nasa-gesdisc/es-publications-researchareas huggingface dataset:
Urbanizing the floodplain: global changes of imperviousness in (2022)

Abstract
            Cities have historically developed close to rivers and coasts, increasing human exposure to flooding. That exposure is exacerbated by changes in climate and population, and by urban encroachment on floodplains. Although the mechanisms of how urbanization affects flooding are relatively well understood, there have been limited efforts to assess the magnitude of floodplain encroachment globally and how it has changed in both space and time. Highly resolved global datasets of both flood hazard and changes in urban area from 1985 to 2015 are now available, enabling the reconstruction of the history of floodplain encroachment at high spatial resolutions. Here we show that the urbanized area in floodplains that have an average probability of flooding of 1/100 years, has almost doubled since 1985. Further, the rate of urban expansion into these floodplains increased by a factor of 1.5 after the year 2000. We also find that urbanization rates were highest in the most hazardous areas of floodplains, with population growth in these urban floodplains suggesting an accompanying increase in population density. These results reveal the scope, trajectory and extent of global floodplain encroachment. With tangible implications for flood risk management, these data could be directly used with integrated models to assess adaptation pathways for urban flooding.

Research areas: Floods, Habitat Conversion/Fragmentation
Annual Flood Monitoring Using Synchronized Floodwater Index in 2010 Indus River Flood (2019)

Flood detection algorithms are able to generate near real-time flood proxy maps on regional and global scales on a daily basis through the use of multiple spaceborne sensors. In this chapter, we introduce a new algorithm for detecting annual floodwater changes using a hybrid conditional process that is incorporated into the Synchronized Multiple-Floodwater Index (SfWI2), a threshold-based statistical flood detection approach, coupled with in situ hydrological data. SfWI2 was applied to the 2010 Indus flood event, a recent extreme flood case, in order to achieve more accurate flood detection at a transboundary river-basin level and an effective emergency response in the early stage of a flood disaster. The resultant flood map shows good agreement between the MODIS-derived flood proxy map and high-resolution satellite images at the representative barrage stations.

Research areas: Floods
Global-scale human pressure evolution imprints on sustainability of river systems (2019)

Abstract. Human pressures on river systems pose a major threat to the sustainable development of human societies in the twenty-first century. Previous studies showed that a large part of global river systems was already exposed to relevant anthropogenic pressures at the beginning of this century. A relevant question that has never been explained in the literature so far is whether these pressures are increasing in time, therefore representing a potential future challenge to the sustainability of river systems. This paper proposes an index we call Differential Human Pressure on Rivers (DHPR) to quantify the annual evolution of human pressure on river systems. DHPR identifies a per-year percentage increment (or decrement) of normalized human pressures on river systems (i.e., ratio of annual values to long-term average). This index, based on annual nightlights and stationary discharge data, is estimated for 2195 major river basins over a period of 22 years, from 1992 to 2013. The results show that normalized annual human pressure on river systems increased globally, as indicated by an average DHPR value of 1.9 % per year, whereby the greatest increase occurred in the northern tropical and equatorial areas. The evaluation of DHPR over this 22-year period allows the identification of hot-spot areas, therefore offering guidance on where the development and implementation of mitigation strategies and plans are most needed (i.e., where human pressure is strongly increasing).

Research areas: Habitat Conversion/Fragmentation
Metamodeling for uncertainty quantification of a flood wave model for concrete dam breaks (2020)

Uncertainties in instantaneous dam-break floods are difficult to assess with standard methods (e.g., Monte Carlo simulation) because of the lack of historical observations and high computational costs of the numerical models. In this study, polynomial chaos expansion (PCE) was applied to a dam-break flood model reflecting the population of large concrete dams in Switzerland. The flood model was approximated with a metamodel and uncertainty in the inputs was propagated to the flow quantities downstream of the dam. The study demonstrates that the application of metamodeling for uncertainty quantification in dam-break studies allows for reduced computational costs compared to standard methods. Finally, Sobol sensitivity indices indicate that reservoir volume, length of the valley, and surface roughness contributed most to the variability of the outputs. The proposed methodology, when applied to similar studies in flood risk assessment, allows for more generalized risk quantification than conventional approaches.

