This file contains shapefiles depicting the extent of 5-, 25-, and 100- year flood recurrence events. This data was created by the World Bank and should be credited as follows: Plan d’Élaboration propre basée sur les données de la Banque Mondiale: Inondation de Récurrence (5 ans / 25 ans / 100 ans)

This file contains flood hazard extent for Garissa, Kilifi and Tana River counties for the period 2018, 2019 and 2023. This data was obtained by merging flood extent from different sources among them UNOSAT. This data is in vector format.

For the floods in Southern Malawi of March 2019, we have combined flood extent maps (Sentinel) with HRSL settlement/population grid. This results in a calculation of # of affected buildings/people per district. The results is shared through maps and in a shapefile. 1. Data sources Sentinel 1 Imagery from 7th of March 2017 Sentinel 2 Imagery from 10th/12th/14th of March 2017 HRSL population data Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 9 March 2019. 2. Good to know The flood extent for Nsanje district was separately added on March 14th, to the existing flood extent for the main area from March 12th. 3. Methodology A. Flood Extent Based on SAR The following steps were used to detect flood extent(water/no water). In SNAP tool the raw data downloaded from sci-hub Copernicus was processed to calibrate image for atmospheric correction, spike filter and terrain correction(This is mainly for Sentinel 1 data). Finally defining water no water based on a threshold applied on the corrected image. Defining a threshold is always a challenge in SAR image analysis for flood detection, we collected data from the field to define this threshold. For Sentinel 2 as a first step cloud filter was calculated by applying a combined threshold on Band 2 and Band 10. The cloud mask shown in the figure below didn’t capture shadows of clouds, these were miss interpreted by the flood algorithm as water/flood. To correct this areas with more cloud cover were clipped out with a polygon. To define water no water based on sentinel data we used NDWI index, the treshold is adjusted based on data collected from the field Validation points were collected by Field team tested different values and check if the threshold identified fits with observation. The complete methodology how to detect flooding based on Sentinel 1 data and SNAP toolbox is documented in ESA website. B. Affected People To calculate number of affected people per each admin level, flood extent map is combined with HRSL population data. This is done in two steps: First, in step 1, we calculate a raster, which multiplies the population grid with the flood grid, such that we are left with only "population in flooded area". This is done using raster calculator where population density raster was multiplied by flood extent raster, which has a value of 0 for no flood and 1 for flood areas. Note that the flood extent grid was first resampled to match it to the population grid. This whole exercise is repeated for settlement/buildings instead of population. Step 2: We apply zonal statistics per TA to calculate total number of buildings/people affected in each admin level. For each Admin level2 estimated number of affected people and affected houses are plotted in the map. The zonal statistics data used for plotting can be found in the shape file.

Product This priority index was derived by combining a detailed flood extent mapping with detailed human settlement geo-data. Both sources were combined to produce the location and magnitude of population living in flooded areas. This was subsequently aggregated to admin-4 areas (GND) as well as admin-3 areas (DS divisional). The flood extent mapping was derived in turn by combining two sources: Flood extent maps could be produced rather faster using satellite imageries captured by either optical sensors or Synthetic Aperture Radar (SAR) sensors. In most places flood is cause by heavy rainfall which means in most cases cloud is present, this is a limitation for optical sensors as they can’t penetrate clouds. Radar sensors are not affected by cloud, which make them more useful in presence of cloud. In This analysis we analyzed sentinel2 optical image from May 28th and Sentinel 1 SAR image from May 30th. Then we combine the two results adding up the flood extents. Main cloud covered areas and permanent water bodies are removed from the flood extent map using the Sentinel 2 cloud mask. The scale/resolution of the flood extent map is 30mts where as the permanent water body map has 250m scale resolution. This will introduce some discrepancy: part of flood extent map could be permanent water body. Scope Analysis focused on 4 districts in South-West Sri Lanka based on news reports (https://www.dropbox.com/s/n0qdqe7qfgq6fyv/special_situation.pdf?dl=0). Based on the admin-3-level analysis, highest percentages of population living in flooded areas were seen in Matara district. Admin-4 level analysis concentrated only on Matara district for that reason. Caveats The dataset is showing percentage flooded. The data has not yet been corrected for small populations. We believe the product is currently pointing to the high priority areas. In the shp or csv files the user of this data could easily correct for small populations, if there is a wish to target on the amount of people affected. Data used from partners The human settlement data was retrieved from http://ciesin.columbia.edu/data/hrsl/. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-06-2017. The Radar imagery analysis was done by NASA JPL, whose input in this product has been crucial. Visualization An example map is available here: http://bit.ly/SriLankaFloodMap Linked data Admin boundaries 3 and 4 can be found here (link on OBJECT_ID): https://data.humdata.org/group/lka?q=&ext_page_size=25&sort=score+desc%2C+metadata_modified+desc&tags=administrative+boundaries#dataset-filter-start How to use The ratio column in the SHPs or CSVs can be multiplied by 100 to get the percentage of flooding in the area.

