Drought is a major driver of vegetation activity in Spain, with
significant impacts on crop yield, forest growth, and the occurrence of
forest fires. Nonetheless, the sensitivity of vegetation to drought
conditions differs largely amongst vegetation types and climates. We used a
high-resolution (1.1 km) spatial dataset of the normalized difference
vegetation index (NDVI) for the whole of Spain spanning the period from 1981 to
2015, combined with a dataset of the standardized precipitation
evapotranspiration index (SPEI) to assess the sensitivity of vegetation types
to drought across Spain. Specifically, this study explores the drought timescales at which vegetation activity shows its highest response to drought
severity at different moments of the year. Results demonstrate that – over
large areas of Spain – vegetation activity is controlled largely by the
interannual variability of drought. More than 90 % of the land areas
exhibited statistically significant positive correlations between the NDVI
and the SPEI during dry summers (JJA). Nevertheless, there are some
considerable spatio-temporal variations, which can be linked to differences
in land cover and aridity conditions. In comparison to other climatic regions
across Spain, results indicate that vegetation types located in arid regions
showed the strongest response to drought. Importantly, this study stresses
that the timescale at which drought is assessed is a dominant factor in
understanding the different responses of vegetation activity to drought.
Introduction
Drought is one of the major hydroclimatic hazards impacting land surface
fluxes (Baldocchi et al., 2004; Fischer et al., 2007; Hirschi et al., 2011),
vegetation respiration (Ciais et al., 2005), net primary production
(Reichstein et al., 2007; Zhao and Running, 2010), primary and secondary
forest growth (Allen et al., 2015), and crop yield (Lobell et al., 2015;
Asseng et al., 2015). Recently, numerous studies suggested an accelerated
impact of drought on vegetation activity and forest mortality under different
environmental conditions (Allen et al., 2010, 2015; Breshears et al., 2005)
with a reduction in vegetation activity and higher rates of tree decay (e.g.
Carnicer et al., 2011; Restaino et al., 2016). Nevertheless, a comprehensive
assessment of the impacts of drought on vegetation activity is a challenging
task. This is particularly because data on forest conditions and growth are
partial, spatially sparse, and restricted to a small number of sampled
forests (Grissino-Mayer and Fritts, 1997). Furthermore, the temporal
resolution of forest data is insufficient to provide deep insights into the
impacts of drought on vegetation activity (e.g. the official forest
inventories; Jenkins et al., 2003). In addition to these challenges, the
spatial and temporal data on crops are often limited, as they are mostly
aggregated to administrative levels and provided at the annual scale, with
minor information on vegetation activity across the different periods of the
year (FAO, 2018). To handle these limitations, numerous studies have
alternatively employed the available remotely sensed data to assess the
impacts of drought on vegetation activity (e.g. Ji and Peters, 2003; Wan et
al., 2004; Rhee et al., 2010; Zhao et al., 2017).
Several space-based products allow for quantifying vegetation conditions,
given that active vegetation responds dissimilarly to the electromagnetic
radiation received in the visible and near-infrared parts of the vegetation
spectrum (Knipling, 1970). As such, with the available spectral information
recorded by sensors on board satellite platforms, it is possible to
calculate vegetation indices and accordingly assess vegetation activity
(Tucker, 1979). In this context, several studies have already employed
vegetation indices not only to develop drought-related metrics (e.g. Kogan,
1997; Mu et al., 2013), but to determine the impacts of drought on vegetation
conditions as well (García et al., 2010; Vicente-Serrano et al., 2013;
Zhang et al., 2017). An inspection of these studies reveals that drought
impacts can be characterized using vegetation indices, albeit with a
different response of vegetation dynamics as a function of a wide-range of
factors, including – among others – vegetation type, bioclimatic
conditions, and drought severity (Bhuiyan et al., 2006; Vicente-Serrano,
2007; Quiring and Ganesh, 2010; Ivits et al., 2014).
Given the high interannual variability of precipitation, combined with the
prevailing semi-arid conditions across vast areas of the territory, Spain has
suffered from frequent, intense, and severe drought episodes during the past
decades (Vicente-Serrano, 2006). Nonetheless, in the era of temperature rise,
the observed increase in atmospheric evaporative demand (AED) during the last
decades has accelerated the severity of droughts (Vicente-Serrano et al.,
2014c), in comparison to the severity caused only by precipitation deficits
(Vicente-Serrano et al., 2014b; González-Hidalgo et al., 2018). Over
Spain, the hydrological and socio-economic impacts of droughts are
well-documented. Hydrologically, droughts are often associated with a
decrease in streamflow and reservoir storages (Lorenzo-Lacruz et al., 2010, 2013). The impacts of drought can extend further to
crops, leading to crop failure due to deficit in irrigation water (Iglesias
et al., 2003), and even in arable unirrigated lands (Austin et al., 1998;
Páscoa et al., 2017). Over Spain, numerous investigations also
highlighted the adverse impacts of drought on forest growth (e.g. Camarero et
al., 2015; Gazol et al., 2018; Peña-Gallardo et al., 2018a) and forest
fires (Hill et al., 2008; Lasanta et al., 2017; Pausas, 2004; Pausas and
Fernández-Muñoz, 2012).
