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In global hydrological models, groundwater storages and flows are generally simulated by linear reservoir models. Recently, the first global gradient-based groundwater models were developed in order to improve the representation of groundwater-surface water interactions, capillary rise, lateral flows and human water use impacts. However, the reliability of model outputs is limited by a lack of data as well as model assumptions required due to the necessarily coarse spatial resolution. The impact of data quality is presented by showing the sensitivity of a groundwater model to changes in the only available global hydraulic conductivity data-set. To better understand the sensitivity of model output to uncertain spatially distributed parameter inputs, we present the first application of a global sensitivity method for a global-scale groundwater model using nearly 2000 steady-state model runs of the global gradient-based groundwater model G3M. By applying the Morris method in a novel domain decomposition approach that identifies global hydrological response units, spatially distributed parameter sensitivities are determined for a computationally expensive model. Results indicate that globally simulated hydraulic heads are equally sensitive to hydraulic conductivity, groundwater recharge and surface water body elevation, though parameter sensitivities vary regionally. For large areas of the globe, rivers are simulated to be either losing or gaining, depending on the parameter combination, indicating a high uncertainty of simulating the direction of flow between the two compartments. Mountainous and dry regions show a high variance in simulated head due to numerical difficulties of the model, limiting the reliability of computed sensitivities in these regions. This instability is likely caused by the uncertainty in surface water body elevation. We conclude that maps of spatially distributed sensitivities can help to understand complex behaviour of models that incorporate data with varying spatial uncertainties. The findings support the selection of possible calibration parameters and help to anticipate challenges for a transient coupling of the model.
Reduction of greenhouse gas (GHG) emissions to minimize climate change requires very significant societal effort. To motivate this effort, it is important to clarify the benefits of avoided emissions. To this end, we analysed the impact of four emissions scenarios on future renewable groundwater resources, which range from 1600 GtCO2 during the 21st century (RCP2.6) to 7300 GtCO2 (RCP8.5). Climate modelling uncertainty was taken into account by applying the bias-corrected output of a small ensemble of five CMIP5 global climate models (GCM) as provided by the ISI-MIP effort to the global hydrological model WaterGAP. Despite significant climate model uncertainty, the benefits of avoided emissions with respect to renewable groundwater resources (i.e. groundwater recharge (GWR)) are obvious. The percentage of projected global population (SSP2 population scenario) suffering from a significant decrease of GWR of more than 10% by the 2080s as compared to 1971–2000 decreases from 38% (GCM range 27–50%) for RCP8.5 to 24% (11–39%) for RCP2.6. The population fraction that is spared from any significant GWR change would increase from 29% to 47% if emissions were restricted to RCP2.6. Increases of GWR are more likely to occur in areas with below average population density, while GWR decreases of more than 30% affect especially (semi)arid regions, across all GCMs. Considering change of renewable groundwater resources as a function of mean global temperature (GMT) rise, the land area that is affected by GWR decreases of more than 30% and 70% increases linearly with global warming from 0 to 3 ° C. For each degree of GMT rise, an additional 4% of the global land area (except Greenland and Antarctica) is affected by a GWR decrease of more than 30%, and an additional 1% is affected by a decrease of more than 70%.
Local climate change risk assessments (LCCRAs) are best supported by a quantitative integration of physical hazards, exposures and vulnerabilities that includes the characterization of uncertainties. We propose to use Bayesian Networks (BNs) for this task and show how to integrate freely-available output of multiple global hydrological models (GHMs) into BNs, in order to probabilistically assess risks for water supply. Projected relative changes in hydrological variables computed by three GHMs driven by the output of four global climate models were processed using MATLAB, taking into account local information on water availability and use. A roadmap to set up BNs and apply probability distributions of risk levels under historic and future climate and water use was co-developed with experts from the Maghreb (Tunisia, Algeria, Morocco) who positively evaluated the BN application for LCCRAs. We conclude that the presented approach is suitable for application in the many LCCRAs necessary for successful adaptation to climate change world-wide.
Wetlands such as bogs, swamps, or freshwater marshes are hotspots of biodiversity. For 5.1 million km2 of inland wetlands, the dynamics of area and water storage, which strongly impact biodiversity and ecosystem services, were simulated using the global hydrological model WaterGAP. For the first time, the impacts of both human water use and man‐made reservoirs (WUR) and future climate change (CC) on wetlands around the globe were quantified. WUR impacts are concentrated in arid/semiarid regions, where WUR decreased mean wetland water storage by more than 5% on 8.2% of the mean wetland area during 1986–2005 (Am), with highest decreases in groundwater depletion area. Using output of three climate models, CC impacts on wetlands were quantified, distinguishing unavoidable impacts [i.e., at 2 °C global warming (GW)] from avoidable impacts (difference between 3 °C and 2 °C impacts). Even unavoidable CC impacts are projected to be much larger than WUR impacts, also in arid/semiarid regions. On most wetland area with reliable estimates, avoidable CC impacts are more than twice as large as unavoidable impacts. In case of 2 °C GW, half of Am is estimated to be unaffected by mean storage changes of more than 5%, but only one third in case of 3 °C GW. Temporal variability of water storage will increase for most wetlands. Wetlands in dry regions will be affected the most, particularly by water storage decreases in the dry season. Different from wealthier countries, low‐income countries will dominantly suffer from a decrease in wetland water storage due to CC.