Dimensionner la récupération d'eau de pluie pour être autonome/en : Différence entre versions

Version actuelle datée du 3 août 2024 à 16:57

Tutorial de Aurelpere | Catégories : Eau

Aurelpere

https://wiki.lowtechlab.org/wiki/Dimensionner_la_r%C3%A9cup%C3%A9ration_d%27eau_de_pluie_pour_%C3%AAtre_autonome/en

Dernière modification le 03/08/2024

Difficulté

Moyen

Durée

1 heure(s)

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0 EUR (€)

Description

Offgrid rainwater harvesting sizing

Difficulté

Moyen

Durée

1 heure(s)

Coût

0 EUR (€)

Autres langues :

English • ‎français

Sommaire

1 Description
2 Sommaire
3 Introduction
4 Étape 1 - Software prerequisites
5 Étape 2 - Needs evaluation
6 Étape 3 - Retrospective calculus of daily and seasonal precipitations
7 Étape 4 - Harvesting surface sizing and storage sizing
8 Étape 5 - Storage optimisation and extra water use in summer season
9 Étape 6 - Use climate change data
10 Commentaires

Licence : Attribution (CC BY)

Introduction

In cases where we want to be offgrid, the water issue is essential

It is actually the first element to consider for example when considering site settlement in permaculture (observation stage).

I initially made the piece of logic below to make a mobilhome offgrid with the idea to use photovoltaic modules to harvest rainwater, as in the ulta chaata realisation (https://www.facebook.com/weultachaata/?locale=fr_FR et https://fr.futuroprossimo.it/2023/03/ulta-chaata-ombrello-magico-che-puo-dare-acqua-e-luce-allindia/)

We can wonder on the correct way to size rainwater harvesting devices

To do that, we can use meteorological data (meteo france in france) to get a retrospective view on the seasonal precipitations and adjust the harvesting device sizes

Interactive web demo here:

https://vpn.matangi.dev/water

Étape 1 - Software prerequisites

In this tutorial, we use meteorological synop data available here:

http://data.cquest.org/meteo-france/synop/ with description here:

http://data.cquest.org/meteo-france/synop/doc_parametres_synop_168.pdf

You can also download the data on Meteo France website:

https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32

Download all csv files with months and years with which you want to make the computing, put them in a directory of your choice and unzip them (archive format is gz). Also put in this directory the file processing.py containing the code shared in this tutorial.

Example in debian linux command line to download and unzip all csv of year 2020 in a directory ~/synop:

(In the tutorial we use all data from 2010 to 2020)


cd ~

mkdir -p synop && cd synop

wget http://data.cquest.org/meteo-france/synop/synop.202001.csv.gz && gzip -d synop.202001.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202002.csv.gz && gzip -d synop.202002.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202003.csv.gz && gzip -d synop.202003.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202004.csv.gz && gzip -d synop.202004.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202005.csv.gz && gzip -d synop.202005.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202006.csv.gz && gzip -d synop.202006.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202007.csv.gz && gzip -d synop.202007.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202008.csv.gz && gzip -d synop.202008.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202009.csv.gz && gzip -d synop.202009.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202010.csv.gz && gzip -d synop.202010.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202011.csv.gz && gzip -d synop.202011.csv.gz

wget http://data.cquest.org/meteo-france/synop/synop.202012.csv.gz && gzip -d synop.202012.csv.gz

To use python under another operating system, please get by with your proprietary and intrusive crap.

Under linux, python is usually installed and to use the code shared here, you'll just have to copy and paste the code in a text file called processing.py and then enter


python processing.py

However, you will have to install pandas library which is massively used in finance and science industries, in particular for its efficient timeseries handling and vectorisation capacities.

To do so, here are the commands to enter in a linux debian system before running processing.py to be ok:


sudo apt install python3 python3-venv python3-pip python-is-python3

cd ~ && python -m venv venv

source venv/bin/activate

pip install pandas

Mind to activate virtual environment where pandas is installed each time you use the script (after a reboot or if you close and open again the terminal) with this command:


cd ~ && source venv/bin/activate

We are in 2024 and if you are being targetted and shackled as ecoterrorists like me, you will want to inspect your measurements instruments before use, so you can inspect the source code of pandas, which is of course free software, here: https://github.com/pandas-dev/pandas Or you can arguably suppose you can trust a software so massively used in finance and science industries.

Python relies on C libraries for some basics operations, and hack, including scientific hack, is never impossible, but we will let these pro-lowtech considerations aside that are out-of-scope of the perspectives of this tutorial.

Étape 2 - Needs evaluation

To evaluate the needs for domestic installations, nothing is more efficient than a water meter. A first approach is to make a rule of 3 from your weekly consumption. You can also measure individually each consumption entry (shower, washing machine, cooking, gardening, toilet, etc.) so you can make more accurate seasonal projections.

For a mobilhome with dry toilets we have:

conso solo - 2 showers weekly (L)

shower (L)			50

drinks				4

dish washing			10

cooking				4

washing machine		50

week  (2 showers, 1 machine)	276

daily			39

quarter (13 weeks)		3588

conso for two - 4 showers a week (L)

shower				50

drinks				4

dish washing			10

cooking				4

washing machine			50

week (4 showers, 1 machine)	752

daily			107

quarter (13 weeks)		9776

Étape 3 - Retrospective calculus of daily and seasonal precipitations

For correct storage sizing, we first need mean precipitations on previous years.

To do so, we provide the following piece of logic coded in python (fitting meteo france data but adapted to other meteorological data, as synop is an encoding standard used by OMM)

import math
import os
import pandas as pd

# Watch out if you use this piece of code in other countries, you have to add adhoc meteorological stations

# data processing
print("\ndata processing\n")
files=os.listdir('.')
csv=[a for a in files if a[-3:]=='csv']
combined_df = pd.concat((pd.read_csv(f,sep=';') for f in csv), ignore_index=True)
#07510 bordeaux
#07535 gourdon

#"hard coded" meteorological stations 
stations=[{'ID': '07005', 'Nom': 'ABBEVILLE', 'Latitude': '50.136000', 'Longitude': '1.834000', 'Altitude': '69'}, {'ID': '07015', 'Nom': 'LILLE-LESQUIN', 'Latitude': '50.570000', 'Longitude': '3.097500', 'Altitude': '47'}, {'ID': '07020', 'Nom': 'PTE DE LA HAGUE', 'Latitude': '49.725167', 'Longitude': '-1.939833', 'Altitude': '6'}, {'ID': '07027', 'Nom': 'CAEN-CARPIQUET', 'Latitude': '49.180000', 'Longitude': '-0.456167', 'Altitude': '67'}, {'ID': '07037', 'Nom': 'ROUEN-BOOS', 'Latitude': '49.383000', 'Longitude': '1.181667', 'Altitude': '151'}, {'ID': '07072', 'Nom': 'REIMS-PRUNAY', 'Latitude': '49.209667', 'Longitude': '4.155333', 'Altitude': '95'}, {'ID': '07110', 'Nom': 'BREST-GUIPAVAS', 'Latitude': '48.444167', 'Longitude': '-4.412000', 'Altitude': '94'}, {'ID': '07117', 'Nom': "PLOUMANAC'H", 'Latitude': '48.825833', 'Longitude': '-3.473167', 'Altitude': '55'}, {'ID': '07130', 'Nom': 'RENNES-ST JACQUES', 'Latitude': '48.068833', 'Longitude': '-1.734000', 'Altitude': '36'}, {'ID': '07139', 'Nom': 'ALENCON', 'Latitude': '48.445500', 'Longitude': '0.110167', 'Altitude': '143'}, {'ID': '07149', 'Nom': 'ORLY', 'Latitude': '48.716833', 'Longitude': '2.384333', 'Altitude': '89'}, {'ID': '07168', 'Nom': 'TROYES-BARBEREY', 'Latitude': '48.324667', 'Longitude': '4.020000', 'Altitude': '112'}, {'ID': '07181', 'Nom': 'NANCY-OCHEY', 'Latitude': '48.581000', 'Longitude': '5.959833', 'Altitude': '336'}, {'ID': '07190', 'Nom': 'STRASBOURG-ENTZHEIM', 'Latitude': '48.549500', 'Longitude': '7.640333', 'Altitude': '150'}, {'ID': '07207', 'Nom': 'BELLE ILE-LE TALUT', 'Latitude': '47.294333', 'Longitude': '-3.218333', 'Altitude': '34'}, {'ID': '07222', 'Nom': 'NANTES-BOUGUENAIS', 'Latitude': '47.150000', 'Longitude': '-1.608833', 'Altitude': '26'}, {'ID': '07240', 'Nom': 'TOURS', 'Latitude': '47.444500', 'Longitude': '0.727333', 'Altitude': '108'}, {'ID': '07255', 'Nom': 'BOURGES', 'Latitude': '47.059167', 'Longitude': '2.359833', 'Altitude': '161'}, {'ID': '07280', 'Nom': 'DIJON-LONGVIC', 'Latitude': '47.267833', 'Longitude': '5.088333', 'Altitude': '219'}, {'ID': '07299', 'Nom': 'BALE-MULHOUSE', 'Latitude': '47.614333', 'Longitude': '7.510000', 'Altitude': '263'}, {'ID': '07314', 'Nom': 'PTE DE CHASSIRON', 'Latitude': '46.046833', 'Longitude': '-1.411500', 'Altitude': '11'}, {'ID': '07335', 'Nom': 'POITIERS-BIARD', 'Latitude': '46.593833', 'Longitude': '0.314333', 'Altitude': '123'}, {'ID': '07434', 'Nom': 'LIMOGES-BELLEGARDE', 'Latitude': '45.861167', 'Longitude': '1.175000', 'Altitude': '402'}, {'ID': '07460', 'Nom': 'CLERMONT-FD', 'Latitude': '45.786833', 'Longitude': '3.149333', 'Altitude': '331'}, {'ID': '07471', 'Nom': 'LE PUY-LOUDES', 'Latitude': '45.074500', 'Longitude': '3.764000', 'Altitude': '833'}, {'ID': '07481', 'Nom': 'LYON-ST EXUPERY', 'Latitude': '45.726500', 'Longitude': '5.077833', 'Altitude': '235'}, {'ID': '07510', 'Nom': 'BORDEAUX-MERIGNAC', 'Latitude': '44.830667', 'Longitude': '-0.691333', 'Altitude': '47'}, {'ID': '07535', 'Nom': 'GOURDON', 'Latitude': '44.745000', 'Longitude': '1.396667', 'Altitude': '260'}, {'ID': '07558', 'Nom': 'MILLAU', 'Latitude': '44.118500', 'Longitude': '3.019500', 'Altitude': '712'}, {'ID': '07577', 'Nom': 'MONTELIMAR', 'Latitude': '44.581167', 'Longitude': '4.733000', 'Altitude': '73'}, {'ID': '07591', 'Nom': 'EMBRUN', 'Latitude': '44.565667', 'Longitude': '6.502333', 'Altitude': '871'}, {'ID': '07607', 'Nom': 'MONT-DE-MARSAN', 'Latitude': '43.909833', 'Longitude': '-0.500167', 'Altitude': '59'}, {'ID': '07621', 'Nom': 'TARBES-OSSUN', 'Latitude': '43.188000', 'Longitude': '0.000000', 'Altitude': '360'}, {'ID': '07627', 'Nom': 'ST GIRONS', 'Latitude': '43.005333', 'Longitude': '1.106833', 'Altitude': '414'}, {'ID': '07630', 'Nom': 'TOULOUSE-BLAGNAC', 'Latitude': '43.621000', 'Longitude': '1.378833', 'Altitude': '151'}, {'ID': '07643', 'Nom': 'MONTPELLIER', 'Latitude': '43.577000', 'Longitude': '3.963167', 'Altitude': '2'}, {'ID': '07650', 'Nom': 'MARIGNANE', 'Latitude': '43.437667', 'Longitude': '5.216000', 'Altitude': '9'}, {'ID': '07661', 'Nom': 'CAP CEPET', 'Latitude': '43.079333', 'Longitude': '5.940833', 'Altitude': '115'}, {'ID': '07690', 'Nom': 'NICE', 'Latitude': '43.648833', 'Longitude': '7.209000', 'Altitude': '2'}, {'ID': '07747', 'Nom': 'PERPIGNAN', 'Latitude': '42.737167', 'Longitude': '2.872833', 'Altitude': '42'}, {'ID': '07761', 'Nom': 'AJACCIO', 'Latitude': '41.918000', 'Longitude': '8.792667', 'Altitude': '5'}, {'ID': '07790', 'Nom': 'BASTIA', 'Latitude': '42.540667', 'Longitude': '9.485167', 'Altitude': '10'}, {'ID': '61968', 'Nom': 'GLORIEUSES', 'Latitude': '-11.582667', 'Longitude': '47.289667', 'Altitude': '3'}, {'ID': '61970', 'Nom': 'JUAN DE NOVA', 'Latitude': '-17.054667', 'Longitude': '42.712000', 'Altitude': '9'}, {'ID': '61972', 'Nom': 'EUROPA', 'Latitude': '-22.344167', 'Longitude': '40.340667', 'Altitude': '6'}, {'ID': '61976', 'Nom': 'TROMELIN', 'Latitude': '-15.887667', 'Longitude': '54.520667', 'Altitude': '7'}, {'ID': '61980', 'Nom': 'GILLOT-AEROPORT', 'Latitude': '-20.892500', 'Longitude': '55.528667', 'Altitude': '8'}, {'ID': '61996', 'Nom': 'NOUVELLE AMSTERDAM', 'Latitude': '-37.795167', 'Longitude': '77.569167', 'Altitude': '27'}, {'ID': '61997', 'Nom': 'CROZET', 'Latitude': '-46.432500', 'Longitude': '51.856667', 'Altitude': '146'}, {'ID': '61998', 'Nom': 'KERGUELEN', 'Latitude': '-49.352333', 'Longitude': '70.243333', 'Altitude': '29'}, {'ID': '67005', 'Nom': 'PAMANDZI', 'Latitude': '-12.805500', 'Longitude': '45.282833', 'Altitude': '7'}, {'ID': '71805', 'Nom': 'ST-PIERRE', 'Latitude': '46.766333', 'Longitude': '-56.179167', 'Altitude': '21'}, {'ID': '78890', 'Nom': 'LA DESIRADE METEO', 'Latitude': '16.335000', 'Longitude': '-61.004000', 'Altitude': '27'}, {'ID': '78894', 'Nom': 'ST-BARTHELEMY METEO', 'Latitude': '17.901500', 'Longitude': '-62.852167', 'Altitude': '44'}, {'ID': '78897', 'Nom': 'LE RAIZET AERO', 'Latitude': '16.264000', 'Longitude': '-61.516333', 'Altitude': '11'}, {'ID': '78922', 'Nom': 'TRINITE-CARAVEL', 'Latitude': '14.774500', 'Longitude': '-60.875333', 'Altitude': '26'}, {'ID': '78925', 'Nom': 'LAMENTIN-AERO', 'Latitude': '14.595333', 'Longitude': '-60.995667', 'Altitude': '3'}, {'ID': '81401', 'Nom': 'SAINT LAURENT', 'Latitude': '5.485500', 'Longitude': '-54.031667', 'Altitude': '5'}, {'ID': '81405', 'Nom': 'CAYENNE-MATOURY', 'Latitude': '4.822333', 'Longitude': '-52.365333', 'Altitude': '4'}, {'ID': '81408', 'Nom': 'SAINT GEORGES', 'Latitude': '3.890667', 'Longitude': '-51.804667', 'Altitude': '6'}, {'ID': '81415', 'Nom': 'MARIPASOULA', 'Latitude': '3.640167', 'Longitude': '-54.028333', 'Altitude': '106'}, {'ID': '89642', 'Nom': "DUMONT D'URVILLE", 'Latitude': '-66.663167', 'Longitude': '140.001000', 'Altitude': '43'}]

