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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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NASA NTRS2019

Investigating Sources of Variability and Error in Simulations of Carbon Dioxide in an Urban Region

Greenhouse gas (GHG) emissions estimation methods that use atmospheric trace gas observations, including inverse modeling techniques, perform better when carbon dioxide (CO2) fluxes are more accurately transported and dispersed in the atmosphere by a numerical model. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric CO2 from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations).

CO2↗
DOE OSTI2020

Uncovering a membrane-distal conformation of KRAS available to recruit RAF to the plasma membrane

The small GTPase KRAS is localized at the plasma membrane where it functions as a molecular switch, coupling extracellular growth factor stimulation to intracellular signaling networks. In this process, KRAS recruits effectors, such as RAF kinase, to the plasma membrane where they are activated by a series of complex molecular steps. Defining the membrane-bound state of KRAS is fundamental to understanding the activation of RAF kinase and in evaluating novel therapeutic opportunities for the inhibition of oncogenic KRAS-mediated signaling. We combined multiple biophysical measurements and computational methodologies to generate a consensus model for authentically processed, membrane-anchored KRAS. In contrast to the two membrane-proximal conformations previously reported, we identify a third significantly populated state using a combination of neutron reflectivity, fast photochemical oxidation of proteins (FPOP), and NMR. In this highly populated state, which we refer to as “membrane-distal” and estimate to comprise ~90% of the ensemble, the G-domain does not directly contact the membrane but is tethered via its C-terminal hypervariable region and carboxymethylated farnesyl moiety, as shown by FPOP. Subsequent interaction of the RAF1 RAS binding domain with KRAS does not significantly change G-domain configurations on the membrane but affects their relative populations. Overall, our results are consistent with a directional fly-casting mechanism for KRAS, in which the membrane-distal state of the G-domain can effectively recruit RAF kinase from the cytoplasm for activation at the membrane.

KRAS↗
DOE OSTI2023

Tree-Based Ensemble Learning Models for Wall Temperature Predictions in Post-Critical Heat Flux Flow Regimes at Subcooled and Low-Quality Conditions

Accurately predicting post-critical heat flux (CHF) heat transfer is an important but challenging task in water-cooled reactor design and safety analysis. Although numerous heat transfer correlations have been developed to predict post-CHF heat transfer, these correlations are only applicable to relatively narrow ranges of flow conditions due to the complex physical nature of the post-CHF heat transfer regimes. In this paper, a large quantity of experimental data is collected and summarized from the literature for steady-state subcooled and low-quality film boiling regimes with water as the working fluid in vertical tubular test sections. In addition, a low-quality water film boiling (LWFB) database is consolidated with a total of 22,813 experimental data points, which cover a wide flow range of the system pressure from 0.1 to 9.0 MPa, mass flux from 25 to 2750 kg/m 2 s, and inlet subcooling from 1 to 70 °C. Two machine learning (ML) models, based on random forest (RF) and gradient boosted decision tree (GBDT), are trained and validated to predict wall temperatures in post-CHF flow regimes. The trained ML models demonstrate significantly improved accuracies compared to conventional empirical correlations. To further evaluate the performance of these two ML models from a statistical perspective, three criteria are investigated and three metrics are calculated to quantitatively assess the accuracy of these two ML models. For the full LWFB database, the root-mean-square errors between the measured and predicted wall temperatures by the GBDT and RF models are 5.7% and 6.2%, respectively, confirming the accuracy of the two ML models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Many-Body Collective Neutrino Oscillations: Recent Developments

Neutrino flavor transformations in core-collapse supernovae and binary neutron star mergers represent a complex and unsolved problem that is integral to our understanding of the dynamics and nucleosynthesis in these environments. The high number densities of neutrinos present in these environments can engender various collective effects in neutrino flavor transformations, driven either by neutrino-neutrino coherent scattering, or in some cases, through collisional (incoherent) interactions. An ensemble of neutrinos undergoing coherent scattering among themselves is an interacting quantum many-body system -- as such, there is a tantalising prospect of quantum entanglement developing between the neutrinos, which can leave imprints on their flavor evolution histories. Here, in this study, we seek to summarize recent progress that has been made towards understanding this phenomenon.

