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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Who Has the Upper HAND in a Hurricane? Flood Risk, Weather Shocks, and Property Prices

Flood risk is increasing in the United States, but how property markets respond to this hazard is unclear. Focusing on Houston, Texas, we analyze changes in flood risk capitalization in property prices after three major Gulf Coast hurricanes. Here, we compare two measures of flood risk–floodplain designations from the Federal Emergency Management Agency (FEMA), and a hydrologically-imputed, continuous measure, Height Above the Nearest Drainage (HAND). HAND affects property prices after a storm in intuitive ways, while we observe counterintuitive post-storm price premiums within FEMA floodplains. Plausibly exogenous measures like HAND may be useful tools for characterizing flood risk in economic analyses.

Plough, Julian A. [University of North Carolina, C↗
DOE OSTI2025

Yesterday’s extremes, today’s new normal: flood risk in the Kathmandu Valley, Nepal

Unplanned urban growth has left many cities increasingly vulnerable to extreme rainfall events, particularly in regions with inadequate drainage infrastructures and development encroaching on natural floodplains. Here, in this perspective paper, we examine the September 2024 floods that struck Central Nepal, triggered by a persistent low-pressure system and enhanced by converging moisture flows from the Arabian Sea and the Bay of Bengal which led to widespread catastrophic damage. In the Kathmandu Valley, floodwaters expanded to more than 2.5 times the bankfull water extent, causing significant damage to housing, transportation network, and critical infrastructure, displacing thousands of residents, and severely disrupting urban services. This event highlights the urgent need for improved flood management strategies that integrate both structural and non-structural measures into the infrastructure development. While early warning systems provided critical lead time, challenges remain in reducing forecasting uncertainties and improving communication across government agencies and with local communities. A forward-looking approach is essential, including probabilistic flood forecasting systems, sustainable floodplain management, risk-sensitive land use planning, climate- and disaster- resilient infrastructure development, and the integration of nature-based solutions like urban green and blue spaces to mitigate flood impacts. By involving local communities in planning and preparedness efforts, particularly through citizen science initiatives, and engagement with underserved and disadvantaged communities, Nepal can better adapt to the growing risks posed by extreme rainfall and urban flooding and enhance long-term disaster resilience in rapidly urbanizing areas like Kathmandu Valley.

Kathmandu Valley↗
DOE OSTI2025

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. Finally, this underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

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 OSTI2026

Mapping Philadelphia's Floodscape: A 35‐Year Analysis of Coastal Urban Flood Hazards and Drivers

Low-lying coastal urban cities face significant flooding risks from river flooding (fluvial), storm surges and high tides (coastal), and intense local rainfall (pluvial). Accurately assessing these hazards requires modeling frameworks capable of capturing both the individual and combined effects of multiple flood drivers, as well as the diversity of flood scenarios that can arise from their interactions. In this study, we implemented a physics-based, high-resolution modeling approach to assess flood hazards in Philadelphia, PA, a coastal city exemplifying multi-driver flood risks over multiple decades (1985–2019), by simulating a wide range of flood events at a 10-m resolution. By integrating watershed, coastal, and urban flood models, we explicitly resolved the interaction between fluvial, pluvial, and coastal processes across the city. From these simulations, we identified flood hazard hotspots and systematically attributed the primary drivers of flooding for each event. The results suggested that 44% of Philadelphia's flood events were compound floods, primarily driven by fluvial-surge and fluvial-pluvial combinations. Notably, 77% of these events involved fluvial flooding, either as a single flood driver or in combination with other drivers, underscoring the dominant role of riverine processes in the city's flood hazard. Overall, this study demonstrates the value of a comprehensive, process-based approach for urban flood hazard assessment and highlights the importance of considering the full spectrum of flood scenarios to inform targeted and adaptive flood management strategies in coastal cities.

Compound flooding↗
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 OSTI2024

MSD CoP Webinar: Accounting for distributive justice in model-based decision support

