described in https://tidesandcurrents.noaa.gov/publications/techrpt83_Global_and_Regional_SLR_Scenarios_for_the_US_final.pdf
In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import re
import pandas as pd
In [2]:
df = pd.read_csv('techrpt083.csv', skiprows=15)
In [3]:
df.shape
Out[3]:
(35046, 20)
In [4]:
df2 = df[df['Site'].str.contains("HALIFAX")]
df2
Out[4]:
Site
PSMSL ID
Latitude
Longitude
Scenario
Background RSL rate (mm/yr)
RSL in 2000 (cm)
RSL in 2010 (cm)
RSL in 2020 (cm)
RSL in 2030 (cm)
RSL in 2040 (cm)
RSL in 2050 (cm)
RSL in 2060 (cm)
RSL in 2070 (cm)
RSL in 2080 (cm)
RSL in 2090 (cm)
RSL in 2100 (cm)
RSL in 2120 (cm)
RSL in 2150 (cm)
RSL in 2200 (cm)
54
HALIFAX
96
44.67
-63.58
0.3 - MED
1.32
0
5
11
16
20
27
32
36
40
44
47
50.0
58.0
66.0
55
HALIFAX
96
44.67
-63.58
0.3 - LOW
1.18
0
2
6
10
13
15
17
18
19
21
22
22.0
26.0
19.0
56
HALIFAX
96
44.67
-63.58
0.3 - HIGH
1.46
0
7
14
21
29
36
45
51
57
62
66
74.0
88.0
110.0
57
HALIFAX
96
44.67
-63.58
0.5 - MED
1.32
0
6
13
19
25
33
39
45
51
56
60
70.0
85.0
105.0
58
HALIFAX
96
44.67
-63.58
0.5 - LOW
1.18
0
4
8
14
18
23
28
33
37
42
45
51.0
60.0
65.0
59
HALIFAX
96
44.67
-63.58
0.5 - HIGH
1.46
0
7
16
24
32
40
49
55
62
68
74
85.0
106.0
142.0
60
HALIFAX
96
44.67
-63.58
1.0 - MED
1.32
0
8
18
28
39
52
66
81
97
114
130
149.0
199.0
285.0
61
HALIFAX
96
44.67
-63.58
1.0 - LOW
1.18
0
6
12
21
30
40
51
63
76
90
104
117.0
159.0
223.0
62
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
0
10
22
33
47
61
78
96
114
133
152
178.0
236.0
356.0
63
HALIFAX
96
44.67
-63.58
1.5 - MED
1.32
0
11
24
37
53
72
93
115
141
168
195
249.0
351.0
565.0
64
HALIFAX
96
44.67
-63.58
1.5 - LOW
1.18
0
6
14
24
37
51
66
84
102
126
149
187.0
260.0
400.0
65
HALIFAX
96
44.67
-63.58
1.5 - HIGH
1.46
0
13
27
42
62
82
107
134
163
193
226
299.0
418.0
677.0
66
HALIFAX
96
44.67
-63.58
2.0 - MED
1.32
0
14
29
46
70
97
128
162
197
236
278
377.0
550.0
913.0
67
HALIFAX
96
44.67
-63.58
2.0 - LOW
1.18
0
13
26
43
62
84
107
133
165
203
243
331.0
489.0
808.0
68
HALIFAX
96
44.67
-63.58
2.0 - HIGH
1.46
0
15
31
51
75
103
135
170
210
255
299
412.0
589.0
972.0
69
HALIFAX
96
44.67
-63.58
2.5 - MED
1.32
0
15
30
50
77
110
149
191
236
289
343
471.0
707.0
1154.0
70
HALIFAX
96
44.67
-63.58
2.5 - LOW
1.18
0
6
14
28
46
70
98
132
171
217
267
376.0
578.0
1031.0
71
HALIFAX
96
44.67
-63.58
2.5 - HIGH
1.46
0
16
34
57
86
120
157
202
252
304
365
537.0
780.0
1267.0
In [5]:
df3 = df2[df2['Scenario'].str.contains("1.0 - HIGH")]
df3
Out[5]:
Site
PSMSL ID
Latitude
Longitude
Scenario
Background RSL rate (mm/yr)
RSL in 2000 (cm)
RSL in 2010 (cm)
RSL in 2020 (cm)
RSL in 2030 (cm)
RSL in 2040 (cm)
RSL in 2050 (cm)
RSL in 2060 (cm)
RSL in 2070 (cm)
RSL in 2080 (cm)
RSL in 2090 (cm)
RSL in 2100 (cm)
RSL in 2120 (cm)
RSL in 2150 (cm)
RSL in 2200 (cm)
62
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
0
10
22
33
47
61
78
96
114
133
152
178.0
236.0
356.0
In [ ]:
In [6]:
df3.columns.values[0:6]
Out[6]:
array(['Site', 'PSMSL ID', 'Latitude', 'Longitude', 'Scenario',
