Rade de Villefranche-sur-Mer

Données satellitaires

Tendances de température

##                       Corrected Zc                        new P-value 
##              7.4251181901928537954              0.0000000000001126793 
##                               N/N*                         Original Z 
##             84.7254781944381534231             68.3455724331707727970 
##                        old P.value                                Tau 
##              0.0000000000000000000              0.3664793433848033688 
##                        Sen's slope                       old.variance 
##              0.0001230563042762353   410688326923.3333129882812500000 
##                       new.variance 
## 34795764887453.1640625000000000000

## 
## Call:
## lm(formula = ano_day ~ decimal_date(t), data = rdg)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0444 -0.5895 -0.0472  0.5621  4.2374 
## 
## Coefficients:
##                    Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)     -89.2313906   1.2861764  -69.38 <0.0000000000000002 ***
## decimal_date(t)   0.0445490   0.0006421   69.38 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9639 on 15334 degrees of freedom
## Multiple R-squared:  0.2389, Adjusted R-squared:  0.2389 
## F-statistic:  4813 on 1 and 15334 DF,  p-value: < 0.00000000000000022
## 
## Call:
## lm(formula = ano_day ~ decimal_date(t), data = filter(rdg, year >= 
##     1995))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.8135 -0.5914 -0.0719  0.5542  4.3922 
## 
## Coefficients:
##                    Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)     -126.386137    2.216094  -57.03 <0.0000000000000002 ***
## decimal_date(t)    0.063017    0.001103   57.14 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9497 on 10586 degrees of freedom
## Multiple R-squared:  0.2357, Adjusted R-squared:  0.2357 
## F-statistic:  3265 on 1 and 10586 DF,  p-value: < 0.00000000000000022

