Malawi environment data

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.

**ADAM ID: FL-20230217-MWI-01** Flood covering 106653.0 sq m on Feb 17 2023 in Malawi. It impacted 182158 people.

**ADAM ID: **FL-20250314-MWI-00 A flood occurred on Mar 14 2025 in Malawi. It affected an estimated 262271 people and 41646 ha of cropland. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

Drone orthomosaic. Post Tropical Storm Chalane Mapping, Malawi conducted with Crowddroning by GLOBHE. More maps and data available on demand upon request from locations globally at https://globhe.com/ MORE CROWDDRONING BY GLOBHE Web: https://globhe.com/ Facebook: https://www.facebook.com/Crowddroning Twitter: https://twitter.com/globhedrones Instagram: https://www.instagram.com/globhedrones/ LinkedIn: https://www.linkedin.com/company/globhedrones/

Drone orthomosaic. Post Tropical Storm Chalane Mapping, Malawi conducted with Crowddroning by GLOBHE. More maps and data available on demand upon request from locations globally at https://globhe.com/ MORE CROWDDRONING BY GLOBHE Web: https://globhe.com/ Facebook: https://www.facebook.com/Crowddroning Twitter: https://twitter.com/globhedrones Instagram: https://www.instagram.com/globhedrones/ LinkedIn: https://www.linkedin.com/company/globhedrones/

Drone orthomosaic. Post Tropical Storm Chalane Mapping, Malawi conducted with Crowddroning by GLOBHE. More maps and data available on demand upon request from locations globally at https://globhe.com/ MORE CROWDDRONING BY GLOBHE Web: https://globhe.com/ Facebook: https://www.facebook.com/Crowddroning Twitter: https://twitter.com/globhedrones Instagram: https://www.instagram.com/globhedrones/ LinkedIn: https://www.linkedin.com/company/globhedrones/

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.

High exposure, High sensitivity and low adaptive capacity leads to high vulnerability. The severity of vulnerability determines the likely impacts to a system that changing climate can have. This data has been developed by RCMRD and Malawi Department of Disaster Management Affairs (DoDMA). SERVIR is a joint USAID-NASA project. For more information on SERVIR, visit http://www.servirglobal.net

Flood extend data of Malawi floods in January 2015

2018 Malawi Traditional Authority ( Admin3 Border) dataset shared by National Statistical Office during Cyclone Idai Response, March 2019

Drone orthomosaic. Post Tropical Storm Chalane Mapping, Malawi conducted with Crowddroning by GLOBHE. More maps and data available on demand upon request from locations globally at https://globhe.com/ MORE CROWDDRONING BY GLOBHE Webb: https://globhe.com/ Facebook: https://www.facebook.com/Crowddroning Twitter: https://twitter.com/globhedrones Instagram: https://www.instagram.com/globhedrones/ LinkedIn: https://www.linkedin.com/company/globhedrones/

The Soil and Terrain database for Malawi (version 1.0), at scale 1:1 million, was compiled based on the soil map of Malawi at scale 1:250,000 (compiled by the Land Resources Evaluation Project) that was complemented with soil boundary information from the provisional soil map at scale 1:1 million. The 90m SRTM-DEM was used to define the various landform types of the SOTER units and also to adjust their boundaries. The SOTER map units were attributed with soil information obtained from the Africa Soil Profiles Database. The SOTER compilation followed the methodology described in the SOTER Procedures Manual Version 2. SOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts. DOI for dataset (submitted): 10.17027/isric.wdcsoils.20160002