Albeit with these adverse drought-driven impacts, there is a lack of
comprehensive studies that assess the impacts of drought on vegetation
activity over the entire Spanish territory, with a satisfactorily temporal
coverage. While numerous studies employed remotely sensed imagery and
vegetation indices to analyse spatial and temporal variability and trends in
vegetation activity over Spain (e.g. del Barrio et al., 2010; Julien et al.,
2011; Stellmes et al., 2013), few attempts have been made to link the
temporal dynamics of satellite-derived vegetation activity with climate
variability and drought evolution (e.g. Vicente-Serrano et al., 2006;
Udelhoven et al., 2009; Gouveia et al., 2012; Mühlbauer et al., 2016). An
example is González-Alonso and Casanova (1997), who analysed the spatial
distribution of droughts in 1994 and 1995 over Spain, concluding that the
most affected areas are semi-arid regions. In their comparison of the MODIS
normalized difference vegetation index (NDVI) data and the standardized
precipitation index (SPI) over Spain, García-Haro et al. (2014)
indicated that the response of vegetation dynamics to climate variability is
highly variable, according to the regional climate conditions, vegetation
community, and growth stages. A similar finding was also confirmed by
Vicente-Serrano (2007) and Contreras and Hunink (2015) in their assessment of
the response of NDVI to drought in semi-arid regions of northeast and
southeast Spain, respectively. With these comprehensive efforts, a
detailed spatial assessment of the links between droughts and vegetation
activity, which covers a long time period (decades), is highly desired for
Spain to explore the differences in the response of vegetation activity to
drought under different environments with various land cover and vegetation
types.
The overriding objectives of this study are (i) to determine the possible
differences in the response of vegetation activity to drought over Spain, as
a function of the different land cover types and climatic conditions, and
(ii) to explore the drought timescales at which vegetation activity highly
responds to drought severity. An innovative aspect of this study is that it
provides – for the first time – a comprehensive assessment of the response
of vegetation activity to drought using a multidecadal (1981–2015) high-spatial-resolution (1.1 km) NDVI dataset over the study region.
Data and methodsDatasetsNDVI data
Globally, there are several NDVI datasets, which have been widely used to
analyse NDVI variability and trends (e.g. Slayback et al., 2003; Herrmann et
al., 2005; Anyamba and Tucker, 2005) and to assess the links between NDVI and
climate variability and drought (e.g. Dardel et al., 2014; Vicente-Serrano et
al., 2015; Gouveia et al., 2016). Amongst these global datasets, the most
widely used are those derived from the Advanced Very High Resolution
Radiometer (AVHRR) sensor on board the NOAA satellites and those retrieved
from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Both
products have been widely employed to evaluate the possible influence of
drought on vegetation dynamics in different regions worldwide (e.g. Tucker et
al., 2005; Gu et al., 2007; Sona et al., 2012; Pinzon and Tucker, 2014; Ma et
al., 2015). While the Global Inventory Modeling and Mapping Studies (GIMMS)
dataset from NOAA AVHRR is available at a semi-monthly temporal resolution
for the period from 1981 onwards (Tucker et al., 2005; Pinzon and Tucker,
2014), its spatial resolution is quite low (64 km2), which makes it
difficult to capture the high spatial variability of vegetation cover over
Spain. However, the NDVI dataset derived from MODIS dates back only
to 2001 (Huete et al., 2002), which is insufficient to give insights into the
long-term response of vegetation activity to drought. To overcome these
spatial and temporal limitations, our decision was made to employ a recently
developed high-resolution spatial NDVI dataset (Sp_1Km_NDVI), which is
available at grid intervals of 1.1 km, spanning the period from 1981 onwards.
In accordance with the GIMMS dataset, Sp_1Km_NDVI is available at a
semi-monthly temporal resolution. This dataset has already been validated
(Vicente-Serrano et al., 2018), showing high performance in comparison to
other available NDVI datasets. As such, it can be used – with confidence –
to provide a multidecadal assessment of NDVI variability at high spatial
resolution, especially in areas of highly variable vegetation. Herein, it is
noteworthy to indicate that the data from the Sp_1Km_NDVI dataset was
standardized (sNDVI), so that each series has an average equal to zero and a
standard deviation equal to 1. This procedure is motivated by the strong
seasonality and spatial differences of vegetation activity over Spain.
Following this procedure, the magnitudes of all NDVI time series are
comparable over space and time. To accomplish this task, the data were fitted
to a log-logistic distribution, which shows better skill in standardizing
environmental variables, in comparison to other statistical distributions
(Vicente-Serrano and Beguería, 2016).
In order to limit the possible impact of changes in land cover on the
dependency between drought and vegetation cover, we assumed that strong
changes in NDVI can be seen as an indicator of changes in land cover. As
such, those pixels with strong changes in NDVI during the study period were
excluded from the analysis. These pixels were defined after an exploratory
analysis in which we tested different thresholds. Specifically, we excluded
those pixels that exhibited a decrease in the annual NDVI higher than 0.05
units or an increase higher than 0.15 units between 1981 and 2015. The
spatial distribution of these pixels (not shown here) concurs well with the
areas identified in earlier studies over Spain in which there was an abrupt
modification of the land cover type: creation of new irrigated lands (Lasanta
and Vicente-Serrano, 2012; Lecina et al., 2010; Stellmes et al., 2013;
Vicente-Serrano et al., 2018), urban expansion (Gallardo and
Martínez-Vega, 2016; Palazón et al., 2016; Serra et al., 2008),
agricultural abandonment (Lasanta et al., 2017), deforestation (Camarero et
al., 2015; Carnicer et al., 2011), reforestation (Ortigosa et al., 1990),
etc. Furthermore, to avoid the possible influence of spatial autocorrelation,
which can occur in areas with dominant positive changes in NDVI due to
excessive rural exodus and natural revegetation processes (Hill et al., 2008;
Vicente-Serrano et al., 2018), we detrended the standardized NDVI series by
means of a linear model. We then add the residuals of the linear trend to the
average of NDVI magnitude over the study period. A similar approach has been
adopted in several environmental studies (Olsen et al., 2013; Xulu et al.,
2018; Zhang et al., 2016). Correlations with the drought dataset were based
on the sNDVI.
