applicable to the whole SNASPE. Following the framework of the Group on Earth Observation - Biodiversity Observation Network (GEO-BON), we used the Essential Biodiversity Variable (EBV) Phenology and MODIS Enhanced Vegetation Index (EVI) data to detect in near-real-time anomalies from the normal annual phenological cycle of vegetation. The platform is based on a flexible non-parametric probabilistic algorithm (the “npphen” R package) capable to reconstruct any type of leaf phenology and to quantify its inter-annual variation by means of confidence intervals around the most probable annual curve. Phenological anomalies are then calculated as a deviation from the expected annual cycle and judged based on their location within the confidence intervals. Anomalies located above 95% confidence interval trigger a “red alert” which is displayed on the web application as soon as the MODIS data become available. This user-friendly platform was implemented in the La Campana National Park giving early alerts of a severe drought in 2019, warning Conaf to implement actions to protect the park from potential wild fires.