Creación de una base de datos de precipitación y temperatura a escala

diaria enel noreste de la Península Ibérica:

Aplicación en estudios de tendencias y extremos climáticos

Instituto Pirenaico de Ecología y Estación Experimental de Aula Dei, Instituto Pirenaico de Ecología y Estación Experimental de Aula Dei, CSIC,CSIC,Campus de Aula Dei, P.O. Box 202, Zaragoza 50080, Spain; Campus de Aula Dei, P.O. Box 202, Zaragoza 50080, Spain; e-mail: svicen@ipe.csic.ese-mail: svicen@ipe.csic.es

Vicente-Serrano SM, El Kenawy AM, Beguería S, López-Moreno JI, Angulo MVicente-Serrano SM, El Kenawy AM, Beguería S, López-Moreno JI, Angulo M

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The need of detailed spatial studies:

Climatic hazardsClimate variabilityTrends

The need of high temporal resolution:

Extreme precipitationDry spellsHeat waves...

Few studies at a daily time-scale and few homogenised data-sets

Commonly the series are fragmentary, with numerous data gaps

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50 0 50 100 Kilometers

N

# Original observatories# Reconstructed observatories

Selection of observatories according the lenght of the series and the number of gaps.

Manual reconstruction of the series according to the distance between observatories (radius < 15 km.)

Among four tested methods for reconstruction, the nearest neighbour method provides better results in terms of magnitude and frequency distributions.

934 were reconstructed from a total of 3106. The rest were used for reconstruction.

Quality control was based on the compariosn of each daily data to the data of neighbour observatories.

On average, the proportion of data substituted was 0.1%

Temporal homogeneity of each reconstructed series was checked

Winter

Pre

cipi

tatio

n (m

m.)

0

100

200

300

400

Candidate seriesReference series

1940 1950 1960 1970 1980 1990 2000 2010

T-v

alue

0

10

20

30

Spring

Pre

cipi

tatio

n (m

m.)

0

100

200

300

400

500

1940 1950 1960 1970 1980 1990 2000 2010

T-v

alue

0

10

20

30

Summer

Pre

cipi

tatio

n (m

m.)

0

50

100

150

200

250

300

350

1940 1950 1960 1970 1980 1990 2000 2010

T-v

alue

0

10

20

30

Autumn

Pre

cipi

tatio

n (m

m.)

0

50

100

150

200

250

300

350

1940 1950 1960 1970 1980 1990 2000 2010T

-val

ue0

10

20

30

Annual

Pre

cipi

tatio

n (m

m.)

0

200

400

600

800

1000

1940 1950 1960 1970 1980 1990 2000 2010

T-v

alue

0

10

20

30

40

Seasonal and annual series of monthly precipitation amounts at the El Burgo de Osma (La Rasa) observatory. The series of

T-values and the limit of confidence (dotted line) are also shown.

Annual series of precipitation amounts and number of rainy days at the l’Ametlla de Mar observatory. The series of T-values and the limit of confidence (dotted line) are also

shown.

HOMOGENEITY TESTING PROCEDURE NUMBER OF INHOMOGENEITIES

SERIES ELIMINATED

Precipitation amount 260 74 Number of days with precipitation > 1 mm. 157 32 Monthly maxima and number of days with precipitation above 99.5 percentile

25 0

RECONSTRUCTED PERIOD TOTAL INHOMOGENEOUS % TOTAL % INHOMOGENEOUS > 20 years 383 192 41.0% 43.4% > 5 years < 20 years 229 113 24.5% 25.5% < 5 years 322 137 34.5% 30.1% Total 934 442 100.0% 100.0%

% o

f se

ries

0.0

0.5

1.0

1.5

2.0

2.5

1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

% o

f se

ries

0

1

2

3

4

% o

f se

ries

0.0

0.5

1.0

1.5

2.0

2.5

% o

f se

ries

0.0

0.5

1.0

1.5

2.0

2.5

1

2

3

4

Results from homogeneity process

Percentage of inhomogeneous series with respect to the number of series available for each year: 1) precipitation amount, 2) number of rainy days, 3) monthly maximum and number of days above the 99.5th percentile, 4) total.

