Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data landscape”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18

DART: Recent Advances in Remote Sensing Data Modeling With Atmosphere, Polarization, and Chlorophyll Fluorescence

To better understand the life-essential cycles and processes of our planet and to further develop remote sensing (RS) technology, there is an increasing need for models that simulate the radiative budget (RB) and RS acquisitions of urban and natural landscapes using physical approaches and considering the three-dimensional (3-D) architecture of Earth surfaces. Discrete anisotropic radiative transfer (DART) is one of the most comprehensive physically based 3-D models of Earth-atmosphere radiative transfer, covering the spectral domain from ultraviolet to thermal infrared wavelengths. It simulates the optical 3-DRB and optical signals of proximal, aerial, and satellite imaging spectrometers and laser scanners, for any urban and/or natural landscapes and for any experimental and instrumental configurations. It is freely available for research and teaching activities. In this paper, we briefly introduce DART theory and present recent advances in simulated sensors (LiDAR and cameras with finite field of view) and modeling mechanisms (atmosphere, specular reflectance with polarization and chlorophyll fluorescence). A case study demonstrating a novel application of DART to investigate urban landscapes is also presented.

Gastellu-Etchegorry, Jean-Phil↗

A comparative interregional analysis of selected data from LANDSAT-1 and EREP for the inventory and monitoring of natural ecosystems

Comparative statistics were presented on the capability of LANDSAT-1 and three of the Skylab remote sensing systems (S-190A, S-190B, S-192) for the recognition and inventory of analogous natural vegetations and landscape features important in resource allocation and management. Two analogous regions presenting vegetational zonation from salt desert to alpine conditions above the timberline were observed, emphasizing the visual interpretation mode in the investigation. An hierarchical legend system was used as the basic classification of all land surface features. Comparative tests were run on image identifiability with the different sensor systems, and mapping and interpretation tests were made both in monocular and stereo interpretation with all systems except the S-192. Significant advantage was found in the use of stereo from space when image analysis is by visual or visual-machine-aided interactive systems. Some cost factors in mapping from space are identified. The various image types are compared and an operational system is postulated.

Poulton, C. E.↗

Observed relation between thermal emission and reflected spectral radiance of a complex vegetated landscape

An investigation is conducted, for a complex vegetated land area, into the statistical relationship between remotely sensed thermal emissions and reflected spectral radiance. The Kauth-Thomas Tasseled Cap transformation is employed to infer the albedo and amount of green vegetation present from Landsat multispectral scanner (MSS) observations. Reflective data and thermal infrared data were acquired from the Heat Capacity Mapping Mission (HCMM) satellite along with reflective data from the Landsat 3 MSS for a site near Hartford, CT on a single date. Results are presented which indicate that thermal emissions had the greatest association with the amount of vegetation as indicated by a multispectral index, while albedo did not exhibit any substantial relationship with these emissions. These findings are explained in terms of the enhanced latent heat flux to the atmosphere associated with actively transpiring vegetation.

Goward, S. N.↗

Inferring total canopy APAR from PAR bidirectional reflectances and vegetation indices in tallgrass prairie

The fraction of photosynthetically active radiation (PAR) absorbed by a vegetated canopy (APARc) or landscape (APARs) is a critical parameter in climate processes. A grassland study examined: 1) whether APARs can be estimated from PAR bidirectional exitance fractions; and 2) whether APARs is correlated with spectral vegetation indices (SVIs). Data were acquired with a high resolution continuous spectroradiometer at 4 sun angles on grassland sites. APARs was computed from the scattered surface PAR exitance fractions. The nadir APARs value was the most variable diurnally; it provided a good estimate of the average surface APARs at ~95 percent. APARc was best represented by exitance factors between 30-60* forward.

Middleton, Elizabeth M.↗

Remote Sensing Characterization of the Urban Landscape for Improvement of Air Quality Modeling

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, in moderating ground-level ozone and air temperature, compared to "business as usual" simulations in which heat island mitigation strategies are not applied. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data has been found to better characterize low densityhburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for fiture scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the state Environmental Protection agency to evaluate how these transportation plans will affect fbture air quality.

Quattrochi, Dale A.↗

Remote Sensing and Spatial Growth Modeling Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 80 percent of the world s population will live in cities. Directly aligned with the expansion of cities is urban sprawl. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes. A reduction in air quality over cities is a major result of these impacts. Strategies that can be directly or indirectly implemented to help remediate air quality problems in cities and that can be accepted by political decision makers and the general public are now being explored to help bring down air pollutants and improve air quality. The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban spatial growth modeling (SGM) projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how ozone and air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission, the regional planning agency for the area. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rationale decisions on urban growth and sustainability for the metropolitan area in the future.

