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Impact model: WEB-DHM-SG

Sector
Water (global)
Region
global

The Water and Energy Budget-based Distributed biosphere Hydrological Model with improved Snow physics for Global simulation (WEB-DHM-SG) combined the SiB2 land surface model, the GB hydrological model, a physically based snowmelt module from SSiB3, a physically based rain-snow temperature threshold dataset, and spatial distributed river depth and width data. A modifed CaMa-Flood model based on the DDM30 routing scheme is incorporated into WEB-DHM-SG.

Information for the model WEB-DHM-SG 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.

Confirm data for simulation round ISIMIP3b
Person responsible for model simulations in this simulation round
Wei Qi: QiWei_WaterResources@hotmail.com, Southern University of Science and Technology (China)
Output Data
Experiments: (*) picontrol_histsoc_default, historical_2015soc_default, ssp126_2015soc_default, ssp585_2015soc_2015co2, historical_histsoc_default, picontrol_2015soc_default, ssp370_2015soc_default, ssp585_2015soc_default
Climate Drivers: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL
Date: 2023-05-16
Basic information
Model Version: 1.0
Reference Paper: Main Reference: Qi W, Feng L, Yang H, Liu J, Zheng Y, Shi H, Wang L, Chen D et al. Economic growth dominates rising potential flood risk in the Yangtze River and benefits of raising dikes from 1991 to 2015. Environmental Research Letters,17,034046,2022
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Vertically resolved: Yes
Number of vertical layers: Three.
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
Simulated atmospheric climate data sets used: MRI-ESM2-0, IPSL-CM6A-LR, MPI-ESM1-2-HR, UKESM1-0-LL, GFDL-ESM4
Emissions data sets used: Atmospheric composition (ISIMIP3b)
Socio-economic data sets used: Gridded historical land transformation
Land use data sets used: Historical, gridded land use
Other data sets used: Land-sea mask, Drainage direction map for river routing
Additional input data sets: LAI is from VISIT model.
Climate variables: huss, sfcWind, tas, rlds, rsds, ps, pr
Exceptions to Protocol
Exceptions: WEB-DHM-SG does not have “2015soc-from-histsoc” simulations. This is because the current version of WEB-DHM-SG takes months to do such long time simulation. To avoid disturbance because of facility failure, it was separated into two simulations.
Spin-up
Was a spin-up performed?: Yes
Spin-up design: The first 15 years are used to generate initial variables for the model run.
Natural Vegetation
Natural vegetation partition: Multiple vegetation types in each grid.
Natural vegetation dynamics: Dynamic LAI is from the VISIT model. Photosynthesis and leaf stomatal conductance effects are considered through the Ball-Berry model.
Natural vegetation cover dataset: From the SiB2 classification
Soil layers: Three layers. The first layer is 5 cm. The second is the root zone. The second and third layers vary with land use type. Total depth of three moisture layers (m) Broadleaf Evergreen Trees = 3.5 Broadleaf Deciduous Trees = 2.0 Broadleaf and Needleleaf Trees = 2.0 Needleleaf Evergreen Trees = 2.0 Needleleaf Deciduous Trees = 2.0 Short Vegetation/C4 Grassland = 2.0 Shrubs with Bare Soil = 2.0 Dwarf Trees and Shrubs = 2.0 Agriculture or C3 Grassland = 2.0 Rooting depth (m) Broadleaf Evergreen Trees = 2.0 Broadleaf Deciduous Trees = 1.5 Broadleaf and Needleleaf Trees = 1.5 Needleleaf Evergreen Trees = 1.5 Needleleaf Deciduous Trees = 1.5 Short Vegetation/C4 Grassland = 1.5 Shrubs with Bare Soil = 1.5 Dwarf Trees and Shrubs = 1.5 Agriculture or C3 Grassland = 1.5
Management & Adaptation Measures
Management: Time varying land-use and LAI.
Soil
Soil layers: Three layers. The first layer is 5 cm. The second is the root zone. The second and third layers vary with land use type. Total depth of three moisture layers (m) Broadleaf Evergreen Trees = 3.5 Broadleaf Deciduous Trees = 2.0 Broadleaf and Needleleaf Trees = 2.0 Needleleaf Evergreen Trees = 2.0 Needleleaf Deciduous Trees = 2.0 Short Vegetation/C4 Grassland = 2.0 Shrubs with Bare Soil = 2.0 Dwarf Trees and Shrubs = 2.0 Agriculture or C3 Grassland = 2.0 Rooting depth (m) Broadleaf Evergreen Trees = 2.0 Broadleaf Deciduous Trees = 1.5 Broadleaf and Needleleaf Trees = 1.5 Needleleaf Evergreen Trees = 1.5 Needleleaf Deciduous Trees = 1.5 Short Vegetation/C4 Grassland = 1.5 Shrubs with Bare Soil = 1.5 Dwarf Trees and Shrubs = 1.5 Agriculture or C3 Grassland = 1.5
