Nitrate isotopic values are often used as a tool to understand sources of contamination in order to effectively manage groundwater quality. However, recent literature describes that biogeochemical reactions may modify these values. Therefore, data interpretation is difficult and often vague. We provide a discussion on this topic and complement the study using halides as comparative tracers assessing an aquifer underneath a sub-humid to humid region in NE Mexico. Hydrogeological information and stable water isotopes indicate that active groundwater recharge occurs in the 8000km(2) study area under present-day climatic and hydrologic conditions. Nitrate isotopes and halide ratios indicate a diverse mix of nitrate sources and transformations. Nitrate sources include organic waste and wastewater, synthetic fertilizers and soil processes. Animal manure and sewage from septic tanks were the causes of groundwater nitrate pollution within orchards and vegetable agriculture. Dairy activities within a radius of 1,000 m from a sampling point significantly contributed to nitrate pollution. Leachates from septic tanks caused nitrate pollution in residential areas. Soil nitrogen and animal waste were the sources of nitrate in groundwater under shrubland and grassland. Partial denitrification processes helped to attenuate nitrate concentration underneath agricultural lands and grassland, especially during summer months.

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Regional Climate Models (RCMs) are an essential tool for analysing regional climate change impacts as they provide simulations with more small-scale details and expected smaller errors than global climate models. There has been much effort to increase the spatial resolution and simulation skill of RCMs, yet the extent to which this improves the projection of hydrological change is unclear. Here, we evaluate the skill of five reanalysis-driven Euro-CORDEX RCMs in simulating precipitation and temperature, and as drivers of a hydrological model to simulate river flow on four UK catchments covering different physical, climatic and hydrological characteristics. We use a comprehensive range of evaluation indices for aspects of the distribution such as means and extremes, as well as for the structure of time series. We test whether high-resolution RCMs provide added value, through analysis of two RCM resolutions, 0.44° (50 km) and 0.11° (12.5 km), which are also bias-corrected employing the parametric quantile-mapping (QM) method, using the normal distribution for temperature, and the Gamma (GQM) and Double Gamma (DGQM) distributions for precipitation. The performance of these is considered for a range of meteorological variables and for the skill in simulating hydrological impacts.In a small catchment with complex topography, the 0.11° RCMs outperform their 0.44° version for precipitation and temperature, but when used in combination with the hydrological model, fail to capture the observed river flow distribution. In the other (larger) catchments, only one high-resolution RCM consistently outperforms its low-resolution version, implying that in general there is no added value from using the high-resolution RCMs in those catchments. GQM decreases most of the simulation biases, except for extreme precipitation and high flows, which are further decreased by DGQM. Bias correction does not improve the representation of daily temporal variability, but it does for monthly variability, in particular when applying DGQM, which reduces most of the simulation biases. Overall, an increase in RCM resolution does not imply a better simulation of hydrology and bias-correction represents an alternative to ease decision-making.An important driver for increasing RCM resolution is the need to improve the analysis of climate change impacts for decision-making (e.g. Macadam et al., 2016;Qian et al., 2015). For hydrology, the standard analysis of climate change impacts generally involves coupling uncorrected or bias-corrected GCM or RCM precipitation and temperature outputs with hydrological models to simulate river flow scenarios (e.g.

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