Analyse de données de pluie

Manipulation de données en provenance du site https://hydrometrie.wallonie.be

Import de modules

[2]:

import matplotlib.pyplot as plt
import numpy as np
import datetime as dt

from wolfhece import is_enough
if not is_enough('2.2.63'):
     print('Please update wolfhece to version 2.2.63 or higher to run this notebook')

import wolfhece.irm_qdf as qdf
from wolfhece.hydrometry.rain_SPW import *
from wolfhece.hydrometry.flow_SPW import *

Instanciation des objets

Les séries temporelles n’ont pas la même durée selon le pas d’acquisition.

Pour faciliter la manipulation, des classes spécifiques sont proposées pour les pas de 5 minutes et 1 heure.

[3]:

#objet de gestion des pluvios SPW
precip_5min = SPW_pluviographs_5min()
precip_1h = SPW_pluviographs_1h()

Listing des stations disponibles

[4]:

names_5min = precip_5min.get_names()
print('Available 5min pluviographs : {0}'.format(names_5min))

names_1h = precip_1h.get_names()
print('Available 1h pluviographs : {0}'.format(names_1h))

print('Number of stations with 5min pluviographs : {0}'.format(len(names_5min)))
print('Number of stations with 1h pluviographs : {0}'.format(len(names_1h)))

print('Differences between 5min and 1h pluviographs : {0}'.format(len(set(names_5min) - set(names_1h))))

Available 5min pluviographs : ['WAVRE', 'LOUVAIN LA NEUVE', 'BOUSVAL', 'PERWEZ', 'HELECINE', 'UCCLE', 'TUBIZE', 'SOIGNIES', 'LILLOIS', 'SENEFFE', 'DERGNEAU', 'ENGHIEN', 'CHIEVRES', 'KAIN', 'MOUSCRON', 'WASMUEL', 'TRIVIERES', 'ROISIN', 'ROUVEROY', 'BLAREGNIES', 'PERUWELZ', 'COMINES Barrage-Ecl', 'GEMMENICH', 'WAREMME', 'AWANS', 'BATTICE', 'LANAYE', 'RACHAMPS-NOVILLE', 'ORTHO', 'SAINT-HUBERT Aéro', 'TERNELL', 'MONT-RIGI', 'SPA aerodrome', 'JALHAY', 'LOUVEIGNE', 'COO INF.', 'COO SUP.', 'VIELSALM', 'TAILLES', 'ROBERTVILLE', 'BUTGENBACH', 'SART-TILMAN', 'OUFFET', 'MEAN', 'EREZEE', 'MARCHE', 'LANDENNE', 'MODAVE', 'BRAIVES', 'VEDRIN', 'MORNIMONT Bar-Ecluse', 'CHATELET', 'MONCEAU Bar-Ecluse', 'SOLRE S/S Bar-Ecluse', 'MOMIGNIES', 'LIGNY', 'GERPINNES', 'PLATE TAILLE', 'SENZEILLES', 'SIVRY', 'ANSEREMME', 'SAINT-GERARD', 'CRUPET', 'CINEY', 'FLORENNES', 'DAVERDISSE', 'LIBIN', 'BEAURAING', 'ROCHEFORT', 'NASSOGNE', 'GEDINNE', 'CROIX-SCAILLE', 'BOUSSU-EN-FAGNE', 'PETIGNY Barrage', 'CUL-DES-SARTS', 'VRESSE', 'BOUILLON', 'FRATIN', 'MEIX-LE-TIGE', 'ARLON', 'SUGNY', 'BERTRIX', 'STRAIMONT', 'NAMOUSSART', 'TORGNY', 'ATHUS', 'AUBANGE', 'SELANGE', 'ORVAL', 'STEFFESHAUSEN', 'SANKT-VITH', 'BASTOGNE', 'MARTELANGE']
Available 1h pluviographs : ['WAVRE', 'LOUVAIN LA NEUVE', 'BOUSVAL', 'PERWEZ', 'HELECINE', 'UCCLE', 'TUBIZE', 'SOIGNIES', 'LILLOIS', 'SENEFFE', 'DERGNEAU', 'ENGHIEN', 'CHIEVRES', 'KAIN', 'MOUSCRON', 'WASMUEL', 'TRIVIERES', 'ROISIN', 'ROUVEROY', 'BLAREGNIES', 'PERUWELZ', 'COMINES Barrage-Ecl', 'GEMMENICH', 'WAREMME', 'AWANS', 'BATTICE', 'LANAYE', 'RACHAMPS-NOVILLE', 'ORTHO', 'SAINT-HUBERT Aéro', 'TERNELL', 'MONT-RIGI', 'SPA aerodrome', 'JALHAY', 'LOUVEIGNE', 'COO INF.', 'COO SUP.', 'VIELSALM', 'TAILLES', 'ROBERTVILLE', 'BUTGENBACH', 'SART-TILMAN', 'OUFFET', 'MEAN', 'EREZEE', 'MARCHE', 'LANDENNE', 'MODAVE', 'BRAIVES', 'VEDRIN', 'MORNIMONT Bar-Ecluse', 'CHATELET', 'MONCEAU Bar-Ecluse', 'SOLRE S/S Bar-Ecluse', 'MOMIGNIES', 'LIGNY', 'GERPINNES', 'PLATE TAILLE', 'SENZEILLES', 'SIVRY', 'ANSEREMME', 'SAINT-GERARD', 'CRUPET', 'CINEY', 'FLORENNES', 'DAVERDISSE', 'LIBIN', 'BEAURAING', 'ROCHEFORT', 'NASSOGNE', 'GEDINNE', 'CROIX-SCAILLE', 'BOUSSU-EN-FAGNE', 'PETIGNY Barrage', 'CUL-DES-SARTS', 'VRESSE', 'BOUILLON', 'FRATIN', 'MEIX-LE-TIGE', 'ARLON', 'SUGNY', 'BERTRIX', 'STRAIMONT', 'NAMOUSSART', 'TORGNY', 'ATHUS', 'AUBANGE', 'SELANGE', 'ORVAL', 'STEFFESHAUSEN', 'SANKT-VITH', 'BASTOGNE', 'MARTELANGE']
Number of stations with 5min pluviographs : 93
Number of stations with 1h pluviographs : 93
Differences between 5min and 1h pluviographs : 0