'Background RSL rate (mm/yr)'], dtype=object)
In [7]:
df4 = pd.melt(df3, id_vars=df3.columns.values[0:6],
var_name="Date", value_name="Value")
df4
Out[7]:
Site
PSMSL ID
Latitude
Longitude
Scenario
Background RSL rate (mm/yr)
Date
Value
0
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2000 (cm)
0.0
1
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2010 (cm)
10.0
2
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2020 (cm)
22.0
3
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2030 (cm)
33.0
4
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2040 (cm)
47.0
5
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2050 (cm)
61.0
6
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2060 (cm)
78.0
7
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2070 (cm)
96.0
8
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2080 (cm)
114.0
9
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2090 (cm)
133.0
10
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2100 (cm)
152.0
11
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2120 (cm)
178.0
12
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2150 (cm)
236.0
13
HALIFAX
96
44.67
-63.58
1.0 - HIGH
1.46
RSL in 2200 (cm)
356.0
In [8]:
df5 = df4.copy(deep=True)
for scenario in df2['Scenario'].values:
df3 = df2[df2['Scenario'].str.contains(scenario)]
var = scenario.replace(' ','')
df4 = pd.melt(df3, id_vars=df3.columns.values[0:6],
var_name="Date", value_name=var)
df5[var] = df4[var]
In [9]:
df5['Date'] = [int(re.findall(r'\d+', v)[0]) for v in df5['Date'].values]
In [10]:
df5.rename(columns = {'Site':'id'}, inplace = True)
In [11]:
def df_station(df, station):
df2 = df[df['Site'].str.contains(station)]
df3 = df2[df2['Scenario'].str.contains("1.0 - HIGH")]
df4 = pd.melt(df3, id_vars=df3.columns.values[0:6],
var_name="Date", value_name="Value")
df5 = df4.copy(deep=True)
for scenario in df2['Scenario'].values:
df3 = df2[df2['Scenario'].str.contains(scenario)]
var = scenario.replace(' ','')
df4 = pd.melt(df3, id_vars=df3.columns.values[0:6],
var_name="Date", value_name=var)
df5[var] = df4[var]
return df5
In [12]:
df5 = df_station(df, 'HALIFAX')
In [13]:
df2 = df[~df['Site'].str.contains('grid_')]
df3 = df2[~df2['Site'].str.contains('GMSL')]
df3
Out[13]:
Site
PSMSL ID
Latitude
Longitude
Scenario
Background RSL rate (mm/yr)
RSL in 2000 (cm)
RSL in 2010 (cm)
RSL in 2020 (cm)
RSL in 2030 (cm)
RSL in 2040 (cm)
RSL in 2050 (cm)
RSL in 2060 (cm)
RSL in 2070 (cm)
RSL in 2080 (cm)
RSL in 2090 (cm)
RSL in 2100 (cm)
RSL in 2120 (cm)
RSL in 2150 (cm)
RSL in 2200 (cm)
18
SAN FRANCISCO
10
37.81
-122.47
0.3 - MED
-0.07
0
3
6
10
13
17
21
25
28
31
34
40.0
47.0
53.0
19
SAN FRANCISCO
10
37.81
-122.47
0.3 - LOW
-0.16
0
1
3
6
8
10
13
15
17
19
19
23.0
25.0
17.0
20
SAN FRANCISCO
10
37.81
-122.47
0.3 - HIGH
0.02
0
4
8
13
18
23
28
33
38
43
49
55.0
65.0
83.0
21
SAN FRANCISCO
10
37.81
-122.47
0.5 - MED
-0.07
0
3
8
12
17
22
28
33
38
43
48
56.0
70.0
92.0
22
SAN FRANCISCO
10
37.81
-122.47
0.5 - LOW
-0.16
0
2
5
9
