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DOE OSTI2025

Representing lateral groundwater flow from land to river in Earth system models

Lateral groundwater flow (LGF) is an important hydrologic process in controlling water table dynamics. Due to the relatively coarse spatial resolutions of land surface models, the representation of this process is often overlooked or overly simplified. In this study, we developed a hillslope-based lateral groundwater flow model. Specifically, we first developed a hillslope definition model based on an existing watershed delineation model to represent the subgrid spatial variability in topography. Building upon this hillslope definition, we then developed a physical-based lateral groundwater flow using Darcy’s equation. This model explicitly considers the relationships between the groundwater table along the hillslope and the river water table levels. We coupled this intra-grid model to the land component (E3SM Land Model: ELM) and river component (MOdel for Scale Adaptive River Transport: MOSART) of the Energy Exascale Earth System Model (E3SM). We tested both the hillslope definition model and the lateral groundwater flow model and performed sensitivity experiments using different configurations. Simulations for a single grid cell at 0.5°×0.5° within the Amazon basin show that the definition of hillslope is the key to modeling lateral flow processes and the runoff partition between surface and subsurface can be dramatically changed using the hillslope approach. Although our method provides a pathway to improve the lateral flow process, future improvements are needed to better capture the subgrid structure to account for the spatial variability in hillslopes within the simulated grid of land surface models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Kinetic theory of particle-in-cell simulation plasma and the ensemble averaging technique

Abstract We derive the kinetic theory of fluctuations in physically and numerically stable particle-in-cell (PIC) simulations of electrostatic plasmas. The starting point is the single-time correlation at the start of the simulation between the statistical fluctuations of the weighted densities of macroparticle centers in the plasma particle phase-space. The fluctuations are associated with different initial conditions, typically due to the random initial conditions (in velocity space) of the macroparticles/simulation plasma, assigned according to their initial distribution of probability. The single-time correlations at all time steps and in each spatial grid cell are then determined from the Laplace–Fourier transforms of the discretized Klimontovich-like equation for the macroparticles and Maxwell’s equations for the fields, as computed by modern PIC codes. We recover the expressions for the electrostatic field and the plasma particle density fluctuation autocorrelation spectra as well as the kinetic equations describing the average evolution of PIC-simulated plasma particles, first derived by Langdon (1970b Proc. 4th Conf. Numerical Simulation of Plasmas ) using a test macroparticle approach perturbing a discretized Vlasovian plasma and then averaging the obtained physical quantity over the initial macroparticle velocity distribution. We generalize and extend these results to the modern algorithms in PIC codes using arbitrary macroparticle weights. Analytical estimates of statistical fluctuation amplitudes are derived as a function of the plasma simulation parameters, using the central limit theorem in the limit of a large number of macroparticles per cell. The theory is then used to analyze the ensemble averaging technique of PIC simulations where statistical averages are performed over ensembles of PIC simulations, modeling the same plasma physics problem but using different statistical realizations of the initial distribution functions of the macroparticles. This method is illustrated by linear Landau damping uncovering (from noise, which is usually considered numerical) the physical fluctuations driven by a single small amplitude electrostatic wave perturbing a PIC simulation plasma in equilibrium.

fluctuations correlations↗

DOE OSTI2021

Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation

Abstract Understanding the complex interrelationships between wildfire and its environmental and anthropogenic controls is crucial for wildfire modeling and management. Although machine learning (ML) models have yielded significant improvements in wildfire predictions, their limited interpretability has been an obstacle for their use in advancing understanding of wildfires. This study builds an ML model incorporating predictors of local meteorology, land‐surface characteristics, and socioeconomic variables to predict monthly burned area at grid cells of 0.25° × 0.25° resolution over the contiguous United States. Besides these predictors, we construct and include predictors representing the large‐scale circulation patterns conducive to wildfires, which largely improves the temporal correlations in several regions by 14%–44%. The Shapley additive explanation is introduced to quantify the contributions of the predictors to burned area. Results show a key role of longitude and latitude in delineating fire regimes with different temporal patterns of burned area. The model captures the physical relationship between burned area and vapor pressure deficit, relative humidity (RH), and energy release component (ERC), in agreement with the prior findings. Aggregating the contribution of predictor variables of all the grids by region, analyses show that ERC is the major contributor accounting for 14%–27% to large burned areas in the western US. In contrast, there is no leading factor contributing to large burned areas in the eastern US, although large‐scale circulation patterns featuring less active upper‐level ridge‐trough and low RH two months earlier in winter contribute relatively more to large burned areas in spring in the southeastern US.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Wildfire Risk Evaluation Framework for Grid Operations and Planning

