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

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Influence of Global Climate on Freshwater Changes in Africa’s Largest Endorheic Basin Using Multi-Scaled Indicators

The poor investments in gauge measurements for hydro-climatic research in Africa has necessitated the need to investigate how decision makers can leverage on sophisticated spaceborne measurements to improve knowledge on surface water hydrology that can feed directly into water accounting processes and risk assessment from extreme droughts and its impacts. To demonstrate such potential, a suite of satellite earth observations (Sentinel-2, altimetry, Landsat, GRACE, and TRMM) and model data are combined with the standardized precipitation evapotranspiration index to assess the impacts of global climate on freshwater dynamics over the LCB (Lake Chad basin), Africa’s largest endorheic basin. As shown in the results of this study, the significant relationship of climate modes (AMO; r = 0.68 and 0.59; and AMM; r = 0.2 and 0.47) with drought patterns in the LCB highlights the evidence of global climate influence in the region. The significant declines in drought extents and their intensities (2004 - 2015) over LCB coincide with the rise in surface water extent of the Lake Chad during the same period. Change detection analysis of open water features in the southern pool of Lake Chad during the 2015 - 2019 period shows that on the average, only 28.4% of inundated areas within the vicinity of the Lake persisted during the period. While the association of terrestrial water storage (TWS) with model-derived surface water storage (SWS) is strongest (r = 0.89) in the catchments that provide the most nourishment to the Lake Chad, the relationship of rainfall (2002 - 2017) with TWS (r = 0.85), model TWS (r = 0.87) and SWS (r = 0.88) confirm that the LCB’s hydrology is predominantly climate-driven. This notion is further reinforced as the predicted SWS over the LCB using a support vector machine regression scheme was found to be strongly correlated (r = 0.95 at = 0.05) with observed SWS.

Sentinel-2↗
NASA NTRS2021

Northern Great Plains Disasters: Using Earth Observations to Enhance Flood Monitoring on Tribal Lands in the Northern Great Plains

In 2019, the Great Plains experienced unprecedented catastrophic flooding. Large flood events are predicted to increase in frequency and severity, posing risks to communities in this region, particularly Tribal Nations. We used data from Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), imagery from the Sentinel-2 MultiSpectral Instrument (MSI), and digital elevation models (DEMs) from the Shuttle Radar Topography Mission (SRTM) within Google Earth Engine to map historical floods in the region beginning in 2014 with particular attention to the Rosebud Sioux Reservation and the tribal lands of other Great Plains Tribal Water Alliance members. This historical mapping used C-SAR for a combined method approach with a Z-score algorithm in addition to an index for flooded short vegetation. We also developed a flood risk map by weighting different flood predictor variables according to flood risk literature. These variables included soil drainage from the Soil Survey Geographic Database (SSURGO); elevation, slope, and Topographic Wetness Index (TWI) derived from digital elevation models; precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS); land cover from the National Land Cover Database (NLDC); and Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 Operational Land Imager (OLI). From the flood extent and risk maps, we identified widespread flooding in short vegetation (including cropland) and noted flood susceptibility in regions exhibiting high social vulnerability and low community resilience (FEMA indices). We created an ArcGIS Online StoryMap to share project background, results, and data. Additionally, we provided a written tutorial so partners may replicate the flood mapping for future flood events.

