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Drought evolution, severity and trends in mainland China

over 1961–2013

Ning Yao

a,b

,YiLi

a,b,

⁎

, Tianjie Lei

⁎⁎

,LinglingPeng

a,b

College of Water Resources and Architectural Engineering, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China

Institute of Water Saving Agriculture in Arid Areas of China, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China

China Institute of Water Resources and Hydropower Research (IWHR), Beijing 100038, China.

HIGHLIGHTS

• The Pa, SPI, SPEI and EDDI consistently

implied an overall relief of drought con-

ditions.

• Droughts evolutions were site-, region-

al-speciﬁc and complicated although re-

lieved in recent 53 years in mainland

China.

• The Pa, SPI and SPEI are used for deter-

mining continuous droughts and EDDI

for identifying ﬂash droughts.

GRAPHICAL ABSTRACT

abstractarticle info

Article history:

Received 22 August 2017

Received in revised form 31 October 2017

Accepted 31 October 2017

Available online 16 November 2017

Editor: Ouyang Wei

Droughts have destructive impacts on crop yields and water supplies, and researching droughts is vital for societal

stability and human life. This work aimed to assess the spatiotemporal evolution of droughts in mainland China

over 1961–2013 using four drought indices. These indices were the percentage of precipitation anomaly (Pa), stan-

dard precipitation index (SPI), standard precipitation evapotranspiration index (SPEI) and evaporative demand

drought index (EDDI) at multiple timescales ranging from 1-week to 24-month. The variations of the SPI, SPEI

and EDDI were compared with historical severe or extreme droughts. The general increases of the Pa, SPI and

SPEI, and general decrease of the EDDI, consistently implied an overall relief of drought conditions over 1961–

2013. The different drought indices revealed historical drought conditions, including the national extreme

droughts in 1961, 1965, 1972, 1978, 1986, 1988, 1992, 1994, 1997, 1999 and 2000, but various drought severity

levels were classiﬁed for each drought event since the classiﬁcation standards differed. Although the SPI and

SPEI performed better than the EDDI and there were higher correlations between the SPI and the SPEI, all the in-

dices were regional- or station-speciﬁc and have identiﬁed historical severe or extreme drought events. At shorter

timescales, the EDDI revealed earlier onsets and ends of ﬂash droughts, unlike those indicated by the SPI and SPEI.

Keywords:

Drought index

Historical extreme drought event

Trend test

Timescale

Science of the Total Environment 616–617 (2018) 73–89

Abbreviations: E

, evaporative demand; EDDI, Evaporative Demand Drought Index; EMC, Entire mainland China; FAO56-PM, FAO56 Penma n-Monteith equation; InsigDec,

insigniﬁcant decrease; InsigInc, insigniﬁcant increase; METRIC, Mapping evapotranspiration with Internalized Calibration; MMK, modiﬁed Mann–Kendall; M, month; P, precipitation;

Pa, percentage of precipitation anomaly; PDSI, Palmer Drought Severity Index; R, Correlation coefﬁcient; RDTTA, ratio of drought-threatened area to total arable land area; RH,relative

humidity; sc-PDSI, self-calibrating PDSI; SEBAL, Surface Energy Balance Algorithm for Land; SEBS, Surface Energy Balance System; SigDec, signiﬁcant increase; SigInc, signiﬁcant

increase; SPEI, Standardized Precipitation Evapotranspiration Index; SPI, Standardized Precipitation Index; T

max

, maximum temperature; T

mean

, mean temperature; T

min

, minimum

temperature; U

, wind speed; W, week.

⁎ Correspondence to: Y. Li, College of Water Resources and Architectural Engineering, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, China.

⁎⁎ Corresponding author.

