Introduction
Drought is a naturally occurring environmental phenomenon and a major natural
hazard that can have devastating impacts on regional water resources,
agriculture, industry, and other social-ecological systems, with far-reaching
impacts in an increasingly globalized and uncertain world (IPCC, 2013;
Sternberg, 2011). Although still among the least understood extreme weather
events affecting larger areas, droughts have proved to be the costliest and
most widespread of natural disasters (Bryant, 2005; Wilhite, 2000b). This is
primarily due to their usually lengthy duration, severity, and large spatial
extent, sometimes reaching continental scales and lasting for many years
(Sheffield et al., 2009). Generally, droughts can affect all components of
the hydrological cycle, from its origin as a deficit in precipitation (P;
Dai, 2011; Palmer, 1965; McKee et al., 1993), to its combination with high
evapotranspiration losses that can lead to a deficit in soil moisture and
subsequent manifestation into a hydrological drought (Tallaksen and Stahl,
2014).
A review of key drought concepts (e.g. classification and indices) and the
relation between droughts and large-scale climate indices has been carried
out by Mishra and Singh (2010). However, due to the wide variety of sectors
affected by droughts, their diverse spatial and temporal structures, the
interdependence across climatic, hydrologic, geomorphic, ecological, and
societal variables, and the demand placed on water supply by different users,
there is no universal definition of droughts and associated impacts. The most
used drought classification is that initially proposed by Dracup et
al. (1980) and later integrated by Wilhite and Glantz (1985), and
Wilhite (2000a). Based on the degree of water deficit, droughts are often
classified into three types including (1) meteorological, (2) agricultural,
and (3) hydrological. Further details on drought classification and
definitions are found in Mishra and Singh (2010), Dai (2011), and Van Loon et
al. (2016).
Studies on regional drought characteristics are important and should be
incorporated in water resource management efforts (Mishra and Singh, 2011;
Wheater and Gober, 2013). Of particular interest is the analysis of drought
occurrence over Canada, a country in which drought is among the most costly
natural hazards, particularly in the interior Prairie region; see e.g. Bonsal et
al. (2011a). During the period 1950–2010, nationwide annual mean surface air
temperature, T, increased by 1.5 ∘C (Vincent et al., 2012). Being
the second largest country in the world and with a large continental
interior, this rapid warming has been accompanied by significant changes in
many other hydroclimatic elements in different parts of the country,
including increases in P (Mekis and Vincent, 2011), decreases in the
duration of snow cover (Brown and Braaten, 1998), and decreases in annual
streamflow (Zhang et al., 2001). Climate projections also indicate that many
regions of Canada will likely experience increasing drought risk by the end
of the 21st century (Masud et al., 2017; Bonsal et al., 2013; Dibike et
al., 2017).
Historically, most areas of Canada have experienced periodic droughts with
different durations, severities, and marked spatial extent, but the
agricultural belt of the Canadian Prairies has tended to be highly
susceptible to droughts due in part to its location in the lee of the Rocky
Mountains and its strong dependence on rain-fed agriculture (Shabbar and Skinner, 2004). In particular, devastating drought
events over western Canada during the 1890s, 1910s, 1930s, 1960s, 1980s,
1999–2005, and most recently in 2015 have been identified by using a
variety of drought indicators at various scales (Bonsal et al.,
2011b; Bonsal and Regier, 2007; Szeto et al., 2016). The 1961 drought (the
worst single year drought on the Prairies, with about 50 % of normal
growing season precipitation) led to a total net farm income drop of 48 %
(USD 300 million) compared with the previous year (Bonsal et al., 1999).
The drought of 1988 had many impacts on the agricultural sectors of Canada,
including wind erosion, livestock, incomes, farm management, crop
production, and prices. In addition, the sparse snow cover and high spring
Ts resulted in little or no spring runoff from Prairie watersheds in 1988,
such that the mean runoff volume was 60 to 70 % of the normal amount (Wheaton
et al., 1992). Furthermore, the 1999–2005 drought which was at its most
severe between 2001 and 2002 was felt across Canada but was concentrated in the
Prairies, and cost the regional economy an estimated USD 3.6 billion in lost
agricultural output (Council of Canadian Academies, 2013).
The uncertainty of drought characterization in Canada in an era of changing
climate and increasing pressure from competing water users poses a major
challenge to sustainable water management. A better understanding of the
spatial distribution of drought, and its frequency, intensity, and duration
is thus required. Increased knowledge of these drought characteristics and
their relationship to large-scale ocean–atmosphere forcing is necessary for
predicting seasonal drought severity, as well as for planning for impacts
due to future climate change. Previous studies have documented significant
links between low-frequency internal climate variability and the Canadian
hydroclimate. For example, positive phases of the Pacific Decadal
Oscillation (PDO) and El Niño–Southern Oscillation (ENSO) have been
associated with warm winter T in western and central Canada (Bonsal et
al., 2001; Shabbar and Yu, 2012; Shabbar and Khandekar, 1996) and a reduction
of snow cover in western Canada (Brown and Braaten, 1998). A
review of the association of large-scale variability and low streamflows
over Canada was conducted by Bonsal and Shabbar (2008). They found a higher
frequency of low-flow events to coincide with warmer/drier conditions during
El Niño events and positive phases of the PDO and the Pacific–North
American (PNA) pattern. Nazemi et al. (2017) investigated the
major drivers of annual streamflow variability in the headwaters of the
Canadian Prairies during the 20th century and found the PDO to
significantly determine monotonic trends and shifts in the central tendency
of annual mean streamflow.
Fleming and Quilty (2006) quantified the effects of
organized modes of climate variability upon groundwater resources, by
examining the influence of ENSO on water levels in shallow aquifers in
British Columbia. They found water levels to be above average during La
Niña years and below average during El Niño years, an indication of
variability in winter and spring P that recharges the aquifer systems.
Similarly, Tremblay et al. (2011) analyzed the variability of
groundwater systems for three different regions across Canada and their
linkage to the North Atlantic Oscillation (NAO), the Arctic Oscillation
(AO), the PNA, and ENSO. Their findings indicated that groundwater
variability in the Prince Edward Island region is mostly modulated by the
NAO and AO, in Manitoba it is influenced by the PNA, while for Vancouver
Island NAO, AO, and ENSO showed the highest influence. Perez-Valdivia et al. (2012) found variability in groundwater levels in the Canadian Prairies
in the 2–7-year and 7–10-year bands to be highly influenced by ENSO while
oscillation modes in the 18–22-year band reflected a negative correlation
with the PDO index.
