This dataset is extracted from AMP CSV file and de-normalized to include location data in separate rows for mapping. The dataset is extracted using the following code https://gist.github.com/anjesh/11110737

OpenStreetMap contains roughly 8.1 million buildings in this region. Based on AI-mapped estimates, this is approximately 90% of the total buildings.The average age of data for this region is 3 years ( Last edited 5 days ago ) and 1% buildings were added or updated in the last 6 months. Read about what this summary means : indicators , metrics

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['building'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• building
• building:levels
• building:materials
• addr:full
• addr:housenumber
• addr:street
• addr:city
• office
• source
• name:ne

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap contains roughly 282.8 million km of roads in this region. Based on AI-mapped estimates, this is approximately 85 % of the total road length in the dataset region. The average age of data for the region is 3 years ( Last edited 5 days ago ) and 4% of roads were added or updated in the last 6 months. Read about what this summary means : indicators , metrics

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['highway'] IS NOT NULL

Features may have these attributes:

• name
• name:en
• highway
• surface
• smoothness
• width
• lanes
• oneway
• bridge
• layer
• source
• name:ne

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.

"People Displaced" refers to the number of people living in displacement as of the end of each year.

"New Displacement" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once.

Contains data from IDMC's data portal.

This archive contains data produced for a study assessing the importance and vulnerability of the world’s water towers. Code (R-scripts) used to process these files is available on the MountainHydrology Github page

The archive is organized in directories with specific topics. Each directory contains input files (optional) and output/processed files. The input files can be used in combination with the R-scripts published on Github to generate the processed files included in this archive. In many cases external published data is used as input data for the calculations. In that case the data is not included in this archive but literature references and links to the specific files are provided in the description below. Files which have been preprocessed before use in the R-scripts are included in this archive. For calculation details please see the publication, in particular Extended Data Tables 3 and 4.

Archive contents

The archives contents are organized in eight separate directories, which are listed here, along with their contents:

• ERA5

Precipitation and evaporation data are extracted from ERA5 reanalysis available online in the Copernicus Climate Data Store at https://cds.climate.copernicus.eu

This directory includes:

Input

ERA5_evaporation_avgannual_2001_2017.nc - Average annual evaporation (mm) for 2001-2017
ERA5_evaporation_ymonmean_2001_2017.nc - Multi-year mean monthly evaporation (mm) for 2001-2017
era5_total-precipitation_ymonmean_2001-2017_global.tif - Multi-year mean monthly precipitation (mm) for 2001-2017
era5_total-precipitation_yearsum_2001-2017.tif - Average annual precipitation (mm) for 2001-2017

Output

P_avg_annual_basin_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to basins
P_avg_annual_DS_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to downstream basins
P_avg_annual_mm.tif - Average annual precipitation 2001-2017 (mm)
P_avg_annual_WT_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to Water Tower Units
P_var_interannual.tif - Interannual variablity in precipitation 2001-2017
P_var_interannual_basin.tif - Interannual variablity in precipitation 2001-2017 aggregated to basins
P_var_interannual_DS.tif - Interannual variablity in precipitation 2001-2017 aggregated to downstream basins
P_var_interannual_WT.tif - Interannual variablity in precipitation 2001-2017 aggregated to Water Tower Units
P_var_intraannual.tif - Intra-annual variablity in precipitation 2001-2017
P_var_intraannual_basin.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to basins
P_var_intraannual_DS.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to downstream basins
P_var_intraannual_WT.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to Water Tower Units
WTU_P_indicators.csv - Table listing all calculated precipition indicators per Water Tower Unit

• Glaciers

Glacier volume and mass balance are derived from published datasets. This directory includes:

Output

Glac_area_WT_km2.tif - Glacier area (km2) aggregated for Water Tower Units
Glac_volume_WT_km3.tif - Glacier volume (km3) aggregated for Water Tower Units
WTU_Glacier_indicators.csv - Table listing all derived glacier indicators per Water Tower Unit
WTU_MB.shp - shapefile of Water Tower Units including the glacier mass balance per Water Tower Units as attribute

External data

Glacier volume data published in Farinotti et al., 2019, Nature Geoscience, were used.
Reference: Farinotti, D. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019).
Glacier volume (km3) and glacier area (km2) at 0.05 degrees spatial resolution were used, which are available here.
The used files are p05_degree_glacier_area_km2.tif and p05_degree_glacier_volume_km3.tif

Glacier mass balance data published by the World Glacier Monitoring Service were used to derive an average glacier mass balance per Water Tower Unit.
References:
Zemp, M. et al. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature 568, 382–386 (2019).
World Glacier Monitoring Service. Fluctuations of Glaciers (FoG) Database. (2018). doi:10.5904/wgms-fog-2018-06

• HydroLAKES

Surface lake and water storage per Water Tower Unit was calculated. This directory includes:

Output

WTU_lake_storage_volume.csv - Table listing lake and reservoir volume (km3) per Water Tower Unit
WTU_surface_water_storage_km3.tif - Lake and reservoir storage volume (km3) aggregated to Water Tower Units

External data

For surface water lakes and reservoirs the HydroLAKES dataset is used. The shapefile HydroLAKES_polys_v10.shp can be downloaded from HydroSheds

Reference: Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 1–11 (2016).

• Indicators

All indicators and subindicators calculated for the Water Tower Index calculation are stored per Water Tower Unit.

