Impact model: PCR-GLOBWB

The global hydrological model PCR-GLOBWB simulates for each grid cell (0.5 degree globally over the land) and for each time step (daily) the water storage in two vertically stacked soil layers and an underlying groundwater layer, as well as the water exchange between the layers (infiltration, percolation, and capillary rise) and between the top layer and the atmosphere (rainfall, evapotranspiration, and snow melt). The model also calculates canopy interception and snow storage. Water use for agriculture, industry and households is dynamically linked to hydrological simulation at a daily time step. The simulated local direct runoff, interflow, and baseflow are routed along the river network that is also linked to water allocation and reservoir operation scheme. PCR-GLOBWB is one of the 13 global hydrology models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a global water sector outputs; for a full technical description of the ISIMIP2a Simulation Data from Water (global) Sector, see this DOI link: http://doi.org/10.5880/PIK.2017.010

Sector
Water (global)
Region
global
Contact Person
  • Yoshihide Wada (wada@iiasa.ac.at), International Institute for Applied Systems Analysis (Austria)

Information for the model PCR-GLOBWB is provided for the simulation rounds shown in the tabs below. Click on the appropriate tab to get the information for the simulation round you are interested in.

Basic information
Model Version: version 2
Reference Paper: Main Reference: Wada Y, Wisser D, Bierkens M et al. Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources. Earth System Dynamics,5,15-40,2014
Reference Paper: Other References:
Output Data
Experiments: I, II, III
Climate Drivers: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Date: 2017-10-05
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Additional Spatial Aggregation & Resolution Information: sub-grid variability for vegetation, land cover, etc
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Co2: annual
Temporal Resolution Of Input Data: Land Use/Land Cover: annual
Temporal Resolution Of Input Data: Soil: constant
Input data sets used
Simulated Atmospheric Climate Data Sets Used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Emissions Data Sets Used: CO2 concentration
Socio-Economic Data Sets Used: Historical, country-level population, Future, gridded Gross Domestic Product (GDP), Future, country-level Gross Domestic Product (GDP), Future, country-level population, Future, gridded population, Historical, country-level Gross Domestic Product (GDP), Historical, gridded population
Land Use Data Sets Used: Historical, gridded land use (HYDE 3.2)
Other Human Influences Data Sets Used: Water abstraction for domestic and industrial uses
Other Data Sets Used: Land-sea mask, GRanD reservoirs & dams, River-routing network
Climate Variables: ta, tasmax, tas, tasmin, pr
Additional Input Data Sets: GLWD for lakes and wetlands combined with GRanD reservoir dataset
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: 100 year spin-up before the model simulation with climate forcing provided
Natural Vegetation
Natural Vegetation Partition: Land use and land cover prescribed by HYDE dataset and MIRCA, GLOBCOVER (annually prescribed, not dynamic)
Natural Vegetation Dynamics: Land use and land cover prescribed by HYDE dataset and MIRCA, GLOBCOVER (annually prescribed, not dynamic)
Natural Vegetation Cover Dataset: GLOBCOVER
Management & Adaptation Measures
Management: no
Technological Progress
Technological Progress: GDP changes and technological development have been integrated in the caluclation of water use (industrial and domestic sector)
Soil
Soil Layers: 2 soil layers and 1 groundwater layer
Water Use
Water-Use Types: Irrigation, domestic, industry, livestock
Water-Use Sectors: Irrigation, domestic, industry, livestock
Routing
Runoff Routing: Travel time routing (charateristic distance) linked with dynamic reservoir operation
Routing Data: DDM30 combined with GLWD dataset
Land Use
Land-Use Change Effects: Irrigation, rainfed, natural vegetation (tall and short classes)
Dams & Reservoirs
Dam And Reservoir Implementation: GRanD data
Vegetation
How Is Vegetation Represented?: Annually prescribed (daily characteristics) consistently with land cover change
Methods
Potential Evapotranspiration: Hamon method
Snow Melt: Degree-day method
Basic information
Model Version: version 2
Reference Paper: Main Reference: Wada Y, Wisser D, Bierkens M et al. Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources. Earth System Dynamics,5,15-40,2014
Reference Paper: Other References:
Output Data
Experiments: historical
