Evidence of increasing drought severity caused by temperature rise ...
Transcript of Evidence of increasing drought severity caused by temperature rise ...
Supplementary material
Evidence of increasing drought severity caused by temperature rise in southern Europe
Sergio M. Vicente-Serrano1, Juan-I. Lopez–Moreno1, Santiago Beguería2, Jorge Lorenzo–Lacruz1, Arturo Sanchez–Lorenzo3, José M. García–Ruiz1, Cesar Azorin–Molina1, Enrique Morán-Tejeda1, Jesús Revuelto1, Ricardo Trigo4, Fatima Coelho5, Francisco Espejo6 1 Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC), Spain, 2 Estación Experimental de Aula Dei (EEAD–CSIC), Zaragoza, 3 Department of Physics, University of Girona, Girona, Spain, 4 Instituto Dom Luiz, Universidade de Lisboa, Lisboa, Portugal, 5 Instituto Português do Mar e da Atmosfera, I.P. Departamento de Meteorologia e Geofísica, Portugal, 6Agencia Estatal de Meteorologia (AEMET), Spain
* Corresponding author: [email protected]
This document contains:
1. Supplemental material and methods
1.1. Climate Datasets
1.2. Calculation of the Reference Evapotranspiration (ET0)
1.3. Drought index calculation
2. Supplemental Figures 1 to 28 and Supplementary Tables 1 and 2
3. Supplementary references
1. Supplementary material and methods
1.1. Climate Datasets
Only the first‐order meteorological stations (113) in the weather observation networks of Spain and Portugal
measure all the variables necessary to calculate drought indices, taking into account both precipitation
inputs and robust estimates of the evaporative demand by the atmosphere. Using the Spanish records,
Sanchez‐Lorenzo et al. (2007) created a homogeneous dataset of sunshine duration since the beginning of
the 20th century. González‐Hidalgo et al. (2011) developed a dense and homogeneous precipitation dataset
for Spain, which was extended to Portugal by Lorenzo‐Lacruz et al. (2013). For Spain, Vicente‐Serrano et al.
(2014) obtained 50 homogeneous time series of relative humidity, maximum and minimum temperature,
and surface pressure. These studies used common methodologies for quality control, and reconstruction and
homogenization of the series. Quality control was based on comparison of the rank of each data record with
the average rank for the data recorded at adjacent meteorological stations (Vicente‐Serrano et al., 2010).
The standard normal homogeneity test (SNHT) for single breaks, developed by Alexandersson (1986), was
used to detect inhomogeneities. As reliability in detecting inhomogeneities is only possible through the use
of relative homogeneity methods, based on information from neighboring meteorological stations, reference
series were calculated for each station. The approach of Peterson and Easterling (1994) was used; this relies
on data from several neighboring stations, which are used to create a reference series for each station and
variable. The AnClim software (Stepánek, 2007) (available at ; last accessed 1 September 2013) was used in
applying the SNHT to each meteorological station. Data identified as nonhomogeneous were corrected using
monthly coefficients, and temporal gaps were filled using linear regressions based on the respective
reference series.
Azorin‐Molina et al. (2014) have recently developed a homogeneous dataset of wind speed at 10 m height
for the entire IP. Although the quality control for wind speed data was similar to that applied to the other
variables cited above, homogeneity testing was slightly different because the reference series were derived
from a climate version of the Pennsylvania State University/National Center for Atmospheric Research
numerical model, also known as the MM5 suite. This procedure was followed because in areas of complex
topography surrounded by ocean/sea surfaces, as is the case for the IP, the spatial dependency among
meteorological stations can markedly degrade over short distances.
We used the datasets cited above, updated to 2011, as they were the most reliable available for the
meteorological variables needed to obtain robust estimates of ET0. The series needed for Portugal were
processed following the same approaches indicated above. A total of 46 stations providing data on all the
necessary meteorological variables were available for continental Spain and the city of Melilla, in northern
Africa, and 8 series were available for Portugal. Therefore, 54 series were available for the period 1961–
2011. The station names, coordinates and elevations are shown in Supplementary Table 1, and the spatial
distribution of the series is shown in Supplementary Figure 1.
To test the reliability of the estimates of ET0 used in the drought indices, we used data series for pan and
Piché evaporimeters in Spain (Sanchez‐Lorenzo et al., 2013). Although the measurements obtained from
these evaporimeters do not correspond directly to the ET0 (they correspond to direct evaporation
measurements under no water restrictions), they were assumed to be consistent with ET0 data. Sánchez‐
Lorenzo et al. (2013) developed a homogeneous dataset of the available pan and Piché series for Spain.
Given the low spatial density of the pan evaporation series (19), a recently developed method (HOMER –
HOMogenization software in R‐) (Mestre et al., 2013) was used to test inhomogeneity in the series. This
method compares each candidate series with other available series, without the need to create a reference
series. Piché measurements were also available for the 19 meteorological stations having pan evaporation
series from 1984, enabling comparison between the two evaporation measurements. The Piché
evaporimeter series are longer, and are available from the 1960s for 32 of the 46 meteorological stations
having data on the variables necessary to calculate ET0. This enabled a comparison between the evolution of
the ET0 and Piché evaporation for these meteorological stations. Supplementary Figure 26 shows the source
locations of the Piché and pan evaporation series, and the meteorological stations having common Piché
data and data on the variables necessary to calculate ET0.
