Content uploaded by D. H. Yan
Author content
All content in this area was uploaded by D. H. Yan on Jul 08, 2016
Content may be subject to copyright.
ORIGINAL PAPER
Changes in extreme precipitation in the Huang-Huai-Hai
River basin of China during 1960-2010
Dong-Dong Zhang &Deng-Hua Yan &Yi-Cheng Wang &
Fan Lu &Di Wu
Received: 30 May 2013 /Accepted: 15 April 2014
#Springer-Verlag Wien 2014
Abstract With the increasing exposure of populations and
economy to the natural hazards, it is of vital importance to
study the spatiotemporal characteristics of extreme precipita-
tion. Based on daily precipitation at 154 meteorological sta-
tions in the Huang-Huai-Hai River basin of China during
1960–2010, the spatial and temporal changes in extreme
precipitation were analyzed using twelve indices. The basin
was divided into five climate areas using clustering analysis to
detect the spatial changes of the extreme indices, and the
temporal changes in the probability distributions of the ex-
treme indices were also examined. The results showed that
maximum 5-day precipitation, wet days and consecutive wet
days decreased significantly while consecutive dry days
showed a weak increasing trend. The other precipitation indi-
ces had insignificant decreasing trends. The probability distri-
bution functions of simple daily intensity index and consecu-
tive dry days were positively shifted while the rest of the
indices were negatively shifted. The temporal changes of
extreme indices implied that the frequency of extreme precip-
itation was decreasing, but the intensity of extreme precipita-
tion was increasing in the Huang-Huai-Hai River basin. The
spatial changes in the aspect of precipitation extreme events
showed obvious spatial differences between different climate
areas. In addition, the series including maximum 1-day pre-
cipitation and maximum 5-day precipitation were fitted by
generalized extreme-value distribution for risk analysis and
the results showed that the generalized extreme-value distri-
bution could fit the series well. The amounts of extreme
precipitation for different return periods were calculated and
high risk areas for flooding disaster were presented.
1 Introduction
Recently, extreme weather and climate events have become more
frequent than ever before due to climate warming, which has a
significant impact on human society and even causes serious
losses to people’s lives and property (Fu et al. 2013; Goudie
2006;Groismanetal.2013; Jhajharia et al. 2012; Kunkel et al.
1999; Martinez et al. 2012; Pittock et al. 2006; Suppiah and
Hennessy 1998). The changes in the magnitude and frequency of
extreme events are associated with the spatial and temporal
characteristics in extreme precipitation as they may trigger floods
and droughts (Shao et al. 2013). The fourth IPCC assessment
report pointed out that the frequency and intensity of extreme
precipitation in many areas showed an increasing trend, which
drew concerns from meteorologists and hydrologists of the world
(Dash et al. 2012;Gaffinetal.2004; Stock et al. 2011).
Recently, precipitation extremes have been studied across
different geographic regions and timescales (Bartholy and
Pongrácz 2007,2010; Cavalcanti 2012; Dodla and Ratna
2010; Hidalgo-Muñoz et al. 2011; Tramblay et al. 2012;
Zhang et al. 2012). Sheffield and Wood (2008) found that
the precipitation extremes have already been amplified due to
climate changes in some regions of Africa. Easterling et al
(2000) indicated that though the total precipitation may de-
crease or be unchanged, more extreme precipitation events
had occurred over large areas of landespecially in the midland
high-latitude regions including Eastern Russia, Norway, and
the north of Japan. Although the evidence for increasing
trends appears in most regions, statistically decreasing trends
in extreme rainfall events have been found in Western
Australia (Haylock and Nicholls 2000), Southeast Asia and
parts of the central Pacific (Griffiths et al. 2003; Manton et al.
2001), northern and eastern New Zealand (Salinger and
Griffiths 2001), and in Poland (Bielec 2001). These studies
have concluded that the changes in the aspect of precipitation
extreme events showed obvious spatial differences.
