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  • 100+ Downloads
    Updated May 15, 2019 | Dataset date: May 1, 2018
    This dataset updates: Every day
    UNOSAT produced satellite-detected flood water extent in Somali Region, Ethiopia. The analysis was conducted by analysing a Sentinel-1 image acquired on the 1 May 2018. As observed from the satellite radar image, a total of 9,200 ha of land were inundated in the area of interest. By using WorldPop data, we estimate that at least 12,000 people are potentially affected or living close to the flooded area. This corresponds to about 7% of the population living in the area of interest. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks and within built-up urban areas because of the special characteristics of the satellite data used. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR UNOSAT. Satellite Data: Sentinel-1
  • 100+ Downloads
    Updated May 15, 2019 | Dataset date: May 6, 2018
    This dataset updates: Every day
    UNOSAT produced satellite-detected flood water extent and IDP distribtuion within the town of Belet Weyne in Belet Weyne District,Hiiran Region, Somalia. The analysis was conducted analyzing GeoEye-1 & WorldView-3 images acquired on the 30 April & 1 May 2018. As observed from the satellite image, the town of Belet Weyne is completely affected by the floods. Around 70% of the extension of the town is totally inundated, being the districts of Bulahabley, Bundaweyn, Dhagahjebis, Hilac, Hindab and Lamagalay Regional Military Based, Radar and Kutimbo completely submerged in water. The flood waters inside areas of partially flooded districts are receding. More than 110 IDP sites are located inside the town, and 50% of them are inside areas completly flooded. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR UNOSAT.
  • This map illustrates the satellite detected surface waters in Manicaland Province, Zimbabwe, as observed from the Sentinel-1 data imagery acquired on 12 and 24 March 2019. Within the analysis extent, over Manicaland Province, 164,130 ha of surface waters were observed the 12 March 2019. and about of 406,600 ha of surface waters were observed the 24 March 2019. It represents an increase of 40 %. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Satellite data (pre-event) : Sentinel-1 Imagery date: 12 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Satellite data (post-event) : Sentinel-1 Imagery date: 24 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Boundary data: OCHA ROSEA Water body & waterway: COD Analysis : UNITAR-UNOSAT Production: UNITAR - UNOSAT
  • 100+ Downloads
    Updated April 4, 2019 | Dataset date: Mar 21, 2019
    This dataset updates: As needed
    This dataset is a compilation of various sources such as Copernicus, Sentinel-1 and Atmospheric and Environmental Research, A Verisk Business, & African Risk Capacity using several days to calculate the maximum flood extent for the whole event.
  • 200+ Downloads
    Updated March 15, 2019 | Dataset date: Mar 13, 2019
    This dataset updates: Never
    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.
  • 300+ Downloads
    Updated December 31, 2018 | Dataset date: Jan 1, 2013-Dec 31, 2013
    This dataset updates: Never
    1) Natural disaster events include avalanches, extreme winter conditions, flooding, heavy rainfall, landslides & mudflows, and extreme weather (sandstorms, hail, wind, etc) as recorded by OCHA field offices and IOM Afghanistan Humanitarian Assistance Database (HADB). 2) A natural disaster incident is defined as an event that has affected (i.e. impacted) Afghans, who may or may not require humanitarian assistance. 3) HADB information is used as a main reference and supplemented by OCHA Field Office reports for those incidents where information is not available from the HADB. OCHA information includes assessment figures from OCHA, ANDMA, Red Crescent Societies, national NGOs, international NGOs, and ERM.
  • 200+ Downloads
    Updated September 11, 2018 | Dataset date: Nov 13, 2015
    This dataset updates: Never
    This dataset shows the Shabelle and Juba Riverine Basin Population Displacement Estimates - 2015. Working assumptions: • Displaced population defined as direct displacement through flood inundation • Displaced population calculated by multiplying the number of hh's by hh size of 6 • If a range is provided to quantify displacement the upper figure is used
  • 200+ Downloads
    Updated September 11, 2018 | Dataset date: Nov 9, 2015
    This dataset updates: Never
    This dataset shows the reported flooded areas in somalia
  • 100+ Downloads
    Updated September 11, 2018 | Dataset date: Nov 13, 2015
    This dataset updates: Never
    Dataset shows the reported flooded Areas in Somalia
  • 200+ Downloads
    Updated September 6, 2018 | Dataset date: May 31, 2016
    This dataset updates: Never
    This dataset contains an active archive of flood event records from 1985 to present. Details such as the country affected, the number of people killed, the number of people displaced, the cost of damages, and a measure of the magnitude of the flood are included for each flood event. The archive is updated on an ongoing basis and new flood event are added immediately. The information presented in this Archive is derived from news, governmental, instrumental, and remote sensing sources.
  • 100+ Downloads
    Updated July 21, 2018 | Dataset date: Jul 21, 2018
    This dataset updates: Every month
    North East Nigeria Camp Management partner operational presence by Local Government Area or LGA (Admin 2). Dataset covers partner operational presence in the three crisis-affected states of Borno, Yobe and Adamawa, up to LGA level; Camp Coordination and Camp Management (CCCM) coverage by Local Government Area or LGA (Admin 2). Includes number of households covered and not covered; total households and individuals covered, percentage and overall coverage in the three crisis-affected states of Borno, Yobe and Adamawa, up to LGA level; Camp Coordination and Camp Management (CCCM) and Emergency Shelter and Non-Food Items (ESNFI) activity coverage up to Local Government Area or LGA (Admin 2) level in the three crisis-affected states of Borno, Yobe and Adamawa. Also includes Camp Management partner operational presence by LGA, as of June 2018.
