Impact model: DLEM (Dynamic Land Ecosystem Model)

DLEM is one of the 8 global models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a biome sector outputs; for a full technical description of the ISIMIP2a Simulation Data from Biomes Sector, see this DOI link: http://doi.org/10.5880/PIK.2017.002

Sector
Biomes
Region
global
Contact Person

Information for the model DLEM (Dynamic Land Ecosystem Model) 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: v2.0
Reference Paper: Main Reference: Tian, Hanqin, Guangsheng Chen, Chaoqun Lu, Xiaofeng Xu, Daniel J. Hayes, Wei Ren, Shufen Pan, Deborah N. Huntzinger, and Steven C. Wofsy et al. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget. Climatic change,129,,
Reference Paper: Other References:
Person Responsible For Model Simulations In This Simulation Round: Hanqin Tian
Output Data
Experiments: I, II, IIa, III, IIIa
Climate Drivers: IPSL-CM5A-LR, GFDL-ESM2M, MIROC5
Date: 2017-09-18
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: annual
Temporal Resolution Of Input Data: Land Use/Land Cover: annual
Temporal Resolution Of Input Data: Soil: constant
Input data sets used
Simulated Atmospheric Climate Data Sets Used: IPSL-CM5A-LR, GFDL-ESM2M, MIROC5
Emissions Data Sets Used: CO2 concentration
Land Use Data Sets Used: Future land-use patterns, Historical, gridded land use (HYDE 3.2)
Other Human Influences Data Sets Used: N-Fertilizer, Nitrogen deposition
Other Data Sets Used: Land-sea mask
Climate Variables: tasmax, tas, tasmin, rsds, pr
Additional Input Data Sets: Vegetation data and nitrogen deposition are from the SYNMAP BIOME data, provided by MstMIP project (https://daac.ornl.gov/NACP/guides/NACP_MsTMIP_Model_Driver.html)
Exceptions to Protocol
Exceptions: selected monthly output only, grid-level output, rather than PFT level output
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: Before the transient simulation, we have equilibrium run and model spin-up. In the equilibrium run, we used the average climate condition during 1661-1690, and other input data (CO2 concentration, nitrogen deposition, vegetation type, land use and land management) at the level of year 1660 to run the model. We assumed the equilibrium state was reached when the differences of carbon, nitrogen, and water content at grid level are less than 0.5 g C/m2, 0.5 g N/m2, and 0.5 mm between two consecutive 50-year periods. The model determines how many model years the equilibrium run takes. The 100-year spin-up run follows equilibrium run. During this stage, model randomly selected climate conditions from the 30-year period of 1661-1690 to drive the model, while other driving forces (CO2 concentration, nitrogen deposition, land use and land cover) were kept at the level of 1661. When the equilibrium run and spin-up were done, the model started the transient run from 1661 to the last year of climate data. The transient run is forced by all the time-series of driving factors.
Natural Vegetation
Natural Vegetation Partition: The DLEM assume up to four natural vegetation types coexist in one grid. The DLEM did not simulate “natural vegetation”, but use SYNMAP BIOME classification data to drive our model, which provided the percentage of various vegetation types in each grid. For cropland, we use a crop system map to determine crop types (eg. wheat, rice, corn, etc) and rotation.
Natural Vegetation Dynamics: The fraction of natural vegetation was estimated according to cropland area in each grid.
Natural Vegetation Cover Dataset: SYNMAP
Management & Adaptation Measures
Management: For cropland simulation, we used prescribed data on nitrogen fertilizer use and irrigation to force DLEM model.
Extreme Events & Disturbances
Key Challenges: The current version of DLEM is capable of catching drought, heat wave and extreme cold, but unable to simulate flooding impact on ecosystems.
Model output specifications
Output Format: DLEM output is based on per grid-cell area. when calculating global or regional total, one should multiple by grid area.
Output Per Pft?: DLEM output is based on grid-cell area. We did not provide output for each PFT type.
