Over the last half-century, land use changes, including deforestation, urban sprawl, and open-pit surface mining, have accelerated across the Susurluk Basin in northwestern T & uuml;rkiye. This study analysed how land use changes, damming and mining activities affected basin hydrology using empirical and analytical methods and the process-based Water Supply Stress Index Model (WaSSI). The monthly WaSSI water balance model was validated using streamflow data from gaging stations between 1980 and 2005. Two of the eight subbasins exhibited streamflow reductions of about 32%-42%, with mean annual discharge decreasing between 1980-1989 and 1990-2005, primarily due to land use change rather than climate variability. The runoff coefficient (Runoff/Precipitation) dropped from 22% during 1980-1989 to 12% during 1990-2005 in one rural subbasin containing several surface-mine ponds. Overall, empirical and process-based modelling indicated that land use dynamics, rather than climate, were responsible for the hydrological change. The monthly WaSSI showed satisfactory performance in subbasins with low human impacts (NSE > 0.50) but considerably lower performance (NSE < 0.20) in highly human-modified areas. This integrated study concludes that land use activities, especially pond creation for pit mining, were the dominant drivers of hydrological changes in the study area.

Extreme precipitation events, intensified by climate change, are increasingly disrupting nutrient dynamics in large river systems worldwide. Beyond altering the magnitude of individual nutrient fluxes, extreme precipitation may fundamentally reshape nutrient composition and stoichiometric balances, with critical implications for aquatic ecosystem health. Here, we apply an integrated data-model framework to assess how these events have altered nitrogen (N) and phosphorus (P) fluxes across the Mississippi River Basin from 1980 to 2018. We find that extreme rainfall disproportionately increases P export relative to N, driven primarily by enhanced soil erosion and mobilization of particulate-bound nutrients. Concurrent temporal and spatial changes in extreme precipitation regimes have induced declining N:P ratios in headwater streams and cumulative nutrient loads, shifting export stoichiometry toward the Redfield ratio. Therefore, extreme precipitation can increase nutrient fluxes that fuel harmful algal blooms, yet at the same time reduce N:P ratios that may favor less toxic communities. This trade-off calls for watershed management strategies that go beyond managing nutrient quantity alone.

The "Nature-based Solutions" strategy for combatting global land degradation and climate change through forestation has drawn increasing concerns regarding its potential tradeoffs with water resources, especially in dry regions. China is "greening up" due to decades of large-scale tree planting and ecological restoration campaigns. However, national-level assessments of the individual and combined effects of forest cover and climate changes on water yield at a fine scale are still lacking. Here, we show several lines of evidence indicating that the unprecedented forestation has exacerbated water availability decline during 1980-2020 amid climate change across China. Meta-analysis identifies forestation as the dominant driver of the observed large declines in river flows for about half of the 112 reviewed watersheds. Scenario simulations using the Budyko framework and a monthly ecohydrological model, WaSSI-CN, all suggest that forestation substantially reduced water yield nation-wide, especially in the humid southern China and during the growing season. Forestation exacerbated the water yield decline caused by climate change of reduction in precipitation and rising air temperature through enhanced evapotranspiration in over 80% of the 168 large basins modeled. Effects of forestation could exert more impacts on water yield than climate change over 17 large basins. Forest management practices such as stand thinning, native tree species planting, and prescribed burning are encouraged to increase forest resilience and improve water supply functions.

Bottomland hardwood wetland forests along the Atlantic Coast of the United States have been changing over time; this change has been exceptionally apparent in the last two decades. Tree mortality is one of the most visually striking changes occurring in these coastal forests today. Using 2009–2019 tree mortality data from a bottomland hardwood forest monitored for long-term flux studies in North Carolina, we evaluated species composition and tree mortality trends and partitioned variance among hydrologic (e.g., sea level rise (SLR), groundwater table depth), biological (leaf area index (LAI)), and climatic (solar radiation and air temperature) variables affecting tree mortality. Results showed that the tree mortality rate rose from 1.64% in 2009 to 45.82% over 10 years. Tree mortality was primarily explained by a structural equation model (SEM) with R2 estimates indicating the importance of hydrologic (R2 = 0.65), biological (R2 = 0.37), and climatic (R2 = 0.10) variables. Prolonged inundation, SLR, and other stressors drove the early stages of ‘ghost forest’ formation in a formerly healthy forested wetland relatively far inland from the nearest coastline. This study contributes to a growing understanding of widespread coastal ecosystem transition as the continental margin adjusts to rising sea levels, which needs to be accounted for in ecosystem modeling frameworks.

