-
Notifications
You must be signed in to change notification settings - Fork 1
/
drought-stats-fullrange.py
executable file
·224 lines (201 loc) · 10.3 KB
/
drought-stats-fullrange.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 25 21:26:17 2021
Plot range of drought stats as well as mean
@author: lizz
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import gSPEI as gSPEI
## Labels: (P)arametric or (NP)nonparametric;
## Standardization (1) lumped or (2) split by starting month
fpath_NP2 = './data/SPEI_Files/nonparametric-var_stom_c/'
## Settings in filenames
integration_times = np.arange(3, 28, 4) # all SPEI integration times used
modelnames = ['CanESM2', 'CCSM4', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'GISS-E2-R', 'INMCM4', 'MIROC-ESM', 'NorESM1-M'] # all models used in comparison
scenarios = ['Rcp4p5', 'Rcp8p5'] # climate scenarios
## Basins in the order they are written
basin_names = ['INDUS','TARIM','BRAHMAPUTRA','ARAL SEA','COPPER','GANGES','YUKON','ALSEK','SUSITNA','BALKHASH','STIKINE','SANTA CRUZ',
'FRASER','BAKER','YANGTZE','SALWEEN','COLUMBIA','ISSYK-KUL','AMAZON','COLORADO','TAKU','MACKENZIE','NASS','THJORSA','JOEKULSA A F.',
'KUSKOKWIM','RHONE','SKEENA','OB','OELFUSA','MEKONG','DANUBE','NELSON RIVER','PO','KAMCHATKA','RHINE','GLOMA','HUANG HE','INDIGIRKA',
'LULE','RAPEL','SANTA','SKAGIT','KUBAN','TITICACA','NUSHAGAK','BIOBIO','IRRAWADDY','NEGRO','MAJES','CLUTHA','DAULE-VINCES',
'KALIXAELVEN','MAGDALENA','DRAMSELV','COLVILLE']
BasinArea=[1139075,1051731,518011,1233148,64959,1024462,829632,28422,49470,423657,51147,30599,
239678,30760,1745094,258475,668561,191032,5880854,390631,17967,1752001,21211,7527,7311,
118114,97485,42944,2701040,5678,787256,793704,1099380,73066,54103,190522,42862,988062,341227,
25127,15689,11882,7961,58935,107215,29513,24108,411516,130062,18612,17118,41993,
17157,261204,17364,57544] # area of each basin in km2
basin_glacier_area = [26893.8, 24645.4, 16606.7, 15176.7, 12998., 11216., 9535.4, 5614.8, 4304.,
3945.4, 3467.6, 3027.8, 2495.1, 2372.3, 2317.4, 2295.9, 1878.4, 1677.3,
1634.1, 1601.2, 1583.6, 1519.2, 1337.3, 1251.8, 1098.6, 1032.8, 904.2, 742.3,
739.5, 683.4, 485.7, 408.4, 374.7, 347.3, 312.7, 285.0, 269.4, 267.9, 248.4,
247.2, 238.1, 198.9, 159.5, 146., 134.5, 86.4, 76.2, 71.2, 64.1, 57.3, 46.5,
40.6, 37.9, 33.3, 32.1, 31.9]
yrs = np.linspace(1900, 2101, num=2412)
SPEI_by_model_C = {m: {} for m in modelnames} # create dictionary indexed by model name
for m in modelnames:
norunoff_f_m = fpath_NP2+'NRunoff_{}_{}_{}_Conduct.txt'.format(integration_times[0], m, scenarios[0])
wrunoff_f_m = fpath_NP2+'WRunoff_{}_{}_{}_Conduct.txt'.format(integration_times[0], m, scenarios[0])
SPEI_by_model_C[m]['NRunoff'] = np.loadtxt(norunoff_f_m)
SPEI_by_model_C[m]['WRunoff'] = np.loadtxt(wrunoff_f_m)
SPEI_by_model_C[m]['diff'] = SPEI_by_model_C[m]['WRunoff'] - SPEI_by_model_C[m]['NRunoff']
## Re-structure dictionary and create pandas DataFrames aggregated by basin
SPEI_by_basin = gSPEI.sort_models_to_basins(SPEI_by_model_C)
for b in basin_names:
for t in ('NRunoff', 'WRunoff', 'diff'):
SPEI_by_basin[b][t] = SPEI_by_basin[b][t].fillna(-3)
