This study examined the relative importance of climate change and drinking-water treatment for gastrointestinal illness incidence in children (age <5 years) from period 2046–2065 compared to 1991–2010. The northern Wisconsin (USA) study focused on municipalities distributing untreated groundwater. A time-series analysis first quantified the observed (1991–2010) precipitation and gastrointestinal illness associations after controlling for seasonality and temporal trends. Precipitation likely transported pathogens into drinking-water sources or into leaking water-distribution networks. Building on observed relationships, the second analysis projected how climate change and drinking-water treatment installation may alter gastrointestinal illness incidence. Future precipitation values were modeled by 13 global climate models and three greenhouse-gas emissions levels. The second analysis was rerun using three pathways: (1) only climate change, (2) climate change and the same slow pace of treatment installation observed over 1991–2010, and (3) climate change and the rapid rate of installation observed over 2011–2016. The results illustrate the risks that climate change presents to small rural groundwater municipalities without drinking water treatment. Climate-change-related seasonal precipitation changes will marginally increase the gastrointestinal illness incidence rate (mean: ∼1.5%, range: −3.6–4.3%). A slow pace of treatment installation somewhat decreased precipitation-associated gastrointestinal illness incidence (mean: ∼3.0%, range: 0.2–7.8%) in spite of climate change. The rapid treatment installation rate largely decreases the gastrointestinal illness incidence (mean: ∼82.0%, range: 82.0–83.0%).

A geographic information system (GIS)-based water-budget framework has been developed to study the climate-change impact on regional groundwater recharge, and it was applied to the Southern Hills aquifer system of southwestern Mississippi and southeastern Louisiana, USA. The framework links historical climate variables and future emission scenarios of climate models to a hydrologic model, HELP3, to quantify spatiotemporal potential recharge variations from 1950 to 2099. The framework includes parallel programming to divide a large amount of HELP3 simulations among multiple cores of a supercomputer, to expedite computation. The results show that a wide range of projected potential recharge for the Southern Hills aquifer system resulted from the divergent projections of precipitation, temperature and solar radiation using three scenarios (B1, A2 and A1FI) of the National Center for Atmospheric Research’s Parallel Climate Model 1 (PCM) and the National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Lab’s (GFDL) model. The PCM model projects recharge change ranging from −33.7 to +19.1 % for the 21st century. The GFDL model projects less recharge than the PCM, with recharge change ranging from −58.1 to +7.1 %. Potential recharge is likely to increase in 2010–2039, but likely to decrease in 2070–2099. Projected recharge is more sensitive to the changes in the projected precipitation than the projected solar radiation and temperature. Uncertainty analysis confirms that the uncertainty in projected precipitation yields more changes in the potential recharge than in the projected temperature for the study area.

Numerous modeling approaches are available to provide insight into the relationship between climate change and groundwater recharge. However, several aspects of how hydrological model choice and structure affect recharge predictions have not been fully explored, unlike the well-established variability of climate model chains—combination of global climate models (GCM) and regional climate models (RCM). Furthermore, the influence on predictions related to subsoil parameterization and the variability of observation data employed during calibration remain unclear. This paper compares and quantifies these different sources of uncertainty in a systematic way. The described numerical experiment is based on a heterogeneous two-dimensional reference model. Four simpler models were calibrated against the output of the reference model, and recharge predictions of both reference and simpler models were compared to evaluate the effect of model structure on climate-change impact studies. The results highlight that model simplification leads to different recharge rates under climate change, especially under extreme conditions, although the different models performed similarly under historical climate conditions. Extreme weather conditions lead to model bias in the predictions and therefore must be considered. Consequently, the chosen calibration strategy is important and, if possible, the calibration data set should include climatic extremes in order to minimise model bias introduced by the calibration. The results strongly suggest that ensembles of climate projections should be coupled with ensembles of hydrogeological models to produce credible predictions of future recharge and with the associated uncertainties.