Recent Advances in the Modelling of Extremes and FLoodsVan-Thanh-Van Nguyen (and Students)
*
OUTLINE
INTRODUCTION
Extreme Rainfall Estimation Issues?
OBJECTIVES
INTRODUCTION
Extreme storms (and floods) account for more losses than any other natural disaster (both in terms of loss of lives and economic costs).
Damages due to Saguenay flood in Quebec (Canada) in 1996: $800 million dollars.
Average annual flood damages in the U.S. are US$2.1 billion dollars. (US NRC)
Information on extreme rainfalls is essential for planning, design, and management of various water-resource systems.
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December 19, 2007, Climate Change Symposium, Singapore
The choice of an estimation method depends on the availability of historical data:
Gaged Sites Sufficient long historical records (> 20 years?) At-site Methods.
Partially-Gaged Sites Limited data records Regionalization Methods.
Ungaged Sites Data are not available Regionalization Methods.
Design Rainfall Estimation Methods
Design Rainfall and Design Storm Estimation
At-site Frequency Analysis of Precipitation
Regional Frequency Analysis of Precipitation
⇒ Intensity-Duration-Frequency (IDF) Relations
⇒ DESIGN STORM CONCEPT for design of hydraulic structures
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Extreme Rainfall Estimation Issues (1)
Current practices:
At-site Estimation Methods (for gaged sites): Annual maximum series (AMS) using 2-parameter Gumbel/Ordinary moments method, or using 3-parameter GEV/ L-moments method.
⇒ Which probability distribution?
⇒ Which estimation method?
Problems: Uncertainties in Data, Model and Estimation Method
*
Extreme Rainfall Estimation Issues (2)
Regionalization methods
GEV/Index-flood method.
Similarity (or homogeneity) of point rainfalls?
How to define groups of homogeneous gages? What are the classification criteria?
(WMO Guides to Hydrological Practices: 1st Edition 1965 → 6th Edition: Section 5.7, in press)
Proposed Regional Homogeneity:
PCA of rainfall amounts at different sites for different time scales.
PCA of rainfall occurrences at different sites.
4
3
2
1
Hydrologic neighborhood type regions
The “scale” problem
The properties of a variable depend on the scale of measurement or observation.
Are there scale-invariance properties? And how to determine these scaling properties?
Existing methods are limited to the specific time scale associated with the data used.
Existing methods cannot take into account the properties of the physical process over different scales.
Extreme Rainfall Estimation Issues (3)
*
December 19, 2007, Climate Change Symposium, Singapore
Climate Variability and Change will have important impacts on the hydrologic cycle, and in particular the precipitation process!
How to quantify Climate Change?
General Circulation Models (GCMs):
A credible simulation of the “average” “large-scale” seasonal distribution of atmospheric pressure, temperature, and circulation. (AMIP 1 Project, 31 modeling groups)
Climate change simulations from GCMs are “inadequate” for impact studies on regional scales:
Spatial resolution ~ 50,000 km2
Reliability of some GCM output variables (such as cloudiness precipitation)?
Extreme Rainfall Estimation Issues (4)
*
…
How to develop Climate Change scenarios for impacts studies in hydrology?
Spatial scale ~ a few km2 to several 1000 km2
Temporal scale ~ minutes to years
A scale mismatch between the information that GCM can confidently provide and scales required by impacts studies.
“Downscaling methods” are necessary!!!
*
IDF Relations
⇒ Intensity-Duration-Frequency (IDF) Relations
Traditional IDF estimation methods:
Time scaling problem: no consideration of rainfall properties at different time scales;
Spatial scaling problem: results limited to data availability at a local site;
Climate change: no consideration.
Summary
Successful applications of the scale invariant concept in precipitation modeling to permit statistical inference of precipitation properties between various durations.
Global climate models (GCMs) could reasonably simulate some climate variables for current period and could provide various climate change scenarios for future periods.
Various spatial downscaling methods have been developed to provide the linkage between (GCM) large-scale data and local scale data.
Scale Issues:
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OBJECTIVES
To review recent progress in downscaling methods from both theoretical and practical viewpoints.
To assess the performance of statistical downscaling methods to find the “best” method in the simulation of daily precipitation time series for climate change impact studies.
To develop an approach that could link daily simulated climate variables from GCMs to sub-daily precipitation characteristics at a regional or local scale (a spatial-temporal downscaling method).
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DOWNSCALING METHODS
Statistical Models (Statistical Downscaling)
*
(SPATIAL) DYNAMIC DOWNSCALING METHODS
Variable resolution GCM (high resolution over the area of interest)
GCM + RCM or LAM (Nested Modeling Approach)
More accurate downscaled results as compared to the use of GCM outputs alone.
Spatial scales for RCM results ~ 20 to 50 km still larges for many hydrologic models.
Considerable computing resource requirement.
(SPATIAL) STATISTICAL DOWNSCALING METHODS
Weather Typing or Classification
Classification schemes are somewhat subjective.
Stochastic Weather Generators
Generation of realistic statistical properties of daily weather series at a local site.
Inexpensive computing resources
Climate change scenarios based on results predicted by GCM (unreliable for precipitation)
Regression-Based Approaches
Results limited to local climatic conditions.
Long series of historical data needed.
Large-scale and local-scale parameter relations remain valid for future climate conditions.
Simple computational requirements.
APPLICATIONS
LARS-WG Stochastic Weather Generator (Semenov et al., 1998)
Generation of synthetic series of daily weather data at a local site (daily precipitation, maximum and minimum temperature, and daily solar radiation)
Procedure:
Use semi-empirical probability distributions to describe the state of a day (wet or dry).
Use semi-empirical distributions for precipitation amounts (parameters estimated for each month).
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Statistical Downscaling Model (SDSM)
(Wilby et al., 2001)
Generation of synthetic series of daily weather data at a local site based on empirical relationships between local-scale predictands (daily temperature and precipitation) and large-scale predictors (atmospheric variables)
Procedure:
Identify large-scale predictors (X) that could control the local parameters (Y).
Find a statistical relationship between X and Y.
Validate the relationship with independent data.
Generate Y using values of X from GCM data.
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Geographical locations of sites under study.
Geographical coordinates of the stations
Station
DATA:
Observed daily precipitation and temperature extremes at four sites in the Greater Montreal Region (Quebec, Canada) for the 1961-1990 period.
NCEP re-analysis daily data for the 1961-1990 period.
Calibration: 1961-1975; validation: 1976-1990.
Evaluation indices and statistics
2
SDII
mm/r.day
Season
Daily Mean: sum of daily precipitations / number of wet days
3
CDD
days
Season
Maximum number of consecutive dry days (daily precipitation < 1 mm)
4
R3days
mm
Season
6
Precip_mean
mm/day
Month
Sum of daily precipitation in a month / number of days in that month
7
Precip_sd
mm
Month
*
The mean of daily precipitation for the period of 1961-1975
BIAS = Mean (Obs.) – Mean (Est.)
