Impact model: SWIM-NVE

SWIM 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
  • Shaochun Huang (shh@nve.no), The Norwegian Water Resources and Energy Directorate (NVE) (Norway)

Information for the model SWIM-NVE 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: v. 7
Model output license: CC BY 4.0
Reference Paper: Main Reference: Huang S, Kumar R, Rakovec O, Aich V, Wang X, Samaniego L, Liersch S, Krysanova V et al. Multimodel assessment of flood characteristics in four large river basins at global warming of 1.5, 2.0 and 3.0 K above the pre-industrial level. Environmental Research Letters,13,124005,2018
Person Responsible For Model Simulations In This Simulation Round: Shaochun Huang
Output Data
Experiments: I, II, III (all for Rhine, Yellow, and Mississippi)
Climate Drivers: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Date: 2018-02-16
Resolution
Spatial Aggregation: subbasins
Temporal Resolution Of Input Data: Climate Variables: daily
Input data sets used
Simulated Atmospheric Climate Data Sets Used: MIROC5 (rcp45), HadGEM2-ES (rcp45), IPSL-CM5A-LR (rcp45), GFDL-ESM2M (rcp45), IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Observed Atmospheric Climate Data Sets Used: EWEMBI
Climate Variables: tasmax, tas, tasmin, rhs, pr
Spin-up
Was A Spin-Up Performed?: No
Natural Vegetation
Natural Vegetation Partition: Natural vegetation and crops are simulated using a simplified EPIC approach and the vegetation parameter database attached to the model (as in SWAT )
Management & Adaptation Measures
Management: Water management can be included if data are available
Soil
Soil Layers: Up to 10 soil layers, 11 soil parameters
Routing
Runoff Routing: Muskingum method + reservoirs and irrigation
Calibration
Was The Model Calibrated?: True
Which Years Were Used For Calibration?: Different number of subcatchments and calibration period depending on the observed discharge data availability
Methods
Potential Evapotranspiration: Priestley-Taylor or Turc-Ivanov
Snow Melt: An extended degree-day method
Modelled catchments
Modelled catchments: Rhine, Upper Yellow and Upper Mississippi
Basic information
Output Data
Experiments: historical (Rhine, Yellow, Mississippi)
Climate Drivers: WATCH (WFD)
Date: 2017-02-20
Resolution
Spatial Aggregation: subbasins
Spatial Resolution: Hydrotopes within subbasins
Temporal Resolution Of Input Data: Climate Variables: daily
Input data sets used
Observed Atmospheric Climate Data Sets Used: WATCH (WFD)
Climate Variables: tasmax, tas, tasmin, rhs, pr
Additional Input Data Sets: DEM, Land use map, soil map, subbasin map, river network, discharge data
Spin-up
Was A Spin-Up Performed?: No
Natural Vegetation
Natural Vegetation Partition: Natural vegetation and crops are simulated using a simplified EPIC approach and the vegetation parameter database attached to the model (as in SWAT
Management & Adaptation Measures
Management: Water management can be included if data are available
Extreme Events & Disturbances
Key Challenges: If the input data are of a good quality, SWIM is able to reproduce hydrological extreme events: floods and droughts.
Soil
Soil Layers: Up to 10 soil layers, 11 soil parameters
Routing
Runoff Routing: Muskingum method + reservoirs and irrigation
Vegetation
How Is Vegetation Represented?: A simplified EPIC approach
Methods
Potential Evapotranspiration: Priestley-Taylor or Turc-Ivanov
Snow Melt: An extended degree-day method