1.2. Calculation of the Reference Evapotranspiration (ET0)
Estimating the evaporative demand of the atmopshere is really only possible locally, because in addition to
atmospheric (meteorological) conditions it is also influenced by surface conditions (e.g. type of surface,
vegetation type, soil conditions). For this reason the parameter Reference Evapotranspiration (ET0) has been
established. This is defined as the evapotranspiration rate from a reference surface with no water
constraints. Allen et al. (1998) strongly discouraged the use of other concepts including potential ET, because
of ambiguities in their definitions. Moreover, ET0 enables assessment of the evaporative demand of the
atmosphere independently of vegetation type and growth conditions, and it is spatially comparable among
different climate regions. This is because ET0 measurements refer to the ET from the same reference surface,
and the only factors affecting ET0 are climatic parameters. Consequently, it can be considered to equate to
the evaporative demand by the atmosphere, and according to Allen et al. (1998) “ET0 expresses the
evaporating power of the atmosphere at a specific location and time of the year and does not consider the
crop characteristics and soil factors.” Although transpiration accounts for the majority of water loss to the
atmosphere (Jasechko et al., 2013), evaporation and transpiration occur simultaneously and there is no easy
way of distinguishing these two processes; they are considered together when atmospheric water demand is
estimated.
Evapotranspiration is a climate phenomenon because solar radiation, air temperature, air humidity and wind
speed determine the evaporation demand by the atmosphere. Thus, ET0 can be computed from
meteorological data. From the 1940s numerous methods were developed for computing ET0. The
International Commission for Irrigation (ICID), the Food and Agriculture Organization of the United Nations
(FAO), and the American Society of Civil Engineers (ASCE) have adopted the Penman‐Monteith (PM) method
(Allen et al., 1998; Penman, 1948 and 1963; Walter et al., 2000) as the standard for computing ET0 from
climate data. The PM method is widely used because it is predominantly a physically‐based approach that
can be used globally, and has been widely tested using lysimeter data obtained under a broad range of
climate conditions (Allen et al., 1994; Ventura et al., 1999; Itenfisu et al., 2000; López‐Urrea et al., 2006).
Therefore, the FAO PM method is recommended as the standard, and the use of older FAO or other
reference ET methods is no longer encouraged (Allen et al., 1998). In this study we used the PM method to
calculate ET0 from 1961 to 2011 for each of the 54 meteorological stations.
The FAO PM method was developed by defining the reference crop as a hypothetical crop with an assumed
height of 0.12 m, a surface resistance of 70 s m–1 and an albedo of 0.23. This closely approximates the
evaporation expected from an extensive surface of actively growing and adequately watered green grass of
uniform height, and is defined by the equation:
0.408∆900273
∆ 1 0.34
where ET0 is the reference evapotranspiration (mm day–1), Rn is the net radiation at the crop surface (MJm–2
day–1), G is the soil heat flux density (MJ m–2 day–1), T is the mean air temperature at a height of 2 m (°C), u2
is the wind speed at 2 m height (m s–1), es is the saturation vapor pressure (kPa), ea is the actual vapor
pressure (kPa), es–ea is the saturation vapor pressure deficit (kPa), is the slope vapor pressure curve
(dependent on temperature) (kPa °C–1) and is the psychrometric constant (kPa °C–1).
The main drawback of the PM method is the relatively large amount of data involved, as it requires data on
solar radiation, temperature, wind speed and relative humidity (Valiantzas, 2006). For this reason numerous
alternative methods have been developed to calculate ET0 using less data. In this study we also used six
other widely used methods that require much less information. This was done to assess the possible
influence of the selected ET0 method on the computation of drought severity in the IP. These methods are
described below:
a) The Thornthwaite equation (Thorthwaite, 1948)
This is one of the simplest and most widely used approaches to calculation of ET0, and only requires data on
monthly mean temperature. The ET0 (mm month–1) is obtained using the equation:
1610
where I is a heat index (calculated as the sum of 12 monthly index values i, which is derived from the mean
monthly temperature as
), m is a coefficient depending on I (
),and K is a correction coefficient computed as a function of
the latitude and month using the equation , where NDM is the number of days of the
month and N is the total daytime hours for the month.
b) The Linacre equation (Linacre, 1977)
Linacre simplified the PM equation in relation to a vegetation surface having an albedo of 25% and no water
limitations. In this method ET0 (mm day–1) is calculated using the equation:
514.1
5
Ti
492.079.171.775.6 22537 IEIEIEm
3012
NDMNK
500 100 15 0.0023 0.37 0.53 0.35 10.9⁄
80
where Tm = T + 0.006h, h is the elevation above sea level (m), A is the latitude in degrees, and Ran is the
difference between the average mean temperature of the warmest and coldest month.