Récupération d’une série temporelle depuis le site web

Il faut bien assimiler que les données temporelles sont liées à un timezone.

Si les dates passées en arguments ne contiennent pas de timezone spécifique (“naive” timezone), elles seront supposées être en heure locale belge (‘Europe/Brussels”).

Par défaut, les valeurs retournées le seront en “GMT+1”/”UTC+1”, soit l’heure d’hiver. Ce timezone est la valeur utilisée en interne par le SPW dans la base de données Kiwis.

[5]:

station = 'Jalhay'

january = precip_5min.get_month_data(month=1, year=2021,
                                     name = station)
print('Total rain in January 2021 at {0} : {1} mm'.format(station, january.sum()))
print('Start date of the data : {0}'.format(january.index[0]))
print('End date of the data : {0}'.format(january.index[-1]))
print('Time zone of the data : {0}'.format(january.index.tzinfo))

print('')

july = precip_5min.get_month_data(month=7, year=2021,
                                  name = station)
print('Total rain in July 2021 at {0} : {1} mm'.format(station, july.sum()))
print('Start date of the data : {0}'.format(july.index[0]))
print('End date of the data : {0}'.format(july.index[-1]))
print('Time zone of the data : {0}'.format(july.index.tzinfo))
print('')

july_local = precip_5min.get_month_data(month=7, year=2021,
                                        name = station,
                                        timezone='Europe/Brussels')
print('Total rain in July 2021 at {0} : {1} mm'.format(station, july_local.sum()))
print('Start date of the data : {0}'.format(july_local.index[0]))
print('End date of the data : {0}'.format(july_local.index[-1]))
print('Time zone of the data : {0}'.format(july_local.index.tzinfo))

