Bangladesh environment data

# EOS-RS Flood Proxy Map: Bangladesh, Floods, 10 Aug 2023, v0.5 The Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS) created this preliminary Flood Proxy Map (FPM) depicting areas that are likely flooded in Bangladesh due to heavy rains. This map was derived from synthetic aperture radar (SAR) images acquired by the Copernicus Sentinel-1 satellite operated by the European Space Agency (ESA) before (29 July 2023) and during (10 Aug 2023) the event. The image covers an area indicated by the large white polygon. Each light blue pixel measures about 30 meters across. Preliminary validation was done in selected areas using high resolution optical imagery and news reports. This flood proxy map should be used as a guide to identify areas that are likely flooded, and is less reliable over urban and vegetated areas. The product contains modified Copernicus Sentinel data (2023), processed by ESA and analyzed by the Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), using the Advanced Rapid Imaging and Analysis (ARIA) system originally developed at NASA's Jet Propulsion Laboratory, California Institute of Technology, and modified at EOS-RS. **More map details and files:** **More map details and files:** **Credits:** Earth Observatory of Singapore - Remote Sensing Lab (EOS-RS), Advanced Rapid Imaging and Analysis (ARIA). Contains modified Copernicus Sentinel data (2023).

GLIDEnumbers are GLobal unique IDEntifiers for Disasters. Each disaster is registered in a record that contains a GLIDEnumber that uniquely identifies it and allows information to be interoperable across multiple disaster information sources, both structured and textual. Accessing disaster information can be a time consuming and laborious task. Not only is data scattered but frequently identification of the disaster can be confusing in countries with many disaster events and across borders. To address both of these issues, Asian Disaster Reduction Center (ADRC) proposed a globally common Unique ID code for disasters. Among the institutions using actively GLIDEnumbers are UN-OCHA (ReliefWeb, FSCC, HDX), UNDRR, UNDP, WMO, IFRC, FAO, the European Commission and the Centre for Research on the Epidemiology of Disasters (CRED), among many others. Country datasets are only produced for countries with at least one GLIDE event since 1 January 2022. The global dataset contains all events available from the GLIDE API, including those not assigned to a country.

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).

**UNOSAT code: FL20210729BGD** This is a web map showing analysis and results related to floods in Cox's Bazar area, Chittagong division, Bangladesh

**UNOSAT code: FL20240502BGD, GDACS ID: 1001064** This map illustrates satellite-detected surface waters in Sylhet Sadar, Bishwanath, Dakshin Surma Upazilas, Sylhet district, Sylhet division, as observed from a SAOCOM-1A satellite image acquired on 26 June 2024 at 17:45 local time. Within the analysed area, about 134 km² of land appears to be flooded. Furthermore, within the analysed area, about 70,000 structures are identified as potentially affected by the floods (60% from the total structures in the analysed area), along with about 254 km of roads, as of 26 June 2024 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: FL20240502BGD, GDACS ID: 1102700** This map illustrates satellite-detected surface waters in Chhatak Upazila, Sunamganj District, Sylhet division, as observed from a GLOBAL satellite image acquired on 26 June 2024 at 05:58 local time. Within the analysed area, about 20 km² of land appears to be flooded. Furthermore, within the analysed area, about 3,350 structures are identified as potentially affected by the floods (30% of the total structures in the analysed area), along with about 40 km of roads, as of 26 June 2024 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: FL20240502BGD, GDACS ID: 1102700** This map illustrates satellite-detected surface waters in Sunamganj Sadar and Bishwambarpur Upazilas, Sunamganj District, Sylhet Division, as observed from a Pleiades satellite image acquired on 26 June 2024 at 11:01 local time. Within the analysed area, about 120 km² of land appears to be flooded. Furthermore, within the analysed area, about 25,800 structures are identified as potentially affected by the floods (42% of the total structures in the analysed area), along with about 260 km of roads, as of 26 June 2024 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).

**ADAM ID: 1001034\_4** Cyclone (tropical storm) during the period Nov 16 2023-Nov 17 2023 in India, Bangladesh. It impacted 0 people.

**ADAM ID: 1000976\_2** Cyclone (tropical storm) during the period Jun 09 2023-Jun 10 2023 in Bangladesh, Myanmar, India. It impacted 0 people.

**ADAM ID: 1001029\_6** Cyclone (category 1) during the period Oct 23 2023-Oct 24 2023 in Bangladesh, India, Myanmar. It impacted 3946522 people.