a new tool for monthly precipitation analysis in spain: mopredas database (monthly precipitation...

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. 31: 715–731 (2011)Published online 10 March 2010 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/joc.2115

A new tool for monthly precipitation analysis in Spain:MOPREDAS database (monthly precipitation trends

December 1945–November 2005)

Jose Carlos Gonzalez-Hidalgo,a,b* Michele Brunettib and Martın de Luisa

a Department of Geography, University of Saragossa, 50009, Saragossa, Spainb Instituto di Scienze dell’Atmosfera e del Clima ISAC-CNR, Bologna, Italy

ABSTRACT: A new monthly precipitation database has been developed for conterminous provinces in Spain by exploitingthe total amount of data available at Spanish Meteorological Agency (AEMET, formerly INM). The new MOPREDAS(monthly precipitation database of Spain) database has been constructed by using all 6821 original data series that havebeen recorded for at least 10 years. These provide a total of 2670 complete, homogeneous series for the period 1946–2005,and are the most complete and extensive monthly precipitation dataset available in Spain at present. MOPREDAS hasbeen created with the aim of analysing the behaviour of precipitation in the conterminous provinces of Spain, and to helpvalidate the downscaling of climate models on a detailed spatial level. To this end, the station data were also interpolatedon a regular grid, at 1/10 of degree of resolution, over the whole Spain.

Trend analysis confirms great spatial and temporal variability in the behaviour of precipitation across Spain. The monthlyprecipitation trends vary from month to month, from coherent spatial trend patterns in March, June (both with a generaland significant negative trend) and October (general positive trends), to highly regionalized trend patterns in July (withpositive trends in north-west and mainly negative in the remainder), February and April (positive and negative trends inthe south-east, respectively). These results suggest that both global and local factors affect the spatial distribution of trendsin the Iberian Peninsula. Mountain ranges seem to be the most significant geographical factor in determining the spatialdistribution of monthly trends on a detailed, sub-regional spatial scale. These results show that it is possible to accuratelydelineate the areas affected by different precipitation trends if a dense spatial database is available. Copyright 2010Royal Meteorological Society

KEY WORDS precipitation database; rainfall trends; Spain; Western Mediterranean

Received 28 May 2009; Revised 14 January 2010; Accepted 14 January 2010

1. Introduction

Precipitation is one of the most important climate ele-ments directly affecting human society (water availabil-ity, human consumption, political and social stability),economic activities (location of dams, water planning,irrigation, demand for industry) and natural systems(water stress, fires, erosion) (Randall et al., 2007).

Many authors have indicated that precipitation isthe most variable (in space and time) climate element(New et al., 2001; Mitchell and Jones, 2005; Karagian-nidis et al., 2008), and spatial variability is inherentlyon a smaller scale for precipitation than for tempera-ture (Giorgi, 2002). Thus, precipitation changes can bedetected only if a spatially dense network of observationsis used (Vinnikov et al., 1990; Groisman and Legates,1994; Hulme et al., 1995; New et al., 2001; Auer et al.,2005; Brunetti et al., 2006; Valero et al., 2009). Thisfact is particularly true in convective pluvial regimes,

* Correspondence to: Jose Carlos Gonzalez-Hidalgo, Department ofGeography, University of Saragossa, 50009 Saragossa, Spain.E-mail: [email protected]

where the amount of precipitation is concentrated in timeand space, and station data are only representative of avery small area (Cosgrove and Garstang, 1995; Mosmannet al., 2004; del Rıo et al., 2005).

Due to this, the last AR4 report (IPCC, 2007) renewedinterest in the study of precipitation, following the pathindicated by previous TAR (IPCC, 2001). The AR4particularly suggests focussing on detailed sub-regionalstudies, with a preference for those areas where water isa scarce resource with heavy demands placed on it.

The Mediterranean basin is a climate transition areabetween tropical and middle latitude climates and, there-fore, displays wide local climate variability and ratherlarge gradients, especially in the south–north direc-tion. It is a small-scale coupled sea-atmosphere sys-tem with quite a short time response to climatic forc-ing (Xoplaki et al., 2004). The climate of the Mediter-ranean is also influenced by weather conditions overthe Atlantic and sometimes by polar outbreaks; it ismarked by a strong annual precipitation cycle oscillatingbetween dry and wet seasons (Dunkeloh and Jacobeit,2003), and precipitation variability is one of the main

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716 J. C. GONZALEZ-HIDALGO et al.

climate characteristics (Xoplaki et al., 2004; Lionelloet al., 2006).

There is general agreement on the effects of globalwarming on hydrological cycles (Huntington, 2006), butstudies on precipitation trends do not highlight a clear,generalized pattern on either a global or regional scale(Lawrimore et al., 2001; New et al., 2001; Giorgi, 2002;Klein-Tank et al., 2002).

The same is true for the Mediterranean basin, whereno significant generalized pattern has been detected forprecipitation (Xoplaki et al., 2004; Norrant and Dougue-droit, 2005) and where the hydrological cycle has par-ticular, specific characteristics (Mariotta et al., 2002).Nevertheless, a mostly non-significant general annualdecrease in precipitation from the 1950s has beenreported (Schonwise and Rapp, 1997). The sub-regionalanalyses also confirm a non-significant decrease in theeastern (Giuffrida and Conte, 1989; Maheras and Kolyva-Machera, 1990; Amanatidis et al., 1993; Kutiel et al.,1996; Feidas et al., 2007; Tayanc et al., 2009), central(Piervitali et al., 1998; Delitala et al., 2000; Brunettiet al., 2006) and western (Esteban-Parra et al., 1998; Ser-rano et al., 1999b; Gonzalez Rouco et al., 2001) parts ofthe basin over the last half of the century. However, thespatial results, in many cases, deal with different peri-ods and the spatial density of stations is usually low;so results for precipitation variability and change are notdirectly comparable.

Furthermore, the most recent global revision overthe whole Mediterranean basin has not detected anyclear spatial pattern in precipitation trends during theperiod 1951–2000 (Norrant and Douguedroit, 2005), anda generalized increase in precipitation during the last10–15 years of the 20th century has been reported ina context of secular non-significant decrease (Xoplakiet al., 2004). Seasonal analyses also confirm the highsub-regional spatial variability (Quadrelli et al., 2001;Dunkeloh and Jacobeit, 2003; Jacobeit, 2000).

Within the Mediterranean region, the Iberian Peninsula(hereafter IP) is a special case that has often attractedthe attention of researchers, due to its extreme lati-tude–longitude position in the south-west of Europe(Romero et al., 1998). Thus, it has been considered as anarea isolated from the other parts of Mediterranean basin.The IP is located between two contrasting water masses,its topography is highly complex with mountain rangesoriented from west to east, and from north to south, and,finally, it has high mean altitude. In Valero et al. (2009),Morata et al. (2006), Martin-Vide (2004), Romero et al.(1998), among other papers, the most recent, exhaustivereview of how these factors affect global precipitation inthe IP can be found. The several mountain ranges causethe IP to exhibit a high variety of rainfall regimes (deLuis et al., 2008a), and is why the recent National Cli-mate Report has indicated that ‘precipitation is the mostimportant climate element in Spain’ (de Castro et al.,2005).

