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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Earthquake hazard analysis for the different regions in and around Ağrı

We investigated earthquake hazard parameters for Eastern part of Turkey by determining the a and b parameters in a Gutenberg–Richter magnitude–frequency relationship. For this purpose, study area is divided into seven different source zones based on their tectonic and seismotectonic regimes. The database used in this work was taken from different sources and catalogues such as TURKNET, International Seismological Centre (ISC), Incorporated Research Institutions for Seismology (IRIS) and The Scientific and Technological Research Council of Turkey (TUBITAK) for instrumental period. We calculated the a value, b value, which is the slope of the frequency–magnitude Gutenberg–Richter relationship, from the maximum likelihood method (ML). Also, we estimated the mean return periods, the most probable maximum magnitude in the time period of t-years and the probability for an earthquake occurrence for an earthquake magnitude ≥ M during a time span of t-years. We used Zmap software to calculate these parameters. The lowest b value was calculated in Region 1 covered Cobandede Fault Zone. We obtain the highest a value in Region 2 covered Kagizman Fault Zone. This conclusion is strongly supported from the probability value, which shows the largest value (87%) for an earthquake with magnitude greater than or equal to 6.0. The mean return period for such a magnitude is the lowest in this region (49-years). The most probable magnitude in the next 100 years was calculated and we determined the highest value around Cobandede Fault Zone. According to these parameters, Region 1 covered the Cobandede Fault Zone and is the most dangerous area around the Eastern part of Turkey.

58 GEOSCIENCES↗
DOE OSTI2024

Quantifying the compound hazard of freezing rain and wind gusts across CONUS

Abstract The co-occurrence of freezing rain, ice accumulation and wind gusts (FZG) poses a significant hazard to infrastructure and transportation. However, quantification of the frequency and intensity of FZG is challenged by the lack of direct icing measurements. In this work, we evaluate and then apply an energy balance model to high-frequency data collected during 2005–2022 to derive hourly ice accumulation at 883 stations across the contiguous USA. These estimates are combined with wind gust observations to compute time series of hourly FZG hazard magnitude using the Sperry–Piltz Ice Accumulation (SPIA) index. Results are evaluated using Storm Reports of damage and economic disruption. The hourly SPIA estimates are also used to (i) derive a geospatial atlas of the hazard including the 50 yr return period event intensities for each US state derived using superstations, and (ii) describe storylines of significant events in terms of meteorological drivers and socioeconomic impacts. The highest values of SPIA during the 18 yr study period occur in a region extending from the Southern Great Plains over the Midwest into the densely populated Northeast. States in these regions also have high 50 yr return period maximum radial ice accumulation of 3–5 cm and co-occurring wind gusts >30 ms −1 . These values are comparable to past estimates for the 500 yr event which may imply this hazard has been previously underestimated. This atlas can be used to inform optimal FZG hazard mitigation strategies for each state/region.

58 GEOSCIENCES↗
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 OSTI2016

On the hydrologic-hydraulic revaluation of large dams

The aim of the present work is to develop a criteria to evaluate the safety level of large dams. This is executed by comparing the discharge capability of a dam with the peak flow estimates, with respect to 100-, 200-, 500-, and 1000- year return periods. The methodology consists of estimating the intensity duration frequency (IDF) curves, starting from observational time series, collected by rain gauges. Then, through a rainfall-runoff transformation, the design hydrographs are obtained for different return periods. The results are compared to trends of peak discharges, obtained previously by the handler of the dam. It can be noted that, if the basin area is smaller than 1 km{sup 2}, the peak discharges estimated are always lower than those calculated by the handler, regardless of the antecedent moisture conditions (AMC) assumed. Otherwise, the peak discharges obtained by the handler range between those we obtained for AMCII and AMCIII.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Anthropogenic warming impacts on California snowpack during drought

Sierra Nevada climate and snowpack is simulated during the period of extreme drought from 2011 to 2015 and compared to an identical simulation except for the removal of the twentieth century anthropogenic warming. Anthropogenic warming reduced average snowpack levels by 25%, with middle-to-low elevations experiencing reductions between 26 and 43%. In terms of event frequency, return periods associated with anomalies in 4 year 1 April snow water equivalent are estimated to have doubled, and possibly quadrupled, due to past warming. Here, we also estimate effects of future anthropogenic warmth on snowpack during a drought similar to that of 2011–2015. Further snowpack declines of 60–85% are expected, depending on emissions scenario. The return periods associated with future snowpack levels are estimated to range from millennia to much longer. Therefore, past human emissions of greenhouse gases are already negatively impacting statewide water resources during drought, and much more severe impacts are likely to be inevitable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Gulf of Mexico Offshore Wind Energy Hurricane Risk Assessment