def distance(lat1, lon1, lat2, lon2):
    """
    distance computing between two geographic points using euclidian distance formula.
    """
    return math.sqrt((lat2 - lat1)**2 + (lon2 - lon1)**2)

def station_la_plus_proche(x, y, stations):
    """
    Find closest meteorological station using x and y coordinates (latitude and longitude).
    """
    distance_min = float('inf')
    station_proche = None
    
    for station in stations:
        lat_station = float(station['Latitude'])
        lon_station = float(station['Longitude'])
        d = distance(x, y, lat_station, lon_station)
        if d < distance_min:
            distance_min = d
            station_proche = station
    
    return station_proche

# Ask user to enter latitude and longitude
x_input = input("Enter your latitude: ")
y_input = input("Enter your longitude: ")

# Replace comma with points
x_input = float(x_input.replace(',', '.'))
y_input = float(y_input.replace(',', '.'))

# Use values entered by user as x and y variables to find the closest meteorological station
station_proche = station_la_plus_proche(x_input, y_input, stations)
print("The closest meteorological station is :", station_proche['Nom'])

result=combined_df[combined_df['numer_sta']==int(station_proche['ID'])]

# Convert 'date_column'in a datetime format and make it a sorted index
result['datetime'] = pd.to_datetime(result['date'], format='%Y%m%d%H%M%S')
result.set_index('datetime', inplace=True)
result = result.sort_index()

# replace missing data with 0
result['rr3']=result['rr3'].replace('mq','0')
result['rr3']=result['rr3'].astype('float')

# Only keep precipitations columns of last 3 hours
result=result['rr3']

# Calculate daily precipitations sums
resultday=result.resample('D').sum()
print("\nDaily mean (mm):\n", resultday.mean())
print("Daily minimum (mm):\n", resultday.min())
print("Daily maximum (mm):\n", resultday.max())

# Calculate weekly precipitations sums
resultweek=result.resample('W').sum()

# Calculate montly precipitation sums
resultmonth=result.resample('ME').sum()

# Calculate quarterly precipitations sum
resulttrim=result.resample('QE').sum()
resulttrim=resulttrim.rename_axis('trimestre')
print(resulttrim)

# Calculate yeraly precipitations sum
resultyear=result.resample('YE').sum()
print("\nYearly mean precipitations (mm):\n",resultyear.mean())

# Calculate maximum consecutive rainless days
max_streak = 0
current_streak = 0
for value in resultday:
    if value == 0:
        current_streak += 1
        max_streak = max(max_streak, current_streak)
    else:
        current_streak = 0  # Reset the streak if the value is not zero
print(f"\nMaximum number of rainless consecutive days: {max_streak}")

# Quarterly mean for each quarter
moyenne_trimestrielle_par_trimestre = resulttrim.groupby(resulttrim.index.quarter).mean()

# Quarterly minimum for each quarter
min_trimestrielle_par_trimestre = resulttrim.groupby(resulttrim.index.quarter).min()

# Quarterly maximum for each quarter
max_trimestrielle_par_trimestre = resulttrim.groupby(resulttrim.index.quarter).max()

# Print results
print("\nQuarterly mean for each quarter (mm):\n", moyenne_trimestrielle_par_trimestre)
print("\nQuarterly minimum for each quarter (mm):\n", min_trimestrielle_par_trimestre)
print("\nQuarterly maximum for each quarter (mm):\n", max_trimestrielle_par_trimestre)

# Daily minimum for each quarter
min_par_jour_par_trimestre = resultday.groupby(resultday.index.quarter).min()
min_par_jour_par_trimestre=min_par_jour_par_trimestre.rename_axis('trimestre')

# Daily maximum for each quarter
max_par_jour_par_trimestre = resultday.groupby(resultday.index.quarter).max()
max_par_jour_par_trimestre=max_par_jour_par_trimestre.rename_axis('trimestre')

# Daily mean for each quarter
moyenne_par_jour_par_trimestre = resultday.groupby(resultday.index.quarter).mean()
moyenne_par_jour_par_trimestre=moyenne_par_jour_par_trimestre.rename_axis('trimestre')

# Print results
print("\n Daily minimum for each quarter (mm):\n", min_par_jour_par_trimestre)
print("\n Daily maximum for each quarter (mm):\n", max_par_jour_par_trimestre)
print("\n Daily mean for each quarter (mm):\n", moyenne_par_jour_par_trimestre)

For latitude 44.2 and longitude 0.6 we get:

processing des data

Enter your latitude: 44.2
Enter your longitude: 0.6
The closest meteorological station is : GOURDON

Daily mean (mm):
 2.022896963663514
Daily minimum (mm):
 -0.6000000000000001
Daily maximum (mm):
 55.0
trimestre
2010-03-31    202.0
2010-06-30    245.4
2010-09-30    132.2
2010-12-31    201.2
2011-03-31    126.7
2011-06-30    102.2
2011-09-30    164.6
2011-12-31    207.0
2012-03-31     99.8
2012-06-30    341.0
2012-09-30    100.0
2012-12-31    188.8
2013-03-31    248.4
2013-06-30    307.5
2013-09-30    136.6
2013-12-31    247.8
2014-03-31    253.8
2014-06-30    201.8
2014-09-30    192.8
2014-12-31    139.0
2015-03-31    176.4
2015-06-30    155.7
2015-09-30    184.6
2015-12-31     82.6
2016-03-31    322.1
2016-06-30    300.4
2016-09-30     29.0
2016-12-31    115.1
2017-03-31    213.0
2017-06-30    216.3
2017-09-30    133.2
2017-12-31    155.3
2018-03-31    252.8
2018-06-30    251.9
2018-09-30    103.7
2018-12-31    199.3
2019-03-31    100.1
2019-06-30    203.5
2019-09-30    138.8
2019-12-31    350.9
2020-03-31    149.5
2020-06-30    150.9
2020-09-30     66.9
2020-12-31    237.4
Freq: QE-DEC, Name: rr3, dtype: float64

Yearly mean precipitations (mm):
 738.9090909090909

Maximum rainless consecutive days: 44

Quarterly mean for each quarter (mm):
 trimestre
1    194.963636
2    225.145455
3    125.672727
4    193.127273
Name: rr3, dtype: float64

Quarterly minimum for each quarter (mm):
 trimestre
1     99.8
2    102.2
3     29.0
4     82.6
Name: rr3, dtype: float64

Quarterly maximum for each quarter (mm):
 trimestre
1    322.1
2    341.0
3    192.8
4    350.9
Name: rr3, dtype: float64

Daily minimum for each quarter (mm):
 trimestre
1   -0.6
2   -0.4
3   -0.3
4   -0.5
Name: rr3, dtype: float64

Daily maximum for each quarter (mm):
 trimestre
1    42.8
2    55.0
3    50.2
4    28.0
Name: rr3, dtype: float64

Daily mean for each quarter (mm):
 trimestre
1    2.159718
2    2.474126
3    1.366008
4    2.099209
Name: rr3, dtype: float64

Étape 4 - Harvesting surface sizing and storage sizing

For correct storage sizing we recall usefully that 1m2 gives an equivalent of 1L for 1mm of precipitations.

We can then do the calculus of mean precipitations previously estimated and mean consumption previously measured

Example for 1m2:

yearly (L)				739

daily max (L)				55

quarterly min (L)			29

quarterly max (L)			350

minimum quarterly mean (L)		125

Needs:

Max rainless 44d  solo (L)		1735

Max rainless 44 d duo (L)		4727

quarterly solo consumption		3549

quarterly duo consumption		9669

Gross estimate:

quarterly consumption/minimum quarterly mean precipitations=

solo : 3588/125=29

duo: 9776/125=78

=> We need 29m2 to satisfy the solo needs with stage 1 hypothesis

=> We need 78m2 to satisfy duo needs with hypothesis stage 1

The strong precipitations are usually regrouped (high standard deviation to the mean),

and we will consequently take a minimum storage sized two times and a half

what is necessary for daily maximum precipitation

Important precipitation constraints:

We need a minimum storage of 3987L in solo (2.5*max daily precipitations*29)

and 10725L in duo (2.5*max precipitations *78)

But we also need a minimum for dry periods;

1735L in solo (44 maximum consecutive rainless days) of storage with stage 1 hypothesis

4727L in duo (44 maximum consecutive rainless days) of storage with stage 1 hypothesis

Which is satisfying with the previous constraint.

We will now use these minimum surface and minimum storage results

as basic hypothesis and add a "data-test" with iterations (50% of minimum surface

and 100% of minimum volume as starting points for the iteration) on the harvesting surface

and storage to verify we dont have drying up

(we make the hypothesis there is an overflow management and we dont

have storage overlows problems) and we statisfy the consumption needs.