Drought dataset
Due to its complicated physiological strategies to cope with water stress,
vegetation can show specific and even individual resistance and vulnerability
to drought (Chaves et al., 2003; Gazol et al., 2017, 2018). As such, it is
quite difficult to directly assess the impacts of drought on vegetation
activity and forest growth. Alternatively, drought indices can be an
appropriate tool to make this assessment, particularly with their calculation
at multiple timescales. These timescales summarize the accumulated climatic
conditions over different periods, which make drought indices closely related
to impact studies. Overall, to calculate drought indices, we employed data
for a set of meteorological variables (i.e. precipitation, maximum and
minimum air temperature, relative humidity, sunshine duration, and wind
speed) from a recently developed gridded climatic dataset (Vicente-Serrano et
al., 2017). This gridded dataset was developed using a dense network of
quality-controlled and homogenized meteorological records. Data are available
for the whole Spanish territory at a spatial resolution of 1.1 km, which is
consistent with the resolution of the NDVI dataset (Sect. 2.1.1). Based on
this gridded dataset, we computed the atmospheric evaporative demand (AED)
and the standardized precipitation evapotranspiration index (SPEI). We used
the reference evapotranspiration (ETo) as the most reliable way of
estimating the AED. ETo was calculated using the physically based FAO-56
Penman–Monteith equation (Allen et al., 1998). Conversely, the SPEI
was computed using precipitation and ETo data (Vicente-Serrano et al., 2010).
The SPEI is one of the most widely used drought indices and has thus been
employed to quantify drought in a number of agricultural (e.g.
Peña-Gallardo et al., 2018b), environmental (e.g. Vicente-Serrano et al.,
2012; Bachmair et al., 2018), and socio-economic applications (e.g. Bachmair
et al., 2015; Stagge et al., 2015). The SPEI is advantageous compared to the
Palmer Drought Severity Index (PDSI), as it is calculated at different timescales. In comparison to the standardized precipitation index (SPI) (McKee et
al., 1993), the SPEI does not account only for precipitation, but it also
considers the contribution of ETo in drought evolution.
In this work, the SPEI was calculated for the common 1- to 24-month
timescales, but here, given the semi-monthly availability of the data, we
calculated the corresponding 1- to 48-semi-monthly timescales. The preference
to use various timescales is motivated by our intention to characterize the
response of different hydrological and environmental systems to drought. It
is well-recognized that natural systems can show different responses to the
timescales of drought (Vicente-Serrano et al., 2011, 2013). The timescale
refers to the period in which antecedent climate conditions are accumulated
and it allows adaptation of the drought index to the drought impacts since
different hydrological and environmental systems show different response
sensitivities to the timescales of climate variability. This has been shown
for hydrological systems (López-Moreno et al., 2013; Barker et al.,
2016), but ecological and agricultural systems also show strong differences
in the response to different timescales of climatic droughts (Pasho et al.,
2011; Peña-Gallardo et al., 2018b) given different biophysical conditions
and the different strategies of vegetation types to cope with water stress
(Chaves et al., 2003; McDowell et al., 2008), which are strongly variable in
complex Mediterranean ecosystems. For instance, drought indices can
be calculated on flexible timescales since it is not known a priori the most
suitable period at which the NDVI responds. Herein, we also detrended and
standardized the semi-monthly SPEI data to be comparable with the de-trended
sNDVI.
Finally, we used the CORINE Land Cover for 2000
(https://land.copernicus.eu/pan-european/corine-land-cover, last
access: 21 May 2019) to determine how land
cover can impact the response of NDVI to drought severity. This map is
representative of the main classes of land cover in the study domain over the
period of investigation.
Statistical analysis
We used the Pearson's r correlation coefficient to assess the relationship
between the interannual variability of the sNDVI and SPEI. This association
was evaluated independently for each semi-monthly period of the year. Specifically, we calculated the correlation between the sNDVI for each
semi-monthly period and SPEI recorded in the same period, at 1- and 48-semi-monthly timescales. Significant correlations were set at p<0.05. Importantly, as the data of the sNDVI and SPEI were
de-trended, the possible impact of serial correlation on the correlation
between sNDVI and SPEI is minimized, with no spurious correlation effects
that can be expected from the co-occurrence of the trends. Similarly, as the
data were analysed for each semi-monthly period independently, our results
are free from any seasonality effect. Given that it is not possible to know a
priori the best cumulative period to explain the response of the vegetation
activity to drought variability, we retained for further analysis the maximum
correlation, independently of the timescale at which this is obtained.
Based on the correlation coefficients between the sNDVI and SPEI in the
study domain, we determined the semi-monthly period of the year and the SPEI
timescale at which the maximum correlation is found. This information was
then used to determine the spatial and seasonal variations according to the
different land cover categories. Finally, the average climate conditions
over the study domain, including aridity (precipitation minus ETo) and
average temperature, were related to the timescales at which the maximum
correlation between the sNDVI and SPEI was found.
Spatial distribution of the Pearson's r correlation coefficient
calculated between the sNDVI and different SPEI timescales for different
semi-monthly periods.
ResultsGeneral influence of drought on the sNDVI
Figure 1 shows an example of the spatial distribution of the Pearson's r
correlation coefficients calculated between the sNDVI and the SPEI at the
timescales of 1, 3, 6, and 12 months (2, 6, 12 and 24 semi-monthly
periods). Results are shown only for the second semi-monthly period of each
month between April and July. The differential response of the NDVI to the
different timescales of the SPEI is illustrated. As depicted, the 6-month
timescale was more relevant to vegetation activity in large areas of
southwestern and southeastern Spain during the second half of April. Conversely, vegetation activity was more determined by the 12-month SPEI
across the Ebro basin in northeastern Spain. This stresses the need to
consider different drought timescales to know the climate cumulative
period that mostly affects vegetation activity. The 6-month and 12-month
SPEIs
produced similar results during the second period of May, while the 12-month
timescale is more related to vegetation activity in June and July.
Spatial distribution of the maximum correlation between the sNDVI
and the SPEI during the different semi-monthly periods.