Study area showing topographic and hydrographic features of
Canada.
The development of a comprehensive drought monitoring system capable of
providing early warning of a drought's onset, severity, persistence, and
spatial extent in a timely manner is a critical component in establishing a
national drought policy or strategy. However, such nationwide drought
assessments in Canada are hampered partly by observational uncertainties.
The paucity and heterogeneous distribution of P and T estimates are an
important limitation of drought characterization. Ground-based measurements
(e.g. gauges) are limited, especially over the Rocky Mountains and north of
the 60th parallel, and suffer from inaccuracies associated with cold
climate processes (Wang and Lin, 2015; Wong et al., 2016; Asong et al.,
2017; Asong et al., 2016). For this purpose, it is worthwhile to study the
long-term time series of P and T regarding their non-homogeneous climatic and
hydrological properties. It is also important to identify homogeneous
regions within Canada with distinct drought features for improved drought
risk assessment and for a more efficient water resource management at the
regional level. So far, most studies on droughts have been limited to Canada
south of 60∘ N (Masud et al., 2015; Bonsal et al., 2013; Dibike et al.,
2017). Nevertheless, we have not come across studies that attempt to
establish the link between nationwide drought characteristics (e.g. spatial
structure, temporal patterns, periodicities) and the large-scale
ocean–atmospheric modes of variability in a comprehensive manner.
This study aims to fill these gaps by providing a comprehensive analysis of
historical droughts over the whole of Canada. Drought events are
characterized by the Standardized Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2010) over various temporal scales
(1, 3, 6, and 12 consecutive months, 6 months from April to September,
and 12 months from October to September). First, trends in the SPEI are
investigated by means of the Mann–Kendall test. Major patterns of long-term
change and periodicity of drought events are then characterized using the
rotated empirical orthogonal function and continuous wavelet transform
techniques, respectively. In addition, potential key drivers of drought are
investigated using wavelet coherence analysis, with a special emphasis on
the role played by large-scale modes of climate variability. Finally, due to
the uncertainty associated with climate variables, especially in the northern
and mountainous regions where ground-based measurements are inevitably
limited (Zhang et al., 2000), this study utilizes and compares
two common Canada-wide gridded data sets (monthly P and T) for the period 1950–2013.
The paper is organized as follows. Section 2 provides a description of the
data and analysis methods. Section 3 discusses the detailed characteristics
of drought over different regions of Canada by applying the different
aforementioned statistical analyses to the drought index. This section also
discusses the physical and dynamical mechanisms driving the observed dry
episodes in the country. Finally, a summary and conclusions are given in
Sect. 4.
Materials and methods
Study area
The study area comprises the entire Canadian landmass (Fig. 1). The region
includes several major river systems including the Great Lakes–St.
Lawrence River system, which is one of the largest freshwater resources
globally. Topography plays an important role in shaping regional climates,
ranging from wet maritime on the coasts to dry continental across the
Prairies and Boreal Plain. Snowfall is restricted to winter months
(approximately October to April depending on region). The occurrence,
intensity, and timing of seasonal P greatly influence the functioning of
ecosystems in various terrestrial ecozones in this region (Hogg et al., 2000). Based on the period 1950–2013,
mean annual P varied from more than 2460 mm on the west and 1260 mm on the
east coastal regions to less than 360 mm in the interior Prairie (southern
central) and northern (above 60∘ N) regions (Fig. 2). The long-term
monthly (January–December) minimum and maximum Ts ranged from -30 to
+15 ∘C (see Sect. 2.2 below for data sources). Characterized by a
highly variable hydroclimate and diminishing water resources, southern parts
of Canada are home to cities with the highest population densities and
support a vibrant agro-based economy that was hard-hit by the most severe
and prolonged droughts of 1988, 1999–2005, and 2015, as well as severe
floods of 2011, 2013, and 2014 (Wheater and Gober, 2015; Pomeroy et al.,
2016).
Mean annual climatology of precipitation, minimum temperature (Tmin), and
maximum temperature (Tmax) for the period 1950–2013.
Data sources
Gridded observations – ANUSPLIN
The Australian National University Spline (ANUSPLIN) implementation of
trivariate thin plate smoothing splines (Hutchinson et al.,
2009) has been used to provide gridded climate data over continental Canada,
available on a 0.0833 grid spacing (∼ 10 km). Variables include monthly
minimum T (Tmin), maximum T (Tmax), and P amounts. Station data from Environment
and Climate Change Canada observing sites were interpolated onto the
high-resolution grid using the ANUSPLIN smoothing splines, with longitude,
latitude, and elevation as interpolation predictors (McKenney
et al., 2011). Prior to interpolation, observed station data (Fig. S1 in the Supplement) were
quality-controlled and corrected for station relocation, changes in the
definition of the climate day, and trace P amounts. Hopkinson et al. (2011) showed that annual mean absolute interpolation errors in
ANUSPLIN were limited to 1.0 ∘C for Tmax, 1.3 ∘C for Tmin, and about
9 % for annual P over southern Canada.
Gridded observations – CANGRD
The Canadian gridded (CANGRD) data originate from the second generation of
Adjusted and Homogenized Canadian Climate Data – Daily Temperature and Precipitation
(http://open.canada.ca/data/en/dataset/d6813de6-b20a-
46cc-8990-01862ae15c5f, last access: 25 April 2018), with over 330
locations (Fig. S2) for T and 460 for total P (note that these numbers
are not constant over time). These data have been quality-controlled and
adjusted to account for known changes in measurement practices. In
particular, records from stations separated by less than 10 km were merged so
that correlations between stations would be small. See Mekis and Vincent (2011) for a detailed discussion on merging techniques and trends in
the mean climatologies of these data. For CANGRD, these data were
interpolated to evenly spaced (50 km) grids using Gandin's optimal
interpolation (Gandin, 1966) technique. As in the case
of ANUSPLIN, monthly P, Tmax, and Tmin values were extracted from
1950–2013 and used in the analyses.