Hypernuclei↗
DOE OSTI2021

Accounting for uncertainty in complex alluvial aquifer modeling by Bayesian multi-model approach

Alluvial aquifers by nature are complex caused by varied depositional environments. Developing a reliable groundwater model to represent an alluvial aquifer is non-trivial. Also, relying on a single best calibrated model may not be sufficient because of an inadequate choice of model parameter values. To better understand groundwater dynamics and improve model prediction reliability, this study presents a Bayesian multi-model uncertainty quantification (BMMUQ) framework to account for model parameter uncertainty in complex alluvial groundwater modeling. The methodology was applied to the agriculturally intensive Mississippi River alluvial aquifer (MRAA), Northeast Louisiana. An aquifer architecture was first constructed using 7,259 well logs in the MRAA area which covers three fluvial deposits (alluvium, braided-stream terrace, and braided-stream terrace-loess). A 12-layer MODFLOW model was then developed to address the alluvial aquifer complexity and well calibrated through a genetic algorithm. This study quantified model parameter uncertainty in hydraulic conductivity and specific storage of sand facies. Bayesian model averaging (BMA) with the Expectation Maximization (EM) algorithm was adopted to derive posterior model weights and head variances of 50 alternative conceptual groundwater flow models, and thereby obtains BMA ensemble model predictions instead of only relying on the best calibrated conceptual model. Overall, results show that an estimated around 950 million m3 of groundwater storage loss occurs in 2015 with respect to the beginning of 2004, due to high groundwater demand for irrigation in the MRAA area. Explicitly quantifying model uncertainty can produce more reliable groundwater level predictions from BMA ensemble model. The presented groundwater modeling framework improves our understanding of the MRAA and provides a valuable tool to assist agricultural water management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration

Abstract Beavers ( Castor canadensis ) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model‐data integration workflow to address uncertainty in groundwater response to beaver‐induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning‐based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down‐valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down‐valley underflow, from 2 during dry pond periods to 20 during wet periods, serving as an analogy for conditions without and with beaver ponds. The study highlights the influence of floodplain structure on groundwater storage, water balance, and water quality impacted by beaver ponds. A thick gravel bed layer, with a large down‐valley underflow, minimizes the effect of beaver‐induced inundation on water quality. We emphasize the need for field‐scale measurements of floodplain structure and improved characterization of evapotranspiration changes to reduce uncertainty in groundwater response. Plain Language Summary Beavers change the flow of water in river corridors by creating ponds, expanding wetlands, and flooding floodplains. This increases surface water area, promotes plant growth, and enhances biodiversity. However, the impact of this flooding on groundwater flow is not well understood, especially in mountainous areas with gravel layers where water moves easily beneath soil. In this study, we used numerical modeling to investigate how beaver ponds influence groundwater in a mountainous floodplain of the Upper Colorado River Basin. We adapted a machine learning method to validate our numerical models using multiple field data sets. Our findings show that beaver ponds significantly increase vertical water flow from the soil to the gravel during wet periods, compared to when the ponds are fully drained. The study also highlights the importance of floodplain structure in controlling both water flow in gravel layers along the river direction and vertical flow from the soil to the gravel with the presence of beavers. To reduce uncertainty in groundwater response, we emphasize the need for more field‐scale measurements of floodplain structure, hydraulic properties, and evapotranspiration changes. Key Points Floodplain structures and hydraulic conductivities are important for groundwater response with beaver ponds in mountainous floodplains Large down‐valley underflow in permeability‐stratified floodplains reduces beaver‐induced impacts on groundwater storage and water quality Machine learning‐based model calibration methods are effective for estimating posterior distributions of groundwater model parameters