D.<D. Zhang :D.<H. Yan (*):Y.<C. Wang :F. Lu :D. Wu
State Key Laboratory of Simulation and Regulation of Water Cycle
in River Basin, China Institute of Water Resources and Hydropower
Research, 1-A Fuxing RoadHaidian District Beijing 100038,
People’sRepublicofChina
e-mail: denghuay@gmail.com
Theor Appl Climatol
DOI 10.1007/s00704-014-1159-2
In China, Zhai et al (2005) have studied the trends in annual
and seasonal total precipitation and in extreme daily precipita-
tion (defined as those larger than its 95th percentile for the year)
during 1951–2000 with 740 stations in China. The results
indicated that there was little trend in total precipitation for
China as a whole, but there were distinctive regional and sea-
sonal patterns of trends. Several subsequent studies showed a
rising trend in the occurrence of extreme precipitation events in
the western part of Northwest China, the middle and lower
reaches of the Yangtze River, South China and the Tibetan
Plateau and a significant decrease trend in north China and the
Sichuan Basin (Wang et al. 2008). Furthermore, based on the
data of CDD (the maximum number of consecutive dry days)
and CWD (the maximum number of consecutive wet days),
Zhang et al (2011) have demonstrated that increasing fractional
contribution of shorter consecutive wet days may imply intensi-
fying precipitation in China. Many researchers analyzed the
trend of extreme precipitation events with a threshold value in
the past 50 years which showed a significant increasing trend in
mean extreme precipitation days. Xia et al. (2011) examined
changes in extreme precipitation events in the Huai River Basin
and found insignificant increase in annual maximum rainfall and
extreme precipitation events. In the case of Northeast China,
Wan g e t al. (2013a,b,c) have demonstrated an overall decreas-
ing trend in the frequency of extreme precipitation and the
change patterns were not spatially clustered. However, few
studies on changes in extreme precipitation conducted detailed
analyses over the Huang-Huai-Hai River basin, even though it is
a region that could be significantly impacted by possible future
changes in climate. And there are limitations in using the com-
prehensive indices to analyze precipitation extreme events in the
Huang-Huai-Hai River basin.
The objectives of this study are (1) to quantify spatial and
temporal changes in extreme precipitation during 1960–2010
over the Huang-Huai-Hai River basin in China based on 12
indices; (2) to analyze the trends of the extreme precipitation
in different climate areas and to identify if the considered area
is getting more extreme in terms of precipitation; (3) to predict
the extreme precipitation for different return periods by the
generalized extreme value (GEV) distribution to provide basic
data for the risk assessment of disasters in this area.
2Materialsandmethods
2.1 Study area
Located in 30°∼43° N, 100°∼123° E as shown in Fig. 1,the
Huang-Huai-Hai River basin usually refers to three sub-river
basins, namely the Yellow River basin, Huai River basin, and
Hai River basin including 13 provinces and two municipali-
ties: Qinghai, Sichuan, Gansu, Ningxia, Inner Mongolia,
Shaanxi, Shanxi, Henan, Hebei, Shandong, Anhui, Jiangsu,
Beijing, and Tianjin. The geography of the study area varies
from west to east which consists of the Qinghai-Tibet Plateau,
Inner Mongolia plateau, the Loess Plateau, and the Huang-
Huai-Hai Plain with an area of 1.445×10
6
km
2
(Table 1). The
plain area is 5.24×10
6
km
2
and the mountainous area covers
0.921×10
6
km
2
. Due to a vast territory, the climate in this area
is influenced by many factors and can be classified as four
types including temperate continental, temperate monsoon,
subtropical monsoon, and highland climates. The precipita-
tion, in general, decreases from south tonorth and from eastto
west and the average annual precipitation is 556.0 mm. As the
main agricultural area and quick economic development area,
the Huang-Huai-Hai watershed accounts for 25 % of the total
GDP in China. However, frequent droughts and floods in this
area had caused serious losses to the agriculture and industry
recently (Du et al. 2014; Liu et al. 2008;Wangetal.2011).
Therefore, studies in assessing and predicting the influence of
extreme precipitation are essential to offer theoretical and
technical support for the water resource management and
comprehensive response to extreme meteorological disasters.
2.2 Data
There are 173 meteorological stations in Huang-Huai-Hai
River basin. On the basis of the length of records, 159 mete-
orological stations are selected. Daily precipitation data of 159
stations in the Huang-Huai-Hai River basin is collected from
the National Meteorological Information Center of China
Meteorological Administration (CMA). The time series is
from January 1960 to December 2010. Before constructing
the precipitation indices series, data quality control and ho-
mogeneity assessment have to be performed on the initial data
set of daily precipitation amounts from each station.
The RclimDex (http://www.pcic.uvic.ca/tools-and-data/
climdex) which was developed by Xuebin Zhang at the
Climate Research Branch of the Environment Canada was
used for data quality control. The precipitation values which
were below 0 mm would be taken as missing values. The
outliers were defined as the mean value of the year plus or
minus four times the standard deviation of the value for that
calendar day in this study (Tian et al. 2012). If the values were
outside the thresholds, they were marked as potentially errone-
ous. The potentially erroneous values were checked and set to
missing values. We completed the missing data through the
average precipitation values of the neighboring stations, which
had been used to reconstruct the missing precipitation data in the
Hai River Basin and proved to be effective (Wang et al. 2011).
Climate data series usually contain artificial shifts due to
inevitable changes in observing instrument (or observer),
location, environment, and observing practices/procedures
taking place in the period of data collection (Cao and Yan
2012). It is important to detect artificial shifts in climate data
series, because these artificial changes could considerablybias
D.-D. Zhang et al.
the results of climate trends, variability, and extremes analysis.