Teleconnection indices
To analyze the key drivers of drought events over Canada, six large-scale
climate anomalies that have been linked to hydroclimatic variability over
Canada (Shabbar and Khandekar, 1996; Asong et al., 2015; Zhao et al., 2013;
Perez-Valdivia et al., 2012; Bonsal and Shabbar, 2008; Fleming and Quilty,
2006; Nazemi et al., 2017) and/or North America (Ropelewski and Halpert,
1986) are investigated. They include the Pacific Decadal Oscillation (PDO;
Mantua and Hare, 2002), North Atlantic Oscillation (NAO; Hurrell and Van
Loon, 1997), Pacific–North American (PNA; Barnston and Livezey, 1987),
Arctic Oscillation (AO; Zhou et al., 2001), Atlantic Multidecadal Oscillation
(AMO; Enfield et al., 2001), and Multivariate El Niño/Southern
Oscillation Index (MEI; Wolter, 1987; Wolter and Timlin, 2011). Monthly
values of all indices are sourced from https://www.esrl.noaa.gov/psd/
(last access: 25 April 2018) for the period 1950–2013.
Drought index calculation and drought identification
Many quantitative metrics have been developed and used for identification and
monitoring of droughts (Mishra and Singh, 2011; Raible et al., 2017). A
variety of these indices measure, in most cases, how much P and T for a
given period deviate from historical averages. Examples include the Palmer
drought severity index (PDSI; Palmer, 1965), Palmer hydrological drought index
(Palmer, 1965), self-calibrated Palmer drought severity index (Wells et al.,
2004), standardized precipitation index (SPI; McKee et al., 1993), the SPEI
(Vicente-Serrano et al., 2010), and multivariate standardized drought index
(Hao and AghaKouchak, 2013). In Canada, most drought analyses (Dibike et al.,
2016; Masud et al., 2017) have utilized climate-based indices since the T
and P variables are readily available for longer periods and span larger
areas, compared to hydrologic variables such as soil moisture and streamflow.
In this study, we make use of the SPEI as a meteorological proxy for drought
quantification.
As detailed in Vicente-Serrano et al. (2010), the SPEI is a multi-scalar
drought index based on a water balance approach that uses the monthly
difference between P and potential evapotranspiration (PET) to analyze
wet/dry spells over multiple timescales. SPEI involves calculating monthly PET and then subtracting this from the corresponding monthly P to
obtain the climatic water balance. Several derivations have been put forth
for calculating PET, including the widely used Penman (Penman, 1948),
Thornthwaite (Thornthwaite, 1948), Priestley–Taylor (Priestley and Taylor,
1972), and Hargreaves (Hargreaves et al., 1985) methods. However, most of
these approaches require long records for solar radiation, Ts, wind speed,
and air pressure, which are not readily available in Canada and many regions
of the world. Reanalysis products are an alternative data set for historical
drought analysis in Canada and could lead to robust estimates of PET based on
the Penman–Monteith algorithm (Maidment, 1993). However, they have been
found to be uncertain compared to observations (Wong et al., 2017). A more
suitable product with close performance to observations is the Global
Environmental Multiscale (GEM) numerical weather prediction model output
(Côté et al., 1998). However, it is limited in record length
(2002–present). Therefore, the Hargreaves method (Hargreaves, 1994) which
simply uses Tmin and Tmax for estimating PET is employed in this study.
Once PET is calculated, the difference between P and PET for the month j
is calculated as in Eq. (1):
Categories of dryness/wetness degree according to the SPEI values (McKee et al.,
1993).
Categories
SPEI classifications
Extremely dry
≤-2.00
Severely dry
-1.99 to -1.50
Moderately dry
-1.49 to -1.00
Near normal
-0.99 to 0.99
Moderately wet
1.00 to 1.49
Severely wet
1.50 to 1.99
Extremely wet
≥2.00
Qj=Pj-PETj,
where Qjvalues represent monthly water surplus or deficit.
To compute SPEI, the monthly Qjvalues are first standardized
with respect to the long-term monthly mean values. The 1-month SPEI is
generally representative of meteorological drought, while timescales between
3 and 6 months are considered as an agricultural drought index. Longer scales
such as 6 and 12 months are used to represent hydrological drought and are
useful for monitoring surface water resources (Beguería et al., 2014;
Hayes et al., 2011). To ascertain the variability of spatio-temporal patterns
for different types of droughts, the SPEI was used at different timescales,
namely, at 1 (SPEI1), 3 (SPEI3), 6 (SPEI6), and 12 (SPEI12) consecutive
months from January–December, at 6 months during the warm season
(April–September, SPEI6Apr-Sep), and at 12 months during the
hydrologic year (October–September, SPEI12Oct-Sep). SPEI1,
SPEI3, SPEI6, and SPEI12 account for the sub-annual variability of droughts,
while SPEI6Apr-Sep and SPEI12Oct-Sep account for the
inter-annual variability. A drought event occurs any time the SPEI is
continuously negative and reaches an intensity of -1.0 or less, and it ends
when SPEI becomes positive. Whenever a drought event has been detected with a
start and termination month, drought properties such as duration, magnitude,
and frequency can be determined. SPEI categories are shown in Table 1. Unless
indicated, we focus mostly on other SPEI timescales except SPEI1, which at 1
month is mainly a meteorological drought index.
Trend analysis
Long-term trends in drought intensity and variability (inter-annual) are
analyzed using the SPEI time series at each grid point. This enables
investigation of the percentage of grid points with increasing/decreasing
trends during the period 1950–2013 based on the ANUSPLIN- and CANGRD-derived
SPEI values. Pre-whitening as described in Yue et al. (2002) is first applied
to the monthly SPEI anomalies to remove lag-1 autocorrelation, since serial
correlation is generally recognized to influence trends in auto-correlated
time series, which may distort the power of the Mann–Kendall test.
Pre-whitening is limited to a low-order autoregressive model, i.e. AR(1),
since it can falsify the structure of variability in time series across
timescales (particularly with higher order models) (Razavi and Vogel, 2018).
The pre-whitened SPEI values on different timescales are then used for
detecting statistically significant (p<0.05) trends on a pixel
basis. Further details on Mann–Kendall trends can also be found in Hamed and
Rao (1998).
