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data-use-github-style\u003e\u003ch2 id=\"global-flood-database-scripts--data\"\u003eGlobal Flood Database Scripts \u0026amp; Data\u003c/h2\u003e\n\u003cp\u003eThis repository includes code and supporting data for the Global Flood Database. Below are descriptions of the data and code and how they relate to \u003cem\u003eTellman et al, Satellite observations indicate increasing proportion of population exposed to floods\u003c/em\u003e\u003c/p\u003e\n\u003ch3 id=\"data\"\u003eData\u003c/h3\u003e\n\u003ch4 id=\"flood-maps\"\u003eFlood Maps\u003c/h4\u003e\n\u003cp\u003eThe flood maps (.tif files) can be accessed through a visualization and data portal at: \u003ca href=\"http://global-flood-database.cloudtostreet.info/\"\u003ehttp://global-flood-database.cloudtostreet.info/\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eYou can also download the entire database as GeoTIFF files directly from Google Cloud Storage (GCS) using the \u003ca href=\"https://cloud.google.com/storage/docs/gsutil_install\"\u003egsutil\u003c/a\u003e \u003ca href=\"https://cloud.google.com/storage/docs/gsutil/commands/cp\"\u003ecp command\u003c/a\u003e from the GCS bucket \"gfd_v3\". You can use the following command to download the entire database to a local directory:\u003c/p\u003e\n\u003cp\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003egsutil -m cp gs://gfd_v3 your/local/directory/to/save/to\u003c/code\u003e\u003c/p\u003e\n\u003ch4 id=\"flood-mapping\"\u003eFlood Mapping\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\shp_files\\dfo_polys_20191203.shp\u003c/code\u003e: the Dartmouth Flood Observatory (DFO) flood polygon dataset used in our analyses and processing of satellite imagery.\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_qcdatabase_2019_08_01.csv\u003c/code\u003e: the Quality Control (QC) database described in Tellman et al.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4 id=\"validation\"\u003eValidation\u003c/h4\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_validation_points_2018_12_17.csv\u003c/code\u003e: validation data of 123 selected flood events that includes geo-location of each assessment point, the classified data for different methods (e.g. 3day Standard), analyst initials and spectral data from the interpretation imagery (i.e. Landsat-5, 7 \u0026amp; 8). Field values are explained in the \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003emain_validation.pynb\u003c/code\u003e (see below)\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_validation_sensitivity.csv\u003c/code\u003e: assessed validation points up to 400 points for selected flood events to test appropiate sampling intensity\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_validation_metrics.csv\u003c/code\u003e: summarized validation metrics (e.g. commission error) for each validation flood\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\sample_frame_CC20_D1_051618.csv\u003c/code\u003e: a summary of available Landsat images (5, 7 \u0026amp; 8) for each flood event. Used to determine which flood events can be used to collect validation data. The field \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eDELTA\u003c/code\u003e is the number of days following max flood extent, \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eCLOUD_COVER\u003c/code\u003e is the maximum allowable percent cloud cover for a validation image, \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eX\u003c/code\u003e and \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eY\u003c/code\u003e are the centroid of the flood event from the DFO polygon.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4 id=\"exposed-population-estimates\"\u003eExposed Population Estimates\u003c/h4\u003e\n\u003col start=\"7\"\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\SSP2010.csv\u003c/code\u003e: 2010 population estimates from the SSP2 (Socioeconomic pathways scenario)\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\SSP2030.csv\u003c/code\u003e: 2030 population estimates from the SSP2 (Socioeconomic pathways scenario)\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\aqueductcountrydata.csv\u003c/code\u003e: WRI Aqueduct flood exposure estimates for various return periods for 2010 and 2030\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\aqueduct_dictionary.xlsx\u003c/code\u003e: data dictionary to explain columns in WRI Aqueduct flood exposure estimates\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_popsummary.csv\u003c/code\u003e: Global Flood Database population exposure estimates per country, in 2000 and 2015, and associated statistics.\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\GFDabove_13_wBias.csv\u003c/code\u003e: Global Flood Database population exposure estimates per country, in 2000 and 2015, with bias correction factor based on comparison to HRSL data.