E-mail addresses: liyi@nwsuaf.edu.cn (Y. Li), leitj@iwhr.com (T. Lei).

https://doi.org/10.1016/j.scitotenv.2017.10.327

Contents lists available at ScienceDirect

Science of the Total Environment

journal homepage: www.elsevier.com/locate/scitotenv

The comparison of the different indices based on the historical drought events conﬁrmed the uses of the Pa, SPI and

SPEI for determining continuous droughts and that of the EDDI for identifying ﬂash droughts.

1. Introduction

Droughts are one of the most destructive and costly natural disasters,

with serious impacts on agriculture, water resources, ecology, and socie-

ty (Lei et al., 2016; Mishra and S ingh, 2010; Schubert et al., 2016). With

recent global warming (IPCC, 2013), the percentage of dry areas in the

world has increased by approximately 1.74% per decade from 1950–

2008 (Dai, 2011). China has suffered long-lasting and severe droughts

in the past (Chen and Sun, 2015; Dai, 2011, 2013; Zhang B. et al.,

2016). The inﬂuences of drought have evoked high levels of interest out-

side of the scientiﬁc community (Leuzinger et al., 2005).

Based on their timescales and impacts, droughts are generally catego-

rized into ﬁve types: meteorological droughts, agricultural droughts, hy-

drological droughts, socio-economical droughts, and droughts that

impact stream health (Esfahanian et al., 2017; Heim, 2002; Wilhite and

Glantz, 1985). Of these categories, meteorological droughts usually pre-

cede the others and are deﬁned in terms of the magnitude of the lack

of precipitation over a region over time. There are many meteorological

drought indices (Heim, 2002; Wu et al., 2015), which can be generally

classiﬁed as non-standardized (Arora, 2002; Budyko, 1974; Erinç, 1965;

Sahin, 2012; UNEP, 1993), standardized, or combination indices. The

non-standardized indices are always simple to compute using some

available data but their ranges for classifying drought level are different.

The standardized indices are complicated in computation but their

drought level classiﬁcations are similar and comparable. The combina-

tion indices are the combination of the two. Non-standardized indices in-

clude the percentage of precipitation anomaly (Pa) (Van Rooy, 1965),

aridity index (Arora, 2002; Budyko, 1974; Erinç, 1965), UNEP index

(UNEP, 1993) and speciﬁc humidity-based index (Sahin, 2012). The

China composite index is a typical combination drought index (China

Meteorological Administration, 2006). There are several standardized in-

dices, such as the Palmer drought severity index (PDSI) (Palmer, 1965),

standardized precipitation index (SPI) (McKee et al., 1993), and stan-

dardized precipitation evapotranspiration index (SPEI) (Vicente-

Serrano et al., 2010a). Each drought index has its own adaptability, ad-

vantages, and limitations. The PDSI was originally developed to measure

the cumulative departure from the surface water balance and has been

applied worldwide (Dai et al., 2004; Zhang et al., 2017). However, the

PDSI lacks the multiscale characteristics of droughts (Wang Q. et al.,

2015). The SPI is less complex than the PDSI and can be applied to any

location or month-based timescale (Hayes et al., 1999)butdoesnotre-

ﬂect the drought conditions caused by warming because it only con-

siders precipitation (Vicente-Serrano et al., 2010b; Zhang and He,

2016). The SPEI was proposed as an improved drought index compared

to the SPI, which is suitable for studying the effects of global warming on

ought severity (Begueria et al., 2014 ).

However, the abovementioned drought indices have difﬁculty cap-

turing the rapidly evolving drought events, commonly called “ﬂash

droughts” (Senay et al., 2008; Svoboda et al., 2002). The rapid onset of

ﬂash droughts signiﬁcantly reduces the time available for disaster prep-

aration measures (Otkin et al., 2015). Soil moisture anomalies are useful

for characterizing ﬂash droughts (Mozny et al., 2012). However, the lack

of spatial soil moisture data precludes its inclusion in ﬂash drought mon-

itoring (Ford et al., 2015). The evaporative stress index provides an early

warning for ﬂash drought development (Anderson et al., 2011; Otkin et

al., 2015) but it has some limitations when applied to different regions

(McEvoy et al., 2016). The newly proposed Evaporative Demand Drought

Index (EDDI) is physically-based and multiscalar (Hobbins et al., 2016). It

can serve as an indicator of both ﬂash (at 1-, 2-, …,12-weektimescales;