Wang, Lijing↗
DOE OSTI2021

Modeling laser-driven ion acceleration with deep learning

Developments in machine learning promise to ameliorate some of the challenges of modeling complex physical systems through neural-network-based surrogate models. High-intensity, short-pulse lasers can be used to accelerate ions to mega-electronvolt energies, but to model such interactions requires computationally expensive techniques such as particle-in-cell simulations. Multilayer neural networks allow one to take a relatively sparse ensemble of simulations and generate a surrogate model that can be used to rapidly search the parameter space of interest. In this study, we created an ensemble of over 1,000 simulations modeling laser-driven ion acceleration and developed a surrogate to study the resulting parameter space. A neural-network-based approach allows for rapid feature discovery not possible for traditional parameter scans given the computational cost. A notable observation made during this study was the dependence of ion energy on the pre-plasma gradient length scale. While this methodology harbors great promise for ion acceleration, it has ready application to all topics in which large-scale parameter scans are restricted by significant computational cost or relatively large, but sparse, domains.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Exascale Computing and Data Handling: Challenges and Opportunities for Weather and Climate Prediction

The emergence of exascale computing and artificial intelligence offer tremendous potential to significantly advance Earth system prediction capabilities. However, enormous challenges must be overcome to adapt models and prediction systems to use these new technologies effectively. A 2022 WMO report on exascale computing recommends “urgency in dedicating efforts and attention to disruptions associated with evolving computing technologies that will be increasingly difficult to overcome, threatening continued advancements in weather and climate prediction capabilities.” Further, the explosive growth in data from observations, model and ensemble output, and postprocessing threatens to overwhelm the ability to deliver timely, accurate, and precise information needed for decision-making. Artificial intelligence (AI) offers untapped opportunities to alter how models are developed, observations are processed, and predictions are analyzed and extracted for decision-making. Given the extraordinarily high cost of computing, growing complexity of prediction systems, and increasingly unmanageable amount of data being produced and consumed, these challenges are rapidly becoming too large for any single institution or country to handle. This paper describes key technical and budgetary challenges, identifies gaps and ways to address them, and makes a number of recommendations.

Atmosphere↗
NASA NTRS1995

Repeating sources of classical gamma-ray bursts

From an analysis of the first catalog of the Burst and Transient Source Experiment (BATSE) experiment (Fishman et al. 1993, 1994a) on the Compton Gamma Ray Observatory (CGRO), we find an excessive number of pairs of gamma-ray bursts which are clustered in both a space and time . The angular separation between the two bursts in each pair is less than their positional uncertainties, and the interval between their occurrence times is within several days. Optimizing the signal, we find that the probability of observing such a clustered excess froma Poisson ensemble is approximately 2 x 10(exp -5). We suggest that these bursts arise from repeating sources. A detailed study of the most prolific source, GBS 0855-00, is presented in a separate paper (Wang & Lingenfelter 1993a, b). Unlike most of the 'soft' gamma-ray repeaters, these repeating bursts have relatively hard spectra, complex light curves, and widely varying durations, that are indistinguishable from classical gamma-ray bursts. Although the significance of the clustered excess was optimized a posteriori, because it depends on temporal and spatial bin sized that could not be defined a priori, we can use the optimizations from the first catalog to test subsequent BATSE data sets. Unfortunately, the failure of the on-board tape recorders during the second catalog period seriously reduced the number of accurately positioned bursts (Fishman et al. 1994b), so that we can neither confirm, nor refute, the predicted repitition in that sample, and we must await the results of the third catalog.

Wang, V. C.↗
DOE OSTI2023

Deducing subnanometer cluster size and shape distributions of heterogeneous supported catalysts

Abstract Infrared (IR) spectra of adsorbate vibrational modes are sensitive to adsorbate/metal interactions, accurate, and easily obtainable in-situ or operando. While they are the gold standards for characterizing single-crystals and large nanoparticles, analogous spectra for highly dispersed heterogeneous catalysts consisting of single-atoms and ultra-small clusters are lacking. Here, we combine data-based approaches with physics-driven surrogate models to generate synthetic IR spectra from first-principles. We bypass the vast combinatorial space of clusters by determining viable, low-energy structures using machine-learned Hamiltonians, genetic algorithm optimization, and grand canonical Monte Carlo calculations. We obtain first-principles vibrations on this tractable ensemble and generate single-cluster primary spectra analogous to pure component gas-phase IR spectra. With such spectra as standards, we predict cluster size distributions from computational and experimental data, demonstrated in the case of CO adsorption on Pd/CeO 2 (111) catalysts, and quantify uncertainty using Bayesian Inference. We discuss extensions for characterizing complex materials towards closing the materials gap.