MHWs

Table 1 - Résumé par année pour les données satellitaires
Année Nombre Durée moyenne (jours) Durée maximale Moyenne IM Moyenne des IMax IMax Moyenne des VarI Moyenne des IC Taux de croissance (°C/j) Taux de déclin (°C/j) Nombre de jours total Intensité cumulée totale
1982 3 6.666667 8 2.019300 2.425367 2.7486 0.2608667 13.32773 0.2310667 0.3373667 20 39.9832
1983 2 6.000000 7 1.206550 1.446850 1.9287 0.2182500 7.63180 0.3870000 0.2369500 12 15.2636
1984 1 6.000000 6 2.006300 2.263300 2.2633 0.2226000 12.03770 0.2731000 0.3629000 6 12.0377
1985 1 13.000000 13 1.776700 2.311500 2.3115 0.3036000 23.09700 0.0759000 0.3821000 13 23.0970
1986 1 13.000000 13 2.355200 2.907800 2.9078 0.3912000 30.61760 0.1426000 0.3623000 13 30.6176
1987 1 12.000000 12 1.985000 2.146800 2.1468 0.1860000 23.81970 0.0842000 0.1078000 12 23.8197
1988 0 NA NA NA NA NA NA NA NA NA NA NA
1989 1 8.000000 8 1.487000 1.787000 1.7870 0.2077000 11.89570 0.1686000 0.1576000 8 11.8957
1990 3 18.666667 39 1.421400 1.672700 2.3784 0.1872667 21.33513 0.0708000 0.2933000 56 64.0054
1991 2 7.000000 8 1.372000 1.801450 1.8115 0.2483000 9.71180 0.1659500 1.0342500 14 19.4236
1992 1 5.000000 5 1.803800 1.958600 1.9586 0.1227000 9.01920 0.3703000 0.1391000 5 9.0192
1993 0 NA NA NA NA NA NA NA NA NA NA NA
1994 2 9.000000 10 1.917500 2.135750 2.3375 0.1537500 17.48360 0.2283500 0.0966500 18 34.9672
1995 1 7.000000 7 1.940100 2.144600 2.1446 0.1496000 13.58060 0.2402000 0.0871000 7 13.5806
1996 0 NA NA NA NA NA NA NA NA NA NA NA
1997 0 NA NA NA NA NA NA NA NA NA NA NA
1998 0 NA NA NA NA NA NA NA NA NA NA NA
1999 2 6.000000 7 1.671750 1.891900 1.9616 0.1782000 10.13065 0.1779000 0.2049500 12 20.2613
2000 0 NA NA NA NA NA NA NA NA NA NA NA
2001 2 7.500000 8 1.153150 1.312400 1.7232 0.1140500 8.82875 0.1047500 0.2286500 15 17.6575
2002 2 10.000000 10 2.136650 2.693350 3.7438 0.3453500 21.36605 0.1899000 0.2927000 20 42.7321
2003 2 33.000000 39 3.135900 4.024200 4.6770 0.5784500 100.35220 0.1199500 0.2953000 66 200.7044
2004 0 NA NA NA NA NA NA NA NA NA NA NA
2005 3 9.000000 12 2.165667 2.555900 3.6284 0.2688333 20.69913 0.1371333 0.3206000 27 62.0974
2006 8 11.375000 25 1.864800 2.240975 4.0284 0.2566375 22.15974 0.2768250 0.1832250 91 177.2779
2007 4 14.250000 31 1.644150 2.146700 3.2684 0.3350000 27.23355 0.1935500 0.2740500 57 108.9342
2008 1 10.000000 10 2.775500 3.356900 3.3569 0.3336000 27.75470 0.2665000 0.2166000 10 27.7547
2009 3 23.000000 39 2.187533 3.161400 3.6789 0.4755667 45.39343 0.3077667 0.2911333 69 136.1803
2010 1 24.000000 24 3.027800 4.295200 4.2952 0.4991000 72.66830 0.2059000 0.1643000 24 72.6683
2011 8 12.125000 27 2.055063 2.611175 3.4178 0.4209125 23.37836 0.2155250 0.4202625 97 187.0269
2012 7 12.285714 25 1.893700 2.384357 4.0164 0.3455714 21.83336 0.2506571 0.2757571 86 152.8335
2013 4 8.000000 10 1.699275 2.000975 2.5124 0.1913750 13.42950 0.1024750 0.3813500 32 53.7180
2014 6 31.000000 63 1.904450 2.452800 4.4106 0.3340000 58.44450 0.3714167 0.1685833 186 350.6670
2015 8 15.000000 30 2.096975 2.878137 5.0584 0.3998250 31.55809 0.2740375 0.4361000 120 252.4647
2016 5 17.600000 49 1.750600 2.281280 3.4775 0.3072600 33.70274 0.1411400 0.2819200 88 168.5137
2017 7 14.714286 43 1.879486 2.614871 5.1752 0.4286000 29.39341 0.2231857 0.2704000 103 205.7539
2018 6 37.500000 120 2.123400 2.886633 4.3012 0.4511000 85.34360 0.1598500 0.1798167 225 512.0616
2019 8 22.500000 57 1.928600 2.501362 5.4016 0.3466000 42.79745 0.1813000 0.1590375 180 342.3796
2020 8 17.375000 35 1.696962 2.121412 3.1471 0.2411125 30.37866 0.1712125 0.1600875 139 243.0293
2021 6 12.833333 19 1.632267 2.076417 3.5870 0.2761667 23.43730 0.1618167 0.1592333 77 140.6238
2022 3 96.000000 133 2.009100 2.808267 4.6376 0.4047333 223.24343 0.0366333 0.0310667 288 669.7303
2023 8 23.375000 42 1.795012 2.392587 4.1183 0.3444250 49.73246 0.1369125 0.0705375 187 397.8597
2024 2 38.000000 40 1.309150 2.277800 3.2135 0.3993500 50.32425 0.0700000 0.1511000 76 100.6485
Table III - Tendances pour quelques métriques des MHWs
Mesures Theil-Sen (pente) T-S (p-val) Point de rupture Pettitt (p-val) Theil-Sen (avant) p-val (avant) Theil-Sen (après) p-val (après)
Nombre de jours de vague de chaleur par an 3.3928571 3.0e-07 2004 7.70e-06 -0.1250000 0.4199534 8.3636364 0.0009747
Nombre d’évènements par an 0.2000000 3.4e-06 2004 1.62e-05 0.0000000 0.4401483 0.4000000 0.0053619
Moyenne des IM (°C) 0.0452354 0.0e+00 2002 6.00e-07 0.0103043 0.3811873 0.0526577 0.0006577
Moyenne des IMax (°C) 0.0478887 4.0e-07 2001 3.40e-06 -0.0165967 0.2561450 0.0567108 0.0121987
IC annuelle (°C.j) 16.5327267 0.0e+00 2002 6.00e-07 3.7785299 0.3811873 19.2213311 0.0005175

