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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Climate change–induced stress disrupts ectomycorrhizal interaction networks at the boreal–temperate ecotone

The interaction networks formed by ectomycorrhizal fungi (EMF) and their tree hosts, which are important to both forest recruitment and ecosystem carbon and nutrient retention, may be particularly susceptible to climate change at the boreal–temperate forest ecotone where environmental conditions are changing rapidly. Here, we quantified the compositional and functional trait responses of EMF communities and their interaction networks with two boreal ( Pinus banksiana and Betula papyrifera ) and two temperate ( Pinus strobus and Quercus macrocarpa ) hosts to a factorial combination of experimentally elevated temperatures and reduced rainfall in a long-term open-air field experiment. The study was conducted at the B4WarmED (Boreal Forest Warming at an Ecotone in Danger) experiment in Minnesota, USA, where infrared lamps and buried heating cables elevate temperatures (ambient, +3.1 °C) and rain-out shelters reduce growing season precipitation (ambient, ~30% reduction). EMF communities were characterized and interaction networks inferred from metabarcoding of fungal-colonized root tips. Warming and rainfall reduction significantly altered EMF community composition, leading to an increase in the relative abundance of EMF with contact-short distance exploration types. These compositional changes, which likely limited the capacity for mycelial connections between trees, corresponded with shifts from highly redundant EMF interaction networks under ambient conditions to less redundant (more specialized) networks. Further, the observed changes in EMF communities and interaction networks were correlated with changes in soil moisture and host photosynthesis. Collectively, these results indicate that the projected changes in climate will likely lead to significant shifts in the traits, structure, and integrity of EMF communities as well as their interaction networks in forest ecosystems at the boreal–temperate ecotone.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Global scale assessment of urban precipitation anomalies

Urbanization has accelerated dramatically across the world over the past decades. Urban influence on surface temperatures is now being considered as a correction term in climatological datasets. Although prior research has investigated urban influences on precipitation for specific cities or selected thunderstorm cases, a comprehensive examination of urban precipitation anomalies on a global scale remains limited. This research is a global analysis of urban precipitation anomalies for over one thousand cities worldwide. We find that more than 60% of the global cities and their downwind regions are receiving more precipitation than the surrounding rural areas. Moreover, the magnitude of these urban wet islands has nearly doubled in the past 20 y. Urban precipitation anomalies exhibit variations across different continents and climates, with cities in Africa, for example, exhibiting the largest urban annual and extreme precipitation anomalies. Cities are more prone to substantial urban precipitation anomalies under warm and humid climates compared to cold and dry climates. Cities with larger populations, pronounced urban heat island effects, and higher aerosol loads also show noticeable precipitation enhancements. This research maps global urban rainfall hotspots, establishing a foundation for the consideration of urban rainfall corrections in climatology datasets. This advancement holds promise for projecting extreme precipitation and fostering the development of more resilient cities in the future.

Science & Technology - Other Topics↗
DOE OSTI2016

Unraveling the mechanisms underlying pulse dynamics of soil respiration in tropical dry forests

Tropical dry forests are already undergoing changes in the quantity and timing of rainfall, but there is great uncertainty over how these shifts will affect belowground carbon (C) cycling. While it has long been known that dry soils quickly release carbon dioxide (CO 2 ) upon rewetting, the mechanisms underlying the so-called 'Birch effect' are still debated. Here, we quantified soil respiration pulses and their biotic predictors in response to simulated precipitation events in a regenerating tropical dry forest in Costa Rica. We also simulated the observed rewetting CO 2 pulses with two soil carbon models: a conventional model assuming first-order decay rates of soil organic matter, and an enzyme-catalyzed model with Michaelis–Menten kinetics. We found that rewetting of dry soils produced an immediate and dramatic pulse of CO 2 , accompanied by rapid immobilization of nitrogen into the microbial biomass. However, the magnitude of the rewetting CO 2 pulse was highly variable at fine spatial scales, and was well correlated with the size of the dissolved organic C pool prior to rewetting. Both the enzyme-catalyzed and conventional models were able to reproduce the Birch effect when respiration was coupled directly to microbial C uptake, although models differed in their ability to yield realistic estimates of SOC and microbial biomass pool sizes and dynamics. Lastly, our results suggest that changes in the timing and intensity of rainfall events in tropical dry forests will exert strong influence on ecosystem C balance by affecting the dynamics of microbial biomass growth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Interannual precipitation controls on soil CO 2 fluxes in high elevation conifer and aspen forests

