Hyperresolution Global Operational Hydrological Modelling and Forecasting: enhancing reproducability, skill and workflows setup

Operational hydrological modelling and forecasts are based on complex simulation workflows that include, a.o. input data acquisition, pre-processing, hydrologic simulations, post-processing, publication and dissemination of the results. Stakeholders expect regular updates of the information at specified times and in high quality. Therefore, it must be ensured that in the event of an interruption in the workflow, the error can be quickly identified and rectified. Simultaneously, practitioners have high expectations of the model results, that should profit from continuous development of the hydrologic model and other components.

The open-source mesoscale Hydrologic Model mHM (mhm-ufz.org) is a spatially distributed hydrologic model that conceptualizes dominant hydrological processes on the land surface. The unique feature of mHM is the Multiscale Parameter Regionalization (MPR) [1] that relates geophysical properties of the land (e.g., soil and land cover properties) to model parameters via transfer functions at a high spatial resolution (typically less than 250 m cell size). Subsequently, model parameters are aggregated to the spatial resolution at which the model runs are conducted (over 1 km). MPR allows seamless model application at different spatial resolutions and model parameters to be transferred in space [2]. mHM has been applied at different scales ranging from catchments to continents ([3], [4], [5]). mHM is written in Fortran programming language and is available under the GNU Lesser General Public License v3.

mHM has been in continuous development for more than a decade now. In the past year, the following technical and methodological features have been added to the model:

• Installation via conda: mHM installation can be cumbersome because a Fortran compiler and netCDF4 library is required. We have now created a conda package (ananconda.org) for mHM that allows installing release versions of mHM.
• Reading of hourly meteorological input files: Traditionally, mHM was designed to read daily meteorological files. However, its internal time step is hourly. As higher resolved observational datasets become available, mHM can now read hourly data. This feature is critical for flood forecasting.

Recently, mHM was applied globally at 0.1 deg grid resolution within the EU Copernicus-funded ULYSSES project. It took 36 hours to simulate 1.4 million  grid cells for 30 years of daily values at 18 compute cores (using OpenMP parallelization). Although the run time provides an acceptable CO2 footprint of  the simulations, it was challenging to organize a 51 member global hydrological forecast ensemble of six terrestrial environmental variables (Q, ET, SM, SWE, PET, GWR). We used the ecFlow workflow manager (https://confluence.ecmwf.int/display/ECFLOW) to submit the simulations to an HPC cluster. ecFlow allows to monitor the status of jobs and build complex workflows that include various tasks. Using a workflow manager like ecFlow allows creating reproducible simulation results more easily. We developed a general-purpose python package (ecPy) to interact with ecFlow functionalities for a wide range of software applications. We will present these new features and design of ecPy in this presentation.

References:

[1] https://doi.org/10.1029/2008WR007327

[2] https://doi.org/10.5194/gmd-2021-103

[3] https://doi.org/10.1002/wrcr.20431

[4] https://doi.org/10.1175/bams-d-17-0274.1

[5] https://doi.org/10.1061/(asce)he.1943-5584.0002097