TRENDS IN DROUGHT INTENSITY AND VARIABILITY IN THE
MIDDLE EBRO VALLEY (NE OF THE IBERIAN PENINSULA) DURING
THE SECOND HALF OF THE TWENTIETH CENTURY
S.M. Vicente-Serrano (1)*, J.M. Cuadrat-Prats (2)
(1) Instituto Pirenaico de Ecología, CSIC (Spanish Research Council), Campus de Aula Dei,
P.O. Box 202, Zaragoza 50080, Spain, (2), Departamento de Geografía, Universidad de
Zaragoza, Pedro Cerbuna 12, 50009, Zaragoza, Spain
* svicen@ipe.csic.es
Abstract: Here we analyse trends in drought magnitude in the middle Ebro valley, a semi-arid
area of the Iberian Peninsula, between 1951 and 2000. A significant increase in the severity of
drought was identified from 1951 to 2000, and principal components analysis revealed three
general patterns of drought evolution. Trend analysis of these patterns indicated that trend is
significant only in northern areas (p<0.01). Trends in drought variability were also analysed; a
positive trend was recorded between 1951 and 2000. However, the overall results show a high
degree of spatial variability. We show that this variability is determined by several
geographic/topographic factors, mainly the distance to the Mediterranean Sea and the Bay of
Biscay, water bodies that regulate the origin and direction of air masses and flows. It should also
be noted that spatial variability of drought was detected because we used a dense database. Our
results indicate that at the sub-regional level, drought patterns should be studied using a large
amount of empirical data, since spatial variability may be relevant.
Key words: Precipitation, drought, trends, Standardized Precipitation Index, Mediterranean
region, Iberian Peninsula
1. Introduction
Approximately 85% of natural disasters are related to extreme meteorological events (Obasi,
1994), drought being the one that causes most damage (CRDE, 2003). Non-spatial synchronicity
in dry periods has been reported, even at regional scales (Oladipo, 1995; Vicente-Serrano et al.,
2004). Moreover, in addition to normal spatial variability, the great uncertainty about climate
evolution must be considered and its relation to temporal variability and extreme climate events,
including droughts, in the context of an increased atmospheric concentration of greenhouse gases
(Houghton et al., 1990).
Large climatic variations were recorded during the twentieth century. Although increased
precipitation has been reported on a global scale (New et al., 2001), regional variations are
considerable (Bradley et al., 1987). There are clear regional differences in precipitation trends in
Europe, with a general increase occurring in the North and a decrease in the South (Schönwise
and Rapp, 1997). However, general models show a number of limitations when analysing
climate evolution at regional or local scales (De Luis et al., 2000; Gonzalez-Hidalgo et al., 2003).
One of the main objectives of climate research is to determine evolution at the sub-regional level
by means of dense climatic networks. Thus, droughts must be analysed at detailed spatial scales
in order to determine spatial variations.
In the South of Europe some climate models predict a future increase in droughts as a result of a
decrease in the magnitude and frequency of precipitation (Houghton et al., 2001; Jones et al.,
1996). As a consequence of atmospheric changes, southern Mediterranean areas could be greatly
affected by climatic change as a result of increased evapotranspiration and reduced rainfall
(Gibelin and Déqué, 2003); present models indicate a displacement of the Polar front to the
North (Quereda et al., 2000).
Another key aspect related to the evolution of drought variability must also be considered.
Extreme events are more sensitive to changes in variability than in average values (Katz and
Brown, 1992 and Meehl et al., 2000). The last report of the IPCC (Houghton et al., 2001)
describes an increase in variability over the last decade, mainly in transitional climates such
those in Mediterranean areas. Therefore, a link between climatic variability and drought must be
evaluated at the sub-regional level.
Here we analysed trends in drought intensity and temporal variability during the second half of
the twentieth century. The analysis was conducted in the middle Ebro valley, the northernmost
semi-arid region in Europe in which droughts are frequent and where they have a negative
impact (Vicente-Serrano and Begueria, 2003; Vicente-Serrano, 2006). Here we aimed:
1) To determine whether droughts have distinct spatial patterns regarding temporal evolution and
variability in a Mediterranean region in which the geographic and climatic characteristics have a
high degree of diversity.
2) To identify the factors those determine the spatial variability of droughts.
These issues have a high theoretical and applied interest. Confirmation of large spatial variability
of this meteorological phenomenon would imply the need to develop early drought warning
plans at a local level. The design of these plans would be a priority management task in areas
with more frequent and intense droughts.
2. Database and methods
2.1. Database
For drought analysis, 41 precipitation series covering the period 1951-2000 were used (Figure 1).
To ensure the final quality of the precipitation series, the homogeneity of each was checked
against an independent reference series generated by selecting the five series whose difference
series correlated best with the series under study (Peterson and Easterling, 1994). The Standard
Normal Homogeneity Test was used to check the homogeneity of the series (SNHT,
Alexandersson, 1986). For this purpose, we used the ANCLIM program (Štìpánek, 2004). A
statistical inhomogeneity was detected in only one observatory and was corrected following
Alexandersson and Moberg (1997).
An overall precipitation series was also obtained in order to evaluate the general evolution in the
study area. This series was generated from averages of monthly records in each observatory, and
was weighted for the size of the territory it represented using the Thiessen polygons method,
following Jones and Hulme (1996).
