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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Nickel Accounting for the Psyche Spacecraft

(16) Psyche is a metallic asteroid located in the asteroid belt between Mars and Jupiter. The composition of Psyche is unknown, but the scientific community believes it is composed primarily of iron, nickel, and silicates. The Psyche mission will be the first space mission to study this asteroid in close proximity, and as such, determining the elemental composition of the asteroid is one of the primary objectives of this mission. The spacecraft measures this elemental composition using the Gamma Ray Spectrometer (GRS), which detects cosmic-ray induced gamma rays from Psyche’s surface. In addition to gamma rays from Psyche, the GRS will also detect background gamma rays from spacecraft material nearby the GRS. Because Ni is such a key element for achieving the mission science objectives, a special effort has been made to minimize background gamma rays from Ni, thus maximizing sensitivity to the Ni gamma rays from Psyche. The Psyche mission approached this potential Ni background issue with two methods, locating the GRS away from the spacecraft/source of Ni and setting a threshold requirement against the total Ni mass of the spacecraft. Verifying that the total amount of Ni on the spacecraft is within this threshold is thus a key part of ensuring the GRS can meet its science objectives. Ni alloys are commonly used on spacecraft, but the mass and location of elemental Ni is not typically accounted for. Ni can be found in many materials all over the spacecraft – in fasteners, optical assemblies, magnetic shields, electromagnetic interference (EMI) tape, under gold plating, in connectors, and so on. The task proved further challenging because of the distributed nature of the build of this spacecraft and the use of vendors external to JPL. While the analysis was atypical and source information difficult to cull, the approach described in this paper used tools and data available to the project in some form to achieve this goal. For Psyche, current analysis shows there is margin in meeting the Ni mass requirement. This paper explains the systematic approach used to accurately and precisely record Ni content on the Psyche spacecraft. While this approach was defined for Ni on the Psyche mission, it can be used for any future project with the need to accurately estimate the total mass of a single element or material on a spacecraft.

Cullinan, Joe↗
NASA NTRS2022

Nickel Accounting for the Psyche Spacecraft

(16) Psyche is a metallic asteroid located in the asteroid belt between Mars and Jupiter. The composition of Psyche is unknown, but the scientific community believes it is composed primarily of iron, nickel, and silicates. The Psyche mission will be the first space mission to study this asteroid in close proximity, and as such, determining the elemental composition of the asteroid is one of the primary objectives of this mission. The spacecraft measures this elemental composition using the Gamma Ray Spectrometer (GRS), which detects cosmic-ray induced gamma rays from Psyche’s surface. In addition to gamma rays from Psyche, the GRS will also detect background gamma rays from spacecraft material nearby the GRS. Because Ni is such a key element for achieving the mission science objectives, a special effort has been made to minimize background gamma rays from Ni, thus maximizing sensitivity to the Ni gamma rays from Psyche. The Psyche mission approached this potential Ni background issue with two methods, locating the GRS away from the spacecraft/source of Ni and setting a threshold requirement against the total Ni mass of the spacecraft. Verifying that the total amount of Ni on the spacecraft is within this threshold is thus a key part of ensuring the GRS can meet its science objectives. Ni alloys are commonly used on spacecraft, but the mass and location of elemental Ni is not typically accounted for. Ni can be found in many materials all over the spacecraft – in fasteners, optical assemblies, magnetic shields, electromagnetic interference (EMI) tape, under gold plating, in connectors, and so on. The task proved further challenging because of the distributed nature of the build of this spacecraft and the use of vendors external to JPL. While the analysis was atypical and source information difficult to cull, the approach described in this paper used tools and data available to the project in some form to achieve this goal. For Psyche, current analysis shows there is margin in meeting the Ni mass requirement. This paper explains the systematic approach used to accurately and precisely record Ni content on the Psyche spacecraft. While this approach was defined for Ni on the Psyche mission, it can be used for any future project with the need to accurately estimate the total mass of a single element or material on a spacecraft.

