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Flooding affects more people than any other environmental hazard and hinders sustainable development1,2. Investing in flood adaptation strategies may reduce the loss of life and livelihood caused by floods3. Where and how floods occur and who is exposed are changing as a result of rapid urbanization4, flood mitigation infrastructure5 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 uncertainty3,7–11. 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 estimates7. 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. Satellite imagery for the period 2000–2018 reveals that population growth was greater in flood-prone regions than elsewhere, thus exposing a greater proportion of the population to floods.

Flooding is the most common natural hazard worldwide, affecting over a billion people and costing $100 billion every year. This will likely increase in the future due to increasing population and assets in the flood-prone zones and climate change. Earth observation data have been utilized to map flooding on a global scale. This was mostly done by utilizing optical bands, as for these satellites the return period is relatively short; often daily. However, the usage of optical data can be restrictive due to e.g. cloud cover and nighttime. With synthetic aperture radar data most of these limitations can be overcome. As such, many entities now provide remotely sensed flood products, such that it becomes difficult for users to determine the best available flood data source. Here we describe the long-time development and implementation of a semi operational ‘one-stop-shop’ portal that contains globally scoped, flood prediction, monitoring capabilities and risk evaluations by leveraging on efforts of the entire flood community.

Flooding affects more people than any other environmental hazard and hinders sustainable development1–3. Investing in flood adaptation can reduce loss of life and livelihoods 4,5. However, where and how floods occur, and who is exposed, is changing due to rapid urbanization 6, new flood mitigation infrastructure 7,8, and increasing human settlements in floodplains9. Previous studies estimating population exposed to floods are limited by lack of observational data, relying instead on global models with high uncertainty4,10–14. Here we use nearly two decades of daily satellite imagery at 250 meter resolution (MODIS) to develop new estimates of flood extent and population exposure for 913 large flood events from 2000-2018, estimating 2.23 million km2 of flooded area and 290 million people directly affected. We find the at least 86 million people newly settled in a location where satellites observed inundation in the past 20 years. This represents a 20% increase in the proportion of population exposed to floods, ten times higher than previous estimates10. Near-future climate change projections (2030), indicate the proportion of population exposed to floods will continue to expand. Our findings can aid prioritization of global flood adaptation investment and inform adaptation strategies, from insurance to managed retreat, at local scales15–17. As satellite coverage and resolution increase, we anticipate observation of flood events to aid understanding where floods change and how to adapt. This flood event database will contribute to improved accuracy of global and local flood models, vulnerability assessments, efficacy of adaptation intervention, and understanding interactions of land change, climate, and floods.

Flooding affects more people than any other environmental hazard and hinders sustainable development

The Global Flood Detection Systems (GFDS) currently operated at the European Commission’s Joint Research Centre (JRC) is a satellite-based observation system that provides daily stream flow measurements of global rivers. The system was initially established using NASA Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) Ka-band passive microwave satellite data. Since its initiation in 2006, the methodology and the GFDS database have been further adapted for data acquired by the Tropical Rainfall Measuring Mission (TRMM) GOES Precipitation Index (GPI), the AMSR2 sensor onboard the Global Change Observation Mission – Water satellite (GCOM-W1), and the Global Precipitation Measurement (GPM) GPM Microwave Imager (GMI) sensor. This paper extends the same flow monitoring methodology to low frequency (L-band) passive microwave observations obtained by the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) sensor that was launched in 2009. A primary focus is tropical climate regions with dense rainforest vegetation (the Amazon, the Orinoco, and the Congo basins) where high-frequency microwave observations from GFDS reveal a significant influence of vegetation cover and atmospheric humidity. In contrast, SMOS passive microwave signatures at the much lower L-band frequency exhibit deeper penetration through the dense vegetation and minimal atmospheric effects, enabling more robust river stage retrievals in these regions. The SMOS satellite river gauging observations are for 2010–2018 and are compared to single-sensor GFDS data over several river sites. To reduce noise, different filtering techniques were tested to select the one most suitable for analysis of the L-band time series information. In-situ water level (stage) measurements from the French Observation Service SO Hybam database were used for validation to further evaluate the performance of the SMOS data series. In addition to GFDS data, water stage information from Jason-2 and Jason-3 altimetry was compared to the microwave results. Correlation of SMOS gauging time series with in-situ stage data revealed a good agreement (r = 0.8–0.94) during the analyzed period of 2010–2018. Moderate correlation was found with both high frequency GFDS data series and altimetry data series. With lower vegetation attenuation, SMOS signatures exhibited a robust linear relationship with river stage without seasonal bias from the complex hysteresis effects that appeared in the Ka-band observations, apparently due to different attenuation impacts through dense forests at different seasonal vegetation stages.