In the IP, mostly Spain, studies of precipitation duringthe 20th century reveal no clear pattern on annual, sea-sonal and monthly scales, but it is difficult to comparethe results as different length of periods, methods andspatial density are used. Millan (1996) analysed 53 sec-ular series up to 1990 and found a slight increase in thenorth, a negative trend in the Mediterranean areas (mostlynon-significant) and no clear signal in central areas. Sim-ilar results were also reported by Esteban-Parra et al.(1998) from 65 stations for 1880–1992: they detectedthree different sectors with different long-term precipita-tion trends (mostly non-significant): north (mainly posi-tive on an annual scale), central-south and Mediterranean(negative). Serrano et al. (1999b) analysed 40 stationsbetween 1921 and 1995 and did not find any spatialpattern in annual and monthly trends over the whole ofcontinental Spain, except for March (negative). GonzalezRouco et al. (2001) analysed the effect of quality controlon trend analysis and they did not find any significantannual trends in the IP during the period 1899–1989,while the most prominent seasonal result was a positivetrend in winter and negative in autumn; however, trendswere substantially modified after discarding anomalousdata, and the area affected by the significant trend in win-ter was reduced. The same results (no significant trend)on an annual and seasonal scale were observed in sevenstations along Mediterranean coast, with data gatheredfor over 100 years, at an altitude below 100 m a.s.l., byLana and Burgueno (2000), who analysed the discrepancybetween their results and those from Schonwise and Rapp(1997) and concluded that a dense dataset was importantin detecting a clear sign of the spatial distribution of lin-ear trends. Sotillo et al. (2006) and Valero et al. (2009),using the massive information from the Iberian PeninsulaDataset (IPD, a 25-km resolution gridded dataset obtainedfrom more than 4000 stations), analysed the principalcomponents of seasonal regionalization and detected nosignificant trends in either winter or autumn between1961 and 2003. No general, clear pattern was detectedby Norrant and Douguedroit (2005) in the IP in theirglobal analyses of the Mediterranean basin, except for adecrease in March precipitation in central-southwest IP,and on the Mediterranean coast of the IP during October.Finally, using an area-average for the entire IP, Nietoet al. (2006) suggested a slight decrease in precipitationduring 1949–2003 in the winter months.

The sub-regional analysis did not detect a significantannual trend over the long-term in the Ebro basin (Abau-rrea et al., 2002), south-east (Chazarra and Almarza,2002), south inland plateau (Galan et al., 1999), Catalo-nia (Saladie et al., 2004), Mediterranean coast and Ebrobasin (de Luis et al., 2009). Seasonal analysis shows thesame results as global Mediterranean studies: no sig-nificant trend. Goodess and Jones (2002) analysed 18stations between 1958 and 1997 and detected a gen-eral tendency towards decreasing mean seasonal rain-fall across the seasons, but few of these trends arestatistically significant, with the main exception beingalong the south-eastern Mediterranean area. Rodrigo and

Copyright 2010 Royal Meteorological Society Int. J. Climatol. 31: 715–731 (2011)

MONTHLY PRECIPITATION TRENDS IN SPAIN (DECEMBER 1945–NOVEMBER 2005) 717

Trigo (2007) analysed 18 selected stations from 1951 to2002, and no significant trend was detected, but mostof the winter, spring and summer trends were negative,while a positive trend was observed in autumn. Sum-mer trends were studied by Mosmann et al. (2004) in333 stations (period 1961–1990) and they found signif-icant positive values during July and August in inlandsouthern and central areas, whereas there were mostlysignificant negative values during June and September.No significant seasonal trends were detected by Cebal-los et al. (2004) in the Duero basin in 15 stations during1967–2000, even though winter and spring presented ageneral negative tendency, and autumn a positive trend,while del Rio et al. (2005) in the same area found a non-significant negative trend in winter, spring and autumnduring 1961–1997 using a denser database (171 sta-tions): the only significant trend detected was a decreasein March, and a slight positive summer trend. In thelower Ebro basin, Saladie et al. (2002) found a non-significant seasonal trend in precipitation, although wintergave a positive signal between 1949 and 1998. Marchwas the only generalized monthly pattern described forthe Mediterranean coast and the Ebro basin in Spain dur-ing 1951–2000 in the monthly precipitation data baseof Mediterranean Spain (MOPREDAMES) database byGonzalez-Hidalgo et al. (2009), although a small, homo-geneous area on the east coast was found with a positivetrend in winter precipitation during 1951–2000, whereasRomero et al. (1998) detected a decrease in winter precip-itation during 1963–1994 from a database of 410 stations.

To give an overview, no generalized pattern of monthlyprecipitation trends has been found except the negativetrend in March, and the above results can be summarizedin agreement with the Spanish National Report on climatechange, which states, ‘The trend toward a decrease in thetotal amount of precipitation [. . .] is not easy to verify inSpain [. . .] (the analysis) should be done by a massiveuse of climate series not yet available. Thus, at present,there is no detailed, high resolution analysis covering thecountry’ (de Castro et al., 2005, p. 20).

In this paper, we present the new monthly precipitationdatabase for conterminous Spain (MOPREDAS) and amonthly trend analysis for the period 1946–2005. Wecarried out a monthly analysis because seasonal trendanalysis often masks very different monthly tendenciesin IP precipitation (Serrano et al., 1999a and b; Goodessand Jones, 2002). The new database updates the previousversion for the Mediterranean fringe MOPREDAMES

(Gonzalez-Hidalgo et al., 2009) and extends it to thewhole of conterminous Spain.

2. Study area and data source

2.1. Study area

The IP spreads over 500 000 km2 on the south-west edgeof Europe, at 36° –44° latitude north and between 10 °Wto 3 °E. This location at the transition of the subtropicalfringe makes it particularly interesting from a climatic

point of view, mainly because it is surrounded by twocompletely different water masses: the Atlantic Ocean inthe North and West and the Mediterranean Sea in theSouth and East, and also because of mountain rangesdistributed from west to east (starting from the north,these are the Cantabrian Mountains, Pyrenees, SistemaCentral, Sistema Betico), hemmed in on the east by theSistema Iberico running roughly from north to south(Figure 1). As a result there are latitudinal variationsfrom the north-west to the south-east in the total annualamounts of precipitation (de Castro et al., 2005).