2.2. Calculation of the time series of drought indices and the drought variability series
A number of indices have been developed for the analysis and monitoring of drought (see e.g.,
Heim, 2002). In the last decade the most popular index has been the Standardized Precipitation
Index (SPI), valued for its theoretical development, robustness and versatility in drought analysis
(McKee et al., 1993; Keyantash and Dracup, 2002; Wu et al., 2005). Technically, the SPI is the
number of standard deviations that the observed value deviates from the long-term mean for a
normally distributed random variable. Since precipitation does not show a normal distribution,
data are transformed to follow a normal distribution. The Pearson III distribution is the most
suitable method to model precipitation records (Guttman, 1999; Vicente-Serrano, 2006b).
Therefore, here we obtained the SPI using this distribution of probability and the L-moment
method was used to calculate the parameters of the distribution (See details in Vicente-Serrano,
2006b). A temporal scale of three months was used to calculate the SPI due to the strong
relationship with agricultural droughts (Ji and Peters, 2003; Sims et al., 2002).
The SPI has been applied to develop early drought warning systems (Svoboda et al., 2002;
Tsakiris and Vangelis, 2004) and to determine the spatial extent and magnitude of droughts
(Hayes et al., 1999). However, this index has not been widely used to analyse the spatial (e.g.,
Bonaccorso et al., 2003; Bordi et al., 2004) or temporal patterns (e.g., Lloyd-Hughes and
Saunders, 2002; Rouault and Richard, 2003) of this phenomenon. Nevertheless, the spatial and
temporal analysis of droughts using the SPI has some advantages regarding the use of
precipitation series because the SPI series are comparable in time and space (Lana et al., 2001).
The monthly records are comparable (within the same series and also between areas),
independently of precipitation seasonality. Therefore, trends can be calculated on the basis of a
wide range of monthly homogeneous records.
To analyse the temporal evolution of drought variability, we obtained a moving standard
deviation statistic from the SPI series. To obtain time series that characterise precipitation
variability is difficult, particularly in areas in which precipitation is highly seasonal and shows
spatial diversity. Consequently, few studies have addressed the temporal variability of climate
analysed by means of moving window procedures. This is a very useful approach because trends
in climate variability can be calculated, and periods with distinct variability can be identified.
The SPI allowed us to obtain time series that reflect the temporal variability of
precipitation/droughts continuously over time because this index is a normal standard variable
with mean 0 and standard deviation 1. Thus, the analysis of variability was simplified, as the
simple calculation of mobile deviations allowed the determination of the general evolution of
temporal variability. Likewise, the standardised character of the SPI implies that the seasonality
of precipitation and its spatial differences do not affect the comparison of the series of drought
variability.
Here we used a moving window of five years (60 months) to calculate the series of drought
variability, a similar period to that described by Gruza et al. (1999), who considered annual data.
The series obtained in each observatory were denominated Temporal Variability of Drought
(TVD) series.
2.3. Spatial and temporal analysis of droughts
Principal components analysis (PCA) was used to determine temporal differences in drought
patterns by means of the original SPI series and the TVD series. PCA allows common features to
be identified and specific local characteristics to be determined (Richman, 1986; Jollife, 1990).
PCA reduces a large number of interrelated variables to a few independent principal components
that capture much of the variance of the original data set (Hair et al. 1998). Therefore, this
analysis provides a graphical display of coherent modes of spatially variability.
PCA has been used in several studies to analyse the spatial and temporal modes of drought
variation (Eder et al., 1987; Bonaccorso et al., 2003). When it is performed and components are
obtained, its rotation is common (Varimax) in order to redistribute the variance and obtain more
separability between the components, while maintaining their orthogonality (Richman, 1986).
Using PCA, spatial rotated Empirical Orthogonal Function (rEOF) values can be obtained. These
represent the correlation between each temporal component and the individual time series, and
determine the similarity between time series of droughts indices and the main temporal patterns
of drought represented by components. High rEOF values indicate a high similarity, while low
values (near 0) show a low relationship between the two series. In summary, the rEOF values
indicate the spatial representativeness of each temporal component.
Finally, we analysed the trends in drought intensity and variability. For this purpose, the most
widely used approaches are non-parametric tests, such as Mann-Kendall or Rho-Spearman. We
chose the latter because of its robustness in the presence of extreme values (Lanzante, 1996).
3. Results
3.1. Trends in drought intensity and in surface affected by droughts
Figure 2 shows the evolution of the SPI from regional series between 1951 and 2000. Several dry
periods were recorded, the most severe between 1952 and 1954, 1957 and 1958, and mainly
between November of 1993 and November of 1995. A negative and significant trend (p<0.01)
was observed from 1951 to 2000, which was very intense from 1960 to 2000 (p<0.001). The
mean duration of the droughts (consecutive months in which the SPI was < 0) was 3.9 months.
Nevertheless, extended dry periods were recorded between 1950 and 2000, with 11 months with
an SPI < 0 between September 1957 and July 1958, and 12 months at this value between January
and December 1995 and between March 1998 and February 1999.
Figure 3 shows the evolution of the percentage of the study area affected by droughts of varying
intensity, following the classification of Agnew (2000). The extreme droughts that affected a
higher proportion of the study area occurred in the 1950s and 1990s, but usually not more than
20-30% of the total area was affected in either decade. This observation indicates that drought
intensity can be highly diverse spatially and extreme drought can be recorded in some areas
while in other sectors it may have a moderate or severe intensity. In relation to trends, the surface
affected by droughts showed a significant increase only for moderate droughts, while the
increase in surface affected by severe and intense droughts was not significant. This indicates
that the general negative and significant trend shown by the regional SPI series is not driven by
more intense droughts but mainly by a higher frequency of moderate droughts.
