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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Impacts of Pasture Conversion to Sugarcane on Water Fluxes and Water Use Efficiency in the Southeastern US

The expansion of sugarcane (cane), a high-yielding perennial crop, will likely reshape the bioenergy landscape in the Southeastern US. However, its ecohydrological implications, particularly following conversion from grazed pastures, a dominant land use in the region, remain highly uncertain. We investigated the impact of cane expansion on evapotranspiration (ET) and its partitioning, and the mechanisms influencing both ET components and water use efficiency (WUE) across multiple scales and growth cycles in subtropical Florida. We combined eddy covariance, biometric measurements, and process-based stomatal conductance (g s ) models. ET was 1.7% lower in cane than in improved pasture (IMP) but exceeded that in semi-native pasture (SN) by 21%. Transpiration (T) followed a similar pattern, consistent with lower g s in cane relative to IMP. Cane had more conservative water use and greater sensitivity of g s to vapor pressure deficit (VPD) compared to IMP pasture, suggesting cane may be more tolerant of increasing atmospheric water demand. In contrast, SN showed lower g s and weaker stomatal sensitivity to VPD compared to cane, resulting in lower T. In cane, stomatal regulation and T varied across growth cycles, with stomata becoming less water conservative as stands matured, highlighting the importance of incorporating stand age-dependent stomatal regulation into hydrological models. Evaporation (E) was higher in cane than pastures (19%–26%), partially offsetting WUE gains. Cane exhibited higher intrinsic WUE (GPP/g s ; Gross Primary Productivity), ecosystem WUE (GPP/ET), and harvest WUE (harvest/ET) than both pasture types. Large-scale pasture-to-cane conversion could produce widely contrasting hydrological outcomes. The net regional impact will depend on the proportion of each pasture type converted and on cane's high g s sensitivity to VPD, which triggers tight stomatal regulation and conservative water use, both of which will become increasingly consequential under intensifying atmospheric water demand.

bioenergy↗
DOE OSTI2026

Raw soil carbon dioxide, moisture, temperature and micrometeorological data in the East River Watershed, Colorado June 2021-June 2024. (DE-SC0021139)

This dataset contains raw data from four tripod stations along an elevation gradient on Snodgrass Mountain in the East River Watershed, CO, USA. Each station contains a datalogger connected to 3 soil Carbon Dioxide CO2 gas probes, 3 soil temperature/moisture sensors and a micrometeorological station. Sensors are scanned every minute, and the 30 minute average is reported. The file snodgrass_soil_ESS.csv contains raw data, a row of column descriptors, and units of measurements. some data processing and QA/QC was done to filter out data from sensors that went bad and extreme outliers. CO2 sensors that went bad were replaced with new sensors as soon as possible. This research was performed to investigate the ecohydrological linkages of belowground carbon processes in the East River watershed forested communities to better understand how these ecosystems will respond to a changing cold-season moisture input. This is the second version of this data set and was modified on 10/01/2024. The primary change in the data was the addition of data from the fall of 2022 to June of 2024. In addition, minor QA/QC was done to filter out data from sensors that went bad and extreme outliers. THe filtered data are now NA's in this data frame and primarily the CO2 sensors. Limited to no QA/QC has been done on the other environmental data. This is now the third version of the data set, and was modified 03/25/2026. The primary change in the data was the addition of data from the June of 2024 to December 2025. Further r QA/QC was done with the new data to filter out bad data from faulty sensors and extreme outliers. The filtered data are now NA's in this data frame and primarily the CO2 sensors. Limited to no QA/QC has been done on the other environmental data. ##This additional data was funded under DE-SC0024218( Responses of Plant and Microbial Respiration Sources to Changing Cold Season Climate Drivers in the East River Watershed)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CO2 and CH4 leaf-level fluxes and soil porewater concentrations from common vegetation patches in Louisiana’s coastal wetlands

This dataset contains leaf-level flux and soil porewater concentration measurements of carbon dioxide (CO2) and methane (CH4 ) in plots in the footprint of Ameriflux sites US-LA2 and US-LA3. Leaf fluxes in US-LA2 were measured on patches dominated by Sagittaria lancifolia and co-dominated by Sagittaria lancifolia and Typha latifolia. In US-LA3, fluxes were measured from distinct Juncus roemerianus and Spartina alterniflora patches. The porewater concentrations were collected across a vertical profile (~50 cm depth) at centric locations within 25 m2 plots where we measured the leaf fluxes. US-LA3 included an additional set of measurements in open water spots. We aimed to evaluate differences in leaf fluxes and porewater pools of CO2 and CH4 of representative ecohydrological patches across a salinity gradient. We also used this dataset to help develop ELM-Wet, a more realistic representation of wetland carbon biogeochemical processes within the U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) Land Model version 1 (ELM v.1). The files can be opened with regular text editors or spreadsheet programs. Version 2.0 (8/26/2025): This is the latest version of this dataset. The update includes additional samples of soil porewater CH4/CO2 concentrations from June-2021 to November-2022, as well as minor adjustments made to V1 samples via changing Henry's solubility to account for porewater salinity. Additionally leaf-level measurments of spectral indices, PSRI, NDVI, and PRI have been added to complement Leaf-level flux measurements. All V1 data sets have been integrated into V2 sheets, ensuring data from the previous version is contained with the additional samples and consistent with V2 metadata.