Étape 5 - Storage optimisation and extra water use in summer season

The improved piece of puthon code is this one (the comments explain each stages)

To explain a bit the iteration stage: We start at volume0 storage capcity and surface0 harvesting surface precalculated in the previous stage. We do iteration loops on the precipitations data and each day we do a substract haversted water-daily consumption+summer consumption when summer En case of drying up: We have a first iteration loop 6 times of +33% of the sruface, and for each surface iteration, we have a secon iteration loop 40 times +50% of volume. We stop the iteration loops each time the sizing fits and we register the result. We display the result at the end of the iterations.

import math
import os
import pandas as pd
import time
# Watch out if you use this piece of code in other countries, you have to add adhoc meteorological stations

# data processing
print("\nprocessing des data\n")
files=os.listdir('.')
csv=[a for a in files if a[-3:]=='csv']
combined_df = pd.concat((pd.read_csv(f,sep=';') for f in csv), ignore_index=True)
#07510 bordeaux
#07535 gourdon

#"hard coded" meteorological stations 
stations=[{'ID': '07005', 'Nom': 'ABBEVILLE', 'Latitude': '50.136000', 'Longitude': '1.834000', 'Altitude': '69'}, {'ID': '07015', 'Nom': 'LILLE-LESQUIN', 'Latitude': '50.570000', 'Longitude': '3.097500', 'Altitude': '47'}, {'ID': '07020', 'Nom': 'PTE DE LA HAGUE', 'Latitude': '49.725167', 'Longitude': '-1.939833', 'Altitude': '6'}, {'ID': '07027', 'Nom': 'CAEN-CARPIQUET', 'Latitude': '49.180000', 'Longitude': '-0.456167', 'Altitude': '67'}, {'ID': '07037', 'Nom': 'ROUEN-BOOS', 'Latitude': '49.383000', 'Longitude': '1.181667', 'Altitude': '151'}, {'ID': '07072', 'Nom': 'REIMS-PRUNAY', 'Latitude': '49.209667', 'Longitude': '4.155333', 'Altitude': '95'}, {'ID': '07110', 'Nom': 'BREST-GUIPAVAS', 'Latitude': '48.444167', 'Longitude': '-4.412000', 'Altitude': '94'}, {'ID': '07117', 'Nom': "PLOUMANAC'H", 'Latitude': '48.825833', 'Longitude': '-3.473167', 'Altitude': '55'}, {'ID': '07130', 'Nom': 'RENNES-ST JACQUES', 'Latitude': '48.068833', 'Longitude': '-1.734000', 'Altitude': '36'}, {'ID': '07139', 'Nom': 'ALENCON', 'Latitude': '48.445500', 'Longitude': '0.110167', 'Altitude': '143'}, {'ID': '07149', 'Nom': 'ORLY', 'Latitude': '48.716833', 'Longitude': '2.384333', 'Altitude': '89'}, {'ID': '07168', 'Nom': 'TROYES-BARBEREY', 'Latitude': '48.324667', 'Longitude': '4.020000', 'Altitude': '112'}, {'ID': '07181', 'Nom': 'NANCY-OCHEY', 'Latitude': '48.581000', 'Longitude': '5.959833', 'Altitude': '336'}, {'ID': '07190', 'Nom': 'STRASBOURG-ENTZHEIM', 'Latitude': '48.549500', 'Longitude': '7.640333', 'Altitude': '150'}, {'ID': '07207', 'Nom': 'BELLE ILE-LE TALUT', 'Latitude': '47.294333', 'Longitude': '-3.218333', 'Altitude': '34'}, {'ID': '07222', 'Nom': 'NANTES-BOUGUENAIS', 'Latitude': '47.150000', 'Longitude': '-1.608833', 'Altitude': '26'}, {'ID': '07240', 'Nom': 'TOURS', 'Latitude': '47.444500', 'Longitude': '0.727333', 'Altitude': '108'}, {'ID': '07255', 'Nom': 'BOURGES', 'Latitude': '47.059167', 'Longitude': '2.359833', 'Altitude': '161'}, {'ID': '07280', 'Nom': 'DIJON-LONGVIC', 'Latitude': '47.267833', 'Longitude': '5.088333', 'Altitude': '219'}, {'ID': '07299', 'Nom': 'BALE-MULHOUSE', 'Latitude': '47.614333', 'Longitude': '7.510000', 'Altitude': '263'}, {'ID': '07314', 'Nom': 'PTE DE CHASSIRON', 'Latitude': '46.046833', 'Longitude': '-1.411500', 'Altitude': '11'}, {'ID': '07335', 'Nom': 'POITIERS-BIARD', 'Latitude': '46.593833', 'Longitude': '0.314333', 'Altitude': '123'}, {'ID': '07434', 'Nom': 'LIMOGES-BELLEGARDE', 'Latitude': '45.861167', 'Longitude': '1.175000', 'Altitude': '402'}, {'ID': '07460', 'Nom': 'CLERMONT-FD', 'Latitude': '45.786833', 'Longitude': '3.149333', 'Altitude': '331'}, {'ID': '07471', 'Nom': 'LE PUY-LOUDES', 'Latitude': '45.074500', 'Longitude': '3.764000', 'Altitude': '833'}, {'ID': '07481', 'Nom': 'LYON-ST EXUPERY', 'Latitude': '45.726500', 'Longitude': '5.077833', 'Altitude': '235'}, {'ID': '07510', 'Nom': 'BORDEAUX-MERIGNAC', 'Latitude': '44.830667', 'Longitude': '-0.691333', 'Altitude': '47'}, {'ID': '07535', 'Nom': 'GOURDON', 'Latitude': '44.745000', 'Longitude': '1.396667', 'Altitude': '260'}, {'ID': '07558', 'Nom': 'MILLAU', 'Latitude': '44.118500', 'Longitude': '3.019500', 'Altitude': '712'}, {'ID': '07577', 'Nom': 'MONTELIMAR', 'Latitude': '44.581167', 'Longitude': '4.733000', 'Altitude': '73'}, {'ID': '07591', 'Nom': 'EMBRUN', 'Latitude': '44.565667', 'Longitude': '6.502333', 'Altitude': '871'}, {'ID': '07607', 'Nom': 'MONT-DE-MARSAN', 'Latitude': '43.909833', 'Longitude': '-0.500167', 'Altitude': '59'}, {'ID': '07621', 'Nom': 'TARBES-OSSUN', 'Latitude': '43.188000', 'Longitude': '0.000000', 'Altitude': '360'}, {'ID': '07627', 'Nom': 'ST GIRONS', 'Latitude': '43.005333', 'Longitude': '1.106833', 'Altitude': '414'}, {'ID': '07630', 'Nom': 'TOULOUSE-BLAGNAC', 'Latitude': '43.621000', 'Longitude': '1.378833', 'Altitude': '151'}, {'ID': '07643', 'Nom': 'MONTPELLIER', 'Latitude': '43.577000', 'Longitude': '3.963167', 'Altitude': '2'}, {'ID': '07650', 'Nom': 'MARIGNANE', 'Latitude': '43.437667', 'Longitude': '5.216000', 'Altitude': '9'}, {'ID': '07661', 'Nom': 'CAP CEPET', 'Latitude': '43.079333', 'Longitude': '5.940833', 'Altitude': '115'}, {'ID': '07690', 'Nom': 'NICE', 'Latitude': '43.648833', 'Longitude': '7.209000', 'Altitude': '2'}, {'ID': '07747', 'Nom': 'PERPIGNAN', 'Latitude': '42.737167', 'Longitude': '2.872833', 'Altitude': '42'}, {'ID': '07761', 'Nom': 'AJACCIO', 'Latitude': '41.918000', 'Longitude': '8.792667', 'Altitude': '5'}, {'ID': '07790', 'Nom': 'BASTIA', 'Latitude': '42.540667', 'Longitude': '9.485167', 'Altitude': '10'}, {'ID': '61968', 'Nom': 'GLORIEUSES', 'Latitude': '-11.582667', 'Longitude': '47.289667', 'Altitude': '3'}, {'ID': '61970', 'Nom': 'JUAN DE NOVA', 'Latitude': '-17.054667', 'Longitude': '42.712000', 'Altitude': '9'}, {'ID': '61972', 'Nom': 'EUROPA', 'Latitude': '-22.344167', 'Longitude': '40.340667', 'Altitude': '6'}, {'ID': '61976', 'Nom': 'TROMELIN', 'Latitude': '-15.887667', 'Longitude': '54.520667', 'Altitude': '7'}, {'ID': '61980', 'Nom': 'GILLOT-AEROPORT', 'Latitude': '-20.892500', 'Longitude': '55.528667', 'Altitude': '8'}, {'ID': '61996', 'Nom': 'NOUVELLE AMSTERDAM', 'Latitude': '-37.795167', 'Longitude': '77.569167', 'Altitude': '27'}, {'ID': '61997', 'Nom': 'CROZET', 'Latitude': '-46.432500', 'Longitude': '51.856667', 'Altitude': '146'}, {'ID': '61998', 'Nom': 'KERGUELEN', 'Latitude': '-49.352333', 'Longitude': '70.243333', 'Altitude': '29'}, {'ID': '67005', 'Nom': 'PAMANDZI', 'Latitude': '-12.805500', 'Longitude': '45.282833', 'Altitude': '7'}, {'ID': '71805', 'Nom': 'ST-PIERRE', 'Latitude': '46.766333', 'Longitude': '-56.179167', 'Altitude': '21'}, {'ID': '78890', 'Nom': 'LA DESIRADE METEO', 'Latitude': '16.335000', 'Longitude': '-61.004000', 'Altitude': '27'}, {'ID': '78894', 'Nom': 'ST-BARTHELEMY METEO', 'Latitude': '17.901500', 'Longitude': '-62.852167', 'Altitude': '44'}, {'ID': '78897', 'Nom': 'LE RAIZET AERO', 'Latitude': '16.264000', 'Longitude': '-61.516333', 'Altitude': '11'}, {'ID': '78922', 'Nom': 'TRINITE-CARAVEL', 'Latitude': '14.774500', 'Longitude': '-60.875333', 'Altitude': '26'}, {'ID': '78925', 'Nom': 'LAMENTIN-AERO', 'Latitude': '14.595333', 'Longitude': '-60.995667', 'Altitude': '3'}, {'ID': '81401', 'Nom': 'SAINT LAURENT', 'Latitude': '5.485500', 'Longitude': '-54.031667', 'Altitude': '5'}, {'ID': '81405', 'Nom': 'CAYENNE-MATOURY', 'Latitude': '4.822333', 'Longitude': '-52.365333', 'Altitude': '4'}, {'ID': '81408', 'Nom': 'SAINT GEORGES', 'Latitude': '3.890667', 'Longitude': '-51.804667', 'Altitude': '6'}, {'ID': '81415', 'Nom': 'MARIPASOULA', 'Latitude': '3.640167', 'Longitude': '-54.028333', 'Altitude': '106'}, {'ID': '89642', 'Nom': "DUMONT D'URVILLE", 'Latitude': '-66.663167', 'Longitude': '140.001000', 'Altitude': '43'}]

def distance(lat1, lon1, lat2, lon2):
    """
    distance computing between two geographic points using euclidian distance formula.
    """
    return math.sqrt((lat2 - lat1)**2 + (lon2 - lon1)**2)

def station_la_plus_proche(x, y, stations):
    """
    Find closest meteorological station using x and y coordinates (latitude and longitude).
    """
    distance_min = float('inf')
    station_proche = None
    
    for station in stations:
        lat_station = float(station['Latitude'])
        lon_station = float(station['Longitude'])
        d = distance(x, y, lat_station, lon_station)
        if d < distance_min:
            distance_min = d
            station_proche = station
    
    return station_proche

# Ask user to enter latitude and longitude
x_input = input("Enter your latitude: ")
y_input = input("Enter your longitude: ")

# Replace comma with points
x_input = float(x_input.replace(',', '.'))
y_input = float(y_input.replace(',', '.'))

# Use values entered by user as x and y variables to find the closest meteorological station
station_proche = station_la_plus_proche(x_input, y_input, stations)
print("\nThe closest meteorological station is:", station_proche['Nom'])

# Ask user to enter his/her weekly water consumption
waterconsohebdo = input("Entrez la consommation d'eau hebdomadaire constante(L): ")

# Replace comma with points
waterconsohebdo = float(waterconsohebdo.replace(',', '.'))

# Mean daily consumption calculus
waterconsojour = waterconsohebdo/7

# Ask a user to enter the starting month of the summer periodmoisdebutete = input("Enter the starting mont for the weekly extra use of water in summer (1,2,3,4,5,6,7,8,9,10,11,12): ")
try:
    _=int(moisdebutete)
    moisdebutete=_
except Exception as err:
    moisdebutete=5
    print(f"\ntype error or empty user value, continuing with moisdebutete={moisdebutete}")

# Ask user to enter the final month of the summer period
moisfinete = input("Enter the ending month for the extra water consumption of summer period (1,2,3,4,5,6,7,8,9,10,11,12): ")
try:
    _=int(moisfinete)
    moisfinete=_
except Exception as err:
    moisfinete=9
    print(f"\ntype error or empty user value, continuing with moisfinete={moisfinete}")

# Ask user to enter extra water consumption in summer period
waterconsohebdoete = input("Enter extra weekly water consumption in summer (L) - 0 L by default: ")
try:
    _=float(waterconsohebdoete.replace(',', '.')) # Replace commas with points

waterconsohebdoete=_
except Exception as err:
    waterconsohebdoete=0
    print(f"\ntype error or empty user value, continuing with waterconsohebdoete={waterconsohebdoete}L")

# Mean daily consumption calculus
waterconsojourete = waterconsohebdoete/7

result=combined_df[combined_df['numer_sta']==int(station_proche['ID'])]

# Convert 'date_column' in a datetime format and put it as index
result['datetime'] = pd.to_datetime(result['date'], format='%Y%m%d%H%M%S')
result.set_index('datetime', inplace=True)
result = result.sort_index()

# Replace missing values with 0
result['rr3']=result['rr3'].replace('mq','0')
result['rr3']=result['rr3'].astype('float')

# Only keep precipitations of last 3 hours
result=result['rr3']

# Calculate daily precipitation sums
resultday=result.resample('D').sum()
resultdaymonthindex=resultday.copy()
resultdaymonthindex.index=resultdaymonthindex.index.month
print("\Daily mean (mm):\n", resultday.mean())
print("Daily minimum (mm):\n", resultday.min())
print("Daily maximum  (mm):\n", resultday.max())

# Calculate weekly precipitation sums
resultweek=result.resample('W').sum()

# Calculate monthly precipitation sums
resultmonth=result.resample('ME').sum()

# Calculate quarterly precipitation sums
resulttrim=result.resample('QE').sum()
resulttrim=resulttrim.rename_axis('trimestre')
print(resulttrim)

# Calculate yearly precipitation sums
resultyear=result.resample('YE').sum()
print("\nPrécipitations annuelles moyennes (mm):\n",resultyear.mean())