The United States (US) environmental protection agency's (EPA's) climate change indicators for wildfires show a long-term trend of increased annual wildfire activity, larger wildfire size, and more variable dynamics in wildfire behavior. This has caused more frequent preemptive public safety power shutoff (PSPS) events in the regions with recognized high wildfire risk. These preemptive power shutoffs attempt to prevent the ignition of a wildfire but it nonetheless renders the transmission line non-operational, which often sheds load of downstream communities. As wildfires and PSPS events become more frequent, it is crucial to find the communities most at risk of load shedding. To that end, this paper proposes the wildfire risk evaluation of the system (WiRES) framework, which is a performance-based framework that translates extreme weather-related and PSPS event probabilities into a cumulative probability of a non-operational transmission line. This study also provides a geospatial visualization tool that breaks down the entire western electrical coordinating council (WECC) region into 50 km grid cells which can be used to 1) filter out transmission lines with higher than a threshold outage probability, and 2) graphically discover the affected regions and their biophysical and socioeconomic metrics. %such as the social vulnerability index, population density, gross domestic product, etc. Lastly, an impact assessment study is conducted which connects the results of the proposed framework to python and powerworld-based contingency analysis to highlight the applicability of the framework.

Wildfire, Resilience, Extreme Events, Grid Reslien↗

DOE OSTI2022

Enhancing Distribution System Resiliency Using Grid-Forming Fuel Cell Inverter: Preprint

Legacy inverters interfacing distributed energy re-sources are traditionally grid-following (GFL) in nature. GFL assets typically follow real power and reactive power set points. Recently, inverters with grid-forming (GFM) capability are gaining attention as GFM assets can increase the resiliency of the distribution system under stressed conditions. These GFM inverters can use photovoltaics, batteries, or fuel cells as their energy source. In this paper, we present information on inverters interfacing fuel cell assets, specifically with GFM capability. By introducing a fuel cell powered GFM coupled with hydrogen production and storage, the GFM can continuously provide GFM activities during periods of low renewable resource availability, and/or during power outages exceeding typical electric battery duration. Finally, we present information on the need for updates on interconnection and interoperability standards that can be leveraged by utilities for including fuel cell inverters in their asset mix.

fuel cell inverters↗

DOE OSTI2025

Measurement of Contact Resistivity In Symmetric Polycrystalline Si/SiO x /Monocrystalline Si Test Structures Using Variable Light Illumination

While different methods exist to determine the contact resistivity in semiconductor devices, these methods are limited to measurement of the majority carrier contacts. The measurement of p‐ or n‐type contacts on n‐ or p‐type crystalline silicon, respectively, is challenging due to the blocking diode formed by the p–n junction. In this article, we address this problem for tunneling oxide passivating contacts used in high‐efficiency Si solar cells. We propose a universal method to extract contact resistivity on symmetric test structures with polycrystalline silicon on SiO x (poly‐Si/SiO x ) passivating contacts under illumination, both for p–n and high–low‐junction passivated contacts. In this method, we demonstrate that the total contact resistance of each cell grid finger to the base wafer is governed by its effective contact area, defined by the transfer length extending from both sides of the finger. Therefore, the grid contact resistance of a poly‐Si contact depends on the ratio of the doped poly‐Si sheet resistance to the tunneling contact resistivity.

14 SOLAR ENERGY↗

DOE OSTI2020

A new spatially distributed added value index for regional climate models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A model for discrete fracture-clay rock interaction incorporating electrostatic effects on transport

