# -*- coding: utf-8 -*-
"""
* Copyright (C) 2023-2025 Alexandre Gauvain, Ronan Abhervé, Jean-Raynald de Dreuzy
*
* This program and the accompanying materials are made available under the
* terms of the Eclipse Public License 2.0 which is available at
* http://www.eclipse.org/legal/epl-2.0, or the Apache License, Version 2.0
* which is available at https://www.apache.org/licenses/LICENSE-2.0.
*
* SPDX-License-Identifier: EPL-2.0 OR Apache-2.0
"""
#%% LIBRAIRIES
# Python
import pandas as pd
import numpy as np
import os
from scipy.optimize import curve_fit
from hydromodpy.watershed import sim2
import re
from hydromodpy.tools import get_logger
logger = get_logger(__name__)
#%% CLASS
[docs]
class Climatic:
"""
Class to initialize the climate forcing data (recharge, runoff).
"""
def __init__(self, out_path: str=None):
"""
Parameters
----------
out_path : str
Path of the HydroModPy outputs.
"""
logger.info('Initializing climatic module parameters')
self.data_folder = os.path.join(out_path, 'results_stable/climatic/')
self.drias_folder = os.path.join(out_path, 'results_stable/drias/')
self.recharge = None
self.runoff = None
#%% UPDATE FROM OWN MANUAL DATA
[docs]
def update_recharge(self, values, sim_state):
"""
Function to update recharge based on its own values.
Parameters
----------
values
Recharge values, float or list of float.
sim_state : str
Select the simulation type, steady-state or transient.
"""
self.recharge = values # recharge
if isinstance(values,(dict))==False:
if sim_state == 'steady':
self.recharge = np.mean(self.recharge)
if isinstance(self.recharge,(int,float))==False:
self.recharge = self.recharge[0]
[docs]
def update_runoff(self, values, sim_state):
"""
Function to update runoff based on its own values.
Parameters
----------
values
Runoff values, float or list of float.
sim_state : str
Select the simulation type, steady-state or transient.
"""
self.runoff = values # recharge
if isinstance(values,(dict))==False:
if sim_state == 'steady':
try:
self.runoff = np.mean(self.runoff)
if isinstance(self.runoff,(int,float))==False:
self.runoff = self.runoff[0]
except:
pass
[docs]
def update_first_clim(self, first_clim):
"""
Define the first value of the recharge list values.
Parameters
----------
first_clim
Choice between a float, the mean or the first value in the list of values.
"""
self.first_clim = first_clim # 'mean', 'first' or value
#%% UPDATE FROM CREATED SYNTHETIC DATA
[docs]
def update_recharge_synthetic(self, rech, shape, years, start_date="2020-08",
time_step=None, dis='normal'):
"""
Create synthetic recharge values from mathematical function.
Parameters
----------
rech : float
Total annual recharge requested by the user.
shape : float
Value to build the mathematical function.
years : float
Number of years to generate.
start_date : str
Year and month for the beginning. The default is "2020-08".
freq : str
Inform the time step requested ('D','M','Y'). The default is None.
dis : str
Distribution of the mathematical function. The default is 'normal'.
"""
self.freq = time_step
days = years*365
date = pd.date_range(start_date, periods=days)
t = np.linspace(1,365,365)
time = []
for y in range(0,years):
time = np.concatenate((time,t))
mean = 180
if dis == 'inverse-gaussian':
pdf = (((mean*shape)/(2*np.pi*time**3))**0.5*np.exp(-(shape*(time-mean)**2)/(2*mean*time)))*rech
if dis == 'normal':
pdf = ((1/(shape*np.sqrt(2*np.pi)))*np.exp(-((time-mean)**2/(2*shape**2))))*rech
if dis == 'uniform':
pdf = np.zeros(len(time))
pdf[(time >= (mean-(shape/2))) & (time < ((shape/2)+mean))] = rech/shape
self.recharge = pd.Series(data = pdf, index=date)
if self.freq != None:
self.recharge = self.recharge.resample(self.freq).mean()
[docs]
def update_recharge_sinusoid(self, serie, period, amplitude, offset, omega, phase):
"""
Create synthetic recharge values from sinusoidal function.
