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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Idaho National Laboratory Site Environmental Surveillance Program Report: Third Quarter 2022

This report for the third quarter of 2022 contains results from the INL Site environmental surveillance program’s monitoring of the U.S. Department of Energy’s Idaho National Laboratory (INL) Site’s onsite, boundary and distant location environment, July 1 through September 30, 2022. All sample types (media) and the sampling schedule followed during 2022 are listed in Appendix A. This report contains results for the following sample types: • Air, including particulate air filters, charcoal cartridges, and atmospheric moisture, • Precipitation, • Milk, • Lettuce, • Grain, and • Large Game Animal Sampling.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Evaluating the Water Cycle Over CONUS at the Watershed Scale for the Energy Exascale Earth System Model Version 1 (E3SMv1) Across Resolutions

Abstract The water cycle is an important component of the earth system and it plays a key role in many facets of society, including energy production, agriculture, and human health and safety. In this study, the Energy Exascale Earth System Model version 1 (E3SMv1) is run with low‐resolution (roughly 110 km) and high‐resolution (roughly 25 km) configurations—as established by the High Resolution Model Intercomparison Project protocol—to evaluate the atmospheric and terrestrial water budgets over the conterminous United States (CONUS) at the large watershed scale. The warm season water cycle slows down in the HR experiment relative to the LR, with decreasing fluxes of precipitation, evapotranspiration, atmospheric moisture convergence, and runoff. The reductions in these terms exacerbate biases for some watersheds, while reducing them in others. For example, precipitation biases are exacerbated at HR over the Eastern and Central CONUS watersheds, while precipitation biases are reduced at HR over the Western CONUS watersheds. The most pronounced changes with resolution to the water cycle come from reductions in precipitation and evapotranspiration. The reduction in evapotranspiration reduces the biases across nearly all of the CONUS. Additional exploratory metrics show improvements to water cycle extremes (both in precipitation and streamflow), fractional contributions of different storm types to total precipitation, and mountain snowpack.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Arctic extreme precipitation changes from 1980 to 2022 in response to sea ice decline and enhanced atmospheric rivers

Arctic extreme precipitation (EP) broadly impacts permafrost degradation, glacier and snow cover changes, and ice sheet mass balance as well as ecosystems. However, investigation of EP spatiotemporal variations over the Arctic remains challenging, and their primary drivers are still poorly understood. Performance estimation of three state-of-the-art reanalysis products (Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA-5), and Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2)) against gauge-based precipitation observations reveals that MERRA-2 outperforms other reanalysis for Arctic EP changes. Based on MERRA-2 data, both annual EP amount and occurrence days averaged over the Arctic show statistically significant positive trends during 1980–2022 (3.37 ± 1.03 mm dec −1 and 0.42 ± 0.17 d dec −1 , respectively), with the most pronounced increase in the autumn. Spatial heterogeneity in annual and seasonal EP trends is found across the Arctic, with the largest positive annual trends of 30 mm dec −1 over the Bering Sea and the Denmark Strait. The significant EP increase is closely associated with intensified atmospheric rivers (ARs) and widespread decline in sea ice concentration (SIC). Specially, SIC and ARs are responsible for 12% and 50% of Arctic EP inter-annual variance, respectively, while ARs directly contribute 28.3% of the total annual EP amounts. These findings explain the mechanistic controls on Arctic EP, providing critical insights for projecting polar weather and climate extremes and their impacts on the Arctic environment.

atmospheric rivers↗
DOE OSTI2021

Effects of coupling a stochastic convective parameterization with the Zhang–McFarlane scheme on precipitation simulation in the DOE E3SMv1.0 atmosphere model

Abstract. A stochastic deep convection parameterization is implemented into the US Department of Energy (DOE) Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1.0 (EAMv1). This study evaluates its performance in simulating precipitation. Compared to the default model, the probability distribution function (PDF) of rainfall intensity in the new simulation is greatly improved. The well-known problem of “too much light rain and too little heavy rain” is alleviated, especially over the tropics. As a result, the contribution from different rain rates to the total precipitation amount is shifted toward heavier rain. The less frequent occurrence of convection contributes to suppressed light rain, while more intense large-scale and convective precipitation contributes to enhanced heavy total rain. The synoptic and intraseasonal variabilities of precipitation are enhanced as well to be closer to observations. The sensitivity of the rainfall intensity PDF to the model vertical resolution is examined. The relationship between precipitation and dilute convective available potential energy in the stochastic simulation agrees better with that in the Atmospheric Radiation Measurement (ARM) observations compared with the standard model simulation. The annual mean precipitation is largely unchanged with the use of the stochastic scheme except over the tropical western Pacific, where a moderate increase in precipitation represents a slight improvement. The responses of precipitation and its extremes to climate warming are similar with or without the stochastic deep convection scheme.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A Moist Physics Parameterization Based on Deep Learning