\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_popdictionary.xlsx\u003c/code\u003e: data dictionary to explain columns in Global Flood Database exposure estimates\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePopulation Exposed Per Event\u003c/strong\u003e: Population exposure estimate per event. To access click on the \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eINFO\u003c/code\u003e button on our data portal at: \u003ca href=\"http://global-flood-database.cloudtostreet.info/\"\u003ehttp://global-flood-database.cloudtostreet.info/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePopulation Exposed Per Country Per Event\u003c/strong\u003e: Population exposure estimates per country by event. To access click on the \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eINFO\u003c/code\u003e button on our data portal at: \u003ca href=\"http://global-flood-database.cloudtostreet.info/\"\u003ehttp://global-flood-database.cloudtostreet.info/\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4 id=\"pop-sensitivity--uncertainty\"\u003ePop Sensitivity \u0026amp; Uncertainty\u003c/h4\u003e\n\u003col start=\"15\"\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_popsensitivity.csv\u003c/code\u003e: Global Flood database population exposure estimates per country using the Global Human Settlement Layer (GHSL), High Resolution Settlement Layer (HRSL) and GridPop3. Countries are limited to those with HRSL data.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4 id=\"flood-mechanism\"\u003eFlood Mechanism\u003c/h4\u003e\n\u003col start=\"16\"\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_floodmechanism.csv\u003c/code\u003e: Global Flood database disaggregated by \"flood type\" (data from the Dartmouth Flood Observatory) and estimated population exposure estimate per in 2000 and 2015.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3 id=\"code\"\u003eCode\u003c/h3\u003e\n\u003cp\u003eOur code includes modules written in Python, Javascript and R. In the case of Javascript, this code is stored as a \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003e.txt\u003c/code\u003e file (\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003e.js\u003c/code\u003e files are prohibited as Gmail attachments) and can be run by \"copy and pasting\" into \u003ca href=\"https://code.earthengine.google.com/\"\u003eGoogle Earth Engine's code editor\u003c/a\u003e. Python scripts are based on \u003ca href=\"https://developers.google.com/earth-engine/guides/python_install\"\u003eGoogle Earth Engine's Python API\u003c/a\u003e and require installation before running. Additional code in R require publicly available downloads of \u003ca href=\"https://rstudio.com/\"\u003eR or RStudio\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eBelow is a short description of scripts within our repository and how they relate to \u003cem\u003eTellman et al, Satellite observations indicate increasing proportion of population exposed to floods\u003c/em\u003e\u003c/p\u003e\n\u003ch4 id=\"flood-mapping-1\"\u003eFlood Mapping\u003c/h4\u003e\n\u003col\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003emain_gfd.py\u003c/code\u003e - uses GEE Python API to create flood maps for each Dartmouth Flood Observatory flood event. This script relies on modules found in the \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eflood_detection\u003c/code\u003e folder. The exports are stored in Google Cloud Storage which can be accessed as described above.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4 id=\"validation-1\"\u003eValidation\u003c/h4\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003egee_sampleFrameLandsat.txt\u003c/code\u003e - uses GEE Code Editor to determine what floods have available Landsat imagery coincident within 1-day of the max extent of a flood event. This code produces \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\sample_frame_CC20_D1_051618.csv\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003egee_validationGUI.txt\u003c/code\u003e  - used GEE Code Editor to collect validation data using a custom tool designed in GEE that retrieves a flood event, coincident Landsat imagery and creates a statrified sample. An example of our validation GUI can be seen below in \u003cstrong\u003eFigure 1\u003c/strong\u003e. Analysts can then interpret sample points based on Landsat imagery and results are recorded. This code relies upon \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003egee_landsatTools.txt\u003c/code\u003e and \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003egee_misc.txt\u003c/code\u003e sub-modules. The outputs of assessment points by each analyst were stored in Google Cloud Storage and is compiled here \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_validation_points_2018_12_17.csv\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003emain_validation.pynb\u003c/code\u003e - This script uses the accuracy assessment points (i.e. \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_validation_points_2018_12_17.csv\u003c/code\u003e) to calculate various accuracy metrics including ommission and commission errors. The results are stored in \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_validation_metrics.csv\u003c/code\u003e. This script also analyzes the validation sensitivity (Extended Data Fig 8).