Predicting Localized Flooding during the 2015 North Alabama Christmas Flood Event Using WRF-Hydro Simulated Streamflow
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WRF-Hydro↗
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Using NASA Earth Observations to Assess Coastal Flood Risk with Measures of Land Cover Change, Flood Extent, and Vulnerability for Adaptation and Mitigation Planning on Hawai‘i Island
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Garren Kalter↗
Mexico Disasters: Comparing Feasibility of Flood Detection Methods Using Google Earth Engine and Open Data Cube for Flood Mitigation in Mexico
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Philip Casey↗
Youngstown & Warren Disasters: Mapping Flood Susceptibility, Vulnerability, and Risk and Tree Canopy Coverage in Northern Ohio to Inform Stormwater Management and Flood Mitigation Efforts
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Lauren Mahoney↗
Youngstown & Warren Disasters: Mapping Flood Susceptibility, Vulnerability, and Risk and Tree Canopy Coverage in Northern Ohio to Inform Stormwater Management and Flood Mitigation Efforts
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Lauren Mahoney↗
Multi-Mechanism Flood Hazard Assessment: Critical Review of Current Practice and Approaches
This report documents the initial findings from the Nuclear Regulatory commission (NRC)-sponsored research project Methods for Estimating Joint Probabilities of Coincident and Correlated Flooding Mechanisms for Nuclear Power Plant Flood Hazard Assessments.1 This research project is a part of NRC’s Probabilistic Flood Hazard Assessment (PFHA) Research Program and will aid the development of guidance on the use of PFHA methods to evaluate infrastructure safety for existing and proposed US nuclear power plants (NPPs). More specifically, this project intends to provide technical background for the development of flood hazard curves for multi-mechanism floods (MMFs). MMFs are flood events caused by more than one flooding mechanism (e.g., flood events due to the simultaneous occurrence of precipitation-induced river flooding and storm surge). Project activities include three main tasks: Task 1—Survey of current concepts and methods in assessing MMF hazards; Task 2—Critical assessment of selected methods and approaches for quantifying probabilistic MMF hazard risk; Task 3—Development of example case studies to illustrate best practices for quantifying probabilistic MMF hazard risk The initial findings from Tasks 1 and 2 are documented in this report. Task 1 comprised a survey of approaches and methods that have been applied to understand and assess flood hazards due to MMFs. Task 2 involved a critical review of the selected approaches and methods. To that end, the scope of this report includes documentation of (1) a reconnaissance-level survey of the current state of concepts and practice for MMF hazard assessment; (2) a generalized MMF assessment framework to address the distinctions among various types of flood-forcing phenomena, flood mechanisms (grouped into three mechanism types), and flood severity metrics; (3) a wide-ranging survey of approaches and methods that have been applied to various flooding phenomena and settings; and (4) a critical assessment of MMF hazard assessment methods. Studies were identified involving MMFs related to coastal flooding mechanisms, fluvial (rivers/streams) flooding mechanisms, and associated combinations of coastal and fluvial flooding mechanisms. Studies were also identified that address MMFs involving coastal and fluvial flooding mechanisms as well as coastal flooding mechanisms along with extreme precipitation (without specific attribution to fluvial or pluvial mechanisms). The studies identified for review in this report included assessments at varying spatial scales (from local to global) with differing geographic regions of focus using both observed and synthetic data. The majority of studies identified and reviewed were site-specific assessments focusing on relatively short return periods. Studies considered a range of flood severity metrics, made differing assumptions regarding the occurrence of extrema, and used multiple statistical techniques; the use of copulas for the development of joint distributions was a particularly popular analysis technique. The literature review highlighted the differences among existing studies relative to terminology used, means of presenting results, framework and techniques employed, and level of sophistication regarding the number and types of variables considered. Despite the significant diversity in existing studies, the review identified several promising techniques that will be considered in future work under this project, including the development of joint distributions for MMFs using copula and Bayesian-motivated approaches.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
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↗
Research to Develop Flood Barrier Testing Strategies for Nuclear Power Plants