Abstract Current moist physics parameterization schemes in general circulation models (GCMs) are the main source of biases in simulated precipitation and atmospheric circulation. Recent advances in machine learning make it possible to explore data‐driven approaches to developing parameterization for moist physics processes such as convection and clouds. This study aims to develop a new moist physics parameterization scheme based on deep learning. We use a residual convolutional neural network (ResNet) for this purpose. It is trained with 1‐year simulation from a superparameterized GCM, SPCAM. An independent year of SPCAM simulation is used for evaluation. In the design of the neural network, referred to as ResCu, the moist static energy conservation during moist processes is considered. In addition, the past history of the atmospheric states, convection, and clouds is also considered. The predicted variables from the neural network are GCM grid‐scale heating and drying rates by convection and clouds, and cloud liquid and ice water contents. Precipitation is derived from predicted moisture tendency. In the independent data test, ResCu can accurately reproduce the SPCAM simulation in both time mean and temporal variance. Comparison with other neural networks demonstrates the superior performance of ResNet architecture. ResCu is further tested in a single‐column model for both continental midlatitude warm season convection and tropical monsoonal convection. In both cases, it simulates the timing and intensity of convective events well. In the prognostic test of tropical convection case, the simulated temperature and moisture biases with ResCu are smaller than those using conventional convection and cloud parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Robust late twenty-first century shift in the regional monsoons in RegCM-CORDEX simulations

Abstract We use an unprecedented ensemble of regional climate model (RCM) projections over seven regional CORDEX domains to provide, for the first time, an RCM-based global view of monsoon changes at various levels of increased greenhouse gas (GHG) forcing. All regional simulations are conducted using RegCM4 at a 25 km horizontal grid spacing using lateral and lower boundary forcing from three General Circulation Models (GCMs), which are part of the fifth phase of the Coupled Model Inter-comparison Project (CMIP5). Each simulation covers the period from 1970 through 2100 under two Representative Concentration Pathways (RCP2.6 and RCP8.5). Regional climate simulations exhibit high fidelity in capturing key characteristics of precipitation and atmospheric dynamics across monsoon regions in the historical period. In the future period, regional monsoons exhibit a spatially robust delay in the monsoon onset, an increase in seasonality, and a reduction in the rainy season length at higher levels of radiative forcing. All regions with substantial delays in the monsoon onset exhibit a decrease in pre-monsoon precipitation, indicating a strong connection between pre-monsoon drying and a shift in the monsoon onset. The weakening of latent heat driven atmospheric warming during the pre-monsoon period delays the overturning of atmospheric subsidence in the monsoon regions, which defers their transitioning into deep convective states. Monsoon changes under the RCP2.6 scenario are mostly within the baseline variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

TRACER-ACE: Aerosol Characterization Experiment Field Campaign Report

An objective for the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Tracking Aerosol Convection Interactions Experiment (TRACER) is to provide high-temporal-and-spatial resolution observations of convective clouds in the Houston region, over a broad range of environmental and aerosol regimes (Jensen et al. 2022). The TRACER siting strategy was to deploy the first ARM Mobile Facility (AMF1), with its full suite of cloud, aerosol, precipitation, and atmospheric state measurement capabilities, in a region that experiences the full diversity of aerosol properties from the Houston domain including rural, urban, and industrial atmospheric aerosol environments (see Figure 1).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A first-principles machine-learning force field for heterogeneous ice nucleation on microcline feldspar