Abstract A model based on the code CrunchClay is presented for a fracture-clay matrix system that takes electrostatic effects on transport into account. The electrostatic effects on transport include those associated with the development of a diffusion potential as captured by the Nernst-Planck equation, and the formation of a diffuse layer bordering negatively charged clay particles within which partial anion exclusion occurs. The model is based on a dual continuum formulation that accounts for diffuse layer and bulk water pore space, providing a more flexible framework than is found in the classical mean electrostatic potential models. The diffuse layer model is obtained by volume averaging ion concentrations in the Poisson-Boltzmann equation, but also includes the treatment of longitudinal transport within this continuum. The calculation of transport within the bulk and diffuse layer porosity is based on a new formulation for the Nernst-Planck equation that considers averaging of diffusion coefficients and accumulation factors at grid cell interfaces. Equations for function residuals and the associated Jacobian matrix are presented such that the system of nonlinear differential-algebraic equations can be solved with Newton’s method. As an example, we consider a 2D system with a single discrete fracture within which flow and advective transport occurs that is coupled to diffusion in the clay-rich matrix. The simulation results demonstrate the lack of retardation for anions (e.g., 36 Cl − ) of the contaminant plume within the fracture flow system because they are largely excluded from the charged clay rock, while the migration of cations (e.g., 90 Sr ++ ) is more strongly attenuated. The diffusive loss of divalent cations in particular from the fracture is accentuated by their accumulation in the diffuse layer within the clay-rich matrix.

58 GEOSCIENCES↗

DOE OSTI2020

Zonal-Based Emission Source Term Model for Predicting Particulate Emission Factors in Wildfire Simulations

A physics/chemistry-based numerical model for predicting the emission of fine particles from wildfires is proposed. This model implements the fundamental mechanisms of soot formation in a combustion environment: soot nucleation, surface growth, agglomeration, oxidation, and particle fragmentation. These mechanisms occur on a scale too fine for the discretization of most wildfire models, which need to simulate landscape-scale dynamics. As a result this model implements a zonal approach, where the computed soot particle distribution is partitioned into process zones within a single resolved grid cell. These process zones include: an inception zone (for nucleation), a heating zone (for coagulation, surface growth, and fragmentation), a reaction zone (for oxidation), and a quenched zone (for atmospheric processes). Governing mechanisms are applied to the appropriate zones to predict total particle growth and emission. Additionally, the proposed model is implemented into HIGRAD/FIRETEC, a physics-based wildfire simulation code which couples interactions between fire, fuels, atmosphere, and topography on a landscape scale. Fire simulations among grasslands and conifer forests are performed and compared against experimental data for emission factors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Potential reductions in fine particulate matter and premature mortality following implementation of air pollution controls on coal-fired power plants in India

Coal-fired power plants (CFPPs) account for > 70% of electricity generation in India, but < 5% of facilities have installed technologies for sulfur dioxide (SO 2 ) and nitrogen oxide (NO X ) removal. Emissions of these pollutants lead to the formation of fine particulate matter (PM 2.5 ) and an increased risk of premature mortality for exposed populations. Here, we use a nested version of the GEOS-Chem global chemical transport model (0.5° × 0.625° resolution) for India to estimate reductions in PM 2.5 concentrations that could have been achieved by implementing existing emission control technologies like flue-gas desulfurization (FGD) and/or selective catalytic reduction (SCR). We quantify the associated burden of disease using the integrated exposure response (IER) and global exposure mortality model (GEMM) functions and compare the costs of premature mortality to those for FGD installation. Model simulations for 2010 suggest installation of FGD would have reduced mean annual PM 2.5 concentrations across India by 8%, compared to 3% with SCR installation, and 11% with both FGD and SCR. A 7–28% reduction in PM 2.5 was simulated for local communities closest to CFPPs (same model grid cell), leading to up to 17% reduction in annual premature mortality. Overall, more than 0.21–0.48 million premature deaths would have been avoided over a 10-year period if FGD had been implemented on all CFPPs, compared to 0.09–0.21 million with SCR and 0.22–0.72 million with both FGD and SCR. Benefits associated with such actions are approximately $\$18.1$–$\$604$ billion USD per year, which is equivalent to ~ 0.44 to 10% of India’s GDP. These results suggest that monetary benefits from avoided premature mortality far outweigh the capital and operational costs of FGD and/or SCR installation of $\$19.5$ billion and/or $\$32.8$ billion per year, respectively. This information is essential because the high costs of installation and operation are often given as reasons for delaying installation and commissioning. Finally, we conclude that policy actions to control air pollution from CFPPs are economically justifiable.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2020

Growth modeling to evaluate alternative cultivation strategies to enhance national microalgal biomass production

