ORI GIN AL PA PER

Evaluation of drought and wetness episodes in a coldregion (Northeast China) since 1898 with differentdrought indices

Binquan Li • Zhongmin Liang • Zhongbo Yu • Kumud Acharya

Received: 26 September 2013 / Accepted: 7 December 2013 / Published online: 15 December 2013� Springer Science+Business Media Dordrecht 2013

Abstract Drought identification and drought severity characterization are crucial to

understand water scarcity processes. Evolution of drought and wetness episodes in the

upper Nen River (UNR) basin have been analyzed for the period of 1951–2012 using

meteorological drought indices and for the period of 1898–2010 using hydrological

drought indices. There were three meteorological indices: one based on precipitation [the

Standardized Precipitation Index (SPI)] and the other two based on water balance with

different formulations of potential evapotranspiration (PET) in the Standardized Precipi-

tation Evapotranspiration Index (SPEI). Moreover, two hydrological indices, the Stan-

dardized Runoff Index and Standardized Streamflow Index, were also applied in the UNR

basin. Based on the meteorological indices, the results showed that the main dry period of

1965–1980 and wet periods of 1951–1964 and 1981–2002 affected this cold region. It was

also found that most areas of the UNR basin experienced near normal condition during the

period of 1951–2012. As a whole, the UNR basin mainly had the drought episodes in the

decades of 1910, 1920, 1970 and 2000 based on hydrological indices. Also, the severity of

droughts decreased from the periods of 1898–1950 to 1951–2010, while the severity of

floods increased oppositely during the same periods. A correlation analysis showed that

hydrological system needs a time lag of one or more months to respond to meteorological

conditions in this cold region. It was also found that although precipitation had a major role

in explaining temporal variability of drought, the influence of PET was not negligible.

B. Li (&) � Z. Liang � Z. Yu (&)State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University,Nanjing 210098, Chinae-mail: libinquan@hhu.edu.cn

Z. Yue-mail: zyu@hhu.edu.cn

Z. YuDepartment of Geoscience, University of Nevada Las Vegas, Las Vegas, NV 89154, USA

K. AcharyaDivision of Hydrologic Sciences, Desert Research Institute, Las Vegas, NV 89119, USA

123

Nat Hazards (2014) 71:2063–2085DOI 10.1007/s11069-013-0999-x

However, the sole temperature driver of PET had an opposite effect in the UNR basin (i.e.,

misestimating the drought detection) and was inferior to the SPI, which suggests that the

PET in the SPEI should be determined by using underlying physical principles. This

finding is an important implication for the drought research in future.

Keywords Drought � Meteorological drought index � Hydrological drought

index � Upper Nen River � Northeast China

1 Introduction

Drought is an insidious hazard of nature that is difficult to detect and monitor (Hayes et al.

1999). Compared with flood disasters, drought develops slowly but with long duration and

large scale and is jointly driven by climatic variations and hydrological conditions.

Drought can be categorized in terms of four basic approaches for measuring drought:

meteorological, hydrological, agricultural, and socioeconomic (Wilhite and Glantz 1985).

In addition, Mishra and Singh (2010) suggested that groundwater drought (defined or

quantified by the decrease in groundwater level, or groundwater storage, or groundwater

recharge or discharge) should be treated as a different type of drought. Meteorological and

hydrological drought assessments are of great concerns to hydrologists as they directly

affect water budgets at the watershed scale. In recent decades, droughts observed on all

continents have produced large-scale impacts on economic and social sectors. In China,

long-lasting and severe droughts in recent decades, such as the drying-up (zero flow) in

1997 of the Yellow River (Cong et al. 2009), the drought in northern China in 2000 (Zhang

2003), and the lowest level of basin runoff in the Yangtze River in 2006 during the last

50 years (Dai et al. 2008), have caused large economic and societal losses. In another

example, a drought with a-hundred-year return period swept across southwest China

(including Yunan, Guizhou, Guangxi, and Sichuan Provinces, and Chongqing City) during

summer 2009 to spring 2010, resulting in a large decrease in most river levels (Lu et al.

2011). Furthermore, due to high temperature and a lack of precipitation, severe droughts

have hit Yunnan Province every year since 2009 even in the rainy seasons. The consecutive

drought was expected to end in April 2013, affecting more than 10 million people in this

region (Wang 2013).

Many previous studies have been conducted for evaluating dryness or wetness spells

over China using different drought indices (Zhang et al. 2012; Zhai et al. 2010; Zou et al.

2005; Wu et al. 2001; Wang et al. 2011), including the Palmer drought severity index

(PDSI; Palmer 1965), the China-Z index (Wang et al. 2003), and the Standardized Pre-

cipitation Index (SPI; McKee et al. 1993). With precipitation and temperature data ana-

lyzed in a water-balance model, the PDSI is one of the most widely used drought indices

over the world for regional drought monitoring (Mishra and Singh 2010). Zou et al. (2005)

found that the successive large increase in dry areas from the late 1990s to 2003 in

Northeast, North and Eastern Northwest China were unprecedented during 1951–2003.

Zhai et al. (2010) calculated the frequency of dry and wet years using PDSI and SPI,

finding upward dry trends for three northeastern basins during the period of 1961–2005.

However, there are criticisms of PDSI that its assumptions are not universal (Mishra and

Singh 2010). The PDSI cannot be used for hydrological drought assessment during winter

months or in snowmelt regions as all precipitation are assumed as rain. The SPI is widely

2064 Nat Hazards (2014) 71:2063–2085

123

accepted due to its multiple simultaneous timescales and simplicity of calculation; it is

only based on precipitation and sensitivity to the length of the precipitation record (Heim

2002; Wu et al. 2005). This consideration in SPI may produce unrealistic estimates.

Compared with the SPI, the extra input of potential evapotranspiration (PET) is needed for

calculating the newly developed Standardized Precipitation Evapotranspiration Index

(SPEI; Vicente-Serrano et al. 2010). The SPEI combines the sensitivity of the PDSI to

changes in evaporation demand and the robustness of the multi-temporal nature of the SPI.

Vicente-Serrano et al. (2010) originally used a simplified calculation of PET that responds

only to temperature, i.e., Thornthwaite equation (Thornthwaite 1948). However, the sim-

plified Thornthwaite equation may produce biased PET estimates and further results in

overestimation of SPEI in response to global warming. A recent study verified that the

previously reported increase in global drought was overestimated because of the biased

PET calculation in PDSI, and little change in drought over the past 60 years was found

when underlying physical principles (changes in available energy, humidity and wind

speed) were considered (Sheffield et al. 2012). In many regions, global warming has

resulted in a significant increase in air temperature in recent decades (IPCC 2007). Thus,

air temperature should not be the sole driver of PET calculation in drought indices (e.g.,

PDSI and SPEI). In addition to these indices based on meteorological condition, indices

based on runoff (or streamflow) are also used in the assessment of hydrological drought.

Based on the concept of SPI, Shukla and Wood (2008) derived the Standardized Runoff

Index (SRI) which uses seasonal runoff loss driven by influences of climate and hydro-

logical initial conditions to evaluate hydrological droughts. Vicente-Serrano et al. (2011a)

developed another streamflow drought index, the Standardized Streamflow Index (SSI),

which allows spatial and temporal comparisons of hydrological conditions of a stream or

set of streams.

The Nen River Basin, a cold region in the northeastern China, is one of the most

important crop-production regions of the country. During the past century, this cold region

has experienced substantial changes in climate and land use/cover, which has led to serious

water resource problems (e.g., drought and flood hazards). Recent studies have shown that

the regional climate has become warmer and drier, and the runoff in this cold region has

declined since 1950s (Feng et al. 2011). In this study, long-term variations of dryness and

wetness spells in the upper Nen River (UNR) basin were investigated using different

meteorological and hydrological drought indices. The objectives of this paper are to

(a) investigate evolutions of drought and wetness episodes in the UNR basin for the period

of 1951–2012 using meteorological drought indices and for the period of 1898–2010 using

hydrological drought indices; (b) intercompare drought indices and evaluate their depiction

of observed droughts and floods; and (c) explore the responses of hydrological systems to

meteorological conditions.