UNOSAT code FL20231105SOM This map illustrates satellite-detected surface waters in Beledweyne City, Beledweyne District, Hiraan Region, Somalia, 12 November 2023 at 07:32 UTC. Within the analysed area of about 50 km2 about 14 km2 of land appear to be flooded. Furthermore, within the analysed area about 22,000 buildings are identified as potentially affected by the floods. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

UNOSAT code: FL20231120DOM, GDACS ID: 1102342 This map illustrates cumulative satellite-detected water extent in Valdesia Regions, Dominican Republic as observed from a Sentinel-2 image acquired on the 21 November 2023 at 11:17 local time. Within the cloud free analysed area of about 320 km² , less than 5 km² of land appears to be affected with flood waters and 2,500 people are potentially exposed to flood waters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

UNOSAT code: FL20231120DOM, GDACS ID: 1102342 This map illustrates cumulative satellite-detected water extent in Higuamo & Valdesia Regions, Dominican Republic as observed from a Sentinel-2 image acquired on the 21 November 2023 at 11:17 local time. Within the cloud free analysed area of about 1,800 km² , about 35 km² of land appears to be affected with flood waters and 14,000 people are potentially exposed to flood waters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

UNOSAT code FL20231105SOM This map illustrates satellite-detected surface waters in Bardere City, Baardheere District, Gedo Region, Somalia as observed from a Pleiades image acquired on 12 and 13 November 2023. Within the analysed area of about 32 km2 about 5.3 km2 of land appears to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

UNOSAT code: FL20231105SOM This map illustrates satellite-detected surface waters in Jowhar City, Jowhar District, Middle Shabelle Region, Somalia as observed from a Worldview-2 image acquired on 22 November 2023. Within the analysed area of about 13 km2 about 1,4 km2 of land appears to be flooded. Furthermore, within the analysed area about 300 buildings are identified as potentially affected by the floods. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

UNOSAT code: FL20231120DOM, GDACS ID: 1102342 This map illustrates cumulative satellite-detected water extent in Cibao Nordeste Region, Dominican Republic as observed from a Sentinel-2 image acquired on the 21 November 2023 at 11:17 local time. Within the cloud free analysed area of about 920 km² , about 190 km² of land appears to be affected with flood waters and 13,000 people are potentially exposed to flood waters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

UNOSAT code FL20231018GHA This map illustrates cumulative satellite-detected water extent as observed from a Sentinel-1 imagery acquired on the 23 October 2023 at 18:10 local time. Within the analysed area of about 4,300 km², a total of about 53 km² of lands appear to be affected with flood waters. Based on Worldpop population data and the detected surface waters, about 12,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

UNOSAT code: FL20231105SOM This map illustrates satellite-detected surface waters in Bardere City, Baardheere District, Gedo Region, Somalia as observed from a WorldView-3 image acquired on 22 November 2023. Within the analysed area of about 32 km2 about 6 km2 of land appears to be flooded. The water extent appears to have been stable since 13 November 2023. Furthermore, within the analysed area about 2,600 buildings are identified as potentially affected by the floods. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

UNOSAT code: FL20231105SOM This map illustrates satellite-detected surface waters in Bardere City, Baardheere District, Gedo Region, Somalia as observed from a WorldView-3 image acquired on 29 November 2023. Within the analysed area of about 32 km2 about 4.5 km2 of land appears to be flooded. The water extent appears to have receded about 1.5 km2 since 22 November 2023. Furthermore, within the analysed area about 1,200 buildings are identified as potentially affected by the floods as of 29 November 2023. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

UNOSAT code FL20231105SOM This map illustrates satellite-detected surface waters in Baardheere District, Gedo Region and Saakow District, Juba Dhexe Region, Somalia as observed from a Sentinel-1 image acquired on 07 November 2023 at 02:54 UTC. Within the map extent of about 4,900 km², about 50 km² of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

UNOSAT code: FL20240102COG This map illustrates cumulative satellite-detected minimum floodwater using VIIRS in Republic of Congo between 29 December 2023 and 2 January 2024. Within the cloud free analysed area of about 330,000 km2, a total of about 1,000 km2 of lands appear to be affected with flood waters. Based on Worldpop population data and the minimum flood water coverage, ~114,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please provide ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Minimum floodwater extent indicates the portion of the pixel (375m) covered by 80 to 100% of flood water.