Figure 2 summarizes the maximum correlation between the sNDVI and the SPEI,
providing insights into the differential response of the NDVI to drought. It
can be noted that there are clear seasonal and spatial differences in the
response of sNDVI to the SPEI. The sNDVI is more related to the SPEI during
the warm season (MJJA). In contrast, the response of the sNDVI to drought is
less pronounced from September to April, albeit with some exceptions. One
example is the response of vegetation to drought alongside the southeastern
Mediterranean coastland, where the correlation between sNDVI and SPEI is
almost high all year. Table 1 summarizes the percentage of the
total area exhibiting significant or non-significant correlations over Spain
during the different semi-monthly periods. Positive (lower sNDVI with
drought) and statistically significant correlations are dominant across the
entire territory, but with a seasonal component. In particular, a higher
percentage of the territory shows positive and significant correlations
during the warm season (MJJA). From the middle of May to middle of September, more than
80 % of the study domain shows positive and significant correlations
between the sNDVI and the SPEI. A similar finding is also found between the
middle of June and the beginning of August. Figure 3 summarizes the average
correlations between the SPEI and sNDVI. As illustrated, there is a gradual
increase in the response of the sNDVI to the SPEI from the beginning of May
to the end of July, when the maximum average correlation is recorded. In
contrast, the correlations between the SPEI and sNDVI decrease progressively
from August to December.
Spatial average and standard error of the Pearson's r correlation
coefficient between the sNDVI and SPEI time series.
Percentage of the total surface area according to the different
significance categories of Pearson's r correlations between the sNDVI and
SPEI.
The response of the sNDVI to different timescales of the SPEI and seasons
is quite complex. Figure 4 shows the spatial distribution of the SPEI timescale at which the maximum correlation was found for each one of the 24
semi-monthly periods of the year. It can be noted that there are considerable
seasonal and spatial differences. Nonetheless, these differences are masked
with the estimated average values of the SPEI timescale recorded for the
semi-monthly periods (Fig. 5), which are less variable (oscillating between 18
and 22 semi-monthly periods – 9–11 months) throughout the year. In general,
the areas and periods with higher correlations are recorded at 7- and 24-semi-monthly timescales (3–12 months).
Spatial distribution of the SPEI timescales at which the maximum
correlation between the sNDVI and SPEI is found for each one of the
semi-monthly periods.
Average and standard error of the SPEI timescale at which the
maximum Pearson's r correlation coefficient between the sNDVI and SPEI is
found.
Land cover differences
There are differences in the magnitude and seasonality of the Pearson's r
correlation coefficients among all land cover types. Figure 6 shows the
average and standard error of the mean of the maximum Pearson's r
coefficients between the sNDVI and SPEI for the different land cover types
and the 24 semi-monthly periods. The magnitudes of correlation vary
considerably, as a function of land cover type, as well as the period of the
year in which the highest correlations are recorded. The unirrigated arable
lands show a peak of significant correlation between April and June. However,
this correlation decreases towards the end of the year. The majority of this
land cover shows positive and significant correlations between May and
September (Supplement Table S1), with percentages almost close to
100 %. Conversely, irrigated lands do not show such a strong
response to drought during the warm season. Even with the presence of a
seasonal pattern, it is less pronounced than the one observed for
unirrigated arable lands. Overall, irrigated areas are characterized by
positive and significant correlations between sNDVI and SPEI during
summertime (Table S2). Similarly, vineyards show a clear seasonal pattern,
albeit with a peak of maximum correlations during the late summer
(July to August) and early autumn (September to October) (Table S3). Conversely, olive groves show the highest correlation between the sNDVI and SPEI
during the second half of May and in October, suggesting a quasi-bimodal
response of the NDVI to drought. This pattern is also revealed in the
percentage of the surface area with significant correlations (Table S4). In
the same context, the areas of natural vegetation exhibit their maximum
correlation between the sNDVI and SPEI during summer months. The highest
correlations are found in July and August for the forest types, compared to
earlier June for the natural grasslands and the areas of sclerophyllous
vegetation. Conversely, the mixed forests tend to show lower
correlations than broad-leaved and coniferous forests. A quick inspection of
all these types of land cover indicates that the correlations between the
sNDVI and SPEI are generally positive and significant during summer months
(Tables S5 to S11).
Average and standard error of the Pearson's r correlation
coefficient between the sNDVI and SPEI for the different land cover types.
Large differences across vegetation types were found for the SPEI timescales
at which maximum correlations between sNDVI and the SPEI are found (Fig. 7).
For example, for unirrigated arable lands, the maximum correlation between
SPEI and sNDVI is found for timescales between 11 and 21 semi-monthly
periods. This indicates that crops in May–June (the period in which higher
correlations are recorded) respond mostly to the climate conditions recorded
between June and December of the preceding year. Irrigated lands show a clear
seasonal pattern, as maximum correlations are recorded at timescales between
12 and 18 semi-monthly periods (i.e. 6 to 9 months), mainly between November
and May. Conversely, the maximum correlations between sNDVI and SPEI during
summer are found for timescales between 25 and 28 semi-monthly periods.
Similar to irrigated lands, vineyards show a strong seasonality, responding
to longer timescales at the end of summertime. In contrast, natural
vegetation areas show a less seasonal response to SPEI timescales, which
mostly impact the interannual variability of sNDVI. The SPEI timescales, at
which the maximum correlation is found between sNDVI and SPEI, vary from 20
semi-monthly periods during the warm season (MJJAS) to 30 semi-monthly
periods during the cold season (ONDJFMA). This finding is evident for all
forest types and areas of sclerophyllous vegetation and mixed wood–scrub.
The only exception corresponds to natural grasslands, which show a response
to shorter SPEI timescales (i.e. 20 semi-monthly periods in winter and 15 in
spring and early summer).
Average and standard error of the SPEI timescale at which the
maximum Pearson's r correlation coefficient was found between the sNDVI and
SPEI for the different land cover types.