3.2. Spatial patterns in drought trends
PCA from SPI series extracted three components, which accounted for 81% of the total variance
(Table 1); however, the third explained a small percentage compared to the first two components.
This observation indicates that there are two very clear regions with a distinct drought evolution
between 1950 and 2000 within the study area. Figure 4 shows the spatial distribution of the rEOF
for the three components. A clear spatial differentiation in the distribution of Components 1 and
2 was observed. Component 1 represents the northern areas (rEOF values > 0.75), whereas
Component 2 is representative of southern areas. This behaviour in the context of the spatial and
temporal patterns of drought can be explained by the distinct evolution of this phenomenon
between 1950 and 2000 between the northern and southern sectors. The third component is more
local, which explains the lower percentage of variance in the drought variability that it
accumulates (9.2%).
Figure 5 depicts the temporal evolution of the three components. The evolution of the two main
components (1 and 2) differed greatly during the study period. For example, the long and intense
drought that affected the North between 1981 and 1983 had a lower duration and intensity in the
South. Also between 1988 and 1992, several long droughts occurred in the North while in the
South they were shorter. In contrast, between 1963 and 1967 the drought in the South coincided
with a humid period in the North. Therefore, a considerable non-synchrony in the occurrence of
droughts between the North and the South of the middle Ebro valley was detected. Although
some drought periods affected the two areas, the occurrence of this phenomenon in one area
usually coincided with humid conditions in the other. Trend analysis indicated a negative trend
in Component 1 (North areas) (p<0.01), whereas in the South and East (Components 2 and 3,
respectively) the trend was not significant. In the North, drought increased significantly from
1960, which explains the general trend observed from regional series.
3.3. Evolution of drought variability
The TVD regional series showed that temporal variability of droughts increased significantly in
the second half of the twentieth century (Figure 6). The highest values were recorded during the
1990s. The trend from 1950 was positive and significant (p<0.01), indicating higher climatic
uncertainty caused by rapid changes between wet and dry periods.
3.4. Spatial patterns in drought variability trends
The result of the PCA analysis from the TVD series is shown in Table 2. The first four
components were retained, and accounted for 62.5% of the total variance. A lower percentage of
variance was explained by the selected components in relation to the SPI series. This finding
indicates that the spatial variability of the TVD series is higher than that of the SPI series. Also,
the series of the distinct components represent smaller areas than those of the SPI series because
the total percentage of variance explained for each component was much lower.
In general, the spatial representativeness of the components did not overlap (Figure 7). Thus, as
for drought evolution, clear regionalisation and spatial diversity in the TVD series was observed,
although the spatial differences in the temporal evolution of variability were higher than in the
SPI. Component 1 of the TVD series covered the highest percentage of the study area and was
representative of the Northeast. Component 2 represented the temporal evolution of series in the
Northwest and Component 3 some areas in the centre of the valley. Component 4 was patchier
but the highest rEOF values were obtained in the East and South-eastern sectors. On the basis of
significant rEOF values, several areas were unrepresented by these components. This
observation indicates that although some areas showed homogeneous temporal variability of
droughts, spatial diversity was very high in this region.
The temporal series of the four components are presented in Figure 8. Component 1 (North-East)
showed a general positive increase in the TVD series, mainly from the 1980s on (positive trend,
p<0.01). However, Component 2 (Northeast) showed a progressive and significant decrease in
the TVD series (negative trend, p<0.01). Component 3 did not show any trend and Component 4
presented a large increment in TVD series from 1960 on (positive trend, p<0.001). Therefore, the
highest increase in temporal variability was recorded in the Northeast and Southeast of the study
area, while in the other areas no changes or even a moderate decrease in temporal variability
were detected.
3.5. Geographic factors that control the spatial patterns of drought
To explain the factors that control the spatial diversity of the SPI and TVD series in the middle
Ebro valley, we used Geographical Information Systems (GIS) and Digital Terrain Models
(DTM). A number of geographical and topographical factors noticeably affect the spatial
distribution of climatic elements (Daly et al., 1994 and 2002; Ninyerola et al., 2000). Several
climatic classifications in the Iberian Peninsula based on homogeneous areas of precipitation
variability have shown that the topography and the influence of atmospheric circulation
modes/flows play a major role on spatial patterns (Fernández-Mills, 1995; Rodríguez-Puebla et
al., 1998; Vicente-Serrano, 2005 and 2006c).
Using GIS and DTMs, it is possible perform continuous quantification of a range of topographic
and geographic variables. For this purpose, we used a Digital Elevation Model (DEM) that
provides information on the elevation in each point, at a spatial resolution of 100 meters. From
this model we obtained other derived models that provided data on the slope, which can affect air
flow and determine the spatial distribution of precipitation (Agnew and Palutikof, 2000), and
potential solar radiation, which informs about the land exposure (See details in Vicente-Serrano
et al., 2003). Also the distance to the Mediterranean Sea and the Bay of Biscay was calculated
continuously by means of GIS because most air flows that affect the climate in the Ebro valley
derive from these water masses (Creus, 1982).
We related the rEOF values of the components obtained from the SPI (3 components) and the
TVD series (4 components) in the distinct observatories with the distribution of the spatial
variables modelled by means of GIS. Following this procedure, we determined the geographic
factors that regulate drought patterns.
Table 3 shows the correlations between the spatial distribution of the rEOF values and the
geographic variables. Non-significant relationships were found between the rEOFs and slope. In
relation to the SPI patterns, distance to the Mediterranean Sea and the Bay of Biscay showed the
highest correlations. Component 1 was positively related to distance to the Mediterranean and
negatively to distance to the Bay of Biscay, while Component 2 showed the opposite pattern,
with significant and strong correlations (p < 0.01). As an example of these relationships, Figure 9
shows the close relationship between the rEOFs in Components 2 and 3 and distance to the Bay
of Biscay.
