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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Evaluating the Impact of Agricultural Soil NOx Emissions on Air Quality Using Advanced Satellite, Ground-based, and Model Data

Soil moisture can also influence the concentration of atmospheric trace gases by moderating the emission of nitrogen oxides (NOx = NO + NO2) from agricultural fields. Human activities can play an important role in controlling the available soil moisture depending on irrigation demand and agricultural management. To further complicate matters, nitrogen fertilizer use has also been found to be a significant source of NOx emissions in agricultural areas. In fact, some of the highest emissions have been reported from fertilized soils in high-temperature agricultural regions, such as the Imperial Valley of California. Since NO2 is an important precursor to ozone formation in the troposphere, which is a leading cause of premature death in humans, it is critical to understand how natural and anthropogenic factors contribute to NOx emissions in these areas. O3 pollution is also a growing threat to global food security due to its detrimental impacts on crop production. This work uses a suite of satellite, ground-based, and model data to investigate how agricultural soil NOx emissions from natural (rainfall) and anthropogenic factors (irrigation, nitrogen fertilizer) govern air quality over California. We evaluate the capabilities of using SMAP retrievals and SMAP Land Information System (LIS) output for monitoring irrigation activities over the major agricultural areas of California by conducting intensive analyses of soil moisture and precipitation data. High-resolution trace gas observations from the new generation TROPOspheric Monitoring Instrument (TROPOMI) sensor are used to characterize and monitor the variability of trace gases and air quality conditions associated with soil NOx emissions. We also synthesize satellite, ground-based, and agriculture data to estimate the separate contributions from rainfall, irrigation, and nitrogen fertilizer practices on soil NOx emissions and associated air quality conditions. Finally, unprecedented hourly trace gas retrievals from the NASA Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary satellite sensor are used to monitor the diurnal evolution of NO2 and O3 concentrations from soil NOx emissions.

Aaron R Naeger↗
NASA NTRS2022

A Waveform Skewness Index for Measuring Time Series Nonlinearity and Its Applications to the ENSO-Indian Monsoon Relationship

Many geophysical time series possess nonlinear characteristics that reflect the underlying physics of the phenomena the time series describe. The nonlinear character of times series can change with time, so it is important to quantify time series nonlinearity without assuming stationarity. A common way of quantifying the time evolution of time series nonlinearity is to compute sliding skewness time series, but it is shown here that such an approach can be misleading when time series contain periodicities. To remedy this deficiency of skewness, a new waveform skewness index is proposed for quantifying local nonlinearities embedded in time series. A waveform skewness spectrum is proposed for determining the frequency components that are contributing to time series waveform skewness. The new methods are applied to the El Niño–Southern Oscillation (ENSO) and the Indian monsoon to test a recently proposed hypothesis that states that changes in the ENSO–Indian monsoon relationship are related to ENSO nonlinearity. We show that the ENSO–Indian rainfall relationship weakens during time periods of high ENSO waveform skewness. The results from two different analyses suggest that the breakdown of the ENSO–Indian monsoon relationship during time periods of high ENSO waveform skewness is related to the more frequent occurrence of strong central Pacific El Niño events, supporting arguments that changes in the ENSO–Indian rainfall relationship are not solely related to noise.

Justin Schulte↗
NASA NTRSDate not supplied

Assessing Carbon Properties in Coastal Waters with a New Observing System Testbed