Influence of average climatic conditions
In addition to the impact of the timescale at which drought is quantified,
the response of vegetation activity to drought can also be closely related to
the prevailing climatic conditions. Figure 8 summarizes the spatial
correlation between aridity (P-ETo) and the maximum correlation between the
sNDVI and SPEI. For most of the semi-monthly periods of the year aridity is
negatively correlated with the maximum correlation between sNDVI and SPEI,
indicating that vegetation activity at arid sites is more responsive to
drought variability. This correlation is more pronounced for the period
between December and June. In contrast, this negative association becomes
weaker and statistically non-significant during warmer months (July to
August). Figure 9 illustrates the spatial correlation between mean air
temperature and the maximum correlation between the sNDVI and SPEI. Results
demonstrate similar results to those found for aridity, with a general
positive and significant correlation from March to June, followed by a
non-significant and weak correlation during summer months.
Scatterplots showing the relationships between the maximum
correlation obtained between the sNDVI and the SPEI and the climate aridity
(precipitation minus ETo). Given the high number of data, the significance of
the correlation was obtained using a bootstrap method. A total of 1000 random
samples of 30 data points each were extracted, from which correlations and
p values were obtained. The final significance was assessed by means of the
average of the obtained correlation coefficients and p values, which are
indicated in the figure.
Scatterplots showing the relationships between the maximum
correlation obtained between the sNDVI and the SPEI and the average air
temperature. Given the high number of points, the significance of correlation
was obtained by means of 1000 random samples of 30 cases from which
correlations and p values were obtained. The final significance was
assessed by means of the average of the obtained p values.
Nonetheless, these general patterns vary largely as a function of land cover
type (Supplement Figs. S1 to S11). For
example, in unirrigated arable lands, there is strong negative correlation
between aridity and the sNDVI–SPEI maximum correlation from March to May: a
period that witnesses the peak of vegetation activity in this land cover
type. This also coincides with the period of the highest average correlations
between the sNDVI and SPEI. Taken together, this demonstrates that
unirrigated arable lands located in the most arid areas are more sensitive
to drought variability than those located in humid regions. As opposed to
unirrigated arable lands, the correlations with aridity are found to be
statistically non-significant in all periods of the year for irrigated lands,
vineyards, and olive groves. Nevertheless, for the different natural
vegetation categories, the correlations are negative and statistically
significant during large periods. The mixed agricultural–natural vegetation
areas show a significant correlation between October and July, with stronger
association at the beginning of the summer season. Broadleaved and coniferous
forests, scrub, and pasturelands also show a negative relationship between
the spatial patterns of the sNDVI–SPEI correlations and aridity.
As depicted in Fig. 9, the relationship between the sNDVI–SPEI correlation
and air temperature shows that the response of vegetation activity to drought
is modulated by air temperature during springtime. This implies that warmer
areas are those in which the sNDVI is more controlled by drought. A
contradictory pattern is found during warmer months, in which the role of air
temperature in modulating the impact of drought on vegetation activity is
minimized. The relationships between air temperature and the NDVI–SPEI
correlation vary among the different land cover types (Figs. S12 to S22). For
example, in unirrigated arable lands, the positive and statistically
significant correlation is found in the period from March to May, indicating
that the response of the sNDVI to SPEI tends to coincide spatially with areas
of warmer conditions. As observed for aridity, the relationship between the
sNDVI and SPEI in irrigated lands is less associated with the spatial
patterns of air temperature. A similar pattern is recorded for vineyards and
olive groves. Nevertheless, the areas of natural vegetation show a clear
relationship between air temperature and the sNDVI–SPEI correlations. In the
mixed agriculture and natural vegetation areas, we found a statistically
significant positive association between the sNDVI and SPEI from October to
May. Conversely, this association is less evident during summer months.
This general association during springtime, combined with the lack of
association during summertime, can also be seen for other natural vegetation
types such as broad-leaved and coniferous forests, natural grasslands,
sclerophyllous vegetation, and mixed wood–scrub.
Box plots showing the climate aridity values, as a function of the
SPEI timescales at which the maximum correlation between the sNDVI and SPEI
is recorded.
We also analysed the dependency between climatic conditions (i.e. aridity and
air temperature) and the SPEI timescale(s) at which the maximum correlation
between the sNDVI and SPEI is recorded. Figure 10 shows the values of aridity
corresponding to SPEI timescales at which the maximum correlation between
the sNDVI and SPEI is found for each semi-monthly period. The different
box plots indicate complex patterns, which are quite difficult to interpret.
Overall, less arid areas show stronger correlations at longer timescales
(25–42 semi-monthly periods) during springtime. In the same context, the
regions with maximum correlations at short timescales (1–6 months) tend to
be located in less arid regions that record their maximum correlations at
timescales between 7 and 24 semi-monthly periods. This suggests that the
most arid areas mostly respond to the SPEI timescales between 6 and
12 months, compared to short (1–3 months) or long (> 12 months)
SPEI timescales in more humid regions. In contrast, during the summer season,
the interannual variability of the sNDVI in the arid areas is mostly
determined by the SPEI recorded at timescales higher than 6 months
(12 semi-monthly periods), while responding to short SPEI timescales
(< 3 months) over the most humid regions.
Box plots showing air temperature values, as a function of the
SPEI timescales at which the maximum correlation between the sNDVI and SPEI
is recorded.
Also, we found links between the spatial distribution of air temperature and
the SPEI timescales at which maximum correlation between the sNDVI and SPEI
is recorded (Fig. 11). In early spring, short SPEI timescales dominate in
warmer areas, compared to long SPEI timescales in colder regions. A
contradictory pattern is observed from June to September, with a dominance of
shorter SPEI timescales in colder areas and longer SPEI timescales in
warmer regions.
The spatial distribution of all land cover types, after excluding irrigated
lands in which the anthropogenic factors dominate, is illustrated in Fig. 12.
Mixed forests are located in the most humid areas, while vineyards, olive
groves, unirrigated arable lands, and the sclerophyllous natural vegetation
are distributed at the most arid sites. Nevertheless, there is a gradient of
these land cover types in terms of their response to drought, as those types
located under more arid conditions show a stronger response of vegetation
activity to drought than those located in humid environments. For example,
the mixed forests show lower correlations than crop types and other
vegetation areas. This pattern is more evident during the different
semi-monthly periods, albeit with more differences during spring and autumn.