Empirical orthogonal function analysis
Hydroclimatological data are often characterized by non-linearity and high
dimensionality. Thus, the challenging task is to find ways to reduce the
dimensionality of the system to as few modes as possible by expressing the
data in such a way as to highlight their similarities and differences
(Hannachi et al., 2007). The empirical orthogonal function (EOF) analysis (also
known as principal component analysis, PCA) is among the most widely and
extensively used method in hydroclimate sciences for accomplishing such tasks
(Richman, 1986; Wilks, 2011; Preisendorfer and Mobley, 1988). EOF is employed
to find hydroclimate subregions that experienced similar drought features
during the period 1950–2013. The EOF consists of computing the covariance
matrix of the SPEI series with the corresponding eigenvalues and eigenvectors
(Uvo, 2003).
Following the rule by North et al. (1982), the sampling errors at 95 %
confidence level of the eigenvalues associated with the leading components
were estimated in order to establish how many modes to retain for rotation.
To achieve more stable and physically explainable patterns, a rotation of the
retained components with the varimax procedure (Richman, 1986) was applied.
The patterns defined in this way are referred to as rotated empirical
orthogonal functions (REOFs). In summary, the REOF (spatial patterns) values
indicate the spatial representativeness of each rotated temporal component (RPC; temporal patterns). Subsequently, we obtained the most revealing
patterns of drought evolution across Canada and determined the spatial extent
of each component series by mapping the factorial matrix values (i.e.
correlation between each REOF and the original SPEI series). Finally, the
time variability of the selected RPCs of SPEI was examined for possible
trends in the identified subregions using linear regression. The slopes of
the trends were computed by applying the method of least squares linear
fitting to the time series.
Wavelet analysis
After delimitating hydroclimate subregions that experienced similar drought
features, the RPC time series corresponding to the REOFs was then analyzed
in a time–frequency domain (to reveal dominant oscillations) by means of the
continuous wavelet transform (CWT). Subsequently, by utilizing the wavelet
coherence (WCO) technique (Grinsted et al., 2004; Torrence and Compo,
1998; Addison, 2016), the relationships between the dominant oscillations and
large-scale climate indices that have possibly modulated historical drought
patterns across Canada are investigated. The CWT projects the
spectral-temporal characteristics of a time series onto a time–frequency
plane from which the dominant periodicities and their duration can be
distinguished (Fugal, 2009; Grinsted et al., 2004).
The wavelet power spectrum (WPS) is defined as the squared modulus of the
CWT (Jiang et al., 2014; Hao et al., 2012), and represents the signal
energy at a specific scale (period) and time. From the WPS, the various
periodicities and the time intervals of their occurrence can be determined.
For a given time series yn, with n =1,2,3,…,N,
the CWT is given by
Wns=∑n′=1Nδts0.5yn′ψ*n′-nδts,
where Wns are the wavelet coefficients, n is the time
index describing the location of the wavelet in time, s is the wavelet
scale, and δt is the sampling interval. The function ψ is the
mother wavelet, and the asterisk (*) denotes its complex conjugate.
The Morlet wavelet was used since it provides a good balance between time
and frequency domains and is suitable when the purpose is to extract
dominant signals (Grinsted et al., 2004). The statistical
significance of the CWT was assessed against a red noise background at a
95 % confidence interval. The CWT function creates a cone of influence
(COI) that delimitates a region of the WPS beyond which edge effects become
important and the power could be suppressed (Torrence and
Compo, 1998).
Wavelet analysis can also be used to identify the covariance between two
time series. This can be done using the concept of wavelet coherence (Grinsted et al., 2004). Wavelet coherence (WCO) reveals
local similarities between two time series and may be considered a local
correlation coefficient in the time–frequency (time period) plane. For
climatological time series, WCO can be used to identify their possible
teleconnection with large-scale atmospheric drivers. The WCO between two
time series can be computed by normalizing and smoothing their cross wavelet
spectrum:
WnXYs=WnXsWnY*s,
where WnXs and WnYs represent
the WPS of the time series xi and yi, respectively. The statistical significance (p<0.05) of
the WCO is estimated using Monte Carlo methods with respect to a red-noise
spectrum, resulting in significant periodicities of coherence delineated by
significance contours. As in the CWT, regions outside of the COI should be
interpreted with caution (Torrence and Compo, 1998).
Results and discussion
Spatial structure of long-term climatic water balance
Figure 3 shows the average monthly (January–December) water surplus/deficit
(mm) as defined in Eq. (1) for ANUSPLIN and CANGRD. It is evident that,
apart from the Pacific/Atlantic maritime, most of the continental Canadian
interior experienced moisture deficits, with the Prairie being the most
affected region (this region also has the highest climatological Tmin and
Tmax, and some of the least mean annual P during the study period – Fig. 2).
Other than the coastal areas, there is a general east to west moisture
deficit pattern, mostly determined by the P and T pattern. For comparison,
5.8 % (33.3 %) of CANGRD points experienced moisture surplus (deficit),
and 4.3 % (41.6 %) for ANUSPLIN. This implies that ANUSPLIN showed a
relatively higher tendency towards dryness relative to CANGRD.
Spatial structure of long-term mean monthly water surplus/deficit
(mm), i.e. P-PET, derived from ANUSPLIN (a) and CANGRD (b).
Percentage of grids with decreasing (Dec.) and increasing (Inc.)
trends for the SPEI.
ANUSPLIN
CANGRD
Inc.
Dec.
Inc.
Dec.
SPEI1
8.9
14.7
8.6
5.9
SPEI3
4.1
10.6
5.4
3.0
SPEI6
2.8
9.6
3.7
2.3
SPEI12
1.4
10.0
2.9
4.0
SPEI6Apr-Sep
0.9
6.2
2.2
4.0
SPEI12Oct-Sep
1.2
10.0
2.7
3.1
Trends in SPEI at each grid point for ANUSPLIN and CANGRD. Only
significant values are shown on the map. Brown indicates decreasing and
green is increasing. (a) SPEI at 1 month, (b) SPEI at 3 months, (c) SPEI at
6 months, (d) SPEI at 12 months, (e) SPEI at 6 months from
Apr–Sep, and (f) SPEI at 12 months of the water/hydrologic year, i.e. Oct–Sep. Trends
are significant at 95 % confidence level.