Large rainfall events over land can lead to a substantial flux of carbon and nutrients to estuaries and the coastal ocean. In the mid-Atlantic on the east coast of North America (35° - 42° N), these events often happen due to tropical storm activity as well as less predictable anomalously large midlatitude storms or abrupt spring snow melt runoff. Storms can directly impact the coastal carbon cycle via export of carbon from land to sea, while also stimulating phytoplankton production due to the influx of nutrients to the coastal ocean. To assess the impact of high precipitation and river flow events on the coastal carbon cycle, we have integrated multiple observing platforms in an analytical framework to dynamically observe carbon-related properties. The new observing system testbed (NOS-T) allows for an estimate of phytoplankton and organic carbon stocks in the surface ocean, with a goal of providing near real time analytical capability. A case study of high river discharge in the mid-Atlantic in the summer and fall of 2018 and 2021 were used to examine how riverine carbon manifests along the land-estuary-ocean continuum particularly in Chesapeake Bay. Chesapeake Bay was chosen as the study site because of long term monitoring by the Chesapeake Bay Program and a robust and well-developed regional biogeochemical modeling system, the Chesapeake Bay Environmental Forecast System (CBEFS), that is publicly available. There is also new capability in Chesapeake Bay to combine hyperspectral radiometric data from the Aerosol Robotic Network site that is online in 2022 with in-water observations of optical properties. Precipitation data from the Global Precipitation Measurement IMERG data set was used to establish triggering criteria for storm carbon flux observation. USGS and in-water carbon data were used to establish statistical models to estimate the mass flux of organic carbon into Chesapeake Bay using measured river discharge. The year 2018 was examined as there was record rainfall and near record river flow in the late summer. The CBEFS modeling was compared with in-water estimates of dissolved organic carbon (DOC) to assess the model’s ability to capture storm fluxes of carbon to the ecosystem. Multiple satellite platforms were also used to assess how remote sensing using passive Earth orbiting sensors can be used to observe carbon in this complex coastal region. A set of recommendations have been established to improve sensing capability to measure aquatic carbon during storm events. A unique challenge in these dynamic inland waters is how rapidly carbon fluxes can evolve in space and time, with many sources contributing to the water leaving reflectance that satellites can observe. To improve our ability to quantify carbon stocks and fluxes in near real time, a suite of satellite sensors and high-resolution modeling capability is needed, all supplemented by in situ monitoring. Future inclusion of in-water observations that would be deployed when the system reaches triggering criteria, as well as taskable orbital instruments, will improve estimates of ocean carbon properties and provide the ability to calculate major carbon stocks and fluxes in near real time following episodic storm events.

land-ocean continuum↗
NASA NTRS2023

Territory and Population Attributes Affect Florida Scrub-Jay Fecundity in Fire Adapted Ecosystems

Fecundity, the number of young produced by a breeding pair during a breeding season, is a primary component in evolutionary and ecological theory and applications. Fecundity can be influenced by many environmental factors and requires long-term study due to the range of variation in ecosystem dynamics. Fecundity data often include a large proportion of zeros when many pairs fail to produce any young during a breeding season due to nest failure or when all young die independently after fledging. We conducted color banding and monthly censuses of Florida scrub-jays (Aphelocoma coerulescens) across 31 years, 15 populations, and 761 territories along central Florida’s Atlantic coast. We quantified how fecundity (juveniles/pair-year) was influenced by habitat quality, presence/absence of nonbreeders, population density, breeder experience, and rainfall, with a zero-inflated Bayesian hierarchical model including both a Bernoulli (e.g., brood success) and a Poisson (counts of young) submodel, and random effects for year, population, and territory. The results identified the importance of increasing “strong” quality habitat, which was a mid-successional state related to fire frequency and extent, because strong territories, and the proportion of strong territories in the overall population, influenced fecundity of breeding pairs. Populations subject to supplementary feeding also had greater fecundity. Territory size, population density, breeder experience, and rainfall surprisingly had no or small effects. Different mechanisms appeared to cause annual variation in fecundity, as estimates of random effects were not correlated between the success and count submodels. The increased fecundity for pairs with nonbreeders, compared to pairs without, identified empirical research needed to understand how the proportion of low-quality habitats influences population recovery and sustainability, because dispersal into low-quality habitats can drain nonbreeders from strong territories and decrease overall fecundity. We also describe how long term study resulted in reversals in our understanding because of complications involving habitat quality, sociobiology, and population density.

