1. Homepage
  2. Impact Model Settings & Characteristics
  3. Impact model: SWIM-NVE

Impact model: SWIM-NVE

Sector
Water (regional)
Region
regional

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

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.

Person responsible for model simulations in this simulation round
Shaochun Huang: shh@nve.no, 0000-0001-7426-5181, The Norwegian Water Resources and Energy Directorate (NVE) (Norway)
Additional persons involved: Shaochun Huang
Output Data
Experiments: I, II, III (all for Rhine, Yellow, and Mississippi)
Climate Drivers: None
Date: 2018-02-16
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
Resolution
Spatial aggregation: subbasins
Temporal resolution of input data: climate variables: daily
Input data
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
Methods
Potential evapotranspiration: Priestley-Taylor or Turc-Ivanov
Snow melt: An extended degree-day method
Routing
Runoff routing: Muskingum method + reservoirs and irrigation
Calibration
Was the model calibrated?: Yes
Which years were used for calibration?: Different number of subcatchments and calibration period depending on the observed discharge data availability
Modelled catchments
Modelled catchments: Rhine, Upper Yellow and Upper Mississippi
Person responsible for model simulations in this simulation round
Fred Hattermann: hattermann@pik-potsdam.de, 0000-0002-6046-4670, Potsdam Institute for Climate Impacts Research (Germany)
Shaochun Huang: shh@nve.no, 0000-0001-7426-5181, The Norwegian Water Resources and Energy Directorate (NVE) (Norway)
Valentina Krysanova: krysanova@pik-potsdam.de, 0000-0002-9481-0148, Potsdam Institute for Climate Impact Research (Germany)
Additional persons involved: Shaochun Huang
Output Data
Experiments: historical (Rhine, Yellow, Mississippi)
Climate Drivers: None
Date: 2017-02-20
Basic information
Model Version: v. 7
Model Output License: CC0
Reference Paper: Main Reference: Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds.
Reference Paper: Other References:
Resolution
Spatial aggregation: subbasins
Horizontal resolution: Hydrotopes within subbasins
Temporal resolution of input data: climate variables: daily
Input data
Observed atmospheric climate data sets used: WATCH (WFD)
Additional input data sets: DEM, Land use map, soil map, subbasin map, river network, discharge data
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
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.
Methods
Potential evapotranspiration: Priestley-Taylor or Turc-Ivanov
Snow melt: An extended degree-day method
Routing
Runoff routing: Muskingum method + reservoirs and irrigation
Calibration
Was the model calibrated?: Yes
Which years were used for calibration?: 1971-1980 for the Mississippi; 1981-1990 for the Rhine and 1971-1980 for the Yellow
Which dataset was used for calibration?: WATCH
How many catchments were callibrated?: only at the outlet of the 3 catchments
Modelled catchments
Modelled catchments: Upper Mississippi, Rhine and Upper Yellow




Website consultancy, design and implementation:
Matthias Brück
brueck.io

Except where otherwise noted, content on this site is licensed under a Creative Commons Attribution 4.0 International license.