Total rain in January 2021 at Jalhay : 198.77 mm
Start date of the data : 2021-01-01 00:00:00+01:00
End date of the data : 2021-01-31 23:55:00+01:00
Time zone of the data : UTC+01:00

Total rain in July 2021 at Jalhay : 362.59999999999997 mm
Start date of the data : 2021-06-30 23:00:00+01:00
End date of the data : 2021-07-31 22:55:00+01:00
Time zone of the data : UTC+01:00

Total rain in July 2021 at Jalhay : 362.59999999999997 mm
Start date of the data : 2021-07-01 00:00:00+02:00
End date of the data : 2021-07-31 23:55:00+02:00
Time zone of the data : UTC+02:00

[6]:

july_1h = precip_1h.get_month_data(month=7, year=2021, name = station)

print('Time steps must be 1h : {0}'.format(july_1h.index[1] - july_1h.index[0]))
# check if the time step is uniform
print('Time steps are uniform : {0}'.format(np.all(july_1h.index[1:] - july_1h.index[:-1] == july_1h.index[1] - july_1h.index[0])))

Time steps must be 1h : 0 days 01:00:00
Time steps are uniform : True

Téléchargement de toutes les données disponibles

Il est possible de charger toutes les données en local (environ 150 Mo pour les données 1h).

Elles seront par défaut stockées dans le répertoire du paquet wolfhece.

Le format de stockage par défaut est “parquet”, format binaire compressé. Il est toutefois possible de sauver des “csv”.

Si les fichiers existent, les données ne seront pas rechargées.

[7]:

precip_1h.download_all(fromyear=2002, toyear=2026,
                       timezone='GMT+1', format='parquet')

100%|██████████| 93/93 [00:00<00:00, 2019.67it/s]

Chargement de toutes les données

Une fois en local, le chargement de toutes les données ne prend que quelques secondes.

Le format parquet est particulièrement efficace dans ce cadre.

La routine “load_all” retourne un dictionnaire dont les clés sont les ts_id et les valeurs sont les séries pandas.

Ce dictionnaire est également disponible comme attribut de l’objet. L’accès à une station peut s’y faire grâce à son nom, la conversion vers le ts_id étant gérée pour vous.

[8]:

all_data = precip_1h.load_all()

print('Type of the data : {0}'.format(type(all_data)))

93it [00:01, 53.42it/s]

Type of the data : <class 'dict'>

Mise à jour des données locales

Il n’est pas nécessaire de recharger toutes les données.

Les fichiers seront analysés et les données manquantes seront ajoutées.

Les fichiers seront par contre réécrits, le format parquet ne permettant pas facilement une mise à jour de sa structure.

Si toutes les données ont déjà été chargées, elles seront relues après la mise à jour.

[9]:

precip_1h.update_all()

100%|██████████| 93/93 [00:58<00:00,  1.60it/s]
93it [00:01, 51.73it/s]

[9]:

