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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

A new estimate of the China temperature anomaly series and uncertainty assessment in 1900–2006

While global warming during the last century has been well recognized, the magnitude of the climate warming in regions such as China over the past 100 years still has some uncertainty due to limited observations during the early years. Several series of temperature anomalies for the 20th century in China have been independently developed by different groups. The uncertainty arises mainly from the sparse observations before 1950, where statistics are sensitive to the small and potentially biased sample. In this study, BSHADE–MSN (Biased Sentinel Hospitals Areal Disease Estimation and Means of Stratified Nonhomogeneous Surface), a combination of two novel distinct statistical methods that are applicable with different sample situations to a spatial heterogeneous surface, is applied to estimate annual mean temperature anomalies for China. This method takes into account prior knowledge of geographical spatial autocorrelation and nonhomogeneity of target domains, remedies the biased sample, and maximizes an objective function for the best linear unbiased estimation (BLUE) of the regional mean quantity. For the period 1900–1999, the overall trend estimated by BSHADE–MSN is 0.80°C with a 95% confidential interval between 0.41°C and 1.18°C. This is significantly lower than that calculated by Climate Anomaly Method (CAM) and Block Kriging. The new temperature anomaly series for China exhibits slightly warmer conditions for the period before 1950 than existing studies. All the methods applied so far agree well with each other for the period after 1950, when there are sufficient stations across the country for the estimation of temperature anomaly series. Cross validation shows that the new regional mean temperature anomaly series has smaller estimation error variance and higher accuracy than those based on the other methods assessed in this study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Estimating groundwater dynamics at a Colorado River floodplain site using historical hydrological data and climate information

Long-term prediction of groundwater dynamics is important for assessing water resources and their impacts on biogeochemical cycling. However, estimating future groundwater dynamics is challenging due to the wide range of spatiotemporal scales in hydrological processes and uncertainty in future climate conditions. In this study, we develop a Bayesian model to combine small-scale historical hydrological data with large-scale climate information to estimate groundwater dynamics at a floodplain site in Rifle, Colorado. Although we have only a few years of groundwater elevation measurements, we have 47 years of streamflow data from a gaging station approximately 43 km upstream and long-term climate prediction on the Upper Colorado River Basin. To estimate future daily groundwater dynamics, we first develop a time series model to downscale the monthly streamflow derived from climate information to daily streamflow, and then transform the daily streamflow to groundwater dynamics at the downstream floodplain site. We use Monte Carlo methods to estimate future groundwater dynamics at the site through sampling from the joint posterior probability distribution. The results suggest that although future groundwater levels are expected to be similar to the current levels, the timing of the high groundwater levels is predicted to occur about 1 month earlier. The developed framework is extendable to other sites to estimate future groundwater dynamics given disparate data sets and climate projections. Additionally, the obtained estimates are being used as input to a site-specific watershed reactive transport models to predict how climate-induced changes will influence future biogeochemical cycling relevant to a variety of ecosystem services.

58 GEOSCIENCES↗
DOE OSTI2018

Comparison of Big-Leaf, Two-Big-Leaf, and Two-Leaf Upscaling Schemes for Evapotranspiration Estimation Using Coupled Carbon-Water Modeling

Evapotranspiration (ET) is commonly estimated using the Penman-Monteith equation, which assumes that the plant canopy is a big leaf (BL) and the water flux from vegetation is regulated by canopy stomatal conductance (G s ). However, BL has been found to be unsuitable for terrestrial biosphere models built on the carbon-water coupling principle because it fails to capture daily variations of gross primary productivity (GPP). A two-big-leaf scheme (TBL) and a two-leaf scheme (TL) that stratify a canopy into sunlit and shaded leaves have been developed to address this issue. However, there is a lack of comparison of these upscaling schemes for ET estimation, especially on the difference between TBL and TL. We find that TL shows strong performance (r 2 = 0.71, root-mean-square error = 0.05 mm/h) in estimating ET at nine eddy covariance towers in Canada. BL simulates lower annual ET and GPP than TL and TBL. The biases of estimated ET and GPP increase with leaf area index (LAI) in BL and TBL, and the biases of TL show no trends with LAI. BL miscalculates the portions of light-saturated and light-unsaturated leaves in the canopy, incurring negative biases in its flux estimation. TBL and TL showed improved yet different GPP and ET estimations. This difference is attributed to the lower Gsand intercellular CO 2 concentration simulated in TBL compared to their counterparts in TL. Finally, we suggest to use TL for ET modeling to avoid the uncertainty propagated from the artificial upscaling of leaf-level processes to the canopy scale in BL and TBL.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

