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DOE OSTI2019

Simulating 10,000 Years of Erosion to Assess Nuclear Waste Repository Performance

Long-term environmental performance assessments of natural processes, including erosion, are critically important for waste repository site evaluation. Yet, assessing a site’s ability to continuously function is challenging due to parameter uncertainty and compounding nonlinear processes. In lieu of unavailable site data for model calibration, we present a workflow to include multiple sources of surrogate data and reduced-order models to validate parameters for a long-term erosion assessment of a low-level radioactive nuclear waste repository. We apply this new workflow to a low-level waste repository on mesas in Los Alamos National Laboratory in New Mexico. To account for parameter uncertainty, we simulate high-, moderate-, and low-erosion cases. The assessment extends to 10,000 years, which results in large erosion uncertainties, but is necessary given the nature of the interred waste. Our long-term erosion analysis shows that high-erosion scenarios produce rounded mesa tops and partially filled canyons, diverging from the moderate-erosion case that results in gullies and sharp mesa rims. Our novel model parameterization workflow and modeling exercise demonstrates the utility of long-term assessments, identifies sources of erosion forecast uncertainty, and demonstrates the utility of landscape evolution model development. We end with a discussion on methods to reduce assessment uncertainty and increase model confidence.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Improving Representation of Deforestation Effects on Evapotranspiration in the E3SM Land Model

Abstract Evapotranspiration (ET) plays an important role in land‐atmosphere coupling of energy, water, and carbon cycles. Following deforestation, ET is typically observed to decrease substantially as a consequence of decreases in leaf area and roots and increases in runoff. Changes in ET (latent heat flux) revise the surface energy and water budgets, which further affects large‐scale atmospheric dynamics and feeds back positively or negatively to long‐term forest sustainability. In this study, we used observations from a recent synthesis of 29 pairs of adjacent intact and deforested FLUXNET sites to improve model parameterization of stomatal characteristics, photosynthesis, and soil water dynamics in version 1 of the Energy Exascale Earth System Model (E3SM) Land Model (ELMv1). We found that default ELMv1 predicts an increase in ET after deforestation, likely leading to incorrect estimates of the effects of deforestation on land‐atmosphere coupling. The calibrated model accurately represented the FLUXNET observed deforestation effects on ET. Importantly, the search for global optimal parameters converged at values consistent with recent observational syntheses, confirming the reliability of the calibrated physical parameters. Applying this improved model parameterization to the globe scale reduced the bias of annual ET simulation by up to ~600 mm/year. Analysis on the roles of parameters suggested that future model development to improve ET simulation should focus on stomatal resistance and soil water‐related parameterizations. Finally, our predicted differences in seasonal ET changes from deforestation are large enough to substantially affect land‐atmosphere coupling and should be considered in such studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2014

Parametric Sensitivity and Calibration for Kain-Fritsch Convective Parameterization Scheme in the WRF Model

Convective parameterizations used in weather and climate models all display sensitivity to model resolution and variable skill in different climatic regimes. Although parameters in convective schemes can be calibrated using observations to reduce model errors, it is not clear if the optimal parameters calibrated based on regional data can robustly improve model skill across different model resolutions and climatic regimes. In this study, this issue is investigated using a regional modeling framework based on the Weather Research and Forecasting (WRF) model. To quantify the response and sensitivity of model performance to model parameters, we identified five key input parameters and specified their ranges in the Kain-Fritsch (KF) convection scheme in WRF and calibrated them across different spatial resolutions, climatic regimes, and radiation schemes using observed precipitation data. Results show that the optimal values for the five input parameters in the KF scheme are close and model sensitivity and error exhibit similar dependence on the input parameters for all experiments conducted in this study despite differences in the precipitation climatology. We found that the model overall performances in simulating precipitation are more sensitive to the coefficients of downdraft (Pd) and entrainment (Pe) mass flux and starting height of downdraft (Ph). However, we found that rainfall biases, which are probably more related to structural errors, still exist over some regions in the simulation even with the optimal parameters, suggesting further studies are needed to identify the sources of uncertainties and reduce the model biases or structural errors associated with missed or misrepresented physical processes and/or potential problems with the modeling framework.

