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NASA NTRS2006

Mineralogy at Gusev Crater and Meridiani Planum from the Moessbauer Spectrometers on the Mars Exploration Rovers

The Moessbauer spectrometers on the twin MER rovers Spirit and Opportunity have provided significant new information on the distribution of iron among its oxidation states, the identification of the mineralogical composition of iron-bearing phases, and the distribution of iron among those phases for rock and soil at Gusev Crater and Meridiani Planum. The plains of Gusev Crater are dominated by olivine-bearing basalt (approximately Fo(60)) and Fe(3+)/Fe(total)=0.1 - 0.5. The oxide mineral generally present is magnetite. In contrast, initial results for the Columbia Hills are consistent with the presence of hematite and a ferrous iron phase, possibly pyroxene. Gusev spectra also have a ferric doublet (not jarosite) that is tentatively associated with nano-phase ferric oxide. A wider diversity of material is present at Meridiani Planum. Significantly, jarosite-bearing outcrop is present throughout the region, with good exposures in impact craters such as Eagle and Endurance (Fe(3+)/Fe (total) approx. 0.9). The Moessbauer identification of jarosite (a hydroxyl-bearing sulfate mineral) is evidence for aqueous, acid-sulfate processes on Mars. Hematite is observed within the outcrop matrix and in the spheroidal particles (Blueberries) found within the outcrop and as a surface lag. An isolated rock (Bounce Rock) was the only sample at either landing site whose iron-bearing phase was dominated by pyroxene. The basaltic sand in the central portion of Eagle crater, in the intercrater plains, and between slabs of outcrop at both Eagle and Endurance craters is olivine-bearing basalt. The widespread occurrence of olivine-bearing basalt at both MER landing sites implies that physical, rather than chemical, weathering processes dominate at the surface of contemporary Mars.

Morris, Richard V.↗

NASA NTRS2012

Distributed Assimilation of Satellite-based Snow Extent for Improving Simulated Streamflow in Mountainous, Dense Forests: An Example Over the DMIP2 Western Basins

Snow cover area affects snowmelt, soil moisture, evapotranspiration, and ultimately streamflow. For the Distributed Model Intercomparison Project - Phase 2 Western basins, we assimilate satellite-based fractional snow cover area (fSCA) from the Moderate Resolution Imaging Spectroradiometer, or MODIS, into the National Weather Service (NWS) SNOW-17 model. This model is coupled with the NWS Sacramento Heat Transfer (SAC-HT) model inside the National Aeronautics and Space Administration's (NASA) Land Information System. SNOW-17 computes fSCA from snow water equivalent (SWE) values using an areal depletion curve. Using a direct insertion, we assimilate fSCAs in two fully distributed ways: 1) we update the curve by attempting SWE preservation, and 2) we reconstruct SWEs using the curve. The preceding are refinements of an existing simple, conceptually-guided NWS algorithm. Satellite fSCA over dense forests inadequately accounts for below-canopy snow, degrading simulated streamflow upon assimilation during snowmelt. Accordingly, we implement a below-canopy allowance during assimilation. This simplistic allowance and direct insertion are found to be inadequate for improving calibrated results, still degrading them as mentioned above. However, for streamflow volume for the uncalibrated runs, we obtain: (1) substantial to major improvements (64-81 %) as a percentage of the control run residuals (or distance from observations), and (2) minor improvements (16-22 %) as a percentage of observed values. We highlight the need for detailed representations of canopy-snow optical radiative transfer processes in mountainous, dense forest regions if assimilation-based improvements are to be seen in calibrated runs over these areas.

Yatheendradas, Soni↗

NASA NTRS2020

Gila Water Resources III - Modeling the Impacts of Post-fire Restoration Methods on Vegetation Recovery in the Gila National Forest

