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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Soil Temperature and Moisture within the Kougarok Fire Complex, Kougarok Road Mile Marker 86, Seward Peninsula, Alaska, 2019-2023

Daily averages of soil temperature and moisture measured once every hour at different heights located at Intensive Monitoring Stations within the Kougarok Fire Complex, Kougarok Road Mile Marker 86 site. Data were retrieved annually from 2019-2023. Package contains 21 *.CSV data files plus a file level metadata *.CSV, data dictionary *.CSV, data file inventory *.CSV, and sensor location site map *.JPG. Data files have header rows, NaN fields indicate invalid or missing data, and negative vertical offsets are above ground.The Kougarok tundra fire complex (KFC) is located north of Nome and the Kigluaik Mountains, near Quartz Creek and the Kougarok River. The site is accessed by foot from the end of the Nome-Taylor Highway (mile marker 86; also called the Kougarok or Beam Road). The KFC burned in six major fires in the decades since 1950 (Alaska Interagency Coordination Center, unpublished data). Lightning ignited five of these fires (1971, 1997, 2015, and 2019) and one was human caused (2002). The mosaic of overlapping fire scars allows for the study of repeat fires in the tundra which, until recently, was not a common phenomenon outside the boreal forest in Alaska. Our reference unburned tundra fire site is south of the KFC located at mile marker 80 of the Nome-Taylor Highway.The two most recent fires are the Mingvk Lake (2015; 21,698 acres burned from 7/27/2015 to 9/28/2015) and Garfield Creek (2019; 422 acres burned from 7/31/2019 to 8/20/19). The Mingvk Lake fire scar includes areas that burned 1-4x (1971, 1997, 2002), while the entirety of the Garfield Creek fire scar has burned 2x previously (1971, 2002).Previous research at the KFC focused on permafrost (Liljedahl et al. 2007; Narita et al. 2015; Iwahana et al. 2016; Tsuyuzaki, Iwahana, and Saito 2017) and vegetation (Narita et al. 2015; Hollingsworth et al. 2021) response to fire. The central Seward Peninsula is characterized by continuous permafrost with a thickness of 15 to 30 m and a mean active layer thickness of 56 cm (Hinzman et al. 2003). Sloping hills with mixed shrub–tussock tundra and tussock tundra vegetation in the uplands are characteristic of the region. Three micrometeorological towers near the Kougarok field site recorded a mean annual temperature of −2.4°C, mean January temperature of −23.1°C, mean July temperature of +11°C, and mean summer rainfall (June–August) of 94 mm from 2000 to 2006 (Liljedahl et al. 2007).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↗
NASA NTRS1999

The MSP 2001 Mars Environmental Compatibility Assessment (MECA)