Percentage of variance explained by the first two varimax rotated
loadings (REOFs) of the SPEI at various timescales, computed using
observations from ANUSPLIN and CANGRD data sets.
ANUSPLIN
CANGRD
REOF1
REOF2
REOF1
REOF2
SPEI1
15.0
13.2
19.5
11.9
SPEI3
15.8
13.8
18.9
12.9
SPEI6
17.9
14.4
19.1
12.7
SPEI12
20.6
13.3
19.1
13.0
SPEI6Apr-Sep
19.5
12.6
16.5
11.7
SPEI12Oct-Sep
20.5
13.4
19.3
13.1
Long-term trends
Figure 4 depicts the spatial structure of long-term (1950–2013) SPEI trends
at various timescales. Only grid points with significant trends are shown.
It is noteworthy that significant trends largely occur in spatially isolated
blocks. Decreasing trends are observable in the southern parts of the
Prairies, the foothills of the Rocky Mountains, and Pacific maritime regions,
whereas increasing trends are limited to a small area in the north and
parts of the Atlantic coast to the east, similar to the water balance shown in
Fig. 3.
Table 2 shows the percentage of grids with decreasing and increasing trends
for ANUSPLIN and CANGRD. For both data sets, the percentage generally
decreases with increasing SPEI timescale. ANUSPLIN has a higher tendency
towards dryness (decreasing trends) unlike CANGRD. Using SPEI12 as an
example, 10 % of ANUSPLIN pixels experienced decreasing trends compared to
4 % in the case of CANGRD. Conversely, apart from SPEI1, CANGRD showed a
strong tendency towards wetness (increasing trends) relative to ANUSPLIN.
These differences can be attributed partly to the inputs (e.g. the number of
stations considered for gridding ANUSPLIN is larger than the number of
stations of the CANGRD dataset), estimation methods, and spatial resolution,
which are different for both data sets (see Sect. 2.2).
Spatial patterns of drought
Concerning the EOF analysis, and taking into account the percentage of
variance explained by each rotated component (REOF), two main patterns were
identified for subsequent discussion and analysis. Table 3 presents the
explained variances of varimax rotated components relative to the considered
SPEI timescales and data sets. The first two components (for both data
sets) explain about 28 to 33.9 % of the total variance depending on
the timescale, with the minimum and maximum variances observed for SPEI1
and SPEI12, respectively, in the case of ANUSPLIN. For CANGRD,
SPEI6Apr-Sep and SPEI12 explained the minimum and maximum variances,
respectively.
Figure 5 shows that between the two main components (REOF), the regions with
higher correlations (>0.7) do not overlap, with clearly
spatially disjunctive structure. For all SPEI timescales, the loading
patterns of the first component (REOF1) with a maximum explained temporal
variance of 20.6 % (SPEI12, ANUSPLIN) and 19.5 % (SPEI1, CANGRD)
highlights mainly the drought evolution on the interior Prairie ecozone of
Canada. This semi-arid region is relatively low-lying and characterized by
high hydroclimate variability and some of the least annual mean P. The second
component (REOF2) explains a maximum variance of around 13.8 % (SPEI3) in
the case of ANUSPLIN and 13.1 % (SPEI12Oct-Sep) for CANGRD. REOF2 is
mainly representative of the northern central part of Canada (with the least
annual mean P), including the Taiga Shield, Taiga Plains, Southern Arctic, and
Taiga Cordillera ecozones, with positive correlations across all timescales
and data sets. In summary, the foregoing analyses suggest that the Prairies
and northern central regions are the leading droughts modes for Canada, in
that they are well captured by the two data sets, with high positive loadings
at all investigated timescales.
Temporal drought characteristics
The RPCs of REOF1 and REOF2 for ANUSPLIN and CANGRD, as well as correlation
coefficients between the data sets, are shown in Fig. 6. It is evident that
the RPCs from the two data sets are strongly correlated (cor>0.75)
for all SPEI timescales, except for RPC2 of SPEI3 where cor=0.45
(the centroids of SPEI3 REOF2 in Fig. 5 are slightly different for both data
sets). The primary focus is on SPEI6Apr-Sep and
SPEI12Oct-Sep, which reflect the agricultural season (when
droughts are most critical for rain-fed and irrigated agriculture) and
hydrological year in Canada, respectively. Fig. 6 reveals that the Prairies
(REOF1 and RPC1) experienced moderate to extreme (Table 1) drought episodes
starting in the mid-1950s, early and late 1960s, the 1970s and 1980s, the
period from mid-1997–2005, and the summer of 2009. Some of the drought
episodes were extremely dry and severe as they were prolonged in time such as
the ones in the late 1970s, mid-1980s, and 1997–2005. These identified
drought events correspond well with the findings of Bonsal et al. (2011a),
who identified large-scale Prairie droughts in 1961, 1967, 1988, and 2001
using ANUSPLIN and CANGRD data. In the northern central region (REOF2 and
RPC2), although fewer droughts were detected for the hydrological year
(SPEI12Oct-Sep), with the most intense occurring in 2000 (based
on the ANUSPLIN data set), the seasonal SPEI series
(SPEI6Apr-Sep) detected several drought events in this region.
Extremely dry episodes in this region occurred in the early 1960s, 1980s,
early 1990s, and 2000s.
Spatial patterns of the first two REOFs of SPEI at various timescales. The spatial extent of the first two REOFs was characterized by
mapping the values of the factorial matrix. See Table 3 for information on
variances explained by each REOF pattern.
Linear trends of the RPCs for the two subregions are depicted in Fig. 7 and
Table 4. For the Prairie subregion, both ANUSPLIN and CANGRD showed
insignificant decreasing trends for SPEI6, SPEI6Apr-Sep, and
SPEI12Oct-Sep. However, in the northern central region, the trend
direction is not the same for both data sets at all SPEI timescales. For
ANUSPLIN (CANGRD), decreasing (increasing) trends are found in
SPEI6Apr-Sep. Conversely, CANGRD (ANUSPLIN) shows decreasing
(increasing) trends in SPEI3. Regionally, the SPEI at different timescales
tends to display more significant trends in the northern central relative to the
Prairies. Most of the trends are significant in the case of CANGRD compared
to ANUSPLIN. The apparent differences in trends between the two data sets
and subregions may be attributed to the low station density in areas above
60∘ N which can introduce higher uncertainty in the gridded P and T fields (Vincent et al., 2015).