Données in situ

Bouée EOL

Climatologie satellitaire

Pour calculer la climatologie, on utilise ici des données satellitaires (de 1982 à 2012), mais la détection d’évènements se fait sur les données issues de la bouée EOL uniquement. Note: si on choisit comme période de référence pour calculer la climatologie (toujours à partir des données satellitaires) la même période que pour les données EOL seules (2014-2024), on obtient sensiblement les mêmes résultats qu’en la calculant avec les données EOL. Pour ces dernières, on utilise une moyenne de toutes les valeurs de la journée. Le point utilisé comme référence est au large de Villefranche (c’est le centre du pixel de 0.25x0.25° qui contient la rade, situé à 43.625°N, 7.375°E)

Table IV - Résumé par année de quelques métriques pour les données EOL avec climatologie satellitaire
Année Nombre Durée moyenne (j) Moyenne IM (°C) Moyenne des IMax (°C) Moyenne des IC (°C.j) Nombre de jours total (j) Intensité cumulée totale (°C.j)
2014 4 38.25000 2.026850 2.727000 82.91030 153 331.6412
2015 9 13.55556 1.958767 2.426256 30.13709 122 271.2338
2016 8 15.50000 1.601550 1.924338 27.88276 124 223.0621
2017 10 12.90000 1.883910 2.506460 26.36788 129 263.6788
2018 4 61.25000 2.325975 3.158725 148.58378 245 594.3351
2019 3 86.00000 1.539933 2.769400 171.35583 258 514.0675
2020 6 20.33333 1.852250 2.348867 44.06693 122 264.4016
2021 8 26.25000 1.611600 1.967912 39.11744 210 312.9395
2022 2 81.50000 2.944800 3.934050 277.52310 163 555.0462
2023 6 35.66667 2.244033 3.050800 75.36787 214 452.2072

En utilisant les données EOL seules

Ici, la climatologie est calculée à partir des données EOL seules (à partir de 2014).

Table V - Résumé par année de quelques métriques pour les données EOL seules
Année Nombre Durée moyenne (j) Moyenne IM (°C) Moyenne des IMax (°C) Moyenne des IC (°C.j) Nombre de jours total (j) Intensité cumulée totale (°C.j)
2014 3 35.33333 1.19890 1.477033 40.86373 106 122.5912
2015 1 5.00000 2.25770 2.405800 11.28850 5 11.2885
2016 0 NA NA NA NA NA NA
2017 1 6.00000 2.20880 2.429700 13.25270 6 13.2527
2018 3 8.00000 1.41810 1.636633 11.89370 24 35.6811
2019 2 8.00000 2.04240 2.553750 17.11225 16 34.2245
2020 1 8.00000 0.45860 0.637200 3.66880 8 3.6688
2021 0 NA NA NA NA NA NA
2022 5 18.80000 1.99270 2.593520 38.77490 94 193.8745
2023 2 9.50000 1.81235 2.079950 16.27585 19 32.5517

CTD - SOMLIT

Evènements

Ces graphiques montrent, par profondeur (de 5 à 80m), l’évènement le plus important sur toute la période d’échantillonnage (de mai 1995 à avril 2023) d’après trois critères : celui qui contient le pic d’anomalie (l’intensité maximale) le plus élevé, le plus long, et celui dont l’intensité cumulée (au dessus du seuil, sur tout l’évènement) est la plus importante (en °C.j).