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50 0 50 100 Kilometers

N

# Original# Reconstructed% Homogeneous 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Nu

mb

er

of

ob

se

rva

tori

es

0

100

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Reconstructed

Homogenization (precipitation amount)

Homogenization (number of days of precipitation)

Final series:Homogenization (monthly maximum anddays above 99.5 pcnt.)

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a) 1920 b) 1935

d) 1965c) 1950

N

50 0 50 100 150 Kilometers

Results

Average amount (mm.) each precipitation day

0 50 100 150 200

Se

miv

ari

an

ce

0

1

2

3

4

5

6

ReconstructedQuality controlHomogeneous

Average duration of dry spells

0 50 100 150 2000

1

2

3

4

5

Average duration of wet spells

0 50 100 150 200

Se

miv

ari

an

ce

0.00

0.05

0.10

0.15

0.20Number of days with precipitation > 0

Distance (km.)

0 50 100 150 2000.0

2.0e+5

4.0e+5

6.0e+5

8.0e+5

1.0e+6

1.2e+6

Days with precipitation > 75 mm.

Distance (km.)

0 50 100 150 200 250 300

Se

miv

ari

an

ce

0

50

100

150

200

ReconstructedQuality controlHomogeneous

Distance

0 10 20 30 40 50 60 70 80 90 100 110 120

Ave

rage

R-P

ears

on

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Final series

Reconstructed series

Higer spatial coherence of the final dataset

Annual and seasonal mapping of peak intensity, magnitude and duration of extreme precipitation events across a climatic gradient, North-East Spain

459 complete daily precipitation series with continuous data between 1970 and 2002

A declustering process was applied to the original daily series to obtain series of rainfall events. A rainfall event was defined as a series of consecutive days with

registered rainfall, so a period of one or more days without precipitation was the criteria to separate

between events.

Three parameters were determined for each precipitation event: its maximum intensity (in mm per day), total magnitude (accumulated precipitation, in

mm), and duration (in days).

Each event was assigned to the last date of the cluster, which allowed for constructing time series of

precipitation events.

L-moment plots: comparison between theoretical (lines) and empirical (dots) L-skewness (x acis) and L-kurtosis (y axis).

Several theoretical distributions are shown: Generalized Pareto (continuous line), Exponential (intersection between the vertical and horizontal lines), Lognormal (dashed line) and Pearson III

(dotted line).

The methodology adopted to extract the extreme observations was based on exceedance, or peaks-over-

threshold. Given an original variable X, a derived exceedance variable Y is constructed by taking only

the exceedances over a pre-determined threshold value, x0:

A threshold value corresponding to the 90th centile of each series was used to construct the exceedance

series. This means that only the ten percent highest events, in terms of intensity, magnitude and duration,

were retained for the analysis.

The probability distribution of an exceedance or peaks over threshold variate with random occurrence times

belongs to the Generalised Pareto (GP) family.

Although the GP distribution is very flexible due to its three parameters, there is a large uncertainty involved in estimating the shape parameter and it is frequently difficult to determine whether the estimates of differ

significantly from zero for a given sample. For this reason it is advisable to use the simpler Exponential

distribution instead of the GP, due its highest robustness.

L-moment plots where obtained as a graphical confirmation of the goodness of fit of the GP and the

Exponential distribution to the data series.

0xX=Y 0x>X

Under the Exponential distribution, and assuming Poisson distributed arrival times, the T-year return

period exceedance, YT, can be obtained as the (1 - 1/T) quantile in the distribution of the exceedances:

In our case, parameter estimates at ungauged locations were obtained as a mixture of a linear

regression and a local autoregressive component

T

xXT 1

log0

01

00

0ˆ xε+pλ+xzβ=xp j

m

=j

n

=iii

GIS-layers

LAT Latitude (km)

LONG Longitude (km)

D_SEA Distance to the Sea (km)

D_MED Distance to Mediterranean Sea (km)

D_ATL Distance to Atlantic Ocean (km)

ELEV Elevation (m)

ELEVx Average elevation (m) within x, where x is a circular window with radii of 2.5, 5, and 25 km

RAD Annual average incoming solar radiation (MJ × day)