The U.S. Nuclear Regulatory Commission has developed regulations regarding the siting and design of nuclear power plants (NPPs) that are aimed at addressing various natural hazards, including flooding. Flood barriers are designed to prevent water from entering NPP areas containing structures, systems, and components (SSCs) important to safety. The barriers are used at NPPs along with drains, sumps, pumps, valves, plugs, and site grading as part of the plant flood protection features that protect SSCs from experiencing external or internal flooding and mitigate the effects of flooding on NPP operations. The performance of flood protection features, including flood barriers at NPPs, has been an ongoing concern. Domestic and international operational experience provides clear indications that flood barrier performance has significant safety implications, especially for aging NPPs. The observed deficiencies show that flood barriers should be designed and installed properly, then adequately tested, inspected, and maintained in order to ensure that they perform their intended functions during flooding events. Here, this paper reviews available information related to flood barriers employed at U.S. NPPs and provides an overview and categorization of NPP flood barriers. It identifies potential domestic and international flood barrier testing facilities, including operating and decommissioned U.S. NPPs. Finally, this paper presents the technical and logistical considerations that should be made when developing specific testing strategies and protocols for flood barriers, such as the selection of flood barriers, test locations, testing approach, performance criteria, and testing parameters.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Enhancing Heavy-Oil-Recovery Efficiency by Combining Low-Salinity-Water and Polymer Flooding
Summary Combining low-salinity-water (LSW) and polymer flooding was proposed to unlock the tremendous heavy-oil resources on the Alaska North Slope (ANS). The synergy of LSW and polymer flooding was demonstrated through coreflooding experiments at various conditions. The results indicate that the high-salinity polymer (HSP) (salinity = 27,500 ppm) requires nearly two-thirds more polymer than the low-salinity polymer (LSP) (salinity = 2,500 ppm) to achieve the target viscosity at the condition of this study. Additional oil was recovered from LSW flooding after extensive high-salinity-water (HSW) flooding [3 to 9% of original oil in place (OOIP)]. LSW flooding performed in secondary mode achieved higher recovery than that in tertiary mode. Also, the occurrence of water breakthrough can be delayed in the LSW flooding compared with the HSW flooding. Strikingly, after extensive LSW flooding and HSP flooding, incremental oil recovery (approximately 8% of OOIP) was still achieved by LSP flooding with the same viscosity as the HSP. The pH increase of the effluent during LSW/LSP flooding was significantly greater than that during HSW/HSP flooding, indicating the presence of the low-salinity effect (LSE). The residual-oil-saturation (Sor) reduction induced by the LSE in the area unswept during the LSW flooding (mainly smaller pores) would contribute to the increased oil recovery. LSP flooding performed directly after waterflooding recovered more incremental oil (approximately 10% of OOIP) compared with HSP flooding performed in the same scheme. Apart from the improved sweep efficiency by polymer, the low-salinity-induced Sor reduction also would contribute to the increased oil recovery by the LSP. A nearly 2-year pilot test in the Milne Point Field on the ANS has shown impressive success of the proposed hybrid enhanced-oil-recovery (EOR) process: water-cut reduction (70 to less than 15%), increasing oil rate, and no polymer breakthrough so far. This work has demonstrated the remarkable economical and technical benefits of combining LSW and polymer flooding in enhancing heavy-oil recovery.
Engineering↗
Climate and Human Impacts on Hydrological Processes and Flood Risk in Southern Louisiana
Satellite observations of coastal Louisiana indicate an overall land loss over recent decades, which could be attributed to climate and human-induced factors, including sea level rise (SLR). Climate induced hydrological change (CHC) has impacted the way flood control structures are used, altering the spatiotemporal water distribution. Based on “what-if” scenarios, we determine relative impacts of SLR and CHC on increased flood risk over southern Louisiana and examine the role of water management, via flood control structures, in mitigating flood risk over the region. Our findings show that CHC has increased flood risk over the past 28 years. The number of affected people increases as extreme hydrological events become more exceptional. Water management reduces flood risk to urban areas and croplands, especially during exceptional hydrological events. For example, currently (i.e., 2016-2020 period), CHC-induced flooding puts an additional 73km2 of cropland under flood risk at least half of the time (median flood event) and 65km2 once a year (annual flood event), when compared to a past period (1993-1997). A ten- to twenty-fold increase relative to SLR-induced flooding. CHC also increases population vulnerability in southern Louisiana to flooding; additional 9900 residents currently live under flood risk at least half of the time, and that number increases to 27,400 for annual flood events. Residents vulnerable to SLR induced flooding is lower (6000 and 3300 residents, respectively). Conclusions are that CHC is a major factor that should be accounted for flood resilience and that water management interventions can mitigate risks to human life and activities.