# Calculate maximum consecutive rainless days 
max_streak = 0
current_streak = 0
for value in resultday:
    if value == 0:
        current_streak += 1
        max_streak = max(max_streak, current_streak)
    else:
        current_streak = 0  # Reset the streak if the value is not zero
print(f"\nMaximum consecutive rainless days: {max_streak}")

# Quarterly mean for each quarter
moyenne_trimestrielle_par_trimestre = resulttrim.groupby(resulttrim.index.quarter).mean()

# Quarterly minimum for each quarter
min_trimestrielle_par_trimestre = resulttrim.groupby(resulttrim.index.quarter).min()

# Quarterly maximum for each quarter
max_trimestrielle_par_trimestre = resulttrim.groupby(resulttrim.index.quarter).max()

# Print results
print("\nQuarterly mean for each quarter (mm):\n", moyenne_trimestrielle_par_trimestre)
print("\nQuarterly minimum for each quarter(mm):\n", min_trimestrielle_par_trimestre)
print("\nQuarterly maximum for each quarter (mm):\n", max_trimestrielle_par_trimestre)

#Climate change consideration (conservative hypothesis from multimodels drias precipitations modelisations):
# Define impacts on seasonal precipitation volumes
adjustments = {0: -15, 1: -10, 2: -50, 3: -15}  # Adjust quarter 1 by -15%, quarter 2 by -10%, quarter 3 by -50%, quarter 4 by -15%

# Impacts on quarterly mean precipitations:
cc_moyenne_trimestrielle_par_trimestre=moyenne_trimestrielle_par_trimestre.copy()
for line, adjustment in adjustments.items():
    cc_moyenne_trimestrielle_par_trimestre.iloc[line] = cc_moyenne_trimestrielle_par_trimestre.iloc[line]+cc_moyenne_trimestrielle_par_trimestre.iloc[line]*adjustment/100

print("\nQuarterly mean for each quarter with consideration for climate change (mm):\n", cc_moyenne_trimestrielle_par_trimestre)

# Daily mean for each quarter
min_par_jour_par_trimestre = resultday.groupby(resultday.index.quarter).min()
min_par_jour_par_trimestre=min_par_jour_par_trimestre.rename_axis('trimestre')

# Daily maximum for each quarter
max_par_jour_par_trimestre = resultday.groupby(resultday.index.quarter).max()
max_par_jour_par_trimestre=max_par_jour_par_trimestre.rename_axis('trimestre')

# Daily mean for each quarter
moyenne_par_jour_par_trimestre = resultday.groupby(resultday.index.quarter).mean()
moyenne_par_jour_par_trimestre=moyenne_par_jour_par_trimestre.rename_axis('trimestre')

# Print results
print("\nDaily Minimum for each quarter (mm):\n", min_par_jour_par_trimestre)
print("\nDaily Maximum for each quarter (mm):\n", max_par_jour_par_trimestre)
print("\nDaily Mean for each quarter (mm):\n", moyenne_par_jour_par_trimestre)

#Minimum surface treshold calculus:
surf0=(1/2)*math.ceil((13*waterconsohebdo)/min(moyenne_trimestrielle_par_trimestre))
print(f"""Harvesting surface treshold with hypothesis and user input data (m2)
hypothesis:(quarterly consumption / quarterly min precipitations)
{int(math.ceil(surf0))} m2""")

#Minimum storage treshold calculus:
contraintejourmax=(resultday.max())*surf0
contraintejourszero=max_streak*waterconsojour
volume0=math.ceil(max(2.5*contraintejourmax,contraintejourszero))
print(f"""\nVolum treshold with hypothesis and user input data(L)
hypothesis: max((2.5*max daily precipitation*harvesting surface treshold),(44d max consecutive rainless days*daily consumption))
{int(math.ceil(volume0))} L""")

surf0_input = input("\n\nIf you want to correct the initial surface value (m2) for the iterations, you can enter your value, otherwise hit enter")
try:
    _=float(surf0_input)
    surf0=_
except Exception as err:
    print(f"\ntype error or empty value, continuing with surf0={surf0}m2")

volume0_input = input("\n\nIf you want to correct the initial volume value(L) for iterations, enter your value, otherwise hit enter")
try:
    _=float(volume0_input)
    volume0=_
except Exception as err:
    print(f"\ntype error or empty user value, continuing with volume0={volume0}L")

# Storage&Consumption algorithmic iterations

#hypothesis storage  2/3 full at t0
water=(2/3)*volume0
resultsurfvolume=(volume0,surf0)
#iteration loop
listsurf0=[surf0*(1+i*0.33) for i in range(0,999)]
listvolume0=[volume0*(1+i*0.5) for i in range(0,999)]
listeday=list(resultday)
listemonth=list(resultdaymonthindex.index)
listresult=[]
#function to check surface volume
def iterv(data, v0,s0):
    "function to check surface volume"
    water=(2/3)*v0
    for k in range(0,len(data)):
        recupday=data[k]*s0
        #print(f'recupday:{recupday}')
        if listemonth[k] in range(moisdebutete, moisfinete + 1):
            consoday = waterconsojour + waterconsojourete
        else:
            consoday = waterconsojour
        water=water+recupday-consoday
        #print(f'water:{water}')
        if water>v0:
            print("storage full")
            water=v0 #hypothesis overflow ok
            continue
        if water<0:
            print("storage empty")
            #time.sleep(1)
            return (0,0)
    print("surface and volume allow to meet the consumption need on the dataset")
    return (v0,s0)

for i in range(0,6): # surface iteration loop
    for k in range(0,len(listeday)):
        recupday=listeday[k]*listsurf0[i]
        print(f'recupday:{recupday}')
        if listemonth[k] in range(moisdebutete,moisfinete+1):
            consoday=waterconsojour+waterconsojourete
        else:
            consoday = waterconsojour
        water=water+recupday-consoday
        print(f'water:{water}')
        if water>volume0:
            print("storage full")
            water=volume0 #hypothesis overflow ok
            continue
        if water<0:
            print("storage empty, iteration with hypothesis higher storage volume")
            #time.sleep(1)
            for j in range (1,i+40):
                resultsurfvolume=iterv(listeday,listvolume0[j],listsurf0[i])
                if resultsurfvolume!=(0,0):
                    listresult.append(resultsurfvolume)
                    break
                else:
                    continue
            break

for k in listresult:
    print(f"""with data provided by the user, and a volume of  {int(k[0])}L and 
a surface of {int(k[1])}m2,
we meet user needs({waterconsohebdo}L/week constant) 
and {waterconsohebdoete}L/to add for week during the summer period(from month {moisdebutete} to month {moisfinete})
entered as hypothesis\n""")

We do the test with above hypothesis (latitude 44.2, longitude 0.6, solo 276L weekly, duo 752L weekly)

We get the following results:

solo:

with data provided by the user, and 
a volume of 7976L and 
a surface area of 19m2,
we meet user needs (276.0L/week constant) 
and 0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

 <pre>
with data provided by the user, and 
a volume of 5982L and 
a surface area of 24m2,
we meet user needs (276.0L/week constant) 
and 0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

<pre>
with data provided by the user, and 
a volume of 4985L and 
a surface area of 28m2,
we meet user needs (276.0L/week constant) 
and 0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

<pre>
with data provided by the user, and 
a volume of 3988L and 
a surface area of 33m2,
we meet user needs (276.0L/week constant) 
and 0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

<pre>
with data provided by the user, and 
a volume of 3988L and 
a surface area of 38m2,
we meet user needs (276.0L/week constant) 
and 0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

solo with 600L/week in summer period:

with data provided by the user, and 
a volume of 15642L and 
a surface area of 45m2,
we meet user needs (276.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 15642L and 
a surface area of 60m2,
we meet user needs (276.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 12514L and 
a surface area of 75m2,
we meet user needs (276.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 12514L and 
a surface area of 90m2,
we meet user needs (276.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 12514L and 
a surface area of 105m2,
we meet user needs (276.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 12514L and 
a surface area of 120m2,
we meet user needs (276.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

duo: 
<pre>
with data provided by the user, and 
a volume of 24133L and 
a surface area of 51m2,
we meet user needs (752.0L/week constant) 
and 0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 16089L and 
a surface area of 64m2,
we meet user needs (752.0L/week constant) 
and 0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 13407L and 
a surface area of 77m2,
we meet user needs (752.0L/week constant) 
and 0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 10726L and 
a surface area of 90m2,
we meet user needs (752.0L/week constant) 
and 0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 10726L and 
a surface area of 103m2,
we meet user needs (752.0L/week constant) 
and 0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

duo with 600L/week in summer period: :

with data provided by the user, and 
a volume of 33687L and 
a surface area of 70m2,
we meet user needs (752.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 24062L and 
a surface area of 93m2,
we meet user needs (752.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 19250L and 
a surface area of 116m2,
we meet user needs (752.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 19250L and 
a surface area of 139m2,
we meet user needs (752.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 19250L and 
a surface area of 162m2,
we meet user needs (752.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

with data provided by the user, and 
a volume of 19250L and 
a surface area of 185m2,
we meet user needs (752.0L/week constant) 
and 600.0L/to add for week during the summer period (from month 5 to month 9)
entered as hypothesis

Étape 6 - Use climate change data

As well explained in this other tutorial : Estimer la quantité d'eau de pluie récupérable grâce à une toiture, dimensionner son stockage en prenant en compte les changements climatiques, climate change will modify precipitations in terms of quantity but also and mainly frequencies.

For harvesting and storing, it is particularly important for droughts that might be more intense.

Technically, we can "data-test" on a daily basis with the prospective data on the drias website (https://drias-climat.fr/). However, we would need one the one hand to test with multiple datasets because the models are very different from one another, and on the other hand they are climate models and not meteorological models, which limit relevance to use these data as meteorological inputs.

Before scientists can make their models more precise for prospective with finer spatial and temporal grids, we can summarize impact estimates on the precipitation volumes on each season from the more conservative scenario: drias 2070-2100 rcp 8.5 (+10% to -10% in spring according to model taken, -50% to +20% in summer,-15% to +5% in autumn, -15% to +30% in winter)

Being careful for each season, ie with conservative hypothesis for each, we get the following reduction on precipitation volumes:

-10% in spring

-50% en summer

-15% en autumn

-15% in winter

We can therefore update the algorithm :

You will notice we already hav added these lines in the code of stage 5 just before the calculus of the #Quarterly mean for each quarter:

#Climate change consideration (conservative hypothesis from multimodels drias precipitations modelisations):
# Define impacts on seasonal precipitation volumes
adjustments = {0: -15, 1: -10, 2: -50, 3: -15}  # Adjust quarter 1 by -15%, quarter 2 by -10%, quarter 3 by -50%, quarter 4 by -15%

# Impacts on quarterly mean precipitations:
cc_moyenne_trimestrielle_par_trimestre=moyenne_trimestrielle_par_trimestre.copy()
for line, adjustment in adjustments.items():
    cc_moyenne_trimestrielle_par_trimestre.iloc[line] = cc_moyenne_trimestrielle_par_trimestre.iloc[line]+cc_moyenne_trimestrielle_par_trimestre.iloc[line]*adjustment/100

print("\nQuarterly mean for each quarter with consideration for climate change (mm):\n", cc_moyenne_trimestrielle_par_trimestre)

Which displays

Quarterly mean for each quarter with consideration for climate change (mm):
 trimestre
1    165.719091
2    202.630909
3     62.836364
4    164.158182
Name: rr3, dtype: float64

We dont do the data-test on a daily basis considered the strong bias induced by choosing only one prospective model.

In first approximation, the impact on the results is to double the necessary harvesting surface with the methode described in the previous stages.