NREL's feasibility assessment of offshore wind in the Gulf of Mexico concluded that hurricane risk was one of the major challenges that would need to be overcome for a mature offshore wind industry to develop in the Gulf of Mexico. To ensure the robust design of wind turbines in the Gulf of Mexico, it is critical to understand the added risk posed by the threat of major hurricanes, as those affecting the Gulf of Mexico region have a significant potential to exceed design limits prescribed by the International Electrotechnical Commission (IEC) wind design standards. To satisfy this charge, this project defines the wind hazard for the Gulf of Mexico Offshore Wind Energy area using the hurricane hazard model develop by Applied Research Associates and published extensively in the open literature. In doing so, the return periods associated with the IEC Class 1A and Typhoon Class limit-state hurricanes are estimated on a grid with nominal resolution of 10 km to determine where hurricane risk results in the exceedance of the IEC design criteria. On the same grid, wind speeds hazard contours associated with return periods varying from 50 to 1,000 years are also estimated. An additional challenge in assessing hurricane wind speed risk in the Gulf of Mexico arises from inconsistent terminology across the Saffir-Simpson hurricane scale and the IEC design criteria. Saffir-Simpson definitions are based on 1-minute sustained wind speeds estimated at 10-m height over marine terrain, while the IEC uses a different averaging period (3-second versus 1-minute) and reference height (assumed herein a hub height of 150 m versus 10 m). Employing the latest research on turbulence characteristics of the hurricane boundary layer, conversions between various durations (e.g., 3-seconds, 1-minute, 10-minutes, 1-hour) and between elevations near the surface (10 m) to near hub height (assumed herein 150 m) are developed. IEC Class 1A and Typhoon Class limit states are also provided in terms of an equivalent Saffir-Simpson hurricane wind speed category.

17 WIND ENERGY↗
DOE OSTI2016

Hazard function theory for nonstationary natural hazards

Impact from natural hazards is a shared global problem that causes tremendous loss of life and property, economic cost, and damage to the environment. Increasingly, many natural processes show evidence of nonstationary behavior including wind speeds, landslides, wildfires, precipitation, streamflow, sea levels, and earthquakes. Traditional probabilistic analysis of natural hazards based on peaks over threshold (POT) generally assumes stationarity in the magnitudes and arrivals of events, i.e., that the probability of exceedance of some critical event is constant through time. Given increasing evidence of trends in natural hazards, new methods are needed to characterize their probabilistic behavior. The well-developed field of hazard function analysis (HFA) is ideally suited to this problem because its primary goal is to describe changes in the exceedance probability of an event over time. HFA is widely used in medicine, manufacturing, actuarial statistics, reliability engineering, economics, and elsewhere. HFA provides a rich theory to relate the natural hazard event series ( X ) with its failure time series ( T ), enabling computation of corresponding average return periods, risk, and reliabilities associated with nonstationary event series. This work investigates the suitability of HFA to characterize nonstationary natural hazards whose POT magnitudes are assumed to follow the widely applied generalized Pareto model. We derive the hazard function for this case and demonstrate how metrics such as reliability and average return period are impacted by nonstationarity and discuss the implications for planning and design. As a result, our theoretical analysis linking hazard random variable X with corresponding failure time series T should have application to a wide class of natural hazards with opportunities for future extensions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

On seasonal and semi-annual approach for flood frequency analysis

As a supplementary method to the conventional flood frequency analysis based on annual peak flows, we propose an approach in this paper to infer the flood frequency distribution on quarterly and semi-annual time scale, which are then converted to annual time scale to obtain the floods corresponding to return periods in unit of year. Two criteria for test of data independence, namely, minimum 7 and 15-day interval between two consecutive peak flows, are tested. The proposed approach was applied to Des Moines River at Fort Dodge, Iowa, USA using its 62 years of observation daily flows. The results show that the estimated floods for given return periods from quarter-annual data series are in general higher than the corresponding estimated floods from semi-annual data series, which is further larger than estimated floods from annual peak flows. In conclusion, the floods estimated from semi-annual data series agree well with the results of previous US Geological Survey study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

CONUS-wide Projected Flood Frequency and Uncertainty Estimates, Version 1.0

This dataset presents a large-ensemble of CONUS-wide projected flood frequency and uncertainty estimates across ~2.7 million NHDPlusV2 river reaches over the CONUS. The framework producing this dataset leverages a multi-model, uncertainty-aware modeling framework that allows evaluating shifts in flood frequences at the stream reach level across the CONUS. CONUS-wide ensemble streamflow projections generated from hydrologic simulations driven by downscaled and bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs are used to derive these flood frequency and uncertainty estimates over the period 1980 - 2099. A spatially consistent regional L-moment algorithm is applied across clusters defined by the US Hydrologic Unit Code Subregions (HUC4s and HUC8s) and NHDPlusV2 stream orders to estimate flood frequencies. The dataset also includes at-site based flood estimates that allow for the comparison between local and regional approach-based estimates, assess projected changes, and characterize their uncertainties. For more reliable estimation of rare flood frequencies such as 500 and 1000-year return periods, super-ensemble based estimates are also included in the dataset. This dataset is derived to support the "Impact-Informed Dam Safety Risk Assessment for Securing Hydropower Assests" project for the US Department of Energy (DOE) Hydropower and Hydrokinetic Office (H2O). For further details, refer to Kao et al. (2022), Ghimire et al. (2023), Ghimire et al. (2025), and Hosking and Wallis (1997).