Augusto Getirana↗
Potential Benefits in Remapping the Special Flood Hazard Area: Evidence from the U.S. Housing Market
A typical U.S. homebuyer's understanding of whether a property faces flood risk is based on whether the property is located inside the National Flood Insurance Program's (NFIP) Special Flood Hazard Area (SFHA). The SFHA boundary, however, may bias homebuyers’ perceptions of flood risk relative to unobserved true risk because the SFHA is an incomplete, and sometimes inaccurate, representation of flood hazards. Using a national dataset of property transactions, flood hazard data from the First Street Foundation, state-level measures of flood disclosure laws, and a spatially restricted triple-difference design, we distinguish capitalization effects of policy-driven (SFHA) versus quasi-objective (First Street) indicators of flood hazard. We identify these effects by assessing thousands of local spatial interactions between property SFHA designations, measures of quasi-objective flood hazard, and being in a state that mandates flood history disclosures. Being inside the SFHA generates a risk discount, but the signal is muted relative to underlying hazard exposure. Further, the SFHA signal can result in inefficient discounts for properties erroneously mapped in the SFHA. Disclosure requirements about flood history accentuate price effects for hazardous properties and result in more adequate risk internalization. In the absence of disclosures, however, we find either no or a weak risk signal for houses outside the SFHA that face flood hazard. Our results highlight potential benefits of updating SFHA boundaries to include all houses that may experience flooding, and argue in favor of requiring flood-related disclosures from sellers to improve the market's ability to internalize flood risk.
54 ENVIRONMENTAL SCIENCES↗
Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City
In coastal-urban cities facing an elevated risk of nuisance flooding (by rain and tide) due to increased heavy rainfall, sea level rise, urbanization, and aging drainage systems, real-time flood forecasting at the street-scale can provide useful information to transportation decision-makers. Physics-Based Models (PBMs) that offer high accuracy come with high computational runtimes and costs that limit their application for real-time flood forecasting. To address this challenge, Machine Learning (ML) surrogate models trained from PBMs have been proposed to provide street-scale flood forecasts. Previous related studies have focused on using Long Short-Term Memory (LSTM) architectures to model hourly flood depth on streets. While LSTM models can capture input sequences effectively, they fall short in accurately preserving output sequences, limiting their suitability for multi-step-ahead forecasts. The seq2seq LSTM architecture offers a key advantage here by capturing the full sequence of input–output, making it potentially more suitable for multi-step-ahead flood forecasts compared to traditional LSTM models. However, seq2seq LSTM has not been tested for street-scale flood forecasting, particularly for rapidly fluctuating nuisance flooding events which require special attention to its temporal sequences. Hence, in this study, we applied the seq2seq LSTM model to explore multi-step-ahead street-scale nuisance flooding and compared its results to the traditional LSTM model as a benchmark model. LSTM and seq2seq LSTM surrogate models were applied to 22 flood-prone streets in Norfolk, Virginia, as a case study with a 4-hr (short-term) and 8-hr (long-term) lead time. The models were trained with environmental (rainfall and tide) and topographic (elevation, Topographic Wetness Index, and Depth-To-Water) features along with PBM-derived water depths for different storm events. The results demonstrated satisfactory performance of both LSTM and seq2seq LSTM surrogate models throughout the forecast period compared to the PBM. However, the seq2seq LSTM showed lower Mean Absolute Error (MAE)/ Root Mean Square Error (RMSE) and higher Nash–Sutcliffe Efficiency (NSE)/ correlation than the LSTM across most lead times, particularly for long-term forecasting due to its supremacy in handling both input–output sequences together, which is missing in the traditional LSTM. For example, in the long-term, the average RMSE ranges were 0.0268–0.0373 m for LSTM and 0.0226–0.0319 m for seq2seq LSTM, while in the short-term, they were 0.0263–0.0293 m and 0.0261–0.0283 m, respectively. Additionally, while both models exhibited similar performance in distinguishing flooded and non-flooded streets for flood depth ≥ 0.1 m, the seq2seq LSTM model demonstrated superior performance for higher flood depths (such as ≥ 0.2 m and ≥ 0.3 m). Once trained, inference took only 0.09 to 0.11 s (short-term) and 0.30 to 0.35 s (long-term) per storm event for the 22 streets, making the application highly suitable for real-time decision-making during nuisance flood events.