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 2002-01-01 00:00:00+01:00    0.0
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                             ...
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                             ...
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                             ...
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                             ...
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                             ...
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                             ...
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                             ...
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                             ...
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 2002-01-01 04:00:00+01:00    0.0
                             ...
 2026-02-13 13:00:00+01:00    0.0
 2026-02-13 14:00:00+01:00    0.0
 2026-02-13 15:00:00+01:00    0.0
 2026-02-13 16:00:00+01:00    0.0
 2026-02-13 17:00:00+01:00    0.0
 Name: Data, Length: 211434, dtype: float64,
 '256496010': Timestamp
 2002-01-01 00:00:00+01:00    0.0
 2002-01-01 01:00:00+01:00    0.0
 2002-01-01 02:00:00+01:00    0.0
 2002-01-01 03:00:00+01:00    0.0
 2002-01-01 04:00:00+01:00    0.0
                             ...
 2026-02-13 13:00:00+01:00    0.0
 2026-02-13 14:00:00+01:00    0.4
 2026-02-13 15:00:00+01:00    0.0
 2026-02-13 16:00:00+01:00    0.0
 2026-02-13 17:00:00+01:00    0.0
 Name: Data, Length: 211434, dtype: float64,
 '399076010': Timestamp
 2025-01-16 00:00:00+01:00    0.0
 2025-01-16 01:00:00+01:00    0.0
 2025-01-16 02:00:00+01:00    0.0
 2025-01-16 03:00:00+01:00    0.0
 2025-01-16 04:00:00+01:00    0.0
                             ...
 2026-02-13 13:00:00+01:00    0.0
 2026-02-13 14:00:00+01:00    0.1
 2026-02-13 15:00:00+01:00    0.0
 2026-02-13 16:00:00+01:00    0.2
 2026-02-13 17:00:00+01:00    0.0
 Name: Data, Length: 9450, dtype: float64,
 '403325010': Timestamp
 2025-08-01 00:00:00+01:00    0.0
 2025-08-01 01:00:00+01:00    1.8
 2025-08-01 02:00:00+01:00    5.9
 2025-08-01 03:00:00+01:00    0.9
 2025-08-01 04:00:00+01:00    9.9
                             ...
 2026-02-13 13:00:00+01:00    0.3
 2026-02-13 14:00:00+01:00    0.0
 2026-02-13 15:00:00+01:00    0.0
 2026-02-13 16:00:00+01:00    0.0
 2026-02-13 17:00:00+01:00    0.0
 Name: Data, Length: 4722, dtype: float64}

Tracer un graphique

Il est possible de tracer un graphique superposant les données pour une même période de l’année définie par la date de début et le nombre de mois à afficher.

[10]:

figax = precip_1h.plot_periodic(precip_1h['jalhay'],          # series to plot
                                origin=dt.datetime(2002,7,1), # origin of the plot (can be any date, but must be in the data)
                                length_in_months=3,           # length of the period to plot in months
                                toshow=True)                  # whether to show the plot or not

C:\Users\pierre\Documents\Gitlab\HECEPython\docs\source\tutorials\../../..\wolfhece\hydrometry\rain_SPW.py:604: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
  fig.show()

../_images/tutorials_analyse_pluie_hydrometry_17_1.png

On peut également comparer les données avec les valeurs statistiques fournies par l’IRM et accessibles via la classa “Qdf_IRM”.

[11]:

#Récupération des données de 2021 à Ternell et Jalhay

# en spécifiant les dates au chargement
Ternell= precip_1h.load(name='ternell', fromdate=dt.datetime(2021,1,1), todate=dt.datetime(2022,1,1))

# en spécifiant les dates sur la série après le chargement
Jalhay = precip_1h['jalhay']
Jalhay = Jalhay[(Jalhay.index >= dt.datetime(2021,1,1, tzinfo=Jalhay.index.tz)) & (Jalhay.index < dt.datetime(2022,1,1, tzinfo=Jalhay.index.tz))]

# Récupération des données de juillet 2008 au Sart-Tilman
SartTilman= precip_1h.load(name='sart-tilman', fromdate=dt.datetime(2008,1,1), todate=dt.datetime(2009,1,1))

# Stockage des données de 2002 à 2025 pour les trois stations
JalhayYear=[]
for curyear in range(2002,2026):
    JalhayYear.append(precip_1h.load(name='jalhay', fromdate=dt.datetime(curyear,1,1), todate=dt.datetime(curyear+1,1,1)))
TernellYear=[]
for curyear in range(2002,2026):
    TernellYear.append(precip_1h.load(name='Ternell', fromdate=dt.datetime(curyear,1,1), todate=dt.datetime(curyear+1,1,1)))
SartYear=[]
for curyear in range(2002,2026):
    SartYear.append(precip_1h.load(name='Sart-Tilman', fromdate=dt.datetime(curyear,1,1), todate=dt.datetime(curyear+1,1,1)))