calibration↗

DOE OSTI2014

On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models

The present study examines the correspondence between short- and long-term systematic errors in five atmospheric models by comparing the 16 five-day hindcast ensembles from the Transpose Atmospheric Model Intercomparison Project II (Transpose-AMIP II) for July–August 2009 (short term) to the climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and AMIP for the June–August mean conditions of the years of 1979–2008 (long term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach. The analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by day 5 in ensemble average hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitudes comparable to the climate errors, and the impacts of initial conditions on the simulated ensemble mean errors are relatively small. This robust bias correspondence suggests that these systematic errors across different models likely are initiated by model parameterizations since the atmospheric large-scale states remain close to observations in the first 2–3 days. However, biases associated with model physics can have impacts on the large-scale states by day 5, such as zonal winds, 2-m temperature, and sea level pressure, and the analysis further indicates a good correspondence between short- and long-term biases for these large-scale states. Therefore, improving individual model parameterizations in the hindcast mode could lead to the improvement of most climate models in simulating their climate mean state and potentially their future projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Toward Rapid Transient Identification and Characterization of Kilonovae

With the increasing sensitivity of advanced gravitational-wave (GW) detectors, the first joint detection of an electromagnetic and GW signal from a compact binary merger will hopefully happen within this decade. However, current GW likelihood sky areas span ∼100--1000 deg{sup 2}, and thus it is a challenging task to identify which, if any, transient corresponds to the GW event. In this study, we make a comparison between recent kilonova/macronova light-curve models for the purpose of assessing potential light-curve templates for counterpart identification. We show that recent analytical and parameterized models for these counterparts result in qualitative agreement with more complicated radiative transfer simulations. Our analysis suggests that with improved light-curve models with smaller uncertainties it will become possible to extract information about ejecta properties and binary parameters directly from the light-curve measurement. Even tighter constraints are obtained in cases for which GW and kilonova parameter estimation results are combined. It will therefore be important to make comparisons and potentially combine parameter estimation with the kilonova and GW results. However, to be prepared for upcoming detections, more realistic kilonova models are needed. These will require numerical relativity with more detailed microphysics, better radiative transfer simulations, and a better understanding of the underlying nuclear physics.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

The response of stomatal conductance to seasonal drought in tropical forests

Stomata regulate CO2 uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (gs) in models vary. In particular, current understanding of drivers of the variation in a key parameter in those models, the slope parameter (i.e. a measure of intrinsic plant water-use-efficiency), is still limited, particularly in the tropics. Here we collected diurnal measurements of leaf gas exchange and water potential (?leaf), and a suite of plant traits from the upper canopy of 15 tropical trees in two contrasting Panamanian forests throughout the dry season of the 2016 El Niño. The plant traits included wood density, leaf-mass-per-area (LMA), leaf carboxylation capacity (Vc,max25), leaf water content, the degree of isohydry, and predawn ?leaf. We first investigated how the choice of four commonly used leaf-level gs models with and without the inclusion of ?leaf as an additional predictor variable influence the ability to predict gs, and then explored the abiotic (i.e. month, site-month interaction) and biotic (i.e. tree-species-specific characteristics) drivers of slope parameter variation. Our results show that the inclusion of ?leaf did not improve model performance and that the models that represent the response of gs to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each gs model, we found large variation in the slope parameter, and this variation was attributable to the biotic driver, rather than abiotic drivers. We further investigated potential relationships between the slope parameter and the six available plant traits mentioned above, and found that only one trait, LMA, had a significant correlation with the slope parameter (R2=0.66, n=15), highlighting a potential path towards improved model parameterization. This study advances understanding of gs dynamics over seasonal drought, and identifies a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forests.