In summer, these differences are much smaller between land cover categories,
irrespective of aridity conditions.
Scatterplots showing the relationship between the mean annual
aridity and the maximum correlation found between the sNDVI and the SPEI in
the different land cover types analysed in this study. Vertical and
horizontal bars represent one-fourth the standard deviation around the mean
values.
There are also differences in the average SPEI timescale at which the
maximum sNDVI–SPEI correlation is obtained (Fig. 13). However, these
differences are complex, with noticeable seasonal differences in terms of the
relationship between climate aridity and land cover types. In spring and late
autumn, land cover types located in more arid conditions tend to respond to
shorter SPEI timescales than those located in more humid areas. This pattern
can be seen in late summer and early autumn, in which the most arid land
cover types (e.g. vineyards and olive groves) tend to respond at longer SPEI
timescales, compared to forest types (mostly the mixed forests), which are
usually located under more humid conditions.
Scatterplots showing the relationship between the mean annual
aridity and the SPEI timescale at which the maximum correlation is found
between the sNDVI and SPEI for the different land cover types. Vertical and
horizontal bars represent one-fourth the standard deviation around the mean
values.
Discussion
This study assesses the response of vegetation activity to drought in Spain
using a high-resolution (1.1 km) spatial NDVI dataset that dates back to
1981 (Vicente-Serrano et al., 2018). Based on another high-resolution
semi-monthly gridded climatic dataset, drought was quantified using the
standardized precipitation evapotranspiration index (SPEI) at different timescales (Vicente-Serrano et al., 2017).
Results demonstrate that vegetation activity over large parts of Spain is
closely related to the interannual variability of drought. In summer more
than 90 % of the study domain shows statistically significant positive
correlations between the NDVI and SPEI. A similar response of the NDVI to
drought is confirmed in earlier studies in different semi-arid and subhumid
regions worldwide, including northeastern Brazil (e.g. Barbosa et al., 2006),
the Sahel (e.g. Herrmann et al., 2005), central Asia (e.g. Gessner et al.,
2013), Australia (e.g. De Keersmaecker et al., 2017), and California (e.g.
Okin et al., 2018). Albeit with this generalized response, our results also
show noticeable spatial and seasonal differences in this response. These
differences can be linked to the timescale at which the drought is
quantified, in addition to the impact of other dominant climatic conditions (e.g.
air temperature and aridity).
The response of vegetation activity to drought variability
This study stresses that the response of vegetation activity to drought is
more pronounced during the warm season (MJJAS), in which vast areas of the
Spanish territory show statistically significant positive correlation between
the sNDVI and SPEI. This seasonal pattern can be attributed to the phenology
of vegetation under different land cover types. In the cold season, some
areas, such as pastures and non-permanent broadleaf forests, do not have any
vegetation activity. Other areas, with coniferous forests, shrubs, and cereal
crops, show a low vegetation activity. As such, irrespective of the recorded
drought conditions, the response of vegetation to drought would be low during
wintertime. This behaviour is also enhanced by the atmospheric evaporative
demand (AED), which is generally low in winter in Spain (Vicente-Serrano et
al., 2014d), with a lower water demand of vegetation and accordingly low
sensitivity to soil water availability. Austin et al. (1998) indicated that
soil water recharge occurs mostly during winter months, given the low water
consumption by vegetation. However, in spring, vegetation becomes more
sensitive to drought due to temperature rise. Accordingly, the photosynthetic
activity, which determines NDVI, is highly controlled by soil water
availability (Myneni et al., 1995). In this study, the positive spatial
relationship found between air temperature and the sNDVI–SPEI correlation
reinforces this explanation. In spring, we found low correlations between the
NDVI and SPEI, even in cold areas. In contrast, summer warm temperatures
reinforce vegetation activity, but with some exceptions such as cereal
cultivations, dry pastures, and shrubs. This would explain why the response of
vegetation activity to the SPEI is stronger during summer in vast areas of
Spain.
Also, this study suggests clear seasonal differences in the response of the
NDVI to drought, and in the magnitude of the correlation between the NDVI and
the SPEI, as a function of the dominant land cover. These differences are
confirmed at different spatial scales, ranging from regional and local (e.g.
Ivits et al., 2014; Zhao et al., 2015; Gouveia et al., 2017; Yang et al.,
2018) to global (e.g. Vicente-Serrano et al., 2013), Over Spain, the
unirrigated arable lands, natural grasslands, and sclerophyllous vegetation
show an earlier response to drought, mainly in late spring and early summer.
This response is mainly linked to the vegetation phenology dominating in
these land covers, which usually reach their maximum activity in late spring
to avoid dryness and temperature rise during summer months. The root systems
of herbaceous species are not very deep, so they depend on the water storage
in the most superficial soil layers (Milich and Weiss, 1997), and they could
not survive during the long and dry summer in which the surface soil layers
are mostly depleted (Martínez-Fernández and Ceballos, 2003). This
would explain an earlier and stronger sensitivity to drought also shown in
other semi-arid regions (Liu et al., 2017; Yang et al., 2018; Bailing et
al., 2018). Conversely, maximum correlations between the NDVI and the
SPEI are recorded during summer months in the forests but also in wood
cultivations like vineyards and olive groves. In this case, the maximum
sensitivity to drought coincides with the maximum air temperature and
atmospheric evaporative demand (Vicente-Serrano et al., 2014d). This pattern
would be indicative of a different adaptation strategy of trees in comparison
to herbaceous vegetation, since whilst herbaceous cover would adapt to the
summer dryness generating the seed bank before the summer (Peco et al., 1998;
Russi et al., 1992), the trees and shrubs would base their adaptation on
deeper root systems, translating the drought sensitivity to the period of
highest water demand and water limitation.