In recent years, wildfires in New Mexico’s Gila National Forest have become increasingly common and more severe. Wildfires can have powerful impacts on hydrology and soil stability, including erosion, flooding, and debris-flows that threaten lives and infrastructure downstream. Vegetation restoration treatments like seeding and mulching can mitigate these effects and facilitate ecosystem recovery. Understanding the effectiveness of various restoration methods is vital to planning a cost-effective and successful post-fire recovery strategy. The immediate response to a fire on US Forest Service land is coordinated by a Burned Area Emergency Response (BAER) team, a group responsible for mitigating immediate post-fire risks to human life, property, and critical natural and cultural resources. This study created a proof-of-concept methodology for a decision-support tool designed to help BAER teams identify the restoration treatments most likely to succeed in a given burned area. Leveraging random forest regression, Google Earth Engine, and Landsat 7 and 8 Earth observations, this study modeled vegetation recovery after the 2013 Silver Fire for seeded areas, seeded/mulched areas, and untreated areas. Treatment type and initial burn severity were the largest drivers of vegetation recovery across the landscape. Seeded/mulched areas showed higher recovery levels than untreated areas three months post-fire, but by four years post-fire, treated and untreated areas displayed similar recovery levels. To produce a robust predictive tool for the Gila National Forest, the model should be trained on many more fires and incorporate post-fire weather conditions into the process. Such a model will help partners ensure efficient resource use and plan effective post-fire restoration strategies.

DEVELOP Project Summary↗

DOE OSTI2021

Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon

The Weather Research and Forecasting (WRF) model can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations but subjected to uncertainties associated with model physical parameterizations. In this study, we propose a framework to quantify and reduce model physical parameterization uncertainties associated with surface fluxes and L-A coupling. An ensemble of WRF simulations with different physical schemes is used to simulate surface fluxes and land-atmosphere coupling strength over the Amazon region. The physical parameterizations investigated include cloud microphysics (MP), land surface processes (LSM), planetary boundary layer (PBL), surface layer (SL), and cumulus (CU). We perform 120 ensemble simulations using the WRF model and different combinations of six MPs, three LSMs, six PBLs and SLs and three CUs. The measurements from the GoAMAZON field campaign and satellite data are used to evaluate model performance. A Multi-way analysis of variance (ANOVA) approach is applied to quantify the relative importance of different physics processes on L-A coupling. The Tukey’s test is used to sort schemes that have no significant differences into one group. The suite of physics that result in the best simulations of the corresponding variables are selected based on the Taylor skill score. Results show that the relative importance of processes and their interaction vary with the variables of interest. For example, CU was the most important process in modulating soil moisture, 2 m-humidity, latent heat, and net radiation. LSM showed dominant effects on 2 m-temperature and also has the largest impact on sensible heat and the lifting condensation level. The best physical parameterization ensembles show much narrower ranges of the variables of interest than the priori ensemble. Results of this study show the roles of different physical processes in modulating L-A interactions, quantify model uncertainties from physical processes, and provide insights for improving the model physics parameterizations.

Wang, Chen↗

NASA NTRSDate not supplied

Determining Surface Exposure Ages of Regolith Grains in Lunar Core Sample 73002 By Investigating Solar Particle Irradiation Damage

Space weathering causes the surface soils of airless bodies like the Moon to be micro-structurally and chemically altered by exposure to micrometeoroid bombardment and solar wind irradiation. These alterations accumulate in surface grains with continued residence in the uppermost millimeters of regolith. Low energy solar wind ions (largely H+ and He+) produce a layer of radiation damage as they become implanted in grain surfaces. Also present are solar energetic particle (SEP) tracks, which are nanoscale lineations within grain interiors formed by heavy, high-energy ions (mainly Fe group nuclei) penetrating up to millimeters below the surface. Recent work has determined that the width of solar wind-damaged rims on anorthite and olivine, and their respective SEP track densities are correlated with each other and with grain surface exposure age. Core sample 73002, recently released under the Apollo Next Generation Sample Analysis (ANGSA) Program, has provided an opportunity to study material collected from the light mantle formation in the Taurus-Littrow Valley during Apollo 17. The light mantle is thought to have been deposited vial and slide originating from the neighboring South Massif. Spectral analysis of bulk core soils found a plagioclase rich composition, confirming a highlands origin of the formation. Spectral profiles and ferromagnetic resonance (FMR) measurements of bulk soils sampled at cm-intervals from 73002 indicate that regolith up to~9.5 cm in depth have maturity indices consistent with longer durations of surface processing than samples deeper in the core. Thus, this upper ~9 cm layer likely represents the maximum extent of an in-situ reworking zone which may have persisted over 5 to 17million years. Here we present an analysis of grain exposure ages within the in-situ reworking zone of 73002derived from SEP track density measurements via transmission electron microscopy.