RADx Annual average incoming solar radiation (MJ × day) within x, where x is a circular window with radii of 2.5, 5, and 25 km

SLOPE Slope gradient (%)

SLOPEx Average slope gradient (%) within x, where x is a circular window with radii of 2.5, 5, and 25 km

GIS-layers Spatial distribution of the Exponential distribution parameters and corresponding to annual series: 1) peak intensity ;

2) x0 peak intensity ; 3) magnitude ; 4) x0 magnitude ; 5) duration ; 6) x0 duration .

a) Average MAE MBE D

annual 4.39 0.24 0.03 0.92

autumn 1.09 0.06 0.01 0.92

winter 1.06 0.07 0.01 0.93

spring 1.24 0.07 <0.01 0.90

summer 1.01 0.08 0.02 0.95

b)

annual )(ˆ x 14.56 1.57 0.34 0.95

annual )(ˆ x 28.89 2.35 -0.44 0.95

autumn )(ˆ x 15.12 2.82 0.43 0.92

autumn )(ˆ x 34.38 3.07 -0.23 0.96

winter )(ˆ x 11.68 2.20 0.75 0.91

winter )(ˆ x 28.64 3.56 -1.34 0.93

spring )(ˆ x 10.71 1.96 -0.24 0.87

spring )(ˆ x 26.00 2.23 -0.51 0.93

summer )(ˆ x 11.92 2.05 -0.11 0.79

summer )(ˆ x 28.02 2.01 -0.32 0.89

c)

annual )(ˆ x 38.31 9.81 -7.22 0.75

annual )(ˆ x 41.02 8.58 5.27 0.82

autumn )(ˆ x 36.60 8.83 -4.28 0.85

autumn )(ˆ x 44.09 14.40 10.78 0.75

winter )(ˆ x 34.03 7.78 -3.13 0.84

winter )(ˆ x 41.89 14.69 9.52 0.78

spring )(ˆ x 29.08 7.31 -4.78 0.79

spring )(ˆ x 39.34 8.41 5.13 0.84

summer )(ˆ x 16.92 4.71 2.39 0.65

summer )(ˆ x 35.07 5.10 3.13 0.71

d)

annual )(ˆ x 1.69 0.19 0.04 0.94

annual )(ˆ x 4.01 0.31 0.04 0.96

autumn )(ˆ x 1.42 0.30 0.06 0.86

autumn )(ˆ x 3.96 0.30 0.02 0.95

winter )(ˆ x 1.53 0.26 0.05 0.92

winter )(ˆ x 4.50 0.41 0.09 0.96

spring )(ˆ x 1.58 0.32 0.02 0.86

spring )(ˆ x 4.37 0.42 0.00 0.94

summer )(ˆ x 1.22 0.35 -0.07 0.52

summer )(ˆ x 3.24 0.21 0.03 0.93

A good agreement was found in general between the regionalised parameters and the ones obtained by at-site analysis,

d) annual frequency of events;a) peak intensity; b) magnitude; c) duration

Spatial distribution of corresponding to seasonal series of peak intensity: 1) winter; 2) spring; 3)

summer; 4) autumn.

Average MAE MBE D

a)

annual 99.84 8.60 1.28 0.96 autumn 87.01 11.00 1.27 0.95 winter 68.73 8.94 1.26 0.95 spring 64.70 7.84 -1.46 0.91 summer 68.58 7.19 -0.43 0.88 b) annual 228.41 43.74 -29.78 0.85 autumn 172.03 25.26 -4.12 0.94 winter 159.05 25.51 -0.60 0.93 spring 144.82 23.54 -12.19 0.90 summer 92.79 20.44 11.85 0.70

c)

annual 12.27 1.17 0.26 0.96 autumn 8.92 1.25 0.23 0.92 winter 9.84 1.16 0.28 0.96 spring 10.10 1.31 0.08 0.93 summer 7.37 1.32 -0.14 0.70

2

Comparison between the quantile estimates of peak intensity, magnitude and duration for a return period of 30 years using spatially modelled (y axis) and at-site (x axis)

Exponential distribution parameter estimates, line of perfect fit (continuous) and regression line (dashed).