Key model processes
Dynamic Vegetation: no
Nitrogen Limitation: yes
Co2 Effects: yes, Farquhar/Collatz photosynthesis
Light Interception: big-leaf apporach
Light Utilization: Farquhar/Collatz photosynthesis
Phenology: Fixed annual phenology based on satellite observation
Water Stress: Influence on photosynthesis, carbon allocation, evapotranspiration, and soil biogeochemical processes
Heat Stress: Influence on photosynthesis and repisration
Evapo-Transpiration Approach: Penman-Monteith equation, with the consideration of soil moisture availability
Differences In Rooting Depth: no
Root Distribution Over Depth: PFT-specific root distribution by following Zeng 2001, "Global vegetation root distribution for land modeling"
Permafrost: yes
Closed Energy Balance: DLEM does not calculate energy balance at land surface
Coupling/Feedback Between Soil Moisture And Surface Temperature: yes
Latent Heat: yes, latent heat is based on ET (Penman-monteith equation)
Sensible Heat: no
Causes of mortality in vegetation models
Age: yes, DLEM set a maximum age (AGEmax) for each PFT type. In each year, DLEM assume 1/AGEmax of this vegetation die due to aging effect.
Fire: no
Drought: no
Insects: no
Storm: no
Stochastic Random Disturbance: no
Other: no
Remarks: DLEM has a process-based fire module to estimate burned area, fire emissions, and fire mortality. However, we did not switch on Fire module in the ISIMIP simulation.
NBP components
Fire: no
Land-Use Change: yes, DLEM assumes that part of biomass transferred to litter and woody debris pool; part of them transferred to product pools; and the left directly released to the atmosphere due to burning.
Harvest: 1. harvested crop biomass tranferred to product pool. Crop residue and straw biomass tranferred to litter pool. 2. no forest harvest. 3. no grassalnd harvest.
Species / Plant Functional Types (PFTs)
List Of Species / Pfts: Tundra (T); Boreal Broadleaf Deciduous Forest (BBDF); Boreal Needleleaf Evergreen Forest (BNEF); Boreal Needleleaf Deciduous Forest (BNDF); Temperate Broadleaf Deciduous Forest (TBDF); Temperate Broadleaf Evergreen Forest (TBEF); Temperate Needleleaf Evergreen Forest (TNEF); Temperate Needleleaf Deciduous Forest (TNDF); Tropical Broadleaf Deciduous Forest (TrBDF); Tropical Broadleaf Evergreen Forest (TrBEF); Deciduous Shrub (Dshrub); Evergreen Shrub (Eshrub); C3 grassland (C3G); C4 grassland (C4G); Cropland
Comments: Cropland (14 types including: Barly, Cassava, Cotton, Corn, Millet, Potato, Rapeseed, Rice, Sorghum, Soybeen, Sugarcane, Sunflower, Wheat, Oat), provided by Hanqin Tian - 30-10-2015
Land-use change implementation
Is crop harvest included? If so, how?: yes. Grain was be harvested at the end of growing season
Is cropland soil management included? If so, how?: no
Is grass harvest included? If so, how?: no
Is shifting cultivation included?: no
Is wood harvest included? If so, how?: no
Carbon-cycle benchmarking
Does your model reach a (near) steady state after spin up (characterized by NBP of < 0.2 PgC y-1)? (Yes/no, provide number): yes. global NBP < 0.1 Pg C yr-1.
What is your modeled NBP for the 1990-2000 decade? Is it within 1.2 +/- 0.8 GtC/yr (1-sigma) of observed data from O2/N2 trends (Keeling and Manning 2014) for 1990-1999 (Yes/no, provide number): yes.
Basic information
Model Version: v2.0
Reference Paper: Main Reference: Tian, Hanqin, Guangsheng Chen, Chaoqun Lu, Xiaofeng Xu, Daniel J. Hayes, Wei Ren, Shufen Pan, Deborah N. Huntzinger, and Steven C. Wofsy et al. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget. Climatic change,129,,
Reference Paper: Other References:
Output Data
Experiments: historical
Climate Drivers: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Date: 2016-04-28
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: annual
Temporal Resolution Of Input Data: Land Use/Land Cover: annual
Temporal Resolution Of Input Data: Soil: constant
Input data sets used
Observed Atmospheric Climate Data Sets Used: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Climate Variables: tasmax, tas, tasmin, rsds, pr
Additional Input Data Sets: Vegetation data and nitrogen deposition are from the SYNMAP BIOME data, provided by MstMIP project (https://daac.ornl.gov/NACP/guides/NACP_MsTMIP_Model_Driver.html)
Exceptions to Protocol
Exceptions: selected monthly output only, grid-level output, rather than PFT level output
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: Before the transient simulation, we have equilibrium run and model spin-up. In the equilibrium run, we used the average climate condition during 1901-1930, and other input data (CO2 concentration, nitrogen deposition, vegetation type, land use and land management) at the level of year 1901 to run the model. We assumed the equilibrium state was reached when the differences of carbon, nitrogen, and water content at grid level are less than 0.5 g C/m2, 0.5 g N/m2, and 0.5 mm between two consecutive 50-year periods. The model determines how many model years the equilibrium run takes. The 100-year spin-up run follows equilibrium run. During this stage, model randomly selected climate conditions from the 30-year period of 1901-1930 to drive the model, while other driving forces (CO2 concentration, nitrogen deposition, land use and land cover) were kept at the level of 1901. When the equilibrium run and spin-up were done, the model started the transient run from 1901 to the last year of climate data. The transient run is forced by all the time-series of driving factors.