Long-term studies↗
NASA NTRS2023

Youngstown and Warren Disasters: Mapping Flood Susceptibility, Vulnerability, and Risk and Tree Canopy Coverage in Northern Ohio to Inform Stormwater Management and Flood Mitigation Efforts

Both pluvial and fluvial flooding events pose direct challenges on urban infrastructure and communities across the United States. Heavy rainfall events oversaturate the ground, overflow waterbodies, and overwhelm stormwater infrastructure. Vulnerable areas receive heavy damage from flooding events due to physical factors like increased impervious surfaces, poor stormwater systems, and limited greenspaces. These vulnerable neighborhoods are comprised of aging populations, minority communities, and lower income levels. Lack of data in these communities have made it difficult to implement policymaking and flood mitigation strategies. Using the Urban Flood Risk Mitigation model (InVEST) and the PlanetScope satellite constellation, the team visualized historical flooding and tree canopy coverage as a measure of flood susceptibility. The team also used the Arc-Malstrom model to provide further insight into where flooding accumulates via surface elevation depressions in the study area. To validate these models, the team explored the spatial variation of rainfall events using NASA’s Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM IMERG). The resulting maps highlight areas surrounding the cities of Youngstown and Warren as being the most flood susceptible and socially vulnerable, while the city centers contain the lowest tree canopy coverage. The DEVELOP team collaborated with the Environmental Collaborative of Ohio (ECO) to create products for end users within the City of Warren’s Water Pollution Control Department, the Eastgate Regional Council of Governments and the Healthy Community Partnership of Mahoning Valley. These products help identify target areas for preventative flood mitigation measures as well as areas ideal for green infrastructure intervention.

InVEST Urban Flood Risk Mitigation Model↗
NASA NTRSDate not supplied

Investigating Convective Gustiness of Winds and Fluxes Using Microwave Remote Sensing

Tropical atmospheric convection is known to be associated with enhanced winds over the oceans that can drive increased rates of mass and energy transfer through turbulent latent and sensible heat fluxes. However, convection and its associated rainfall usually obscures the surface wind signals observed using passive microwave observations. Air-sea flux estimates based on the use of microwave imagers typically mask in areas of rainfall and thus are likely underrepresenting the true turbulent flux transfer. However, both monostatic and bistatic radar scatterometer systems are allowing for more detailed analysis of the convective near-surface environment. While rain still impact C-band scatterometer observations, the L-band observables from the Cyclone Global Navigation Satellite System (CYGNSS) are providing improved observing capabilities in all-weather conditions. In this study, we will present ongoing research to leverage CYGNSS observations in addition to other scatterometer, in situ, and satellite-based precipitation information to provide an improved characterization of surface wind enhancement within tropical oceanic convection. The results will specifically highlight current capabilities and limitations to assess convective wind enhancement and wind gustiness and their relationship to the surface turbulent fluxes of heat and water.

J Brent Roberts↗
NASA NTRS2019

Alternative Vegetation States in Tropical Forests and Savannas: The Search for Consistent Signals in Diverse Remote Sensing Data