Seasonal rainfall regimes also have specific character-istics in the IP. Summer is usually dry whereas there isusually torrential rain in autumn. Northern and southernareas are mainly dependent on winter precipitation, whilebimodal Autumn–Spring (Spring–Autumn) patterns pre-dominate in central and eastern areas. Finally, monthly,seasonal and annual values depend on a few daily events(Gonzalez-Hidalgo et al., 2004) making the interannualvariability quite high.

In Spain, precipitation has been recently consideredas the most important element not only from the pointof view of climate, but also as a natural resource, due toscarcity and high spatial and temporal variability (de Cas-tro et al., 2005). This variability is the most evident char-acteristic of Mediterranean precipitation, mostly relatedto local orography (Perez-Cueva, 1995; de Luis, 2000;Mosmann et al., 2004), and it is one of the reasons whyno general whole-IP patterns in rainfall trend analysishave been observed. There is a great deal of informationon this spatial variability on the Mediterranean fringe ofthe IP (Llasat and Puigcerver, 1994; Romero et al., 1998;Lana and Burgueno, 2000; Rodrigo et al., 2000; Sotilloet al., 2003; Martın et al., 2004; Gonzalez-Hidalgo et al.,2009), but information is more scarce, on a detailed spa-tial scale for inland areas. This is one of the reasonswhy we decided to set up a new high-density dataset ofmonthly precipitation to study precipitation trends at thehighest spatial resolution possible.

2.2. Sources for precipitation analysis from theSpanish Meteorological Agency Archives

The archives of Spanish Meteorological Agency(AEMET, formerly INM) store information from morethan 10 000 stations including the Balearic Islands,Canary Islands, the former provinces of Ifni (until 1968,at present Morocco), Guinea (until 1968, at present Equa-torial Guinea), Sahara (until 1975) and information fromnorthern Morocco (until the 1950s). The informationis organized by hydrological divisions called ‘Cuencas’(catchments), from 0 to 9. Global information about theirsize and general characteristics can be seen in Table I andin Figure 1, where we present the spatial distribution ofcatchments, together with the main topographic featuresof IP.

Until now, complete information from conterminousSpain has scarcely been used. However, Cano and Gutier-rez (2004) applied a daily quality control and producedan interpolated daily dataset. Luna and Almarza (2004)



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EbroValley

Sistema Bético

Figure 1. Catchment division of National Meteorological Agency of Spain (a). Norte division (Catchment 1) subdivided into four sub-catchmentsare not analysed separately in this paper. Principal relief features quoted in text (b).

Table I. Characteristics of the 10 hydrological divisions of conterminous Spain.

Catchment Code Area (km2) Stations (total) Stations reconstructed Density (stations/km2) Population

Pirineos Orientales 0 16 493 385 126 1/128.9 7 037 206Nortea 1 53 804 916 320 1/168.1 6 293 922Duero 2 78 972 994 491 1/160.8 2 557 330Tagus 3 54 769 493 195 1/280.9 7 592 126Guadiana 4 59 873 685 279 1/214.6 1 715 504Guadalquivir 5 63 085 962 335 1/188.3 5 463 409Andalucıa Oriental 6 18 391 326 134 1/137.2 2 230 896Segura 7 18 254 237 125 1/146.0 1 426 109Jucar 8 42 904 658 234 1/183.4 5 642 368Ebro 9 86 098 1165 429 1/200.7 3 001 303

a Norte division subdivided into four sub-catchments is not analysed separately in this paper. Stations (total) column refers to the number ofstations from AEMET contained in MOPREDAS (i.e. >10 years), and Stations reconstructed the number of stations analysed for 1946–2005.Density is referred to reconstructed stations. Populations are calculated by provinces, and data for hydrological divisions are only approximatebecause of boundaries of hydrological divisions not coinciding with administrative census areas.

performed a gridded 25 × 25 km resolution dataset forthe period 1961–2003. This grid was called the IberianMonthly Precipitation Data set (IPD) in the HYPOCASTproject, and was used for several seasonal precipitationanalyses (Morata et al., 2006; Sotillo et al., 2006; Valeroet al., 2009). Finally, Hernandez et al. (1999) analysedthe original mass of information in their study of similar-ities (4374 obs., period 1960–1991, density 1/112 km2),and recently Ninyerola et al. (2007) have produced anew climatology for 1950–1999 with 1999 stations (den-sity 1/246 km2). In addition to the above, GonzalezRouco et al. (2001) presented the South-western Euro-pean Dataset (SED, with about 100 stations for IP)

for precipitation trend analysis. A higher spatial den-sity of information was achieved by Martın et al. (2004)and Mosmann et al. (2004) by studying the relationshipbetween spring precipitation and North Atlantic Oscilla-tion (NAO), and summer trends with 333 stations, andby Fernandez and Martın-Vide (2004), in their analysisof summer circulation and rainfall based on 203 stationseries. Many other research papers covering different top-ics dealing with precipitation series for the whole IPor Spain have been done by using less than 100 sta-tions, such as trend analyses (Millan, 1996; Esteban-Parra et al., 1998; Serrano et al., 1999a), seasonalanalysis (Munoz-Diaz and Rodrigo, 2006; Nieto and



Rodrıguez-Puebla, 2006; Paredes et al., 2006), atmo-spheric circulation and tele-connection (Rodo et al.,1997; Rodriguez-Puebla et al., 1998, 2001; Gomez et al.,2001; Goodess and Jones, 2002), droughts (Martın-Videand Gomez, 1999; Cuadrat and Vicente, 2004; Vicente,2006), regionalization (Serrano et al., 1999b; Morataet al., 2003; Munoz-Diaz and Rodrigo, 2004), daily pre-cipitation (Martın-Vide, 2004; Rodrigo and Trigo, 2007),to give but a few examples. In many cases, they haveanalysed what are called first-order stations. These seriesare usually located in the main cities (capitals) and air-ports, and constitute a high-quality database (mostly ona daily scale), but there is a very low spatial resolution(less than 100 stations), and few stations are located over1000 m a.s.l.

The above research produces a general picture ofIberian precipitation, but spatial details, as demandedby AR4, cannot be highlighted because of low spatialresolution. Moreover, stations used are very scarce ataltitudes higher than 500 m a.s.l. and any informationfrom above 1000 m a.s.l. is practically non-existent. Asan example, among 65 first-order meteorological stationsin conterminous Spain (Aupi, 2005) only 6 are locatedabove 1000 m a.s.l., and 14 over 750 m a.s.l., while 43%of Spanish conterminous land has an elevation greaterthan 750 m, and 19% is over 1000 m.

In addition to these works, there are many regionaland sub-regional analyses of precipitation in Spain basedon higher resolution datasets, many of them developedin administrative regions that do not necessarily overlapwith climatic regions.

