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DOE OSTI2022

Hyperspectral Reflectance for Measuring Canopy‐Level Nutrients and Photosynthesis in a Salt Marsh

Abstract Salt marsh ecosystems are underrepresented in process‐based models due to their unique location across the terrestrial–aquatic interface. Particularly, the role of leaf nutrients on canopy photosynthesis (F A ) remains unclear, despite their relevance for regulating vegetation growth. We combined multiyear information of canopy‐level nutrients and eddy covariance measurements with canopy surface hyperspectral remote sensing (CSHRS) to quantify the spatial and temporal variability of F A in a temperate salt marsh. We found that F A showed a positive relationship with canopy‐level N at the ecosystem scale and for areas dominated by Spartina cynosuroides , but not for areas dominated by short S. alterniflora . F A showed a positive relationship with canopy‐level P, K, and Na, but a negative relationship with Fe, for areas associated with S. cynosuroides , S. alterniflora , and at the ecosystem scale. We used partial least squares regression (PLSR) with CSHRS and found statistically significant data–model agreements to predict canopy‐level nutrients and F A . The red‐edge electromagnetic region and ∼770 nm showed the highest contribution of variance in PLSR models for canopy‐level nutrients and F A , but we propose that underlying sediment biogeochemistry can complicate interpretation of reflectance measurements. Our findings highlight the relevance of spatial variability in salt marshes vegetation and the promising application of CSHRS for linking information of canopy‐level nutrients with F A . We call for further development of canopy surface hyperspectral methods and analyses across salt marshes to improve our understanding of how these ecosystems will respond to global environmental change.

Environmental Sciences & Ecology↗

DOE OSTI2026

Dynamical Downscaling of Earth System Model Data for Energy System Analysis

Assessing energy resources (e.g., solar, wind, and hydro) under future scenarios requires datasets with sufficient spatial and temporal detail to capture variability and extreme events. While global-scale Earth System Model (ESM) projections are widely used, their coarse resolution limits direct application to regional energy system analyses. Dynamical downscaling offers a robust approach to generate physically consistent, fine-scale datasets that better represent local atmospheric processes impacting energy resources. In this work, we present a two-stage approach for producing high-resolution historical and future projections over the contiguous United States (CONUS). First, we optimize the Weather Research and Forecasting (WRF) model configuration for energy-relevant variables - solar irradiance, wind speed, and precipitation - by conducting ERA5-driven simulations at 8-km and 28-km resolution. Multiple physics schemes and model configurations within the WRF are evaluated against observational datasets including the National Solar Radiation Database (NSRDB), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the Stage IV multi-radar/multi-sensor precipitation product for the CONUS domain. Using the best-performing configuration, we dynamically downscale MPI-ESM1-2-HR simulations for 2000-2060 under SSP2-4.5 and SSP5-8.5 scenarios at 4-km spatial and hourly temporal resolution. This presentation will provide a comprehensive analysis of the results from multiple numerical experiments and high-resolution ESM projections. In addition, we will discuss potential applications of our high-resolution datasets within the energy sector and outline future research avenues dedicated to evaluating how extreme weather events influence system performance and resilience.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1997

Bio-Optical Properties of the Arabian Sea as Determined by In Situ and Sea WiFS Data

The overall objective of this work was to characterize optical and fluorescence properties in the euphotic zone during two British Ocean Flux Study (BOFS) Arabian Sea cruises. This was later expanded in 1995 to include three U.S. JGOFS Arabian Sea Cruises. The region was to be divided into one or more "bio-optical provinces," within each of which a single set of regression models was to be developed to relate the vertical distribution of irradiance attenuation and normalized fluorescence (SF and NF) to remote sensing reflectance and diffuse attenuation coefficient. The working hypothesis was that over relatively large spatial and temporal scales, the vertical profiles of bio-optical properties were predictable. The specific technical objectives were: (1) To characterize the vertical distribution of the inherent and apparent optical properties by measuring downwelling and upwelling irradiances, upwelling radiances, scalar irradiance of PAR, and beam transmissions at each station - from these data, spectral diffuse attenuation coefficients, irradiance reflectances, remote sensing reflectances, surface-leaving radiances and beam attenuation coefficients were determined; (2) To characterize the spectral absorption of total particulate, detrital, and dissolved organic material at each station from discrete water samples; (3) To describe the vertical distribution of photoadaptive properties in the water column by measuring profiles of stimulated (SF) and natural (NF) fluorescence and examining relationships between SF and NF as a function of diffuse optical depth, pigment biomass and primary productivity; and (4) To establish locally derived, in-water algorithms relating remote sensing reflectance spectra to diffuse attenuation coefficients, phytoplankton pigment concentrations and primary productivity, through intercomparisons with in situ measurements, for application to SeaWiFS data.