The Mars Environmental Compatibility Assessment (MECA) will evaluate the Martian environment for soil and dust-related hazards to human exploration as part of the Mars Surveyor Program 2001 Lander. Sponsored by the Human Exploration and Development of Space (HEDS) enterprise, MECA's goal is to evaluate potential geochemical and environmental hazards that may confront future Martian explorers, and to guide HEDS scientists in the development of high fidelity Mars soil simulants. The integrated MECA payload contains a wet-chemistry laboratory, a microscopy station, an electrometer to characterize the electrostatics of the soil and its environment, and arrays of material patches to study the abrasive and adhesive properties of soil grains. The instrument will acquire soil samples with a robotic arm equipped with a camera. MECA will examine surface and subsurface soil and dust in order to characterize particle size, shape, hardness, and also physical characteristics that may provide clues to mineralogy. MECA will characterize soil/water mixtures with respect to pH, redox potential, total dissolved ions, and trace toxins. MECA will determine the nature of electrostatic charging associated with excavation of soil, and the influence of ionizing radiation on material properties. It will also observe natural dust accumulation on engineering materials. To accomplish these objectives, MECA is allocated a mass of 10 kg within an enclosure of 35 x 25 x 15 cm. The Wet Chemistry Laboratory (WCL) consists of four identical cells that will accept samples from surface and subsurface regions accessible to the Lander's robotic arm, mix them with water, and perform extensive analysis of the solution. Ion-selective electrodes and related sensors will evaluate total dissolved solids, redox potential, pH, and the concentration of many soluble ions and gases in wet Martian soil. These electrodes can detect potentially dangerous heavy-metal ions, emitted pathogenic gases, and the soil's corrosive potential. Experiments will include cyclic voltammetry and anodic stripping voltammetry. Complementary to the Viking experiments, the chemical laboratory will characterize the water-soil solution rather than emitted gases. Nonetheless, through analysis of dissolved gases it will be able to replicate many of the Viking observations related to oxidants. MECA's microscopy station combines optical and atomic-force microscopy (AFM) in an actively focused, controlled illumination environment to image particles from millimeters to nanometers in size. Careful selection of substrates allows controlled experiments in adhesion, abrasion, hardness, aggregation, magnetic and other properties. Special tools allow primitive manipulation (brushing and scraping) of samples. Soil particle properties including size, shape, color, hardness, adhesive potential (electrostatic and magnetic), will be determined using an array of sample receptacles and collection substrates. The simple, rugged atomic-force microscope will image in the submicron size range and has the capability of performing a particle-by-particle analysis of the dust and soil. On Earth, the earliest forms of life are preserved as microfossils. The atomic-force microscope will have the required resolution to image down to the scale of terrestrial microfossils and beyond. Mounted on the end of the robot arm, MECA's electrometer actually consists of four types of sensors: an electric field meter, several triboelectricity monitors, an ion gauge, and a thermometer. Tempered only by ultraviolet-light-induced ions and a low-voltage breakdown threshold, the dry, cold, dusty martian environment presents an imposing electrostatic hazard to both robots and humans. The field meter will measure the ambient field on nearby objects while the triboelectric sensors, using identical circuitry, will measure the charge accumulated on test substances as they are dragged through the soil by the arm. The ion chamber, open to the environment, will sense both charged dust and free ions in the air. Over and above the potential threat to electronics, the electrostatic environment holds one of the keys to transport of dust and, consequently, Martian meteorology. Viewed with the robot arm camera, the abrasion and adhesion plates are strategically placed to allow direct observation of the interaction between materials and soils on a macroscopic scale. Materials of graded hardness are placed directly under the robot arm scoop to sense wear and soil hardness. A second array, placed on the lander deck, is deployed after the dust plume of landing has settled. It can be manipulated in a primitive fashion by the arm, first having dirt deposited on it from the scoop and subsequently shaken clean. A third array will passively collect dust from the atmosphere. In addition to objectives related to human exploration, the MECA data set will be rich in information relevant to basic geology, paleoclimate, and exobiology issues. To understand both contemporaneous and ancient processes on Mars, the mineralogy, petrology, and reactivity of Martian surface materials should be constrained. The MECA experiment will shed light on these quantities through its combination of chemistry and microscopy. MECA will be capable of measuring the composition of ancient surface water environments, observing microscopic evidence of geological (and biological?) processes, inferring soil and dust transport, comminution and weathering mechanisms, and characterizing soil horizons that might be encountered during excavation.

Hecht, M. H.↗
DOE OSTI2025

COMPASS-FME Synoptic Site Characterization

This dataset contains soil biogeochemical and physicochemical characterization data for the COMPASS-FME synoptic sites.This dataset also contains data for the paper Patel et al. 2025 "Transition zones at the changing coastal terrestrial-aquatic interface", https://doi.org/10.1029/2025JG008978.Coastal soils are a significant but highly uncertain component of global biogeochemical cycles. These systems experience unique spatial and temporal variability in biogeochemical processes, driven by wetland-to-upland gradients and hydrological fluctuations. We studied drivers of coastal soil variability (a) at regional scales and (b) across transects from upland forest to wetland, in two contrasting regions — Lake Erie, a freshwater lacustrine system, and Chesapeake Bay, a saltwater estuarine system. Salinity-related analytes were a key driver of soil variability, not just in the saltwater system, but surprisingly, also in the freshwater system. We had hypothesized linear trends in biogeochemical parameters along the TAI – however, contrary to expectations, transition soils were not consistently intermediate between upland and wetland endmembers; the non-monotonic trends of carbon, phosphorus, iron along our transects suggest that these are key analytes to study in our regions. Rapidly changing soil factors across coastal gradients provide insights into which soil processes may act as precursors to ecosystem shifts. Our comprehensive soil characterization across the coastal transects provides essential data for mechanistic modeling of ecosystem dynamics.The data are provided as processed, csv files. Raw data and processing scripts can be accessed on GitHub (https://github.com/COMPASS-DOE/cmps-soil_characterization).A note on the nomenclature: the experimental design represents three points along the coastal gradient -- upland, transition, and wetland. "wetland" is referred to as "marsh" in the corresponding paper. The two terms can be used interchangeably for the sites in this study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Onshore U.S. Carbon Pipeline Deployment: Siting, Safety, and Regulation

