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DOE OSTI2023

Collaborative Proposal: Improving Understanding of the Internal Structure and Dynamics of Deep Convection Using ARM Observations and Large Eddy Simulations (Final progress report)

This is the final technical report for the DOE Atmospheric System Research funded project entitled "Collaborative Proposal: Improving Understanding of the Internal Structure and Dynamics of Deep Convection Using ARM Observations and Large Eddy Simulations". This project addressed several science questions related to the structure and behavior of deep convective clouds in the atmosphere. It applied knowledge gained from this work to improve the deep cumulus convection parameterization in the Community Earth System Model (CESM). Specific outcomes related to the project include an improved understanding of: (1) how convective cloud environments regulate the size of thermals reaching the upper troposphere, and hence the shallow-to-deep convective transition; (2) the role of dynamic pressure forcing in thermal and convective cloud evolution; and (3) the role of vertical wind shear on the behavior of moist thermals and the transition from shallow to deep convection. Although the primary motivation of this project was to improve basic understanding of thermal dynamics in atmospheric convection, an important component of this work was using this improved understanding to modify the representation of entrainment and vertical velocity profiles in the Zhang-McFarlane cumulus parameterization used in CESM. This work also had a strong observational component, with analyses of Atmospheric Radiation Measurement (ARM) observations to characterize thermal characteristics and behavior. Observations collected during the ARM-supported CACTI field project were used with high-resolution simulations to explore environmental controls on deep convection initiation. Data from the ARM-supported MC3E field project were also used to evaluate the representation of updraft vertical velocities in the Zhang-McFarlane cumulus parameterization. This project directly supported the training of two post-doctoral researchers and led to 16 peer-reviewed published papers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Exploring the Sensitivity of Photosynthesis and Stomatal Resistance Parameters in a Land Surface Model

Abstract Land surface models, like the Common Land Model component of the ParFlow integrated hydrologic model (PF-CLM), are used to estimate transpiration from vegetated surfaces. Transpiration rates quantify how much water moves from the subsurface through the plant and into the atmosphere. This rate is controlled by the stomatal resistance term in land surface models. The Ball–Berry stomatal resistance parameterization relies, in part, on the rate of photosynthesis, and together these equations require the specification of 20 input parameters. Here, the active subspace method is applied to 2100 year-long PF-CLM simulations, forced by atmospheric data from California, Colorado, and Oklahoma, to identify which input parameters are important and how they relate to three quantities of interest: transpiration, stomatal resistance from the sunlit portion of the canopy, and stomatal resistance from the shaded portion. The slope (mp) and intercept (bp) parameters associated with the Ball–Berry parameterization are consistently important for all locations, along with five parameters associated with ribulose bisphosphate carboxylase/oxygenase (RuBisCO)- and light-limited rates of photosynthesis [CO2 Michaelis–Menten constant at 25°C (kc25), maximum ratio of oxygenation to carboxylation (ocr), quantum efficiency at 25°C (qe25), maximum rate of carboxylation at 25°C (vcmx25), and multiplier in the denominator of the equation used to compute the light-limited rate of photosynthesis (wj1)]. The importance of these input parameters, quantified by the active variable weight, and the relationship between the input parameters and quantities of interest vary seasonally and diurnally. Input parameter values influence transpiration rates most during midday, summertime hours when fluxes are large. This research informs model users about which photosynthesis and stomatal resistance parameters should be more carefully selected. Quantifying sensitivities associated with the stomatal resistance term is necessary to better understand transpiration estimates from land surface models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Bioenergy crop models: Descriptions, data requirements and future challenges

Field studies that address the production of lignocellulosic biomass as a potential source of renewable energy are making available critical information for the development, validation, and use of bioenergy crop models. A literature survey revealed that 14 models have been developed and validated for herbaceous and woody bioenergy crops, and for Crassulacean acid metabolism (CAM) crops adapted to arid lands. These models simulate field-scale production of biomass for switchgrass (ALMANAC, EPIC, and Agro-BGC), miscanthus (MISCANFOR, MISCANMOD, and WIMOVAC), sugarcane (APSIM, AUSCANE, and CANEGRO), and poplar and willow (SECRETS and 3PG). Two models are adaptations of dynamic global vegetation models and simulate biomass yields of miscanthus and sugarcane as plant function types at regional scales (Agro-IBIS and LPJmL). A model of biomass production in CAM plants has been developed (EPI), but lacks the sophistication of the other models. Except for CAM plants, all the models include representations of leaf area dynamics, radiation interception and utilization, biomass production, and partitioning of biomass to roots and shoots. A few of the models are capable of simulating soil water, nutrient, and carbon cycle processes, making them especially useful for assessing environmental consequences (e.g., erosion and nutrient losses) associated with the field-scale deployment of bioenergy crops. Similar to other process-based models, simulations are challenged by computing and data management issues and an integrated framework for model testing and inter-comparison is needed. Considerable work remains concerning the development of models for unconventional bioenergy crops like CAM plants, generation and distribution of high-quality field data for model development and validation, and development of an integrated framework for efficient execution of large-scale simulations for use in planning regional to global sustainable bioenergy production systems.