Dorval
0
2
4
6
8
10
12
14
J
F
M
A
M
J
J
A
S
O
N
D
(mm)
Analysis Period: 01/01/1961 - 31/12/1975
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Observed
January
6.4971830986
31
3.6
34.2804175407
14.2
61.5066666667
30.5376344086
3
5.8549481245
February
6.7687022901
32.5
4.3
35.6103975338
15.7
58.6940898345
30.9692671395
3
5.9674448078
March
8.4628099174
36.6
5.1
67.7116053719
19.8
68.2666666667
26.0215053763
4
8.2287061785
April
8.8577235772
32.3
6.4
42.9694275623
17.5
72.6333333333
27.3333333333
4
6.5551069833
May
6.6585034014
23.4
4.3
26.1429237722
13.7
65.2533333333
31.6129032258
4
5.1130151351
June
9.5046511628
49.5
6.4
79.5745094477
23.4
81.74
28.6666666667
6
8.9204545539
July
9.4347222222
45.2
6.1
76.8970376845
20.3
90.5733333333
30.9677419355
3
8.7690956024
August
10.7776978417
51.6
6.1
109.1353685747
24.4
99.8733333333
29.8924731183
4
10.4467874763
September
10.7789915966
73.4
7.1
151.9806566016
21.8
85.5133333333
26.4444444444
3
12.3280435026
October
7.5541984733
30.5
5.6
33.0171937757
15.2
65.9733333333
28.1720430108
4
5.7460589777
November
8.0246987952
35.6
5.1
41.1805377875
16.3
88.8066666667
36.8888888889
5
6.417206385
December
7.8528089888
42.7
5.1
51.7244270933
18.3
93.1866666667
38.2795698925
4
7.1919696254
Winter
7.0395647925
35.4
4.3333333333
40.5384140559
16.0666666667
71.1291410559
33.2621571469
3.3333333333
6.3669784086
Spring
7.9930122987
30.7666666667
5.2666666667
45.6079855688
17
68.7177777778
28.3225806452
4
6.7533684609
Summer
9.9056904089
48.7666666667
6.2
88.535638569
22.7
90.7288888889
29.8422939068
4.3333333333
9.409337839
Autumn
8.785962955
46.5
5.9333333333
75.3927960549
17.7666666667
80.0977777778
30.5017921147
4
8.6829025133
Annual
8.4310576138
40.3583333333
5.4333333333
62.5187085622
18.3833333333
77.6683963751
30.4822059534
3.9166666667
7.9068772953
Statistics computed from 100 ensembles (copy from those in the file: Arrange_criteria.xls)
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Mean
Jan
7.652
39.098
5.732
44.428
15.662
71.572
30.187
4.710
6.6229334757
7.150
31.399
5.598
32.225
14.316
60.951
30.454
4.550
5.6372387109
10.110
50.356
7.354
83.373
21.189
84.121
26.852
4.100
9.058
9.296
38.841
7.457
49.740
18.238
79.797
28.611
4.510
7.013166658
6.787
28.818
5.408
26.516
13.451
67.579
32.125
5.210
5.1192199648
8.956
44.044
6.644
63.081
18.766
78.065
29.067
5.270
7.8958857477
8.064
43.361
5.722
56.803
17.358
78.726
31.473
4.440
7.4799080738
10.867
60.169
7.732
106.533
23.114
104.118
30.916
4.450
10.234577299
9.612
45.832
7.057
73.231
20.246
79.266
27.487
3.970
8.4821434497
7.173
33.033
5.578
33.928
14.505
65.307
29.374
4.450
5.7885581271
7.834
37.542
6.001
42.178
16.061
85.991
36.604
8.030
6.4560638359
7.560
40.190
5.578
45.303
15.873
87.696
37.449
4.940
6.6902764022
7.454
36.896
5.636
40.652
15.284
73.406
32.697
4.733
6.3596226625
8.731
39.338
6.740
53.210
17.626
77.166
29.196
4.607
7.2699349991
9.296
49.191
6.699
75.472
19.746
86.970
30.485
4.720
8.6620310788
8.206
38.802
6.212
49.779
16.937
76.855
31.155
5.483
7.0360085387
8.422
41.057
6.322
54.778
17.398
78.599
30.883
4.886
7.3945998196
STD
0.432
8.186
0.517
9.922
1.583
5.119
1.649
1.289
0.7553245179
0.478
7.399
0.542
7.631
1.321
5.060
1.675
1.095
0.671741079
0.780
11.975
0.759
21.754
2.405
7.791
1.558
1.259
1.160
0.621
7.822
0.722
10.595
1.728
7.161
1.689
1.141
0.7489161373
0.406
7.381
0.419
5.875
1.147
5.245
1.693
1.526
0.5594858317
0.651
8.676
0.722
13.803
1.878
7.130
1.833
1.100
0.8621896361
0.635
10.558
0.596
14.224
1.994
8.117
1.819
1.085
0.9285705355
0.810
15.128
0.765
28.252
2.295
9.191
1.656
1.201
1.3434413489
0.873
10.767
0.706
19.638
2.439
8.777
1.686
1.087
1.1388491882
0.505
7.434
0.506
7.715
1.387
6.133
1.899
1.218
0.6520452738
0.448
8.868
0.496
9.379
1.335
5.902
1.742
2.646
0.7088104613
0.544
8.889
0.497
9.993
1.576
6.525
1.770
1.118
0.7408123184
0.282
5.218
0.302
5.801
0.891
3.079
0.923
0.639
0.4576156815
0.352
5.161
0.349
8.982
1.084
3.904
0.908
0.786
0.6009926209
0.393
7.066
0.401
11.659
1.141
4.863
1.093
0.740
0.6678269371
0.347
4.797
0.317
7.453
0.909
3.889
1.000
0.930
0.5257610587
0.165
2.815
0.169
4.687
0.509
1.917
0.465
0.359
0.314875329
Max
9.237
63.808
6.970
68.123
20.439
82.639
34.839
8.000
8.2536676696
8.240
60.329
6.945
55.208
18.376
74.514
34.752
8.000
7.4302308174
11.857
91.101
10.105
156.106
27.602
105.675
30.108
9.000
12.494
11.146
73.315
9.307
81.323
23.812
98.084
33.111
7.000
9.0179182742
7.653
65.680
6.648
48.551
16.670
82.685
36.344
11.000
6.9678626565
10.831
68.135
8.457
106.660
23.960
99.655
34.222
8.000
10.3276376776
9.700
80.998
7.035
98.931
23.139
98.166
35.914
8.000
9.9464159374
12.929
133.213
9.963
203.746
29.546
122.225
35.054
9.000
14.2739447946
11.972
78.024
8.662
127.444
27.110
102.978
31.556
8.000
11.2891230837
8.275
57.781
7.274
62.494
18.380
79.419
33.763
8.000
7.905336805
9.061
73.644
7.421
69.150
19.930
98.012
41.333
15.000
8.3156641346
9.545
76.425
7.265
68.435
19.912
108.172
43.226
9.000
8.272544953
8.098
58.725
6.403
54.332
17.974
80.373
34.954
6.333
7.3710460587
9.570
56.590
7.678
85.861
20.296
86.877
31.611
7.333
9.266099503
10.383
81.520
7.808
115.272
23.211
97.616
32.956
7.000
10.7364565849
9.207
49.269
7.177
69.572
19.100
86.699
33.422
8.000
8.3409903489
8.868
49.788
6.711
70.957
18.910
83.023
32.203
5.667
8.423575844
Min
6.838
22.506
4.378
22.924
12.162
57.894
26.452
3.000
4.7879369252
6.110
19.714
4.421
17.425
11.571
49.227
27.187
3.000
4.1743586334
8.126
30.759
5.947
41.984
15.383
65.009
22.581
2.000
6.479
7.983
26.545
5.572
27.497
14.330
64.334
24.667
2.000
5.2437658224
5.769
18.114
4.394
14.875
11.097
55.001
27.742
3.000
3.8567784484
7.430
29.025
5.154
34.530
13.214
59.937
24.444
3.000
5.8762385928
6.782
24.891
4.410
29.482
13.900
62.271