c) The Hargreaves equation (Hargreaves and Samani, 1985)
This method only requires information on the maximum and minimum temperatures, and extraterrestrial
solar radiation. The ET0 (mm month–1) is calculated using the equation:
0.0023 . 17.8
where R is the difference between the maximum and minimum monthly average temperatures (°C), and Ra is
the extraterrestrial solar radiation (mm day–1)
d) The Turc equation (Turc, 1955)
This method is based on an empirical relationship in which ET0 is calculated using the relative humidity, the
average temperature, and the solar radiation. ET0 (mm month–1) is a function of the average relative
humidity. If the monthly average relative humidity is > 50%, ET0 = 0.40 [T/(T +15)] (23.884RS + 50). If the
monthly average relative humidity is < 50%, ET0 = 0.40 [T/(T +15)] (23.884RS + 50)[1+(50–RH)/70]. In these
equations Rs is the solar radiation (MJ m–2 day–1) and RH is the mean relative humidity (%).
e) The FAO‐Blaney‐Criddle equation (Doorenbos and Pruitt, 1975)
This is a modification of the original Blaney‐Criddle method, which includes the influence of radiation, wind
speed and relative humidity. The equation is derived from a calibration using lysimeter measurements. The
ET0 (mm day–1) is calculated using the equations (Frevert et al., 1983):
,
0.46 8.13 ,
0.0043 1.41 and
0.81917 0.0040922 1.0705 0.065649 0.0059684
0.000597
where RHmin is the minimum monthly average relative humidity (%) and n is the observed monthly average
number of sun hours (h).
f) The radiation method (Doorenbos and Pruitt, 1975)
The equation proposed for this method is:
∆∆
where Rs is the solar radiation (mm day–1). The coefficients a and b can be obtained according to Frevert et al.
(1983), where a = –0.3 and 1.0656 0.0012795 0.044953 0.00020033
0.000031508 0.0011026 .
Allen et al. (2008) detailed (chapter 3 of the FAO‐56 publication) the variables required to obtain the various
parameters in the equations listed above. These include: i) monthly average maximum and minimum air
temperatures (°C); ii) monthly average actual vapor pressure (ea; kPa); iii) average monthly net radiation (MJ
m–2 day–1); and iv) monthly average wind speed (m s–1) measured 2 m above ground level. Among these, ea is
not measured at meteorological stations, but can be calculated from relative humidity and temperature. In
addition, the monthly average net solar radiation is not commonly available from meteorological stations,
and this parameter is usually estimated from the monthly average of daily sunshine hours, measured using
sunshine duration recorders (e.g. the Campbell‐Stokes recorder). Among the other necessary parameters,
the soil heat flux density (G) was estimated using monthly mean temperatures. The extraterrestrial radiation
(Ra) was calculated based on the day of the year, and net and surface solar radiation (Rn and Rs, respectively)
were obtained using the extraterrestrial radiation and the relative sunshine duration. The psychrometric
constant was obtained using data for atmospheric pressure (kPa). The mean saturation pressure (es) and the
slope of the saturation vapor pressure curve () were obtained using monthly maximum and minimum
temperatures. Wind speed is measured at the meteorological stations, but commonly at 10 m above the
ground (the standard anemometer height). To adjust wind speed data to the standard height of 2 m above
ground, a logarithmic relationship was used (Allen et al., 1998).
1.3. Drought index calculation
Drought is among the most complex climatic phenomena affecting society and the environment (Wilhite,
1993). At the root of this complexity is the difficulty of quantifying drought severity. Droughts are identified
by their effects on various systems (e.g., agriculture, water resources, ecology, forestry, economy), but no
physical variable can be used as a measure to quantify drought severity. Thus, droughts are difficult to
pinpoint in time and space because of the complexity of establishing the time when a drought starts and
ends, and quantifying its duration, magnitude and spatial extent (Wilhite, 2000; Burton et al., 1978).
These problems explain the vast scientific effort devoted to developing tools to enable objective and
quantitative evaluation of drought severity. Drought impacts are commonly quantified using so‐called
drought indices, which are proxies based on climate information and are assumed to adequately quantify
the degree of drought hazard for sensitive systems. Recent reports have reviewed the development of
drought indices and compared their advantages and drawbacks (Heim, 2002; Keyantash and Dracup, 2002;
Mishra and Singh, 2010; Sivakumar et al., 2010).
The most widely used drought indices for drought analysis and monitoring are the SPI and the Palmer
Drought Severity Index (PDSI). The SPI was increasingly used during the last two decades because of its solid
theoretical development, robustness and versatility in drought analyses (Redmond, 2002). It is based on the
conversion of precipitation data to probabilities based on long‐term precipitation records computed for
different time scales. Probabilities are transformed to standardized series with an average of 0 and a
standard deviation of 1. The main advantage of the SPI is that it facilitates analysis of drought impacts at
various temporal scales (Edwards and McKee, 1997), which is advantageous because different systems and
regions can respond to drought conditions at very different time scales (Vicente‐Serrano and López‐Moreno,
2005; Szalai et al., 2000; Fiorillo and Guadagno, 2010; Lorenzo‐Lacruz et al., 2010; Khan et al., 2008; Vicente‐
Serrano et al., 2011). Several studies have demonstrated variation in the response of agricultural (Vicente‐
Serrano et al., 2006; Quiring and Ganesh, 2010; Potop et al., 2012) and ecological variables (Ji and Peters,
2003; Vicente‐Serrano, 2007; Pasho et al., 2011; Vicente‐Serrano et al., 2011) to different drought time
scales. Given its substantial advantages in quantifying and monitoring droughts, the SPI has been accepted
by the World Meteorological Organization (WMO) as the reference drought index. In the 'Lincoln Declaration
on Drought Indices', 54 experts from all regions of the world agreed on the use of a universal meteorological
drought index for more effective drought monitoring and climate risk management. They reached the
significant consensus agreement that the SPI should be used by national meteorological and hydrological
services worldwide to characterize meteorological droughts (Hayes et al., 2011). The WMO provides
standard guidelines and software to calculate the SPI (WMO, 2012).