Ghimire, Ganesh [ORNL] (ORCID:0000000242843941)↗
DOE OSTI2021

Extreme Wind and Waves in U.S. East Coast Offshore Wind Energy Lease Areas

The Outer Continental Shelf along the U.S. east coast exhibits abundant wind resources and is now a geographic focus for offshore wind deployments. This analysis derives and presents expected extreme wind and wave conditions for the sixteen lease areas that are currently being developed. Using the homogeneous ERA5 reanalysis dataset it is shown that the fifty-year return period wind speed (U50) at 100 m a.s.l. in the lease areas ranges from 29.2 to 39.7 ms -1 . After applying corrections to account for spectral smoothing and averaging period, the associated pseudo-point U50 estimates are 34 to 46 ms -1 . The derived uncertainty in U50 estimates due to different distributional fitting is smaller than the uncertainty associated with under-sampling of the interannual variability in annual maximum wind speeds. It is shown that, in the northern lease areas, annual maximum wind speeds are generally associated with intense extratropical cyclones rather than cyclones of tropical origin. Extreme wave statistics are also presented and indicate that the 50-year return period maximum wave height may substantially exceed 15 m. From this analysis, there is evidence that annual maximum wind speeds and waves frequently derive from the same cyclone source and often occur within a 6 h time interval.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

Temporal Coherence Importance Sampling for Wind Turbine Extreme Loads Estimation

Estimating long return period extreme wind turbine loads is made especially difficult by the large response variability for 'the same' environmental conditions. To alleviate this, we have 'opened up the black box' of the turbulent wind generation stage of the simulations. Exploiting the notion of 'temporal coherence' allows us to manipulate the turbulent inflow to target extreme wind conditions, while at the same time quantifying 'how probable these are.' The resulting importance sampling load estimates achieve a significantly lower exceedance probability (i.e., they represent much longer return periods) than estimates using the same number of samples (i.e., the same computational resources) but only a standard Monte Carlo estimate.

17 WIND ENERGY↗
DOE OSTI2019

The El Niño event of 2015–2016: climate anomalies and their impact on groundwater resources in East and Southern Africa

The impact of climate variability on groundwater storage has received limited attention despite widespread dependence on groundwater as a resource for drinking water, agriculture and industry. Here, we assess the climate anomalies that occurred over Southern Africa (SA) and East Africa, south of the Equator (EASE), during the major El Niño event of 2015–2016, and their associated impacts on groundwater storage, across scales, through analysis of in situ groundwater piezometry and Gravity Recovery and Climate Experiment (GRACE) satellite data. At the continental scale, the El Niño of 2015–2016 was associated with a pronounced dipole of opposing rainfall anomalies over EASE and Southern Africa, north–south of ~12°S, a characteristic pattern of the El Niño–Southern Oscillation (ENSO). Over Southern Africa the most intense drought event in the historical record occurred, based on an analysis of the cross-scale areal intensity of surface water balance anomalies (as represented by the standardised precipitation evapotranspiration index – SPEI), with an estimated return period of at least 200 years and a best estimate of 260 years. Climate risks are changing, and we estimate that anthropogenic warming only (ignoring changes to other climate variables, e.g. precipitation) has approximately doubled the risk of such an extreme SPEI drought event. These surface water balance deficits suppressed groundwater recharge, leading to a substantial groundwater storage decline indicated by both GRACE satellite and piezometric data in the Limpopo basin. Conversely, over EASE during the 2015–2016 El Niño event, anomalously wet conditions were observed with an estimated return period of ~10 years, likely moderated by the absence of a strongly positive Indian Ocean zonal mode phase. The strong but not extreme rainy season increased groundwater storage, as shown by satellite GRACE data and rising groundwater levels observed at a site in central Tanzania. We note substantial uncertainties in separating groundwater from total water storage in GRACE data and show that consistency between GRACE and piezometric estimates of groundwater storage is apparent when spatial averaging scales are comparable. These results have implications for sustainable and climate-resilient groundwater resource management, including the potential for adaptive strategies, such as managed aquifer recharge during episodic recharge events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Pooling Data Improves Multimodel IDF Estimates over Median-Based IDF Estimates: Analysis over the Susquehanna and Florida

