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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Validation of SAS4A/SASSYS-1 for predicting steady-state single-phase natural circulation

The validation of system analysis codes for nuclear reactor systems is required for the development and application of these computational tools. Designed as a comprehensive system analysis code for advanced nuclear reactors, SAS4A/SASSYS-1 requires validation of its physics model for capturing single-phase natural circulation behavior. To support the validation of SAS4A/SASSYS-1, high-precision experiments are performed capturing steady-state single-phase natural circulation on a scaled facility with comprehensive instrumentation. In this work, dedicated tests are performed quantifying the critical modeling parameters, and a single-phase natural circulation benchmark dataset is obtained with well-documented uncertainty and comprehensive facility description. The validation is then performed against the dataset examining the capability of SAS4A/SASSYS-1 in simulating steady-state single-phase natural circulation. The experimental facility is modeled in the candidate code. Solution verification is performed using Richardson-extrapolation-based estimators which quantify and restrict numerical errors from discretization. Input uncertainty provided by the benchmark dataset is forward propagated through the candidate code, quantifying the output uncertainty in a Monte Carlo approach. The composition of the output uncertainty is also quantified through a variance-based sensitivity analysis. With the uncertainty quantified for each individual condition, a detailed comparison between the simulation results and experimental data is performed covering the whole dataset. The results show consistent agreement for all primary parameters. The current validation activity provides a valuable benchmark dataset for the validation of system analysis codes in capturing single-phase natural circulation and demonstrates satisfactory prediction capability of SAS4A/SASSYS1 for steady-state single-phase natural circulation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Cs-137 Body-burdens in Wild Pigs on the Department of Energy's Savannah River Site: Determination of an Appropriate Framework for Spatiotemporal Analysis of Hunter Dose Tracking System Data

The Savannah River Site (SRS) is a U.S. Department of Energy facility with an area of 310 square miles. Since its opening in 1951, SRS has produced and processed nuclear materials for national defense and industrial purposes. These activities have resulted in the release of radiocesium (Cs-137) which has led to contamination of associated streams, floodplains, man-made reservoirs, soil and vegetation. Contamination is spread to wild pigs as they move about SRS, consume vegetation, drink water and wallow in soil. SRS provides annual deer hunts open to the public in which wild pigs may be taken as well. The Cs-137 concentration of each animal is determined before it is released to the hunter for consumption and a cumulative dose is retained for each individual hunter. These tasks are accomplished using the Hunter Dose Tracking System (HDTS). The data collected on SRS by the HDTS for White-Tailed deer has been extensively analyzed. A repeatable method for calculating the background radiocesium body burden for the SRS deer population was established by Gaines and Novak (2009,2016). This model was framed using Cs-137 contamination obtained via gamma over-flight data, the decay rate of radiocesium and the historical behavior observed in the SRS deer population. These parameters were used in a spatiotemporal analysis to estimate background Cs-137 body burdens (from global fallout, etc.), as well as the effect SRS activities may have. The purpose of this project is to modify the framework to represent the behavior of the wild pig population on SRS such that HDTS data collected may be analyzed similarly. The 2018 data were collected via the HDTS in the field to screen wild pigs dispatched by Forest Service - Savannah River. These data will provide insight for an analysis of seasonal variance in the body burdens of the SRS wild pig population. The HDTS quickly provided Cs-137 concentrations in the field, as well as automatically updated the electronic database. Space: Historically, the average home range of wild pigs on the Savannah River Site has been reported at approximately six square miles. This range was confirmed accurate as of July 2018, with the observed minimum and maximum at one and ten square miles respectively. This range was geoprocessed using ArcMap to project buffers based on 1991 gamma over-flight data. The buffers were constructed to originate at all Cs-137 contamination point coordinates and protrude outward in all directions for 1.5, 2.45 and 6 square miles. While the 2.45 mile buffer is theoretically representative of potential hog exposure based on the average home range, it does not consider the possibility of home ranges not uniform in shape. Therefore, the 1.5 and 6 mile buffers were also constructed as visual aids for determining the appropriate hunt compartments for background level considerations. The SRS is divided into numbered hunt compartments of varying size and shape. These compartment numbers are used during sponsored hunts to record the location in which a wild pig was dispatched. Time: The Gaines report determined deer analysis should be carried out at the half-decade level for the best representation of true body burdens. This is due to the impact natural factors, such as drought or flood, can have on the bioaccumulation of Cs-137. To determine the appropriate time scale for hog analysis, all available data was totaled and averaged at 1, 2, 3 and 7 year increments. The annual averages and running average are provided. The appropriate compartments for the determination of the SRS hog population background level should be: 2, 3, 4, 9, 10, 14 and 15. Hogs dispatched within other compartments are most likely exposed to Cs-137 contamination and should not be included in the background level. The current dataset is small, but was still able to demonstrate the relationship between where hogs on SRS and the Cs-137 body burdens they incur. Geoprocessing the data using the average home range of wild pigs highlighted the importance of collecting additional samples from areas surrounding SRS if an accurate background is to be determined. When the average concentration of Cs-137 in pigs from background compartments is compared against that of pigs from contaminated compartments, a significant difference can be seen. A Table provides a summary of the analyzed data from the 15 years processed thus far.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Using Bayesian Networks for Sensitivity Analysis of Complex Biogeochemical Models