To eliminate the possible effect of artificial shifts caused by
relocations of measurement sites or other unknown reasons,
the time series of daily precipitation in Huang-Huai-Hai River
basin from each station was checked for temporal homogene-
ity using the software package RHtestsV3. The results re-
vealed that no shifts had been detected in the data sets for all
the stations except five stations. After rejecting five stations
with inhomogeneous series, 154 stations were finally selected
in this study (Fig. 1). There are 78 stations located in the
Yellow River basin, 44 stations in the Huai River basin, and
32 stations in the Hai River basin.
2.3 Climate areas
The basin is divided into five climate areas (Fig. 2, Table 2)
using clustering analysis via the self-organizing map (SOM)
neural network algorithm based on the data of the latitude and
longitude (i.e., Xand Ygeodetic coordinate values), elevation,
and annual average precipitation of the 154 meteorological
stations. Compared with other classification methods, SOM is
a network capable of self-organization and self-learning; and
its other advantages include that it realizes real-time learning,
possesses network stability, does not require external evalua-
tion functions, recognizes the most significant characteristics
of the vector space, has good anti-noise ability, and etc. (Yan
et al. 2013; Yang and Yan 2006).
In area I, there are 32 representative meteorological stations,
accounting for 21 % of the total number of stations, which are
mainly distributed in the area surrounding Bo Sea and the
Shandong Peninsula as well as the downstream of Huai River.
Area II has 33 stations, accounting for 21 %, which are mainly
distributed in the upper and middle reaches of the Huai River as
well as the Daqing River, Ziya River, and Zhangwei River plains
in the Hai River basin. Area III has 38 stations, accounting for
25 %, which are mainly distributed on the Hetao Plain in
Ningxia-Inner Mongolia and Inner Mongolia Plateau, the Fen-
Wei Valley basin in Shanxi and Shaanxi Provinces as well as on
the Loess Plateau. Area IV has 28 representative meteorological
stations, accounting for 18 %, which are mainly located in the
high mountains and hilly regions at altitudes of 1,500–3,000 m.
And in area V, there are 23 stations, accounting for 15 %, which
Fig. 1 Study area and location of
the meteorological stations
Tabl e 1 Area of sub-River basin
and the characteristic of
precipitation (1961–2010)
River basin Total area (1×10
6
km
2
) Annual precipitation
Mean value/mm Range/mm Coefficient of variation
Hai 0.320 538.1 481.3 0.18
Yellow 0.795 439.9 325.9 0.14
Huai 0.330 854.2 631.8 0.16
Huang-Huai-Hai 1.445 556.0 389.3 0.12
Changes in extreme precipitation in the Huang-Huai-Hai River
are mainly located on the Tibetan Plateau in the Yellow River
basin at altitudes above 3,000 m.
2.4 Methodology
In this paper, the Mann–Kendall test method was employed to
test the significance of trends (at the 95 % level of confidence
in the study), and the annual trend rates were calculated using
Kendall slope estimator (Akritas et al. 1995). Regional aver-
ages were calculated as an arithmetic mean of values at all
stations in the study and the correlations among these indices
were also analyzed. The number of stations with the same
trend as that for the whole region had been counted for each
index, and stations with significant trends had also been
identified. In addition, the weighted 11-year binomial moving
average was used to show the interannual variation of climatic
extremes (Böhm et al. 2001). Factor analysis was used to
partition the data set into clusters with in-cluster similarities
and between-cluster dissimilarities (Li et al. 2011). The gen-
eralized extreme value distribution (GEV) was used to model
the extreme precipitation distributions and predict the extreme
precipitation for different return periods.
2.4.1 Extreme precipitation indices
The Expert Team on Climate Change Detection and Indices
(ETCCDI) defined 27 core extreme indices based on daily
temperature and precipitation. Exact definitions of all the indices
are available from the ETCCDI website (www.clivar.org/
organization/etccdi/etccdi.php). In this study, twelve indices
(Table 3) which can reflect the changes of extreme
precipitation in different aspects are chosen. An R-based pro-
gram, RClimDexV3 developed at the Climate Research Branch
of the Meteorological Service of Canada, is applied to calculate
these extreme indices. The precipitation indices are divided into
two types. One is precipitation indices including maximum1-
day precipitation, maximum 5-day precipitation, very wet day
precipitation, extremely wet day precipitation, and simple daily
intensity. The other is the number of days with precipitation
including wet days, consecutive wet days, consecutive dry days,
Fig. 2 Climate areas in Huang-
Huai-Hai River basin
Tabl e 2 Basic information of
climate areas in the study area Climate areas Climate types Number of meteorological stations Ratio (%)
I Subtropical monsoon climate 32 21
II Temperate monsoon climate 33 21
III Temperate continental climate 38 25
IV Temperate continental climate 28 18
V Plateau mountain climate 23 15
D.-D. Zhang et al.
heavy precipitation days, very heavy precipitation days, and
extremely heavy precipitation days.