carbon and water exchange, stomatal conductance mo↗

DOE OSTI2020

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model’s rule set. The model’s internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Characterization of Grain‐Size Distribution, Thermal Conductivity, and Gas Diffusivity in Variably Saturated Binary Sand Mixtures

Core Ideas Parameterized models are relevant to applications where different porous media mixtures are used. Models include a grain‐size distribution function to describe bimodal behavior for binary mixtures. Improved model describes observed thermal conductivity–saturation relations for binary mixtures. Combined Buckingham–Penman model used to describe observed gas diffusivity–air content relations. This work is relevant for proper simulation of mixed porous media. Characterization of differently textured porous materials, as well as different volumetric porous media mixtures, in relation to mass and heat transport is vital for many engineering and research applications. Functional relations describing physical (e.g., grain‐size distribution, total porosity), thermal, and gas diffusion properties of porous media and mixtures are necessary to optimize the design of porous systems that involve heat and gas transport processes. However, only a limited number of studies provide characterization of soil physical, thermal, and gas diffusion properties and the functional relationships of these properties under varying soil water contents, especially for soil mixtures, complicating optimization efforts. To better understand how mixing controls the physical, thermal, and gas diffusion properties of porous media, a set of laboratory experiments was performed using five volumetric mixtures of coarse‐ and fine‐grained sand particles. For each mixture, the grain‐size distribution (GSD), thermal conductivity, and gas diffusivity were obtained and parameterized using existing and suggested parametric models. Results show that the extended, two‐region Rosin–Rammler particle‐size distribution model proposed in this study could successfully describe the bimodal behavior of the GSD of binary mixtures. Further, the modified Côté and Konrad thermal conductivity model adequately described the thermal conductivity–water saturation relations observed in different mixtures. The proposed simple soil‐gas diffusivity descriptive model parameterized the upper limit, average, and lower limit behavior in gas diffusivity–air content relations in apparently texture‐invariant gas diffusivity data. Results further show a close analogy between gas diffusivity and thermal conductivity and their variation with saturation across different binary mixtures. Overall, the results of the study provide useful numerical insight into the physical, thermal, and gas transport characteristics of binary mixtures, with wide implications for future engineering and research applications that involve multicomponent porous systems.

Deepagoda, T. K. K. Chamindu↗

DOE OSTI2022

Stabilized Hyperfoam Modeling of the General Plastics EF4003 (3 PCF) Flexible Foam

Constitutive model parameterizations for the General Plastics EF4003 low density 3 pound per cubic foot are needed for design and qualification purposes in normal and abnormal mechanical simulations. The material is expected to be deformed in two ways: first during preloading, and second under impact conditions of the system (transient dynamic). All analyses are to be performed at room temperature. The goal is to provide the analysis community a robust constitutive model parameterization to represent the compression behavior of the EF4003 foam from small deformations up to massive compressive deformations when the foam is densifying. It is worth noting the EF4003 exhibits anisotropy in its stress-strain behavior between the rise and transverse directions (See figure 2.8c-d) as well as plateau behavior that is very likely to cause material stability issues, due to the buckling transition, (and has historically done so) when using Sandia’s current workhorse models for flexible foams, Hyperfoam and Flex Foam. A Stability-informed Hyperfoam parameterization procedure is developed and executed to calibrate a hyperfoam model for the EF4003 room temperature, transversely loaded data. A rise orientation parameterization was not attempted due to localization in the experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Simulation of Hurricane Harvey flood event through coupled hydrologic-hydraulic models: Challenges and next steps