#Lecture des données QDF pour Jalhay, Eupen et Sart-Tilman
JalhayQDF= qdf.Qdf_IRM(name='Jalhay', import_as_needed=True)
EupenQDF = qdf.Qdf_IRM(name='Eupen',  import_as_needed=True)
SartQDF  = qdf.Qdf_IRM(name='Seraing', import_as_needed=True)

[12]:

#Hyétogramme sur le mois de juillet et sur les 4 jours de crues
figD = precip_1h.plot(Jalhay,
                      xbounds=[dt.datetime(2021,7,1), dt.datetime(2021,7,31)],
                      ticks='D',
                      label='Jalhay')

precip_1h.plot(Ternell,
               xbounds=[dt.datetime(2021,7,1), dt.datetime(2021,7,31)],
               ticks='D',
               figax=figD,
               label='Ternell')

figH = precip_1h.plot(Jalhay,
                      xbounds=[dt.datetime(2021,7,13), dt.datetime(2021,7,16)],
                      ticks='2H',
                      label='Jalhay')
precip_1h.plot(Ternell,
               xbounds=[dt.datetime(2021,7,13), dt.datetime(2021,7,16)],
               ticks='2H',
               figax=figH,
               label='Ternell')

[12]:

(<Figure size 1500x800 with 1 Axes>,
 <Axes: xlabel='Date', ylabel='Précipitation moyenne horaire [mm/h]'>)

../_images/tutorials_analyse_pluie_hydrometry_20_1.png

../_images/tutorials_analyse_pluie_hydrometry_20_2.png

Superposition aux valeurs statistiques

[13]:

#Calcul des volumes cumulés pour toutes les durées
mystatsqJal = precip_1h.compute_stats_Q(Jalhay, STATS_HOURS_IRM)
mystatsqTer = precip_1h.compute_stats_Q(Ternell, STATS_HOURS_IRM)
mystatsqST  = precip_1h.compute_stats_Q(SartTilman, STATS_HOURS_IRM)

mystatsqJalYear=[]
mystatsqTerYear=[]
mystatsqSartYear=[]
for curJal in JalhayYear:
    mystatsqJalYear.append(precip_1h.compute_stats_Q(curJal, STATS_HOURS_IRM))
for curTer in TernellYear:
    mystatsqTerYear.append(precip_1h.compute_stats_Q(curTer, STATS_HOURS_IRM))
for curTer in SartYear:
    mystatsqSartYear.append(precip_1h.compute_stats_Q(curTer, STATS_HOURS_IRM))

#Calcul des intensités moyennes pour toutes les durées
mystatsiJal = precip_1h.compute_stats_i(Jalhay, STATS_HOURS_IRM)
mystatsiTer = precip_1h.compute_stats_i(Ternell, STATS_HOURS_IRM)
mystatsiST  = precip_1h.compute_stats_i(SartTilman, STATS_HOURS_IRM)

[14]:

#Plot des QDF vs événement de juillet 2021
fig, ax = JalhayQDF.plot_qdf(color=[.8,.8,.8])

ax:plt.Axes
ax.scatter(STATS_MINUTES_IRM, mystatsqJal,color='r', label='Juillet 2021')
ax.scatter(STATS_MINUTES_IRM, mystatsqST, color='b', marker='X', label='Mai 2008 - Sart-Tilman')
ax.legend()

fig,ax = JalhayQDF.plot_qdf(which='Montana', color=[.8,.8,.8])
year = 2002
for curJal in mystatsqJalYear:
    col = (year-2002) / (2020-2002) *.7
    ax.scatter(STATS_MINUTES_IRM, curJal, color=[col,col,col], label=str(year))
    year+=1

# ax.scatter(STATS_MINUTES_IRM, mystatsqJal, color='r', label='Juillet 2021')
# maxval=[np.max([cur[k] for cur in mystatsqJalYear]) for k in range(len(STATS_MINUTES_IRM))]
# pos=[np.argmax([cur[k] for cur in mystatsqJalYear]) for k in range(len(STATS_MINUTES_IRM))]
# for k in range(len(STATS_MINUTES_IRM)):
#     ax.text(STATS_MINUTES_IRM[k],maxval[k]+10,str(pos[k]+2002),horizontalalignment='center')