Surendran Nair, Sujith↗
DOE OSTI2016

Green Ocean Amazon 2014/15 Terrestrial Ecosystem Project (Geco). Field Campaign Report

In conjunction with the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility GoAmazon campaign, the Terrestrial Ecosystem Science (TES)-funded Green Ocean Amazon (GoAmazon 2014/15) terrestrial ecosystem project (Geco) was designed to: evaluate the strengths and weaknesses of leaf-level algorithms for biogenic volatile organic compounds (BVOCs) emissions in Amazon forests near Manaus, Brazil, and conduct mechanistic field studies to characterize biochemical and physiological processes governing leaf- and landscape-scale tropical forest BVOC emissions, and the influence of environmental drivers that are expected to change with a warming climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Idealized simulations of wind farm interactions with intermittent turbulence in stable boundary layer conditions

Stable atmospheric boundary layer conditions typically correspond to weak turbulence levels, but intermittent periods of elevated turbulence can occur during otherwise quiescent conditions. The interaction between intermittent turbulence and wind turbines is not well understood because of sparse observations, as well as the difficulty in realistically resolving small-scale turbulence during strongly stable conditions with numerical simulations. In this study, an explicit filtering and reconstruction approach for large-eddy simulation (LES) is used to simulate weakly and strongly stable conditions, with surface cooling rates of −0.2 and −2.0 K h −1 , respectively. This approach can sustain resolved background turbulence at relatively coarse grid spacing and stronger stratification compared to conventional closures, permitting more realistic intermittent stable boundary layer (SBL) turbulence. The idealized LES capability of the Weather Research and Forecasting model is employed with turbine rotors parameterized using generalized actuator disks to examine (1) how the presence of turbine wakes affects SBL evolution and (2) the effect of intermittent turbulence on power production and wake recovery. Wakes increase mixing and deepen the SBL, with a stronger effect under strongly stable conditions, primarily because the SBL is shallower and closer to the top of the wind turbine rotor layer. Intermittent turbulence does not have a significant impact on mean power generation and wake recovery because the relevant intermittent turbulent structures in this study only affect the bottom half of the rotor disk. Power production is, however, more variable during periods of elevated turbulence, demonstrating the impact of SBL intermittency. This study uses an idealized configuration, focusing on LES model performance and physical understanding, with the goal of informing future simulations of the conditions observed during the American Wake Experiment.

Energy - Wind↗
DOE OSTI2012

On parameterization of the inverse problem for estimating aquifer properties using tracer data

We consider a field-scale tracer experiment conducted in 2007 in a shallow uranium-contaminated aquifer at Rifle, Colorado. In developing a reliable approach for inferring hydrological properties at the site through inverse modeling of the tracer data, decisions made on how to parameterize heterogeneity (i.e., how to represent a heterogeneous distribution using a limited number of parameters that are amenable to estimation) are of paramount importance. We present an approach for hydrological inversion of the tracer data and explore, using a 2D synthetic example at first, how parameterization affects the solution, and how additional characterization data could be incorporated to reduce uncertainty. Specifically, we examine sensitivity of the results to the configuration of pilot points used in a geostatistical parameterization, and to the sampling frequency and measurement error of the concentration data. A reliable solution of the inverse problem is found when the pilot point configuration is carefully implemented. In addition, we examine the use of a zonation parameterization, in which the geometry of the geological facies is known (e.g., from geophysical data or core data), to reduce the non-uniqueness of the solution and the number of unknown parameters to be estimated. When zonation information is only available for a limited region, special treatment in the remainder of the model is necessary, such as using a geostatistical parameterization. Finally, inversion of the actual field data is performed using 2D and 3D models, and results are compared with slug test data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period