AimWe model and map the climatically suitable habitats and migration potential of 326 tree species by combining forest inventories of the United States, Canada and to a lesser extent, Mexico, with the goal of providing a continental perspective of species ranges and migration potential to facilitate better forest stewardship under a changing climate.LocationUnited States, Canada, and Mexico.Major Taxa StudiedTree species.MethodsWe use a multi-model ensemble technique to assess climatic habitat suitability under current and future climates, and a migration model to compute colonisation likelihoods to simulate end of century tree species migration. We combine and synthesise these outputs to provide various products relevant to range-wide assessment of tree species.ResultsFor 326 tree species, we provide maps of: current habitat suitability, future habitat suitability under SSP2-4.5 and SSP5-8.5 scenarios, and combined habitat suitability and colonisation likelihoods for the end of the current century. In addition, we provide synthesis outputs of (a) climate and topographic statistical range assessments, (b) maps of potential differences in species richness from current to future climates, (c) assessment of model performance, (d) climate-topographic variable-importance groupings and (e) climatic disequilibrium trends across genera. A continent-wide assessment of both individual and combined species responses showed evidence of climatic disequilibrium for species with smaller ranges, a projected potential reduction in species richness in the middle and lower mid-continental regions, and an increase across the southeastern, northeastern, and northwestern regions of the continent. Also, we found habitat suitability of most eastern species were mainly driven by moisture, while western species showed strong associations with heat and moisture.ResultsFor 326 tree species, we provide maps of: current habitat suitability, future habitat suitability under SSP2-4.5 and SSP5-8.5 scenarios, and combined habitat suitability and colonisation likelihoods for the end of the current century. In addition, we provide synthesis outputs of (a) climate and topographic statistical range assessments, (b) maps of potential differences in species richness from current to future climates, (c) assessment of model performance, (d) climate-topographic variable-importance groupings and (e) climatic disequilibrium trends across genera. A continent-wide assessment of both individual and combined species responses showed evidence of climatic disequilibrium for species with smaller ranges, a projected potential reduction in species richness in the middle and lower mid-continental regions, and an increase across the southeastern, northeastern, and northwestern regions of the continent. Also, we found habitat suitability of most eastern species were mainly driven by moisture, while western species showed strong associations with heat and moisture.Main ConclusionsOur study provides, for the first time, a baseline for understanding the overall continental dynamics of shifting climatic habitats and migration potential of tree species across their entire range, facilitating improved management of North American forested ecosystems.

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R2 = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m−2 d−1, and Rn = 167.54 W m−2. These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise.

Wetlands are the largest and most climate‐sensitive natural sources of methane. Accurately estimating wetland methane emissions involves reconciling inversion (“top‐down”) and process‐based (“bottom‐up”) models within the global methane budget. However, estimates from these two model types are inherently interdependent and often reveal substantial discrepancies. To enhance the reliability of both approaches, we need a comprehensive understanding of wetland methane emissions and an independent high‐resolution long‐term flux data set. Here, we employed a data‐driven random forest approach to identify key variables influencing methane emissions from subtropical freshwater wetlands in the Southeastern United States. The model‐estimated monthly mean methane fluxes fit well with measured methane fluxes ( R 2 = 0.67) at four representative FLUXNET‐CH4 wetland sites across the region. Variable importance analysis highlighted the sensitivity of subtropical freshwater wetland methane emissions to variations in both temperature and water levels. High temperatures facilitate methanogenesis by enhancing microbial activities, while elevated water levels maintain anaerobic conditions necessary for methane production. Notably, the response of methane emissions to water level fluctuations is contingent on temperature conditions, and vice versa. Moreover, we constructed the first high‐spatial‐resolution (∼1 km × 1 km) and long‐term (1982–2010) gridded regional wetland methane flux product for the Southeastern United States, estimating annual methane emissions from subtropical freshwater wetlands in the region at 4.93 ± 0.11 Tg CH 4 yr −1 for 1982–2010. This new benchmark product holds promise for validating and parameterizing uncertain wetland methane emission processes in bottom‐up models and provides improved prior information for top‐down models.