Natural Vegetation
Natural Vegetation Partition: The DLEM assume up to four natural vegetation types coexist in one grid. The DLEM did not simulate “natural vegetation”, but use SYNMAP BIOME classification data to drive our model, which provided the percentage of various vegetation types in each grid. For cropland, we use a crop system map to determine crop types (eg. wheat, rice, corn, etc) and rotation.
Management & Adaptation Measures
Management: For cropland simulation, we used prescribed data on nitrogen fertilizer use and irrigation to force DLEM model.
Extreme Events & Disturbances
Key Challenges: The current version of DLEM is capable of catching drought, heat wave and extreme cold, but unable to simulate flooding impact on ecosystems.
Model output specifications
Output Format: DLEM output is based on per grid-cell area. when calculating global or regional total, one should multiple by grid area.
Output Per Pft?: DLEM output is based on grid-cell area. We did not provide output for each PFT type.
Key model processes
Dynamic Vegetation: no
Nitrogen Limitation: yes
Co2 Effects: yes, Farquhar/Collatz photosynthesis
Light Interception: big-leaf apporach
Light Utilization: Farquhar/Collatz photosynthesis
Phenology: Fixed annual phenology based on satellite observation
Water Stress: Influence on photosynthesis, carbon allocation, evapotranspiration, and soil biogeochemical processes
Heat Stress: Influence on photosynthesis and repisration
Evapo-Transpiration Approach: Penman-Monteith equation, with the consideration of soil moisture availability
Differences In Rooting Depth: no
Root Distribution Over Depth: PFT-specific root distribution by following Zeng 2001, "Global vegetation root distribution for land modeling"
Permafrost: yes
Closed Energy Balance: DLEM does not calculate energy balance at land surface
Coupling/Feedback Between Soil Moisture And Surface Temperature: yes
Latent Heat: yes, latent heat is based on ET (Penman-monteith equation)
Sensible Heat: no
Causes of mortality in vegetation models
Age: yes, DLEM set a maximum age (AGEmax) for each PFT type. In each year, DLEM assume 1/AGEmax of this vegetation die due to aging effect.
Fire: no
Drought: no
Insects: no
Storm: no
Stochastic Random Disturbance: no
Other: no
Remarks: DLEM has a process-based fire module to estimate burned area, fire emissions, and fire mortality. However, we did not switch on Fire module in the ISIMIP simulation.
NBP components
Fire: no
Land-Use Change: yes, DLEM assumes that part of biomass transferred to litter and woody debris pool; part of them transferred to product pools; and the left directly released to the atmosphere due to burning.
Harvest: 1. harvested crop biomass tranferred to product pool. Crop residue and straw biomass tranferred to litter pool. 2. no forest harvest. 3. no grassalnd harvest.
Species / Plant Functional Types (PFTs)
List Of Species / Pfts: Tundra (T); Boreal Broadleaf Deciduous Forest (BBDF); Boreal Needleleaf Evergreen Forest (BNEF); Boreal Needleleaf Deciduous Forest (BNDF); Temperate Broadleaf Deciduous Forest (TBDF); Temperate Broadleaf Evergreen Forest (TBEF); Temperate Needleleaf Evergreen Forest (TNEF); Temperate Needleleaf Deciduous Forest (TNDF); Tropical Broadleaf Deciduous Forest (TrBDF); Tropical Broadleaf Evergreen Forest (TrBEF); Deciduous Shrub (Dshrub); Evergreen Shrub (Eshrub); C3 grassland (C3G); C4 grassland (C4G); Cropland
Comments: Cropland (14 types including: Barly, Cassava, Cotton, Corn, Millet, Potato, Rapeseed, Rice, Sorghum, Soybeen, Sugarcane, Sunflower, Wheat, Oat), provided by Hanqin Tian - 30-10-2015