13
17
21
26
31
36
38
46.0
52.0
57.0
23
SAN FRANCISCO
10
37.81
-122.47
0.5 - HIGH
0.02
0
4
9
15
20
26
32
38
43
49
55
68.0
87.0
123.0
24
SAN FRANCISCO
10
37.81
-122.47
1.0 - MED
-0.07
0
5
10
17
25
36
47
59
73
87
104
123.0
176.0
278.0
25
SAN FRANCISCO
10
37.81
-122.47
1.0 - LOW
-0.16
0
3
8
14
20
29
40
52
64
78
91
114.0
158.0
242.0
26
SAN FRANCISCO
10
37.81
-122.47
1.0 - HIGH
0.02
0
6
12
19
28
40
52
66
81
97
117
141.0
202.0
325.0
27
SAN FRANCISCO
10
37.81
-122.47
1.5 - MED
-0.07
0
7
13
22
34
51
69
90
114
141
174
210.0
318.0
541.0
28
SAN FRANCISCO
10
37.81
-122.47
1.5 - LOW
-0.16
0
4
9
17
27
41
58
76
98
122
147
191.0
286.0
476.0
29
SAN FRANCISCO
10
37.81
-122.47
1.5 - HIGH
0.02
0
8
15
25
38
57
77
100
127
155
191
247.0
370.0
630.0
30
SAN FRANCISCO
10
37.81
-122.47
2.0 - MED
-0.07
0
8
16
28
46
70
97
127
163
204
253
324.0
500.0
872.0
31
SAN FRANCISCO
10
37.81
-122.47
2.0 - LOW
-0.16
0
7
14
25
41
63
88
117
151
188
232
299.0
459.0
804.0
32
SAN FRANCISCO
10
37.81
-122.47
2.0 - HIGH
0.02
0
9
18
32
49
73
101
133
170
211
261
361.0
551.0
933.0
33
SAN FRANCISCO
10
37.81
-122.47
2.5 - MED
-0.07
0
8
18
32
54
83
118
158
202
252
311
433.0
669.0
1130.0
34
SAN FRANCISCO
10
37.81
-122.47
2.5 - LOW
-0.16
0
3
10
21
37
60
90
124
163
207
257
368.0
573.0
1014.0
35
SAN FRANCISCO
10
37.81
-122.47
2.5 - HIGH
0.02
0
9
20
36
57
88
125
166
215
268
334
471.0
725.0
1207.0
36
NEW YORK
12
40.70
-74.01
0.3 - MED
1.29
0
5
11
15
20
25
31
36
39
44
46
51.0
58.0
69.0
37
NEW YORK
12
40.70
-74.01
0.3 - LOW
1.20
0
2
6
10
13
15
18
19
21
21
22
27.0
28.0
26.0
38
NEW YORK
12
40.70
-74.01
0.3 - HIGH
1.38
0
7
14
21
28
36
42
49
54
60
65
75.0
88.0
110.0
39
NEW YORK
12
40.70
-74.01
0.5 - MED
1.29
0
6
13
19
25
31
39
45
50
56
61
70.0
85.0
110.0
40
NEW YORK
12
40.70
-74.01
0.5 - LOW
1.20
0
4
8
13
18
24
29
33
39
43
47
54.0
63.0
70.0
41
NEW YORK
12
40.70
-74.01
0.5 - HIGH
1.38
0
7
16
23
31
39
47
54
60
66
72
85.0
106.0
145.0
42
NEW YORK
12
40.70
-74.01
1.0 - MED
1.29
0
9
19
29
39
51
65
80
96
114
130
148.0
202.0
295.0
43
NEW YORK
12
40.70
-74.01
1.0 - LOW
1.20
0
6
12
21
29
40
52
64
78
92
106
122.0
167.0
240.0
44
NEW YORK
12
40.70
-74.01
1.0 - HIGH
1.38
0
11
23
34
47
60
76
93
111
130
150
176.0
238.0
359.0
45
NEW YORK
12
40.70
-74.01
1.5 - MED
1.29
0
12
25
39
53
71
92
114
139
167
197
247.0
353.0
567.0
46
NEW YORK
12
40.70
-74.01
1.5 - LOW
1.20
0
7
14
24
36
51
68
87
108
130
154
195.0
281.0
442.0
47
NEW YORK
12
40.70
-74.01
1.5 - HIGH
1.38
0
15
31
45
61
80
104
130
159
191
225
297.0
420.0
673.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
4272
CAPE HATTERAS
2294
35.22
-75.64
1.0 - MED
1.68
0
9
17
27
38
50
64
79
94
112
130
149.0
204.0
304.0
4273
CAPE HATTERAS
2294
35.22
-75.64
1.0 - LOW
1.35
0
5
13
20
29
40
52
64
78
92
106
128.0
173.0
251.0
4274
CAPE HATTERAS
2294
35.22
-75.64
1.0 - HIGH
2.01
0
10
21
33
45
59
74
92
109
128
149
174.0
240.0
369.0
4275
CAPE HATTERAS
2294
35.22
-75.64
1.5 - MED
1.68
0
11
22
36
51
68
89
114
139
168
203
242.0
354.0
577.0
4276
CAPE HATTERAS
2294
35.22
-75.64
1.5 - LOW
1.35
0
