RESUMEN
Precipitation plays a crucial role in the natural hydrological cycle. Understanding the spatial and temporal variations of precipitation isotopes is essential for identifying hydrological, meteorological, and ecological processes. In high mountain areas with arid and semi-arid conditions, especially in regions with endorheic basins, the portion of precipitation that infiltrates the groundwater as the primary source of water recharge. However, estimating this recharge is challenging and prone to high uncertainty. The main objective of this study was to implement a robust and detailed methodology to analyze the influence of meteorological variables and the origin of moisture sources on the stable isotopic composition (δ18O and δ2H) of precipitation. As an illustrative case, we focused on the Los Pozuelos Basin, an endorheic basin in the Altiplano-Puna region of the Andes. The analysis incorporated precipitation samples collected over a 3-year period (January 2020 to April 2023) along with comprehensive monitoring of local atmospheric variables, satellite imagery, and HYSPLIT backward trajectory models. The examination involved a multivariate analysis of meteorological and stable isotope data and atmospheric transport pattern. Precipitation characteristics exhibited seasonal variability, with summer precipitation being depleted in heavy isotopes due to its extended continental journey and the recycling it undergoes while crossing the Amazon basin with convective activity. Another moisture path from the Atlantic Ocean, via the Río de la Plata or Gran Chaco basin, represented an intermediate isotopic stage. La Niña events intensified westerly winds, drawing moist air masses from the Pacific Ocean and causing rainfall in the study area. In winter, precipitation comes from the Pacific Ocean and isotopically enriched due to the low amount of precipitation and lower convective activity. By employing a meticulous methodology and multivariate statistical analysis, the study contributes positively to the broader discourse on water resource management and conservation in arid and semi-arid environments.
RESUMEN
Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that estimates isotopic compositions through machine learning algorithms with user-defined variables. Isocompy includes dataset preprocessing, outlier detection, statistical analysis, feature selection, model validation and calibration and postprocessing. This tool has the flexibility to operate with discontinuous inputs in time and space. The automatic decision-making procedures are knitted in different stages of the algorithm, although it is possible to manually complete each step. The extensive output reports, figures and maps generated by Isocompy facilitate the comprehension of stable water isotope studies. The functionality of Isocompy is demonstrated with an application example involving the meteorological features and isotopic composition of precipitation in N Chile, which are compared with the results produced in previous studies. In essence, Isocompy offers an open source foundation for isotopic studies that ensures reproducible research in environmental fields.