Abstract In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge‐based, satellite‐related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high‐latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

A new approach to estimate fecundity rate from inter‐birth intervals

Abstract Accurate estimates of fecundity rate are key to population assessments and effectively direct conservation efforts. We present a new approach to estimate fecundity rate based on the probability of a female giving birth, conditional on a previous birth t years ago, from which an expected inter‐birth interval ( IBI ) can be estimated. We use generalized linear mixed‐effects models to account for individual and temporal variability and apply the approach to individual reproductive histories of bottlenose dolphins ( Tursiops truncatus ) from the east coast of Scotland. We estimate a fecundity rate of 0.222 (95% CI = 0.218–0.253) and an expected IBI of 4.49 yr (95% CI = 3.94–4.93 yr). We use simulated data samples to show that the approach produces estimates with a minimum bias of <3%. Simulations are also used to investigate the effect of the most common data‐driven biases in the estimates of birth intervals and fecundity rate; we recommend longitudinal studies of at least 10 yr and capture probabilities of at least 0.3 when using this methodology. The approach may be modified to incorporate other parameters of interest and should be applicable to any population with comprehensive data on birth intervals.

Arso Civil, Mònica↗
DOE OSTI2021

Benchmark estimate of the effect of anthropogenic emissions on the ocean surface

Abstract Investigations into the role of anthropogenic emissions in the occurrence of extreme weather often use a method that compares simulations of atmospheric climate models run under a factual scenario of historical boundary conditions observed during the period of the event against simulations run under a counterfactual scenario of what those boundary conditions might naturally have been over that same period in the absence of anthropogenic emissions. A particular requirement for this experiment design is an accurate estimation of ocean surface boundary conditions for use by the counterfactual natural simulations. Here we use output from the CMIP5 multi‐climate‐model archive to develop a robust estimate of sea surface temperatures and sea ice conditions for use in counterfactual natural simulations, intended as a benchmark estimate to facilitate comparison across climate models and across studies. This development includes tests to ensure that the final estimate is stable from year‐to‐year and stable against other perturbations to the methodology, as well as consideration of the strengths and weaknesses in comparison to other available attributable warming estimates. While this estimate is tailored specifically for the International CLIVAR C20C+ Detection and Attribution Project, it can be used by related projects as well.

Stone, Dáithí A.↗
DOE OSTI2021

Estimating basis functions in massive fields under the spatial mixed effects model

Abstract Spatial prediction is commonly achieved under the assumption of a Gaussian random field by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the expectation–maximization algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the spatial mixed effects model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an alternating expectation conditional maximization algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature dataset archived by the United States National Climate Data Center, with improved results over previous methodology.

Pazdernik, Karl↗
DOE OSTI2026

Estimating and Evaluating Roughness Length and Displacement Height in Heterogeneous Urban Environments

The roughness length (z 0 ) and displacement height (z d ) are essential surface-layer parameters in numerical models (e.g., weather, climate, wall-modeled LES, etc.). This work evaluates the consistency of z 0 and z d estimates from morphometric and anemometric methods using data from two eddy-covariance flux towers (AmeriFlux US-INg and US-INc) in Indianapolis, IN. Results show inconsistencies in estimated z 0 and z d values depending on the chosen method. The two evaluated anemometric methods estimate non-physical values of z d when compared to roughness elements surrounding both towers. Additionally, predictions of mean wind speed using surface-layer similarity theory with morphometric estimates exhibit a bias during near-neutral and stable conditions relative to observations. The overestimation of mean wind speed by surface layer similarity theory is consistent with previous observational and modeling studies in urban areas, suggesting that the application of similarity theories to urban environments may have limitations. Differentiation of vegetation from built structures appears to impact morphometric z 0 and z d estimates, particularly where vegetation is abundant; however, it has little impact on correcting biases in the similarity theory. Specifically, we find that existing similarity theories using morphometric estimates underestimate integral velocity and length scales, and the degree of underestimation depends on the stability conditions. Accounting for the degree of anisotropy in surface-layer turbulence helps reduce the biases between similarity theories and observations during unstable conditions, but not in near-neutral cases. Future work is needed to identify the cause of such biases for near-neutral conditions.