J A McFadden↗

NASA NTRS2019

Definition of a Technology Validation Mission for P-band Reflectometry using Signals of Opportunity

Root-Zone Soil Moisture (RZSM) (moisture profile in the top meter of soil) and Snow Water Equivalent (SWE) (total snow pack water content) are identified as priority target variables in the ESAS 2017 decadal survey [1] with critical roles in hydrology and water management. RZSM estimates are vital for understanding multiple Earth system processes and forecasting (for example, droughts [2]). Simultaneous knowledge of surface and RZSM could enable a breakthrough in estimating key unobserved hydrologic fluxes and reduce uncertainty in net ecosystem exchange (NEE), carbon balance [3] discharge estimates, and crop yield forecasts [4] .With the high albedo and insulating properties of snow, monitoring, SWE accumulation would provide a key constraint on the potential runoff during spring ablation while monitoring SWE disappearance rates would provide a key constraint on SWE partition into runoff vs. infiltration/recharge. [5] demonstrated that knowledge of early-spring SWE generally contributes most to streamflow forecast skill in the Western U.S. SWE is also a source of water storage that provides the water resources during spring snowmelt. Despite such potentially transformative contributions, accurate RZSM and SWE measurements are unattainable with current technology. While active/passive L-band methods (e.g. SMAP, SMOS) can reliably retrieve surface soil moisture in the top 5 cm of soil [6], [7]. RZSM estimates are only available through model assimilation of brightness temperatures with a radiative transfer and land surface models [8]. SWE estimation uses multi-frequency passive microwave techniques (e.g. [9]-[11]), which have significant problems with deeper snow and in forested and mountainous environments [12]. Signals of opportunity (SoOp) in P-band (200-400 MHz) is a new remote sensing technique with the capability of estimating both essential hydrologic variables, RZSM and SWE, circumventing many of the aforementioned limitations under all weather conditions day and night. SoOp is the re-utilization of existing powerful satellite transmissions within bands allocated for communications or navigation. P-band SoOp sensitivity to soil moisture has been demonstrated in an airborne experiment over Oklahoma in 2016 [13]. Recent theory [14] and experiments [15] have also confirmed that the reflection coefficient phase is proportional to SWE.

Garrison, J. L.↗

NASA NTRS1999

Estimates of Long Term Surface Soil Moisture in the Midwestern U.S. Derived from Satellite Microwave Observations

Soil moisture is a key component of the water and energy balances of the Earth's surface, and has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, soil moisture is often somewhat difficult to measure accurately in both space and time, especially at large spatial scales. Soil moisture is highly variable, and while point measurements are typically quite accurate, subsequent areal averaging of these measurements often leads to large errors. Since remotely sensed land surface observations are already a spatially averaged or areally integrated value, they are a logical input parameter to regional or larger scale land process models. A database of long-term soil moisture was compared to satellite microwave observations over test sites in the Midwestern United States. Ground measurements of average volumetric surface soil moisture in the top ten cm were made bimonthly at 19 locations throughout the state of Illinois. Nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and a temporal frequency over the test area of about 3 nighttime orbits per week. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Because the time of satellite coverage does not always coincide with the ground measurements of soil moisture, the existing ground data were used to calibrate a water balance for the top IO cm surface layer in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area average soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture, Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.

Owe, M.↗

DOE OSTI2022

Effects of physical, chemical, and biological ageing on the mineralization of pine wood biochar by a Streptomyces isolate

If biochar is to be used for carbon (C) management, we must understand how weathering or ageing affects biochar C mineralization. Here, we incubated aged and unaged eastern white pine wood biochar produced at 350 and 550°C with a Streptomyces isolate, a putative biochar-decomposing microbe. Ageing was accelerated via three different processes, namely, (a) physical ageing–subjecting biochar to alternating freeze-thaw and wet-dry cycles, (b) chemical ageing–treating biochar with concentrated hydrogen peroxide and (c) biological ageing–incubating biochar in the presence of nutrients and microorganisms. Elemental composition and surface chemistry (Fourier Transform Infrared spectroscopy) of biochar samples were compared before and after ageing. Biochar C mineralization between ageing treatments was significantly different in the case of 350°C biochar ( p value = 0.03). Among the 350°C biochars, physical ageing resulted in the greatest increase (by 103%) in biochar C mineralization ( p value = 0.05). However, in the case of 550°C biochar, ageing did not result in a significant change in biochar C mineralization ( p value = 0.40). Biochar C mineralization was positively correlated with an increase in O/C ratio post-ageing ( r s = 0.86, p value = 0.01). In the case of 350°C biochar, surface oxidation during ageing enhanced biochar degradation by the isolate. For 550°C biochar, however, ageing did not significantly increase biochar C mineralization, likely due to high condensed aromatic C content and lower surface oxidation during ageing. The results from our study suggest that low temperature aged biochar is more susceptible to biological degradation by soil microbes. These findings have implications for the use of biochar for long term C storage in soils.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Basin-Scale Assessment of the Land Surface Water Budget in the National Centers for Environmental Prediction Operational and Research NLDAS-2 Systems