## Analyse multi-model ensemble mean & quantiles for drought statistics
r_w = gSPEI.basin_ensemble_mean(SPEI_by_basin, 'TARIM', 'WRunoff')
r_n = gSPEI.basin_ensemble_mean(SPEI_by_basin, 'TARIM', 'NRunoff')
basin_stats_bymodel_hist = {m: {b: gSPEI.basin_summary_stats(SPEI_by_basin, basin_name=b, modelnames=[m], period=(1980,2010)) for b in basin_names}
for m in modelnames}
basin_stats_bymodel_midC = {m: {b: gSPEI.basin_summary_stats(SPEI_by_basin, basin_name=b, modelnames=[m], period=(2030,2060)) for b in basin_names}
for m in modelnames}
basin_stats_bymodel_endC = {m: {b: gSPEI.basin_summary_stats(SPEI_by_basin, basin_name=b, modelnames=[m], period=(2070,2100)) for b in basin_names}
for m in modelnames}
## Composite of all stats over time
color_fam = cm.get_cmap('tab20b')
inc_color=color_fam(5)
dec_color=color_fam(17)
count_pbuff_num = 0
count_pbuff_sev = 0
count_nbuff_num = 0
count_nbuff_sev = 0
fig3, ((ax1,ax2, ax3),
# (ax4,ax5,ax6),
(ax7,ax8,ax9)) = plt.subplots(2,3, sharex=True, sharey='row', figsize=(10,12))
for b, a, ag in zip(basin_names, BasinArea, basin_glacier_area):
pg = ag/a # percent glaciated
number_b = []
duration_b = []
severity_b = []
number_midC = []
number_endC = []
duration_midC = []
duration_endC = []
severity_midC = []
severity_endC = []
for m in modelnames:
number_b.append(basin_stats_bymodel_hist[m][b][0][1]-basin_stats_bymodel_hist[m][b][0][0])
duration_b.append(basin_stats_bymodel_hist[m][b][1][1]-basin_stats_bymodel_hist[m][b][1][0])
severity_b.append(-1*(basin_stats_bymodel_hist[m][b][2][1]-basin_stats_bymodel_hist[m][b][2][0]))
number_midC.append(basin_stats_bymodel_midC[m][b][0][1]-basin_stats_bymodel_midC[m][b][0][0])
number_endC.append(basin_stats_bymodel_endC[m][b][0][1]-basin_stats_bymodel_endC[m][b][0][0])
duration_midC.append(basin_stats_bymodel_midC[m][b][1][1]-basin_stats_bymodel_midC[m][b][1][0])
duration_endC.append(basin_stats_bymodel_endC[m][b][1][1]-basin_stats_bymodel_endC[m][b][1][0])
severity_midC.append(-1*(basin_stats_bymodel_midC[m][b][2][1]-basin_stats_bymodel_midC[m][b][2][0]))
severity_endC.append(-1*(basin_stats_bymodel_endC[m][b][2][1]-basin_stats_bymodel_endC[m][b][2][0]))
## Count pos vs neg buffering
if np.nanmean(number_b)>0:
count_pbuff_num +=1
elif np.nanmean(number_b)<0:
count_nbuff_num +=1
if np.nanmean(severity_b)>0:
count_pbuff_sev +=1
elif np.nanmean(severity_b)<0:
count_nbuff_sev +=1
## Color code changes over time
midC_v_hist_n = np.nanmean(number_midC)-np.nanmean(number_b)
if midC_v_hist_n >0.1: # buffering on number increasing
midC_color_n=inc_color
midC_marker_n='^'
elif midC_v_hist_n<-0.1:
midC_color_n=dec_color
midC_marker_n='v'
else:
midC_color_n='k'
midC_marker_n='o'
endC_v_hist_n = np.nanmean(number_endC)-np.nanmean(number_b)
if endC_v_hist_n >0.1:
endC_color_n=inc_color
endC_marker_n='^'
elif endC_v_hist_n<-0.1:
endC_color_n=dec_color
endC_marker_n='v'
else:
endC_color_n='k'
endC_marker_n='o'
midC_v_hist_d = np.nanmean(duration_midC)-np.nanmean(duration_b)
if midC_v_hist_d >0.1: # buffering on duration increasing
midC_color_d=inc_color
midC_marker_d='^'
elif midC_v_hist_d<-0.1:
midC_color_d=dec_color
midC_marker_d='v'
else:
midC_color_d='k'