The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic and yielded an excellent data set to test the parameterizations of ocean/sea-ice/atmosphere interaction processes in regional climate models (RCMs). In the present paper, near-surface data during MOSAiC are used for the verification of the RCM COnsortium for Small-scale MOdel–Climate Limited area Mode (COSMO-CLM or CCLM). CCLM is used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data with around 6 km resolution, sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead fraction data for the winter period. CCLM simulations show a good agreement with the measurements. Relatively large negative biases for temperature occur for November and December, which are likely associated with a too large ice thickness used by CCLM. The consideration of sea ice leads in the sub-grid parameterization in CCLM yields improved results for the near-surface temperature. ERA5 data show a large warm bias of about 2.5°C and an underestimation of the temperature variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

SU-E-T-549: Modeling Relative Biological Effectiveness of Protons for Radiation Induced Brain Necrosis

Purpose: To develop a model of the relative biological effectiveness (RBE) of protons as a function of dose and linear energy transfer (LET) for induction of brain necrosis using clinical data. Methods: In this study, treatment planning information was exported from a clinical treatment planning system (TPS) and used to construct a detailed Monte Carlo model of the patient and the beam delivery system. The physical proton dose and LET were computed in each voxel of the patient volume using Monte Carlo particle transport. A follow-up magnetic resonance imaging (MRI) study registered to the treatment planning CT was used to determine the region of the necrosis in the brain volume. Both, the whole brain and the necrosis volumes were segmented from the computed tomography (CT) dataset using the contours drawn by a physician and the corresponding voxels were binned with respect to dose and LET. The brain necrosis probability was computed as a function of dose and LET by dividing the total volume of all necrosis voxels with a given dose and LET with the corresponding total brain volume resulting in a set of NTCP-like curves (probability as a function of dose parameterized by LET). Results: The resulting model shows dependence on both dose and LET indicating the weakness of the constant RBE model for describing the brain toxicity. To the best of our knowledge the constant RBE model is currently used in all clinical applications which may Result in increased rate of brain toxicities in patients treated with protons. Conclusion: Further studies are needed to develop more accurate brain toxicity models for patients treated with protons and other heavy ions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

RACORO aerosol data processing

The RACORO aerosol data (cloud condensation nuclei (CCN), condensation nuclei (CN) and aerosol size distributions) need further processing to be useful for model evaluation (e.g., GCM droplet nucleation parameterizations) and other investigations. These tasks include: (1) Identification and flagging of 'splash' contaminated Twin Otter aerosol data. (2) Calculation of actual supersaturation (SS) values in the two CCN columns flown on the Twin Otter. (3) Interpolation of CCN spectra from SGP and Twin Otter to 0.2% SS. (4) Process data for spatial variability studies. (5) Provide calculated light scattering from measured aerosol size distributions. Below we first briefly describe the measurements and then describe the results of several data processing tasks that which have been completed, paving the way for the scientific analyses for which the campaign was designed. The end result of this research will be several aerosol data sets which can be used to achieve some of the goals of the RACORO mission including the enhanced understanding of cloud-aerosol interactions and improved cloud simulations in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantifying Uncertainties in Modeling Wind Resource Data from Different PBL Schemes in the WRF Model: A Case Study Over the Puerto Rico Region

This study examines the modeling uncertainty of wind resource data stemming from the use of various planetary boundary layer (PBL) parameterizations available in the Weather Research and Forecasting (WRF) model. WRF-based wind simulations spanning 20 years at 3-km resolution using 11 different PBL schemes are used to objectively investigate the uncertainty in modeling wind speed for land-based wind (LBW) and offshore wind (OSW) locations in Puerto Rico. The uncertainty in the wind modeling for the 20-year dataset is quantified using the spread index (SI) and standard deviation (SD). For virtual LBW and OSW sites, the SI and SD values are analyzed as calculated across various spatial and temporal scales. Because the PBL's atmospheric stability conditions can be characterized into two dominant categories, the study focuses on analyzing the SI and SD for daytime (mainly unstable PBL conditions) and nighttime (mainly stable PBL conditions). For wind shear (10 m-200 m) at the OSW and LBW sites, WRF-based numerical experiments indicate the following SI (or SD) ranges: 39%-94% (0.74 m/s-1.44 m/s) during the daytime for OSW, 50%-75% (0.68 m/s-1.19 m/s) during the daytime for LBW, 37%-60% (0.73 m/s-1.12 m/s) during the nighttime for OSW, and 57%-143 % (0.65 m/s-1.43 m/s) during the nighttime for LBW. While a high SI is observed when modeling LBW during the nighttime, there are notable modeling uncertainties during the daytime on the leeward side of the orographic barriers for Puerto Rico.

