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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Projected changes in extreme rainfall and temperature events and possible implications for Cameroon's socio-economic sectors

Extreme events like flooding, droughts and heatwave are among the factors causing huge socio-economic losses to Cameroonians. Investigating the potential response of rainfall and temperature extremes to global warming is therefore critically needed for tailoring and adjusting the country's policies. Recent datasets have been developed for this purpose within the Coordinated Output for Regional Evaluations (CORDEX-CORE) initiative, at ~25 km grid spacing. These regional climate models were used to dynamically downscaled four global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), under the optimistic and pessimistic representative concentration pathways (RCPs) 2.6 and 8.5, respectively. These models were employed in this study for characterizing the response of Cameroon's extreme precipitation and temperature events to global warming, using seven indices defined by the Expert Team on Cli mate Change Detection and Indices. Under global warming, the maximum number of consecutive dry (wet) days' is expected to increase (decrease). However, the annual total rainfall amount is expected to increase, mainly due to the intensification of very wet days and daily rainfall intensity. Furthermore, the temperature-based indices reveal an increase (decrease) in the total annual hot (cold) days, and overall, changes intensify with increased radiative forcing. The high-mitigated low-emission pathway RCP2.6 features attenuated changes, and even sometimes adapts to reverse the sign of changes. Designing reliable policies to limit the risks associated with the above changes is required, as their socio-economic consequences are likely to include food insecurity, heat-related illness, population impoverishment, price rises and market instability.

54 ENVIRONMENTAL SCIENCES↗
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↗
DOE OSTI2023

A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment

This study presents a framework to quantify changes in soil saturation in response to flooding caused by extreme hydrologic perturbation on coastal ecosystems at the interfaces and transition between terrestrial and aquatic systems. Subsurface heterogeneity limits the use of in situ measurements to quantify subsurface flow during flooding due to the spatial discontinuity in the measured data. While geophysical methods, including time-lapse electrical resistivity imaging (ERI), are increasingly used to monitor soil hydrological processes, their abilities to parameterize flow models have been underutilized. This study combines background ERI, ground penetrating radar (GPR), time-lapse ERI, soil characterization, and a numerical flow model developed using an Advanced Terrestrial Simulator (ATS) code to quantify the infiltration pathway and describe the hydrological dynamics during a simulated flooding experiment. We assessed the use of two conceptual models developed using [1] ERI and GPR data that described the stratigraphic distribution, and time-lapse ERI that mapped permeability contrast, and [2] information from a national soil database for capturing changes in saturation. Combining the ERI and GPR results with soil core data revealed the stratigraphic heterogeneity at the site with a silty clay layer from 1 to 2 m between an overlying loamy topsoil and an underlying saturated silty sand. This silty clay layer could restrict deep infiltration. The time-lapse ERI showed up to a 35% decrease in resistivity, which correlated with soil moisture data (R 2 value > 0.53) and revealed preferential infiltration zones used to inform the flow model. Numerical simulation results from both the geophysics- and soil database-informed models quantified changes in soil saturation with calculated soil moistures that agreed with field data. The geophysics-informed model captured more of the system’s variability, reflective of shallow subsurface heterogeneities. The framework presented will serve as a precursor for a robust ecohydrological model that can describe the impacts of extreme events induced by climate change on coastal ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

How extreme rainfall and failing dams unleashed the Derna flood disaster

On September 11, 2023, Storm Daniel unleashed unprecedented rainfall over the Wadi Derna watershed, triggering one of the most devastating floods in modern history, striking Derna, a coastal city in Libya. This study reconstructs the disaster using an integrated modeling approach that combines satellite imagery, hydrologic, hydraulic, and geotechnical simulations, machine learning, eyewitness accounts, and digital elevation data to assess the impact of cascading dam failures. Our findings reveal that the region’s dams, even if structurally sound, would have provided minimal protection against the extreme runoff. However, their failure unleashed a destructive surge wave, amplifying the disaster’s magnitude and devastation. Here, we show that the collapse of aging flood control infrastructures, compounded by inadequate risk assessment and emergency preparedness, dramatically escalated the disaster’s impact. Our findings underscore the urgent need for systematic dam safety evaluations, enhanced flood forecasting, and adaptive risk management strategies that address climate extremes and infrastructure vulnerabilities.