Parameters
----------
serie
Pandas series of the initial recharge values.
period : str
Inform the time step requested ('D','M').
amplitude : float
Control the mxaimal value of the sinusoidal function.
offset : float
Control the vertical shift of the sinusoidal function.
omega : float
Control the number of cycles of the sinusoidal function.
phase : TYPE
Control the horizontal shift of the sinusoidal function.
"""
def sinusoid(x, A , offset, omega, phase):
return A*np.sin(omega*x+phase) + offset
def get_p0(Y, T):
A0 = (max(Y[0:T]) - min(Y[0:T]))/2
offset0 = Y[0]
phase0 = 0
omega0 = 2.*np.pi/T
return [A0, offset0, omega0, phase0]
if period=='D':
T=365
if period=='M':
T=12
date = serie.index
serie = serie.reset_index(drop=True)
X = serie.index
Y = serie.values
param, covariance = curve_fit(sinusoid, X, Y, p0=get_p0(Y, T))
param[0] = param[0] * amplitude # Amplitude : max
param[1] = param[1] * offset # Offset : shift v
param[2] = param[2] * omega # Omega : cycles
param[3] = param[3] * phase # Phase : shift h
sinus = sinusoid(X, *param)
self.recharge = pd.Series(data = sinus, index=date)
self.recharge[self.recharge < 0] = 0
#%% UPDATE FROM REANALYSIS DATA SET
# Adpated for :
# Historical reanalysis SAFRAN-SURFEX
# https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.2003
[docs]
def update_recharge_reanalysis(self, path_file, clim_mod, clim_sce, first_year,
last_year, time_step, sim_state=None):
"""
Update the recharge from a hydrometeorological reanalysis at the France scale.
From an inital REA.h5 file, and after using safransurfex.py class
Parameters
----------
path_file : str
Path of the specific file .csv, generated by a specific climatics model.
clim_mod : str
Label of the climatic model (e.g. 'REA').
clim_sce : str
Label of the scenario forecast model (e.g. 'historic').
first_year : int
First year of selected period.
last_year : int
Last year of selected period.
time_step : str
Frequency of the period ('D','W','M','Y')
sim_state : TYPE, optional
Select the simulation type, steady-state or transient.
"""
self.freq = time_step
climatic = pd.read_csv(path_file, sep=';', index_col=0,
# parse_dates=True
)
test_str = climatic.index[0]
pattern_str_tir = r'^\d{4}-\d{2}-\d{2}$'
pattern_str_sla = r'^\d{2}/\d{2}/\d{4}$'
if re.match(pattern_str_tir, test_str):
date_obj = pd.to_datetime(climatic.index, format="%Y-%m-%d")
if re.match(pattern_str_sla, test_str):
date_obj = pd.to_datetime(climatic.index, format="%d/%m/%Y")
climatic.index = date_obj
climatic = climatic['REC_'+clim_mod+'_'+clim_sce]
climatic = climatic[(climatic.index.year >= first_year) & (climatic.index.year <= last_year)]
self.recharge = climatic # recharge in meters
# self.recharge.index = self.recharge.asfreq(self.freq).index
self.recharge = self.recharge.resample(self.freq).mean()
self.recharge = self.recharge.ffill()
# self.recharge.index = self.recharge.index.to_period(self.freq)
if sim_state == 'steady':
self.recharge = self.recharge.mean()
[docs]
def update_runoff_reanalysis(self, path_file, clim_mod, clim_sce, first_year,
last_year, time_step, sim_state=None):
"""
Update the runoff from a hydrometeorological reanalysis at the France scale.
From an inital REA.h5 file, and after using safransurfex.py class
Parameters
----------
path_file : str
Path of the specific file .csv, generated by a specific climatics model.
clim_mod : str
Label of the climatic model (e.g. 'REA').
clim_sce : str
Label of the scenario forecast model (e.g. 'historic').
first_year : int
First year of selected period.
last_year : int
Last year of selected period.
sim_state : TYPE, optional
Select the simulation type, steady-state or transient.
