holmes-rs 0.2.1

HOLMES (HydrOLogical Modeling Educationnal Software) is a software developped to teach operational hydrology. It is developed at the university Laval, Québec, Canada. These are the models written in rust.

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.