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Impact model: MATSIRO

Sector
Water (global)
Region
global

MATSIRO 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

Information for the model MATSIRO 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 ISIMIP2b
Person responsible for model simulations in this simulation round
Yusuke Satoh: yusuke.satoh@kaist.ac.kr, 0000-0001-6419-7330, Korea Advanced Institute of Science and Technology (KAIST) (Japan)
Output Data
Experiments: I, II, III, VIII
Climate Drivers: None
Date: 2019-09-10
Basic information
Model Version: MIROC-INTEG
Reference Paper: Main Reference: Takata K, Emori S, Watanabe T et al. Development of the minimal advanced treatments of surface interaction and runoff. Global and Planetary Change,38,209-222,2003
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
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
Observed atmospheric climate data sets used: EWEMBI
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: Reservoirs and dams, Land-sea mask, Drainage direction map for river routing, GRanD reservoirs & dams
Climate variables: ta, huss, sfcWind, tasmax, tas, tasmin, rlds, rsds, prsn, ps, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 60-year spin-up before 1661. For the spin-up, we gave MATSIRO forcing data starting from 1661 to 1691 and then backword from 1690 to 1662.
Natural Vegetation
Natural vegetation partition: 11 static types with fixed characteristics
Natural vegetation dynamics: No
Natural vegetation cover dataset: Land cover and vegetation parameters are taken from the GSWP2 (Dirmeyer et al. 2006).
Soil
Soil layers: 13 layers
Water Use
Water-use types: demand and consumption x actual and potential
Water-use sectors: Agricultural (Irrigation), domestic and industrial water use
Routing
Runoff routing: TRIP model
Routing data: DDM30
Land Use
Land-use change effects: Irrigation area
Dams & Reservoirs
Dam and reservoir implementation: GRanD
Calibration
Was the model calibrated?: No
Vegetation
Is co2 fertilisation accounted for?: Yes
Methods
Potential evapotranspiration: The stomatal resistance is prescribed at a constant rate in the bucket-type model. That is calculated from a photosynthetic scheme on the basis of physiology (Farquhar-type model) after SiB2 (Sellers et al., 1996)
Snow melt: surface energy-balance methods
Person responsible for model simulations in this simulation round
Hyungjun Kim: hjkim@rainbow.iis.u-tokyo.ac.jp, Institute of Industrial Science, University of Tokyo (Japan)
Taikan Oki: taikan@iis.u-tokyo.ac.jp, 0000-0003-4067-4678, Institute of Industrial Science, University of Tokyo (Japan)
Yadu Pokhrel: ypokhrel@egr.msu.edu, 0000-0002-1367-216X, Department of Civil and Environmental Engineering, Michigan State University (USA)
Yusuke Satoh: yusuke.satoh@kaist.ac.kr, 0000-0001-6419-7330, Korea Advanced Institute of Science and Technology (KAIST) (Japan)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-08-23
Basic information
Model Version: HiGW-MAT
Model Output License: CC BY 4.0
Reference Paper: Main Reference: Pokhrel Y, Koirala S, Yeh P, Hanasaki N, Longuevergne L, Kanae S, Oki T et al. Incorporation of groundwater pumping in a global Land Surface Model with the representation of human impacts. Water Resources Research,51,78-96,2014
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5’ x 0.5’
Temporal resolution of input data: climate variables: 3hrly
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
Land use data sets used: Historical, gridded land use (HYDE 3.0)
Other data sets used: Reservoirs and dams, Land-sea mask, Drainage direction map for river routing, GRanD reservoirs & dams
Climate variables: tas, rlds, rhs, rsds, prsn, ps, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 1951-1970
Natural Vegetation
Natural vegetation partition: SiB2 global land cover dataset. fixed vegetation characteristics for 12 types of natural land cover
Management & Adaptation Measures
Management: no
Technological Progress
Technological progress: Industrial and domestic water demand which relfect GDP growth are one of input of the model
Soil
Soil layers: 13 layers
Water Use
Water-use types: Irrigation (simulated), doemstic (prescribed), industrial (precribed)
Water-use sectors: Under process of simulation: Irrigation (simulated), doemstic (prescribed), industrial (precribed)
Routing
Runoff routing: TRIP (Oki et al. 1999), constant flow velocity (5m/s), DDM30 river routine, GRanD resevoirs (pressoc and varsoc)
Land Use
Land-use change effects: Irrigation area
Dams & Reservoirs
Dam and reservoir implementation: Use GranDR resevoir data. Reservoir operation is Monthly base operation and dynamically linked with routing. For varsoc run, the construction year of each dam/reservoir was taken into accounted.
Calibration
Was the model calibrated?: No
Vegetation
Is co2 fertilisation accounted for?: No
How is vegetation represented?: Monthly LAI, and fixed vegetation characteristics for 12 types of natural land cover
Methods
Potential evapotranspiration: Penman-Monteith
Snow melt: Energy balance
Person responsible for model simulations in this simulation round
Hyungjun Kim: hjkim@rainbow.iis.u-tokyo.ac.jp, Institute of Industrial Science, University of Tokyo (Japan)
Taikan Oki: taikan@iis.u-tokyo.ac.jp, 0000-0003-4067-4678, Institute of Industrial Science, University of Tokyo (Japan)
Yadu Pokhrel: ypokhrel@egr.msu.edu, 0000-0002-1367-216X, Department of Civil and Environmental Engineering, Michigan State University (USA)
Yusuke Satoh: yusuke.satoh@kaist.ac.kr, 0000-0001-6419-7330, Korea Advanced Institute of Science and Technology (KAIST) (Japan)
Shinta Seto: seto@rainbow.iis.u-tokyo.ac.jp, Institute of Industrial Science, University of Tokyo (Japan)
Natushi Yoshida: yoshida@rainbow.iis.u-tokyo.ac.jp, Institute of Industrial Science, University of Tokyo (Japan)
Kei Yoshimura: kei@aori.u-tokyo.ac.jp, Institute of Industrial Science, University of Tokyo (Japan)
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-17




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