This study aims to determine if there is variation in precipitation concentrations in Chile. We analyzed daily and monthly records from 89 pluviometric stations in the period 1970–2016 and distributed between 29°12′ S and 39°30′ S. This area was divided into two climatic zones: arid–semiarid and humid–subhumid. For each station, the Gini coefficient or Gini Index (GI), the precipitation concentration index (PCI), and the maximum annual precipitation intensity in a 24-h duration were calculated. These series of annual values were analyzed with the Mann–Kendall test with 5% error. Overall, it was noted that positive trends in the GI are present in both areas, although most were not found to be significant. In the case of PCI, the presence of positive trends is only present in the arid–semiarid zone; in the humid–subhumid zone, negative trends were mostly observed, although none of them were significant. Although no significant changes in all indices are evident, the particular case of the GI in the humid–subhumid zone stands out, where mostly positive trends were found (91.1%), of which 35.6% were significant. This would indicate that precipitation is more likely to be concentrated on a daily scale.

The arid Coquimbo region of Chile has experienced a significant economic growth in recent decades, fueled in large part by water-intensive activities such as mining and agriculture. Under this context, a monthly and annual trend analysis of precipitation, streamflow, and piezometric levels was carried out. Thus, 43 pluviometric stations, 11 fluviometric stations, and 11 wells were selected. These stations were evaluated for their temporal trends using the Mann–Kendall test. Results revealed a significant decrease in river flows, with negative and significant trends concentrated in the mean and maximum flows, both at annual and monthly levels. Likewise, positive trends were found in the depth to water table on wells, with significant trends in 81.8% of the monthly cases, and in 72.7% of the annual cases. While also decreasing over the same period, rainfall trends exhibit high variability and lacked significance. Although the amounts of precipitation have decreased, this does not seem to be the main factor responsible for the scarcity of water in the region, but rather an excessive consumption of this resource. This is endorsed by the increase in GDP (Gross Domestic Product), which is explained by activities that consume water (mining and agriculture). Similarly, an increase in the granting of underground water rights was verified, which speaks of the high demands for the resource. However, future modeling is advised to better understand the regional hydrology of the area and quantify the anthropic effects on water resources more precisely.

A rainfall simulator is an important tool for the study of runoff generation and soil loss because it can be used either under laboratory conditions, or in disturbed or natural soil. The objective of this study was to describe the design and operation of a rainfall simulator to evaluate soil loss in situ. The rainfall simulator has four full-cone spray nozzles with a Unijet system mounted in a straight line pipe configuration, and easily transported and assembled. Simulated rainfall uniformity was evaluated in the laboratory, whereas the applicability of the simulator in natural soil erosion plot conditions in an experimental field with different slopes was tested by quantifying runoff and evaluating soil erosion. Twenty simulations were carried out in the laboratory and sixteen in the field with slopes of 11, 21, and 39%. Four water-sprinklers in a straight line generated rainfall with uniformity close to 90%. The constructed simulator was easy to use and low cost, facilitating the necessary experimental replicates to achieve a suitable spatial representation of superficial runoff and soil loss on hillsides.

The availability of water in Chile has shown signs of decline in recent decades. This is problematic because Chile’s economy depends on mining, forestry, and agricultural activities, all limited by the availability of water resources. In this study, daily, monthly and annual flows in 31 basins located in the arid–semiarid zones (29°12′ S–33°58′ S) and in the humid–subhumid zones (34°43′ S–38°30′ S) of Chile were evaluated using the Mann–Kendall trend test and the quantile–Kendall procedure during three periods: 1984–2021 (31 stations), 1975–2021 (20 stations), and 1969–2021 (18 stations). Results showed that, at the annual level, trends were predominantly negative in both climatic zones and over the three periods analyzed. In the arid–semiarid zone, a higher frequency of annual significant negative trends was found in maximum flows in 1969–2021 and 1975–2021, compared to the last period under study. The humid–subhumid zone showed significant annual negative trends in all series analyzed. At the monthly level, on the other hand, the arid-semiarid zone showed a decrease in significant negative trends as the number of years analyzed increased, for all flow types. The humid–subhumid zone did not indicate a similar defined pattern. Likewise, the quantile–Kendall procedure showed a reduction in the significant trends as the length of the time series was increased in the arid-semiarid zone, but no such pattern was observed in the humid–subhumid zone. Furthermore, a relationship was observed for the PDO and the summer month flows for both zones. Consequently, it is concluded that the flow trends are generally negative, and their statistical significance depends on the period studied.

The lack of reliable continuous rainfall records can exacerbate the negative impact of extreme storm events. The inability to describe the continuous characteristics of rainfall from storm events increases the likelihood that the design of hydraulic structures will be inadequate. To mitigate extreme storm impacts and improve water governance at the catchment scale, it is vital to improve the availability of data and the array of tools used to model and forecast hydrological processes. In this paper, we describe and discuss the implementation of a web-based system for the estimation of intensity–duration–frequency (IDF) curves (WEBSE_IDF) in Chile. The web platform was constructed using records from 47 pluviographic gauges available in central Chile (30–40° S), with at least 15 years of reliable records. IDF curves can be generated for durations ranging from 15 min to 24 h. In addition, the extrapolation of rainfall intensity from pluviograph to pluviometric gauges (i.e., 24-h rainfall accumulation) can be carried out using the storm index (S_I) method. IDF curves can also be generated for any spatial location within central Chile using the ordinary Kriging method. These procedures allow the generation of numerical and graphical displays of IDF curves, for any selected spatial location, and for any combination of probability distribution function (PDF), parameter estimation method, and type of IDF model. One of the major advantages of WEBSE_IDF is the flexibility of its database, which can be easily modified and saved to generate IDF curves under user-defined scenarios, that is, changing climate conditions. The implementation and validation of WEBSE_IDF serves as a decision support system, providing an important tool for improving the ability of the Chilean government to mitigate the impact of extreme hydrologic events in central Chile. The system is freely available for students, researchers, and other relevant professionals, to improve technical decisions of public and private institutions.

Wildfires pose an escalating threat to ecosystems and human settlements, making accurate forecasting essential for early mitigation. This study compared three deep learning models for wildfire prediction: Deep Reinforcement Learning (DRL) with Actor–Critic architecture, Convolutional Neural Network (CNN), and Transformer-based models. The models were trained and evaluated using historical data from Chile (2000–2023), including wildfire occurrences, meteorological variables, topography, and vegetation indices. After preprocessing and class balancing, each model was tested over 100 experimental runs. All models achieved outstanding performance, with F1-Scores exceeding 0.999 and perfect AUC-ROC scores. The Transformer model showed a slight advantage over the CNN (99.94%) and Actor–Critic DRL (99.93%) in accuracy. Feature importance analysis identified wind speed, temperature, and vegetation indices as the most influential variables. While DRL offers theoretical benefits for adaptive decision-making, Transformer architectures more effectively capture spatiotemporal dependencies in wildfire dynamics. The findings can support the integration of deep learning models into early warning systems, contributing to proactive wildfire risk management. Future work will include validation with diverse regional datasets, real-time deployment, and collaboration with emergency response agencies.