Aerodynamic roughness length↗
DOE OSTI2018

Conditions of Asymptotic Normality of One-Step M-Estimators

In the case of independent identically distributed observations, we study the asymptotic properties of one-step M-estimators served as explicit approximations of consistent M-estimators. We find rather general conditions for the asymptotic normality of one-step M-estimators. We consider Fisher’s approximations of consistent maximum likelihood estimators and find general conditions guaranteeing the asymptotic normality of the Fisher estimators even in the case where maximum likelihood estimators do not necessarily exist or are not necessarily consistent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Cost Implications of Uncertainty in CO{sub 2} Storage Resource Estimates: A Review

Carbon capture from stationary sources and geologic storage of carbon dioxide (CO{sub 2}) is an important option to include in strategies to mitigate greenhouse gas emissions. However, the potential costs of commercial-scale CO{sub 2} storage are not well constrained, stemming from the inherent uncertainty in storage resource estimates coupled with a lack of detailed estimates of the infrastructure needed to access those resources. Storage resource estimates are highly dependent on storage efficiency values or storage coefficients, which are calculated based on ranges of uncertain geological and physical reservoir parameters. If dynamic factors (such as variability in storage efficiencies, pressure interference, and acceptable injection rates over time), reservoir pressure limitations, boundaries on migration of CO{sub 2}, consideration of closed or semi-closed saline reservoir systems, and other possible constraints on the technically accessible CO{sub 2} storage resource (TASR) are accounted for, it is likely that only a fraction of the TASR could be available without incurring significant additional costs. Although storage resource estimates typically assume that any issues with pressure buildup due to CO{sub 2} injection will be mitigated by reservoir pressure management, estimates of the costs of CO{sub 2} storage generally do not include the costs of active pressure management. Production of saline waters (brines) could be essential to increasing the dynamic storage capacity of most reservoirs, but including the costs of this critical method of reservoir pressure management could increase current estimates of the costs of CO{sub 2} storage by two times, or more. Even without considering the implications for reservoir pressure management, geologic uncertainty can significantly impact CO{sub 2} storage capacities and costs, and contribute to uncertainty in carbon capture and storage (CCS) systems. Given the current state of available information and the scarcity of (data from) long-term commercial-scale CO{sub 2} storage projects, decision makers may experience considerable difficulty in ascertaining the realistic potential, the likely costs, and the most beneficial pattern of deployment of CCS as an option to reduce CO{sub 2} concentrations in the atmosphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2018

Quantum estimation in an expanding spacetime

Highlights: • FI and QFI of estimation on Hubble parameter of de Sitter space are explored. • QFI degrades due to Gibbons–Hawking radiation. • QFI can be enhanced for proper coupling of scalar field to curvature. • Optimal QFI depends on the squeezed nature of de Sitter α-vacua. - Abstract: We investigate the quantum estimation on the Hubble parameter of an expanding de Sitter space by quantum metrological techniques. By exploring the dynamics of a freely falling Unruh–DeWitt detector, which interacts with a scalar field coupling to curvature, we calculate the Fisher information (FI) and quantum Fisher information (QFI) for the detector, which bound the highest precision of the estimation on Hubble parameter. In standard Bunch–Davies vacuum, we show that the maxima of FI/QFI are located for particular initial state of probe. Beside its dependence on the evolving time of detector and the energy spacing of atom ω, we show that the maxima of FI/QFI can be significantly enhanced once a proper coupling of scalar field to curvature is chosen. For instance, we show numerically that the estimation in the scenario with minimally/nearly minimally coupling scalar field can always outperform that with conformally coupling scalar field, corresponding to a higher FI/QFI in estimation. Moreover, we find that for general α-vacua of de Sitter space, a further improvement of estimation can be achieved, attributed to the squeezed nature of α-vacua that heavily constrains the measurement uncertainty. Some implications of our results are also discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A reforecasting-based dynamic reserve estimation for variable renewable generation and demand uncertainty

