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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Where should we put flood sensors?

Flooding is a major and growing problem in Southeast Texas, especially after storms like Hurricane Harvey and Tropical Storm Imelda. Communities need good flood sensor networks to know when and where flooding happens. Sensor networks have primarily been used and located to assess the physical aspects of flooding. This approach provides an incomplete picture for decisions intended to reduce risk to people. our budget for flood sensors is limited, so we should make the best possible use of each sensor we can afford. Poorly placed sensors can create misunderstanding of who is at risk, while better placement helps communities protect lives and homes and support recovery efforts. Because local budgets for flood sensors are limited, using a holistic approach for sensor placement can support more effective decision making.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Field surveying data of low-cost networked flood sensors in southeast Texas

Floods are common natural disasters worldwide and pose substantial risks to life, property, food production, and natural resources. Effective measures for flood mitigation and warning are essential. Southeast Texas is still at significant risk of flooding, and Lamar University is assisting the region with asset management of a flood sensor network for flooding events. This network provides real-time water stage information. Lamar University developed a survey program to measure elevation and coordinates at each sensor site location to make this data more useful for flood monitoring and mapping. This paper overviews the measurement of the elevation and coordinates of 74 networked flood sensors and various flood stage thresholds at critical points that flood decision-makers can use for reference at each site. In the first phase of this program, these sensors were deployed throughout a 7-county region spanning nearly 6,000 square miles in Southeast Texas. The latitude and longitude of the sensors and their elevations were determined using survey-grade Global Navigation Satellite System (GNSS) technology. Various Continually Operating Reference Stations (CORS) were utilized for post-processing to achieve sub-inch resolution. The flood stage thresholds, water level sensors elevation, and the elevations and positions of other critical surrounding points are viewable to the public through two online repositories and a web-based sensor management dashboard. The data is used to aid with decisions related to road closures or modeling efforts by mitigation decision-makers, emergency managers, and the public, including the Texas Department of Transportation, Houston Transtar, the National Weather Service, and the Sabine River Authority of Texas (SRA).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data Repository for Multi-Objective Urban Observational Strategies: A risk-based framework for expanding flood sensor networks.

These data support the manuscript "Multi-Objective Urban Observational Strategies: A risk-based framework for expanding flood sensor networks." These data are generated to allow water managers to reason about optimal locations to expand a flood observation system from multiple perspectives, specifically focusing on flood hazards, and population exposure to flooding. The data included are a) a shapefile of individual sensor locations b) a shapefile of river reach catchments, c) raster of FEMA flood likelihood layers d) shapefile of population locations and population socioeconomic characteristics. The code is written in R and includes all files necessary to generate the figures for the associated manuscript. Interactive maps of the final calculated maps of hazard, vulnerability, exposure, and risk are also included as html files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Multi‐Objective Urban Observational Strategies: A Risk‐Based Framework for Expanding Flood Sensor Networks

In coupled human and natural systems, developing an observation strategy which maximizes insight into both the natural system and the human system is a challenging multi-objective optimization problem. In this article, we describe the expansion of a flood risk observation system in Southeast Texas designed to improve our understanding of both physical and socioeconomic exposure to hydrological hazards at fine spatial scales, in the context of a structured hazard-exposure-vulnerability risk framework. We describe a new approach for assessing the spatial extent through which a flood sensor's observations can be assumed to be relevant, and estimate the population served within each sensor's area of information using downscaled socio-demographic data. As hydrological observations and modeling move to ever finer scale, assessing the information they contain in the context of both social and natural systems becomes increasingly important for developing actionable scientific insights.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Flood Susceptibility Mapping Using Machine Learning and Geospatial-Sentinel-1 SAR Integration for Enhanced Early Warning Systems

This study presents a comprehensive framework for flood susceptibility mapping by integrating geospatial factors with both statistical and machine learning models. Thirteen Flood-related factors, including DEM, slope, TWI, NDVI, etc., are extracted as features of models, and historical flood data derived from Sentinel-1 SAR from 2018 to 2023 are used as the target variables of the models. These datasets are analyzed using a frequency-based statistical model and three machine learning models, including Random Forest, XGBoost, and CNN, to generate flood susceptibility maps. The performance of each model is evaluated through AUC; and SHAP scores are separately generated for Machine learning (ML) models to explain each feature contribution in the ML model. The generated susceptibility maps are validated by high-flood-risk locations monitored by flood sensors, BLE inundation models, and flood-prone areas suggested by the Local Community Task Force. The results indicate that the XGBoost model outperforms all other models, with an AUC of 0.92 and demonstrates the highest alignment with recommended high-flood-risk locations, while the frequency-based statistical model showed the weakest performance with an AUC of 0.65. SHAP value graphs highlight the elevation, slope, and TWI as the most influential features across all models. The susceptibility maps generated by the machine learning model show strong agreement with the BLE map and high-flood-risk areas identified by the local Community Task Force.