###

fig, ax = SartQDF.plot_qdf(color=[.8,.8,.8])
ax:plt.Axes
ax.scatter(STATS_MINUTES_IRM,mystatsqST,color='b',marker='X', label='Mai 2008 - Sart-Tilman')
ax.legend()

fig,ax=SartQDF.plot_qdf(which='Montana',color=[.8,.8,.8])
year=2002
ax.scatter(STATS_MINUTES_IRM, mystatsqSartYear[-1], color='r', label='Juillet 2021')
ax.scatter(STATS_MINUTES_IRM, mystatsqST, color='b', marker='X', label='Mai 2008')
ax.legend()

# for cur in mystatsqSartYear:
#     col=(year-2002)/(2021-2002)*.7
#     ax.scatter(STATS_MINUTES_IRM, cur, color=[col,col,col], label=str(year))
#     year+=1
# ax.scatter(STATS_MINUTES_IRM, mystatsqSartYear[-1], color='r', label='Juillet 2021')
# ax.scatter(STATS_MINUTES_IRM, mystatsqST,color='b', marker='X', label='Mai 2008')
# maxval=[np.max([cur[k] for cur in mystatsqSartYear]) for k in range(len(STATS_MINUTES_IRM))]
# pos=[np.argmax([cur[k] for cur in mystatsqSartYear]) for k in range(len(STATS_MINUTES_IRM))]
# for k in range(len(STATS_MINUTES_IRM)):
#     ax.text(STATS_MINUTES_IRM[k],maxval[k]+10,str(pos[k]+2002),horizontalalignment='center')

# ###

fig,ax=EupenQDF.plot_qdf(color=[.8,.8,.8])
ax.scatter(STATS_MINUTES_IRM,mystatsqTer,color='r',label='Juillet 2021')
ax.scatter(STATS_MINUTES_IRM,mystatsqST,color='b',marker='X', label='Mai 2008 - Sart-Tilman')
ax.legend()

# fig,ax=EupenQDF.plot_qdf(which='Montana',color=[.8,.8,.8])
# year=2002
# for curTer in mystatsqTerYear:
#     col=(year-2002)/(2020-2002)*.7
#     ax.scatter(STATS_MINUTES_IRM,curTer,color=[col,col,col],label=str(year))
#     year+=1
# ax.scatter(STATS_MINUTES_IRM,mystatsqTer,color='r',label='Juillet 2021')
# maxval = [np.max([cur[k] for cur in mystatsqTerYear]) for k in range(len(STATS_MINUTES_IRM))]
# pos = [np.argmax([cur[k] for cur in mystatsqTerYear]) for k in range(len(STATS_MINUTES_IRM))]
# for k in range(len(STATS_MINUTES_IRM)):
#     ax.text(STATS_MINUTES_IRM[k], maxval[k]+10, str(pos[k]+2002), horizontalalignment='center')


# #Plot des IDF vs événement de juillet 2021
# fig,ax = JalhayQDF.plot_idf(which='Montana',color=[.8,.8,.8])
# ax.scatter(STATS_MINUTES_IRM,mystatsiJal,color='r',label='Juillet 2021')
# ax.scatter(STATS_MINUTES_IRM,mystatsiST,color='b',marker='X', label='Mai 2008 - Sart-Tilman')
# ax.legend().set_draggable(True)

# fig,ax = EupenQDF.plot_idf(which='Montana',color=[.8,.8,.8])
# ax.scatter(STATS_MINUTES_IRM,mystatsiTer,color='r',label='Juillet 2021')
# ax.scatter(STATS_MINUTES_IRM,mystatsiST,color='b',marker='X', label='Mai 2008 - Sart-Tilman')
# ax.legend().set_draggable(True)

