Atmospheric warming is reducing snowpack, with uncertain effects on mountainous streamflow, a crucial water resource. Despite limited historical observations of groundwater-streamflow interactions above 2,500 m, new measurements in the Upper Colorado River headwaters indicate declining groundwater storage that is dated decades to millennia old. Here we use integrated hydrologic modelling spanning water years 2015-2021 to determine whether the loss of old-age groundwater buffers streamflow during low-snow years and whether that loss is exacerbated with warming. Results show that old-groundwater contributions to streams remain relatively steady through time, unlike the more variable contributions from young groundwater. Numerical experiments of increased surface air temperatures (+2.5 degrees C and +4 degrees C) increase rain-snow fractions and evapotranspiration and decrease runoff ratio by 2-3% per degree Celsius increase. As streamflow declines with warming, the age of groundwater supporting it gets older, in part owing to intermediate-aged (1-3 year) groundwater declining twice as fast. Simulations show that water table depths at higher elevations (>3,700 m) decline disproportionately and fail to recover even during wet years. These findings suggest altered groundwater-streamflow interactions with warming and snow loss, with implications for water resources.

Ongoing atmospheric warming and declines in snow are expected to continue with anthropogenic climate change, with unknown impacts on mountainous water budgets that provide out-sized water resources to lower elevations. In a headwater catchment of the Upper Colorado River Basin (USA), six years of high-frequency groundwater observations at a lower montane well show >1m decline in baseflow water table levels since 2016 with corresponding mean ages from environmental tracers (CFC-12, SF6, 3H, and 4He) ranging from decades to millennia. Meanwhile, 100+ years of observed streamflow with reconstructed precipitation estimates suggests a long-term decline in annual runoff efficiency, but with interannual variability that remains high. This begs the question, is old-aged groundwater buffering streamflow? Using an integrated hydrologic model that allows for three-dimensional groundwater interaction with surface-water and land-surface fluxes of water and energy, we quantify spatio-temporal trends in water partitioning in the East River Watershed over the recent, observational period. Over half of the simulated water years show basin-wide groundwater loss, especially after low-snow years. Simulated runoff efficiency is inversely related to groundwater storage efficiency (what we define as the annual change in subsurface storage expressed as a fraction of precipitation), suggesting an underlying physical mechanism linking the two responses. We test a conceptual model where relative declines in groundwater storage accompany either a) new water input (precipitation or snowmelt) bypassing groundwater, instead feeding streamflow and/or b) groundwater reserves that are consistently being drained, also effectively subsidizing streamflow. With a Lagrangian particle tracking method, we quantify the groundwater age distributions that contribute to streamflow under different conditions. Results show substantial old-aged groundwater exports that are invariant to contemporary snow or melt conditions. This is unlike the young-aged groundwater contributions to streams, which are more transient. Numerical experiments of +2.5 and +4 degrees C of surface air temperature show higher rain-to-snow fractions, higher evapotranspiration rates, and losses to total streamflow yield. Together, these changes result in declines in runoff efficiency by ~2-3% per degree C of warming. Notably, the model shows disproportionate impacts to the highest elevations of the watershed with warming (10-30% change in water table depth, with local changes as high as 5 m), suggesting these regions will be most impacted by a warmer climate. Ongoing work uses the transient particle tracking age distributions, precipitation and snow stable isotope measurements, and the convolution integral to predict streamwater stable isotope dynamics, which can be compared to measurements from the past ~6 years at biweekly frequencies. This comparison will better constrain model performance and improve understanding of future water budget partitioning under warming and low-to-no snow conditions.

Deciphering dissolved organic matter (DOM) molecular complexity is crucial for understanding ecosystem function. Using the continental-scale Worldwide Hydrobiogeochemistry Observation Network for Dynamic Rivers Systems (WHONDRS) Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) dataset, we reveal fundamental scaling patterns of DOM chemodiversity with watershed characteristics. Analysis of 54 river sites shows local and regional watershed features significantly influence DOM chemodiversity (2500-8718 unique formulae), exhibiting consistent scaling patterns across compound classes and a novel latitudinal gradient (decreasing diversity with increasing latitude). Scaling relationships for DOM composition vary by compound class. Crucially, the scaling parameters (B, baseline chemodiversity; Z, sensitivity) are linearly interrelated. This B-Z relationship is most robust for potentially bio-labile carbohydrates (coefficient of determination R2 ≈ 0.85), diminishing for recalcitrant, plant-derived molecules (such as lignin), and indicates (potential) biolability-dependent coupling between baseline diversity and environmental responsiveness. These quantitative scaling relationships, with scaling exponents ranging from - 2.1 to 2.2 across compound classes, enable prediction of DOM composition across watersheds, offering a framework to understand ecosystem responses to environmental change. This research bridges biogeochemistry and ecology, providing tools to anticipate molecular transformations across scales.