"""
self.freq = time_step
climatic = pd.read_csv(path_file, sep=';', index_col=0,
# parse_dates=True
)
test_str = climatic.index[0]
pattern_str_tir = r'^\d{4}-\d{2}-\d{2}$'
pattern_str_sla = r'^\d{2}/\d{2}/\d{4}$'
if re.match(pattern_str_tir, test_str):
date_obj = pd.to_datetime(climatic.index, format="%Y-%m-%d")
if re.match(pattern_str_sla, test_str):
date_obj = pd.to_datetime(climatic.index, format="%d/%m/%Y")
climatic.index = date_obj
climatic = climatic['RUN_'+clim_mod+'_'+clim_sce]
climatic = climatic[(climatic.index.year >= first_year) & (climatic.index.year <= last_year)]
self.runoff = climatic # recharge in meters
# self.runoff.index = self.runoff.asfreq(self.freq).index
self.runoff = self.runoff.resample(self.freq).mean()
self.runoff = self.runoff.ffill()
# self.runoff.index = self.runoff.index.to_period(self.freq)
if sim_state == 'steady':
self.runoff = self.runoff.mean()
#%% UPDATE FROM EXPLORE1 DATA SET
# Adpated for :
# EXPLORE 2070 : SURFEX projections (downscaled from DAYON 2015)
# https://professionnels.ofb.fr/fr/node/44
[docs]
def update_recharge_explore1(self, path_file, clim_mod, clim_sce,
first_year, last_year,
time_step, sim_state=None):
"""
Update the recharge from a hydrometeorological hydroclimatic projection EXPLORE1 at the France scale.
Greenhouse effect scenarios: RCP2.6, RCP4.5, RCP6.0, RCP8.5.
Parameters
----------
path_file : str
Path of the specific file .csv, generated by a specific climatics model.
clim_mod : str
Label of the climatic model (e.g. 'REA').
clim_sce : str
Label of the scenario forecast model (e.g. 'historic').
first_year : int
First year of selected period.
last_year : int
Last year of selected period.
time_step : str
Frequency of the period ('D','W','M','Y')
sim_state : TYPE, optional
Select the simulation type, steady-state or transient.
"""
self.freq = time_step
climatic = pd.read_csv(path_file, sep=';', index_col=0,
# parse_dates=True
)
test_str = climatic.index[0]
pattern_str_tir = r'^\d{4}-\d{2}-\d{2}$'
pattern_str_sla = r'^\d{2}/\d{2}/\d{4}$'
if re.match(pattern_str_tir, test_str):
date_obj = pd.to_datetime(climatic.index, format="%Y-%m-%d")
if re.match(pattern_str_sla, test_str):
date_obj = pd.to_datetime(climatic.index, format="%d/%m/%Y")
climatic.index = date_obj
climatic = climatic['REC_'+clim_mod+'_'+clim_sce]
climatic = climatic[(climatic.index.year >= first_year) & (climatic.index.year <= last_year)]
self.recharge = climatic/1000 # recharge in meters
# self.recharge.index = self.recharge.asfreq(self.freq).index
self.recharge = self.recharge.resample(self.freq).mean()
self.recharge.fillna(method = 'ffill', inplace = True)
# self.recharge.index = self.recharge.index.to_period(self.freq)
if sim_state == 'steady':
self.recharge = self.recharge.mean()
[docs]
def update_runoff_explore1(self, path_file, clim_mod, clim_sce, first_year, last_year, time_step, sim_state=None):
"""
Update the runoff from a hydrometeorological hydroclimatic projection EXPLORE1 at the France scale.
Greenhouse effect scenarios: RCP2.6, RCP4.5, RCP6.0, RCP8.5.