2 Study area

The Nen River is the northern source of the Songhuajiang River in Northeast China. The

UNR basin (Fig. 1) is located between the Great and Lesser Khingan Mountain Ranges

(121�450–127�050E, 48�290–50�400N). This basin is covered by lush forests with the Nierji

Reservoir as its outlet covering an area of 66,382 km2. There is no major water conser-

vancy project in the basin. Annual streamflow volume from the basin is about

104.7 9 108 m3 and 45.7 % of that can be attributed to the Nen River. The UNR basin is

located in a mountainous and hilly area that results in a narrow river valley with an average

Nat Hazards (2014) 71:2063–2085 2065

123

riverbed slope [0.3 %. Annual mean precipitation amounts (1951–2012) are 487, and

500 mm at the NJ and SHY stations, respectively. In addition, about 80 % of annual

precipitation occurs during the period of June–September, and precipitation in the winter

season only contributes about 5 % to the total. In the northern latitudes, the study area

experiences a cold climate almost for half of the year. At the NEJ station, annual mean

air temperature is 1.5 �C, while the extreme maximum (minimum) value is 39.5 �C

(-40.4 �C). In the cold environment, the frozen soil develops up to 2 m below the surface

in this region.

3 Methods and data processing

3.1 Drought indices

3.1.1 SPI

SPI is an indicator of meteorological drought which is mainly caused by precipitation

deficiency. Thus, its calculation requires that consecutive data record length be at least

30 years. The long-term precipitation data are first fitted by a probability distribution, and

then the cumulative distribution of precipitation is determined. Initially, the Gamma dis-

tribution was used by McKee et al. (1993), but the Pearson Type III distribution was found

to be more robust after testing for different probability models (Vicente-Serrano 2006;

Guttman 1999). In this paper, a complete formulation of SPI including the Pearson Type III

Fig. 1 The study basin map. Subbasins with IDs from 1 to 12 have their outlet stations of: 1 SL, 2 ALH, 3JW, 4 JGDQ, 5 SHY, 6 GL, 7 HLM, 8 KMT, 9 LJT, 10 NJ, 11 KH, and 12 NEJ

2066 Nat Hazards (2014) 71:2063–2085

123

distribution and the L-moments method for calculating parameters was used. Then the

cumulative distribution is transformed equiprobably into a standard normal distribution so

that the mean SPI at the specific location becomes zero. The transformed probability is the

SPI value, which varies between ?2.0 and -2.0, with extremes outside this range

occurring 5 % of the time (Edwards 1997).

3.1.2 SPEI

PET in the original SPEI (Vicente-Serrano et al. 2010) was calculated based on the

simplified Thornthwaite equation (Thornthwaite 1948), which only requires monthly mean

temperature data. This original version of SPEI is denoted as ‘‘SPEI-TE’’ hereafter. A time

series of the difference between precipitation and PET was fitted to a three-parameter log-

logistic probability distribution to take into account common negative values (Lorenzo-

Lacruz et al. 2010). This is done because the log-logistic distribution shows a very close fit

to data series (Vicente-Serrano et al. 2011b). The SPEI values are accumulated to different

time scales, following the similar approach to that for SPI, and converted to standard

deviations with respect to average values.

As mentioned above, however, the Thornthwaite equation may produce biased PET

estimates, and further results in overestimation of SPEI in response of global warming. In

order to overcome this problem, PET was estimated using the Penman–Monteith equation

recommended by the Food and Agriculture Organization (FAO) (Allen et al. 1998). This

version of SPEI was denoted as ‘‘SPEI-PM’’ in this paper. Formulation of SPEI-PM

depends on the availability of relevant data such as air temperature, solar radiation, wind

velocity and humidity.

3.1.3 SRI

This index is based on the concept of SPI, described earlier. Shukla and Wood (2008)

developed this hydrological drought index which incorporates hydrological processes that

determine seasonal loss in streamflow due to the influence of climate. In this study, the SPI

program was employed to calculate SRI values at the hydrological stations, which rep-

resent an average dry or wet regime in the controlled drainage areas of the stations.

3.1.4 SSI

In the development of SSI, six three-parameter distributions that are widely used in

hydrological analysis (lognormal, Pearson Type III, log-logistic, general extreme value,

generalized Pareto, and Weibull) were tested for fitting to monthly streamflow series

(Vicente-Serrano et al. 2011a). The L-moment method was used to calculate the param-

eters of six probability distributions. Complete formulation of SSI allows the use of dif-

ferent distributions for each monthly streamflow series based on optimizing criteria.

Drought categories defined originally for the SPI values were employed for all of the

drought indices in the study (McKee et al. 1993; Hayes et al. 1999): (a) 2.00 and above:

extremely wet; (b) 1.50–1.99: very wet; (c) 1.00–1.49: moderately wet; (d) -0.99 to 0.99:

near normal; (e) -1.00 to -1.49: moderately dry; (f) -1.50 to -1.99: severely dry; and

(g) -2.00 and less: extremely dry.

Nat Hazards (2014) 71:2063–2085 2067

123

3.2 Trend analysis

The nonparametric Mann–Kendall (MK) test (Kendall 1975; Mann 1945) was employed in

this study to test trends in time series. A positive (negative) value of the MK test statistic Z

signifies an increasing (decreasing) trend. In order to eliminate the effect of significant

serial correlation in a time series on trend analysis, a trend-free pre-whitening procedure

(Yue et al. 2002) was employed before applying the MK test. In addition, the increasing or

decreasing slope (change per unit time) of a time series can be estimated using a Sen’s

slope method (Sen 1968). A positive (negative) value of slope indicates the increasing

(decreasing) trend in a time series.

3.3 Data processing

Meteorological and hydrological data used in this study are collected from administrative

agencies and subjected to rigorous quality control. Monthly precipitation at 44 stations

and daily maximum/minimum/mean air temperature, relative humidity, wind speed and

sunshine duration at 11 weather stations were collected for the period of 1951–2012.

Observed monthly streamflow for 11 hydrological stations were collected for the period

of 1951–2006. In addition, the monthly inflow data (1898–2010) for the Nierji Reservoir

was also used in analysis. Meteorological data were interpolated to 2,000 square meter

grids using the inverse distance weighting method after cross-validation. The lapse rate

for air temperature was -0.6 �C (100 m)-1, which was recommended by a previous

comprehensive study on climate change in northeast China (Sun 2008).

The selected distributions of the drought indices for fitting observed monthly data series

were examined in the UNR basin (Fig. 2). The fitting results showed that observations can

be well expressed in terms of percentile (top axes) or the standardized index (bottom axes).

In the calculation of the SPEI-PM, daily meteorological data was used to calculate PET at

each grid via the Penman–Monteith equation, and then daily PET was re-sampled to the

monthly scale. The meteorological drought indices were calculated at each grid for drought

trend, area, duration and magnitude analysis. To assess the possible impact of climate

variations on surface water resources, we used monthly river discharge from the hydro-

logical stations in the UNR basin for calculating two hydrological drought indices (the SRI

and SSI). Hydrological drought indices calculated at a specific station represent a general

drought/wetness condition of the entire drainage basin. A correlation analysis was applied

between hydrological drought indices and averaged meteorological indices for the entire

UNR basin. Time scales from 1 to 24 months were used in the correlation analysis, since

the optional time scale may vary notably among different hydrological systems (Lorenzo-

Lacruz et al. 2010; Vicente-Serrano et al. 2011b). In addition, time lags from 0 to

24 months for hydrological response to meteorological condition were also considered in

the correlation analysis between meteorological and hydrological drought indices.

4 Results

4.1 Hydrometeorological trends

To better understand the evolutions of these drought indices with effects of climate change,

trend analyses for precipitation, air temperature, PET and streamflow were first performed

for stations with consecutive data record length more than 30 years. Results for

2068 Nat Hazards (2014) 71:2063–2085

123

precipitation are contrasting, with increases at seven stations and decreases in the

remaining four (i.e., SHY, KMT, NJ and KH). Moreover, increasing trends in three western

mountainous stations (JW, ALH and JGDQ) were statistically significant while the KH

station in the eastern plain decreased significantly (90 % confidence level). The largest

positive trend with a Sen’s slope of 3.69 mm per year (mm a-1) was identified for JW, a

station with the highest altitude, 499 m. As a whole, positive trends were observed at

western mountainous locations; and negative trends were in the eastern locations.