Anna Ballasiotes↗
NASA NTRS2007

Generating a Long-Term Land Data Record from the AVHRR and MODIS Instruments

The goal of NASA's Land Long Term Iiata Record (LTDR) project is to produce a consistent long term data set from the AVHRR and MODIS instruments for land climate studies. The project will create daily surface reflectance and normalized difference vegetation index (NDVI) products at a resolution of 0.05 deg., which is identical to the Climate Modeling Grid (CMG) used for MODIS products from EOS Terra and Aqua. Higher order products such as burned area, land surface temperature, albedo, bidirectional reflectance distribution function (BRDF) correction, leaf area index (LAI), and fraction of photosyntheticalIy active radiation absorbed by vegetation (fPAR), will be created. The LTDR project will reprocess Global Area Coverage (GAC) data from AVHRR sensors onboard NOAA satellites by applying the preprocessing improvements identified in the AVHRR Pathfinder Il project and atmospheric and BRDF corrections used in MODIS processing. The preprocessing improvements include radiometric in-flight vicarious calibration for the visible and near infrared channels and inverse navigation to relate an Earth location to each sensor instantaneous field of view (IFOV). Atmospheric corrections for Rayleigh scattering, ozone, and water vapor are undertaken, with aerosol correction being implemented. The LTDR also produces a surface reflectance product for channel 3 (3.75 micrometers). Quality assessment (QA) is an integral part of the LTDR production system, which is monitoring temporal trands in the AVHRR products using time-series approaches developed for MODIS land product quality assessment. The land surface reflectance products have been evaluated at AERONET sites. The AVHRR data record from LTDR is also being compared to products from the PAL (Pathfinder AVHRR Land) and GIMMS (Global Inventory Modeling and Mapping Studies) systems to assess the relative merits of this reprocessing vis-a-vis these existing data products. The LTDR products and associated information can be found at http://ltdr.nascom.nasa.gov/ltdr/ltdr.html.

Pedelty, Jeffrey↗
DOE OSTI2022

Statistical framework to assess long-term spatio-temporal climate changes: East River mountainous watershed case study

Abstract Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ET o ), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ET o , was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

LAI, EVI, NDVI, and kNDVI in 23 pantropical forests affected by 21 cyclones

Statement of purpose: Cyclones alter the function and composition of tropical forests, making effects of intensifying cyclones on carbon-rich forests a critical topic of study. Here, we quantified cyclone-induced damage and recovery of 21 cyclone disturbances affecting 23 pantropical forest sites between 1988-2017 utilizing leaf area index (LAI), enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and transformed NDVI (kNDVI) values from Google Earth Engine. Field observations collected in a meta-analysis (Bomfim et al., 2022, in review) were used to ground-truth and test effects of soil resource availability and disturbance factors on damage and recovery. This meta-analysis also served as the basis to begin vegetation index extraction, utilizing unique site and date combinations, from tropical forests effect by cyclone disturbances. We began collecting NDVI (5km resolution) from the NOAA Climate Data Record (CDR) of AVHRR Normalized Difference Vegetation Index (NDVI), Version 5 data product (Vermote, 2019) for all case studies included, 42. Next, we began extracting Landsat data from Landsat 4, 5, and 8, courtesy of the U.S. Geological Survey, in search of higher resolution data. We selected a 3 by 3 Landsat pixel area, leading to a 90m resolution data extraction. The specific imagery used includes Landsat 4 USGS Landsat 4 TM Collection 1 Tier 1 TOA (top of atmosphere) Reflectance, Landsat 5 USGS Landsat 5 TM (thematic mapper) Collection 1 Tier 1 TOA Reflectance, and Landsat 8 USGS Landsat 8 Collection 1 Tier 1 TOA Reflectance. Within Google Earth Engine, we selected the date and location (latitude and longitude), calculated NDVI, kNDVI, and EVI utilizing Landsat bands (see metadata_NGEE-tropics_cyclones), and extracted post- and pre-cyclone values for each case study to calculate cyclone-induced change in the vegetative index. Due to limited spatial resolution of Landsat remote sensing data, MODIS products were investigated next. First, the MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m product was used to extract 250m EVI and NDVI (Didan, 2015) and then the MCD15A3H.006 MODIS Leaf Area Index/FPAR 4-Day Global 500m product product was used to extract LAI 500m (Myneni et al., 2015). Pre- and post-cyclone values, change in the vegetative index, and standard deviation for all values are included in the main csv (see case_study_data.csv) for all vegetative indices collected, including LAI 500m, EVI 250m, NDVI 250m, NDVI 90m, kNDVI 90m, EVI 90m, and NDVI 5km. Lastly, recovery values were calculated utilizing a standardization method (see metadata_NGEE-tropics_cyclones) and recovery values for MODIS (see MODIS_recovery.csv) and Landsat (Landsat_recovery.csv) data are included.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The global Sahel monsoon ocean-pressure index reconciles its regional and large-scale features