One of the main challenges for global hydrological modelling is the limited availability of observational data for calibration and model verification. This is particularly the case for real time applications. This problem could potentially be overcome if discharge measurements based on satellite data were sufficiently accurate to substitute for ground-based measurements. The aim of this study is to test the potentials and constraints of the remote sensing signal of the Global Flood Detection System for converting the flood detection signal into river discharge values. The study uses data for 322 river measurement locations in Africa, Asia, Europe, North America and South America. Satellite discharge measurements were calibrated for these sites and a validation analysis with in situ discharge was performed. The locations with very good performance will be used in a future project where satellite discharge measurements are obtained on a daily basis to fill the gaps where real time ground observations are not available. These include several international river locations in Africa: Niger, Volta and Zambezi rivers. Analysis of the potential factors affecting the satellite signal was based on a classification decision tree (Random Forest) and showed that mean discharge, climatic region, land cover and upstream catchment area are the dominant variables which determine good or poor performance of the measurement sites. In general terms, higher skill scores were obtained for locations with one or more of the following characteristics: a river width higher than 1 km; a large floodplain area and in flooded forest; with a potential flooded area greater than 40%; sparse vegetation, croplands or grasslands and closed to open and open forest; Leaf Area Index > 2; tropical climatic area; and without hydraulic infrastructures. Also, locations where river ice cover is seasonally present obtained higher skill scores. The work provides guidance on the best locations and limitations for estimating discharge values from these daily satellite signals.

This paper presents an automated technique which ingests orbital synthetic-aperture radar (SAR) imagery and outputs surface water maps in near real time and on a global scale. The service anticipates future open data dissemination of water extent information using the European Space Agency's Sentinel-1 data. The classification methods used are innovative and practical and automatically calibrated to local conditions per 1 × 1° tile. For each tile, a probability distribution function in the range between being covered with water or being dry is established based on a long-term SAR training dataset. These probability distributions are conditional on the backscatter and the incidence angle. In classification mode, the probability of water coverage per pixel of 1 km × 1 km is calculated with the input of the current backscatter – incidence angle combination. The overlap between the probability distributions of a pixel being wet or dry is used as a proxy for the quality of our classification. The service has multiple uses, e.g. for water body dynamics in times of drought or for urgent inundation extent determination during floods. The service generates data systematically: it is not an on-demand service activated only for emergency response, but instead is always up-to-date and available. We validate its use in flood situations using Envisat ASAR information during the 2011 Thailand floods and the Pakistan 2010 floods and perform a first merge with a NASA near real time water product based on MODIS optical satellite imagery. This merge shows good agreement between these independent satellite-based water products.

The measurement and recording of the height and spatial extent reached by coastal storm surges is fundamental to scientific progress in understanding these phenomena. Such information is required for better prediction and for risk assessment. Model-based evaluation of increasing delta vulnerability, for example, cannot be tested without long-term, consistent, and sustained observation of actual events. Also, storm surges occur within the temporal context of tidal variation, which must first be characterized through observation. Present standard approaches for measuring storm surges are not optimum. Thus, tidal gauges provide information at one point, whereas the heights reached by surges vary spatially. Also, post-surge ground surveys are expensive, laborious, and commonly lack comparison to similar data obtained for previous surges or for high tides. The advent of moderate spatial resolution, high temporal resolution remote sensing initiated by the launch of the two NASA MODIS sensors greatly reduces these constraints. For over a decade, daily coverage of most coastal land areas, though restricted by cloud cover, has systematically captured the maximum extents reached by both high tides and by storm surges. Automated water classification algorithms are now transforming the incoming image data into GIS water boundary files, again at daily or near-daily time steps. This paper provides a retrospective view of sample storm surges as mapped via these sensors and describes: (a) the present, MODIS-based surface water surveillance system, (b) the mapping enhancement to be provided by frequent-repeat, wide-swath satellite radar imaging, and (c) the emerging prospects for routine global surveillance of storm surge events. Such will be necessary if long-term trends are to be recognized, characterized, and understood, along coastal zones now being affected by both increasing subsidence and rising sea level.

