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Abstract Accurate groundwater representation in land surface models (LSMs) is vital for water and energy cycle studies, water resource assessments, and climate projections. Yet, many LSMs do not consider key processes including lateral groundwater flow and aquifer pumping, especially at the global scale. This study simulates these processes using an enhanced version of the Community Land Model (CLM5) and evaluates their roles at three spatial resolutions (0.5°, 0.25°, 0.1°). Results show that lateral flow strongly modulates water table depth and capillary rise at all resolutions. The magnitude of mean lateral flow increases from 25 mm/year at 0.5° to 36 mm/year at 0.25°, and 52 mm/year at 0.1° resolution, with pumping inducing lateral flow even at 0.5° (∼50 km), a typical grid size in global LSMs. Further, lateral flow alters runoff in regions with high recharge and shallow water table (e.g., eastern North America and Amazon basin), and soil moisture and ET in regions with comparatively low recharge and deeper water table (e.g., western North America, central Asia, and Australia) through enhanced capillary rise. Runoff alteration by lateral flow increases substantially with resolution, from a maximum of 15 mm/month at 0.5° to 20 mm/month and 25 mm/month at 0.25° and 0.1°, respectively; the impact of resolution on soil moisture and ET is less pronounced. While the model does not fully capture deeper water tables—warranting further enhancements—it provides valuable insights on how lateral groundwater flow impacts land surface processes, highlighting the importance of lateral groundwater flow and pumping in global LSMs. 

Characterizing the impact of human actions on terrestrial water fluxes and storages at multi‐basin, continental, and global scales has long been on the agenda of scientists engaged in climate science, hydrology, and water resources systems analysis. This need has resulted in a variety of modeling efforts focused on the representation of water infrastructure operations. Yet, the representation of human‐water interactions in large‐scale hydrological models is still relatively crude, fragmented across models, and often achieved at coarse resolutions (10–100 km) that cannot capture local water management decisions. In this commentary, we argue that the concomitance of four drivers and innovations is poised to change the status quo: “hyper‐resolution” hydrological models (0.1–1 km), multi‐sector modeling, satellite missions able to monitor the outcome of human actions, and machine learning are creating a fertile environment for human‐water research to flourish. We then outline four challenges that chart future research in hydrological modeling: (a) creating hyper‐resolution global data sets of water management practices, (b) improving the characterization of anthropogenic interventions on water quantity, stream temperature, and sediment transport, (c) improving model calibration and diagnostic evaluation, and (d) reducing the computational requirements associated with the successful exploration of these challenges. Overcoming them will require addressing modeling, computational, and data development needs that cut across the hydrology community, thereby requiring a major communal effort. 

Global Water Models (GWMs) are critical tools for understanding the Earth's water cycle and water resource management under changing climate and accelerating human interventions. While GWMs have been evaluated for hydrologic fluxes (e.g., river discharge) and the role of representing human activities, there is a persistent gap in understanding models’ ability to simultaneously reproduce fluxes and storages (e.g., terrestrial water storage; TWS). Here, we show that eight state-of-the-art GWMs do not consistently reproduce discharge and TWS with same efficacy across varied geographic and climatic regions. Further, model performance for discharge deteriorates as human impacts intensify. While a general agreement between simulated and observed TWS trends is found in two-third of major global river basins, models tend to underestimate the trends in both directions. Likewise, no single model simulates TWS trends and seasonality accurately and uniformly across major global river basins. While improvements in capturing basin-averaged TWS trends, spatial distributions, and seasonal fluctuations have been achieved compared to previous reports, challenges remain in accurately reproducing both fluxes and storages, owing primarily to inadequate representation of human activities in heavily managed regions. This study underscores critical disparities in GWM performance, emphasizing the need for further model enhancements which is crucial for improved and more robust hydrologic assessments and predictions under climate change. 

Abstract. Groundwater serves as a crucial freshwater resource for people and ecosystems, playing a vital role in adapting to climate change. Yet, its availability and dynamics are affected by climate variations, changes in land use, and abstraction. Despite its importance, our understanding of how global change will influence groundwater in the future remains limited. Multi-model ensembles are powerful tools for impact assessments; compared to single-model studies, they provide a more comprehensive understanding of uncertainties and enhance the robustness of projections by capturing a range of possible outcomes. However, to date, no ensemble of groundwater models has been available to assess the impacts of global change. Here, we present the new Groundwater sector within ISIMIP, which combines multiple global, continental, and regional-scale groundwater models. We describe the rationale for the sector, the sectoral output variables that underpinned the modeling protocol, and showcase current model differences and possible future analysis. Currently, eight models are participating in this sector, ranging from gradient-based groundwater models to specialized karst recharge models, each producing up to 19 out of 23 modeling protocol-defined output variables. To showcase the benefits of a joint sector, we utilize available model outputs of the participating models to show the substantial differences in estimating water table depth (global arithmetic mean 6–127 m) and groundwater recharge (global arithmetic mean 78–228 mm yr−1), which is consistent with recent studies on the uncertainty of groundwater models, but with distinct spatial patterns. We further outline synergies with 13 of the 17 existing ISIMIP sectors and specifically discuss those with the global water and water quality sectors. Finally, this paper outlines a vision for ensemble-based groundwater studies that can contribute to a better understanding of the impacts of climate change, land use change, environmental change, and socio-economic change on the world's largest accessible freshwater store – groundwater. 