54 ENVIRONMENTAL 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 OSTI2024

Functional-type modeling approach and data-driven parameterization of methane emissions in wetlands (Final Technical Science Report)

Our goals are to improve understanding and quantitative representation of the multiple processes that affect methane emissions at a high (patch level, vertically detailed) spatial resolution, and translate this understanding to improved modeling capability of coastal wetland fluxes using the E3SM Land Model (ELM v1) wetland CH4 biogeochemistry module. We propose an experimental approach to identify and parameterize uncertainties in ELM. Understanding of methane emissions can be improved along three conceptual axes: (i) horizontal (ecohydrological patch resolution), (ii) vertical (through the depth of the soil column), and (iii) process level (e.g., resolving microbial pathways, vegetation specific transport pathways). Along each of the three axes, we will characterize, quantify, and model, the key ecological, hydrological, and meteorological controls of methane (CH4) flux heterogeneity in four model coastal wetlands.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Chapter 14: 100 Years of Progress in Hydrology

The focus of this chapter is progress in hydrology for the last 100 years. During this period, we have seen a marked transition from practical engineering hydrology to fundamental developments in hydrologic science, including contributions to Earth system science. The first three sections in this chapter review advances in theory, observations, and hydrologic prediction. Building on this foundation, the growth of global hydrology, land-atmosphere interactions and coupling, ecohydrology, and water management are discussed, as well as a brief summary of emerging challenges and future directions. Although the review attempts to be comprehensive, the chapter offers greater coverage on surface hydrology and hydrometeorology for readers of this American Meteorological Society (AMS) Monograph.

Peters-Lidard, Christa D.↗
NASA NTRS2021

Managing wetlands to solve the water crisis in the Katuma River ecosystem, Tanzania

The formerly perennial Katuma River in western Tanzania starts in a protected forest, it then flows through irrigated rice farms before reaching Lake Katavi, a floodplain wetland whose outflow regulates the river flow through the Katavi National Park (KNP) down to its outlet at Lake Rukwa, which has no outlet. In recent years, due to overexploitation of water for irrigation, the Katuma River dried out for up to four months per year and this greatly degraded the KNP ecosystem, the siltation of river lead to flooding of the adjacent areas during heavy rains, and the water level of Lake Rukwa has decreased by 4 m since 1992 while its fishery yield and water quality also deteriorated. In 2016, a total of 46 illegal weirs were removed from the Katuma River upstream of KNP. Following that, the river zero-flow periods were reduced by two months and Lake Rukwa water level rose by 1 m. We suggest that the construction of a low V-notch weir at the outlet of the Lake Katavi wetlands would further reduce the Katuma River zero-flow periods by an additional month, thus returning the river nearly to its former perennial status. The enforcement of regulations governing the construction of irrigation weirs is essential. These ecohydrology solutions do not eliminate the threats, but they amplify the opportunities for sustainable development at the basin scale. This example of active governance of water resources at the basin scale can be applied throughout Tanzania and in semi-arid East Africa in general.

Satellite altimetry↗
NASA NTRS2021

Recovery: Fast and Slow—Vegetation Response During the 2012–2016 California Drought

The 2012–2016 California Drought severely impacted natural vegetation across a wide range of environmental gradient. Although several studies have reported an increase in plant water stress and mortality, the spatiotemporal variations of ecosystem productivity responses and the associated environmental and biological drivers remain unclear. Here, using Enhanced Vegetation Index from the Moderate resolution imaging spectrometer, we found that 45% of the natural ecosystems showed an abrupt change (breakpoint [BP]) in productivity during 2012–2016. There were three major contrasting temporal patterns of productivity responses: (i) a steady increase under higher temperature followed by a decline due to accumulated moisture depletion (high elevation forest) or temperature decrease (high elevation nonforest), (ii) gradual decline during the drought followed by a rapid recovery within 1 year after drought stress was partially relieved, and (iii) both a gradual decline and an abrupt decline. The magnitude of abrupt changes was negatively correlated (r = −0.80, p < 0.001) with initial gradual changes. Overall, changes during BP offset, on average, 57% of the preceding gradual responses. The spatial variability in ecosystem response patterns is driven by both environmental and biological factors. Particularly, for forests, positive BP was driven by increasing rainfall and decreasing temperature, while negative BP was mainly driven by the precipitation anomaly. By 2019, 33% of the natural vegetation have recovered to the level of EVI in 2010. Ecosystem responses to multiyear droughts can influence ecosystem dynamics in a complex pattern. Multiple ecohydrological factors should be considered to understand and predict the long-term drought impacts on ecosystems.

