Hydrological models - data pre-processing

Overview

We provide a collection of scripts that pre-processes environmental data for use in several hydrological models:

PCR-GLOBWB

PCR-GLOBWB (PCRaster Global Water Balance) is a large-scale hydrological model intended for global to regional studies and developed at the Department of Physical Geography, Utrecht University (Netherlands). The recipe pre-processes ERA-Interim reanalyses data for use in the PCR-GLOBWB.

MARRMoT

MARRMoT (Modular Assessment of Rainfall-Runoff Models Toolbox) is a rainfall-runoff model comparison framework that allows objective comparison between different conceptual hydrological model structures https://github.com/wknoben/MARRMoT. The recipe pre-processes ERA-Interim and ERA5 reanalyses data for use in the MARRMoT.

MARRMoT requires potential evapotranspiration (evspsblpot). The variable evspsblpot is not available in ERA-Interim. Thus, we use the debruin function (De Bruin et al. 2016) to obtain evspsblpot using both ERA-Interim and ERA5. This function needs the variables tas, psl, rsds, and rsdt as input.

wflow_sbm and wflow_topoflex

Forcing data for the wflow_sbm and wflow_topoflex hydrological models can be prepared using recipe_wflow.yml. If PET is not available from the source data (e.g. ERA-Interim), then it can be derived from psl, rsds and rsdt using De Bruin’s 2016 formula (De Bruin et al. 2016). For daily ERA5 data, the time points of these variables are shifted 30 minutes with respect to one another. This is because in ERA5, accumulated variables are recorded over the past hour, and in the process of cmorization, we shift the time coordinates to the middle of the interval over which is accumulated. However, computing daily statistics then averages the times, which results in 12:00 UTC for accumulated variables and 11:30 UTC for instantaneous variables. Therefore, in this diagnostic, the time coordinates of the daily instantaneous variables are shifted 30 minutes forward in time.

LISFLOOD

LISFLOOD is a spatially distributed water resources model, developed by the Joint Research Centre (JRC) of the European Commission since 1997. We provide a recipe to produce meteorological forcing data for the Python 3 version of LISFLOOD.

LISFLOOD has a separate preprocessor LISVAP that derives some additional variables. We don’t replace LISVAP. Rather, we provide input files that can readily be passed to LISVAP and then to LISFLOOD.

HYPE

The hydrological catchment model HYPE simulates water flow and substances on their way from precipitation through soil, river and lakes to the river outlet. HYPE is developed at the Swedish Meteorological and Hydrological Institute. The recipe pre-processes ERA-Interim and ERA5 data for use in HYPE.

Available recipes and diagnostics

Recipes are stored in esmvaltool/recipes/hydrology

  • recipe_pcrglobwb.yml
  • recipe_marrmot.yml
  • recipe_wflow.yml
  • recipe_lisflood.yml
  • recipe_hype.yml

Diagnostics are stored in esmvaltool/diag_scripts/hydrology

  • pcrglobwb.py
  • marrmot.py
  • wflow.py
  • lisflood.py
  • hype.py

User settings in recipe

  1. recipe_pcrglobwb.yml

    Required preprocessor settings:

    • start_year: 1979
    • end_year: 1979
  2. recipe_marrmot.yml

    There is one diagnostic diagnostic_daily for using daily data.

    Required preprocessor settings:

    The settings below should not be changed.

    extract_shape:

    • shapefile: Meuse.shp (MARRMoT is a hydrological Lumped model that needs catchment-aggregated forcing data. The catchment is provided as a shapefile, the path can be relative to auxiliary_data_dir as defined in config-user.yml.).
    • method: contains
    • crop: true

    Required diagnostic script settings:

    • basin: Name of the catchment
  3. recipe_wflow.yml

    Optional preprocessor settings:

    • extract_region: the region specified here should match the catchment

    Required diagnostic script settings:

    • basin: name of the catchment
    • dem_file: netcdf file containing a digital elevation model with elevation in meters and coordinates latitude and longitude.
    • regrid: the regridding scheme for regridding to the digital elevation model. Choose area_weighted (slow) or linear.
  4. recipe_lisflood.yml

    Required preprocessor settings:

    • extract_region: A region bounding box slightly larger than the shapefile. This is run prior to regridding, to save memory.

    • extract_shape:*

      • shapefile: A shapefile that specifies the extents of the catchment.

      These settings should not be changed

      • method: contains
      • crop: true
    • regrid:*

      • target_grid: Grid of LISFLOOD input files

      These settings should not be changed

      • lon_offset: true
      • lat_offset: true
      • scheme: linear

    There is one diagnostic diagnostic_daily for using daily data.

