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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

AmeriFlux FLUXNET-1F US-KS2 Kennedy Space Center (scrub oak)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-KS2 Kennedy Space Center (scrub oak). This is the FLUXNET version of the carbon flux data for the site US-KS2 Kennedy Space Center (scrub oak) produced by applying the standard ONEFlux (1F) software. Site Description - The Kennedy Space Center Scrub Oak site is located within the Merritt Island National Wildlife Refuge at the Kennedy Space Center (KSC) on the east coast of central Florida. Situated in a 10 ha scrub oak ecosystem, the surrounding stand was completely burned by a prescribed fire in 1996. The purpose of the burn was to control understory fuel load, which has been a common practice since 1969. Within a few weeks of the 1996 burn, the stand began to naturally regenerate from roots and rhizomes. Most scrub oak stands in the region undergo a 7 to 10 year disturbance cycle, mostly related to fire or hurricane activity. A severe drought gripped most of Florida beginning in 1998 until the later half of 2001 resulting in four years of relatively low amount of annual rainfall. Exceptionally high annual rainfall amount in 2004 was the result of a pair of hurricanes that hit the area in August and September of 2004. Prevaling wind directions for the site are as follows: W to NW in the winter, afternoon E sea breeze in the summer.

Drake, Bert↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-LS1 San Pedro River Lewis Springs Sacaton Grassland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-LS1 San Pedro River Lewis Springs Sacaton Grassland. This is the FLUXNET version of the carbon flux data for the site US-LS1 San Pedro River Lewis Springs Sacaton Grassland produced by applying the standard ONEFlux (1F) software. Site Description - This is a riparian grassland located along an old alluvial terrace along the banks of the entrenched San Pedro River in southeastern Arizona. Site climate is semiarid with monsoonal (Jul-Sep) rainfall. The grasses access groundwater (depth of 2-4 m)from an alluvial aquifer recharged elsewhere. Site water balance is not closed and ET greatly exceeds precipitation. The perennial bunchgrasses use both rainfall, groundwater and occasional flood water. This groundwater-using ecosystem is far more ebulliant and productive than the upland vegetation around it (shrublands and grasslands) outside of the San Pedro River corridor.

Scott, Russell [USDA-ARS]↗
DOE OSTI2026

AmeriFlux FLUXNET-1F US-CMW Charleston Mesquite Woodland

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-CMW Charleston Mesquite Woodland. This is the FLUXNET version of the carbon flux data for the site US-CMW Charleston Mesquite Woodland produced by applying the standard ONEFlux (1F) software. Site Description - Site is a riparian mesquite woodland located along an old alluvial terrace along the banks of the San Pedro River in southeastern Arizona. Site climate is semiarid with monsoonal (Jul-Sep) rainfall. The mesquite trees access groundwater from an alluvial aquifer recharged elsewhere. Site water balance is not closed and ET greatly exceeds precipitation. The understory grasses and forbs are rainfall dependent. This groundwater-dependent ecosystem is far more ebulliant and productive than the upland vegetation around it (shrublands and grasslands).

Scott, Russell [USDA-ARS]↗
DOE OSTI2023

The Seismic Signature of a High-Energy Density Physics Laboratory and Its Potential for Measuring Time-Dependent Velocity Structure

The Z Machine at Sandia National Laboratories is a pulsed power facility for high-energy density physics experiments that can shock materials to extreme temperatures and pressures through a focused energy release of up to ~ 25 MJ in < 100 nanoseconds. It has been in operation for more than two decades and conducts up to ~ 100 experiments, or “shots,” per year. Based on a set of 74 known shot times from 2018, we determined that Z Machine shots produce detectable ~ 3–17 Hz ground motion 12 km away at the Albuquerque Seismological Laboratory, New Mexico (ANMO), borehole seismograph, with peak signal at ~ 7 Hz. The known shot waveforms were used to create a three-component template, leading to the detection of 2339 Z Machine shots since 1998 through single-station cross-correlation. Local seismic magnitude estimates range from local magnitude (M L ) —2 to —1.3 and indicate that only a small fraction of the shot energy is transmitted by seismic phases observable at 12 km distance. The most recent major facility renovation, which was intended to decrease mechanical dissipation, is associated with an abrupt decrease in observed seismic amplitudes at ANMO despite stable maximum shot energy. The highly repetitive impulsive sources are well suited to coda-wave interferometry to investigate time-dependent velocity structures. Relative velocity variations (dv/v) show an annual cycle with amplitude of ~ 0.2%. Local minima are observed in the late spring, and dv/v increases through the summer monsoon rainfall, possibly reflecting patchy saturation as rainfall infiltrates near the eastern edge of the Albuquerque basin. Importantly, the cumulative results demonstrate that forensic seismology can provide insight into long-term operation of facilities such as pulsed-power laboratories, and that their recurring signals may be valuable for studies of time-dependent structure