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Model-based analyses play an ever-increasing role in informing policy and decision-making. However, many large-scale societal challenges unavoidably involve questions about the fair distribution of positive and negative consequences. How are the costs of the energy transition distributed over households and businesses? How are flood risks redistributed under different flood risk management plans? How are the impacts of climate change and climate mitigation distributed over different parts of the world?These kinds of questions play an important role when deliberating public policy, and the lack, in many cases, of clear answers becomes an obstacle to decision-making. At recent COPs, for example, wicked questions about loss and damages and the phase-out versus phase-down of coal became obstacles to global climate action, illustrating the misalignment of the direction of scientific research and decision-makers' information needs. In this talk, I'll explore advances that are being made that enable analysts to start providing grounded model-based answers to questions of distributive justice, as well as argue that analysts should embrace and explicate the normative nature of their work instead of hiding behind the purported neutrality of science. Presenters: Dr. Jan Kwakkel (Delft University of Technology) Moderator(s): Rebecca Saari (MSD CoP Working Group Co-Chair; Univ. of Waterloo), Matt Sparks (Univ. of Waterloo); Sarah Fletcher (MSD CoP Working Group Co-Chair; Stanford University), Juan Moreno-Cruz (Univ. of Waterloo), Patrick Reed (MSD CoP Facilitation Team Member, Moderator and Organizer) This webinar was held on: November 13, 2024 from 1 PM - 2:15 PM ET

Kwakkel, Jan H.↗
DOE OSTI2025

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗
DOE OSTI2025

Increased flood exposure in the Pacific Northwest following earthquake-driven subsidence and sea-level rise

Climate-driven sea-level rise is increasing the frequency of coastal flooding worldwide, exacerbated locally by factors like land subsidence from groundwater and resource extraction. However, a process rarely considered in future sea-level rise scenarios is sudden (over minutes) land subsidence associated with great (>M8) earthquakes, which can exceed 1 m. Along the Washington, Oregon, and northern California coasts, the next great Cascadia subduction zone earthquake could cause up to 2 m of sudden coastal subsidence, dramatically raising sea level, expanding floodplains, and increasing the flood risk to local communities. Here, we quantify the potential expansion of the 1% floodplain (i.e., the area with an annual flood risk of 1%) under low (~0.5 m), medium (~1 m), and high (~2 m) earthquake-driven subsidence scenarios at 24 Cascadia estuaries. If a great earthquake occurred today, floodplains could expand by 90 km 2 (low), 160 km 2 (medium), or 300 km 2 (high subsidence), more than doubling the flooding exposure of residents, structures, and roads under the high subsidence scenario. By 2100, when climate-driven sea-level rise will compound the hazard, a great earthquake could expand floodplains by 170 km 2 (low), 240 km 2 (medium), or 370 km 2 (high subsidence), more than tripling the flooding exposure of residents, structures, and roads under the high subsidence scenario compared to the 2023 floodplain. Our findings can support decision-makers and coastal communities along the Cascadia subduction zone as they prepare for compound hazards from the earthquake cycle and climate-driven sea-level rise and provide critical insights for tectonically active coastlines globally.

Science & Technology - Other Topics↗
DOE OSTI2026

Small Reservoirs Offer a New Perspective on Flood Reduction in Large Basins

The flood reduction potential of individual reservoirs within a large river network continuum remains poorly understood due to the complex interplay between reservoir characteristics and network properties. Here we investigate whether a collection of relatively small reservoirs can play a significant role in mediating downstream floods and assess how that role may be influenced by reservoir network properties compared to traditionally known reservoir characteristics. Our unique contribution was the simulation of downstream flood inundation maps alongside peak flows for each of the 81 major reservoirs (6 × 104 to 8 × 109 m3) across 15,000 river reach segments, integrated into a process-based hydrologic model covering a 415,000 km2 region in the Texas Gulf Coast, United States. The three key takeaways from our study are as follows. (a) Smaller reservoirs can substantially reduce downstream flooding, suggesting that inclusion of large reservoirs—the traditional approach in flood risk management studies—may present only a partial picture. (b) Flood reduction by smaller reservoirs is more effective upstream, although this phenomenon may be linked with aridity and overall water availability. (c) While reservoir size matters, it is not the primary factor determining its downstream flood reduction potential; the influence of network properties, such as catchment area, the count of upstream reservoirs, the cumulative maximum storage capacity of upstream reservoirs, and Stream Order (i.e., location), is equally and often more important. The broader impact of our findings goes beyond just floods, providing foundational insights for addressing emerging challenges such as aging dams and river connectivity.

Patel, Krutikkumar [University of Texas at Arlingt↗
DOE OSTI2026

Unlocking the benefits of transparent and reusable science for climate-risk management

People around the world seek climate-risk information to guide their decisions. For instance, projections about future flood risk inform where households choose to live, how lenders manage credit risks, and which communities receive federal funding. Yet data limitations and fundamental validation challenges raise important concerns about the reliability of such projections. The principles of transparency and reusability help address these concerns by enabling scrutiny of assumptions and methods, development of foundational data and tools, and consistent application of evaluation standards. While there is ongoing debate about how much transparency commercial climate-risk services should provide, many expect non-commercial actors to lead the way on operationalizing transparency and reusability to fulfill their knowledge-building role in the climate-risk ecosystem. However, despite prominent success stories, we find a substantial gap between principles and practice: only four percent of the most-cited peer-reviewed climate-risk studies in recent years fully share their data and code despite this being a widely accepted minimum standard for transparency. We highlight low-cost measures that non-commercial researchers can take now to improve transparency and reusability. We also emphasize that transformative progress requires substantial investment, cross-sector collaboration, and careful consideration of tradeoffs, data rights, and multiple perspectives on equity. We hope this perspective accelerates both immediate actions and longer-term conversations to improve the ability of science to effectively support timely, evidence-based, and sound climate-risk management.