Mountainous watersheds are characterized by variability in functional traits, including vegetation, topography, geology, and geomorphology, which together determine nitrogen (N) retention, and release. Coal Creek and East River are two contrasting catchments within the Upper Colorado River Basin that differ markedly in total nitrate (NO3-) export. The East River has a diverse vegetation cover, sinuous floodplains, and is underlain by N-rich marine shale, resulting in a three to twelve times greater total NO3- export relative to the conifer-dominated Coal Creek. While this can partly be explained by the larger size of the East River, the distinct watershed traits of these two catchments imply different mechanisms controlling the aggregate N-export signal. A causality analysis shows biogenic and geogenic processes were critical in determining NO3- export from the East River catchment. Stable isotope ratios of NO3- (δ15NNO3 and δ18ONO3) show the East River catchment is a strong hotspot for biogeochemical processing of NO3- at the soil-saprolite interface and within the floodplain prior to export. By contrast, the conifer-dominated Coal Creek retained nearly all (~97 %) atmospherically-deposited NO3-, and its export was controlled by catchment hydrological traits (i.e., snowmelt periods and water table depth). The conservative N-cycle within Coal Creek is likely due to the abundance of conifer trees, and a smaller riparian region, retaining more NO3- overall and reduced processing prior to export. This study highlights the value of integrating isotope systematics to link watershed functional traits to mechanisms of watershed element retention and release.

Cities are concentrators of complex, multi-sectoral interactions. As keystones in the interconnected human-Earth system, cities have an outsized impact on the Earth system. We describe a multi-lens framework for organizing our understanding of the complexity of urban systems and scientific research on urban systems, which may be useful for natural system scientists exploring the ways their work can be made more actionable. We then describe four critical dimensions along which improvements are needed to advance the urban research that addresses urgent climate challenges: (a) solutions-oriented research, (b) equity-centered assessments which rely on fine-scale human and ecological data, (c) co-production of knowledge, and (d) better integration of human and natural systems occurring through theory, observation, and modeling.

Drylands are some of the most sensitive areas to climate change and human activities around the globe. Assessment of future climate trend scenarios provides valuable practical information for dryland management decision-making. According to Huang et al . (2016), Amore than 50% of global drylands will expand by this century, with a maximum (78%) of newly expanded dryland occurring in developing countries. To understand the potential for expansion of drylands and desertification, we examine critical predictor variables (temperature and precipitation) of Bengal dryland expansion to guide early actions to mitigate and prevent desertification. Using trend analysis of bias- corrected CMIP6 projected climate changeAdata for temperature and precipitation (2022-2041), Aresults indicate that future dryland expansion is possible from increases in temperature and declines in monsoonal precipitation. Over the next two decades (2022-2041), ABengal dryland areas will be +0.1 to +0.5 degrees C warmer and rainfall will decrease by-2.57 to-13.43 mm total during the monsoon period. Given that these variables are critical predictors of dryland expansion due to their role in driving evapotranspiration and soil moisture deficits, we anticipate an increase in the population affected by water scarcity, land degradation and desertification mayAbe a potential outcome. Our work provides information critical for effective dryland management, biodiversity conservation, and land-use planning under future climate conditions.

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

Wildfires naturally occur in many landscapes, however they are undergoing rapid regime shifts. Despite the emphasis in the literature on the most severe hydrological responses to wildfire, there remains a knowledge gap on the thresholds of wildfire (i.e., burned area/drainage area ratio, BAR) required to initiate hydrological responses. We investigated hydrological changes in the Russian River Watershed (RRW) in California, a coastal, Mediterranean, drought‐prone, wildfire‐adapted ecosystem, following ten wildfires that burned 30% of the watershed. Our findings suggest that sub‐watersheds of the RRW have not burned beyond an intrinsic, unknown, threshold required to initiate change. Using paired watersheds, we examined spatiotemporal patterns of pre‐and‐post wildfire hydrology with a rainfall‐runoff hydrological model. Even though these successive wildfires burned 1%–50% of each sub‐watershed (1%–30% at moderate/high severity), we found little evidence of wildfire‐related shifts in hydrology. As a function of BAR, wildfire imposed limited effects on runoff ratios (runoff/precipitation) and runoff residuals (observations—model simulations). Our findings that post‐wildfire runoff enhancements asymptote beyond 30% burn indicate that when a watershed is burned beyond a certain threshold, the magnitude of the hydrologic response no longer increases. Drought and storm conditions explained much of the variability observed in streamflow, whereas wildfire explained only moderate variability in streamflow even when wildfire accounted for >45% BAR. While the BAR in the RRW was sufficiently beyond previously reported minimum disturbance thresholds (>20% burned forest), the lack of hydrological response is attributed to buffering effects of wildfire adaptation and drought factors that are unique to Mediterranean ecoregions.