Sensitivity analysis is a vital tool in numerical modeling to identify important parameters and processes that contribute to the overall uncertainty in model outputs. We developed a new sensitivity analysis method to quantify the relative importance of uncertain model processes that contain multiple uncertain parameters. The method is based on the concepts of Bayesian networks (BNs) to account for complex hierarchical uncertainty structure of a model system. We derived a new set of sensitivity indices using the methodology of variance-based global sensitivity analysis with the Bayesian inference. The framework is capable of representing the detailed uncertainty information of a complex model system using BNs and affords flexible grouping of different uncertain inputs given their characteristics and dependency structures. We have implemented the method on a real-world biogeochemical model at the groundwater-surface water interface within the Hanford Site’s 300 Area. The uncertainty sources of the model were first grouped into forcing scenario and three different processes based on our understanding of the complex system. The sensitivity analysis results indicate that both the reactive transport and groundwater flow processes are important sources of uncertainty for carbon-consumption predictions. Within the groundwater flow process, the structure of geological formations is more important than the permeability heterogeneity within a given geological formation. Our new sensitivity analysis framework based on BNs offers substantial flexibility for investigating the importance of combinations of interacting uncertainty sources in a hierarchical order, and it is expected to be applicable to a wide range of multi-physics models for complex systems.

Dai, Heng↗
DOE OSTI2019

Hierarchical sensitivity analysis for simulating barrier island geomorphologic responses to future storms and sea-level rise

This paper presents a new application of an advanced hierarchical sensitivity analysis of a new climate model of barrier island geomorphological evolution. The implemented sensitivity analysis in this study integrates a hierarchical uncertainty framework with a variance-based global sensitivity analysis to decompose the different model input uncertainties. The analysis can provide quantitative and accurate measurements for the relative importance of uncertain model input factors while considering their dependence relationships. The climate model used in this research was the barrier island profile (BIP) model, which is a new computer code developed to simulate barrier island morphological evolution over periods ranging from years to decades under the impacts of accelerated future sea-level rise and long-term changes in the storm climate. In the application of the model, the BIP model was used to evaluate the responses of a series of barrier island cross-sections derived for Santa Rosa Island, Florida, to random storm events and five potential accelerated rates of sea-level rise projected over the next century. The uncertain model input factors thus include the scenario uncertainty caused by alternative future sea-level rise scenarios and the parametric uncertainties of random storm parameters and dune characteristics. The study results reveal that the occurrence of storms is the most important factor for the evolution of sand dunes on the barrier island and the impact of sea-level rise is essential to the morphological change of the island backshore environment. The analysis can provide helpful insights for coastal management and planning. This hierarchical sensitivity analysis is mathematically general and rigorous and can be applied to a wide range of climate models.

