pyquasoare

Python and C package to solve the reservoir differential equation using a piecewise quadratic interpolation following the QuaSoARe method.

What is pyquasoare?

This package implements the Quadratic Solution of the Approximate Reservoir Equation (QuaSoARe) method described in the following paper: Lerat, J. (2024),
"Technical note: Quadratic solution of the approximate reservoir equation (QuaASOARE)", HESS, Submitted.

Installation

• Create a suitable python environment. We recommend using miniconda combined with the environment specification provided in the env_pyquasoare.yml file in this repository.
• Git clone this repository and run pip install .

Basic use

Solution of the production store from the GR4J daily rainfall-runoff model using QuaSoAre:

from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyquasoare import benchmarks, approx, models
from hydrodiy.io import csv

# Package root path (might need modification)
froot = Path(__file__).parent.parent

# Get flux functions for the GR4J production store
# The store capacity is X1 (mm) and its filling level is S. 
#
# P is the daily rainfall, E is the daily evapotranspiration.
# infiltrated rainfall : fp(S/X1) = P(1-[S/X1]^2)
# actual evapotranspiration : fe(S/X1) = E S/X1 (2-S/X1)
# percolation : fr = -2.25^4/4 (S/X1)^5
#
fluxes, _ = benchmarks.gr4jprod_fluxes_noscaling()

# Definition of interpolation points
nalphas = 20
alphas = np.linspace(0., 1.2, nalphas)

# Quadratic piecewise interpolation of the flux functions
amat, bmat, cmat, cst = approx.quad_coefficient_matrix(fluxes, alphas)

# Loading data from a test site
siteid = "203014"
df = data_reader.get_data(siteid, "daily")
df = df.loc[start:end]
nval = len(df)
rain = df.loc[:, "RAINFALL[mm/day]"]
evap = df.loc[:, "PET[mm/day]"]

# input accounting for interception
rain_intercept = np.maximum(rain-evap, 0.)
evap_intercept = np.maximum(evap-rain, 0.)

# Defines scaling factors applied to flux functions for
# each time step. There are 3 scalings corresponding to 
# the 3 flux functions. 
scalings = np.column_stack([rain_intercept/X1, \
                            evap_intercept/X1, \
                            np.ones(nval)])

# Run the model using QuaSoare
X1 = 500
s0 = 1./2
niter, s1, fx = models.quad_model(alphas, scalings, \
                                amat, bmat, cmat, s0, 1.)

sims = np.column_stack([s1*X1, fx[:, 0]*X1, \
                            -fx[:, 1]*X1, -fx[:, 2]*X1])

# Plot results
plt.close("all")
fig, axs = plt.subplots(nrows=4, figsize=(15, 10), layout="constrained")
for iax, ax in enumerate(axs):
    ax.plot(time, sims[:, iax])

plt.show()

Attribution

This project is licensed under the MIT License, which allows for free use, modification, and distribution of the code under the terms of the license.

For proper citation of this project, please refer to the CITATION.cff file, which provides guidance on how to cite the software and relevant publications.