Exposure and elevation also showed significant and contrary correlations for Components 1 and
2. The former would represent drought evolution in more elevated areas and with Southern
exposures while the latter would represent areas more exposed to the North. Therefore, the
geographic and topographic variables considered here contribute to explaining the spatial
differences of drought evolution in the middle Ebro valley.
On the contrary, the correlations between the rEOFs of the TVD series and the distinct
geographic and topographic variables were, in general, weaker than those obtained from the SPI
series. This observation indicates a lower geographic control and more spatial random
characteristic of the spatial patterns of the TVD series, as indicated by the lower percentage of
variance accumulated for the components selected.
4. Discussion and Conclusions
This study describes the spatial and temporal patterns of droughts in the middle Ebro valley (NE
Spain) between 1950 and 2000. This region is one of the driest in Europe and droughts are highly
relevant, causing considerable socio-economic and environmental impact (Vicente-Serrano,
2006).
The most general evolution of droughts is consistent with that in other regions of Spain (e.g.
Rodríguez et al., 1999; Raso, 1993; Esteban-Parra et al., 1998; Peréz-Cueva, 2001) and other
Mediterranean countries (Briffa et al., 1994; Maheras, 1988; Delitala et al., 2000), with the most
severe droughts recorded during the 1950s, 1980s and 1990s.
In the middle Ebro valley, we have reported a general increase in drought severity and temporal
variability. This increase is reflected by the progressive decrease in the SPI values, mainly since
the 1960s, which is consistent with the findings of other studies performed in Spain (e.g., Lana et
al., 2001 in Catalonia) and in the Mediterranean region (Szinell et al., 1998; Hisdal et al., 2001;
Brunetti et al., 2000). If the occurrence of drought continues to increase in this region, it may
lead to several negative consequences. A number of recent studies have also shown that climate
models predict a large decrease in precipitation during the second half of the twenty-first century
in the Mediterranean area (Gibelin and Déqué, 2003; Raisanen et al., 2004). This decrease could
have serious environmental and socio-economic consequences as a result of changes in
biodiversity (Sala et al., 2000) and a decrease in suitable areas for cultivation (Rounsevell et al.,
2005).
In addition, the increase in drought occurrence is linked to an increment in the temporal
variability of this phenomenon, which leads to greater uncertainty about future water availability
in the middle Ebro valley. If this pattern continues, the risk of droughts may increase related to a
higher uncertainty in the drought occurrence (Riebsame, 1988). Houghton et al. (2001) reported
the increase in precipitation variability as one of the most important aspects related to climatic
change, mainly in regions of climatic transition such as the Mediterranean area. Some studies in
other areas of the Mediterranean have described an increase in precipitation variability
(Riebsame, 1988; Granger, 1979; De Luis et al., 2000).
The changes in the variability and the higher frequency of drought extremes could explain the
increase in this phenomenon in the middle Ebro valley during the second half of the twentieth
century. Extreme events are more sensitive to changes in variability than to changes in average
values; therefore it is possible to infer a connection between precipitation variability and
droughts (Katz and Brown, 1992).
Nevertheless, we must also consider that drought is spatially very complex because the changes
in precipitation variability are more local than other elements such as temperature (Díaz and
Quayle, 1980). Consequently, detailed spatial scales are required to perform studies on drought.
Here we have also demonstrated that in the middle Ebro valley the drought patterns show
considerable spatial variability and also with large non-synchrony in evolution between some
areas. This finding is consistent with the spatial pattern of droughts in the whole of the Iberian
Peninsula (Vicente-Serrano, 2006c) and even at a regional scale, as observed in regions of the
East of the Iberian Peninsula such as Valencia (Estrela et al., 2000; Vicente-Serrano et al., 2004)
and Catalonia (Martín-Vide, 2001; Lana et al., 2001).
In the middle Ebro valley, the largest differences in drought patterns were recorded between
areas in the North and South. Our results show that these differences are related to a number of
geographic/topographic variables, but mainly to distance to the Mediterranean Sea and the Bay
of Biscay. These are the origin of the main air masses and flows that affect the climate of this
valley. Regions in the Southeast are highly affected by Mediterranean perturbations, mainly
during the autumn (Millán et al., 1995; Serra et al., 1996). These perturbations become weaker as
they ascend the Ebro valley (Creus and Ferraz, 1995) and their influence in the central areas and
in the North is lower.
Also, the northern areas of the valley are more affected by the perturbations associated with the
Polar fronts. The effect of these fronts on precipitation is greatly increased by the mountain
ranges that oppose the westerly flows, thereby creating a great contrast with the centre of the
valley (Creus and Ferraz, 1995; Ruiz, 1982). Moreover, areas in the North of the valley are
affected by Southwest flows associated with the negative phase of the North Atlantic Oscillation
in winter (Martin-Vide and Fernandez, 2001). These are regional flows that have an influence on
the whole of the Iberian Peninsula and they are reactivated in the Mountains of the North
because of vertical movements of air masses (Esteban et al., 2002; Vicente-Serrano, 2005b). This
would explain the positive correlation between the rEOF values corresponding to Component 1
and elevation.
Our results show that the spatial patterns of drought variability are more complex and random in
the space and less associated with the distinct geographic/topographic factors than patterns of
drought series. This introduces more spatial uncertainty in the drought patterns in this region.