27.742
3.000
5.4297348002
9.157
30.272
6.018
54.083
17.292
85.072
27.312
3.000
7.3541253729
7.676
29.583
5.346
29.724
13.805
60.893
22.222
2.000
5.4519317677
6.050
20.832
4.382
17.541
11.834
52.009
24.301
2.000
4.1881965092
6.907
24.106
4.565
25.815
13.442
71.642
31.778
4.000
5.0808768928
6.180
25.025
4.566
26.451
12.381
68.836
33.333
3.000
5.1430788444
7.026
24.490
5.074
26.629
13.472
66.659
30.811
3.333
5.1603023167
7.860
29.763
6.046
34.930
15.131
69.096
27.087
3.333
5.9101751243
8.528
32.498
5.851
50.547
17.359
75.405
27.577
3.333
7.109664549
7.390
27.463
5.583
34.895
14.662
68.759
28.738
3.667
5.907235394
8.053
34.210
5.891
45.663
16.362
74.742
29.911
4.167
6.757448483
X25
7.394
32.844
5.337
37.864
14.726
67.966
29.032
4.000
6.1532891119
6.852
26.070
5.233
26.786
13.393
57.458
29.314
4.000
5.1754830953
9.598
41.828
6.819
68.508
19.925
77.587
25.806
3.000
8.277
8.809
33.925
6.975
43.083
17.178
75.342
27.556
4.000
6.5637792118
6.479
24.189
5.162
22.579
12.620
63.905
30.753
4.000
4.7517237623
8.520
37.617
6.248
53.617
17.718
73.633
27.556
4.750
7.3223539969
7.574
36.280
5.338
45.806
15.728
72.729
30.323
4.000
6.7679845465
10.242
49.753
7.268
85.642
21.748
97.329
29.677
4.000
9.2542691374
9.101
38.432
6.680
58.769
18.730
73.686
26.444
3.000
7.6661045973
6.760
28.099
5.253
27.779
13.533
60.978
28.118
4.000
5.2705723074
7.497
31.748
5.667
35.456
15.146
81.801
35.333
6.000
5.9545203373
7.228
34.368
5.301
37.730
14.890
83.357
36.344
4.000
6.1424110672
7.220
33.463
5.352
36.288
14.611
71.135
32.099
4.333
6.0239294469
8.481
35.726
6.473
47.815
17.000
74.069
28.614
4.000
6.9147999588
9.036
45.586
6.347
68.778
19.002
83.436
29.821
4.333
8.2932530007
7.994
35.019
6.021
44.960
16.452
73.909
30.461
4.667
6.7051867072
8.301
39.337
6.207
51.141
16.996
77.282
30.587
4.583
7.151264541
X50
7.690
38.104
5.693
43.895
15.652
71.134
30.323
4.500
6.6253019512
7.171
30.266
5.535
31.360
14.350
61.284
30.496
4.000
5.6000066889
10.054
48.970
7.184
81.798
21.191
84.433
26.882
4.000
9.044
9.343
37.391
7.462
48.328
18.122
79.457
28.444
4.000
6.9518183491
6.740
27.200
5.352
25.715
13.293
67.634
32.043
5.000
5.0709798759
8.873
43.606
6.573
61.282
18.688
78.299
29.222
5.000
7.8282432613
8.068
41.990
5.732
55.942
17.417
78.415
31.398
4.000
7.4794102887
10.945
58.820
7.632
101.115
23.096
103.161
30.968
4.000
10.0555893874
9.546
43.520
7.053
70.538
19.963
78.634
27.556
4.000
8.3986530514
7.207
32.027
5.548
32.601
14.329
65.406
29.462
4.000
5.7097617575
7.779
35.296
5.959
40.431
16.058
85.880
36.667
7.000
6.3585488119
7.565
39.187
5.585
44.427
15.844
88.097
37.419
5.000
6.6653780168
7.389
35.703
5.597
40.458
15.218
73.193
32.607
4.667
6.3606876791
8.717
38.307
6.717
52.051
17.639
77.446
29.137
4.333
7.2146346102
9.301
48.455
6.720
73.884
19.576
87.299
30.527
4.667
8.5955870176
8.209
38.791
6.206
49.625
16.830
76.611
31.179
5.333
7.0445240194
8.414
40.974
6.315
54.802
17.390
78.544
30.908
4.917
7.4028530482
X75
7.914
45.095
6.119
51.743
16.359
75.589
31.237
5.250
7.1931982135
7.374
34.689
5.969
38.277
15.128
63.820
31.678
5.000
6.1868517906
10.704
55.975
7.872
93.366
22.567
89.569
28.172
5.000
9.663
9.725
42.717
7.988
56.689
19.290
84.232
30.000
5.000
7.5291818462
7.051
31.808
5.695
30.019
14.127
71.095
33.118
6.000
5.4789282262
9.286
48.772
7.170
71.274
19.922
81.934
30.278
6.000
8.4423432216
8.552
47.748
6.114
63.497
18.613
83.931
32.688
5.000
7.9684895468
11.446
67.501
8.228
125.770
24.730
112.217
31.828
5.000
11.21471952
10.063
51.417
7.550
86.168
21.694
84.492
28.444
5.000
9.2826223561
7.558
36.109
5.906
38.803
15.341
70.334
30.806
5.000
6.2292108654
8.138
42.070
6.372
48.353
16.914
90.260
37.778
10.000
6.9536039966
7.859
43.975
5.885
53.188
16.717
91.612
38.333
6.000
7.2930183692
7.644
40.066
5.856
44.778
15.797
75.945
33.171
5.333
6.691658888
8.994
42.339
6.991
56.637
18.342
79.891
29.790
5.000
7.5257270243
9.528
51.481
7.008
83.738
20.426
90.256
31.177
5.333
9.1508649465
8.406
41.314
6.404
54.395
17.607
79.453
31.768
6.000
7.375317744
8.526
42.641
6.441
57.836
17.725
79.930
31.134
5.167
7.6049936474
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Monthly box-plot for Variance (column G)
Seasonal box-plot for Variance (column G)
Annual box-plot for Variance (column G)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Monthly box-plot for Skewness (column J)
Seasonal box-plot for Skewness (column J)
Annual box-plot for Skewness (column J)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Fig.___ The 90th percentile of observed and SDSM-generated daily precipitation
Fig.___ The 90th percentile of observed and SDSM-generated daily precipitation
Fig.___ The 90th percentile of observed and SDSM-generated daily precipitation
Fig.___ The total monthly amount of observed and SDSM-generated daily precipitation
Fig.___ The total monthly amount of observed and SDSM-generated daily precipitation
Fig.___ The total monthly amount of observed and SDSM-generated daily precipitation
Fig.___ The percentage of wet-day of observed and SDSM-generated daily precipitation
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Fig.___ The percentage of wet-day of observed and SDSM-generated daily precipitation
Fig.___ The percentage of wet-day of observed and SDSM-generated daily precipitation
Fig.___ The maximum wet-spell length of observed and SDSM-generated daily precipitation
Fig.___ The maximum wet-spell length of observed and SDSM-generated daily precipitation
Fig.___ The maximum wet-spell length of observed and SDSM-generated daily precipitation
Fig.___ The standard deviation of observed and SDSM-generated daily precipitation
Fig.___ The standard deviation of observed and SDSM-generated daily precipitation