Hydrology↗
DOE OSTI2026

Record-Breaking Atmospheric River Drives April 2024 Extreme Precipitation in the United Arab Emirates and the Surrounding Gulf Region

In mid-April 2024, the United Arab Emirates (UAE) and the surrounding Gulf region experienced unprecedented rainfall and catastrophic flooding, causing widespread damage, loss of life, and significant economic costs. During the 3-day period from 15 to 17 April, rainfall in the UAE exceeded 100 mm in the hardest-hit areas, with more than 170% of the typical annual precipitation recorded in just 72 h. The event was associated with exceptionally strong integrated water vapor transport (IVT), driven by a persistent low pressure system and a focused corridor of moisture transport. This environment, combined with favorable dynamical and convective conditions, triggered intense thunderstorms and widespread flooding. This article examines the role of an atmospheric river (AR) in the April 2024 extreme precipitation event, emphasizing the contribution of extreme IVT to preconditioning and amplifying the heavy rainfall. While this event has previously been described primarily in terms of a mesoscale convective system (MCS) and potential vorticity (PV) streamers, here we document and illustrate its close relationship to a record-breaking moisture transport corridor. This AR-based perspective highlights how large-scale moisture-transport frameworks can complement synoptic and mesoscale analyses in understanding extreme rainfall in arid and semiarid regions such as the UAE and the Gulf. The event serves as a stark example of the vulnerabilities faced by arid regions, where atmospheric conditions conducive to extreme flooding may become more frequent in the future.

Massoud, Elias [ORNL] (ORCID:0000000217725361)↗
DOE OSTI2021

Climate Change and Changes in Compound Coastal-Riverine Flooding Hazard Along the U.S. Coasts

The cooccurrence of coastal and riverine flooding leads to compound events with substantial impacts on people and property in low-lying coastal areas. Climate change could increase the level of compound flood hazard through higher extreme sea levels and river flows. Here, a bivariate flood hazard assessment method is proposed to estimate compound coastal-riverine frequency under current and future climate conditions. A copula-based approach is used to estimate the joint return period (JRP) of compound floods by incorporating sea-level rise (SLR) and changes in peak river flows into the marginal distributions of flood drivers. Specifically, the changes in JRP of compound major coastal-riverine flooding defined based on simultaneous exceedances above major coastal and riverine thresholds, are explored by midcentury. Subsequently, the increase in the probability of occurrence of at least one compound major coastal-riverine flooding for a given period of time is quantified. The proposed compound flood hazard assessment is conducted at 26 paired tidal-riverine stations along the Contiguous United States coast with long-term data and defined flood thresholds. We show that the northeast Atlantic and the western part of the Gulf coasts are experiencing the highest compound major coastal-riverine flood probability under current conditions. However, future SLR scenarios show the highest frequency amplification along the southeast Atlantic coast. The impact of changes in peak river flows is found to be considerably less than that of SLR. Climate change impacts, especially SLR, may lead to more frequent compound events, which cannot be ignored for future adaptation responses in estuary regions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2024

Vegetation Greening Mitigates the Impacts of Increasing Extreme Rainfall on Runoff Events

Future flood risk assessment has primarily focused on heavy rainfall as the main driver, with the assumption that projected increases in extreme rain events will lead to subsequent flooding. However, the presence of and changes in vegetation have long been known to influence the relationship between rainfall and runoff. Here, we extract historical (1850–1880) and projected (2070–2100) daily extreme rainfall events, the corresponding runoff, and antecedent conditions simulated in a prominent large Earth system model ensemble to examine the shifting extreme rainfall and runoff relationship. Even with widespread projected increases in the magnitude (78% of the land surface) and number (72%) of extreme rainfall events, we find projected declines in event-based runoff ratio (runoff/rainfall) for a majority (57%) of the Earth surface. Runoff ratio declines are linked with decreases in antecedent soil water driven by greater transpiration and canopy evaporation (both linked to vegetation greening) compared to areas with runoff ratio increases. Using a machine learning regression tree approach, we find that changes in canopy evaporation is the most important variable related to changes in antecedent soil water content in areas of decreased runoff ratios (with minimal changes in antecedent rainfall) while antecedent ground evaporation is the most important variable in areas of increased runoff ratios. Our results suggest that simulated interactions between vegetation greening, increasing evaporative demand, and antecedent soil drying are projected to diminish runoff associated with extreme rainfall events, with important implications for society.