For mean annual air temperature, a positive significant trend was detected in both

considered weather stations (JW and NJ), with the same Sen’s slope of 0.04 �C per year

(�C a-1). As the representatives of real evapotranspiration, PET demonstrated different

trends for two locations (JGDQ and NJ) with negative and positive rates, respectively. A

significant positive rate of 2.95 mm a-1 was detected at NJ with PET calculated by the

Penman–Monteith equation. Since the 1950s the runoff trends at most hydrological stations

decreased with an average value of -0.76 mm a-1, except for three northern stations

(ALH, SL and GL). Two negative trending stations (SHY and KMT) and one positive

trending station (SL) were statistically significant in this period starting as early as 1951. In

addition, at the NJ station the river water level had a significant negative rate of

0.5 2 5 10 30 50 70 90 95 98 99.5

percentile (%)

(a) SPI at NJ station

prec

ipita

tion,

P (

mm

)

400

600

SPI

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

0.5 2 5 10 30 50 70 90 95 98 99.5

percentile (%)

stre

amflo

w (

m3 s

-1)

0

200

400

600

800

SRI

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

0.5 2 5 10 30 50 70 90 95 98 99.5

percentile (%)

P -

PE

T (

mm

)

−400

−200

0

SPEI-TE

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

0.5 2 5 10 30 50 70 90 95 98 99.5

percentile (%)

(d) SPEI-PM at NJ stationP

- P

ET

(m

m)

−600

−400

−200

SPEI-PM

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

(b) SRI at UNR basin outlet

(c) SPEI-TE at NJ station

Fig. 2 Simulated historical (1951–2012 for precipitation/PET, and 1898–2010 for streamflow) distributionsof 12-month observations in the UNR basin. Samples are fitted with a and b Pearson Type III, and c andd three-parameter Log-logistic distributions. The indices on the bottom axes are the unit standard normaldeviation associated with the percentile of observations (top axes)

Nat Hazards (2014) 71:2063–2085 2069

123

-4.37 mm a-1. During the period of 1898–2010, the increasing trend of runoff from the

entire UNR basin was statistically significant, with a Sen’s slope of 0.47 mm a-1.

4.2 Evolution of meteorological drought indices (1951–2012)

Figure 3 shows the evolution of the SPI, SPEI-TE, and SPEI-PM over 3, 12, and

24 months intervals in the period of 1951–2012. In the entire UNR basin, short timescales

(e.g., 3-month indices) showed a higher temporal frequency of dryness and wetness periods

for these three indices. With increasing timescales (e.g., 12- and 24-month), drought and

wetness periods showed a lower temporal frequency and a longer duration. All indices

showed a similar evolution with no notable differences except for the SPEI-PM in the

period from the mid-1960s to the early 1980s. Alternant dominant dry and wet conditions

were evident between 1951 and 2012 for all meteorological indices. Persistent drought

spells occurred in the periods of 1965–1980 and 2002–2012, whereas wet conditions

dominated in the other periods. The SPEI-PM, which included more than just precipitation

and temperature, showed consistent drought and wetness spells within these considered

periods. In the last decade (2003–2012), three indices demonstrated the different climatic

conditions: (a) predominant drought periods were found using the SPI and SPEI-TE, with

particularly severe drought in 2007 and (b) alternating drought and wetness periods were

detected by using the SPEI-PM. As a whole, the SPI was similar, in a high degree, to the

SPEI-TE with extra temperature input. The SPEI-PM with consideration of underlying

physical principles, however, tended to show a low temporal frequency and a long duration

of drought episodes.

Based on the drought categories described above, the individual drought episodes were

determined from the index series using a threshold of -1, which represents 15.9 % of the

probability distribution of the standardized variable, and a threshold of 1 for wetness

episodes identification. Moreover, the duration and magnitude of each drought/wetness

event were further determined (Dracup et al. 1980): the duration is the number of con-

secutive months with values \-1 ([1) for drought (wetness) and the sum of the index

values is the drought (wetness) magnitude. Table 1 shows the occurrence number, average

(a) 3-month SPI

SP

I

−3

−2

−1

0

1

2

3

1951 1960 1970 1980 1990 2000 2010

(d) 3-month SPEI-TE

SP

EI-

TE

−3

−2

−1

0

1

2

3

1951 1960 1970 1980 1990 2000 2010

(g) 3-month SPEI-PM

SP

EI-

PM

−3

−2

−1

0

1

2

3

1951 1960 1970 1980 1990 2000 2010

(b) 12-month SPI

−3

−2

−1

0

1

2

3

1951 1960 1970 1980 1990 2000 2010

(e) 12-month SPEI-TE

−3

−2

−1

0

1

2

3

1951 1960 1970 1980 1990 2000 2010

(h) 12-month SPEI-PM

−3

−2

−1

0

1

2

3

1951 1960 1970 1980 1990 2000 2010

(c) 24-month SPI

−3

−2

−1

0

1

2

3

1951 1960 1970 1980 1990 2000 2010

(f) 24-month SPEI-TE

−3

−2

−1

0

1

2

3

1951 1960 1970 1980 1990 2000 2010

(i) 24-month SPEI-PM

−3

−2

−1

0

1

2

3

1951 1960 1970 1980 1990 2000 2010

Fig. 3 Evolution of the 3-, 12-, and 24-month SPI, SPEI-TE, and SPEI-PM for the entire UNR basin from1951 to 2012

2070 Nat Hazards (2014) 71:2063–2085

123

duration and magnitude of drought and wetness periods determined by using the meteo-

rological drought indices at the timescale of 12-month. For the period of 1951–2012, the

SPI and SPEI-TE series show a large difference in the number of drought episodes (15 and

22, respectively), whereas the SPEI-PM series show a minimum number of drought epi-

sodes (11). The average duration for the SPEI-TE based drought episodes was 5.0 months

which was the shortest, but for the SPI and SPEI-PM they were 5.9 and 13.1 months,

respectively. The average magnitudes of the SPI and SPEI-TE show a similar contrast

(-8.7 vs. -7.1), whereas the SPEI-PM shows a value of -19.7. On the decadal scale, no

drought event was identified in 1990s using these three indices and only one moderate

drought event was found in the 1980s with the SPEI-TE. In the 1950s, the SPEI-PM also

did not identify any drought but the other two indices found two episodes. In the 1970s, the

SPEI-PM, which performed very differently with other two indices, showed four severe

drought spells with long durations. The severest drought with longest duration by the SPEI-

PM started in August, 1975 (59 months duration and -92.7 magnitudes). As for the

wetness episodes the SPI, SPEI-TE and SPEI-PM series show a larger number than drought

episodes for the period of 1951–2012 (20, 24 and 18, respectively). The SPEI-PM series

show the largest duration and magnitude of wetness episodes, while the SPI and SPEI-TE

show a similar duration and magnitude. In each decade, wetness episodes can be found

except that the SPEI-PM did not identify any wetness event in the 1970s. No clear trend

can be found for drought and wetness severity, in terms of duration and magnitude.

Spatial distributions of the MK trend statistic of seasonal and annual SPI, SPEI-TE, and

SPEI-PM of subbasins from 1951 to 2012 were investigated (Table 2). On annual scale, the

SPI-based series showed the wetter (positive) trends for the right bank areas (subbasin IDs:

1–4, 6 and 9) and drier (negative) trends for the left bank areas (subbasin IDs: 5, 7, 8 and

10–12) of the mainstem of UNR, respectively, but all trends were not statistically sig-

nificant. Seasonally, significant drier trends of all subbasins were in fall while significant

wetter trends in five subbasins (IDs: 1–4 and 6) were observed by the SPI in summer. For

the SPEI-TE significant drier trends were found in the entire basin of the spring and fall

seasons, and also in seven subbasins on the annual scale. As for the SPEI-PM, three

subbasins (IDs: 10–12) had the significant drier trend in fall and on annual scale, only a

downriver subbasin became drier significantly. Obviously, the SPEI-TE strengthened the

drier trends or weakened the wetter trends in all cases. This was mainly driven by climate

Table 1 Occurrence number (ON), average duration and magnitude of drought and wetness periodsdetermined by using the meteorological drought indices at the 12-month timescale

Periods Drought episodes (ON/duration/magnitude) Wetness episodes (ON/duration/magnitude)

SPI SPEI-TE SPEI-PM SPI SPEI-TE SPEI-PM

1950s 2/5.5/-9.2 2/5.0/-6.5 0/0/0 5/5.4/7.8 4/8.0/12.9 4/10.3/15.2

1960s 3/8.7/-12.4 5/3.8/-5.0 5/4.6/-6.4 1/1.0/1.0 4/3.0/3.5 1/3.0/3.7

1970s 6/3.8/-4.9 5/2.6/-3.3 4/27.0/-41.8 1/1.0/1.1 1/8.0/9.6 0/0/0

1980s 0/0/0 1/1.0/-1.1 0/0/0 6/4.2/5.4 6/5.7/6.8 5/6.4/8.8

1990s 0/0/0 0/0/0 0/0/0 6/7.2/9.7 7/5.0/6.4 5/6.8/9.4

2000s 4/7.0/-11.4 7/7.4/-11.7 2/6.5/-8.6 1/10.0/12.9 2/4.0/5.0 2/3.5/4.6

1951–2012 15/5.9/-8.7 22/5.0/-7.1 11/13.1/-19.7 20/5.4/7.2 24/5.4/7.1 18/6.6/9.2

The numbers separated by the symbols ‘‘/’’ represent the ON, average duration (months) and magnitude,respectively

