Impact model: WaterGAP2-2c

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 will use 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
Contact Person

Information for the model WaterGAP2-2c 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: WaterGAP 2.2c
Model output license: CC-BY-NC-4.0
Model Homepage: http://watergap.de/
Model License: CC-BY-NC-4.0
Reference Paper: Main Reference: Müller Schmied, H., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., Oki, T., Portmann, F. T., Reinecke, R., Riedel, C., Song, Q., Zhang, J., and Döll, P et al. Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use. Hydrology and Earth System Sciences,20,2877-2898,
Reference Paper: Other References:
Person Responsible For Model Simulations In This Simulation Round: Hannes Müller Schmied
Output Data
Experiments: I, II, III, VIII
Climate Drivers: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Date: 2017-08-29
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: MIROC5 (rcp45), HadGEM2-ES (rcp45), IPSL-CM5A-LR (rcp45), GFDL-ESM2M (rcp45), IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Observed Atmospheric Climate Data Sets Used: EWEMBI
Other Data Sets Used: Reservoirs and dams, Land-sea mask, GRanD reservoirs & dams, River-routing network
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.
Additional Input Data Sets: GLWD for lakes and wetlands as well as a WaterGAP version of GRanD for lakes and reservoirs, for differences in ISIMIP2a see: https://www.arcgis.com/home/item.html?id=d966db9c7b2949ac8380458d7020adf9
Exceptions to Protocol
Exceptions: Land use / land cover change is only considered in terms of annual varying irrigation areas (up to the year 2008). Different version of GRanD is used that fits to the requirements of WaterGAP.
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)
Management & Adaptation Measures
Management: The model includes yearly varying irrigation area up to the year 2008. After 2008, irrigation areas for year 2008 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)
Additional Comments: No, details to the model and used input data are described in the reference papers.
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, manufactoring, 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
Calibration
Was The Model Calibrated?: True
Which Years Were Used For Calibration?: Years where GRDC data are available for the specific basin (from 1920-2009) but max. 30 years with a preferred time span from 1979-2008
Which Dataset Was Used For Calibration?: We calibrated WaterGAP for a homogenized WFD_EWEMBI dataset following the approach described in Müller Schmied et al 2016, HESS (10.5194/hess-20-2877-2016)using all water sectors.
How Many Catchments Were Callibrated?: 1319 basins, covering ~54% 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
Basic information
Model Version: WaterGAP 2.2c
Model output license: CC BY-NC 4.0
Model Homepage: http://watergap.de/
Model License: CC-BY-NC-4.0
Reference Paper: Main Reference: Müller Schmied, H., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., Oki, T., Portmann, F. T., Reinecke, R., Riedel, C., Song, Q., Zhang, J., and Döll, P et al. Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use. Hydrology and Earth System Sciences,20,2877-2898,
Reference Paper: Other References:
Person Responsible For Model Simulations In This Simulation Round: Hannes Müller Schmied
Output Data
Experiments: None
Climate Drivers: GSWP3-W5E5, GSWP3-EWEMBI, GSWP3, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Date: 2021-08-25
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, GSWP3-EWEMBI, GSWP3, Historical observed climate data, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Other Data Sets Used: Reservoirs and dams, Land-sea mask, GRanD reservoirs & dams, River-routing network
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.
Additional Input Data Sets: GLWD for lakes and wetlands as well as a WaterGAP version of GRanD for lakes and reservoirs, for differences in ISIMIP2a see: https://www.arcgis.com/home/item.html?id=d966db9c7b2949ac8380458d7020adf9
Exceptions to Protocol
Exceptions: Land use / land cover change is only considered in terms of annual varying irrigation areas (up to the year 2008). Different version of GRanD is used that fits to the requirements of WaterGAP.
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)
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)
Additional Comments: No, details to the model and used input data are described in the reference papers.
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, manufactoring, 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
Calibration
Was The Model Calibrated?: True
Which Years Were Used For Calibration?: Years where GRDC data are available for the specific basin (from 1920-2009) but max. 30 years with a preferred time span from 1979-2008
Which Dataset Was Used For Calibration?: We calibrated WaterGAP for a all climate input dataset individually following the approach described in Müller Schmied et al 2016, HESS (10.5194/hess-20-2877-2016) using all water sectors.
How Many Catchments Were Callibrated?: 1319 basins, covering ~54% 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