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Impact model: HYPE

Sector
Water (regional)
Region
regional

The HYPE model is a dynamic rainfall–runoff model which describes the hydrological processes at the catchment scale. The model represents processes for snow/ice, evapotranspiration, soil moisture and flow paths, groundwater fluctuations, aquifers, human alterations (reservoirs, regulation, irrigation, abstractions), and routing through rivers and lakes.

HYPE 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 HYPE 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
Ilias Pechlivanidis: ilias.pechlivanidis@smhi.se, 0000-0002-3416-317X, Swedish Meteorological and Hydrological Institute (SMHI) (Sweden)
Additional persons involved: Ilias Pechlivanidis
Output Data
Experiments: I, II, III (all for Mackenzie and Lena)
Climate Drivers: None
Date: 2017-12-04
Basic information
Model Version: HYPE v 4.5.1
Reference Paper: Main Reference: Lindstrom G., Pers C., Rosberg J., Stromqvist J., Arheimer B. et al. Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrology Research,41,295-319,2010
Reference Paper: Other References:
Resolution
Spatial aggregation: subbasins
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
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
Other data sets used: GRanD reservoirs & dams
Climate variables: ta, pr
Exceptions to Protocol
Exceptions: Consideration of human-impacts on the model setups
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 3 years spin-up
Natural Vegetation
Natural vegetation partition: Depending on the model setup and geographic domain
Natural vegetation dynamics: No dynamics
Natural vegetation cover dataset: CORINE, GLC2000
Management & Adaptation Measures
Management: Reservoir management using information from GLWD and GranD databases
Extreme Events & Disturbances
Key challenges: Based on the performance metrics used and monthly evaluation, the performance is considered acceptable.
Methods
Potential evapotranspiration: Potential evapotranspiration depends on temperature and a seasonal adjustment factor. It is zero for temperatures below a threshold value (as for snow fall and snow melt).
Snow melt: For land classes precipitation is assumed to fall as snow below an air temperature threshold. The air temperature depends on the elevation of the class. For temperatures within an interval around the threshold, a mixture of rain and snowfall is assumed. Snow is accumulated for each land class until melting. Snow melt is calculated with a degree-day method, and uses the same threshold temperature as snowfall. The degree-day parameter depends on land use.
Vegetation
Is co2 fertilisation accounted for?: No
Routing
Runoff routing: Based on topography
Routing data: HydroSHEDS, Hydro1K
Calibration
Was the model calibrated?: Yes
Which years were used for calibration?: E-HYPE Rhine (1985-2000); E-HYPE Tagus (1983-1986); Arctic-HYPE Lena (1987-1994); Arctic-HYPE MacKenzie (1991-2001); Niger-HYPE (1990-2001); India-HYPE (1969-1971)
Which dataset was used for calibration?: WFD
How many catchments were callibrated?: A single catchment at the outlet
Modelled catchments
Modelled catchments: Rhine (at Lobith), Tagus (at Almourol), Lena (at Stolb), MacKenzie (at Arctic Red River), Niger (at Lokoja and Tossaye), Ganges (at Farakka)
Confirm data for simulation round ISIMIP2a
Person responsible for model simulations in this simulation round
Berit Arheimer: berit.arheimer@smhi.se, 0000-0001-8314-0735, Swedish Meteorological and Hydrological Institute (Sweden)
Chantal Donnelly: chantal.donnelly@bom.gov.au, 0000-0002-0086-4453, Bureau of Meteorology, Australia (Australia)
David Gustafsson: David.Gustafsson@smhi.se, 0000-0002-2754-7415, Swedish Meteorological and Hydrological Institute (SMHI) (Sweden)
Yeshewatesfa Hundecha: yeshewatesfa.hundecha@smhi.se, 0000-0002-9225-1485, Swedish Meteorological and Hydrological Institute (SMHI) (Sweden)
Ilias Pechlivanidis: ilias.pechlivanidis@smhi.se, 0000-0002-3416-317X, Swedish Meteorological and Hydrological Institute (SMHI) (Sweden)
Output Data
Experiments: historical (Rhine, Tagus, Niger, Ganges, Lena, Mackenzie)
Climate Drivers: None
Date: 2017-02-20
Basic information
Model Version: The model version depends on the regional application
Reference Paper: Main Reference: Lindstrom G., Pers C., Rosberg J., Stromqvist J., Arheimer B. et al. Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrology Research,41,295-319,2010
Reference Paper: Other References:
Resolution
Spatial aggregation: subbasins
Horizontal resolution: depends on region
Additional spatial aggregation & resolution information: Spatial resolution depends on the region, mean area for the European basins is 215 km2, for the Arctic basins is 715 km2, for the Ganges is 800 km2, and for the Niger is 2650 km2.
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: co2: -
Temporal resolution of input data: land use/land cover: time-constant
Temporal resolution of input data: soil: constant
Input data
Observed atmospheric climate data sets used: WATCH (WFD)
Climate variables: tasmax, tas, tasmin, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: Same as in protocol
Natural Vegetation
Natural vegetation partition: we do not include dynamic modelling in the model
Natural vegetation dynamics: we do not include dynamic modelling in the model
Management & Adaptation Measures
Management: Yes
Extreme Events & Disturbances
Key challenges: A good model performance would result in higher confidence on model robustness to reproduce extremes
Methods
Potential evapotranspiration: Priestley-Taylor, modified Hargreaves-Semani and other
Snow melt: Degree-day method or simplified energy approaches
Vegetation
Is co2 fertilisation accounted for?: No
Routing
Runoff routing: Reservoir cascade from ground and surface discharge




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