Nat Hazards (2014) 71:2063–2085 2071

123

Ta

ble

2S

en’s

slop

eso

fse

aso

nal

and

ann

ual

met

eoro

log

ical

dro

ugh

tin

dic

esin

the

UN

Rb

asin

for

the

per

iod

of

19

51–

20

12

IDs

Sp

rin

g(3

–5

)S

um

mer

(6–

8)

Fal

l(9

–1

1)

Win

ter

(12

–2)

Yea

r(1

2m

on

ths)

SP

IT

EP

MS

PI

TE

PM

SP

IT

EP

MS

PI

TE

PM

SP

IT

EP

M

1-

0.3

-1

.40

.40

.90

.50

.9-

1.1

-1

.5-

0.4

-0

.30

.20

.00

.3-

0.6

0.2

2-

0.1

-1

.60

.20

.80

.40

.8-

1.4

-1

.9-

0.5

0.1

0.2

-0

.10

.3-

0.9

0.3

30

.0-

1.2

0.1

0.7

0.4

0.8

-1

.3-

1.7

-0

.70

.50

.40

.10

.1-

0.9

-0

.2

4-

0.1

-1

.80

.21

.00

.61

.0-

1.3

-2

.0-

0.8

0.1

0.1

-0

.20

.3-

0.8

0.2

50

.4-

1.9

0.6

0.4

-0

.20

.6-

1.2

-2

.0-

0.6

0.7

0.7

0.3

-0

.2-

1.7

0.0

60

.1-

1.8

0.4

0.9

0.5

1.0

-1

.1-

1.8

-0

.50

.20

.30

.10

.5-

0.8

0.2

70

.1-

2.2

0.0

-0

.3-

0.9

0.0

-1

.6-

2.4

-0

.90

.90

.90

.3-

0.7

-2

.0-

0.4

80

.4-

1.9

0.1

0.1

-0

.70

.2-

1.4

-2

.2-

0.8

0.5

0.5

0.4

-0

.3-

1.9

-0

.4

90

.4-

1.7

0.3

0.5

-0

.10

.6-

1.3

-1

.9-

0.7

0.4

0.6

0.1

0.0

-1

.5-

0.1

10

0.5

-1

.8-

0.8

0.0

-0

.8-

0.5

-1

.6-

2.5

-1

.50

.40

.40

.2-

0.8

-2

.4-

1.5

11

0.5

-1

.70

.3-

0.2

-1

.00

.0-

1.9

-2

.8-

1.3

0.5

0.9

0.8

-0

.7-

2.5

-0

.8

12

0.6

-1

.50

.00

.0-

0.8

-0

.1-

1.4

-2

.3-

1.2

0.4

0.8

0.5

-0

.4-

2.0

-0

.7

UN

R0

.4-

1.7

0.3

0.5

-0

.20

.4-

1.3

-2

.0-

0.9

0.5

0.7

0.3

-0

.1-

1.6

-0

.2

TE

and

PM

are

for

the

SP

EI-

TE

and

SP

EI-

PM

,re

spec

tivel

y;

Sen

’ssl

opes

are

dim

ensi

onle

ss(9

10

-2),

and

bo

ldsl

op

esin

dic

ate

90

%co

nfi

den

cele

vel

bas

edo

nM

Kte

st

2072 Nat Hazards (2014) 71:2063–2085

123

warming in the UNR basin. Correspondingly, the SPEI-PM showed a moderate drought

trend.

Figure 4 shows the temporal variations of the percent area of the UNR basin experi-

encing annual dry and wet conditions during the period of 1951–2012. It was clear that

most of the grids in the basin experienced near normal condition during the period of

1951–2012 based on all three meteorological drought indices. The SPI and SPEI-TE

showed a similar pattern in all drought categories with an extremely dry event in 2007.

However, based on the SPEI-PM, most of the grids in the UNR basin only experienced a

moderate dry condition in 2007. No drought event occurred in most grids prior to 1965 and

after 1980 (except for the 2007 drought), and drought mainly occurred during 1966–1979.

In addition, most of the grids experienced the wetness episodes in the years of 1951–1953,

1956, 1983, 1984, 1988, 1991, 1993, 1998 and 2003.

4.3 Evolution of hydrological drought indices (1898–2010)

Two hydrological drought indices, 12-month SRI and SSI, were also investigated using

monthly inflow to the Nierji Reservoir (i.e., NEJ station) from 1898–2010, and the

1-25% 26-50% 51-75% 76-100%(c) Dry/wet area percent from SPEI-PM

extremely dry

severely dry

moderately dry

near normal

moderately wet

very wet

extremely wet

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

(b)

(a)

Fig. 4 Temporal variations of percent area of the UNR basin experiencing the 12-month a SPI, b SPEI-TE,and c SPEI-PM for the period of 1951–2012. Indices values at each grid were calculated in statistics of dryand wet percent areas

Nat Hazards (2014) 71:2063–2085 2073

123

evolution of the difference between these two indices (SRI-SSI) was also calculated

(Fig. 5). During the 113 year period, the SRI and SSI series show a very similar evolution

of drought and wetness episodes, and both detected the main drought episodes in the

decades of 1910, 1920, 1970 and 2000, suggesting a high degree of similarity between the

two series. The calculation of SRI-SSI identified some difference values greater than one;

these were in the years of 1908, 1921, 1922 and 1930. Two long duration of dry period

during the period of 1898–1930 were recorded by both indices.

The duration and magnitude of drought and wetness episodes were investigated for the

hydrological drought series. We focused on the period of 1898–1950, during which the

meteorological drought indices cannot be obtained, and we compared the pattern with the

last 60 years (1951–2010). Figure 6 shows the duration and magnitude of the drought/

wetness events recorded between these two periods from the SRI and SSI. For the period

of 1898–1950, the SRI and SSI series show a similar number of drought episodes (10 and

12, respectively) and wetness episodes (8 and 9, respectively). The average duration for

the SRI-based drought (wetness) episodes was 17.1 months (10.5 months), but for the SSI

it was 13.1 months (7.9 months). The average magnitudes were -24.4 and -21.1 for the

SRI and SSI drought series, respectively; and 15.6 and 12.7 for the SRI and SSI wetness

series, respectively. The numbers of drought series are larger than the averages for the

period of 1951–2010 (7 and 7 episodes, 8.7 and 8.4 duration, and -11.7 and -11.4

magnitude, for the SRI and SSI, respectively). It indicated that the drought severity

decreased, in terms of both duration and magnitude, between the first and second time

period. This was confirmed by the increased wetness (or flood) episodes between 1898

and 1950 and between 1951 and 2010. For the period of 1951–2010, 17 and 14 flood

episodes were detected by using the SRI and SSI, respectively. The SSI-based flood series

showed a wetter trend in terms of both duration (9.5 months) and magnitude (13.5),

between these two periods, whereas the numbers of the SRI series for the period of

1951–2010 (8.4 duration and 11.8 magnitude) were lower than that in the period of

1898–1950. As a whole, the differences between these two hydrological indices were low

in detecting the drought/wetness events.

The trend variations of 12-month hydrological drought indices were investigated at 10

hydrological stations. Two stations, HLM and NJ, were excluded because HLM only

recorded discharge data from April to December, and NJ was a water level station without

(a) 12-month SRI

(b) 12-month SSI

(c) SRI-SSI

Diff

eren

ce

−1

0

1

SS

I

−3−2−1

0123

SR

I

−3−2−1

0123

Year

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Fig. 5 The 12-month a SRI and b SSI from the inflow series for the Nierji Reservoir. The differencesbetween these two indices series are also shown in c

2074 Nat Hazards (2014) 71:2063–2085

123

discharge observations. On the basin scale, both hydrological drought indices increased

remarkably with slope of 0.007 during the period of 1898–2010 which indicated that the

basin was becoming wetter. This was mainly produced by a significant upward (wetter)

trend in the first period (1898–1950) with 0.021 slope (for both indices), while a significant

downward trend for the second period (1951–2010) with -0.015 slope (also for both) was

detected at the NEJ station. For interior stations, the SRI and SSI also showed a very high

correlation in terms of both Sen’s slope and MK statistic except that the SRI-based trends

at the SHY and KH stations were significant but for the SSI they were nonsignificant. The

drier or wetter trends at all stations were highly correlated with the SPEI-PM based

changing trends in the subbasins. Four stations of SL, ALH, JGDQ and GL had positive

trends suggesting their drainage areas were becoming wetter for the considered periods

while the other stations had drier trends.