midC_marker_d='o'
endC_v_hist_d = np.nanmean(duration_endC)-np.nanmean(duration_b)
if endC_v_hist_d >0.1:
endC_color_d=inc_color
endC_marker_d='^'
elif endC_v_hist_d<-0.1:
endC_color_d=dec_color
endC_marker_d='v'
else:
endC_color_d='k'
endC_marker_d='o'
midC_v_hist_s = np.nanmean(severity_midC)-np.nanmean(severity_b)
if midC_v_hist_s >0.1: # buffering on duration increasing
midC_color_s=inc_color
midC_marker_s='^'
elif midC_v_hist_s<-0.1:
midC_color_s=dec_color
midC_marker_s='v'
else:
midC_color_s='k'
midC_marker_s='o'
endC_v_hist_s = np.nanmean(severity_endC)-np.nanmean(severity_b)
if endC_v_hist_s >0.1:
endC_color_s=inc_color
endC_marker_s='^'
elif endC_v_hist_s<-0.1:
endC_color_s=dec_color
endC_marker_s='v'
else:
endC_color_s='k'
endC_marker_s='o'
## First column: historical
ax1.errorbar(pg, np.nanmean(number_b),
yerr=((np.nanmean(number_b)-np.nanmin(number_b), np.nanmax(number_b)-np.nanmean(number_b)),),
color='k', marker='o', lw=1.0)
# ax4.errorbar(pg, np.nanmean(duration_b),
# yerr=(((np.nanmean(duration_b)-np.nanmin(duration_b), np.nanmax(duration_b)-np.nanmean(duration_b)),)),
# color='k', marker='o', lw=1.0, alpha=0.8)
ax7.errorbar(pg, np.nanmean(severity_b),
yerr=(((np.nanmean(severity_b)-np.nanmin(severity_b), np.nanmax(severity_b)-np.nanmean(severity_b)),)),
color='k', marker='o', lw=1.0)
## Second column: mid-c
ax2.errorbar(pg, np.nanmean(number_midC),
yerr=(((np.nanmean(number_midC)-np.nanmin(number_midC), np.nanmax(number_midC)-np.nanmean(number_midC)),)),
color=midC_color_n, marker=midC_marker_n, lw=1.0)
# ax5.errorbar(pg, np.nanmean(duration_midC),
# yerr=(((np.nanmean(duration_midC)-np.nanmin(duration_midC), np.nanmax(duration_midC)-np.nanmean(duration_midC)),)),
# color=midC_color_d, marker=midC_marker_d, lw=1.0)
ax8.errorbar(pg, np.nanmean(severity_midC),
yerr=(((np.nanmean(severity_midC)-np.nanmin(severity_midC), np.nanmax(severity_midC)-np.nanmean(severity_midC)),)),
color=midC_color_s, marker=midC_marker_s, lw=1.0)
## Third column: end of century
ax3.errorbar(pg, np.nanmean(number_endC),
yerr=(((np.nanmean(number_endC)-np.nanmin(number_endC), np.nanmax(number_endC)-np.nanmean(number_endC)),)),
color=endC_color_n, marker=endC_marker_n, lw=1.0)
# ax6.errorbar(pg, np.nanmean(duration_endC),
# yerr=(((np.nanmean(duration_endC)-np.nanmin(duration_endC), np.nanmax(duration_endC)-np.nanmean(duration_endC)),)),
# color=endC_color_d, marker=endC_marker_d, lw=1.0)
ax9.errorbar(pg, np.nanmean(severity_endC),
yerr=(((np.nanmean(severity_endC)-np.nanmin(severity_endC), np.nanmax(severity_endC)-np.nanmean(severity_endC)),)),
color=endC_color_s, marker=endC_marker_s, lw=1.0)
ax1.set(ylabel=r'$\Delta$ Number', title='Historical (1980-2010)', xscale='log')
ax2.set(title='Mid-21st Cent. (2030-2060)')
ax3.set(title='End 21st Cent. (2070-2100)')
# ax4.set(ylabel=r'$\Delta$ Duration', xscale='log')
ax7.set(ylabel=r'$\Delta$ Severity',
xscale='log',xlabel='Glacier area fraction', xlim=(1E-4, 0.23))
ax8.set(xlabel='Glacier area fraction', xlim=(1E-4, 0.23))
ax9.set(xlabel='Glacier area fraction', xlim=(1E-4, 0.23))
for ax in (ax1,ax2,ax3,
# ax4,ax5,ax6,
ax7,ax8,ax9):
ax.axhline(0, ls=':', lw=1.0, color='k')