Long-term (1950–2013) trends of monthly time series (RPCs) for the
first two REOFs of SPEI at various timescales. The slope year-1 (a) and
p value (b) is indicated, with significant (p<0.05) trends shown in
bold.
ANUSPLIN
CANGRD
REOF1
REOF2
REOF1
REOF2
(a)
(b)
(a)
(b)
(a)
(b)
(a)
(b)
SPEI3
1.3×10-4
0.432
1.6×10-4
0.334
1.3×10-4
0.410
-1.0×10-4
0.539
SPEI6
-2.3×10-4
0.157
-1.3×10-5
0.935
-4.5×10-5
0.787
-5.1×10-4
0.002
SPEI12
-1.8×10-5
0.909
5.4×10-4
0.001
2.6×10-5
0.873
6.4×10-4
1×10-4
SPEI6Apr-Sep
-8.5×10-5
0.855
-3.2×10-4
0.492
-1.7×10-3
1×10-4
1×10-3
0.026
SPEI12Oct-Sep
-1.2×10-4
0.456
-3.7×10-4
0.029
-4.6×10-5
0.785
-4.6×10-4
0.006
Frequency estimation/periodicity of drought
To detect the dominant frequencies in the RPCs of the SPEI series in Fig. 6,
the time series were further analyzed using a CWT. The wavelet power spectrum
(WPS) of the RPCs is shown in Fig. 8. Periodicities of drought will be
identified for SPEI6Apr-Sep and SPEI12Oct-Sep and
their relationship to teleconnection indices examined. These two timescales
correspond to the warm season and water year, respectively, and represent
periods when moisture shortages are most critical for various sectors in
Canada. Other SPEI timescales which explain sub-annual variability are
included in Fig. 8 for the interested reader. For SPEI6Apr-Sep,
from the WPS of RPC1 in Fig. 8, significant inter-annual variability of
between 8 and 32 months is evident throughout much of the entire lengths of
the SPEI time series. However, it is concentrated most heavily during the
mid-1950s, early and late 1960s, the 1970s and 1980s, and the period from
mid-1997–2010. For SPEI12Oct-Sep, a strong frequency band is
centred mostly around 16–66 months (∼4 years) and is concentrated
during the mid-1950s and late 1960s. Moreover, 32–64-month frequencies are
dominant in the mid-1980s and the years between 1990 and 2010.
Temporal patterns (RPCs) of the first two rotated principal
components (REOFs) of SPEI at various timescales. Indicated within each box
is the pattern correlation between ANUSPLIN and CANGRD.
In the northern central region (RPC2), for SPEI6Apr-Sep, a significant
periodicity of a cycle of between 8 and 40 months as a dominant period of
variability is evident. It is concentrated in the early 1960s, 1980s, late
1990s, and 2000s. For SPEI12Oct-Sep, the WPS indicates a significant
high power for relatively low-frequency (16–100 months, i.e. ∼ 7 years) signals, concentrated over the period 1956–1975
and 1995–2013. The foregoing analysis reveals that the Prairie region
(REOF1) of Canada is dominated by high-frequency power signals with high
cycles of oscillation for both SPEI6Apr-Sep and SPEI12Oct-Sep,
while the northern central region (REOF2) is dominated by relatively
low-frequency power signals and low cycles of oscillation. The analysis
further indicates that significant inter-annual periodicities (<10 years) dominate drought variability over the two identified subregions
across Canada. The dominant periodicities and the intervals during which
they occurred are summarized in Table 5.
Dominant periods and the intervals of significant variance for
SPEI6Apr-Sep and SPEI12Oct-Sep during 1950–2013.
Dominant periods (months)
Intervals of variance
SPEI6Apr-Sep
REOF1 (Prairie)
8–32
1955–2001; 2002–2013
REOF2 (Northern)
8–40
1955–2000
SPEI12Oct-Sep
REOF1 (Prairie)
16–60; 32–64
1955–1968; 1970–2002
REOF2 (Northern)
16–100
1956–1975; 1995–2013
Long-term (1950–2013) trends (red line) of the RPCs for each
drought subregion and data set.
Coherence between drought and large-scale climate drivers
Wavelet power spectrum of the time series (RPCs) shown in Fig. 6.
The black contour designates the 95 % confidence level against red noise,
and the cone of influence (COI), where edge effects might distort the picture, is shown as a lighter grey shade.
The WCO technique was used to identify both frequency bands and time
intervals at which SPEI6Apr-Sep and SPEI12Oct-Sep, and
large-scale climate indices are co-varying (Torrence and
Webster, 1999). The results of WCO coefficients between the RPCs of
SPEI6Apr-Sep and SPEI12Oct-Sep and teleconnection indices
are shown in Figs. 9–11. In these plots, the coloured shading represents
the magnitude in the coherence as shown in the colour bar, which varies from
0 to 1 and indicates the timescale variability in the correlation between
the two time series. As in Fig. 8, the black contours represent the
significant regions. The relative phase relationships are shown as arrows,
with in phase pointing right (positive correlation) and antiphase pointing
left (negative correlation), whereas a vertical upward (downward) arrow indicates
that the second time series lags (leads) the first in phase by 90∘. If
an association exists between two time series, a slowly varying phase lag would
be expected, and the phenomenon would be phase-locked; i.e. the phase arrows
point only in one direction for a given wavelength (Grinsted et al.,
2004; Gobena and Gan, 2006). For this study, phase angle associations were
noted strictly as either being in-phase-locked or antiphase-locked. To simplify and
limit the length of the paper, only results for large-scale climate indices
with the strongest correlations with frequencies of drought are shown.
Other results are included as supplementary material.