Carbon capture, utilization, and storage (CCUS) technology has significant potential to reduce greenhouse gas (GHG) emissions and mitigate the impact of climate change, particularly in hard to decarbonize industrial and commercial sectors. CCUS involves capturing carbon dioxide (CO 2 ) from industrial processes or power generation and utilizing it for other purposes, such as enhanced oil recovery (EOR), or storing the captured CO 2 underground. CCUS technology can reduce the environmental impact of continued fossil fuel use while smoothing the transition to a low-carbon economy. CCUS can create new economic opportunities, such as the development of new industries and job creation, and can enhance energy security by diversifying energy sources. For these reasons, enabling CCUS has become a key objective of the Biden-Harris administration’s clean energy policy and has received bipartisan support. Despite its environmental and economic potential, CCUS faces multiple barriers to widespread deployment. One of the main challenges is the high cost and technical difficulty of implementing and operating large-scale CCUS infrastructure. CCUS remains a relatively expensive way to reduce carbon emissions (e.g., compared to solar photovoltaic technology’s displacement of coal generation). Additionally, financial incentives and supportive policies like those enacted to support solar photovoltaic development, especially at the state level, are inconsistent or nonexistent, which can discourage investment in CCUS projects. There are also technical challenges associated with safe and secure underground CO 2 storage and the development of new carbon utilization technologies. Public opposition to various aspects of CCUS technologies, ranging from concerns that CCUS will extend reliance on fossil fuels to CCUS infrastructure being sited in disadvantaged communities, is a growing challenge. This paper focuses on another significant barrier to broad CCUS deployment: the need for considerable expansion of the dedicated land-based CO 2 pipeline network in the United States to meet CCUS goals and the unique regulatory challenges to its development. To reach carbon emissions targets in the United States by 2050, CCUS technology will need to be supported by tens of thousands of miles of CO 2 pipelines. Estimates range from a minimum of roughly 29,000 pipeline miles (according to a 2020 Great Plains Institute study) to 66,000 pipeline miles (as per a 2021 Princeton University–led study). As of October 2022, however, the U.S. Department of Transportation (U.S. DOT) reports fewer than 5,400 miles of U.S. pipelines carrying CO 2 . This deficit—and what it means for the prospect of moving substantially larger quantities of CO 2 from source to use or storage—threatens to stifle the development of CCUS projects and technologies identified as an important tool to meet emissions targets. The current regulatory landscape facing CO 2 pipeline development can best be described as uncertain. At the federal level, the U.S. DOT Pipeline and Hazardous Materials Safety Administration (PHMSA) oversees safety regulation of pipelines transporting hazardous materials, including CO 2 upon commencement of operation. However, PHMSA’s definition of CO 2 as “a fluid consisting of more than 90 percent CO 2 molecules compressed to a supercritical state” has not been updated since its 1991 addition to the Federal Register. Because CO 2 can be transported in a gaseous, liquid, or supercritical state (indeed, the physical state of CO 2 can fluctuate within a single pipeline due to environmental changes), doubts persist about the extent of PHMSA’s purview—and raise questions about what, if anything, states should do to address this apparent gap. PHMSA has begun a major revision of its existing rules, but the agency does not expect a first draft before 2024. Economic oversight of CO 2 pipelines is even less clear. The Federal Energy Regulatory Commission (FERC) and Surface Transportation Board (STB)—which regulate the rates of interstate oil/natural gas and non-energy pipelines, respectively—have both declined jurisdiction over interstate CO 2 pipelines. This presumably leaves economic regulation to state and/or local governments, but few if any states have the laws or resources in place to oversee just and reasonable rates. Further, the interstate nature of CO 2 pipeline development creates questions around how different states should align their rate-making decisions. Onshore U.S. Carbon Pipeline Deployment: Siting, Safety, and Regulation Currently, regulatory responsibilities regarding CO 2 pipeline siting and permitting fall to state and local governments. The variety of laws and regulations across the country, however, creates a maze of requirements for pipeline developers to navigate. To secure necessary permits, most states require pipeline companies to be “common carriers” that provide transport service to the public at uniform rates. However, the specific definition of that term varies. Some states require clear evidence that a pipeline services the public, while others automatically deem any pipeline company transporting energy products or hazardous materials to be a “common carrier”—with little consideration for accessibility to third parties. Other states have eschewed common-carrier terminology entirely, placing private and publicly accessible pipelines on equal footing. Much like the variation in common-carrier requirements, laws governing eminent domain authority to secure rights-of-way (ROW) to commence construction on a planned pipeline route differ by state. Several states have no laws or rules governing CO 2 pipelines. In addition to creating questions about whether long-standing rules for other pipelines (e.g., natural gas or petroleum products) apply to CO 2 , this policy vacuum leaves local governments as the sole authority over sections of pipe within their boundaries. With dozens of counties along a given route, the probability of inconsistent regulation of the same pipeline is significant. Even in states with CO 2 pipeline laws in place, local regulatory attempts to address rising concerns over pipeline routing and safety have triggered lawsuits by pipeline companies seeking to delimit areas of federal, state, and local government responsibility. Meanwhile, legislators across the country have introduced bills to restrict the application of eminent domain to CO 2 pipeline projects, which could threaten a key means of securing ROW that companies cannot secure through negotiation with landowners. Taken separately, any of these regulatory issues—the narrow federal definition of CO 2 , FERC’s and STB’s decisions that CO 2 pipelines are not within their jurisdiction, and the considerable variation in state and local governments’ laws regulating CO 2 pipeline technologies—are extremely difficult to resolve. Adding the required scale of CO 2 pipeline expansion and the currently identified narrow window of time in which to reach climate target goals, the task becomes even more difficult—and raises a host of urgent questions for regulators. How should CO 2 be defined in federal regulations to ensure consistent safety standards across the country? What is the potential impact radius of a CO 2 pipeline rupture, and how should that inform local emergency response? In the absence of centralized federal oversight, what should state legislatures do to increase alignment for interstate CO 2 pipeline projects? This paper intends to serve as a primer for regulators and stakeholders who seek to better understand the regulatory challenges and opportunities facing this critical infrastructure.