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models (Final Technical Report)

This is the final report for a DOE-funded research project describing the outcome of research on non-homogeneous hidden Markov models (NHMMs) and coupled ocean-atmosphere (O-A) intermediate-complexity models (ICMs) to identify the potentially predictable modes of climate variability, and to investigate their impacts on the regional-scale. The main results consist of extensive development of the hidden Markov models for rainfall simulation and downscaling specifically within the non-stationary climate change context together with the development of parallelized software; application of NHMMs to downscaling of rainfall projections over India; identification and analysis of decadal climate signals in data and models; and, studies of climate variability in terms of the dynamics of atmospheric flow regimes.

58 GEOSCIENCES↗
DOE OSTI2014

Future projection of mean and variability of the Asian Summer Monsoon and Indian Ocean Climate systems

The overall goal of this project is to assess the ability of the CMIP3/5 models to simulate the Indian-Ocean monsoon systems. The PI along with post-docs investigated research issues ranging from synoptic systems to long-term trends over the Asian monsoon region. The PI applied diagnostic tools such as moist static energy (MSE) to isolate: the moist and radiative processes responsible for extended monsoon breaks over South Asia, precursors in the ENSO-monsoon association, reasons for the drying tendency over South Asia and the possible effect on tropical Indian Ocean climate anomalies influencing certain aspects of ENSO characteristics. By diagnosing various observations and coupled model simulations, we developed working hypothesis and tested them by carrying out sensitivity experiments with both linear and nonlinear models. Possible physical and dynamical reasons for model sensitivities were deduced. On the teleconnection front, the ability of CMIP5 models in representing the monsoon-desert mechanism was examined recently. Further more, we have applied a suite of diagnostics and have performed an in depth analysis on CMIP5 integrations to isolate the possible reasons for the ENSO-monsoon linkage or lack thereof. The PI has collaborated with Dr. K.R. Sperber of PCMDI and other CLIVAR Asian-Australian monsoon panel members in understanding the ability of CMIP3/5 models in capturing monsoon and its spectrum of variability. The objective and process-based diagnostics aided in selecting models that best represent the present-day monsoon and its variability that are then employed for future projections. Two major highlights were an invitation to write a review on present understanding monsoons in a changing climate in Nature Climate Change, and identification of an east-west shift in observed monsoon rainfall (more rainfall over tropical western Pacific and drying tendency over South Asia) in the last six decades and attributing that shift to SST rise over the tropical western Pacific. On the training of post-doctoral scientists: the PI spent considerable amount of time and efforts in introducing the post-docs into climate modeling and designing the numerical experiments. With training provided and knowledge gained, post-docs worked in the project obtained long term positions elsewhere. The PI also enjoyed the experience in managing the works and educating work ethics to the younger generation. Based on the research achievements and publications, the PI gave invited talks in major international monsoon conferences/workshops, and gave lectures in various research organizations in the last six years. Finally, during the project period, the PI attended all the DOE organized PIs meeting and presented the major results. Some of the major implications of the project include: (i) Sustained observational efforts are necessary to monitor the three-dimensional moisture distribution over the Asian monsoon region that would aid in better understanding, modeling and predicting severe monsoons well in advance and (ii) process-based diagnostics lead pathways for model improvements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Lotic Water Hydrodynamic Model