54 ENVIRONMENTAL SCIENCES↗
Flood Extent Mapping for Namibia Using Change Detection and Thresholding with SAR
A new method for flood detection change detection and thresholding (CDAT) was used with synthetic aperture radar (SAR) imagery to delineate the extent of flooding for the Chobe floodplain in the Caprivi region of Namibia. This region experiences annual seasonal flooding and has seen a recent renewal of severe flooding after a long dry period in the 1990s. Flooding in this area has caused loss of life and livelihoods for the surrounding communities and has caught the attention of disaster relief agencies. There is a need for flood extent mapping techniques that can be used to process images quickly, providing near real-time flooding information to relief agencies. ENVISAT/ASAR and Radarsat-2 images were acquired for several flooding seasons from February 2008 to March 2013. The CDAT method was used to determine flooding from these images and includes the use of image subtraction, decision based classification with threshold values, and segmentation of SAR images. The total extent of flooding determined for 2009, 2011 and 2012 was about 542 km2, 720 km2, and 673 km2 respectively. Pixels determined to be flooded in vegetation were typically <0.5 % of the entire scene, with the exception of 2009 where the detection of flooding in vegetation was much greater (almost one third of the total flooded area). The time to maximum flooding for the 2013 flood season was determined to be about 27 days. Landsat water classification was used to compare the results from the new CDAT with SAR method; the results show good spatial agreement with Landsat scenes.
Flood↗
Satellite imaging reveals increased proportion of population exposed to floods
Flooding affects more people than any other environmental hazard and hinders sustainable development. Investing in flood adaptation strategies may reduce the loss of life and livelihood caused by floods. Where and how floods occur and who is exposed are changing as a result of rapid urbanization4, flood mitigation infrastructure and increasing settlements in floodplains6. Previous estimates of the global flood-exposed population have been limited by a lack of observational data, relying instead on models, which have high uncertainty. Here we use daily satellite imagery at 250-metre resolution to estimate flood extent and population exposure for 913 large flood events from 2000 to 2018. We determine a total inundation area of 2.23 million square kilometres, with 255–290 million people directly affected by floods. We estimate that the total population in locations with satellite-observed inundation grew by 58–86 million from 2000 to 2015. This represents an increase of 20 to 24 per cent in the proportion of the global population exposed to floods, ten times higher than previous estimates. Climate change projections for 2030 indicate that the proportion of the population exposed to floods will increase further. The high spatial and temporal resolution of the satellite observations will improve our understanding of where floods are changing and how best to adapt. The global flood database generated from these observations will help to improve vulnerability assessments, the accuracy of global and local flood models, the efficacy of adaptation interventions and our understanding of the interactions between landcover change, climate and floods.
B.Tellman↗
Assessing Flooding Vulnerability to Assist High Water Intervention and Urban Planning Programs in the Charles River Watershed with NASA DEVELOP
The Charles River watershed intersects 35 municipalities within the Boston Metropolitan Area and has a total population of 1.2 million, making it one of the most densely populated watersheds in New England. In recent years, the watershed has observed higher rates of flood inundation, mainly due to increased development, extreme precipitation events, and increased surface runoff. As the frequency of flooding events increases and a changing climate poses an ongoing threat to local communities, governments and organizations in Massachusetts are in need of accurate flood risk assessments. NASA DEVELOP partnered with the Charles River Watershed Association, the Town of Natick’s Office of Sustainability, and the Massachusetts Audubon Society to assess flood vulnerability and susceptibility in the watershed. The team used Landsat 5 Thematic Mapper, Landsat 8 Operational Land Imager, Sentinel-1 C-Band Synthetic Aperture Radar, and Sentinel-2 MultiSpectral Instrument to assess the feasibility of identifying the extent of past flood events using remote sensing. After identifying images that overlapped with reported flood events, the team concluded that it was not feasible to use Earth observation data to detect localized flooding in the time available for this study. Instead, the Federal Emergency Management Agency (FEMA) 100-year floodplain was used as a proxy for areas where flooding may occur. The team used statistical regression analysis and validation and supervised classification to develop a flood susceptibility map, incorporating several flood conditioning factors. The susceptibility maps were calibrated to various thresholds, including two that highlight hypothetical flooding under more liberal and more conservative planning scenarios. These were overlaid with demographic and socioeconomic data to create flood vulnerability maps. The team’s flood susceptibility maps showed an improvement in capturing known flood events over the FEMA 100-year and 500-year floodplain maps. These results will be improved with the addition of stormwater drainage mapping and precipitation data. Results can be used to fill in the gaps to help the stakeholders understand their communities’ vulnerability and susceptibility to flooding and improve their preparedness plans.
Trista Brophy↗