Impact model: SWAP

SWAP is one of the 15 regional hydrology models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a regional water sector outputs; for a full technical description of the ISIMIP2a Simulation Data from Water (regional) Sector, see this DOI link: http://doi.org/10.5880/PIK.2018.007

Sector
Water (regional)
Region
regional
Contact Person
  • Georgy Ayzel (hydrogo@yandex.ru), Water Problems Institute, Russian Academy of Sciences (RAS) (Russia)
  • Evgeny E. Kovalev (eekovalev@gmail.com), Water Problems Institute, Russian Academy of Sciences (RAS) (Russia)
  • Olga Nasonova (olniknas@yandex.ru), Institute of Water Problems of the Russian Academy of Sciences (Russia)

Information for the model SWAP 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: SWAP. The model was evaluated in ISIMIP2a before application in ISIMIP2b.
Model License: CC BY 4.0
Person Responsible For Model Simulations In This Simulation Round: Olga Nasonova, Yeugeny Gusev
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Land Use/Land Cover: constant
Temporal Resolution Of Input Data: Soil: constant
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: The first year was used for spin-up
Natural Vegetation
Natural Vegetation Partition: Parameters for different types of vegetation within a calculational grid cell were averaged with taking into account their fraction.
Natural Vegetation Dynamics: Many-year means of monthly values of vegetation parameters were prescribed.
Natural Vegetation Cover Dataset: ECOCLIMAP
Management & Adaptation Measures
Management: no
Methods
Potential Evapotranspiration: Simulated by SWAP. See papers
Snow Melt: Energy balance
Vegetation
How Is Vegetation Represented?: prescribed monthly values of vegetation parameters
Calibration
Was The Model Calibrated?: True
Modelled catchments
Modelled catchments: 11 catchments
Basic information
Model Version: The same model version as in ISIMIP2a
Model License: CC BY 4.0
Reference Paper: Main Reference: Gelfan A., Kalugin A., Krylenko I., Nasonova O., Gusev Ye., Kovalev E. et al. Does a successful comprehensive evaluation increase confidence in а hydrological model intended for climate impact assessment? . Climatic change,163,1165-1185,2020
Person Responsible For Model Simulations In This Simulation Round: Olga Nasonova, Yeugeny Gusev, Evgeny Kovalev
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Soil: constant
Input data sets used
Simulated Atmospheric Climate Data Sets Used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Other Data Sets Used: Land-sea mask
Climate Variables: hurs, sfcWind, tas, rlds, rsds, ps, pr
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: The first year (1971) was simulated 4 times for spin-up (according to our experience it is enough to reach equilibrium by our model).
Natural Vegetation
Natural Vegetation Dynamics: no
Natural Vegetation Cover Dataset: We aggregated parameters taken from ECOCLIMAP for 0.5 deg grid cells
Management & Adaptation Measures
Management: no
Methods
Potential Evapotranspiration: SWAP simulates potential evapotranspiration. See papers.
Snow Melt: Energy balance
Vegetation
How Is Vegetation Represented?: Fixed monthly vegetation characteristics.
Routing
Runoff Routing: Kinematic wave equation for water transfer within a grid cell and a linear routing scheme by Oki et al. (1999) for water transfer in a river channel.
Routing Data: We prepared routing data by ourselves
Calibration
Was The Model Calibrated?: True
Modelled catchments
Modelled catchments: Lena, MacKenzie
Basic information
Model Version: SWAP
Model License: CC BY 4.0
Reference Paper: Main Reference: Gusev Ye.M., Nasonova O.N. et al. Modelling heat and water exchange in the boreal spruce forest by the land-surface model SWAP. J. Hydrology,280,162-191,2003
Reference Paper: Other References:
Person Responsible For Model Simulations In This Simulation Round: Olga Nasonova
Output Data
Experiments: historical (Lena, Darling, MacKenzie, Mississippi, Amazon, Rhine, Tagus, Niger, Ganges, Yellow, Yangtze)
Climate Drivers: WATCH (WFD)
Date: 2017-02-20
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Soil: constant
Input data sets used
Observed Atmospheric Climate Data Sets Used: WATCH (WFD)
Other Data Sets Used: Land-sea mask
Climate Variables: tas, rlds, wind, rhs, rsds, ps, pr
Additional Input Data Sets: Vegetation parameters were taken or derived from ECOCLIMAP
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: We started the simulations from 1July 1969 and the first year was simulated 4 times for spin-up (according to our experience it is enough to reach equilibrium by our model).
Natural Vegetation
Natural Vegetation Partition: We aggregated parameters taken from ECOCLIMAP for 0.5 deg grid cells
Natural Vegetation Dynamics: Prescribed monthly vegetation parametes.
Natural Vegetation Cover Dataset: We aggregated parameters taken from ECOCLIMAP for 0.5 deg grid cells
Management & Adaptation Measures
Management: no
Extreme Events & Disturbances
Key Challenges: Time step should be finer to reproduce high flow more accurately.
Methods
Potential Evapotranspiration: See papers
Snow Melt: Energy balance
Vegetation
How Is Vegetation Represented?: Fixed monthly vegetation characteristics.
Routing
Runoff Routing: Kinematic wave equation for water transfer within a grid cell and a linear routing scheme by Oki et al. (1999) for water transfer in a river channel.
Routing Data: Routing data were prepared by ourselves
Calibration
Was The Model Calibrated?: True
Modelled catchments
Modelled catchments: Lena, Darling, MacKenzie, Mississippi, Amazon, Rhine, Tagus, Niger, Ganges, Yellow, Yangtze