Using the 2017 Hurricane Harvey flood event as a test case, this study set up a series of sensitivity analyses to highlight three challenges associated with large-scale flood inundation modeling, including (a) model parameterization, (b) errors in digital elevation models, and (c) effects of reservoir retention. Driven by radar-based hourly rainfall data, a series of hydrologic-hydraulic models including the VIC hydrologic model, RAPID routing model, and Flood2D-GPU hydrodynamic model are set up over Harris County, Texas, to simulate flood inundation and hazards. The results demonstrate the importance of hydrologic parameters in improving flood modeling. For a large flood event such as Hurricane Harvey, the effect of the initial water depths is insignificant. The Manning's n values may increase the peak water depth by ~1%, the flood extents by 65km 2 , and the high danger zone by ~6%. On the contrary, the bathymetry correction factors may reduce the flood extent by ~1.4% and the high-danger zone by ~4%. Reducing the reservoir storage capacity to 1% may increase the flood extent by ~4% and the high-danger zone by ~17%. This study may provide supporting information to guide and prioritize the development of future high-performance computing hydrodynamic large-scale flood simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Collaborative Research: ARM observations for the development and evaluation of models and parameterizations of cloudy boundary layers

This is a collaborative project with Dr. Ping Zhu at Florida International University. It was designed to address key issues regarding the treatment of boundary layer cloud processes in climate models with UM’s research focusing on the analyses of ARM cloud radar observations from MMCR and WACR and FIU’s research focusing on numerical simulations of boundary layer clouds. This project capitalized on recent advancements in the ARM Millimeter Cloud Radar (MMCR) processing and the development of the WACR (at the SGP) to provide high temporal and spatial resolution Doppler cloud radar measurements for characterizing in-cloud turbulence, large-eddy circulations, and high resolution cloud structures of direct relevance to high resolution numerical modeling studies. The principal focus of the observational component of this collaborative study during this funding period was on stratocumulus clouds over the SGP site and fair-weather cumuli over the Nauru site. The statistical descriptions of the vertical velocity structures in continental stratocumulus clouds and in the Nauru shallow cumuli that are part of this study represents the most comprehensive observations of the vertical velocities in boundary layer clouds to date and were done in collaboration with Drs. Virendra Ghate and Pavlos Kollias.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

A global model–measurement evaluation of particle light scattering coefficients at elevated relative humidity

The uptake of water by atmospheric aerosols has a pronounced effect on particle light scattering properties, which in turn are strongly dependent on the ambient relative humidity (RH). Earth system models need to account for the aerosol water uptake and its influence on light scattering in order to properly capture the overall radiative effects of aerosols. Here we present a comprehensive model–measurement evaluation of the particle light scattering enhancement factor f(RH), defined as the particle light scattering coefficient at elevated RH (here set to 85 %) divided by its dry value. The comparison uses simulations from 10 Earth system models and a global dataset of surface-based in situ measurements. In general, we find a large diversity in the magnitude of predicted f(RH) amongst the different models, which can not be explained by the site types. Based on our evaluation of sea salt scattering enhancement and simulated organic mass fraction, there is a strong indication that differences in the model parameterizations of hygroscopicity and model chemistry are driving at least some of the observed diversity in simulated f(RH). Additionally, a key point is that defining dry conditions is difficult from an observational point of view and, depending on the aerosol, may influence the measured f(RH). The definition of dry also impacts our model evaluation, because several models exhibit significant water uptake between RH = 0 % and 40 %. The multisite average ratio between model outputs and measurements is 1.64 when RH = 0 % is assumed as the model dry RH and 1.16 when RH = 40 % is the model dry RH value. The overestimation by the models is believed to originate from the hygroscopicity parameterizations at the lower RH range which may not implement all phenomena taking place (i.e., not fully dried particles and hysteresis effects). This will be particularly relevant when a location is dominated by a deliquescent aerosol such as sea salt. Our results emphasize the need to consider the measurement conditions in such comparisons and recognize that measurements referred to as dry may not be dry in model terms. Recommendations for future model–measurement evaluation and model improvements are provided.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A library of AI-assisted FAIR water cycle and related disturbance datasets to enable model training, parameterization and validation

This whitepaper is responsive to focal area Data acquisition and assimilation enabled by machine learning, AI, and advanced methods. Here we describe how FAIR (Findable, Accessible, Reusable, Interoperable) datasets related to water cycle extremes are essential for successful implementation of ML in Earth System and other models. We also describe how AI can be used to acquire and integrate water cycle data related to extreme events to create a library of FAIR datasets for training and evaluating algorithms.

