Building a model#

This plugin allows to build a complete model from available data. Once the configuration and data libraries are set, you can build a model by using:

hydromt build wflow path/to/built_model "{'basin': [x, y]}" -i wflow_build.ini -d data_sources.yml -vvv

The recommended region options for a proper implementation of this model are:

  • basin

  • subbasin

Configuration file#

Settings to build or update a Wflow model are managed in a configuration file. In this file, every option from each model method can be changed by the user in its corresponding section.

Note that the order in which the components are listed in the ini file is important:

  • setup_basemaps should always be run first to determine the model domain

  • setup_rivers should be run right after setup_basemaps as it influences several other setup components (lakes, reservoirs, riverwidth, gauges)

Below is an example ini file that can be used to build a complete Wflow model .ini file. Each section corresponds to a model component with the same name.

[global]
data_libs       = []            # add optional paths to data yml files

[setup_config]                  # options parsed to wflow ini file <section>.<option>
starttime = 2010-01-01T00:00:00
endtime = 2010-03-31T00:00:00
timestepsecs = 86400
input.path_forcing = inmaps-era5-2010.nc

[setup_basemaps]
hydrography_fn     = merit_hydro   # source hydrography data {merit_hydro, merit_hydro_1k}
basin_index_fn  = merit_hydro_index # source of basin index corresponding to hydrography_fn
upscale_method  = ihu           # upscaling method for flow direction data, by default 'ihu'

[setup_rivers]
hydrography_fn   = merit_hydro      # source hydrography data, should correspond to hydrography_fn in setup_basemaps
river_geom_fn    = rivers_lin2019_v1 # river source data with river width and bankfull discharge
river_upa        = 30               # minimum upstream area threshold for the river map [km2]
rivdph_method    = powlaw           # method to estimate depth {'powlaw', 'manning', 'gvf'}
min_rivdph       = 1                # minimum river depth [m]
min_rivwth       = 30               # minimum river width [m]
slope_len        = 2000             # length over which tp calculate river slope [m]
smooth_len       = 5000             # length over which to smooth river depth and river width [m]

# [setup_hydrodem]                  # uncomment for river and/or land local-inertial routing.
# elevtn_map      = dem_subgrid     # {'dem_subgrid', 'wflow_dem'}
# river_routing   = local-inertial  # {'kinematic-wave', 'local-inertial'}
# land_routing    = kinematic-wave  # {'kinematic-wave', 'local-inertial'}

[setup_reservoirs]
reservoirs_fn   = hydro_reservoirs  # source for reservoirs based on GRAND: {hydro_reservoirs}; None to skip
min_area        = 1.0           # minimum lake area to consider [km2]
priority_jrc    = True          # if True then JRC data from hydroengine is used to calculate some reservoir attributes instead of the GRanD and HydroLAKES db.   

[setup_lakes]
lakes_fn        = hydro_lakes   # source for lakes based on hydroLAKES: {hydro_lakes}; None to skip
min_area        = 10.0          # minimum reservoir area to consider [km2]

[setup_glaciers]
glaciers_fn     = rgi           # source for glaciers based on Randolph Glacier Inventory {rgi}; None to skip
min_area        = 1.0           # minimum glacier area to consider [km2]

[setup_lulcmaps]
lulc_fn         = globcover     # source for lulc maps: {globcover, vito, corine}

[setup_laimaps]
lai_fn          = modis_lai     # source for LAI: {modis_lai}

[setup_soilmaps]
soil_fn         = soilgrids     # source for soilmaps: {soilgrids}
ptf_ksatver     = brakensiek    # pedotransfer function to calculate hydraulic conductivity: {brakensiek, cosby}

[setup_gauges]
gauges_fn       = grdc          # if not None add gaugemap. Either a path or known gauges_fn: {grdc}
snap_to_river   = True          # if True snaps gauges from source to river
derive_subcatch = False         # if True derive subcatch map based on gauges.

[setup_precip_forcing]
precip_fn       = era5          # source for precipitation.
precip_clim_fn  = None          # source for high resolution climatology to correct precipitation.

[setup_temp_pet_forcing]
temp_pet_fn     = era5          # source for temperature and potential evapotranspiration.
press_correction= True          # if True temperature is corrected with elevation lapse rate.
temp_correction = True          # if True pressure is corrected with elevation lapse rate.
dem_forcing_fn  = era5_orography # source of elevation grid corresponding to temp_pet_fn. Used for lapse rate correction.
pet_method      = debruin       # method to compute PET: {debruin, makkink}
skip_pet        = False         # if True, only temperature is prepared.

[setup_constant_pars]
KsatHorFrac=100
Cfmax = 3.75653
cf_soil = 0.038
EoverR = 0.11
InfiltCapPath = 5
InfiltCapSoil = 600 
MaxLeakage = 0
rootdistpar = -500
TT = 0
TTI = 2
TTM = 0
WHC = 0.1
G_Cfmax = 5.3
G_SIfrac = 0.002
G_TT = 1.3

Selecting data#

Data sources in HydroMT are provided in one of several yaml libraries. These libraries contain required information on the different data sources so that HydroMT can process them for the different models. There are three ways for the user to select which data libraries to use:

  • If no yaml file is selected, HydroMT will use the data stored in the hydromt-artifacts which contains an extract of global data for a small region around the Piave river in Northern Italy.

  • Another options for Deltares users is to select the deltares-data library (requires access to the Deltares P-drive). In the command lines examples below, this is done by adding either –dd or –deltares-data to the build / update command line.

  • Finally, the user can prepare its own yaml libary (or libraries) (see HydroMT documentation to check the guidelines). These user libraries can be added either in the command line using the -d option and path/to/yaml or in the ini file with the data_libs option in the [global] sections.