SRI SSI(a)

Dro

ught

dur

atio

n

0

10

20

30

40

50

60

70SRI SSI(b)

Dro

ught

mag

nitu

de

-20

-40

-60

-80

-100

-120

SRI SSI(c)

Wet

ness

dur

atio

n

0

5

10

15

20

25SRI SSI(d)

Wet

ness

mag

nitu

de

0

10

20

30

40

1898-1950 1951-2010 1898-1950 1951-2010 1898-1950 1951-2010 1898-1950 1951-2010

1898-1950 1951-2010 1898-1950 1951-2010 1898-1950 1951-2010 1898-1950 1951-2010

Fig. 6 Box plots showing the duration and magnitude of drought and wetness for the SRI and SSI series inthe periods of 1898–1950 and 1951–2010. The closed and open dots represent the mild and extreme outliers,respectively

Nat Hazards (2014) 71:2063–2085 2075

123

4.4 Correlation analysis of drought indices

In order to intercompare the meteorological drought indices used in this paper, the cor-

relation analysis was performed with no time lag. Figure 7 shows the Pearson R correlation

coefficients between the series of meteorological drought indices for the entire UNR basin

at different timescales (1–24 months). Correlations were positive for the various time-

scales, and in general, the indices in the same category (meteorological or hydrological)

had a high correlation at all timescales. On the whole, the hydrological drought indices

demonstrated a very high correlation ([0.98) at different timescales, while the correlation

values for the meteorological indices were between 0.72 and 0.95 (Fig. 7). The SPI and

SPEI-TE showed the greatest correlation (range 0.91–0.95), while the SPEI-TE and SPEI-

PM had the lowest correlation (range 0.72–0.80). The SPI had a lower degree of similarity

with the SPEI-PM than with the SPEI-TE. This indicated that precipitation dominated the

calculation of meteorological indices and that there was a large PET difference between

the two different formulations. There was an obvious decreased tendency of Pearson

R correlation with accumulated timescales in SPI vs. SPEI-TE and SPEI-TE vs. SPEI-PM,

while the comparison between SPI and SPEI-PM had a relatively stable correlation value

from 0.78 to 0.80.

Responses of hydrological systems to meteorological conditions were identified for the

river flow of the entire UNR basin with the time lag from 0 to 24 months (Fig. 8). In

general, the time lag of hydrological response to meteorological condition is determined by

climate feature, basin size and geographical and geomorphic conditions, etc. In such cold

regions with good vegetation condition, the range 0–24 month lag of hydrological indices

should be suitable for investigating the correlation with meteorological indices. The cor-

relation 3D diagrams for meteorological and hydrological drought indices were of similar

shape. Higher correlation values occurred within the time ranges of 8–24 months (scale)

and 0–3 months (lag), while lower correlations were of shorter time scale. In addition, two

hydrological indices demonstrated a high degree of similarity for the response to meteo-

rological droughts. This was in agreement with the previous results (e.g., Fig. 7). It is

noteworthy that correlation was slightly higher with the SPEI-TE rather than the SPI,

indicating that a combined consideration of precipitation and PET better explained the

variability of hydrological regime than did precipitation alone. Obviously, the SPEI-PM

SPI ~ SPEI-TESPI ~ SPEI-PMSPEI-TE ~ SPEI-PMSRI ~ SSI

R-P

ears

on

0.8

0.9

1.0

Time scale (months)2 4 6 8 10 12 14 16 18 20 22 24

Fig. 7 Pearson R correlationvalues for the 1- to 24-monthdrought indices in the UNRbasin. Meteorological droughtindices were calculated for theentire basin while thehydrological drought indiceswere determined at the basinoutlet

2076 Nat Hazards (2014) 71:2063–2085

123

showed the highest correlations with both hydrological indices for most time scales and

lags. For two water-balance-based meteorological indices at shorter time lags

(0–3 months), the timescale of 7 months seems to be a turning point: the SPEI-PM had

higher correlations with hydrological indices (\7 months scale) and the SPEI-TE turned

over with time scale[7 months. For all time scales, 1-month time lag produced the highest

increase in Pearson R correlation values among all considered time lags. Thus, climatic

condition in the previous month was the most significant variable contributing to the

hydrological regime on the basin scale of the UNR.

Furthermore, correlation analysis was examined in nine subbasins for better under-

standing the performances of these indices. It was found that the two hydrological drought

indices also experienced a very high degree of correlation ([0.95) in all considered sub-

basins, and meteorological indices had similar correlations against both hydrological

Fig. 8 3D diagrams of Pearson R correlation values for the entire UNR basin with the consideration of bothtime scale (1–24 months) and time lag (0–24 months)

Nat Hazards (2014) 71:2063–2085 2077

123

Ta

ble

3C

orr

elat

ion

anal

ysi

sb

etw

een

thre

em

eteo

rolo

gic

ald

rou

gh

tin

dic

esan

dth

eS

RI

atn

ine

hy

dro

logic

alst

atio

ns

Hydro

logic

alst

atio

ns/

(subbas

inID

s)P

ears

on

Rco

rrel

atio

nra

nges

and

aver

age

val

ues

(tim

ela

g=

1)

Tim

ela

g*

SP

Iv

ersu

sS

RI

SP

EI-

TE

ver

sus

SR

IS

PE

I-P

Mv

ersu

sS

RI

SL

/(1

)[0

.37

,0

.69];

0.6

3[0

.33

,0

.63];

0.5

2[0

.36

,0

.75];

0.6

91

–2

mon

th

AL

H/(

2)

[0.3

7,

0.5

7];

0.5

4[0

.38,

0.6

8];

0.5

9[0

.41,

0.5

4];

0.4

91

month

JW/(

3)

[0.3

4,

0.6

6];

0.5

9[0

.35

,0

.69];

0.6

3[0

.31

,0

.54];

0.5

01

mon

th

JGD

Q/(

2–

4)

[0.4

2,

0.8

9];

0.8

1[0

.40

,0

.88];

0.7

9[0

.47

,0

.86];

0.7

91

mon

th

SH

Y/(

5)

[0.2

9,

0.7

0];

0.6

2[0

.34

,0

.76];

0.6

6[0

.33

,0

.77];

0.6

81

–5

mon

th

GL

/(1,

6)

[0.4

2,

0.9

1];

0.8

3[0

.41

,0

.87];

0.8

0[0

.49

,0

.92];

0.8

51

mon

th

KM

T/(

1,

5–

8)

[0.2

8,

0.7

7];

0.6

7[0

.30

,0

.79];

0.7

1[0

.36

,0

.74];

0.6

81

mon

th

LJT

/(2

–4,

9)

[0.3

7,

0.8

2];

0.7

5[0

.38

,0

.83];

0.7

5[0

.42

,0

.85];

0.7

71

mon

th

KH

/(1

1)

[0.3

6,

0.8

1];

0.7

2[0

.40

,0

.87];

0.7

9[0

.39

,0

.72];

0.6

71

mon

th

Pea

rso

nR

corr

elat

ion

ran

ge

amon

gd

iffe

ren

tti

me

scal

es(1

–2

4m

on

ths)

are

sep

arat

edw

ith

aco

mm

aw

ith

ina

bra

cket

foll

ow

edb

yth

eav

erag

ev

alue

*T

ime

lag

isth

ela

gfo

rth

ep

eak

corr

elat

ion

val

ue

for

each

tim

esc

ale

(1–

24

mo

nth

);1

mon

thin

dic

ates

that

all

tim

esc

ales

hav

eth

eir

pea

kco

rrec

tio

nv

alu

esat

the

tim

ela

go

fo

ne

mo

nth

2078 Nat Hazards (2014) 71:2063–2085

123

indices. Thus, only comparison results between three meteorological indices and SRI are

presented (Table 3). Similarly, for three meteorological indices, lower and higher corre-

lation values with the SRI were for small and large timescales, respectively, in all drainage

areas of these hydrological stations. But, in fact, it is difficult to discern between good or

bad among these metrological indices for all time scales and drainage areas. Relatively

high correlation (*0.80) existed in the drainage areas of JGDQ and GL stations for all

indices. For all time scales, the peak correlation values were found with 1 month time lag

at all considered stations except for the SL (subbasin 1) and SHY (subbasin 5) stations. In

subbasin (1), the 18- and 19-month time scales were with 2 months lag whereas the rest

were with 1 month. In subbasin (5), 1-month lag for peak correlation was found for 1–12-

month time scales; however, it increased to 2–5-months lag for [12-month time scales.