Monitoring Sahelian rainfall variability is increasingly critical as climate extremes intensify across the region. Here, we develop the Sahelian Monsoon Ocean-Pressure Index (SMOPI), a novel global synthetic indicator constructed from five dynamically coherent sea-level pressure regions statistically linked to June-September Sahel monsoon rainfall. SMOPI captures intra-seasonal and interannual variability, and crucially, reflects the influence of both regional processes and large-scale teleconnections on monsoon dynamics. It aligns with the dominant rainfall variability mode in reanalyses and 29 CMIP6 models. Strong/positive SMOPI phases coincide with wet years and are associated with enhanced convergence, favorable jet configurations, and robust Pacific, Atlantic, and Indian Ocean teleconnections. Conversely, weak/negative SMOPI phases correspond to drought conditions and divergent moisture fluxes. SMOPI exposes model failures in reproducing historical droughts and offers new physical insights into rainfall-driving mechanisms. It stands out as a scalable, potentially transferable diagnostic tool for monitoring/forecasting and evaluating Sahelian monsoon rainfall under global warming.

Tamoffo, Alain T. [Helmholtz-Zentrum Hereon GmbH, ↗
DOE OSTI2024

Identifying Disadvantaged Communities in the United States: An Energy-Oriented Mapping Tool that Aggregates Environmental and Socioeconomic Burdens

This paper defines a policy-relevant nationwide composite index to identify communities disproportionately impacted by environmental, energy, and climate injustices in the United States. We review existing vulnerability indicators and indices to assess the tradeoffs of different design parameters, including variable selection, geographic unit, dimensionality reduction, weighting, and aggregation methods. From this methodological review, we create the first nationwide, census tract-level index of cumulative burden that includes energy-relevant indicators alongside climate, social, environmental, and economic indicators, and is flexible to the inclusion of additional data sources. We provide a summary of the sources of inputs used to develop a definition for "disadvantaged communities" that can be used to prioritize energy investments. We discuss use-cases for this index including the implementation of the Justice40 Initiative, which calls for 40% of certain federal clean energy benefits to flow to disadvantaged communities in the United States. We use our results to examine historic allocations of federal energy investments and show that communities that we identify as disadvantaged received about 37% fewer funds per capita than non-disadvantaged communities.

cumulative burden↗
NASA NTRS1993

Determination of the Excess Length-of-Day Since 1630

The Kalman Earth Orientation Filter (KEOF) is used at JPL to combine independent observations of the Earth's rotation parameters, producing smoothed, interpolated estimates of polar motion (PM) and UT1-TAI, as well as estimates of their excitation functions such as the length-of-day (lod). Prior to their combination, adjustments to the data sets are made in order to place them within a common reference frame. Recently, a number of lod data sets have been determined at JPL by combining the observations summarized in Table 1. The resulting lod data sets, summarized in Table 2, span different time intervals depending upon the particular subset of observations being combined, with the longest series, spanning 1630-1990, being obtained by combining all of the observations. The determination of these lod data sets and their comparison with series of climate indicators such as the Southern Oscillation Index will be discussed.

Gross, R. S.↗
NASA NTRS2013

Intensification of North American Megadroughts through Surface and Dust Aerosol Forcing