Flood footprint of historical events at a 200m x 200m resolution based on the cloud to street database with events ranging from the years 2002-2018 (see https://floodbase.com). The events have been processed into one hazard dataset per country.

UNOSAT code: FL20240102COG This map illustrates satellite-detected surface waters and landslides/mudflow extent in the Brazzaville Department, Republic of Congo, as observed from a WorldView-2 image acquired on 8 February 2024, at 10:31 local time. Within the analyzed area of 30,000 hectares, approximately 190 hectares of landslide scars/ mudflow extent are observed, and about 400 hectares of land appear to be flooded. Based on Worldpop population data, the detected surface waters and the extent of landslides/mudflows indicate that about 30,000 people are potentially exposed or living close to landslide and flooded areas. UNOSAT identified a total of 458 structures potentially affected by landslide and flood, along with 19 affected road. This is a preliminary analysis and has not yet been validated in the field. Please provide ground feedback to the United Nations Satellite Centre (UNOSAT).

UNOSAT code: FL20240102COG This map illustrates satellite-detected surface waters and landslides/mudflow extent in Brazzaville and Pool Departments, Republic of Congo, as observed from a Sentinel-2 image acquired on 28 December 2023, at 10:21 local time. Within the analysed area of 30,000 ha, approximately 190 ha of landslide scars/mudflow extent are observed and about 230 ha of land appear to be flooded. Based on Worldpop population data, the detected surface waters and the landslide scar/mudflow extent, about 15,000 people are potentially exposed or living close to landslide and flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please provide ground feedback to the United Nations Satellite Centre (UNOSAT).

UNOSAT code: FL20231120DOM, GDACS ID: 1102342 This map illustrates cumulative satellite-detected water extent in CIbao Nordeste and Cibao Sur Regions, Dominican Republic as observed from a Sentinel-1 image acquired on the 18 November 2023 at 18:53 local time. Within the extent of this map, about 55 km² of land appears to be affected with flood waters and about 10,500 people are potentially exposed to flood waters This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

UNOSAT code FL20231105SOM This map illustrates satellite-detected surface waters in Jilib District, Juba Dhexe & Afmadow Jamaame District, Juba Hoose Region, Somalia as observed from a Sentinel-1 image acquired on 07 November 2023 at 02:54 UTC. Within the analysed area of about 7,300 km², about 60 km² of land appear to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT). Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.

UNOSAT code: FL20231124KEN This map illustrates satellite-detected water extent in Garissa City, Dujis County, Garissa Province, Kenya as observed from a Sentinel-2 image acquired on 26 November 2023 at 10:50 local time. Within analysed area of about 220 km², a total of about 45 km² of lands appear to be affected with flood waters. Furthermore, within the analysed area, about 6,500 buildings are identified as affected by the floods. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

UNOSAT code FL20231105SOM Status: Increased water extent along the Jubba River Further action(s): continue monitoring Summary of findings: - Increase of water extent along the Jubba River observed as of 12 November 2023; - Affected structures and agricultural areas. We also observe inundated roads in Bardere City, in the Gedo Region as of 12 November 2023;

UNOSAT code: FL20231105SOM This map illustrates satellite-detected surface waters in Bu'aale City, Bu'aale District, Middle Juba Region, Somalia as observed from a Worldview-3 image acquired on 22 November 2023 at 07:35 UTC. Within the analysed area of about 1,000 hectares about 800 hectares of land appear to be flooded. Compared to the situation on 13 November, the flood-affected area in the analysed extent increased about 20 hectares and the number of flood-affected buildings increased about 330. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

UNOSAT code: FL20231124KEN This map illustrates satellite-detected water extent in Dadaab Town, Dadaab County, Garissa Province, Kenya as observed from a Sentinel-2 image acquired on 26 November 2023 at 10:50 local time and a Landsat 9 image acquired on 21 November 2023 at 10:30 local time. Within analysed area of about 1,000 km², a total of about 110 km² of lands appear to be affected with flood waters. Furthermore, within the analysed area, about 4,600 buildings are identified as affected by the floods. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

UNOSAT code: FL20231105SOM This map illustrates satellite-detected surface waters in Juba Hoose and Juba Dhexe Region, Somalia as observed from a Sentinel-2 image acquired on 3 December 2023 at 07:39 UTC. Within the analysed area of about 20,000 km2 about 1,300 km2 of land appears to be flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).