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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Derivation and verification of the direct-sampling method for simulating Monte Carlo flight paths in tetrahedral meshes with linear finite-element cross sections

This paper provides a derivation of a direct-sampling approach for modeling continuously varying cross sections in tetrahedral-mesh-based Monte Carlo codes. Specifically, cross sections are spatially approximated using linear nodal finite elements. A linearization strategy is provided for non-linearly varying cross sections. The method is verified against seven analytical pure-absorber test problems. These test problems also highlight the benefit of using linear finite elements over element-wise-constant cross sections.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Sampling Rare Events in Aqueous Systems Using Molecular Simulations

Birth of a new distinct phase is a phenomenon encountered in a myriad of processes, and has wide ranging consequences in material processing, biological self-assembly, separations and several other processes. Several phase transitions are nucleation driven. The nucleation events occur over nanosecond timescales and involve hundreds to thousands of molecules. These length and timescales are difficult to access in experiments, thereby making experimental studies of nucleation challenging. On the other hand, molecular simulations sample the nanosecond and nanometer scales making them ideal to study nucleation. However, nucleation is a rare event, meaning that the waiting time to observe one nucleation event is significant. This makes simulation studies of rare events challenging. The project focused on a multi-pronged approach to address such challenges to develop the next generation rare event sampling methods for molecular simulations. The key outcomes of our work include developing more effective methods for sampling rare events, utilizing machine learning to better elucidate nucleation mechanisms, development of software for easy implementation of the methodologies, and applications of the methods to realistic systems to push the method applicability beyond model systems. Overall, this work has enabled pushing the frontiers of molecular simulations to study rare events with a focus on nucleation in aqueous solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Compressed sensing methods with applications to advanced air sampling

Environmental sampling methods developed by the Savannah River National Laboratory (SRNL) employ collectors with sorbent media tubes set at various locations to collect airborne emissions. Laboratory analyses of these tubes results in one-dimensional signals regarding what chemicals are being released and transported within the atmosphere. The analysis process is time consuming especially when analyzing a full year’s worth of tubes (hourly sample collection results in nearly 9,000 tubes per year). Using a signal processing method such as compressed sensing allows for recreation of the full signal while greatly reducing the number of analyzed samples required. Due to the sparsity of data retrieved from the air tubes, it is possible to use measurements a fraction of the size of the original data to gain much of the same information. This would improve the overall time and cost of analysis when modeling one-dimensional sampling signals.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Compressed Sensing Methods with Applications to Advanced Air Sampling [Poster]

Environmental sampling methods developed by the Savannah River National Laboratory (SRNL) employ collectors with sorbent media tubes set at various locations to collect airborne emissions. Laboratory analyses of these tubes results in one-dimensional signals regarding what chemicals are being released and transported within the atmosphere. The analysis process is time consuming especially when analyzing a full year’s worth of tubes (hourly sample collection results in nearly 9,000 tubes per year). Using a signal processing method such as compressed sensing allows for recreation of the full signal while greatly reducing the number of analyzed samples required. Due to the sparsity of data retrieved from the air tubes, it is possible to use measurements a fraction of the size of the original data to gain much of the same information. This would improve the overall time and cost of analysis when modeling one-dimensional sampling signals.

Campbell, Cassidy [Savannah River National Laborat↗
DOE OSTI2024

Using low volume eDNA methods to sample pelagic marine animal assemblages

Environmental DNA (eDNA) is an increasingly useful method for detecting pelagic animals in the ocean but typically requires large water volumes to sample diverse assemblages. Ship-based pelagic sampling programs that could implement eDNA methods generally have restrictive water budgets. Studies that quantify how eDNA methods perform on low water volumes in the ocean are limited, especially in deep-sea habitats with low animal biomass and poorly described species assemblages. Using 12S rRNA and COI gene primers, we quantified assemblages comprised of micronekton, coastal forage fishes, and zooplankton from low volume eDNA seawater samples (n = 436, 380–1800 mL) collected at depths of 0–2200 m in the southern California Current. We compared diversity in eDNA samples to concurrently collected pelagic trawl samples (n = 27), detecting a higher diversity of vertebrate and invertebrate groups in the eDNA samples. Differences in assemblage composition could be explained by variability in size-selectivity among methods and DNA primer suitability across taxonomic groups. The number of reads and amplicon sequences variants (ASVs) did not vary substantially among shallow (<200 m) and deep samples (>600 m), but the proportion of invertebrate ASVs that could be assigned a species-level identification decreased with sampling depth. Using hierarchical clustering, we resolved horizontal and vertical variability in marine animal assemblages from samples characterized by a relatively low diversity of ecologically important species. Low volume eDNA samples will quantify greater taxonomic diversity as reference libraries, especially for deep-dwelling invertebrate species, continue to expand.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Pump House in East River Watershed, Colorado 2019-2024

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Pump House at Mount Crested Butte in the East River Watershed. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format ER-X-Y, where ER refers to East River, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, ER-PHS, ER-LMC, ER-LMF, and ER-SMN are associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and ER-RBTn (upslope n=1) are sampling transects during the 2019 Rootball Campaign. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Trail Creek in Taylor River Watershed, Colorado 2024-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors near Trail Creek. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format TR-X-Y, where TR refers to Trail Creek, X is the treatment block identifier, and Y is the location identifier. Specifically, TR-ASCC1 is the control treatment block under the Adaptive Silviculture for Climate Change (ASCC) project, and TR-ASCC2 is the clear-cut treatment block. TR-ASCC-EHSn is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and TR-ASCC-ERTn (upslope n=1) are ecohydrology sites along the electrical resistivity tomography transects. The sample and location information can be found in metadata.csv, and the data from the soil sensors will be included in a future data version when the observation period becomes sufficiently long for data analysis. Sampling and Measurements Each sample falls into one of the two sampling methods – (1) intact cores or (2) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. The intact cores were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

2022 Central California Travel Study

# 2022 Central California Travel Study The 2022 Central California Travel Study collected demographic and travel pattern information to better understand household travel behavior in the state’s San Joaquin Valley region. ## Data Collection Agency The Fresno Council of Governments conducted the household travel study in collaboration with metropolitan planning organizations in Fresno, Kern, Kings, Madera, Merced, San Joaquin, Stanislaus, and Tulare counties in California. ## Survey Methodology The two-part study consisted of a survey and a travel diary. The survey gathered data on household demographic composition and typical travel behaviors. The travel diary gathered individual travel data during a specified travel period for all members of a given household. ## Travel Diary Methods Households with smartphones were encouraged to complete their travel diaries using the rMove smartphone app for up to seven consecutive days. Households without smartphones or those unwilling to participate via smartphone completed their travel diaries online (using the web-version of rMove) or by calling the survey call center. These households reported travel for one day (Tuesday, Wednesday, or Thursday). ## Recruitment Sampling Methods The majority of the recruitment was conducted via address-based sampling, a type of probability sampling, with a focus on reaching county-level targets in collaboration with metropolitan planning organizations in the region. Supplemental sampling methods, primarily non-probability, were employed during all waves of data collection to improve survey representation. The supplemental sample included targeted outreach to hard-to-survey populations via transit rider email lists; local housing authorities; Nichols Research, a California-based market research firm; and the Ipsos™ KnowledgePanel, an online random probability panel. See the documentation for more information about sampling and weighting. ## Survey Records, Data, and Documentation In total, 7,406 households completed 19,084 surveys representing 150,012 trips across 42,567 person-days.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