The area that has been studied most is the easternMediterranean coast from Catalonia to Cadiz, whereRomero et al. (1998, 1999) constructed a daily precip-itation database (PRECLIME) for 1964–1993 with 321stations on the mainland (density 1/321 km2) and 410on the Balearic Islands. In the same area, Gonzalez-Hidalgo et al. (2009) and de Luis et al. (2009) producedthe MOPREDAMES, with 1113 complete series recon-structed for the 1951–2000 period (density 1/162 km2)from an original dataset of 2658 series. This database alsocovers the entire area draining into the MediterraneanSea, and so it extends quite far inland, mainly along theEbro catchment. Moreover, MOPREDAMES also containsinformation from higher elevations, at least up to 1500a.s.l.

Other sub-regional databases covering only part of theMediterranean coastland have been made for differentareas. In the north-west (Catalonia), Saladie et al. (2004)created a daily precipitation dataset (NESAP) for thesecond part of the 20th century with 121 stations.Planchon and Mizrahi (1999) and Fernandez-Mills (1995)have analysed about 150–200 stations in the north-eastof IP (densities 1/300–400 km2). Other research withhigh spatial density (between 50 and 100 stations) hasbeen done by Periago et al. (1991), Gibergans and Llasat(1999), Burgueno et al. (2005), Lana et al. (1995, 2001,2004) and Esteban et al. (2002). In the north-easterninland (Ebro Valley), de Luis et al. (2008c) analysed

the spatial trends using the MOPREDAEBRO (>400stations) and Vicente et al. (2004) analysed the technicalproblems of interpolation using 99 rainfall observatories.To the south of the Mediterranean coastland (Regionof Valencia), Perez-Cueva (1995) included 200 stations(for the 1941–1990 period) in the Atlas climatico de laComunidad Valenciana (density 1/116 km2). In the samearea, Penarrocha et al. (2002) analysed 250 stations on adaily scale.

Besides the Mediterranean area, in Northern Spain,Marquinez et al. (2003) analysed 117 stations to calibratea climate model in the northern Cantabrian Mountainarea (density 1/91 km2), and Pejenaute (2002) used 38stations in his research on Navarre (density 1/273 km2).In Castilla-Leon (Northern Plateau), Andres et al. (2000)and del Rio et al. (2005) analysed 119 and 171 stations,respectively (with a spatial density of 1/792 km2 and1/551 km2, respectively). Labajo and Piorno (2001) stud-ied 44 stations at a lower density during 1931–1996. Inthe Sistema Central mountain chain, Gavilan et al. (1998)analysed potential plant cover distribution with 255 sta-tions (density 1/71 km2); in the Southern Plateau, Egidoet al. (1991) studied seasonal rainfall with 161 stationsduring 1970–1985 (density 1/435 km2); and Cano et al.(2001) made a database from the Tagus River catchmentwith 508 stations during 1950–1998 (density 1/137 km2).Finally, in the southern area (Andalusia and the surround-ing area), Ramos-Calzado et al. (2008) have recentlypresented a reconstruction of 932 rainfall stations during1961–2000 (with a density of 1/93 km2).

From this list of works dealing with precipitation data(even though some of them are not aimed at a climatolog-ical analysis of precipitation), a high disparity betweenthe most exploited area of Mediterranean coastland andthe other regions is quite clear. In this context, the abovequote from de Castro et al. (2005) on the absence ofdetailed studies on a country-wide scale aimed at an accu-rate understanding, with high resolution, of precipitationtrends in Spain, is very relevant.

On the other hand, these regional and sub-regionalstudies demonstrate that the AEMET archive offers anopportunity to create a national database of precipitationon a monthly scale with high spatial density over longperiods, and these results should be highly valuable formany purposes, such as studies on the regional impact ofglobal climate change, model validation and calibration,water planning, etc. Finally, we noticed the recent paperby Durao et al. (2009) analysing 105 stations in southernPortugal (1960–1999), which offers the possibility forfuture analysis of the entire IP.

2.3. The history of the Spanish precipitation networkin the 20th century

The evolution of the Spanish precipitation network duringthe 20th century is similar to that described by Newet al. (2001) on a global scale, by Auer et al. (2005)in the Alps and by Brunetti et al. (2006) in Italy: therehas been continuous growth during the 20th century withsome breaks due to social instability, and over the last



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Figure 2. Evolution of Spanish rain gauges (1850–2005). Number of stations and minimum distance average between stations (MDA) in km.This figure is available in colour online at wileyonlinelibrary.com/journal/joc

20 years, a substantial decrease has been observed. Ifwe leave aside the 19th century, which provides scantinformation, from 1900 onwards the main characteristicsin the evolution of the Spanish precipitation network canbe summarized as follows (Figure 2):

• From 1900 until the Civil War (1936–1939, hereinafterCW) there was a steady increase in the number ofstations. In 1900, there were 29 stations operating inthe territory, increasing to 111 in 1911, doubling in1914, and finally, 1 year before the CW (1935), thetotal amount had risen to 944 stations. The increasealso produced a decrease in the mean minimumdistance among stations from 91.7 km in 1900 to11.8 km in 1935.

• The CW caused an abrupt collapse of the nationalstation network, and from 1936 to 1939 the number ofstations decreased from 944 to 871, 596, 576 and 544(in 1936, 1937, 1938 and 1939, respectively), affectingthe mean distance value, which rose to 14.2 km at theend of the CW.

• The third step in the history of the national meteoro-logical network was an immediate recovery after theCW. Only one year after it ended (1940), the num-ber of stations had increased to 670, and to 1120 in1945, up to its maximum of 5241 stations in 1975. Atthe same time, the mean minimum distance betweenstations decreased from 13.8 km in 1940 to 5.9 km in1975.

• After 1975 the number of stations decreased uninter-ruptedly until the present. From 1975 to 2005, thenumber of stations fell, reaching the minimum in 2005(3401), and increasing the mean minimum inter-stationdistance from 5.9 to 7.3 km. In relative terms, in thelast 30 years, 35% of stations have been lost (a totalamount of 1840 stations), slightly fewer than the rela-tive decrease that occurred during the CW.

If there is a continuing fall in the number of stations,we do not know how this will affect future updating pro-cesses, because the closed stations are mainly located in

depopulated rural and mountain areas. Thus, it is possi-ble that, in the near future, information on precipitationin such areas will be scarce.

The amount of stations discussed above refers tothe number of stations simultaneously active over theterritory year on year. It must be emphasized that manystations were abandoned, while many others were set upthroughout the history of the Spanish network, producinga large amount of sites (approximately 10 000) with atleast one month of observation.

3. Quality control, homogenization and theconstruction of the MOPREDAS dataset

It is very common im Spain that station of climate obser-vations changes in time from place to place around thesame environment. The meteorological agency assigneda new station code to each new location (station). Thus,archives preserve a large amount of records, scattered intime, from different stations very close to one another.This is the information that we have analysed and whichwe shall hold to be the original series. Furthermore, it isvery common that, after one station stopped taking mea-surements, the new station carrying on with records doesnot usually overlap the previous one; therefore, whendirectly joining the series, there is no doubt that artifi-cial inhomogeneities (significant or not) were entered intothe series. To overcome this problem, we adopted a newapproach to produce a set of completely homogeneousseries as long (in time) and dense (spatially) as possi-ble. The approach is based on taking the best availablereference series at each step, and on iterative processes.A similar philosophy is presented in Mitchell and Jones(2005). Specific software Anclim and ProclimDB wereused in the processes (Stepanek, 2008a, b).

