Creación de una base de datos de precipitación y temperatura a escala diaria en el noreste de la Península Ibérica: Aplicación en estudios de tendencias y extremos climáticos Instituto Pirenaico de Ecología y Estación Experimental de Aula Dei, CSIC, Campus de Aula Dei, P.O. Box 202, Zaragoza 50080, Spain; Vicente-Serrano SM, El Kenawy AM, Beguería S, López-Moreno JI, Angulo M

The need of detailed spatial studies: Climatic hazards Climate variability Trends The need of high temporal resolution: Extreme precipitation Dry spells Heat waves... Few studies at a daily time-scale and few homogenised data-sets Commonly the series are fragmentary, with numerous data gaps

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 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 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.

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.

Results

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, x 0 : 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.

Under the Exponential distribution, and assuming Poisson distributed arrival times, the T-year return period exceedance, Y T, 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 GIS-layers GIS-layers Spatial distribution of the Exponential distribution parameters and corresponding to annual series: 1)  peak intensity ; 2) x 0 peak intensity ; 3)  magnitude ; 4) x 0 magnitude ; 5)  duration ; 6) x 0 duration.

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.

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

NAO WeMO 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.

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

Acronyms and definition of the nine selected precipitation indices. 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, 1955  2006

Spatial distribution of annual trendsSeasonal differences Winter Autumn

Daily maximum and minimum temperature records