The purpose of this study is to evaluate the components of the land surface water budget in the four land surface models (Noah, SAC-Sacramento Soil Moisture Accounting Model, (VIC) Variable Infiltration Capacity Model, and Mosaic) applied in the newly implemented National Centers for Environmental Prediction (NCEP) operational and research versions of the North American Land Data Assimilation System version 2 (NLDAS-2). This work focuses on monthly and annual components of the water budget over 12 National Weather Service (NWS) River Forecast Centers (RFCs). Monthly gridded FLUX Network (FLUXNET) evapotranspiration (ET) from the Max-Planck Institute (MPI) of Germany, U.S. Geological Survey (USGS) total runoff (Q), changes in total water storage (dS/dt, derived as a residual by utilizing MPI ET and USGS Q in the water balance equation), and Gravity Recovery and Climate Experiment (GRACE) observed total water storage anomaly (TWSA) and change (TWSC) are used as reference data sets. Compared to these ET and Q benchmarks, Mosaic and SAC (Noah and VIC) in the operational NLDAS-2 overestimate (underestimate) mean annual reference ET and underestimate (overestimate) mean annual reference Q. The multimodel ensemble mean (MME) is closer to the mean annual reference ET and Q. An anomaly correlation (AC) analysis shows good AC values for simulated monthly mean Q and dS/dt but significantly smaller AC values for simulated ET. Upgraded versions of the models utilized in the research side of NLDAS-2 yield largely improved performance in the simulation of these mean annual and monthly water component diagnostics. These results demonstrate that the three intertwined efforts of improving (1) the scientific understanding of parameterization of land surface processes, (2) the spatial and temporal extent of systematic validation of land surface processes, and (3) the engineering-oriented aspects such as parameter calibration and optimization are key to substantially improving product quality in various land data assimilation systems.

NLDAS-2↗

NASA NTRS2003

Modeling, Simulation, and Forecasting of Subseasonal Variability

A planning workshop on "Modeling, Simulation and Forecasting of Subseasonal Variability" was held in June 2003. This workshop was the first of a number of meetings planned to follow the NASA-sponsored workshop entitled "Prospects For Improved Forecasts Of Weather And Short-Term Climate Variability On Sub-Seasonal Time Scales" that was held April 2002. The 2002 workshop highlighted a number of key sources of unrealized predictability on subseasonal time scales including tropical heating, soil wetness, the Madden Julian Oscillation (MJO) [a.k.a Intraseasonal Oscillation (ISO)], the Arctic Oscillation (AO) and the Pacific/North American (PNA) pattern. The overarching objective of the 2003 follow-up workshop was to proceed with a number of recommendations made from the 2002 workshop, as well as to set an agenda and collate efforts in the areas of modeling, simulation and forecasting intraseasonal and short-term climate variability. More specifically, the aims of the 2003 workshop were to: 1) develop a baseline of the "state of the art" in subseasonal prediction capabilities, 2) implement a program to carry out experimental subseasonal forecasts, and 3) develop strategies for tapping the above sources of predictability by focusing research, model development, and the development/acquisition of new observations on the subseasonal problem. The workshop was held over two days and was attended by over 80 scientists, modelers, forecasters and agency personnel. The agenda of the workshop focused on issues related to the MJO and tropicalextratropical interactions as they relate to the subseasonal simulation and prediction problem. This included the development of plans for a coordinated set of GCM hindcast experiments to assess current model subseasonal prediction capabilities and shortcomings, an emphasis on developing a strategy to rectify shortcomings associated with tropical intraseasonal variability, namely diabatic processes, and continuing the implementation of an experimental forecast and model development program that focuses on one of the key sources of untapped predictability, namely the MJO. The tangible outcomes of the meeting included: 1) the development of a recommended framework for a set of multi-year ensembles of 45-day hindcasts to be carried out by a number of GCMs so that they can be analyzed in regards to their representations of subseasonal variability, predictability and forecast skill, 2) an assessment of the present status of GCM representations of the MJO and recommendations for future steps to take in order to remedy the remaining shortcomings in these representations, and 3) a final implementation plan for a multi-institute/multi-nation Experimental MJO Prediction Program.