2.4.2 Mann–Kendall trend test
In this study, the Mann–Kendall (MK) statistical test (Mann
1945), a nonparametric approach, is applied to characterize
the trends for the twelve indices and to test their significance.
The Mann–Kendall test is a rank-based procedure, which is
less sensitive to outliers than parametric approaches and it is
widely used in hydrology and climatology (Hamed 2009; Tian
et al. 2012;Wangetal.2008).
In the MK test, the test statistic is calculated as follows:
S¼X
n−1
k¼1X
j¼kþ1
n
sgn xj−xk
ð1Þ
where
sgn xj−xk
¼
1if xj−xk>0
0if xj−xk¼0
−1if xj−xk<0
8
<
:
ð2Þ
x
j
and x
k
are the sequential data values, nis the length of the
data series, and the normally distributed variate zis computed
as follows:
z¼
S−1
Var SðÞ if S >0
0if S ¼0
Sþ1
Var SðÞ if S <0
8
>
>
>
<
>
>
>
:
ð3Þ
Var SðÞ¼
nn−1ðÞ2nþ5ðÞ−Xtt−1ðÞ2tþ5ðÞ
18 ð4Þ
Va r ( S) is the variance of the test statistic S(Hamed 2009)andt
is the extent of any given tie. A tie is the sample data having
the same value and the summation is over all ties (Hirsch et al.
1982). The null hypothesis is that a series x
1
,…,x
n
is indepen-
dent and identically distributed. At the 95 % confidence level,
if |z|> 1.96, the null hypothesis of no trend is rejected.
In Mann–Kendall test, another very useful index is the
Kendall slope, which is the magnitude of the monotonic
change and is given as follows:
β¼Median xj−xi
j−i
∀j<ið5Þ
In which 1< i<j<n: The estimator βis the median over all
combination of record pairs for the whole data set.
2.4.3 The generalized extreme value distribution
The generalized extreme value (GEV) distribution is widely
employed for modeling extremes in the meteorology and
many other fields (Coles et al. 2003;Khaliqetal.2006;Lu
et al. 2013). It was introduced into meteorology by Jenkinson
(1955) and is used extensively to model extremes of natural
phenomena such as precipitation (Gellens 2002), temperature
(Nogaj et al. 2007), and wind speed (Coles and Casson 1998).
The GEV distribution is a family of continuous probability
distributions developed to combine the Gumbel, Fréchet, and
Tabl e 3 Definition of extreme climate indices
Acronym Name of the index Definition Units
RX1DAY Maximum 1-day precipitation Annual maximum 1-day precipitation mm
RX5DAY Maximum 5-day precipitation Annual maximum consecutive 5-day precipitation mm
R95T Very wet day precipitation Annual total precipitation when RR >95th percentile of 1960–2010 daily rainfall mm
R99T Extremely wet day precipitation Annual total precipitation when RR >99thpercentile of 1960–2010 daily rainfall mm
PRCPTOT Wet day precipitation Annual total PRCP in wet days (RR ≥1mm) mm
SDII Simple daily intensity index Average precipitation on wet days mm/day
R10mm Number of heavy precipitation days Annual count of days when RR ≥10 mm days
R20mm Number of very heavy precipitation days Annual count of days when RR ≥20 mm days
R30mm Number of extremely heavy precipitation days Annual count of days when RR ≥30 mm
a
days
NW Wet days Annual count of days when RR ≥1mm days
CDD Consecutive dry
b
days Maximum number of consecutive dry days days
CWD Consecutive wet
c
days Maximum number of consecutive wet days days
a
30 mm was the threshold defined by the authors
b
Drydaysarethosedayswhentheamountrecordedwas<1mm
c
Wet days are those days when the amount recorded was ≥1mm
Changes in extreme precipitation in the Huang-Huai-Hai River
Weibull families. The distribution function of the GEV distri-
bution is as follows:
FxðÞ¼
exp −1þθ3
x−θ1
ðÞ
θ2
−1=θ3
()
;θ3≠0;
exp −exp −x−θ1
ðÞ
θ2
;θ3¼0;
8
>
>
>
<
>
>
>
:
ð6Þ
where θ
1
,θ
2
and θ
3
are the location parameter, the scale
parameter, and the shape parameter, respectively, θ
1
∈R,θ
2
>
0,θ
3
∈Rand 1+ θ
3
(x−θ
1
)/θ
2
>0.
