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SUMMA workflow

This repository contains scripts to install, set up and run the Structure for Unifying Multiple Modeling Alternatives (SUMMA, Clark et al., 2015a,b) and mizuRoute (Mizukami et al., 2016) to generate hydrologic simulations for a given domain. The workflow uses open-source data with global coverage to determine model parameters and forcing, thus enabling transparent and efficient hydrologic science.

The code in this repository is the outcome of stepwise modification of an existing SUMMA instantiation developed by Andy Wood and colleagues at NCAR. This existing setup served as a testbed for consecutive changes to model input data, domain discretization and domain size, resulting in globally applicable model setup code.

Note on cyber security

Use of this workflow requires accounts with various data providers. Login details with these providers are stored as plain text in the user's home directory. It is therefore strongly recommended that you choose unique, new passwords for these accounts. Do not use passwords that are identical to any other passwords you may be using.

Scope

A basic SUMMA+mizuRoute setup requires:

  • Compiled executables for SUMMA and mizuRoute;
  • A shapefile that discretizes the modelling domain into Grouped Responses Units (GRUs) and Hydrological Response Units (HRUs) for use by SUMMA;
  • A shapefile that discretizes the river network that connects the GRUs and, optionally, a shapefile that discretizes the routing basins if these are not the same as the SUMMA GRUs for use by mizuRoute;
  • Time series of forcing data for each HRU
  • Various settings files for SUMMA and mizuRoute that provide parameter values, initial conditions and runtime settings.

This workflow requires the user to provide the catchment and river network shapefiles with certain required contents (see the relevant readme's for details). The scripts in the repository provide all the necessary code to download and pre-process forcing and parameter data, create SUMMA's and mizuRoute's required input files, and run hydrologic and routing simulations. This generates a basic SUMMA + mizuRoute setup upon which the user can improve by, for example, swapping global datasets for higher quality local ones or connecting the model setup to a calibration algorithm.

Data coverage

The workflow uses the following data sources:

  • ERA5 forcing data (Copernicus Climate Change Service, 2017), available globally from 1979 to current minus five days;
  • SOILGRIDS-derived (Benham et al., 2009; Hengl et al., 2017; Knoben, 2021) maps of global soil classes;
  • MODIS maps (Friedl et al., 2019) of global vegetation types;
  • MERIT Hydro Hydrologically Adjusted Elevations (Yamazaki et al., 2019) as a DEM.

The workflow can thus generate model setups with global coverage and for the past half century.

Shapefile requirements

The workflow assumes the user can provide shapefiles that delineate the (sub-)catchments used by SUMMA and the river network used by mizuRoute. These shapefiles should include certain mandatory elements. The folder 0_example contains example shapefiles that can be used to create a model setup for the Bow at Banff, Canada. This folder also contains a detailed description of shapefile requirements.

Getting started

Typical workflow

The workflow is organized around the idea that the code that generates data (i.e. the scripts that form this repo) is kept in a separate directory from the data that is downloaded and created. The connection between repository scripts and data directory is given in the control_file as control setting root_path. We strongly recommend to not put the data directory specified in root_path inside any of the repository folders, but to use a dedicated and separate location for the data instead. Note that the size requirement of the data directory depends on the size of the domain and the length and number of simulations (see below).

A typical application would look as follows:

  1. Fork this repository to your own GitHub account and clone your fork into an arbitrary folder on your operating platform (e.g. local machine with Linux capabilities, a high performance cluster).
  2. Navigate to summaWorkflow_public/0_control_files. Copy and rename control_BowAtBanff.txt to something more descriptive of your modeling domain.
  3. Update all relevant settings in your newly made control file. Initially, this is mainly:
    • The path to your own data directory, sepcified by setting root_path;
    • The names of your shapefiles and the names of the columns in your shapefiles;
    • The spatial extent of your modelling domain;
    • The temporal extent of your period of interest.
  4. Navigate to summaWorkflow_public/1_folderPrep and run the notebook or Python code there to create the basic layout of your data directory.
  5. Copy your catchment, river network and routing basin shapefiles (.shp) into the newly created your/data/path/domain_[yourDomain]/shapefiles folder, placing the shapefiles in the catchment and river_network folders respectively.
  6. Run through the various scripts in order.