Globally, the spatial distribution of vegetation is governed primarily by climatological factors (rainfall and temperature, seasonality, and inter-annual variability). The local distribution of vegetation, however, depends on local edaphic conditions (soils and topography) and disturbances (fire, herbivory, and anthropogenic activities). Abrupt spatial or temporal changes in vegetation distribution can occur if there are positive (i.e., amplifying) feedbacks favoring certain vegetation states under otherwise similar climatic and edaphic conditions. Previous studies in the tropical savannas of Africa and other continents using the MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF) satellite data product have focused on discontinuities in the distribution of tree cover at different rainfall levels, with bimodal distributions (e.g., concentrations of high and low tree cover) interpreted as alternative vegetation states. Such observed bimodalities over large spatial extents may not be evidence for alternate states, as they may include regions that have different edaphic conditions and disturbance histories. In this study, we conduct a systematic multi-scale analysis of diverse MODIS data streams to quantify the presence and spatial consistency of alternative vegetation states in Sub-Saharan Africa. The analysis is based on the premise that major discontinuities in vegetation structure should also manifest as consistent spatial patterns in a range of remote sensing data streams, including, for example, albedo and land surface temperature (LST). Our results confirm previous observations of bimodal and multimodal distributions of estimated tree cover in the MODIS VCF. However, strong disagreements in the location of multimodality between VCF and other data streams were observed at 1 km scale. Results suggest that the observed distribution of VCF over vast spatial extents are multimodal, not because of local-scale feedbacks and emergent bifurcations (the definition of alternative states), but likely because of other factors including regional scale differences in woody dynamics associated with edaphic, disturbance, and/or anthropogenic processes. These results suggest the need for more in-depth consideration of bifurcation mechanisms and thus the likely spatial and temporal scales at which alternative states driven by different positive feedback processes should manifest.

Savanna↗
NASA NTRS2023

Global and Zonal-Mean Hydrological Response to Early Eocene Warmth

Earth's hydrological cycle is expected to intensify in response to global warming, with a “wet-gets-wetter, dry-gets-drier” response anticipated over the ocean. Subtropical regions (∼15°–30°N/S) are predicted to become drier, yet proxy evidence from past warm climates suggests these regions may be characterized by wetter conditions. Here we use an integrated data-modeling approach to reconstruct global and zonal-mean rainfall patterns during the early Eocene (∼56–48 million years ago). The Deep-Time Model Intercomparison Project (DeepMIP) model ensemble indicates that the mid- (30°–60°N/S) and high-latitudes (>60°N/S) are characterized by a thermodynamically dominated hydrological response to warming and overall wetter conditions. The tropical band (0°–15°N/S) is also characterized by wetter conditions, with several DeepMIP models simulating narrowing of the Inter-Tropical Convergence Zone. However, the latter is not evident from the proxy data. The subtropics are characterized by negative precipitation-evaporation anomalies (i.e., drier conditions) in the DeepMIP models, but there is surprisingly large inter-model variability in mean annual precipitation (MAP). Intriguingly, we find that models with weaker meridional temperature gradients (e.g., CESM, GFDL) are characterized by a reduction in subtropical moisture divergence, leading to an increase in MAP. These model simulations agree more closely with our new proxy-derived precipitation reconstructions and other key climate metrics and imply that the early Eocene was characterized by reduced subtropical moisture divergence. If the meridional temperature gradient was even weaker than suggested by those DeepMIP models, circulation-induced changes may have outcompeted thermodynamic changes, leading to wetter subtropics. This highlights the importance of accurately reconstructing zonal temperature gradients when reconstructing past rainfall patterns.

Margot J. Cramwinckel↗
NASA NTRSDate not supplied

Do Better Satellite Precipitation Algorithms Improve Landslide Hazard Assessment?

Satellites make it possible to estimate precipitation in near real time. Given the challenges of achieving global coverage by other means, these data are used widely. However, few systems for landslide hazard assessment rely on satellite precipitation estimates. This could be due in part to perceptions of accuracy, although latency, spatial resolution, and other factors may also be important. We test whether recent changes to data streams from the Global Precipitation Measurement mission (GPM) have improved its potential for use in landslide prediction. Specifically, we examine data produced by the Integrated Multi-satellitERetrievals for the GPM (IMERG) algorithm, which was upgraded to version 7 this year. IMERG relies upon other algorithms, including the Goddard Profiling Algorithm (GPROF) and the GPM Combined Radar-Radiometer Algorithm (CORRA). Many changes have been made during the switch from IMERG version 6 to version 7. These include upgrading CORRA and GPROF to version 7, to improve the accuracy of precipitation in frozen, mountainous, and coastal areas. The measured intensity of some storms has been enhanced with a new algorithm, the Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood. Combined with many others, these changes to IMERG should improve its utility for landslide hazard assessment in a variety of contexts. To test this idea, we retrain the global Landslide Hazard Assessment for Situational Awareness (LHASA) model twice—first with data from IMERG version 6B and second with 7B. Since current daily rainfall is the most important variable in determining outcomes predicted by LHASA, it should reflect changes made to that input. First, we grid the landslides at a daily, thirty-arcsecond resolution. This serves as the response variable. At each of these sites current and antecedent rainfall are extracted, along with antecedent snow mass and soil moisture, slope, and PGA. In addition, one million grid cells are selected at random points to represent conditions under which landslides (probably) do not occur. After merging these data, we hold back 20% of the dataset for validation purposes and train a machine-learning model with the rest. We assess both the model’s overall ability to identify landslides and its ability to predict specific large landslide disasters.