Water-related natural disasters, for example, floods and droughts, are among the most frequent and costly natural hazards, both socially and economically. Many of these floods are a result of excess rainfall collecting in streams and rivers, and subsequently overtopping banks and flowing overland into urban environments. Floods can cause physical damage to critical infrastructure and present health risks through the spread of waterborne diseases. Los Alamos National Laboratory (LANL) has developed Lotic, a state-of-the-art surface water hydrodynamic model, to simulate propagation of flood waves originating from a variety of events. Lotic is a two-dimensional (2D) flood model that has been used primarily for simulations in which overland water flows are characterized by movement in two dimensions, such as flood waves expected from rainfall-runoff events, storm surge, and tsunamis. In 2013, LANL developers enhanced Lotic through several development efforts. These developments included enhancements to the 2D simulation engine, including numerical formulation, computational efficiency developments, and visualization. Stakeholders can use simulation results to estimate infrastructure damage and cascading consequences within other sets of infrastructure, as well as to inform the development of flood mitigation strategies.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Atmospheric Radiation Measurement Madden-Julian Oscillation Investigation Experiment Field Campaign Report

Every 30–90 days during the Northern Hemisphere winter, the equatorial tropical atmosphere experiences pulses of extraordinarily strong deep convection and rainfall. This phenomenon is referred to as the Madden–Julian Oscillation, or MJO, named after the scientists who identified this cycle. The MJO significantly affects weather and rainfall patterns around the world (Zhang 2013). To improve predictions of the MJO—especially about how it forms and evolves throughout its lifecycle—an international group of scientists collected an unprecedented set of observations from the Indian Ocean and western Pacific region from October 2011 through March 2012 through several coordinated efforts. The coordinated field campaigns captured six distinct MJO cycles in the Indian Ocean. The rich set of observations capturing several MJO events from these efforts will be used for many years to study the physics of the MJO. Here we highlight early research results using data from the Atmospheric Radiation Measurement (ARM) Madden-Julian Oscillation Investigation Experiment (AMIE), sponsored by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Observations and Modeling of the Green Ocean Amazon 2014/15: Parsivel2 Field Campaign Report

One of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Parsivel2 disdrometers was deployed at the first ARM Mobile Facility (AMF1) T3 site in Manacapuru, Brazil at the beginning of the second Green Ocean Amazon (GoAmazon)2014/15 intensive operational period (IOP2) in September 2014 through the end of the field campaign in December 2015. The Parsivel2 provided one-minute drop-size distribution (DSD) observations that have already been used for a number of applications related to GoAmazon2014/15 science objectives. The first use was the creation of a reflectivity-rain rate (Z-R) relation enabling the calculation of rain rates from the Brazilian Sistema de Protecao da Amazonia (SIPAM) S-band operational radar in Manaus. The radar-derived rainfall is an important constraint for the variational analysis of a large-scale forcing data set, which was recently released for the two IOPs that took place in the 2014 wet and transition seasons, respectively. The SIPAM radar rainfall is also being used to validate a number of cloud-resolving model simulations being run for the campaign. A second use of the Parsivel2 DSDs has been to provide a necessary reference point to calibrate the vertical velocity retrievals from the AMF1 W Band ARM Cloud Radar (WACR) cloud-profiling and ultra-high-frequency (UHF) wind-profiling instruments. Accurate retrievals of in-cloud vertical velocities are important to understand the microphysical and kinematic properties of Amazonian convective clouds and their interaction with the land surface and atmospheric aerosols. Further use of the Parsivel2 DSD observations can be made to better understand precipitation characteristics and their variability during GoAmazon2014/15.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Cloud, Aerosol, and Complex Terrain Interactions (CACTI) Preliminary Science Plan