Trees, Charles C.↗

NASA NTRS2021

Space-Borne Cloud-Native Satellite-Derived Bathymetry (SDB) Models Using ICESat-2 And Sentinel-2

Shallow nearshore coastal waters provide a wealth of societal, economic and ecosystem services, yet their topographic structure is poorly mapped due to a reliance upon expensive and time intensive methods. Space‐borne bathymetric mapping has helped address these issues, but has remained largely dependent upon in situ measurements. Here we fuse ICESat‐2 lidar data with Sentinel‐2 optical imagery, within the Google Earth Engine cloud platform, to create openly available spatially continuous high‐resolution bathymetric maps at regional‐to‐national scales in Florida, Crete and Bermuda. ICESat‐2 bathymetric classified photons are used to train three Satellite Derived Bathymetry (SDB) methods, including Lyzenga, Stumpf and Support Vector Regression algorithms. For each study site the Lyzenga algorithm yielded the lowest RMSE (approx. 10‐15%) when compared with validation data. We demonstrate a means of using ICESat‐2 for both model calibration and validation, thus cementing a pathway for fully space‐borne estimates of nearshore bathymetry in shallow, clear water environments.

N. Thomas↗

DOE OSTI2020

Projecting Life-Cycle Environmental Impacts of Corn Production in the U.S. Midwest under Future Climate Scenarios using machine learning approach

Emission factors and process-based mechanism models are popular approaches used to estimate life-cycle environmental impacts. However, emission factors are often incapable of describing spatial and temporal heterogeneity of agricultural emissions, whereas process-based mechanism models, capable of capturing the heterogeneity, tend to be very complicated, and time-consuming. Efficient predictions of life-cycle environmental impacts from agricultural production are lacking. This study develops a rapid predictive model to quantify life-cycle global warming (GW), eutrophication (EU) and acidification (AD) impacts of corn production using a novel machine learning approach. We used the boosted regression tree (BRT) model to estimate current and future life-cycle environmental impacts of corn production in 12 U.S. Midwest at county scale under four Intergovernmental Panel on Climate Change (IPCC)’s climate scenarios (RCP2.6. 4.5, 6.0, and 8.5) for years 2009-2100. Results show that future life-cycle GW, EU and AD impacts of corn production will increase under all four scenarios, with the worst environmental impacts shown under the RCP 8.5 scenario. Significant spatial variation were estimated to be 3.6, 29.9 and 1.8-folds under the RCP4.5 scenario, and 6.6, 152.9 and 1.6-folds under the RCP8.5 scenario, respectively. Findings from this study demonstrate the importance of considering county-level life-cycle environmental impacts and the influences of climate change in future years to aid in developing potential adaptation and mitigation programs and policies.

Machine learning, climate change, projections, env↗

NASA NTRS1994

A componential model of human interaction with graphs: 1. Linear regression modeling

Task analyses served as the basis for developing the Mixed Arithmetic-Perceptual (MA-P) model, which proposes (1) that people interacting with common graphs to answer common questions apply a set of component processes-searching for indicators, encoding the value of indicators, performing arithmetic operations on the values, making spatial comparisons among indicators, and repsonding; and (2) that the type of graph and user's task determine the combination and order of the components applied (i.e., the processing steps). Two experiments investigated the prediction that response time will be linearly related to the number of processing steps according to the MA-P model. Subjects used line graphs, scatter plots, and stacked bar graphs to answer comparison questions and questions requiring arithmetic calculations. A one-parameter version of the model (with equal weights for all components) and a two-parameter version (with different weights for arithmetic and nonarithmetic processes) accounted for 76%-85% of individual subjects' variance in response time and 61%-68% of the variance taken across all subjects. The discussion addresses possible modifications in the MA-P model, alternative models, and design implications from the MA-P model.

Gillan, Douglas J.↗

NASA NTRS1986

Tropical synoptic interpretation of inter-channel correlations from Tiros N

Synoptic variability contained in a set of Tiros N channel brightness temperatures was analyzed for a nine-day period during January 1979, between the dateline and 90 deg W and between 30 deg N and 20 deg S. In the procedure, the spatial coherence of each channel is first described in terms of functions of interchannel differences and geographic and synoptic variations. Second, horizontal and vertical pattern of individual channels are examined through empirical orthogonal functional decomposition. Finally, response surface regression models are developed to predict missing Tiros N channels from available Tiros N and NOAA-5 data. The percentage variance was predicted for each channel from the Tiros N microwave and stratospheric channels. Generally speaking, about 80 percent of the variance in each channel can be predicted for the Tiros model, excepting the two tropospheric moisture channels, where prediction was less successful.