Thomas A Stanley↗
NASA NTRS2026

Incremental Learning for Passive Microwave Precipitation Retrievals using Advanced Technology Microwave Sounder

Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized for atmospheric temperature and moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, and systematic misclassification of precipitation phase. These limitations are further compounded by the scarcity of globally accurate and representative precipitation observations, as training data from the Dual-frequency Precipitation Radar (DPR) and the Cloud Profiling Radar (CPR) are spatially sparse, lack uniform global coverage, and exhibit heterogeneous error characteristics across precipitation regimes. To address these challenges, this study presents a supervised retrieval algorithm that incrementally trains an ensemble of extreme gradient-boosted decision trees by augmenting base learners with pre-training on reanalysis data and post-training on coincident DPR and CPR observations matched with the Advanced Technology Microwave Sounder (ATMS). By transferring prior information from reanalysis to posterior constraints from radar observations and adopting a sequential detection–estimation strategy for precipitation phase and rate retrieval, the proposed approach yields retrievals across the full ATMS swath that are largely free from persistent deficiencies in current Global Precipitation Measurement (GPM) passive microwave operational products. In particular, the method resolves bimodal artifacts in rainfall retrievals and mitigates systematic high-latitude snowfall biases, including overestimation across the Arctic and underestimation across the Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) data over the Contiguous United States (CONUS) further demonstrates improved performance in precipitation phase detection and rate estimation relative to both reanalysis and current GPM PMW products.

Mahyar Garshasbi↗
DOE OSTI2019

Multi-scale processes driving tropical convection and influence of the aerosol (Final Technical Report)