The formation of ice in the atmosphere affects precipitation and cloud properties, and plays a key role in the climate of our planet. Although ice can form directly from liquid water at deeply supercooled conditions, the presence of foreign particles can aid ice formation at much warmer temperatures. Over the past decade, experiments have highlighted the remarkable efficiency of feldspar minerals as ice nuclei compared to other particles present in the atmosphere. However, the exact mechanism of ice formation on feldspar surfaces has yet to be fully understood. Here, we develop a first-principles machine-learning model for the potential energy surface aimed at studying ice nucleation at microcline feldspar surfaces. The model is able to reproduce with high-fidelity the energies and forces derived from density-functional theory (DFT) based on the SCAN exchange and correlation functional. Our training set includes configurations of bulk supercooled water, hexagonal and cubic ice, microcline, and fully-hydroxylated feldspar surfaces exposed to vacuum, liquid water, and ice. In this work, we apply the machine-learning force field to study different fully-hydroxylated terminations of the (100), (010), and (001) surfaces of microcline exposed to vacuum. Our calculations suggest that terminations that do not minimize the number of broken bonds are preferred in vacuum. We also study the structure of supercooled liquid water in contact with microcline surfaces, and find that water density correlations extend up to around 10 Å from the surfaces. Finally, we show that the force field maintains a high accuracy during the simulation of ice formation at microcline surfaces, even for large systems of around 30,000 atoms. Future work will be directed towards the calculation of nucleation free energy barriers and rates using the force field developed herein, and understanding the role of different microcline surfaces on ice nucleation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Storm and Annual Time Scale Hydrological Data for the Russian River Watershed 1996-2022

These files contains observed and simulated hydrological data (discharge, precipitation) that have been aggregated to the storm-event scale and to the annual time scale for the Russian River Watershed (RRW), California. Observed data are obtained from 12 different USGS hydrological stations located throughout the watershed, and discharge is simulated using the GR5H hourly hydrological model. Precipitation data are obtained from the NASA NLDAS NOAH Community Land Surface Model. All simulated and observed discharge are aggregated to the storm and annual time scales. We investigated changes in watershed hydrological conditions in the Russian River Watershed, a Mediterranean, drought prone, wildfire-adapted ecosystem, following eleven wildfires that occurred from 2017-2020. We ask two research questions: 1) How do interacting wildfire events, drought, and atmospheric rivers impact hydrological conditions of the watershed and in particular streamflow?, and 2) What percentage of new and/or cumulative wildfire disturbance is required to initiate hydrological change? We hypothesize that sub-watersheds of the RRW Mediterranean ecoregion have not burned beyond an intrinsic, and still unknown, threshold required to initiate change. Using a series of paired burned/unburned catchments nested within the larger watershed, we examined temporal and spatial patterns of pre-and-post wildfire water hydrology using a rainfall runoff hydrological model compared with data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Microphysical-macrophysical interactions in low cloud systems over the Eastern North Atlantic

How low clouds over the oceans respond to changes in atmospheric greenhouse gases and aerosol particles is poorly understood. Climate model disagreement in these responses limits the accuracy of predictions of future climate. Processes governing the behavior of marine low cloud need to be improved in models and comprehensive long-term observations provide important constraints. Cloud structure and impacts on climate are shaped by processes operating on a wide range of time and space scales, which interact to modulate cloud dynamics, boundary layer structure, and precipitation. The Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site at Graciosa Island (Azores, 28°W 39°N) began taking measurements in late 2013 to address the need for comprehensive cloud, aerosol, and meteorological observations in a remote marine low cloud environment. The site straddles the boundary between the subtropical and midlatitude climate zones in the eastern North Atlantic and experiences a great diversity of meteorological and cloud conditions. In addition, the ENA site is downwind of the North American continent and is periodically impacted by continental aerosol particles transported there on the prevailing winds. In response to a 2014 proposal call, our team (ENA Site Science Team) laid out a strategy to accelerate research focused on understanding the multi-scale interactions that shape cloud properties by maximizing the utility of the observational capabilities of the ENA site. The proposed work aligned with the scientific directions of the ASR Working Groups. The team, together with collaborators, conducted a program of research focused upon multi-scale cloud processes. The team worked with scientists both within and outside the Atmospheric System Research (ASR) program, and with the ARM infrastructure to ensure that the full potential of the ENA site is realized. The suite of instruments at the ENA site has been increasing since the site’s inception and is now essentially complete. In 2019, we laid out a two-year extension of the research, and were granted an extension of the Site Science award.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Dust pollution in China affected by different spatial and temporal types of El Niño