6
14
24
36
52
68
87
110
133
160
204.0
295.0
460.0
4277
CAPE HATTERAS
2294
35.22
-75.64
1.5 - HIGH
2.01
0
13
26
41
58
78
101
128
156
189
224
286.0
415.0
679.0
4278
CAPE HATTERAS
2294
35.22
-75.64
2.0 - MED
1.68
0
13
26
45
65
89
121
154
189
232
282
367.0
554.0
928.0
4279
CAPE HATTERAS
2294
35.22
-75.64
2.0 - LOW
1.35
0
11
23
39
58
81
107
135
170
209
254
333.0
497.0
827.0
4280
CAPE HATTERAS
2294
35.22
-75.64
2.0 - HIGH
2.01
0
14
29
48
69
95
127
162
203
246
299
395.0
589.0
980.0
4281
CAPE HATTERAS
2294
35.22
-75.64
2.5 - MED
1.68
0
14
28
49
73
104
142
184
228
282
345
461.0
708.0
1162.0
4282
CAPE HATTERAS
2294
35.22
-75.64
2.5 - LOW
1.35
0
5
14
27
45
69
98
132
173
218
268
380.0
591.0
1042.0
4283
CAPE HATTERAS
2294
35.22
-75.64
2.5 - HIGH
2.01
0
15
30
52
77
110
150
194
243
298
366
518.0
772.0
1259.0
4284
BEAUFORT
2295
34.72
-76.67
0.3 - MED
1.28
0
4
10
15
20
24
30
35
39
43
46
52.0
61.0
74.0
4285
BEAUFORT
2295
34.72
-76.67
0.3 - LOW
1.02
0
2
6
9
13
15
18
19
21
23
24
30.0
30.0
32.0
4286
BEAUFORT
2295
34.72
-76.67
0.3 - HIGH
1.54
0
7
13
20
27
34
41
47
52
58
64
73.0
88.0
111.0
4287
BEAUFORT
2295
34.72
-76.67
0.5 - MED
1.28
0
5
12
18
24
30
37
43
49
55
60
70.0
87.0
114.0
4288
BEAUFORT
2295
34.72
-76.67
0.5 - LOW
1.02
0
4
8
13
18
23
28
33
38
42
46
54.0
63.0
73.0
4289
BEAUFORT
2295
34.72
-76.67
0.5 - HIGH
1.54
0
8
15
23
30
38
45
52
58
65
72
85.0
108.0
151.0
4290
BEAUFORT
2295
34.72
-76.67
1.0 - MED
1.28
0
8
17
26
36
47
61
76
91
108
126
143.0
197.0
293.0
4291
BEAUFORT
2295
34.72
-76.67
1.0 - LOW
1.02
0
5
12
19
28
38
49
62
74
89
103
123.0
167.0
243.0
4292
BEAUFORT
2295
34.72
-76.67
1.0 - HIGH
1.54
0
10
19
31
42
56
71
88
105
124
144
167.0
232.0
357.0
4293
BEAUFORT
2295
34.72
-76.67
1.5 - MED
1.28
0
10
21
35
49
66
88
111
137
166
199
235.0
346.0
561.0
4294
BEAUFORT
2295
34.72
-76.67
1.5 - LOW
1.02
0
6
13
22
34
49
66
84
106
129
155
198.0
288.0
451.0
4295
BEAUFORT
2295
34.72
-76.67
1.5 - HIGH
1.54
0
12
24
39
55
75
97
124
152
185
220
278.0
406.0
666.0
4296
BEAUFORT
2295
34.72
-76.67
2.0 - MED
1.28
0
12
25
42
62
86
118
150
188
230
279
360.0
545.0
915.0
4297
BEAUFORT
2295
34.72
-76.67
2.0 - LOW
1.02
0
11
22
39
56
79
105
135
169
207
251
327.0
490.0
809.0
4298
BEAUFORT
2295
34.72
-76.67
2.0 - HIGH
1.54
0
14
27
45
66
93
124
159
200
243
294
387.0
580.0
968.0
4299
BEAUFORT
2295
34.72
-76.67
2.5 - MED
1.28
0
12
26
47
70
102
140
181
225
280
339
455.0
698.0
1151.0
4300
BEAUFORT
2295
34.72
-76.67
2.5 - LOW
1.02
0
5
13
26
44
67
96
129
169
214
263
373.0
582.0
1029.0
4301
BEAUFORT
2295
34.72
-76.67
2.5 - HIGH
1.54
0
15
29
49
74
107
146
191
241
296
360
507.0
759.0
1244.0
4284 rows × 20 columns
In [14]:
df3['Site'].unique()
Out[14]:
array(['SAN FRANCISCO', 'NEW YORK', 'HALIFAX', 'FERNANDINA BEACH',
'TROIS-RIVIERES', 'SEATTLE', 'PHILADELPHIA', 'PORT-SAINT-FRANCOIS',
'POINTE-AU-PERE', 'BATISCAN', 'BALTIMORE', 'HONOLULU', 'SAN DIEGO',
'GALVESTON II', 'TOFINO', 'VICTORIA', 'PRINCE RUPERT', 'CRISTOBAL',
'QUEBEC', 'VANCOUVER', 'HARRINGTON HBR', 'ATLANTIC CITY',