Waliser, Duane↗

NASA NTRS2024

Kentucky Disasters: Multi-Hazard Approach to Mapping Flood Susceptibility and Vulnerability in Kentucky

Flooding is the most common and costly natural disaster in Kentucky, with major flood events in 2022 and 2023 highlighting the need for flood risk assessment. In partnership with the National Weather Service Jackson and Paducah Forecast Offices and the Kentucky Climate Center, we mapped flood risk in Kentucky using a multi-hazard approach that considered two dimensions of risk: flood susceptibility based on a weighted combination of seven physical factors and flood vulnerability based on 13 socioeconomic and infrastructure factors. We additionally analyzed NASA Soil Moisture Active Passive (SMAP) observations of surface soil moisture to explore the utility of SMAP observations for future analysis of flood risk. By analyzing flood susceptibility, we found that with equal rainfall, western Kentucky generally displays a higher propensity to flood than eastern Kentucky. In contrast, our flood vulnerability analysis indicated that more vulnerable areas were generally concentrated in the eastern part of the state. Through a combined perspective, our flood risk analysis identified much of the state as having moderate degrees of flood susceptibility and vulnerability. Our parallel analysis of antecedent soil moisture found that SMAP soil moisture levels were variable in the months leading up to each flood event but were drier than normal in the month prior to the 2023 event, as shown by negative soil moisture anomalies. These results were limited by challenges with weighting input parameters and a lack of validation but overall demonstrate the feasibility of using GIS and Earth observations for mapping flood risk and soil moisture.

analytic hierarchy process↗

DOE OSTI2026

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

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

Chicago↗

NASA NTRS2014

The Global Albedo of the Moon at 1064 nm from LOLA

The Lunar Orbiter Laser Altimeter (LOLA) measures the backscattered energy of the returning altimetric laser pulse at its wavelength of 1064 nm, and these data are used to map the reflectivity of the Moon at zero-phase angle with a photometrically uniform data set. Global maps have been produced at 4 pixels per degree (about 8 kilometers at the equator) and 2 kilometers resolution within 20 deg latitude of each pole. The zero-phase geometry is insensitive to lunar topography, so these data enable characterization of subtle variations in lunar albedo, even at high latitudes where such measurements are not possible with the Sun as the illumination source. The geometric albedo of the Moon at 1064 nm was estimated from these data with absolute calibration derived from the Kaguya Multiband Imager and extrapolated to visual wavelengths. The LOLA estimates are within 2 sigma of historical measurements of geometric albedo. No consistent latitude-dependent variations in reflectance are observed, suggesting that solar wind does not dominate space weathering processes that modify lunar reflectance. The average normal albedo of the Moon is found to be much higher than that of Mercury consistent with prior measurements, but the normal albedo of the lunar maria is similar to that of Mercury suggesting a similar abundance of space weathering products. Regions within permanent shadow in the polar regions are found to be more reflective than polar surfaces that are sometimes illuminated. Limiting analysis to data with slopes less than 10 deg eliminates variations in reflectance due to mass wasting and shows a similar increased reflectivity within permanent polar shadow. Steep slopes within permanent shadow are also more reflective than similar slopes that experience at least some illumination. Water frost and a reduction in effectiveness of space weathering are offered as possible explanations for the increased reflectivity of permanent shadow; porosity is largely ruled out as the sole explanation. The south polar crater Shackleton is found to be among the most reflective craters in its size range globally but is not the most reflective, so mass wasting cannot be ruled out as a cause for the crater's anomalous reflectance. Models of the abundance of ice needed to account for the reflectance anomaly range from 3 to 14% by weight or area depending on assumptions regarding the effects of porosity on reflectance and whether ice is present as patches or is well mixed in the regolith. If differences in nanophase iron abundances are responsible for the anomaly, the permanently shadowed regions have between 50 and 80% the abundance of nanophase iron in mature lunar soil.