Finally, it is important to highlight that although several approaches have been designed to
model drought at European spatial scales (i.e., Briffa et al., 1994; Lloyd-Hugues and Saunders,
2002), true spatial diversity is not well recorded (e.g., the middle Ebro valley is represented by 15 cells of the models). Therefore, continental and global models show serious limitations when it
comes to identifying spatial and temporal drought patterns in the Mediterranean region, an area
with a high temporal and spatial climatic variability (Bordi et al., 2004b; Martín-Vide, 2001). In
the middle Ebro valley the spatial diversity of drought was detected because we used a dense
database. Our results indicate that for appropriate spatial identification and regionalisation of the
drought phenomenon more detailed studies are required at regional or local scales.
Acknowledgements
The authors want to acknowledge financial support from the projects BSO2002-02743 and
CGL2005-04508/BOS (Financed by Ministerio de Educación y Ciencia, Spain and FEDER), and
"Programa de grupos de investigación consolidados" (grupo Clima, Cambio Global y Sistemas
Naturales, BOA 48 of 20-04-2005), financed by Aragón Government.
References
Agnew CT (2000) Using the SPI to Identify drought. Drought Network News 12: 6-12.
Agnew MD, Palutikof JP (2000) GIS-based construction of base line climatologies for the
Mediterranean using terrain variables. Climate Research 14: 115-127.
Alexandersson H (1986) A homogeneity test applied to precipitation data. Journal of
Climatology 6: 661-675.
Alexandersson H, Moberg A (1997) Homogenization of Swedish temeperature data. Part I:
Homogeneity test for lineal trends. International Journal of Climatology 17: 25-34.
Bonaccorso B, Bordi I, Cancielliere A, Rossi G, Sutera A (2003) Spatial variability of drought:
an analysis of the SPI in Sicily. Water Resources Management 17: 273-296.
Bordi I, Fraedrich K, Jiang JM, Sutera A (2004) Spatio-temporal variability of dry and wet
periods in eastern China. Theoretical and Applied Climatology DOI 10.1007/s00704-0040053-8.
Bordi I, Fraedrich K, Gerstengarbe FW, Werner PC, Sutera A (2004) Potential predictability of
dry and wet periods: Sicily and Elbe-Basin (Germany). Theoretical and Applied
Climatology 77: 125-138.
Bradley RS, Díaz HF, Eischeid JK, Jones PD, Kelly PM, Goodess CM (1987) Precipitation
fluctuations over northers hemisphere land areas since the mid-19th century. Science 237:
171.175.
Briffa KR, Jones PD, Hulme M (1994) Summer moisture variability across Europe, 1892-1991:
an analysis based on the Palmer drought severity index. International Journal of
Climatology 14: 475-506.
Brunetti M, Mangeri M, Nanni T (2000) Variations of temperature and precipitation in Italy from
1866 to 1995. Theoretical and Applied Climatology 65: 165-174.
CRDE (2003) The Centre for Research on the Epidemiology of Disasters. Disasters Database.
http://www.cred.be/emdat/intro.htm. Universite Catholique de Louvain – Brussels –
Belgium.
Creus J (1982) El clima del alto Aragón occidental. Monografías del Instituto de Estudios
Pirenaicos, 109. 233 pp. Jaca.
Creus J, Ferraz J (1995) Irregularidad pluviométrica y continentalidad térmica en el valle medio
del Ebro. Lucas Mallada 7: 147-164.
Daly C, Neilson RP, Phillips DL (1994) A statistical-topographical model for mapping
climatological precipitation over mountainous terrain. Journal of Climate and Applied
Meteorology 33: 140-158.
Daly C, Gibson WP, Taylor GH, Johnson GL, Pasteris P (2002) A knowledge-based approach to
the statistical mapping of climate. Climate Research 22: 99-113.
Delitala AMS, Cesari D, Chessa PA, Ward MN (2000) Precipitation over Sardinia (Italy) during
the 1946-1993 rainy season and associated large-scales climate variations. International
Journal of Climatology 20: 519-541.
De Luis M, Raventós J, González-Hidalgo JC, Sánchez JR, Cortina J (2000) Spatial analysis of
rainfall trends in the region of Valencia (East Spain). International Journal of
Climatology 20: 1451-1469.
Díaz HF, Quayle R (1980) The climate of the United States since 1895: spatial and temporal
changes. Monthly Weather Review 108: 249-266.
Eder BK, Davis JM, Monahan JF (1987) Spatial and temporal analysis of the Palmer drought
severity index over the south-eastern united States. Journal of Climatology 7: 31-51.
Esteban P, Soler X, Prohom M, Planchon O (2002) La distribución de la precipitación a través
del índice NAO. El efecto del relieve a escala local: el Pirineo Oriental. In El agua y el
clima. (Guijarro J.A., Grimalt, M., Laita, M. y Alonso, S Eds.): 25-34.
Esteban Parra MJ, Rodrigo FS, Castro-Díez Y (1998) Spatial and temporal patterns of
precipitation in Spain for the period 1880-1992. International Journal of Climatology 18:
1557-1574.
Estrela MJ, Peñarrocha D, Millán M (2000) Multi-annual drought episodes in the mediterranean
(Valencia region) from 1950- 1996. A spatio-temporal analysis. International Journal of
Climatology 20: 1599-1618.
Fernández Mills G (1995) Principal component analysis of precipitation and rainfall
regionalization in Spain. Theoretical and Applied Climatology 50: 169-183.
Gibelin A, Deque M (2003). Anthropogenic climate change over the mediter-ranean region
simulated by a global variable resolution model. Climate Dynamics 20: 327–339.
