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
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:
wget https://raw.githubusercontent.com/eWaterCycle/ewatercycle/main/environment.yml
conda install mamba -n base -c conda-forge -y
mamba env create --file environment.yml
conda activate ewatercycleThe ewatercycle package is installed with
pip install ewatercycleBesides 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.
Example using the Marrmot M14 (TOPMODEL) hydrological model on Merrimack catchment to generate forcing, run it and produce a hydrograph.
import pandas as pd
import ewatercycle.analysis
import ewatercycle.forcing
import ewatercycle.models
import ewatercycle.observation.grdc
forcing = ewatercycle.forcing.generate(
target_model='marrmot',
dataset='ERA5',
start_time='2010-01-01T00:00:00Z',
end_time='2010-12-31T00:00:00Z',
shape='Merrimack/Merrimack.shp'
)
model = ewatercycle.models.MarrmotM14(version="2020.11", forcing=forcing)
cfg_file, cfg_dir = model.setup(
threshold_flow_generation_evap_change=0.1,
leakage_saturated_zone_flow_coefficient=0.99,
zero_deficit_base_flow_speed=150.0,
baseflow_coefficient=0.3,
gamma_distribution_phi_parameter=1.8
)
model.initialize(cfg_file)
observations_df, station_info = ewatercycle.observation.grdc.get_grdc_data(
station_id=4147380,
start_time=model.start_time_as_isostr,
end_time=model.end_time_as_isostr,
column='observation',
)
simulated_discharge = []
timestamps = []
while (model.time < model.end_time):
model.update()
value = model.get_value('flux_out_Q')[0]
# flux_out_Q unit conversion factor from mm/day to m3/s
area = 13016500000.0 # from shapefile in m2
conversion_mmday2m3s = 1 / (1000 * 24 * 60 * 60)
simulated_discharge.append(value * area * conversion_mmday2m3s)
timestamps.append(model.time_as_datetime.date())
simulated_discharge_df = pd.DataFrame({'simulated': simulated_discharge}, index=pd.to_datetime(timestamps))
ewatercycle.analysis.hydrograph(simulated_discharge_df.join(observations_df), reference='observation')
model.finalize()More examples can be found in the documentation.
If you want to contribute to the development of ewatercycle package, have a look at the contribution guidelines.
Copyright (c) 2018, Netherlands eScience Center & Delft University of Technology
Apache Software License 2.0
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