The installed capacity of renewables-based energy sources has been increasing in traditional power systems. In order to accommodate the increased variability and uncertainty associated with the deeper penetration of renewable sources like solar and wind, adjusted amounts of dynamic reserve are needed. Although probabilistic dynamic reserve estimation methods have been previously developed, most of them consider the uncertainty to be represented by parametric density functions that tend to perform poorly under extreme events and, moreover, neglect uncertainty introduced by the forecasting model itself. Toward addressing these limitations, this work presents, for the first time, a dynamic reserve estimation method for flexibility that incorporates nonparametric density estimation and a machine learning based reforecasting to provide a day-ahead prediction of the mean and spread of uncertainty around the base forecast. The prediction is, in turn, used to estimate the up and down reserve relative to the base forecast. Here, the present method takes various endogenous and exogenous features, including the calendar variables, as input to estimate the day-ahead reserve. Using a combination of reforecasting and dynamic reserve estimation techniques, the method is shown to adjust better to the dynamic nature of reserve requirements providing only what is needed to accommodate the expected deviations. Considering California Independent System Operator (CAISO) solar, wind and load data over an 18 month period, up to 67% reduction in the amount of reserve capacity needed for a one day reserve and reserve penalty for solar uncertainty is demonstrated. Additionally, the risk of reserve insufficiency in meeting the net demand is reduced by 20% with the proposed method.

14 SOLAR ENERGY↗
DOE OSTI2026

Neural Posterior Estimation for Scalable and Accurate Inverse Parameter Inference in Li-Ion Batteries

Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE can infer parameters equally or more accurately than Bayesian calibration, even if it leads to higher voltage reconstruction errors. We also demonstrate that the higher computational costs for data generation are tractable even in high-dimensional cases (ranging from 6 to 27 estimated parameters). The NPE method also offers several interpretability advantages over Bayesian calibration, such as local parameter sensitivity to specific regions of the voltage curve. The NPE method is demonstrated using an experimental fast charge dataset, with parameter estimates validated against measurements of loss of lithium inventory and loss of active material. The implementation is made available in a companion repository (https://github.com/NatLabRockies/BatFIT).

25 ENERGY STORAGE↗
DOE OSTI2012

Consolidating Risk Estimates for Radiation-Induced Complications in Individual Patient: Late Rectal Toxicity

Purpose: To test the feasibility of a new approach to synthesize published normal tissue complication data using late rectal toxicity in prostate cancer as an example. Methods and Materials: A data survey was performed to identify the published reports on the dose-response relationships for late rectal toxicity. The risk estimates for Grade 1 or greater, Grade 2 or greater, and Grade 3 or greater toxicity were obtained for a test cohort of patients treated at our institution. The influence of the potential factors that might have affected the reported toxicity levels was investigated. The studies that did not conform to the general data trends were excluded, and single, combined risk estimates were derived for each patient and toxicity level. Results: A total of 21 studies of nonoverlapping patient populations were identified. Three studies provided dose-response models for more than one level of toxicity. Of these 21 studies, 6, 14, and 5 were used to derive the initial risk estimates for Grade 1, 2, and 3 or greater toxicity, respectively. A comparison of risk estimates between the studies reporting rectal bleeding and rectal toxicity (bleeding plus other symptoms) or between studies with follow-up <36 months and {>=}36 months did not reveal significant differences (p {>=} .29 for all comparisons). After excluding three reports that did not conform to the general data trends, the combined risk estimates were derived from 5 reports (647 patients), 11 reports (3,369 patients), and 5 reports (1,330 patients) for Grade 1, 2, and 3 or greater toxicity, respectively. Conclusions: The proposed approach is feasible and allows for more systematic use of published dose-response data to estimate the complication risks for the individual patient.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Magnetic Resonance Imaging-Based Radiation-Absorbed Dose Estimation of {sup 166}Ho Microspheres in Liver Radioembolization

Purpose: To investigate the potential of magnetic resonance imaging (MRI) for accurate assessment of the three-dimensional {sup 166}Ho activity distribution to estimate radiation-absorbed dose distributions in {sup 166}Ho-loaded poly (L-lactic acid) microsphere ({sup 166}Ho-PLLA-MS) liver radioembolization. Methods and Materials: MRI, computed tomography (CT), and single photon emission CT (SPECT) experiments were conducted on an anthropomorphic gel phantom with tumor-simulating gel samples and on an excised human tumor-bearing liver, both containing known amounts of {sup 166}Ho-PLLA-MS. Three-dimensional radiation-absorbed dose distributions were estimated at the voxel level by convolving the {sup 166}Ho activity distribution, derived from quantitative MRI data, with a {sup 166}Ho dose point-kernel generated by MCNP (Monte Carlo N-Particle transport code) and from Medical Internal Radiation Dose Pamphlet 17. MRI-based radiation-absorbed dose distributions were qualitatively compared with CT and autoradiography images and quantitatively compared with SPECT-based dose distributions. Both MRI- and SPECT-based activity estimations were validated against dose calibrator measurements. Results: Evaluation on an anthropomorphic phantom showed that MRI enables accurate assessment of local {sup 166}Ho-PLLA-MS mass and activity distributions, as supported by a regression coefficient of 1.05 and a correlation coefficient of 0.99, relating local MRI-based mass and activity calculations to reference values obtained with a dose calibrator. Estimated MRI-based radiation-absorbed dose distributions of {sup 166}Ho-PLLA-MS in an ex vivo human liver visually showed high correspondence to SPECT-based radiation-absorbed dose distributions. Quantitative analysis revealed that the differences in local and total amounts of {sup 166}Ho-PLLA-MS estimated by MRI, SPECT, and the dose calibrator were within 10%. Excellent agreement was observed between MRI- and SPECT-based dose-volume histograms. Conclusions: Quantitative MRI was demonstrated to provide accurate three-dimensional {sup 166}Ho-PLLA-MS activity distributions, enabling localized intrahepatic radiation-absorbed dose estimation by convolution with a {sup 166}Ho dose point-kernel for liver radioembolization treatment optimization and evaluation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Influence of urban extent discrepancy on the estimation of surface urban heat island intensity: A global-scale assessment in 892 cities