Open Science↗
DOE OSTI2026

Strategies for Flood Resilience and Grid Investment Among Iowa's Electric Distribution Utilities

Flooding poses a growing threat to Iowa's electric distribution system, yet utilities face significant data and modeling challenges in planning effective resilience investments. This report provides a foundational assessment of how distribution utilities in Iowa, investor-owned, municipal, and cooperative, approach resilience planning, with a focus on flood risk. It combines hazard characterization, review of state and utility practices, and application of NLR's Energy Resilience Analysis for Distribution Systems (ERAD) and Capacity Expansion Decision Support for Distribution Networks (CADET) tools. Using FEMA floodplains, Iowa Flood Center depth grids, and utility infrastructure data, the analysis quantifies asset exposure, simulates outage risks, and evaluates resilience strategies such as pole hardening, undergrounding, and substation protection. Results indicate that while feeder-level upgrades provide incremental benefits, the most significant reductions in outage risk are achieved through targeted substation interventions. The report highlights key data gaps, such as limited elevation data and fragility functions, and underscores the need for probability-weighted investment frameworks to address both frequent and catastrophic flood events. These insights aim to support utilities, state agencies, and federal partners in prioritizing resilience investments that safeguard Iowa's electric grid against future flooding.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Divergent responses of historic rain-on-snow flood extremes to a warmer climate

Global warming is altering flood risks induced by rain-on-snow events. However, decision-makers lack guidance on how rain-on-snow induced extreme floods could be altered with warming. Here, storyline analyses using a kilometer-scale land surface model reveal diverse responses of four historically-impactful, decision-relevant rain-on-snow induced extreme flood events over the contiguous U.S. to warming, due to alterations in their water budgets. For the 2017-Feb California floods, runoff first increases and then decreases with warming, peaking under the +3 K scenario, while runoff of the 2017-Jan California floods increases monotonically by ~53%/K. Contrastingly, runoff of the 1996-Jan Mid-Atlantic floods decreases gradually with warming. Despite these differences, warming generally shifts flood-generating regimes along elevation profiles. High elevations could experience notably increased runoff, while low elevations encounter a shift from rain-on-snow-driven to rainfall-dominated runoff. These findings underscore the need for flood control planning to quantify region- and elevation-specific changes in rain-on-snow events in a warmer climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Temporal persistence of postfire flood hazards under present and future climate conditions in southern Arizona, USA

Changes to soil hydraulic properties that reduce infiltration capacity following fire can increase flash flood risks. These risks are exacerbated by rainfall intensification associated with a warming climate. However, the potential effects of climate-change-driven rainfall intensification on postfire floods remain largely unexplored. Using rainfall and runoff observations from a 49.4 km 2 watershed in southern Arizona, USA, and a hydrologic model (KINEROS2), we examined the temporal evolution following a historic fire of three crucial hydrologic parameters: soil saturated hydraulic conductivity (K sp ), net capillary drive (G p ), and hydraulic roughness (n c ). We explored how the effect of fire on these parameters may influence peak flow under future climate scenarios derived from CMIP6, specifically the medium emissions scenario (SSP2-4.5) and high emissions scenario (SSP5-8.5). Results demonstrate an increase in K sp from 11 mm h −1 in the first postfire year to 60 mm h −1 in postfire year 3. G p similarly increased from 19 mm in the first postfire year to 30 mm in the third, while nc was relatively constant. The highest simulated Q p occurred in the first postfire year. Under the SSP2-4.5 scenario, the likelihood of a 100-year flood is projected to be twice as large by the middle of the century relative to its historical magnitude. Simulations further indicate that the maximum expected discharge associated with a postfire flood, as derived from historical data, could be triggered by a 10-year rainstorm under the SSP5-8.5 scenario by the late century. Simulations also demonstrate that rainfall intensification will lead to greater persistence of elevated flood hazards following fire by the late century under both the SSP2-4.5 and the SSP5-8.5 scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Can Restoring Tidal Wetlands Reduce Estuarine Nuisance Flooding of Coasts Under Future Sea‐Level Rise?