\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4 id=\"exposed-population-estimates-1\"\u003eExposed Population Estimates\u003c/h4\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003emain_popstats.py\u003c/code\u003e - uses GEE Python API to estimate exposed populations for each flood event and country. This script relies on modules found in the \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eflood_stats\u003c/code\u003e folder. Outputs are available on our \u003ca href=\"http://global-flood-database.cloudtostreet.info/\"\u003edata portal\u003c/a\u003e by clicking on the \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eINFO\u003c/code\u003e button. These population estimates do not filter out isolated pixels as described in the methods.\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003emain_popchange.txt\u003c/code\u003e - uses GEE Code Editor to calculate population change in areas of observed inundation from GFD between years 2000 and 2015 for each country. This method removed isolated pixels for a conservative estimate of change. This script yields \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_popsummary.csv\u003c/code\u003e. Additional fields in \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_popsummary.csv\u003c/code\u003e are described in \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_popdictionary.xlsx\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eext.datafig10.R\u003c/code\u003e-  This script was used to make extended data figure 10, which compares the population exposed to at least one flood event between 2000-2018 from the Global Flood Database to floods in 2010 in the WRI Aqueduct flood exposure 100 year return period at the country scale.\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003eext.datafig7.R\u003c/code\u003e - This script was used to make extended data figure 7, which is a sensitivity analysis of the proportion of population exposed to floods under climate change and population growth across return period\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003emain_gfdsummarystats.R\u003c/code\u003e- This script was used to generate summary statistics from the Global Flood Database for the paper.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4 id=\"pop-sensitivity--uncertainty-1\"\u003ePop Sensitivity \u0026amp; Uncertainty\u003c/h4\u003e\n\u003col start=\"10\"\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003emain_popsensitivity.txt\u003c/code\u003e - uses GEE Code Editor to calculate population exposure  using the Global Human Settlement Layer (GHSL), High Resolution Settlement Layer (HRSL) and GridPop3. This method removed isolated pixels for a conservative estimate of change. This script yields per region files that are later compiled into \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_popsensitivity.csv\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003emain_sensitivityanalysis.R\u003c/code\u003e - R script that compiles individual region files generated from \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003emain_popsensitivity.txt\u003c/code\u003e and then calculates a bias factor. This script additionally joins the bias factor to a number of datasets including \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_popsummary.csv\u003c/code\u003e and \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_floodmechanism.csv\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003euncertaintyanalysis.R\u003c/code\u003e - R script that estimates uncertainty in population trend estimates per country using the population dataset \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\GFDabove_13_wBias.csv\u003c/code\u003e. It identifies countries we deem uncertainty and reproduces Figure 2 in the Supplementary discussion. This script recalcualtes the global flood exposure trend analysis removing the \"uncertain\" countries.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch4 id=\"flood-mechanism-1\"\u003eFlood Mechanism\u003c/h4\u003e\n\u003col start=\"12\"\u003e\n\u003cli\u003e\u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003emain_floodmechanism.txt\u003c/code\u003e - uses GEE Code Editor to disaggregate the Global Flood database into flood plains representing different causes/ drivers. Population exposure is calculated using the Global Human Settlement Layer (GHSL) for 2000 and 2015. This script yields per mechanism files that are later compiled into \u003ccode style=\"font-family: Menlo, Consolas, \u0026quot;DejaVu Sans Mono\u0026quot;, monospace;\"\u003edata\\gfd_floodmechanism.csv\u003c/code\u003e.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cimg alt=\"\" src=\"C:\\Users\\jonat\\Documents\\github\\projects\\gfd_nature\\assets\\validation_gui.jpg\"\u003e\n\u003cem\u003eFigure 1.\u003c/em\u003e Example of the GFD Validaiton GUI\u003c/p\u003e\u003c/body\u003e\n      \u003c/html\u003e\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/1113","html_url":"https://ost.ecosyste.ms/projects/1113"}