Abstract Long-term soil CO 2 emission measurements are necessary for detecting trends and interannual variability in the terrestrial carbon cycle. Such records are becoming increasingly valuable as ecosystems experience altered environmental conditions associated with climate change. From 2013 to 2021, we continuously measured soil CO 2 concentrations in the two dominant high elevation forest types, mixed conifer and aspen, in the upper Colorado River basin. We quantified the soil CO 2 flux during the summer months, and found that the mean and total CO 2 flux in both forests was related to the prior winter’s snowfall and current summer’s rainfall, with greater sensitivity to rainfall. We observed a decline in surface soil CO 2 production, which we attributed to warming and a decrease in amount and frequency of summer rains. Our results demonstrate strong precipitation control on the soil CO 2 flux in mountainous regions, a finding which has important implications for carbon cycling under future environmental change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A storyline analysis of Hurricane Irma’s precipitation under various levels of climate warming

Abstract Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the community atmosphere model, we explore how Irma’s precipitation would change under various levels of climate warming. Analysis is focused on a 48 h period where the simulated hurricane tracks reasonably represent Irma’s observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3–7% K −1 compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10–13% K −1 and 17–21% K −1 , respectively. Over Florida, the simulated mean rainfall accumulations increase by 16–26% K −1 , with local maxima increasing by 18–43% K −1 . All percent changes increase monotonically with warming level.

Environmental Sciences & Ecology↗
DOE OSTI2026

Flood Estimation under Snowmelt and Rain-on-Snow Processes in Alaska: A Military Installation Perspective

Accurate flood estimation in snow-dominated and high-latitude regions remains challenging due to complex interactions among rainfall, snowmelt, and rain-on-snow (ROS) processes, which are not captured in conventional precipitation-based intensity-duration-frequency (PREC-IDF) curves. This study evaluates the Next-Generation IDF (NG-IDF) framework, an extension of PREC-IDF that incorporates total water available for runoff (precipitation minus changes in snow water equivalent), in two contrasting Alaskan watersheds of Department of Defense (DoD) significance: the Little Chena River Basin (LCRB) in interior Alaska, a tributary of the Chena River that flows through Fort Wainwright and near Eielson Air Force Base, and the Upper Ship Creek Basin (USCB), which drains Joint Base Elmendorf-Richardson near Anchorage. Using long-term SNOTEL observations and event-based rainfall-runoff modeling, NG-IDF and PREC-IDF flood estimates were compared against observation-based flood frequency analyses. Results show that NG-IDF consistently reduces flood-estimation bias by 15–20% relative to PREC-IDF, particularly for snowmelt- and ROS-dominated events. In the interior LCRB, permafrost conditions can substantially amplify flood responses during snowmelt events, an effect not explicitly represented in standard design tools. These findings demonstrate that NG-IDF provides a more physically consistent and transferable framework for flood estimation in cold regions, with potential relevance to mission-critical DoD installation resilience. Projected increases in ROS frequency and permafrost degradation across Alaska further emphasize the need to integrate physics-based hydrologic models that explicitly represent snow and permafrost processes to enhance design resilience and operational readiness for military and other critical infrastructure.

Yan, Hongxiang (ORCID:000000022387403X)↗
DOE OSTI2025

Normalized Solar-Induced Fluorescence Responds Earlier Than Vegetation Indices to the 2019 North China Plain Drought

Recently, solar-induced chlorophyll fluorescence (SIF) from satellites has shown potential for evaluating vegetation status and stress responses. Fluorescence quantum yield ($Φ_F$) is essentially linked to vegetation stress. However, the complex physiological and structural responses of SIF and $Φ_F$ to drought need further study. This study normalized SIF as SIFn to account for angular variations and fluctuations in photosynthetically active radiation (PAR), aiming for more accurate drought monitoring. SIFn anomalies were compared to historical baselines (2019–2021 averages) of vegetation indices (VIs), raw SIF, and $Φ_F$ during a 2019 drought in the North China Plain (NCP). Here, the results show SIFn provides an effective method for drought monitoring, showing the earliest decline compared to raw SIF, VIs, and $Φ_F$. In the first two weeks of drought, SIFn decreased by 8.2%, 7.0%, 12.5%, and 8.2% across the four NCP subdivisions. SIFn outperformed other indicators, proving sensitive to early drought detection. SIFn was also examined for tracking drought alleviation by rainfall. The uncertainty under different viewing geometries was quantified. SIFn anomalies showed a strong correlation with rainfall anomalies (R: 0.45 ~ 0.52) and meteorological factors like PAR (R: 0.80 ~ 0.84) and relative humidity (R:0.52 ~ 0.54). The correlation of near-infrared reflectance (NIRv) and $Φ_F$ anomalies with SIF was weak during drought onset (R: 0.16 ~ 0.32) but strong at the end (R: 0.83 ~ 0.87). These suggest both canopy structure (mainly characterized by NIRv) and vegetation chlorophyll ($Φ_F$) are impacted by drought and influence SIF at different stages.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Soil–plant–atmosphere conditions regulating convective cloud formation above southeastern US pine plantations