Figure 9 illustrates the squared WCO between the temporal patterns of drought
and MEI. It is apparent that, for SPEI6Apr-Sep, the MEI had
significant coherence with PC1 mainly concentrated in the 8–16-month band
and from 1960–1980 in the Prairie region, indicating that the two time
series are phase-locked over this time interval. Also, the strongest
coherence between PC1 and MEI occurred over the 32–50-month scale, spanning
the period 1986–2002. In the case of SPEI6Apr-Sep PC2 (northern
central region), discontinuous in-phase coherence patterns were detected in
the 2–32-month bands. The dominant high energy coherence occurred in the
16–32-month scale, spanning the period 1985–2005. Similarly, for the
hydrological year timescale (SPEI12Oct-Sep), drought in the
Prairies (SPEI12Oct-SepPC1) experienced significant high power
with the MEI at the 16–32-month scale from 1980 to 2005 based on ANUSPLIN
(1986–2005 based on CANGRD), whereas in the northern central region
(SPEI12Oct-SepPC2), significant in-phase cross power between
drought and the MEI occurred in the 32–64-month period over the years
1978–2000 (in the case of ANUSPLIN). As in Fig. 8, it can be seen that the
Prairie region has more short-term periodicities compared to the northern
central region. It is also evident that although the MEI co-varied with
drought events, the frequency is higher but shorter lived in the Prairies
relative to the northern central region.
Figure 10 shows that for both the seasonal (SPEI6Apr-Sep) and annual
(SPEI12Oct-Sep) drought time series, sporadic but significant
coherence is observed intermittently from year to year with the PDO. For
SPEI6Apr-Sep, dominant strong coherence occurred between 16 and 32 months over the period 1988–2003 for the Prairies region, whereas
fluctuations were intermittently observed between 1955 and 2000 for
SPEI12Oct-Sep over the Prairie and northern central regions ranging
from 16 to 64 months. Unlike the PDO, the PNA showed a strong in-phase
relationship with drought over Canada (Fig. 11). It is apparent that the PNA
co-varied significantly with SPEI6Apr-SepPC1 over most of the years
during the study period. Oscillations in the PNA are manifested in the
SPEI6Apr-SepPC1 on wavelengths varying from 2 to 108 months
(∼9 years), suggesting that the PNA actively mirrors
SPEI6Apr-SepPC1. Particularly, the PNA was phase-locked with drought
during the period 1980–2001 at the 16–32-month scale. Apart from the late
1990s and early 2000s at the 8–16 month scale, no significant coherence
was found between the PNA and SPEI6Apr-SepPC2. Although with less
coherence relative to SPEI6Apr-Sep, the PNA also co-varied with
SPEI12Oct-Sep, more so for the northern Central region compared to the
Prairie region. In the Prairie region, the strongest coherence between
SPEI12Oct-SepPC1 and the PNA occurred over the 16–32-month scale,
while for the northern central region, significant cross power between the
PNA and SPEI12OctApr-SepPC2 occurred over the 32–64-month scale,
spanning the periods 1960–1973 and 1985–2000.
The relationship between drought and the AMO is shown in Fig. S3. It is clear
that the AMO did not co-vary with SPEI6Apr-Sep over both
homogenous drought regions. However, it is important to note that Shabbar and
Skinner (2004) found a significant correlation pattern between the winter AMO
index and following summer PDSI in the north of the Prairies province. Here, we only made use
of the AMO index for the months April–September of each year. For
SPEI12Oct-Sep, significant and high energy existed for the
Prairie region only (SPEI12Oct-SepPC1), mainly distributed in the
8–64-month (∼5 years) band and spanning the period 1970–2005. For the
AO (Fig. S4), in-phase significant coherence existed with
SPEI6Apr-Sep PC2 and SPEI12Oct-SepPC1. In the
northern central region (SPEI6Apr-Sep PC2), 16–32-month
high-energy regions were found over the period 1963–1978, and 8–16-month
significant coherence also existed from 1994 to 2002. In the Prairie region
(SPEI12Oct-SepPC1), drought was in phase with the AO especially
from 1981 to 2003, where significant cross power and coherence were mainly
concentrated in the 64–128-month band (based on CANGRD). The results further
indicate that the AO was in antiphase with SPEI6Apr-Sep PC1 and
SPEI12Oct-SepPC2 during the study period from 1950 to 2013. In
terms of the NAO (Fig. S5), sporadic significant coherence is noticed with
the seasonal SPEI6Apr-Sep at a higher frequency, ranging between
4 and 20 months and mainly concentrated over the period 1975–1990. There is
also statistically significant in-phase coherence between
SPEI12Oct-SepPC1 and the NAO in the 70–128-month band in the
late 1970s–late 1990s, based on CANGRD.
Squared wavelet coherence between the MEI and the temporal
patterns of drought (SPEI6Apr-Sep and SPEI12Oct-Sep). Phase arrows pointing right indicate
signals are in phase, whereas left-pointing arrows indicate an antiphase
relationship. Arrows deviating from the horizontal are indicative of
lead–lag relationships between the two signals. The black contour designates
the 95 % confidence level against red noise, and the cone of influence
(COI), where edge effects might distort the picture, is shown as a lighter
grey shade.
The foregoing analysis has shown significant covariance between drought
variability over Canada and large-scale climate indices, especially the MEI,
PNA and PDO. This is in line with previous studies (e.g. Bonsal and
Shabbar, 2008, Fleming and Quilty, 2006, Tremblay et al., 2011, and Perez-Valdivia
et al., 2012) that have established links between Canadian hydroclimate and
teleconnection indices. Furthermore, one should expect most of the climate
indices to yield similar findings given that they appear to be interrelated
at several timescales (Sheridan, 2003; Gan et al., 2007; Ng and Chan,
2012). We recommend that future studies examine the degree to which such
interrelations can affect the findings reported here by eliminating, for
example, the influence of the PDO on the MEI via partial wavelet coherence
analysis (Ng and Chan, 2012). Also, a lead/lag response of the identified
drought frequencies as well their correlations to positive and negative
phases of various teleconnections constitute an area for future research. In
addition, only one PET estimation method was used in this study and its
selection was constrained by data availability during the study period. We
recommend the use of other simple or complex methods to calculate PET and
assess their impact on drought analysis over Canada since the Hargreaves
method has been known to underestimate PET relative to the Penman–Monteith
method (McMahon et al., 2013). Furthermore, the
Hargreaves method responds only to changes in temperature and can lead to
misleading results under global warming. For example, Sheffield et al. (2012) found little change in global drought
over the past 60 years (1950–2008). In terms of data sources, the
findings reported here should be validated against other data sets such as
long-term satellite products as they become available.