Dai, Heng↗
DOE OSTI2022

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Local-scale panmixia in the lichenized fungus Xanthoria parietina contrasts with substantial genetic structure in its Trebouxia photobionts

Microsatellite markers can provide valuable information about gene flow and population history. We developed and tested new microsatellites for the nitrophilic lichenized fungus Xanthoria parietina and studied its genetic diversity and structure within the urban area of Munich, Bavaria. We compared its local genetic pattern with that of its photobiont partner Trebouxia decolorans, for which existing microsatellites were applied. For comparison, a reference site with clean air was included in the sampling. We found support for three genetic clusters in the fungus X. parietina, which occurred intermingled in collecting sites. There was a high degree of admixture within fungal populations and individuals, and analysis of molecular variance revealed a lack of population structure in the mycobiont. The Trebouxia photobiont, in contrast, exhibited structured populations which grouped into two to five genetic clusters, and individuals showed less admixture than in the mycobiont. This indicates that the two lichen partners differ in their ability to move around in the landscape. The microsatellite markers we report are polymorphic and are suitable for population genetic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Modeling of gap conductance for LWR fuel rods applied in the BISON code

A new gap conductance model is proposed in this study as a combination of Toptan’s model and the Ross-Stoute model. A variance-based sensitivity analysis is performed to understand how simulation results depend on all input parameters of the proposed model. Additionally, new modeling options (e.g. fill gas thermal conductivity, temperature jump distance, thermal accommodation coefficient, etc.) are added into the nuclear fuel performance code, BISON. The need for further investigation of the gap heat transfer between fuel and cladding in BISON motivated this study to evaluate its impact on the code’s predictions. New gap conductance modeling is proposed. A series of integral-effects validation tests is performed: (1) to demonstrate the impact of the proposed model on the code’s fuel temperature predictions at the beginning of life and through the reactor’s life; (2) to ensure that the proposed model is capable of accurately modeling gap heat transfer characteristics in real-world problems; and (3) to investigate the impact of the estimation of fission gas release on the fuel temperature predictions with the proposed model. Overall, the results indicate that the proposed gap conductance model improves BISON’s predictions.

42 ENGINEERING↗
DOE OSTI2018

A non-parametric distance-based method using all available indicators for integrated environmental assessment – a case study of the Mid-Atlantic Region, USA

The study presents a multivariate measure useful for integrated environmental assessments. It is a weighted distance measure applied to metric data but based on nonparametric statistical procedures. The proposed measure allows all environmental indicators to be used directly without any reduction in dimension (e.g. as in principal component analysis) nor losing variance while being able to tolerate possible non-normality of the indicators, as well as non-linear relationships among them. Finally, results of the hypothetical example and the Mid-Atlantic case study show that the proposed measure is suitable and valuable for integrating multiple indicators into a single index, an important task in integrated environmental assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Enabling High-Dimensional Hierarchical Uncertainty Quantification by ANOVA and Tensor-Train Decomposition

Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit simulation by employing spectral methods at different levels. This paper presents an efficient framework to simulate hierarchically some challenging stochastic circuits/systems that include high-dimensional subsystems. Due to the high parameter dimensionality, it is challenging to both extract surrogate models at the low level of the design hierarchy and to handle them in the high-level simulation. In this paper, we develop an efficient analysis of variance-based stochastic circuit/microelectromechanical systems simulator to efficiently extract the surrogate models at the low level. In order to avoid the curse of dimensionality, we employ tensor-train decomposition at the high level to construct the basis functions and Gauss quadrature points. As a demonstration, we verify our algorithm on a stochastic oscillator with four MEMS capacitors and 184 random parameters. This challenging example is efficiently simulated by our simulator at the cost of only 10min in MATLAB on a regular personal computer.

