Results for “Disaster Decision Support” — Page 8

DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗

NASA NTRSDate not supplied

The Challenger tragedy was caused by an Apollo mistake, terminating risk analysis

NASA’s view of risk changed between early Apollo and the Space Shuttle. Risk was a known serious problem at the beginning of Apollo and the risk estimates were disturbingly high. To avoid public concern, risk analysis was discontinued. Risk analysis was avoided in Shuttle, leading to an unnecessarily risky design. The immediate cause of the Challenger tragedy was the mistaken decision to launch in cold weather. The fundamental cause was the high risk of the Shuttle design. Before Challenger, management thought and testified that the probability of an accident was 1 in 100,000. After Challenger, Probabilistic Risk Analysis (PRA) found a roughly 1 in 100 chance of a Shuttle failure. The recent Orion design uses the safer Apollo approach, with a hardened capsule, launch abort escape, and the crew placed above the rocket tanks and engines. During Apollo it was estimated that, “assuming all elements from propulsion to rendezvous and life support were done as well or better than ever before, that 30 astronauts would be lost before 3 were returned safely to the Earth.” The chance of astronaut survival was only 10%. After the Apollo 1 tragedy, the awareness of risk led to an intense focus on achieving safety. “The only possible explanation for the astonishing success – no losses in space and on time – was that every participant at every level in every area far exceeded the norm of human capabilities.” During Apollo, a NASA PRA found that the chance of success was “less than 5 percent.” The NASA Administrator felt that “the numbers could do irreparable harm,” and discontinued numerical risk assessment. This led to decreasing understanding of risk. The head of Apollo reliability and safety decided, “Statistics don’t count for anything,” and that risk is reduced by “attention taken in design.” The great and initially unexpected success of Apollo appeared to validate the neglect of PRA. Continuing to neglect the mathematical estimation of risk led Shuttle into a high risk design that produced tragic results. The initial design of the Shuttle emphasized increasing capability and reducing cost without analysis or even mention of risk. A retired NASA official stated, “some NASA people began to confuse desire with reality. … One result was to assess risk in terms of what was thought acceptable without regard for verifying the assessment. … Note that under such circumstances real risk management is shut out.” Not computing risk led to removing launch abort, removing crew escape, selecting less reliable Solid Rocket Boosters, placing the crew compartment next to the rocket boosters, and accepting more stressed shielding tile designs. Accepting these specific risks directly caused the shuttle disasters. The Challenger tragedy is frequently taught as a case of management failure. The focus is on the Challenger launch decision hours before, which is a dramatic example of bad management. However, the true cause of the Challenger disaster occurred decades earlier in the Apollo era. When the easily predictable failures occurred, failure investigations focused on how they might have been avoided. The Shuttle was cancelled after the space station was completed because of its high risk. The ultimate cause of the Shuttle tragedies was the choice by the Apollo-era NASA administrator to avoid a negative public reaction to realistic risk analysis.

Harry W. Jones↗

NASA NTRSDate not supplied

HydroSAR: A Cloud-based SAR Data Analysis Service to Monitor Hydrological Disasters and their Impact on Population and Agriculture

Weather-related hazards are ubiquitous around the world including: 1) hurricane storm surges, 2) rapid snowmelt and heavy rainfall, 3) severe weather leading to flash floods, and 4) seasonal freeze and thaw of rivers that may lead to ice jams. Each of these hazards affects human settlements and has the potential to impact agricultural productivity. In each setting, end-users in disaster management need access to data processing tools helpful in mapping past and current disasters. Analysis of past events supports risk mitigation by understanding what has already occurred and how to alleviate those impacts in the future. Having capabilities to generate the same products in a response setting means that lessons learned from risk analysis will carry forward to event response. Synthetic aperture radar (SAR) data are particularly useful for these activities due to their all-weather 24/7 monitoring capabilities. In this effort we present HydroSAR, a cloud-based SAR data analysis service for the mapping of meteorological and hydrological disasters as well as their impact on population and agriculture. As part of this project we have developed a series of SAR-based value added products for the monitoring of surface hydrology (image time series, change detection, flood extent, flood depth) and the assessment of impacts on population (flood depth) and agriculture (active agriculture, inundated agriculture, flood duration). We also developed a cloud-based platform for generating these products over affected areas and are working with end-users to integrate derived product into decision-making workflows The paper will briefly introduce the SAR-based products that were developed for this effort. We describe the cloud-based production pipeline that was built to automatically generate these products in near-real time over extended regions. The integration of SAR-based information into hazard preparation and response activities is described for a number of recent disasters including the 2019 forest fires in Alaska, 2019 flooding in the U.S. Midwest, the 2020 U.S. severe weather easter outbreak, 2020 tropical storm Christobal, 2020 cyclone Amphan, 2020, Alaska Spring breakup flooding, and the 2020 flood season in Eastern India, Bangladesh, and Nepal.