In this paper, we present a model-based assessment of alternative cultivation strategies on algal biomass production within the conterminous United States. Our assessment focuses on two basic cultivation strategies: 1) seasonal rotation of three representative freshwater algal strains that are well suited for warm weather, cold weather, and all-season diverse weather conditions, respectively; and 2) variation between three open pond water depths (15cm, 20cm, and 25cm). The enhanced Biomass Assessment Tool (BAT) is applied on a site-specific, hourly basis at 5,832 North American Land Data Assimilation System Phase 2 1/8° meteorological model grid cells over a 30-year period (1980-2009) to evaluate the operational strategies. Recognizing that resource management decisions may also consider scales beyond the individual site, we also conduct a regional assessment of focused on 7 representative climate zones in the conterminous United States. Results demonstrate that the spatial variability of algal productivity is largely affected by the strain-specific growth response to light and temperature that vary significantly by climate zone and latitude. Therefore, with a limited set of alternative cultivation strategies, the BAT identified the best performing combination of cultivation strategies on a site-specific basis that considerably enhances national annual biomass productivity. Specifically, the appropriate choice of seasonal strain rotation is able to significantly dampen climate-driven seasonal and spatial variability.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models

Here, we propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, predictive capabilities, and cost reductions of the proposed method.

42 ENGINEERING↗

DOE OSTI2025

A cohesive zone treatment for the material point method involving problems of large deformation and damage

A new algorithm is described that permits the use of cohesive zones in the material point method for problems involving large deformation and fracture. In contrast to previous cohesive zone implementations, this method does not utilize massless surface-element particles. Instead, cohesive tractions are computed using the shape function mappings from a reference grid configuration in combination with explicitly defined particle surface normals and surface positions. These normals and relative surface positions are updated each time step according to particle deformation. The tractions are converted to cohesive forces using the nodal areas and mapped back to particles using the same reference shape function mappings. These forces are then remapped by conventional particle-to-grid interpolation as external forces using the current-configuration shape-function mappings. This allows highly compliant cohesive zones to function over jump displacements larger than a grid cell. Upon damage, these interfaces can revert to conventional multi-field contact surfaces. This approach is general and readily applies to two and three dimensions as well as being compatible with damage-field gradient partitioning offering exceptional computational flexibility. The framework for this method enables other capabilities, such as improved contact precision using explicitly defined surface normals and positions, and a method to mitigate spurious material damage at weak discontinuities between stiff brittle materials and soft or compliant materials.

Cohesive zone↗

DOE OSTI2023

Areal interpolation of population projections consistent with different SSPs from 1-km resolution to block level based on USA Structures dataset

Population data are normally collected at various census administrative levels, and areal interpolation of population is often required to transform population to the desired spatial resolution. Building footprint datasets, such as Microsoft building footprints, have proven to be useful in estimating population distribution and can therefore be used for areal interpolation of population. In addition to Microsoft building footprints, the recently released USA Structures dataset provides additional information such as building type and building height for some regions, which may provide valuable information for a better depiction of population distribution and improved population areal interpolation accuracy. In this study, we have conducted areal interpolation of population projections consistent with three different Shared Socioeconomic Pathways (SSP2, SSP3, and SSP5) from 1-km grid cells to block level in Washington state for every ten years from 2020 to 2040 based on USA Structures. We assessed USA Structures-based population downscaling accuracy using U.S. decennial survey data in 2020 under three different downscaling schemes, including population downscaling from census tracts to block groups, from census tracts to blocks, and from block groups to blocks. The resulting accuracies were compared with those based on Microsoft building footprints. The comparison showed that USA Structures achieved higher accuracies across different population density regions and areas with different urbanization extent within our study area.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

Toward more mechanistic representations of biogeochemical processes in river networks: Implementation and demonstration of a multiscale model

A recently introduced multiscale model for representing the effects of small-scale hyporheic-zone biogeochemical processes is extended from the reach scale to river network scales. The model uses advection-dispersion-reaction equations for the channel network and one-dimensional advection-reaction subgrid models for the hyporheic zone. We summarize implementation in the integrated surface/subsurface hydrology modeling system Amanzi-ATS. The extension uses topologically defined meshes to represent stream/river networks and associates a hyporheic subgrid model with each channel grid cell. Biogeochemical reaction modeling capability residing in community software is accessed through an application programming interface. The implementation is verified against independent numerical solutions on a single reach. Mesh convergence studies show that commonly used semi-distributed representations can introduce significant spatial discretization error. Denitrification of farm runoff in a subbasin of the Portage River Basin in Ohio, USA is used to demonstrate the general-purpose reactive transport capability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Development of a River Dynamical Core for E3SM to simulate compound flooding on Exascale-class heterogeneous supercomputers

