holmes-rs

A fast, production-ready collection of hydrological models implemented in Rust with Python bindings via PyO3.

holmes-rs is the computational engine behind HOLMES (HydrOLogical Modeling Educational Software), but is designed to be usable as a standalone library for anyone needing efficient hydrological simulations.

Features

  • Hydrological Models: GR4J and Bucket rainfall-runoff models
  • Snow Modeling: CemaNeige snow accumulation and melt model
  • PET Calculation: Oudin method for potential evapotranspiration
  • Calibration: SCE-UA (Shuffled Complex Evolution) optimization algorithm
  • Metrics: RMSE, NSE, and KGE objective functions
  • Performance: Pure Rust with SIMD-friendly array operations via ndarray
  • Python Integration: Full NumPy interoperability through PyO3

Installation

From PyPI (when published)

pip install holmes-rs

From Source

Requires Rust 1.70+ and Python 3.11+.

cd src/holmes-rs
pip install maturin
maturin develop --release

Quick Start

Python

import numpy as np
from holmes_rs.hydro import gr4j
from holmes_rs.pet import oudin
from holmes_rs.metrics import calculate_nse

# Generate PET from temperature
temperature = np.random.uniform(5, 25, 365)
day_of_year = np.arange(1, 366)
latitude = 45.0
pet = oudin.simulate(temperature, day_of_year, latitude)

# Initialize GR4J with default parameters
defaults, bounds = gr4j.init()
# defaults: [350.0, 0.0, 90.0, 1.7]  (x1, x2, x3, x4)
# bounds: [[10, 1500], [-5, 3], [10, 400], [0.8, 10]]

# Run simulation
precipitation = np.random.uniform(0, 20, 365)
streamflow = gr4j.simulate(defaults, precipitation, pet)

# Evaluate against observations
observations = np.random.uniform(0, 10, 365)
nse = calculate_nse(observations, streamflow)
print(f"NSE: {nse:.3f}")

Rust

use holmes_rs::hydro::gr4j;
use holmes_rs::metrics::calculate_nse;
use ndarray::array;

let (defaults, _bounds) = gr4j::init();
let precipitation = array![5.0, 10.0, 0.0, 15.0, 2.0];
let pet = array![3.0, 3.5, 4.0, 3.2, 3.8];

let streamflow = gr4j::simulate(
    defaults.view(),
    precipitation.view(),
    pet.view()
).unwrap();

let nse = calculate_nse(observations.view(), streamflow.view()).unwrap();

Models

GR4J

A parsimonious 4-parameter rainfall-runoff model widely used in operational hydrology.

Parameter Range Description
x1 10–1500 Production store capacity (mm)
x2 -5–3 Groundwater exchange coefficient (mm/day)
x3 10–400 Routing store capacity (mm)
x4 0.8–10 Unit hydrograph time base (days) 
from holmes_rs.hydro import gr4j

defaults, bounds = gr4j.init()
streamflow = gr4j.simulate(params, precipitation, pet)

Bucket

A 6-parameter conceptual model with explicit soil, routing, and transpiration reservoirs.

Parameter Range Description
c_soil 10–1000 Soil storage capacity (mm)
alpha 0–1 Infiltration partitioning
k_r 1–200 Routing decay constant
delta 2–10 Routing delay (timesteps)
beta 0–1 Baseflow coefficient
k_t 1–400 Transpiration parameter 
from holmes_rs.hydro import bucket

defaults, bounds = bucket.init()
streamflow = bucket.simulate(params, precipitation, pet)

CemaNeige

A degree-day snow model with multi-layer elevation distribution.