Traditional multimodel methods for estimating future changes in precipitation intensity, duration, and frequency (IDF) curves rely on mean or median of models’ IDF estimates. Such multimodel estimates are impaired by large estimation uncertainty, shadowing their efficacy in planning efforts. Here, assuming that each climate model is one representation of the underlying data generating process, i.e., the Earth system, we propose a novel extension of current methods through pooling model data: (i) evaluate performance of climate models in simulating the spatial and temporal variability of the observed annual maximum precipitation (AMP), (ii) bias-correct and pool historical and future AMP data of reasonably performing models, and (iii) compute IDF estimates in a nonstationary framework from pooled historical and future model data. Pooling enhances fitting of the extreme value distribution to the data and assumes that data from reasonably performing models represent samples from the “true” underlying data generating distribution. Through Monte Carlo simulations with synthetic data, we show that return periods derived from pooled data have smaller biases and lesser uncertainty than those derived from ensembles of individual model data. We apply this method to NA-CORDEX models to estimate changes in 24-h precipitation intensity–frequency (PIF) estimates over the Susquehanna watershed and Florida peninsula. Our approach identifies significant future changes at more stations compared to median-based PIF estimates. The analysis suggests that almost all stations over the Susquehanna and at least two-thirds of the stations over the Florida peninsula will observe significant increases in 24-h precipitation for 2–100-yr return periods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III® model with a grid resolution of 4 arc-minutes (6–7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200–300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Analysis of the Frequency of Extreme Precipitation Events at Los Alamos National Laboratory

The Environmental Protection and Compliance (EPC) Storm Water Permitting and Compliance Program is currently updating its stormwater compliance standards for the Los Alamos National Laboratory (LANL) Engineering Standards Manual (2015) and is addressing the required current meteorological data needed for storm water applications. Information on the frequency of extreme precipitation events at LANL is needed. In response to this request, LANL Meteorology & Air Quality (MAQ) was tasked to estimate the frequency of extreme precipitation events. An earlier study by Foley (2012) which estimated extreme precipitation rates at LANL for a set of duration periods and average recurrence intervals (return periods) was evaluated. However, it was determined that this study would not be helpful since its technical support documentation was unavailable and the meteorological data and the methodology employed were not well understood. This led to the need to develop a new peer-reviewed methodology. This report documents the development of the new methodology and the application of a temporally representative precipitation dataset from February 1990 through December 2018, monitored at the official LANL meteorological station, Technical Area (TA)-6, to develop the frequency of extreme precipitation events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Advanced Laboratory and Field Arrays: Accelerate the development of next-generation marine energy conversion systems (Task 3)

This task successfully improved the representation of extreme conditions in wave resource characterization. The previous state of the art to characterize extreme conditions was a purely statistical representation of buoy data; Generalized Extreme Value (GEV) techniques are used to extrapolate joint histograms of significant wave height Hs and energy period T e for a given return period. The task confirmed that H s and T e alone are a poor representation of the actual hazards in extreme events, during which WECs many experience significant impacts from wave groups and breaking waves (i.e., it is not simply the largest wave that may produce the worst condition for a WEC). This task improved both prediction of extreme conditions with “third generation” spectral models, such as WAVEWATCHIII and WAM, and observational quantification of the wave breaking impacts during extreme conditions.

13 HYDRO ENERGY↗
DOE OSTI2019

climextremes v0.2.1

Software package for R and Python for analysis of extremes in climate data. Functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from 'extRemes', including for fitting the statistical models.

Krishnan, Harinarayan↗
DOE OSTI2021

The FLOod Probability Interpolation Tool (FLOPIT): A Simple Tool to Improve Spatial Flood Probability Quantification and Communication

Understanding flood probabilities is essential to making sound decisions about flood-risk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling real-estate, flood-proofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example the 1 in 100 or 1 in 500-year flood zones) to communicate flooding probabilities. However, this choice of communication format can miss important details and lead to biased risk assessments. Here we develop, test, and demonstrate the FLOod Probability Interpolation Tool (FLOPIT). FLOPIT interpolates flood probabilities between water surface elevation to produce continuous flood-probability maps. FLOPIT uses water surface elevation inundation maps for at least two return periods and creates Annual Exceedance Probability (AEP) as well as inundation maps for new return levels. Potential advantages of FLOPIT include being open-source, relatively easy to implement, capable of creating inundation maps from agencies other than FEMA, and applicable to locations where FEMA published flood inundation maps but not flood probability. Using publicly available data from the Federal Emergency Management Agency (FEMA) flood risk databases as well as state and national datasets, we produce continuous flood-probability maps at three example locations in the United States: Houston (TX), Muncy (PA), and Selinsgrove (PA). We find that the discrete flood zones generally communicate substantially lower flood probabilities than the continuous estimates.

54 ENVIRONMENTAL SCIENCES↗