Growing forests has been proposed as a cornerstone of natural climate solutions, and some national scale policies facilitating this have been in place for decades. However, not all carbon that is assimilated remains sequestered in biological pools for equal length of time. Carbon retention in ecosystems depends on the balance of inputs, outputs, and turnover, which in turn depends on the chemical composition of the biomass produced, its lifetime as part of a living tissue, its exposure to different decomposer communities and extracellular enzymes upon death, the biological and physicochemical environment, and disturbances. That is, carbon retention in the long term is an ecosystem property, the result of the interacting properties of plants, microbes, and the environment, which all affect the different steps of carbon assimilation, retention, and decomposition. Attempts to increase C sequestration in forests must consider not only productivity, but also decomposition and stabilization. Recent evidence suggests that carbon is retained in the soil when the decomposer community (free-living saprotrophs) is resource-limited and unable to produce the necessary enzymes. This limitation is imposed by vigorous mycorrhizal community that thrives under overall nutrient limitation. High nutrient inputs typical for production-oriented forests reduce that competition and promote the production of carbon mining enzymes by saprotrophs. Furthermore, plant biomass produced in nutrient-rich conditions tends to be lower in secondary compounds and therefore easier to decompose. Thus, the production-oriented cultivation practices appear contradictory to what is required for high carbon retention and long-term sequestration. Here we review the latest literature about plant–microbe and microbe–microbe interactions in coordinating the interlinked carbon, nutrient, and water cycles, and we estimate how these may respond to the key global change factors of CO2, temperature, and precipitation.

As the second most significant greenhouse gas (GHG), methane (CH4) contributes ~20% to the cumulative GHG-induced global warming. Among all methane sources, wetlands are the single largest and climate-controlled natural source. Estimating wetland methane emissions typically involves inversion (“top-down”) or process-based (“bottom-up”) models. Nevertheless, estimates derived from these two model types are not independent and exhibit large disparities. To better understand wetland methane emissions and refine the process-based and inversion models, independent high-resolution and long-term wetland methane flux data are needed. Here, we develop a high-spatial-resolution (0.0083° × 0.0083°, approximately 1 km × 1 km) monthly wetland CH4 flux dataset for the Southeastern (SE) United States (US) from 1982 to 2010 using a data-driven random forest (RF) approach. We utilize CH4 flux measurements from four FLUXNET-CH4 wetland sites to develop the RF regression model along with 11 explanatory variables, including air temperature, precipitation, vapor pressure deficit, incoming shortwave radiation, wind speed, Palmer Drought Severity Index, water table level, leaf area index, the fraction of absorbed photosynthetically active radiation, season, and wetland type (tidal versus non-tidal). Wetland CH4 fluxes estimated using the RF model fit well with the measured CH4 fluxes (R2 = 0.91) from four representative FLUXNET-CH4 wetland sites across the SE US, outperforming previous CH4 flux upscaling studies. Leveraging the developed RF model and wetland distribution data from the National Wetland Inventory, we map the spatial distribution of CH4 emissions in the study region. Our mapping reveals large spatial variability in CH4 emissions, ranging from 0 to 266.0 nmolCH4 m-2 s-1, with the coastal wetland areas, the Mississippi Delta, and the Everglades being the predominant sources of CH4. Our dataset demonstrates good agreement with the remote sensing-derived wetland CH4 fluxes from the Carbon Monitoring System Methane Flux for North America product, confirming the credibility of our wetland CH4 flux estimations. Variable importance analysis highlights that air temperature and the Palmer Drought Severity Index are key environmental predictors, explaining 88% of the variance in measured methane emissions from SE US wetlands. This first-ever high-spatial-resolution (0.0083° × 0.0083°) and long-term (1982-2010) monthly gridded regional wetland CH4 flux product over the SE US provide a benchmark and an added constraint for future wetland CH4 flux modeling and upscaling studies in the study region. The insights gained from this study contribute valuable understanding of the environmental controls on CH4 emissions, offering guidance for quantifying GHG emissions at the regional scale.

