Published December 9, 2019 | Version 1.0
Dataset Open

Supplementary data to: Importance and vulnerability of the world's water towers

  • 1. Utrecht University
  • 2. Utrecht University, FutureWater
  • 3. Universidad Mayor de San Andrés, University of Maryland
  • 4. National Geographic Society
  • 5. Wageningen University & Research
  • 6. University of St. Andrews
  • 7. Royal Holloway University of London
  • 8. Global Change Research Institute
  • 9. Institute of Tibetan Plateau Research
  • 10. Universidad de Concepción
  • 11. University of Dayton
  • 12. Planetary Science Institute
  • 13. University of British Columbia
  • 14. Divecha Center for Climate Change
  • 15. University of Maine
  • 16. International Centre for Integrated Mountain Development
  • 17. Agua Sustentable
  • 18. University of California
  • 19. Swiss Federal Research Institute WSL
  • 20. Johns Hopkins University
  • 21. University of Utah
  • 22. University of Zurich
  • 23. International Institute for Applied Systems Analysis
  • 24. State Key Laboratory of Earth Surface Processes and Resource Ecology

Description

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 demand 2001-2014 aggregated to basins
Water_gap_average_annual_basin.tif - Average annual water gap 2001-2014 aggregated to basins
WTU_Demand_DS_P_available.csv - Table listing dowstream water availability per sector per basin
WTU_Demand_indicators.csv - Table listing demand per sector per basin
WTU_Domestic_Water_Gap_monthly.csv - Table listing multi-year average monthly domestic water gap per basin
WTU_Industrial_Water_Gap_monthly.csv - Table listing multi-year average monthly industrial water gap per basin
WTU_Irrigation_Water_Gap_monthly.csv - Table listing multi-year average monthly irrigation water gap per basin
WTU_Natural_Water_Gap_monthly.csv - Table listing multi-year average monthly natural water gap per basin
WTU_Total_Water_Gap_monthly.csv - Table listing multi-year average monthly water gap per basin
  • WTU units

The spatial units for all calculations are the Water Tower Units, their downstream basins, and the entire basins (Water Tower Unit + downstream basin). They are extracted using definitions of basins and mountain ranges. This directory includes:

Output

basins.tif - Definition of basins with Water Tower Units at 0.05 degrees spatial resolution
basins_downstream.tif - Definition of downstream basins at 0.05 degrees spatial resolution
basins_vector.shp - Definition of basins with Water Tower Units as vector data
downstream_vector.shp - Definition of downstream basins as vector data
gmba_all.shp - All GMBA mountain ranges including glacier volume and snow persistence
gmba_ss.shp - GMBA mountain ranges included in Water Tower Units
WTU.tif - Definition of Water Tower Units as 0.05 degrees spatial resolution
WTU_specs.csv - Table with set of specifications of Water Tower Units
WTU_vector.shp - Definition of Water Tower Units as vector data

External data

FAO's classification of major hydrological basins and FAO's classification of subbasins per continent are used. These are based on HydroSheds and are available as shapefiles at FAO Aquamaps

The specific shapefiles used are:

major_hydrobasins.shp
hydrobasins_asia.shp
hydrobasins_southam.shp
hydrobasins_northam.shp
hydrobasins_neareast.shp
hydrobasins_europe.shp
hydrobasins_centralam.shp
hydrobasins_austpacific.shp:

Notes

This project was funded as part of the National Geographic Society and Rolex partnership to support a Perpetual Planet

Files

data_ngs_watertowers.zip

Files (990.2 MB)

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Additional details

Related works

Is supplement to
Journal article: 10.1038/s41586-019-1822-y (DOI)