Fig.___ The standard deviation of observed and SDSM-generated daily precipitation
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Observed
Jan
6.1
31
3.6
39.28
14.2
57.77
30.54
0.5
3.7682887363
Feb
6.49
32.5
4.3
39.96
16.5
56.22
30.95
0.6
4.0620192023
Mar
8.16
36.6
5.1
72.71
19.8
65.86
26.02
0.56
4.4497190923
Apr
8.6
32.3
6.4
47.42
17.5
70.56
27.33
0.69
4.1833001327
May
6.47
23.4
4.3
28.62
13.7
63.42
31.61
0.83
3.7013511047
Jun
9.34
49.5
6.4
82.75
23.4
80.3
28.67
0.6
4.837354649
Jul
9.29
45.2
6.1
79.66
20.3
89.17
30.97
0.53
4.5055521304
Aug
10.57
51.6
6.1
113.5
24.4
97.99
29.89
0.45
4.9396356141
Sep
10.56
73.4
7.1
156.75
21.8
83.75
26.44
0.55
4.669047012
Oct
7.31
30.5
5.6
36.66
15.2
63.85
28.17
0.63
3.8987177379
Nov
7.81
35.6
5.1
44.62
16.3
86.42
36.89
0.96
4.0373258476
Dec
7.57
42.7
5.1
56.17
18.3
89.79
38.28
0.75
4.2778499272
Winter
6.72
35.4
4.33
45.14
16.33
67.93
33.26
0.62
4.0410394702
Spring
7.75
30.77
5.27
49.58
17
66.61
28.32
0.69
4.1231056256
Summer
9.73
48.77
6.2
91.97
22.7
89.15
29.84
0.52
4.7644516998
Autumn
8.56
46.5
5.93
79.34
17.77
78.01
30.5
0.71
4.2154477817
Annual
8.19
40.36
5.43
66.51
18.45
75.43
30.48
0.64
4.295346319
Statistics computed from 100 ensembles (copy from those in the file: Arrange_criteria.xls)
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Mean
Jan
6.161
29.779
3.649
39.840
14.559
59.013
30.886
0.581
3.8080276099
Feb
6.498
32.506
4.149
41.484
14.825
51.029
28.062
0.583
3.8405529085
Mar
8.313
36.917
5.194
77.823
21.435
73.640
28.586
0.537
4.598
Apr
8.778
34.466
6.719
50.737
17.872
69.590
26.409
0.698
4.2190652141
May
6.519
24.147
4.415
30.019
13.792
62.980
31.181
0.805
3.7080695684
Jun
9.608
47.470
6.608
86.227
22.523
85.104
29.553
0.642
4.7391948785
Jul
9.451
42.608
5.976
82.144
21.435
90.343
30.808
0.535
4.6244494156
Aug
10.640
52.463
6.523
116.638
24.923
96.652
29.325
0.445
4.9866403074
Sep
10.701
68.748
7.060
153.666
21.472
86.838
27.085
0.567
4.6062071023
Oct
7.343
28.331
5.622
37.981
16.725
64.546
28.362
0.652
4.0843733339
Nov
7.763
33.993
5.270
44.922
16.644
81.542
34.998
0.917
4.0759919335
Dec
7.831
41.637
5.272
58.841
17.896
96.684
39.824
0.737
4.2250394601
Winter
6.830
34.641
4.356
46.722
15.760
68.909
32.924
0.633
3.9677935459
Spring
7.870
31.844
5.442
52.860
17.700
68.736
28.725
0.680
4.2010999358
Summer
9.900
47.513
6.369
95.003
22.960
90.700
29.895
0.540
4.7900037797
Autumn
8.602
43.691
5.984
78.856
18.281
77.643
30.148
0.712
4.2708138248
Annual
8.300
39.423
5.539
68.360
18.676
76.497
30.423
0.641
4.3204678601
STD
0.527
1.113
0.363
7.850
1.800
6.342
1.807
0.081
0.2418901209
0.560
3.589
0.535
8.553
2.085
6.645
2.851
0.090
0.2755221986
0.797
1.007
0.587
14.862
4.947
9.027
2.303
0.085
0.543
0.702
3.023
0.591
10.511
2.297
8.436
2.244
0.093
0.2687207244
0.433
0.743
0.367
4.991
1.568
5.978
2.345
0.126
0.2065103904
0.814
5.563
0.799
16.450
2.340
9.913
2.653
0.115
0.2523263554
0.784
3.318
0.730
16.032
2.091
10.171
2.069
0.095
0.2235336112
0.955
2.652
0.888
23.888
2.391
10.814
2.251
0.076
0.2387222826
1.126
6.912
0.773
46.203
4.766
10.425
2.064
0.089
0.5073760291
0.499
2.239
0.457
5.962
1.683
7.167
2.514
0.102
0.208153182
0.546
3.356
0.728
6.956
1.447
8.002
2.152
0.142
0.1749163094
0.518
3.827
0.545
9.894
1.811
8.702
2.366
0.124
0.2132991456
0.285
1.753
0.251
4.770
1.029
4.216
1.418
0.058
0.1291556785
0.372
1.042
0.324
6.294
1.894
4.721
1.425
0.064
0.2262101669
0.483
2.172
0.458
10.251
1.209
5.763
1.368
0.053
0.1265860622
0.456
2.515
0.396
15.399
1.746
4.647
1.269
0.063
0.2026323578
0.215
0.944
0.184
4.995
0.836
2.705
0.718
0.030
0.0967004643
Max
7.690
31.000
4.400
64.520
18.900
73.330
34.410
0.760
4.3474130239
7.820
38.000
5.500
66.900
20.100
70.170
36.190
0.820
4.4833023543
10.180
38.000
6.800
112.010
29.100
95.030
33.550
0.780
5.394
10.680
37.900
9.000
73.840
24.000
89.080
31.780
0.900
4.8989794856
7.540
25.000
5.500
42.270
18.600
80.230
36.340
1.190
4.3127717306
11.520
54.800
8.500
131.140
27.300
111.690
36.220
0.930
5.224940191
11.540
46.000
8.200
134.400
28.800
110.700
35.700
0.770
5.366563146
13.760
55.000
9.300
189.080
31.600
131.460
34.620
0.640
5.621387729
13.930
76.000
8.800
266.740
33.400
113.330
31.780
0.830
5.7792733107
8.660
31.000
6.700
53.040
20.200
85.430
33.550
0.990
4.4944410108
9.200
38.000
7.200
66.210
21.700
99.770
40.670
1.380
4.6583258795
9.560
45.900
7.000
93.050
23.100
125.380
47.100
1.020
4.8062459363
7.700
37.870
5.130
58.540
18.430
79.500
36.610
0.770
4.2930175867
8.950
33.570
6.400
70.390
22.200
79.600
32.370
0.840
4.7116875958
11.150
51.500
7.670
118.250
26.930
107.530
33.570
0.650
5.1894122981
9.730
47.600
6.900
117.940
23.230
91.290
33.870
0.900
4.8197510309
8.840
41.740
6.010
81.620
20.830
85.440
32.070
0.740
4.5639894829
Min
5.090
26.500
2.900
23.940
9.300
45.790
25.810
0.350
3.0495901364
5.130
25.900
2.700
22.660
9.800
34.690
19.760
0.410
3.1304951685
6.190
33.000
3.800
43.800
12.800
53.450
21.940
0.330
3.578
7.330
26.300
5.400
27.080
13.800
50.350
20.440
0.490
3.7148351242
5.570
21.300
3.600
17.850
10.800
45.260
25.590
0.570
3.286335345
7.510
32.900
4.600
46.340
15.300
65.230
24.440
0.350
3.9115214431
7.370
32.900
4.500
45.890
16.700
60.410
26.450
0.340
4.0865633483
8.420
41.900
4.900
66.390
19.400
75.770
24.090
0.280
4.4045431091
7.860
44.300
4.800
49.700
13.800
62.080
21.780
0.420
3.7148351242
6.170
21.100
4.600
22.000
11.700
50.050
21.510
0.450
3.4205262753
6.140
22.400
4.000
27.820
13.700
60.150
30.440
0.560
3.7013511047
6.780
32.100
4.200
38.590
14.700
77.210
33.760
0.460
3.8340579025
6.220
29.700
3.800
36.360
13.700
56.150
28.970
0.490
3.7013511047
6.940
29.500
4.430
37.340
14.000
57.600
25.350
0.510
3.7416573868
8.720
40.300
5.600
71.060
19.670
76.480
26.580
0.420
4.4350873723
7.390
36.200
5.030
43.090
15.070
66.440
27.170
0.570