'PORTLAND ', 'KEY WEST', 'NEUVILLE', 'POINT ATKINSON', 'SAINT JOHN',
'DESCHAILLONS', 'LEWES', 'KETCHIKAN', 'CHARLESTON I', 'BOSTON',
'LOS ANGELES', 'PENSACOLA', 'LA JOLLA', 'ASTORIA', 'SEWARD',
'DAYTONA BEACH', 'SEAVEY ISLAND', 'SEWELLS POINT', 'HILO',
'ANNAPOLIS', 'MAYPORT', 'EASTPORT', 'NEWPORT', 'WASHINGTON DC',
'WILLETS POINT', 'MIAMI BEACH', 'SANDY HOOK', 'WOODS HOLE',
'ST. GEORGES', 'SANTA MONICA', 'CRESCENT CITY', 'FRIDAY HARBOR',
'NEAH BAY', 'GRONDINES', 'PORT AUX BASQUES', "ST. JOHN'S",
'FORT PULASKI', 'WILMINGTON', 'PORTSMOUTH', 'JUNEAU',
"SOLOMON'S ISLAND", 'GUANTANAMO BAY', 'SITKA', 'CHARLOTTETOWN',
'CEDAR KEY II', 'NEW LONDON', 'PROVIDENCE', 'ALAMEDA',
'EUGENE ISLAND', 'YAKUTAT', 'CHURCHILL', 'RICHMOND',
'ADAK SWEEPER COVE', 'MASSACRE BAY', 'SKAGWAY', 'PORT ISABEL',
'PORT SAN LUIS', 'KWAJALEIN', 'MONTAUK', 'ST. PETERSBURG',
'KAHULUI HARBOR', 'MIDWAY ISLAND', 'BAR HARBOR', 'GRAND ISLE',
'PORT ALBERNI', 'CHUUK', 'ROCKPORT', 'PAGO PAGO', 'APRA HARBOUR',
'PUERTO LIMON', 'ALERT BAY', 'PUERTO CORTES', 'GIBARA', 'CORDOVA',
'KODIAK ISLAND', 'CARTAGENA', 'KANTON ISLAND', 'WAKE ISLAND',
'GLOUCESTER POINT', 'JOHNSTON ISLAND', 'KIPTOPEKE BEACH',
'ENEWETOK', 'ACAPULCO', 'FULFORD HARBOUR', 'SALINA CRUZ',
'PROGRESO', 'COATZACOALCOS', 'GUAYMAS', 'LA PAZ', 'MAZATLAN',
'FREEPORT', 'MANZANILLO', 'NAWILIWILI BAY', 'UNALASKA',
'MAGUEYES ISLAND', 'NEWPORT BAY', 'ALVARADO', 'SANDWICH MARINA',
'BUZZARDS BAY', 'REEDY POINT', 'ENSENADA', 'CIUDAD DEL CARMEN',
'MOKUOLOE ISLAND', 'GALVESTON I', 'QUEEN CHARLOTTE CITY',
'PORT RENFREW', 'PORT JEFFERSON', 'NOUMEA-CHALEIX', 'NEW ROCHELLE',
'TUXPAN', 'PADRE ISLAND', 'PORTNEUF', 'BELLA BELLA', 'ST-FRANCOIS',
'SAN JUAN', 'CHAMPLAIN', 'RINCON ISLAND', 'CIUDAD MADERO',
'PORT MANSFIELD', 'ANCHORAGE', 'BRIDGEPORT', 'SELDOVIA',
'PORT HARDY', 'SANTO TOMAS DE CASTILLA', 'FORT MYERS', 'NAPLES',
'NANTUCKET ISLAND', 'PICTOU', 'PATRICIA BAY', 'CAPE MAY',
'DAUPHIN ISLAND', 'YARMOUTH', 'APALACHICOLA', 'SOUTH BEACH',
'STE-ANNE-DES-MONTS', 'RIVIERE-AU-RENARD', 'MAJURO-B',
'BAIE COMEAU', 'TADOUSSAC', 'BAMFIELD', 'ST-JOSEPH-DE-LA-RIVE',
'NEW WESTMINSTER', 'MALAKAL-B', 'RIKITEA', 'STEVESTON',
'CHARLESTON II', 'EASTER ISLAND-E', 'CAMBRIDGE II',
'CABO DE SAN ANTONIO', 'NORTH SYDNEY', 'ARGENTIA', 'SEPT-ILES',
'CAMPBELL RIVER', 'PORT TOWNSEND', 'SUVA-A', 'KANTON ISLAND-B',
'LOWER ESCUMINAC', 'NIKISKI', 'MONTEREY', 'VALDEZ', 'TOKE POINT',
'CABO SAN LUCAS', 'POHNPEI-B', 'CHRISTMAS ISLAND II',
'FRENCH FRIGATE SHOALS', 'HONIARA II', 'PORT-ALFRED',
'CHARLOTTE AMALIE', 'POINT REYES', 'PAPEETE-B', 'NORTH SOUND',
'SOUTH SOUND', 'SPRINGMAID PIER', 'LIME TREE BAY', 'PENRHYN',
'FUNAFUTI', 'KAPINGAMARANGI', 'SAIPAN', 'CUTLER II', 'RIMOUSKI',
'CHERRY POINT', 'SAND POINT', 'CHESAPEAKE BAY BR. TUN.',
'DUCK PIER OUTSIDE', 'BERGEN POINT', 'CLEARWATER BEACH', 'N. SPIT',
'PORT ORFORD', 'PANAMA CITY', 'VACA KEY', 'BECANCOUR',
'WINTER HARBOUR', 'NOME', 'BETIO', 'LAUTOKA', 'SABINE PASS NORTH',
'MAJURO-C', 'FUNAFUTI B', 'APIA B', 'PORT VILA B', "NUKU'ALOFA B",