Seasonal comparisons between meteorological and hydrological indices are presented in

Table 4. Results found that lower correlations at small time scales were produced by

weaker correlations in the cold winter season. For all combinations of meteorological and

hydrological indices, stronger correlations can be found in summer. It was obvious that, in

the UNR basin, cold environment considerably slowed down the hydrological cycle. In

cold months (e.g., spring and winter), most precipitation falls as snow. The solid precip-

itation is certainly used to calculate meteorological indices but cannot transform to

streamflow in hydrological indices until it melts completely. In addition, water re-freezes

in cold soils with temperatures below zero. This further extends the time lag between

meteorological and hydrological indices.

5 Discussion

Several meteorological and hydrological drought indices were used in the UNR basin,

producing some differences in the identification of drought and wetness episodes between

1898 and 2012. Thus, for evaluating these drought indices, we collected the incomplete

historical records of droughts and floods from the local chorography datasets (http://www.

zglz.gov.cn/) and public website (http://www.cws.net.cn/flood/ChinaFlood.html). Some

drought and flood disasters occurring in the left bank area of mainstem of the UNR, the

Jiagedaqi region (subbasin 4) and the entire UNR basin were presented in Table 5. Drought

indices were averaged from the gridded values at different timescales determined by the

period of each event. If no specified periods of drought and flood were recorded, both 1-

and 12-month timescales were used. In the calculation of 1-month meteorological indices,

the month with maximum successfully detected cases were used; and the month for the

Table 4 Pearson R correlation between meteorological and hydrological drought indices of the entire UNRbasin with no time lag. Time series used in analysis are 3-month scale at May, August, November andFebruary for spring, summer, fall and winter seasons, respectively, and at all months for ‘‘whole year’’

Spring Summer Fall Winter Whole year

SPI versus SRI 0.55 0.70 0.59 0.19 0.44

SPI versus SSI 0.56 0.68 0.60 0.20 0.44

SPEI-TE versus SRI 0.56 0.72 0.62 0.12 0.47

SPEI-TE versus SSI 0.58 0.69 0.63 0.12 0.48

SPEI-PM versus SRI 0.57 0.77 0.63 0.30 0.52

SPEI-PM versus SSI 0.62 0.75 0.62 0.30 0.53

Nat Hazards (2014) 71:2063–2085 2079

123

Ta

ble

5In

com

ple

tere

cord

so

fd

rou

gh

tan

dfl

ood

dis

aste

rsd

ocu

men

ted

inth

elo

cal

hy

dro

logic

alan

dm

eteo

rolo

gic

alch

oro

gra

ph

yd

atas

ets

du

rin

g1

89

8an

d2

01

2,

and

the

calc

ula

ted

resu

lts

of

five

dro

ught

indic

es(s

ucc

essf

ull

y-d

etec

ted

case

sof

dro

ught

and

flood

inbold

)

Yea

rM

onth

sD

escr

ipti

on

SP

IS

PE

I-T

ES

PE

I-P

MS

RI

SS

I

1904

Dec

.aD

1:

Lef

tban

kar

ea-

1.0

5/-

1.0

8-

1.0

3/-

0.9

6

1906

Jan.a

D2:

Lef

tban

kar

ea-

1.8

2/-

1.3

2-

1.8

6/-

1.2

5

1909

Jul.

–A

ug.

D3:

Lef

tban

kar

ea0.2

90.2

8

1921

May

–S

ep.

D4:

Lef

tban

kar

ea-

1.6

7-

2.8

9

1923

Jul.

–A

ug.

D5:

Lef

tban

kar

ea-

0.9

1-

0.8

4

1925

Jan.a

D6:

Lef

tban

kar

ea-

1.8

2/-

1.3

3-

1.8

6/-

1.3

1

1954

May

–S

ep.

D7:

Lef

tban

kar

ea-

1.8

2-

1.4

1-

0.3

40.3

00.2

9

1967

Jul.

–A

ug.

D8:

Jiag

edaq

ire

gio

n0.5

0.6

20.1

2

1969

Apr.

–Ju

n.

D9:

Jiag

edaq

ire

gio

n-

1.8

1-

0.7

5-

1.5

9

1970

Apr.

–Ju

n.

D10:

Jiag

edaq

ire

gio

n-

2.0

1-

2.0

6-

2.4

8

1970

Jun.–

Aug.

D11:

Lef

tban

kar

ea-

0.6

3-

0.8

1-

1.3

0-

1.2

9-

1.2

9

1971

Apr.

–Ju

n.

D12:

Lef

tban

kar

ea-

0.4

7-

0.4

1-

1.1

20.5

40.5

6

1971

Jul.

–A

ug.

D13:

Jiag

edaq

ire

gio

n-

0.5

3-

0.4

2-

1.0

6-

0.7

-0.7

1972

Mar

.–M

ayD

14:

Lef

tban

kar

ea-

0.4

9-

0.2

7-

1.9

1-

0.6

3-

0.7

6

1973

Sep

.–O

ct.

D15:

Jiag

edaq

ire

gio

n-

1.4

2-

1.6

0-

2.1

5-

1.3

1-

1.3

2

1974

Jul.

–A

ug.

D16:

Jiag

edaq

ire

gio

n-

1.1

4-

1.3

2-

1.6

2-

1.7

9-

1.7

4

1975

Dec

.b,

Dec

.aD

17:

Lef

tban

kar

ea-

1.4

6/0

.03

-0.1

0/-

0.2

5-

1.8

9/-

1.6

40.4

2/-

0.5

40.2

1/-

0.4

3

1976

Mar

.b,

Apr.

aD

18:

Lef

tban

kar

ea-

1.2

8/-

0.6

6-

1.1

9/-

0.1

8-

1.4

9/-

1.6

7-

1.3

0/-

0.5

7-

0.5

8/-

0.4

6

1977

Sep

.–O

ct.

D19:

Jiag

edaq

ire

gio

n-

0.9

8-

0.8

3-

1.7

6-

0.7

9-

0.8

8

1977

Aug.b

,S

ep.a

D20:

Lef

tban

kar

ea-

1.2

3/-

0.7

7-

1.1

4/-

0.6

9-

1.7

6/-

1.8

6-

1.5

7/-

1.1

6-

0.8

2/-

1.0

6

1978

Jul.

–A

ug.

D21:

Jiag

edaq

ire

gio

n-

1.4

8-

1.2

9-

1.7

8-

0.6

3-

0.6

3

1978

Aug.b

,S

epa

D22:

Lef

tban

kar

ea-

2.0

9/-

0.2

8-

1.7

7/-

0.1

3-

2.1

8/-

1.6

3-

0.7

4/-

0.5

1-

0.3

6/-

0.4

0

1979

May

b,

Aug.a

D23:

Lef

tban

kar

ea-

1.8

0/-

0.2

6-

1.8

7/-

0.0

7-

2.3

7/-

1.5

6-

3.0

9/-

0.9

2-

1.4

2/-

0.7

8

1910

Jul.

–A

ug.

F1:

Lef

tban

kar

ea1.2

61.2

1

1914

May

aF

2:

Lef

tban

kar

ea2.3

0/0

.49

2.1

8/0

.41

2080 Nat Hazards (2014) 71:2063–2085

123

Ta

ble

5co

nti

nu

ed

Yea

rM

onth

sD

escr

ipti

on

SP

IS

PE

I-T

ES

PE

I-P

MS

RI

SS

I

1932

Jun.–

Aug.

F3:

Lef

tban

kar

ea2.2

72.5

0

1934

Jul.

–A

ug.

F4:

Lef

tban

kar

ea1.5

51.5

1

1938

Aug.a

F5:

UN

Rbas

in1.1

7/0

.91

1.1

2/0

.79

1956

Jul.

–S

ep.

F6:

Lef

tban

kar

ea0.4

60.6

20.8

20.2

70.2

8

1957

Jul.

–A

ug.

F7:

Lef

tban

kar

ea0.6

50.9

61.0

51.8

21.8

1

1960

May

–A

ug.

F8:

Lef

tban

kar

ea-

0.6

7-

0.3

20.2

50.6

90.7

1

1967

Jul.

–A

ug.

F9:

Jiag

edaq

ire

gio

n0.5

00.6

20.1

2

1969

Jul.

–A

ug.

F10:

Jiag

edaq

ire

gio

n1.6

41.7

21.0

3

1969

Aug.

F11:

Lef

tban

kar

ea2.1

32.0

71.6

62.0

92.0

4

1970

Sep

.–O

ct.