The main problem with the SPI is that it is based only on precipitation. Its calculation relies on two
assumptions: i) that variability in precipitation is much higher than that of other variables that also affect
drought severity, including temperature and ETo; and ii) the other variables are stationary (i.e., they have no
temporal trend). In this scenario the importance of these other variables is negligible, and droughts are
controlled by temporal variability in precipitation. This explains why although the WMO recommends the SPI
for drought quantification.
However, studies focused on drought trends and changes under the current global warming scenario mainly
use the PDSI. The PDSI represents a landmark in the development of drought indices. It enables
measurement of both wetness (positive value) and dryness (negative values), based on the supply and
demand concepts of the water balance equation, and thus incorporates prior precipitation, moisture supply,
runoff and evaporation demand at the surface level (Karl, 1983 and 1986; Alley, 1984). The PDSI is calculated
based on precipitation and temperature data, as well as the water content of the soil. All the basic terms of
the water balance equation can be determined from those inputs, including evapotranspiration, soil
recharge, runoff, and moisture loss from the surface layer. Thus, the PDSI uses both precipitation and
evaporative demand of the atmosphere as the main inputs for calculation, and is sensitive to variations in
both terms. Hu and Willson (2000) assessed the effect of precipitation and temperature on the PDSI, and
found that the index responded equally to changes of similar magnitude in each variable. Only where the
temperature fluctuation was smaller than that of precipitation was variability in the PDSI controlled by
precipitation.
In a global‐scale study based on observed temperature evolution for the period 1900–2008, Dai (2011) used
the PDSI to confirm that drought severity is increasing as a consequence of observed warming. The
numerous known problems and deficiencies in use of the PDSI for drought quantification and monitoring
have been reviewed (Karl, 1986; Alley, 1984; Soulé, 1992; Akinremi et al., 1996; Weber and Nkemdirim
1998). One of the main problems is that the parameters necessary to calculate the PDSI are determined
empirically, and most of the data used in testing the index were derived in the USA and are not applicable to
other regions (Akinremi et al., 1996), significantly limiting geographical comparisons (Heim, 2002; Guttman
et al., 1992). This problem was partially solved by development of the self‐calibrated (sc) PDSI (Welles et al.,
2004), but the problems in spatial comparability in drought severity have not been completely solved. Wells
et al. (2004) stated that: “It is important to note that, while the (sc)PDSI is more spatially comparable than
the PDSI…, it is not as comparable as an index computed using nonlinear methods (e.g., the Standardized
Precipitation Index).” The problems of spatial comparability for the PDSI and (sc)PDSI were clearly illustrated
by Vicente‐Serrano et al. (2010), who showed that the PDSI represents water deficits at different time scales,
depending on the region under consideration. This problem was initially investigated by Guttman (1998),
who showed that the spectral characteristics of the PDSI varied from site to site. In other words, the time
scales of the PDSI and the sc‐PDSI are not fixed because they depend on the characteristics of the sites, and
vary spatially. This makes it difficult to assess what kind of deficit the index is representing, and makes
spatial comparisons problematic. The non‐normality of the (sc)PDSI series (which shows more frequent dry
than humid periods in different regions of the world), and uncertainties in estimates of the water field
capacity also limit its use. Thus, Karl (1986) analyzed the sensitivity of the PDSI to the water field capacity
parameter and, consistent with the findings of Weber and Nkemdirim (1998), reported that areas of greater
water field capacity are more likely to be affected by drought. Moreover, the PDSI lacks flexibility to adapt to
the intrinsic multi‐scalar nature of drought, which is necessary in determining the varied impacts of drought
for hydrological, ecological and agricultural systems (Vicente‐Serrano et al., 2011).