42 ENGINEERING↗
DOE OSTI2021

Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products

Here we assess the accuracy of estimating daily grass reference evapotranspiration (PM-ET o ) using daily shortwave radiation (R s ) and reference evapotranspiration (ET REF ) products provided by the Meteosat Second Generation (MSG) geostationary satellite delivered by the Satellite Applications Facility on Land Surface Analysis (LSA-SAF) framework. The accuracy of using reanalysis ERA5 shortwave radiation data (R s ERA5 ) provided by the European Center for Medium-Range Weather Forecasts (ECMWF) is also evaluated. The assessments were performed using observed weather variables at 37 weather stations distributed across continental Portugal, where climate conditions range from semi-arid to humid, and 12 weather stations located in Azores islands, characterized by humid, windy and often cloudy conditions. This study&#x27;s use of data from a variety of climate conditions contributed to a unique and innovative assessment of the usability of LSA-SAF and ERA5 products for ET o estimation. The first assessment focused on comparing LSA-SAF estimates of R s (R s LSA-SAF ) against ground stations (R s ground ). The results showed a good matching between the two R s data sets for continental Portugal but a tendency for R s LSA-SAF to under-estimate R s ground in the cloudy islands of Azores. ET o values computed using R s LSA-SAF data and observed temperature, humidity and wind speed (ET o LSA-SAF ) were then compared with PM-ET o estimates with ground-based data, which were used as benchmark; input data of temperature and humidity needed for PM-ET o were quality checked for surface aridity effects. It was observed that ET o LSA-SAF is strongly correlated with PM-ET o (R 2 > 0.97) for most locations in continental Portugal, with regression coefficient of a linear regression forced to the origin ranging between 0.95 and 1.05, mean root mean square error (RMSE) of 0.13 mm d –1 , and Nash and Sutcliff efficiency of modeling (EF) above 0.95. For most Azores locations, ET o LSA-SAF over-estimated PM-ET o . This is likely a consequence of the high spatio-temporal heterogeneity of weather conditions that occur in these oceanic islands together with the different footprints of satellite (averaged over the pixel) and station observations. Reanalysis ERA5 shortwave radiation data presented similar behavior to the LSA-SAF products, however with slightly lower accuracy. The daily LSA-SAF ET REF product (ET REF LSA-SAF ) was assessed and results have shown a good accuracy of this product, with acceptable RMSE and high EF values, for continental Portugal but a low accuracy for the Azores islands. A simplified bias correction approach was shown to improve both ET o derived from the LSA-SAF products, namely for Azores stations, which seem to be representative of smaller areas. The use of the FAO-PM temperature approach (PMT) was also assessed using the R s LSA-SAF and R s ERA5 data, which showed a superiority of the LSA-SAF product for ET o estimations (ET o PMT LSA-SAF ). No significant differences (p < 0.05) were observed in terms of the median value of the RMSE when adopting ET o PMT and ET REF LSA-SAF . Differently, results showed that using the R s LSA-SAF in the PMT approach (ET o PMT LSA-SAF ) produces significantly better RMSE results than ET o PMT and ET REF LSA-SAF . Overall, the performed assessment allows concluding that the use of R s LSA-SAF, and to a lesser extent the use of the R s ERA5 , highly improves the accuracy of computation of ET o when R s observations are not available, including when only temperature data are accessible. The use of the ET REF LSA-SAF product is a good alternative when observed weather data are not available.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Solid-state Architecture Batteries for Enhanced Rechargeability and Safety (SABERS) for Extended Deep Space Applications