Parameters
----------
path_file : str
Path of the specific file .csv, generated by a specific climatics model.
clim_mod : str
Label of the climatic model (e.g. 'IPS1').
clim_sce : str
Label of the scenario forecast model (e.g. 'RPC8.5').
first_year : int
First year of selected period.
last_year : int
Last year of selected period.
time_step : str
Frequency of the period ('D','W','M','Y')
sim_state : TYPE, optional
Select the simulation type, steady-state or transient.
"""
self.freq = time_step
climatic = pd.read_csv(path_file, sep=';', index_col=0,
# parse_dates=True
)
test_str = climatic.index[0]
pattern_str_tir = r'^\d{4}-\d{2}-\d{2}$'
pattern_str_sla = r'^\d{2}/\d{2}/\d{4}$'
if re.match(pattern_str_tir, test_str):
date_obj = pd.to_datetime(climatic.index, format="%Y-%m-%d")
if re.match(pattern_str_sla, test_str):
date_obj = pd.to_datetime(climatic.index, format="%d/%m/%Y")
climatic.index = date_obj
climatic = climatic['RUN_'+clim_mod+'_'+clim_sce]
climatic = climatic[(climatic.index.year >= first_year) & (climatic.index.year <= last_year)]
self.runoff = climatic/1000 # recharge in meters
# self.runoff.index = self.runoff.asfreq(self.freq).index
self.runoff = self.runoff.resample(self.freq).mean()
self.runoff.fillna(method = 'ffill', inplace = True)
# self.runoff.index = self.runoff.index.to_period(self.freq)
if sim_state == 'steady':
self.runoff = self.runoff.mean()
#%% UPDATE FROM EXPLORE2 DATA SET
# Adpated for :
# EXPLORE2-2021-SIM2 : SURFEX projections (available on DRIAS website)
# https://professionnels.ofb.fr/fr/node/1244
[docs]
def update_recharge_explore2(self, path_file, gcm_mod, rcm_mod, sce_mod,
first_year, last_year, sim_state=None):
"""
Update the recharge from a hydrometeorological hydroclimatic projection EXPLORE2 at the France scale.
Greenhouse effect scenarios: RCP2.6, RCP4.5, RCP6.0, RCP8.5.
Parameters
----------
path_file : str
Path of the specific file .csv, generated by a specific climatics model.
gcm__mod : str
Label of the global climatic model (e.g. 'CNR').
rcm_mod : str
Label of the regional climatic model (e.g. 'ALA').
clim_mod : str
Label of the scenario forecast model (e.g. 'historic').
first_year : int
First year of selected period.
last_year : int
Last year of selected period.
sim_state : TYPE, optional
Select the simulation type, steady-state or transient.
"""
data = pd.read_csv(path_file, sep=';', index_col=0,
# parse_dates=True
)
test_str = data.index[0]
pattern_str_tir = r'^\d{4}-\d{2}-\d{2}$'
pattern_str_sla = r'^\d{2}/\d{2}/\d{4}$'
if re.match(pattern_str_tir, test_str):
date_obj = pd.to_datetime(data.index, format="%Y-%m-%d")
if re.match(pattern_str_sla, test_str):
date_obj = pd.to_datetime(data.index, format="%d/%m/%Y")
data.index = date_obj
data = data[(data.index.year >= first_year) & (data.index.year <= last_year)]
self.recharge = data['REC'+'_'+gcm_mod+'-'+rcm_mod+'_'+sce_mod] / 1000 # mm to m
if sim_state == 'steady':
self.recharge = self.recharge.mean()
[docs]
def update_runoff_explore2(self, path_file, gcm_mod, rcm_mod, sce_mod, first_year, last_year, sim_state=None):
"""
Update the runoff from a hydrometeorological hydroclimatic projection EXPLORE2 at the France scale.
Greenhouse effect scenarios: RCP2.6, RCP4.5, RCP6.0, RCP8.5.
