Global monthly sector-specific clean water gaps and driving mechanisms (QUAlloc v2.0, PCR-GLOBWB2, DynQual v1.0) at 10 km spatial resolution

General This repository provides simulated data on (clean) water gaps and their driving mechanisms for five key water use sectors (i.e., domestic, irrigation, livestock, manufacturing and thermoelectric). The simulations were performed using the sectoral water use and allocation model QUAlloc v2.0, fully integrated to the hydrological model PCR-GLOBWB2 and the surface water quality model DynQual v1.0. QUAlloc v2.0 can be found at: https://github.com/SustainableWaterSystems/QUAlloc. Dataset properties spatial resolution: 10 km (global-scale) temporal resolution: monthly and monthy time-step period: 1980 - 2019 units: m3/month or m3/year (for water gap datasets) Folder structure main folder level: groups datasets in water gaps (i.e., "water_gaps" folder) and their driving mechanisms (i.e., "drivers" folder). subfolder level: groups datasets by their temporal resolution (i.e., "monthly" or "yearly" folders). water_gaps monthly <data_type>water_gap_<sector_name>_m3_month_1980_2019.nc yearly <data_type>water_gap_<sector_name>_m3_year_1980_2019.nc drivers monthly water_gap_drivers_<sector_name>_monthly_1980_2019.nc yearly water_gap_drivers_<sector_name>_yearly_1980_2019.nc Folder content Generic output dataset name: <data_type>water_gap_<sector_name>_m3_<time_step>_1980_2019.nc <data_type> "clean_": refers to the water gaps estimated when accounting for sector-specific water quality requirements; absence of this tag refers to water gaps disregarding water quality. <sector_name> "total": aggregation of all sectoral water gaps "domestic" "irrigation" "livestock" "manufacture" "thermoelectric" Formats All datasets are in netCDF4 format, so they can be opened with Python (e.g., xarray package) or with the Climate Data Operators (CDO) command-line tools.