3.8820097888
7.730
37.470
5.160
56.950
16.530
70.400
28.240
0.570
4.0657102701
X25
5.798
29.200
3.400
34.768
13.600
54.708
29.890
0.530
3.6878177829
6.170
29.400
3.800
35.673
13.575
46.423
26.190
0.520
3.6844219907
7.780
36.600
4.900
66.775
16.250
66.830
26.880
0.470
4.031
8.265
32.575
6.300
42.183
16.200
63.993
24.890
0.638
4.0249223595
6.218
23.800
4.200
26.633
12.800
59.660
29.838
0.700
3.577708764
9.100
43.700
5.975
75.338
21.000
77.140
27.503
0.570
4.582575695
8.933
41.100
5.475
70.390
20.400
84.328
29.195
0.468
4.5166359163
9.900
51.575
5.900
101.335
23.275
89.373
27.905
0.398
4.8244149737
10.138
65.875
6.500
122.073
17.875
80.083
26.000
0.510
4.2278805257
6.983
26.975
5.200
33.800
15.600
59.868
26.450
0.578
3.9496835316
7.398
32.175
4.775
39.973
15.700
75.543
33.560
0.808
3.9623225512
7.478
39.675
4.900
53.083
16.725
90.788
38.438
0.640
4.0895900304
6.660
33.445
4.200
43.120
15.023
66.080
32.020
0.600
3.8758866328
7.608
31.100
5.230
48.580
16.093
66.150
27.913
0.640
4.0115455098
9.590
46.310
6.030
88.890
22.268
87.668
28.980
0.500
4.7188414226
8.268
42.700
5.700
67.653
17.083
74.720
29.343
0.670
4.1330964833
8.150
38.750
5.390
64.570
18.158
74.590
29.975
0.620
4.2611610556
X50
6.215
30.100
3.700
40.135
14.900
59.325
30.540
0.580
3.8600518131
6.470
32.300
4.200
40.110
14.900
51.000
27.860
0.580
3.8600518131
8.240
37.100
5.300
78.520
23.300
73.575
28.280
0.530
4.827
8.770
35.200
6.700
50.995
17.550
70.815
26.110
0.705
4.1892677627
6.470
24.200
4.400
30.255
13.500
62.985
31.400
0.810
3.6742346142
9.595
48.850
6.600
86.405
23.000
85.380
29.560
0.640
4.7958315233
9.460
43.650
6.000
79.805
21.200
89.985
30.860
0.530
4.6043457733
10.675
53.300
6.400
114.520
25.100
95.380
29.030
0.450
5.00999002
10.590
71.250
7.050
153.575
21.200
86.060
26.890
0.550
4.6043329674
7.310
28.850
5.700
37.825
16.800
64.305
28.390
0.650
4.0987803064
7.770
34.850
5.100
45.310
16.500
81.390
34.890
0.910
4.0620192023
7.770
42.950
5.300
56.850
17.950
96.285
39.570
0.735
4.2367399237
6.780
34.615
4.330
45.900
15.585
68.800
32.960
0.635
3.9477837315
7.870
32.130
5.400
53.140
17.835
69.155
28.840
0.690
4.2231484498
9.845
47.565
6.270
95.745
22.970
90.845
29.700
0.550
4.7927027865
8.595
44.230
5.970
77.705
18.085
77.430
30.160
0.715
4.2526458693
8.280
39.630
5.530
67.760
18.650
76.280
30.435
0.640
4.3185645763
X75
6.553
30.700
3.900
43.820
15.700
62.770
32.260
0.640
3.9623225512
6.808
36.000
4.500
47.218
16.025
54.775
30.000
0.640
4.0031201324
8.875
37.600
5.500
86.900
25.625
79.750
30.110
0.600
5.062
9.268
37.025
7.100
57.888
19.225
74.938
27.835
0.753
4.3846294769
6.848
24.700
4.600
33.395
14.525
66.973
32.690
0.893
3.8111635734
10.223
52.000
7.200
95.505
23.925
90.405
31.388
0.713
4.8913168399
9.970
45.150
6.425
93.173
22.525
98.315
32.040
0.590
4.7460488001
11.158
54.225
7.100
126.578
26.400
105.888
30.970
0.500
5.1380930315
11.355
73.625
7.600
181.955
24.525
93.390
28.440
0.610
4.952270278
7.693
30.125
5.900
42.250
17.825
68.325
30.163
0.710
4.2219632565
8.100
36.700
5.800
49.578
17.300
87.230
36.275
1.010
4.1593268686
8.135
44.725
5.600
65.125
18.950
101.123
41.345
0.830
4.3531484371
6.983
35.908
4.478
49.570
16.478
71.255
34.020
0.670
4.0592483837
8.083
32.700
5.630
56.470
19.378
71.633
29.590
0.720
4.40198794
10.180
49.248
6.618
100.260
23.778
94.225
30.675
0.580
4.8762176147
8.920
45.148
6.208
89.788
19.478
80.405
30.968
0.760
4.4133318292
8.440
40.073
5.670
71.893
19.093
77.990
30.883
0.660
4.3694965104
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Fig.___ The maximum of observed and LARS-WG simulated daily precipitation
Fig.___ The maximum of observed and LARS-WG simulated daily precipitation
Fig.___ The maximum of observed and LARS-WG simulated daily precipitation
Fig.___ The median of observed and LARS-WG simulated daily precipitation
Fig.___ The median of observed and LARS-WG simulated daily precipitation
Fig.___ The median of observed and LARS-WG simulated daily precipitation
Fig.___ The variance of observed and LARS-WG simulated daily precipitation
Fig.___ The variance of observed and LARS-WG simulated daily precipitation
Fig.___ The variance of observed and LARS-WG simulated daily precipitation
Fig.___ The 90th percentile of observed and LARS-WG simulated daily precipitation
Fig.___ The 90th percentile of observed and LARS-WG simulated daily precipitation
Fig.___ The 90th percentile of observed and LARS-WG simulated daily precipitation
Fig.___ The total monthly amount of observed and LARS-WG simulated daily precipitation
Fig.___ The total monthly amount of observed and LARS-WG simulated daily precipitation
Fig.___ The total monthly amount of observed and LARS-WG simulated daily precipitation
Fig.___ The percentage of wet-day observed and LARS-WG simulated daily precipitation
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Fig.___ The percentage of wet-day observed and LARS-WG simulated daily precipitation
Fig.___ The percentage of wet-day observed and LARS-WG simulated daily precipitation
Fig.___ The mean wet-spell length of observed and LARS-WG simulated daily precipitation
Fig.___ The mean wet-spell length of observed and LARS-WG simulated daily precipitation
Fig.___ The mean wet-spell length of observed and LARS-WG simulated daily precipitation
Fig.___ The standard deviation of observed and LARS-WG simulated daily precipitation
Fig.___ The standard deviation of observed and LARS-WG simulated daily precipitation
Fig.___ The standard deviation of observed and LARS-WG simulated daily precipitation
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Dorval, 1961-1975
Bias = observed - modelled
Bias = observed - modelled
Bias = observed - modelled
Bias = observed - modelled
BIAS = Mean (Obs.) – Mean (Est.)
