Dr. Muñoz-Ortiz, Enrique

Wetlands occupy a small percentage of the Earth's surface but provide essential ecosystem services, such as water regulation, carbon cycling and habitat support. Patagonian “Vegas” are unique wetland ecosystems characterised by their groundwater recharge and hydrological dynamics, distinct from the surrounding steppe. These ecosystems play a critical role in supporting livestock with up to six times the forage productivity of the surrounding steppe and in storing over 69 g kg−1 of organic carbon. However, the influence of soil structure parameters (e.g., pore size distribution, bulk density) and soil shrinkage behaviour on soil moisture variability and ecosystem functions in Patagonian wetlands remains poorly understood. This study aimed to assess the physical capacity and intensity parameters of soils, including shrinkage properties, within a micro-watershed in southern Patagonia. Our findings reveal significant spatial variability in soil properties, with bulk density (BD) ranging from 0.12 to 1.81 Mg m−3 across topographic positions. Mineral soils on summits and footslopes exhibited high macroporosity (up to 18.1% of total pore volume at 5 cm depth), which facilitates water movement, while organic soils in the Vega centre had a higher total porosity (up to 88.8%) that enhances water and air retention. The coefficient of linear extensibility (COLE) for organic soils reached a level of 0.078, indicating a high shrinkage capacity. This shrinkage influenced the functionality of the porous system, shifting pore roles between air conduction and water storage as larger pores contracted. These dynamics, driven by climate change and increased drying cycles, may lead to significant shifts in soil functionality and ecosystem resilience. Enhanced understanding of soil physical states and their response to environmental changes can support sustainable management strategies, benefiting local agriculture and preserving these critical ecosystems.

Aquifers are complex systems that present significant challenges in terms of characterization due to the lack or absence of watershed-scale hydrogeological information. An alternative to address the need to characterize watershed-scale aquifer behavior is recession flow analysis. Recession flows are flows sustained by groundwater release from the aquifer. Aquifer behavior can be characterized using recession flow records available from gauging stations, and therefore an indirect measure of aquifer behavior is obtained through watershed-scale recession flow records and analysis. This study seeks to identify the minimum time period necessary to characterize the behavior of groundwater storage systems in watersheds with different geological, morphological, and hydrological characteristics. To this end, various watersheds in south-central Chile underwent recession flow analysis, with eight time periods considered (2, 3, 4, 5, 10, 15, 20, and 25 years). The results indicate that 25 years of records are sufficient for the characterization of watershed-scale aquifer behavior, along with the representation of the groundwater storage-release (S-Q) process in watersheds with different geological, morphological, and hydrological characteristics. Additionally, the results show that an initial characterization of the groundwater system behavior in watersheds with different geological characteristics can be carried out with two years of records. This information could be important for practical engineering and the study of groundwater systems in watersheds with limited hydrological and hydrogeological information.

The accuracy of determining arsenic and lead using the optical technique Slurry–Total Reflection X-ray Fluorescence (Slurry-TXRF) was significantly enhanced through the application of a machine learning method, aimed at improving the ecological risk assessment of agricultural soils. The overlapping of the arsenic Kα signal at 10.55 keV with the lead Lα signal at 10.54 keV due to the relatively low resolution of TXRF could compromise the determination of lead. However, by applying a Partial Least Squares (PLS) machine learning algorithm, we mitigated interference variations, resulting in improved selectivity and accuracy. Specifically, the average percentage error was reduced from 15.6% to 9.4% for arsenic (RMSEP improved from 5.6 mg kg−1 to 3.3 mg kg−1) and from 18.9% to 6.8% for lead (RMSEP improved from 12.3 mg kg−1 to 5.03 mg kg−1) compared to the previous univariable model. This enhanced predictive accuracy, within the set of samples concentration range, is attributable to the efficiency of the multivariate calibration first-order advantage in quantifying the presence of interferents. The evaluation of X-ray fluorescence emission signals for 26 different synthetic calibration mixtures confirmed these improvements, overcoming spectral interferences. Additionally, the application of these models enabled the quantification of arsenic and lead in soils from a viticultural subregion of Chile, facilitating the estimation of ecological risk indices in a fast and reliable manner. The results indicate that the contamination level of these soils with arsenic and lead ranges from moderate to considerable.