17 WIND ENERGY↗
DOE OSTI2025

Inferring Plant Acclimation and Improving Model Generalizability With Differentiable Physics‐Informed Machine Learning of Photosynthesis

Net photosynthesis (A N ) is a key component of the global carbon cycle influencing climate feedback over decadal scales. Although plant acclimation to environmental changes can modify A N , traditional vegetation models in Earth system models (ESMs) often rely on plant functional type (PFT)-specific parameterizations or simplified acclimation assumptions limiting generalizability across time, space, and PFTs. In this study, we developed a differentiable photosynthesis model to learn the environmental dependencies of V c,max25 (maximum carboxylation rate at 25°C, representing photosynthetic capacity), as this genre of hybrid physics-informed machine learning can seamlessly train neural networks and process-based equations together. Compared to PFT-specific parameterization of V c,max25 , learning the environment dependencies of key photosynthetic parameters improved model spatiotemporal generalizability. Applying environmental acclimation to V c,max25 led to substantial variations in global mean A N indicating the need to address acclimation in ESMs. The model effectively captured multivariate observations (V c,max25 , A N , and stomatal conductance (g s )) simultaneously with multivariate constraints, improving generalization across space and PFTs. It also learned sensible acclimation relationships of V c,max25 to different environmental conditions. The model explained more than 54%, 57%, and 62% of the variance of A N , g s , and V c,max25 , respectively, presenting a first global-scale spatial test benchmark of A N and g s . These results highlight the potential for differentiable modeling to enhance process-based modules in ESMs and effectively leverage information from large, multivariate data sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

General F-theory models with tuned (SU(3) × SU(2) × U(1))/ Z 6 symmetry

We construct a general form for an F-theory Weierstrass model over a general base giving a 6D or 4D supergravity theory with gauge group (SU(3) × SU(2) × U(1))/Z 6 and generic associated matter, which includes the matter content of the standard model. The Weierstrass model is identified by unHiggsing a model with U(1) gauge symmetry and charges q ≤ 4 previously found by the first author. This model includes two distinct branches that were identified in earlier work, and includes as a special case the class of models recently studied by Cvetič, Halverson, Lin, Liu, and Tian, for which we demonstrate explicitly the possibility of unification through an SU(5) unHiggsing. We develop a systematic methodology for checking that a parameterized class of F-theory Weierstrass models with a given gauge group G and fixed matter content is generic (contains all allowed moduli) and confirm that this holds for the models constructed here.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Meta-analysis of high-latitude nitrogen-addition and warming studies implies ecological mechanisms overlooked by land models

Accurate representation of ecosystem processes in land models is crucial for reducing predictive uncertainty in energy and greenhouse gas feedbacks with the climate. Here we describe an observational and modeling meta-analysis approach to benchmark land models, and apply the method to the land model CLM4.5 with two versions of belowground biogeochemistry. We focused our analysis on the aboveground and belowground responses to warming and nitrogen addition in high-latitude ecosystems, and identified absent or poorly parameterized mechanisms in CLM4.5. While the two model versions predicted similar soil carbon stock trajectories following both warming and nitrogen addition, other predicted variables (e.g., belowground respiration) differed from observations in both magnitude and direction, indicating that CLM4.5 has inadequate underlying mechanisms for representing high-latitude ecosystems. On the basis of observational synthesis, we attribute the model–observation differences to missing representations of microbial dynamics, aboveground and belowground coupling, and nutrient cycling, and we use the observational meta-analysis to discuss potential approaches to improving the current models. However, we also urge caution concerning the selection of data sets and experiments for meta-analysis. For example, the concentrations of nitrogen applied in the synthesized field experiments (average = 72 kg ha -1 yr -1 ) are many times higher than projected soil nitrogen concentrations (from nitrogen deposition and release during mineralization), which precludes a rigorous evaluation of the model responses to likely nitrogen perturbations. Overall, we demonstrate that elucidating ecological mechanisms via meta-analysis can identify deficiencies in ecosystem models and empirical experiments.