Cullinan, Joe↗
DOE OSTI2023

Ensemble Spread Behavior in Coupled Climate Models: Insights From the Energy Exascale Earth System Model Version 1 Large Ensemble

AbstractAssessing uncertainty in future climate projections requires understanding both internal climate variability and external forcing. For this reason, single‐model initial condition large ensembles (SMILEs) run with Earth System Models (ESMs) have recently become popular. Here we present a new 20‐member SMILE with the Energy Exascale Earth System Model version 1 (E3SMv1‐LE), which uses a “macro” initialization strategy choosing coupled atmosphere/ocean states based on inter‐basin contrasts in ocean heat content (OHC). The E3SMv1‐LE simulates tropical climate variability well, albeit with a muted warming trend over the twentieth century due to overly strong aerosol forcing. The E3SMv1‐LE's initial climate spread is comparable to other (larger) SMILEs, suggesting that maximizing inter‐basin ocean heat contrasts may be an efficient method of generating ensemble spread. We also compare different ensemble spread across multiple SMILEs, using surface air temperature and OHC. The Community Earth system Model version 1, the only ensemble which utilizes a “micro” initialization approach perturbing only atmospheric initial conditions, yields lower spread in the first ∼30 years. The E3SMv1‐LE exhibits a relatively large spread, with some evidence for anthropogenic forcing influencing spread in the late twentieth century. However, systematic effects of differing “macro” initialization strategies are difficult to detect, possibly resulting from differing model physics or responses to external forcing. Notably, the method of standardizing results affects ensemble spread: control simulations for most models have either large background trends or multi‐centennial variability in OHC. This spurious disequlibrium behavior is a substantial roadblock to understanding both internal climate variability and its response to forcing.

Stevenson, Samantha↗
DOE OSTI2023

Microdiverse bacterial clades prevail across Antarctic wetlands

Antarctica's extreme environmental conditions impose selection pressures on microbial communities. Indeed, a previous study revealed that bacterial assemblages at the Cierva Point Wetland Complex (CPWC) are shaped by strong homogeneous selection. Yet which bacterial phylogenetic clades are shaped by selection processes and their ecological strategies to thrive in such extreme conditions remain unknown. Here, we applied the phyloscore and feature-level βNTI indexes coupled with phylofactorization to successfully detect bacterial monophyletic clades subjected to homogeneous (HoS) and heterogenous (HeS) selection. Remarkably, only the HoS clades showed high relative abundance across all samples and signs of putative microdiversity. The majority of the amplicon sequence variants (ASVs) within each HoS clade clustered into a unique 97% sequence similarity operational taxonomic unit (OTU) and inhabited a specific environment (lotic, lentic or terrestrial). Our findings suggest the existence of microdiversification leading to sub-taxa niche differentiation, with putative distinct ecotypes (consisting of groups of ASVs) adapted to a specific environment. We hypothesize that HoS clades thriving in the CPWC have phylogenetically conserved traits that accelerate their rate of evolution, enabling them to adapt to strong spatio-temporally variable selection pressures. Variable selection appears to operate within clades to cause very rapid microdiversification without losing key traits that lead to high abundance. Variable and homogeneous selection, therefore, operate simultaneously but on different aspects of organismal ecology. The result is an overall signal of homogeneous selection due to rapid within-clade microdiversification caused by variable selection. It is unknown whether other systems experience this dynamic, and we encourage future work evaluating the transferability of our results.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1999

Preliminary Assessment of JERS-1 SAR to Discriminating Boreal Landscape Features for the Boreal Forest Mapping Project

This paper presents an overview of the JERS-1 North American Boreal Forest Mapping Project and a preliminary assessment of JERS-1 SAR imagery for application to discriminating features applicable to boreal landscape processes. The present focus of the JERS-1 North American Boreal Forest Mapping Project is the production of continental scale wintertime and summertime SAR mosaics of the North American boreal forest for distribution to the science community. As part of this effort, JERS-1 imagery has been collected over much of Alaska and Canada during the 1997-98 winter and 1998 summer seasons. To complete the mosaics, these data will be augmented with data collected during previous years. These data will be made available to the scientific community via CD ROM containing these and similar data sets compiled from companion studies of Asia and Europe. Regional landscape classification with SAR is important for the baseline information it will provide about distribution of woodlands, positions of treeline, current forest biomass, distribution of wetlands, and extent of major rivercourses. As well as setting the stage for longer term change detection, comparisons across several years provides additional baseline information about short-term landscape change. Rapid changes, including those driven by fire, permafrost heat balance, flooding, and insect outbreaks can dominate boreal systems. We examine JERS-1 imagery covering selected sites in Alaska and Canada to assess quality and applicability to such relevant ecological and hydrological issues. The data are generally of high quality and illustrate many potential applications. A texture-based classification scheme is applied to selected regions to assess the applicability of these data for distinguishing distribution of such landcover types as wetland, tundra, woodland and forested landscapes.

