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 uncertainty3,7,8,9,10,11. 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.
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The MODIS Collection 6 datasets analysed here are available in the NASA LP DAAC at the USGS EROS Center (https://lpdaac.usgs.gov/products/mod09gav006/, https://lpdaac.usgs.gov/products/mod09gqv006/) and are mirrored in the Google Earth Engine data catalogue (https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD09GA, https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD09GQ). The MODIS NRT Global Flood Product is available in the NASA LANCE Near Real-Time Data and Imagery service (https://earthdata.nasa.gov/earth-observation-data/near-real-time/mcdwd-nrt). The Landsat 5 TM, 7 ETM and 8 OLI surface reflectance products used for the accuracy assessment are available from USGS (https://earthexplorer.usgs.gov/) and are mirrored in the Google Earth Engine data catalogue (https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LT05_C01_T1_SR, https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LE07_C01_T1_SR, https://developers.google.com/earth-engine/datasets/catalog/LANDSAT_LC08_C01_T1_SR) The datasets generated for this study from the Global Flood Database are available on the Cloud to Street website (http://global-flood-database.cloudtostreet.ai) and are mirrored in Google Earth Engine (https://developers.google.com/earth-engine/datasets/catalog/GLOBAL_FLOOD_DB_MODIS_EVENTS_V1). Supplementary Tables provide summary estimates for each event, and all data may be downloaded from http://global-flood-database.cloudtostreet.ai/. Source data are provided with this paper.
Google Earth Engine’s web interface allows the flood mapping algorithm defined in equations (1) and (2) to be applied on any MODIS images. Code to make all figures and flood maps are publicly available at https://github.com/cloudtostreet/MODIS_GlobalFloodDatabase.
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We thank students at the University of Washington for quality checking and validating the Global Flood Database: S. Lau, C. C. Wu, P. K. Kamaluddin, W. Pietsch, S. Lau, G. J. Kim, H. Tran and J. Crinkey. We thank students from the University of Texas at Austin, including C. Norton. We thank the Cloud to Street staff for quality checking additional portions of the database: S. Weber and E. Glinskis. We thank B. L. Turner II, U. Lall, V. Lakshmi, P. Kareiva, C. Tuholkse, J. Doss-Gollin, D. Farnham and A. Massmann for providing comments on the manuscript. Funding for this project was provided by a Google Earth Engine Research Award and NASA award 80NSSC18K0426, 16-GEO16-0068, Integrating Global Remote Sensing and Modeling Systems for Local Flood Prediction and Impact Assessment. This research made use of the LandScan 2010 High Resolution global Population Data Set, copyrighted by UT-Battelle, LLC, operator of Oak Ridge National Laboratory under contract no. DE-AC05-00OR22725 with the United States Department of Energy. The United States Government has certain rights in this dataset. Neither UT-Battelle, LLC nor the United States Department of Energy, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of the dataset.
Two of the authors (B.T. and C.D.) are employed by and hold stock in a company, Cloud to Street, that sells flood observation and satellite monitoring technology. The data in this paper are free and open, and the company expects no direct financial benefit from this study.
Peer review information Nature thanks Brenden Jongman, Albert van Dijk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Global distribution of flood events catalogued by the DFO and the Global Flood Database.
a–c, Flood events in the DFO from 1 January 1985 to 31 December 2018 (n = 4,712; a), in the DFO and coincident with MODIS imagery (n = 3,127; b) and that passed the quality control evaluation for the Global Flood Database (n = 913; c). Base map: US government Large Scale International Boundaries (LSIB) Polygons (2017).
Extended Data Fig. 2 Example of bimodal histograms used to calculate adaptive thresholds for water classifications that approximate, on average, the standard versions of the water classification thresholds.
a, b, Example bimodal histograms (left axes) with interclass variance (ICV; blue lines, right axes) extracted from MODIS imagery used to determine optimal thresholds for B2B1ratio (K1; a) and DNSWIR (K2; b). The dashed red and black lines reflect the estimated Otsu and standard thresholds, respectively. c, d, Distribution of estimated Otsu thresholds calculated for each flood event across the Global Flood Database (n = 913), for B2B1ratio (K1; c) and DNSWIR (K2; d). The average Otsu threshold across the Global Flood Database for B2B1ratio (K1 = 0.77; dashed red line in c) and DNSWIR (K2 = 599; dashed red line in d) are comparable to the standard thresholds (K1 = 0.70, dashed black line in a; K2 = 675, dashed black line in b).