For these reasons the use of a robust non‐linear drought index, which can be calculated on different time‐
scales and can account for the effect of both precipitation and ET0 on drought severity, appears preferable to
use of the PDSI (or scPDSI). The SPEI resolves the main criticism of the SPI, namely that it is based on
precipitation data alone. It also combines the sensitivity of the PDSI to changes in evaporation demand
(caused by temperature fluctuations and trends), and the simplicity of calculation and the multi‐temporal
nature of the SPI. As the SPI, the SPEI is perfectly comparable in time and space, and across different time‐
scales, as it corresponds to a standard normal variable. Thus, the same SPEI values occur with the same
frequency in all regions of the world, independent of the climate characteristics of the region. This index
provides objective information on climatic drought conditions, as it relies only on climate data and is not
influenced by external variables. It is able to identify climate change processes related to changes in
precipitation and/or temperature, and can be used to assess the possible influences of warming. Moreover,
as with the PDSI it is not constrained by the method used to determine ET0 (Beguería et al., 2014). The SPEI
adapts the varied response times of hydrological variables to the climate variability (Vicente‐Serrano et al.,
2011), and it facilitates identification of the complexity of the vegetation response to various drought time
scales (Vicente‐Serrano et al., 2013). A recent comparison of the capacity of the SPI, SPEI and PDSI to identify
hydrological, ecological and agricultural droughts at the global scale (Vicente‐Serrano et al., 2012) showed
that, independently of the system analyzed, the drought indices calculated at different time scales (the SPEI
and the SPI) show greater correlation with the temporal variability of streamflow, soil moisture, tree‐ring
growth and crop production. The Palmer index, which lacks the flexibility of reflecting the intrinsic multi‐
scalar nature of droughts, systematically performed worse than the SPI and SPEI. Moreover, independently
of the variable of interest, the SPEI was more highly correlated than the SPI; this was particularly the case in
summer, when soil moisture and forests are affected by drought stress. Consequently, in the season in which
most drought‐related impacts occur (water supply restrictions, decreased soil moisture, reduced tree
growth, forest fires), and in which drought monitoring is most critical, the SPEI is better able to identify
drought impacts than the SPI. However, the SPI and SPEI are comparable in time and space, and therefore
comparison between these indices is a robust approach to assessing the effect of variability and change in
ET0 on drought severity.
In this study we calculated the SPI (from monthly precipitation series) and the SPEI (from monthly
precipitation and ET0 series) for 54 meteorological stations covering the IP and for regional weighted series
for the entire study area. For the SPI, among the various models evaluated the Pearson III distribution
showed greater adaptability to precipitation series at different time scales (Guttman, 1999; Vicente‐Serrano,
2006; Quiring, 2009) than other distributions. Therefore, we used the algorithm described by Vicente‐
Serrano (2006) to calculate 1‐ to 48‐month SPI values based on the Pearson III distribution and the L‐
moments approach to obtain the distribution parameters. For the SPEI we used a 3‐parameter log‐logistic
distribution.
2. Supplemental Tables and Figures
Station elevation latitude longitude
Melilla 47 35.27 ‐2.95
Jerez De La Frontera Aeropuerto 27 36.75 ‐6.05
Faro 7 37.01 ‐7.96
Granada Base Aérea 690 37.13 ‐3.63
Morón De La Frontera 87 37.15 ‐5.60
Sevilla Aeropuerto 34 37.42 ‐5.87
San Javier Aeropuerto 4 37.78 ‐0.80
Córdoba Aeropuerto 90 37.83 ‐4.83
Alcantarilla, Base Aérea 75 37.95 ‐1.22
Beja 246 38.02 ‐7.87
Alicante/Alacant 81 38.37 ‐0.48
Lisboa/Geofísico 77 38.72 ‐9.15
Badajoz Aeropuerto 185 38.88 ‐6.80
Albacete Base Aérea 702 38.95 ‐1.85
Portalegre 597 39.28 ‐7.42
Valencia 11 39.47 ‐0.35
Valencia Aeropuerto 69 39.48 ‐0.47
Toledo 515 39.88 ‐4.03
Cuenca 945 40.07 ‐2.12
Coimbra/Geofísico 141 40.20 ‐8.42
Getafe 620 40.30 ‐3.72
Madrid, Cuatro Vientos 690 40.37 ‐3.78
Madrid 667 40.40 ‐3.67
Madrid Aeropuerto 609 40.47 ‐3.55
Torrejón De Ardoz 607 40.48 ‐3.45
Puerto De Navacerrada 1894 40.78 ‐4.00
Tortosa 44 40.82 0.48
Molina De Aragón 1056 40.83 ‐1.87
Segovia, Instituto 990 40.93 ‐4.10
Salamanca Aeropuerto 790 40.95 ‐5.48
Daroca 779 41.10 ‐1.40
Porto/S. Pilar 93 41.13 ‐8.60
Vila Real 561 41.27 ‐7.72
Barcelona Aeropuerto 4 41.28 2.07
Zamora 656 41.50 ‐5.73
Lleida 192 41.62 0.58
Zaragoza Aeropuerto 263 41.65 ‐1.00
Valladolid Aeropuerto 846 41.70 ‐4.85
Soria 1082 41.77 ‐2.47
Bragança 690 41.80 ‐6.73
Huesca Aeropuerto 541 42.08 ‐0.32
Vigo Aeropuerto 261 42.23 ‐8.62
Burgos Aeropuerto 891 42.35 ‐3.62
Logroño Aeropuerto 353 42.45 ‐2.32
Ponferrada 534 42.55 ‐6.60
León Aeropuerto 916 42.58 ‐5.65
Pamplona Aeropuerto 459 42.77 ‐1.65
Santiago De Compostela Aeropuerto 370 42.88 ‐8.40
Bilbao Aeropuerto 42 43.28 ‐2.90
San Sebastián, Igueldo 251 43.30 ‐2.03
Hondarribia, Malkarroa 4 43.35 ‐1.78
A Coruña 58 43.35 ‐8.42
Santander Aeropuerto 5 43.42 ‐3.82
Santander, Ciudad 64 43.45 ‐3.82
Supplementary Table 1: Elevation and location of the meteorological stations used in this
study.
