Abstract: There is a wide variety of drought indices, yet a consensus on suitable indices and temporal scales for monitoring streamflow drought remains elusive across diverse hydrological settings. Considering the growing interest in spatially distributed indices for ungauged areas, this study addresses the following questions: (i) What temporal scales of precipitation-based indices are most suitable to assess streamflow drought in catchments with different hydrological regimes? (ii) Do soil moisture indices outperform meteorological indices as proxies for streamflow drought? (iii) Are snow indices more effective than meteorological indices for assessing streamflow drought in snow-influenced catchments? To answer these questions, we examined 100 near-natural catchments in Chile with four hydrological regimes, using the standardised precipitation index (SPI), standardised precipitation evapotranspiration index (SPEI), empirical standardised soil moisture index (ESSMI), and standardised snow water equivalent index (SWEI), aggregated across various temporal scales. Cross-correlation and event coincidence analysis were applied between these indices and the standardised streamflow index at a temporal scale of 1 month (SSI-1), as representative of streamflow drought events. Our results underscore that there is not a single drought index and temporal scale best suited to characterise all streamflow droughts in Chile, and their suitability largely depends on catchment memory. Specifically, in snowmelt-driven catchments characterised by a slow streamflow response to precipitation, the SPI at accumulation periods of 12-24 months serves as the best proxy for characterising streamflow droughts, with median correlation and coincidence rates of approximately 0.70-0.75 and 0.58-0.75, respectively. In contrast, the SPI at a 3-month accumulation period is the best proxy over faster-response rainfall-driven catchments, with median coincidence rates of around 0.55. Despite soil moisture and snowpack being key variables that modulate the propagation of meteorological deficits into hydrological ones, meteorological indices are better proxies for streamflow drought. Finally, to exclude the influence of non-drought periods, we recommend using the event coincidence analysis, a method that helps assessing the suitability of meteorological, soil moisture, and/or snow drought indices as proxies for streamflow drought events.

Abstract: Modeling human behavior within mathematical models of infectious diseases is a key component to understand and control disease spread. We present a mathematical compartmental model of Susceptible-Infectious-Removed to compare the infected curves given by four different functional forms describing the transmission rate. These depend on the distance that individuals keep on average to others in their daily lives. We assume that this distance varies according to the balance between two opposite thrives: the self-protecting reaction of individuals upon the presence of disease to increase social distancing and their necessity to return to a culturally dependent natural social distance that occurs in the absence of disease. We present simulations to compare results for different society types on point prevalence, the peak size of a first epidemic outbreak and the time of occurrence of that peak, for four different transmission rate functional forms and parameters of interest related to distancing behavior, such as: the reaction velocity of a society to change social distance during an epidemic. We observe the vulnerability to disease spread of close contact societies, and also show that certain social distancing behavior may provoke a small peak of a first epidemic outbreak, but at the expense of it occurring early after the epidemic onset, observing differences in this regard between society types. We also discuss the appearance of temporal oscillations of the four different transmission rates, their differences, and how this oscillatory behavior is impacted through social distancing; breaking the unimodality of the actives-curve produced by the classical SIR-model.

Abstract: Rainfall-induced landslides (RILs) are an issue in the southern Andes nowadays. RILs cause loss of life and damage to critical infrastructure. Rainfall-induced landslide early warning systems (RILEWSs) can reduce and mitigate economic and social damages related to RIL events. The southern Andes do not have an operational-scale RILEWS yet. In this contribution, we present a pre-operational RILEWS based on the Weather and Research Forecast (WRF) model and geomorphological features coupled to logistic models in the southern Andes. The models have been forced using precipitation simulations. We correct the precipitation derived from WRF using 12 weather stations through a bias correction approach. The models were trained using 57 well-characterized RILs and validated by ROC analysis. We show that WRF has strong limitations in representing the spatial variability in the precipitation. Therefore, accurate precipitation needs a bias correction in the study zone. We used accurate precipitation simulation and slope, demonstrating a high predicting capacity (area under the curve, AUC, of 0.80). We conclude that our proposal could be suitable at an operational level under determined conditions. A reliable RIL database and operational weather networks that allow real-time correction of the mesoscale model in the implemented zone are needed. The RILEWSs could become a support to decision-makers during extreme-precipitation events related to climate change in the south of the Andes.

Abstract: The exposure to environmental variations in pH and temperature has proven impacts on benthic ectotherms calcifiers, as evidenced by tradeoffs between physiological processes. However, how these stressors affect structure and functionality of mollusk shells has received less attention. Episodic events of upwelling of deep cold and low pH waters are well documented in eastern boundary systems and may be stressful to mollusks, impairing both physiological and biomechanical performance. These events are projected to become more intense, and extensive in time with ongoing global warming. In this study, we evaluate the independent and interactive effects of temperature and pH on the biomineral and biomechanical properties of Argopecten purpuratus scallop shells. Total organic matter in the shell mineral increased under reduced pH (similar to 7.7) and control conditions (pH similar to 8.0). The periostracum layer coating the outer shell surface showed increased protein content under low pH conditions but decreasing sulfate and polysaccharides content. Reduced pH negatively impacts shell density and increases the disorder in the orientation of calcite crystals. At elevated temperatures (18 degrees C), shell microhardness increased. Other biomechanical properties were not affected by pH/temperature treatments. Thus, under a reduction of 0.3 pH units and low temperature, the response of A. purpuratus was a tradeoff among organic compounds (biopolymer plasticity), density, and crystal organization (mineral plasticity) to maintain shell biomechanical performance, while increased temperature ameliorated the impacts on shell hardness. Biopolymer plasticity was associated with ecophysiological performance, indicating that, under the influence of natural fluctuations in pH and temperature, energetic constraints might be critical in modulating the long-term sustainability of this compensatory mechanism.

Abstract: Achieving a local understanding of fire regimes requires high-resolution, systematic and dynamic databases. High-quality information can help to transform evidence into decision-making in the context of rapidly changing landscapes, particularly considering that geographical and temporal patterns of fire regimes and their trends vary locally over time. Global fire scar products at low spatial resolutions are available, but high-resolution wildfire data, especially for developing countries, are still lacking. Taking advantage of the Google Earth Engine (GEE) big-data analysis platform, we developed a flexible workflow to reconstruct individual burned areas and derive fire severity estimates for all reported fires. We tested our approach for historical wild-fires in Chile. The result is the Landscape Fire Scars Database, a detailed and dynamic database that reconstructs 8153 fires scars, representing 66.6% of the country's officially recorded fires between 1985 and 2018. For each fire event, the database contains the following information: (i) the Landsat mosaic of pre- and post-fire images; (ii) the fire scar in binary format; (iii) the remotely sensed estimated fire indexes (the normalized burned ratio, NBR, and the relative delta normalized burn ratio, RdNBR); and two vector files indicating (iv) the fire scar perimeter and (v) the fire scar severity reclassification, respectively. The Landscape Fire Scars Database for Chile and GEE script (JavaScript) are publicly available. The framework developed for the database can be applied anywhere in the world, with the only requirement being its adaptation to local factors such as data availability, fire regimes, land cover or land cover dynamics, vegetation recovery, and cloud cover. The Landscape Fire Scars Database for Chile is publicly available in https://doi.org/10.1594/PANGAEA.941127 (Miranda et al., 2022).