'RAROTONGA B', 'NAURU-B', 'PRUDHOE BAY', 'VIRGINIA KEY',
'HONIARA-B', 'CORPUS CHRISTI', 'CABO CRUZ', 'CASILDA II',
'TRIDENT PIER', 'ARENA COVE', 'SANTA BARBARA', 'PORT ANGELES',
'KAWAIHAE', 'CAPE HATTERAS', 'BEAUFORT'], dtype=object)
In [15]:
dfs = [df_station(df3,station) for station in df3['Site'].unique()]
In [16]:
type(dfs)
Out[16]:
list
In [17]:
df6 = pd.concat(dfs)
In [18]:
del df6['Value']
del df6['Scenario']
del df6['PSMSL ID']
In [20]:
df6['Date'] = [int(re.findall(r'\d+', v)[0]) for v in df6['Date'].values]
df6.rename(columns = {'Site':'id'}, inplace = True)
In [21]:
df6.to_csv('major_stations.csv', index=False)
In [19]:
df6
Out[19]:
Site
Latitude
Longitude
Background RSL rate (mm/yr)
Date
0.3-MED
0.3-LOW
0.3-HIGH
0.5-MED
0.5-LOW
...
1.0-HIGH
1.5-MED
1.5-LOW
1.5-HIGH
2.0-MED
2.0-LOW
2.0-HIGH
2.5-MED
2.5-LOW
2.5-HIGH
0
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2000 (cm)
0.0
0.0
0.0
0.0
0.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2010 (cm)
3.0
1.0
4.0
3.0
2.0
...
6.0
7.0
4.0
8.0
8.0
7.0
9.0
8.0
3.0
9.0
2
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2020 (cm)
6.0
3.0
8.0
8.0
5.0
...
12.0
13.0
9.0
15.0
16.0
14.0
18.0
18.0
10.0
20.0
3
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2030 (cm)
10.0
6.0
13.0
12.0
9.0
...
19.0
22.0
17.0
25.0
28.0
25.0
32.0
32.0
21.0
36.0
4
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2040 (cm)
13.0
8.0
18.0
17.0
13.0
...
28.0
34.0
27.0
38.0
46.0
41.0
49.0
54.0
37.0
57.0
5
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2050 (cm)
17.0
10.0
23.0
22.0
17.0
...
40.0
51.0
41.0
57.0
70.0
63.0
73.0
83.0
60.0
88.0
6
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2060 (cm)
21.0
13.0
28.0
28.0
21.0
...
52.0
69.0
58.0
77.0
97.0
88.0
101.0
118.0
90.0
125.0
7
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2070 (cm)
25.0
15.0
33.0
33.0
26.0
...
66.0
90.0
76.0
100.0
127.0
117.0
133.0
158.0
124.0
166.0
8
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2080 (cm)
28.0
17.0
38.0
38.0
31.0
...
81.0
114.0
98.0
127.0
163.0
151.0
170.0
202.0
163.0
215.0
9
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2090 (cm)
31.0
19.0
43.0
43.0
36.0
...
97.0
141.0
122.0
155.0
204.0
188.0
211.0
252.0
207.0
268.0
10
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2100 (cm)
34.0
19.0
49.0
48.0
38.0
...
117.0
174.0
147.0
191.0
253.0
232.0
261.0
311.0
257.0
334.0
11
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2120 (cm)
40.0
23.0
55.0
56.0
46.0
...
141.0
210.0
191.0
247.0
324.0
299.0
361.0
433.0
368.0
471.0
12
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2150 (cm)
47.0
25.0
65.0
70.0
52.0
...
202.0
318.0
286.0
370.0
500.0
459.0
551.0
669.0
573.0
725.0
13
SAN FRANCISCO
37.81
-122.47
0.02
RSL in 2200 (cm)
53.0
17.0
83.0
92.0
57.0
...
325.0
541.0
476.0
630.0
872.0
804.0
933.0
1130.0
1014.0
1207.0
0
NEW YORK
40.70
-74.01
1.38
RSL in 2000 (cm)
0.0
0.0
0.0
0.0
0.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1
NEW YORK
40.70
-74.01
1.38
RSL in 2010 (cm)
5.0
2.0
7.0
6.0
4.0
...