The nature of science involves the discovery of unknowns. Questions are raised, hypotheses are developed and tested, results are analyzed, and conclusions are made from studies and observations. For thousands of years, this process has steadily advanced human knowledge of our world. Conversely, the lack of study can lead to unwanted consequences (e.g., the impacts of lead paint and asbestos in building construction). Currently, Earth is undergoing a unique experiment through anthropogenic global warming. Many negative impacts are already being observed (e.g., increased heatwaves, flooding, and droughts), and others are hypothesized. However, the potentially most disruptive changes associated with climate change may not even be imagined. The changing climate interacts with hundreds of other constantly evolving environmental shifts (e.g., atmospheric CO2, nitrogen deposition, ozone, landuse change) in ways that have never previously occurred (i.e., nonantecedent factors). The factors could interact to produce events that humans have never seen (i.e., nonantecedent events). These events may be the most troubling because we do not have a historical context from which to predict and prepare for their occurrence. This chapter examines how nonantecedent factors and events interact and how we might better predict (and therefore prepare) their likelihood of happening.

Future water availability is influenced by both climate and associated vegetation dynamics. This study coupled vegetation projections from a dynamic global vegetation model (MC2) with an ecohydrological model, Water Supply Stress Index (WaSSI), to predict water yield at the 8-digit Hydrologic Unit Code (HUC8) watershed level for the conterminous United States (CONUS) for the 21st century. We considered two contrasting warming scenarios (Representative Concentration Pathways 8.5 and 4.5) and accounted for simulation uncertainty by using a large ensemble of climate model outputs. The coupled model projects a decrease in water yield across much of CONUS, especially towards end-century (2080-2099) under RCP 8.5 (warmer scenario), reaching up to -30% at the regional level, relative to the 2008-2027 baseline period. Overall, the projected water yield reduction under RCP 8.5 is roughly twice as high as under RCP 4.5. Substantial changes in water yield for watersheds in the central and southeastern United States are expected by mid-century (2040-2059), reaching up to -40% (RCP 4.5) and -75% (RCP 8.5) at at the century's end (2080-2099), relative to 2008-2027, respectively. Climate change, rather than vegetation change, strongly dominates the projected future changes in water yield, with contributions typically one order of magnitude higher. For a small number of watersheds, the effects of vegetation change can mitigate or exacerbate the effects of climate change on water yield. Our simulation results suggest a widespread increase in aridity and evaporative indices and a decrease in soil moisture, especially under RCP 8.5. Our integrated modeling results can inform policy makers and resource development planners quantitative information of future water availability.

There are twenty experimental forest and range sites (EFRs) across the southeastern United States that are currently maintained by the USDA Forest Service (Forest Service) to conduct forest ecosystem research for addressing ecosystem management challenges. The overall objective of this study was to use multiple gridded datasets to assess the extent to which the twenty EFRs represent the climate, ecosystem structure, and ecosystem functions of southeastern forests. The EFRs represent the large variability of climate conditions across the region relatively well, but we identified small representation gaps. The representativeness of ecosystem structure by these EFRs can be improved by establishing EFRs in forests with relatively low tree cover, leaf area index, or tree canopy height. The current EFRs also represent the forest ecosystem functions of the region relatively well, although areas with intermediate and low aboveground biomass and water yield are not well represented. The trends in climate, ecosystem structure, and ecosystem functions were generally consistent between the region and the EFRs. Our study indicates that the current EFRs represent the region relatively well, but establishing additional EFRs in specific areas within the region could help more completely assess how southeastern forests respond to climate change, disturbance, and management practices.Study Implications This study across the experimental forests and ranges (EFRs) and the southeastern forest region fills the knowledge gap regarding climate, ecosystem structure, and ecosystem functions of EFRs in the context of the broader southeastern forest region. Understanding ecosystem functions and structures across the EFR network can help the Southern Research Station to address new research questions. Our study indicates that the current EFRs represent the climate, ecosystem structure, and ecosystem functions of southeastern forests well. However, establishing additional EFRs in certain regions could help more completely assess how southeastern forests respond to climate change, disturbance, and management practices.