Water Use
Water-use types: No water use.
Water-use sectors: No water use.
Routing
Runoff routing: The CaMa-Flood model is modified based on the DDM30 scheme, and the modified CaMa-Flood model is incorporated into WEB-DHM-SG for runoff routing.
Routing data: DDM30
Land Use
Land-use change effects: Time varying land use. Dynamic LAI is from the Visit model. Photosynthesis and leaf stomatal conductance effects are considered through the Ball-Berry model.
Dams & Reservoirs
Dam and reservoir implementation: No.
Calibration
Was the model calibrated?: No
Vegetation
Is co2 fertilisation accounted for?: Yes
How is vegetation represented?: Dynamic LAI is from the Visit model. Photosynthesis and leaf stomatal conductance effects are considered through the Ball-Berry model. The LAI is influenced by CO2, and therefore CO2 fertilisation influence on water is considered.
Methods
Potential evapotranspiration: Energy Balance
Snow melt: Energy Balance
Confirm data for simulation round ISIMIP3a
Person responsible for model simulations in this simulation round
Wei Qi: QiWei_WaterResources@hotmail.com, Southern University of Science and Technology (China)
Output Data
Experiments: (*) counterclim_histsoc_default, counterclim_1901soc_default, obsclim_1901soc_1901co2, obsclim_histsoc_1901co2, obsclim_2015soc_default, obsclim_histsoc_default, obsclim_2015soc_1901co2, counterclim_2015soc_default, obsclim_1901soc_default
Climate Drivers: GSWP3-W5E5
Date: 2023-03-17
Basic information
Model Version: 1.0
Reference Paper: Main Reference: Qi W, Feng L, Yang H, Liu J, Zheng Y, Shi H, Wang L, Chen D et al. Economic growth dominates rising potential flood risk in the Yangtze River and benefits of raising dikes from 1991 to 2015. Environmental Research Letters,17,034046,2022
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Vertically resolved: Yes
Number of vertical layers: Three.
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-W5E5 (ISIMIP3a)
Emissions data sets used: Atmospheric composition (ISIMIP3a)
Land use data sets used: Historical, gridded land use
Other data sets used: Land-sea mask, Drainage direction map for river routing
Additional input data sets: Soil Hydraulic Properties are from the Future Water HiHydroSoil dataset.
Climate variables: huss, sfcWind, tas, rlds, rsds, ps, pr
Additional information about input variables: LAI from VISIT model is used for obsclim, counterclim, 2015soc, histsoc, 1901soc, default and 1901co2 scenarios.
Spin-up
Was a spin-up performed?: Yes
Spin-up design: The first 15 years are used to generate initial variables for the model run.
Natural Vegetation
Natural vegetation partition: Multiple vegetation types in each grid.
Natural vegetation dynamics: Dynamic LAI is from the VISIT model. Photosynthesis and leaf stomatal conductance effects are considered through the Ball-Berry model.
Natural vegetation cover dataset: The SiB2 land use classification scheme.
Soil layers: Three layers. The first layer is 5 cm. The second is the root zone. The second and third layers vary with land use type. Total depth of three moisture layers (m) Broadleaf Evergreen Trees = 3.5 Broadleaf Deciduous Trees = 2.0 Broadleaf and Needleleaf Trees = 2.0 Needleleaf Evergreen Trees = 2.0 Needleleaf Deciduous Trees = 2.0 Short Vegetation/C4 Grassland = 2.0 Shrubs with Bare Soil = 2.0 Dwarf Trees and Shrubs = 2.0 Agriculture or C3 Grassland = 2.0 Rooting depth (m) Broadleaf Evergreen Trees = 2.0 Broadleaf Deciduous Trees = 1.5 Broadleaf and Needleleaf Trees = 1.5 Needleleaf Evergreen Trees = 1.5 Needleleaf Deciduous Trees = 1.5 Short Vegetation/C4 Grassland = 1.5 Shrubs with Bare Soil = 1.5 Dwarf Trees and Shrubs = 1.5 Agriculture or C3 Grassland = 1.5
Soil
Soil layers: Three layers. The first layer is 5 cm. The second is the root zone. The second and third layers vary with land use type. Total depth of three moisture layers (m) Broadleaf Evergreen Trees = 3.5 Broadleaf Deciduous Trees = 2.0 Broadleaf and Needleleaf Trees = 2.0 Needleleaf Evergreen Trees = 2.0 Needleleaf Deciduous Trees = 2.0 Short Vegetation/C4 Grassland = 2.0 Shrubs with Bare Soil = 2.0 Dwarf Trees and Shrubs = 2.0 Agriculture or C3 Grassland = 2.0 Rooting depth (m) Broadleaf Evergreen Trees = 2.0 Broadleaf Deciduous Trees = 1.5 Broadleaf and Needleleaf Trees = 1.5 Needleleaf Evergreen Trees = 1.5 Needleleaf Deciduous Trees = 1.5 Short Vegetation/C4 Grassland = 1.5 Shrubs with Bare Soil = 1.5 Dwarf Trees and Shrubs = 1.5 Agriculture or C3 Grassland = 1.5