Research areas: Floods, Validation
Sensitivity of Probable Maximum Flood in a Changing Environment (2018)

With likely increases in probable maximum precipitation (PMP) in a changing environment, critical infrastructures such as major reservoirs and nuclear power plants are subject to elevated risk. To understand how factors such as PMP variability, climate change, land use land cover (LULC) change, antecedent soil moisture conditions, and reservoir storage may individually or jointly affect the magnitude of probable maximum flood (PMF), we conducted integrated hydrometeorological simulations involving both the Weather Research Forecasting model and the distributed hydrologic model (DHSVM) over the Alabama-Coosa-Tallapoosa (ACT) River Basin in the southeastern United States. A total of 120 relative humidity-maximized PMP storms under historic and projected future climate conditions were used to drive DHSVM in current and projected future LULC conditions. Overall, PMP and PMF are projected to increase significantly over the ACT River Basin. Sources of meteorological forcing data sets and climate change were found to be the most sensitive factors affecting PMF, followed by antecedent soil moisture, reservoir storage, and then LULC change. The ensemble of PMP and PMF simulations, along with their sensitivity, allows us to better quantify the potential risks associated with hydroclimatic extreme events to critical infrastructures for energy-water security.

Research areas: Floods
The uncertainty of flood frequency analyses in hydrodynamic model simulations (2021)

Abstract. Assessing the risk of a historical-level flood is essential for regional flood protection and resilience establishment. However, due to the limited spatiotemporal coverage of observations, the impact assessment relies on model simulations and is thus subject to uncertainties from cascade physical processes. This study assesses the flood hazard map with uncertainties subject to different combinations of runoff inputs, variables for flood frequency analysis and fitting distributions based on estimations by the CaMa-Flood global hydrodynamic model. Our results show that deviation in the runoff inputs is the most influential source of uncertainties in the estimated flooded water depth and inundation area, contributing more than 80 % of the total uncertainties investigated in this study. Global and regional inundation maps for floods with 1-in-100 year return periods show large uncertainty values but small uncertainty ratios for river channels and lakes, while the opposite results are found for dry zones and mountainous regions. This uncertainty is a result of increasing variation at tails among various fitting distributions. In addition, the uncertainty between selected variables is limited but increases from the regular period to the rarer floods, both for the water depth at points and for inundation area over regions. The uncertainties in inundation area also lead to uncertainties in estimating the population and economy exposure to the floods. In total, inundation accounts for 9.1 % [8.1 %10.3 %] of the land area for a 1-in-100 year flood, leading to 13.4 % [12.1 %15 %] of population exposure and 13.1 % [11.8 %14.7 %] of economic exposure for the globe. The flood exposure and uncertainties vary by continent and the results in Africa have the largest uncertainty, probably due to the limited observations to constrain runoff simulations, indicating a necessity to improve the performance of different hydrological models especially for data-limited regions.

Research areas: Floods
Global costs of protecting against sea-level rise at 1.5 to 4.0° C (2021)

Sea levels will rise, even with stringent climate change mitigation. Mitigation will slow the rate of rise. There is limited knowledge on how the costs of coastal protection vary with alternative global warming levels of 1.5 to 4.0 C. Analysing six sea-level rise scenarios (0.74 to 1.09 m, 50th percentile) across these warming levels, and five Shared Socioeconomic Pathways, this paper quantifies the economic costs of flooding and protection due to sea-level rise using the Dynamic Interactive Vulnerability Assessment (DIVA) modelling framework. Results are presented for World Bank income groups and five selected countries from the present to 2100. Annual sea flood damage costs without additional adaptation are more influenced by socio-economic development than sea-level rise, indicating that there are opportunities to control risk with development choices. In contrast, annual sea dike investment costs are more dependent on the magnitude of sea-level rise. In terms of total costs with adaptation, upper middle, low middle and low income groups are projected to have higher relative costs as a proportion of GDP compared with high income groups. If low income countries protected now, flood costs could be reduced after 2050 and beyond. However, without further adaptation, their coasts will experience growing risks and costs leaving them increasingly reliant on emergency response measures. Without mitigation or adaptation, greater inequalities in damage costs between income groups could result. At country level, annual sea flood damage costs without additional adaptation are projected to rapidly increase with approximately 0.2 m of sea-level rise, leaving limited time to plan and adapt.

Research areas: Floods, Atmospheric/Ocean Indicators
The tale of three floods: From extreme events and cascades of highs to (2022)

Right after a devastating multi-year drought, a number of flood events with unprecedented spatial extent hit different parts of Iran over the 2-week period of March 17th to April 1st<SUP>,</SUP> 2019, causing a human disaster and substantial loss of assets and infrastructure across urban and rural areas. Here, we investigate natural (e.g., rainfall, snow accumulation/melt, soil moisture) and anthropogenic drivers (e.g., deforestation, urbanization, and management practices) of these events using a range of ground-based data and satellite observations. These drivers can range from exceptionally extreme rainfall intensities, to cascades of several extreme and moderate events, and various anthropogenic interventions that exacerbated flooding. Our results reveal strong compounding impacts of natural drivers and anthropogenic triggers in escalating flood risks to unprecedented levels. We argue that a new form of floods, i.e. anthropogenic floods, is becoming more common and should be recognized during the "Anthropocene". This specific form of floods refers to high to extreme streamflow/runoff events that are primarily caused, or largely exacerbated, by anthropogenic drivers. We demonstrate how the growing risk of anthropogenic floods can be assessed using a wide range of climatic and non-climatic satellite and in-situ data.