3.1. Quality control

The quality control we applied consists of two steps: sus-picious data identification and in-homogeneity detection.To this end, a set of reference series was calculated for



each original station. Global procedures and details canbe found in Gonzalez-Hidalgo et al. (2009), and we willonly briefly present them here. To produce a referenceseries for one candidate series (original series), we calcu-lated a monthly correlation matrix between the candidateseries and all the others, and selected the neighbour-ing series with the highest correlation within a criticalthreshold distance of 50 km. The minimum overlappingperiod required for the correlation computation was setat 10 years. Finally, any station involved in the calcula-tion of the reference series had to satisfy the condition ofhaving only positive monthly correlation coefficients anda mean monthly correlation greater then 0.5 (July andAugust were excluded from these evaluations becauseof the effect of 0 values, very common in south andinland of Spain). A weighted average series (using thesquared inverse of distances in km as weights) was thencalculated using the selected neighbouring stations afterrescaling their means to a common overlapping periodwith the candidate original series. Thus, this method com-bines the inverse distance, the normal ratio and the singlebest estimator reviewed by Ramos-Calzado et al. (2008).

Suspicious data detection has been highlighted as anecessary preliminary and fundamental step in qualitycontrol of climate data, particularly in the case of false 0(Peterson et al., 1998; Auer et al., 2005; Begert et al.,2005) because they can affect not only the detectionof in-homogeneity but also the trend analysis (GonzalezRouco et al., 2001). We looked for suspicious data bycomparing each original series with its own referenceseries, using both ratio and inter-quartile methods, andwe applied direct and inverse ratios to avoid the zeroeffect (see details in Gonzalez-Hidalgo et al., 2009).The procedure was iteratively applied until no apparentsuspicious data were detected (7 runs), and after eachrun, a new reference series was calculated to prevent thediscarded data from biasing the new reference series. Thefinal reference series was considered the best availabledataset with no suspicious data, and was then comparedwith the original dataset (including the previous discardeddata) for final detection and elimination of any previouslyundetected suspicious data.

A new correlation matrix was recalculated without thesuspicious data from the final dataset, and a new finalset of reference series was calculated to check inhomo-geneities. The detection of inhomogeneities used one ofthe most common and best-known tests, the standardnormal homogeneity test (SNHT) (Alexanderson, 1986).Once the series found to be non-homogeneous had beencorrected, a new set of reference series was calculatedand SNHT re-run. Correction of non-homogeneity wasdone by applying statistically significant detections, usingmoving windows and accumulation threshold of detec-tion (Gonzalez-Hidalgo et al., 2009), as no metadata areavailable at present.

To find out the effect of suspicious data on homo-geneity analysis, we applied the SNHT before and afterfinal discarding. The total amount of monthly inhomo-geneities detected before discarding the suspicious data

was 4643, affecting 2652 (38.9%) series. After discardinga total amount of 12 399 monthly original values consid-ered as suspicious (<1% of total original data), the totalamount of monthly inhomogeneities decreased to 3291and the series affected by statistical inhomogeneities werereduced to 2117 (31.4%).

The final dataset consists of 6821 original homoge-neous series without suspicious data from the differ-ent stations of AEMET. These data sets are not usefulfor analysis because different lengths, gaps and miss-ing data still affect the series. Figure 3 shows the fre-quency distribution of total months in the original seriesbefore reconstruction. The modal value is about 20 years(240 months).

3.2. Missing data reconstruction

The reconstruction processes had to contend with twomain problems: the spatial variability of precipitation, andthe non-overlapping period between neighbouring series.

In areas of high spatial variability of precipitation, thespatial density of stations should be as high as possiblenot only to ensure quality control, but also any attempt toreconstruct a long series of precipitation from individualstations. This spatial variability in the surrounding areasof Mediterranean basin has been evaluated by analysingthe mean de-correlation distance among pairs of stationsby Auer et al. (2005) and by Brunetti et al. (2006). In theAlpine region and in Northern Italy, they found a criticalthreshold of about 100 km at which the common variancedecreased to below 50%, while in Central and SouthernItaly this value decreased to below 50 km, which is moresimilar to the range between 96 and 35 km found by deLuis et al. (2008b) in the Mediterranean fringe of the IP.These results for the IP are more restrictive, and denote ahigher spatial variability of monthly precipitation in theIP than in relatively close areas around Mediterraneanbasin.

Taking into account this spatial variability and theabsence of overlapping periods between the closeststations, the approach for reconstructing complete seriesas far as possible can be summarized as follows:

1. From the final homogeneized data set we calculatednew reference series following the previous procedure.Please note that reference series calculations aremainly affected by the nearest neighbours, the seriesinvolved in its construction being weighted by theinverse of the squared distance from the series to becompleted.

2. With these reference series we produced a ‘pseudo-reconstruction’ of each original series. The purposeof this procedure was to create a single pseudo-original series covering as much time as possible toproduce overlapping periods between the series to becompleted and the nearest neighbours.

3. These pseudo-reconstructed series were then usedto evaluate the correlation between the series to becompleted and the neighbours, and to rescale these tothe correct mean monthly precipitation amount for the



0

5

10

15

20

25

120 180 240 300 360 420 480 540 600 660 720 780 840 900 960

Months

Per

cent

age

of o

rigi

nal s

erie

s

Figure 3. Frequency distribution of original stations by number of monthly original values. This figure is available in colour online atwileyonlinelibrary.com/journal/joc

series to be completed, in order to calculate the finalreference series using all the neighbouring stationswithin a maximum distance of 25 km from the seriesto be reconstructed.

4. Finally, a new set of reference series was calculated.To avoid the potential artificial smoothing inducedin final reconstruction process, these final referencesseries were standardized throughout their commonperiod to have the same mean and standard deviationas target candidate station.

The result of such procedure is a set of 2670 completeseries from 1946 to 2005 (60 years), in which the percent-age contribution of the original data is 69.2%, with 21.7%of data being reconstructed using information from neigh-bours less than 10 km apart; finally, 9.1% of data werereconstructed using information from neighbours between10 and 25 km away from the candidate series. Figure 4shows the spatial distribution of the reconstructed 2670series and the original 6821 series for 1946–2005. Fur-ther research is in progress to produce MOPREDASfor 1931–2005 (a 75-year period, about 900 series) and1916–2005 (90-year period, about 400 series).