Abstract Loblolly pine trees ( Pinus taeda L.) occupy more than 20% of the forested area in the southern United States, represent more than 50% of the standing pine volume in this region, and remove from the atmosphere about 500 g C m per year through net ecosystem exchange. Hence, their significance as a major regional carbon sink can hardly be disputed. What is disputed is whether the proliferation of young plantations replacing old forest in the southern United States will alter key aspects of the hydrologic cycle, including convective rainfall, which is the focus of the present work. Ecosystem fluxes of sensible ( ) and latent heat (LE) and large‐scale, slowly evolving free atmospheric temperature and water vapor content are known to be first‐order controls on the formation of convective clouds in the atmospheric boundary layer. These controlling processes are here described by a zero‐order analytical model aimed at assessing how plantations of different ages may regulate the persistence and transition of the atmospheric system between cloudy and cloudless conditions. Using the analytical model together with field observations, the roles of ecosystem and LE on convective cloud formation are explored relative to the entrainment of heat and moisture from the free atmosphere. Our results demonstrate that cloudy–cloudless regimes at the land surface are regulated by a nonlinear relation between the Bowen ratio and root‐zone soil water content, suggesting that young/mature pines ecosystems have the ability to recirculate available water (through rainfall predisposition mechanisms). Such nonlinearity was not detected in a much older pine stand, suggesting a higher tolerance to drought but a limited control on boundary layer dynamics. These results enable the generation of hypotheses about the impacts on convective cloud formation driven by afforestation/deforestation and groundwater depletion projected to increase following increased human population in the southeastern United States.

Manoli, Gabriele↗
DOE OSTI2023

The ecosystem wilting point defines drought response and recovery of a Quercus‐Carya forest

Abstract Soil and atmospheric droughts increasingly threaten plant survival and productivity around the world. Yet, conceptual gaps constrain our ability to predict ecosystem‐scale drought impacts under climate change. Here, we introduce the ecosystem wilting point (Ψ EWP ), a property that integrates the drought response of an ecosystem's plant community across the soil–plant–atmosphere continuum. Specifically, Ψ EWP defines a threshold below which the capacity of the root system to extract soil water and the ability of the leaves to maintain stomatal function are strongly diminished. We combined ecosystem flux and leaf water potential measurements to derive the Ψ EWP of a Quercus‐Carya forest from an “ecosystem pressure–volume (PV) curve,” which is analogous to the tissue‐level technique. When community predawn leaf water potential (Ψ pd ) was above Ψ EWP (=−2.0 MPa), the forest was highly responsive to environmental dynamics. When Ψ pd fell below Ψ EWP , the forest became insensitive to environmental variation and was a net source of carbon dioxide for nearly 2 months. Thus, Ψ EWP is a threshold defining marked shifts in ecosystem functional state. Though there was rainfall‐induced recovery of ecosystem gas exchange following soaking rains, a legacy of structural and physiological damage inhibited canopy photosynthetic capacity. Although over 16 growing seasons, only 10% of Ψ pd observations fell below Ψ EWP , the forest is commonly only 2–4 weeks of intense drought away from reaching Ψ EWP , and thus highly reliant on frequent rainfall to replenish the soil water supply. We propose, based on a bottom‐up analysis of root density profiles and soil moisture characteristic curves, that soil water acquisition capacity is the major determinant of Ψ EWP , and species in an ecosystem require compatible leaf‐level traits such as turgor loss point so that leaf wilting is coordinated with the inability to extract further water from the soil.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Toward a coordinated understanding of hydro‐biogeochemical root functions in tropical forests for application in vegetation models

Summary Tropical forest root characteristics and resource acquisition strategies are underrepresented in vegetation and global models, hampering the prediction of forest–climate feedbacks for these carbon‐rich ecosystems. Lowland tropical forests often have globally unique combinations of high taxonomic and functional biodiversity, rainfall seasonality, and strongly weathered infertile soils, giving rise to distinct patterns in root traits and functions compared with higher latitude ecosystems. We provide a roadmap for integrating recent advances in our understanding of tropical forest belowground function into vegetation models, focusing on water and nutrient acquisition. We offer comparisons of recent advances in empirical and model understanding of root characteristics that represent important functional processes in tropical forests. We focus on: (1) fine‐root strategies for soil resource exploration, (2) coupling and trade‐offs in fine‐root water vs nutrient acquisition, and (3) aboveground–belowground linkages in plant resource acquisition and use. We suggest avenues for representing these extremely diverse plant communities in computationally manageable and ecologically meaningful groups in models for linked aboveground–belowground hydro‐nutrient functions. Tropical forests are undergoing warming, shifting rainfall regimes, and exacerbation of soil nutrient scarcity caused by elevated atmospheric CO 2 . The accurate model representation of tropical forest functions is crucial for understanding the interactions of this biome with the climate.