Commentaires

en fr 1 Published

@@ Ligne 13 : / Ligne 13 : @@
 }}
 {{Introduction
 |Introduction=In cases where we want to be offgrid, the water issue is essential
 It is actually the first element to consider for example when considering site settlement in permaculture (observation stage).
 I initially made the piece of logic below to make a mobilhome offgrid with the idea to use photovoltaic modules to harvest rainwater, as in the ulta chaata realisation (https://www.facebook.com/weultachaata/?locale=fr_FR et https://fr.futuroprossimo.it/2023/03/ulta-chaata-ombrello-magico-che-puo-dare-acqua-e-luce-allindia/)
 We can wonder on the correct way to size rainwater harvesting devices
 To do that, we can use meteorological data (meteo france in france) to get a retrospective view on the seasonal precipitations and adjust the harvesting device sizes
+Interactive web demo here:
-Interactive web demo here:
 https://vpn.matangi.dev/water
@@ Ligne 37 : / Ligne 34 : @@
 {{Tuto Step
 |Step_Title=Software prerequisites
 |Step_Content=In this tutorial, we use meteorological synop data available here:
 http://data.cquest.org/meteo-france/synop/ with description here:
@@ Ligne 43 : / Ligne 40 : @@
 http://data.cquest.org/meteo-france/synop/doc_parametres_synop_168.pdf
+You can also download the data on Meteo France website:
-You can also download the data on Meteo France website:
 https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32
 Download all csv files with months and years with which you want to make the computing, put them in a directory of your choice and unzip them (archive format is gz).
 Also put in this directory the file processing.py containing the code shared in this tutorial.
+Example in debian linux command line to download and unzip all csv of year 2020 in a directory ~/synop:
-Example in debian linux command line to download and unzip all csv of year 2020 in a directory ~/synop:
 (In the tutorial we use all data from 2010 to 2020)
 <pre>
@@ Ligne 63 : / Ligne 56 : @@
 mkdir -p synop && cd synop
 wget http://data.cquest.org/meteo-france/synop/synop.202001.csv.gz && gzip -d synop.202001.csv.gz
@@ Ligne 88 : / Ligne 80 : @@
 wget http://data.cquest.org/meteo-france/synop/synop.202012.csv.gz && gzip -d synop.202012.csv.gz
 </pre>
 To use python under another operating system, please get by with your proprietary and intrusive crap.
+Under linux, python is usually installed and to use the code shared here, you'll just have to copy and paste the code in a text file called processing.py and then enter
-Under linux, python is usually installed and o use the code shared here, you'll just have to copy and paste the code in a text file called processing.py and then enter
 <pre>
@@ Ligne 104 : / Ligne 92 : @@
 </pre>
 However, you will have to install pandas library which is massively used in finance and science industries, in particular for its efficient timeseries handling and vectorisation capacities.
 To do so, here are the commands to enter in a linux debian system before running processing.py to be ok:
 <pre>
@@ Ligne 124 : / Ligne 109 : @@
 </pre>
+Mind to activate virtual environment where pandas is installed each time you use the script (after a reboot or if you close and open again the terminal) with this command:
-Mind to activate virtual environment where pandas is installed each time you use the script (after a reboot or if you close and open again the terminal) with this command:
 <pre>
@@ Ligne 133 : / Ligne 116 : @@
 </pre>
 We are in 2024 and if you are being targetted and shackled as ecoterrorists like me, you will want to inspect your measurements instruments before use, so you can inspect the source code of pandas, which is of course free software, here:
@@ Ligne 143 : / Ligne 125 : @@
 {{Tuto Step
 |Step_Title=Needs evaluation
-|Step_Content=To evaluate the needs for domestic installations, nothing is more efficient thant a water meter. A first approach is to make a rule of 3 from your weekly consumption. You can also measure individually each consumption entry (shower, washing machine, cooking, gardening, toilet, etc.) so you can make more accurate seasonal projections.
+|Step_Content=To evaluate the needs for domestic installations, nothing is more efficient than a water meter. A first approach is to make a rule of 3 from your weekly consumption. You can also measure individually each consumption entry (shower, washing machine, cooking, gardening, toilet, etc.) so you can make more accurate seasonal projections.
 For a mobilhome with dry toilets we have:
 <pre>
 conso solo - 2 showers weekly (L)
 shower (L)			50
@@ Ligne 166 : / Ligne 148 : @@
 quarter (13 weeks)		3588
+conso for two - 4 showers a week (L)
-conso for two - 4 showers a week (L)
 shower				50
@@ Ligne 190 : / Ligne 171 : @@
 |Step_Content=For correct storage sizing, we first need mean precipitations on previous years.
 To do so, we provide the following piece of logic coded in python (fitting meteo france data but adapted to other meteorological data, as synop is an encoding standard used by OMM)
 <pre>
@@ Ligne 209 : / Ligne 190 : @@
 #"hard coded" meteorological stations
 stations=[{'ID': '07005', 'Nom': 'ABBEVILLE', 'Latitude': '50.136000', 'Longitude': '1.834000', 'Altitude': '69'}, {'ID': '07015', 'Nom': 'LILLE-LESQUIN', 'Latitude': '50.570000', 'Longitude': '3.097500', 'Altitude': '47'}, {'ID': '07020', 'Nom': 'PTE DE LA HAGUE', 'Latitude': '49.725167', 'Longitude': '-1.939833', 'Altitude': '6'}, {'ID': '07027', 'Nom': 'CAEN-CARPIQUET', 'Latitude': '49.180000', 'Longitude': '-0.456167', 'Altitude': '67'}, {'ID': '07037', 'Nom': 'ROUEN-BOOS', 'Latitude': '49.383000', 'Longitude': '1.181667', 'Altitude': '151'}, {'ID': '07072', 'Nom': 'REIMS-PRUNAY', 'Latitude': '49.209667', 'Longitude': '4.155333', 'Altitude': '95'}, {'ID': '07110', 'Nom': 'BREST-GUIPAVAS', 'Latitude': '48.444167', 'Longitude': '-4.412000', 'Altitude': '94'}, {'ID': '07117', 'Nom': "PLOUMANAC'H", 'Latitude': '48.825833', 'Longitude': '-3.473167', 'Altitude': '55'}, {'ID': '07130', 'Nom': 'RENNES-ST JACQUES', 'Latitude': '48.068833', 'Longitude': '-1.734000', 'Altitude': '36'}, {'ID': '07139', 'Nom': 'ALENCON', 'Latitude': '48.445500', 'Longitude': '0.110167', 'Altitude': '143'}, {'ID': '07149', 'Nom': 'ORLY', 'Latitude': '48.716833', 'Longitude': '2.384333', 'Altitude': '89'}, {'ID': '07168', 'Nom': 'TROYES-BARBEREY', 'Latitude': '48.324667', 'Longitude': '4.020000', 'Altitude': '112'}, {'ID': '07181', 'Nom': 'NANCY-OCHEY', 'Latitude': '48.581000', 'Longitude': '5.959833', 'Altitude': '336'}, {'ID': '07190', 'Nom': 'STRASBOURG-ENTZHEIM', 'Latitude': '48.549500', 'Longitude': '7.640333', 'Altitude': '150'}, {'ID': '07207', 'Nom': 'BELLE ILE-LE TALUT', 'Latitude': '47.294333', 'Longitude': '-3.218333', 'Altitude': '34'}, {'ID': '07222', 'Nom': 'NANTES-BOUGUENAIS', 'Latitude': '47.150000', 'Longitude': '-1.608833', 'Altitude': '26'}, {'ID': '07240', 'Nom': 'TOURS', 'Latitude': '47.444500', 'Longitude': '0.727333', 'Altitude': '108'}, {'ID': '07255', 'Nom': 'BOURGES', 'Latitude': '47.059167', 'Longitude': '2.359833', 'Altitude': '161'}, {'ID': '07280', 'Nom': 'DIJON-LONGVIC', 'Latitude': '47.267833', 'Longitude': '5.088333', 'Altitude': '219'}, {'ID': '07299', 'Nom': 'BALE-MULHOUSE', 'Latitude': '47.614333', 'Longitude': '7.510000', 'Altitude': '263'}, {'ID': '07314', 'Nom': 'PTE DE CHASSIRON', 'Latitude': '46.046833', 'Longitude': '-1.411500', 'Altitude': '11'}, {'ID': '07335', 'Nom': 'POITIERS-BIARD', 'Latitude': '46.593833', 'Longitude': '0.314333', 'Altitude': '123'}, {'ID': '07434', 'Nom': 'LIMOGES-BELLEGARDE', 'Latitude': '45.861167', 'Longitude': '1.175000', 'Altitude': '402'}, {'ID': '07460', 'Nom': 'CLERMONT-FD', 'Latitude': '45.786833', 'Longitude': '3.149333', 'Altitude': '331'}, {'ID': '07471', 'Nom': 'LE PUY-LOUDES', 'Latitude': '45.074500', 'Longitude': '3.764000', 'Altitude': '833'}, {'ID': '07481', 'Nom': 'LYON-ST EXUPERY', 'Latitude': '45.726500', 'Longitude': '5.077833', 'Altitude': '235'}, {'ID': '07510', 'Nom': 'BORDEAUX-MERIGNAC', 'Latitude': '44.830667', 'Longitude': '-0.691333', 'Altitude': '47'}, {'ID': '07535', 'Nom': 'GOURDON', 'Latitude': '44.745000', 'Longitude': '1.396667', 'Altitude': '260'}, {'ID': '07558', 'Nom': 'MILLAU', 'Latitude': '44.118500', 'Longitude': '3.019500', 'Altitude': '712'}, {'ID': '07577', 'Nom': 'MONTELIMAR', 'Latitude': '44.581167', 'Longitude': '4.733000', 'Altitude': '73'}, {'ID': '07591', 'Nom': 'EMBRUN', 'Latitude': '44.565667', 'Longitude': '6.502333', 'Altitude': '871'}, {'ID': '07607', 'Nom': 'MONT-DE-MARSAN', 'Latitude': '43.909833', 'Longitude': '-0.500167', 'Altitude': '59'}, {'ID': '07621', 'Nom': 'TARBES-OSSUN', 'Latitude': '43.188000', 'Longitude': '0.000000', 'Altitude': '360'}, {'ID': '07627', 'Nom': 'ST GIRONS', 'Latitude': '43.005333', 'Longitude': '1.106833', 'Altitude': '414'}, {'ID': '07630', 'Nom': 'TOULOUSE-BLAGNAC', 'Latitude': '43.621000', 'Longitude': '1.378833', 'Altitude': '151'}, {'ID': '07643', 'Nom': 'MONTPELLIER', 'Latitude': '43.577000', 'Longitude': '3.963167', 'Altitude': '2'}, {'ID': '07650', 'Nom': 'MARIGNANE', 'Latitude': '43.437667', 'Longitude': '5.216000', 'Altitude': '9'}, {'ID': '07661', 'Nom': 'CAP CEPET', 'Latitude': '43.079333', 'Longitude': '5.940833', 'Altitude': '115'}, {'ID': '07690', 'Nom': 'NICE', 'Latitude': '43.648833', 'Longitude': '7.209000', 'Altitude': '2'}, {'ID': '07747', 'Nom': 'PERPIGNAN', 'Latitude': '42.737167', 'Longitude': '2.872833', 'Altitude': '42'}, {'ID': '07761', 'Nom': 'AJACCIO', 'Latitude': '41.918000', 'Longitude': '8.792667', 'Altitude': '5'}, {'ID': '07790', 'Nom': 'BASTIA', 'Latitude': '42.540667', 'Longitude': '9.485167', 'Altitude': '10'}, {'ID': '61968', 'Nom': 'GLORIEUSES', 'Latitude': '-11.582667', 'Longitude': '47.289667', 'Altitude': '3'}, {'ID': '61970', 'Nom': 'JUAN DE NOVA', 'Latitude': '-17.054667', 'Longitude': '42.712000', 'Altitude': '9'}, {'ID': '61972', 'Nom': 'EUROPA', 'Latitude': '-22.344167', 'Longitude': '40.340667', 'Altitude': '6'}, {'ID': '61976', 'Nom': 'TROMELIN', 'Latitude': '-15.887667', 'Longitude': '54.520667', 'Altitude': '7'}, {'ID': '61980', 'Nom': 'GILLOT-AEROPORT', 'Latitude': '-20.892500', 'Longitude': '55.528667', 'Altitude': '8'}, {'ID': '61996', 'Nom': 'NOUVELLE AMSTERDAM', 'Latitude': '-37.795167', 'Longitude': '77.569167', 'Altitude': '27'}, {'ID': '61997', 'Nom': 'CROZET', 'Latitude': '-46.432500', 'Longitude': '51.856667', 'Altitude': '146'}, {'ID': '61998', 'Nom': 'KERGUELEN', 'Latitude': '-49.352333', 'Longitude': '70.243333', 'Altitude': '29'}, {'ID': '67005', 'Nom': 'PAMANDZI', 'Latitude': '-12.805500', 'Longitude': '45.282833', 'Altitude': '7'}, {'ID': '71805', 'Nom': 'ST-PIERRE', 'Latitude': '46.766333', 'Longitude': '-56.179167', 'Altitude': '21'}, {'ID': '78890', 'Nom': 'LA DESIRADE METEO', 'Latitude': '16.335000', 'Longitude': '-61.004000', 'Altitude': '27'}, {'ID': '78894', 'Nom': 'ST-BARTHELEMY METEO', 'Latitude': '17.901500', 'Longitude': '-62.852167', 'Altitude': '44'}, {'ID': '78897', 'Nom': 'LE RAIZET AERO', 'Latitude': '16.264000', 'Longitude': '-61.516333', 'Altitude': '11'}, {'ID': '78922', 'Nom': 'TRINITE-CARAVEL', 'Latitude': '14.774500', 'Longitude': '-60.875333', 'Altitude': '26'}, {'ID': '78925', 'Nom': 'LAMENTIN-AERO', 'Latitude': '14.595333', 'Longitude': '-60.995667', 'Altitude': '3'}, {'ID': '81401', 'Nom': 'SAINT LAURENT', 'Latitude': '5.485500', 'Longitude': '-54.031667', 'Altitude': '5'}, {'ID': '81405', 'Nom': 'CAYENNE-MATOURY', 'Latitude': '4.822333', 'Longitude': '-52.365333', 'Altitude': '4'}, {'ID': '81408', 'Nom': 'SAINT GEORGES', 'Latitude': '3.890667', 'Longitude': '-51.804667', 'Altitude': '6'}, {'ID': '81415', 'Nom': 'MARIPASOULA', 'Latitude': '3.640167', 'Longitude': '-54.028333', 'Altitude': '106'}, {'ID': '89642', 'Nom': "DUMONT D'URVILLE", 'Latitude': '-66.663167', 'Longitude': '140.001000', 'Altitude': '43'}]
 def distance(lat1, lon1, lat2, lon2):
@@ Ligne 244 : / Ligne 224 : @@
 # Use values entered by user as x and y variables to find the closest meteorological station
 station_proche = station_la_plus_proche(x_input, y_input, stations)
 print("The closest meteorological station is :", station_proche['Nom'])
 result=combined_df[combined_df['numer_sta']==int(station_proche['ID'])]