Franz Josef Meyer↗

NASA NTRS2012

Customizing NASA's Earth Science Research Products for addressing MENA Water Challenges

As projected by IPCC 2007 report, by the end of this century the Middle East North Mrica (MENA) region is projected to experience an increase of 3 C to 5 C rise in mean temperatures and a 20% decline in precipitation. This poses a serious problem for this geographic zone especially when majority of the hydrological consumption is for the agriculture sector and the remaining amount is for domestic consumption. In late 2011, the World Bank, USAID and NASA have joined hands to establishing integrated, modem, up to date NASA developed capabilities for various countries in the MENA region for addressing water resource issues and adapting to climate change impacts for improved decision making for societal benefits. The main focus of this undertaking is to address the most pressing societal issues which can be modeled and solved by utilizing NASA Earth Science remote sensing data products and hydrological models. The remote sensing data from space is one of the best ways to study such complex issues and further feed into the decision support systems. NASA's fleet of Earth Observing satellites offer a great vantage point from space to look at the globe and provide vital signs necessary to maintain healthy and sustainable ecosystem. NASA has over fifteen satellites and thirty instruments operating on these space borne platforms and generating over 2000 different science products on a daily basis. Some of these products are soil moisture, global precipitation, aerosols, cloud cover, normalized difference vegetation index, land cover/use, ocean altimetry, ocean salinity, sea surface winds, sea surface temperature, ozone and atmospheric gasses, ice and snow measurements, and many more. All of the data products, models and research results are distributed via the Internet freely through out the world. This project will utilize several NASA models such as global Land Data Assimilation System (LDAS) to generate hydrological states and fluxes in near real time. These LDAS products will then be further compared with other NASA satellite observations (MODIS, VIIRS, TRMM, etc.) and other discrete models to compare and optimize evapotranspiration, soil moisture and crop irrigation, drought assessment and water balance. The floods being a critical disaster in many of the MENA countries, NASA's global flood mapping and modeling framework (CREST) will be customized for country specific needs and delivered to the remote sensing organizations for their future use. Training is an important component under this activity and adequate level of training will be offered to build basic capacity to work with NASA provided data products, models for their future use. This paper provides a comprehensive introduction to NASA's Earth Science mission for understanding the behavior of our home Planet, projecting its health for future generations and applying research results solving societal issues.

Habib, Shahid↗

NASA NTRS2020

Ellicott City Disasters II - Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗

NASA NTRS2020

Ellicott City Disasters III: Building a Real-Time Statistical Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the uses of applied remote sensing analyses are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters III project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems in Ellicott City, Maryland. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, this term built on the predictive capability of the long-short term memory (LSTM) model created by the second term of this DEVELOP project to create a Sequentially Trained Real-time EstimAted Model (STREAM). Enhancements to the model included the integration of both real-time and predicted weather products from the National Weather Service to increase predictive capacity. These weather products were supplemented by stream gauge data from the OEM as well as real-time radar products. The resultant flood risk model was trained to evaluate input variables and predict stage height in Ellicott City in real time. The model, upgraded to predict stage height up to 8 hours in advance, was incorporated into an online dashboard in a user-friendly interface. The project demonstrated the potential for integration of open data and NASA Earth observations into a flood risk forecasting tool capable of informing real-time decision-making.