Research areas: Floods
Global exposure to flooding from the new CMIP6 climate model projections (2021)

Estimates of future flood risk rely on projections from climate models. The relatively few climate models used to analyze future flood risk cannot easily quantify of their associated uncertainties. In this study, we demonstrated that the projected fluvial flood changes estimated by a new generation of climate models, the collectively known as Coupled Model Intercomparison Project Phase 6 (CMIP6), are similar to those estimated by CMIP5. The spatial patterns of the multi-model median signs of change (+ or ) were also very consistent, implying greater confidence in the projections. The model spread changed little over the course of model development, suggesting irreducibility of the model spread due to internal climate variability, and the consistent projections of models from the same institute suggest the potential to reduce uncertainties caused by model differences. Potential global exposure to flooding is projected to be proportional to the degree of warming, and a greater threat is anticipated as populations increase, demonstrating the need for immediate decisions.

Research areas: Floods
Spatial Analyses of Cyclone Amphan Induced Flood Inundation Mapping Using Sentinel-1A SAR Images Through GEE Cloud (2022)

The study focused on mapping flood inundation in the lower Indo-Gangetic plains in Purba Medinipur district, West Bengal, India, using synchronized C-band Sentinel-1A synthetic aperture radar satellite images with a cloud computing API on the GEE. The study showed that a considerable proportion of the district 3978.93 km2 was flood inundated during May 2020 due to heavy rainfall and severe cyclone Amphan. Impact of flood inundation was found in agricultural areas (35.93% of the total agricultural land), followed by built-up area (5.03% of the built-up area) that affected a large population (30.85% of the total population), in the study area. Validation assessment is carried out for final flood layer mapping with seven flood status parameters such as VV, VH backscatter of Sentinel-1A, precipitation, NDVI, NDWI, soil moisture, and elevation data acquired in the month of May 2020. The validation accuracy estimation of the flood inundation map through the AUROC method along with the four machine learning models showed that the Naive Bayes (AUROC = 92.6%) outperformed the SVM (AUROC = 89.9%), RF (AUROC = 89.4%), and logistic regression (AUROC = 88.5%) models. Cloud-based GEE services provide quick flood inundation mapping with associated damage information that can be beneficial for disaster management and emergency response. This will support crucial policy makers and stakeholders in creating urgent decisions for flood investigation for sustainable environment disaster risk mitigation management. The study requires implementing a cohesive, multi-decision-making approach with importance on self-reliance of the community for nourishment with local practices and resources.

Research areas: Floods, Severe Storms
Key factors influencing the severity of fluvial flood hazard from tropical cyclones (2021)

Abstract Knowledge of the key drivers of the severity of river flooding from tropical cyclones (TCs) is vital for emergency preparedness and disaster risk reduction activities. This global study examines landfalling TCs in the decade from 2010 to 2019 to identify those characteristics that influence whether a storm has an increased flood hazard. The highest positive correlations are found between flood severity and the total precipitation associated with the TC. Significant negative correlations are found between flood severity and the translation speed of the TC, indicating that slower-moving storms that rain over an area for longer tend to have higher flood severity. Larger and more intense TCs increase the likelihood of having a larger area affected by severe flooding but not its duration or magnitude, and it is found that the fluvial flood hazard can be severe in all intensity categories of TC, including those of tropical storm strength. Catchment characteristics such as antecedent soil moisture and slope also play a role in modulating flood severity, and severe flooding is more likely in cases in which multiple drivers are present. The improved knowledge of the key drivers of fluvial flooding in TCs can help to inform research priorities to help with flood early warning, such as increasing the focus on translation speed in model evaluation and impact-based forecasting.

Research areas: Floods, Severe Storms
Rapid monitoring of cyclone induced flood through an automated approach using multi–temporal Earth Observation (EO) images in RSS CloudToolbox platform (2021)

The new era of cloud platform technologies opens up many opportunities for near real-time dissemination of disaster information to the end-users. The present study utilizes the European Space Agency (ESA) Research and Service Support (RSS) CloudToolbox platform to monitor the spatio-temporal dynamics of a flood event. A collective flood monitoring framework is formulated to rapidly assess cyclone-induced flood in the CloudToolbox platform. The outputs of the framework are spatio-temporal maps of flood extent, depth, and hot spot zones. The framework utilizes Earth Observation (EO) images such as optical and C-band Synthetic Aperture Radar (SAR) images and an automatic Kittler and Illingworth thresholding algorithm for rapid flood mapping. The temporal flood depth maps are created with the Floodwater Depth Estimation Tool (FwDET) which requires only two input parameters, viz. flood extent, and Digital Elevation Model (DEM). Subsequently, flood hotspot zones are also identified. We tested the flood monitoring framework on Amphan cyclone-induced flood event at both regional and local levels. The spatio-temporal flood extent, depth, and hot spot maps are generated for the Amphan cyclone event and a 97% overall accuracy is achieved at the local level. The entire process took less than one hour for regional and local level analysis.