Example

To assist in understanding the process described above, example shapefiles and a control file for the Bow river at Banff, AB, Canada, are provided as part of this repository. Shapefiles can be found in the folder 0_example. The control file can be found in 0_control_files. We strongly recommend to first use the provided shapefiles and control file to create your own setup for the Bow river at Banff. This domain is relatively small and the control file only specifies 1 year of data, which limits the download requirements. Instructions:

  1. Obtain a copy of the repository code;
  2. Ensure your computational environment has the correct packages and modules installed (see below);
  3. Modify the setting root_path in the file control_BowAtBanff.txt to point to your desired data directory location;
  4. Run the scripts in order, starting with the one in folder ./1_folder_prep. This creates a basic folder structure in your specified data directory.
  5. Copy the Bow at Banff shapefiles from the ./0_examples/shapefiles folder in this repo into the newly generated basic folder structure in your data directory. The remaining scripts in the workflow will look for the shapefiles there.
  6. Run the remaining scripts in the workflow in order and try to trace which information each script needs and how it obtains this from the control file. Understanding how the workflow operates will make it much easier to create your own control file.

Software requirements

The workflow uses a combination of Python and Bash. This section lists how to setup your system to use this workflow. We recommend you contact your system administrator if none of this makes sense. Note that this section is a work in progress.

Python

The Python code requires various packages, which may be installed through either pip or conda. It is typically good practice to create a clean (virtual) environment and install the required packages through a package manager. The workflow was developed on Python 3.7.7. and successfully tested on Python 3.8.8.

Pip: Package requirements specified in requirements.txt. Assumes a local install of the GDAL library is available. Scripts for topographic analysis are set up to interact with a stand-alone install of QGIS (see below). Basic instructions to create a new virtual environment:

cd /path/to/summaWorkflow_public
virtualenv summa-env
source summa-env/bin/activate
pip install -r requirements.txt

Conda: Package requirements specified in environment.yml. Installs GDAL as a Conda package. Scripts for topographic analysis are set up to use the Conda QGIS package (see below). Basic instructions to create a new virtual environment:

cd /path/to/summaWorkflow_public
conda env create -f environment.yml
conda activate summa-env

If summa-env is not automatically added as a kernel, close the notebook, run the following from a conda terminal and restart the notebook:

python -m ipykernel install --name summa-env

Note on differences between conda and pip

Please note that while conda automatically installs the necessary underlying libraries for a given package, pip does not. The user must take care to have local installs of the required libraries if using pip. Assumed to exist locally are:

module load proj/7.0.1
module load geos/3.8.1
module load gdal/3.0.4
module load libspatialindex/1.8.5

Interaction with QGIS

The scripts used for geospatial analysis use several functions from QGIS. Depending on your system, you may be able to get QGIS as a Conda package (https://anaconda.org/conda-forge/qgis) or require a stand-alone install of QGIS (https://qgis.org/en/site/). The provided notebooks in folder /summaWorkflow_public/4b_remapping/1_topo/ are designed to use QGIS as a Conda package; the Python scripts in this folder show how to use a standalone install. This folder also contains a more detailed description of QGIS setup.

Bash

The Bash code requires various libraries and command line utilities. These are (tested versions in brackets):

Note on deprecation warnings in Python packages

At the time of writing (12-04-2021) numpy issues warnings about a deprecated feature. netCDF4 uses this feature and as a result any script that uses netCDF4 currently floods the screen with warnings. These are safe to ignore. See:

Disk space requirements

Disk space requirements are largely dependent on the size of the modeling domain (in time and space) and the number of output variables saved by SUMMA. Minimum requirements for the Bow at Banff example are as follows:

  • SUMMA source code: < 50 MB
  • mizuRoute source code: < 50 MB
  • Initial forcing data download: < 20 MB (5 years at hourly resolution, 20 ERA5 grid cells)
  • Initial soil map download: < 500 MB (downloads a global map that is later subset to the region of interest)
  • Initial DEM data download: ~3.5 GB (downloads a 30x30 degree region that is later subset to the region of interest)
  • Initial MODIS data download: ~22 GB (downloads global data that is later subset to the region of interest)
  • SUMMA simulation: < 150 MB (18 model variables simulated with hourly timesteps but saved as daily values)
  • mizuRoute simulation: < 100 MB (both routing schemes)

Note that data generated in intermediate steps in the workflow is saved in corresponding directories. Users may wish to manually delete these intermediate results if disk space is an issue.