Climate Drivers: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Date: 2016-05-10
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Additional Spatial Aggregation & Resolution Information: sub-grid variability for vegetation, land cover, etc
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Co2: not used
Temporal Resolution Of Input Data: Land Use/Land Cover: annual
Temporal Resolution Of Input Data: Soil: constant
Input data sets used
Observed Atmospheric Climate Data Sets Used: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Emissions Data Sets Used: CO2 concentration
Land Use Data Sets Used: Historical, gridded land use (HYDE 3.0)
Other Data Sets Used: Land-sea mask, GRanD reservoirs & dams, River-routing network
Climate Variables: tas, pr
Additional Input Data Sets: GLWD for lakes and wetlands combined with GRanD reservoir dataset
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: 50 year spin-up before the model simulation starts at 1901 with climate forcing provided
Natural Vegetation
Natural Vegetation Partition: Land use and land cover prescribed by HYDE dataset and MIRCA, GLOBCOVER (annually prescribed, not dynamic)
Management & Adaptation Measures
Management: no
Extreme Events & Disturbances
Key Challenges: Discharge variability is relatively well reproduced (in relative sense of max-min) but absolute amounts of low flows in arid regions tend to be less well reproduced due to model-specific uncertainty.
Technological Progress
Technological Progress: GDP changes and technological development have been integrated in the caluclation of water use (industrial and domestic sector)
Soil
Soil Layers: 2 soil layers and 1 groundwater layer
Water Use
Water-Use Types: Irrigation, domestic, industry, livestock
Water-Use Sectors: Irrigation, domestic, industry, livestock
Routing
Runoff Routing: Travel time routing (charateristic distance) linked with dynamic reservoir operation (pressoc, varsoc)
Land Use
Land-Use Change Effects: Irrigation, rainfed, natural vegetation (tall and short classes)
Dams & Reservoirs
Dam And Reservoir Implementation: Reservoir operation is dynamically linked with routing
Vegetation
How Is Vegetation Represented?: Annually prescribed (daily characteristics) consistently with land cover change
Methods
Potential Evapotranspiration: Hamon method
Snow Melt: Degree-day method
Basic information
Model Version: version 2
Reference Paper: Main Reference: Wada Y, Wisser D, Bierkens M et al. Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources. Earth System Dynamics,5,15-40,2014
Reference Paper: Other References:
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Additional Spatial Aggregation & Resolution Information: sub-grid variability for vegetation, land cover, etc
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Co2: not used
Temporal Resolution Of Input Data: Land Use/Land Cover: annual
Temporal Resolution Of Input Data: Soil: constant
Input data sets used
Simulated Atmospheric Climate Data Sets Used: GCM atmospheric climate data (Fast Track)
Climate Variables: tas, pr
Additional Input Data Sets: GLWD for lakes and wetlands combined with GRanD reservoir dataset
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: 50 year spin-up before the model simulation with climate forcing provided
Natural Vegetation
Natural Vegetation Partition: Land use and land cover prescribed by MIRCA fixed at the present condition.
Natural Vegetation Dynamics: Static.
Natural Vegetation Cover Dataset: GLCC
Management & Adaptation Measures
Management: no
Extreme Events & Disturbances
Key Challenges: Discharge variability is relatively well reproduced (in relative sense of max-min) but absolute amounts of low flows in arid regions tend to be less well reproduced due to model-specific uncertainty.
Technological Progress
Technological Progress: Dynamic irrigation scheme has feedback with hydrology (soil moisture, groundwater recharge, runoff).
Soil
Soil Layers: 2 soil layers and 1 groundwater layer
Water Use
Water-Use Types: Irrigation
Water-Use Sectors: Irrigation
Routing
Runoff Routing: Travel time routing (charateristic distance) linked with dynamic reservoir operation (pressoc)
Routing Data: DDM30 modified to accommodate GLWD v3 data set
Land Use
Land-Use Change Effects: Irrigation (paddy and non-paddy), and natural vegetation (tall and short classes)
Dams & Reservoirs
Dam And Reservoir Implementation: Reservoir operation is dynamically linked with routing
Vegetation
How Is Vegetation Represented?: Annually prescribed (daily charateristics) consistently with land cover change
Methods
Potential Evapotranspiration: Hamon method
Snow Melt: Degree-day method