Dust is an important aerosol affecting air quality in China in the winter and spring seasons. Dust in China is potentially influenced by the interannual climate variability associated with El Niño. Here, the impacts of El Niño with different temporal and spatial types on dust pollution in boreal winter and spring in China and the potential mechanisms are investigated using a state-of-the-art Earth system model (E3SMv1). We find that the eastern Pacific (EP) and central Pacific (CP) El Niño both increase wintertime dust concentrations by 5–50 µg m –3 over central-eastern China. Due to a stronger wind and lower relative humidity, which favor dust emissions near sources, and a strengthened northwesterly and reduced precipitation, which are conducive to dust transport, dust concentrations during the CP El Niño are 5–20 µg m –3 higher in northern China than during the EP El Niño, although the changes are mostly insignificant. El Niño with a short duration (SD) increases boreal winter dust concentrations by 20–100 µg m –3 over northern China relative to the climatological mean, while there is a decrease of 5–50 µg m –3 during the long-duration (LD) El Niño, which is also related to the El Niño-induced changes in atmospheric circulation, precipitation, and relative humidity. In the following spring season, all types of El Niño events enhance dust over northern China, but only the increase during the LD El Niño is statistically significant, suggesting that the weaker intensity but longer duration of the LD El Niño events can significantly affect spring dust in China. Our results contribute to the current knowledge of the influence of El Niño on dust pollution, which has profound implications for air pollution control and dust storm prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Data for publication: "A fresh take: Seasonal changes in terrestrial freshwater inputs impact salt marsh hydrology and vegetation dynamics"

This data repository contains data associated with the manuscript "A fresh take: Seasonal changes in terrestrial freshwater inputs impact salt marsh hydrology and vegetation dynamics". This study was conducted at the Elkhorn Slough National Estuarine Research Reserve in Watsonville, California from October 2019 - June 2022. We sought to understand the role of shallow freshwater inputs from adjacent uplands on salt marsh hydrologic behavior and vegetation productivity. This dataset contains CSV files of the following: daily salt marsh subsurface water level and pore water conductivity, monthly vegetation survey measurements, soil core data. Estuary surface water level, conductivity, and local precipitation were downloaded from the National Estuarine Research Reserve System (Centralized Data Management Office) at https://cdmo.baruch.sc.edu/.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Radon Isotopes and Stable Water Isotopes from Coal Creek Watershed, Colorado (2021)

The radon isotope and stable water isotope data for Coal Creek Watershed, Colorado, consists of d2H, d18O, and 222Rn values from samples collected at 8 stream location along Coal Creek, samples from 7 groundwater springs within the watershed, and precipitation isotope samples collected by Next Generation Water Observing System (NGWOS) from a collector within the watershed. All stream and spring samples were collected between June and October, 2021, and precipitation isotope samples were collected between November 2020 and September 2021. These data were collected to evaluate how groundwater contributions to Coal Creek originating from a fractured hillslope and alluvial fan respond to summer monsoon rains and seasonal drying. Understanding of groundwater-surface water interactions in montane systems in critical for the future of water availability in the Western US as groundwater contributions are expected to become more important for sustaining summer stream flows. This data package contains: (1) a csv of all radon samples; (2) a csv of all stream and spring isotope samples; (3) a csv of precipitation isotope samples; and (4) a csv of locations for each sampling site. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Mesoscale Convective Systems Dominate the Energetics of the South Asian Summer Monsoon Onset

The onset of the South Asian summer monsoon (SASM) is featured as an abrupt increase of precipitation and atmospheric column-integrated moist static energy (MSE). Using convection permitting simulations, a novel cloud tracking technique, and an MSE budget, the relative roles of mesoscale convective systems (MCSs) and non-MCS isolated deep convection in the SASM onset are studied. The analysis shows MCSs play a crucial role in the sudden increases of rainfall, diabatic MSE sources, and the subsequent enhanced export of column-integrated MSE during the onset. A “diabatic sources-energy export” plane analysis delineates the different roles of MCSs and non-MCS deep convection in the SASM onset. Although deep convection always leads to a net energy export, mesoscale organization of deep convection dominates the convection associated positive feedbacks that accelerate the energy cycle of the SASM onset. Sensitivity experiment shows that the results are insensitive to the model resolutions.

Chen, Xingchao↗
DOE OSTI2020

Modeling Streamflow and Sediment Transport Under Current and Future Climates at the West Valley Site - 20231