Disclaimer

This workflow (“the program”) is licensed under the GNU GPL v3.0 license. You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/. Please take note of the following: This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. In practical terms, this means that:

  1. The developers do not and cannot warrant that the program meets your requirements or that the program is error free or bug free, nor that these errors or bugs can be corrected;
  2. You install and use the program at your own risk;
  3. The developers do not accept responsibility for the accuracy of the results obtained from using the program. In using the program, you are expected to make the final evaluation of any results in the context of your own problem.

Acknowledgements

Our thanks to those who have contributed to improving this repository (in order of first reports):

  • Dave Casson
  • Hannah Burdett
  • Hongli Liu
  • Guoqiang Tang
  • Jim Freer

References

Benham, E., Ahrens, R. J., & Nettleton, W. D. (2009). Clarification of Soil Texture Class Boundaries. United States Department of Agriculture. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/ks/soils/?cid=nrcs142p2_033171

Clark, M. P., B. Nijssen, J. D. Lundquist, D. Kavetski, D. E. Rupp, R. A. Woods, J. E. Freer, E. D. Gutmann, A. W. Wood, L. D. Brekke, J. R. Arnold, D. J. Gochis, R. M. Rasmussen, 2015a: A unified approach for process-based hydrologic modeling: Part 1. Modeling concept. Water Resources Research, doi:10.1002/2015WR017198

Clark, M. P., B. Nijssen, J. D. Lundquist, D. Kavetski, D. E. Rupp, R. A. Woods, J. E. Freer, E. D. Gutmann, A. W. Wood, D. J. Gochis, R. M. Rasmussen, D. G. Tarboton, V. Mahat, G. N. Flerchinger, D. G. Marks, 2015b: A unified approach for process-based hydrologic modeling: Part 2. Model implementation and case studies. Water Resources Research, doi:10.1002/2015WR017200

Clark, M. P., B. Nijssen, J. D. Lundquist, D. Kavetski, D. E. Rupp, R. A. Woods, J. E. Freer, E. D. Gutmann, A. W. Wood, L. D. Brekke, J. R. Arnold, D. J. Gochis, R. M. Rasmussen, D. G. Tarboton, V. Mahat, G. N. Flerchinger, D. G. Marks, 2015c: The structure for unifying multiple modeling alternatives (SUMMA), Version 1.0: Technical Description. NCAR Technical Note NCAR/TN-514+STR, 50 pp., doi:10.5065/D6WQ01TD

Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), 2020-03-26. https://cds.climate.copernicus.eu/cdsapp#!/home

Friedl, M., Sulla-Menashe, D. (2019). MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2020-05-20 from https://doi.org/10.5067/MODIS/MCD12Q1.006

Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748. https://doi.org/10.1371/journal.pone.0169748

Knoben, W. J. M. (2021). Global USDA-NRCS soil texture class map, HydroShare, https://doi.org/10.4211/hs.1361509511e44adfba814f6950c6e742

Mizukami, N., Clark, M. P., Sampson, K., Nijssen, B., Mao, Y., McMillan, H., Viger, R. J., Markstrom, S. L., Hay, L. E., Woods, R., Arnold, J. R., and Brekke, L. D., 2016: mizuRoute version 1: a river network routing tool for a continental domain water resources applications, Geosci. Model Dev., 9, 2223–2238, https://doi.org/10.5194/gmd-9-2223-2016

Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P.D., Allen, G.H., Pavelsky, T.M., 2019. MERIT Hydro: A High‐Resolution Global Hydrography Map Based on Latest Topography Dataset. Water Resour. Res. 55, 5053–5073. https://doi.org/10.1029/2019WR024873

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