Quattrochi, Dale A.↗

Contrasting Patterns of Damage and Recovery in Logged Amazon Forests From Small Footprint LiDAR Data

Tropical forests ecosystems respond dynamically to climate variability and disturbances on time scales of minutes to millennia. To date, our knowledge of disturbance and recovery processes in tropical forests is derived almost exclusively from networks of forest inventory plots. These plots typically sample small areas (less than or equal to 1 ha) in conservation units that are protected from logging and fire. Amazon forests with frequent disturbances from human activity remain under-studied. Ongoing negotiations on REDD+ (Reducing Emissions from Deforestation and Forest Degradation plus enhancing forest carbon stocks) have placed additional emphasis on identifying degraded forests and quantifying changing carbon stocks in both degraded and intact tropical forests. We evaluated patterns of forest disturbance and recovery at four -1000 ha sites in the Brazilian Amazon using small footprint LiDAR data and coincident field measurements. Large area coverage with airborne LiDAR data in 2011-2012 included logged and unmanaged areas in Cotriguacu (Mato Grosso), Fiona do Jamari (Rondonia), and Floresta Estadual do Antimary (Acre), and unmanaged forest within Reserva Ducke (Amazonas). Logging infrastructure (skid trails, log decks, and roads) was identified using LiDAR returns from understory vegetation and validated based on field data. At each logged site, canopy gaps from logging activity and LiDAR metrics of canopy heights were used to quantify differences in forest structure between logged and unlogged areas. Contrasting patterns of harvesting operations and canopy damages at the three logged sites reflect different levels of pre-harvest planning (i.e., informal logging compared to state or national logging concessions), harvest intensity, and site conditions. Finally, we used multi-temporal LiDAR data from two sites, Reserva Ducke (2009, 2012) and Antimary (2010, 2011), to evaluate gap phase dynamics in unmanaged forest areas. The rates and patterns of canopy gap formation at these sites illustrate potential issues for separating logging damages from natural forest disturbances over longer time scales. Multi-temporal airborne LiDAR data and coincident field measurements provide complementary perspectives on disturbance and recovery processes in intact and degraded Amazon forests. Compared to forest inventory plots, the large size of each individual site permitted analyses of landscape-scale processes that would require extremely high investments to study using traditional forest inventory methods.

Morton, D. C.↗

Lambayeque Water Resources: Assessing Hydrologic Patterns Using NASA Earth Observations to Address Tree Mortality in Peru’s Coastal Mesquite Forests

The mesquite (Prosopi ssp.) forests in Northwestern Peru have had a significant increase in tree mortality in the past fifty years. Within this time frame, 17% of the forest extent was lost and the forest saw an average annual declination rate of 0.33% (Ektvedt, Vetaas, & Lundberg, 2012). These habitats support the region's rich biodiversity and play an important role in local community economies. Several hydrologic causes for mesquite mortality have been hypothesized, but local researchers lack spatially comprehensive techniques to address the problem. While in situ research is currently being utilized in an attempt to explain this recent anomaly, landscape-level visualizations through remote sensing have not been produced to find connections between hydrologic trends and the health of Northwestern Peru’s mesquite forests. Climate Hazards Group InfraRed Precipitation with Station dataand Global Land Data Assimilation Systemdata were analyzed to assess hydrologic patterns pertaining to precipitation and soil moisture in the Lambayeque region of Peru. These data were then paired with vegetation indices derived from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to display how changing hydrologic patterns relate to the health of mesquite trees. These data were then compiled on a monthly basis over the 30-year study period to create a time series product, which was later referenced with background research to find the likely causes of recent forest decline. The results will assist in the Lambayeque Regional Government’s understanding of recent biological declination and will help them design strategies to mitigate forest decline in Lambayeque, Peru and surrounding regions.