McDonald, Kyle↗
NASA NTRS2007

NASA Turbulence Technologies In-Service Evaluation: Delta Air Lines Report-Out

Concluding an in-service evaluation of two new turbulence detection technologies developed in the Turbulence Prediction and Warning Systems (TPAWS) element of the NASA Aviation Safety and Security Program's Weather Accident Prevention Project (WxAP), this report documents Delta's experience working with the technologies, feedback gained from pilots and dispatchers concerning current turbulence techniques and procedures, and Delta's recommendations regarding directions for further efforts by the research community. Technologies evaluated included an automatic airborne turbulence encounter reporting technology called the Turbulence Auto PIREP System (TAPS), and a significant enhancement to the ability of modern airborne weather radars to predict and display turbulence of operational significance, called E-Turb radar.

Amaral, Christian↗
NASA NTRSDate not supplied

Assessment of Quantum ML Applicability for Climate Actions: Comparison of the Variational Quantum Classifier and the Quantum Support Vector Classifier with Classical ML Models

Climate change refers to significant and long-term alterations in the Earth’s climate patterns, typically resulting from human activities that increase greenhouse gas emissions. Addressing climate change is not merely an option but a necessity, demanding creative solutions and efforts from individuals, researchers, communities, and governments. Despite the capabilities of machine learning (ML) with data-driven solutions promising to combat climate change-related problems, they face challenges stemming from traditional computational methods and prolonged training times, impeding their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering and pharmaceutical discovery to the latest frontier of detecting climate anomalies. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features will enable the concurrent exploration of expansive solution spaces, making it well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from NASA Giovannis. We used two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC) models, using the IBM Qiskit ML 0.7.2 ecosystem. We used an actual 127-Qubit IBM Quantum Computer (IBM 127-qubit Eagle) in this study. The methodology and results sections describe the experiences gained from applying and evaluating quantum ML results on climate and weather data obtained from NASA satellites as a novel practical application of quantum computing.

Earth Observational Data↗
NASA NTRS1999

Space Acceleration Measurement System-II: Microgravity Instrumentation for the International Space Station Research Community

The International Space Station opens for business in the year 2000, and with the opening, science investigations will take advantage of the unique conditions it provides as an on-orbit laboratory for research. With initiation of scientific studies comes a need to understand the environment present during research. The Space Acceleration Measurement System-II provides researchers a consistent means to understand the vibratory conditions present during experimentation on the International Space Station. The Space Acceleration Measurement System-II, or SAMS-II, detects vibrations present while the space station is operating. SAMS-II on-orbit hardware is comprised of two basic building block elements: a centralized control unit and multiple Remote Triaxial Sensors deployed to measure the acceleration environment at the point of scientific research, generally within a research rack. Ground Operations Equipment is deployed to complete the command, control and data telemetry elements of the SAMS-II implementation. Initially, operations consist of user requirements development, measurement sensor deployment and use, and data recovery on the ground. Future system enhancements will provide additional user functionality and support more simultaneous users.

Sutliff, Thomas J.↗
DOE OSTI2022

Wildfire aerosol deposition likely amplified a summertime Arctic phytoplankton bloom

Summertime wildfire activity is increasing in boreal forest and tundra ecosystems in the Northern Hemisphere. However, the impact of long range transport and deposition of wildfire aerosols on biogeochemical cycles in the Arctic Ocean is unknown. Here, we use satellite-based ocean color data, atmospheric modeling and back trajectory analysis to investigate the transport and fate of aerosols emitted from Siberian wildfires in summer 2014 and their potential impact on phytoplankton dynamics in the Arctic Ocean. We detect large phytoplankton blooms near the North Pole (up to 82°N in the eastern Eurasian Basin). Our analysis indicates that these blooms were induced by the northward plume transport and deposition of nutrient-bearing wildfire aerosols. We estimate that these highly stratified surface waters received large amounts of wildfire-derived nitrogen, which alleviated nutrient stress in the phytoplankton community and triggered an unusually large bloom event. Our findings suggest that changes in wildfire activity may strongly influence summertime productivity in the Arctic Ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Square Kilometre Array Science Data Challenge 1: analysis and results

ABSTRACT As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality data sets for testing and evaluating new techniques. In this paper, we present a description and results from the first such Science Data Challenge 1 (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560, 1400, and 9200 MHz) at three depths (8, 100, and 1000 h), SDC1 asked participants to apply source detection, characterization, and classification methods to simulated data. The challenge opened in 2018 November, with nine teams submitting results by the deadline of 2019 April. In this work, we analyse the results for eight of those teams, showcasing the variety of approaches that can be successfully used to find, characterize, and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.