3Results
3.1 Precipitation indices
Figure 3shows the regional annual series for precipitation
indices in the Huang-Huai-Hai River basin during 1960–
2010, and Fig. 4demonstrates the spatial distribution pattern
of temporal trends in precipitation extremes for the 154 mete-
orological stations. Table 4shows the results of Mann–
Kendall test. Percentages of stations which show positive
(negative) trend and significant positive (negative) trend are
also present in Table 4. Table 5gives an overview of percent-
ages of stations with the same trend as the study area for
precipitation indices in different climate areas. Regional aver-
ages of maximum 1-day precipitation (RX1DAY) had no
significant decreasing trends with fluctuations during 1960–
2010 over the study region (Fig. 3a). About 51.6 % of the
stations, most of which were located in the areas I, III, and IV,
showed decreasing trends, but some stations in the Huai River
basin and upper Yellow River basin (mainly in areas II and V)
displayed increasing trends (Fig. 4a). The trend pattern of
maximum 5-day precipitation (RX5DAY) resembles closely
that of maximum 1-day precipitation (RX1DAY) in Figs. 3b
and 4b. Regional averages of RX5DAY had significant de-
creasing trends with 57.8 % of the stations showing negative
trend (Table 4), most of which were in the areas I, III, and IV.
The regional average trends of very wet day precipitation
(R95T) and extremely wet day precipitation (R99T) had weak
decreasing trends from 1960 to 2010 (Fig. 3c, d). The propor-
tion of stations with negative trend was 52.6 and 47.4 %,
respectively. The proportion of stations showing significant
negative trends was 3.2 % for both of the indices (Table 4).
For the R95T index, the largest decreasing trends were ob-
served in the Hai River basin, Yellow River basin, and the
eastern part of Huai River basin (Fig. 4c), and the increasing
trends were detected in the west of Huai River basin and the
northwest of area V (Fig. 4d). Decreasing and increasing
regions for R99T were almost the same as the regions for
R95T.
The wet day precipitation (PRCPTOT) had no significant
decreasing trends and mean precipitation amount on a wet day
(SDII) had increased slightly during 1960–2010 (Fig. 3e, f).
The trend rate of PRCPTOT varied from −64.21 to 37.63 mm/
decade, and 64.3 % of stations had decreasing trends, partic-
ularly in the areas I, III and IV (Fig. 4e). As to SDII, most of
the study areas increased except the northern part of the Hai
River basin and the central region of the Yellow River basin
(Fig. 4f). The index of SDII is dependent on two variables,
namely total annual precipitation and number of wet days. As
is shown in Fig. 7d, almost the whole region is dominated by
negative trend of NW, and the areas with decreasing SDII
should be characterized by a stronger negative trend rate of
total annual precipitation than that of NW.
Fig. 3 Interannual variation of precipitation extremes in Huang-Huai-Hai River basin during 1960–2010 (The dotted red line is the linear trend, the blue
line is the 11-year smoothing average, and Tis the decadal trend rate)
D.-D. Zhang et al.
The contribution of extreme precipitation to total precipi-
tation decreased for extremely wet day precipitation (R99T)
while it increased for very wet day precipitation (R95T)
between 1960 and 2010, indicating that very wet day precip-
itation (R95T) may reflect the variation in total wet day
precipitation in the Huang-Huai-Hai River basin. This result
is similar to that of the IPCC Fourth Assessment Report which
indicates the increase in the proportion of total precipitation
from heavy rainfall over most area (Climate Change 2007).
Very wet days accounted for an average of 70.3 % of the total
precipitation amount (range 43.4 to 87.7 %) (Fig. 5a). The
average contribution of extremely wet days was 25.2 % (range
17.6 to 32.3 %) (Fig. 5b).
3.2 Indices of days with precipitation
Over the period of 1960–2010, the regional average occur-
rence of heavy precipitation days (R10mm), very heavy pre-
cipitation days (R20mm), and extremely heavy precipitation
days (R30mm), all had insignificant decreasing trends (Fig. 6
and Table 4). Wet days (NW) and consecutive wet days
(CWD) had a significant decreasing trend while the
Fig. 4 Spatial distribution of decadal trends in precipitation extremes in Huang-Huai-Hai River basin during 1960–2010
Changes in extreme precipitation in the Huang-Huai-Hai River
consecutive dry days (CDD) showed an insignificant increas-
ing trend. The spatial distributions of the changes in trends of
the number days with precipitation were very different. For
the index of R10mm, 56.5 % of stations showed decreasing
trend, and only six stations were significant. The regions with
decreasing trend were found in the Yellow River basin and the
eastern coastal areas in the Hai River basin and Huai River
basin (Fig. 7a). Similarly, for the indices of R20mm and
R30mm, more than half of the stations experienced a decrease
(Table 4). Stations in areas I, III, and IV displayed decreasing
trends (Fig. 7b, c). In case of the NW index, the negative trend
was found in most part (84.4 % of the stations) of the Huang-
Huai-Hai River basin (Fig. 7d, Table 5). Similar result was
found while analyzing the CWD index. Up to 78.6 % of
stations in the Huang-Huai-Hai River basin experienced a
decrease especially in areas II, III, IV, and V (Fig. 7f).
