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Resources","sub_category":"Water Supply and Quality","monthly_downloads":203,"total_dependent_repos":1,"total_dependent_packages":0,"readme":"[![test](https://github.com/IMMM-SFA/mosartwmpy/actions/workflows/test.yml/badge.svg)](https://github.com/IMMM-SFA/mosartwmpy/actions/workflows/test.yml)\n[![codecov](https://codecov.io/gh/IMMM-SFA/mosartwmpy/branch/main/graph/badge.svg?token=IPOY8984MB)](https://codecov.io/gh/IMMM-SFA/mosartwmpy)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.03221/status.svg)](https://doi.org/10.21105/joss.03221)\n[![DOI](https://zenodo.org/badge/312114600.svg)](https://zenodo.org/badge/latestdoi/312114600)\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mosartwmpy)](https://pypi.org/project/mosartwmpy/)\n\n\n## mosartwmpy\n\n`mosartwmpy` is a python translation of MOSART-WM, a model for water routing and reservoir management written in Fortran. The original code can be found at [IWMM](https://github.com/IMMM-SFA/iwmm) and [E3SM](https://github.com/E3SM-Project/E3SM), in which MOSART is the river routing component of a larger suite of earth-science models. The motivation for rewriting is largely for developer convenience -- running, debugging, and adding new capabilities were becoming increasingly difficult due to the complexity of the codebase and lack of familiarity with Fortran. This version aims to be intuitive, lightweight, and well documented, while still being highly interoperable.\nFor a quick start, check out the [Jupyter notebook tutorial](https://github.com/IMMM-SFA/mosartwmpy/blob/main/notebooks/tutorial.ipynb)!\n\n## getting started\n\nEnsure you have Python v3.9 - v3.12 (consider using a [virtual environment](https://github.com/pyenv/pyenv), see the docs [here](https://mosartwmpy.readthedocs.io/en/latest/virtualenv.html) for a brief tutorial), then install `mosartwmpy` with:\n```shell\npip install mosartwmpy\n```\n\nAlternatively, install via conda with:\n```shell\nconda install -c conda-forge mosartwmpy\n```\n\nDownload a sample input dataset spanning May 1981 by running the following and selecting option `1` for \"tutorial\". This will download and unpack the inputs to your current directory. Optionally specify a path to download and extract to instead of the current directory.\n\n```shell\npython -m mosartwmpy.download\n```\n\nSettings are defined by the merger of the `mosartwmpy/config_defaults.yaml` and a user specified file which can override any of the default settings. Create a `config.yaml` file that defines your simulation (if you chose an alternate download directory in the step above, you will need to update the paths to point at your data):\n\n\u003e `config.yaml`\n\u003e ```yaml\n\u003e simulation:\n\u003e   name: tutorial\n\u003e   start_date: 1981-05-24\n\u003e   end_date: 1981-05-26\n\u003e\n\u003e grid:\n\u003e   path: ./input/domains/mosart_conus_nldas_grid.nc\n\u003e\n\u003e runoff:\n\u003e   read_from_file: true\n\u003e   path: ./input/runoff/runoff_1981_05.nc\n\u003e\n\u003e water_management:\n\u003e   enabled: true\n\u003e   demand:\n\u003e     read_from_file: true\n\u003e     path: ./input/demand/demand_1981_05.nc\n\u003e   reservoirs:\n\u003e     enable_istarf: true\n\u003e     parameters:\n\u003e       path: ./input/reservoirs/reservoirs.nc\n\u003e     dependencies:\n\u003e       path: ./input/reservoirs/dependency_database.parquet\n\u003e     streamflow:\n\u003e       path: ./input/reservoirs/mean_monthly_reservoir_flow.parquet\n\u003e     demand:\n\u003e       path: ./input/reservoirs/mean_monthly_reservoir_demand.parquet\n\u003e ```\n\n`mosartwmpy` implements the [Basic Model Interface](https://csdms.colorado.edu/wiki/BMI) defined by the CSDMS, so driving it should be familiar to those accustomed to the BMI. To launch the simulation, open a python shell and run the following:\n\n```python\nfrom mosartwmpy import Model\n\n# path to the configuration yaml file\nconfig_file = 'config.yaml'\n\n# initialize the model\nmosart_wm = Model()\nmosart_wm.initialize(config_file)\n\n# advance the model one timestep\nmosart_wm.update()\n\n# advance until the `simulation.end_date` specified in config.yaml\nmosart_wm.update_until(mosart_wm.get_end_time())\n```\n\n## model input\n\nInput for `mosartwmpy` consists of many files defining the characteristics of the discrete grid, the river network, surface and subsurface runoff, water demand, and dams/reservoirs.\nCurrently, the gridded data is expected to be provided at the same spatial resolution.