Bouskill, N. J.↗
DOE OSTI2018

Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking

Dynamical downscaling with high-resolution regional climate models may offer the possibility of realistically reproducing precipitation and weather events in climate simulations. As resolutions fall to order kilometers, the use of explicit rather than parametrized convection may offer even greater fidelity. However, these increased resolutions both allow and require increasingly complex diagnostics for evaluating model fidelity. Here we focus on precipitation evaluation and analyze five 2-month-long dynamically downscaled model runs over the continental United States that employ different convective and microphysics parameterizations, including one high-resolution convection-permitting simulation. All model runs use the Weather Research and Forecasting Model driven by National Center for Environmental Prediction reanalysis data. We show that employing a novel rainstorm identification and tracking algorithm that allocates essentially all rainfall to individual precipitation events allows new insights into model biases. Results include that, at least in these runs, model wet bias is driven by excessive areal extent of individual precipitating events, and that the effect is time-dependent, producing excessive diurnal cycle amplitude. This amplified cycle is driven not by new production of events but by excessive daytime enlargement of long-lived precipitation events. Here, we further show that in the domain average, precipitation biases appear best represented as additive offsets. Of all model configurations evaluated, convection-permitting simulations most consistently reduced biases in precipitation event characteristics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Ice Nucleating Particles, Aerosols and Clouds over the Higher Latitude Southern Ocean

Improved understanding of aerosol-cloud-precipitation interactions is a key goal of the DOE-ASR program. Ice Nucleating Particles (INPs) are rare aerosol particles that trigger ice formation in clouds. By doing so they may initiate precipitation in clouds, and this impacts cloud lifetime. Nowhere may the role of INPs be manifested so dramatically as in the Southern Ocean (SO). This vast region was the focus of the DOE-ARM MARCUS (Measurement of Aerosols, Radiation and CloUds over the Southern Oceans) and MICRE (Macquarie Island Cloud and Radiation Experiment) campaigns. In the SO, a deficit of INPs has been a leading hypothesis for why liquid clouds persist, which underlays the bias of global climate models in predicting excess shortwave radiation reaching the ocean surface between 55°S and Antarctica. In this study, we used the 6-month acquisition of INP data on four MARCUS cruises from Hobart, Tasmania, to Antarctica, and the single-location full annual INP cycle observed in MICRE, to gain a holistic, quantitative picture of the INP number concentrations as a function of temperature and their variability over SO latitudes from 43°S to Antarctica. Augmenting initial processing of filter collections of aerosol particles during MARCUS and MICRE, additional freezing studies following thermal treatment (to removes INPs associated with microbes) and peroxide treatment (to remove all organics), as well as ionic chemistry and total organic carbon analysis, were applied to samples to improve time and space resolution, and differentiate marine from terrestrial contributions. We also performed Next Generation Sequencing to determine that bacterial composition can tag periods of enhanced and degraded marine organic INPs and the relative lack of occurrence of land-sourced bio-particles. Through these and other analyses, INP data were categorized into representative source types (e.g., marine versus terrestrially-influenced). The products of our analyses are being used to parameterize ice formation for use in numerical modeling studies to determine if, and when, ocean-derived INPs control the microphysical composition and radiative balance of SO clouds in a manner not presently captured by global models. The specific objectives and approaches proposed were largely accomplished, including: 1) Additional and value-added chemical and biological analyses of archived samples of aerosols were completed to categorize and quantify INP types and concentrations over the SO. 2) Analyses were completed to place INP data in meteorological and aerosol context. 3) Sea spray aerosol INPs were demonstrated to dominate the SO region in all but episodic events. 4) New parameterizations for INP sources relevant to the SO were constructed. 5) Collaborative modeling studies demonstrating the crucial role of INPs in determining cloud radiative and precipitation properties were conducted with MARCUS and MICRE partners. Collaborative publication submissions in this regard ensued within the two year study. This work will advance the science of interactions of aerosols, clouds and precipitation, to improve their representation in regional and global climate models. Results have informed representation of primary ice crystal nucleation, give inference to the role of secondary processes, and improve investigations of aerosol influences on clouds via ice nucleation over SO high latitudes and similar vast ocean regions. Application will improve representation of such interactions for clouds in both regional and global climate models. The data base and analyses methods applied will continue to serve research studies in the future.