PSR↗

NASA NTRS2011

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite-and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected as a co-winner of NASA?s 2005 Software of the Year award.LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has e volved from two earlier efforts -- North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations.In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins". LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling be enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation, who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs.LIS has also recently been demonstrated for multi-model data assimilation using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature.Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation.Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems

Peters-Lidard, Christa D.↗

NASA NTRS2000

Estimating Long Term Surface Soil Moisture in the GCIP Area From Satellite Microwave Observations

Soil moisture is an important component of the water and energy balances of the Earth's surface. Furthermore, it has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, accurate estimates of surface soil moisture are often difficult to make, especially at large spatial scales. Soil moisture is a highly variable land surface parameter, and while point measurements are usually accurate, they are representative only of the immediate site which was sampled. Simple averaging of point values to obtain spatial means often leads to substantial errors. Since remotely sensed observations are already a spatially averaged or areally integrated value, they are ideally suited for measuring land surface parameters, and as such, are a logical input to regional or larger scale land process models. A nine-year database of surface soil moisture is being developed for the Central United States from satellite microwave observations. This region forms much of the GCIP study area, and contains most of the Mississippi, Rio Grande, and Red River drainages. Daytime and nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and an orbital frequency over any pixel-sized area of about 2 daytime and 2 nighttime passes per week. Ground measurements of surface soil moisture from various locations throughout the study area are used to calibrate the microwave observations. Because ground measurements are usually only single point values, and since the time of satellite coverage does not always coincide with the ground measurements, the soil moisture data were used to calibrate a regional water balance for the top 1, 5, and 10 cm surface layers in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area spatially averaged soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.

Owe, Manfred↗

NASA NTRS2010

Allophane on Mars: Evidence from IR Spectroscopy and TES Spectral Models

Allophane is an alteration product of volcanic glass and a clay mineral precursor that is commonly found in basaltic soils on Earth. It is a poorly-crystalline or amorphous, hydrous aluminosilicate with Si/Al ratios ranging from approx.0.5-1 [Wada, 1989]. Analyses of thermal infrared (TIR) spectra of the Martian surface from TES show high-silica phases at mid-to-high latitudes that have been proposed to be primary volcanic glass [Bandfield et al., 2000; Bandfield, 2002; Rogers and Christensen, 2007] or poorly-crystalline secondary silicates such as allophane or aluminous amorphous silica [Kraft et al., 2003; Michalski et al., 2006; Rogers and Christensen, 2007; Kraft, 2009]. Phase modeling of chemical data from the APXS on the Mars Exploration Rover Spirit suggest the presence of allophane in chemically weathered rocks [Ming et al., 2006]. The presence of allophane on Mars has not been previously tested with IR spectroscopy because allophane spectra have not been available. We synthesized allophanes and allophanic gels with a range of Si/Al ratios to measure TIR emission and VNIR reflectance spectra and to test for the presence of allophane in Martian soils. VNIR reflectance spectra of the synthetic allophane samples have broad absorptions near 1.4 m from OH stretching overtones and 1.9 m from a combination of stretching and bending vibrations in H2O. Samples have a broad absorption centered near 2.25 microns, from AlAlOH combination bending and stretching vibrations, that shifts position with Si/Al ratio. Amorphous silica (opaline silica or primary volcanic glass) has been identified in CRISM spectra of southern highland terrains based on the presence of 1.4, 1.9, and broad 2.25 m absorptions [Mustard et al., 2008]; however, these absorptions are also consistent with the presence of allophane. TIR emission spectra of the synthetic allophanes show two spectrally distinct types: Si-rich and Al-rich. Si-rich allophanes have two broad absorptions centered near 1080 and 430 cm-1 from Si(Al)-O stretching and Si(Al)-O bending vibrations, respectively, and Al-rich allophanes have three broad absorptions centered near 950, 540, and 430 cm-1. We used a spectral library commonly used to deconvolve TES spectra and four allophane spectra to model nine spectrally distinct regions on Mars [from Rogers et al., 2007]. Regions previously modeled with high-silica phases contain significant amounts of allophane (>10 vol.%) in our models. Our models of northern Acidalia, the type locality for surface type 2 materials, contain 40 vol.% Si-rich allophane. The presence of allophane in multiple surface regions of Mars indicates a more widespread occurrence of low-temperature aqueous alteration at moderate pH than has been previously recognized. The regional variations in modeled abundances and the types of allophane (Si- vs. Al-rich) suggest regional differences in Mars weathering processes.