58 GEOSCIENCES↗

DOE OSTI2017

Quantitative impact of changes in marrow cellularity, skeletal size, and bone mineral density on active marrow dosimetry based upon a reference model

Purpose The hematopoietically active tissues of skeletal bone marrow are a prime target for computational dosimetry given potential risks of leukemia and, at higher dose levels, acute marrow toxicity. The complex three‐dimensional geometry of trabecular spongiosa, however, complicates schema for dose assessment in such a way that only a few reference skeletal models have been developed to date, and which are based upon microimaging of a limited number of cadaveric bone spongiosa cores. The question then arises as to what degree of accuracy is achievable from reference skeletal dose models when applied to individual patients or specific exposed populations? Methods Patient variability in marrow dosimetry were quantified for three skeletal sites — the ribs, lumbar vertebrae, and cranium — for the beta‐emitters 45 Ca, 153 Sm, and 90 Y, and the alpha‐particle emitters 223 Ra, 219 Rn, and 215 Po, the latter two being the immediate progeny of the former. For each radionuclide and bone site, three patient parameters were altered from their values in the reference model: (1) bone size as a surrogate for patient stature, (2) marrow cellularity as a surrogate for age‐ or disease‐related changes in marrow adiposity, and (3) the trabecular bone volume fraction as a surrogate for bone mineral density. Marrow dose variability is expressed as percent differences in the radionuclide S value given by the reference model and the patient‐parameterized model. The impact of radionuclide biokinetics on marrow dosimetry was not considered. Results Variations in overall bone size play a very minor role in active marrow dose variability. Marrow cellularity is a significant factor in dose variability for active marrow self‐irradiation, but it plays no role for radionuclides localized to the trabecular bone matrix. Variations in trabecular bone volume fractions impact the active marrow dose variability for short‐range particle emitters 45 Ca, 223 Ra, 219 Rn, and 215 Po in the vertebrae and ribs, skeletal sites with small spongiosa proportions of trabecular bone. In the cranium, with its relative high proportion of trabecular bone, significant differences in marrow dosimetry from the reference model were noted for all radionuclides. Conclusions Skeletal models of active marrow dosimetry should be more fully parameterized to permit closer matching to patient bone density and marrow cellularity, particularly when considering short‐range particle emitters localized to either the bone trabeculae or active marrow, respectively.

Geyer, Amy M.↗

DOE OSTI2021

Modeling Noise: Paths toward AI-Enabled Stochastic Earth System Models and Parameterizations

Some of the key challenges in Earth system prediction arise from an uncertain representation of unpredictable natural variability, across time and space scales. This variability, hereafter referred to as noise, represents the quantity against which the strength of a signal of interest is measured to assess predictability. Underestimating or overestimating this noise in Earth system models can lead to issues ranging between overconfidence in an erroneous prediction and a lack thereof in an otherwise accurate one. Noise is part of the physical processes in the Earth system and can amplify or damp a signal of interest in a complex manner that is not systematically characterized. Specifically, while there has been significant progress in our understanding of multiscale interactions among known modes of variability (potentially predictable signals), how these are impacted by the noise is not clear. Recent effort toward stochastic parametrization schemes that provide a representation of noise due to uncertain sub-grid processes in climate models (Berner et al., 2017 and references therein) has shown promise not only in reducing biases and improving probabilistic prediction but also in improving the representation of natural variability in the model toward what is observed. However, today’s standard schemes for stochastic parametrization are still simplistic as they do not take the state of the atmosphere into account (other than by the use of multiplicative noise for model tendencies). Furthermore they require hand-tuning and trial-and-error testing for the magnitude of the added noise, which is difficult in a chaotic system that is as complex as the Earth system.