Mcguirk, J. P.↗

NASA NTRS1998

Bio-Optical Properties of the Arabian Sea as Determined by In-Situ and SeaWifs Data

Trees, Charles C.↗

DOE OSTI2022

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

A Climate and History Case Study of 18th- and 19th-Century Multidecadal Droughts in East Africa Using a new Tree-Ring Drought Atlas

Historians and paleoclimatologists have both identified the decades spanning the late-18th and early-19th centuries in many East African regions as a period of prolonged and severe drought. A challenge that emerges from both the historical evidence and the paleolimnological data that have been primarily used to characterize this drought period, however, concerns the dating of events and the characterization of their spatial character; both oral traditions and lake-core proxies cannot typically offer hydroclimate estimates with seasonal or annual resolution, nor can they provide gridded reconstructions on the order of 1° latitude and longitude. The new East African Drought Atlas (EADA) incorporates far-field dendroclimatic records and several local East African tree-ring chronologies to provide estimates of the self-calibrating Palmer Drought Severity Index (scPDSI) on a 0.5° latitude-longitude grid. We present the Point-by-Point Regression technique that was used to create the EADA, the rationale for the employed input data network, and the calibration and validation skill of the derived hydroclimatic field reconstruction. We use the EADA to characterize the timing of two decadal-scale droughts, centered over the Uganda and Kenya regions, which occurred in the late 18th century and early in the 19th century and were separated by a shorter, but pronounced wet period. We characterize the spatial patterns of the associated events and the dynamical causes that that the patterns imply. The reconstructed droughts are also compared to historical and paleolimnological data to evaluate the robustness of the reconstruction and the degree of agreement across the different sources of information.

Droughts↗

NASA NTRSDate not supplied

A Climate and History Case Study of 18th- and 19th-Century Multidecadal Droughts in East Africa Using a new Tree-Ring Drought Atlas

Droughts↗

NASA NTRS1981

Repeatability and measurement uncertainty of the United States meteorological rocketsonde

It is pointed out that rocketsondes provide the only in situ 'ground truth' data for altitudes above 30 km. The need for precise temperature information has, therefore, led to a study of rocketsonde instrument repeatability and measurement uncertainty. Results obtained from root-mean-square difference and linear regression techniques are consistent and strongly suggest that the Datasonde precision is better than 1 C up to an altitude of 53 or 54 km. This result excludes atmospheric variability, which may occur within 5 min, and effects owing to spatial differences, which are small for paired measurements. At altitudes of 55 km and higher it is possible that sensor and flight-related anomalies, differences in radiation corrections owing to differing solar angles because of varying launch times, or even seasonal variations may create conditions that affect the magnitude of the standard corrections.

Schmidlin, F. J.↗

NASA NTRS2007

Distribution of Aboveground Live Biomass in the Amazon Basin

The amount and spatial distribution of forest biomass in the Amazon basin is a major source of uncertainty in estimating the flux of carbon released from land-cover and land-use change. Direct measurements of aboveground live biomass (AGLB) are limited to small areas of forest inventory plots and site-specific allometric equations that cannot be readily generalized for the entire basin. Furthermore, there is no spaceborne remote sensing instrument that can measure tropical forest biomass directly. To determine the spatial distribution of forest biomass of the Amazon basin, we report a method based on remote sensing metrics representing various forest structural parameters and environmental variables, and more than 500 plot measurements of forest biomass distributed over the basin. A decision tree approach was used to develop the spatial distribution of AGLB for seven distinct biomass classes of lowland old-growth forests with more than 80% accuracy. AGLB for other vegetation types, such as the woody and herbaceous savanna and secondary forests, was directly estimated with a regression based on satellite data. Results show that AGLB is highest in Central Amazonia and in regions to the east and north, including the Guyanas. Biomass is generally above 300Mgha(sup 1) here except in areas of intense logging or open floodplains. In Western Amazonia, from the lowlands of Peru, Ecuador, and Colombia to the Andean mountains, biomass ranges from 150 to 300Mgha(sup 1). Most transitional and seasonal forests at the southern and northwestern edges of the basin have biomass ranging from 100 to 200Mgha(sup 1). The AGLB distribution has a significant correlation with the length of the dry season. We estimate that the total carbon in forest biomass of the Amazon basin, including the dead and below ground biomass, is 86 PgC with +/- 20% uncertainty.

rainforest↗

NASA NTRS1980

Trophic classification of Tennessee Valley area reservoirs derived from LANDSAT multispectral scanner data

