Impact model: WaterGAP2

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 WaterGAP2 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 For participating in ISIMIP2b we are using the most up-to-date calibrated WaterGAP 2.2c model version.

Sector
Water (global)
Region
global
Contact Person

Information for the model WaterGAP2 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
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:
Output Data
Experiments: I, II, III
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: Co2: not used
Temporal Resolution Of Input Data: Land Use/Land Cover: annual varying irrigation area
Temporal Resolution Of Input Data: Soil: constant, total water capacity input based on Batjes (2012)
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: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Observed Atmospheric Climate Data Sets Used: EWEMBI
Other Human Influences Data Sets Used: Water abstraction for domestic and industrial uses
Other Data Sets Used: Land-sea mask, 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. Different version of GRanD is used that fits to the needs 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 repeating initial years by repeating the first year. 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)
Management & Adaptation Measures
Management: The model includes yearly varying irrigation area.
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
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.
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 2.2 (ISIMIP2a)
Reference Paper: Other References:
Output Data
Experiments: historical
Climate Drivers: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Date: 2016-11-09
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
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: constant
Temporal Resolution Of Input Data: Soil: Not used (used instead WISE Available Water Capacity (Batjes, 1996))
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, 1996)
Input data sets used
Observed Atmospheric Climate Data Sets Used: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Climate Variables: tas, rlds, rsds, pr
Additional Input Data Sets: GLWD for lakes and wetlands as well as GRanD for lakes and reservoirs
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 in 1901 with 5 repeating initial years. 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 and have a very simplified LAI development model for canopy evaporation.
Management & Adaptation Measures
Management: The model includes yearly varying irrigation area.
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)
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
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.
Dams & Reservoirs
Dam And Reservoir Implementation: Water balance of reservoirs and big lakes are calculated in the grid cell that represents the outlet; reservoirs are in operation in its start year; 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 1971-2000 for WFD, PGFv2, GSWP3; and 1979-2008 for WFD_WFDEI
Which Dataset Was Used For Calibration?: We calibrated WaterGAP for each climate dataset using all water sectors. Additionally, we have homogenized temperature and radiation from WFD for WFDEI to overcome the offset between both forcings (method described in Müller Schmied et al 2016 (HESS, doi:10.5194/hess-2015-527) and calibrated with that; but used the not homogenized forcing WFD_WFDEI from ISI-MIP to run the simulation
How Many Catchments Were Callibrated?: All: 1319 basins, WFD (due to time limitation: 1312 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