week is hereon simpliﬁed as W) and sustained droughts (at 1-, 2-, …,48-

month timescales; month is hereon simpliﬁed as M). The EDDI is a pow-

erful tool for drought preparedness and capturing the precursor signals

of water stress. The development, evolution, and spatiotemporal distri-

bution of sustained droughts in China were previously investigated

using the PDSI, SPI, and SPEI (Chen and Sun, 2015; Wang et al., 2016;

Wang W. et al., 2015; Yu et al., 2014). However, few studies have

assessed the applicability of the widely applied multi-scalar indices of

the SPI and SPEI with Pa, and the newly proposed EDDI, in China.

In this research, meteorological data from 552 stations throughout

the entirety of mainland China (EMC) over 1961–2013 were used to cal-

culatePa,theSPI,SPEIandEDDIatthe1-,6-,12-,and24-Mtimescales,

and EDDI at 1-, 2-, …, to 12-W timescales, respectively. The objectives

are: (1) to analyze the spatiotemporal variations of the EDDI, SPI and

SPEI at different timescales in different sub-regions of EMC; (2) to com-

pare the effectiveness of the four drought indices in identifying historical

severe or extreme drought events; and (3) to objectively assess the ap-

plicability of EDDI for characterizing ﬂash droughts in China.

2. Data and methodology

2.1. The studied stations and the data sets

China has a descending 3-terrace-topography from the west to the

east with much diversiﬁed topography (Fig. 1). There are di fferent

kinds of landscapes, such as plains, valleys, hills, mountainous, river sys-

tems, water bodies, plateaus, deserts, glaciers, basins, etc. (Zhao et al.,

1999). The highest terrace is the Qinghai-Tibet Plateau with an average

altitude of 4000 m. The lowest terrace consists of east China, adjacent to

oceans. A total of 552 stations were selected to analyze drought condi-

tions in EMC.

The observed weather variables including daily and monthly precip-

ation (P), relative humidity (RH), minimum (T

min

), mean (T

mean

)and

maximum temperatures (T

max

), wind speed (U

), sunshine hour (n)

and geographical data over the period 1961–2013 were collected from

the Meteorological Data Sharing Service Network in China with strict

quality control. The data quality and reliability were cross-examined

using nonparametric tests including the Kendall autocorrelation test

and Mann-Whitney homogeneity tests (Helsel and Hirsch, 1992). The re-

sults indicated that randomness and stationary of the weather data were

ﬁxed between the critical points at a 5% statistical signiﬁcance level. The

temporal coverage exceeded 99.7%. The missing data were interpolated

using the arithmetic average of the neighboring days or months using a

linear regression equation.

Considering the multi-year average T

mean

, P and moisture conditi ons

(Table 1), Chin a is divided int o seven natural sub-regions (Zhao, 1983),

which include the temperate and warm-temperate desert of northw est

China (sub-region 1), the temperate grassland of Inner Mongolia (sub-

region 2), the temperate humid and sub-humid northeast China (sub-re-

gion 3), the warm-temperate humid and sub-humid north China (sub-

region 4), the subtropical humid central and south China (sub-region

5), the Qinghai-Tibetan Plateau (sub-region 6), and the tropic humid

south China (sub-region 7). The annual mean T

mean

, RH, and P were

lower in sub-regions 1, 2, 3 and 6 than in the other 3 sub-regions. The an-

nual mean E

was lower in sub-regions 3 and 5 but higher in sub-regions

2and7.