Changes in the consecutive dry days (CDD) further reinforce
this pattern, with 53.9 % of stations having an increasing trend
(mostly in areas I and II and the middle of Yellow River basin)
with four stations significant at the 0.05 level (Fig. 7e).
In addition, the changes in trends of precipitation indices in
different climate areas during 1960–2010 are further analyzed
(Table 5). For most of the indices, more than 50 % of stations in
areas I, III, and IV show the same sign of the trend as the study
area. For the indices of SDII, NW, and CWD, more than 50 %
of stations in areas II and V show the same sign of trend as the
study area while for the other indices, the trend in areas II and V
is opposite to that of the study area. For the indices of NW and
CWD, more than 55 % stations in all the five climate areas
show the same sign of trend as the study area, indicating that
the influence of climate on the two indices was not obvious.
3.3 Patterns of distribution changes between 1960 and 2010
To further research the temporal changes in precipitation
extremes, the probability distribution functions (PDFs) of
Tabl e 4 Results of the Mann–Kendall test and percentages of stations with positive or negative trends for regional indices of precipitation extremes in
Huang-Huai-Hai River basin during 1960–2010
Index Kendall slope Range Positive trend Negative trend
Tot al SS No n-S S Tot al S S N on- SS
Precipitation indices (mm/decade) (mm/decade) % % % % % %
RX1DAY −0.56 −11.46 to 6.92 49.4 2.6 46.8 51.6 1.9 48.7
RX5DAY −1.87* −26.94 to 12.59 42.2 1.3 40.9 57.8 8.4 49.4
R95T −0.94 −35.56 to 25.18 47.4 4.5 42.9 52.6 3.2 49.4
R99T −0.60 −25.00 to 14.42 52.6 3.2 49.4 47.4 3.2 44.2
PRCPTOT −4.97 −64.21 to 37.63 35.7 2.6 33.1 64.3 7.1 57.2
Precipitation indices (mm/day/decade) (mm/day/decade) % % % % % %
SDII 0.04 −0.80 to 0.57 62.9 4.5 58.4 37.0 1.3 35.7
Day indices (day/decade) (day/decade) % % % % % %
R10mm −0.15 −2.42 to 0.95 43.5 1.3 42.2 56.5 3.9 52.6
R20mm −0.04 −0.70 to 0.67 43.5 0.6 42.9 56.5 0.0 56.5
R30mm −0.02 −0.59 to 0.44 40.9 0.6 40.3 59.1 5.2 53.9
NW −0.81* −7.83 to 2.00 15.6 1.3 14.3 84.4 18.8 65.6
CDD 0.05 −1.43 to 3.33 53.9 2.6 51.3 46.1 3.2 42.9
CWD −0.11* −0.49 to 0.25 21.4 0.0 21.4 78.6 8.4 70.2
SS statistical significance, α=0.05
*Significant
Tabl e 5 Percentages of stations with the same trend as the study area for
precipitation indices in different climate areas during 1960–2010 (%)
Regional trend I (%) II (%) III (%) IV (%) V (%)
RX1DAY D 62.5 24.2 73.7 57.1 21.7
RX5DAY D 62.5 33.3 73.7 67.9 47.8
R95T D 68.8 21.2 55.2 71.4 39.1
R99T D 56.2 21.2 60.5 50.0 39.1
PRCPTOT D 81.3 42.4 63.1 96.4 30.4
SDII I 46.8 87.9 63.1 50.0 65.2
R10mm D 75.0 30.3 55.2 85.7 34.8
R20mm D 81.3 39.4 55.3 60.7 43.4
R30mm D 71.9 42.4 71.0 75.0 26.1
NW D 93.8 84.8 81.5 100 56.5
CDD I 71.9 66.7 39.4 57.1 34.8
CWD D 65.6 72.7 78.9 96.4 73.9
Dindicates that the regional trend in Huang-Huai-Hai River basin has
decreased, Iindicates that the regional trend in Huang-Huai-Hai River
basin has increased
D.-D. Zhang et al.
each index for two different time periods were calculated. The
data have been split into two periods, i.e., 1960–1984 and
1985–2010. The probability density function plots are closely
examined to determine if the distributions of two time periods
are the same.