This DOE GoAmazon collaborative project between scientists in the US and Brazil addresses fundamental processes that drive tropical deep convection including aerosol effects. The results provide guidance for improving the parameterizations of cloud and aerosol effects by increasing the knowledge of processes at work in tropical convection, especially over tropical land. Highlights include: • GoAmazon2014/5 observations permitted, for the first time, a comprehensive evaluation of the strong relationship between lower free tropospheric moisture and deep convective onset over the Amazon (Schiro et al. 2016, JAS; Schiro et al. 2018b, PNAS; Chakraborty et al. 2018, ACP; Schiro and Neelin 2018c). A set of diagnostics for deep convective onset has been established using data from the GoAmazon2014/5 campaign (Schiro et al. 2016, JAS), highlighting similarities and differences between the convective transition statistics over tropical land versus corresponding statistics from ARM campaigns in the tropical Western Pacific. The initial set of diagnostics has been provided as a module to be incorporated into the ARM Diagnostics Package of Shaocheng Xie and coworkers, which is being integrated into the PCMDI and E3SM diagnostics packages. Additionally, a set of downdraft diagnostics to inform model parameterizations has been developed (Schiro and Neelin 2018a, ACP). • These diagnostics have been used in constraining the behavior of modeled convective parameterizations. This includes using them with parameter perturbation experiments in the Community Earth System Model to show which parameter ranges are consistent with observed onset statistics, and to show that the primary causal direction of the water vapor-precipitation relationship is via the impact of mixing processes and free tropospheric moisture on convection (Kuo et al. 2017, JAS). Lintner et al. (2017, GRL) used this impact of free tropospheric moisture to systematically analyze issues in the simulation of Amazon rainfall in the CMIP5 model ensemble. • Leveraging the multi-instrument GoAmazon observations, Schiro et al. (2018, PNAS) are able to provide for the first time an empirical mixing using radar wind profiler data that provides a good predictor of precipitation for both mesoscale convective systems and smaller-scale convection. In addition, buoyancy estimated from these results is a good predictor of precipitation across the seasonal and diurnal cycles (Schiro and Neelin 2018c, JAS) and permits the effects of aerosols to be distinguished from those of the thermodynamic environment in the transition to deep convection (Chakraborty et al. 2018, ACP). • Placing the GoAmazon campaign in a larger context, in collaboration with U. São Paulo investigators, Rehbein et al. (2018) analyzed the characteristics of the mesoscale convective systems (MCS) that affect Amazon rainfall, including origins, lifetimes, propagation speed and displacement. de Barros Soares et al. (2017) establish statistically significant observed warming trends over much of South America and are able to attribute the origin of this to anthropogenic forcing based on the agreement of climate model simulations with and without anthropogenic forcings. This analysis left open the attribution of precipitation trends due to large natural variability. Barkhardorian et al. (2018) provide evidence indicating that the observed regional precipitation decrease and drying during recent over northern South America during recent austral springs will continue and intensify in the course of unfolding anthropogenic climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Cloud, Aerosol, and Complex Terrain Interactions (CACTI) 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 multiscale 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 life cycle 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 U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF1), mobile C-band Scanning ARM Precipitation Radar (C-SAPR2), ARM Aerial Facility (AAF) Gulfstream-1 (G-1), and guest instrument observations: How are the properties and life cycles 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? 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 7-month extended operational period (EOP, 1 October, 2018–30 April, 2019) as well as a 1.5-month intensive operational period (IOP, 30 October–13 December) that will include G-1 observations coinciding with the international multi-agency Remote Sensing of Electrification, Lightning, and Meso-scale/Micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign.

Convection, Andes, Argentina, cloud life cycle, cl↗
DOE OSTI2013

Assessment of External Hazards at Radioactive Waste and Used Fuel Management Facilities - 13505

One of the key lessons from the Fukushima accident is the importance of having a comprehensive identification and evaluation of risks posed by external events to nuclear facilities. While the primary focus has been on nuclear power plants, the Canadian nuclear industry has also been updating hazard assessments for radioactive waste and used fuel management facilities to ensure that lessons learnt from Fukushima are addressed. External events are events that originate either physically outside the nuclear site or outside its control. They include natural events, such as high winds, lightning, earthquakes or flood due to extreme rainfall. The approaches that have been applied to the identification and assessment of external hazards in Canada are presented and analyzed. Specific aspects and considerations concerning hazards posed to radioactive waste and used fuel management operations are identified. Relevant hazard identification techniques are described, which draw upon available regulatory guidance and standard assessment techniques such as Hazard and Operability Studies (HAZOPs) and 'What-if' analysis. Consideration is given to ensuring that hazard combinations (for example: high winds and flooding due to rainfall) are properly taken into account. Approaches that can be used to screen out external hazards, through a combination of frequency and impact assessments, are summarized. For those hazards that cannot be screened out, a brief overview of methods that can be used to conduct more detailed hazard assessments is also provided. The lessons learnt from the Fukushima accident have had a significant impact on specific aspects of the approaches used to hazard assessment for waste management. Practical examples of the effect of these impacts are provided. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Water Management Successes at the DoE's Environmental Management Waste Management Facility - 19194