Parameter Range Description
ctg 0–1 Thermal state time constant
kf 0–20 Snowmelt rate coefficient (mm/°C/day)
qnbv 50–800 Degree-day factor threshold 
from holmes_rs.snow import cemaneige

defaults, bounds = cemaneige.init()

# elevation_layers: fraction of catchment at each elevation band
# median_elevation: reference elevation (m)
effective_precip = cemaneige.simulate(
    params, precipitation, temperature, day_of_year,
    elevation_layers, median_elevation
)

# Chain with hydro model
streamflow = gr4j.simulate(hydro_params, effective_precip, pet)

Oudin PET

Temperature-based potential evapotranspiration using extraterrestrial radiation.

from holmes_rs.pet import oudin

pet = oudin.simulate(temperature, day_of_year, latitude)

Calibration

SCE-UA (Shuffled Complex Evolution - University of Arizona) for automatic parameter optimization.

from holmes_rs.calibration.sce import Sce

# Create calibrator
sce = Sce(
    hydro_model="gr4j",
    snow_model=None,            # or "cemaneige"
    objective="nse",            # "rmse", "nse", or "kge"
    transformation="none",      # "none", "log", or "sqrt"
    n_complexes=3,
    k_stop=5,
    p_convergence_threshold=0.1,
    geometric_range_threshold=0.0001,
    max_evaluations=1000,
    seed=42                     # for reproducibility
)

# Initialize with data
sce.init(precip, temp, pet, doy, elevation_layers, median_elev, observations)

# Run calibration
done = False
while not done:
    done, best_params, criteria, objectives = sce.step(
        precip, temp, pet, doy, elevation_layers, median_elev, observations
    )
    print(f"Best NSE so far: {max(objectives):.4f}")

print(f"Optimal parameters: {best_params}")

Objective Functions

Objective Formula Optimal
RMSE √(Σ(O-S)²/n) 0
NSE 1 - Σ(O-S)²/Σ(O-μ)² 1
KGE 1 - √((r-1)² + (α-1)² + (β-1)²) 1

Transformations

Apply transformations to emphasize different flow regimes:

  • none: Raw values (emphasizes high flows)
  • log: Log-transformed (emphasizes low flows)
  • sqrt: Square-root (balanced)

Metrics

Standalone metric functions for model evaluation:

from holmes_rs.metrics import calculate_rmse, calculate_nse, calculate_kge

rmse = calculate_rmse(observations, simulations)
nse = calculate_nse(observations, simulations)
kge = calculate_kge(observations, simulations)

Error Handling

holmes-rs provides informative exceptions for debugging:

from holmes_rs import HolmesValidationError, HolmesNumericalError

try:
    # Invalid: negative precipitation
    gr4j.simulate(params, np.array([-1.0, 5.0]), pet)
except HolmesValidationError as e:
    print(f"Validation failed: {e}")

try:
    # Edge case: constant observations (zero variance)
    calculate_nse(np.array([5.0, 5.0, 5.0]), simulations)
except HolmesNumericalError as e:
    print(f"Numerical issue: {e}")

Module Structure

holmes_rs
├── hydro
│   ├── gr4j      # GR4J model
│   └── bucket    # Bucket model
├── snow
│   └── cemaneige # CemaNeige snow model
├── pet
│   └── oudin     # Oudin PET method
├── calibration
│   └── sce       # SCE-UA optimizer
└── metrics       # RMSE, NSE, KGE

Development

# Run Rust tests
cargo test

# Run Rust tests with coverage
cargo +nightly llvm-cov

# Run Python integration tests
pytest tests/python_integration

# Format and lint
cargo fmt
cargo clippy

Performance Notes

  • All numerical operations use ndarray with optimized BLAS
  • Calibration uses Rayon for parallel objective function evaluation
  • Release builds use LTO for maximum performance

License

MIT License - see LICENSE for details.

Part of HOLMES

This library powers the computational backend of HOLMES v3, a web-based hydrological modeling tool for teaching operational hydrology. While developed primarily for HOLMES, holmes-rs is designed as a general-purpose hydrological modeling library suitable for research, education, and operational applications.

Project links

Data verified by PyPI on Aug 8, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.7.0)

1 maintainer

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Avatar for antoinelb from gravatar.com antoinelb

Credits

Author: Antoine Lefebvre-Brossard

GitHub Statistics

Data verified by PyPI on Aug 8, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT

Requires

Python >=3.11

Classifiers

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0.7.0

21 files

This release

0.6.0 This release

21 files

0.5.1

21 files

0.5.0

21 files

0.4.1

21 files

0.4.0

21 files

0.3.0

21 files

0.2.3

21 files

0.2.2

21 files

0.2.1

21 files

0.2.0

21 files

0.1.2

21 files

0.1.1

10 files

0.1.0

9 files

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