soil moisture↗
NASA NTRSDate not supplied

Utilizing Open-Source Earth Observations to Inform the Toa Baja Municipality’s Flood Risk Mitigation Efforts and Educate the Public

Global climate changes contribute to more intense and frequent tropical storms, subjecting places like Toa Baja, Puerto Rico to critical damage. Known as “the underwater city” due to its propensity to flood, residents of Toa Baja face constant flood risk. During extreme tropical storm events, such as Hurricane Maria in 2017, residents experienced up to 20 feet of inundation. The NASA DEVELOP National Program collaborated with the Municipio Autónomo de Toa Baja, ResilientSEE-PR, and the MIT Urban Risk Lab to supplement 2018 FEMA HEC-RAS flood maps that designate 63% of Toa Baja as a flood plain. This analysis provides a high-resolution interpretation of flood risk through two lenses; susceptibility and vulnerability. For this analysis, susceptibility consists of nine weighted layers: NDVI, landcover, slope, elevation, topographic wetness index, height above nearest drainage, saturated hydraulic conductivity, distance to water, and storm surge. These factors are consistently used to evaluate susceptibility to flood, but their weights vary by analysis. Vulnerability consists of population, informal settlements, and building density, which were given equal weight. Susceptibility and vulnerability were combined to map flood risk. This analysis used a bivariate legend to understand the different levels of risk along a spectrum from low susceptibility and low vulnerability (low risk) to high susceptibility and high vulnerability (high risk). Data processed in Google Earth Engine, which identified historical inundation on various occasions, were used to validate the flood susceptibility layers. Results showed 89% of areas designated as high susceptibility are located within the floodway designated by the FEMA HEC-RAS maps. The eastern region of Toa Baja is most at risk for flooding due to high susceptibility to flooding along with a high density of population, buildings, and informal settlements. The resulting map also reveals the presence of smaller high-risk areas all around the municipality. This analysis provides scientific evidence for flood risk mitigation in Toa Baja by highlighting areas that might be impacted by strong floods in the future. Additionally, these results are communicated in an Esri ArcGIS StoryMap, an accessible platform that can easily inform the public about the flood risk in their neighborhood.

Adriana Le Compte↗
NASA NTRS2024

Central California Disasters: Incorporating Satellite-Derived Precipitation and Soil Moisture Products into Flood Preparedness and Emergency Management in California

Atmospheric rivers are a major contributor to extreme precipitation events and flooding in California. This study assessed the feasibility of using Earth observation data to monitor precipitation, soil moisture, and flooding from atmospheric rivers in the Salinas River Watershed in central California during the winter of 2022-2023. The team analyzed satellite derived precipitation estimates from the Global Precipitation Measurement mission, soil moisture data from the Soil Moisture Active Passive radiometer, and synthetic aperture radar imagery from Sentinel-1 to detect flood inundation. Global Precipitation Measurement precipitation estimates were found to underestimate rainfall by 33% to 52% compared to rain gauge data. The team found the Soil Moisture Active Passive dataset to be a valuable flooding indicator that they could visualize through various graphical representations, although its accuracy still needs verification with in situ flood maps. Sentinel-1 synthetic aperture radar imagery provided mapping of flood inundation extent along the Salinas River and surrounding areas and the Blue Spot model appears to be a good predictor of pluvial flood zones. The findings suggest Earth observations can enhance precipitation and flooding monitoring by the California Department of Water Resources, though further verification is needed. Integration of these datasets with vulnerability indices highlighted communities at highest risk during the 2022-2023 flooding events.