74 N. Yao et al. / Science of the Total Environment 616–617 (2018) 73–89

2.2. Computation of the four selected drought indices

2.2.1. Estimation of E

The FAO56 Penman-Monteith equation (FAO56-PM) is a standard-

ized and widely accepted estimate of evaporative demand E

(mm day

−1

). Daily E

is calculated by (Allen et al., 1998):

0:408Δ R

−GðÞ

Δ þ γ 1 þ 0:34U

ðÞ

900

mean

þ 273

sat

−e

ðÞ

Δ þ γ 1 þ 0:34U

ðÞ

ð1Þ

where Δ is slope of saturated vapor pressure, kPa °C

− 1

; T

mean

0.5(T

max

+ T

min

)for2m,°C;γ is psychrometric constant, kPa °C

−1

; R

is net radiation, MJ m

− 2

day

− 1

; G is downward ground heat ﬂux,

MJ m

−2

day

−1

; e

sat

and e

are saturated and actual vapor pressures, re-

spectively, kPa. Monthly G is estimated by:

¼ 0 :14 T

−T

K−1

ðÞ ð2Þ

where K and K − 1 are month orders, annual E

is cumulated by E

values of 12 months.

is calculated by:

¼ R

−R

ð3Þ

¼ 1−αðÞR

ð4Þ

¼ a

þ b

hi

ð5Þ

¼ σ

max;k

þ T

min;k

0:34−0:14

ﬃﬃﬃﬃﬃ

ðÞ1:35

−0:35



ð6Þ

where R

, R

and R

are net shortwave, net longwave, and clear sky ra-

diations (MJ m

−2

month

−1

), respectively; n and N are actual and maxi-

mum possible sunshine durations, respectively (h); R

is Solar radiation;

is the extraterrestrial radiation (MJ m

− 2

month

− 1

); σis Stefan-

Boltzmann constant (4.903 × 10

− 9

MJ K

−4

− 2

− 1

); α is albedo

(=0.23); T

max,k

and T

min,k

are maximum and minimum absolute tem-

peratures, respectively. Originally, a

=0.25andb

=0.50.Forbetterac-

curacy, the calibrated values of a

and b

at 48 stations using measured

daily R

reported by Chen et al. (2004) were used here for the nearby sta-

tions using the Thiessen polygon method in ArcGIS 10.2 software.

2.2.2. Computation of Pa

The percentage of precipitation anomaly (Pa) is estimated by (China

Meteorological Administration, 2006):

Pa ¼

P−P

 100% ð7Þ

where

Pis average monthly or annual P.

2.2.3. Computation of SPI and SPEI

Monthly P series for each station, station-averaged for each sub-

region and EMC at the 1-, 6-, 12- and 24-M timescales were used

for SPI estimation. Computation of the SPI involves ﬁtting a Gamma prob-

ability density function to P series. The procedures suggested by McKee

et al. (1993 ) and Lloyd-Hughes and Saunders (2002) were followed.

SPI ¼ ‐ t−

þ c

t þ c

1 þ d

t þ d

þ d



ð8Þ

where c

= 2.515517, c

= 0.80285 3, c

= 0.010328, d

= 1.432788, d

= 0.189269, and d

= 0.001308. H(x) is cumulative probability. In order

to account for zero value probability, H(x)=q+(1− q)G(x), here q is

probability of zero P, G(x) is Gamma distribution function deﬁned for x

≠ 0(Zarch et al., 2015). For H(x) ≤ 0.5, t ¼

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

ln ð

HðxÞ

, and for H(x) N

0.5, H(x)isreplacedby1-H(x) and the sign of SPI is reversed.

SPEI is calculated using P and E

series for each station, each sub-

region and EMC. The computation procedure of SPEI f ollows Vicente-

Serrano et al. (2010a):(1)ToestimateE

at the monthly timescale

using Eq. (1); (2) To determine the accumulation of water d eﬁcit D

(=P-E

) at the 1-, 6-, 12- and 24-M timescales; (3) To normalize D

into a log-logistic probability distribution to obtain SPEI using Eqs.

Fig. 1. Geological information, DEM, station distribution and sub-region division in China. The numbers in the map denotes different sub-regions.