Ming, Douglas W.↗

DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗

NASA NTRS1999

The MECA Payload as a Dust Analysis Laboratory on the MSP 2001 Lander

In a companion abstract, the "Mars Environmental Compatibility Assessment" (MECA) payload for Mars Surveyor Program 2001 (MSP 2001) is described in terms of its capabilities for addressing exobiology on Mars. Here we describe how the same payload elements perform in terms of gathering data about surface dust on the planet. An understanding of the origin and properties of dust is important to both human exploration and planetary geology. The MECA instrument is specifically designed for soil/dust investigations: it is a multifunctional laboratory equipped to assess particulate properties with wet chemistry, camera imagery, optical microscopy (potentially with LTV fluorescence capability), atomic force microscopy (AFM; potentially with mineral-discrimination capabilities), electrometry, active & passive external materials-test panels, mineral hardness testing, and electrostatic & magnetic materials testing. Additionally, evaluation of soil chemical and physical properties as a function of depth down to about 50 cm will be facilitated by the Lander/MECA robot arm on which the camera (RAC) and electrometer are mounted. Types of data being sought for the dust include: (1) general textural and grain-size characterization of the soil as a whole --for example, is the soil essentially dust with other components or is it a clast-supported material in which dust resides only in the clast interstices, (2) size frequency distribution for dust particles in the range 0.01 to 10.00 microns, (3) particle-shape distribution of the soil components and of the fine dust fraction in particular, (4) soil fabric such as grain clustering into clods, aggregates, and cemented/indurated grain amalgamations, as well as related porosity, cohesiveness, and other mechanical soil properties, (5) cohesive relationship that dust has to certain types of rocks and minerals as a clue to which soil materials may be prime hosts for dust "piggybacking", (6) particle, aggregate, and bulk soil electrostatic properties, (7) particle hardness, (8) particle magnetic properties, (9) bulk dust geochemistry (solubility, reactivity, ionic and mineral species). All of these quantities are needed in order for the human exploration program to make assessments of hazards on Mars, and to better enable the production on earth, of soil/dust simulants that can act as realistic test materials in terms of those properties that render dust a contaminant.Such properties include the small grain size that enables penetration of space-suit joints, mechanical interfaces and bearings, seals, etc., and presents difficulty for filtration systems. Size also plays a critical role in the potential for lung disease in long-term habitats. The properties of grain shape and hardness are important parameters in determining the abrasiveness of dust as it enters mechanical systems, or bombards helmet visors and habitat windows in dust-laden winds. Adhesive electrostatic and magnetic properties of dust will be prime causes of contamination of space suits and equipment. Contamination causes mechanical malfunction, tracking of dirt into habitats, "piggybacking" of toxins on dust into habitats, changes in albedo and efficiency of solar arrays and heat exchangers, and changes in electrical conductivity of suit surfaces and other materials that may have specific safety requirements regarding electrical conductivity. Other potentially hazardous properties of dust include the possibility of high solubility of some component grains (rendering them reactive), and toxicity of some materials --grains of superoxidants and heavy metals (there is always the slim, but not inconceivable possibility of biogenic components such as spores). Because Mars has no active surface aqueous regime, volcanic emissions, meteoritic debris, weathering products, and photochemical products of Mars have nowhere to go except reside in the surface; there are few mechanical or chemical (buffering) processes to remove the accumulation of eons. From a planetology perspective, there are many enigmatic issues relating to dust and the aeolian regime in general. MECA will be able to address many questions in this area. For example, if MECA determines a particular particle size distribution (size and sorting values), it will be possible to make inferences about the origin of the dust - - is it all aeolian, or a more primitive residue of weathering, volcanic emissions, and meteoritic gardening? Trenching with the Lander/MECA robot arm will enable local stratigraphy to be determined in terms of depositional rates, amounts and cyclicity in dust storms and/or local aeolian transport. Grain shape will betray the origin of the dust fragments as being the product of recent or ancient weathering, or the comminution products of aeolian transport --the dust-silt ratio might be a measure of aeolian comminution energy. Additional information is contained in the original.

Marshall, J.↗