Wetland restoration is an increasingly popular nature‐based method for flood risk mitigation in coastal communities. In this study, we present a novel method using hydrodynamic modeling and harmonic analysis to quantify wetlands' ability to reduce future nuisance flooding. The method leverages a hydrodynamic model calibrated to present day data and was run for a range of future sea‐level rise (SLR) and wetland restoration scenarios to quantify changes to tidal harmonic amplitudes and phases. The harmonic constituents are used to generate water surface elevations over a time period of interest (e.g., one year) and compared to critical exceedance thresholds such as levee elevations. Then, changes to nuisance flooding are calculated by counting the number of hours critical thresholds are exceeded under different SLR and wetland restoration scenarios. We applied the method to Coos Bay, Oregon, USA as a test case. We found restoration reduces the number of hours nuisance flooding occurs in downtown Coos Bay from 15 hr (present day conditions) to 0 hr (fully restored condition) under median SLR (82 cm by 2100). Restoration had spatially variable impacts on reducing peak flood elevations with minimal impacts near the estuary mouth and greatest impact 32 km inland. The effectiveness of restoration was heavily dependent on future SLR. Restoration was maximally effective in 2050 under all SLR scenarios, less effective in 2100 under median SLR, and not effective under high SLR. Modeling results suggest increased tidal prism and accommodation space are driving restoration‐associated reductions in tidal amplitudes.

Brand, Matthew W. [Louisiana State Univ., Baton Ro↗
DOE OSTI2025

Concentrations and Health Implications of As, Hg, and Cd and Micronutrients in Rice and Emissions of CH 4 From Variably Flooded Paddies

Abstract The flooded soil conditions under which rice is typically grown are beneficial for boosting yield and decreasing herbicide inputs but may pose a food safety and environmental health risk. Flooded soils lead to reducing conditions and anaerobic metabolisms of soil microorganisms, which mobilizes arsenic from soil into soil solution, where it can be absorbed by rice roots and transported to grain. These conditions also promote the production and emission of methane (CH 4 )—a potent greenhouse gas. To evaluate how water management affects metal(loid) grain concentrations and CH 4 emissions, we conducted a 2‐year field study in which rice paddy water was managed under a range of soil redox conditions that spanned from flooded to non‐flooded. We observed that growing rice under less flooded conditions decreased CH 4 emissions and concentrations of grain total As, grain inorganic As, grain total Hg, and grain inorganic Hg relative to flooded conditions, with more reductions observed as conditions were drier; grain organic As and Hg (MeHg) species also decreased with drier conditions particularly in Year 1. However, the driest conditions tested led to a 50%–97% increase in grain Cd concentrations that exceeded the CODEX limit and grain yield reductions as high as 25% and 40% in Year 1 and 2, respectively. While concentrations of toxic metal(loid)s could be manipulated by water management, micronutrient concentrations were similar or decreased with drier conditions, potentially increasing grain Cd bioaccessibility to humans. Because practices for rice water management are gaining momentum, more research should monitor grain Cd levels along with micronutrients.

Environmental Sciences & Ecology↗
DOE OSTI2025

Integrated Modeling Driven Evaluation of Opportunities for Climate‐Resilient Perennial Biomass Crop Plantings in Flood‐Prone Agricultural Landscapes

Adapting to future climate change in flood-prone landscapes will require climate-resilient agricultural systems. Planting perennial crops, like switchgrass and willow, along river corridors can mitigate future flooding while supporting bioenergy markets. We developed an integrated assessment linking climate, hydrologic, and inundation model results to assess future flood risk to river-adjacent agricultural lands in the Mid-Atlantic Region (MAR) and explore this opportunity. We produced ensemble streamflow projections for every MAR stream using a hydrologic model driven by a suite of downscaled and bias-corrected Coupled Model Intercomparison Project Phase 6 climate projections. We then conducted high-resolution inundation mapping based on projected flood frequencies for baseline and future periods. Results show that in the near-term future, at least two-thirds of the streams will experience 100-year floods more severe than the baseline 200-year floods. Riparian zones are projected to face a median rise of inundation by 9.5%–24.1%. Results show that there is an opportunity to mitigate flooding in over half of MAR's counties with the quantities of switchgrass and willow plantings anticipated for mature bioenergy markets, even under the most extreme (200-year) flood events. Our integrated modeling framework can guide similar regions to evaluate opportunities for flood-resilient agricultural systems under climate change.

60 APPLIED LIFE SCIENCES↗