PERFORMANCE OF DROUGHT INDICES FOR ECOLOGICAL, AGRICULTURAL AND
HYDROLOGICAL APPLICATIONS
Sergio M. Vicente-Serrano
1,
*, Santiago Beguería
2
, Jorge Lorenzo-Lacruz
1
, Jesús Julio Camarero
3
,
Juan I. López-Moreno
1
, Cesar Azorin-Molina
1
, Jesús Revuelto
1
, Enrique Morán-Tejeda
1
and Arturo
Sánchez-Lorenzo
4
1
Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE-CSIC), Campus de
Aula Dei, P.O. Box 13034, E-50059, Zaragoza, Spain
2
Estación Experimental de Aula Dei, Consejo Superior de Investigaciones Científicas (EEAD-CSIC),
Zaragoza, Spain
3
ARAID-Instituto Pirenaico de Ecología, CSIC (Consejo Superior de Investigaciones Científicas), Campus
de Aula Dei, P.O. Box 13034, E-50059, Zaragoza, Spain
4
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
* Corresponding author: svicen@ipe.csic.es
Abstract: In this study we provide a global assessment of the performance of different drought
indices for monitoring drought impacts on several hydrological, agricultural and ecological
response variables. For this purpose, we compare the performance of several drought indices (the
Standardized Precipitation Index, SPI; four versions of the Palmer Drought Severity Index, PDSI;
and the Standardized Precipitation Evapotranspiration Index, SPEI) to predict changes in
streamflow, soil moisture, forest growth and crop yield. We found a superior capability of the SPEI
and the SPI drought indices, which are calculated on different time-scales, than the Palmer indices
to capture the drought impacts on the aforementioned hydrological, agricultural and ecological
variables. We detected small differences in the comparative performance of the SPI and the SPEI
indices, but the SPEI was the drought index that best captured the responses of the assessed
variables to drought in summer, the season in which more drought-related impacts are recorded and
in which drought monitoring is critical. Hence, the SPEI index shows improved capability to
identify drought impacts as compared with the SPI one. In conclusion, it seems reasonable to
recommend the use of the SPEI if the responses of the variables of interest to drought are not known
a priori.
Key-words: Drought index, drought vulnerability, agricultural droughts, dendrochronology,
hydrological droughts, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized
Precipitation Index (SPI), Palmer Drought Severity Index (PDSI).
1. Introduction
Drought is among the most complex climatic phenomena affecting society and the environment
(Wilhite, 1993). The root of this complexity is related to the difficulty of quantifying drought
severity since we identify a drought by its effects or impacts on different types of systems
(agriculture, water resources, ecology, forestry, economy, etc.), but there is not a physical variable
we can measure to quantify droughts. Thus, droughts are difficult to pinpoint in time and space
since it is very complex to identify the moment when a drought starts and ends, and also to quantify
its duration, magnitude and spatial extent (Burton et al., 1978; Wilhite, 2000).
These characteristics explain the vast scientific effort devoted to develop tools providing an
objective and quantitative evaluation of drought severity. The quantification of drought impacts is
commonly done by using the so-called drought indices, which are proxies based on climatic
information and assumed to adequately quantify the degree of drought hazard exerted on sensitive
systems. Many studies have shown strong relationships between the temporal variability of different
drought indices and response variables of natural systems such as tree growth (e.g., Orwig and
Abrams, 1997; Copenheaver et al., 2011; Pasho et al., 2011), river discharge (e.g., Vicente-Serrano
and López-Moreno, 2005; Hannaford et al., 2011), groundwater level (Khan et al., 2008; Fiorillo
and Guadagno, 2010), crop yields (e.g., Vicente-Serrano et al., 2006; Vergni and Todisco, 2011),
vegetation activity (e.g., Lotsch et al., 2003; McAuliffe and Hamerlynck, 2010; Vicente-Serrano,
2007), the frequency of forest fires (Littell et al., 2009; Drobyshev et al., 2012), etc. Drought
indices are currently used to monitor drought conditions in real time manner that is easily
understood by end users (Svoboda et al., 2002; Shukla et al., 2011). Indeed, drought monitoring has
been recognized as crucial for the implementation of drought plans (Wilhite, 1996; Wilhite et al.,
2007).