González-Hidalgo JC, De Luis M, Raventos J, Sánchez R (2003) Daily rainfall trend in the
Valencia Region of Spain. Theoretical and Applied Climatology 75: 117-130.
Granger O (1979) Increasing variability in California precipitation. Annals of the Association of
American Geographers 69: 533-543.
Gruza G, Rankova E, Razuvaev V, Bulygina M (1999) Indicators of climate change for the
Russian federation. Climatic Change 42: 219-242.
Guttman NB (1999) Accepting the standardized precipitation index: a calculation algorithm.
Journal of the American Water Resources Association 35: 311-322.
Hair JF, Anderson RE, Tatham RL, Black WC (1998) Análisis multivariante. Prentice Hall. 799
pp.
Hayes M, Wilhite DA, Svoboda M, Vanyarkho O (1999) Monitoring the 1996 drought using the
Standardized Precipitation Index. Bulletin of the American Meteorological Society 80:
429-438.
Heim RR (2002) A review of twentieth-century drought indices used in the United States.
Bulletin of the American Meteorological Society 83: 1149-1165.
Hisdal H, Stahl K, Tallaksen LM, Demuth S (2001) Have streamflow droughts in Europe
become more severe or frequent?. International Journal of Climatology 21: 317-333.
Houghton JL, Jenkins GJ, Ephramus JJ (1990) Climatic Change: the IPCC Scientific
Assessment. Cambridge University Press. Cambridge, 365 pp.
Houghton JT, Ding Y, Giggs D, Noguet M, van del Linden P, Dai X, Maskell A, Johnson CA
(2001) Climate Change 2001: The scientific Basis. Eds. Cambridge University Press.
Cambridge.
Ji L, Peters AJ (2003) Assessing vegetation response to drought in the northern Great Plains
using vegetation and drought indices. Remote Sensing of Environment 87: 85-98.
Jollife IT (1990) Principal component analysis: a beginner’s guide. Part I: Introduction and
application. Weather 45: 375-382.
Jones PD, Hulme M (1996) Calculating regional climatic time series for temperature and
precipitation: methods and illustrations. International Journal of Climatology 16: 361377.
Jones PD, Hulme M, Briffa KR, Jones CG (1996) Summer moisture availability over Europe in
the Hadley centre general circulation model based on the Palmer drought severity index.
International Journal of Climatology 16: 155-172.
Katz RW, Brown BG (1992) Extreme events in a changing climate. Variability is more important
than averages. Climatic Change 21: 289-302.
Keyantash J, Dracup J (2002) The quantification of drought: an evaluation of drought indices.
Bulletin of the American Meteorological Society 83: 1167-1180.
Lana X, Serra C, Burgueño A (2001) Patterns of monthly rainfall shortage and excess in terms of
the Standardied Precipitation Index for Catalonia (NE Spain). International Journal of
Climatology 21: 1669-1691.
Lanzante JR (1996) Resistant, robust and non-parametric techniques for the analysis of climate
data: theory and examples, including applications to historical radiosonde station data.
International Journal of Climatology 16: 1197-1226.
Lloyd-Hughes B, Saunders MA (2002) A drought climatology for Europe. International Journal
of Climatology 22: 1571- 1592.
Maheras P (1988) Changes in precipitation conditions in the Western Mediterranean over the last
century. Journal of Climatology 8: 179-189.
Martín-Vide J (2001) Diez consideraciones sobre las sequías en Cataluña y Baleares. In (Gil, A.
y Morales, A. Eds.) Causas y consecuencias de las sequías en España. CAM, Alicante:
207-230.
Martín-Vide J, Fernández D (2001) El índice NAO y la precipitación mensual en la España
peninsular. Investigaciones Geográficas 26: 41-58.
McKee TBN, Doesken J, Kleist J (1993) The relationship of drought frecuency and duration to
time scales. Eight Conf. On Applied Climatology. Anaheim, CA, Amer. Meteor. Soc.
179-184.
Meehl GA, Karl T, Easterling DR, Changnon S, et al. (2000) An introduction to trends in
extreme weather and climate events: observations, socioeconomic impacts, terrestrial
ecological impacts, and model projections. Bulletin of the American Meteorological
Society 81: 413-416.
Millán M, Estrela MJ, Caselles V (1995) Torrential precipitations on the Spanish east coast: the
role of the Mediterranean sea surface temperature. Atmospheric Research 36: 1-16.
New M, Todd M, Hulme M, Jones P (2001) Precipitation measurements and trends in the
twentieth century. International Journal of Climatology 21: 1899-1922.
Ninyerola M, Pons X, Roure JM (2000) A methodological approach of climatological modelling
of air temperature and precipitation through GIS techniques. International Journal of
Climatology 20: 1823-1841.
Obasi GOP (1994) WMO`s role in the international decade for natural disaster reduction.
Bulletin of the American Meteorological Society 75: 1655-1661.
Oladipo EO (1995) Some statistical characteristics of drought area variations in the savanna
region of Nigeria. Theoretical and Applied Climatology 50: 147-155.
Pérez Cueva A (2001) Las sequías en tierras valencianas. In (Gil, A. y Morales, A. Eds.) Causas
y consecuencias de las sequías en España. CAM, Alicante: 131-160.
Peterson TC, Easterling DR (1994) Creation of homogeneous composite climatological reference
series. International Journal of Climatology 14: 671-679.
Quereda J, Montón E, Escrig J (2000) La evolución de las precipitaciones en la cuenca
occidental del Mediterráneo: ¿tendencias o ciclos?. Investigaciones Geográficas 24: 1735.