Due to the climatic conditions in central-southern Chile, there are high heating energy consumption and PM2.5 emissions. Among the alternatives to mitigate it, the Chilean government has implemented subsidies to improve the housings envelope and to replace firewood stoves by pellet stoves and air-to-air heat pumps. Accordingly, for evaluating the effectivity of above-mentioned initiatives, this study proposes to identify the optimal solutions that minimize the energy demand, the environmental impacts, and the global costs, for social housing using different insulation materials and heating systems in four Chilean cities located in central-southern Chile. Results reveal pellet stoves with lower environmental impacts but higher global costs, while heat pumps offer an intermediate solution that can be enhanced with a greener electricity grid, but the global costs are still too high. Firewood stoves could be optimal solution depending on optimization weighting factors. The study emphasizes prioritizing housing envelope improvements in energy policies, followed by heating system enhancements. Although replacing firewood poses challenges due to costs, it is crucial for Chile’s 2050 decarbonization goal. This research provides valuable insights into the complexities and potential solutions for transitioning away from firewood in Chilean social housing.

The use of unmanned aerial vehicles (UAVs) is steadily increasing due to their capacity to capture terrain elevation data with remarkable precision and cost-effectiveness. Nonetheless, their application for estimating water surface elevations and submerged terrain, such as channel bathymetry, remains constrained. Consequently, the development of a digital terrain model that relies on UAV data during low-water periods assumes a more extensive dry channel surface area, thus alleviating the information gap regarding submerged terrain. The objective of this brief report is to validate a hydraulic model for flood calculation. To this end, a 1D steady-state hydrological model of the Ñuble River based on a UAV survey in the low-water period of 2016 was constructed in HEC-RAS v.5.0.3 and compared to water surface elevation observations of the flood on 24 June 2023. The model tends to overestimate the flood, but the errors are considered tolerable for flood calculation (on average, a 10.6% depth error was obtained for a 30-year return period flood); therefore, the hydraulic model derived from remote sensing seems to be an effective alternative for the construction of hydraulic models for flood studies.

Understanding the groundwater storage and release (S-Q) process and its contribution to river flows is essential for different hydrological applications, especially in periods of water scarcity. The S-Q process can be characterized based on recession parameter b, which is the slope of the power–law relationship −dQ/dt = aQb of the recession flow analysis, where recession parameter b represents the linearity of the S-Q process. In various studies, it has been found that this parameter can present high variability, which has been associated with the approach or spatial variability of basin characteristics. However, the variability of parameter b and its relationship with geology and the behavior of groundwater storage over time (evolution over time) have not been sufficiently studied. The objective of this study is to analyze the variability of recession parameter b and its relationship with geological and morphological characteristics and climate variability at different time scales. To this end, 72 drainage basins located in south central Chile were examined via recession flow analysis, considering five different time scales (5 years, 10 years, 15 years, 20 years, and 25 years). In addition, to analyze spatial variability patterns and generate groups of basins with similar characteristics, a cluster analysis was carried out. Clusters were obtained using the principal component analysis (PCA) and K-means methods. The results show that in wet periods, the slope of recession parameter b tends to increase (fast drainage process), while in dry periods, the recession slope tends to decrease (slow drainage processes). In general, the results suggest that the variability of recession coefficient b indicates changes in S-Q behavior; therefore, it could be used as an indicator of the sensitivity of a basin to climate variability.

