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Groundwater provides nearly half of irrigation water supply, and it enables resilience during drought, but in many regions of the world, it remains poorly, if at all managed. In heavily agricultural regions like California’s Central Valley, where groundwater management is being slowly implemented over a 27-year period that began in 2015, groundwater provides two-thirds or more of irrigation water during drought, which has led to falling water tables, drying wells, subsiding land, and its long-term disappearance. Here we use nearly two decades of observations from NASA’s GRACE satellite missions and show that the rate of groundwater depletion in the Central Valley has been accelerating since 2003 (1.86 km3 /yr, 1961-2021; 2.41 km3 /yr, 2003- 2021; 8.58 km3 /yr, 2019-2021), a period of megadrought in southwestern North America. Results suggest the need for expedited implementation of groundwater management in the Central Valley to ensure its availability during the increasingly intense droughts of the future.

Groundwater is the most ubiquitous source of liquid freshwater globally, yet its role in supporting diverse ecosystems is rarely acknowledged 1 , 2 . However, the location and extent of groundwater-dependent ecosystems (GDEs) are unknown in many geographies, and protection measures are lacking 1 , 3 . Here, we map GDEs at high-resolution (roughly 30 m) and find them present on more than one-third of global drylands analysed, including important global biodiversity hotspots 4 . GDEs are more extensive and contiguous in landscapes dominated by pastoralism with lower rates of groundwater depletion, suggesting that many GDEs are likely to have already been lost due to water and land use practices. Nevertheless, 53% of GDEs exist within regions showing declining groundwater trends, which highlights the urgent need to protect GDEs from the threat of groundwater depletion. However, we found that only 21% of GDEs exist on protected lands or in jurisdictions with sustainable groundwater management policies, invoking a call to action to protect these vital ecosystems. Furthermore, we examine the linkage of GDEs with cultural and socio-economic factors in the Greater Sahel region, where GDEs play an essential role in supporting biodiversity and rural livelihoods, to explore other means for protection of GDEs in politically unstable regions. Our GDE map provides critical information for prioritizing and developing policies and protection mechanisms across various local, regional or international scales to safeguard these important ecosystems and the societies dependent on them. Mapping of groundwater-dependent ecosystems, which support biodiversity and rural livelihoods, shows they occur on more than one-third of global drylands analysed, but lack protections to safeguard these critical ecosystems and the societies dependent upon them from groundwater depletion.

Bangladesh lies at the intersection of the Ganges, Brahmaputra, and Meghna rivers with a combined average discharge of 38,000 m3s-1 ranking fourth globally. Despite the volume of water flowing through and seasonally inundating parts of the landscape, groundwater reliance is necessary to support an intensive agricultural industry. Here we use newly-developed open-source software to combine observations from the Gravity Recovery and Climate Experiment (GRACE) satellites with hydrologic estimates of land water storage from the Global Land Assimilation Data System (GLDAS) to isolate basin-scale groundwater anomalies in Northwest Bangladesh from 2002 to 2016. We place our estimates in the context of previously-published water management estimates and our results suggest the largest losses in water storage are due to groundwater abstractions with groundwater storage decreasing at a rate of 0.88 cm yr-1. We estimate basin-averaged total water storage loss from 2002 to 2016 at 27.92 cm with groundwater and surface water storage loss accounting for 12.46 cm or 44.6%. For Bangladesh, a region where 80% of landcover is dedicated for agricultural use and over half of the country’s population is employed in the agricultural sector, the estimated declines in water storage hold long-term implications for the livelihood and food supply of the region.

Accurate and detailed knowledge of California’s groundwater is of paramount importance for statewide water resources planning and management, and to sustain a multi-billion-dollar agriculture industry during prolonged droughts. In this study, we use water supply and demand information from California’s Department of Water Resources to develop an aggregate groundwater storage model for California’s Central Valley. The model is evaluated against 34 years of historic estimates of changes in groundwater storage derived from the United States Geological Survey’s Central Valley Hydrologic Model (USGS CVHM) and NASA’s Gravity Recovery and Climate Experiment (NASA GRACE) satellites. The calibrated model is then applied to predict future changes in groundwater storage for the years 2015–2050 under various precipitation scenarios from downscaled climate projections. We also discuss and project potential management strategies across different annual supply and demand variables and how they affect changes in groundwater storage. All simulations support the need for collective statewide management intervention to prevent continued depletion of groundwater availability.