11.0
12.0
7.0
15.0
14.0
13.0
17.0
14.0
6.0
15.0
2
NEW YORK
40.70
-74.01
1.38
RSL in 2020 (cm)
11.0
6.0
14.0
13.0
8.0
...
23.0
25.0
14.0
31.0
31.0
25.0
35.0
29.0
14.0
33.0
3
NEW YORK
40.70
-74.01
1.38
RSL in 2030 (cm)
15.0
10.0
21.0
19.0
13.0
...
34.0
39.0
24.0
45.0
48.0
43.0
53.0
50.0
28.0
55.0
4
NEW YORK
40.70
-74.01
1.38
RSL in 2040 (cm)
20.0
13.0
28.0
25.0
18.0
...
47.0
53.0
36.0
61.0
67.0
63.0
75.0
76.0
45.0
82.0
5
NEW YORK
40.70
-74.01
1.38
RSL in 2050 (cm)
25.0
15.0
36.0
31.0
24.0
...
60.0
71.0
51.0
80.0
92.0
85.0
100.0
105.0
68.0
114.0
6
NEW YORK
40.70
-74.01
1.38
RSL in 2060 (cm)
31.0
18.0
42.0
39.0
29.0
...
76.0
92.0
68.0
104.0
124.0
107.0
130.0
144.0
97.0
150.0
7
NEW YORK
40.70
-74.01
1.38
RSL in 2070 (cm)
36.0
19.0
49.0
45.0
33.0
...
93.0
114.0
87.0
130.0
154.0
134.0
166.0
185.0
130.0
194.0
8
NEW YORK
40.70
-74.01
1.38
RSL in 2080 (cm)
39.0
21.0
54.0
50.0
39.0
...
111.0
139.0
108.0
159.0
189.0
168.0
206.0
230.0
170.0
241.0
9
NEW YORK
40.70
-74.01
1.38
RSL in 2090 (cm)
44.0
21.0
60.0
56.0
43.0
...
130.0
167.0
130.0
191.0
234.0
205.0
250.0
286.0
214.0
296.0
10
NEW YORK
40.70
-74.01
1.38
RSL in 2100 (cm)
46.0
22.0
65.0
61.0
47.0
...
150.0
197.0
154.0
225.0
279.0
248.0
298.0
342.0
262.0
357.0
11
NEW YORK
40.70
-74.01
1.38
RSL in 2120 (cm)
51.0
27.0
75.0
70.0
54.0
...
176.0
247.0
195.0
297.0
368.0
334.0
405.0
459.0
373.0
530.0
12
NEW YORK
40.70
-74.01
1.38
RSL in 2150 (cm)
58.0
28.0
88.0
85.0
63.0
...
238.0
353.0
281.0
420.0
561.0
493.0
586.0
695.0
574.0
772.0
13
NEW YORK
40.70
-74.01
1.38
RSL in 2200 (cm)
69.0
26.0
110.0
110.0
70.0
...
359.0
567.0
442.0
673.0
926.0
826.0
958.0
1139.0
1011.0
1237.0
0
HALIFAX
44.67
-63.58
1.46
RSL in 2000 (cm)
0.0
0.0
0.0
0.0
0.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1
HALIFAX
44.67
-63.58
1.46
RSL in 2010 (cm)
5.0
2.0
7.0
6.0
4.0
...
10.0
11.0
6.0
13.0
14.0
13.0
15.0
15.0
6.0
16.0
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
12
KAWAIHAE
20.04
-155.83
1.05
RSL in 2150 (cm)
67.0
40.0
88.0
92.0
70.0
...
242.0
376.0
351.0
409.0
556.0
520.0
585.0
709.0
650.0
762.0
13
KAWAIHAE
20.04
-155.83
1.05
RSL in 2200 (cm)
75.0
27.0
114.0
119.0
77.0
...
387.0
630.0
580.0
699.0
949.0
894.0
1011.0
1225.0
1147.0
1334.0
0
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2000 (cm)
0.0
0.0
0.0
0.0
0.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2010 (cm)
5.0
2.0
7.0
6.0
4.0
...
10.0
11.0
6.0
13.0
13.0
11.0
14.0
14.0
5.0
15.0
2
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2020 (cm)
10.0
7.0
15.0
12.0
9.0
...
21.0
22.0
14.0
26.0
26.0
23.0
29.0
28.0
14.0
30.0
3
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2030 (cm)
16.0
10.0
22.0
19.0
14.0
...
33.0
36.0
24.0
41.0
45.0
39.0
48.0
49.0
27.0
52.0
4
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2040 (cm)
22.0
13.0
29.0
26.0
19.0
...
45.0
51.0
36.0
58.0
65.0
58.0
69.0
73.0
45.0
77.0
5
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2050 (cm)
26.0
17.0
37.0
32.0
25.0
...
59.0
68.0
52.0
78.0
89.0
81.0
95.0
104.0
69.0
110.0
6
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2060 (cm)
32.0
19.0
44.0
39.0
30.0
...
