hydrosensenet
An Open-Source Python Package for Optimal Hydrological Monitoring Network Design
⚠️ In active development
Installation
Requires Python >= 3.9.
pip install hydrosensenet
# With optional extras:
pip install "hydrosensenet[viz]" # map plotting (matplotlib, cartopy)
pip install "hydrosensenet[nwm]" # NWM streamflow download stack (fsspec, dask, pynhd, ...)
The core install covers network design, evaluation, and I/O.
NetworkDesignResult.plot() and the NWM download utilities tell you
which extra to install if it is missing.
Development
git clone https://github.com/future-water/hydrosensenet.git
cd hydrosensenet
pip install -e ".[dev]" # editable install with tests, linting, build tooling
pytest # run the test suite
Quick Start
Examples
- design_texas_gulf_baseline.ipynb - Baseline HUC2-scale design with data download and USGS network comparison
- design_texas_gulf_flexible.ipynb - Flexible planning: per-HUC6 batch design across sub-basins
- design_texas_gulf_risk.ipynb - Risk-aware design using FEMA flood-risk weights
Basic Usage
Try it immediately on the bundled sample basin (368 reaches of the lower Colorado River, Texas — NWM v3.0 daily streamflow):
from hydrosensenet import SensorNetworkDesigner, load_example_basin
streamflow_df, locations_gdf = load_example_basin()
designer = SensorNetworkDesigner(streamflow_df, locations_gdf)
result = designer.design_network(n_sensors=20)
result.print_summary()
Or with your own data:
from hydrosensenet import SensorNetworkDesigner
from hydrosensenet.io import load_streamflow
import geopandas as gpd
# Load data
streamflow_df = load_streamflow("streamflow_2000.parquet")
locations_gdf = gpd.read_file("gauges.geojson")
# Design network
designer = SensorNetworkDesigner(
streamflow_data=streamflow_df.values,
locations=locations_gdf,
location_labels=list(streamflow_df.columns)
)
result = designer.design_network(n_sensors=50, evaluate=True)
result.print_summary()
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