@@ Ligne 265 : / Ligne 245 : @@
 print("Daily minimum (mm):\n", resultday.min())
 print("Daily maximum (mm):\n", resultday.max())
 # Calculate weekly precipitations sums
@@ Ligne 326 : / Ligne 305 : @@
 </pre>
 For latitude 44.2 and longitude 0.6 we get:
 <pre>
@@ Ligne 445 : / Ligne 424 : @@
 |Step_Title=Harvesting surface sizing and storage sizing
 |Step_Content=For correct storage sizing we recall usefully that 1m2 gives an equivalent of 1L for 1mm of precipitations.
 We can then do the calculus of mean precipitations previously estimated and mean consumption previously measured
@@ Ligne 461 : / Ligne 439 : @@
 minimum quarterly mean (L)		125
 Needs:
@@ Ligne 482 : / Ligne 459 : @@
 duo: 9776/125=78
 => We need 29m2 to satisfy the solo needs with stage 1 hypothesis
 => We need 78m2 to satisfy duo needs with hypothesis stage 1
 The strong precipitations are usually regrouped (high standard deviation to the mean),
@@ Ligne 494 : / Ligne 469 : @@
 what is necessary for daily maximum precipitation
 Important precipitation constraints:
@@ Ligne 501 : / Ligne 475 : @@
 and 10725L in duo (2.5*max precipitations *78)
 But we also need a minimum for dry periods;
@@ Ligne 510 : / Ligne 483 : @@
 Which is satisfying with the previous constraint.
 We will now use these minimum surface and minimum storage results
 as basic hypothesis and add a "data-test" with iterations (50% of minimum surface
 and 100% of minimum volume as starting points for the iteration) on the harvesting surface
 and storage to verify we dont have drying up
 (we make the hypothesis there is an overflow management and we dont
 have storage overlows problems) and we statisfy the consumption needs.
@@ Ligne 534 : / Ligne 506 : @@
 We have a first iteration loop 6 times of +33% of the sruface, and for each surface iteration, we have a secon iteration loop 40 times +50% of volume. We stop the iteration loops each time the sizing fits and we register the result.
 We display the result at the end of the iterations.
 <pre>
@@ Ligne 553 : / Ligne 524 : @@
 #"hard coded" meteorological stations
 stations=[{'ID': '07005', 'Nom': 'ABBEVILLE', 'Latitude': '50.136000', 'Longitude': '1.834000', 'Altitude': '69'}, {'ID': '07015', 'Nom': 'LILLE-LESQUIN', 'Latitude': '50.570000', 'Longitude': '3.097500', 'Altitude': '47'}, {'ID': '07020', 'Nom': 'PTE DE LA HAGUE', 'Latitude': '49.725167', 'Longitude': '-1.939833', 'Altitude': '6'}, {'ID': '07027', 'Nom': 'CAEN-CARPIQUET', 'Latitude': '49.180000', 'Longitude': '-0.456167', 'Altitude': '67'}, {'ID': '07037', 'Nom': 'ROUEN-BOOS', 'Latitude': '49.383000', 'Longitude': '1.181667', 'Altitude': '151'}, {'ID': '07072', 'Nom': 'REIMS-PRUNAY', 'Latitude': '49.209667', 'Longitude': '4.155333', 'Altitude': '95'}, {'ID': '07110', 'Nom': 'BREST-GUIPAVAS', 'Latitude': '48.444167', 'Longitude': '-4.412000', 'Altitude': '94'}, {'ID': '07117', 'Nom': "PLOUMANAC'H", 'Latitude': '48.825833', 'Longitude': '-3.473167', 'Altitude': '55'}, {'ID': '07130', 'Nom': 'RENNES-ST JACQUES', 'Latitude': '48.068833', 'Longitude': '-1.734000', 'Altitude': '36'}, {'ID': '07139', 'Nom': 'ALENCON', 'Latitude': '48.445500', 'Longitude': '0.110167', 'Altitude': '143'}, {'ID': '07149', 'Nom': 'ORLY', 'Latitude': '48.716833', 'Longitude': '2.384333', 'Altitude': '89'}, {'ID': '07168', 'Nom': 'TROYES-BARBEREY', 'Latitude': '48.324667', 'Longitude': '4.020000', 'Altitude': '112'}, {'ID': '07181', 'Nom': 'NANCY-OCHEY', 'Latitude': '48.581000', 'Longitude': '5.959833', 'Altitude': '336'}, {'ID': '07190', 'Nom': 'STRASBOURG-ENTZHEIM', 'Latitude': '48.549500', 'Longitude': '7.640333', 'Altitude': '150'}, {'ID': '07207', 'Nom': 'BELLE ILE-LE TALUT', 'Latitude': '47.294333', 'Longitude': '-3.218333', 'Altitude': '34'}, {'ID': '07222', 'Nom': 'NANTES-BOUGUENAIS', 'Latitude': '47.150000', 'Longitude': '-1.608833', 'Altitude': '26'}, {'ID': '07240', 'Nom': 'TOURS', 'Latitude': '47.444500', 'Longitude': '0.727333', 'Altitude': '108'}, {'ID': '07255', 'Nom': 'BOURGES', 'Latitude': '47.059167', 'Longitude': '2.359833', 'Altitude': '161'}, {'ID': '07280', 'Nom': 'DIJON-LONGVIC', 'Latitude': '47.267833', 'Longitude': '5.088333', 'Altitude': '219'}, {'ID': '07299', 'Nom': 'BALE-MULHOUSE', 'Latitude': '47.614333', 'Longitude': '7.510000', 'Altitude': '263'}, {'ID': '07314', 'Nom': 'PTE DE CHASSIRON', 'Latitude': '46.046833', 'Longitude': '-1.411500', 'Altitude': '11'}, {'ID': '07335', 'Nom': 'POITIERS-BIARD', 'Latitude': '46.593833', 'Longitude': '0.314333', 'Altitude': '123'}, {'ID': '07434', 'Nom': 'LIMOGES-BELLEGARDE', 'Latitude': '45.861167', 'Longitude': '1.175000', 'Altitude': '402'}, {'ID': '07460', 'Nom': 'CLERMONT-FD', 'Latitude': '45.786833', 'Longitude': '3.149333', 'Altitude': '331'}, {'ID': '07471', 'Nom': 'LE PUY-LOUDES', 'Latitude': '45.074500', 'Longitude': '3.764000', 'Altitude': '833'}, {'ID': '07481', 'Nom': 'LYON-ST EXUPERY', 'Latitude': '45.726500', 'Longitude': '5.077833', 'Altitude': '235'}, {'ID': '07510', 'Nom': 'BORDEAUX-MERIGNAC', 'Latitude': '44.830667', 'Longitude': '-0.691333', 'Altitude': '47'}, {'ID': '07535', 'Nom': 'GOURDON', 'Latitude': '44.745000', 'Longitude': '1.396667', 'Altitude': '260'}, {'ID': '07558', 'Nom': 'MILLAU', 'Latitude': '44.118500', 'Longitude': '3.019500', 'Altitude': '712'}, {'ID': '07577', 'Nom': 'MONTELIMAR', 'Latitude': '44.581167', 'Longitude': '4.733000', 'Altitude': '73'}, {'ID': '07591', 'Nom': 'EMBRUN', 'Latitude': '44.565667', 'Longitude': '6.502333', 'Altitude': '871'}, {'ID': '07607', 'Nom': 'MONT-DE-MARSAN', 'Latitude': '43.909833', 'Longitude': '-0.500167', 'Altitude': '59'}, {'ID': '07621', 'Nom': 'TARBES-OSSUN', 'Latitude': '43.188000', 'Longitude': '0.000000', 'Altitude': '360'}, {'ID': '07627', 'Nom': 'ST GIRONS', 'Latitude': '43.005333', 'Longitude': '1.106833', 'Altitude': '414'}, {'ID': '07630', 'Nom': 'TOULOUSE-BLAGNAC', 'Latitude': '43.621000', 'Longitude': '1.378833', 'Altitude': '151'}, {'ID': '07643', 'Nom': 'MONTPELLIER', 'Latitude': '43.577000', 'Longitude': '3.963167', 'Altitude': '2'}, {'ID': '07650', 'Nom': 'MARIGNANE', 'Latitude': '43.437667', 'Longitude': '5.216000', 'Altitude': '9'}, {'ID': '07661', 'Nom': 'CAP CEPET', 'Latitude': '43.079333', 'Longitude': '5.940833', 'Altitude': '115'}, {'ID': '07690', 'Nom': 'NICE', 'Latitude': '43.648833', 'Longitude': '7.209000', 'Altitude': '2'}, {'ID': '07747', 'Nom': 'PERPIGNAN', 'Latitude': '42.737167', 'Longitude': '2.872833', 'Altitude': '42'}, {'ID': '07761', 'Nom': 'AJACCIO', 'Latitude': '41.918000', 'Longitude': '8.792667', 'Altitude': '5'}, {'ID': '07790', 'Nom': 'BASTIA', 'Latitude': '42.540667', 'Longitude': '9.485167', 'Altitude': '10'}, {'ID': '61968', 'Nom': 'GLORIEUSES', 'Latitude': '-11.582667', 'Longitude': '47.289667', 'Altitude': '3'}, {'ID': '61970', 'Nom': 'JUAN DE NOVA', 'Latitude': '-17.054667', 'Longitude': '42.712000', 'Altitude': '9'}, {'ID': '61972', 'Nom': 'EUROPA', 'Latitude': '-22.344167', 'Longitude': '40.340667', 'Altitude': '6'}, {'ID': '61976', 'Nom': 'TROMELIN', 'Latitude': '-15.887667', 'Longitude': '54.520667', 'Altitude': '7'}, {'ID': '61980', 'Nom': 'GILLOT-AEROPORT', 'Latitude': '-20.892500', 'Longitude': '55.528667', 'Altitude': '8'}, {'ID': '61996', 'Nom': 'NOUVELLE AMSTERDAM', 'Latitude': '-37.795167', 'Longitude': '77.569167', 'Altitude': '27'}, {'ID': '61997', 'Nom': 'CROZET', 'Latitude': '-46.432500', 'Longitude': '51.856667', 'Altitude': '146'}, {'ID': '61998', 'Nom': 'KERGUELEN', 'Latitude': '-49.352333', 'Longitude': '70.243333', 'Altitude': '29'}, {'ID': '67005', 'Nom': 'PAMANDZI', 'Latitude': '-12.805500', 'Longitude': '45.282833', 'Altitude': '7'}, {'ID': '71805', 'Nom': 'ST-PIERRE', 'Latitude': '46.766333', 'Longitude': '-56.179167', 'Altitude': '21'}, {'ID': '78890', 'Nom': 'LA DESIRADE METEO', 'Latitude': '16.335000', 'Longitude': '-61.004000', 'Altitude': '27'}, {'ID': '78894', 'Nom': 'ST-BARTHELEMY METEO', 'Latitude': '17.901500', 'Longitude': '-62.852167', 'Altitude': '44'}, {'ID': '78897', 'Nom': 'LE RAIZET AERO', 'Latitude': '16.264000', 'Longitude': '-61.516333', 'Altitude': '11'}, {'ID': '78922', 'Nom': 'TRINITE-CARAVEL', 'Latitude': '14.774500', 'Longitude': '-60.875333', 'Altitude': '26'}, {'ID': '78925', 'Nom': 'LAMENTIN-AERO', 'Latitude': '14.595333', 'Longitude': '-60.995667', 'Altitude': '3'}, {'ID': '81401', 'Nom': 'SAINT LAURENT', 'Latitude': '5.485500', 'Longitude': '-54.031667', 'Altitude': '5'}, {'ID': '81405', 'Nom': 'CAYENNE-MATOURY', 'Latitude': '4.822333', 'Longitude': '-52.365333', 'Altitude': '4'}, {'ID': '81408', 'Nom': 'SAINT GEORGES', 'Latitude': '3.890667', 'Longitude': '-51.804667', 'Altitude': '6'}, {'ID': '81415', 'Nom': 'MARIPASOULA', 'Latitude': '3.640167', 'Longitude': '-54.028333', 'Altitude': '106'}, {'ID': '89642', 'Nom': "DUMONT D'URVILLE", 'Latitude': '-66.663167', 'Longitude': '140.001000', 'Altitude': '43'}]
 def distance(lat1, lon1, lat2, lon2):
@@ Ligne 563 : / Ligne 533 : @@
 def station_la_plus_proche(x, y, stations):
      """
-     Trouve la station météo la plus proche en utilisant les coordonnées x et y (latitude et longitude).
+     Find closest meteorological station using x and y coordinates (latitude and longitude).
      """
      distance_min = float('inf')
@@ Ligne 578 : / Ligne 548 : @@
      return station_proche
-# Demander à l'utilisateur d'entrer la latitude et la longitude
+# Ask user to enter latitude and longitude
-x_input = input("Entrez la latitude de votre lieux: ")
+x_input = input("Enter your latitude: ")
-y_input = input("Entrez la longitude de votre lieux: ")
+y_input = input("Enter your longitude: ")
-# Remplacer les virgules par des points
+# Replace comma with points
 x_input = float(x_input.replace(',', '.'))
 y_input = float(y_input.replace(',', '.'))
-# Utilisez les valeurs entrées par l'utilisateur comme variables x et y pour trouver la station météo la plus proche
+# Use values entered by user as x and y variables to find the closest meteorological station
 station_proche = station_la_plus_proche(x_input, y_input, stations)
-print("\nLa station météo la plus proche est:", station_proche['Nom'])
+print("\nThe closest meteorological station is:", station_proche['Nom'])
-# Demander à l'utilisateur d'entrer sa consommation d'eau hebdomadaire
+# Ask user to enter his/her weekly water consumption
 waterconsohebdo = input("Entrez la consommation d'eau hebdomadaire constante(L): ")
-# Remplacer les virgules par des points
+# Replace comma with points
 waterconsohebdo = float(waterconsohebdo.replace(',', '.'))
-# Calcul consommation journaliere moyenne
+# Mean daily consumption calculus
 waterconsojour = waterconsohebdo/7
-# Demander à l'utilisateur d'entrer le mois de debut de la periode estivale
+# Ask a user to enter the starting month of the summer periodmoisdebutete = input("Enter the starting mont for the weekly extra use of water in summer (1,2,3,4,5,6,7,8,9,10,11,12): ")
-moisdebutete = input("Entrez le mois de début pour la consommation d'eau hebdomadaire supplémentaire en periode estivale (1,2,3,4,5,6,7,8,9,10,11,12): ")
 try:
      _=int(moisdebutete)
@@ Ligne 606 : / Ligne 575 : @@
 except Exception as err:
      moisdebutete=5
-     print(f"\nerreur de type ou valeur utilisateur vide, poursuite avec utilisation de moisdebutete={moisdebutete}")
+     print(f"\ntype error or empty user value, continuing with moisdebutete={moisdebutete}")
-# Demander à l'utilisateur d'entrer le mois de fin de la periode estivale
+# Ask user to enter the final month of the summer period
-moisfinete = input("Entrez le mois de fin pour la consommation d'eau hebdomadaire supplémentaire en periode estivale (1,2,3,4,5,6,7,8,9,10,11,12): ")
+moisfinete = input("Enter the ending month for the extra water consumption of summer period (1,2,3,4,5,6,7,8,9,10,11,12): ")
 try:
      _=int(moisfinete)
@@ Ligne 615 : / Ligne 584 : @@
 except Exception as err:
      moisfinete=9
-     print(f"\nerreur de type ou valeur utilisateur vide, poursuite avec utilisation de moisfinete={moisfinete}")
+     print(f"\ntype error or empty user value, continuing with moisfinete={moisfinete}")
-# Demander à l'utilisateur d'entrer la consommation d'eau supplémentaire en periode estivale
+# Ask user to enter extra water consumption in summer period