URS-CH2M Oak Ridge LLC (UCOR) manages the cleanup of the 2,200-acre East Tennessee Technology Park (ETTP) for its client, the U.S. Department of Energy (DOE), in Oak Ridge, Tennessee. The ETTP site was contaminated with radioactive, hazardous and industrial wastes generated by more than 40 years of national defense and energy missions. Stormwater management is a critical activity at the DoE's Oak Ridge Reservation Environmental Management Waste Management Facility (EMWMF) due to the high average rainfall and the consequences if inadequately managed. EMWMF is located in East Tennessee with an average rainfall of 54.25 inches/year. EMWMF has implemented a successful, multipart strategy to maximize the margin of safety, reduce the volume of stormwater requiring management, and otherwise minimize operational impacts from large volumes of landfill water. The strategy consists of: - Pre-planning and preparation; - Landfill storm water prevention; - Inventory minimization; - Accelerated storm water removal. Since implementation of this aggressive water management strategy in 2017, EMWMF has reduced the volume of leachate requiring management by approximately 27%. This reduction increased the safety and compliance for the EMWMF, while at the same time, reduced costs. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Port Hope Are Initiative Port Granby Project - Water Management in Extreme Weather Conditions - 19257

The Port Granby Project is part of the Port Hope Area Initiative (PHAI) through which Canadian Nuclear Laboratory (CNL) is managing long-term historic low level radioactive waste (LLRW) resulting from 60 years of uranium and radium processing operations in the urban setting of Port Hope, Ontario. Port Granby remediation scope includes 1) constructing a new, secure long-term waste management facility (LTWMF) and supporting infrastructure, and 2) excavating and relocating LLRW and marginally contaminated soil from the Port Granby Waste Management Facility (PGWMF), which accepted waste until the 1980's, to the newly constructed LTWMF. This paper provides an overview of the challenges of water management (control of storm and groundwater at the LTWMF and PGWMF) on a large-scale construction project with changing and unpredictable weather patterns. Consistent with increased observations of extreme weather events, over two consecutive construction seasons, weather changed from one of the driest on record (high temperatures, dry conditions and strong wind) to one of the wettest on record (short duration high-intensity rainfall that flooded the LTWMF, excavations and on-site PGWMF and LTWMF retention ponds). This paper describes the collaborative approach among Wood (Constructor), AECOM (Engineer of Record), and CNL to adjust to extremes in weather to control dust and excessive water during construction. The summer of 2016, one of the driest summers on record in southern Ontario, was followed by one of the wettest in 2017. In 2016, the project team used water from the uncontaminated on-site storm water ponds, applied a polymer product for dust control across the site to augment water applications, obtained a Permit to Take Water from the provincial government to pump water from Lake Ontario, procured large tanks for storage of Lake Ontario water to meet peak demand, and retained additional water trucks/operators for day to day site operations. In stark contrast, to accommodate heavy rains in 2017 the project team installed ∼6 million liter aboveground storage tanks with secondary containment for contaminated water storage; procured 100+ mobile water storage tanks to supplement the on-site storage, utilized LTWMF cells sumps, excavations and retention ponds to store and manage onsite water; and adjusted additional staff resources (more staff and longer shifts) during anticipated periods of high-intensity rainfall, at times managing water around the clock. Recognizing the need to minimize the release of contaminated soil and water from the construction site at all times, these efforts illustrate the importance of anticipating and reacting to extreme weather events on a more regular basis. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Long-term hydrological, biogeochemical, and climatological data from Walker Branch Watershed, East Tennessee, USA