This directory includes:

indicators.csv - Table with all indicators and subindicators per Water Tower Unit

• Snow

The MODIS MOD10CM006 snow cover product was used to derive snow persistence.
Reference: Hall, D. K. & Riggs, G. A. MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG, Version 6. (2015). doi:10.5067/MODIS/MOD10CM.006

This archive includes:
Input

MOD10CM006_yearmean_2001-2017.tif - Annual mean snow cover 2001-2017
MOD10CM006_ymonmean_2001-2017.tif - Multi-year mean monthly snow cover 2001-2017

Output

Snow_persistence_avg_annual.tif - Average annual snow persistence 2001-2017
Snow_persistence_avg_annual_WT.tif - Average annual snow persistence 2001-2017 aggregated to Water Tower Units
Snow_persistence_var_interannual.tif - Interannaul variability in snow persistence 2001-2017
Snow_persistence_var_interannual_WT.tif - Interannaul variability in snow persistence 2001-2017 aggregated to Water Tower Units
Snow_persistence_var_intraannual.tif - Intra-annaul variability in snow persistence 2001-2017
Snow_persistence_var_intraannual_WT.tif - Intra-annaul variability in snow persistence 2001-2017 aggregated to Water Tower Units
WTU_Snow_indicators.csv - Table listing all derived snow indicators per Water Tower Unit

• Uncertainty

The directory contains the uncertainty ranges used in the uncertainty analysis
The directory includes:

ET_uncertainty_per_downstream.csv - Table listing SD in evaporation per downstream basin
ET_uncertainty_per_WTU.csv - Table listing SD in evaporation per Water Tower Unit
P_uncertainty_per_downstream.csv - Table listing SD in precipitation per downstream basin
P_uncertainty_per_WTU.csv - Table listing SD in precipitation per Water Tower Unit
WTU_IceVol_uncertainty.csv - Table listing uncertainty in ice volume per Water Tower Unit

• Water demands

Net water demands for irrigation, industrial and domestic water use, as well as the environmental flow requirement are extracted from PCR-GLOBWB hydrological model output.
Reference: Wada, Y., De Graaf, I. E. M. & van Beek, L. P. H. High-resolution modeling of human and climate impacts on global water resources. J. Adv. Model. Earth Syst. 8, 735–763 (2016).

The directory includes:
Input

Dom_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net domestic water demand 2001-2014 at 0.05 degrees resolution (km3)
Ind_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net industrial water demand 2001-2014 at 0.05 degrees resolution (km3)
Irr_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net irrigation water demand 2001-2014 at 0.05 degrees resolution (km3)
Tot_use_ymonmean_2001_2014_005.tif - Sum of the three above
global_historical_riverdischarge_ymonmean_m3second_5min_2001_2014.nc4 - Multi-year mean monthly natural discharge (m3/s) 2001-2014

Output

Domestic_use_avg_annual_basin_km3.tif - Average annual net domestic water demand 2001-2014 aggregated to basins
Domestic_use_avg_annual_km3.tif - Average annual net domestic water demand 2001-2014
Industrial_use_avg_annual_basin_km3.tif - Average annual net industrial water demand 2001-2014 aggregated to basins
Industrial_use_avg_annual_km3.tif - Average annual net industrial water demand 2001-2014
Irrigation_use_avg_annual_basin_km3.tif - Average annual net irrigation water demand 2001-2014 aggregated to basins
Irrigation_use_avg_annual_km3.tif - Average annual net irrigation water demand 2001-2014
Natural_demand_avg_annual_basin_km3.tif - Average annual natural water demand 2001-2014 aggregated to basins
Total_human_demand_avg_annual_basin_km3.tif - Average annual net human (sum of domestic, industrial and irrigation) water

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:
- macroregion (admin-1 including region)
- region (admin-2 including state, province, department, governorate)
- macrocounty (admin-3 including arrondissement)
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)

The dataset also contains human settlement points and polygons for:
- localities (city, town, and village)
- neighbourhoods (borough, macrohood, neighbourhood, microhood)

The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names.

Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

This data was developed as part of the Modelling Exposure Through Earth Observation Routines (METEOR) project and is a Level 1, or a global-quality exposure data set. Minimal country-specific data was collected. The data is intended for CAT modeling and loss estimation. Repurposing this data for any reason other than assessing risk is not recommended. The data presents the estimated number of buildings, building area, and rebuilding value at a 15-arcsecond grid resolution (approximately 500 meters at the equator). This data set is in point shapefile format where the points represent the centroids of the 15-arcsecond grid. The results were created through a process of spreading the number of buildings to the 15-arcsecond level by a statistical assessment of moderate resolution EO data, which is described in more detail in the dasymetric mapping lineage processing step. The estimated building count at any given area is a result of statistical processes and should not be mistaken as a building count. The structural classes of buildings used for risk assessment are estimated given the building wall, floor, and roof material classes surveyed through 2002 Population and Housing Census - Volume 1. Analytical report. Additionally, the data is provided in Open Exposure Data (OED) import format, as a pair of CSV files. One CSV file contains the location details, and the other is an "account" file that is filled with default information to satisfy OED format requirements. The OED input files are set to use "All perils" (i.e. "AA1"). All required OED account-related fields are populated with "1" by default (such as PortNumber, AccNumber, PolNumber).

If you find this data useful please provide feedback via our questionnaire; it should take only a few minutes: https://forms.gle/DQjhE89CRegNKB3X8

Please see the METEOR project page for information about the METEOR Project: http://meteor-project.org/

Please see the METEOR map portal for interactive maps: https://maps.meteor-project.org/

For more information about the Open Exposure Data (OED) standard, please see https://github.com/OasisLMF/OpenDataStandards