F12:

Jiag

edaq

ire

gio

n1.0

81.3

10.2

11.9

91.9

5

1980

Sep

.–O

ct.

F13:

Jiag

edaq

ire

gio

n0.5

30.7

50.8

90.9

0.9

7

1981

Apr.

–Ju

n.

F14:

Jiag

edaq

ire

gio

n0.3

80.2

90.3

3-

0.6

6-

0.5

7

1981

Jun.–

Aug.

F15:

Lef

tban

kar

ea1.3

41.4

51.3

82.0

82.0

1

1983

Apr.

b,

May

aF

16:

Lef

tban

kar

ea2.6

1/0

.45

2.4

5/0

.87

2.4

3/1

.01

2.3

4/1

.14

1.2

3/1

.03

1984

Jul.

–A

ug.

F17:

Lef

tban

kar

ea1.5

71.6

31.6

31.3

61.4

0

1988

Apr.

–A

ug.

F18:

Jiag

edaq

ire

gio

n1.3

71.3

61.6

10.3

71.4

9

1988

Apr.

b,

Aug.a

F19:

UN

Rbas

in1.6

7/0

.92

1.7

6/1

.05

1.8

8/0

.80

1.9

7/1

.50

1.9

9/1

.47

1989

Jul.

–A

ug.

F20:

Jiag

edaq

ire

gio

n0.7

20.7

10.6

91.4

11.4

1

1989

Jun.b

,Ju

l.a

F21:

UN

Rbas

in1.4

4/1

.18

1.4

9/0

.91

1.5

1/0

.86

2.1

7/1

.33

2.1

8/1

.24

1990

Feb

.b,

Mar

.aF

22:

UN

Rbas

in1.2

7/1

.37

1.3

6/1

.34

1.0

7/1

.27

1.5

3/0

.47

1.5

1/0

.39

1998

Jun.–

Aug.

F23:

Lef

tban

kar

ea1.0

00.9

61.3

41.5

51.6

1

Dro

ught

and

flood

types

wer

est

ated

by

usi

ng

‘‘D

’’an

d‘‘

F’’

,re

spec

tivel

y,

foll

ow

edby

the

num

ber

s.S

ym

bols

aan

db

repre

sent

the

month

sw

hen

met

eoro

logic

alan

dhydro

logic

alin

dic

es

calc

ula

ted,

resp

ecti

vel

y;

and

indic

ate

the

choro

gra

phy

did

not

reco

rdth

eper

iods

of

dro

ughts

or

floods.

For

the

dis

aste

rsw

ithout

spec

ified

per

iods,

the

1-

and

12-m

onth

tim

esca

les

wer

e

use

dan

dse

par

ated

by

‘‘/’

’.H

ydro

logic

alin

dic

esw

ere

calc

ula

ted

atth

eK

H,

JGD

Qan

dN

EJ

stat

ions

for

the

left

ban

kar

eaof

UN

R,

Jiag

edaq

ire

gio

nan

dth

eU

NR

bas

in,

resp

ecti

vel

y

(flow

seri

esat

the

NE

Jst

atio

nw

ere

also

use

dfo

rth

edro

ughts

and

floods

occ

urr

edin

the

left

ban

kar

eaof

UN

Ran

dJi

aged

aqi

regio

npri

or

to1951)

Nat Hazards (2014) 71:2063–2085 2081

123

hydrological indices was determined in the same way. For the meteorological indices, the

SPEI-PM and SPEI-TE showed the best and worst success rates (i.e., dividing the number

of successfully detected cases by the total) of 74.3 and 54.3 %, respectively. Moreover,

similar comparison results of these three meteorological indices were found in the single

considerations of drought and flood events. The SPEI-PM identified correctly all drought

events except for the 1954 and 1967 droughts, with the highest success rate of 88.2 %.

Although recent studies have illustrated the importance of temperature in explaining the

increased drought severity and the availability of water resources (Vicente-Serrano et al.

2011b; Nicholls 2004), the SPEI-TE with temperature consideration only produced the

success rate of 54.3 % in the UNR basin (52.9 and 55.6 % for drought and flood cases,

respectively). In contrast, the SPI had a moderate performance (62.9 % success rate for all

cases) considering that precipitation is the main driver of drought conditions. Results in the

UNR basin indicated that, even though the PET should be considered in drought index

calculation, the simplified formulation with sole input of temperature may have the

opposite effects (i.e., lower success rates of the SPEI-TE than the SPI), at least in cases of

the UNR basin. The PET calculation, based on the underlying physical principles (changes

in available energy, humidity and wind speed), was recommended for being considered in

the meteorological drought index.

For the hydrological drought identification, both hydrological indices showed a general

success rate (both 63.4 %) for all events and had better performance for flood events than

for drought events. More than half of flood events were identified by using the SRI and SSI

with success rates of 76.2 and 81.0 %, respectively, while the rates for droughts were

50.0 % of SRI and 45.0 % of SSI. This is because the flood records were directly based on

the river discharge, and the drought records were mainly the meteorological or agricultural

drought. The response time from meteorological to hydrological drought events may be

from one to several months, mainly depending on the regional hydrological regime. For

example, 1–3 months were for the events of D3, D12 and D19. Also, it is important to note

that, the river discharges at the NEJ and JGDQ stations were not only from the left area of

the UNR and subbasin (4), respectively, but also used in the calculations. This may have

contributed to the failures of hydrological indices in this metric.

It was found that SPEI-TE may mislead the drought condition detection because of

regional warming. In earlier studies, SPEI-TE was used as a better index than SPI for

considering evapotranspiration as a driver of droughts (e.g., Vicente-Serrano et al. 2010).

For example, the SPEI-TE-based drought analysis revealed that severe and more extreme

droughts have become more serious since late 1990s for all regions of China (Yu et al.

2013). In our work, however, the SPEI-TE produced more failure drought cases compared

with the observations than SPI. Thus, previous analysis based on SPEI-TE should be

revisited. This finding is similar to the Sheffield and colleagues’ research (Sheffield et al.

2012). They quantified drought with two versions of the PDSI model, which used the

Thornthwaite and Penman–Monteith equations, respectively, for the PET estimation, and

found that the PET formulation relying only on temperature resulted in overestimation of

drought. However, Sheffield et al. (2012) also recognized their limitations of lack of

observations for validation, and thus, the results need to be verified by other independent

analyses. Our work confirms their results to some extent. As an alternative formulation of

PET, our results indicated that SPEI-PM is a more useful measure than SPEI-TE. These

findings in the UNR basin should be potentially important as they are not site-specific.

Therefore, the previous studies based on SPEI-TE should be treated with caution.

2082 Nat Hazards (2014) 71:2063–2085

123

6 Summary and conclusions

In this study, five multi-scalar drought indices, three meteorological drought indices (SPI,

SPEI-TE and SPEI-PM) and two hydrological drought indices (SRI and SSI), were used to

investigate drought and wetness episodes in the UNR basin for the periods of 1951–2012

and 1898–2010. The series of meteorological indices showed similar evolution and

identified the main drought episodes that affected this cold region between 1951 and 2012

(mainly in the period of 1965–1980) and also the main wetness spells in the periods of

1951–1964 and 1981–2002. In the identifications of drought and wetness episodes, the sole

precipitation-based index produced the drought events with a 4-year frequency (15 epi-

sodes in the period of 1951–2012), while the combination of precipitation and temperature

in the SPEI-TE greatly increased the drought event frequency during the period of

1951–2012. In contrast, the SPEI-PM index based on underlying physical principles

showed a mild drought frequency with only 11 episodes. The phenomena can also be found

for the wetness events from 1951 to 2012. The SPEI-PM, however, tended to indicate

longer and severe drought and wetness episodes in terms of both duration and magnitude

compared with the two other indices. The MK trend test of the SPI series found wetter and

drier trends in the right and left bank areas, respectively, of the mainstem of the UNR. In

response of climate change, most of the subbasins showed significantly drier trends based

on the SPEI-TE series. It is expected that the SPEI-PM series demonstrated the light

changing trends even though most of the subbasins experienced nonsignificantly drier

conditions. In addition, all three meteorological indices series indicated that most areas in

the UNR basin experienced the near normal condition during the period of 1951–2012.

For the hydrological drought indices, it is evident that the SRI and SSI over the UNR

basin provide better consistency of drought and wetness conditions at all considered

timescales than meteorological indices do. Both indices detected the main drought episodes

in the decades of 1910, 1920, 1970 and 2000. In addition, the drought severity decreased,

in terms of both duration and magnitude, between the periods of 1898–1950 and

1951–2010. Accordingly, the flood severity increased between these two periods. The

correlation analysis of drought indices showed that precipitation dominated the meteoro-

logical indices as the SPI had a lower degree of similarity with the SPEI-PM than with the

SPEI-TE. Results of the comparison between meteorological and hydrological indices

indicated that climate condition in the previous month was the most significant variable

contributing to the hydrological regime on the basin scale. For the subbasin scale, the time

lag between meteorological and hydrological indices increased to 5 months.

