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Impact model: LPJmL

Sector
Water (global)
Region
global

LPJmL is a multi-sectoral Dynamic Global Vegetation Model, suited to address the water sector as it includes the full terrestrial water balance with irrigation modules.
The model version used in ISIMIP2b is different from the ISIMIP2a version. Among other changes, the current version features the differentiation of three irrigation system types and a more process-based representation of irrigation efficiencies.

Information for the model LPJmL 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
Sebastian Ostberg: ostberg@pik-potsdam.de, 0000-0002-2368-7015, Potsdam Institute for Climate Impact Research (Germany)
Output Data
Experiments: I, II, III, IV, V, VI, VII, VIII
Climate Drivers: None
Date: 2018-01-18
Basic information
Model Version: ISIMIP2b version differs from ISIMIP2a version, both in terms of included processes and in terms of the definition of some outputs.
Model Output License: CC BY 4.0
Reference Paper: Main Reference: Sitch S, Smith B, Prentice I, Arneth A, Bondeau A, Cramer W, Kaplan J, Levis S, Lucht W, Sykes M, Thonicke K, Venevsky S et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology,9,161-185,2003
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: co2: annual
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Additional temporal resolution information: Reservoirs are simulated starting from the year they first become operational.
Input data
Simulated atmospheric climate data sets used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Emissions data sets used: Atmospheric CO2 concentration
Land use data sets used: Future land-use patterns, Historical, gridded land use (HYDE 3.2)
Other human influences data sets used: Water abstraction for domestic and industrial uses
Other data sets used: Drainage direction map for river routing, GRanD reservoirs & dams
Climate variables: tas, rsds, pr
Additional information about input variables: Long wave net radiation derived from rlds and tas. If there is more than 1 reservoir in a grid-cell according to GRanD all reservoirs are merged into one and the operational year is set to the first year when at least 50% of the final reservoir capacity is installed.
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 5000 years PNV spin-up recycling picontrol climate 1661-1860 and picontrol CO2 concentration followed by 161 years of land-use spinup also using picontrol climate and CO2.
Natural Vegetation
Natural vegetation partition: dynamic vegetation composition
Natural vegetation dynamics: as in Sitch et al. 2003 (9 PFTs competing for light, water, space)
Management & Adaptation Measures
Management: crop sowing and harvest dates computed internally
Technological Progress
Technological progress: no
Soil
Soil layers: 5 hydrologically and thermally active layers with depth (from surface down) of 20 cm, 30cm, 50cm, 100cm, 100cm. One more thermally active layer of 10m.
Water Use
Water-use types: irrigation (computed internally), domestic and industry prescribed as input provided by ISIMIP2b
Water-use sectors: irrigation, domestic and industry
Routing
Runoff routing: Continuity equation derived from linear reservoir model
Routing data: routing data according to DDM30
Land Use
Land-use change effects: Different partitioning of rainfall depending on vegetation status of managed areas (cropland and pastures)
Dams & Reservoirs
Dam and reservoir implementation: Reservoirs collect water from the river system for distribution to connected irrigation areas. Evaporation from reservoir surface is calculated. Minumum release for environmental flow.
Calibration
Was the model calibrated?: No
Vegetation
Is co2 fertilisation accounted for?: Yes
How is vegetation represented?: Dynamic simulation of growth and productivity (with prescribed spatial distribution of crops and pasture); dynamic vegetation composition on PNV areas
Methods
Potential evapotranspiration: Priestley-Taylor (modified for transpiration)
Snow melt: Degree-day method with precipitation factor
Confirm data for simulation round ISIMIP2a
Person responsible for model simulations in this simulation round
Dieter Gerten: gerten@pik-potsdam.de, 0000-0002-6214-6991, Potsdam Institute for Climate Impact Research (Germany)
Sebastian Ostberg: ostberg@pik-potsdam.de, 0000-0002-2368-7015, Potsdam Institute for Climate Impact Research (Germany)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-04-28
Basic information
Reference Paper: Main Reference: Sitch S, Smith B, Prentice I, Arneth A, Bondeau A, Cramer W, Kaplan J, Levis S, Lucht W, Sykes M, Thonicke K, Venevsky S et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology,9,161-185,2003
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: co2: annual
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Observed atmospheric climate data sets used: GSWP3, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Additional input data sets: GGCMI harmonized planting and maturity datasets (for a subset of simulations)
Climate variables: tas, lwnet, rsds, pr
Additional information about input variables: lwnet derived from tas and rlds
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 5000 years of PNV spin-up, followed by 390 years of land-use spin-up, both recycling 120-year random climate sequence (taken from 1901-1930), spin-up leads into transient run 1901 - end of climate dataset; 278 ppm CO2 used before 1765, after 1765 CO2 from provided input file
Natural Vegetation
Natural vegetation partition: dynamic vegetation distribution
Natural vegetation dynamics: as in Sitch et al. 2004 (9 PFTs competing for light, water, space)
Management & Adaptation Measures
Management: crop sowing dates computed internally but fixed after 1960 in biome, permafrost and water sector runs
Technological Progress
Technological progress: no
Soil
Soil layers: Five hydrologically active soil layers, coupled to carbon and thermal balance
Water Use
Water-use types: irrigation (computed internally), household, industry, livestock (prescribed)
Water-use sectors: two versions available: one with irrigation only, the other including additional water uses; existing runs on server have to be replaced because of setup errors; upload of new data pending on naming decision
Routing
Runoff routing: Continuity equation derived from linear reservoir model, routing data according to DDM30
Land Use
Land-use change effects: Different partitioning of rainfall depending on vegetation status of managed areas (cropland and pastures)
Dams & Reservoirs
Dam and reservoir implementation: Reservoirs collect water from the river system for distribution to connected irrigation areas. Evaporation from reservoir surface is calculated. Minumum release for environmental flow.
Calibration
Was the model calibrated?: No
Vegetation
Is co2 fertilisation accounted for?: Yes
How is vegetation represented?: Dynamic simulation of growth and productivity (with prescribed spatial distribution of crops and pasture); dynamic vegetation composition on PNV areas
Methods
Potential evapotranspiration: Priestley-Taylor (modified for transpiration)
Snow melt: Degree-day method with precipitation factor
Person responsible for model simulations in this simulation round
Dieter Gerten: gerten@pik-potsdam.de, 0000-0002-6214-6991, Potsdam Institute for Climate Impact Research (Germany)
Jens Heinke: heinke@pik-potsdam.de, International Food Policy Research Institute (USA)
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-17




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