Impact model: mHM

The mesoscale hydrologic model (mHM) developed by our group is a spatially explicit distributed hydrologic model that uses grid cells as a primary hydrologic unit, and accounts for the following processes: canopy interception, snow accumulation and melting, soil moisture dynamics, infiltration and surface runoff, evapotranspiration, subsurface storage and discharge generation, deep percolation and baseflow and discharge attenuation and flood routing. The model is driven by hourly or daily meteorological forcings (e.g., precipitation, temperature), and it utilizes observable basin physical characteristics (e.g., soil textural, vegetation, and geological properties) to infer the spatial variability of the required parameters. To date, the model has been successfully applied and tested in more than 300 Pan EU basins, as well as India, and USA, ranging in size from 4 to 550,000 km2 at spatial resolutions (or grid size) varied between 1 km and 100 km. Shown below is the model performance for stream flow simulations over the EU basins. For more info, please see: www.ufz.de/mhm mHM 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

Information for the model mHM 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. 5.7
Reference Paper: Other References:
Output Data
Experiments: I, II, III (all for Rhine, Niger, Ganges, Yellow, Darling, Mississippi, Amazon)
Climate Drivers: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Date: 2017-09-29
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: regular or irregular grid, any size from 100 m to 100 km
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Land Use/Land Cover: yearly
Temporal Resolution Of Input Data: Soil: soil texture time-constant. Soil porosity, hydraulic conductivity, Van Gennugten constants are time-dependent and depend on land cover (organic matter)
Additional Temporal Resolution Information: Can also handle daily values. Soil texture time-constant. Soil porosity, hydraulic conductivity, Van Gennugten constants are time-dependent and depend on land cover (organic matter)
Input data sets used
Climate Variables: tasmax, tas, tasmin, rhs, pr
Additional Input Data Sets: LAI time-dependent
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: Spin-up is made to generat a restart file at any time point. Spin-ups are made with the longest time series available. It the time series are short, the available data is use until steady-state is reached. Another form of initialization is to estimate the climatology of a given day using available data. Min timeseries to reach stable results is five years.
Natural Vegetation
Natural Vegetation Partition: Corine land cover and LAI (both time-dependent)
Management & Adaptation Measures
Management: No (In version 5.4). Will be included in future versions.
Extreme Events & Disturbances
Key Challenges: If the input data is of good quality, mHM is able to reproduce extreme events and demostrated in the provided references.
Additional Comments: mHM uses a multiscale parameter regionalization (MPR) technique to ease transferability of model constants across scales and locations. Model constants are those scalars (like those in the pedothnasfer fucntions) that are valid for all grid cells and time points, thus can be transfered. Model parameters, on the other, hand are not transferable becuase they are cell specific e.g., soil porosity. See Samaniego et al. WRR 2010 for dertails.
Soil
Soil Layers: N soil layers (here two soil layers were used)
Routing
Runoff Routing: Muskingum method
Land Use
Land-Use Change Effects: Canopy interception, soil moisture dynamics and runoff generation, as well as runoff routing processes
Dams & Reservoirs
Dam And Reservoir Implementation: None
Calibration
Was The Model Calibrated?: True
Which Years Were Used For Calibration?: Varied depending on the data availability in different basins
Which Dataset Was Used For Calibration?: WFD
How Many Catchments Were Callibrated?: 8 to 10
Vegetation
How Is Vegetation Represented?: Fixed monthly plant characteristics
Methods
Potential Evapotranspiration: Hargreaves and Samani method + aspect correction
Snow Melt: Enhanced Degree-day method
Basic information
Model Version: v. 5.4
Reference Paper: Other References:
Output Data
Experiments: historical (Rhine, Niger, Blue Nile, Ganges, Yellow, Darling, Mississippi, Amazon)
Climate Drivers: WATCH (WFD)
Date: 2017-02-20
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: regular or irregular grid, any size from 100 m to 100 km
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Land Use/Land Cover: yearly
Temporal Resolution Of Input Data: Soil: soil texture time-constant. Soil porosity, hydraulic conductivity, Van Gennugten constants are time-dependent and depend on land cover (organic matter)
Additional Temporal Resolution Information: Can also handle daily values. Soil texture time-constant. Soil porosity, hydraulic conductivity, Van Gennugten constants are time-dependent and depend on land cover (organic matter)
Input data sets used
Observed Atmospheric Climate Data Sets Used: WATCH (WFD)
Climate Variables: tasmax, tas, tasmin, wind, rhs, pr
Additional Input Data Sets: LAI time-dependent
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: Spin-up is made to generat a restart file at any time point. Spin-ups are made with the longest time series available. It the time series are short, the available data is use until steady-state is reached. Another form of initialization is to estimate the climatology of a given day using available data. Min timeseries to reach stable results is five years.
Natural Vegetation
Natural Vegetation Partition: Corine land cover and LAI (both time-dependent)
Management & Adaptation Measures
Management: No (In version 5.4). Will be included in future versions.
Extreme Events & Disturbances
Key Challenges: If the input data is of good quality, mHM is able to reproduce extreme events and demostrated in the provided references.
Additional Comments: mHM uses a multiscale parameter regionalization (MPR) technique to ease transferability of model constants across scales and locations. Model constants are those scalars (like those in the pedothnasfer fucntions) that are valid for all grid cells and time points, thus can be transfered. Model parameters, on the other, hand are not transferable becuase they are cell specific e.g., soil porosity. See Samaniego et al. WRR 2010 for dertails.
Soil
Soil Layers: N soil layers (here two soil layers were used)
Routing
Runoff Routing: Muskingum method
Land Use
Land-Use Change Effects: Canopy interception, soil moisture dynamics and runoff generation, as well as runoff routing processes
Calibration
Was The Model Calibrated?: True
Which Years Were Used For Calibration?: Varied depending on the data availability in different basins
Which Dataset Was Used For Calibration?: WFD
How Many Catchments Were Callibrated?: 8 to 10
Vegetation
How Is Vegetation Represented?: Fixed monthly plant characteristics
Methods
Potential Evapotranspiration: Hargreaves and Samani method + aspect correction
Snow Melt: Enhanced Degree-day method