Annual quantiles maps corresponding to a return period of 30 years: 1) peak intensity (mm day-1); 2)

magnitude (mm); and 3) duration (days).

Error/accuracy statistics for annual and seasonal quantile estimates for a 30 years

return period: a) peak intensity; b) magnitude; and d) duration.

Seasonal quantile maps of peak intensity (mm day-1): 1) winter; 2) spring; 3) summer; and 4) autumn.

Seasonal quantile maps of magnitude (mm): 1) winter; 2) spring; 3) summer; and 4) autumn.

Seasonal quantile maps of duration (mm): 1) winter; 2) spring; 3) summer; and 4) autumn.

Regionalization of the study area as a function of the season in which the maximum quantile estimate is recorded: 1) peak

intensity; 2) magnitude; and 3) duration

Daily atmospheric circulation events and extreme precipitation risk in Northeast Spain: the role of the North Atlantic Oscillation, Western Mediterranean Oscillation,

and Mediterranean Oscillation

174 complete series of daily precipitation amounts with continuous data between 1950 and 2006 Sea-level-pressure points used to calculate the

daily atmospheric circulation indices.

Temporal evolution of daily NAO, WeMO, and MO indices between 1 October 2006 and 31 December

2006. Temporal evolution of October–March WeMO, NAO, and MO indices obtained from average daily and

monthly indices.

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5Generalized Pareto Generalized Logistic

General extreme valuePearson III

Lognormal Wakeby

Normal

Exponential

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7L

-Ku

rto

sis

(

0.0

0.1

0.2

0.3

0.4

0.5

POSITIVE NEGATIVE

POSITIVE NEGATIVE

MAGNITUDE

MAXIMUM INTENSITY

NAO

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5Generalized Pareto Generalized Logistic

General extreme valuePearson III

Lognormal Wakeby

Normal

Exponential

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5

POSITIVE NEGATIVE

POSITIVE NEGATIVE

MAGNITUDE

MAXIMUM INTENSITY

WeMO

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5Generalized Pareto Generalized Logistic

General extreme valuePearson III

Lognormal Wakeby

Normal

Exponential

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5

L-Skewness (

-0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

L-K

urt

os

is (

0.0

0.1

0.2

0.3

0.4

0.5

POSITIVE NEGATIVE

POSITIVE NEGATIVE

MAGNITUDE

MAXIMUM INTENSITY

MO

L-Moment diagrams for series of the magnitude and maximum intensity of precipitation for positive and negative atmospheric circulation events. Each point

indicates the statistics for each observatory. Box-plot of the p-values obtained from the Kolmogorov-Smirnov test. Above: precipitation

intensity. Below: precipitation magnitude.