\nRunoff input can be provided at any time resolution; each timestep will select the runoff at the closest time in the past.\nCurrently, demand input is read monthly but will also pad to the closest time in the past.\nEfforts are under way for more robust demand handling.\n\nDams/reservoirs require four different input files: the physical characteristics, the average monthly flow expected during the simulation period, the average monthly demand expected during the simulation period, and a database mapping each GRanD ID to grid cell IDs allowed to extract water from it.\nThese dam/reservoir input files can be generated from raw GRanD data, raw elevation data, and raw ISTARF data using the [provided utility](https://github.com/IMMM-SFA/mosartwmpy/blob/main/mosartwmpy/utilities/CREATE_GRAND_PARAMETERS.md).\nThe best way to understand the expected format of the input files is to examine the sample inputs provided by the download utility: `python -m mosartwmpy.download`.\n\n#### multi-file input\n\nTo use multi-file demand or runoff input, use year/month/day placeholders in the file path options like so:\n* If your files look like `runoff-1999.nc`, use `runoff-{Y}.nc` as the path\n* If your files look like `runoff-1999-02.nc`, use `runoff-{Y}-{M}.nc` as the path\n* If your files look like `runoff-1999-02-03`, use `runoff-{Y}-{M}-{D}.nc` as the path, but be sure to provide files for leap days as well!\n\n\n## model output\n\nBy default, key model variables are output on a monthly basis at a daily averaged resolution to `./output/\u003csimulation name\u003e/\u003csimulation name\u003e_\u003cyear\u003e_\u003cmonth\u003e.nc`. See the configuration file for examples of how to modify the outputs, and the `./mosartwmpy/state/state.py` file for state variable names.\n\nAlternatively, certain model outputs deemed most important can be accessed using the BMI interface methods. For example:\n```python\nfrom mosartwmpy import Model\n\nmosart_wm = Model()\nmosart_wm.initialize()\n\n# get a list of model output variables\nmosart_wm.get_output_var_names()\n\n# get the flattened numpy.ndarray of values for an output variable\nsupply = mosart_wm.get_value_ptr('supply_water_amount')\n```\n\n\n## subdomains\n\nTo simulate only a subset of basins (defined here as a collection of grid cells that share the same outlet cell),\nuse the configuration option `grid -\u003e subdomain` (see example below) and provide a list of latitude/longitude\ncoordinate pairs representing each basin of interest (any single coordinate pair within the basin). For example, to\nsimulate only the Columbia River basin and the Lake Washington regions, one could enter the coordinates for Portland and\nSeattle:\n\n\u003e `config.yaml`\n\u003e ```yaml\n\u003e grid:\n\u003e   subdomain:\n\u003e     - 47.6062,-122.3321\n\u003e     - 45.5152,-122.6784\n\u003e   unmask_output: true\n\u003e ```\n\nBy default, the output files will still store empty NaN-like values for grid cells outside the subdomain, but\nfor even faster simulations and smaller output files set the `grid -\u003e unmask_output` option to `false`. Disabling\nthis option causes the output files to only store values for grid cells within the subdomain. These smaller files\nwill likely take extra processing to effectively interoperate with other models.\n\n\n## visualization\n\n`Model` instances can plot the current value of certain input and output variables (those available from `Model.get_output_var_name` and `Model.get_input_var_names`):\n\n```python\nfrom mosartwmpy import Model\nconfig_file = 'config.yaml'\nmosart_wm = Model()\nmosart_wm.initialize(config_file)\nfor _ in range(8):\n    mosart_wm.update()\n\nmosart_wm.plot_variable('outgoing_water_volume_transport_along_river_channel', log_scale=True)\n```\n![River transport](https://github.com/IMMM-SFA/mosartwmpy/raw/main/docs/_static/river_transport.png)\n\nUsing provided utility functions, the output of a simulation can be plotted as well.