In 1967, the original Walker Branch Watershed (WBW) project was established to study elemental cycling and mass balances in a relatively unimpacted watershed. Over the next 50+ years, findings from additional experimental studies and long-term observations on WBW advanced understanding of catchment hydrology, biogeochemistry, and ecology and established WBW as a seminal site for catchment science. The 97.5-ha WBW is located in East Tennessee, USA, on the U.S. Department of Energy's Oak Ridge Reservation. Vegetation on the watershed is characteristic of an eastern deciduous, second-growth forest. The watershed is divided into two subcatchments: the West Fork (38.4 ha) and the East Fork (59.1 ha). Headwater streams draining these subcatchments are fed by multiple springs, and thus flow is perennial. Stream water is high in base cations due to weathering of dolomite bedrock and nutrient concentrations are low. Long-term observations of climate, hydrology, and biogeochemistry include daily (1969–2014) and 15-min (1994–2014) stream discharge and annual runoff (1969–2014); hourly, daily, and annual rainfall (1969–2012); daily climate and soil temperature (1993–2010); and weekly stream water chemistry (1989–2013). These long-term datasets are publicly available on the WBW website (https://walkerbranch.ornl.gov/long-term-data/). While collection of these data has ceased, related long-term measurements continue through the National Ecological Observatory Network (NEON), where WBW is the core terrestrial and aquatic site in the Appalachian and Cumberland Plateau region (NEON's Domain 7) of the United States. These long-term datasets have been and will continue to be important in evaluating the influence of climatic and environmental drivers on catchment processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of CMIP6 models in simulating the statistics of extreme precipitation over Eastern Africa

We report the Eastern Africa region experiences frequent extreme precipitation events that can cause destruction of property and environment, and loss of lives. Thus, there is a need to understand how these events may change in the future and how well the global climate models that are used to make projections can simulate precipitation extremes in this region before they can be used in downscaling or flood and drought impact assessment studies. In this work, we evaluated the ability of sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models to simulate present-day precipitation extremes over the Eastern Africa region during the two rainy seasons (March–May and September–November). We used nine extreme precipitation indices (including seven (one) indices of wet (dry) extremes) defined by the Expert Team on Climate Change Detection and Indices. The CMIP6 models were evaluated against two gridded observation datasets: Global Precipitation Climatology Project One-Degree Daily Dataset and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42. Three model performance metrics (percentage bias, normalized root-mean-square error, and pattern correlation coefficient) were employed to further assess the strengths and weakness of the models. Our results show that the multi-model ensemble mean generally provides a better representation of observed precipitation and related extremes compared to individual models when considering all metrics and seasons. Several consistent biases are evident across CMIP6 models, which tend to overestimate the total-wet day precipitation and consecutive wet days, and underestimate very wet days and maximum 5-day precipitation in both seasons. Furthermore, no single model consistently performs best, model performance varies with the season and index under consideration and is generally independent of horizontal resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Identifying recharge sources and their impacts on a North Central New Mexico shallow aquifer using unsupervised machine learning

In this article, shallow aquifers are important but highly variable resources in arid to semi-arid regions. Limited shallow aquifer volume results in high sensitivity to recharge fluctuations, which can impact the local fauna and flora, and transport of contaminants in the aquifer or vadose zone. Aquifer response to external forcing (e.g., precipitation) is usually solved by estimating aquifer parameters and running physics-based models to match known fluctuations of hydraulic head. However, this technique is time and computationally expensive. Furthermore, high aquifer complexity decreases precision in physics-based models. Alternatively supervised machine learning is used to predict aquifer dynamics. However, these techniques rely on input data and struggle to interpret aquifer response for missing sources (i.e., snowpack data). To counter these problems, we propose an unsupervised machine learning technique (NMFk) to estimate the impact of different sources on aquifer recharge. NMFk is used to understand the influence of external forcing on shallow aquifer recharge in the Pajarito Plateau (Los Alamos, NM, USA). The results show how NMFk can be used to reduce the data dimension in a complex field dataset to three recharge signals that cause fluctuations within the field data. Here, the source signals are interpreted as rainfall, snowmelt, and a delayed aquifer response to the previous two signals. These results evidence how heterogeneous aquifers delimited by canyons incised into the Pajarito Plateau respond in similar ways to the source signals identified by NMFk. Furthermore, results show the importance of the local geology where faults act as sinks, and anthropogenic disturbances can facilitate infiltration amplifying the interpreted signal.

54 ENVIRONMENTAL SCIENCES↗