Raisanen J, Hansson U, Ullerstig A, Doscher R, Graham LP, Jones C, Meier HEM, Samuelsson
P, Willen U (2004) European climate in the late twenty-first century: regional simulations
with two driving global models and two forcing scenarios. Climate Dynanics 22: 13-31.
Raso JM (1993) Evolución de las precipitaciones anuales en España desde 1870. Notes de
Geografía Física 22: 5-24.
Richman MB (1986) Rotation of Principal Components. Journal of Climatology 6: 29-35.
Riebsame WE (1988) Adjusting water resources management to climate change. Climatic
Change 13: 69-97.
Rodríguez R, Llasat MC, Wheeler D (1999) Analysis of the Barcelona precipitation series, 18501991. International Journal of Climatology 19: 787-801.
Rodríguez-Puebla C, Encinas AH, Nieto S, Garmendia J (1998) Spatial and temporal patterns of
annual precipitation variability over the Iberian Peninsula. International Journal of
Climatology 18: 299-316.
Rouault M, Richard Y (2003) Intensity and spatial extension of droughts in South Africa at
different time scales. Water SA 29: 489-500.
Rounsevell MDA, Ewert F, Reginster I, Leemans R, Carter TR (2005) Future scenarios of
European agricultural land use: II. Projecting changes in cropland and grassland.
Agriculture, Ecosystems & Environment 107: 117-135.
Ruiz E (1982) La transición climática del Cantábrico Oriental al valle medio del Ebro.
Diputación Foral de Álava. 651 pp.
Sala OE, Chapin III FS, Armesto JJ, Berlow E, et al. (2000) Global biodiversity scenarios for the
year 2100. Science 287: 1770-1774.
Serra C, Fernández Mills G, Periago MC, Lana X (1996) Winter and autumn daily precipitation
patterns in catalonia, Spain. Theoretical and Applied Climatology 54: 175-186.
Štìpánek P (2004) AnClim - software for time series analysis (for Windows), Dept. of
Geography, Fac. of Natural Sciences, MU, Brno. 1.47 MB.
Schönwiese CD, Rapp J (1997) Climate trends atlas of Europe based on observations, 18911990. Kluwer Academic Publishers. Dordrecht.
Sims AP, Nigoyi DS, Raman S (2002) Adopting indices for estimating soil moisture: A North
Carolina case study. Geophysical Research Letters 29: 1-4.
Svoboda M, LeCompte D, Hayes M, Heim R, Gleason K, Angel J, et al. (2002) The drought
monitor. Bulletin of the American Meteorological Society 83: 1181-1190.
Szalai S, Szinell CS, Zoboki J (2000) Drought monitoring in Hungary. In Early warning systems
for drought preparedness and drought management. World Meteorological Organization.
Lisboa: 182-199.
Szinell, C.S., Bussay, A. and Szentimrey, T., (1998): Drought tendencies in Hungary.
International Journal of Climatology. 18: 1479-1491.
Tsakiris G, Vangelis H (2004) Towards a drought watch system based on spatial SPI. Water
Resources Management 18: 1-12.
Vicente-Serrano SM, Beguería S (2003) Estimating extreme dry-spell risk in the middle Ebro
valley (Northeastern Spain): A comparative analysis of partial duration series with a
General Pareto distribution and Annual maxima series with a Gumbel distribution.
International Journal of Climatology 23: 1103-1118.
Vicente-Serrano SM, Saz MA, Cuadrat JM (2003) Comparative analysis of interpolation
methods in the middle Ebro valley (Spain): application to annual precipitation and
temperature. Climate Research 24: 161-180.
Vicente-Serrano SM, González-Hidalgo JC, de Luis M, Raventós J (2004) Drought patterns in
the Mediterranean area: the Valencia region (East-Spain). Climate Research 26: 5-15.
Vicente-Serrano SM (2005) El Niño and La Niña influence on drought conditions at different
time scales in the Iberian Peninsula. Water Resources Research 41, W12415,
doi:10.1029/2004WR003908
Vicente-Serrano SM (2005b) Las sequías climáticas en el valle medio del Ebro: Factores
atmosféricos, evolución temporal y variabilidad espacial. Consejo de Protección de la
Naturaleza de Aragón. Zaragoza. 277 pp.
Vicente-Serrano SM (2006) Evaluación de las consecuencias ambientales de las sequías en el
sector central del valle del Ebro mediante imágenes de satélite: Posibles estrategias de
mitigación. Consejo Económico y Social de Aragón. Zaragoza. 325 pp.
Vicente-Serrano SM (2006b) Differences in spatial patterns of drought on different time scales:
an analysis of the Iberian Peninsula. Water Resources Management 20: 37-60.
Vicente-Serrano SM (2006c) Spatial and temporal analysis of droughts in the Iberian Peninsula
(1910-2000). Hydrological Sciences Journal 51: 83-97.
Wu H, Hayes MJ, Wilhite DA, Svoboda MD (2005) The effect of the length of record on the
standardized precipitation index calculation. International Journal of Climatology 25:
505-520.
Table 1: Results of PCA analysis from SPI series
Components
Explained variance
Accumulated variance
1
2
3
37.90
34.01
9.20
37.90
71.90
81.10
Table 2: Results of PCA analysis from TVD series
Components
1
2
3
4
Explained variance
23.66
17.27
11.89
9.72
Accumulated variance
23.66
40.93
52.82
62.54
Table 3. Correlations between the REOFs in each observatory obtained from PCA from the SPI
and TVD series and different geographic and topographic variables obtained from DTMs and
GIS.