The mean of daily precipitation for the period of 1976-1990
Dorval
0
2
4
6
8
10
12
14
J
F
M
A
M
J
J
A
S
O
N
D
(mm)
Analysis Period: 01/01/1976 - 31/12/1990
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Observed
January
6.5006666667
32.5
3.6
36.0780532438
13.2
65.0066666667
32.2580645161
4
6.0065009152
Feb
6.7722689076
24.7
3.6
31.9754109101
15
53.2198113208
28.0660377358
4
5.6546804428
Mar
6.9659722222
27.5
4.2
35.6808619852
16.4
66.8733333333
30.9677419355
3
5.9733459623
Apr
7.901369863
34.5
5.6
41.1609636278
17.2
76.9066666667
32.4444444444
4
6.4156810728
May
7.3356164384
34.4
4.6
44.3588606519
15.1
71.4
31.3978494624
5
6.6602447892
Jun
8.6082758621
61.6
5.6
76.0581254789
18
83.2133333333
32.2222222222
6
8.7211309748
Jul
9.45390625
57.4
5.6
97.4465987943
23.6
80.6733333333
27.5268817204
3
9.8715043835
Aug
10.2074324324
67.2
6.8
94.0923933628
21.8
100.7133333333
31.8279569892
4
9.7001233684
Sep
10.426984127
81.9
6.4
125.7335060317
25.1
87.5866666667
28
3
11.2130952922
Oct
8.1653846154
63.8
5.6
67.0240843672
17.2
84.92
33.5483870968
6
8.1868238266
Nov
8.6970414201
54.6
5.2
66.3364792899
18.8
97.9866666667
37.5555555556
5
8.1447209461
Dec
7.6431372549
37.9
4.6
42.2544427245
17.8
77.96
32.9032258065
4
6.5003417391
Winter
6.9720242764
31.7
3.9333333333
36.7693022928
15.3333333333
65.3954926625
31.0757760195
4
6.0637696438
Spring
7.4009861745
32.1333333333
4.8
40.400228755
16.2333333333
71.7266666667
31.6033452808
4
6.3561174277
Summer
9.4232048482
62.0666666667
6
89.199039212
21.1333333333
88.2
30.5256869773
4.3333333333
9.4445242978
Autumn
9.0964700542
66.7666666667
5.7333333333
86.3646898963
20.3666666667
90.1644444444
33.0346475508
4.6666666667
9.2932604556
Annual
8.2231713383
48.1666666667
5.1166666667
63.183315039
18.2666666667
78.8716509434
31.5598639571
4.25
7.9487933071
Statistics computed from 100 ensembles (copy from those in the file: Arrange_criteria.xls)
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Month
Mean
Maximum
Median
Variance
Percentile
Sum
Wet-days%
Max_wspel
STD
Mean
Jan
8.224
45.371
6.088
53.069
16.617
79.125
31.065
4.390
7.2382807719
Feb
7.522
33.019
5.970
34.472
14.891
66.948
31.788
7.010
5.8340394154
Mar
10.299
53.428
7.513
90.124
22.033
90.887
28.492
4.310
9.426
Apr
9.339
38.349
7.502
49.232
18.395
85.792
30.627
4.230
6.971341137
May
7.262
33.133
5.740
32.577
14.506
78.965
35.084
5.950
5.6837246884
Jun
9.012
43.743
6.645
62.785
18.885
80.635
29.842
5.110
7.8538261015
Jul
7.626
41.938
5.375
52.229
16.383
74.127
31.355
4.010
7.1707735855
Aug
11.353
63.038
7.988
119.533
24.613
106.133
30.166
4.400
10.8346223366
Sep
9.883
49.552
7.262
78.801
20.886
81.862
27.613
3.920
8.8002769899
Oct
7.253
32.289
5.664
33.216
14.391
71.802
31.916
5.450
5.7256484389
Nov
7.758
36.147
5.963
40.810
15.800
79.735
34.247
5.090
6.3549356015
Dec
6.889
35.752
5.046
37.886
14.476
72.927
34.157
4.400
6.1210063726
Winter
7.545
38.047
5.702
41.809
15.328
73.000
32.337
5.267
6.4526474312
Spring
8.967
41.636
6.918
57.311
18.311
85.214
31.401
4.830
7.5521474172
Summer
9.330
49.573
6.669
78.182
19.960
86.965
30.454
4.507
8.8095972019
Autumn
8.298
39.329
6.296
50.943
17.026
77.800
31.259
4.820
7.1177146733
Annual
8.535
42.147
6.396
57.061
17.656
80.745
31.363
4.856
7.5476721627
STD
0.488
11.849
0.544
12.380
1.562
5.627
1.766
1.024
0.8265017349
0.474
7.026
0.488
7.942
1.412
5.472
1.658
1.714
0.6635308064
0.632
11.633
0.736
21.790
2.300
6.892
1.729
1.489
1.132
0.520
8.164
0.569
11.580
1.713
6.383
1.653
1.014
0.7993697337
0.399
6.661
0.470
6.065
1.076
5.709
1.801
1.321
0.5241339817
0.646
11.734
0.648
17.837
1.884
7.184
1.864
1.340
1.0551336106
0.552
10.115
0.595
13.488
1.646
6.599
1.577
0.916
0.9041785633
0.744
17.299
0.890
33.025
2.373
8.653
1.585
0.995
1.4714876771
0.857
10.775
0.785
20.994
2.325
8.659
1.738
1.440
1.1705902858
0.483
6.758
0.537
7.719
1.354
6.816
1.871
1.635
0.66127246
0.452
7.286
0.469
8.712
1.430
6.280
1.526
1.102
0.6553907774
0.450
7.709
0.409
8.001
1.351
5.955
1.903
1.137
0.650864527
0.299
4.563
0.290
5.413
0.875
3.476
1.121
0.746
0.4172219684
0.301
4.629
0.321
8.017
1.032
3.767
1.003
0.727
0.5281423405
0.421
8.109
0.407
13.797
1.240
4.579
1.005
0.642
0.7609520485
0.378
4.762
0.396
7.603
1.005
4.316
1.087
0.816
0.5325354165
0.170
2.889
0.175
4.685
0.554
2.029
0.545
0.356
0.3079451674
Max
9.184
94.727
7.260
92.329
20.176
93.409
35.269
9.000
9.6087793189
9.106
53.941
6.986
63.552
19.202
83.077
34.906
11.000
7.9719815604
11.789
104.045
9.805
178.366
29.841
109.245
32.258
10.000
13.355
11.001
67.354
9.430
89.393
23.765
97.787
34.667
7.000
9.4548056564
8.518
54.261
6.887
50.825
17.654
91.124
39.355
9.000
7.1291689558
11.323
97.205
8.392
142.515
23.140
103.590
34.889
8.000
11.9379772156
9.152
71.116
6.834
89.251
21.711
95.261
36.129
8.000
9.447263625
13.005
121.188
10.308
229.071
29.764
135.249
33.548
8.000
15.135104889
11.964
82.252
9.211
142.351
26.293
100.355
32.000
8.000
