Recently, the satellite images have been used in remote sensing allowing observations with high temporal and spatial distribution. The use of water indices has proved to be an effective methodology in the monitoring of surface water resources. However, precise or automatic methodologies using satellite imagery to determine reservoir volumes are lacking. To fulfill that gap, this methodology proposes three stages: use Google Earth Engine to select images; automatically calculate flooded surface areas applying water indices; determine the volume stored in reservoirs over those years based on the relation between the flooded area and the stored volume. The method was applied in four reservoirs and contemplate Landsat 4 and 5 ETM and Landsat 8 Operational Land Imager. For the calculation of the flooded area the Normalized Difference Water Index Indexes (Gao, 1996, https://doi.org/10.1016/s0034-4257(96)00067-3; McFeeters, 1996, https://doi.org/10.1080/01431169608948714) and the MNDWI index (Xu, 2006, https://doi.org/10.1080/01431160600589179) were applied and tested. The estimation of stored volume of water was made based on the area indices and a cross‐check between real stored volume and calculated volume was made. Finally, an analysis on the selection of the best‐fit water indices was made. The results of every case studies herein displayed showed a quantifiable proficiency and reliability for quite a varied natural conditions. As a conclusion, this methodology could be seen as a tool for water resources management in developing countries, not only, to measure automatically trends of stored volumes and its relation with the precipitation, and could eventually be extended to other types of surface water bodies, as lakes and coastal lagoons.

Today, more than ever, there is a need to implement robust statistical methods to ensure the proper evaluation of water resources data to support decision makers in water resources planning and management. Graphing or mapping data for visualization is the easiest way to communicate trends, especially to a non-technical audience. This paper describes the use of an approach that combines the Mann-Kendall test, Sen slope test and principal component analysis to detect and map the monthly trends of piezometric time series and their magnitude in the period 1979–2008. The data were obtained in 23 shallow wells in the alluvial aquifers of the Elqui River basin in central Chile, an area characterized by scarce water resources and intense agricultural and mining activities. The results show significant downward trends at the majority of the wells. Because groundwater in these shallow wells is highly dependent on the water in the river and its tributaries, the reasons for these downward trends are mainly related to a decrease of streamflow observed in the Elqui River. The streamflow is derived from mountain snowmelt rather than from rainfall, which showed no flow trend during the same period.

This paper describes a simulation-optimization model aimed at helping water utilities determine the best way to operate large-scale multisource water-supply systems. The operation of water systems is optimized taking into account the principal planning objectives defined for interventions that include reducing operating costs, satisfying demand, delivering water of appropriate quality, and not prompting the use of emergency sources. The model allows that these planning objectives may not be completely fulfilled in critical circumstances (e.g., droughts) when goals have to be prioritized by the water utilities. The model is a highly nonlinear programming problem and is solved with the general algebraic modeling system (GAMS), using the MINOS algorithm (GAMS/MINOS). The application of the model to the multimunicipal urban water-supply system of the Algarve shows its capabilities for optimizing the operation considering economic and noneconomic goals included in the objective function and to cope with future shortages in critical circumstances by improving the conjunctive use of the different water sources with interannual planning time horizons.

Mapping the suitability of groundwater-dependent vegetation in semi-arid Mediterranean areas is fundamental for the sustainable management of groundwater resources and groundwater-dependent ecosystems (GDEs) under the risks of climate change scenarios. For the present study the distribution of deep-rooted woody species in southern Portugal was modeled using climatic, hydrological and topographic environmental variables. To do so, Quercus suber, Quercus ilex and Pinus pinea were used as proxy species to represent the groundwater-dependent vegetation (GDV). Model fitting was performed between the proxy species Kernel density and the selected environmental predictors using (1) a simple linear model and (2) a geographically weighted regression (GWR) to account for autocorrelation of the spatial data and residuals. When comparing the results of both models, the GWR modeling results showed improved goodness of fit as opposed to the simple linear model. Climatic indices were the main drivers of GDV density, followed by a much lower influence by groundwater depth, drainage density and slope. Groundwater depth did not appear to be as pertinent in the model as initially expected, accounting only for about 7 % of the total variation compared to 88 % for climate drivers. The relative proportion of model predictor coefficients was used as weighting factors for multicriteria analysis to create a suitability map for the GDV in southern Portugal showing where the vegetation most likely relies on groundwater to cope with aridity. A validation of the resulting map was performed using independent data of the normalized difference water index (NDWI), a satellite-derived vegetation index. June, July and August of 2005 NDWI anomalies, for the years 1999–2009, were calculated to assess the response of active woody species in the region after an extreme drought. The results from the NDWI anomalies provided an overall good agreement with the suitability to host GDV. The model was considered to be reliable for predicting the distribution of the studied vegetation. The methodology developed to map GDVs will allow for the prediction of the evolution of the distribution of GDV according to climate change and aid stakeholder decision-making concerning priority areas of water resource management.

This paper reports on the qualitative and quantitative screening of groundwater sources for integration into the public water supply system of the Algarve, Portugal. The results are employed in a decision support system currently under development for an integrated water resources management scheme in the region. Such a scheme is crucial for several reasons, including the extreme seasonal and annual variations in rainfall, the effect of climate change on more frequent and long-lasting droughts, the continuously increasing water demand and the high risk of a single-source water supply policy. The latter was revealed during the severe drought of 2004 and 2005, when surface reservoirs were depleted and the regional water demand could not be met, despite the drilling of emergency wells. For screening and selection, quantitative criteria are based on aquifer properties and well yields, whereas qualitative criteria are defined by water quality indices. These reflect the well's degree of violation of drinking water standards for different sets of variables, including toxicity parameters, nitrate and chloride, iron and manganese and microbiological parameters. Results indicate the current availability of at least 1100 l s⁻¹ of high quality groundwater (55% of the regional demand), requiring only disinfection (900 l s⁻¹) or basic treatment, prior to human consumption. These groundwater withdrawals are sustainable when compared to mean annual recharge, considering that at least 40% is preserved for ecological demands. A more accurate and comprehensive analysis of sustainability is performed with the help of steady-state and transient groundwater flow simulations, which account for aquifer geometry, boundary conditions, recharge and discharge rates, pumping activity and seasonality. They permit an advanced analysis of present and future scenarios and show that increasing water demands and decreasing rainfall will make the water supply system extremely vulnerable, with a high risk of groundwater salinization and ecosystem degradation.