GR2M

Implementation

LuMod 0.1.3.0 GR2M

Time step: monthly
Backend: installed Python dependencies
Calibrated parameters: 2
Temperature required: no

Parameters and initial configuration

Parameter	Supported calibration range	Default
`x1`	50 to 2000	500
`x2`	0.1 to 2	0.8
`s0`	Fixed initial/configuration value	0.5
`r0`	Fixed initial/configuration value	0.5

Ranges/defaults are implementation contracts, not universal priors or a recommended basin calibration. Consult source comments for parameter units and coupling.

Governing equations

The following source is the exact model kernel used by this adapter. For MARRMoT it includes the state derivative and each referenced flux function; the solver and routing are described above. Original notices and source citations are retained in the files.

# -*- coding: utf-8 -*-
"""
modèle pluie-débit mensuel GR2M

Rain-Runoff Model


Author:
Saul Arciniega Esparza
Hydrogeology Group, Faculty of Engineering,
National Autonomous University of Mexico
zaul.ae@gmail.com | sarciniegae@comunidad.unam.mx

Reference:
Mouelhi, S., 2003. Vers une chaîne cohérente de modèles pluie-débit conceptuels
globaux aux pas de temps pluriannuel, annuel, mensuel et journalier. Thèse de Doctorat,
ENGREF, Cemagref Antony, France, 323 pp.

Mouelhi, S., C. Michel, C. Perrin, and V. Andréassian (2006), Stepwise development of a two-parameter
monthly water balance model, J. Hydrol., 318, 200-214, https://doi.org/10.1016/j.jhydrol.2005.06.014
"""

# Vendored from LuMod 0.1.3.0: kernels only; cached Numba compilation.
import math
from math import tanh
import numpy as np
import numba as nb

@nb.njit(cache=True)
def _gr2m(prec, pet, s0, r0, x1, x2):
    """
    modèle pluie-débit mensuel GR2M
    """
    # Initial parameters
    s0 = s0 * x1
    r0 = r0 * 60

    # Output series
    n = len(prec)
    s = np.zeros(n, dtype=np.float32)
    r = np.zeros(n, dtype=np.float32)
    qt = np.zeros(n, dtype=np.float32)

    # Main loop
    for i in range(n):
        phi = np.tanh(prec[i] / x1)
        psi = np.tanh(pet[i] / x1)
        s1 = (s0 + x1 * phi) / (1.0 + phi * s0 / x1)
        p1 = prec[i] + s0 - s1
        s2 = (s1 * (1.0 - psi)) / (1.0 + psi * (1.0 - s1 / x1))
        s0 = s2 / (1.0 + (s2 / x1) ** 3.0) ** (1.0 / 3.0)
        s[i] = s0 / x1  # save state
        p2 = s2 - s0
        p3 = p1 + p2
        r1 = r0 + p3
        r2 = x2 * r1
        qt[i] = r2 ** 2.0 / (r2 + 60.0)
        r0 = r2 - qt[i]
        r[i] = r0 / 60.0  # save state

    return qt, s, r

Simulation

from basinforge import Basin, get_model

basin = Basin.from_csv("basin.csv", basin_id="A", area_km2=1200,
                       q_unit="m3/s", timestep="monthly")
q_mm = get_model("GR2M").simulate(basin)
q_m3s = basin.to_m3s(q_mm)

Supply your actual data and catchment area; temperature-dependent models require a temperature column. For non-daily models, choose an appropriate warmup in model steps.

See calibration, input requirements, sources and verification limitations.