General circulation models and downscaled regional models exhibit persistent biases in deep convective initiation location and timing, cloud top height, stratiform area and precipitation fraction, and anvil coverage. Despite important impacts on the distribution of atmospheric heating, moistening, and momentum, nearly all climate models fail to represent convective organization, while system evolution is not represented at all. Improving representation of convective systems in models requires characterization of their predictability as a function of environmental conditions, and this characterization depends on observing many cases of convective initiation, non-initiation, organization, and non-organization. The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) experiment in the Sierras de Córdoba mountain range of north-central Argentina is designed to improve understanding of cloud life cycle and organization in relation to environmental conditions so that cumulus, microphysics, and aerosol parameterizations in multi-scale models can be improved. The Sierras de Córdoba range has a high frequency of orographic boundary-layer clouds, many reaching congestus depths, many initiating into deep convection, and some organizing into mesoscale systems uniquely observable from a single fixed site. Some systems even grow upscale to become among the deepest, largest, and longest-lived in the world. These systems likely contribute to an observed regional trend of increasing extreme rainfall, and poor prediction of them likely contributes to a warm, dry bias in climate models downstream of the Sierras de Córdoba range in a key agricultural region. Many environmental factors influence the convective lifecycle in this region including orographic, low-level jet, and frontal circulations, surface fluxes, synoptic vertical motions influenced by the Andes, cloud detrainment, and aerosol properties. Local and long-range transport of smoke resulting from biomass burning as well as blowing dust are common in the austral spring, while changes in land surface properties as the wet season progresses impact surface fluxes and boundary layer evolution on daily and seasonal time scales that feed back to cloud and rainfall generation. This range of environmental conditions and cloud properties coupled with a high frequency of events makes this an ideal location for improving our understanding of cloud-environment interactions. The following primary science questions will be addressed through coordinated first ARM Mobile Facility (AMF1), mobile C-band Scanning ARM Precipitation Radar (C-SAPR2), guest instrumentation, and potential ARM Aerial Facility (AAF) Gulfstream-1 (G-1) observations: 1. How are the properties and lifecycles of orographically generated cumulus humulis, mediocris, and congestus clouds affected by environmental kinematics, thermodynamics, aerosols, and surface properties? How do these cloud types alter these environmental conditions? 2. How do environmental kinematics, thermodynamics, and aerosols impact deep convective initiation, upscale growth, and mesoscale organization? How are soil moisture, surface fluxes, and aerosol properties altered by deep convective precipitation events and seasonal accumulation of precipitation? This multi-faceted experiment involves a long term 8.5-month Extended Observing Period (EOP, 15 August, 2018-30 April, 2019) as well as a 6-week Intensive Observation Period (IOP, 1 November-15 December) that will coincide with the international multi-agency RELAMPAGO field campaign.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Warming-Induced Biome Change at the Temperate-Boreal Ecotone: An Experimental Test of Key Regeneration Processes (Final Technical Report)

This project advanced our understanding of plants’ key physiological and ecological processes that are essential to the survival, success, and carbon cycling of plants from the boreal-temperate forest ecotone in the future climate. A primary goal of the project was to test the effect of warming and water limitation on plants from the Boreal-Temperate Ecotone using a realistic outdoor long-term experiment. Plant at this ecotone is predicted to be particularly sensitive to future climate since the ecotone represents a relatively sharp boundary between many temperate species to the south and boreal species to the north. This work was important, as there was and still is a significant uncertainty surrounding the mechanisms of future climate effects on vegetation compositional and carbon cycling change, especially in forests mostly due to lack of experimental examination of response to warming and limited water availability. Consequently, we tested our central objective, that was to assess the potential for projected climate to alter tree species composition and carbon cycling at the southern boreal-temperate forest ecotone by experimentally warming forest plots and documenting effects on establishment and juvenile performance for tree species near their warmer (boreal species) and colder (temperate species) range limits in northern Minnesota. To accomplish our goals, we designed and built a successful highly manipulative experiment that implemented vegetation-soil warming and rainfall removal in both understory and open habitats at two sites in northern Minnesota (MN). The sites lie at different distances to the north of the ecotone between the southern boreal and northern temperate forest biomes. We focused on ten target species, all of which are near their warmer (Pinus banksiana, Picea glauca, Abies balsamea, Populus tremuloides, Betula papyrifera) or colder (Pinus strobus, Quercus macrocarpa, Quercus rubra, Acer rubrum, Acer saccharum) range edge in northern MN; we also studied other species such as a woody invader and native herbs and shrubs. Three temperature treatments (ambient, +1.7°C, +3.4°C) were imposed using infra-red heating lamps augmented with soil heating cables to simultaneously control soil and vegetation temperatures, enabling assessment of non-linear responses. These temperatures bracket the range of anticipated warming in the region during the next 75-100 years. In addition, we implemented summer rain removal treatment mimicking predicted distribution in precipitation patterns by future climate where we actively removed ~40% of rainfall. Measurements included germinant establishment, plant growth, phenology, physiology and survival, soil, plant and air temperature, soil moisture and nutrient status. These measures enabled us to assess how germination, growth and survival at the establishment stage are influenced by direct climate effects and/or indirect effects of warming, soil resources such as water and nutrients availability or other environmental variables that either directly or indirectly influence plant performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Prediction of Tropical Cyclone Intensity Change using Advance Machine Learning Techniques