Extended duration deep space missions as well as permanent space habitats face numerous technical challenges, key among them is energy generation and energy storage. There are considerable monetary and technical barriers to the generation of power in space. Therefore, it is important to store and be able to access power in an efficient and safe manner over a large number of cycles. Energy storage and in particular, batteries, are vital to the operation of next-generation extraterrestrial shuttles, rovers, habitats and extravehicular activity (EVA) space suits. The performance metrics for extended duration space missions are at least 2 times greater than those set for terrestrial applications such as electric automobiles. Furthermore, safety is essential for operation of space missions particularly involving astronauts such as shuttles, habitats and EVA space suits. Preliminary systems level analysis has indicated that there are five key properties which must be optimized for successful implementation of battery systems. Those five key criteria are: safety, energy density, power, packaging design and scalability. Current state-of-the-art (SOA) lithium-ion batteries can meet or exceed the requirements for certain space applications in the areas of power and scalability, yet are insufficient in the key performance criteria of energy, safety and packaging design. The SABERS concept proposes a battery that meets all five key performance criteria through development of a solid-state architecture battery utilizing high capacity sulfur-selenium cathode and lithium metal anode. The combination of sulfur and selenium offers a balanced energy-to-power density ratio, which can be tailored to the specific application by altering the stoichiometric ratios of sulfur to selenium. This hybrid cathode will be developed by implementing NASA patented holey graphene technology as a highly conductive, ultra-lightweight electrode scaffold. A solid-state electrolyte will be used as a safe, non-flammable replacement to the highly flammable liquid organic electrolytes currently used in SOA lithium-ion batteries. This solid-state lithium-sulfur/selenium cell will be designed into a serial stacking configuration to enable dense packaging of the battery cells. The serial stacking configuration is termed a bipolar stack, which has the advantages of reducing overall cell weight, simplifying the interfaced connections for the cell, and minimizing the cooling requirements for the cell. Lastly, optimization of battery components will occur through a robust and rigorous combination of various computational modeling techniques covering multiple length scales. The expected result will be a fully solid-state battery with operational temperatures up to 150 °C which provides the required energy density, discharge rates, and inherent safety to meet the strict space mission performance criteria. In particular, the wide operational temperature window is required for the large temperature ranges which are experienced across a broad range of potential space missions. This presentation will show initial results that demonstrate the SABERS team has developed a composite carbon-sulfur cathode which exceeds 1100 Wh/kg at a discharge rate of 0.4C, and 804 Wh/kg at a discharge rate of 1C. Additionally, this presentation will show the SABERS team multiscale computational modeling approach and has produced a novel particle dynamics method called Solid Electrolyte Sphere Approximation Model (SESAM). SESAM is on the 1-10 µm scale and provides electromechanical and grain interactions for predictive design guidelines for the experimental team to follow.