-waterconsohebdoete = input("Entrez la consommation d'eau hebdomadaire supplémentaire en periode estivale (L) - 0 L par defaut: ")
+waterconsohebdoete = input("Enter extra weekly water consumption in summer (L) - 0 L by default: ")
 try:
-     _=float(waterconsohebdoete.replace(',', '.')) # Remplacer les virgules par des points
+     _=float(waterconsohebdoete.replace(',', '.')) # Replace commas with points
-    waterconsohebdoete=_
+waterconsohebdoete=_
 except Exception as err:
      waterconsohebdoete=0
-     print(f"\nerreur de type ou valeur utilisateur vide, poursuite avec utilisation de waterconsohebdoete={waterconsohebdoete}L")
+     print(f"\ntype error or empty user value, continuing with waterconsohebdoete={waterconsohebdoete}L")
-# Calcul consommation journaliere moyenne
+# Mean daily consumption calculus
 waterconsojourete = waterconsohebdoete/7
 result=combined_df[combined_df['numer_sta']==int(station_proche['ID'])]
-# Convertir la colonne 'date_column' dans un format datetime et la mettre en index trié
+# Convert 'date_column' in a datetime format and put it as index
 result['datetime'] = pd.to_datetime(result['date'], format='%Y%m%d%H%M%S')
 result.set_index('datetime', inplace=True)
 result = result.sort_index()
-# remplacer les données manquantes par 0
+# Replace missing values with 0
 result['rr3']=result['rr3'].replace('mq','0')
 result['rr3']=result['rr3'].astype('float')
-# Ne garder que la colonne des précipitations des 3 dernieres heures
+# Only keep precipitations of last 3 hours
 result=result['rr3']
-# Calculer les sommes de précipitations par jour
+# Calculate daily precipitation sums
 resultday=result.resample('D').sum()
 resultdaymonthindex=resultday.copy()
 resultdaymonthindex.index=resultdaymonthindex.index.month
-print("\nMoyenne par jour (mm):\n", resultday.mean())
+print("\Daily mean (mm):\n", resultday.mean())
-print("Minimum par jour (mm):\n", resultday.min())
+print("Daily minimum (mm):\n", resultday.min())
-print("Maximum par jour  (mm):\n", resultday.max())
+print("Daily maximum  (mm):\n", resultday.max())
+# Calculate weekly precipitation sums
-# Calculer les sommes de précipitations par semaine
 resultweek=result.resample('W').sum()
-# Calculer les sommes de précipitations par mois
+# Calculate monthly precipitation sums
 resultmonth=result.resample('ME').sum()
-# Calculer les sommes de précipitations par trimestre
+# Calculate quarterly precipitation sums
 resulttrim=result.resample('QE').sum()
 resulttrim=resulttrim.rename_axis('trimestre')
 print(resulttrim)
-# Calculer les sommes de précipitations par an
+# Calculate yearly precipitation sums
 resultyear=result.resample('YE').sum()
 print("\nPrécipitations annuelles moyennes (mm):\n",resultyear.mean())
-# Calculer le nombre de jours consécutifs maximum sans pluie
+# Calculate maximum consecutive rainless days
 max_streak = 0
 current_streak = 0
@@ Ligne 677 : / Ligne 645 : @@
      else:
          current_streak = 0  # Reset the streak if the value is not zero
-print(f"\nNombre de jours consecutifs maximum sans pluie: {max_streak}")
+print(f"\nMaximum consecutive rainless days: {max_streak}")
-# Moyenne par trimestre pour chaque trimestre
+# Quarterly mean for each quarter
 moyenne_trimestrielle_par_trimestre = resulttrim.groupby(resulttrim.index.quarter).mean()
-# Minimum par trimestre pour chaque trimestre
+# Quarterly minimum for each quarter
 min_trimestrielle_par_trimestre = resulttrim.groupby(resulttrim.index.quarter).min()
-# Maximum par trimestre pour chaque trimestre
+# Quarterly maximum for each quarter
 max_trimestrielle_par_trimestre = resulttrim.groupby(resulttrim.index.quarter).max()
-# Imprimer les résultats
+# Print results
-print("\nMoyenne par trimestre pour chaque trimestre (mm):\n", moyenne_trimestrielle_par_trimestre)
+print("\nQuarterly mean for each quarter (mm):\n", moyenne_trimestrielle_par_trimestre)
-print("\nMinimum par trimestre pour chaque trimestre (mm):\n", min_trimestrielle_par_trimestre)
+print("\nQuarterly minimum for each quarter(mm):\n", min_trimestrielle_par_trimestre)
-print("\nMaximum par trimestre pour chaque trimestre (mm):\n", max_trimestrielle_par_trimestre)
+print("\nQuarterly maximum for each quarter (mm):\n", max_trimestrielle_par_trimestre)
-#Prise en comptes changement climatiques (hypothèses conservatrices multimodeles drias precipitations):
+#Climate change consideration (conservative hypothesis from multimodels drias precipitations modelisations):
-# Definir les impacts sur les volumes de précipitations par saison
+# Define impacts on seasonal precipitation volumes
-adjustments = {0: -15, 1: -10, 2: -50, 3: -15}  # Adjust line 1 by -15%, line 2 by -10%, line 3 by -50%, line 4 by -15%
+adjustments = {0: -15, 1: -10, 2: -50, 3: -15}  # Adjust quarter 1 by -15%, quarter 2 by -10%, quarter 3 by -50%, quarter 4 by -15%
-# Appliquer les impacts sur les précipitations moyennes par trimestres:
+# Impacts on quarterly mean precipitations:
 cc_moyenne_trimestrielle_par_trimestre=moyenne_trimestrielle_par_trimestre.copy()
 for line, adjustment in adjustments.items():
      cc_moyenne_trimestrielle_par_trimestre.iloc[line] = cc_moyenne_trimestrielle_par_trimestre.iloc[line]+cc_moyenne_trimestrielle_par_trimestre.iloc[line]*adjustment/100
-print("\nMoyenne par trimestre pour chaque trimestre avec prise en compte du changement climatique (mm):\n", cc_moyenne_trimestrielle_par_trimestre)
+print("\nQuarterly mean for each quarter with consideration for climate change (mm):\n", cc_moyenne_trimestrielle_par_trimestre)
+# Daily mean for each quarter
-# Minimum par jour pour chaque trimestre
 min_par_jour_par_trimestre = resultday.groupby(resultday.index.quarter).min()
 min_par_jour_par_trimestre=min_par_jour_par_trimestre.rename_axis('trimestre')
-# Maximum par jour pour chaque trimestre
+# Daily maximum for each quarter
 max_par_jour_par_trimestre = resultday.groupby(resultday.index.quarter).max()
 max_par_jour_par_trimestre=max_par_jour_par_trimestre.rename_axis('trimestre')
-# Moyenne par jour pour chaque trimestre
+# Daily mean for each quarter
 moyenne_par_jour_par_trimestre = resultday.groupby(resultday.index.quarter).mean()
 moyenne_par_jour_par_trimestre=moyenne_par_jour_par_trimestre.rename_axis('trimestre')
-# Imprimer les résultats
+# Print results
-print("\nMinimum par jour pour chaque trimestre (mm):\n", min_par_jour_par_trimestre)
+print("\nDaily Minimum for each quarter (mm):\n", min_par_jour_par_trimestre)
-print("\nMaximum par jour pour chaque trimestre (mm):\n", max_par_jour_par_trimestre)
+print("\nDaily Maximum for each quarter (mm):\n", max_par_jour_par_trimestre)
-print("\nMoyenne par jour pour chaque trimestre (mm):\n", moyenne_par_jour_par_trimestre)
+print("\nDaily Mean for each quarter (mm):\n", moyenne_par_jour_par_trimestre)
-#Calcul seuil mini surface de recuperation:
+#Minimum surface treshold calculus:
 surf0=(1/2)*math.ceil((13*waterconsohebdo)/min(moyenne_trimestrielle_par_trimestre))
-print(f"""Seuil surface recuperation avec hypothèse entrée et données fournies par l'utilisateur (m2)
+print(f"""Harvesting surface treshold with hypothesis and user input data (m2)
-hypothese:(conso trimestre / precipitations moyenne min trimestre)
+hypothesis:(quarterly consumption / quarterly min precipitations)
 {int(math.ceil(surf0))} m2""")
-#Calcul seuil mini reservoir:
+#Minimum storage treshold calculus:
 contraintejourmax=(resultday.max())*surf0
 contraintejourszero=max_streak*waterconsojour
 volume0=math.ceil(max(2.5*contraintejourmax,contraintejourszero))
-print(f"""\nSeuil volume avec hypothèse entrée et données fournies par l'utilisateur (L)
+print(f"""\nVolum treshold with hypothesis and user input data(L)
-hypothèse: max((2.5*précipitations journaliere maxi*Seuil surface recuperation),(44j consécutifs max sans pluie*conso journaliere))
+hypothesis: max((2.5*max daily precipitation*harvesting surface treshold),(44d max consecutive rainless days*daily consumption))
 {int(math.ceil(volume0))} L""")
-surf0_input = input("\n\nSi vous souhaitez corriger la valeur initiale de surface (m2) pour les itérations, entrer votre valeur, sinon appuyer sur entree")
+surf0_input = input("\n\nIf you want to correct the initial surface value (m2) for the iterations, you can enter your value, otherwise hit enter")
 try:
      _=float(surf0_input)
      surf0=_
 except Exception as err:
-     print(f"\nerreur de type ou valeur utilisateur vide, poursuite avec utilisation de surf0={surf0}m2")
+     print(f"\ntype error or empty value, continuing with surf0={surf0}m2")
-volume0_input = input("\n\nSi vous souhaitez corriger la valeur initiale de volume (L) pour les itérations, entrer votre valeur, sinon appuyer sur entree")
+volume0_input = input("\n\nIf you want to correct the initial volume value(L) for iterations, enter your value, otherwise hit enter")
 try:
      _=float(volume0_input)
      volume0=_
 except Exception as err:
-     print(f"\nerreur de type ou valeur utilisateur vide, poursuite avec utilisation de volume0={volume0}L")
+     print(f"\ntype error or empty user value, continuing with volume0={volume0}L")
-# Itérations algorithmiques stockage&consommation
+# Storage&Consumption algorithmic iterations
-#hypothèse récupérateur 2/3 plein à t0
+#hypothesis storage  2/3 full at t0
 water=(2/3)*volume0
 resultsurfvolume=(volume0,surf0)
-#boucle iteration
+#iteration loop
 listsurf0=[surf0*(1+i*0.33) for i in range(0,999)]
 listvolume0=[volume0*(1+i*0.5) for i in range(0,999)]
@@ Ligne 764 : / Ligne 728 : @@
 listemonth=list(resultdaymonthindex.index)
 listresult=[]
-#fonction check surface volume
+#function to check surface volume
 def iterv(data, v0,s0):
-     "fonction check surface volume"
+     "function to check surface volume"
      water=(2/3)*v0
      for k in range(0,len(data)):
@@ Ligne 778 : / Ligne 742 : @@
          #print(f'water:{water}')
          if water>v0:
-             print("récupérateur plein")
+             print("storage full")
-             water=v0 #hypothese gestion du trop plein ok
+             water=v0 #hypothesis overflow ok
              continue
          if water<0:
-             print("récupérateur vide")
+             print("storage empty")
              #time.sleep(1)
              return (0,0)
-     print("les surfaces et volumes permettent de subvenir à la consommation d'eau sur le dataset")
+     print("surface and volume allow to meet the consumption need on the dataset")
      return (v0,s0)
-for i in range(0,6): #boucle iteration surface
+for i in range(0,6): # surface iteration loop
      for k in range(0,len(listeday)):
          recupday=listeday[k]*listsurf0[i]
@@ Ligne 799 : / Ligne 763 : @@
          print(f'water:{water}')
          if water>volume0:
-             print("récupérateur plein")
+             print("storage full")
-             water=volume0 #hypothese gestion du trop plein ok
+             water=volume0 #hypothesis overflow ok
              continue
          if water<0:
-             print("récupérateur vide, iteration avec hypothèse volume de récupération plus grand")
+             print("storage empty, iteration with hypothesis higher storage volume")
              #time.sleep(1)
              for j in range (1,i+40):
@@ Ligne 815 : / Ligne 779 : @@
 for k in listresult:
-     print(f"""avec les données fournies par l'utilisateur, et
+     print(f"""with data provided by the user, and a volume of  {int(k[0])}L and
-un volume de {int(k[0])}L et
+a surface of {int(k[1])}m2,
-une surface de {int(k[1])}m2,
+we meet user needs({waterconsohebdo}L/week constant)
-on satisfait aux besoins utilisateurs ({waterconsohebdo}L/semaine constant)
+and {waterconsohebdoete}L/to add for week during the summer period(from month {moisdebutete} to month {moisfinete})
-et {waterconsohebdoete}L/semaine en periode estivale (du mois {moisdebutete} au mois {moisfinete})
+entered as hypothesis\n""")
-entrées en hypothèse\n""")
 </pre>
-On fait le test avec les hypothèses ci dessus (latitude 44.2, longitude 0.6, solo 276L semaine, duo 752L semaine)
+We do the test with above hypothesis
+(latitude 44.2, longitude 0.6, solo 276L weekly, duo 752L weekly)
-On obtient les résultats suivants:
+We get the following results:
 solo:
 <pre>
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 7976L et