Google Engine↗
DOE OSTI2025

Identifying Urban Pluvial Frequency Flooding Hotspots Using the Topographic Control Index and Remote Sensing Radar Images for Early Warning Systems

Identifying areas that frequently experience post-rainfall ponding is essential for effective flood mitigation and planning. This study integrates Sentinel-1 radar imagery and the Topographic Control Index (TCI) to identify 378 flood-prone urban depressions in Beaumont, Texas. Out of 159 major rainfall events, only six had Sentinel-1 radar imagery acquired within six hours of peak rainfall, and these were used to generate the flood frequency map; the ground-based flood sensor data were used to verify that these selected events corresponded to actual peak rainfall and to validate radar-detected water pixels. Validation results showed 100% precision, 70.87% recall, an F1-score of 82.95%, and 71.32% overall accuracy. Approximately 84% of medium-to-high TCI depressions overlapped with Beaumont’s two-year inundation map, confirming a strong relationship between TCI and observed flooding. A total of 124 depressions retained significant water, and after excluding 25 engineered detention ponds, 99 natural depressions remained flood vulnerable. Among these, 74 depressions with medium or high TCI were identified as the highest-priority nuisance flooding hotspots. The results demonstrate that combining TCI with radar imagery provides a reliable and cost-effective approach for identifying areas prone to frequent urban ponding. This framework supports practical decision-making for drainage improvements, hotspot identification, and early-warning system development in urban flood-prone regions.

Sentinel-1 radar imagery↗
NASA NTRS2008

Potential Application of Airborne Passive Microwave Observations for Monitoring Inland Flooding Caused by Tropical Cyclones

Inland flooding from tropical cyclones can be a significant factor in storm-related deaths in the United States and other countries. Information collected during NASA tropical cyclone field studies suggest surface water and flooding induced by tropical cyclone precipitation can be detected and therefore monitored using passive microwave airborne radiometers. In particular, the 10.7 GHz frequency of the NASA Advanced Microwave Precipitation Radiometer (AMPR) flown on the NASA ER-2 has demonstrated high resolution detection of anomalous surface water and flooding in numerous situations. This presentation will highlight the analysis of three cases utilizing primarily satellite and airborne radiometer data. Radiometer data from the 1998 Third Convection and Moisture Experiment (CAMEX-3) are utilized to detect surface water during landfalling Hurricane Georges in both the Dominican Republic and Louisiana. A third case is landfalling Tropical Storm Gert in Eastern Mexico during the Tropical Cloud Systems and Processes (TCSP) experiment in 2005. AMPR data are compared to topographic data and vegetation indices to evaluate the significance of the surface water signature visible in the 10.7 GHz information. The results of this study suggest the benefit of an aircraft 10 GHz radiometer to provide real-time observations of surface water conditions as part of a multi-sensor flood monitoring network.

Hood, Robbie E.↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗
NASA NTRS2019

HYDRAFloods Near Real-Time Mapping of Flood Events Using Multiple Satellite Sensors

Information about inundated areas is critical for distributing aid and resources in flood emergency response operations. Conventional methods of monitoring floods, like gauge based observations and reports from local authorities, provide very detailed and accurate information about flood depth and location. However, the geographic coverage of these point-based observations is limited and delays are common. Satellite-based images can help address these challenges, providing near real-time flood extent information over large areas of coverage. The Hydrological Remote Sensing Analysis of Floods (HYDRAFloods) tool, currently being developed by SERVIR-Mekong in collaboration with the Myanmar Department of Disaster Management, is one such example. Generating flood maps, even from satellite imagery, is challenging, given the many disparate sources of information. HYDRAFloods leverages the most recently available remotely sensed data acquired by multiple satellite platforms to automate the creation of daily flood maps. Through combining multiple satellite sources, including optical, microwave, and synthetic aperture radar datasets, near real-time flood maps with reduced cloud impact and increased satellite observations can be generated for use by disaster managers.

Nauman, Claire↗
DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1989

Photosensor with enhanced quantum efficiency

A method to significantly increase the quantum efficiency (QE) of a CCD (or similar photosensor) applied in the UV, far UV and low energy x-ray regions of the spectrum. The increase in QE is accomplished by overthinning the backside of a CCD substrate beyond the epitaxial interface and UV flooding the sensor prior to use. The UV light photoemits electrons to the thinned surface and charges the backside negatively. This in turn forms an accumulation layer of holes near the Si-SiO.sub.2 interface creating an electric field gradient in the silicon which directs the photogenerated signal to the frontside where they are collected in pixel locations and later transferred. An oxide film, in which the backside charge resides, must have quality equivalent to a well aged native oxide which typically takes several years to form under ambient conditions. To reduce the amount of time in growing an oxide of sufficient quality, a process has been developed to grow an oxide by using deionized steam at 95.degree. C. which takes less than one hour to grow.