Erika Munshi↗

NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗

NASA NTRSDate not supplied

GeoAI Advances in Specific Landform Mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation.

machine learning↗

DOE OSTI2019

The Value of Resilience for Distributed Energy Resources: An Overview of Current Analytical Practices

Recent extreme weather events, natural disasters, and cyber incursions have brought the vulnerability of the electric system into sharp focus. These events have demonstrated that planning for long-duration power interruptions caused by high-impact, low-probability events will require new approaches to power system resilience above and beyond previous hardening efforts. At the same time, the rapid growth and declining costs of distributed energy resources (DERs) such as microgrids, solar photovoltaics, and batteries have introduced new technology options for energy resilience. Consequently, state policymakers across the country have established electricity resilience policies and programs, with several states focusing specifically on resilient DERs as part of clean energy programs and grid modernization efforts. Although it is clear that DERs offer resilience benefits, it is unclear how to determine the value of those benefits. Identifying appropriate methodologies to calculate the value of resilience will be an important step toward ensuring that resilient DERs are considered alongside alternatives and integrated into future energy infrastructure and investment planning efforts. This paper reviews current practices for calculating the value of resilience with a focus on valuing resilient DERs installed within the distribution system. It examines both regulatory decision-making and non-regulatory cost-benefit analyses in order to determine if, and how, a value of resilience was calculated and applied. The paper is designed to address questions that utility regulators have identified as being of interest: (1) Have regulators identified and utilized a value of resilience in regulatory decisions related to resilient DERs? (2) Is the value of resilience being used to analyze resilient DERs in venues other than regulatory proceedings? (3) What are the different methods to value energy resilience? (4) What are the pros and cons of different methods used to value resilience? (5) Can regulators adopt or improve value of resilience methods to support their decisions?

14 SOLAR ENERGY↗

NASA NTRS2019

Earth Science Technology Office (ESTO) New Observing Strategies (NOS) and NOS-Testbed (NOS-T)

With the advancement of space hardware technologies such as smaller spacecraft, component and instrument miniaturization and high performance space processors, and with the advancement of software technologies in artificial intelligence, big data analysis and autonomous decision making, Earth Science is looking at novel ways to observe phenomena that previously could not have been studied or would have been too expensive to study with traditional missions. In particular, the New Observing Strategies (NOS) component of the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program aims at leveraging these novel technologies as well as low cost and easy access to space to acquire multi-temporal or simultaneous multi-angular, multi-locations, multi-resolution and multi-spectral observations that will provide better multi-source measurements and will build a more dynamic and comprehensive picture of Earth Science phenomena that need to be studied and analyzed. For applications such as water resources management, air quality monitoring, biodiversity studies or disaster management, NOS will integrate the use of small instruments, small spacecraft, constellations of spacecraft and networks of sensors to design new missions that will provide the necessary measurements to improve future forecast and science modeling systems.Measurement acquisition will therefore be approached as a system of systems rather than on a mission basis, and a system of this complexity should not be expected to work without full integration and experimental characterization. Although most of the individual technologies enabling to link and coordinate multi-source observations are more or less mature, a few technologies need to be developed and all of them need to be integrated and tested as a system. In order for this validation to occur, the AIST Program is developing the NOS Testbed that includes 3 main goals:1.Validate novel NOS technologies, independently and as a system2.Demonstrate novel distributed operations concepts3.Socialize new Distributed Spacecraft Mission (DSM) and SensorWeb (SW) technologies and concepts to the science community by significantly retiring the risk of integrating these new technologies.The NOS Testbed will consist of multiple sensing nodes, simulated or actual, representing space, air and/or ground measurements, that are interconnected by a communications fabric (infrastructure that permits nodes to transmit and receive data between one another and interact with each other). Each node will be supported by hardware capabilities required to perform nodes monitoring and command & control, as well as intelligent "onboard" computing. The nodes will work together in a collaborative manner to demonstrate optimal science capabilities. The testbed will enable to validate technologies such as inter-node communication models, techniques and protocols; inter-node coordination; real-time data fusion and understanding; planning; sensor re-targeting; etc. Additionally, the testbed will have the capability to interact with various mission design tools, OSSEs and one or several forecast models. More details about the NOS Testbed will be presented at the confererence.