Variables Units magnitude of change
Mann‐Kendall tau p value
Annual
Climate
RH % ‐0.95 ‐0.57 0.000
Max. Temp. ºC 0.34 0.48 0.000
Min. Temp. ºC 0.26 0.47 0.000
Precip. mm. ‐20.03 ‐0.24 0.015
Wind speed m/s ‐0.01 ‐0.13 0.170
Sun. Hours hours 0.04 0.14 0.151
ET0
Penman‐Monteith mm. 23.53 0.49 0.000
Thonrthwaite mm. 14.04 0.49 0.000
Hargreaves mm. 13.69 0.40 0.000
Turc mm. 17.91 0.41 0.000
Linacre mm. 41.97 0.41 0.000
FAO‐Radiation mm. 12.86 0.59 0.000
FAO‐Blaney‐Criddle mm. 28.20 0.38 0.000
Summer
Climate
RH % ‐1.24 ‐0.45 0.000
Max. Temp. ºC 0.42 0.35 0.000
Min. Temp. ºC 0.39 0.52 0.000
Precip. mm. ‐2.78 ‐0.08 0.421
Wind speed m/s 0.00 ‐0.04 0.679
Sun. Hours hours ‐0.01 0.02 0.839
ET0
Penman‐Monteith mm. 12.92 0.31 0.001
Thornthwaite mm. 10.79 0.40 0.000
Hargreaves mm. 7.25 0.26 0.008
Turc mm. 9.12 0.27 0.005
Linacre mm. 19.66 0.33 0.001
FAO‐Radiation mm. 7.26 0.44 0.000
FAO‐Blaney‐Criddle mm. 13.01 0.22 0.021
Supplementary Table 2: Magnitude of change (decade‐1) and significance of trends in the
annual and summer regional averages of climate variables and ET0 estimates for the entire
Iberian Peninsula.
Supplementary Figure 1: Spatial distribution of the main meteorological stations in the Iberian
Peninsula and Portugal (hollow circles), and the 54 stations selected for the study (red circles).
Supplementary Figure 2: Spatial distribution of the 287 streamflow gauging stations used in
the study.
Supplementary Figure 3: A) Streamflow basin types based on the impoundment ratio. B) Average annual streamflow (in Hm3) recorded at each streamflow
gauging station (represented as the streamflow basin).
Supplementary Figure 4: Evolution of the SPEI at various time scales for the entire Iberian
Peninsula.
2-month
1960 1970 1980 1990 2000 2010
SP
EI
-3
-2
-1
0
1
2
3
6-month
1960 1970 1980 1990 2000 2010
SP
EI
-3
-2
-1
0
1
2
3
12-month
1960 1970 1980 1990 2000 2010
SP
EI
-3
-2
-1
0
1
2
3
24-month
1960 1970 1980 1990 2000 2010
SP
EI
-3
-2
-1
0
1
2
3
Supplementary Figure 5: A) Upper: Evolution of the regional Standardized Precipitation Index
(SPI) (blue columns) and the Standardized Precipitation Evapotranspiration Index (SPEI) (black
line) for the Iberian Peninsula from 1961 to 2011. The SPEI was determined from the
Hargreaves equation for calculation of ET0. B) Middle: percentage of surface area affected by
drought from 1961 to 2011, based on the SPI (blue) and the SPEI (red). The surface area
affected was selected according to a SPI/SPEI threshold of –1.28, which corresponds to 10% of
the events according to the probability distribution function. C) Bottom: Difference between
the SPI and the SPEI (SPI–SPEI) with respect to the surface area affected by drought. Liner fit is
included.
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
0
20
40
60
80
100
1960 1970 1980 1990 2000 2010
Ano
ma
lies
-3
-2
-1
0
1
2
3
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
-40
-20
0
20
40
60
80
A)
B)
C)
y = 0.021 x - 4.5
Supplementary Figure 6: As in Supplementary Figure 5, but determined using the Thornthwaite
equation.
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
0
20
40
60
80
100
1960 1970 1980 1990 2000 2010
Ano
ma
lies
-3
-2
-1
0
1
2
3
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
-40
-20
0
20
40
60
80
A)
B)
C)
y = 0.025 x - 5.9
Supplementary Figure 7: As in Supplementary Figure 5, but determined using the Linacre
equation.
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
0
20
40
60
80
100
1960 1970 1980 1990 2000 2010
Ano
ma
lies
-3
-2
-1
0
1
2
3
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
-40
-20
0
20
40
60
80
A)
B)
C)
y = 0.05 x - 13.5
Supplementary Figure 8: As in Supplementary Figure 5, but determined using the Turc
equation.
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
0
20
40
60
80
100
1960 1970 1980 1990 2000 2010
Ano
ma
lies
-3
-2
-1
0
1
2
3
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
-40
-20
0
20
40
60
80
A)
B)
C)
y = 0.031 x - 8.0
Supplementary Figure 9: As in Supplementary Figure 5, but determined using the FAO‐Blaney‐
Criddle equation.