Bonaldi, A.↗
DOE OSTI2023

Numerical relativity higher order gravitational waveforms of eccentric, spinning, nonprecessing binary black hole mergers

We use the open source, community-driven, numerical relativity software, the EINSTEIN TOOLKIT to study the physics of eccentric, spinning, nonprecessing binary black hole mergers with mass-ratios q = {2; 4; 6}, individual dimensionless spin parameters chi 1z = +/- 0.6, chi 2z = +/- 0.3, that include higher order gravitational wave modes l <= 4, except for memory modes. Assuming stellar mass binary black hole mergers that may be detectable by the advanced LIGO detectors, we find that including modes up to l = 4 increases the signal-to-noise of compact binaries between 3.5% to 35%, compared to signals that only include the l = ImI = 2 mode. We use two waveform models, TEOBResumS and SEOBNRE, which incorporate spin and eccentricity corrections in the waveform dynamics, to quantify the orbital eccentricity of our numerical relativity catalog in a gauge-invariant manner through fitting factor calculations. Our findings indicate that the inclusion of higher order wave modes has a measurable effect in the recovery of moderately and highly eccentric black hole mergers, and thus it is essential to develop waveform models and signal processing tools that accurately describe the physics of these astrophysical sources.

Joshi, Abhishek V.↗
DOE OSTI2025

Genetic Transfer in Action: Uncovering DNA Flow in an Extremophilic Microbial Community

ABSTRACT Horizontal genetic transfer (HGT) is a significant driver of genomic novelty in all domains of life. HGT has been investigated in many studies however, the focus has been on conspicuous protein‐coding DNA transfers that often prove to be adaptive in recipient organisms and are therefore fixed longer‐term in lineages. These results comprise a subclass of HGTs and do not represent exhaustive (coding and non‐coding) DNA transfer and its impact on ecology. Uncovering exhaustive HGT can provide key insights into the connectivity of genomes in communities and how these transfers may occur. In this study, we use the term frequency‐inverse document frequency (TF‐IDF) technique, that has been used successfully to mine DNA transfers within real and simulated high‐quality prokaryote genomes, to search for exhaustive HGTs within an extremophilic microbial community. We establish a pipeline for validating transfers identified using this approach. We find that most DNA transfers are within‐domain and involve non‐coding DNA. A relatively high proportion of the predicted protein‐coding HGTs appear to encode transposase activity, restriction‐modification system components, and biofilm formation functions. Our study demonstrates the utility of the TF‐IDF approach for HGT detection and provides insights into the mechanisms of recent DNA transfer.

Microbiology↗
DOE OSTI2024

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2008

Science requirements for LISA

Historically, gravitational wave antennas have been characterized by their detection capability. This is measured in terms of signal-to-noise ratio, and implies a rate of false positives and false negatives. But to do useful astrophysics, one would like to measure - or more properly, estimate - astrophysical parameters of the gravitational wave sources. In the interest of strengthening the connection between science objectives and a specific instrument performance, the LISA community has reformulated the Laser Interferometer Space Antenna (LlSA) science requirements around the anticipated uncertainty in astrophysical parameter estimation. The rationale for this characterization of LlSA and a summary of the astrophysics and fundamental physics that LISA can do will be given. LISA will be able to make precision measurements of sources out to z approximately equal to 10.

Stebbins, Robin T.↗
NASA NTRS2010

Influence of Satellite-Based Heterogeneous Vegetation Momentum Roughness on Mesoscale Model Dynamics During IHOP 2002

The sensitivity of mesoscale weather prediction model to a vegetation roughness initialization is investigated for the south central United States. Three different roughness databases are employed: i) a control or standard lookup table roughness that is a function only of land cover type, ii) a spatially heterogeneous roughness database previously derived using a physically based procedure and MODIS imagery, and iii) a MODIS climatologic roughness database that possesses the same spatial heterogeneity as (i) but with mean land class values from (ii). The model used is the Weather Research and Forecast Model (WRF) coupled to the Community Land Model within the Land Information System (LIS). For each simulation, a statistical comparison is made between modeled results and ground observations from meteorological stations within the Oklahoma mesonet and surrounding region during IHOP20O2. A sensitivity analysis on the impact the MODIS-based roughness fields is also made through a time-series intercomparison of temperature bias, probability of detection (POD), average wind speed, boundary layer height, and turbulent kinetic energy (TKE) the results that, for the current replacement of the standard land-cover type based roughness values with the satellite-derived fields statistically improves model performance for most of the observed variables. Further, the satellite-based roughness enhances the surface wind speed, PBL height and TKE production on the order of 3 to l0 percent, with a lesser effect over grassland and cropland domains, and the greater effect over mixed land cover domains

Jasinski, Michael↗
NASA NTRS2015

Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

(AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction.