REOF 1 (SPI)
REOF 2 (SPI)
REOF 3 (SPI)
REOF 1 (TVD)
REOF 2 (TVD)
REOF 3 (TVD)
REOF 4 (TVD)
Exposure
Elevation
0.37*
-0.39**
-0.17
0.37*
0.11
-0.31*
0.05
0.32*
-0.56**
0.17
0.22
0.24
-0.70**
0.00
Distance to
Mediterranean Sea
0.70**
-0.88**
0.62**
0.25
0.82**
-0.38*
-0.25
Distance to
Cantabrian Sea
-0.83**
0.94**
-0.45**
-0.40**
-0.85**
0.42*
0.20
FIGURE CAPTIONS
Figure 1: Location of the study area and weather stations used for analysis
Figure 2: Evolution of regional SPI in the middle Ebro valley from 1951 to 2000
Figure 3: Temporal evolution of the surface affected by droughts of different intensity.
Figure 4: Spatial distribution of the rEOF from PCA of SPI series
Figure 5: Evolution of the three components obtained from SPI series.
Figure 6: Evolution of TVD regional series in the middle Ebro valley from 1951 to 2000.
Figure 7: Spatial distribution of the rEOF from PCA of TVD series
Figure 8: Evolution of the four components obtained from TVD series
Figure 9. Relationship between the rEOF obtained from components 1 and 2 and the distance
(Km) to the Bay of Biscay.
Figure 1: Location of the study area, mean annual precipitation and weather stations used for analysis
3
2
SPI
1
0
-1
-2
-3
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
Figure 2: Evolution of regional SPI in the middle Ebro valley from 1951 to 2000
2000
1
0
0
8
0
M
o
d
e
r
a
t
e
d
r
o
u
g
h
t
s
p
<
0
.
0
1
6
0
%OFSURFACE
4
0
2
0
0
1
9
5
0
1
9
5
5
1
9
6
0
1
9
6
5
1
9
7
0
1
9
7
5
1
9
8
0
1
9
8
5
1
9
9
0
1
9
9
5
2
0
0
0
1
9
9
5
2
0
0
0
1
9
9
5
2
0
0
0
1
0
0
8
0
p
>
0
.
0
1
S
e
v
e
r
e
d
r
o
u
g
h
t
s
6
0
%OFSURFACE
4
0
2
0
0
1
9
5
0
1
9
5
5
1
9
6
0
1
9
6
5
1
9
7
0
1
9
7
5
1
9
8
0
1
9
8
5
1
9
9
0
1
0
0
8
0
p
>
0
.
0
1
E
x
t
r
e
m
e
d
r
o
u
g
h
t
s
6
0
%OFSURFACE
4
0
2
0
0
1
9
5
0
1
9
5
5
1
9
6
0
1
9
6
5
1
9
7
0
1
9
7
5
1
9
8
0
1
9
8
5
1
9
9
0
Figure 3 : Temporal evolution of the surface affected by droughts of different intensity.
Components
Explained variance
Accumulated variance
1
2
3
37.90
34.01
9.20
37.90
71.90
81.10
Table 1: Results of PCA analysis from SPI series
Figure 4: Spatial distribution of the REOF from PCA of SPI series
3
COMPONENT 1 (37.9%)
2
SPI
1
0
-1
-2
-3
3
COMPONENT 2 (34%)
2
SPI
1
0
-1
-2
-3
3
COMPONENT 3 (9.2%)
2
SPI
1
0
-1
-2
-3
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
Figure 5: Evolution of the three components obtained from SPI series.
Standard deviation
1.3
1.2
1.1
1.0
0.9
0.8
0.7
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
Figure 6: Evolution of TVD regional series in the middle Ebro valley from 1951 to 2000.
2000
Components
1
2
3
4
Explained variance
23.66
17.27
11.89
9.72
Accumulated variance
23.66
40.93
52.82
62.54
Table 2: Results of PCA analysis from TVD series
Figure 7: Spatial distribution of the REOF from PCA of TVD series
3
2
1
0
-1
-2
-3
COMPONENT 1
3
2
1
0
-1
-2
-3
COMPONENT 2
3
2
1
0
-1
-2
-3
COMPONENT 3
3
2
1
0
-1
-2
-3
-4
COMPONENT 4
1950
1960
1970
1980
1990
Figure 8: Evolution of the four components obtained from TVD series
2000
REOF 1 (SPI)
REOF 2 (SPI)
REOF 3 (SPI)
REOF 1 (TVD)
REOF 2 (TVD)
REOF 3 (TVD)
REOF 4 (TVD)
Radiation
Elevation
0.37*
-0.39**
-0.17
0.37*
0.11
-0.31*
0.05
0.32*
-0.56**
0.17
0.22
0.24
-0.70**
0.00
Distance to
Mediterranean Sea
0.70**
-0.88**
0.62**
0.25
0.82**
-0.38*
-0.25
Distance to
Cantabrian Sea
-0.83**
0.94**
-0.45**
-0.40**
-0.85**
0.42*
0.20
Table 3. Correlations between the REOFs in each observatory obtained from PCA from the SPI and TVD series and
different geographic and topographic variables obtained from GIS.
Distance to Cantabrian Sea
350
350
300
300
250
250
200
200
150
150
100
100
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
REOF (Component 1-SPI)
0.9
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
REOF (Component 1-SPI)
Figure 9. Relationship between the REOF obtained from components 1 and 2 and the distance (Km) to the
Cantabrian Sea.