Recent works have reviewed the development of drought indices and compared their advantages
and drawbacks (Heim, 2002; Keyantash and Dracup, 2002; Mishra and Singh, 2010; Sivakumar et
al., 2010). However, very few studies have performed robust statistical assessments by comparing
different drought indices which may allow recommending the preferential use of one of them based
on objective criteria (Guttman, 1998; Keyantash and Dracup, 2002; Steinemann, 2003; Paulo and
Pereira, 2006; Quiring, 2009; Vicente-Serrano et al., 2010; Barua et al., 2011; Anderson et al.,
2011). In addition, few researchers have compared the relative performance of different drought
indices to identify drought impacts on several systems. In the case of drought impacts on
hydrological systems, Vasiliades et al., (2011) compared five drought indices in Greece. Lorenzo-
Lacruz et al. (2010) compared the performance of two drought indices to identify hydrological
droughts in river discharges and reservoir storages in central Spain, and Zhai et al. (2010) compared
the relationship between the Standardized Precipitation Index (SPI) and the Palmer Drought
Severity Index (PDSI) and streamflow data in ten regions of China. Sims et al. (2002) compared the
PDSI and the SPI to assess soil moisture variations in North Carolina, USA. In relation to
vegetation activity and crop productivity, Potop (2011) compared different indices to assess drought
impacts on corn yields in Moldava, and Mavromatis (2007) and Quiring and Papakryiakou (2003)
followed a similar approach by quantifying wheat production in Greece and the Canadian prairies,
respectively. Quiring and Ganesh (2010) compared drought indices to assess the responses of
vegetation activity to drought severity in Texas (USA). Kempes et al. (2008) assessed tree-ring
growth response to different drought indices in the southwestern USA. Recently, Drobyshev et al.
(2012) analyzed the correlation between different drought indices and fire frequency in Sweden.
The results of these studies are diverse, since the best drought index for detecting impacts changes
as a function of the analyzed system and the performance of the drought indices varied spatially. As
a result, at present there is high uncertainty among scientists, managers and end users of drought
information when they aim to select one drought index for a specific purpose.
To the best of our knowledge, at present there is no global study analyzing and comparing to which
degree the most widely used drought indices are able to identify drought impacts on vulnerable
systems. This task is necessary in order to have solid and objective criteria for selecting a drought
index to be used for specific tasks. In this study we provide the first global assessment of the
performance of different drought indices for monitoring drought impacts on streamflows, soil
moisture, forest growth and crop yields. For this purpose, we compare two of the most widely used
drought indices, the Standardized Precipitation Index, SPI (McKee et al., 1993), and four versions
of the Palmer Drought Severity Index, PDSI (Palmer, 1965). In addition, we also include in our
comparison the recently developed Standardized Precipitation Evapotranspiration Index (SPEI),
which has been claimed to outperform the two previous indices (Vicente-Serrano et al., 2010b).
2. Datasets and methods
2.1. Drought indices
a) The Palmer Drought Indices
The PDSI was a landmark in the development of drought indices. It enables measuring both wetness
(positive value) and dryness (negative values), based on the supply and demand concepts of the
water balance equation, and thus incorporates prior precipitation, moisture supply, runoff and
evaporation demand at the surface level. Although the PDSI presents several deficiencies (Alley,
1984; Karl, 1986; Soulé, 1992; Akimremi et al., 1996; Weber and Nkemdirim, 1998; Vicente-
Serrano et al., 2011), currently it is still one of the most widely used drought indices. The PDSI is
calculated based on precipitation and temperature data, as well as the water content of the soil. All
the basic terms of the water balance equation can be determined from those inputs, including
evapotranspiration, soil recharge, runoff, and moisture loss from the surface layer. The complete
calculation procedure of the PDSI can be consulted in many publications (e.g., Karl, 1983 and
1986; Alley, 1984).
The modified Palmer Drought Severity Index (WPLM) was proposed by the National Weather
Service Climate Analysis Center for operational meteorological purposes (Heddinghaus and Sabol
1991), modifying the original rules of accumulation during wet and dry spells.
The Palmer Hydrological Drought index (PHDI) was derived from the PDSI to quantify the long-
term impact of drought on hydrological systems. Values of the PHDI tend to be negative for up to
several months after PDSI have returned to normal levels, i.e. it usually returns to near-normal
levels more gradually than the PDSI (Karl et al., 1987). Therefore, the PHDI is considered a
measure of long-term hydrological drought since streamflows, reservoir storages and groundwater
tend to stay below normal values for some time after a meteorological drought ends. Finally, the
Palmer Z-Index is also derived from the Palmer model and it is much more responsive to short-term
moisture deficiencies than the PDSI. The Palmer Z-Index shows how monthly moisture conditions
depart from normal, and it is sensitive to unusual wet (and dry) months even in extended dry (or
wet) spells. Therefore, the Palmer Z-index is usually used for the detection of short term droughts.
One of the main problems of the Palmer indices is that the parameters necessary to calculate them
were determined empirically and mainly tested in the USA, which restricts its use in other regions
(see Akimremi et al., 1996) and limits the geographical comparisons based on the PDSI (Heim,
2002; Guttman et al., 1992). This problem was solved by developing of the self-calibrated Palmer
indices (Wells et al., 2004), which are spatially comparable and report extreme wet and dry events
at frequencies expected for rare conditions. Therefore, in this study we have used the self-calibrated
versions of the four Palmer drought indices, which are more suitable for drought quantification and
monitoring at a global scale than the corresponding Palmer indices.
b) The Standardized Precipitation Index (SPI)