Earth Science missions; Advanced information Syste↗

NASA NTRSDate not supplied

Spaceflight Medical Evacuation Risk Assessment Principles - A Qualitative Investigation

BACKGROUND Future human space exploration beyond Low Earth Orbit (LEO) will require innovative solutions in many areas, primarily those that provide direct medical support to crew on long-duration missions and optimize their health and performance. The associated challenges therein will be numerous, including but not necessarily limited to extended one-way or "asynchronous" communication delays, minimal to non-existent resupply, and a prolonged transit time to "definitive care" ranging from 3-days to 9-months. At the same time, long-duration exploration spacecraft and crew will face restrictions on mass, power, volume, and data far more significant than that seen in current LEO settings. Given these limitations, medical risk assessment is of primary importance, especially evaluating the implications of a medical evacuation of an ill or injured crewmember. Such evacuations are complicated, potentially dangerous, and well may be impossible in certain phases of the mission. Regardless, such issues must be weighed against the risks of the injured crew remaining aboard a spacecraft with limited medical resources. OBJECTIVE This qualitative study drew from the experiences of subject matter experts (SMEs) in spaceflight and appropriate analog environments (i.e., military, disaster, and extreme environment fields) to identify unique principles common amongst medical evacuation considerations helpful in informing future risk assessment tools. Appropriate analog environments included austere operational settings where multiple factors (weather, logistics/limited resupply, denied/extreme environments, and patient condition) resulted in a limited ability to provide definitive local medical care. The fundamental principles in question revolved around scenarios where evacuation became a complicating yet necessary consideration and where life-threatening medical concerns had to be weighed against critical mission objective(s). The primary authors collected semi-structured data gathered through in-depth interviews with 16 subject matter experts (SMEs). Interview questions investigated how these SMEs consider and weigh the attendant risks present in medical evacuation scenarios. Among the critical questions posed were those that sought to understand how the SME balanced the challenges and requirements of medical evacuation (or keeping an injured patient "on-site" aka: "prolonged field care") against the evacuation operation's risks on impacting overarching mission success. The team analyzed interview transcripts for common themes and principles using the qualitative methods of thematic analysis based on consensus, co-occurrence, and comparison. As a result, nine primary risk consideration themes and nine contributing factor themes emerged, all of which will ideally inform future medical evacuation decision-making tools and operational decision-making for exploration class missions. Specific aims for this study included: 1. Identification of common principles used to assess risks and benefits of medical evacuations in extreme environments 2. Identification of common points of friction or complication and challenges in extreme environment evacuations

A T Almand↗

DOE OSTI2025

Life Cycle Cost Analysis of Prestressed Concrete Poles Subjected to Wind, Surges, and Waves

Prestressed concrete (PC) poles are becoming popular choices to support coastal power transmission systems. However, the existing literature does not offer a detailed analysis of the effectiveness of PC poles in terms of long-term vulnerabilities and the direct and indirect costs. This is due to (1) lack of fragility models for PC poles and (2) lack of probabilistic wind, storm surge, and wave models in coastal settings. In this study, we address these gaps through a series of Monte Carlo simulations to estimate fragility of PC poles as a function of age and hazard (wind, surges, and waves) intensity, and the development of a probabilistic hazard model based on 10,000 years of synthetic tropical cyclone data. The probabilistic hazard model is used in conjunction with high-resolution hydrodynamic models to generate realizations of coastal wind, storm surges, and waves for the Louisiana and Mississippi coasts. A comprehensive life cycle cost analysis for a service life of 70 years considering direct and indirect losses is conducted to compare the performance of a transmission line located in Pascagoula, Mississippi, when wood poles are replaced by PC poles. Results showed that aging has a minor effect on the reliability of PC poles, highlighting the advantages of replacing wood poles with PC poles, especially in coastal areas. In addition, PC poles are significantly more cost-effective compared with wood poles over their life cycle, leading to an estimated saving of $11.55 million (68.17% reduction). The results of this study provide key insight to inform decision-making processes to keep the coastal power grids resilient and cost-effective against future storm hazards.

natural disasters↗

NASA NTRS2007

An Experimental System for a Global Flood Prediction: From Satellite Precipitation Data to a Flood Inundation Map