Soil respiration (Rs) and methane (FCH4) fluxes are two important metrics of ecosystem metabolism. An accurate estimate of the budget of these two greenhouse gases is critical to understanding their response to climate and land-use changes. Reconstructing continuous time series of gappy chamber Rs and eddy-covariance derived FCH4 measurements is usually done based on correlative relationships of these fluxes with environmental variables. However, current approaches do not account for the fact that different environmental drivers affect the carbon fluxes at different temporal scales. Here we propose a novel gapfilling technique that accounts for the specific spectral frequencies at which each of the environmental variables covaries with Rs and FCH4 - photosynthetically active radiation at diel scale, soil temperature at synoptic scale, and soil moisture, water table depth and atmospheric pressure at synoptic and seasonal scale. The method was applied on two operational loblolly pine plantations of different ages and a mixed hardwood forested wetland on the lower coastal plain of North Carolina. The time series of these environmental drivers were reconstructed using wavelet decomposition and a Daubechies wavelet filter. Further, to consider the joint influence of the environmental drivers, parametric (elastic net regression, support vector machine, gradient boost and artificial neural network), and nonparametric (Bayesian) statistical models were chosen, and compared the results with Q10 and Marginal Distribution Sampling (MDS) outputs. In all cases, the algorithms were trained on 70 % of the data and validated with the remaining data. Spectral-filtered models did not significantly differ from those driven by unfiltered data with respect to Rs and FCH4 predictions. While all the spectrally driven algorithms achieved high predictive accuracy against Q10, the increase in model fit compared to MDS was minimal. Spectral data filtering modestly improves model accuracy, shedding light on complex environmental and biological factors affecting greenhouse gas flux variability.

Nutrients are essential regulators of the structure and function of natural ecosystems and play a key role in constraining future terrestrial productivity and carbon sequestration in response to rising CO2 concentrations and climate change. Nitrogen (N) and phosphorus (P) widely limit plant growth in global terrestrial ecosystems. Therefore understanding spatial patterns and future trends of N and P limitation can shed light on the future dynamics of forests and other terrestrial biomes. Based on current literature, here we (1) review the concept and mechanisms of nutrient limitation and how vascular plants adapt to nutrient limitation, (2) summarize the direct and indirect approaches to diagnose nutrient limitation, (3) synthesize the current understanding of global patterns of N and P limitation, and (4) discuss the future trends in N and P limitation in boreal, temperate, and tropical forests. Phosphorus limitation mainly occurs in tropical, subtropical, and temperate broadleaved forests, while N limitation mainly occurs in boreal and temperate conifer forests. Therefore P is limiting in a larger area than N in global forests and the area of P-limited forests will likely expand due to continuingly rising atmospheric CO2 and climate change. More observational, experimental, and modeling efforts are needed to better understand forest dynamics in response to changing nutrient limitation in the context of global change.

Measuring water use in co‐occurring loblolly pine (Pinus taeda L.) and shortleaf pine (Pinus echinata Mill.) enhances our understanding of their competitive water use and aids in refining watershed water budget model parameters. This study was conducted in a 12‐ha forested headwater catchment in the Piedmont of North Carolina, southeastern U.S., from 2018 to 2019 (pre‐thinning) to 2020 (post‐thinning). Sap flux density (Js), species‐level transpiration (Ts), and watershed‐level transpiration (Tw) were quantified. Water use efficiency (WUE) in loblolly and shortleaf pines was compared, alongside an investigation into how both species' Js and Ts responded to atmospheric vapor pressure deficit (VPD). Loblolly pine had 19%–36% higher Js than shortleaf pine. Daily Ts for loblolly pine ranged from 15.0 to 29.0 L/day while Ts in shortleaf pine ranged from 3.0 to 6.8 L/day. The Ts was significantly higher in loblolly pine when compared to shortleaf pine likely due to higher canopy position and higher growth rates of the former. WUE, defined by annual tree biomass growth per tree water use, was not significantly different between the two. Daily Js and Ts in both species responded nonlinearly to VPD, with loblolly pine being more sensitive and variable. Species‐specific water use should be considered when quantifying Tw and developing reliable models to predict the effects of forest management practices on water resources.