Plant Sciences↗
DOE OSTI2019

Homeostatic maintenance of nonstructural carbohydrates during the 2015-2016 El Niño drought across a tropical forest precipitation gradient

Non-structural carbohydrates (NSCs) are essential for maintenance of plant metabolism, and may be sensitive to both short- and long-term climatic variation. The NSC variation in NSCs in moist tropical forests has only rarely been studied, however, so regulation of NSCs in these systems is poorly understood. We measured foliar and branch NSC content in 23 tree species at three sites located across a large precipitation gradient in Panama during the 2015-2016 El Niño to examine how short- and long-term climatic variation impacts carbohydrate dynamics. Across all sites, leaf NSCs increased over diurnal time-periods. There was no significant difference in total NSCs over as the progression of the drought progressed (leaf p=0.32, branch p=0.30), nor across the rainfall gradient (leaf p=0.91, branch p=0.96). The Foliar soluble sugars decreased while starch increased :starch ratio did decrease over the duration of the dry period, suggesting greater partitioning of NSCs to storage than metabolism or transport as drought progressed. There was large variation across species at all sites, but total foliar NSCs were positively correlated with leaf mass per area, while branch sugars were positively related to leaf temperature and negatively correlated with daily photosynthesis and wood density. The NSC homeostasis across a wide range of conditions suggests that NSCs are an allocation priority in moist tropical forests.

Dickman, Lee T.↗
DOE OSTI2021

Research on the Influence Mechanism of the High‐Steep Slope on the Deformation Characteristics of Bridge Substructure

With the development of the Chinese railway, the high‐steep slope is irreversible to be faced; especially under severe conditions such as heavy rainfall and earthquake, this kind of slope is prone to geological disasters, which seriously affects the safety and stability of the bridge substructure. Aiming to this, long‐term monitorization and numerical analysis were carried out in this research, and the influence mechanism of the high‐steep slopes on the stress and deformation characteristics of the bridge structure was studied. The research results show that under the effect of rainfall and earthquake, the original stress balance in the high‐steep slope is broken, and the possibility of landslide thrust increases; under the comprehensive impact of residual landslide thrust, traction force at slope foot, vertical gravity of bridge slab and vehicle, and the bridge cap will deform. Besides, the deformation of the bridge pier exceeds the allowable lateral displacement of the top of the bridge pier, reaching 111.7%∼112.4% of the limit, which seriously affects the stability of the bridge structure and the safety of the railway service. Therefore, by increasing the support strength of the slope foot and the diameter of the bridge pile foundation, the traction force of the slope foot can be reduced, and the sliding resistance of the bridge pile foundation can be improved so as the safety of the bridge structure can be promoted.

Zhang, Yufang (ORCID:0000000348912060)↗
DOE OSTI2016

TRMM Latent Heating Retrieval: Applications and Comparisons with Field Campaigns and Large-Scale Analyses

Yanai et al. (1973) utilized the meteorological data collected from a sounding network to present a pioneering work on thermodynamic budgets, which are referred to as the apparent heat source (Q1) and apparent moisture sink (Q2). Latent heating (LH) is one of the most dominant terms in Q1. Yanai’s paper motivated the development of satellite-based LH algorithms and provided a theoretical background for imposing large-scale advective forcing into cloud-resolving models (CRMs). These CRM-simulated LH and Q1 data have been used to generate the look-up tables in Tropical Rainfall Measuring Mission (TRMM) LH algorithms. A set of algorithms developed for retrieving LH profiles from TRMM-based rainfall profiles are described and evaluated, including details concerning their intrinsic space-time resolutions. Included in the paper are results from a variety of validation analyses that define the uncertainty of the LH profile estimates. Also, examples of how TRMM-retrieved LH profiles have been used to understand the lifecycle of the MJO and improve the predictions of global weather and climate models as well as comparisons with large-scale analyses are provided. Areas for further improvement of the TRMM products are discussed.