Meng, Huan↗
NASA NTRSDate not supplied

PLANETARY DEFENSE: FINDINGS AND RECOMMENDATIONS FROM THE NATIONAL ACADEMIES PLANETARY SCIENCE AND ASTROBIOLOGY DECADAL SURVEY 2023 - 2032

The National Academies of Sciences, Engineering, and Medicine was chartered by the NASA Science Mission Directorate (SMD), with support from the National Science Foundation, to conduct a new decadal survey to develop a comprehensive science and mission strategy for planetary science (1). The survey was tasked to determine the current state of knowledge and to identify the most important scientific questions to be addressed in astrobiology and planetary science during the upcoming decade of 2023 - 2032. For the first time, the decadal survey also included and addressed the topic of planetary defense, now recognized as a key element of NASA’s planetary science activities under the direction of NASA’s SMD Planetary Defense Coordination Office. The topic of planetary defense was included in the decadal survey as a dedicated chapter, divided into five sections based on the framework from the U.S. National NEO Preparedness Strategy and Action Plan (2). These sections discussed: NEO Detection, Tracking, and Characterization NEO Modelling, Prediction, and Integration NEO Deflection and Disruption Missions International Cooperation on NEO Preparation NEO Impact Emergency Procedures and Action Protocols Content of these sections included identifying the activities, ground- and space-based assets, facilities, resources, technologies, and missions that should be considered to enable NEO preparedness over the next decade. Supporting inputs to this chapter involved contributions from submitted white papers authored by many members of the NEO science and planetary defense communities. In addition, a planetary defense rapid mission architecture study commissioned by the decadal survey small bodies panel and led by the Jet Propulsion Laboratory examined several different types of in-situ characterization and mitigation demonstration missions. All of this input contributed to the formulation of 42 findings and 11 recommendations that suggest a path forward for increasing our understanding of the NEO population and their associated physical characteristics, modelling the impact probabilities and hazard potential of these objects, and implementing plans to develop and test NEO impact mitigation capabilities. Including planetary defense in the decadal survey has helped sharpen the focus on this international issue and provided key inputs for stakeholders and decision makers to consider when developing future planetary defense-related activities. Such information is vital for developing a long-term integrated planetary defense capability able to protect humanity in the decades to come.

NEO Detection and Characterization↗
NASA NTRSDate not supplied

Assessing Flooding Vulnerability to Assist High Water Intervention and Urban Planning Programs in the Charles River Watershed with NASA DEVELOP

The Charles River watershed intersects 35 municipalities within the Boston Metropolitan Area and has a total population of 1.2 million, making it one of the most densely populated watersheds in New England. In recent years, the watershed has observed higher rates of flood inundation, mainly due to increased development, extreme precipitation events, and increased surface runoff. As the frequency of flooding events increases and a changing climate poses an ongoing threat to local communities, governments and organizations in Massachusetts are in need of accurate flood risk assessments. NASA DEVELOP partnered with the Charles River Watershed Association, the Town of Natick’s Office of Sustainability, and the Massachusetts Audubon Society to assess flood vulnerability and susceptibility in the watershed. The team used Landsat 5 Thematic Mapper, Landsat 8 Operational Land Imager, Sentinel-1 C-Band Synthetic Aperture Radar, and Sentinel-2 MultiSpectral Instrument to assess the feasibility of identifying the extent of past flood events using remote sensing. After identifying images that overlapped with reported flood events, the team concluded that it was not feasible to use Earth observation data to detect localized flooding in the time available for this study. Instead, the Federal Emergency Management Agency (FEMA) 100-year floodplain was used as a proxy for areas where flooding may occur. The team used statistical regression analysis and validation and supervised classification to develop a flood susceptibility map, incorporating several flood conditioning factors. The susceptibility maps were calibrated to various thresholds, including two that highlight hypothetical flooding under more liberal and more conservative planning scenarios. These were overlaid with demographic and socioeconomic data to create flood vulnerability maps. The team’s flood susceptibility maps showed an improvement in capturing known flood events over the FEMA 100-year and 500-year floodplain maps. These results will be improved with the addition of stormwater drainage mapping and precipitation data. Results can be used to fill in the gaps to help the stakeholders understand their communities’ vulnerability and susceptibility to flooding and improve their preparedness plans.

Trista Brophy↗