  • 80+ Downloads
    Updated May 4, 2018 | Dataset date: May 3, 2018
    This dataset updates: Every week
    UNOSAT produced satellite-detected flood water extent in the districts of Jilib, Saakow, and Bu'aale, Somalia. The analysis was conducted analyzing Sentinel-1 images acquired on the 1 May 2018. As observed from the satellite imagery, a total of 92,000 ha of land were inundated in the area of interest. The most affected districts are Saakow, with almost 38,000 ha of flooded land and southern Diinsoor, with almost 27,000 ha. At least 20 settlements are potentially located within the flooded area. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks and within built-up urban areas because of the special characteristics of the satellite data used.
  • 20+ Downloads
    Updated September 20, 2017 | Dataset date: Oct 24, 2015
    This dataset updates: Every year
    El Índice Nacional de Inundación es un índice que toma en cuenta parámetros hidrológicos como la precipitación acumulada, pendiente del terreno, acumulación de escurrimiento y retención máxima de humedad del suelo. Su aplicación principal consiste en la identificación de humedales, definidos como zonas perenes o efímeramente saturadas o inundadas. Dicho índice no relaciona variables hidráulicas como lo es el tirante, velocidad del fluido o tiempo de anegación. Este mapa considera el escurrimiento de la precipitación media acumulada para un periodo de retorno de 100 años. Dicho mapa es la base para realizar modelos de tipo hidráulico que se han integrado al Atlas Nacional de Riesgos, en zonas donde además de aparecer como inundables se han registrado inundaciones históricas, por lo que se sugiere no utilizarlo como la primer fuente de información en la toma de decisiones en cuanto a la planeación del territorio o atención de las emergencias.
  • 700+ Downloads
    Updated August 17, 2017 | Dataset date: Aug 15, 2017
    This dataset updates: Never
    In this analysis we have combined several data sources around the floods in Bangladesh in August 2017. Visualization See attached map for a map visualization of this analysis. See http://bit.ly/2uFezkY for a more interactive visualization in Carto. Situation Currently, in Bangladesh many water level measuring stations measure water levels that are above danger levels. This sets in triggers in motion for the partnership of the 510 Data Intitiative and the Red Cross Climate Centre to get into action. Indicators and sources In the attached map, we combined several sources: Locations of waterlevel stations and their respective excess water levels (cms above danger level) at 14/08/2017 (Source: http://www.ffwc.gov.bd/index.php/googlemap?id=20) Population density in Bangladesh to quickly see where many people live in comaprison to these higher water-level stations. (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00018 >> the People per hectare 2015 UN-adjusted totals file is used.) Vulnerability Index: we constructed a Vulnerability Index (0-10) based on two sources. First poverty incidence was collected from Worldpop (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00020 >> The estimated likelihood of living below $2.50/day). Second, we used a Deprivation Index which is estimated in the report Lagging District Reports 2015 (Source: http://www.plancomm.gov.bd/wp-content/uploads/2015/02/15_Lagging-Regions-Study.pdf > Appendices > Table 20), which combines many socio-economic variables into one Deprivation Index through PCA analysis. Detailed methodology Vulnerability The above-mentioned poverty source file is on a raster level. This raster level poverty was transformed to admin-4 level geographic areas (source: https://data.humdata.org/dataset/bangladesh-admin-level-4-boundaries), by taking a population-weighted average. (Source population also Worldpop). The district-level PCA components from abovementioned reports were matched to the geodata based on district names, and thus joined to the admin-4 level areas, which now contain a poverty value as well as Deprivation Index value. Note that all admin-4 areas within one district (admin-2) obviously all have the same value. The poverty rates do differ between all admin-4 areas. Lastly, both variables were transformed to a 0-10 score (linearly), and a geomean was taken to calculate the final index of the two. A geomean (as opposed to an arithmetic mean) is often used in calculating composite risk indices, for example in the widely used INFORM-framework (www.inform-index.org).
  • 80+ Downloads
    Updated May 22, 2017 | Dataset date: May 22, 2017
    This dataset updates: Every day
    Peru-Floods: damage and needs assessment matrix, GLIDE NUMBER:FL2017-000014
  • 200+ Downloads
    Updated October 28, 2016 | Dataset date: Jan 10, 2015
    This dataset updates: Never
    Flood extend data of Malawi floods in January 2015
  • 200+ Downloads
    Updated October 13, 2016 | Dataset date: Jan 12, 2015-Mar 15, 2015
    This dataset updates: Every year
    Site Assessment conducted as the response to internal displacement due to Floods in 2015
  • 400+ Downloads
    Updated June 13, 2016 | Dataset date: Oct 1, 2015-Jan 19, 2016
    This dataset updates: Never
    This dataset shows the number of people affected by elnino rains per county