The estimation of surface urban heat island intensity (SUHII) is crucial for studying the urban thermal environment, which is influenced not only by the commonly known definition of rural reference but also by the delineation of urban extent. Existing studies relies on various urban extent products defined in different ways, and the influence of urban extent discrepancy (UED) on SUHII estimates still remains unclear. In this study, we collected five open-source global urban extent products (GUEPs) for the year 2015 and corresponding daily land surface temperature (LST) observations (MYD11A1). Based on these products, we quantified the UED-induced uncertainty in SUHII estimates by comparing absolute difference (ΔSUHII AD ) and relative difference (ΔSUHII RD ) in SUHII among GUEPs across 892 global cities. Additionally, we introduced an ISF-constrained (ISF–C) method to reduce SUHII differences among GUEPs by constraining the impervious surface fraction (ISF) within urban and rural extents. The results show that urban extents delineated by different GUEPs are not consistent, leading to their difference in ISF as well as LST, which in turn causes uncertainties in the estimated SUHII. On average for global cities, the annual daytime and nighttime ΔSUHII AD are 0.46 ± 0.02 °C (mean ± 95% confidence interval) and 0.24 ± 0.01 °C, with corresponding ΔSUHII RD of 42.0 ± 2.7% and 35.2 ± 2.3%, respectively. The UED-induced uncertainty in SUHII estimates varies among climate zones, and the annual daytime ΔSUHII RD averaged for cities located in the arid zone reaches up to 60.8 ± 6.6%, which is nearly twice as high as that in other climate zones. More importantly, both ΔSUHII AD and ΔSUHII RD show lower values when using the ISF-C method, implying the effectiveness of this method. This study highlights the non-negligible impact of UED on the estimation of SUHII, which requires more attention due to the inconsistency of urban extents among current products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Re-evaluating probable maximum precipitation estimates: sensitivity to transposition domains and storm rotation using modern datasets

This study examines the sensitivity of Probable Maximum Precipitation (PMP) estimates to key methodological decisions embedded in the legacy approach adopted in the U.S. National Weather Service Hydrometeorological Reports No. 51 and No. 52. Although widely used for infrastructure design and risk regulation, fundamental aspects of PMP estimation—such as storm sample size, transposition domain, maximization procedures, and storm rotation—remain poorly constrained and lack formal guidance. Using the Red Rock watershed in Iowa as a case study, and leveraging the 2002–2023 NOAA Analysis of Record for Calibration (AORC) precipitation dataset, we systematically evaluate how each methodological choice, individually and in combination, influences PMP estimates. Our findings demonstrate that PMP is not a fixed physical upper bound but rather a modeling construct shaped heavily by user-defined assumptions. Notably, PMP values derived from modern gridded rainfall datasets can be substantially higher than the legacy estimate used in the original spillway design for Red Rock Dam. Decisions regarding storm sample size, domain extent, climatological window, and particularly storm rotation all contributed to higher PMP estimates. Storm rotation alone—a loosely constrained element in the current PMP practice—can amplify PMP by more than 25%. These results reveal the lack of standardized bounds in current PMP workflows and the need for systematic sensitivity and uncertainty analysis. As PMP estimation shifts toward probabilistic approaches, incorporating physically meaningful storm attributes will be key to developing more transparent, defensible methods for dam safety and climate-resilient infrastructure.

Probable maximum precipitation↗