Figure 8shows the PDFs for twelve indices for 1960–1984
and 1985–2010, respectively. The PDFs of two different time
periods followed the same distributions for all the indices
except RX5DAY and CWD. The shapes of the distributions
for all the indices except CWD for 1985–2010 were smoother
than those for 1960–1984. For RX5DAY and CWD, the
distributions are significantly different between two periods
with remarkable negative shifts. The PDFs of SDII and CDD
were positively shifted, and the rest of the indices were neg-
atively shifted. Generally, the distribution changes in precip-
itation indices showed a tendency toward drier conditions,
which was in accord with the result of trends analysis above.
3.4 Analysis of correlations of precipitation indices
Factor analysis of the precipitation data revealed that F1,
which included all precipitation indices accounted for
69.1 % of the overall variance (Table 6). This reflected the
similarity of variations in annual precipitation and extreme
precipitation events, and the increasing contribution made by
very wet day precipitation to annual total precipitation.
RX1DAY and CDD dominated F2, which accounted for
10.7 % of the total variance, confirming the decrease of
RX1DAY and the increase of CDD were the main factors
which influenced changes in annual precipitation.
Tab le 7also showed that the correlations between extreme
precipitation indices and total precipitation were significant at
the 0.01 level except CDD. The correlation coefficients be-
tween the total precipitation and precipitation indices, includ-
ing very wet day precipitation (R95T), heavy precipitation
days (R10mm), very heavy precipitation days (R20mm), ex-
tremely heavy precipitation days (R30mm) exceeded 0.9, and
the others exceeded 0.5, indicating that annual total precipita-
tion was well correlated with extreme precipitation. Therefore,
the indices selected in this study could reflect the changes in
annual total precipitation. In addition, Table 7also showed
that there were statistically significant correlations among the
precipitation indices. The result was in agreement with the
findings of previous work (Wang et al. 2011; You et al. 2011).
Fig. 5 Regional series for athe ratio of the index of precipitation on very
wet days (R95T) to total precipitation and bthe ratio of the index of
precipitation on extremely wet days (R99T) to total precipitation in the
Huang-Huai-Hai River basin during 1960–2010 (The dotted red line is
the linear trend, the blue line is the 11-year smoothing average, and Tis
the decadal trend rate)
Fig. 6 Interannual variation of precipitation extremes in Huang-Huai-Hai River basin during 1960–2010 (The dotted red line is the linear trend, the blue
line is the 11-year smoothing average, and Tis the decadal trend rate)
Changes in extreme precipitation in the Huang-Huai-Hai River
3.5 Estimation of extreme precipitation for different return
periods
The series of maximum 1-day precipitation (RX1DAY)
and maximum 5-day precipitation (RX5DAY) for each
station were fitted by generalized extreme-value distribu-
tion (GEV), which was widely employed for modeling
extremes in the meteorology (Lu et al. 2013). The param-
eters of the distributions were estimated using maximum
likelihood method. The goodness of fit of the probability
function was evaluated by Kolmogorov–Smirnov’sstatis-
tic D (K–S D) at 95 % confidence level (Frank and Masse
1951). Results showed that all the series passed the K–S
testandGEVcanbeusedtodescribethedistributionsof
the series of RX1DAY and RX5DAY. According to the
amount of precipitation, estimates of extreme precipitation
indices for different return periods were clarified into six
classes using natural break method and indicated by dif-
ferent colors in the map (Fig. 9). It showed that the
amount of extreme precipitation reduces gradually from
Fig. 7 Spatial distribution of decadal trends in precipitation extremes in Huang-Huai-Hai River basin during 1960–2010
D.-D. Zhang et al.
the east to the west. For the RX1DAY for different return
periods, large amount of precipitation was detected in the
upper reaches and the eastern part of Huai River basin.
For the RX5DAY for return periods of 50 years, the
precipitation in many areas including the upper reaches
and the eastern part of the Huai River, parts of Hebei
province, and the city of Zhengzhou would exceed
250mm/daywhichmayleadtoflashfloodandurban
pluvial flood.
4 Discussion and conclusion
Based on observed data from 154 meteorological stations in
the Huang-Huai-Hai River basin of China during 1960–2010,
12 indices of extreme precipitation were employed to analyze
the spatial and temporal distributions of precipitation ex-
tremes. And the main findings are summarized as follows:
Most part of the Huang-Huai-Hai River Basin showed
mixed positive and negative trends in the precipitation
Fig. 8 Annual PDFs of the extreme precipitation indices for the two sub-periods, 1960–1984 (red lin e) and 1985–2010 (blue line), and Pvalue <0.05
means that the hypothesis of Kolmogorov–Smirnov and the two distributions are the same and can be rejected
Tabl e 6 Factor loadings of the variance of precipitation indices
Factors Total P RX1DAY RX5DAY R95T R99T PRCPTOT SDII R10mm R20mm R30mm NW CDD CWD %Variance
F1 0.97 0.77 0.75 0.98 0.89 0.97 0.73 0.91 0.95 0.96 0.81 0.06 0.57 69.1
F2 −0.20 0.49 0.25 0.04 0.37 −0.19 0.44 −0.33 −0.14 0.02 −0.49 0.54 −0.17 10.7
Changes in extreme precipitation in the Huang-Huai-Hai River
extreme indices, but most of these trends were not significant.