The study aims to evaluate the applicability of the slurry-TXRF method for estimating background contents and ecological indices in a rapid and convenient way. For this reason, the agricultural soils of the Itata Valley were used as a case study, where 48 soil samples were collected and analyzed. This rapid, minimally sample-intensive, and simultaneous multi-element quantification technique presented high accuracy but lower precision (approx. 20% RSD) compared to the classic total reflection X-ray fluorescence and flame/graphite furnace atomic absorption spectrometry methods, which require sample digestion. Due to the analytical characteristics of Slurry-TXRF, it can be concluded that the lower precision is likely compensated for, and this method represents a valuable alternative for the rapid and efficient assessment of trace element contamination in agricultural soils. The regional median concentrations of Cr, Ni, Cu, Zn, and Cd in the Itata Valley surface soils were found to be 63.7, 9.57, 31.0, 41.1, and 0.56 mg kg−1, respectively, with corresponding upper limits of 47.6, 6.82, 17.0, 30.7, and 0.284 mg kg−1. The ecological indices, including the geoaccumulation index, contamination factor, enrichment factor, and degree of contamination, suggest moderate levels of contamination in the region.

The use of unmanned aerial vehicles (UAVs) has been steadily increasing due to their ability to acquire high-precision ground elevation information at a low cost. However, these devices have limitations in estimating elevations of the water surface and submerged terrain (i.e., channel bathymetry). Therefore, the creation of a digital terrain model (DTM) using UAVs in low-water periods means a greater dry channel surface area and thus reduces the lack of information on the wet area not appropriately measured by the UAV. Under such scenarios, UAV-DTM-derived data present an opportunity for practical engineering in estimating floods; however, the accuracy of estimations against current methods of flood estimations and design needs to be measured. The objective of this study is therefore to develop an exploratory analysis for the creation of hydraulic models of river floods using only UAV-derived topographic information. Hydraulic models were constructed based on DTMs created in (i) the traditional manner, considering the bathymetry measured with RTK-GPS and topography, and via (ii) remote sensing, which involves topography measurement with a UAV and assumes a flat bed in the part of the channel covered by water. The 1D steady-state HEC-RAS model v.5.0.3 was used to simulate floods at different return periods. The applied methodology allows a slightly conservative, efficient, economical, and safe approach for the estimation of floods in rivers, with an RMSE of 6.1, 11.8 and 12.6 cm for the Nicodahue, Bellavista and Curanilahue rivers. The approach has important implications for flood studies, as larger areas can be surveyed, and cost-and time-efficient flood estimations can be performed using affordable UAVs. Further research on this topic is necessary to estimate the limitations and precision in rivers with different morphologies and under different geographical contexts.

River flooding is one of the most widespread natural disasters. Projections indicate that climate change will increase flood hazard in many areas around the world. In this study, we investigate the individual and combined effects of sea level rise, flow increase and riparian vegetation encroachment on flood hazard in the lower Biobío River, Chile. Results show that each has the potential to individually increase flood hazard in certain areas, and that individual effects can compound. Encroachment of riparian vegetation onto previously sparsely vegetated areas of the floodplain, likely a result of the Chilean megadrought, causes higher flow resistance and increased flooding during large events. Somewhat counterintuitively, drought has therefore led to an increase in flood hazard in the study area. Drought risk for most land areas across the globe is expected to increase with climate change. Potential future vegetation encroachment should therefore be included as a key variable in riverine flood hazard studies.

In Chile in recent years, changes in precipitation and temperatures have been reported that could affect water resource management and planning. One way of facing these changes is studying and understanding the behavior of hydrological processes at a regional scale and their different temporal scales. Therefore, the objective of this study is to analyze the importance of the hydrological processes of the HBV model at different temporal scales and for different hydrological regimes. To this end, 88 watersheds located in south-central Chile were analyzed using time-varying sensitivity analysis at five different temporal scales (1 month, 3 months, 6 months, 1 year, and 5 years). The results show that the model detects the temporality of the most important hydrological processes. In watersheds with a pluvial regime, the greater the temporal scale, the greater the importance of soil water accumulation processes and the lower the importance of surface runoff processes. By contrast, in watersheds with a nival regime, at greater temporal scales, groundwater accumulation and release processes take on greater importance, and soil water release processes are less important.