For this study (January 1946–December 2005), thecurrent version of MOPREDAS contains 2670 completestations and produces a very high-density cover forSpanish conterminous land, with minimum spatial gapsmainly located in mountain areas or low-population,inland areas, such as Monegros and Bardenas in the Ebrobasin (Catchment 9), and La Mancha on the SouthernPlateau (Catchment 4).

MOPREDAS also includes information on altitude upto at least 1500 m a.s.l. This represents a significantimprovement with respect to previous studies, in whichstations were located mainly below 1000 m. In Table II,we show the altitude distribution (in percentages) of

(a)

(b)

Figure 4. Spatial distribution of MOPREDAS original stations (a).Selected stations for trend analysis 1946–2005 (b). This figure is

available in colour online at wileyonlinelibrary.com/journal/joc

stations in conterminous Spain at different intervals ofelevation.

The overall density of MOPREDAS for the selectedstations is 1 station per 185 km2 with minor variationsamong the different elevation intervals, except for areasabove 1500 m a.s.l. The highest densities are achieved atthe lowest interval and between 500 and 750 m becauseof the altitude distribution in Spain. As a consequence,MOPREDAS is the densest database for monthly precipi-tation ever produced in the IP for the 1946–2005 period,and only some regional or sub-regional databases havea higher density (see previous comments). Please notethe difficulties in comparing MOPREDA with previousresearch, because of the different periods analysed.

3.3. Gridded dataset

To facilitate the spatial analysis of precipitation and tomake our dataset more suitable for comparing model



Table II. Altitude distribution of MOPREDAS database and selected stations (1946–2005).

Altitude (m) <250 250–500 500–750 750–1000 1000–1500 >1500 Total

% of land 13.0 20.7 23.6 23.6 15.6 3.5 100MOPREDAS 1 625 1 314 1 520 1 430 869 63 6 821Selected stations 562 524 653 561 353 17 2 670Density MOPREDAS (km−2) 1/40 1/78 1/77 1/82 1/89 1/275 1/73Density selected stations (km−2) 1/115 1/196 1/179 1/208 1/219 1/1019 1/185

outputs (for model validation and downscaling purposes),we decided to interpolate our dataset onto a regular grid.

The grid resolution was chosen equal to the mean dis-tance between the selected 2670 stations, which is about10 km. A 1/10 of degree resolution gridded version ofthe monthly dataset was then constructed. Before inter-polating the station data onto the grid cells, each serieswas converted into multiplicative anomalies, normaliz-ing each monthly value by its average estimated overthe 1946–2005 period. This choice is justified by thefact that absolute precipitation values present importantspatial gradients, being strongly dependent on the orogra-phy (Roe, 2005), and the available station density, eventhough very good, is not sufficiently high to make thestations involved in each grid point evaluation truly repre-sentative of the amount of grid cell precipitation in termsof absolute values. On the contrary, deviation from themean has a higher spatial coherence.

The grid was built with an improved version ofthe interpolation technique described by Brunetti et al.(2006). This improvement consists of the introduction ofan angular weight, in particular, a radial weight with aGaussian shape. The radial weight of the ith station forthe evaluation of the (x, y) grid cell is as follows:

wri (x, y) = e

−d2i (x, y)

c (1)

with

c = − d2

ln(0.5)(2)

where i runs along the stations and di(x, y) is the distancebetween the station i and the point (x, y) for which thelocal record is being estimated. With this choice of theparameter c, we obtain weights of 0.5 for station distancesequal to d from the point (x, y), where d is defined asthe mean value of the distances among each station ofthe data set and its closest neighbour (i.e. 10 km). Thecomputation is performed by taking all stations within adistance of 2d . In this way, the sets of stations involvedin each grid cell computation are completely independentevery four grid cells. This choice, together with thehigh resolution of the grid, prevents undesired exchangeof information among different climatic regions and, inparticular, between either side of the most importantmountain chains that play a key role in separatingdifferent precipitation regimes.

Besides the radial weight, an angular weight whichaccounts for the geographical separation among the siteswith available station-data has also been considered, andtakes the following form:

wangi (x, y) = 1 +

n∑

i=1

wri (x, y)[1 − cos θ(x,y)(i, l)]

n∑

i=1

wri (x, y)

(3)

where θ(x,y)(i, l) is the angular separation of stations i

and l with the vertex of the angle defined at grid point(x, y), and wr

i (x, y) is the radial weight as defined inEquation (1). The introduction of this angular weightprevents undesired overweighting of the areas with thehighest station density in the evaluation of the grid cell.

The final weight is the product of the radial weight andthe angular term. The mean number of stations involvedin the estimation of each grid point is approximately 6.The final gridded dataset consists of 5334 cells coveringthe whole territory of Spain.

3.4. Monthly trends analyses

We applied the rank-based Mann–Kendall method (MK)to analyse monthly trends. The test is a nonparametric,commonly used method to assess the significance ofmonotonic trends in hydro-meteorological time series.Also the MK test has the advantage of not assumingany distribution form for the data and has similar powerto its parametric competitors (Serrano et al., 1999b).Therefore, it is highly recommended for general use bythe World Meteorological Organization.

In the MK test, for each element xi (i = 1, . . . , n) ofthe series, the number ni of lower elements xj (xj < xi)preceding it (j < i) is calculated (see original text inMann, 1945; also Sneyers, 1990) and the test statistic t

is given by t = �ini .In the absence of any trend (null hypothesis), t

is asymptotically normal, regardless of the distributionfunction of the data and Z(t) = (t− < t >)/

√var(t) has

standard normal distribution, with < t > and var(t) givenby Equations (4) and (5):

< t > = n(n − 1)

4(4)

var(t) = n(n − 1)(2n + 5)

72(5)



Table III. Selected p values for trend analysis.

Qualifying p level Z value

Exceptionally likely <0.01 2.576Extremely likely <0.05 1.960Very likely <0.10 1.645Likely <0.33 1.034About as likely as not >0.33

The null hypothesis can therefore be rejected fordifferent values of |Z(t)|, with the probability α1 ofrejecting the null hypothesis when it is true derived froma standard normal distribution table:

α1 = P(|Z| > |Z(t)|) (6)

For the analyses of results, we follow a similarapproach to the last IPCC (AR4) applying differentselected p values (Table III).

4. Results

The trend analysis of the MOPREDAS highlighted forthe 1946–2005 period shows very different monthlytrend patterns (Figure 5(a)–(j)). In fact, regardless of thetrend level of significance, January, February, March,June, August and December show mainly negative trends;April, September, October and November show predom-inantly positive trends; and May and July present moreor less the same amount of grid cells with positive andnegative trends (Table IV). If we consider the very likelyp level (p < 0.10), the three interesting months with sig-nificant trends over most of the study are: March, Juneand October (Table IV).