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
0
20
40
60
80
100
1960 1970 1980 1990 2000 2010
Ano
ma
lies
-3
-2
-1
0
1
2
3
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
-40
-20
0
20
40
60
80
A)
B)
C)
y = 0.035 x - 9.2
Supplementary Figure 10: As in Supplementary Figure 5, but determined using the FAO‐
Radiation equation.
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
0
20
40
60
80
100
1960 1970 1980 1990 2000 2010
Ano
ma
lies
-3
-2
-1
0
1
2
3
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
-40
-20
0
20
40
60
80
A)
B)
C)
y = 0.021 x - 4.6
Supplementary Figure 11: A) Changes in the SPI (z‐units per decade) determined for each of
the 54 meteorological stations for the period 1961–2011. B) As in (A), but for the SPEI. ET0
input was estimated using the Hargreaves equation. C) Changes in the monthly differences
between the SPEI and the SPI (z‐units per decade) determined for each of the 54 stations for
the period 1961–2011. The changes were estimated using least square regression, with the
series of time as the independent variable.
Supplementary Figure 12: As in Supplementary Figure 11, but with ET0 input estimated using
the Thornthwaite equation.
Supplementary Figure 13: As in Supplementary Figure 11, but with ET0 input estimated using
the Linacre equation.
Supplementary Figure 14: As in Supplementary Figure 11, but with ET0 input estimated using
the Turc equation.
Supplementary Figure 15: As in Supplementary Figure 11, but with ET0 input estimated using
the FAO‐Blaney‐Criddle equation.
Supplementary Figure 16: As in Supplementary Figure 11, but with ET0 input estimated using
the FAO‐Radiation equation.
Supplementary Figure 17: A) Relationship between change in the SPEI (SPEI units decade‐1)
obtained by the SPEI calculated by means of the ET0 Penman‐Monteith and those forced by
Thornthwaite and Hargreaves ET0. Each dot represents the change at each of the 54
meteorological stations used in the study. The dashed line represents the least-square
regression. B) Box-plot showing the difference between the magnitude of change in the SPEI
calculated by Penman-Monteith ET0 and Hargreaves and Thornthwaite ET0.
Hargreaves Thornthwaite
Diff
eren
ce c
hang
e de
cade
-1
-0.20
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
Change (SPEI decade-1) Penman-Monteith
-0.6 -0.4 -0.2 0.0 0.2Cha
nge
(SP
EI
deca
de-1
) H
argr
eave
s
-0.6
-0.4
-0.2
0.0
0.2
Change (SPEI decade-1) Penman-Monteith
-0.6 -0.4 -0.2 0.0 0.2Cha
nge
(SP
EI
deca
de-1
) T
hor
nthw
aite
-0.6
-0.4
-0.2
0.0
0.2
R2 = 0.77R2 = 0.73
A)
B)
Supplementary Figure 18: Annual evolution (1961–2011) in the ET0 and the climate variables
involved in ET0 calculation averaged for 54 meteorological stations distributed across the
Iberian Peninsula.
Relative humidity
1960 1970 1980 1990 2000 2010
%
60
62
64
66
68
70
72Temp. max.
1960 1970 1980 1990 2000 2010
ºC
18.018.519.019.520.020.521.021.522.0
Temp. min.
1960 1970 1980 1990 2000 2010
ºC
7.5
8.0
8.5
9.0
9.5
10.0
10.5Wind speed
1960 1970 1980 1990 2000 2010
m s
-1
2.1
2.2
2.3
2.4
2.5
2.6
Sunshine duration
1960 1970 1980 1990 2000 2010
hou
rs
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
Precipitation
1960 1970 1980 1990 2000 2010
mm
300
400
500
600
700
800
900
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Penman-Monteith
1960 1970 1980 1990 2000 2010
mm
800
1000
1200
1400
1600
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Thornthwaite
1960 1970 1980 1990 2000 2010
mm
800
1000
1200
1400
1600
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Hargreaves
1960 1970 1980 1990 2000 2010
mm
800
1000
1200
1400
1600
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Turc
1960 1970 1980 1990 2000 2010
mm
800
1000
1200
1400
1600
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Linacre
1960 1970 1980 1990 2000 2010
mm
800
1000
1200
1400
1600
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Radiation
1960 1970 1980 1990 2000 2010
mm
800
1000
1200
1400
1600
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Blaney-Criddle
1960 1970 1980 1990 2000 2010
mm
800
1000
1200
1400
1600
1800
Supplementary Figure 19: As in Supplementary Fig. 18, but for summer (May to August).
Relative humidity
1960 1970 1980 1990 2000 2010
%
48505254565860626466
Temp. max.
1960 1970 1980 1990 2000 2010
ºC
232425262728293031
Temp. min.