Summary and conclusions
This study performs a comprehensive analysis of historical droughts over the
whole of Canada, considering the role of teleconnections by analyzing different
monthly P and T products for the period 1950–2013. SPEI, a climatological
drought index, is applied over various temporal scales to evaluate various
drought characteristics such as trends, spatio-temporal patterns of long-term
change, inter/intra-annual variability, and periodicity/frequency. In
addition, potential prominent modes of low-frequency variability such as the
PNA, AO, MEI, PDO, AMO, and NAO which are well established to influence the
hydroclimate of Canada and North America are investigated as precursors to
historical drought occurrence. The main conclusions from the analyses are
given in the following.
Apart from the Pacific/Atlantic maritime regions, most of the continental
Canadian interior experienced moisture deficits, with the Prairie region
being the most affected region between 1950 and 2013. Based on a trend
analysis derived from the water balance, significant decreasing trends in
SPEI values are observed in the southern Prairies, the foothills of the
Rocky Mountains, and Pacific maritime regions, whereas increasing trends are
limited to a small area in the north and parts of the Atlantic coast to the
east. Therefore, southern parts of the country showed a trend towards drier
conditions and vice versa for the northern regions. Note that the northern
region (above 60∘ N) has lower station density and as such higher
uncertainty in the gridded P and T fields.
Squared wavelet coherence between the PDO and the temporal
patterns of drought (SPEI6Apr-Sep and
SPEI12Oct-Sep). Phase arrows pointing right indicate
signals are in phase, whereas left-pointing arrows indicate an antiphase
relationship. Arrows deviating from the horizontal are indicative of
lead–lag relationships between the two signals. The black contour designates
the 95 % confidence level against red noise, and the cone of influence
(COI), where edge effects might distort the picture, is shown as a lighter
grey shade.
Squared wavelet coherence between the PNA and the temporal
patterns of drought (SPEI6Apr-Sep and
SPEI12Oct-Sep). Phase arrows pointing right indicate
signals are in phase, whereas left-pointing arrows indicate an antiphase
relationship. Arrows deviating from the horizontal are indicative of
lead–lag relationships between the two signals. The black contour designates
the 95 % confidence level against red noise, and the cone of influence
(COI), where edge effects might distort the picture, is shown as a lighter
grey shade.
EOF identifies two main spatially disjunctive subregions of drought
variability over Canada – the Prairies and northern central Canada. Based on
both seasonal (SPEI6Apr-Sep) and annual (SPEI12Oct-Sep) SPEI
values, the Prairie subregion experienced moderate to extreme droughts
episodes starting in the mid-1950s, early and late 1960s, the 1970s and
1980s, the period from mid-1997–2005, and the summer of 2009. Some of the
drought episodes were extremely dry and severe as they were prolonged in
time such as the ones in the late 1970s, mid-1980s, and 1997–2005. In the
northern central region, although fewer (likely due to below 0 temperatures
for most of the cold season) droughts were detected for the hydrological
year (SPEI12Oct-Sep), with the most intense occurring in 2000, the
seasonal SPEI series (SPEI6Apr-Sep) detected extremely dry episodes in
this region in the early 1960s, 1980s, early 1990s, and 2000s. However,
drought variability in the Prairies in particular experienced largely
insignificant trends, a finding similar to numerous previous studies.
Wavelet analysis was particularly useful to detect periodical signals in the
SPEI time series patterns for each subregion. For SPEI6Apr-Sep, the
analysis reveals clearly the presence of a dominant periodicity of between 8
and 32 months, persisting approximately from 1955 to 2001 in the Prairie
region, while in the North central region, a significant periodicity of
a cycle between 8 and 40 months as a dominant period of variability spanning
the years 1955–2000 is apparent. For SPEI12Oct-Sep, a strong power
frequency band over the Prairie region, centred mostly around 16–66
months (∼4 years) and spanning the period 1955–1968 is
found. Moreover, 32–64-month periodic high-power signals are dominant
during the years 1970–2002. In the northern central region, a significant
high power for relatively low-frequency (16–100 months, i.e.
∼7 years), spanning the period 1956–1975 and 1995–2013,
is detected. Therefore, the Prairie region of Canada is dominated by
high-frequency (i.e. more frequent and shorter cycles of dry events) power
signals for both SPEI6Apr-Sep and SPEI12Oct-Sep, while the
northern central region is dominated by low-frequency (i.e. less frequent
cycles of dry events) power signals. The analysis further indicates that
significant inter-annual periodicities (with a period of <10 years)
dominate drought variability over the two identified major regions of
drought variability across Canada.
The identified drought short-time (long-time) inter-annual periodicities in
the Prairie (northern central region) are likely associated with the
immediate and significant influence of the MEI and PNA in particular, as
these large-scale climate indices have maximum regions with a 5 %
significance level in the WCO plots. For the MEI index, 8–16 and 32–50 months were the most predominant and effective periods for drought
occurrence in Canada, while for the PNA, the 2–108-month period was the most
predominant over the Prairie region and for SPEI6Apr-Sep compared with
the northern central region. For SPEI12Oct-Sep, the PNA showed
more coherence with drought at the 32–64-month scale.
The foregoing analysis has indicated the need to consider various
observational data sets in drought characterization, given the uncertainty in
data. In terms of trends, the ANUSPLIN data set indicated a higher tendency
for drought over the study period relative to CANGRD. Furthermore,
irrespective of the timescale of accumulation, ANUSPLIN tends to reveal more
drought severity compared to CANGRD although the correlation between the time
series of the two data sets from each of the homogenous drought subregions
is very strong. Therefore, further applications using other gridded data sets
to verify the role played by the spatial resolution of the input data on
regional drought patterns are recommended. The identification of these
subregions with similar drought variability and characteristics can be
useful for drought risk management at a regional scale in Canada. Two of the
most important river basins are both in the Prairies region (Saskatchewan
River basin) and northern region (MacKenzie River basin). Lastly, this study
is the first of its kind to identify dominant periodicities in drought
variability over the whole of Canada in terms of when the drought events
occur, their duration, and how often they do so over the Prairies and northern
central regions.