Xi Yang↗
NASA NTRSDate not supplied

Overview of the NASA Earth Action Strategies Wildland Fire Initiative

As part of NASA’s new Earth Action strategy, the Wildland Fire initiative was established, which includes both the NASA Wildland Fire Program (WFP) and the FireSense project. NASA has over 50 years of experience generating data and technology to enhance fire science and operational management. The WFP’s mission is threefold: 1) assemble communities of practice through collaborative efforts with government, academia, and the private sector; 2) co-develop knowledge and applications with relevant partners and stakeholders in the wildfire community; and 3) improve wildland fire management through the transitioning of NASA data, technology, tools, and science to stakeholder organizations. The WFP is focusing on supporting proactive fire management, including situational awareness, preparedness, and risk mitigation. This will be accomplished through selected projects that identify management challenges, relevant to partners and end users, and the NASA data that will be utilized to deliver innovative solutions to enhance the management of wildland fires. Examples include: i) investigation of evaporative stress from OpenET to help predict the risk of wildfire occurrence in watersheds; ii) incorporation of space based LiDAR for the generation of 3-dimensional forest fuel metrics, used to improve wildfire risk and behavior models; iii) integration of global, multi-platform geostationary active-fire data in near-real-time into NASA’s Fire Information for Resource Management System (FIRMS); and iv) identification of post-fire ecohydrological conditions using thermal, multispectral, synthetic aperture radar (SAR), and hyperspectral remotely-sensed data to improve flood hazard forecast models. The FireSense project is a US-focused 5-year project that will focus on delivering NASA’s unique Earth science and technological capabilities to operational agencies, striving towards enhancing fire fighting and air quality management. The project will include airborne campaigns and new technology that will likely have global implications. Initial stakeholder engagement led FireSense to focus on four use-cases focused on the characterization and measurement of: (i) pre-fire fuels conditions, (ii) active fire-dynamics; (iii) post-fire impact and threats; and iv) air quality impacts and forecasting, each-developed with identified stakeholders.

Wildland Fire program↗
DOE OSTI2025

Stemflow Hydrodynamics

Stemflow hydrodynamics is the study of water movement along the exterior surface area of plants. Its primary goal is to describe water velocity and water depth along the stem surface area. Its significance in enriching the rhizosphere with water and nutrients is not in dispute. Yet, the hydrodynamics of stemflow have been entirely overlooked. This review seeks to fill this knowledge gap by drawing from thin film theories to seek outcomes at the tree scale. The depth‐averaged conservation equations of water and solute mass are derived at a point. These equations are then supplemented with the conservation of momentum that is required to describe water velocities or relations between water velocities and water depth. Relevant forces pertinent to momentum conservation are covered and include body forces (gravitational effects), surface forces (wall friction), line forces (surface tension), and inertial effects. The inclusion of surface tension opens new vistas into the richness and complexity of stemflow hydrodynamics. Flow instabilities such as fingering, pinching of water columns into droplets, accumulation of water within fissures due to surface tension and their sudden release are prime examples that link observed spatial patterns of stemflow fronts and morphological characteristics of the bark. Aggregating these effects at the tree‐ and storm‐ scales are featured using published experiments. The review discusses outstanding challenges pertaining to stemflow hydrodynamics, the use of dynamic similarity and 3D printing to enable the interplay between field studies and controlled laboratory experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Sap Velocity Data for Urban Trees in Chicago, Illinois (2024-2025)

