Climate change threatens the sustainable use of groundwater resources worldwide by affecting future recharge rates. However, assessments of global warming’s impact on groundwater recharge at local scales are lacking. This study provides a continental-scale assessment of groundwater recharge changes in Europe, past, present, and future, at a (5 x 5) km2 resolution under different global warming levels (1.5 K, 2.0 K, and 3.0 K). Utilizing multi-model ensemble simulations from four hydrologic and land-surface models (HMs), our analysis incorporates E-OBS observational forcing data (1970-2015) and five bias-corrected and downscale climate model (GCMs) datasets covering the near-past to future climate conditions (1970-2100). Results reveal a north-south polarization in projected groundwater recharge change: declines over 25-50% in the Mediterranean and increases over 25% in North Scandinavia at high warming levels (2.0-3.0 K). Central Europe shows minimal changes (±5%) with larger uncertainty at lower warming levels. The southeastern Balkan and Mediterranean region exhibited high sensitivity to warming, with changes nearly doubling between 1.5 K and 3.0 K. We identify greater uncertainty from differences among GCMs, though significant uncertainties due to HMs exist in regions like the Mediterranean, Nordic, and Balkan areas. The findings highlight the importance of using multi-model ensembles to assess future groundwater recharge changes in Europe and emphasize the need to mitigate impacts in higher warming scenarios.

Global hydrological models are important decision support tools for policy making in today’s water-scarce world as their process-based nature allows for worldwide water resources assessments under various climate-change and socio-economic scenarios. Although efforts are continuously being made to improve water resource assessments, global hydrological model computational demands have dramatically increased and calibrating them has proven to be difficult. To address these issues, deep-learning approaches have gained prominence in the hydrological community, in particular the development of deep-learning surrogates. Nevertheless, the development of deep-learning global hydrological model surrogates remains constrained, as previous surrogate frameworks only focus on land-surface fluxes for a single spatial resolution. Therefore, we introduce a global hydrological model surrogate framework that includes integrated spatially-distributed runoff routing, human impacts on water resources and the ability to scale across spatial resolutions. To test our framework we develop a deep-learning surrogate for the PCRaster Global Water Balance (PCR-GLOBWB) global hydrological model. Our surrogate performed well when compared to the model outputs, with a median Kling-Gupta Efficiency (KGE) of 0.45, while predictions were at least an order of magnitude faster. Moreover, the multi-resolution surrogate performed similarly to several single-resolution surrogates, indicating limited trade-offs between the surrogate’s broad spatial applicability and its performance. Model surrogates are a promising tool for the global hydrological modeling community, given their potential benefits in reducing computational demands and enhancing calibration. Accordingly, our framework provides an excellent foundation for the community to create their own multi-scale deep-learning global hydrological model surrogates.

Water scarcity represents a critical global challenge, which is driven by diverse complex interactions between natural and anthropogenic factors. Long-term water scarcity often results in depletion of water resources in so-called water scarcity hotspots. To understand the interactions among social, ecological and hydrological components within water scarce systems at such hotspots, we applied causal discovery to observational time series of socio-economic, meteorological, and ecological variables. This resulted in a network representing the causal relations between these variables and Terrestrial Water Storage (TWS). Recognizing the limitations of causal discovery, we supplemented the network with expert knowledge. From this we derived Structural Causal Models (SCMs) that simulate the causal mechanisms influencing TWS trends at the water scarcity hotspots. The resulting SCMs have a variable performance with a median r^2 of 0.52 compared to TWS observations. The SCMs allowed us to estimate the impact of anthropogenic and natural changes on TWS variability at water scarcity hotspots. Our analysis identified population dynamics as the most significant cause of TWS change in hotspots. As such, this study demonstrates how causal discovery and SCMs can enhance modelling of human-water system dynamics affected by water scarcity, improving the understanding of these systems and potential impacts of future changes on water storage and availability. For future research, more detailed data on human-water use is needed to improve the robustness of these models. This is essential for developing effective water management strategies to mitigate water scarcity at hotspots.

Changes in groundwater recharge in areas where irrigated agriculture has increased despite low precipitation and surface water allocation are a big concern. The increasing water demand has raised global drought intensities by 10-500%, worsening hydrological drought. Agriculture accounts for 69% of total groundwater abstraction worldwide, and South America expects to face unprecedented hydrological drought conditions within the next 30 years with uncertain impacts (UNESCO 2022, Wada 2016, Wanders & Wada 2015, Siebert et al. 2015). This study compares coupled and non-coupled versions of PCR-GLOBWB2.0 with MODFLOW regarding model selection and scenario comparison. Natural and human scenarios are presented to understand the effects of groundwater depletion and drought recovery. The scenario comparison evaluation of groundwater fluxes, drought characteristics, and recovery reveals changes in the water cycle due to anthropogenic impacts. This is valuable for identifying drought vulnerability in regions where water management is critical for human consumption and ecosystems.

Multi-year droughts (MYDs), droughts lasting over a year, can have devastating effects on vegetation. Due to climate change, MYDs are expected to become more frequent and intense, making it crucial to assess and understand their impact on vegetation. In this study, we used ERA5 reanalysis and MODIS remote-sensing data to assess vegetation drought sensitivity and quantify the impact of MYDs on seven different vegetation types in specific regions across the globe. We first assessed drought sensitivity by calculating the Enhanced Vegetation Index (EVI) anomaly across different drought timescales. Then, we evaluated the impact of MYDs and normal droughts (NDs) by averaging the EVI anomaly during their respective drought periods. Our analysis shows that croplands, urban areas, and shrublands are highly drought-sensitive, while grasslands and trees are less so. As anticipated, the overall impact of MYDs on vegetation was negative, but there were significant spatial and temporal variations, with some areas showing greening. In general, shrublands experienced the largest decrease in greenness, while trees flourished. Natural water availability was the primary factor influencing vegetation response to MYDs. Vegetation in water-limited areas tends to suffer during MYDs, whereas vegetation in energy-limited areas thrives as long as sufficient water is available. Compared to NDs, MYDs typically have a more negative impact on vegetation. Overall, these findings show that there is no unidirectional vegetation response to MYDs and that local factors, like natural energy and water availability, play a vital role in quantifying the complex interplay between drought and its impacts on vegetation.