1960 1970 1980 1990 2000 2010
ºC
11
12
13
14
15
16Wind speed
1960 1970 1980 1990 2000 2010
m s
-1
2.1
2.2
2.3
2.4
2.5
2.6
2.7
Sunshine duration
1960 1970 1980 1990 2000 2010
hour
s
8.5
9.0
9.5
10.0
10.5
11.0
11.5
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
Precipitation
1960 1970 1980 1990 2000 2010
mm
406080
100120140160180200220240
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Penman-Monteith
1960 1970 1980 1990 2000 2010
mm
400
500
600
700
800
900
1000
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Thornthwaite
1960 1970 1980 1990 2000 2010
mm
400
500
600
700
800
900
1000
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Hargreaves
1960 1970 1980 1990 2000 2010
mm
400
500
600
700
800
900
1000
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Turc
1960 1970 1980 1990 2000 2010
mm
400
500
600
700
800
900
1000
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Linacre
1960 1970 1980 1990 2000 2010m
m
400
500
600
700
800
900
1000
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Radiation
1960 1970 1980 1990 2000 2010
mm
400
500
600
700
800
900
1000
year vs hr_anual year vs hr_anual year vs hr_anual year vs hr_anual
ET0 Blaney-Criddle
1960 1970 1980 1990 2000 2010
mm
400
500
600
700
800
900
1000
Supplementary Figure 20: Box‐plot showing the magnitude of change (1961–2011) in the ET0
and the climate variables involved in ET0 calculation, averaged for 54 meteorological stations
distributed across the Iberian Peninsula.
Relative humidity
Annual Summer
% d
eca
de
-1
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5Temp. max.
Annual Summer
ºC d
eca
de-1
0.1
0.2
0.3
0.4
0.5
0.6
0.7Temp. min.
Annual Summer
ºC d
eca
de-1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7Wind speed
Annual Summer
m/s
de
cad
e-1
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
Sunshine duration
Annual Summer
ho
urs
de
cad
e-1
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20Precipitation
Annual Summer
mm
de
cad
e-1
-140
-120
-100
-80
-60
-40
-20
0
20
40ET0 Penman-Monteith
Annual Summer
mm
de
cad
e-1
0
10
20
30
40
50
60ET0 Thornthwaite
Annual Summer
mm
de
cad
e-1
0
10
20
30
40
50
60
ET0 Hargreaves
Annual Summer
mm
de
cad
e-1
0
10
20
30
40
50
60ET0 Turc
Annual Summer
mm
dec
ad
e-1
0
10
20
30
40
50
60ET0 Linacre
Annual Summer
mm
de
cad
e-1
0
10
20
30
40
50
60ET0 Radiation
Annual Summer
mm
de
cad
e-1
0
10
20
30
40
50
60
ET0 Blaney-Criddle
Annual Summer
mm
de
cad
e-1
0
10
20
30
40
50
60
Supplementary Figure 21: A) Evolution of the average summer pan (blue line) and Piché (red
line) evaporation at 19 meteorological stations in Spain (see Supplementary Fig. 29). The
Pearson’s r correlation for the common period (1985–2011) was 0.80 (p < 0.01). B) Evolution of
the average Penman‐Monteith ET0 (blue line) and Piché evaporation (red line) at 30
meteorological stations in Spain (see Supplementary Fig. 26). The Pearson’s r correlation for
the common period (1965–2011) was 0.89 (p < 0.01).
A)
1960 1970 1980 1990 2000 2010
Pic
hé e
vapo
ratio
n (m
m)
500
550
600
650
700
750
800
850
900
Pan
eva
pora
tion
(mm
)
650
700
750
800
850
900
950
1000
B)
1960 1970 1980 1990 2000 2010
Pic
hé e
vapo
ratio
n (m
m)
550
600
650
700
750
800
850
900
Pen
man
-Mon
teith
ET
0 (
mm
)
540
560
580
600
620
640
660
680
700
720
740
Supplementary Figure 22: Location of the gauging stations closest to the mouth of the main
Iberian Peninsula rivers, and the drainage basin corresponding to each station.
Supplementary Figure 23: Evolution of the average Standardized Streamflow Index (1961–2011) for the main
basins shown in Supplementary Figure 18, and for the natural, regulated and highly regulated basins of the
entire Iberian Peninsula.
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
SS
I
-3-2-10123
NATURAL
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
SS
I
-3-2-10123 REGULATED
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
SS
I
-3-2-10123 HIGHLY REGULATED
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
SS
I
-3-2-10123 MAIN
Supplementary Figure 24: Percentage of surface area affected for streamflow drought from 1961 to 2009,
based on the SSI for the natural, regulated and highly regulated basins of the entire Iberian Peninsula. The
surface area affected was selected based on a SSI threshold of –1.28, which corresponds to 10% of the
events according to the probability distribution function.
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
0
20
40
60
80
All
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
0
20
40
60
80
Natural
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
0
20
40
60
80
Regulated
1960 1970 1980 1990 2000 2010
% o
f su
rfac
e
0
20
40
60
80
Highly regulated
Supplementary Figure 25: A) Spatial distribution of the maximum summer correlation between the SSI and
the SPEI. B) Spatial distribution of the maximum summer correlation between the SSI and the SPI. C)
Difference between the SPEI and the SPI (SPEI – SPI).
Supplementary Figure 26: Spatial distribution of the pan (white circles) and Piché evaporimeters (yellow
circles) in Spain. Black circles indicate the location of stations having the series necessary to obtain Penman‐
Monteith ET0.
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