# fig,ax = SartQDF.plot_idf(which='Montana',color=[.8,.8,.8])
# ax.scatter(STATS_MINUTES_IRM,mystatsiST2021,color='r',label='Juillet 2021 - Sart-Tilman')
# ax.scatter(STATS_MINUTES_IRM,mystatsiST2008,color='b',marker='X', label='Mai 2008 - Sart-Tilman')
# ax.legend().set_draggable(True)

[14]:

<matplotlib.legend.Legend at 0x26fcf504a90>

../_images/tutorials_analyse_pluie_hydrometry_23_1.png

../_images/tutorials_analyse_pluie_hydrometry_23_2.png

../_images/tutorials_analyse_pluie_hydrometry_23_3.png

../_images/tutorials_analyse_pluie_hydrometry_23_4.png

../_images/tutorials_analyse_pluie_hydrometry_23_5.png

Tentative d’association d’une période de retour

ATTENTION: les statistiques de l’IRM sont basées sur des mesures de pluies historiques jusque 2016. L’épisode de juillet 2021 n’est donc pas (encore) inclu dans la série.

L’extrapolation est donc soumise à analyse experte et est fournie ici pour simple illustration.

[13]:

#Calage d'une GEV sur les données
JalhayQDF.fit_cdf(qdf.dur3d,plot=True)
EupenQDF.fit_cdf(qdf.dur3d, plot=True)

#Estimation des périodes de retour pour 3 jours et 2 jours
TJ3d = JalhayQDF.get_Tfromrain(mystatsqJal[7],qdf.dur3d)
TT3d = EupenQDF.get_Tfromrain(mystatsqTer[7], qdf.dur3d)

JalhayQDF.fit_cdf(qdf.dur2d,plot=True)
EupenQDF.fit_cdf(qdf.dur2d, plot=True)

TJ2d = JalhayQDF.get_Tfromrain(mystatsqJal[6],qdf.dur2d)
TT2d = EupenQDF.get_Tfromrain(mystatsqTer[6], qdf.dur2d)

print(f'Jalhay 2d: {TJ2d:.0f} ans - {mystatsqJal[6]:.2f} mm')
print(f'Jalhay 3d: {TJ3d:.0f} ans - {mystatsqJal[7]:.2f} mm')
print(f'Ternell 2d: {TT2d:.0f} ans - {mystatsqTer[6]:.2f} mm')
print(f'Ternell 3d: {TT3d:.0f} ans - {mystatsqTer[7]:.2f} mm')

#Estimation des périodes de retour pour 2 heures en 2008 au Sart-Tilman
SartQDF.fit_cdf(qdf.dur2h,plot=True)
Qrain =mystatsqSartYear[6][np.argwhere(STATS_HOURS_IRM==2)][0][0]
TST2h = SartQDF.get_Tfromrain(Qrain,qdf.dur2h)
print(f'Sart-Tilman 2h: {TST2h:.0f} ans - {Qrain:.2f} mm')

SartQDF.fit_cdf(qdf.dur3h,plot=True)
Qrain = mystatsqSartYear[6][np.argwhere(STATS_HOURS_IRM==3)][0][0]
TST3h = SartQDF.get_Tfromrain(Qrain,qdf.dur3h)
print(f'Sart-Tilman 3h: {TST3h:.0f} ans - {Qrain:.2f} mm')

Jalhay 2d: 101333 ans - 275.40 mm
Jalhay 3d: 126336 ans - 291.70 mm
Ternell 2d: 4112 ans - 198.30 mm
Ternell 3d: 3520 ans - 208.90 mm
Sart-Tilman 2h: 850 ans - 79.80 mm
Sart-Tilman 3h: 647 ans - 83.10 mm

../_images/tutorials_analyse_pluie_hydrometry_25_1.png

../_images/tutorials_analyse_pluie_hydrometry_25_2.png

../_images/tutorials_analyse_pluie_hydrometry_25_3.png

../_images/tutorials_analyse_pluie_hydrometry_25_4.png

../_images/tutorials_analyse_pluie_hydrometry_25_5.png

../_images/tutorials_analyse_pluie_hydrometry_25_6.png