ewatercycle 2.3.0

A Python package for running and validating a hydrology model

ewatercycle

A Python package for running hydrological models.

The eWaterCycle package makes it easier to use hydrological models without having intimate knowledge about how to install and run the models.

• Uses container for running models in an isolated and portable way with grpc4bmi
• Generates rain and sunshine required for the model using ESMValTool
• Supports observation data from GRDC or USGS
• Exposes simple interface to quickly get up and running

Install

The ewatercycle package needs some geospatial non-python packages to generate forcing data. It is preferred to create a Conda environment to install those dependencies:

curl -o conda-lock.yml https://raw.githubusercontent.com/eWaterCycle/ewatercycle/main/conda-lock.yml
conda install mamba conda-lock -n base -c conda-forge -y
conda-lock install --no-dev -n ewatercycle
conda activate ewatercycle

The ewatercycle package is installed with

pip install ewatercycle

The ewatercycle package ships without any models. Models are packaged in plugins. To install all endorsed plugins use

pip install ewatercycle-hype ewatercycle-lisflood ewatercycle-marrmot ewatercycle-pcrglobwb ewatercycle-wflow ewatercycle-leakybucket

Besides installing software you will need to create a configuration file, download several data sets and get container images. See the system setup chapter for instructions.

Usage

Example using the Marrmot M14 (TOPMODEL) hydrological model on Rhine catchment to generate forcing, run it and produce a hydrograph.

In condensed code:

forcing = ewatercycle.forcing.sources['MarrmotForcing'].generate(...)
model = ewatercycle.models.sources['MarrmotM14'](forcing)
model.setup(...)
model.initialize()
while (model.time < model.end_time):
    model.update()
    value = model.get_value_as_xarray('flux_out_Q')
model.finalize()
ewatercycle.analysis.hydrograph(...)

In real code:

import ewatercycle.analysis
import ewatercycle.forcing
import ewatercycle.models
import ewatercycle.observation.grdc
from ewatercycle.testing.fixtures import rhine_shape
import shapefile
import pandas as pd

forcing = ewatercycle.forcing.sources['MarrmotForcing'].generate(
    dataset='ERA5',
    start_time='2010-01-01T00:00:00Z',
    end_time='2010-12-31T00:00:00Z',
    shape=rhine_shape()
)

model = ewatercycle.models.sources['MarrmotM14'](version='2020.11', forcing=forcing)

cfg_file, cfg_dir = model.setup(
    threshold_flow_generation_evap_change=0.1,
)

model.initialize(cfg_file)

# flux_out_Q unit conversion factor from mm/day to m3/s
sf = shapefile.Reader(rhine_shape())
area = sf.record(0)['SUB_AREA'] * 1e6 # from shapefile in m2
conversion_mmday2m3s = 1 / (1000 * 24 * 60 * 60)
conversion = conversion_mmday2m3s * area

simulated_discharge = []
while (model.time < model.end_time):
    model.update()
    simulated_discharge.append(
        model.get_value_as_xarray('flux_out_Q')
    )

observations_ds = ewatercycle.observation.grdc.get_grdc_data(
    station_id=6335020,  # Rees, Germany
    start_time=model.start_time_as_isostr,
    end_time=model.end_time_as_isostr,
    column='observation',
)

# Combine the simulated discharge with the observations
sim_da = xr.concat(simulated_discharge, dim='time') * conversion
sim_da.name = 'simulated'
discharge = xr.merge([sim_da, observations_ds["observation"]]).to_dataframe()
discharge= discharge[["observation", "simulated"]].dropna()

ewatercycle.analysis.hydrograph(simulated_discharge_df.join(observations_df), reference='observation')

model.finalize()

More examples can be found in the plugins listed in the documentation.

Contributing

If you want to contribute to the development of ewatercycle package, have a look at the contribution guidelines.

License

Copyright (c) 2018 - 2024, Netherlands eScience Center & Delft University of Technology

Apache Software License 2.0