Tendances de température

## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 147330, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             969 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 172444, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                            1001 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 122646, p-value = 0.000000000001957
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                            1001 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 98262, p-value = 0.0000000391
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                            1219 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 76434, p-value = 0.00004332
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                            1220 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 75240, p-value = 0.00006043
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                            1220 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 102480, p-value = 0.000000008244
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             611 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 124394, p-value = 0.0000000000008846
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             613 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 136254, p-value = 0.000000000000003011
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             613 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 176396, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             613 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 204196, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             790 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 226904, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             790 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 242160, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             790 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 260284, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             794 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 260260, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             794 
## 
## 
##  Pettitt's test for single change-point detection
## 
## data:  ctdepth_i$temp_DS
## U* = 240012, p-value < 0.00000000000000022
## alternative hypothesis: two.sided
## sample estimates:
## probable change point at time K 
##                             628
Table II - Tendances de températures sur les données satellitaires (0 m) et CTD
Profondeur (m) Mann-Kendall global (p-val) pente de Theil-Sen globale (°C/an) Test de Pettitt (p-val) Date de rupture probable (an) M-K pré-rupture (p-val) T-S pré-rupture (°C/an) M-K post-rupture (p-val) T-S post-rupture (°C/an)
0 0.0000000 0.0449463 0.0000000 2003 0.1199891 0.0116731 0.0000000 0.0645453
5 0.1205614 0.0182985 0.0000000 2014 0.0085169 -0.0291438 0.9514944 -0.0032803
10 0.0349058 0.0234367 0.0000000 2014 0.0059323 -0.0201408 0.7416679 -0.0194380
15 0.1889768 0.0149327 0.0000000 2014 0.0000516 -0.0223796 0.6022333 0.0274447
20 0.4141574 0.0085188 0.0000000 2018 0.3000586 -0.0109065 0.4903573 -0.0552056
25 0.6658341 0.0034348 0.0000433 2018 0.0967179 -0.0120637 0.8932688 -0.0071282
30 0.3214442 0.0070804 0.0000604 2018 0.6365334 -0.0042799 0.9923374 -0.0004237
35 0.0237262 0.0128976 0.0000000 2007 0.5964568 -0.0097090 0.4277986 0.0095766
40 0.0002213 0.0146355 0.0000000 2007 0.4122389 -0.0109865 0.3796748 0.0071207
45 0.0000000 0.0152467 0.0000000 2007 0.0925672 -0.0219622 0.0843385 0.0101949
50 0.0000011 0.0200254 0.0000000 2007 0.0011410 -0.0370958 0.0551926 0.0155698
55 0.0000052 0.0228366 0.0000000 2010 0.0084170 -0.0150125 0.7847413 0.0034574
60 0.0000008 0.0251920 0.0000000 2010 0.0644026 -0.0111221 0.7753609 0.0035012
65 0.0000000 0.0266425 0.0000000 2010 0.2806422 -0.0064150 0.8107427 0.0026948
70 0.0000000 0.0260191 0.0000000 2010 0.1212195 -0.0087411 0.9990016 -0.0000259
75 0.0000098 0.0229793 0.0000000 2010 0.2355906 -0.0079409 0.4268843 -0.0088856
80 0.0000497 0.0214588 0.0000000 2007 0.0453349 -0.0294937 0.0985450 0.0109700

Heatmaps

T-MEDNet

Evènements

Ces graphiques montrent, par profondeur (de 5 à 40m), l’évènement le plus important sur toute la période d’échantillonnage (de juillet 2017 à décembre 2023) d’après trois critères : celui qui contient le pic d’anomalie (l’intensité maximale) le plus élevé, le plus long, et celui dont l’intensité cumulée (au dessus du seuil, sur tout l’évènement) est la plus importante (en °C.j).

Tendances de température

depth MK_pval slope S_p_val adj_R2
0 0.0000000 0.0449463 0.0000000 0.2356662
5 0.0000000 0.1221039 0.0000000 0.0300963
10 0.0000000 0.0974760 0.0000003 0.0156685
15 0.0000004 -0.0932293 0.0002490 0.0077784
20 0.0000000 0.0443820 0.0248297 0.0025346
25 0.0002817 0.0630441 0.0073930 0.0038752
30 0.2498132 0.0377052 0.0725585 0.0013978
35 0.5419588 0.0161625 0.3848496 -0.0001536
40 0.0000000 0.0158582 0.1063192 0.0010109

Heatmaps

Comparaisons

Comparaisons des datasets

Ici, on s’intéresse seulement à rapidement comparer certains jeu de données entre eux. Les données satellites et EOL d’abord, pour confirmer la similitude des données malgré le (relatif) éloignement spatial et ainsi pour justifier de la pertinence de l’utilisation des données satellites pour calculer une climatologie dont on se sert pour détecter des MHWs sur les données EOL. D’autre part, on compare aussi les données CTD et T-MEDNet pour chaque profondeur disponible pour T-MEDNet

Comparaisons des résultats

On compare les résultats obtenus après la détection d’évènements sur les données EOL seules et sur les données EOL+Satellite

Table VI - Tests de Wilcoxon appariés comparant les caractéristiques des MHWs détectées sur les données EOL, avec ou sans données satellites
Mesures Nombre d’évènements Durée moyenne (jours) Durée maximale Moyenne IM Moyenne des IMax Moyenne des IC Taux de croissance (°C/j) Taux de déclin (°C/j) Nombre de jours total Intensité cumulée totale
p-value 0.0184405 0.0078125 0.0207062 0.1484375 0.0078125 0.0078125 0.1953125 0.25 0.0078125 0.0078125

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