74.0
89.0
68.0
101.0
121.0
107.0
127.0
142.0
98.0
150.0
7
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2070 (cm)
38.0
21.0
51.0
46.0
35.0
...
92.0
114.0
87.0
128.0
154.0
135.0
162.0
184.0
132.0
194.0
8
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2080 (cm)
42.0
24.0
57.0
52.0
41.0
...
109.0
139.0
110.0
156.0
189.0
170.0
203.0
228.0
173.0
243.0
9
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2090 (cm)
46.0
25.0
63.0
58.0
46.0
...
128.0
168.0
133.0
189.0
232.0
209.0
246.0
282.0
218.0
298.0
10
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2100 (cm)
50.0
26.0
69.0
64.0
50.0
...
149.0
203.0
160.0
224.0
282.0
254.0
299.0
345.0
268.0
366.0
11
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2120 (cm)
57.0
33.0
79.0
75.0
58.0
...
174.0
242.0
204.0
286.0
367.0
333.0
395.0
461.0
380.0
518.0
12
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2150 (cm)
67.0
35.0
96.0
93.0
68.0
...
240.0
354.0
295.0
415.0
554.0
497.0
589.0
708.0
591.0
772.0
13
CAPE HATTERAS
35.22
-75.64
2.01
RSL in 2200 (cm)
81.0
39.0
119.0
122.0
79.0
...
369.0
577.0
460.0
679.0
928.0
827.0
980.0
1162.0
1042.0
1259.0
0
BEAUFORT
34.72
-76.67
1.54
RSL in 2000 (cm)
0.0
0.0
0.0
0.0
0.0
...
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1
BEAUFORT
34.72
-76.67
1.54
RSL in 2010 (cm)
4.0
2.0
7.0
5.0
4.0
...
10.0
10.0
6.0
12.0
12.0
11.0
14.0
12.0
5.0
15.0
2
BEAUFORT
34.72
-76.67
1.54
RSL in 2020 (cm)
10.0
6.0
13.0
12.0
8.0
...
19.0
21.0
13.0
24.0
25.0
22.0
27.0
26.0
13.0
29.0
3
BEAUFORT
34.72
-76.67
1.54
RSL in 2030 (cm)
15.0
9.0
20.0
18.0
13.0
...
31.0
35.0
22.0
39.0
42.0
39.0
45.0
47.0
26.0
49.0
4
BEAUFORT
34.72
-76.67
1.54
RSL in 2040 (cm)
20.0
13.0
27.0
24.0
18.0
...
42.0
49.0
34.0
55.0
62.0
56.0
66.0
70.0
44.0
74.0
5
BEAUFORT
34.72
-76.67
1.54
RSL in 2050 (cm)
24.0
15.0
34.0
30.0
23.0
...
56.0
66.0
49.0
75.0
86.0
79.0
93.0
102.0
67.0
107.0
6
BEAUFORT
34.72
-76.67
1.54
RSL in 2060 (cm)
30.0
18.0
41.0
37.0
28.0
...
71.0
88.0
66.0
97.0
118.0
105.0
124.0
140.0
96.0
146.0
7
BEAUFORT
34.72
-76.67
1.54
RSL in 2070 (cm)
35.0
19.0
47.0
43.0
33.0
...
88.0
111.0
84.0
124.0
150.0
135.0
159.0
181.0
129.0
191.0
8
BEAUFORT
34.72
-76.67
1.54
RSL in 2080 (cm)
39.0
21.0
52.0
49.0
38.0
...
105.0
137.0
106.0
152.0
188.0
169.0
200.0
225.0
169.0
241.0
9
BEAUFORT
34.72
-76.67
1.54
RSL in 2090 (cm)
43.0
23.0
58.0
55.0
42.0
...
124.0
166.0
129.0
185.0
230.0
207.0
243.0
280.0
214.0
296.0
10
BEAUFORT
34.72
-76.67
1.54
RSL in 2100 (cm)
46.0
24.0
64.0
60.0
46.0
...
144.0
199.0
155.0
220.0
279.0
251.0
294.0
339.0
263.0
360.0
11
BEAUFORT
34.72
-76.67
1.54
RSL in 2120 (cm)
52.0
30.0
73.0
70.0
54.0
...
167.0
235.0
198.0
278.0
360.0
327.0
387.0
455.0
373.0
507.0
12
BEAUFORT
34.72
-76.67
1.54
RSL in 2150 (cm)
61.0
30.0
88.0
87.0
63.0
...
232.0
346.0
288.0
406.0
545.0
490.0
580.0
698.0
582.0
759.0
13
BEAUFORT
34.72
-76.67
1.54
RSL in 2200 (cm)
74.0
32.0
111.0
114.0
73.0
...
357.0
561.0
451.0
666.0
915.0
809.0
968.0
1151.0
1029.0
1244.0
3402 rows × 23 columns
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Content source: rsignell-usgs/sweet_slr
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