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.

Fresh submarine groundwater discharge (SGD) and seawater intrusion (SWI) are complementary processes at the interface of coastal groundwater and oceans. Multiple common drivers enable or limit SGD and SWI. However, we find that SGD and SWI are rarely studied simultaneously. In this meta-analysis, we synthesize 1298 publications, examining drivers of SGD and SWI, where and why they are studied, and at which scales they are impacted by their drivers. Studies of SGD and SWI accumulate in urban coastal basins with high gross domestic product (GDP), and high permeabilities, where measurable groundwater fluxes are expected. We find, that studies investigate various drivers, but rarely assess the scales they act at. Effects of temporally recurring processes (e.g., tides) are studied more often and are better known than effects of spatial variability (e.g., permeability). Future studies should investigate SGD and SWI simultaneously, report impact scales of drivers explicitly and explore unchartered coastlines.

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.

With a large network of dikes that in the future will protect up to 15% of the world’s population from flooding, more extreme river discharges that result from climate change will dramatically increase the flood risk of these protected societies. Precise calculations of dike stability under adverse loading conditions will become increasingly important, though the hydrological impacts on dike stability, particularly the effects of groundwater flow, are often oversimplified in stability calculations. To include these effects, we use a coupled hydro-stability model to indicate relations between the geometry, subsurface materials, groundwater hydrology and stability of a dike regarding soil slip and basal sliding mechanisms. Sensitivity analyses are performed with this model using a large number of parameter combinations, while assessing both the individual sensitivity as combined effects. The analyses show that the material type of the dike and its slope are the more important parameters influencing the stability, followed by the shallow subsurface type and dike crest elevation. The material of the dike and shallow subsurface is additionally important, as a change towards sandier material can either result in either an increase or a decrease of the stability. A database created by an extensive Monte Carlo analysis provides further evidence for these relations and is used to estimate failure probabilities for dike stretches that have not been assessed in detail. Despite the use of a simplified model, not including small-scale heterogeneity, remaining soil strength and transient groundwater flow, the application of the method to a case study proves its applicability.