The Landsat images used to evaluate 123 flood events in the Global Flood Database are globally distributed, cover 15 biomes66 and span 15 years. Flood events for validation were selected on the basis of availability of Landsat imagery. Eligibility of imagery included conditions that Landsat imagery occurred within 24 h of the maximum day of inundation, intersected the flood area and had less than 20% cloud cover. Biome shapefile for the base map (grey shading; https://ecoregions.appspot.com/) indicates varying vegetation patterns that may influence the accuracy of our remote sensing algorithm.
a, Sensitivity plot of accuracy metrics with random sampling of 500 points for 10 flood events. b, Error analysis showing the distribution of true positive (tp), true negative (tn), false positive (fp) and false negative (fn) rates for each of the four water detection methods, where the centre line represents the median, the hinges represent the 25th and 75th percentiles, the upper whisker extends from the hinge to the largest value no further than 1.5*IQR (interquartile range) and the lower whiskers extends from the hinge to the smallest value no further than 1.5*IQR, and the dots show outlier points outside the whisker range. c, Accuracy statistics, given as the mean and standard deviation (s.d), summarized for each thresholding method and image composite choice (metrics per event in Supplementary Table 2).
Extended Data Fig. 5 Global distribution of accuracy metrics based on 123 flood events from the Global Flood Database.
a, b, Overall accuracy (a) is consistently distributed across the globe, whereas errors of commission (b) are inflated at higher latitudes and errors of omission (c) are lower than errors of commission and have no clear spatial pattern. Base maps: GADM (Global Administrative Areas) 2018, version 3.6.
Coverage of the Global Flood Database is well represented in southeast USA, Central America, South America, southeast Asia, Australia, west Africa and east Africa. a–c, Counts of flood events that passed (b) or failed (a) quality control (QC), and the proportion of events that passed as a ratio of total flood events (c). Base map: US government LSIB Polygons (2017).
a, Correction factors determined as the ratio of HRSL flood exposure to GHSL flood exposure per continent, where the centre line represents the median, the hinges represent the 25th and 75th percentiles, the upper whisker extends from the hinge to the largest value no further than 1.5*IQR (interquartile range) and the lower whiskers extends from the hinge to the smallest value no further than 1.5*IQR, and the dots show points outside the whisker range. b, Uncertainty analysis for each country plotted against flood exposure trends (2000–2015). Countries in the upper right quadrant represent regions with plausibly high uncertainty in population estimates, such that we are not confident in their flood exposure trends (unc., uncertainty; inc., increasing trends; dec., decreasing trends). Countries are labelled by their ISO 1366 standard Alpha-2 two-letter country codes (from LSIB) and coloured by continent.
Extended Data Fig. 8 Sensitivity analysis of return periods to changes in flood exposure, 2010–2030.
a, Change in total population exposed to floods (logarithmic scale; 2030 minus 2010), for each return period, summarized by continent. b, Percentage increase in the population exposed to floods from 2010 to 2030, for each return period. c, Percentage increase in total population from 2010 to 2030 for each continent. d, Multiplicative change in the proportion of the population exposed to floods (equation (7), Methods). For all boxplots (a–d), the centre line represents the median, the whiskers represent the 25th and 75th percentile, the upper whisker extends from the hinge to the largest value no further than 1.5*IQR (interquartile range) and the lower whiskers extends from the hinge to the smallest value no further than 1.5*IQR, and the dots show points outside the whisker range.
We find moderate temporal correlation, greater representation of events in DFO than in Em-Dat in USA, Australia and Russia, and a smaller catalogue of events in DFO than in Em-Dat in Africa and Latin America. a, Annual flood events in DFO and Em-Dat. b, Number of flood events in DFO minus that in Em-Dat for each country. Base map: Natural Earth, tmap R package51.
Extended Data Fig. 10 Comparison for each country of the population exposed to floods in a 100-year event (using 2010 climate and population) determined from GLOFRIS and an estimate of the total population exposed from the Global Flood Database, 2000–2018.
r = 0.89, P < 0.001. The blue line represents parity between the population determined from GLOFRIS and from the Global Flood Database line; the black line is the linear regression line. Country names are coloured by continent.
This file contains Supplementary Tables 1-5, 7 and 8, Supplementary Discussion, and Supplementary References.
Population exposed and area inundation estimated per flood event. gfd_area_km2 provides area estimates and gfd_population_exposed represents population exposure estimates from this study. Other information includes GLIDE numbers, DFO ID numbers, event start and end dates, and other metadata from the DFO for each event.
Quality control information for the main questions asked in the quality control questionnaire.
This file contains 250m-resolution raster .tiff images of observed inundation from satellite data (MODIS) for Figure 2 (a and b) and the duration in days of inundation observed for Figure 2 (c and d).
This file contains 250m-resolution raster .tiff images of the population change from 2015 to 2000 in inundated areas observed from satellite data (MODIS) for Figure 3.
This file contains the zipped source data (shapefiles) for Figure 4.
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Tellman, B., Sullivan, J.A., Kuhn, C. et al. Satellite imaging reveals increased proportion of population exposed to floods. Nature 596, 80–86 (2021). https://doi.org/10.1038/s41586-021-03695-w
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