A tropical cyclone (TC) is a rapidly rotating storm system characterized by a lowpressure center, a closed low-level atmospheric circulation, strong winds, and a spiral arrangement of thunderstorms that produce heavy rain or squalls. TC occurs when the sea temperatures are high, and the air above the warm ocean is heated and rises quickly, causing an area of very low pressure, and then drawing more warm moist air, leading to strong winds. The air cools and condenses when rising, so that cumulonimbus clouds formed and produce heavy rainfall. These high winds, excessive rainfall and flooding threaten lives and property.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A trait-based framework for linking microbial communities with carbon transformations under precipitation change

Droughts are common throughout the world. As the climate changes, droughts may become more frequent and intense. Still, scientists are uncertain about how drought will affect the natural world, particularly the bacteria, fungi, and other microbes that live in soils. These tiny life forms are crucial because they control the Earth’s flows of carbon and essential nutrients. Researchers at the University of California, Irvine, and Lawrence Berkeley National Laboratory teamed up to study how the microbiome, or collection of bacteria and fungi in the soil, deals with drought. Since 2007, the researchers have used shelters with retractable roofs to prevent nearly half of normal rainfall from reaching grass and shrub ecosystems, and their soil microbiomes, in Southern California. The study team discovered that microbes have some clever tricks up their sleeve for surviving drought. When growing on dead grass as a food source, microbes ramp up production of specialized chemicals called osmolytes that keep their cells from drying out. But microbes growing on dead shrubs face another problem. Unlike grass, shrub leaves are a lousy food source. To digest and consume shrub leaves, microbes have to exude more enzymes, which act like biochemical chef knives that chop complex leaf molecules into bite-sized pieces. Carbon is the coin of the microbial realm, earned via enzyme action or slurping up dead plant juices. Microbes growing under normal conditions on tasty dead grass have it easy: they can spend most of their carbon currency on growth. With drought, life gets harder as microbes need to pay up for osmolytes and ramp down their growth. It gets worse with shrub leaves because microbes have to multi-task among growth, enzyme secretion, and osmolyte production. When drought hits, microbes on shrub leaves forgo the osmolytes, probably because losing the carbon revenue from enzyme investment would be a deal-breaker for survival. The next question tackled by the researchers asked how the genes controlling microbial lifestyles sort out across the tree of life. Most microbiologists thought these lifestyles would correspond to rather large branches on the tree. But the study team found that in fact, very closely related microbes differ in important and interesting ways. For example, bacteria that have nearly identical housekeeping genes respond distinctively to warming, rainfall, and plant chemistry. As a result, soil microbiomes are much richer in diversity than originally thought. And studying microbial diversity in much greater detail could open up many more possibilities for how microbial life deals with changing environmental conditions. Trying to understand microbial life without the fine details of genetic diversity is like trying to stream Netflix on a dial-up connection. By looking across the landscape, the researchers revealed that microbial diversity is absolutely critical for maintaining the planet’s flows of carbon and nutrients. The study team designed a new technology—microbial cages—for transplanting intact microbiomes. With the cages, the researchers could move microbiomes to novel environments and compare their ability to cycle carbon and nutrients. In some cases, performance tailed off when microbiomes found themselves in a new environment, but in other cases, performance rivaled or even exceeded that of the resident microbiome. And even the low performers eventually caught up to the native microbiomes if given sufficient time. These findings mean that microbiomes—at least in Southern California—may be resilient to climate shifts due to a high diversity of microbial lifestyles. Coping with heat and drought may literally be in their DNA. The last piece of the research puzzle, and a “Holy Grail” for microbial ecologists, is to forecast the behavior of diverse microbiomes. To meet this challenge, the study team developed new theory and computer models. For the first time, these models account for hundreds of different microbes and how their intricate lifestyles cope with drought. The models are starting to connect the tiniest microbes with the global cycles that sustain Earth’s farms, fields, and forests. With that connection, it will be easier for society to plan for a world with more droughts and other climate disruptions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Improving Short Term Predictability of Hydrologic Models with Deep Learning