Water Resources↗

Northern Colorado Water Resources: Using Earth Observations to Assess Relationships between Snowpack and Wildfires for Water Utility Management

In recent years, wildfires in the western U.S. have increased in size, frequency, and severity. Colorado is no exception to this trend, with many recent wildfires raising concerns. The Cameron Peak and East Troublesome Fires, the two largest recorded wildfires in Colorado history, burned over 400,000 acres in 2020. Wildfires pose a significant threat to high-elevation ecosystems, especially in terms of disturbances to watershed resources. Colorado’s annual water supply is dependent upon the melt and runoff of high-elevation snowpack, thus understanding wildfire effects is crucial. This project partnered with Northern Water, a water management company in Colorado, to explore fire impacts on Colorado Front Range watersheds and water supply forecasts. We investigated the use of remote sensing to study the influences of wildfire on snow depth, as Northern Water had previously relied on field-based observations of snowpack for their water supply forecasts. This project utilized airborne LiDAR data from Airborne Snow Observatories, Inc., Landsat 8 OLI derived products, as well as in situ snow survey data from Colorado State University to assess and model changes in snowpack over time. We uncovered key drivers of change in snowpack such as burn severity and snow zone and created a random forest model to determine and investigate their relationships. Despite temporal data availability limitations, analysis also showed that remotely-sensed snow depth measurements were strongly correlated with in situ snow depth measurements, revealing the accuracy and feasibility of using remote sensing technologies to study landscape-scale snowpack characteristics for water utility management purposes.

airborne LiDAR↗

Fragmentation Increases Impact of Wind Disturbance on Forest Structure and Carbon Stocks in a Western Amazonian Landscape

Tropical second-growth forests could help mitigate climate change, but the degree to which their carbon potential is achieved will depend on exposure to disturbance. Wind disturbance is common in tropical forests, shaping structure, composition, and function, and influencing successional trajectories. However, little is known about the impacts of extreme winds in fragmented landscapes, though second-growth forests are often located in mosaics of forest, pasture, cropland, and other land cover types. Though indirect evidence suggests that fragmentation increases risk of wind damage, few studies have found such impacts following severe storms. In this study, we ask whether fragmentation and forest type (old vs. second growth) were associated with variation in wind damage after a severe convective storm in a fragmented production landscape in western Amazonia. We applied linear spectral unmixing to Landsat 8 imagery from before and after the storm, and combined it with field observations of damage to map wind effects on forest structure and biomass (Figure 4, 5). We also used Landsat 8 imagery to map land cover with the goals of identifying old- and second-growth forest and characterizing fragmentation. We used these data to assess variation in wind disturbance across 95,596 hectares of forest, distributed over 6,110 patches. We find that fragmentation is significantly associated with wind damage, with damage severity higher at forest edges and in edgier, more isolated patches (Figure 7). Damage was more severe in old-growth than in second-growth forests, but this effect was weaker than that of fragmentation (Figure 8). These results illustrate the importance of considering spatial configuration and landscape context in planning tropical forest restoration and predicting carbon sequestration in second-growth forests. Future research should address the mechanisms behind these results, to minimize wind damage risk in second-growth forests so their carbon potential can be maximally achieved.

Schwartz, Naomi B.↗

Estimation of photosynthetic capacity using MODIS polarization: 1988 proposal to NASA Headquarters

The remote sensing community has clearly identified the utility of NDVI (normalized difference vegetation index) and SR (simple ratio) and other vegetation indices for estimating such metrics of landscape ecology as green foliar biomass, photosynthetic capacity, and net primary production. Both theoretical and empirical investigations have established cause and effect relationships between the photosynthetic process in plant canopies and these combinations of remotely sensed data. Yet it has also been established that the relationships exhibit considerable variability that appears to be ecosystem-dependent and may represent a source of ecologically important information. The overall hypothesis of this proposal is that the ecosystem-dependent variability in the various vegetation indices is in part attributable to the effects of specular reflection. The polarization channels on MODIS provide the potential to estimate this specularly reflected light and allow the modification of the vegetation indices to better measure the photosynthetic process in plant canopies. In addition, these polarization channels potentially provide additional ecologically important information about the plant canopy.

Vanderbilt, Vern C.↗

Language Model For Earth Science: Exploring Potential Downstream Applications As Well As Current Challenges

The use of deep learning techniques to build transformer language models such as SciBERT and GPT3 have transformed the natural language technology (NLT) landscape. These new NLTs are being used in speech to text and vice versa, auto-mated text classification, sentiment analysis, topic modeling, text summarization, and cognitive assistants. While Earth science has no shortage of unstructured data such as journal and conference papers, little efforts have focused on harnessing NLTs for knowledge extraction and supporting the scientific process. This paper surveys the use of language models in different science. BERT-E, a new Earth science-specific language model, is presented. BERT-E is generated using a transfer learning solution. A language model that has already been trained for general Science (SciBERT) is fine-tuned using abstracts and full text extracted from various Earth science-related articles. A downstream keywords classification application is used for evaluation, and the use of BERT-E shows improved performance. The need to develop a robust set of benchmarks in evaluating the language model such as BERT-E is discussed. Finally, example applications are presented to inspire additional ideas for applications using domain-specific language models.

