Impact model: WaterGAP2-2e

WaterGAP2 is a global water availability and water use model. Some introducing information can be found here: https://en.wikipedia.org/wiki/WaterGAP and at www.watergap.de For participating in ISIMIP FastTrack we have used WaterGAP 2.2 (described in 10.5194/hess-18-3511-2014). WaterGAP2-2ISIMIP2a is a model version which is based on WaterGAP 2.2 (described in 10.5194/hess-18-3511-2014) but applied to the specifics of the simulation round ISIMIP2a. WaterGAP2-2c is a model version (WaterGAP 2.2c, described in 10.5194/hess-20-2877-2016) used for phase ISIMIP2b. We have re-run ISIMIP2a with WaterGAP2-2c to allow consistent assessments with ISIMIP2b simulation outputs. For ISIMIP3, we used WaterGAP2-2e (WaterGAP 2.2e) which is the successor of WaterGAP 2.2d (described in 10.5194/gmd-14-1037-2021).

Sector
Water (global)
Region
global

Information for the model WaterGAP2-2e 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.

Person responsible for model simulations in this simulation round
Tim Trautmann: t.trautmann@em.uni-frankfurt.de, 0000-0001-8652-6836, Goethe University Frankfurt (Germany)
Additional persons involved: Hannes Müller Schmied
Output Data
Experiments: (*) ssp585_2015soc_default, ssp585_1850soc_default, ssp126_2015soc-from-histsoc_default, picontrol_2015soc-from-histsoc_default, ssp126_2015soc_default, picontrol_2015soc_default, historical_histsoc_default, ssp370_1850soc_default, ssp126_1850soc_default, historical_2015soc_default, ssp370_2015soc-from-histsoc_default, ssp370_2015soc_default, ssp585_2015soc-from-histsoc_default, picontrol_histsoc_default, historical_1850soc_default, picontrol_1850soc_default
Climate Drivers: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL
Date: 2022-04-21
Basic information
Model Version: WaterGAP2.2e
Model Output License: CC BY 4.0
Model Homepage: http://watergap.de/
Simulation Round Specific Description: * Data in embargo period, not yet publicly available
Reference Paper: Main Reference: Müller Schmied H, Cáceres D, Eisner S, Flörke M, Herbert C, Niemann C, Peiris T, Popat E, Portmann F, Reinecke R, Schumacher M, Shadkam S, Telteu C, Trautmann T, Döll P et al. The global water resources and use model WaterGAP v2.2d: model description and evaluation. Geoscientific Model Development,14,1037-1079,2021
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Soil: constant
Additional Temporal Resolution Information: land cover from IGBP-classification based on MODIS land cover from the year 2004. Soil data from WISE Available Water Capacity (Batjes, 2012)
Input data sets used
Simulated Atmospheric Climate Data Sets Used: MRI-ESM2-0, IPSL-CM6A-LR, MPI-ESM1-2-HR, UKESM1-0-LL, GFDL-ESM4
Climate Variables: tas, rlds, rsds, pr
Additional Information About Input Variables: water use data described here: Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F., Alcamo, J. (2013): Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study. Global Environ. Change, 23, 144-156. doi:10.1016/j.gloenvcha.2012.10.018.
Exceptions to Protocol
Exceptions: Land use / land cover change is only considered in terms of annual varying irrigation areas (up to the year 2008). Location of reservoirs are adapted to the reqirements of the model.
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: Spin-up is used only for the pre-industrial time periods by repeating 5 times the first year. The remaining periods (historical, future) are initialized by the previous periods. We have no CO2 concentration included in the model.
Natural Vegetation
Natural Vegetation Partition: We only distinguishing IGBP classes based on MODIS data for the year 2004.
Natural Vegetation Dynamics: A very simplified LAI development model for canopy evaporation is included (see appendix of Müller Schmied et al., 2014, HESS and Müller Schmied et al., 2021, GMD)
Soil Layers: 1 soil layer with land-cover dependend static depth
Management & Adaptation Measures
Management: The model includes yearly varying irrigation area up to the year 2008. After 2008, irrigation areas are kept constant.
Extreme Events & Disturbances
Key Challenges: Correct simulation of low flows and high flows, from a climatic perspective modelling in dry regions; (climate) input data uncertainty, process representation in the model(s); WaterGAP tends to underestimate varability (river discharge, total water storage)
Technological Progress
Technological Progress: Via water use models (details in Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F., and Alcamo, J.: Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study, Global Environ. Change, 23, 144–156, doi: 10.1016/j.gloenvcha.2012.10.018, 2013.)
Soil
Soil Layers: 1 layer with 0.1-4 m depth (depending on land use)
Water Use
Water-Use Types: domestic, manufacturing, cooling of thermal power plant, livestock and irrigation sectors
Water-Use Sectors: irrigation, domestic, manufacturing, electricity, livestock
Routing
Runoff Routing: Linear reservoir, flow velocity based on Manning-Strickler
Routing Data: DDM30
Land Use
Land-Use Change Effects: Only static land use map is used (IGBP classification based on MODIS data from the year 2004). Irrigation area is varying from year to year up to 2008, aferwards it is kept constant.
Dams & Reservoirs
Dam And Reservoir Implementation: Water balance of reservoirs and big lakes are calculated in the grid cell that represents the outlet; reservoir operation years are considered (see Müller Schmied 2017, appenix 2 of http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/44073); reservoir algorithm after Hanasaki et al. (2006), described in Döll & Fiedler (2008), HESS, see also Müller Schmied et al. (2021)
Calibration
Was The Model Calibrated?: True