ANOVA↗
DOE OSTI2016

Quantifying uncertainty and sensitivity in sea ice models

The Los Alamos Sea Ice model has a number of input parameters for which accurate values are not always well established. We conduct a variance-based sensitivity analysis of hemispheric sea ice properties to 39 input parameters. The method accounts for non-linear and non-additive effects in the model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Pyrolysis Molecular Beam Mass Spectrometry_Analysis_of_Natural_Variants_of_Poplulus_Trichocarpa_Leaves

Select leaves from natural variants of Poplar (Populus Trichocarpa) grown in a greenhouse at Oak Ridge National Laboratory were analyzed by Pyrolysis-Molecular Beam Mass Spectrometry (Py-MBMS). Leaves were harvested, cryomilled and kept frozen until analysis. Py-MBMS analysis was conducted using approximately 4 mg of biomass and each sample was analyzed in duplicate. A Frontier PY2020 unit pyrolyzed samples at 500°C for 30 s in 80 µL deactivated stainless steel cups. An Extrel Super-Sonic MBMS Model Max 1000 was used to collect mass spectral data fromm/z30 to 450 at 17 eV and processed using Merlin Automation software (V3). Spectral ion intensities were normalized to the total ion chromatogram signal for each sample for analysis of spectral variance. Lignin content (wt %) was estimated based on relative responses from standards of known Klason lignin content using mean-normalized ion intensities ofm/z120, 124 (G), 137 (G), 138 (G), 150 (G), 152, 154 (S), 164 (G), 167 (S), 168 (S), 178 (G), 180, 181, 182 (S), 194 (S), 208 (S) and 210 (S) where G indicates guaiacyl-derived ions, S indicates syringyl-derived ions, and other ions either derive from other lignin monomers or multiple sources. Ratios of S and G lignin monomer units (S/G) were obtained by dividing the sum of S-based ions by the sum of G-based ions using mean-normalized ion intensities.

CBI↗
DOE OSTI2026

Pyrolysis_Molecular_Beam_Mass_Spectrometry_Analysis_of_Specific_Switchgrass_Genotypes

Select natural variant switchgrass genotypes grown in Tifton, GA were analyzed by Pyrolysis-Molecular Beam Mass Spectrometry (Py-MBMS). Biomass was harvested, milled, several genotypes were analyzed with and without being destarched and extracted with ethanol prior to analysis (indicated with -DE if destarched and extracted). Py-MBMS analysis was conducted using approximately 4 mg of biomass and each sample was analyzed in duplicate. A Frontier PY2020 unit pyrolyzed samples at 500°C for 30 s in 80 µL deactivated stainless steel cups. An Extrel Super-Sonic MBMS Model Max 1000 was used to collect mass spectral data fromm/z30 to 450 at 17 eV and processed using Merlin Automation software (V3). Spectral ion intensities were normalized to the total ion chromatogram signal for each sample for analysis of spectral variance. Lignin content (wt %) was estimated based on relative responses from standards of known Klason lignin content using mean-normalized ion intensities ofm/z120, 124 (G), 137 (G), 138 (G), 150 (G), 152, 154 (S), 164 (G), 167 (S), 168 (S), 178 (G), 180, 181, 182 (S), 194 (S), 208 (S) and 210 (S) where G indicates guaiacyl-derived ions, S indicates syringyl-derived ions, and other ions either derive from other lignin monomers or multiple sources. Ratios of S and G lignin monomer units (S/G) were obtained by dividing the sum of S-based ions by the sum of G-based ions using mean-normalized ion intensities.

