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

ISIMIP2a
ISIMIP Fast Track

Confirm data for simulation round ISIMIP2a

Person responsible for model simulations in this simulation round

Maoyi Huang: maoyi.huang@pnnl.gov, 0000-0001-9154-9485, PNNL (USA)

Guoyong Leng: guoyong.leng@ouce.ox.ac.uk, 0000-0001-6345-143X, Environmental Change Institute, University of Oxford (UK)

Output Data

Experiments: historical
Climate Drivers: None
Date: 2016-04-20

Basic information

Model Version: CLM4 driven by satellite phenology with modifications documented in Leng et al.,2015

Reference Paper: Main Reference: A modeling study of irrigation effects on global surface water and groundwater resources under a changing climate.

Reference Paper: Other References: Technical description of version 4.0 of the Community Land Model [CLM].

Resolution

Spatial aggregation: regular grid

Horizontal 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

Observed atmospheric climate data sets used: GSWP3, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI

Additional input data sets: aerosol deposition simulated by CCSM, key for snow albedo simulations

Climate variables: tas, rlds, wind, rsds, ps, pr

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

Calibration

Was the model calibrated?: No

Vegetation

Is co2 fertilisation accounted for?: No

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.