Dr. Muñoz-Ortiz, Enrique

Data on historical extreme events provides information not only for water resources planning and management but also for the design of disaster-prevention measures. However, most basins around the globe lack long-term hydro-meteorological information to derive the trend of hydrological extremes. This study aims to investigate a method to estimate maximum and minimum flow trends in basins with limited streamflow records. To carry out this study, data from the Allipén River watershed (Chile), the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model at a daily time step, and an uncertainty analysis were used. Through a calibration using only five years of records, 21-year mean daily flow series were generated and the extreme values derived. To analyze the effect of the length of data availability, 2, 5, and 10 years of flows were eliminated from the analyses. The results show that in the case of 11 years of simulated flows, the annual maximum and minimum flow trends present greater uncertainty than in the cases of 16 and 19 years of simulated flows. Simulating 16 years, however, proved to properly simulate the observed long-term trends. Therefore, in data-scarce areas, the use of a hydrological model to simulate extreme mean daily flows and estimate long-term trends with at least 16 years of meteorological data could be a valid option.

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 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.

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 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.

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.

The most used global sensitivity analysis (GSA) method is based on variance. This is performed using Monte Carlo Sampling (MCS) or Latin Hypercube Sampling (LHS). It requires a large sample to obtain accurate estimates. Density-based methods, such as the GSA PAWN, have been developed to reduce the sample size without compromising the result. PAWN is simpler than other methods because it uses cumulative density functions (CDF) instead of probability density. This method has been widely used in areas such as environmental engineering with very good results, reducing computation time. However, its use in structural engineering is incipient. The PAWN method was used to classify the design variables of the isolation system in relation to their sensitivity, and in relation to the seismic response of industrial storage racks. The above was analyzed in terms of the effectiveness of each variable to reduce the seismic demand using a novel base isolation kinematic device (BIKD). Racks with different combinations of their structural parameters such as the number of storage levels, the height between them, and isolation period, among others, were studied. The dimensions of the racks were chosen to match those that would later be experimentally tested on shaking table. An earthquake whose response spectrum matched the design spectrum of current Chilean regulations, was considered as seismic forcing. The maximum base shear load, the displacement of the top level of storage and the floor drift were considered as target responses to be studied. Fixed base racks (FBR), as reference, and base-isolated racks (BIR) were analyzed. The results showed the effectiveness of using the BIKD system in reducing all three-target responses up to one order of magnitude. Additionally, it was determined that the parameters that have the greatest influence on the response correspond to the number of storage levels and the height between them, both for FBR and BIR.

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.

Depending on the purpose of the study, aggregated hydrological models are preferred over distributed models because they provide acceptable results in terms of precision and are easy to run, especially in data scarcity scenarios. To obtain acceptable results in terms of hydrological process representativeness, it is necessary to understand and assess the models. In this study, the relative importance of the parameters of the Hydrologiska Byråns Vattenbalansavdelning (HBV) model is analyzed using sensitivity analysis to detect if the simulated processes represent the predominant hydrological processes at watershed scale. As a case study, four watersheds with different hydrological regimes (glacial and pluvial) and therefore different dominant processes are analyzed. The results show that in the case of the rivers with a glacial regime, the model performance depends highly on the snow module parameters, while in the case of the rivers with a pluvial regime, the model is sensitive to the soil and evapotranspiration modules. The results are directly related to the hydrological regime, which indicates that the HBV model, complemented by sensitivity analysis, is capable of both detecting and representing hydrological processes at watershed scale.

Granitic residual soils are soils formed by the in situ weathering of intrusive granitic rocks and are present in different parts of the world. Due to their large presence, many civil engineering projects are carried out on and within these soils. Therefore, a correct characterization of the slopes is necessary for slope stability studies. This investigation aims to study the influence of the values of geomechanical parameters (specific weight, cohesion, and friction angle) and the geometry of a slope (height and inclination) on slope stability of residual granitic soils in dry and static conditions. To this end, an automatic system was developed for the numerical study of cases using the finite element method with limit analysis. The system allows modeling, through Monte Carlo simulation and different slope configurations. With this system, the safety factors of 5000 cases were obtained. The results of the models were processed through the SAFE toolbox, performing a Regional Sensitivity Analysis (RSA). The results of this research concluded that the order of influence of the factors were: slope angle > slope height > cohesion > friction angle > unit weight (β > H > c > ϕ > γ).