Table 1

The multi-year mean values of climatic variables and E

in different sub-regions of China.

Sub-region Station

no.

max

(°C)

min

(°C)

mean

(°C)

(m s

−1

)

(%)

(h)

(mm)

1 61 14.7 1.2 8.0 1.9 51 8.1 135 1065

2 44 11.9 − 1.2 5.4 2.3 53 8.1 304 970

3 72 10.4 − 1.6 4.4 2.1 66 6.9 598 741

4 104 16.7 6.0 11.4 1.9 63 6.8 593 900

5 165 21.2 12.7 17.0 1.5 77 4.6 1269 863

6 50 11.1 − 3.2 3.9 1.8 54 7.2 455 911

7 56 26.3 18.5 22.4 1.7 79 5.2 1605 1055

EMC 552 17.1 6.4 11.7 1.8 66 6.3 812 909

75N. Yao et al. / Science of the Total Environment 616–617 (2018) 73–89

(9) and (10).

FxðÞ¼ 1 þ

x−γ



−1

ð9Þ

SPEI ¼ W−

þ c

W þc

1 þ d

W þd

þ d

ð10Þ

where F(x) is the probability distribution function of D series; α, β

and γ are scale, shape, and origin parameters of the Pear son III distri-

bution, respectively (Singh et al., 1993; Vicente- Serrano et al.,

2010a). For P(D) ≤ 0.5, W ¼

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

−2lnPðDÞ

, P(D)=1− F( x); for P(D)

N 0.5, P(D)isreplacedby1− P(D)andthesignofSPEIisreversed.

2.2.4. Computation of EDDI

The EDD I is calculated using E

series for each station, each sub-

region and EMC at the 1- to 12-W, and 1-, 6-, 12-, 24-M timescales.

Table 2

Drought level classiﬁcations using Pa, SPI, SPEI and EDDI.

Drought level Level no. 1-M Pa (%) 12-M Pa (%) SPI or SPEI EDDI

Normal 0 N− 40 N−15 − 0.5–0.5 − 0.5–0.5

Mild drought 1 − 60–−40 − 30–−15 − 1.0–−0.5 0.5–1.0

Moderate drought 2 − 80–−60 − 40–−30 − 1.5–−1.0 1.0–1.5

Severe drought 3 − 95–−80 − 45–−40 − 2.0–−1.5 1.5–2.0

Extreme drought 4 ≤−95 ≤− 45 ≤− 2.0 ≥ 2.0

Fig. 2. Spatial distributions of the annual mean climatic variables and E

in China.

76 N. Yao et al. / Science of the Total Environment 616–617 (2018) 73–89

The empirical probabilities of E

-P(E

), aggregated across a period of in-

terest, are obtained through the empirical Tukey plotting position

(Wilks, 2011):

ðÞ¼

i−0:33

n þ 0:33

ð11Þ

where i is the rank of the aggregated E

in the historical time series, i =1

for maximum E

;andn is the number of observations in the series. EDDI

is then derived following Eq. (7) by replacing SPEI and P(D) with EDDI

and P(E

), respectively (Hobbins et al., 2016).

Fig. 3. Spatial distributions of the trends in climatic variables and E

tested by the MMK method. SigInc, InsigInc, InsigDec and SigDec represent signiﬁcant increase, insigniﬁcant increase,

insigniﬁcant decrease and signiﬁcant increase trends, respectively (similar below).

Table 3

The number of stations with particular trends for the annual climatic variables and E

EMC.

Variable T

max

(°C)

min

(°C)

mean

(°C)

−1

)

(%)

(h)

(mm)

Trend

SigInc 44 293 135 9 14 8 41 23

InsigInc 497 250 412 75 109 77 226 191

SigDec 1 1 1 212 107 171 19 53

InsigDec 10 8 4 256 322 296 266 285

77N. Yao et al. / Science of the Total Environment 616–617 (2018) 73–89

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