Performance Assessment models often require inputs representing specific hydrologic processes such as streamflow and sediment flux. This paper details the development of a process-level hydrologic model and its use in a Probabilistic Performance Assessment (PPA) system-level model for the West Valley Site in western New York. For this PPA model, a watershed model was developed using SWAT (Soil and Water Assessment Tool) and used to evaluate water and sediment fluxes in nearby streams draining the Site. A particular challenge of probabilistic modeling over long periods of time is scaling parameters appropriately so that outcomes are not grossly over- or underestimated. This challenge is especially important when the spatial and temporal scales of process and system level models differ. In this instance, the SWAT watershed model and GoldSim PPA model have similar spatial scales of stream reaches and sub-catchments within a single watershed (approximately hundreds to tens of thousands of square meters, but the temporal scales differ considerably. The SWAT model calculates streamflow and sediment transport rates at the daily scale, while the PPA model requires long-term average values that are applied over hundreds to thousands of year-long periods. As such, distributions of daily climatic parameters such as average precipitation rate, temperature, wind speed, and relative humidity were developed to force a transient SWAT model that captures the dynamic behavior of the watershed in response to daily changes in the weather. Output from the model, which includes stream flow (m{sup 3}/y) and sediment transport rate (Mg/y) is then averaged over a 100-year period as an approximation for the long-term average values which can be applied in GoldSim. A single SWAT model was run 5000 times with varying climatic inputs and deterministic soil, land use, and elevation data, and the range of outputs were compiled into a distribution which can be implemented stochastically into the PPA Model. Additionally, this paper includes a high-level discussion of how changes in future climate affect weather input distributions, and a summary of how those changes in weather affect the outcomes from the SWAT model and the inputs into the PPA model. In this analysis, future climate distributions were developed based on climate research documenting the expected changes to precipitation and temperature in western New York. The 5,000 SWAT simulations were then repeated with these future climate distributions, and the resulting output was implemented into the PPA Model as hydrologic conditions under a future climate. In the current version of the PPA Model, hydrologic parameters change linearly from the 'current climate' conditions to the 'future climate' conditions over a period of 100 years, after which they remain constant. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Assessment of Spatiotemporal Variability of Evapotranspiration and Its Governing Factors in a Mountainous Watershed at the East River, Colorado, Water: Dataset

This data package contains simulation results of daily evapotranspiration for 17 locations of meteorological stations with different precipitation, air temperature and vegetation within the East River Watershed, Colorado (CLMResult.csv). Daily precipitation data is included for reference. These are the main results of the paper Tran, 2019. Meteorological stations names, latitude and longitude coordinates of meteorological stations, types of lands from the National Land Cover Database, and a comparison with the community land model (CLM) data are also included (Met_LC.csv). The purpose of this data is to analyze the spatio-temporal variations of evapotranspiration at the East River Watershed and understand the factors that control these variations. More information regarding the study, methods, and findings can be found in the Related Reference paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Changes in Precipitation and Air Temperature Contribute Comparably to Permafrost Degradation in a Warmer Climate: Modeling Archive

The dataset contains "ecosys" model outputs reported in Mekonnen et al. (2021) to examine the importance of heat transfer from precipitation on permafrost degradation. The study highlighted the importance of mechanistically representing this process in earth system models (ESMs) and demonstrated that precipitation is as important an environmental control on permafrost degradation as surface air temperature. Included are (1) the modeled spatially averaged annual active layer depth (ALD) and daily maximum soil temperature across the North Slope of Alaska from 1980 to 2100, and (2) 25 km resolution modeled ALD for averaged for two time intervals, 2006 - 2010 and 2096 - 2100. The results are provided for two "ecosys" simulations: S1 with precipitation heat transfer and S2 without precipitation heat transfer across the North Slope of Alaska. These data are available in a NetCDF and csv data formats. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data and scripts associated with “When do Riverine Systems 'Feel the Burn'? Simulating How Burn Extent and Severity Modulate Hydrologic Controls on Biogeochemical Export” (v2)

This data package is associated with the publication “When do Riverine Systems 'Feel the Burn'? Simulating How Burn Extent and Severity Modulate Hydrologic Controls on Biogeochemical Export” published in Water Resources Research (Wampler et al. 2025; preprint: https://doi.org/10.22541/essoar.174438106.63564767/v1). This study used the Soil and Water Assessment Tool (SWAT), a processed based model to explore the impacts of area burned and burn severity on streamflow, nitrate, and dissolved organic carbon (DOC) in two test basins: a semi-arid, mixed land use basin and a humid, primarily forested basin. We developed 1800 wildfire scenarios that we ran in each basin: 20 different burn extents (5 to 100% by 5%), 3 different burn severities (low, moderate, and high), and 30 different post-fire precipitation scenarios. We also ran an additional 30 scenarios associated with no wildfire for the 30 post-fire precipitation scenarios. For each scenario we were interested in the change in runoff ratio (streamflow) and average concentration and annual loads (nitrate and DOC) across the wildfire scenarios. This data package contains the data and scripts required to build SWAT models for the two test basins, create and run the wildfire scenarios, and generate the data summaries and figures used in the associated manuscript. This data package was originally published in March 2025. It was updated in January 2026 (v2; new and modified files) to include the final files after the manuscript went through reviews. See the change history section below for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About.

54 ENVIRONMENTAL SCIENCES↗