Urban Air Mobility (UAM) Vehicles↗
NASA NTRS1993

Space Biology and Medicine: Space and Its Exploration - Volume I

Perhaps one of the greatest gifts that has been given to the people of the world in the last few hundred years has been an emerging sense of the place of our planet and its inhabitants within the context of the vast universe. Our knowledge of the rest of the universe has not come quickly, nor was the process of attaining it only recently begun; however, the unprecedented acceleration of that process has benefitted from a fundamental new aspect of our species that has only manifested itself in the last 30 years or so, the ability to travel in space. Before the space age, the Universe was studied only through observations from the Earth. All that has changed with the beginning of the space age. Machines built by humans have flown to all but one of the nine planets that revolve around our Sun, have ventured billions of miles from the Earth and looked back, and have landed on three other worlds. Spacecraft in orbit around the Earth have viewed the sky at a vast number of electromagnetic wavelengths, detecting the shape of the galaxy and the universe, and even measuring the remnants of the universe's beginning. Human explorers have ventured forth, first for short stays in orbit, then, later, walking upon the Moon and living for long periods in space. As they did so, billions of people on the Earth came to view the Earth in a fundamentally different way, not just as the familiar day to- day backdrop for their lives, but as a small oasis suspended in the night sky above an alien landscape. It is this new view of the Earth that is the true gift of space exploration. Space exploration has at once given us a new perspective on the value of our world, and a new perspective from which to understand how it operates. It has shown us that the Earth is by far the most precious place in the solar system in terms of supporting human life, while revealing that other destinations may still be compelling. The exploration of space has at once become a challenge for humanity to overcome and a path to our common future. But for humanity to embark on this path, we need to understand ourselves in a new environment. As such, an understanding of the biological consequences of and opportunities in space flight is essential. In this, the first volume of a joint U.S./Russian series on space biology and medicine, we describe the current status of our understanding of space and present general information that will prove useful when reading subsequent volumes. Since we are witnesses to the beginning of a new era of interplanetary travel, a significant portion of the first volume will concentrate on the physical and ecological conditions that exist in near and outer space, as well as heavenly bodies from the smallest ones to the giant planets and stars. While space exploration is a comparatively recent endeavor, its foundations were laid much more than 30 years ago, and its history has been an eventful one. In the first part of this volume, Rauschenbach, Sokolskiy, and Gurjian address the "Historical Aspects of Space Exploration" from its beginnings to a present-day view of the events of the space age. The nature of space itself and its features is the focus of the second section of the volume. In the first chapter of the part, "Stars and Interstellar Space," the origin and evolution of stars, and the nature of the portions of space most distant from Earth are described by Galeev and Marochnik. In Chapter 2, Pisarenko, Logachev, and Kurt in "The Sun and Interplanetary Space" bring us to the vicinity of our own solar system and provide a description and discussion of the nearest star and its influence on the space environment that our Earth and the other planets inhabit. In our solar system there are many fascinating objects, remnants of the formation of a rather ordinary star in a rather obscure portion of the galaxy. Historical accident has caused us to be much more curious (and knowledgeable) about "The Inner Planets of the Solar System" than about any of these other objects. In Chapter 3, Marov describes the planets Mercury, Venus, Earth, and Mars, their history and origin, and their environmental conditions, and in Chapter 4 Owen provides similar information about Jupiter, Saturn, Uranus, Neptune, and Pluto, "The Outer Planets of the Solar System." Morrison provides a thorough discussion of "Asteroids, Comets, and Other Small Bodies" in Chapter 5. The understanding of these relics of the formation of the solar system may form the center of our ability to understand the origin of solar systems in general, and of the critical role that the beginning of the solar system had on the prospects for the origin of life and its continued survival and evolution in the face of their recurrent impacts on Earth. In Chapter 6, the first chapter of the third part, Rummel describes the area of "Exobiology," the study of the origin, evolution, and distribution of life in the context of the origin and evolution of the universe. The same processes that have given rise to life on Earth may have given rise to life elsewhere. In Chapter 7, the "Earth and the Biosphere," the nature and function of the Earth are discussed as a specific instance of planetary and biological evolution. The effects of biological processes on the Earth under the influence of human activities are also addressed by Moore and Bartlett in Chapter 7. The final chapter in this section concerns the prospects that life in the universe may be widespread; "SETI," the Search for Extraterrestrial Intelligence, by Billingham and Tarter, presents the arguments for conducting a search for evidence of life elsewhere in the galaxy, and describes the various methods proposed for conducting such a search. While SETI has a distinctly exploration al character, more direct means are available for exploring the solar system around us. The fourth part of the volume addresses this subject of space exploration. Considering the prospects for research on space biology and medicine, the means of providing "Access to Space" are described by Feoktistov and Briggs in Chapter 9. This chapter addresses carriers and launch systems, the unmanned and manned spacecraft that they loft into space, and the task of mission operations by which these precious vessels are monitored, navigated, and controlled. Despite the successes of the past and the capabilities of the present, it is clear that the study of space biology and medicine will be even more rewarding in the future than it has been to date. The work of the next few years that will be undertaken by the U.S. and Russia, both independently and jointly, will focus first on enabling greater capabilities in the exploration of space, and then on using the unique characteristics of the space environment to provide insight and greater understanding into biological systems, their behavior, development, and origin. The chapters of the first volume were written by leaders in their fields from the U.S. and Russia. The material presented summarizes our current understanding of space and its exploration. We understand that the first volume will be of interest not only to medical personnel and biologists, but also to general readers who want information about space beyond their own particular fields of expertise.