Focal Area: Focal Area 2. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Science Challenge: A major challenge exists in the lack of reliable predictive modeling of water-cycle extremes using macro-scale hydrologic models that are driven by atmospheric climate data. This causes a critical knowledge gap in understanding the magnitudes, probabilities, and severity of droughts, rainfall, and flooding. Improved understanding of these extreme events requires more accurate modeling at high spatial and temporal resolutions. This shortcoming is especially apparent when complex couplings between atmospheric quantities and engineered systems emerge, such as during extreme precipitation or drought events and at intersections of atmospheric-land-fluvial-ocean systems. Added complexity around representing operations of engineered infrastructure leads to a limited capability to analyze the temporal and spatial implications of compounding extreme events across inland and coastal regions. This results in profound challenges for water and power systems operators, agriculture production, and regional infrastructure planning. The science challenge can be summarized as How can we use AI/ML to improve short term predictability of extreme water-cycle events and associated risk, especially compounding events and extreme rainfall patterns that result in flooding, to inland and coastal communities under different climate scenarios?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Application of Point Precipitation Frequency Estimates to Watersheds

This report documents work sponsored by the U.S. Nuclear Regulatory Commission (NRC) at the Oak Ridge National Laboratory (ORNL) as part of the RES project, “Application of Point Precipitation Frequency Estimates to Watersheds.” This project was implemented as part of the Probabilistic Flood Hazard Assessment (PFHA) Research Program. The objective of the PFHA Research Program is to develop tools and guidance on the use of PFHA methods to risk-inform NRC’s licensing of new facilities as well as licensing and oversight of currently operating facilities as they relate to flooding hazards. Many nuclear power plants (NPPs) are located on or near rivers so riverine flooding hazards need to be considered in their design and operation. Probabilistic riverine flood models are important tools for realistic assessment of flooding risks. However, these models require areal estimates of the depth, duration, and frequency of rainfall distributed over the watershed, which are not often available. Point precipitation frequency estimates are more widely available. For example, the National Oceanic and Atmospheric Administration (NOAA) has published NOAA Atlas 14, which provides point precipitation frequency estimates for 5-minute through 60-day durations at average recurrence intervals of 1-year through 1,000-year. The research documented in this report addresses areal reduction factors (ARFs), which can be used to convert the widely available point precipitation frequency estimates, to estimates of areal precipitation frequency over a watershed. The most widely used ARF source is Technical Paper 29 (TP-29) published by the then U.S. Weather Bureau in 1958. However, both the methods and the underlying precipitation data used to produce TP-29 are seriously out of date. For example, due to the small gauge network available at the time of TP-29’s compilation, ARF estimates developed are only for watersheds smaller than about 400 square miles. Due to the relatively short record lengths of precipitation data available, frequency considerations could not be accurately determined. Other factors such as regional climate and seasonality were not addressed. Several newer methods have been published since TP-29 was developed and both the type and quantity of precipitation data have increased significantly, along with computational resources and analytical tools such as geographic information systems. This report reviewed and assessed the available precipitation products and methods for conducting ARF analysis. The work applied up-to-date precipitation data products and analysis methods with a novel watershed-based approach to investigate how ARF estimates vary across different methods, data sources, geographical locations, return periods, and seasons. The overall findings reported here regarding basic ARF trends are in line with other recent studies showing that ARFs decrease with increasing area, increase with increasing duration, and decrease with increasing return period. This study found significant differences among the available ARF methods. This work also found a strong geographical variability across different US hydrologic regions, suggesting that the ARF are specific to regional climate patterns and geographical characteristics and should not be applied arbitrarily to other locations. The results also reveal the importance of data record length, especially for high return level ARFs. The work reported in NUREG/CR-7271 will assist NRC staff in assessing different classes of ARF methods in conjunction with available rainfall data sets. It will also support the development of guidance for application of point precipitation data in PFHAs. It should be noted that the ARF values presented in this report for any location or region were developed for the purposes of comparing methods and investigating the factors that influence ARFs. They should not be considered official and should not be used in leu of a site-specific analysis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Carbon and Water Dynamics in the Sandhills of South Carolina

