Steven George McNulty

Land use and land cover changes, especially urbanization, can strongly alter coupled water and energy cycles, intensifying urban heat island effects, stormwater runoff, and flood risk. Process-based land surface models provide an important tool for quantifying these interactions in heterogeneous landscapes with mixed urban, grassland, and forest cover where direct observations are limited. However, few studies have evaluated how models designed for regional-scale applications perform across scales from small watersheds to a broader urban–rural interface. Here, we diagnostically assessed the upgraded Common Land Model -Urban (CoLM-U) parameterization in the Nashville metropolitan area in the State of Tennessee, southern United States. We integrated local precipitation observations, streamflow records from five small watersheds, and remote sensing-derived evapotranspiration (ET) products from 2000 to 2020. CoLM-U reproduced the broad magnitude of annual ET and runoff but showed substantial watershed-to-watershed and seasonal discrepancies. At the monthly scale, runoff root mean square error ranged from 14.4 to 27.4 mm month ⁻¹ and Nash–Sutcliffe Efficiency ranged from 0.3 to 0.6. The model generally underestimated winter runoff and overestimated summer runoff, reflecting the combined effects of precipitation forcing uncertainty, growing-season ET underestimation, and limitations in representing soil water storage and runoff generation. Long-term ET estimated by CoLM-U was broadly consistent with watershed water balance estimates, while remote sensing ET was generally over estimated ET, especially over forested areas. CoLM-U modeling results indicated that forests exhibited the highest ET and lowest sensible heat fluxes during the growing season, whereas urban cores showed lower ET and higher sensible heat. This study demonstrates what a regional land surface model can and cannot capture of ecohydrological processes at local watershed-to-regional scales. Improved local model climate input data and refined soil parameterizations are essential for accurate application in estimating the environmental effects of land cover change.

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.

Biodiversity can enhance forest resistance to drought, but the forms of diversity involved and conditions under which diversity confers resistance remain unclear. We used Forest Inventory and Analysis (FIA) plot data to examine whether four variables—structural richness (variation in tree heights, stand density, canopy cover and/or spatial heterogeneity), species richness (number of tree species), structural evenness and species evenness—are associated with the resistance of net primary productivity to drought and whether these relationships change with drought severity and by ecoregion. Structural richness was positively associated and species evenness was negatively associated with resistance, but the strength of these relationships varied by ecoregion. Relationships between structural richness and resistance and between species evenness and resistance became stronger under more severe droughts, supporting the stress gradient hypothesis. Forest managers cannot readily change abiotic conditions but could increase structural richness and select certain species to enhance forest resistance to drought.

Drought is a natural part of the Southeastern United States climate and a threat to agricultural productivity and profitability. Whether slow developing or rapid onset, droughts can have negative effects on agricultural crops and livestock, depending on timing, severity, and length. Droughts are expected to become increasingly common in the Southeast due to increasing temperatures, longer dry periods between rain events, and growing water demand across the region. To help producers remain resilient and productive in the face of this threat, the U.S. Department of Agriculture (USDA) Southeast Climate Hub co-produced this guide to help beef producers prepare for, manage, and recover from droughts.

Southeastern U.S. longleaf pine savannas have been reduced to less than 5% of their original extent, giving space to fast‐growing, high‐density loblolly pine plantations. Restoring longleaf pine savannas is an alternative that has been discussed to reduce evapotranspiration and increase water yield, which is of great relevance in resource management and ecosystem restoration. Understanding the benefits of forest management and restoration requires advanced tools to better quantify the fundamental processes of energy, water, and carbon interactions at stand, landscape, and regional scales. We implement and assess the Community Land Model Version 5 (CLM5) at two long‐term research sites, one representing a typical loblolly pine ( Pinus taeda ) plantation with a high tree density and another a typical longleaf pine ( Pinus palustris ) savanna with a low tree density. We also carry out numerical experiments exploring potential differences in energy, water, and carbon balances under alternate land cover scenarios at each site. Using tailored parameterizations for loblolly and longleaf pine and an adjusted C4 grass parameterization, we found that CLM5 reasonably captured the overall observed stand structure and functions at both sites, performing substantially better than with the default parameterizations. Our numerical experiments indicated 8%–17% lower evapotranspiration and 30%–125% higher water yield for longleaf pine savanna compared to loblolly pine plantation. However, net ecosystem production (NEP) and NEP‐based water use efficiency were 60% lower for longleaf pine savanna than loblolly pine plantation. Our modeling framework could be implemented regionally in future studies to support forest management decisions and longleaf pine restoration initiatives.

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.

This archive contains research data collected and/or funded by Forest Service Research and Development (FS R&D), U.S. Department of Agriculture. It is a resource for accessing both short and long-term FS R&D research data, which includes Experimental Forest and Range data. It is a way to both preserve and share the quality science of our researchers.

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.