\n\nPlot the storage, inflow, and outflow of a particular GRanD dam:\n```python\nfrom mosartwmpy import Model\nfrom mosartwmpy.plotting.plot import plot_reservoir\nconfig_file = 'config.yaml'\nmosart_wm = Model()\nmosart_wm.initialize(config_file)\nmosart_wm.update_until()\n\nplot_reservoir(\n    model=mosart_wm,\n    grand_id=310,\n    start='1981-05-01',\n    end='1981-05-31',\n)\n```\n![Grand Coulee](https://github.com/IMMM-SFA/mosartwmpy/raw/main/docs/_static/grand_coulee_1981_05.png)\n\nPlot a particular output variable (as defined in `config.yaml`) over time:\n```python\nfrom mosartwmpy import Model\nfrom mosartwmpy.plotting.plot import plot_variable\nconfig_file = 'config.yaml'\nmosart_wm = Model()\nmosart_wm.initialize(config_file)\nmosart_wm.update_until()\n\nplot_variable(\n    model=mosart_wm,\n    variable='RIVER_DISCHARGE_OVER_LAND_LIQ',\n    start='1981-05-01',\n    end='1981-05-31',\n    log_scale=True,\n    cmap='winter_r',\n)\n```\n![River network no tiles](https://github.com/IMMM-SFA/mosartwmpy/raw/main/docs/_static/river_without_tiles_1981_05.png)\n\nIf `cartopy`, `scipy`, and `geoviews` are installed, tiles can be displayed along with the plot:\n```python\nplot_variable(\n    model=mosart_wm,\n    variable='RIVER_DISCHARGE_OVER_LAND_LIQ',\n    start='1981-05-01',\n    end='1981-05-31',\n    log_scale=True,\n    cmap='winter_r',\n    tiles='StamenWatercolor'\n)\n```\n![River network with tiles](https://github.com/IMMM-SFA/mosartwmpy/raw/main/docs/_static/river_with_tiles_1981_05.png)\n\n## model coupling\n\nA common use case for `mosartwmpy` is to run coupled with output from the Community Land Model (CLM). To see an example of how to drive `mosartwmpy` with runoff from a coupled model, check out the [Jupyter notebook tutorial](https://github.com/IMMM-SFA/mosartwmpy/blob/main/notebooks/tutorial.ipynb)!\n\n## testing and validation\n\nBefore running the tests or validation, make sure to download the \"sample_input\" and \"validation\" datasets using the download utility `python -m mosartwmpy.download`.\n\nTo execute the tests, run `./test.sh` or `python -m unittest discover mosartwmpy/tests` from the repository root.\n\nTo execute the validation, run a model simulation that includes the years 1981 - 1982, note your output directory, and then run `python -m mosartwmpy.validate` from the repository root. This will ask you for the simulation output directory, think for a moment, and then open a figure with several plots representing the NMAE (Normalized Mean Absolute Error) as a percentage and the spatial sums of several key variables compared between your simulation and the validation scenario. Use these plots to assist you in determining if the changes you have made to the code have caused unintended deviation from the validation scenario. The NMAE should be 0% across time if you have caused no deviations. A non-zero NMAE indicates numerical difference between your simulation and the validation scenario. This might be caused by changes you have made to the code, or alternatively by running a simulation with different configuration or parameters (i.e. larger timestep, fewer iterations, etc). The plots of the spatial sums can assist you in determining what changed and the overall magnitude of the changes.\n\nIf you wish to merge code changes that intentionally cause significant deviation from the validation scenario, please work with the maintainers to create a new validation dataset.\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.21105/joss.03221"],"works":{"https://doi.org/10.21105/joss.03221":{"id":"https://openalex.org/W3176599056","doi":"https://doi.org/10.21105/joss.03221","title":"mosartwmpy: A Python implementation of the MOSART-WM coupled hydrologic routing and water management model","display_name":"mosartwmpy: A Python implementation of the MOSART-WM coupled hydrologic routing and water management model","publication_year":2021,"publication_date":"2021-06-24","ids":{"openalex":"https://openalex.org/W3176599056","doi":"https://doi.org/10.21105/joss.03221","mag":"3176599056"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.21105/joss.03221","pdf_url":null,"source":{"id":"https://openalex.org/S4210214273","display_name":"Journal of open source software","issn_l":"2475-9066","issn":["2475-9066"],"is_oa":true,"is_in_doaj":true,"host_organization":"https://openalex.org/P4310315853","host_organization_name":"Open Journals","host_organization_lineage":["https://openalex.org/P4310315853"],"host_organization_lineage_names":["Open Journals"],"type":"journal"},"license":"cc-by","version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"journal-article","open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.21105/joss.03221","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058671752","display_name":"Travis Thurber","orcid":"https://orcid.org/0000-0002-4370-9971"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Travis Thurber","raw_affiliation_string":"","raw_affiliation_strings":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006939159","display_name":"Chris R. 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