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

Microscopic Derivation of Transition-state Theory for Complex Quantum Systems

The decay of quantum complex systems through a potential barrier is often described with transition-state theory, also known as RRKM theory in chemistry. Here we derive the basic formula for transition-state theory based on a generic Hamiltonian as might be constructed in a configuration-interaction basis. Two reservoirs of random Hamiltonians from Gaussian orthogonal ensembles are coupled to intermediate states representing the transition states at a barrier. Under the condition that the decay of the reservoirs to open channels is large, an analytic formula for reaction rates is derived. Here, the transition states act as independent Breit–Wigner resonances which contribute additively to the total transition probability, as is well known for electronic conductance through resonant tunneling states. It is also found that the transition probability is independent of the decay properties of the states in the second reservoir over a wide range of decay widths.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2014

Quantification and Mitigation of Long-Term Impacts of Urbanization and Climate Change in the Tropical Coastal City of San Juan, Puerto Rico

Urbanization, along with other cases of land cover and land use changes, has significant climate impacts in tropical regions with the added complexity of occurring within the context of global warming. The individual and combined effects of these two factors on the surface energy balance of a tropical city are investigated by use of an integrated atmospheric modeling approach, taking the San Juan Metropolitan Area (SJMA), Puerto Rico as the test case. To achieve this goal, an ensemble of climate and weather simulations is performed, with the climate scenarios combining urban development and sprawl with regional climate change over the past 50 years, and the short-term simulations designed to test the sensitivity to different urban vegetation configurations as mitigating alternatives. As indicator of change, we use the thermal response number (TRN), which is a measure of the sensible heating to the thermal storage of a surface or region, and the Bowen ratio, which is defined as the ratio of sensible to latent heat fluxes. The TRN of the area occupied by the SJMA has decreased as a consequence of replacing the low land coastal plain vegetation with man made materials, indicating that it takes less energy to raise the surface temperature of the urban area, whereas the TRN of forested regions has remained virtually unchanged. The global warming signal also has effects on the thermal response of the SJMA, where dryer current conditions generate lower TRN values. Differences due to global warming are more evident in the Bowen ratio pattern, mostly associated with the drier present conditions observed and its effects on sensible and latent heat fluxes. In terms of testing different mitigation strategies, the short-term simulations show that the urban area is more efficient in partitioning surface energy balance terms when green roofs are specified, as opposed to including vegetation inside the urban core.

Comarazamy, Daniel↗
DOE OSTI2026

Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints

Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Probing reaction channels via reinforcement learning

Abstract Chemical reactions are dynamical processes involving the correlated reorganization of atomic configurations, driving the conversion of an initial reactant into a result product. By virtue of the metastability of both the reactants and products, chemical reactions are rare events, proceeding fleetingly. Reaction pathways can be modelled probabilistically by using the notion of reactive density in the phase space of the molecular system. Such density is related to a function known as the committor function, which describes the likelihood of a configuration evolving to one of the nearby metastable regions. In theory, the committor function can be obtained by solving the backward Kolmogorov equation (BKE), which is a partial differential equation (PDE) defined in the full dimensional phase space. However, using traditional methods to solve this problem is not practical for high dimensional systems. In this work, we propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of states that concentrate on the transition path ensemble. This configuration ensemble can be effectively employed in a neural network-based PDE solver to obtain an approximation solution of a restricted BKE, even when the dimension of the problem is very high. The resulting solution provides an approximation for the committor function that encodes mechanistic information for the reaction, paving a new way for understanding of complex chemical reactions and evaluation of reaction rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

MCCCS-MN

The MCCCS‒MN (Monte Carlo for Complex Chemical Systems‒Minnesota) software is developed by the Siepmann research group at the University of Minnesota. MCCCS‒MN allows for the simulation of multi-component molecular systems in the canonical, isobaric-isothermal (including constant stress for solids), grand-canonical, semi-grand, and Gibbs (NVT, NPT, and more than two simulation boxes) ensembles. It uses the configurational-bias Monte Carlo method to efficiently sample phase space for linear, branched and cyclic chain molecules, the adiabatic nuclear and electronic sampling Monte Carlo method to treat many-body polarization effects, and the aggregation-volume-bias Monte Carlo algorithm to efficiently sample the spatial distribution of associating molecules. MCCCS-MN employs a molecular representation of the system where force fields contain bonded and non-bonded terms. Funding for the development of MCCCS-MN through grants from the National Science Foundation (simulation of fluid phase equilibria and chromatography) and the Department of Energy (simulation of adsorption equilibria) is gratefully acknowledged.