Tree-ring-based reconstructions of the Palmer drought severity index (PDSI) indicate that, during the Medieval Climate Anomaly (MCA), the central plains of North America experienced recurrent periods of drought spanning decades or longer. These megadroughts had exceptional persistence compared to more recent events, but the causes remain uncertain. The authors conducted a suite of general circulation model experiments to test the impact of sea surface temperature (SST) and land surface forcing on the MCA megadroughts over the central plains. The land surface forcing is represented as a set of dune mobilization boundary conditions, derived from available geomorphological evidence and modeled as increased bare soil area and a dust aerosol source (32deg-44degN, 105deg-95degW). In the experiments, cold tropical Pacific SST forcing suppresses precipitation over the central plains but cannot reproduce the overall drying or persistence seen in the PDSI reconstruction. Droughts in the scenario with dust aerosols, however, are amplified and have significantly longer persistence than in other model experiments, more closely matching the reconstructed PDSI. This additional drying occurs because the dust increases the shortwave planetary albedo, reducing energy inputs to the surface and boundary layer. The energy deficit increases atmospheric stability, inhibiting convection and reducing cloud cover and precipitation over the central plains. Results from this study provide the first model-based evidence that dust aerosol forcing and land surface changes could have contributed to the intensity and persistence of the central plains megadroughts, although uncertainties remain in the formulation of the boundary conditions and the future importance of these feedbacks.

Earth surface↗
NASA NTRS1988

Estimating surface soil moisture from satellite microwave measurements and a satellite derived vegetation index

Normalized 18-GHz microwave brightness temperatures, T(B), and a vegetation index determined from satellite radiometer data are combined with climatically modeled surface moisture estimates to constrain a simple physically based soil moisture model. It is found that the normalized T(B) values correlated well with soil moisture when the data were segregated by vegetation index range, but less so when all the data were combined. By using the vegetation index parameter, the model is shown to account for about 70 percent of the variability in modeled surface soil moisture.

Owe, Manfred↗
NASA NTRS2023

Southern Rockies Western Slope Agriculture: Identifying Drivers of Rangeland Production for Drought Planning on the Western Slope of the Southern Rockies

Over the last decade, the southern Rocky Mountains of the United States experienced severe and variable drought. Local ranchers and landowners have reported strain on their operations, citing decreasing forage for their cattle and a need to adjust their business models. This study identified Major Land Resource Area-48 (MLRA-48) and northwestern Colorado as the key region for analysis. NASA DEVELOP partnered with the BLM Colorado River Field Office, Colorado State University Extension, USDA Forest Service, and the National Drought Mitigation Center to address concerns regarding the efficacy of remotely sensed rangeland production platforms and identify early warning climatic indicators of drought. The study identified two key platforms, The Rangeland Productivity Monitoring Service (RPMS) and Rangeland Analysis Platform (RAP), which use NASA Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2 to estimate rangeland biomass. We regressed these with in situ biomass data to validate their efficacy and found that RAP was more effective than RPMS in estimating rangeland biomass, though it presents a tendency to overestimate. Our study performed a random forest analysis, comparing monthly RAP biomass estimates to a variety of climate variables, including mean precipitation, temperature, Palmer Drought Severity Index, snow water equivalent, snow persistence from Terra MODIS, wind speed and direction, and vapor pressure deficit. We determined that vapor pressure deficit and precipitation are key indicators in predicting forage production in MLRA-48. Our climate analysis provided our partners with greater understanding of the influence of various climate variables in determining rangeland production and allows them to assist land managers in drought mitigation.

remote sensing↗
NASA NTRS2020

Developing a Customized Composite Drought Index for Pakistan

Pakistan experiences frequent and intense agricultural drought, varying spatially and temporally. Prolonged dry conditions often result in failed crop production. Using multiple variables, different components of drought can be captured across a multitude of climatic zones and throughout different seasons. Developing a composite drought index (CDI), specific for each district, will provide a more complete view of agricultural drought and enhance early warning systems.

Schwartz, Caily↗
NASA NTRS1995

Microwave Backscatter and Attenuation Dependence of Leaf Area Index for Flooded Rice Fields

Wetlands are important for their role in global climate as a source of methane and other reduced trace gases. As part of an effort to determine whether radar is suitable for wetland vegetation monitoring, we have studied the dependence of microwave backscatter and attenuation on leaf area index (LAI) for flooded rice fields. We find that the radar return from a flooded rice field does show dependence on LAI. In particular, the C-band VV cross section per unit area decreases with increasing LAI. A simple model for scattering from rice fields is derived and fit to the observed HH and VV data. The model fit provides insight into the relation of backscatter to LAI and is also used to calculate the canopy path attenuation as a function of LAI.

