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.


the Linacre equation.


the Turc equation.


the FAO‐Blaney‐Criddle equation.


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


ET0 Penman-Monteith

1960 1970 1980 1990 2000 2010

mm

800

1000

1200

1400

1600


ET0 Thornthwaite

1960 1970 1980 1990 2000 2010

mm

800

1000

1200

1400

1600


ET0 Hargreaves

1960 1970 1980 1990 2000 2010

mm

800

1000

1200

1400

1600


ET0 Turc

1960 1970 1980 1990 2000 2010

mm

800

1000

1200

1400

1600


ET0 Linacre

1960 1970 1980 1990 2000 2010

mm

800

1000

1200

1400

1600


ET0 Radiation

1960 1970 1980 1990 2000 2010

mm

800

1000

1200

1400

1600


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


Precipitation

1960 1970 1980 1990 2000 2010

mm

406080

100120140160180200220240


ET0 Penman-Monteith

1960 1970 1980 1990 2000 2010

mm

400

500

600

700

800

900

1000


ET0 Thornthwaite

1960 1970 1980 1990 2000 2010

mm

400

500

600

700

800

900

1000


ET0 Hargreaves

1960 1970 1980 1990 2000 2010

mm

400

500

600

700

800

900

1000


ET0 Turc

1960 1970 1980 1990 2000 2010

mm

400

500

600

700

800

900

1000


ET0 Linacre

1960 1970 1980 1990 2000 2010m

m

400

500

600

700

800

900

1000


ET0 Radiation

1960 1970 1980 1990 2000 2010

mm

400

500

600

700

800

900

1000


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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evidence of increasing drought severity caused by temperature rise

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