Parameters
----------
path_file : str
Path of the specific file .csv, generated by a specific climatics model.
gcm__mod : str
Label of the global climatic model (e.g. 'CNR').
rcm_mod : str
Label of the regional climatic model (e.g. 'ALA').
clim_mod : str
Label of the scenario forecast model (e.g. 'historic').
first_year : int
First year of selected period.
last_year : int
Last year of selected period.
sim_state : TYPE, optional
Select the simulation type, steady-state or transient.
"""
data = pd.read_csv(path_file, sep=';', index_col=0,
# parse_dates=True
)
test_str = data.index[0]
pattern_str_tir = r'^\d{4}-\d{2}-\d{2}$'
pattern_str_sla = r'^\d{2}/\d{2}/\d{4}$'
if re.match(pattern_str_tir, test_str):
date_obj = pd.to_datetime(data.index, format="%Y-%m-%d")
if re.match(pattern_str_sla, test_str):
date_obj = pd.to_datetime(data.index, format="%d/%m/%Y")
data.index = date_obj
data = data[(data.index.year >= first_year) & (data.index.year <= last_year)]
self.runoff = data['RUN'+'_'+gcm_mod+'-'+rcm_mod+'_'+sce_mod] / 1000 # mm to m
if sim_state == 'steady':
self.runoff = self.runoff.mean()
#%% UPDATE FROM SIM2 REANALYSIS (online)
[docs]
def update_sim2_reanalysis(self, *, var_list, nc_data_path,
first_year, last_year=None, time_step='D',
sim_state='transient', spatial_mean=False,
geographic, disk_clip=None):
"""
Download SIM2 reanalysis datasets and attach them to the climatic object.
Parameters
----------
var_list : str or list[str]
Variables to download (``['recharge', 'runoff', ...]``). Accepts a
single string which is converted to a list internally.
nc_data_path : str
Folder where the NetCDF files are cached (created if missing).
first_year : int
First year included in the extraction window.
last_year : int, optional
Last year to download (defaults to ``first_year`` if omitted).
time_step : {'D', 'M'}, optional
Temporal resolution requested when querying SIM2 (daily by default).
sim_state : {'transient', 'steady'}, optional
Simulation flavour; used when setting HydroModPy inputs.
spatial_mean : bool, optional
Average each variable over the watershed mask before storing it.
geographic : hydromodpy.watershed.geographic.Geographic
Geographic descriptor providing CRS, bounds, and watershed mask.
disk_clip : str, optional
Either ``'watershed'`` or a shapefile path controlling how cached
NetCDF cubes are spatially clipped to save disk space.
Returns
-------
None
"""
# If a single var name is provided, convert it to a list.
if isinstance(var_list, str): var_list = [var_list]
# Creation of SIM2 reanalysis object:
self.sim2_rea = sim2.Sim2(var_list=var_list, nc_data_path=nc_data_path,
first_year=first_year, last_year=last_year,
time_step=time_step, sim_state=sim_state,
spatial_mean=spatial_mean, geographic=geographic,
disk_clip=disk_clip)
# Note: values are available through reanalysis.data
for var in var_list:
exec(f"self.{var} = self.sim2_rea.values[var]")
# Get the data
data = self.sim2_rea.values[var]
# Construct the file path
file_path = os.path.join(nc_data_path, "_"+var+".csv")
# Save to CSV only for pandas objects (not xarray with spatial dimensions)
# For xarray Datasets with spatial dimensions, skip CSV export to avoid memory issues
if isinstance(data, (pd.DataFrame, pd.Series)):
data.to_csv(file_path, index=True, sep=';')
elif hasattr(data, 'to_dataframe'):
if 'x' not in data.dims and 'y' not in data.dims:
data.to_dataframe().to_csv(file_path, index=True, sep=';')
#%% SET DATA SET TO STEADY INPUTS
[docs]
def set_steady_recharge(self):
"""
Calculate the mean of recharge time series for steady-state simulation.
"""
self.recharge = self.recharge.mean()
[docs]
def set_steady_runoff(self):
"""
Calculate the mean of runoff time series for steady-state simulation.
"""
self.runoff = self.runoff.mean()
#%% NOTES