+a volume of 7976L and
-une surface de 19m2,
+a surface area of 19m2,
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+we meet user needs (276.0L/week constant)
-et 0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+ <pre>
-un volume de 5982L et
+with data provided by the user, and
-une surface de 24m2,
+a volume of 5982L and
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+a surface area of 24m2,
-et 0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+we meet user needs (276.0L/week constant)
-entrées en hypothèse
+and 0L/to add for week during the summer period (from month 5 to month 9)
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+<pre>
-un volume de 4985L et
+with data provided by the user, and
-une surface de 28m2,
+a volume of 4985L and
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+a surface area of 28m2,
-et 0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+we meet user needs (276.0L/week constant)
-entrées en hypothèse
+and 0L/to add for week during the summer period (from month 5 to month 9)
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+<pre>
-un volume de 3988L et
+with data provided by the user, and
-une surface de 33m2,
+a volume of 3988L and
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+a surface area of 33m2,
-et 0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+we meet user needs (276.0L/week constant)
-entrées en hypothèse
+and 0L/to add for week during the summer period (from month 5 to month 9)
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+<pre>
-un volume de 3988L et
+with data provided by the user, and
-une surface de 38m2,
+a volume of 3988L and
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+a surface area of 38m2,
-et 0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+we meet user needs (276.0L/week constant)
-entrées en hypothèse
+and 0L/to add for week during the summer period (from month 5 to month 9)
+entered as hypothesis
 </pre>
-solo avec 600L/semaine en periode estivale:
+solo with 600L/week in summer period:
 <pre>
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 15642L et
+a volume of 15642L and
-une surface de 45m2,
+a surface area of 45m2,
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+we meet user needs (276.0L/week constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 15642L et
+a volume of 15642L and
-une surface de 60m2,
+a surface area of 60m2,
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+we meet user needs (276.0L/week constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 12514L et
+a volume of 12514L and
-une surface de 75m2,
+a surface area of 75m2,
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+we meet user needs (276.0L/week constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 12514L et
+a volume of 12514L and
-une surface de 90m2,
+a surface area of 90m2,
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+we meet user needs (276.0L/week constant)
-et 600.0L/semaine en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 12514L et
+a volume of 12514L and
-une surface de 105m2,
+a surface area of 105m2,
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+we meet user needs (276.0L/week constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 12514L et
+a volume of 12514L and
-une surface de 120m2,
+a surface area of 120m2,
-on satisfait aux besoins utilisateurs (276.0L/semaine constant)
+we meet user needs (276.0L/week constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-</pre>
 duo:
 <pre>
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 24133L et
+a volume of 24133L and
-une surface de 51m2,
+a surface area of 51m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
+we meet user needs (752.0L/week constant)
-et 0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 16089L et
+a volume of 16089L and
-une surface de 64m2,
+a surface area of 64m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
+we meet user needs (752.0L/week constant)
-et 0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 13407L et
+a volume of 13407L and
-une surface de 77m2,
+a surface area of 77m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
+we meet user needs (752.0L/week constant)
-et 0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 10726L et
+a volume of 10726L and
-une surface de 90m2,
+a surface area of 90m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
+we meet user needs (752.0L/week constant)
-et 0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 10726L et
+a volume of 10726L and
-une surface de 103m2,
+a surface area of 103m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
+we meet user needs (752.0L/week constant)
-et 0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
 </pre>
-duo avec 600L/semaine en periode estivale: :
+duo with 600L/week in summer period: :
 <pre>
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 33687L et
+a volume of 33687L and
-une surface de 70m2,
+a surface area of 70m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
+we meet user needs (752.0L/week constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
-un volume de 24062L et
-une surface de 93m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
-entrées en hypothèse
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 19250L et
+a volume of 24062L and
-une surface de 116m2,
+a surface area of 93m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
+we meet user needs (752.0L/week constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 19250L et
+a volume of 19250L and
-une surface de 139m2,
+a surface area of 116m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
+we meet user needs (752.0L/week constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 19250L et
+a volume of 19250L and
-une surface de 162m2,
+a surface area of 139m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
+we meet user needs (752.0L/week constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
-avec les données fournies par l'utilisateur, et
+with data provided by the user, and
-un volume de 19250L et
+a volume of 19250L and
-une surface de 185m2,
+a surface area of 162m2,
-on satisfait aux besoins utilisateurs (752.0L/semaine constant)
+we meet user needs (752.0L/week constant)
-et 600.0L/semaine supplémentaire en periode estivale (du mois 5 au mois 9)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
-entrées en hypothèse
+entered as hypothesis
+with data provided by the user, and
+a volume of 19250L and
+a surface area of 185m2,
+we meet user needs (752.0L/week constant)
+and 600.0L/to add for week during the summer period (from month 5 to month 9)
+entered as hypothesis
 </pre>
 }}
 {{Tuto Step
-|Step_Title=Utiliser des data du changement climatique
+|Step_Title=Use climate change data
-|Step_Content=Comme très bien expliqué dans cet autre tuto : Estimer la quantité d'eau de pluie récupérable grâce à une toiture, dimensionner son stockage en prenant en compte les changements climatiques, le changement climatique va perturber les précipitations en termes de quantités mais surtout de fréquences.
+|Step_Content=As well explained in this other tutorial : [[Estimer la quantité d'eau de pluie récupérable grâce à une toiture, dimensionner son stockage en prenant en compte les changements climatiques]], climate change will modify precipitations in terms of quantity but also and mainly frequencies.
-Pour de la récupération/stockage, c'est particulièrement important notamment pour les périodes de sécheresses qui risques de s'accentuer.
-Techniquement, on peut "data-tester" au jour le jour avec les données de prévisions du portail drias (https://drias-climat.fr/). Cependant, il faudrait d'une part data tester avec plusieurs set de données car les modèles sont tres différents les uns des autres, et d'autres part il s'agit de modèles climatiques et pas météorologiques, ce qui limite la pertinence d'utiliser des résultats comme input meteo.
-En attendant que les scientifiques affinent leurs modèles pour la prospective à échelle plus fine (spatiale et temporelle), on peut reprendre les estimations d'impact sur les volumes de précipitations par saisons à partir des modèles drias 2070-2100 rcp 8.5 (+10% à -10% selon modèles au printemps, -50% à +20% en été,-15% à +5% en automne, -15% à +30% en hiver)
+For harvesting and storing, it is particularly important for droughts that might be more intense.
+Technically, we can "data-test" on a daily basis with the prospective data on the drias website (https://drias-climat.fr/).
+However, we would need one the one hand to test with multiple datasets because the models are very different from one another, and on the other hand they are climate models and not meteorological models, which limit relevance to use these data as meteorological inputs.
-En étant prudent pour chaque saison, c'est à dire en prenant l'hypothèse la plus conservatrice pour chaque saison, on obtient des volumes réduits de :
+Before scientists can make their models more precise for prospective with finer spatial and temporal grids, we can summarize impact estimates on the precipitation volumes on each season from the more conservative scenario: drias 2070-2100 rcp 8.5 (+10% to -10% in spring according to model taken, -50% to +20% in summer,-15% to +5% in autumn, -15% to +30% in winter)
--10% au printemps
+Being careful for each season, ie with conservative hypothesis for each, we get the following reduction on precipitation volumes:
--50% en été
+-10% in spring
--15% en automne
+-50% en summer
--15% en hiver
+-15% en autumn
-On peut alors mettre à jour l'algorithme :
+-15% in winter
+We can therefore update the algorithm :
-Vous noterez qu'on a déjà ajouté les lignes suivantes dans le code de l'etape 5 juste avant le calcul de # Moyenne par trimestre pour chaque trimestre:
+You will notice we already hav added these lines in the code of stage 5 just before the calculus of the #Quarterly mean for each quarter:
 <pre>
-#Prise en comptes changement climatiques (hypothèses conservatrices multimodeles drias precipitations):
+#Climate change consideration (conservative hypothesis from multimodels drias precipitations modelisations):
-# Definir les impacts sur les volumes de précipitations par saison
+# Define impacts on seasonal precipitation volumes
-adjustments = {0: -15, 1: -10, 2: -50, 3: -15}  # Adjust line 1 by -15%, line 2 by -10%, line 3 by -50%, line 4 by -15%
+adjustments = {0: -15, 1: -10, 2: -50, 3: -15}  # Adjust quarter 1 by -15%, quarter 2 by -10%, quarter 3 by -50%, quarter 4 by -15%
-# Appliquer les impacts sur les précipitations moyennes par trimestres:
+# Impacts on quarterly mean precipitations:
 cc_moyenne_trimestrielle_par_trimestre=moyenne_trimestrielle_par_trimestre.copy()
 for line, adjustment in adjustments.items():
      cc_moyenne_trimestrielle_par_trimestre.iloc[line] = cc_moyenne_trimestrielle_par_trimestre.iloc[line]+cc_moyenne_trimestrielle_par_trimestre.iloc[line]*adjustment/100
-print("\nMoyenne par trimestre pour chaque trimestre avec prise en compte du changement climatique (mm):\n", cc_moyenne_trimestrielle_par_trimestre)
+print("\nQuarterly mean for each quarter with consideration for climate change (mm):\n", cc_moyenne_trimestrielle_par_trimestre)
 </pre>
-Ce qui affiche:
+Which displays
 <pre>
-Moyenne par trimestre pour chaque trimestre avec prise en compte du changement climatique (mm):
+Quarterly mean for each quarter with consideration for climate change (mm):
   trimestre
     165.719091
@@ Ligne 1 054 : / Ligne 1 013 : @@
 </pre>
-On n'effectue pas le data-test au jour le jour compte tenu des biais trop importants induits par le choix d'un seul modèle de prévision.
+We dont do the data-test on a daily basis considered the strong bias induced by choosing only one prospective model.
-En première approximation l'impact sur les résultats est de doubler la surface de récupération nécessaire à partir de la méthode décrite dans les étapes précédentes.
+In first approximation, the impact on the results is to double the necessary harvesting surface with the methode described in the previous stages.
 |Step_Picture_00=Dimensionner_la_r_cup_ration_d_eau_de_pluie_pour_tre_autonome_drias.png
 }}