Research areas: Floods, Severe Storms
Urban heat island and thermal comfort of Esfahan City (Iran) during COVID-19 lockdown (2022)

The Urban Heat Island (UHI), driven in part by urban green space transformation, and resulting in bioclimatic stress, is one of the major environmental problems facing cities today. A brief reprise in UHI severity is hypothesised to have occurred for many cities during COVID-19 lockdown, as a result of the marked reduction in individual and industrial activities. This study explores the UHI the city of Esfahan, one of the major metropolises of Iran, comparing the urban temperature for the period 20 March to 20 April 2020, the first lockdown period in Iran, with a long term mean for this month calculated from 2000 to 2019. During the lockdown period, the UHI effect covered a much smaller land area than in all prior years investigated. The land surface area which experienced heat stress reduced significantly during the lockdown period, and the total energy heating requirement (heat degree days) increased by 23,945.2 day calories compared to the long term average. These results demonstrate the significant changes in UHI and related variables during lockdown. While lockdown regulations are not sustainable in the long run, the improvements recorded demonstrate the potential to mitigate anthropogenic heating.

Research areas: Heat
Surface urban heat islands in Italian metropolitan cities: Tree cover and impervious surface influences (2021)

Land surface temperature (LST) predictors, such as impervious and vegetated surfaces, strongly influence the urban landscape mosaic, also changing microclimate conditions and exacerbating the surface urban heat island (SUHI) phenomenon. The aim of this study was to investigate the summer daytime SUHI phenomenon and the role played by impervious and tree cover surfaces in the 10 Italian peninsular metropolitan cities. Summer daytime LST values were assessed by using MODIS data referred to the months of June, July and August from 2016 to 2018. High spatial resolution (10 m) of impervious surface and tree cover layers was calculated based on open-data developed by the Italian National Institute for Environmental Protection and Research. A novel informative urban surface landscape layer was developed combining impervious surfaces and tree cover densities and its mapping for metropolitan cities was performed. Summer daytime SUHI rose significantly, increased especially in inland cities, by increasing the size of areas with low tree cover densities in the metropolitan core (or decreasing areas with low tree cover densities outside the metropolitan core), further increasing its intensity when the impervious density grew. A mitigating effect of the sea on daytime LST and SUHI was observed on coastal cities. The most intense SUHI phenomenon was observed in Turin (the largest Italian metropolitan city): for every 10% increase in areas with highly impervious surfaces and low tree cover densities in the metropolitan core, the SUHI significantly (p 

Research areas: Heat
Policy-relevant indicators for mapping the vulnerability of urban populations to extreme heat events: A case study of Philadelphia (2015)

The recent global increase in extreme heat events linked to climate change is projected to continue. The additive effect of urban heat islands from impervious surfaces and urban heat emissions (e.g., from transportation and building cooling) exacerbates extreme heat events in urban areas, exposing dense populations to extreme heat with implications for human health. Ground- and satellite-based data on urban and suburban temperatures and vegetation over a historical period can help identify temporal and geospatial trends in heat exposure. A set of indicators has been developed to map the exposure, social sensitivity, and vulnerability of urban populations to heat wave health impacts. Guided by an Advisory Group of local planners in the pilot city of Philadelphia, localized trends of increasing urban extreme heat events using MODIS Land Surface Temperature (LST) data, confirmed with urban and non-urban temperature monitor data were identified. For the Philadelphia study area, the number of heat-event days in the urban setting has increased from approximately 4 days in 1980 to almost 12 days in 2013, while the non-urban setting has consistently experienced 5 days of heat events per year across the time period. Warmer micro-climates with limited vegetative cooling and elevated LSTs were also identified. The exposure indicator was combined with areas of high social sensitivity (e.g., low-income and elderly) to create a vulnerability indicator, showing significant overlap between highly exposed and highly sensitive populations. As a measure of the adaptive capacity of local governments to reduce the urban heat island, evidence of targeted vegetation increases or reduced localized temperatures linked to urban greening and cooling programs were sought, though none were of a scale to be identified by the 1 km satellite data utilized. The indicators have helped local decision makers to understand patterns of vulnerability, and may be used in the future to target adaptation actions and measure results (LST reduction or vegetation increase) from existing adaptation actions.

Research areas: Heat