Floods impact more people globally than any other type of natural disaster. It has been established by experience that the most effective means to reduce the property damage and life loss caused by floods is the development of flood early warning systems. However, advances for such a system have been constrained by the difficulty in estimating rainfall continuously over space (catchment-. national-, continental-. or even global-scale areas) and time (hourly to daily). Particularly, insufficient in situ data, long delay in data transmission and absence of real-time data sharing agreements in many trans-boundary basins hamper the development of a real-time system at the regional to global scale. In many countries around the world, particularly in the tropics where rainfall and flooding co-exist in abundance, satellite-based precipitation estimation may be the best source of rainfall data for those data scarce (ungauged) areas and trans-boundary basins. Satellite remote sensing data acquired and processed in real time can now provide the space-time information on rainfall fluxes needed to monitor severe flood events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models, which can be parameterized by a tailored geospatial database. An example that is a key to this progress is NASA's contribution to the Tropical Rainfall Measuring Mission (TRMM), launched in November 1997. Hence, in an effort to evolve toward a more hydrologically-relevant flood alert system, this talk articulates a module-structured framework for quasi-global flood potential naming, that is 'up to date' with the state of the art on satellite rainfall estimation and the improved geospatial datasets. The system is modular in design with the flexibility that permits changes in the model structure and in the choice of components. Four major components included in the system are: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM, topography-derived hydrologic parameters such as flood direction. flow accumulation, basin, and river network etc.; 3) spatially distributed hydrological models to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay thc input data to the models and display the flood inundation results to the users and decision-makers. Early results appear reasonable in terms of location and frequency of events. Case studies of this experimental system are evaluated with surface runoff data and other river monitoring systems. such as Dartmouth Flood Observatory's "Surface Water Watch" array of river reaches that are measured daily via other satellite remote sensing data. A major outcome of this progress will be the availability of a global overview of flood alerts that should consequently improve the performance of Decision Support System. We expect these developments in utilization of satellite remote sensing technology to offer a practical solution to the challenge of building a cost-effective early warning system for data scarce and under-developed areas.

Adler, Robert↗

DOE OSTI2021

Power System Resilience Metrics Augmentation for Critical Load Prioritization

One of the major goals of new grid operation regimes, such as transactive energy systems (TESs), is to make the power grid more resilient to withstand natural or man-made disasters and potential reliability events, and to continue to serve the maximum number of its customers. But it is a well-known fact to system operators that not all customers are the same. This implies that any discussion of TESs’ impacts on the resilience of the power system should consider the needs of its critical customers (such as the power system operation centers, fire and police stations, and hospitals) over those of other customers. When evaluating the resilience of the system, bonus points must be awarded to any system that could maintain its power supply to critical customers during a disturbance that may cause an outage. This report discusses critical infrastructure (CI) as found in the literature and then categorizes it based on the field to which the operations belong (such as human life/safety-related, operations management, necessary city operation, industrial customers, etc.). Each of these CI categories is further divided into types of critical customers (e.g., the human life/safety-related category has different types of customers like hospitals, fire and police stations, etc.). The entire demand of each of the critical customer types is not categorized as critical load (CL); instead, only a portion of the total load of these critical customers is characterized as critical load. This is done based on the categories of equipment, the function of which is crucial in the operation of the overall facility. CL categorization is performed to provide the ratio of the critical load portion to the overall load , so that it can serve as a parameter in the resilience evaluation of the grid through a metrics-based approach. Such categorization is important as it helps to augment the existing quantifiable resilience metrics with CL categorization. The metrics for a power system need to not only consider how well a system performed during a disturbance event, but also how it reduced strain and supplied power to its CLs. The first step in this process is characterize CLs in the system. After CL characterization, the next step is the inclusion of these loads in the resilience metrics. To that end, in this report weight-based augmentation of resilience metrics is proposed, where certain customers (the ones that are categorized as critical) are assigned higher weights than others. Though an overview of assigning weights to customers is discussed, there is no one-size-fits-all approach for every power system. The decisions made about assigning such weights to customers vary greatly from one operator to another, based on their unique systems and the current and predicted states of critical customers. This decision-making can include the type of disturbance event, which might only affect certain parts of the system. In general, analyzing critical customers before an event helps understand system vulnerabilities. It also helps in planning and conducting operations during the event, evaluating system performance after the event, and supporting better planning for future events. An alternative to the current practices of managing the grid for outages is an innovative TES, which has the potential to provide a platform for including distributed energy resources for managing CLs. This report also describes how TES qualities can help (1) to maintain power supply to critical customers for uninterrupted operations and (2) to restore lost power supply to the critical customers rapidly.

24 POWER TRANSMISSION AND DISTRIBUTION↗