This dataset contains uncorrected sap velocity measurements using the heat ratio method (HRM) collected using ICT International SFM1x sensors at five urban sites in Chicago, Illinois, as part of the DOE CROCUS project. The data includes continuous monitoring of sap velocity from various tree species, including Maples (Acer spp.): Sugar Maple (Acer saccharum), Silver Maple (Acer saccharinum), and Red Maple (Acer rubrum); Oaks (Quercus spp.): Swamp White Oak (Quercus bicolor); American Elm (Ulmus americana); Honey Locust (Gleditsia triacanthos); Cottonwood (Populus deltoides); and Tree of Heaven (Ailanthus altissima) across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), and West Woodlawn "Blacks in Green" (BIG). These include both street trees and those in urban park locations. Measurements were collected at 15-20 minute intervals, depending on the sensor, and transmitted via Long Range Wide Area Network (LoRaWAN) protocols. The wireless data was collected by Sage Network (https://sagecontinuum.org/) nodes. The dataset includes sensor ID, Global Positioning System (GPS) coordinates, tree species (common and scientific names), tree identification number, diameter at breast height (DBH in cm), uncorrected sap velocity measurements (cm/hr) from both inner and outer probes, and Sage Node identifiers so the data can be mapped to related variables such as air quality and wind speed that were collected on the Sage nodes. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 3-bit binary system indicating physical range violations (< -10 or > 60 cm/hr), step spikes (absolute difference > 36 cm/hr), and stuck sensor conditions (> 10 consecutive identical values). These are raw data, not corrected for wood anatomy or species-specific characteristics. Data is provided in comma separated (CSV) format. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Multi-Function Research LoRaWAN (MFR) Nodes. DOIs for the supporting data are provided as part of this data package.

Chicago↗
DOE OSTI2026

A network of soil moisture, soil temperature, air temperature, net radiation, ground heat flux and ground water for Chicago, Illinois

This dataset contains environmental monitoring data collected using solar-powered Multi-Function Research (MFR) Long Range Wide Area (LoRaWAN)-enabled nodes at 11 sites in Chicago, Illinois, as part of the DOE Urban Integrated Field Lab CROCUS project. The MFR node system consists of an Input/Output Digital Input Module (IB8) interface box (ICT International) providing wired connections for environmental sensors and an MFR-Node-L data logger that manages power, data processing, and LoRaWAN communication. The wireless data are ingested via Sage network (https://sagecontinuum.org/) nodes that contain LoRaWAN antennae. Measurements were collected from 11 MFR nodes deployed across Chicago State University (CSU), Northeastern Illinois University (NEIU), Northwestern University (NU), University of Illinois Chicago (UIC), West Woodlawn "Blacks in Green" (BIG), and Indian Boundary Prairies (IBP). Each MFR node supports a consistent suite of sensors measuring atmospheric, soil, and hydrological variables. Atmospheric measurements include 2m air temperature (°C), 2m vapor pressure deficit (kPa), and 2m shortwave/longwave radiation (incoming and outgoing, W/m²) measured using ATH-VPD and Apogee SN500 sensors. Soil measurements include volumetric water content (VWC, %) and temperature (°C) at four depths (15, 30, 45, and 60 cm below surface) using Meter Teros54 sensors, and heat flux (W/m²) at 10 cm depth using Huske HFP01-05 sensors. At selected locations, Meter Hydros21 sensors measure groundwater depth (mm), specific conductivity (dS/m), and temperature (°C). The dataset includes timestamps, site identifiers with location names, device IDs, Global Positioning System (GPS) coordinates, variable names with units, measurement depths, values, sensor names, and Sage node identifiers. All timestamps are in local Chicago time (CDT/CST). Quality control flags are provided using a 6-bit binary system indicating physical range violations, step spikes, 24-hour flat-line conditions, 6-hour jitter, 7-day ultra-low variance, and persistent high offset. Data is provided in CSV and CF-compliant NetCDF formats. This dataset is part of a larger collection of CROCUS environmental monitoring data, including linked datasets from Air Quality Transmitter (AQT) sensors, Weather Transmitter (WXT) sensors, and Sap Flow Meter (SFM1x) sensors.

Chicago↗
DOE OSTI2026

Sap Velocity Data for East River Watershed Sites (2023-2025)

This dataset includes sap velocity measurements for aspen (populus tremuloides), fir (abies lasiocarpa), spruce (Engelmann spruce) and lodgepole pine (pinus contorta) trees at nine sites in the East River Watershed near Gothic, CO. This dataset was generated following a similar method as a previous dataset (Dataset. doi:10.15485/1647654) but was conducted at different sites in the area and now includes lodgepole pine. Site selection was done to explicitly improve understanding of topographic controls of tree water use. The data collection began in June 2023 and we provide data until December 2025 - though data collection is ongoing. The sap flux data were collected using ICT SFM1 sensors and are presented in both units of cm h^-1 and as kg h^-1 by multiplying the sap flux by the sapwood area of the tree. All sap flow data has been been corrected using estimates of wounding diameter, water content of wood and sap wood depth. We also provide a normalized sap velocity estimate by subtracting each measurement from that trees' annual minimum and dividing by that trees' annual maximum. This provides data for each tree and year on a 0-1 scale. This data entry contains one CSV file that includes all available sap flow data. The timestamps are provided in local time as year, day of year and hour and each measurement contains an associated latitude, longitude and site number which can be used to identify trees in a given stand. Each species is given a numeric value as: 1= populus tremuloides, 2=Engelmann spruce, 3=abies lasiocarpa and 4=pinus contorta.