Nicogossian, Arnauld E.↗
NASA NTRSDate not supplied

EVA Swab Kit: Tools and Techniques for Collecting Aseptic Samples from Crewed Space Missions

Introduction: When we send humans to search for life on other planets, we'll need to know what we brought with us versus what may already be there. To ensure our crewed spacecraft meet planetary protection requirements—and to protect our science from human contamination—we'll need to assess and verify whether micro-organisms may be leaking/venting from our spacesuits. This requires collecting samples under Extravehicular Activity (EVA) conditions. Detailed, systematic research on forward contamination from robotic spacecraft has been steadily progressing since the Viking missions, but systematic studies of contamination from space suits has not been conducted in many years. The modern EMU (Extravehicular Mobility Unit) suit used by NASA is designed to leak at rates as high as 100 cc/min. Before humans land on Mars there is a critical need to understand the types and quantities of microbes that could be introduced via space suits. The Human Forward Contamination Assessment team at NASA’s Johnson Space Center (JSC) has developed a prototype EVA swab tool [1,2,3,4] designed for use in space to sample cleaned and uncleaned space suits to determine the present day microbial load and eventually the rate of leakage. The ability to assess microbial leakage early in advanced space suit and life support system design cycles will help avoid costly hardware redesign later. Test Objectives: The primary objective of EMU testing was to characterize the type of micro-organisms typically found on or near selected suit pressure joints under suit differential pressure conditions. Most human-borne microbes can fit through a 0.5 to 1.0 µm gap. Knowing which joints are more likely to leak will inform hardware design decisions. Knowing which types of micro-organisms may leak from EVA suits provides a basis for subsequent studies to characterize the viability of those organisms under destination conditions, as well as how far they might spread through natural or human-influenced processes. That data, in turn, will inform exploration mission operations and hardware design. The secondary objective of testing was to evaluate the interface between a fully suited test subject and the EVA swab tool at vacuum. Bulky EVA suits can restrict movement and limit visibility through the helmet visor. Fully suited testing is important for identifying tool design issues prior to flight. At exploration destinations, such as Mars, suited crew may be required to periodically sample their suits as part of an environmental monitoring protocol. Suit Microbial Sampling Results: This report details results of microbial swabs collected from current flight suit configurations worn by crew members assigned to upcoming ISS expedition missions as well as swabs collected from prototype suits intended for use on the Orion spacecraft. These tests were intended to characterize the types of contaminants found on flight suits under current, typical handling conditions. No attempt was made to change suit handling procedures, provide additional sterilization, or to limit typical potential contaminant sources. Using culture based techniques, we cultivated 235 CFU (colony forming units) comprised of 26 bacterial species and one fungal species on the outside of the suits. The fungal species and 14 of the bacterial species were unique to the suit surfaces and were not detected in any of the background samples collected within the chambers. We sequenced 755,434 ribosomal fragments on all of the suit surfaces from swab samples. 557,016 of these sequences represent DNA that survived at least 4 hours at vacuum. These sequences formed 2,464 OTU's (Operational Taxonomic Units, 97% similarity) showing low diversity in the samples. The most abundant sequences that survived vacuum belong to the genera Staphyloccocus, Ralstona, Bacillus and Rhodobacter all of which are common to the human microbiome. [5] See Danko et al., (2021) for more complete details of these first analyses. Further analysis of EVA suit materials with respect to the efficacy of various cleaning protocols and engineered containment solutions is planned to inform suit design for NASA’s Artemis Moon to Mars program crew testing. Swab Tool Function Results: The kit was demonstrated for fit and function in suited subject vacuum tests to determine how well the tool worked as an aseptic