11.9310854494
8.670
51.617
7.434
55.901
18.383
91.332
36.774
12.000
7.476665032
9.225
55.376
7.092
71.064
19.997
103.322
38.222
9.000
8.4299638196
8.121
66.444
6.326
60.251
18.117
87.900
39.224
8.000
7.7621459662
8.325
52.589
6.421
58.170
17.738
82.475
34.743
7.000
7.6268938632
9.762
58.408
7.689
80.824
21.410
96.296
33.689
6.667
8.990237483
10.434
73.761
7.676
117.744
22.888
96.719
32.705
6.333
10.8509815224
9.149
49.031
7.184
69.984
19.844
86.836
34.925
7.667
8.3656243043
8.903
48.427
6.723
68.521
18.760
86.394
32.856
5.750
8.2777533184
Min
7.164
28.047
4.770
30.075
13.562
65.943
26.452
3.000
5.4840222465
6.607
19.039
4.783
18.242
12.477
51.551
27.594
3.000
4.271109926
8.161
33.413
5.640
40.606
16.275
72.566
24.516
2.000
6.372
8.223
26.014
6.165
29.970
14.757
68.017
26.222
3.000
5.4745118504
6.210
21.777
4.739
21.664
12.216
67.335
29.462
3.000
4.6544172568
7.349
27.744
4.941
31.752
14.290
62.707
25.556
3.000
5.634863796
6.470
23.247
3.980
31.998
13.117
60.818
26.882
2.000
5.6566854252
9.474
34.808
6.160
57.868
19.755
89.054
26.882
3.000
7.6071170623
7.926
28.357
5.064
38.588
15.938
61.293
23.111
2.000
6.2119457499
5.904
20.282
4.699
18.719
11.434
51.166
26.452
3.000
4.3265702352
6.598
23.283
4.998
27.239
12.919
62.905
30.444
3.000
5.2191330698
5.639
20.306
4.129
18.935
11.107
58.721
29.741
3.000
4.3513894333
6.799
29.413
5.130
30.717
13.189
64.476
28.910
3.333
5.5422720973
8.256
30.924
6.279
38.226
15.429
77.406
29.417
3.333
6.1826830745
8.411
36.011
5.727
51.773
17.189
77.711
27.018
3.333
7.1953491924
7.366
27.977
5.140
35.626
14.976
66.985
28.418
3.000
5.9687134292
8.113
36.134
5.982
46.005
16.466
73.504
30.021
3.917
6.7827022638
X25
7.865
36.501
5.687
44.075
15.623
75.726
29.892
4.000
6.6389101317
7.178
28.769
5.623
29.916
13.764
64.172
30.425
6.000
5.4695377779
9.908
45.986
7.071
75.503
20.555
85.430
27.312
3.000
8.689
9.051
32.466
7.156
40.818
17.263
81.919
29.556
3.000
6.3888971104
6.985
28.230
5.444
28.528
13.631
74.678
33.978
5.000
5.3411435094
8.606
36.512
6.222
52.779
17.603
75.716
28.444
4.000
7.2648723024
7.259
34.827
4.950
41.749
15.429
69.048
30.323
3.750
6.4613558545
10.856
49.645
7.444
94.842
22.655
100.581
29.032
4.000
9.7386879666
9.213
42.996
6.748
64.210
19.076
75.996
26.444
3.000
8.0131154894
6.947
28.249
5.311
27.627
13.514
67.144
30.699
4.000
5.2560997871
7.478
30.957
5.693
34.662
14.908
75.768
33.111
4.000
5.8874363322
6.658
30.971
4.753
32.298
13.591
69.383
32.759
4.000
5.6831615492
7.318
35.185
5.484
37.603
14.584
70.451
31.632
4.667
6.1321307319
8.764
38.682
6.699
51.375
17.753
82.509
30.651
4.333
7.1676171634
9.037
43.400
6.373
69.287
19.037
83.678
29.750
4.000
8.3238828171
8.042
36.051
6.050
45.531
16.233
75.449
30.543
4.333
6.7476797415
8.394
40.064
6.272
54.266
17.218
79.166
30.925
4.583
7.3665488049
X50
8.226
43.515
6.027
50.602
16.491
78.854
31.183
4.000
7.113524067
7.495
31.311
5.968
33.446
14.675
66.754
31.958
7.000
5.7832517068
10.262
51.394
7.398
89.125
21.918
91.326
28.495
4.000
9.441
9.357
36.591
7.463
47.332
18.281
85.658
30.444
4.000
6.8797636789
7.228
31.774
5.695
31.726
14.510
78.622
35.054
6.000
5.6325342527
9.011
42.255
6.631
58.766
18.758
80.987
29.778
5.000
7.6658912855
7.591
40.093
5.276
49.565
16.227
74.307
31.183
4.000
7.0401724184
11.338
61.565
7.985
116.330
24.598
105.825
30.000
4.000
10.7856453096
9.918
48.653
7.203
77.105
20.621
81.544
27.778
4.000
8.780965433
7.194
31.261
5.559
32.356
14.202
71.307
31.828
5.000
5.6882068243
7.801
34.695
5.983
39.401
15.697
79.861
34.111
5.000
6.2769883184
6.868
34.140
5.021
38.264
14.537
72.491
34.267
4.000
6.1857565658
7.550
38.067
5.702
41.749
15.415
73.191
32.426
5.333
6.4613206089
8.947
41.144
6.938
57.317
18.249
85.562
31.354
4.667
7.5707676208
9.266
48.974
6.646
75.358
19.953
86.689
30.431
4.333
8.6809186977
8.297
39.227
6.320
50.445
16.931
78.144
31.231
4.667
7.1024250823
8.526
42.072
6.425
56.049
17.639
80.938
31.409
4.875
7.4866180852
X75
8.643
51.937
6.459
57.688
17.718
82.668
32.258
5.000
7.5951528935
7.870
37.292
6.319
39.171
15.792
69.951
33.019
8.000
6.2586916393
10.690
58.989
7.979
101.188
23.412
95.356
29.677
5.000
10.059
9.631
42.087
7.884
55.958
19.292
90.699
31.778
5.000
7.4805064528
7.498
36.894
6.023
35.808
15.188
82.452
36.344
7.000
5.9839522859
9.457
47.972
7.012
71.157
20.118
85.342
31.111
6.000
8.4354412747
7.975
46.748
5.772
59.289
16.903
77.993
32.473
4.000
7.699893025
11.763
73.805
8.510
135.611
26.289
111.194
31.398
5.000
11.6451729483
10.501
54.163
7.814
90.430
22.465
88.188
28.722
4.000
9.5094356409
7.554
34.782
5.988
38.573
15.391
75.914
33.118
7.000
6.2106801209
8.051
40.374
6.272
44.024
16.762
83.998
35.389
6.000
6.6349957169
7.128
38.914
5.326
42.292
15.332
77.119
35.399
5.000
6.5032460765
7.753
40.852
5.909
45.577
15.842
75.195
33.243
5.667
6.7510594245
9.146
44.339
7.092
61.181
18.907
87.416
32.168
5.333
7.8218200391
9.590
54.179
6.911
83.934
20.956
90.006
31.142