R Ramachandran↗

Grand Canyon Ecological Forecasting: Using NASA Earth Observations to Monitor and Model Juniper Woodland Mortality in Grand Canyon National Park

Significant die-offs of the drought tolerant species Utah juniper (Juniperus osteosperma) and one-seeded juniper (Juniperus monosperma) have been observed throughout central and northern Arizona, including Grand Canyon National Park (GCNP). As climate models project rising temperatures and continuous drought, land managers are concerned for the future of juniper in and around GCNP. This project incorporated data from Landsat 8 Operational Land Imager (OLI), the Shuttle Radar Topography Mission (SRTM), and ocular samples of the National Agriculture Imagery Program (NAIP) in a random forest model to identify patterns between characteristics of the landscape and locations of juniper woodland mortality and to model areas subject to vulnerability. This study found no significant correlation between ocularly sampled juniper tree mortality and remotely sensed environmental variables used thus, accurately modeling mortality vulnerability in the future was not feasible. The ocular sampling, however, allows the partners at the GCNP’s Science & Resource Management Division to better understand areas of juniper tree woodland mortality and the relative amount of mortality in the park. Additionally, the areas of juniper tree mortality found in this project provide the partners with guidance for future field sampling.

Scarlet Jackson↗

Africa's Megafans and Their Tectonic Setting

Megafans are a really extensive continental sediment bodies, fluvially derived, and fan-shaped in planform. Only those >80 km long were included in this study. Africa's megafans were mapped for purposes of both comprehensive geomorphic description and as a method of mapping by remote sensing large probable fluvial sediment bodies (we exclude sediment bodies deposited in well defined, modern floodplains and coastal deltas). Our criteria included a length dimension of >80 km and maximum width >40 km, partial cone morphology, and a radial drainage pattern. Visible and especially IR imagery were used to identify the features, combined with topographic SRTM data. We identified 99 megafans most of which are unstudied thus far. Their feeder rivers responsible for depositing megafan sediments rise on, and are consequent drainages oriented down the slopes of the swells that have dominated African landscapes since approximately 34 Ma (the high points in Africa's so-called basin-and-swell topography [1]). Most megafans (66%) have developed along these consequent rivers relatively near the swell cores, oriented radially away from the swells. The vast basins between the swells provide accommodation for megafan sediment wedges. Although clearly visible remotely, most megafans are inactive as a result of incision by the feeder river (which then no longer operates on the fan surface). Two tectonic settings control the location of Africa's megafans, 66% on swell flanks, and 33% related to rifts. (i) Swell flanks Most megafans are apexed relatively near the core of the parent swell, and are often clustered in groups: e.g., six on the west and north flanks of the Hoggar Swell (Algeria), seven on the north and south flanks of the Tibesti Swell (Libya-Chad borderlands), twelve on the west flank of the Ethiopian Swell, four on the east flank of the East African Swell (Kenya), Africa's largest, and eight around Angola's Bié Swell (western Zambia, northern Namibia). A cluster of possible fans lies on the western margin of the Congo Basin (Mayombe Swell), and on the coastal slopes of the Namibia Swell. Sheer size may have militated aginst the recognition of many megafans: the largest in the Sahara are the Teghahart (378 km, Hoggar Swell, Algeria), and the Wadi Albalata (340 km, Uweinat Swell, Egypt). In southern Africa the largest are the Cubango (320 km, Bié Swell, Angola/ Namibia), and the Limpopo (230 km, Mozambique). (ii) Rift zones (a) Steer's horns basins-wide depressions centered on rifts. The largest contiguous group (n=14) developed in a steer's-horns basin occupies the wide Muglad depression (200-350 km, South Sudan). Four rift-related megafans lie SE of Lake Chad (Chad). Nine megafans occupy the complex Anza Rift in Kenya/South Somalia. The Salamat megafan (Chad), is unusual because it oriented parallel with the linked Salamat, Doseo and Doba rift axes, and is consequently one of the longest in Africa (465 km). (b) Rift depressions sensu stricto. Most rifts are too narrow to provide a transverse dimension large enough to accommodate megafans. Although well-known, the Okavango Rift (NW Botswana, NE Namibia) is unique in Africa in hosting three megafans within identifiable faulted margins. The Nile megafan is Africa's largest (476 km) and comprises the vast Sudd wetland (South Sudan). An explanation for its remarkable size may be its location in a depression at the junction of two conducive tectonic zones, the East African Swell margin and the Muglad steer's-horns depression. Discharge of the River Nile, the largest in the region, has allowed the Nile megafan to outcompete neighboring megafans for space.