Which Years Were Used For Calibration?: Years where GRDC data are available for the specific basin (from 1920-2019) but max. 30 years with a preferred time span from 1981-2010
Which Dataset Was Used For Calibration?: GSWP3-W5E5
How Many Catchments Were Callibrated?: 1509 basins, covering ~55% of global land surface (except Antarctica and Greenland)
Vegetation
How Is Vegetation Represented?: Temperature and precipitation based LAI-model, fixed rooting depth
Methods
Potential Evapotranspiration: Priestley Taylor with two alpha factors depending on the aridity of the grid cell
Snow Melt: Degree-day method
Person responsible for model simulations in this simulation round
Tim Trautmann: t.trautmann@em.uni-frankfurt.de, 0000-0001-8652-6836, Goethe University Frankfurt (Germany)
Additional persons involved: Hannes Müller Schmied
Output Data
Experiments: (*) obsclim_1901soc_default, counterclim_1901soc_default, obsclim_2015soc_default, obsclim_histsoc_nowatermgt, counterclim_histsoc_default, obsclim_histsoc_default, counterclim_2015soc_default
Climate Drivers: 20CRV3-ERA5, 20CRV3-W5E5, GSWP3-W5E5
Date: 2022-04-12
Basic information
Model Version: WaterGAP2.2e
Model Output License: CC BY 4.0
Model Homepage: http://watergap.de/
Simulation Round Specific Description: * Data in embargo period, not yet publicly available
Reference Paper: Main Reference: Müller Schmied H, Cáceres D, Eisner S, Flörke M, Herbert C, Niemann C, Peiris T, Popat E, Portmann F, Reinecke R, Schumacher M, Shadkam S, Telteu C, Trautmann T, Döll P et al. The global water resources and use model WaterGAP v2.2d: model description and evaluation. Geoscientific Model Development,14,1037-1079,2021
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Soil: constant
Additional Temporal Resolution Information: land cover from IGBP-classification based on MODIS land cover from the year 2004. Soil data from WISE Available Water Capacity (Batjes, 2012)
Input data sets used
Observed Atmospheric Climate Data Sets Used: GSWP3-W5E5
Climate Variables: tas, rlds, rsds, pr
Additional Information About Input Variables: water use data described here: Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F., Alcamo, J. (2013): Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study. Global Environ. Change, 23, 144-156. doi:10.1016/j.gloenvcha.2012.10.018.
Exceptions to Protocol
Exceptions: Land use / land cover change is only considered in terms of annual varying irrigation areas (up to the year 2008). Location of reservoirs are adapted to the reqirements of the model.
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: Spin-up is in our case only to make sure that water storages behaves correctly with respect to the initialisation. We started the simulations with 5 times repeating initial years by repeating the first year (according to the experiment). We have no CO2 concentration included in the model.
Natural Vegetation
Natural Vegetation Partition: We only distinguishing IGBP classes based on MODIS data for the year 2004.
Natural Vegetation Dynamics: A very simplified LAI development model for canopy evaporation is included (see appendix of Müller Schmied et al., 2014, HESS and Müller Schmied et al., 2021, GMD)
Soil Layers: 1 soil layer with land-cover dependend static depth
Management & Adaptation Measures
Management: The model includes yearly varying irrigation area up to the year 2008. After 2008, irrigation areas are kept constant.
Extreme Events & Disturbances
Key Challenges: Correct simulation of low flows and high flows, from a climatic perspective modelling in dry regions; (climate) input data uncertainty, process representation in the model(s); WaterGAP tends to underestimate varability (river discharge, total water storage)
Technological Progress
Technological Progress: Via water use models (details in Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F., and Alcamo, J.: Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study, Global Environ. Change, 23, 144–156, doi: 10.1016/j.gloenvcha.2012.10.018, 2013.)
Soil
Soil Layers: 1 layer with 0.1-4 m depth (depending on land use)
Water Use
Water-Use Types: domestic, manufacturing, cooling of thermal power plant, livestock and irrigation sectors
Water-Use Sectors: irrigation, domestic, manufacturing, electricity, livestock
Routing
Runoff Routing: Linear reservoir, flow velocity based on Manning-Strickler
Routing Data: DDM30
Land Use
Land-Use Change Effects: Only static land use map is used (IGBP classification based on MODIS data from the year 2004). Irrigation area is varying from year to year up to 2008, aferwards it is kept constant.
Dams & Reservoirs
Dam And Reservoir Implementation: Water balance of reservoirs and big lakes are calculated in the grid cell that represents the outlet; reservoir operation years are considered (see Müller Schmied 2017, appenix 2 of http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/44073); reservoir algorithm after Hanasaki et al. (2006), described in Döll & Fiedler (2008), HESS, see also Müller Schmied et al. (2021)
Calibration
Was The Model Calibrated?: True
Which Years Were Used For Calibration?: Years where GRDC data are available for the specific basin (from 1920-2019) but max. 30 years with a preferred time span from 1981-2010
Which Dataset Was Used For Calibration?: GSWP3-W5E5
How Many Catchments Were Callibrated?: 1509 basins, covering ~55% of global land surface (except Antarctica and Greenland)
Vegetation
How Is Vegetation Represented?: Temperature and precipitation based LAI-model, fixed rooting depth
Methods
Potential Evapotranspiration: Priestley Taylor with two alpha factors depending on the aridity of the grid cell
Snow Melt: Degree-day method