field campaigns↗
DOE OSTI2020

PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

Precipitation measurements with high spatiotemporal resolution are a vital input for hydrometeorological and water resources studies; decision-making in disaster management; and weather, climate, and hydrological forecasting. Moreover, real-time precipitation estimation with high precision is pivotal for the monitoring and managing of catastrophic hydroclimate disasters such as flash floods, which frequently transpire after extreme rainfall. While algorithms that exclusively use satellite infrared data as input are attractive owing to their rich spatiotemporal resolution and near-instantaneous availability, their sole reliance on cloud-top brightness temperature (T b ) readings causes underestimates in wet regions and overestimates in dry regions—this is especially evident over the western contiguous United States (CONUS). We introduce an algorithm, the Precipitation Estimations from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain rate model (PDIR), which utilizes climatological data to construct a dynamic (i.e., laterally shifting) T b –rain rate relationship that has several notable advantages over other quantitative precipitation-estimation algorithms and noteworthy skill over the western CONUS. Validation of PDIR over the western CONUS shows a promising degree of skill, notably at the annual scale, where it performs well in comparison to other satellite-based products. Analysis of two extreme landfalling atmospheric rivers show that solely IR-based PDIR performs reasonably well compared to other IR- and PMW-based satellite rainfall products, marking its potential to be effective in real-time monitoring of extreme storms. This research suggests that IR-based algorithms that contain the spatiotemporal richness and near-instantaneous availability needed for rapid natural hazards response may soon contain the skill needed for hydrologic and water resource applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The ATTO-Campina site: A new observatory for tropical convection and gas-aerosol-cloud-precipitation interactions in the Amazon

We present results from ATTO-Campina, a new permanent observational site in central Amazon, about 4 km from the ATTO towers. Operational since 2020, ATTO-Campina characterizes atmospheric, cloud and rainfall properties through remote sensing. The goal is to provide continuous, complementary measurements to the ATTO towers, addressing the rainforest’s complex gas-aerosol-cloud-precipitation dynamics. Using a 3.5-year dataset, we classified convective clouds into three types: shallow cumulus (ShCu), congestus (Con) or (Deep) clouds. The shallow-to-deep transition takes about three hours, starting with ShCu formation at 11:00 local time. The accumulated rainfall peak follows at about 16:00. Only weak downdrafts are present in the upper troposphere where previous studies indicate new particle formation (NPF) occurrence. Strong downdrafts are mostly limited to heights below 5 km. Con and Deep convective days have higher concentrations of ultrafine aerosol and lower concentrations of accumulation-mode particles compared to ShCu. Convective clouds also significantly modify gas mixing ratios. Deep convective clouds are associated with high near-surface O3, consistent with downward transport from the midtroposphere. Our results showcase the added detail achieved by integrating data from the ATTO towers and ATTO-Campina sites. Together, these sites support better understanding of interconnected gas-aerosol-cloud-precipitation processes in the Amazon and their evolution under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Using Lomb–Scargle Analysis to Derive Empirical Orthogonal Functions from Gappy Meteorological Data

The Lomb–Scargle discrete Fourier transform (LSDFT) is a well-known technique for analyzing time series. In this study, a solution for empirical orthogonal functions (EOFs) based on irregularly sampled data is derived from the LSDFT. It is demonstrated that this particular algorithm has no hard limit on its accuracy and yields results comparable to those of complex Hilbert EOF analysis. Two LSDFT algorithms are compared in terms of their performance in evaluating EOFs for precipitation observations from the Tropical Rainfall Measuring Mission satellite. Both are shown to be able to capture the pattern of the diurnal cycle of rainfall over the complex topography and diverse land cover of South America, and both also show other consistent features in the 0–12-day frequency band.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Lagrangian Investigation of the Precipitation Efficiency of Convective Clouds

The precipitation efficiency of cumulus congestus clouds is explored with a new Lagrangian particle framework for large-eddy simulations. The framework is created to track particles representative of individual water molecules. A Monte Carlo approach facilitates the transition of particles between the different water classes (e.g., vapor, rain, or graupel). With this framework, it is possible to reconstruct the pathways of water as it moves from vapor at a specific altitude to rain at the surface. By tracking water molecules through both physical and microphysical space, the precipitation efficiency can be studied in detail as a function of height. Large-eddy simulations of individual cumulus congestus clouds show that the clouds convert entrained vapor to surface precipitation with an efficiency of around 10%. About two-thirds of all vapor that enters the cloud does so by entrainment in the free troposphere, but free-tropospheric vapor accounts for only one-third to one-half of the surface rainfall, with the remaining surface rainfall originating as vapor entrained through the cloud base. The smaller efficiency with which that laterally entrained water is converted into surface precipitation results from the smaller efficiencies with which it condenses, forms precipitating hydrometeors, and reaches the surface.

54 ENVIRONMENTAL SCIENCES↗