microbial sampling device as well as to identify any design elements that could be upgraded for EVA task specific improvement. It was found that sample acquisition efficacy could be enhanced by redesign of the sample canister to end-effector interface. Several modifications of the sample caddy assemblies to optimize EVA safety and functionality were also identified. Consequently, fabrication of the redesigned sample canister to end-effector assembly interfaces and and the sample caddy assemblies are required. Fabrication of sixteen flight sample canister assemblies (8 per each of two EVA Swab Kits) and two sample caddy assemblies are in process to be followed by hardware testing and certification to produce two flight-certified EVA Swab Kits for transport to ISS no earlier than summer of 2022. Sampling Strategy: The International Space Station is an ideal testbed for systematic studies of contamination from crewed vehicles since it has been continuously occupied for 20 years and exposed to non-terrestrial conditions. We will sample the exterior of the ISS during EVA using a purpose-built swab tool capable of maintaining sterility while undergoing temperature changes from -151 to +121°C under hard vacuum. Prior to each EVA, the project team will work with ISS mission managers to identify precise sampling locations, which will vary by EVA based on the translation paths and worksites scheduled for that particular EVA. Ideally, translation path handrails and areas near ECLSS (Environmental Control and Life Support System) external vent openings on a spacecraft would be assessed. There are currently more than a dozen ECLSS external vents on the ISS. Some are connected to systems that vent waste products, while others are intended to equalize cabin pressure. As EVA opportunity allows, microbial samples from any of these external vents would provide a valuable data point, though some will be more useful than others. Four criteria have been identified to help prioritize sampling sites near vents: • EVA Accessibility: To minimize cost, it is desired to piggy-back onto a planned EVA. Therefore, the sampling location must be readily accessible by an EVA crew • Type of Vented Products: Vent products that have been in direct contact with crew, such as cabin air, are more likely to contain microorganisms than vent products associated with isolated systems, such as experiment module combustion products. • Mass of Vented Products: Higher-flow vents are more likely to contain detectible levels of microbial contaminants than lower-flow vents. • Local Environment: Sample locations with relatively benign local conditions, such as warm surfaces shielded from direct ultraviolet (UV) radiation exposure, may be more likely to support microbial growth than locations with harsher local environmental conditions. Because EVA accessibility is the most important criteria, the proposal team worked with an astronaut and flight controllers using the Dynamic Onboard Ubiquitous Graphics (DOUG) tool. The DOUG virtual environment allows an operator to “fly” around the current ISS vehicle configuration to assess EVA translation paths, attach points, and keep-out zones. While analysis on station or rapid return to Earth would be preferable, samples collected from the exterior of the ISS have already been exposed to temperature variations between -157 and +121 °C as well as hard vacuum. Therefore, they should be fairly stable and robust. We hypothesize that samples collected from the ISS exterior could be stored for up to 6 months at -80°C without degradation. Sample canisters will be returned to Earth while frozen at -80°C for analysis, and sterilized canisters can be re-flown back to ISS to support additional sampling opportunities Relevance to NASA Exploration Objectives: These data will allow us to identify new or improved methods, technologies, and procedures for spacecraft sterilization and leakage mitigation to minimize the amount of contamination introduced to the environment by human explorers. This work is funded by NASA research grant: NNH18ZDA001N-PPR References: [1] Bell, M.S. et al. (2015) LPS XLVI, Abst. #1832 [2] Rucker et al. (2018) 42nd COSPAR (PPP.3) [3] Bell, M.S. et al. (2019) Mars Extant Life Conference, Abst. #5096.[4] Bell, M.S. et al., (2020) 43rd COSPAR (BO.2).[5] Danko D, et.al.,(2021)Front.Microbiol.12:608478.

Mary Suzanne Bell↗
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 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↗