Abhinav Banthiya↗
DOE OSTI2023

Mesoscale Convective Systems Modulated by Convectively Coupled Equatorial Waves

Mesoscale convective systems (MCSs) produce over 50% of tropical precipitation and account for the majority of extreme rainfall and flooding events. MCSs are considered the building blocks of larger-scale convectively coupled equatorial waves (CCEWs). While CCEWs can provide favorable environments for convection, how CCEWs can systematically impact organized convection and thereby MCS characteristics is less clear. We examine this question by analyzing a global MCS tracking data set. During the active phase of CCEWs, MCS frequency increases and MCSs rain harder, produce more lifetime total rain, and grow larger in size. The probability of extreme MCSs also elevates. These changes are most pronounced when MCSs are associated with Kelvin waves and tropical depression-type waves while less so with the Madden-Julian Oscillation. Furthermore, these results can be benchmarks to improve model representation of MCS interactions with large-scale circulations and can be leveraged for operational forecasts of high-impact MCSs at extended lead times.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Real-Time Application of Multi-Satellite Precipitation Analysis for Floods and Landslides

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets-- both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers, In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences.

Adler, Robert↗
NASA NTRS2007

Application of Multi-Satellite Precipitation Analysis to Floods and Landslides

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers. In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences. However, difficulties in comparing landslide event information (mostly from news reports) with the satellite-based forecasts are analyzed.

Adler, Robert↗
DOE OSTI2021

Climate hazard indices projections based on CORDEX-CORE, CMIP5 and CMIP6 ensemble

The CORDEX-CORE initiative was developed with the aim of producing homogeneous regional climate model (RCM) projections over domains world wide. In its first phase, two RCMs were run at 0.22° resolution downscaling 3 global climate models (GCMs) from the CMIP5 program for 9 CORDEX domains and two climate scenarios, the RCP2.6 and RCP8.5. The CORDEX-CORE simulations along with the CMIP5 GCM ensemble and the most recently produced CMIP6 GCM ensemble are analyzed, with focus on several temperature, heat, wet and dry hazard indicators for present day and mid-century and far future time slices. The CORDEX-CORE ensemble shows a better performance than the driving GCMs for several hazard indices due to its higher spatial resolution. For the far future time slice the 3 ensembles project an increase in all temperature and heat indices analyzed under the RCP8.5 scenario. The largest increases are always shown by the CMIP6 ensemble, except for Tx > 35 °C, for which the CORDEX-CORE projects higher warming. Extreme wet and flood prone maxima are projected to increase by the RCM ensemble over the la Plata basin in South America, the Congo basin in Africa, east North America, north east Europe, India and Indochina, regions where a better performance is obtained, whereas the GCM ensembles show small or negligible signals. Furthermore, compound hazard hotspots based on heat, drought and wet indicators are detected in each continent worldwide in region like Central America, the Amazon, the Mediterranean, South Africa and Australia, where a linear relation is shown between the heatwave and drought change signal, and region like Arabian peninsula, the central and south east Africa region (SEAF), the north west America (NWN), south east Asia, India, China and central and northern European regions (WCE, NEU) where the same linear relation is found for extreme precipitation and HW increases. Although still limited, the CORDEX-CORE initiative was able to produce high resolution climate projections with almost global coverage and can provide an important resource for impact assessment and climate service activities.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2023

Flood Impacts on Net Ecosystem Exchange in the Midwestern and Southern United States in 2019

Climate extremes such as droughts, floods, heatwaves, frosts, and windstorms add considerable variability to the global year-to-year increase in atmospheric CO(2) through their influence on terrestrial ecosystems. While the impact of droughts on terrestrial ecosystems has received considerable attention, the response to flooding is not well understood. To improve upon this knowledge, the impact of the 2019 anomalously wet conditions over the Midwest and Southern US on CO(2) vegetation fluxes is examined in the context of 2017–2018 when such precipitation anomalies were not observed. CO(2) is simulated with NASA's Global Earth Observing System (GEOS) combined with the Low-order Flux Inversion, where fluxes of CO(2) are estimated using a suite of remote sensing measurements including greenness, night lights, and fire radiative power as well as with a bias correction based on insitu observations. Net ecosystem exchange CO(2) tracers are separated into the three regions covering the Midwest, South, and Eastern Texas and adjusted to match CO(2) observations from towers located in Iowa, Mississippi, and Texas. Results indicate that for the Midwestern region consisting primarily of corn and soybeans crops, flooding contributes to a 15%–25% reduction of annual net carbon uptake in 2019 in comparison to 2017 and 2018. These results are supported by independent reports of changes in agricultural activity. For the Southern region, comprised mainly of non-crop vegetation, annual net carbon uptake is enhanced in 2019 by about 10%–20% in comparison to 2017 and 2018. These outcomes show the heterogeneity in effects that excess wetness can bring to diverse ecosystems.