abies↗
DOE OSTI2026

Automated point dendrometer, soil moisture and temperature, and meteorological variables datasets, Oct 2024 – Nov 2025, G.A. Pearson Natural Area, Flagstaff, AZ, USA

This data package includes parsed, cleaned, and calibrated data from 48 TOMST automated point dendrometers, 48 TOMST 15 cm soil moisture sensors, and 12 TOMST 30 cm soil moisture sensors. The point dendrometers were cleaned with the “dendRoAnalyst” package in RStudio. The soil sensors were cleaned and calibrated for volumetric water content (VWC) with the “myClim” package in RStudio using the soil texture of the site (sandy clay loam). Additionally, this data package also includes raw data from 2 METER weather stations. Dendrometers and soil sensors have both their sensor ID, as well as the ID for the specific tree they were instrumented on at the G.A. Pearson Natural Area (GPNA) site and their experimental group. The purpose of these data is to understand how ponderosa pine trees in restored (thinned and burned) vs. unrestored (no treatment) areas are responding to drought and seasonal precipitation. These data use radial growth and soil moisture data to answer the following question: how are active season length, growth on different time scales (weekly, monthly, seasonally, and annually), growth during dry periods and after precipitation events, and environmental and biological drivers of radial growth different between restored versus unrestored areas?

Air temperature↗
NASA NTRS2018

A Comparative Analysis of Micrometeorological Determinants of Evapotranspiration Rates Within a Heterogeneous Urban Environment

Variability in micrometeorological conditions and their influence on estimated reference evapotranspiration (RET) rates were evaluated across a heterogeneous urban environment. Micrometeorological data sets (incoming solar radiation, air temperature, relative humidity and wind speed) were collected over a one-year period at six weather stations in New York City, NY (USA). Weather stations are located at four new urban green space monitoring sites and two airports. Reference evapotranspiration (RET) rates were estimated from the micrometeorological data sets for a short reference surface at a daily time-step using the ASCE Standardized Reference Evapotranspiration Equation, a Penman-Monteith based combination equation. Nonparametric comparative statistical analyses (Kruskal-Wallis) revealed statistically significant differences (at significance level α = 0.05) in micrometeorological conditions and estimated RET rates between the six sites. On a cumulative annual basis, estimated RET varied by up to 40 percent between the sites. A new technique for adjusting weather data collected at one location (e.g. regional airports) for use at another location (e.g. interior engineered urban green spaces) was evaluated. The study highlights the importance, for accurate estimation of ET, of onsite micrometeorological data sets, but concludes that additional research is needed to more thoroughly characterize micrometeorological variability across heterogeneous urban environments, and also to evaluate the influence of non-meteorological determinants, e.g. vegetation type, soil/media type, media moisture conditions and anthropogenic heat fluxes, on urban ET.

Urban environment↗
NASA NTRS2024

Discounting Water for Optimal Carbon Gain as a Basis of Stomatal Closure

The exchange of carbon dioxide and water vapor between terrestrial ecosystems and the atmosphere is regulated by stomata (small pores in the leaves of plants). Unsurprisingly, environmental factors controlling the opening and closure of stomata has been sought as early as 1800. One approach, popularized in the early 1970s, is a stomatal optimization framework. This framework is based on the hypothesis that plants optimize carbon gain subject to water loss or water availability constraints. This constraint optimization problem was solved in various forms assuming instantaneous adjustments of stomatal aperture to maximize a reward function with no future foresight or legacy effects. Holtzman et al. (2024, https://doi.org/10.1029/2023av001113) offers a novel approach that can diagnose the effective timescale over which the reward function maximization must be time-integrated. The developed method thus optimizes an integrated carbon gain function but adjusted by a discount factor subject to water availability in the root zone. The discount factor considers how the plant values carbon gain to save water and its timescale can be inferred from observations because the model is analytically tractable. The results suggest that the most important climate factor that determines this discount timescale is multi-annual mean of the longest dry period during the growing season. The findings highlight how local climate traits influence the spatial variation in ecosystem-level water use strategies. This sets the stage for expanding such a framework to cases where multiple constraints act in concert while operating at distinct time scales.

stomata↗