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Water balance modeling is an essential aspect of water management projects. This study presents WaterpyBal, a tool that generates spatial–temporal water balance models, focused on diffused precipitation and recharge modeling. WaterpyBal has flexible input data and modeling time intervals and integrates different stages of the water balance assessment, such as data interpolation and evapotranspiration and infiltration calculations, while taking into account soil characteristics and urban water cycle. WaterpyBal calculates water budget parameters such as recharge, deficit, and runoff and can be used to create maps, datasheets, and raster archives. WaterpyBal has a modular design that ensures its further development. This Python library is accompanied by the WaterpyBal Studio, a graphic user interface that facilitates the usage of WaterpyBal in water management projects. The functionality of WaterpyBal is demonstrated using a synthetic example. In essence, WaterpyBal supports sustainable groundwater management projects and ensures reproducible research results in these environmental fields.

This study reports precipitation samples covering a 3-year period (January 2020 to April 2023) with extensive monitoring of meteorological parameters in the local atmosphere, satellite imagery and HYSPLIT backward trajectory models humid air masses causing the sampled reception in the Los Pozuelos basin in NW Argentina.

InSAR coherence-change detection (CCD) is a promising remote sensing technique that is able to map areas affected by torrential sediment transport triggered by flash floods in arid environments. CCD maps the changes in the interferometric coherence between synthetic aperture radar images (InSAR coherence), a parameter that measures the stability of the radar signal between two different SAR images, i.e., data acquisitions. In arid environments, such changes are mainly due to changes in the surface. However, the residual effect of other factors on the InSAR coherence cannot be completely excluded. Therefore, CCD-based maps contain the uncertainty of whether the detected changes are actual changes in the observed surface or just errors related to those residual effects. Thus, in this paper, the results of four CCD mapping methods, with different degrees of complexity and sensitivity to the different factors affecting the InSAR coherence, are compared in order to evaluate the existence of the errors and their importance. The obtained CCD maps are also compared with changes in satellite optical images and a field campaign. The results lead to the conclusion that CCD maps are reliable in the identification of the zones affected by sediment transport, although the precision in the delimitation of the affected area remains an open issue. However, highly rugged relief areas still require a thorough analysis of the results in order to discard the geometric effects related to the perpendicular baseline.

Coherence change detection (CCD) is a remote sensing technique used to map phenomena that, under certain conditions, can be directly related to changes in Interferometric SAR (InSAR) coherence. Mapping the areas affected by sediment transport events in arid environments is one of the most common applications of CCD. However, the reliability of these maps remains an unsolved issue. This paper focuses on verifying that InSAR coherence is indeed able to detect all the fluvial sediment transport events that have actually mobilised sediments in arid environments by building a classification model and validating its results. The proposed methodology is tested in three study areas in Salar de Atacama, Chile, using three years of Sentinel data plus a fourth year for validation, and meteorological records of rainfall, the relative humidity of the air and snow cover. The results prove that InSAR coherence can be used to remotely detect sediment transport events related to flash floods in arid environments, that it might have a greater detection ability than meteorological records and that the perpendicular baseline does have a relevant effect on the InSAR coherence that needs to be considered. All these findings will increase the reliability of maps based on InSAR coherence. In addition, the proposed method will allow focusing the mapping tasks only on the relevant dates and, once calibrated, the classification model will enable the automatised remote detection of new events.

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.

The data is a compilation of information from previous studies and during the study published in “Spatial Distribution of Factors Controlling Stable Isotopes in Precipitation in Northern Chile”.

The data is a compilation of information from previous studies and during the study published in “Spatial Distribution of Factors Controlling Stable Isotopes in Precipitation in Northern Chile”.

SM_6_Executable: This executable is for understand and model the relationships among meteorological parameters, geospatial features and the isotopic composition of precipitation. The results from this executable have been published in “Spatial Distribution of Factors Controlling Stable Isotopes in Precipitation in Northern Chile”.

The hydrologic cycle behaviour in the Bolivian Altiplano and how it interacts with human activity are still not fully understood. The hydraulic cycle is examined in a novel way in this work by considering extended time series of associated hydrologic and climatic data. Results show that human exploitation and losses from the sewage and water supply networks in the urban areas might cause an hydraulic imbalance in the region. These anthropogenic incomes, at the same time, increase concentrations of different pollutants. Water isotopic analysis reinforces the conceptual model showing that the groundwater sampled are mainly from precipitation. Workshops with local municipalities supported and defined the most relevant anthropic pressures in the basin. This information supports a vulnerability analysis in collaboration among them. The decision-making process for government entities might be strengthened by this study, which would help to develop long-term strategies to prevent and mitigate the issues found in the evaluation of water vulnerability in the region.

To reduce uncertainty in the identification of the recharge areas in the Peripheral Aquifer of the Salar de Atacama (SdA), a few studies have investigated the isotopic characteristics and moisture sources of precipitation in the SdA basin. In the present study, the seasonal cycle of meteorological parameters and the relationships of this cycle with sea surface temperature anomalies are shown, the sources of humidity are identified, and the types of clouds producing precipitation are defined. Finally, the isotopic compositions of precipitation, surface water and groundwater in the SdA basin and the Altiplano-Puna Plateau basins are analysed to identify the area recharging the northern, eastern and southern subbasins of the SdA. In summer, when the highest temperature, relative humidity and precipitation values of the year are recorded, the precipitation is due to deep convection. The trajectories of the arriving air masses can be classified into three groups: from the North Atlantic Ocean across the Amazon basin, from the South Atlantic Ocean across the La Plata River basin and the Gran Chaco, and from the Pacific Ocean. In winter, when the temperature, relative humidity and precipitation are lower, the moisture masses come from the Pacific Ocean. Winter precipitation is more depleted in heavy isotopes than summer precipitation. The isotopic analysis of precipitation, surface water and groundwater shows that recharge of the eastern subbasins of the SdA occurs by diffuse infiltration of precipitation and concentrated infiltration of surface water, both within the hydrographic basin of the SdA. The meteoric source of the waters in the Altiplano-Puna Plateau basins is isotopically lighter than the waters found in the side basins of the SdA, so there is no significant water quantity transfer to the peripheral aquifers of the SdA from outside the hydrographic basin.