Mountainous systems cover approximately 23% of Earth’s land and are distributed across all continents. They can capture and store atmospheric moisture that is then cycled through the terrestrial surface and subsurface system, released to downstream communities, and cycled back to the atmosphere. Mountain hydroclimate—characterized by steep gradients, geological, ecological, and biogeochemical diversity—is influenced by topographic forcing and elevated warming and susceptible to large subseasonal to multidecadal variability and rapid changes. Terrestrial hydrological and biogeochemical cycles also experience cascading effects from global warming impacts, such as multidecadal declines in mountain snowpack, longer growing seasons, and increased frequency and severity of extreme events like droughts and wildfires. However, little is known about the effects of these impacts and their feedbacks on climate systems and surface-subsurface compartments. Also unknown are the full implications of changing hydroclimate and extreme events on hydro biogeochemical cycles across atmosphere, terrestrial, and human systems in mountain regions and beyond. This knowledge gap is critical, given human reliance on mountain systems for stable water supply and quality. Mountain systems’ increasing vulnerability to climate change and human perturbations motivates the need to improve understanding of integrated mountain hydroclimate (IMHC) systems and their feedbacks and impacts on humans across scales. However, due to large heterogeneity and strong gradients, coupled natural-human processes in mountain regions present significant challenges for observations, modeling, predictions, and projections. Motivated by gaps in mountain hydroclimate understanding, observations, and modeling and the need for credible projections of future changes, the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program organized a virtual workshop on “Understanding and Predictability of Integrated Mountain Hydroclimate.” Sponsored by BER’s Earth and Environmental Systems Sciences Division (EESSD), the workshop aimed to inform and catalyze EESSD’s growing interests in enhancing predictive understanding of IMHC. Organizers structured the workshop to identify (1) knowledge gaps, (2) observational and modeling challenges, (3) short-term (1 to 3 years) and long-term (10 years and beyond) research opportunities, and (4) strategies for fostering collaboration and coordination. To address the outstanding challenges of IMHC, the workshop included two sessions organized by disciplinary, cross-disciplinary, and crosscutting science topics. The disciplinary and cross-disciplinary topics focused on essential IMHC elements: atmosphere, terrestrial, and human systems and their interactions. Breakout sessions on disciplinary and cross-disciplinary topics facilitated identification of crosscutting topics and central emerging themes. Session 1 focused on connecting existing DOE investments to accelerate progress related to scientific challenges in understanding mountain hydroclimate. In Session 2, participants further explored key Session 1 takeaways through the lens of multiagency collaborations and coordination.

Open and free data underpin a new way of thinking about what is required to advance scientific discoveries. The fourth paradigm for hydrology, i.e. data-intensive science, can only lead to transformative science and groundbreaking findings if data is freely accessible. Open datasets are available online, are accessible in machine-readable formats (i.e. not pdfs or reports), and are obtainable by the public. Free refers to the availability of datasets at no cost to individuals, researchers, institutions, or projects. Here, we provide a broad perspective on the current state of hydrological data including data quality, reliability, uncertainty, national and/or international data policies with a special focus on hydropolitics including current data policies. We also discuss the need for increasing awareness and improving visibility of existing datasets and dedicated projects and repositories. Crowdsourced scientific efforts enable collaborative work and provide potential solutions to these issues. We report on our work initiating a crowdsourced manuscript with an inventory of open and free hydrological datasets from around the world, providing a database of static links, data DOIs, descriptions of datasets, and the general typologies of these data including their usability, uncertainty, quality, and trustworthiness. Our work catalyzes new discussions around data requirements for advancing hydrological research.

Ecosystems at coastal terrestrial–aquatic interfaces play a significant role in global biogeochemical cycles. In this study, we aimed to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region. We investigated the heterogeneity of coastal ecosystem structures as a function of along-shore distance and transverse distance, based on the spatial data layers, including topography, wetland vegetation cover, and the time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. Results showed that the topographic metrics (elevation and slope), soil texture, and plant productivity influence the spatial distribution of wetland land-covers (emergent and phragmites). These results highlight a natural organization along the transverse axis, where the elevation and the EVI increase further away from the coastline. In addition, the clustering analysis allowed us to identify regions with distinct environmental characteristics, as well as the ones that are more sensitive to interannual lake-level variations.

Groundwater levels are gradually declining in basins around the world due to anthropogenic and natural factors.Climate is not the only factor contributing to change in groundwater levels,population growth and economic progress are leading to increased water demand.Areas used for agricultural irrigation are expanding,necessitating the use of artificial groundwater recharge as a method to sustain pumping and enhance storage.The present study delineates potential locations of significant groundwater resources that already exist using a geosta-tistical approach as a method to identify potential groundwater recharge zones.The Multi-Influencing Factors(MIF)technique was applied to determine the relationship between different landscape and climatic factors that influence groundwater recharge.Factors include topogra-phy,climate,hydrogeology,population,economic change,and geology.Integration of these factors enabled the identification of potential locations of groundwater suit-able for artificial recharge efforts based on weights derived through the MIF technique.We applied these weights to derive a groundwater recharge index(GRI)map.The map was delineated into three groundwater recharge zones classified by their potential areal coverage as a metric for recharge suitability,namely low,medium and high suit-ability,occupying areas of 8625 km2(30.06％),9082 km2(31.65％),and 10,989 km2(38.29％),respectively.Our findings have important implications for designing sus-tainable groundwater development and land-use plans for the coming century.