Janesick, James R.↗
NASA NTRS2009

Feature Detection Systems Enhance Satellite Imagery

In 1963, during the ninth orbit of the Faith 7 capsule, astronaut Gordon Cooper skipped his nap and took some photos of the Earth below using a Hasselblad camera. The sole flier on the Mercury-Atlas 9 mission, Cooper took 24 photos - never-before-seen images including the Tibetan plateau, the crinkled heights of the Himalayas, and the jagged coast of Burma. From his lofty perch over 100 miles above the Earth, Cooper noted villages, roads, rivers, and even, on occasion, individual houses. In 1965, encouraged by the effectiveness of NASA s orbital photography experiments during the Mercury and subsequent Gemini manned space flight missions, U.S. Geological Survey (USGS) director William Pecora put forward a plan for a remote sensing satellite program that would collect information about the planet never before attainable. By 1972, NASA had built and launched Landsat 1, the first in a series of Landsat sensors that have combined to provide the longest continuous collection of space-based Earth imagery. The archived Landsat data - 37 years worth and counting - has provided a vast library of information allowing not only the extensive mapping of Earth s surface but also the study of its environmental changes, from receding glaciers and tropical deforestation to urban growth and crop harvests. Developed and launched by NASA with data collection operated at various times by the Agency, the National Oceanic and Atmospheric Administration (NOAA), Earth Observation Satellite Company (EOSAT, a private sector partnership that became Space Imaging Corporation in 1996), and USGS, Landsat sensors have recorded flooding from Hurricane Katrina, the building boom in Dubai, and the extinction of the Aral Sea, offering scientists invaluable insights into the natural and manmade changes that shape the world. Of the seven Landsat sensors launched since 1972, Landsat 5 and Landsat 7 are still operational. Though both are in use well beyond their intended lifespans, the mid-resolution satellites, which provide the benefit of images detailed enough to reveal large features like highways while still broad enough for global coverage, continue to scan the entirety of the Earth s surface. In 2012, NASA plans to launch the Landsat Data Continuity Mission (LDCM), or Landsat 8, to extend the Landsat program s contributions to cartography, water management, natural disaster relief planning, and more.

Source record↗
NASA NTRS1976

Flooding of Monroe County, Michigan - A comparison of three remote sensor data sets

During the spring of 1973, three techniques were used to obtain remotely sensed data pertaining to the flooded shoreline of Monroe County, Michigan. Although digitized data from the Earth Resources Technology Satellite were available on magnetic tapes, analysis was based on interpretation methods normally used for aerial photography, since the black and white IR photography and side-looking airborne radar (SLAR) data were available only as images. Using IR photography as the standard, it was found that ERTS-1 Band 7 imagery had a 93% accuracy in detecting areas in the combined category of open water and flooded fields. X(HH) data from SLAR detected that built-up and wet areas were built-up (89 to 100% accuracy), but not that they were wet. It is suggested that these two sensors used in concert from orbital heights could provide information useful to relief organizations in flooded areas. It is considered likely that SLAR data will improve as higher resolution becomes available.

Bryan, M. L.↗
NASA NTRS1979

Continuation of measurement of hydrologic soil-cover complex with airborne scatterometers

The author has identified the following significant results. Analysis of radar scatterometry data obtained over five flight lines in Texas by NASA C-130 aircraft demonstrated that multivariant radar data can be used to distinguish difference in land use, and hence be an indicator of surface runoff characteristics. The capability of using microwave sensors to detect flood inundation of timbered land was also determined.

Blanchard, B. J.↗
NASA NTRS2020

Leveraging Space and Ground Assets in a Sensorweb for Scientific Monitoring: Early Results and Opportunities for the Future

Increased space and ground sensing is enabling dramaticnew measurements of a wide range of Earth Science andApplied Earth Science phenomena. New space sensors tomonitor volcanism, flooding, wildfires, weather, and manyother phenomena abound.New challenges exist to rapidly assimilate available dataand to optimize measurements (e.g. direct assets) to bestobserve these complex and dynamic spatiotemporalphenomena. Artificial Intelligence offers the potential toassist in data interpretation and resource allocation to bestallocate sensing assets. We describe some efforts to buildand experiment with such “sensorweb” systems as well asoffer some direction for the future sensorweb observationsystems.

Chien, Steve↗
NASA NTRSDate not supplied

Dissemination of Global Surface Water Mapping from SAR and Optical Data to Global Stakeholders

Under NASA Solicitation A.37 “Earth Science Applications: Disaster Risk Reduction and Response,” NASA’s Disasters Program is funding projects focused on flood forecasting, post-event flood mapping, flood depth estimation and pre-event flood severity using Earth observation (EO) datasets (SAR and optical imagery) and derived flood products. A new initiative in the Disasters Program is underway to disseminate the flood products from different sensors to global stakeholders via the Pacific Disaster Center’s DisasterAWARE®, the NASA Disasters Mapping Portal and potentially other mechanisms. This initiative will also generate an integrated flood product(s) using an ensemble approach that will combine outputs from hydrologic models and EO data for different stakeholders globally for decision-making and response efforts.

remote sensing↗
DOE OSTI2024

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

54 ENVIRONMENTAL SCIENCES↗