Impact model: CLM4.0

CLM4.0 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

Information for the model CLM4.0 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: CLM4 driven by satellite phenology with modifications documented in Leng et al.,2015
Output Data
Experiments: historical
Climate Drivers: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Date: 2016-04-20
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Additional Spatial Aggregation & Resolution Information: multiple soil columns co-existing in a grid cell and allow multiple plant functional types (PFTs) to exist in one soil column in which the dynamics for soil water, soil organic carbon, litter, etc. are represented [Lawrence et al., 2011]
Temporal Resolution Of Input Data: Climate Variables: subdaily: met forcing is temporarily interpolated to drive the model at hourly time steps using the algorithm described in Leng,G., and Q. Tang (2014), Modeling the impacts offuture climate change on irrigation over China: sensitivity to adjustedprojections, J. Hydrometeor., 15,2085–2103.
Temporal Resolution Of Input Data: Co2: constant (no effect on the simulation)
Temporal Resolution Of Input Data: Land Use/Land Cover: constant
Temporal Resolution Of Input Data: Soil: constant
Additional Temporal Resolution Information: Constant CO2 has no effect on the simulation. Meteorological forcing is temporarily interpolated to drive the model at hourly time steps using the algorithm described in Leng,G., and Q. Tang (2014), Modeling the impacts of future climate change on irrigation over China: sensitivity to adjusted projections, J. Hydrometeor., 15, 2085–2103.
Input data sets used
Observed Atmospheric Climate Data Sets Used: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Climate Variables: tas, rlds, wind, rsds, ps, pr
Additional Input Data Sets: aerosol deposition simulated by CCSM, key for snow albedo simulations
Exceptions to Protocol
Exceptions: no, but we did not finish the LULCC runs
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: start the spinup from an existing CLM initial condition provided by NCAR. Then the watch dataset was cycled for 200 years to stablized the state variables again. The final state was then used as the initial conditon for the historical simulation
Natural Vegetation
Natural Vegetation Partition: based on satellite data provided by NCAR. Details can be found at Oleson et al. (2010)
Management & Adaptation Measures
Management: Irrigation. See Leng et al., 2015 for details
Technological Progress
Technological Progress: No
Soil
Soil Layers: 15 soil layers (soil moisture is simulated for the first 10 layers)
Water Use
Water-Use Types: Irrigation
Water-Use Sectors: No
Routing
Runoff Routing: Linear reservoir, constant flow velocity
Land Use
Land-Use Change Effects: No
Dams & Reservoirs
Dam And Reservoir Implementation: No
Vegetation
How Is Vegetation Represented?: Fixed monthly plant characteristics
Methods
Potential Evapotranspiration: No, evapotranspiration is solved based on energy balance equations and scaled by soil moisture at the subgrid level, and weighted averaged over each grid cell.
Snow Melt: A physically based snow module.
Basic information
Model Version: CLM4 driven by satellite phenology with modifications documented in Leng et al.,2015
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Additional Spatial Aggregation & Resolution Information: multiple soil columns co-existing in a grid cell and allow multiple plant functional types (PFTs) to exist in one soil column in which the dynamics for soil water, soil organic carbon, litter, etc. are represented [Lawrence et al., 2011]
Temporal Resolution Of Input Data: Soil: constant
Additional Temporal Resolution Information: Constant CO2 has no effect on the simulation. Meteorological forcing is temporarily interpolated to drive the model at hourly time steps using the algorithm described in Leng,G., and Q. Tang (2014), Modeling the impacts of future climate change on irrigation over China: sensitivity to adjusted projections, J. Hydrometeor., 15, 2085–2103.
Input data sets used
Additional Input Data Sets: aerosol deposition simulated by CCSM, key for snow albedo simulations
Spin-up
Was A Spin-Up Performed?: Yes
Natural Vegetation
Natural Vegetation Partition: based on satellite data provided by NCAR. Details can be found at Oleson et al. (2010)
Management & Adaptation Measures
Management: Irrigation. See Leng et al., 2015 for details
Technological Progress
Technological Progress: No
Soil
Soil Layers: 15 soil layers (soil moisture is simulated for the first 10 layers)
Water Use
Water-Use Types: Irrigation
Water-Use Sectors: No
Routing
Runoff Routing: Linear reservoir, constant flow velocity
Land Use
Land-Use Change Effects: No
Dams & Reservoirs
Dam And Reservoir Implementation: No
Vegetation
How Is Vegetation Represented?: Fixed monthly plant characteristics
Methods
Potential Evapotranspiration: No, evapotranspiration is solved based on energy balance equations and scaled by soil moisture at the subgrid level, and weighted averaged over each grid cell.
Snow Melt: A physically based snow module.