Flooding events pose significant risk to human life, property, and infrastructure. Physically-consistent quantification of altered flood risks in global models requires hyper-resolution (~1 km) or fine flood simulations using two-dimensional (2D) physics schemes, both of which are unavailable in the current generation Earth System Models. Here, in this work, we have developed the River Dynamical Core (RDycore), which is an open-source, 2D shallow water equation (SWE) library for the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM). RDycore uses PETSc and libCEED libraries that allows it to run efficiently on CPUs and GPUs, as well as select a time-integration algorithm at runtime without requiring any code modifications. RDycore achieves spatial error convergence rates for problems with analytical and manufactured solutions similar to those reported previously in the literature, or consistent with the implemented first-order spatial discretization scheme. RDycore's accuracy in predicting flooding for a well-studied dam break problem is comparable to existing SWE models. For a problem with 471 million grid cells, RDycore achieves a speedup of 6.6x and 7.6x on GPUs compared to CPUs when using 320 compute nodes on DOE's Perlmutter and Frontier supercomputers, respectively. The one-way coupling of the RDycore library within E3SM is demonstrated by performing multiple 5-day flooding simulations during Hurricane Harvey driven by five precipitation datasets. The E3SM--RDycore simulations at 30 m spatial resolution accurately simulate maximum water height during the hurricane when benchmarked against a previously published study and achieve a speedup of 15x (Perlmutter) and 21x (Frontier) on GPUs relative to CPUs. The work presented here is the foundational step in providing hardware and algorithmic portability framework for simulating kilometer-scale river dynamics within E3SM.

Flood Simulation↗

DOE OSTI2025

A constrained-transport embedded boundary method for compressible resistive magnetohydrodynamics

Motivated by the increased interest in pulsed-power magneto-inertial fusion devices in recent years, we present a method for implementing an arbitrarily shaped embedded boundary on a Cartesian mesh while solving the equations of compressible resistive magnetohydrodynamics. The method is built around a finite volume formulation of the equations in which a Riemann solver is used to compute fluxes on the faces between grid cells, and a face-centered constrained transport formulation of the induction equation. The small time step problem associated with the cut cells is avoided by always computing fluxes on the faces and edges of the Cartesian mesh. We extend the method to model a moving interface between two materials with different properties using a ghost-fluid approach, and show some preliminary results including shock-wave-driven and magnetically-driven dynamical compressions of magnetohydrostatic equilibria. In conclusion, we present a thorough verification of the method and show that it converges at second order in the absence of discontinuities, and at first order with a discontinuity in material properties.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

TROPOMI observations allow for robust exploration of the relationship between solar-induced chlorophyll fluorescence and terrestrial gross primary production

Solar-induced chlorophyll fluorescence (SIF) observed by satellites has advanced the monitoring of terrestrial photosynthesis regionally and globally. The relationship between SIF and gross primary production (GPP) at leaf, canopy, and ecosystem scales has received tremendous attention in recent years. It remains controversial whether the SIF-GPP relationship at the ecosystem scale is universal or dependent upon vegetation type. New SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI) with unprecedented high spatial and temporal resolution provide a new opportunity to elucidate the SIF-GPP relationship. Here, we examine the SIF-GPP relationship for seven major vegetation types across the U.S. with TROPOMI SIF and in-situ GPP data for 83 eddy covariance flux sites. We find that TROPOMI SIF shows a strong and consistent relationship with tower-based GPP at both satellite footprint and grid-cell levels. The slope of the SIF-GPP relationship is similar among all the vegetation types except croplands, demonstrating a nearly universal (converging to ~13.5 g C m -2 d -1 /W m -2 μm -1 sr -1) rather than vegetation type-specific SIF-GPP relationship. This confirms that TROPOMI SIF can be used as a proxy for GPP across a wide variety of vegetation types and can also be used to quantify GPP by avoiding uncertainty associated with land cover maps. The C 4 crops have a much higher slope than the C 3 crops, and therefore croplands tend to have a higher slope than C 3 -dominated vegetation types (e.g., forests, shrublands, savannas). We also find that the TROPOMI SIF is well correlated with GPP under normal or wetter conditions, while their relationship becomes weaker under water stress. In conclusion, our TROPOMI-based study could improve our understanding of the SIF-GPP relationship at the ecosystem scale and advance the mapping of GPP globally with SIF observations from space.

54 ENVIRONMENTAL SCIENCES↗