An investigation was conducted to better understand the relationship between the carbon and precipitation cycles in the atmosphere. The carbon cycle is an important biogeochemical process in which a plant takes in carbon during photosynthesis and releases it during respiration. Why plants take or release carbon can be explained by various environmental conditions throughout the year. By using eddy-covariance measurements of rainfall and evaporation, we can describe the carbon cycle in the sandhills of South Carolina. We examine those measurements in comparison to soil water over the course of five years at an eddy covariance tower, located in a long leaf pine forest at the Savannah River Site in Aiken, South Carolina. The study illustrates how ecosystem fluxes respond to rainfall events and drought periods on site, specifically the lowest and highest moisture periods through the year. Additionally, this study also explores how resistant the store of carbon is during a drought and its resilience when rains return.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Prefire Approach for Probabilistic Assessments of Postfire Debris-Flow Inundation

Increases in wildfire activity and rainfall intensification are driving more postfire debris flows (PFDF) in many regions around the world. PFDFs are most common in the first postfire year and may even occur before a fire is fully controlled. This underscores the importance of assessing postfire hazards before a fire starts. Evaluation of PFDF hazards prior to fire can help strategize interventions lessening the negative effects of future fires. However, debris-flow runout and inundation analyses are not routine in PFDF hazard assessments, partially due to time constraints and substantial uncertainties in boundary conditions. Here, we propose a prefire PFDF inundation assessment framework using a debris-flow runout model based on the Herschel-Bulkley (HB) rheology (HEC-RAS v6.1). We constrain model inputs and parameters using Bayesian posterior analysis, rainfall-runoff simulations, and a debris-flow volume model. We use observations from recent PFDF incidents in northern Arizona, USA, to calibrate model components and then apply our prefire inundation assessment framework in a nearby unburned area. Specifically, we (a) identify yield stress as the most influential factor on inundation extent and arrival time in a HB model, (b) establish posterior distributions for model parameters suitable for forward modeling by leveraging uncertainties in field observations, and (c) implement a predictive forward analysis in an area that has not burned recently to evaluate PFDF inundation under several future fire scenarios. This study improves our ability to assess postfire debris-flow hazards before a fire begins and provides guidance for future applications of single-phase rheological models when assessing PFDF hazards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using satellite data to assess Hurricane Helene’s impact on vegetation by land use in the CSRA

Many regions within Georgia, South Carolina, and North Carolina experienced record-breaking rainfall and catastrophic winds due to Hurricane Helene. Helene made landfall on Florida’s big bend on September 26 th , 2024, as a category 4 hurricane, and tracked northward through Georgia and the southern Appalachian Mountains before dissipating on September 29 th , 2024. One significant impact of the hurricane was severe damage to tree canopies across the southeastern United States. This study utilizes satellite-based remotely sensed data provided by the National Aeronautical and Space Administration (NASA) to examine the resilience of these tree canopies following the hurricane. Specifically, the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Cover Type, Soil Moisture Active Passive (SMAP), and the Global Precipitation Measurement (GPM) mission datasets were employed to study the tree canopy’s response to such events in the Central Savannah River Area (CSRA). A significant increase was observed in the LAI in November of 2024. This increase in LAI was likely influenced by warmer-than-average temperatures throughout October of 2024, along with a second record-breaking rainfall event in the CSRA on November 6th, 2024. The LAI increase was then broken down into land cover type to understand which areas contributed most. The complex nature and resilience of trees became evident, offering opportunities for further exploration and application in urban planning, emergency response, and environmental management.

54 ENVIRONMENTAL SCIENCES↗