In addition to the seasonal differences among land cover types, we have shown
that in Spain herbaceous crops show a higher correlation between the NDVI and
the SPEI than most natural vegetation types (with the exception of the
sclerophyllous vegetation). This behaviour could be explained by three
different factors: (i) a higher adaptation of natural vegetation to the
characteristic climate of the region where drought is a frequent phenomenon
(Vicente-Serrano, 2006), (ii) the deeper root systems that allow shrubs and
trees to obtain water from the deep soil, and (iii) cultivated lands that tend to
be typically located in drier areas than natural vegetation. Different
studies showed that the vegetation of dry environments tends to have a more
intense response to drought than subhumid and humid vegetation (Schultz and
Halpert, 1995; Abrams et al., 1990; Nicholson et al., 1990; Herrmann et al.,
2016). Vicente-Serrano et al. (2013) analysed the sensitivity of the NDVI in
the different biomes at a global scale and found a spatial gradient in the
sensitivity to drought, which was more important in arid and semi-arid
regions.
Response to the average climatology
In this study we have shown a control in the response of the NDVI to drought
severity by the climatic aridity. Thus, there is a significant correlation
between the spatial distribution of the climatic aridity and the sensitivity
of the NDVI to drought, mostly in spring and autumn. This could be explained
because in more humid environments the main limitation to vegetation growth
is temperature and radiation rather than water, so not all the water
available would be used by vegetation reflected in a water surplus as surface
runoff. This characteristic would make the vegetation less sensitive to
drought in the cold season. Drought indices are relative metrics in
comparison to the long-term climate with the purpose of making drought
severity conditions comparable between areas of very different climate
characteristics (Mukherjee et al., 2018). This means that in humid areas the
corresponding absolute precipitation can be sufficient to cover the
vegetation water needs although drought indices provide information on
below-average conditions. Conversely, in arid regions a low value
of a drought index is always representative of limited water availability,
which would explain the closer relationship between the NDVI and the SPEI.
Here we also explored if the general pattern observed in humid and semi-arid
regions is also affected by the land cover, and found that the behaviour in
the unirrigated arable lands is the main reason for the global
pattern. Herbaceous crops show that aridity levels have a clear control of
the response of the NDVI to drought during the period of vegetation activity.
Nevertheless, after the common harvest period (June) this control by aridity
mostly disappears. This is also observed in the grasslands and in the
sclerophyllous vegetation, and it could be explained by the low vegetation
activity of the herbaceous and shrub species during the summer, given the
phenological strategies to cope with water stress with the formation of the
seeds before the period of dryness (Chaves et al., 2003). The limiting
aridity conditions that characterize the regions in which these vegetation
types grow would also contribute to explaining this phenomenon. Conversely,
the forests, both broadleaved and coniferous, also show a control by aridity
in the relationship between the NDVI and the SPEI during the summer months
since these land cover types show the peak of the vegetation activity during
this season.
In any case, it is also remarkable that the spatial pattern of the NDVI
sensitivity to drought in forests is less controlled by aridity during the
summer season, curiously the season in which there are more limiting
conditions. This could be explained by the NDVI saturation under high levels
of leaf area index (Carlson and Ripley, 1997) since once the tree tops are
completely foliated the electromagnetic signal is not sensitive to additional
leaf growth. This could explain the less sensitive response of the forests to
drought in comparison to land cover types characterized by lower leaf area
(e.g. shrubs or grasslands). Nevertheless, we do not think that this
phenomenon can totally explain the decreased sensitivity to drought with
aridity in summer since the dominant coniferous and broadleaved forests in
Spain are usually not characterized by a 100 % leaf coverage
(Castro-Díez et al., 1997; Molina and del Campo, 2012), so large signal
saturation problems are not expected. Conversely, the ecophysiological
strategies of forests to cope with drought may help explain the observed
lower relationship between aridity during the summer months. Experimental
studies suggested that the interannual variability of the secondary growth
could be more sensitive to drought than the sensitivity observed by the
photosynthetic activity and the leaf area (Newberry, 2010). This could be a
strategy to optimize the storage of carbohydrates, suggesting that forests in
dry years would prioritize the development of an adequate foliar area in
relation to the wood formation in order to maintain respiration and
photosynthetic processes. Recent studies by Gazol et al. (2018) and
Peña-Gallardo et al. (2018b) confirmed that, irrespective of forest
species, there is a higher sensitivity of tree-ring growth to drought,
compared to the sensitivity of the NDVI. The different spatial and seasonal
responses of vegetation activity to drought in our study domain can also be
linked to the dominant forest species and species richness, which have been
evident in numerous studies (e.g. Lloret et al., 2007). Moreover, this might
also be attributed to the ecosystem physiological processes, given that
vegetation tends to maintain the same water use efficiency under water stress
conditions, regardless of vegetation types and environmental conditions
(Huxman et al., 2004). This would explain that – independently of the
aridity conditions – the response of the NDVI to drought would be similar.
Here, we demonstrated that the response of the NDVI to drought is similar
during summer months, even with the different land cover types and
environmental conditions.
The importance of drought timescales
A relevant finding of this study is that the response of the NDVI is highly
dependent on the timescale at which drought is quantified. Numerous studies
indicated that the accumulation of precipitation deficits during different
time periods is essential to determine the influence of drought on the NDVI
(e.g. Malo and Nicholson, 1990; Liu and Kogan, 1996; Lotsch et al., 2003; Ji
and Peters, 2003; Wang et al., 2003). This is simply because soil moisture is
impacted largely by precipitation and the atmospheric evaporative demand over
previous cumulative periods (Scaini et al., 2015). Moreover, the different
morphological, physiological, and phenological strategies would also explain
the varying response of vegetation types to different drought timescales.