Results of drought indices calculation agreed with the historical records for different

duration of drought and flood with some exceptions. It is noted that the SPEI-PM had a

high success rate of 88.2 % in the drought identification. Thus, PET should be considered

in the determination of meteorological drought index. It should be noted that the simplified

PET formulation with sole input of temperature had the opposite effects in the UNR basin

(i.e., the SPEI-TE had the lower success rate than the SPI). It indicated that SPEI-TE may

mislead the drought condition detection because of regional warming. As a consequence,

the PET calculation based on underlying physical principles is recommended for drought

indices. This finding has important implications in future applications of SPEI.

Acknowledgments The work was funded by the National Basic Research Program of China(#2010CB951101) and the Natural Science Foundation of China (#51079039). Meteorological datasets werecollected from the Climate Data Center, China Meteorological Administration, and streamflow data werecollected from the Songliao Water Conservancy Commission (SWCC), China. We are grateful to Lixiang

Nat Hazards (2014) 71:2063–2085 2083

123

Chen for collecting streamflow data during the period of 2006–2010. The SPI, SPEI and SSI programs wereprovided by Sergio M. Vicente-Serrano and Santiago Beguerıa. We also extend our special thanks toAndrew W. Wood for reviewing and giving some constructive suggestions for this work.

References

Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing cropwater requirements-FAO irrigation and drainage paper 56. FAO, Rome

Cong Z, Yang D, Gao B, Yang H, Hu H (2009) Hydrological trend analysis in the Yellow River basin using adistributed hydrological model. Water Resources Research 45:W00A13. doi:10.1029/2008WR006852

Dai Z, Du J, Li J, Li W, Chen J (2008) Runoff characteristics of the Changjiang River during 2006: effect ofextreme drought and the impounding of the three Gorges dam. Geophys Res Lett 35 (7):L07406.doi:10.1029/2008GL033456

Dracup JA, Lee K, Paulson EG (1980) On the definition of droughts. Water Resour Res 16:297–302Edwards DC (1997) Characteristics of 20th Century drought in the United States at multiple time scales.

(No. AFIT-97-051). Air Force Inst of Tech Wright-Patterson AFB OHFeng X, Zhang G, Yin X (2011) Hydrological responses to climate change in Nenjiang river basin,

northeastern China. Water Resour Manag 25(2):677–689Guttman NB (1999) Accepting the standardized precipitation index: a calculation algorithm. J Am Water

Resour As 35(2):311–322. doi:10.1111/j.1752-1688.1999.tb03592.xHayes MJ, Svoboda MD, Wilhite DA, Vanyarkho OV (1999) Monitoring the 1996 drought using the

standardized precipitation index. Bull Am Meteorol Soc 80(3):429–438Heim RR Jr (2002) A review of twentieth-century drought indices used in the United States. Bull Am

Meteorol Soc 83(8):1149–1165Intergovernmental Panel on Climate Change (IPCC) (2007) Climate change 2007—Working Group I: the

physical science basis. Cambridge University Press, CambridgeKendall MG (1975) Rand correlation methods. Charles Griffin, LondonLorenzo-Lacruz J, Vicente-Serrano SM, Lopez-Moreno JI, Begueria S, Garcia-Ruiz JM, Cuadrat JM (2010)

The impact of droughts and water management on various hydrological systems in the headwaters ofthe Tagus River (central Spain). J Hydrol 386(1–4):13–26. doi:10.1016/j.jhydrol.2010.01.001

Lu E, Luo Y, Zhang R, Wu Q, Liu L (2011) Regional atmospheric anomalies responsible for the 2009–2010severe drought in China. J Geophys Res Atmos (1984–2012) 116 (D21)

Mann HB (1945) Nonparametric tests against trend. Econometrica 13:245–259McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales.

In: Proceedings of the 8th Conference on Applied Climatology. American Meteorological SocietyBoston, MA, pp 179–183

Mishra AK, Singh VP (2010) A review of drought concepts. J Hydrol 391(1):202–216. doi:10.1016/j.jhydrol.2010.07.012

Nicholls N (2004) The changing nature of Australian droughts. Clim Change 63:323–326Palmer WC (1965) Meteorologic Drought. US Department of Commerce, Weather Bureau, Research Paper

No. 45, p. 58Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat As

63(324):1379–1389Sheffield J, Wood EF, Roderick ML (2012) Little change in global drought over the past 60 years. Nature

491:435–440. doi:10.1038/nature11575Shukla S, Wood AW (2008) Use of a standardized runoff index for characterizing hydrologic drought.

Geophys Res Lett 35 (2) doi: 10.1029/2007gl032487Sun F (2008) Climate change and extreme meteorological events in the northeast China. China Meteorol

Press, BeijingThornthwaite CW (1948) An approach toward a rational classification of climate. Geogr Rev 38(1):55–94Vicente-Serrano SM (2006) Differences in spatial patterns of drought on different time scales: an analysis of

the Iberian Peninsula. Water Resour Manag 20(1):37–60Vicente-Serrano SM, Begueria S, Lopez-Moreno JI (2010) A multiscalar drought index sensitive to global

warming: The Standardized Precipitation Evapotranspiration Index. J Clim 23(7):1696–1718. doi:10.1175/2009jcli2909.1

Vicente-Serrano SM, Lopez-Moreno JI, Beguerıa S, Lorenzo-Lacruz J, Azorin-Molina C, Moran-Tejeda E(2011a) Accurate computation of a streamflow drought index. J Hydrol Eng 17(2):318–332

2084 Nat Hazards (2014) 71:2063–2085

123

Vicente-Serrano SM, Lopez-Moreno JI, Drumond A, Gimeno L, Nieto R, Moran-Tejeda E, Lorenzo-LacruzJ, Begueria S, Zabalza J (2011b) Effects of warming processes on droughts and water resources in theNW Iberian Peninsula (1930–2006). Clim Res 48(2–3):203–212. doi:10.3354/cr01002

Wang Q (2013) Six-month-long drought set to end in Yunnan. http://www.chinadaily.com.cn/china/2013-04/10/content_16391011.htm. Accessed June 1 2013

Wang Z, Zhai P, Zhang H (2003) Variation of drought over northern China during 1950–2000. J Geogr Sci13(4):480–487

Wang A, Lettenmaier DP, Sheffield J (2011) Soil moisture drought in China, 1950–2006. J Clim24(13):3257–3271. doi:10.1175/2011jcli3733.1

Wilhite DA, Glantz MH (1985) Understanding the drought phenomenon: the role of definitions. Water Int10(3):111–120

Wu H, Hayes MJ, Weiss A, Hu Q (2001) An evaluation of the Standardized Precipitation Index, the China-ZIndex and the statistical Z-Score. Int J Climatol 21(6):745–758. doi:10.1002/joc.658

Wu H, Hayes MJ, Wilhite DA, Svoboda MD (2005) The effect of the length of record on the standardizedprecipitation index calculation. Int J Climatol 25(4):505–520

Yu M, Li Q, Hayes MJ, Svoboda MD, Heim RR (2013) Are droughts becoming more frequent or severe inChina based on the Standardized Precipitation Evapotranspiration Index: 1951–2010? Int J Climatol.doi:10.1002/joc.3701

Yue S, Pilon P, Phinney B, Cavadias G (2002) The influence of autocorrelation on the ability to detect trendin hydrological series. Hydrol Process 16:1807–1829

Zhai J, Su B, Krysanova V, Vetter T, Gao C, Jiang T (2010) Spatial variation and trends in PDSI and SPIindices and their relation to streamflow in 10 large regions of China. J Clim 23(3):649–663

Zhang Q (2003) Drought and its impacts. In: Chen H (ed) China Climate Impact Assessment. ChinaMeteorol Press, Beijing, pp 12–18

Zhang Q, Li J, Singh VP, Bai Y (2012) SPI-based evaluation of drought events in Xinjiang, China. NatHazards 64(1):481–492. doi:10.1007/s11069-012-0251-0

Zou X, Zhai P, Zhang Q (2005) Variations in droughts over China: 1951–2003. Geophys Res Lett 32(4):L04707. doi:10.1029/2004GL021853

Nat Hazards (2014) 71:2063–2085 2085

123