Siepmann, J.Ilja [University of Minnesota - Twin C↗
NASA NTRS2005

A Note on the Relationship between Temperature and Water Vapor in Quasi-Equilibrium and Climate States

An ideal and simple formulation is successfully derived that well represents a quasi-linear relationship found between the domain-averaged water vapor, q (mm), and temperature, T (K), fields obtained from a series of quasi-equilibrium (long-term) simulations for the Tropics using the two-dimensional Goddard Cumulus Ensemble (GCE) model. Earlier model work showed that the forced maintenance of two different wind profiles in the Tropics leads to two different equilibrium states. Investigating this finding required investigation of the slope of the moisture-temperature relations, which turns out to be linear in the Tropics. The extra-tropical climate equilibriums become more complex, but insight on modeling sensitivity can be obtained by linear stepwise regression of the integrated temperature and humidity. A globally curvilinear moisture-temperature distribution, similar to the famous Clausius-Clapeyron curve (i.e., saturated water vapor pressure versus temperature), is then found in this study. Such a genuine finding clarifies that the dynamics are crucial to the climate (shown in the earlier work) but the thermodynamics adjust. The range of validity of this result is further examined herein. The GCE-modeled tropical domain-averaged q and T fields form a linearly-regressed "q-T" slope that genuinely resides within an ideal range of slopes obtained from the aforementioned formulation. A quantity (denoted as dC2/dC1) representing the derivative between the static energy densities due to temperature (C2) and water vapor (C1) for various quasi-equilibrium states can also be obtained. A dC2/dC1 value near unity obtained for the GCE-modeled tropical simulations implies that the static energy densities due to moisture and temperature only differ by a pure constant for various equilibrium states. An overall q-T relation also including extra-tropical regions is, however, found to have a curvilinear relationship. Accordingly, warm/moist regions favor change in water vapor faster than temperature, while cold/dry regions favor an increase in temperature quicker than water vapor.

Shie, C.-L.↗
DOE OSTI2025

Resolving Mesoscale Convective Systems: Grid Spacing Sensitivity in the Tropics and Midlatitudes

Abstract Mesoscale convective systems (MCSs) are a critical global water cycle component and drive extreme precipitation events in tropical and midlatitude regions. However, simulating deep convection remains challenging for modern numerical weather and climate models due to the complex interactions of processes from microscales to synoptic scales. Recent models with kilometer‐scale horizontal grid spacings offer notable improvements in simulating deep convection compared to coarser‐resolution models. Still, deficiencies in representing key physical processes, such as entrainment, lead to systematic biases. Additionally, evaluating model outputs using process‐oriented observational data remain difficult. This study presents an ensemble of MCS simulations with spanning the deep convective gray zone ( from 12 km to 125 m) in the Southern Great Plains of the U.S. and the Amazon Basin. Comparing these simulations with Atmospheric Radiation Measurement (ARM) wind profiler observations, we find greater sensitivity in the Amazon Basin compared to the Great Plains. Convective drafts converge structurally at sub‐kilometer scales, but some deficiencies remain. In both regions, simulated up and downdrafts are too deep and extreme downdrafts are not strong enough. Furthermore, Amazonian updrafts are too strong. Overall, we observe higher sensitivity in the tropics, including an artificial buildup in vertical kinetic energy at scales of , suggesting a need for 250 m in this region. Nevertheless, bulk convergence—agreement of storm‐average statistics—is achievable with kilometer‐scale simulations within a 10% error margin with 1 km providing a good balance between accuracy and computational cost.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States

Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large‐scale streamflow projections to inform water resource management and long‐term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications.

CLM5↗