This finding is confirmed in previous works using NDVI and different timescales of a drought index (e.g. Ji and Peters, 2003; Vicente-Serrano, 2007),
but also using other variables like tree-ring growth (e.g. Pasho et al.,
2011; Arzac et al., 2016; Vicente-Serrano et al., 2014a). This study confirms
this finding, given that there is a high spatial diversity in the SPEI timescale at which vegetation has its maximum correlation with the NDVI. These
spatial variations, combined with strong seasonal differences, are mainly
controlled by the dominant land cover types and aridity conditions. In their
global assessment, Vicente-Serrano et al. (2013) found gradients in the
response of the world biomes to drought, which are driven mainly by the timescale at which the biome responds to drought in a gradient of aridity. Again,
the response to these different timescales implies not only different
vulnerabilities of vegetation to water deficits, but also various strategies
from plants to cope with drought. In Spain, we showed that the NDVI responds
mostly to the SPEI at timescales of around 20 semi-monthly periods (10 months),
but with some few seasonal differences (i.e. shorter timescales in spring
and early autumn than in late summer and autumn). Herein, it is also
noteworthy to indicate that there are differences in this response, as a
function of land cover types. Overall, during the periods of highest
vegetation activity, the herbaceous land covers (e.g. unirrigated arable
lands and grasslands) respond to shorter SPEI timescales than other forest
types. This pattern can be seen in the context that herbaceous covers are
more dependent on the weather conditions recorded during short periods. These
vegetation types could not reach deep soil levels, which are driven by
climatic conditions during longer periods (Changnon and Easterling, 1989;
Berg et al., 2017). In contrast, the tree root systems would access these
deeper levels, having the capacity to buffer the effect of short-term
droughts, albeit with more vulnerability to long droughts that ultimately
would affect deep soil moisture levels. This pattern has been recently
observed in southeastern Spain when comparing herbaceous crops and vineyards
(Contreras and Hunink, 2015). Recently, Okin et al. (2018) linked the
different responses to drought timescales between scrubs and chaparral
herbaceous vegetation in California to soil water depletion at different
levels.
Albeit with these general patterns, we also found some relevant seasonal
patterns. For example, irrigated lands responded to long SPEI timescales
(> 15 months) during summer months, whilst they responded to
shorter timescales (< 7 months) during spring and autumn. This
behaviour can be linked to water management in these areas. Specifically,
during spring months, these areas do not receive irrigation and accordingly
vegetation activity is determined by water stored in the soil. Conversely, summer irrigation depends on the water stored in the dense net of
reservoirs existing in Spain; some of them have a multiannual capacity. Water
availability in the reservoirs usually depends on the climate conditions
recorded during long periods (1 or 2 years) (López-Moreno et al.,
2004; Lorenzo-Lacruz et al., 2010), which determine water availability for
irrigation. This explains why vegetation activity in irrigated lands depends
on long timescales of drought. Similarly, vineyards and olive groves respond
to long SPEI timescales during summer. These cultivations are highly
resistant to drought stress (Quiroga and Iglesias, 2009). However, these
adapted cultivations can be sensitive to severe droughts under extreme summer
dryness. In comparison to other natural vegetation, mixed forests show
a response to shorter SPEI timescales. This could be explained by the low
resistance of these forest species to water deficits (e.g. the different fir
species located in humid mountain areas; Camarero et al., 2011, 2018).
Here, we also showed that climate aridity can partially explain the response
of the NDVI to the different SPEI timescales. In Spain, the range of the
mean aridity recorded by the mean land cover types is much lower than that
observed at the global scale for the world biomes (Vicente-Serrano et al.,
2013). This might explain why there are no clear patterns in the response of
the land cover types to the aridity gradients and the SPEI timescales at
which the maximum correlation between the NDVI and SPEI is found.
Nevertheless, we found some seasonal differences between the cold and warm
seasons. In summer, the NDVI responds to longer SPEI timescales, as opposed
to the most humid forests that respond to shorter timescales. This stresses
that – in addition to aridity – the degree of vulnerability to different
duration water deficits, which are well-quantified using the drought timescales, may contribute to explaining the spatial distribution of the main
land cover types across Spain given different biophysical conditions, but
also the different strategies of vegetation types to cope with water stress
(Chaves et al., 2003; McDowell et al., 2008), which are strongly variable in
complex Mediterranean ecosystems.
Conclusions
The main conclusions of this study are as follows.
Vegetation activity over large parts of Spain is closely related to the
interannual variability of drought.
The response of vegetation activity to drought is more pronounced during the
warm season, which is attributed to the phenology of vegetation under
different land cover types.
There are clear seasonal differences in the response of the NDVI to drought.
Natural grasslands and sclerophyllous vegetation show an earlier response to
drought.
There is a control in the response of the NDVI to drought severity by the
climatic aridity, which is partially controlled by the land cover.
The response of the NDVI is highly dependent on the timescale at which
drought is quantified although there are differences in this response, as a
function of land cover types.
Data availability
The drought index dataset is available
at http://monitordesequia.csic.es (Begueria et al., 2019). The NDVI
data are available upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/nhess-19-1189-2019-supplement.
Author contributions
SMVS designed
the research; SMVS performed research. CAM, MTB, NMH, and MPG worked on data
generation. MG, SB, FDC, MPG and IN assisted with data processing and
figure creation. SMVS and AEK drafted the paper and all the authors
contributed to the writing.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Hydroclimatic
extremes and impacts at catchment to regional scales”. It is not associated
with a conference.
Financial support
This research has been supported by the Spanish Commission of Science and Technology and FEDER (grant no. PCIN-2015-220), the Spanish Commission of Science and Technology and FEDER (grant no. CGL2014-52135-C03-01), the Spanish Commission of Science and Technology and FEDER (grant no. CGL2017-83866-C3-3-R), the Spanish Commission of Science and Technology and FEDER (grant no. CGL2017-82216-R), WaterWorks 2014 (grant no. 690462, IMDROFLOOD), the JPI Climate (grant no. 690462, INDECIS), and WaterWorks 2015 (FORWARD grant).
Review statement
This paper was edited by Chris Reason and reviewed by two
anonymous referees.
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