This is an application of a spatial copula model that is fitted to a real world data set. The copula model allows modeling of pure spatial dependence independently of the marginal distribution. Using non‐Gaussian copula models it is demonstrated that the spatial dependence structure of the Borden aquifer is significantly non‐Gaussian—despite the fact that the Borden aquifer is commonly thought of as a relatively homogeneous porous medium with a small variance of hydraulic conductivity. In addition to evaluating the spatial dependence structure of the Borden hydraulic conductivity data set using copulas, goal of this study is to explore if the structure of the hydraulic conductivity field influences a physical property, such as plume evolution as evaluated by second spatial central moments of concentration fields. For this comparison, two types of hydraulic conductivity fields were fitted to the Borden hydraulic conductivity data set: one with a Gaussian and the other with a non‐Gaussian type of dependence. These two types of hydraulic conductivity fields were constructed such that their second‐order spatial moments are identical, and hence they cannot be distinguished by semivariogram‐based geostatistics. This paper illustrates that the spatial dependence structure of the Borden hydraulic conductivity data set is significantly non‐Gaussian. Despite the fact that Borden is a relatively homogeneous porous medium, and despite the fact that both types of spatial fields are not distinguishable by their variograms, the solute transport characteristics based on these two types of isotropic fields differ significantly in two‐dimensional settings. The difference is less pronounced in three‐dimensions with anisotropy. It is postulated that non‐Gaussian spatial dependence of hydraulic conductivity and a more skewed marginal distribution of hydraulic conductivity will have significant implications in the other more heterogeneous aquifers.

Droughts are serious natural hazards, especially in semi-arid regions. They are also difficult to characterize. Various summary metrics representing the dryness level, denoted drought indices, have been developed to quantify droughts. They typically lump meteorological variables and can thus directly be computed from the outputs of regional climate models in climate-change assessments. While it is generally accepted that drought risks in semi-arid climates will increase in the future, quantifying this increase using climate model outputs is a complex process that depends on the choice and the accuracy of the drought indices, among other factors. In this study, we compare seven meteorological drought indices that are commonly used to predict future droughts. Our goal is to assess the reliability of these indices to predict hydrological impacts of droughts under changing climatic conditions at the annual timescale. We simulate the hydrological responses of a small catchment in northern Spain to droughts in present and future climate, using an integrated hydrological model calibrated for different irrigation scenarios. We compute the correlation of meteorological drought indices with the simulated hydrological time series (discharge, groundwater levels, and water deficit) and compare changes in the relationships between hydrological variables and drought indices. While correlation coefficients linked with a specific drought index are similar for all tested land uses and climates, the relationship between drought indices and hydrological variables often differs between present and future climate. Drought indices based solely on precipitation often underestimate the hydrological impacts of future droughts, while drought indices that additionally include potential evapotranspiration sometimes overestimate the drought effects. In this study, the drought indices with the smallest bias were the rainfall anomaly index, the reconnaissance drought index, and the standardized precipitation evapotranspiration index. However, the efficiency of these drought indices depends on the hydrological variable of interest and the irrigation scenario. We conclude that meteorological drought indices are able to identify years with restricted water availability in present and future climate. However, these indices are not capable of estimating the severity of hydrological impacts of droughts in future climate. A well-calibrated hydrological model is necessary in this respect.