Durden, Stephen L.↗
NASA NTRS1999

The Special Sensor Microwave Imager Wind Dataset: A Source of Quantitative Information for the Ocean-to-Land Advection

It is well recognized that advection from the North Atlantic has a profound effect on the climatic conditions in central Europe. A new dataset of the ocean-surface winds, derived from the Special Sensor Microwave Imager, SSM/1, is now available. This satellite instrument measures the wind speed, but not the direction. However, variational analysis developed at the Data Assimilation Office, NASA Goddard Space Flight Center, by combining the SSM/I measurements with wind vectors measured from ships, etc., produced global maps of the ocean surface winds suitable for climate analysis. From this SSM/I dataset, a specific index I(sub na) of the North Atlantic surface winds has been developed, which pertinently quantifies the low-level advection into central Europe. For a selected time-period, the index I(sub na) reports the average of the amplitude of the wind, averaging only the speed when the direction is from the southwest (when the wind is from another direction, the contribution counts to the average as zero speed). Strong correlations were found between February I(sub na) and the surface air temperatures in Europe 50-60 deg N. In the present study, we present the correlations between I(sub na) and temperature I(sub s), and also the sensitivity of T(sub s), to an increase in I(sub na), in various seasons and various regions. We specifically analyze the flow of maritime-air from the North Atlantic that produced two extraordinary warm periods: February 1990, and early-winter 2000/2001. The very cold December 2001 was clearly due to a northerly flow. Our conclusion is that the SSM/I dataset is very useful for providing insight to the forcing of climatic fluctuations in Europe.

Otterman, J.↗
NASA NTRSDate not supplied

Southern Rockies Western Slope Agriculture: Identifying Drivers of Rangeland Production for Drought Planning on the Western Slope of the Southern Rockies

Over the last decade, the southern Rocky Mountains of the United States have experienced increasingly severe and variable drought. Local ranchers and landowners have reported strain on their operations, citing decreasing forage production for their cattle and a need to adjust their business models, even considering abandoning their businesses altogether. The study identified Major Land Resource Area-48 (MLRA-48) and northwestern Colorado as the key region for analysis. NASA DEVELOP partnered with the BLM Colorado River Field Office, Colorado State University Extension, USDA Forest Service, and the National Drought Mitigation Center to address stakeholder concerns of the efficacy of existing remotely sensed rangeland production estimation platforms and explore possible early warning climatic indicators of drought. The study identified two key rangeland platforms, the Rangeland Production Monitoring Service (RPMS) and Rangeland Analysis Platform (RAP) and used in-situ data to statistically validate their efficacy. RAP outperformed RPMS in estimating in-situ biomass and was therefore used in our climate modeling. Our study performed a random forest analysis, sampling 1500 points across the study area, comparing monthly RAP biomass estimates to a variety of climatic variables, including mean precipitation, temperature, palmer drought severity index, snow water equivalent, wind speed and direction, and vapor pressure deficit. After analysis, our study determined that vapor pressure deficit is a key indicator in predicting forage production in MLRA-48. Our study recommends the use of RAP in estimating potential forage, with caution for its tendency to overestimate. Our climate analysis provided our partners with greater understanding of the influence of various climatic factors in determining forage production and allows them to assist landowners in planning for future drought.

Addie Gonzalez↗
DOE OSTI2023

Climate change increases carbon allocation to leaves in early leaf green-up

Global greening, characterized by an increase in leaf area index (LAI), implies an increase in foliar carbon (C). Whether this increase in foliar C under climate change is due to higher photosynthesis or to higher allocation of C to leaves remains unknown. Here, we explored the trends in foliar C accumulation and allocation during leaf green-up from 2000 to 2017 using satellite-derived LAI and solar-induced chlorophyll fluorescence (SIF) across the Northern Hemisphere. The accumulation of foliar C accelerated in the early green-up period due to both increased photosynthesis and higher foliar C allocation driven by climate change. In the late stage of green-up, however, we detected decreasing trends in foliar C accumulation and foliar C allocation. Such stage-dependent trends in the accumulation and allocation of foliar C are not represented in current terrestrial biosphere models. Further, our results highlight that a better representation of C allocation should be incorporated into models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Ohio River Basin Water Resources: Monitoring Flash Drought Potential and Quantifying the Hydrologic Impacts in the Ohio River Basin