LANDSAT MSS data from four different dates were extracted from computer tapes using a semiautomated digital data handling and analysis system. Reservoirs were extracted from the surrounding land matrix by using a Band 7 density level slice of 3; and descriptive statistics to include mean, variance, and ratio between bands for each of the four bands were calculated. Significant correlations ( 0.80) were identified between the MSS statistics and many trophic indicators from ground truth water quality data collected at 35 reservoirs in the greater Tennessee Valley region. Regression models were developed which gave significant estimates of each reservoir's trophic state as defined by its trophic state index and explained in all four LANDSAT frames at least 85 percent of the variability in the data. To illustrate the spatial variations within reservoirs as well as the relative variations between reservoirs, a table look up elliptical classification was used in conjunction with each reservoir's trophic state index to classify each reservoir on a pixel by pixel basis and produce color coded thematic representations.

Meinert, D. L.↗

DOE OSTI2022

How Does Flow Alteration Propagate Across a Large, Highly Regulated Basin? Dam Attributes, Network Context, and Implications for Biodiversity

Abstract Large dams are a leading cause of river ecosystem degradation. Although dams have cumulative effects as water flows downstream in a river network, most flow alteration research has focused on local impacts of single dams. Here we examined the highly regulated Colorado River Basin (CRB) to understand how flow alteration propagates in river networks, as influenced by the location and characteristics of dams as well as the structure of the river network—including the presence of tributaries. We used a spatial Markov network model informed by 117 upstream‐downstream pairs of monthly flow series (2003–2017) to estimate flow alteration from 84 intermediate‐to‐large dams representing >83% of the total storage in the CRB. Using Least Absolute Shrinkage and Selection Operator regression, we then investigated how flow alteration was influenced by local dam properties (e.g., purpose, storage capacity) and network‐level attributes (e.g., position, upstream cumulative storage). Flow alteration was highly variable across the network, but tended to accumulate downstream and remained high in the main stem. Dam impacts were explained by network‐level attributes (63%) more than by local dam properties (37%), underscoring the need to consider network context when assessing dam impacts. High‐impact dams were often located in sub‐watersheds with high levels of native fish biodiversity, fish imperilment, or species requiring seasonal flows that are no longer present. These three biodiversity dimensions, as well as the amount of dam‐free downstream habitat, indicate potential to restore river ecosystems via controlled flow releases. Our methods are transferrable and could guide screening for dam reoperation in other highly regulated basins.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2009

Fault Diagnostics and Prognostics for Large Segmented SRMs

We report progress in development of the fault diagnostic and prognostic (FD&P) system for large segmented solid rocket motors (SRMs). The model includes the following main components: (i) 1D dynamical model of internal ballistics of SRMs; (ii) surface regression model for the propellant taking into account erosive burning; (iii) model of the propellant geometry; (iv) model of the nozzle ablation; (v) model of a hole burning through in the SRM steel case. The model is verified by comparison of the spatially resolved time traces of the flow parameters obtained in simulations with the results of the simulations obtained using high-fidelity 2D FLUENT model (developed by the third party). To develop FD&P system of a case breach fault for a large segmented rocket we notice [1] that the stationary zero-dimensional approximation for the nozzle stagnation pressure is surprisingly accurate even when stagnation pressure varies significantly in time during burning tail-off. This was also found to be true for the case breach fault [2]. These results allow us to use the FD&P developed in our earlier research [3]-[6] by substituting head stagnation pressure with nozzle stagnation pressure. The axial corrections to the value of the side thrust due to the mass addition are taken into account by solving a system of ODEs in spatial dimension.

Luchinsky, Dmitry↗

NASA NTRS1979

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

The spatial characteristics of the data were evaluated. A program was developed to reduce the spatial distortions resulting from variable viewing distance, and geometrically adjusted data sets were generated. The potential need for some level of radiometric adjustment was evidenced by an along track band of high reflectance across different cover types in the Varian imagery. A multiple regression analysis was employed to explore the viewing angle effect on measured reflectance. Areas in the data set which appeared to have no across track stratification of cover type were identified. A program was developed which computed the average reflectance by column for each channel, over all of the scan lines in the designated areas. A regression analysis was then run using the first, second, and third degree polynomials, for each channel. An atmospheric effect as a component of the viewing angle source of variance is discussed. Cover type maps were completed and training and test field selection was initiated.

Hoffer, R. M.↗

NASA NTRS2017

Evaluating Soil Moisture Retrievals from ESA's SMOS and NASA's SMAP Brightness Temperature Datasets

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d'tudes spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Donnes) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40 deg incidence angle, over the 2013 - 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https:ismn.geo.tuwien.ac.at) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE),Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is 48 similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R 0.80) over the Oznet sites and worst (R 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R 0.51 and R 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.

SMOS↗