An innovative flood-prediction framework is developed using Tropical Rainfall Measuring Mission precipitation forcing and a proxy for river discharge from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) onboard the National Aeronautics and Space Administration's Aqua satellite. The AMSR-E-detected water surface signal was correlated with in situ measurements of streamflow in the Okavango Basin in Southern Africa as indicated by a Pearson correlation coefficient of 0.90. A distributed hydrologic model, with structural data sets derived from remote-sensing data, was calibrated to yield simulations matching the flood frequencies from the AMSR-E-detected water surface signal. Model performance during a validation period yielded a Nash-Sutcliffe efficiency of 0.84. We concluded that remote-sensing data from microwave sensors could be used to supplement stream gauges in large sparsely gauged or ungauged basins to calibrate hydrologic models. Given the global availability of all required data sets, this approach can be potentially expanded to improve flood monitoring and prediction in sparsely gauged basins throughout the world.

Floods are among the most catastrophic natural disasters around the globe impacting human lives and infrastructure. Implementation of a flood prediction system can potentially help mitigate flood-induced hazards. Such a system typically requires implementation and calibration of a hydrologic model using in situ observations (i.e., rain and stream gauges). Recently, satellite remote sensing data have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. The focus of this study is to integrate the best available satellite products within a distributed hydrologic model to characterize the spatial extent of flooding and associated hazards over sparsely gauged or ungauged basins. We present a methodology based entirely on satellite remote sensing data to set up and calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas. A raster-based distributed hydrologic model, Coupled Routing and Excess STorage (CREST), was implemented for the Nzoia basin, a subbasin of Lake Victoria in Africa. Moderate Resolution Imaging Spectroradiometer Terra-based and Advanced Spaceborne Thermal Emission and Reflection Radiometer-based flood inundation maps were produced over the region and used to benchmark the distributed hydrologic model simulations of inundation areas. The analysis showed the value of integrating satellite data such as precipitation, land cover type, topography, and other products along with space-based flood inundation extents as inputs to the distributed hydrologic model. We conclude that the quantification of flooding spatial extent through optical sensors can help to calibrate and evaluate hydrologic models and, hence, potentially improve hydrologic prediction and flood management strategies in ungauged catchments.

During the Great Flood of summer 1993 in the Upper Mississippi Valley, the European Remote Sensing Satellite (ERS-1) imaged overbank flooding with a ground resolution of ca 25 m. When combined with topographic information, these data permit measurement of instantaneous longitudinal profiles of individual flooded valleys. Along a major tributary to the Mississippi in southwestern Wisconsin, a measured in-transit flood wave exhibits an amplitude of 1.5-2.0 m, and can be differentiated from a constant discharge, step backwater-modelled water surface for a distance of 5 km along the valley.

This paper presents an automated technique, embedded in an online service, which ingests orbital synthetic aperture radar (SAR) imagery and outputs surface water maps in near real time and on a global scale. The service anticipates future open data dissemination of water extent information using the European Space Agency's Sentinel-1 data. The classification methods used are innovative but practical and different per 1 × 1 degree tile. For each tile, a probability distribution function of a pixel, being covered with water or being dry is established based on a long SAR training dataset. These probability distributions are conditional on the backscatter and the incidence angle. In classification mode the probability of water coverage is calculated, conditional on the current backscatter – incidence angle combination. The overlap between the probability distributions of a pixel being wet or dry is used as a proxy for the quality of our classification. The service has multiple uses, e.g. for water body dynamics in times of drought or for urgent inundation extent determination during floods. The service generates data systematically: it is not an on-demand service activated only for emergency response, but instead is always up-to-date and available. We demonstrate its use in flood situations using Envisat ASAR information during the 2011 Thailand floods. A first merge with a NASA near real time water product based on MODIS optical satellite imagery shows excellent agreement between these independent satellite-based water products.