58 GEOSCIENCES↗

DOE OSTI2016

Proceedings of the 2016 Parameterized Reduced Order Modeling Workshop

The 2016 Parameterized Reduced Order Modeling (PROM) Workshop was held in June, 2016, in Albuquerque, NM. This workshop included 30 researchers who took part in a two day discussion regarding the state of the art for PROMs, complimentary reduced order modeling (ROM) theories, and discussion of the future directions of PROM research. The goals of the workshop were three-fold: to assess the relative accuracy, efficiency, and merits of the different PROM methods; to discuss the state of the art for ROMs and how PROMs can benefit from these advances; and to define the pressing challenges for PROMs and a path for future research collaborations.

42 ENGINEERING↗

DOE OSTI2019

Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model

Abstract. Parameterizations for physical processes in weather and climate models are computationally expensive. We use model output from the Weather Research Forecast (WRF) climate model to train deep neural networks (DNNs) and evaluate whether trained DNNs can provide an accurate alternative to the physics-based parameterizations. Specifically, we develop an emulator using DNNs for a planetary boundary layer (PBL) parameterization in the WRF model. PBL parameterizations are used in atmospheric models to represent the diurnal variation in the formation and collapse of the atmospheric boundary layer – the lowest part of the atmosphere. The dynamics and turbulence, as well as velocity, temperature, and humidity profiles within the boundary layer are all critical for determining many of the physical processes in the atmosphere. PBL parameterizations are used to represent these processes that are usually unresolved in a typical climate model that operates at horizontal spatial scales in the tens of kilometers. We demonstrate that a domain-aware DNN, which takes account of underlying domain structure (e.g., nonlocal mixing), can successfully simulate the vertical profiles within the boundary layer of velocities, temperature, and water vapor over the entire diurnal cycle. Results also show that a single trained DNN from one location can produce predictions of wind speed, temperature, and water vapor profiles over nearby locations with similar terrain conditions with correlations higher than 0.9 when compared with the WRF simulations used as the training dataset.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Multiple soil nutrient competition between plants, microbes, and mineral surfaces: model development, parameterization, and example applications in several tropical forests

Soil is a complex system where biotic (e.g., plant roots, micro-organisms) and abiotic (e.g., mineral surfaces) consumers compete for resources necessary for life (e.g., nitrogen, phosphorus). This competition is ecologically significant, since it regulates the dynamics of soil nutrients and controls aboveground plant productivity. Here we develop, calibrate and test a nutrient competition model that accounts for multiple soil nutrients interacting with multiple biotic and abiotic consumers. As applied here for tropical forests, the Nutrient COMpetition model (N-COM) includes three primary soil nutrients (NH 4 + , NO 3 − and PO x ; representing the sum of PO 4 3− , HPO 4 2− and H 2 PO 4 − ) and five potential competitors (plant roots, decomposing microbes, nitrifiers, denitrifiers and mineral surfaces). The competition is formulated with a quasi-steady-state chemical equilibrium approximation to account for substrate (multiple substrates share one consumer) and consumer (multiple consumers compete for one substrate) effects. N-COM successfully reproduced observed soil heterotrophic respiration, N 2 O emissions, free phosphorus, sorbed phosphorus and NH 4 + pools at a tropical forest site (Tapajos). The overall model uncertainty was moderately well constrained. Our sensitivity analysis revealed that soil nutrient competition was primarily regulated by consumer–substrate affinity rather than environmental factors such as soil temperature or soil moisture. Our results also imply that under strong nutrient limitation, relative competitiveness depends strongly on the competitor functional traits (affinity and nutrient carrier enzyme abundance). We then applied the N-COM model to analyze field nitrogen and phosphorus perturbation experiments in two tropical forest sites (in Hawaii and Puerto Rico) not used in model development or calibration. Under soil inorganic nitrogen and phosphorus elevated conditions, the model accurately replicated the experimentally observed competition among nutrient consumers. Although we used as many observations as we could obtain, more nutrient addition experiments in tropical systems would greatly benefit model testing and calibration. In summary, the N-COM model provides an ecologically consistent representation of nutrient competition appropriate for land BGC models integrated in Earth System Models.

54 ENVIRONMENTAL SCIENCES↗