Nikolay Balashov↗
DOE OSTI2020

Integrating field observations and process-based modeling to predict watershed water quality under environmental perturbations

Watersheds play a critical role in supplying water resources needed for human use and ecosystem health. Understanding and predicting how, when, and where changes in the quantity and quality of water resources occur under different environmental stresses including extreme events is crucial for sustainable management of water resources under a changing environment. However, few studies have attempted to quantify or identify the factors and process interactions controlling the impact of extreme events across water-shed systems. Only few large-scale studies include coordinated monitoring and modeling efforts, which limits our ability to assess the large-scale impact of extreme events on water supply and quality. Methods are lacking to propagate uncertainty in process understanding through an integrated hydro-biogeochemical model framework and evaluate its importance, thus failing to take full advantage of the information potentially available through transformative advances in characterization technologies from high-resolution mass spectrometry to airborne and satellite-based remote sensing. There are consequent risks to our nations water security and to human and ecosystem health that may become exacerbated with the increasing frequency of extreme events that is projected for the coming decades. This paper reviews the current status of watershed science for both water quantity and quality and identifies critical gaps in our current knowledge and modeling capability in addressing the emergent needs in predicting watershed hydrologic and biogeochemical responses (i.e., water quantity and quality) under natural and anthropogenic perturbations. We highlight the need to (1) understand how environmental perturbations including extreme events like floods and droughts propagate through watershed systems and assess their short- and long-term impacts on watershed biogeochemistry, water quality and their recovery pathways; (2) develop and improve a watershed water quality model that reflects the state of scientific understanding gained from observations; and (3) construct a data-model fusion system for watershed characterization, process identification, and mechanistic model parameterization. A large base of modeling, monitoring and data capabilities have been built by various federal government agencies given the relevance of water to their critical missions. An emerging need is to build an integrated national capability for watershed water availability and quality that can address water-related missions across multiple federal agencies.

13 HYDRO ENERGY↗
DOE OSTI2024

Dissipation Scaled Internal Wave Drag in a Global Heterogeneously Coupled Internal/External Mode Total Water Level Model

This study showcases a global, heterogeneously coupled total water level system wherein salinity and temperature outputs from a coarser-resolution (~12 km) ocean general circulation model are used to calculate density-driven terms within a global, higher-resolution (~2.5 km) depth-averaged total water level model. We demonstrate that the inclusion of baroclinic forcing in the barotropic model requires modification of the internal wave drag term to prevent excess degradation of tidal results compared to the barotropic model. By scaling the internal tide dissipation by an easy to calculate dissipation ratio, the resulting heterogeneously coupled model has complex root mean square errors (RMSE) of 2.27 cm in the deep ocean and 12.16 cm in shallow waters for the M 2 tidal constituent. While this represents a 10%–20% deterioration as compared to the barotropic model, the improvements in total water level prediction more than offset this degradation. Global median RMSE compared to observations of total water levels, 30-day sea levels, and non-tidal residuals improve by 1.86 (18.5%), 2.55 (42.5%), and 0.36 (5.3%) cm respectively. The drastic improvement in model performance highlights the importance of including density-driven effects within global hydrodynamic models and will help to improve the results of both hindcasts and forecasts in modeling extreme and nuisance flooding. With only an 11% increase in model run time compared to the fully barotropic total water level model, this approach paves the way for high resolution coastal water level and flood models to be used alongside climate models, improving operational forecasting of total water levels.

Blakely, Coleman Peter [University of Notre Dame, ↗