58 GEOSCIENCES↗
DOE OSTI2013

Two-moment Bulk Stratiform Cloud Microphysics in the Grid-point Atmospheric Model of IAP LASG (GAMIL)

A two-moment bulk stratiform microphysics scheme, including recently developed physically-based droplet activation/ice nucleation parameterizations has been implemented into the Grid-point Atmospheric Model of IAP LASG (GAMIL) as an effort to enhance the model capability for studying aerosol indirect effects. Unlike the previous one-moment cloud microphysics scheme, the new scheme produces reasonable representation of cloud particle size and number concentration. This scheme captures the observed spatial variations in cloud droplet number concentrations. Simulated ice crystal number concentrations in cirrus clouds qualitatively agree with in-situ observations. The longwave and shortwave cloud forcing are in better agreement with observations. Sensitivity tests show that the column cloud droplet number concentrations calculated from two different droplet activation parameterizations are similar. However, ice crystal number concentration in mixed-phased clouds is sensitive to different heterogeneous freezing formulations. The simulation with high ice crystal number concentration in mixed-phase clouds has less liquid water path and weaker cloud forcing. Furthermore, ice crystal number concentration in cirrus clouds is sensitive to different ice nucleation parameterizations. Sensitivity tests also suggest that impact of pre-existing ice crystals on homogeneous freezing in old clouds should be taken into account.

(GAMIL)↗
DOE OSTI2023

Salinity exposure affects lower-canopy specific leaf area of upland trees in a coastal deciduous forest

Sea level rise and increasing storm surges are likely to affect the canopy physiology, ecology, and structure of coastal forests, even well in advance of tree mortality. Laboratory and greenhouse studies have documented that saltwater exposure can trigger changes in leaf-level physiology and morphology, but few in situ studies have examined how tree-specific leaf area (SLA), the ratio of leaf area to mass and a crucial trait and model parameter, is affected by saline soils. We conducted an observational study of SLA in a mid-Atlantic (USA) coastal deciduous forest, taking advantage of a natural gradient in salinity along a tidal creek. Measured SLA of the 239 trees and seven species sampled ranged from Carya glabra (N = 6 trees, mean SLA = 277.9±36.3cm 2 /g) to Fagus grandifolia (N=60, 321.9±62.9cm 2 g); as expected, trees species and canopy position (sun versus shade) significantly affected SLA. For trees (N=100) directly exposed to the tidal creek, salinity was highly significant after accounting for species (P<0.001), with trees in the lower reaches of the creek having lower SLA. Leaf area index (LAI), computed from SLA and litter traps, ranged from 4.8 to 15.8 and was inversely related to salinity exposure; the spatial variability in leaf litter production contributed much more to LAI uncertainty than did SLA variability. These in situ results are correlative but consistent with the hypothesis, based on previous greenhouse studies, that the stress of chronic salinity exposure changes species’ leaf morphology. Our findings are useful for understanding the growing effects of saltwater intrusion into upland forests, as well as parameterizing and testing ecosystem-scale models simulating forest stressors and disturbances at the terrestrial-aquatic interface.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Application of physiologically-based pharmacokinetic modeling to explore the role of kidney transporters in renal reabsorption of perfluorooctanoic acid in the rat

ABSTRACT: Renal elimination and the resulting clearance of perfluorooctanoic acid (PFOA) from the serum exhibit pronounced sex differences in the adult rat. The literature suggests that this is largely due to hormonally regulated expression of organic anion transporters (OATs) on the apical and basolateral membranes of the proximal tubule cells that facilitate excretion and reabsorption of PFOA from the filtrate into the blood. Previously developed PBPK models of PFOA exposure in the rat have not been parameterized to specifically account for transporter-mediated renal elimination. We developed a PBPK model for PFOA in male and female rats to explore the role of Oat1, Oat3, and Oatp1a1 in sex-specific renal reabsorption and excretion of PFOA. Descriptions of the kinetic behavior of these transporters were extrapolated from in vitro studies and the model was used to simulate time-course serum, liver, and urine data for intravenous (IV) and oral exposures in both sexes. Model predicted concentrations of PFOA in the liver, serum, and urine showed good agreement with experimental data for both male and female rats indicating that in vitro derived physiological descriptions of transporter-mediated renal reabsorption can successfully predict sex-dependent excretion of PFOA in the rat. This study supports the hypothesis that sex-specific serum half-lives for PFOA are largely driven by expression of transporters in the kidney and contribute to the development of PBPK modeling as a tool for evaluating the role of transporters in renal clearance. - Highlights: • The PBPK model for PFOA in the rat explores the role of OATs in sex-specific clearance. • Descriptions of OAT kinetics were extrapolated from in vitro studies. • Model predictions showed good fit with experimental data for male and female rats.

60 APPLIED LIFE SCIENCES↗