5.000
9.161549071
8.544
42.662
6.568
57.057
17.718
80.227
32.019
5.333
7.5535727894
8.675
43.792
6.516
60.032
18.068
81.930
31.775
5.083
7.7480541716
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Monthly box-plot for Variance (column G)
Seasonal box-plot for Variance (column G)
Annual box-plot for Variance (column G)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Monthly box-plot for Skewness (column J)
Seasonal box-plot for Skewness (column J)
Annual box-plot for Skewness (column J)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Winter
Spring
Summer
Autumn
Annual
Jan
Feb
Mar
Winter
Annual
Fig.___ The 90th percentile of observed and SDSM-generated daily precipitation
Fig.___ The 90th percentile of observed and SDSM-generated daily precipitation
Fig.___ The 90th percentile of observed and SDSM-generated daily precipitation
Fig.___ The total monthly amount of observed and SDSM-generated daily precipitation
Fig.___ The total monthly amount of observed and SDSM-generated daily precipitation
Fig.___ The total monthly amount of observed and SDSM-generated daily precipitation
Fig.___ The percentage of wet-day of observed and SDSM-generated daily precipitation
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Fig.___ The percentage of wet-day of observed and SDSM-generated daily precipitation
Fig.___ The percentage of wet-day of observed and SDSM-generated daily precipitation
Fig.___ The maximum wet-spell length of observed and SDSM-generated daily precipitation
Fig.___ The maximum wet-spell length of observed and SDSM-generated daily precipitation
Fig.___ The maximum wet-spell length of observed and SDSM-generated daily precipitation
Fig.___ The standard deviation of observed and SDSM-generated daily precipitation
Fig.___ The standard deviation of observed and SDSM-generated daily precipitation
Fig.___ The standard deviation of observed and SDSM-generated daily precipitation
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Dorval, 1976-1990
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Observed
Jan
6.1806666667
32.5
3.6
40.1633149888
13.2
61.8066666667
32.2580645161
0.5531914894
3.6331804249
Feb
6.4085470085
24.7
3.6
36.8956159741
15
49.9866666667
27.8571428571
0.5125
3.8729833462
Mar
6.7715277778
27.5
4.2
38.3707221251
16.4
65.0066666667
30.9677419355
0.5747126437
4.0496913463
Apr
7.7178082192
34.5
5.6
44.0478186112
17.2
75.12
32.4444444444
0.7857142857
4.1472882707
May
7.1369863014
34.4
4.6
47.2533811998
15.1
69.4666666667
31.3978494624
0.7317073171
3.8858718455
Jun
8.4124137931
61.6
5.6
79.4149837165
18
81.32
32.2222222222
0.597826087
4.2426406871
Jul
9.28203125
57.4
5.6
100.6921942667
23.6
79.2066666667
27.5268817204
0.4301075269
4.8579831206
Aug
10.0304054054
67.2
6.8
97.6994093583
21.8
98.9666666667
31.8279569892
0.5473684211
4.669047012
Sep
10.2412698413
81.9
6.4
129.6026031746
25.1
86.0266666667
28
0.6025641026
5.00999002
Oct
7.9884615385
63.8
5.6
69.9005111663
17.2
83.08
33.5483870968
0.8372093023
4.1472882707
Nov
8.4911242604
54.6
5.2
69.896885038
18.8
95.6666666667
37.5555555556
0.9090909091
4.3358966777
Dec
7.368627451
37.9
4.6
46.4024303406
17.8
75.16
32.9032258065
0.6559139785
4.2190046219
Winter
6.6526137087
31.7
3.9333333333
41.1537871011
15.3333333333
62.3177777778
31.0061443932
0.5738684893
3.9157800415
Spring
7.2087740994
32.1333333333
4.8
43.2239739787
16.2333333333
69.8644444444
31.6033452808
0.6973780822
4.0290610982
Summer
9.2416168162
62.0666666667
6
92.6021957805
21.1333333333
86.4977777778
30.5256869773
0.5251006783
4.5971005355
Autumn
8.90695188
66.7666666667
5.7333333333
89.799999793
20.3666666667
88.2577777778
33.0346475508
0.7829547713
4.5129443456
Annual
8.0024891261
48.1666666667
5.1166666667
66.6949891633
18.2666666667
76.7344444444
31.5424560505
0.6448255053
4.2739521133
Statistics computed from 100 ensembles (copy from those in the file: Arrange_criteria.xls)
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Month
Mean
Maximum
Minimum
Median
Variance
POT
Percentile
Skewness
STD
Mean
Jan
6.410
31.071
3.775
43.308
15.199
60.865
30.637
0.637
3.8924914898
Feb
6.011
28.973
3.834
35.130
13.558
48.172
28.602
0.525
3.6748554395
Mar
7.450
33.342
4.660
61.796
18.579
72.082
31.237
0.714
4.276
Apr
7.068
28.270
5.357
33.395
14.522
68.287
32.204
0.727
3.8047844765
May
7.324
27.067
4.942
38.333
15.569
71.065
31.299
0.794
3.9409595396
Jun
8.721
43.553
5.921
71.628
20.869
82.659
31.633
0.609
4.5611274151
Jul
10.464
46.871
6.656
99.530
23.850
90.704
27.978
0.449
4.8788280615
Aug
9.648
47.217
5.910
95.706
22.481
94.825
31.669
0.566
4.7377310305
Sep
9.998
64.767
6.464
135.801
20.728
87.282
29.116
0.674
4.5233198426
Oct
7.843
31.224
6.038
43.542
17.684
78.483
32.295
0.803
4.199933599
Nov
7.889
35.498
5.322
47.103
16.793
91.333
38.580
0.907
4.0958946753
Dec
7.997
42.721
5.288
63.903
18.239
87.090
35.135
0.704
4.2623311463
Winter
6.806
34.255
4.299
47.4