MOPREDAS shows a global decrease of precipitationin March that affects 68.9% of Spain with likely proba-bility (p < 0.10) and 57% of land with extremely likelyprobability (p < 0.05). These negative trends in Marchare clearly evident not only along the central and south-west IP (with the strongest decrease up to −22% per

decade), but also affect extended areas of the Mediter-ranean fringe (Figure 5(c)). The significance in the east-ern part is low except for the northern Ebro basin andnorth-east (where the decrease is stronger than −10%per decade) but the pattern is very clear.

During June, the higher level of significance in trends(with rates ranging from −5 to −15% per decade)concerns the two central plains and the south-west(Figure 5(f)). Also, the extreme north-west (Galicia,with trend exceeding −15% per decade) and the cen-tral north-east (Catalonia) exhibit a very likely negativesignal (p < 0.10). The least affected areas are locatedin the extreme south-east and along a narrow fringeof Ebro basin. The significant decrease of precipita-tion in June that we observed affects 31.8% of Spain(p < 0.10).

In October, the situation is the opposite of March andJune, the trend being positive over almost the wholearea (Figure 5(j)) and affects with very likely probability(p < 0.10) 33.7% of land. The strongest positive signalis located in the northern plateau with trend even greaterthan +15% per decade in some areas, extreme north-west(Galicia) and central Pyrenees. In the Southern Plateauand Andalusia, the signal, although very clear, is lessintense (p level likely) and rarely exceeding +10% perdecade.

Apart from these three months characterized by quiteuniform spatial trend signals, we also detected numeroussub-regional coherent patterns well delineated by topo-graphic factors, and passing unnoticed until now due toinadequate data density. The most noticeable are linkedto the Cantabrian Mountain (to the north), Sistema Cen-tral and Sistema Iberico (inland), and Sistema Betico tothe south-east.

The Cantabrian Mountain effects are remarkable dur-ing January (Figure 5(a)), when a very likely negativetrend signal is detected along the narrow fringe of thenorthern coast (with the highest trend values ranging from−5% to −10% per decade). The negative signal affectsthe Basque Country, Cantabria, Asturias and Northern

Table IV. MOPREDAS monthly trend analysis, percentage of conterminous Spanish land (grid points) as different p levels,Mann–Kendall test.

Trend p Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Positive <0.01 0.0 0.3 0.0 0.1 0.0 0.0 0.2 0.0 0.0 4.4 0.0 0.0<0.05 0.0 1.6 0.0 1.7 0.0 0.0 2.2 0.4 0.2 21.4 1.2 0.0<0.10 0.0 2.4 0.0 5.0 0.0 0.1 6.1 1.6 1.3 33.7 3.3 0.0<0.33 0.4 4.8 0.1 21.6 2.7 0.5 18.8 6.7 13.8 61.6 17.3 0.3>0.30 14.8 17.6 4.4 69.7 44.1 4.3 40.6 34.1 67.9 84.9 68.0 14.4

Negative >0.30 85.2 82.4 95.6 30.3 55.9 95.7 59.4 65.9 32.1 15.1 32.0 85.6<0.33 29.0 32.1 84.0 7.3 4.7 66.5 23.4 22.0 3.5 4.7 2.5 11.9<0.10 6.1 1.9 68.9 3.6 0.6 31.8 6.2 5.1 0.3 1.4 0.0 0.8<0.05 1.9 0.4 57.0 2.8 0.2 16.2 2.2 1.4 0.1 0.1 0.0 0.2<0.01 0.0 0.1 23.6 0.9 0.0 2.0 0.3 0.0 0.0 0.0 0.0 0.0

Qualifying: Exceptionally likely p < 0.01; Extremely likely p < 0.05; Very likely p < 0.10; Likely p < 0.33; About as like as not p > 0.30.In bold p levels (p < 0.10) if they represent more than 5% of total land.



Janu

ary

Mar

ch

Feb

ruar

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pril

May

June

(a)

(c)

(e)

(b)

(d)

(f)

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July

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Galicia, while in the southern lee of Castilla-Leon (catch-ment 2) there is no strong signal, and positive trendspredominate in the basin.

A similar behaviour, with even more clear boundariesand a more marked north–south dipole, is evident dur-ing March (Figure 5(c)), August (Figure 5(h)), October(Figure 5(j)) and November (Figure 5(k)). Differencesbetween north–south areas are expressed by trend signal(positive/negative) or p level (<0.10 and >0.10).

The Sistema Betico separates the behaviour betweenthe extreme south-east and inland region, as in Febru-ary (Figure 5(b)) when the extreme south-east is char-acterized by a very likely positive trend (p < 0.10).Different signal of trend, or p level, between extremesouth-east and the surrounding areas can be observedagain in January (Figure 5(a)), March (Figure 5(c)),April (Figure 5(d)), June (Figure 5(f)) and October(Figure 5(j)).

Less strong effects can be attributed to the SistemaCentral and Sistema Iberico in central-inland areas.The former extends inland from west to east dividingthe IP into two, and the latter extends from northto south and separates the Northern Plateau from theEbro catchment and east coast (Figure 1(b)). In January(Figure 5(a)), even though the signal is very low (rarelythe trend is stronger than ±5% per decade), a positivetrend is detected in the Northern Plateau while theSouthern Plateau is characterized by a negative trend;the same is true for April (Figure 5(d)) where the SistemaCentral again separates the positive signal in the NorthernPlateau from the Southern Plateau (with some few areaspresenting trend even higher than 10% per decade), whichhas low negative signal.

The continuity from Cantabrian Mountain to the Sis-tema Iberico creates a transition from the north coastto the Mediterranean area along the Ebro Valley, wheretrends behave more close related to the northern fringeones than that in the inland (see Figure 5(a) for Januarytrend, and Figure 5(k) for November trend).

Finally, it is worth remarking that high mountain areassometimes behave similarly to the surrounding area, butwith a strong signal, as in December when Sierra Nevadato the south (Sistema Betico), central Pyrenees to thenorth (Pyrenean mountains), and Sierras de Moncayo andTeruel (Sistema Iberico) offer the strongest significanceof negative trend (p < 0.10).

5. Discussion

In many previous researches precipitation trends havebeen analysed showing the interest of such topic in theIP. The well-known negative trend for March has beenpreviously detected at sub-regional scale in different sub-periods between the 1920s and 2000s in Castilla-Leon(del Rıo et al., 2005), in western Andalusia (Aguilaret al., 2006), in the Mediterranean coast and Ebro basin(Gonzalez-Hidalgo et al., 2009), Portugal and central andsouth-western areas of IP (Trigo and Dacamara, 2000;

Norrant and Douguedroit, 2005; Lopez-Bustins, 2006;Paredes et al., 2006), and for the whole IP between 1921and 1995 (Serrano et al., 1999b). This global decrease ofMarch precipitation has been linked to a northward shiftof storms (Paredes et al., 2006). Moreover, if we considerthe existing studies concerning other meteorologicalparameters, it is also consistent with the positive trendin sunshine duration and anticyclonic activity in Marchobserved by Sanchez-Lorenzo et al. (2007).