    Required diagnostic script settings:

    • catchment: Name of the catchment, used in output filenames
  5. recipe_hype.yml

    Required preprocessor settings:

    • start_year: 1979
    • end_year: 1979
    • shapefile: Meuse_HYPE.shp (expects shapefile with subcatchments)

    These settings should not be changed

    • method: contains
    • decomposed: true

Variables

  1. recipe_pcrglobwb.yml

    • tas (atmos, daily, longitude, latitude, time)
    • pr (atmos, daily, longitude, latitude, time)
  2. recipe_marrmot.yml

    • pr (atmos, daily or hourly mean, longitude, latitude, time)
    • psl (atmos, daily or hourly mean, longitude, latitude, time)
    • rsds (atmos, daily or hourly mean, longitude, latitude, time)
    • rsdt (atmos, daily or hourly mean, longitude, latitude, time)
    • tas (atmos, daily or hourly mean, longitude, latitude, time)
  3. recipe_wflow.yml

    • orog (fx, longitude, latitude)
    • pr (atmos, daily or hourly mean, longitude, latitude, time)
    • tas (atmos, daily or hourly mean, longitude, latitude, time)

    Either potential evapotranspiration can be provided:

    • evspsblpot(atmos, daily or hourly mean, longitude, latitude, time)

    or it can be derived from tas, psl, rsds, and rsdt using the De Bruin formula, in that case the following variables need to be provided:

    • psl (atmos, daily or hourly mean, longitude, latitude, time)
    • rsds (atmos, daily or hourly mean, longitude, latitude, time)
    • rsdt (atmos, daily or hourly mean, longitude, latitude, time)
  4. recipe_lisflood.yml

    • pr (atmos, daily, longitude, latitude, time)
    • tas (atmos, daily, longitude, latitude, time)
    • tasmax (atmos, daily, longitude, latitude, time)
    • tasmin (atmos, daily, longitude, latitude, time)
    • tdps (atmos, daily, longitude, latitude, time)
    • uas (atmos, daily, longitude, latitude, time)
    • vas (atmos, daily, longitude, latitude, time)
    • rsds (atmos, daily, longitude, latitude, time)
  5. recipe_hype.yml

    • tas (atmos, daily or hourly, longitude, latitude, time)
    • tasmin (atmos, daily or hourly, longitude, latitude, time)
    • tasmax (atmos, daily or hourly, longitude, latitude, time)
    • pr (atmos, daily or hourly, longitude, latitude, time)

Observations and reformat scripts

Note: see headers of cmorization scripts (in esmvaltool/cmorizers/obs) for download instructions.

  • ERA-Interim (esmvaltool/cmorizers/obs/cmorize_obs_era_interim.py)
  • ERA5 (esmvaltool/cmorizers/obs/cmorize_obs_era5.py)

Output

  1. recipe_pcrglobwb.yml

  2. recipe_marrmot.yml

    The forcing data, the start and end times of the forcing data, the latitude and longitude of the catchment are saved in a .mat file as a data structure readable by MATLAB or Octave.

  3. recipe_wflow.yml

    The forcing data, stored in a single NetCDF file.

  4. recipe_lisflood.yml

    The forcing data, stored in separate files per variable.

References

  • Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., and Bierkens, M. F. P.: PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model, Geosci. Model Dev., 11, 2429-2453, https://doi.org/10.5194/gmd-11-2429-2018, 2018.
  • De Bruin, H. A. R., Trigo, I. F., Bosveld, F. C., Meirink, J. F.: A Thermodynamically Based Model for Actual Evapotranspiration of an Extensive Grass Field Close to FAO Reference, Suitable for Remote Sensing Application, American Meteorological Society, 17, 1373-1382, DOI: 10.1175/JHM-D-15-0006.1, 2016.
  • Arheimer, B., Lindström, G., Pers, C., Rosberg, J. och J. Strömqvist, 2008. Development and test of a new Swedish water quality model for small-scale and large-scale applications. XXV Nordic Hydrological Conference, Reykjavik, August 11-13, 2008. NHP Report No. 50, pp. 483-492.
  • Lindström, G., Pers, C.P., Rosberg, R., Strömqvist, J., Arheimer, B. 2010. Development and test of the HYPE (Hydrological Predictions for the Environment) model – A water quality model for different spatial scales. Hydrology Research 41.3-4:295-319.
  • van der Knijff, J. M., Younis, J. and de Roo, A. P. J.: LISFLOOD: A GIS-based distributed model for river basin scale water balance and flood simulation, Int. J. Geogr. Inf. Sci., 24(2), 189–212, 2010.