CBI↗
DOE OSTI2026

Pyrolysis_Molecular_Beam_Mass_Spectrometry_Analysis_of_hybrid_cross_of_Populus_tremula_x_P_alba_717-1B4_and_overexpression_of_a_lectin_receptor-like_kinase_(PtLecRLK1)

Stem tissues from the hybrid poplarPopulus tremula × P. albaclone 717-1B4 and from lectin receptor-like kinase overexpression lines PP7 and PP19 were individually colonized with the ectomycorrhizal fungiLaccaria bicolorstrain S238N,Hyaloscypha finlandicastrain PMI746, orUmbelopsis vinaceastrain PMI3018, as well as with a mixed fungal inoculum; non-inoculated plants served as controls. Plants were grown in a greenhouse at Oak Ridge National Laboratory and harvested in January 2025. Stem samples were analyzed using Pyrolysis–Molecular Beam Mass Spectrometry (Py-MBMS). Stems were harvested, debarked, dried, milled, destarched and ethanol extracted prior to analysis. Py-MBMS analysis was conducted using approximately 4 mg of wood from biomass and each sample was analyzed in duplicate. A Frontier PY2020 unit pyrolyzed samples at 500°C for 30 s in 80 µL deactivated stainless steel cups. An Extrel Super-Sonic MBMS Model Max 1000 was used to collect mass spectral data fromm/z30 to 450 at 17 eV and processed using Merlin Automation software (V3). Spectral ion intensities were normalized to the total ion chromatogram signal for each sample for analysis of spectral variance. Lignin content (wt %) was estimated based on relative responses from standards of known Klason lignin content using mean-normalized ion intensities ofm/z120, 124 (G), 137 (G), 138 (G), 150 (G), 152, 154 (S), 164 (G), 167 (S), 168 (S), 178 (G), 180, 181, 182 (S), 194 (S), 208 (S) and 210 (S) where G indicates guaiacyl-derived ions, S indicates syringyl-derived ions, and other ions either derive from other lignin monomers or multiple sources. Ratios of S and G lignin monomer units (S/G) were obtained by dividing the sum of S-based ions by the sum of G-based ions using mean-normalized ion intensities.

CBI↗
DOE OSTI2023

Fusing time-varying mosquito data and continuous mosquito population dynamics models

Climate change is arguably one of the most pressing issues affecting the world today and requires the fusion of disparate data streams to accurately model its impacts. Mosquito populations respond to temperature and precipitation in a nonlinear way, making predicting climate impacts on mosquito-borne diseases an ongoing challenge. Data-driven approaches for accurately modeling mosquito populations are needed for predicting mosquito-borne disease risk under climate change scenarios. Many current models for disease transmission are continuous and autonomous, while mosquito data is discrete and varies both within and between seasons. This study uses an optimization framework to fit a non-autonomous logistic model with periodic net growth rate and carrying capacity parameters for 15 years of daily mosquito time-series data from the Greater Toronto Area of Canada. The resulting parameters accurately capture the inter-annual and intra-seasonal variability of mosquito populations within a single geographic region, and a variance-based sensitivity analysis highlights the influence each parameter has on the peak magnitude and timing of the mosquito season. This method can easily extend to other geographic regions and be integrated into a larger disease transmission model. This method addresses the ongoing challenges of data and model fusion by serving as a link between discrete time-series data and continuous differential equations for mosquito-borne epidemiology models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Measuring the β-decay Properties of Neutron-rich Exotic Pm, Sm, Eu, and Gd Isotopes to Constrain the Nucleosynthesis Yields in the Rare-earth Region

Abstract The β -delayed neutron-emission probabilities of 28 exotic neutron-rich isotopes of Pm, Sm, Eu, and Gd were measured for the first time at RIKEN Nishina Center using the Advanced Implantation Detector Array (AIDA) and the BRIKEN neutron detector array. The existing β -decay half-life ( T 1/2 ) database was significantly increased toward more neutron-rich isotopes, and uncertainties for previously measured values were decreased. The new data not only constrain the theoretical predictions of half-lives and β -delayed neutron-emission probabilities, but also allow for probing the mechanisms of formation of the high-mass wing of the rare-earth peak located at A ≈ 160 in the r -process abundance distribution through astrophysical reaction network calculations. An uncertainty quantification of the calculated abundance patterns with the new data shows a reduction of the uncertainty in the rare-earth peak region. The newly introduced variance-based sensitivity analysis method offers valuable insight into the influence of important nuclear physics inputs on the calculated abundance patterns. The analysis has identified the half-lives of 168 Sm and of several gadolinium isotopes as some of the key variables among the current experimental data to understand the remaining abundance uncertainty at A = 167–172.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Unifying soil organic matter formation and persistence frameworks: the MEMS model

Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical models have traditionally been simulated as immeasurable fluxes between conceptually defined pools. This greatly limits how empirical data can be used to improve model performance and reduce the uncertainty associated with their predictions of carbon (C) cycling. Recent advances in our understanding of the biogeochemical processes that govern SOM formation and persistence demand a new mathematical model with a structure built around key mechanisms and biogeochemically relevant pools. Here, we present one approach that aims to address this need. Our new model (MEMS v1.0) is developed from the Microbial Efficiency-Matrix Stabilization framework, which emphasizes the importance of linking the chemistry of organic matter inputs with efficiency of microbial processing and ultimately with the soil mineral matrix, when studying SOM formation and stabilization. Building on this framework, MEMS v1.0 is also capable of simulating the concept of C saturation and represents decomposition processes and mechanisms of physico-chemical stabilization to define SOM formation into four primary fractions. After describing the model in detail, we optimize four key parameters identified through a variance-based sensitivity analysis. Optimization employed soil fractionation data from 154 sites with diverse environmental conditions, directly equating mineral-associated organic matter and particulate organic matter fractions with corresponding model pools. Finally, model performance was evaluated using total topsoil (0–20 cm) C data from 8192 forest and grassland sites across Europe. Despite the relative simplicity of the model, it was able to accurately capture general trends in soil C stocks across extensive gradients of temperature, precipitation, annual C inputs and soil texture. The novel approach that MEMS v1.0 takes to simulate SOM dynamics has the potential to improve our forecasts of how soils respond to management and environmental perturbation. Ensuring these forecasts are accurate is key to effectively informing policy that can address the sustainability of ecosystem services and help mitigate climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Representing methane emissions from wet tropical forest soils using microbial functional groups constrained by soil diffusivity

Tropical ecosystems contribute significantly to global emissions of methane (CH 4 ), and landscape topography influences the rate of CH 4 emissions from wet tropical forest soils. However, extreme events such as drought can alter normal topographic patterns of emissions. Here we explain the dynamics of CH 4 emissions during normal and drought conditions across a catena in the Luquillo Experimental Forest, Puerto Rico. Valley soils served as the major source of CH 4 emissions in a normal precipitation year (2016), but drought recovery in 2015 resulted in dramatic pulses in CH 4 emissions from all topographic positions. Geochemical parameters including (i) dissolved organic carbon (C), acetate, and soil pH and (ii) hydrological parameters like soil moisture and oxygen (O 2 ) concentrations varied across the catena. During the drought, soil moisture decreased in the slope and ridge, and O 2 concentrations increased in the valley. We simulated the dynamics of CH 4 emissions with the Microbial Model for Methane Dynamics-Dual Arrhenius and Michaelis–Menten (M3D-DAMM), which couples a microbial functional group CH 4 model with a diffusivity module for solute and gas transport within soil microsites. Contrasting patterns of soil moisture, O 2 , acetate, and associated changes in soil pH with topography regulated simulated CH 4 emissions, but emissions were also altered by rate-limited diffusion in soil microsites. Changes in simulated available substrate for CH 4 production (acetate, CO 2 , and H 2 ) and oxidation (O 2 and CH 4 ) increased the predicted biomass of methanotrophs during the drought event and methanogens during drought recovery, which in turn affected net emissions of CH 4 . A variance-based sensitivity analysis suggested that parameters related to aceticlastic methanogenesis and methanotrophy were most critical to simulate net CH 4 emissions. This study enhanced the predictive capability for CH 4 emissions associated with complex topography and drought in wet tropical forest soils.

54 ENVIRONMENTAL SCIENCES↗