Zaragoza

Centile

0 20 40 60 80 100

Mag

nitu

de

0

100

200

300

400

500

PostiveNegative

MAGNITUDE

MAXIMUM INTENSITY

Castellón

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Monzón de Campos

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Articutza

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Barcelona

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Zaragoza

Centile

0 20 40 60 80 100

Max

imum

Inte

nsity

0

50

100

150

200

250

PostiveNegative

Castellón

Centile

0 20 40 60 80 100

0

50

100

150

200

250Monzón de Campos

Centile

0 20 40 60 80 100

0

50

100

150

200

250Articutza

Centile

0 20 40 60 80 100

0

50

100

150

200

250Barcelona

Centile

0 20 40 60 80 100

0

50

100

150

200

250

Zaragoza

Centile

0 20 40 60 80 100

Mag

nitu

de

0

100

200

300

400

500

PostiveNegative

MAGNITUDE

MAXIMUM INTENSITY

Castellón

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Monzón de Campos

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Articutza

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Barcelona

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Zaragoza

Centile

0 20 40 60 80 100

Max

imu

m In

tens

ity

0

50

100

150

200

250

PostiveNegative

Castellón

Centile

0 20 40 60 80 100

0

50

100

150

200

250Monzón de Campos

Centile

0 20 40 60 80 100

0

50

100

150

200

250Articutza

Centile

0 20 40 60 80 100

0

50

100

150

200

250Barcelona

Centile

0 20 40 60 80 100

0

50

100

150

200

250

Zaragoza

Centile

0 20 40 60 80 100

Mag

nitu

de

0

100

200

300

400

500

PostiveNegative

MAGNITUDE

MAXIMUM INTENSITY

Castellón

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Monzón de Campos

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Articutza

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Barcelona

Centile

0 20 40 60 80 100

0

100

200

300

400

500

Zaragoza

Centile

0 20 40 60 80 100

Max

imu

m In

tens

ity

0

50

100

150

200

250

PostiveNegative

Castellón

Centile

0 20 40 60 80 100

0

50

100

150

200

250Monzón de Campos

Centile

0 20 40 60 80 100

0

50

100

150

200

250Articutza

Centile

0 20 40 60 80 100

0

50

100

150

200

250Barcelona

Centile

0 20 40 60 80 100

0

50

100

150

200

250

NAO

WeMO

MO

Centile values of precipitation magnitude and maximum intensity for positive and negative NAO,

WeMO and MO events for five representative observatories.

pp-plots between the empirical distribution and the modeled Generalised Pareto distribution for precipitation intensity series in five representative observatories corresponding to the positive and negative phases of the

three atmospheric circulation patterns. a) Zaragoza; b) Castellón; c) Monzón de Campos; d) Articutza; e) Barcelona.

Probability of maximum intensity of precipitation exceeding 50 mm and total magnitude exceeding 100 mm corresponding to positive and negative NAO, WeMO, and MO events following a GP distribution.

Quantile maps of maximum daily precipitation and total magnitude during NOA, WeMO, and MO events corresponding to a return period of 50 years.

Vicente-Serrano S.M., Santiago Beguería, Juan I. López-Moreno, Ahmed M. El Kenawy y Marta Angulo. Daily atmospheric circulation events and extreme precipitation risk in Northeast Spain: the role of the North Atlantic Oscillation, Western Mediterranean Oscillation,

and Mediterranean Oscillation. Journal of Geophysical Research-Atmosphere. In press

Acronym Definitions Unit

P Total precipitation mm

WD Number of wet days (precipitation >1 mm) days

PI Simple daily intensity (P/WD) mm

C90 Annual 90th percentile mm

R90N Nº of events with precipitation greater than long-term 90th percentile (P90)

days

R90T Percentage of total precipitation from events above P90 %

R5GD Greatest 5-day total precipitation mm

WS Max Nº of consecutive wet days (precipitation >1 mm) days

DS Max Nº of consecutive dry days (precipitation <1 mm) days

Acronyms and definition of the nine selected precipitation indices.

Indices Annual DJF MAM JJA SON - o + - o + - o + - o + - o + P 68 32 0 58 41 0 42 57 0 46 53 0 20 73 7 WD 54 39 7 59 38 3 41 52 7 55 43 2 2 72 26 PI 44 42 14 35 49 16 34 55 11 30 55 15 52 44 3 C90 41 48 11 26 61 13 28 60 12 28 56 16 39 57 35 R90N 49 46 5 34 60 6 27 69 4 32 63 5 32 61 7 R90T 29 62 9 17 74 9 12 76 12 21 65 14 31 62 7 R5GD 44 50 6 37 60 3 32 66 2 38 59 3 24 69 7 WS 30 61 9 34 62 4 25 63 12 50 47 3 8 72 19 DS 5 57 38 4 73 23 0.1 85 14 9 60 31 20 71 9 Percentage of observatories with positive (+, < 0.05), unchanged

(o, < 0.05) and negative (, < 0.05) trends in precipitation indices.

Trends in daily precipitation on the northeastern Iberian Peninsula, 19552006

Spatial distribution of annual trends

-1.0 -0.5 0.0 0.5 1.0-1.0 -0.5 0.0 0.5 1.0-1.0

-0.5

0.0

0.5

1.0

WD-1.0 -0.5 0.0 0.5 1.0

P

A B C r=0.14r=0.61 r=0.65

PI R5GD

5A 5B

PI

-1.0 -0.5 0.0 0.5 1.0

C90

-1.0

-0.5

0.0

0.5

1.0

R5GD

-1.0 -0.5 0.0 0.5 1.0

r=0.88 r=0.64A B

Seasonal differences

Winter Autumn

Daily maximum and minimum temperature records