"Despite the water-rich nature of the southeastern United States (US), extended and intense dry periods intermittently occur across the region leading to reduced soil moisture levels and surface water supplies. These drought periods affect the landscape at different scales, with agriculture experiencing impacts earlier than other sectors. State and national entities may use field-based reports of impacts to crops and pasture - in conjunction with onsite and remote sensing data products - to monitor, respond, and provide relief to agricultural producers during drought. Therefore, it is crucial that information on drought impacts on agriculture is documented, credible, and accessible. The United States Department of Agriculture (USDA) Southeast Climate Hub collaborated with the National Oceanic and Atmospheric Administration (NOAA) National Integrated Drought Information System (NIDIS) Southeast Drought Early Warning System (DEWS) on a joint assessment of how Southeastern states record, report, and utilize information about drought impacts on agriculture."--

Evapotranspiration (ET) links water, energy, and carbon balances, and its magnitude and patterns are changing due to climate and land use change in the southeastern U.S. Quantifying the environmental controls on ET is essential for developing reliable ecohydrological models for water resources management. Here, we synthesized eddy covariance data from 24 AmeriFlux sites distributed across the southeastern U.S., comprising 162 site-years of flux data representing six representative ecosystems including cropland vegetation mosaic (CVM), deciduous broadleaf forests (DBF), evergreen needle-leaf forests (ENF), grasslands (GRA), savannas (SAV), and wetlands (WET). Our objectives were to assess the daily, seasonal, and annual variability in ET and to develop practical predictive models for regional applications in ecosystem service analysis. We evaluated the response of ET to climatic and biotic forcings including potential evapotranspiration (PET), precipitation (P), and leaf area index (LAI), and compared the performance of these empirical ET models based and those developed using machine learning algorithms. Our results showed that the mean daily ET varied significantly, ranging from 1.36 mm d−1 in GRA to 2.30 mm d−1 in SAV, with a numerical order : GRA < DBF < ENF < WET < CVM < SAV. In this humid region, mean annual PET exceeded P in 16 out of the 24 flux sites. Using the Budyko framework, we showed that ENF had the highest evaporative efficiency (ET/P). PET and leaf area index (LAI) emerged as the most influential factors explaining ET variability. Artificial neural networks (ANN) and random forest (RF) models demonstrated superior capabilities in predicting monthly ET across sites over generalized additive modeling (GAM) and multiple linear regression (MLR) methods. The present study confirmed that the Southeast region is generally 'energy limited', implying that atmospheric demand along with vegetation information can be used to reliably estimate monthly and annual ET. Our study provides valuable insights into how ET of specific ecosystems is controlled by climatic and land surface drivers, enabling the development of reliable predictive models for regional extrapolation of flux measurements in water resource management in the humid southeastern U.S. region.

Forests and forest ecosystems are vital to our social, economic, and environmental well-being. However, climate change and climate-driven disturbances (CDDs) are undermining the health and resilience of forests worldwide and pose significant uncertainty to sustainable forest management. Climate-smart forestry (CSF) remains a grand challenge in practice due to our limited knowledge of how forests respond to climate change and our abilities to collect related information to empower decision making. Rapid advances in artificial intelligence (AI) can offer a timely opportunity to address the challenges in CSF. We argue that the AI-enabled, next-generation CSF can be achievable through synergistically coordinated and transdisciplinary efforts that develop and advance foundational and use-inspired AI technologies that can lead to building next-generation forest decision support systems.