Flash drought conditions emerge in a matter of weeks following persistent weather anomalies, such as high temperatures or large vapor pressure deficits, that drive increased evaporative demand. Vegetation response rapidly depletes soil moisture, threatening surface water supplies, triggering significant crop loss, and increasing wildfire risk. Drought indices sensitive to flash drought are currently not included in drought forecast models in the Ohio River Basin (ORB). The team assessed and compared drought indices, gauge-based data, and satellite measurements over the course of the September 2019 flash drought event in the Ohio River Basin to investigate environmental fingerprints throughout flash drought evolution. Potential leading flash drought indicators were compared to the Standardized Precipitation Index (SPI) to aid the National Weather Service (NWS) Ohio River Forecast Center and Kentucky Climate Center in producing early warning flash drought forecasts. These drought indices included Evaporative Demand Drought Index (EDDI), Standardized Precipitation Evapotranspiration Index (SPEI), and Landscape Evaporative Response Index (LERI) (derived from Terra Moderate Resolution Imaging Spectroradiometer (MODIS)). Drought index behavior inherently varies based on parameters incorporated - EDDI measures evaporative demand through potential evapotranspiration, and demonstrates more variations due to sensitivity to seasonal changes in evaporative demand throughout the 2010-2019 study period. Conversely, LERI measures evaporative response through actual evapotranspiration, representing different environmental changes. Soil moisture response was evaluated using Soil Moisture Active Passive (SMAP) L-band radiometer data and Kentucky Mesonet gauge-based measurements. This analysis enables climatologists and weather forecasters to keep decision-makers and stakeholders better informed about drought risks, and to implement the appropriate actions for preparation of drought onset.

Adelaide Schmid↗
NASA NTRS2020

Ohio River Basin Water Resources: Monitoring Flash Drought Potential and Quantifying the Hydrologic Impacts in the Ohio River Basin Utilizing NASA Earth Observations

Flash drought conditions emerge in a matter of weeks following persistent weather anomalies, such as high temperatures or large vapor pressure deficits, that drive increased evaporative demand. Vegetation response rapidly depletes soil moisture, threatening surface water supplies, triggering significant crop loss, and increasing wildfire risk. Drought indices sensitive to flash drought are currently not included in drought forecast models in the Ohio River Basin (ORB). The team assessed and compared drought indices, gauge-based data, and satellite measurements over the course of the September 2019 flash drought event in the Ohio River Basin to investigate environmental fingerprints throughout flash drought evolution. Potential leading flash drought indicators were compared to the Standardized Precipitation Index (SPI) to aid the National Weather Service (NWS) Ohio River Forecast Center and Kentucky Climate Center in producing early warning flash drought forecasts. These drought indices included Evaporative Demand Drought Index (EDDI), Standardized Precipitation Evapotranspiration Index (SPEI), and Landscape Evaporative Response Index (LERI) (derived from Terra Moderate Resolution Imaging Spectroradiometer (MODIS)). Drought index behavior inherently varies based on parameters incorporated -EDDI measures evaporative demand through potential evapotranspiration, and and demonstrates more variations due to sensitivity to seasonal changes in evaporative demand throughout the 2010-2019 study period. Conversely, LERI measures evaporative response through actual evapotranspiration, representing different environmental changes. Soil moisture response was evaluated using Soil Moisture Active Passive (SMAP) L-band radiometer data and Kentucky Mesonet gauge-based measurements. This analysis enables climatologists and weather forecasters to keep decision-makers and stakeholders better informed about drought risks to implement the appropriate actions for preparation of drought onset.

DEVELOP Project Summary↗