In-depth knowledge about the global patterns and dynamics of land surface net water flux (NWF) is essential for quantification of depletion and recharge of groundwater resources. Net water flux cannot be directly measured, and its estimates as a residual of individual surface flux components often suffer from mass conservation errors due to accumulated systematic biases of individual fluxes. Here, for the first time, we provide direct estimates of global NWF based on near-surface satellite soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. We apply a recently developed analytical model derived via inversion of the linearized Richards’ equation. The model is parsimonious, yet yields unbiased estimates of long-term cumulative NWF that is generally well correlated with the terrestrial water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) satellite. In addition, in conjunction with precipitation and evapotranspiration retrievals, the resultant NWF estimates provide a new means for retrieving global infiltration and runoff from satellite observations. However, the efficacy of the proposed approach over densely vegetated regions is questionable, due to the uncertainty of the satellite soil moisture retrievals and the lack of explicit parameterization of transpiration by deeply rooted plants in the proposed model. Future research is needed to advance this modeling paradigm to explicitly account for plant transpiration.

Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.

Timely identification of canopy decline in commercial strawberry production is challenging because visual scouting often misses subtle or spatially heterogeneous symptoms. We developed a plant-level UAV-based monitoring framework that integrates repeated multispectral imagery, canopy-derived metrics, unsupervised clustering, and Random Survival Forest (RSF) time-to-event modeling. The framework was applied across three commercial strawberry fields in Oxnard, California using nine UAV surveys collected from December 2022 to June 2023, yielding 159,220 plant-level monitoring units. NDRE- and Redness Index-based classifications quantified proportional and absolute canopy dieback within standardized hexagonal units and supported survival-based modeling of canopy decline progression. Across withheld test plants from all survey dates, overall concordance indices ranged from 0.88 to 0.95 across fields, indicating strong ability to rank plants by time-to-decline risk under heterogeneous field conditions. Spatial risk maps revealed localized high-risk clusters that expanded over time in fields with greater canopy deterioration, while fields with minimal visible decline exhibited diffuse but stable risk distributions. Post-hoc comparison with operational fumigation rates (280, 336, and 392 kg Pic-Clor 60/ha) showed no consistent association with predicted canopy decline risk. These results demonstrate that framing repeated UAV observations as a time-to-event process enables fine-scale spatiotemporal modeling of canopy decline dynamics and supports risk stratification for targeted field monitoring in commercial strawberry systems.

The time it takes for water to transit from the ground back to the atmosphere affects weather, climate, biogeochemistry and ecosystem function. The transit time of water through vegetation, defined as the age of water transpiring from vegetation since time of entry, is a particularly understudied aspect of the terrestrial hydrologic cycle. Here we use a synergy of satellite remote sensing measurements over a five-year period to estimate global aboveground vegetation water storage to be on average 484 km 3 , roughly half of which is stored in Earth’s water-limited savannah, grassland and shrubland ecosystems. We then combine these storage estimates with remotely sensed data for transpiration and find that mean transit times of water through aboveground vegetation vary from ~5 days in croplands to ~18 days in evergreen needleleaf forests, with a global median of 8.1 days. In herbaceous-dominated land-cover types with comparatively low water storage and high seasonal water use, such as grasslands, the water stored in biomass may be frequently transiting in less than one day. Our estimates contribute to resolving the role of vegetation in the terrestrial hydrologic cycle; plants store little water compared to other pools, and the time it takes to return that water to the atmosphere is among the fastest components of the hydrologic cycle. Using satellite data, this study presents global estimates of transit times of water through vegetation across ecosystems, highlighting the dynamic role of plants in the hydrologic cycle.

Major Games such as the 2015 Parapan American Games in Toronto (TO2015) generate the potential to bring awareness to sport participation opportunities for people with impairment (Chalip et al, 2017). In the post-games era, it is important to examine the ways in which sport program managers recognize the outcomes of games-related leveraging initiatives. Teleconference interviews were conducted with twelve program managers in the Greater Toronto Area. The study followed an interpretive descriptive methodology and employed a theoretical construct of recognition as a novel approach to assess the legacy and social impacts of hosting parasport games. A form of thematic analysis was used to interpret interview data and bring to surface the perspectives and attitudes of sport program managers involved in any ongoing legacy initiatives. The knowledge acquired from this study suggests that a concept of recognition can support the assessment of the long-term impacts of hosting major games.