Abstract Crucial to the assessment of future water security is how the land model component of Earth System Models partition precipitation into evapotranspiration and runoff, and the sensitivity of this partitioning to climate. This sensitivity is not explicitly constrained in land models nor the model parameters important for this sensitivity identified. Here, we seek to understand parametric controls on runoff sensitivity to precipitation and temperature in a state‐of‐the‐science land model, the Community Land Model version 5 (CLM5). Process‐parameter interactions underlying these two climate sensitivities are investigated using the sophisticated variance‐based sensitivity analysis. This analysis focuses on three snow‐dominated basins in the Colorado River headwaters region, a prominent exemplar where land models display a wide disparity in runoff sensitivities. Runoff sensitivities are dominated by indirect or interaction effects between a few parameters of subsurface, snow, and plant processes. A focus on only one kind of parameters would therefore limit the ability to constrain the others. Surface runoff exhibits strong sensitivity to parameters of snow and subsurface processes. Constraining snow simulations would require explicit representation of the spatial variability across large elevation gradients. Subsurface runoff and soil evaporation exhibit very similar sensitivities. Model calibration against the subsurface runoff flux would therefore constrain soil evaporation. The push toward a mechanistic treatment of processes in CLM5 have dampened the sensitivity of parameters compared to earlier model versions. A focus on the sensitive parameters and processes identified here can help characterize and reduce uncertainty in water resource sensitivity to climate change. 

Abstract. Flow regimes in major global river systems are undergoing rapid alterations due to unprecedented stress from climate change and human activities. The Mekong River basin (MRB) was, until recently, among the last major global rivers relatively unaltered by humans, but this has been changing alarmingly in the last decade due to booming dam construction. Numerous studies have examined the MRB's flood pulse and its alterations in recent years. However, a mechanistic quantification at the basin scale attributing these changes to either climatic or human drivers is lacking. Here, we present the first results of the basin-wide changes in natural hydrological regimes in the MRB over the past 8 decades and the impacts of dams in recent decades by examining 83 years (1940–2022) of river regime characteristics simulated by a river–floodplain hydrodynamic model that includes 126 major dams in the MRB. Results indicate that, while the Mekong River's flow has shown substantial decadal trends and variabilities, the operation of dams in recent years has been causing a fundamental shift in the seasonal volume and timing of river flow and extreme hydrological conditions. Even though the dam-induced impacts have been small so far and most pronounced in areas directly downstream of major dams, dams are intensifying the natural variations in the Mekong's mainstream wet-season flow. Further, the additional 65 dams commissioned since 2010 have exacerbated drought conditions by substantially delaying the MRB's wet-season onset, especially in recent years (e.g., 2019 and 2020), when the natural wet-season durations are already shorter than in normal years. Further, dams have shifted by up to 20 % of the mainstream annual volume between the dry and wet seasons in recent years. While this has a minimal impact on the MRB's annual flow volume, the flood occurrence in many major areas of Tonlé Sap and the Mekong Delta has been largely altered. This study provides critical insights into the long-term hydrological variabilities and impacts of dams on the Mekong River's flow regimes, which can help improve water resource management in light of intensifying hydrological extremes. 

Abstract Agricultural irrigation has experienced rapid expansion, and its growing freshwater consumption is potentially exacerbating water scarcity issues. Previous studies predominantly relied on observations or land-only simulations, often neglecting land–atmosphere interactions or failing to capture long-term evolution. We therefore analyse the effects of historical irrigation expansion on water fluxes and resources using seven Earth system models. Here we show that irrigation expansion in many regions substantially decreases the net water influx from the atmosphere to land, further aggravating the existing drying trends caused by climate change. For example, irrigation expansion changed the trend of this net influx from −0.664 ( ± 0.283) to −1.461 ( ± 0.261) mm yr⁻²in South Asia after 1960. Consequently, the local terrestrial water storage depletion rate is substantially enlarged by irrigation expansion (for example, from −2.559 ( ± 0.094) to −16.008 ( ± 0.557) mm yr⁻¹). Our results attribute the land water loss to irrigation expansion and climate change, calling for immediate solutions to tackle the negative trends.