Routing
Runoff routing: The CaMa-Flood model is modified based on the DDM30 scheme, and the modified CaMa-Flood model is incorporated into WEB-DHM-SG for runoff routing.
Routing data: DDM30
Land Use
Land-use change effects: Dynamic LAI is from the Visit model. Photosynthesis and leaf stomatal conductance effects are considered through the Ball-Berry model.
Calibration
Was the model calibrated?: No
Vegetation
Is co2 fertilisation accounted for?: Yes
How is vegetation represented?: Dynamic LAI is from the Visit model. Photosynthesis and leaf stomatal conductance effects are considered through the Ball-Berry model. The LAI is influenced by CO2, and therefore CO2 fertilisation influence on water is considered.
Methods
Potential evapotranspiration: Energy Balance
Snow melt: Energy Balance
Person responsible for model simulations in this simulation round
Wei Qi: QiWei_WaterResources@hotmail.com, Southern University of Science and Technology (China)
Output Data
Experiments: II, III
Climate Drivers: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5 (historical and future)
Date: 2021-01-19
Basic information
Model Output License: CC0
Reference Paper: Main Reference: Qi, W., Feng, L., Liu, J., Yang, H. et al. Snow as an important natural reservoir for runoff and soil moisture in Northeast China. Journal of Geophysical Research: Atmospheres,None,,2020
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
Simulated atmospheric climate data sets used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Emissions data sets used: CO2 concentration
Land use data sets used: Historical, gridded land use (HYDE 3.2)
Climate variables: huss, sfcWind, tas, rlds, rsds, ps, pr
Spin-up
Was a spin-up performed?: No
Natural Vegetation
Natural vegetation partition: Multiple vegetation types in each grid.
Natural vegetation dynamics: Vegetation LAI and photosynthesis variations. LAI data are from the Visit model which consiered CO2 influence on LAI.
Natural vegetation cover dataset: From the SiB2 classification
Soil
Soil layers: Three layers. The first layer is 0.05 m.
Dams & Reservoirs
Dam and reservoir implementation: Vegetation LAI and photosynthesis variations. LAI data are from the Visit model which consiered CO2 influence on LAI.
Calibration
Was the model calibrated?: No
Vegetation
Is co2 fertilisation accounted for?: Yes
How is vegetation represented?: LAI data are from the Visit model which consiered CO2 influence on LAI.
Methods
Potential evapotranspiration: Water and energy budget
Snow melt: A physically based snowmelt module from SSiB3
Person responsible for model simulations in this simulation round
Wei Qi: QiWei_WaterResources@hotmail.com, Southern University of Science and Technology (China)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2021-01-20
Basic information
Model Output License: CC0
Reference Paper: Main Reference: Qi, W., Feng, L., Liu, J., Yang, H. et al. Snow as an important natural reservoir for runoff and soil moisture in Northeast China. Journal of Geophysical Research: Atmospheres,None,,2020
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-W5E5 (ISIMIP2a), GSWP3-EWEMBI, GSWP3, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Emissions data sets used: CO2 concentration
Land use data sets used: Historical, gridded land use (HYDE 3.0)
Other data sets used: Land-sea mask
Climate variables: huss, sfcWind, tas, rlds, rsds, ps, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: The first 10 years.
Natural Vegetation
Natural vegetation partition: One vegetation type in each grid.
Natural vegetation dynamics: Vegetation LAI and photosynthesis variations. LAI data are from the Visit model, MODIS and LAI3g data.
Natural vegetation cover dataset: From the SiB2 classification
Soil
Soil layers: Three, first layer is 0.05 m
Land Use
Land-use change effects: Vegetation LAI and photosynthesis variations.
Calibration
Was the model calibrated?: No
Vegetation
Is co2 fertilisation accounted for?: Yes
How is vegetation represented?: Dynamic of LAI is from the Visit model, MODIS and LAI3g data. Photosynthesis variations are considered. The LAI is influenced by CO2.
Methods
Potential evapotranspiration: Watre and energy budget
Snow melt: A physically based snowmelt module from SSiB3




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