Wilkinson, M. J.↗

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↗

Estimating Everglades Peat Reserves and Losses on a Landscape-Scale

The Florida Everglades formed from sawgrass and other aquatic plant remains that have accumulated over thousands of years and is a patterned peatland. The region was initially drained for agricultural and urban development in the late 1800s by the lowering of Lake Okeechobee water levels through canal drainage. Over the course of the past century, the Everglades has further been altered by the construction of canals and levees to provide control of Lake Okeechobee and Everglades water levels. The drainage has caused the loss of much of the peat of the Everglades by peat fires and microbial oxidation, much of it occurring between the 1880s and the 1950s. Plans for Everglades restoration are designed to provide additional water to flow through the remaining Everglades. Additional water into the Everglades would prevent further peat loss by reducing oxidation and encouraging the accretion of peat that has been lost. This potentially could result in the sequestration of significant quantities of atmospheric carbon. We used historic records to determine how much peat was there originally and how much has been lost. Over the past century and a half, approximately 50% of the original Everglades has been lost to agricultural and urban development. In addition, much of the peat in the remaining Everglades Protection Area has been lost. We used Geographic Information Systems technologies to determine the volume and mass of peat, the mass of carbon for each of the predrainage landscapes as well as the current Everglades regions. To do this, historical and current surveys within the Everglades were used to create elevation maps. With these maps, along with a bedrock contour map of south Florida, and soils data from the USEPA, we estimated Everglades peat characteristics. These calculations provide rough, quantitative estimates of the historical Everglades, its current condition, and the changes in the peat soil that have taken place since the 19th century. Given the uncertainties in the hindcasting of some of these data sets, this analysis provides rough estimates for the peat volumes and related constituents. Our calculations indicate that the current Everglades roughly contains less than 24% of theoriginal peat volume, 17% of its mass and 19% of its carbon. The work described was fully supported by the South Florida Water Management District, West Palm Beach, FL

Thomas Walter Dreschel↗

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↗

Bird Migration Under Climate Change - A Mechanistic Approach Using Remote Sensing

The broad-scale reductions and shifts that may be expected under climate change in the availability and quality of stopover habitat for long-distance migrants is an area of increasing concern for conservation biologists. Researchers generally have taken two broad approaches to the modeling of migration behaviour to understand the impact of these changes on migratory bird populations. These include models based on causal processes and their response to environmental stimulation, "mechanistic models", or models that primarily are based on observed animal distribution patterns and the correlation of these patterns with environmental variables, i.e. "data driven" models. Investigators have applied the latter technique to forecast changes in migration patterns with changes in the environment, for example, as might be expected under climate change, by forecasting how the underlying environmental data layers upon which the relationships are built will change over time. The learned geostatstical correlations are then applied to the modified data layers.. However, this is problematic. Even if the projections of how the underlying data layers will change are correct, it is not evident that the statistical relationships will remain the same, i.e. that the animal organism may not adapt its' behaviour to the changing conditions. Mechanistic models that explicitly take into account the physical, biological, and behaviour responses of an organism as well as the underlying changes in the landscape offer an alternative to address these shortcomings. The availability of satellite remote sensing observations at multiple spatial and temporal scales, coupled with advances in climate modeling and information technologies enable the application of the mechanistic models to predict how continental bird migration patterns may change in response to environmental change. In earlier work, we simulated the impact of effects of wetland loss and inter-annual variability on the fitness of migratory shorebirds in the central fly ways of North America. We demonstrated the phenotypic plasticity of a migratory population of Pectoral sandpipers consisting of an ensemble of 10,000 individual birds in response to changes in stopover locations using an individual based migration model driven by remotely sensed land surface data, climate data and biological field data. With the advent of new computing capabilities enabled hy recent GPU-GP computing paradigms and commodity hardware, it now is possible to simulate both larger ensemble populations and to incorporate more realistic mechanistic factors into migration models. Here, we take our first steps use these tools to study the impact of long-term drought variability on shorebird survival.

Smith, James A.↗