Furthermore, the spring precipitation decrease detectedin previous researches in some parts of southern andcentral plateau of the IP (Galan et al., 1999), in the Duerobasin (del Rıo et al., 2005) and in the north-eastern area(Saladie et al., 2002, 2006) can be related to the negativeMarch trend, and seems to be the only clear signal on aseasonal scale (de Castro et al., 2005). This is a cleardemonstration of the fact that climate analysis basedon a seasonal scale could mask the real behaviour ofprecipitation.

The decrease of precipitation in June agrees withthe negative trend previously detected in the normal1961–1990 by Mosmannn et al. (2004) and del Rıo et al.(2005) during 1961–1997 in inland and south-westernIP. The increase in anticyclonic conditions detectedover inland IP during 1957–1996 by Fernandez andMartin-Vide (2004) could justify the generalized negativetrend during June observed here, even though, in thiscase, there is no clear evidence of a relationship withsunshine duration (Sanchez-Lorenzo et al., 2007) as washighlighted for March, because in June the increase insunshine duration is not statistically significant, althoughfor the last 20 years a slight increase has been recorded.

Finally, the positive trends of precipitation in Octoberwere briefly noticed by Paredes et al. (2006) between1941 and 1997 for the whole IP, while Norrant andDouguedroit (2005) suggested a negative trend in theMediterranean areas between 1951 and 2000, which canbe delineated by the surrounding areas of Valencia andAlicante provinces. On the other hand, the positive signalof precipitation particularly agrees with the negativetrend of sunshine duration in the western area of the IP(Sanchez-Lorenzo et al., 2007).

In all other months, characterized by less uniformspatial trend signals, we detect an interesting role oftopography on spatial distribution of trends from Northto South, and West to East. To the North, the mainclimatic feature is the role of Cantabrian Mountain inseparating the two basic climate types affecting Spain:the oceanic temperate mid-latitude climate to the North,and the Mediterranean one to the South. Thus, theCantabrian Mountains act as a barrier that isolates therainfall behaviour of northern Spanish regions (mainlyAsturias, Cantabria and the Basque Country). We noticealso that the extreme north-west (Galicia) is more closelyrelated to the Northern Plateau, while the northern fringeis more closely related to the Mediterranean areas throughthe Ebro basin (see, e.g. Figure 5(c) and (k)). Thus,the Cantabrian Mountains extending from west to eastparallel to the north coast can act as a very effective



barrier, together with the Iberian mountain system, inchannelling the spatial distribution of trend signals to theMediterranean coast.

The continuity from Cantabrian Mountain to the Sis-tema Iberico and the Sistema Betico creates a transitionfrom the north coast to the Mediterranean area alongthe Ebro Valley. This transitional characteristic to theMediterranean coast was highlighted by Serrano et al.(1999a) and more recently by Munoz-Diaz and Rodrigo(2004), who suggest that the Ebro basin behaves like theMediterranean coastland, while Fernandez-Mills (1995)linked the Ebro Valley to the Duero and Tajo inland catch-ments, particularly during autumn (September–October),and recently Morata et al. (2006) have stated the sameopinion and focussed on the effect of Mediterranean pro-cesses inland.

Cantabrian Mountain, Sistema Iberico and SistemaBetico separate the central inland and the south-westernareas to the north, north-east and Mediterranean coast-land. The western margins of this long mountainarch (including the northern and southern plateaus andGuadalquivir valley, mostly catchments 2, 3, 4, 5, seeFigure 1) have been related to the effect of NAO (Rodoet al., 1997; Rodriguez-Puebla et al., 1998; Goodess andJones, 2002), and their effects on daily rainfall trends(Rodrigo and Trigo, 2007) and Atlantic storm paths (Pare-des et al., 2006). Thus, the mountain arch seems to bea specific climatic boundary from the point of view ofprecipitation trends.

The effects of mountain chains on trends analysis agreewith the conclusions from Dunkeloh and Jacobeit (2003)on the effects of topography on flow-dependent rain-fall distribution patterns in the Mediterranean basin, andSotillo et al. (2003) on the spatial distribution of precip-itation amounts in the Iberian Peninsula. Thus, in the IP,the main mountain chains divide the peninsular area intowell-defined sectors in which both global (such as atmo-spheric patterns) and middle-local scale factors (such astopography) can act with very effective results throughoutthe year.

5. Conclusions

The description of monthly precipitation trends in Spainusing the new MOPREDAS database fills the gap existinguntil now of a high spatial resolution description of pre-cipitation for a long period, as was demanded by NationalClimate Report. We cannot exclude the fact that someresidual inhomogeneities still affect the data in spite ofthe remarkable homogenization effort. Notwithstandingsome minor problems, which will probably be addressedin the future only if metadata becomes available, itis worth highlighting the fact that the MOPREDASdatabase shows an excellent internal consistency and thatsuch a high-quality database turns out to be a very usefultool to better understand the behaviour of precipitationon a very detailed scale.

The analyses show that there is a high spatial andtemporal variability in trends on a monthly scale in the

period 1946–2005. Consecutive months exhibit differenttrends and the spatial distribution of signals varies a greatdeal from month to month, so caution should be takenwhen interpreting seasonal or annual trend analyses.

Spatial distribution of trends greatly varies from monthto month, and the spatial coherence in the trend signalis different for different months, from a more regionaland general pattern (March, June, October) to sub-regional or local ones (January, February, April, etc).This suggests to us that precipitation trends in the IPseem to be the result of the overlapping of global effectsand local factors that act throughout the year, not onlyin summer months, as usually suggested, but in allseasons, including winter, in spite of the more coherentatmospheric circulation. The analysis shows that, if adense database is available, it is possible to detect anddelineate these sub-regional and local patterns.

The analysis highlighted the high spatial complexityof precipitation behaviour in the IP, and mountain chainsseem to have a primary role in delimiting areas withdifferent trends. Detailed analyses in future researchare necessary to identify the possible reasons for suchcomplex atmospheric circulation changes at sub-regionallevel.

Acknowledgements

The authors wish to thank the following Contract GrantSponsor: Gobierno de Espana, Proyecto CGL2008-05112-C02-01/CLI, Gobierno Regional de Aragon DGA,Grupo de Investigacion Consolidado ‘Clima, Agua, Cam-bio Global y Sistemas Naturales’ (BOA 69, 11-06-2007). EU-COST-ACTION ES0601 “Advances in homo-geneization methods of climate series: an integratedapproach (HOME)”. This paper was written during astage of JC Gonzalez-Hidalgo at ISAC-CNR of Bologna(Italy), under Programa Salvador de Madariaga (Gob-ierno de Espana). Data were provided by AgenciaEstatal de Meteorologia (AEMET, Spanish Meteorologi-cal Agency).

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