For the regional average trends of precipitation extremes, SDII
and CDD had insignificant increasing trends while the other
indices, including RX1DAY, RX5DAY, R95T, R99T,
PRCPTOT, R10mm, R20mm, R30mm, NW, and CWD had
decreasing trends, and RX5DAY, NW, and CWD were statis-
tically significant. The regional average annual total precipi-
tation (PRCPTOT) trend for the whole region was negative
but statistically insignificant at the 95 % confidence level,
which had been revealed in the regional studies (Wang et al.
2013a,b,c;Zhaietal.2005). The decreasing trends in indices
of NW, R10, R20, R30, and CWD dominated for a majority of
the study area which indicated that the frequency and duration
of extreme precipitation in the study area were decreasing.
This climatic evolution is consistent with the results of the
trend analyses in precipitation performed in the study area
Tabl e 7 Correlation coefficients of precipitation extremes
Tot al PRX1DAY RX5DAY R95T R99T PRCPTOT SDII R10mm R20mm R30mm NW CDD CWD
Tot al P1
RX1DAY 0.65* 1
RX5DAY 0.63* 0.80* 1
R95T 0.94* 0.74* 0.68* 1
R99T 0.80
*
0.94* 0.81* 0.90* 1
PRCPTOT 1* 0.64* 0.63* 0.96* 0.79* 1
SDII 0.67* 0.66* 0.49* 0.80* 0.78* 0.63* 1
R10mm 0.95* 0.49* 0.51* 0.89* 0.65* 0.97* 0.57* 1
R20mm 0.95* 0.58* 0.54* 0.96* 0.75* 0.96* 0.72* 0.95* 1
R30mm 0.92* 0.70* 0.64* 0.98* 0.85* 0.94* 0.78* 0.88* 0.96* 1
NW 0.85* 0.42* 0.51* 0.73* 0.54* 0.89* 0.26 0.90* 0.80* 0.73* 1
CDD −0.03 0.14 −0.01 0.06 0.12 0.02 0.17 0.00 0.06 0.08 −0.05 1
CWD 0.50* 0.44* 0.62* 0.47* 0.45* 0.52* 0.23 0.50* 0.43* 0.43* 0.56* −0.04 1
*Significant at the 0.01 level
Fig. 9 Amounts of RX1DAY and
RX5DAY for return periods of 10
and 50 years in Huang-Huai-Hai
River basin
D.-D. Zhang et al.
(Du et al. 2014; Liu et al. 2008;Wangetal.2011). Further
study on the changes of distribution for all the indices between
1960 and 2010 confirmed the conclusion above. In addition,
the contribution of extremely wet precipitation (R99T) to total
precipitation decreased and while it increased for very wet day
precipitation (R95T) between 1960 and 2010, indicating that
R95T may reflect the variation in total wet day precipitation in
the Huang-Huai-Hai River basin.
The study areas were divided into five different climate
areas using SOM. The change patterns for different climate
areas were different. For the area I, all the indices had decreas-
ing trends except CDD, indicating a decrease in both intensity
and frequency of extreme precipitation in this area. For the
area III, all the indices had decreasing trends except SDII,
indicating a decrease in frequency but an increase in intensity
of extreme precipitation in this area. Similar results were
found in area IV, the indices of CDD and SDII in area IV
had increased while the other indices had decreased. For the
area II, all the indices had increased except CWD and NW.
Similar results were found in area V with the increased indices
of CWD, NW, and CDD. The results might indicate that more
total extreme precipitation was accompanied by more ex-
tremes precipitation amount and less days of extremes precip-
itation in areas II and V. For the indices of NW and CWD,
more than 55 % stations in all the five climate areas showed
the same sign of trend as the study area which indicated that
the influence of climate on the two indices was not obvious.
Overall, the changes in trends of indices implied that the
frequency of extreme precipitation was decreasing but the
intensity of extreme precipitation was increasing in the
Huang-Huai-Hai River basin.
In the time series of these extreme precipitation indices,
most of precipitation indices were strongly correlated with
annual total precipitation in the Huang-Huai-Hai River basin.
Factor analysis of the precipitation data revealed that the
decrease of RX1DAY and the increase of CDD were the main
factors which influenced changes in annual precipitation.
Extreme precipitation indices including RX1DAY and
RX5DAY for different return periods were calculated by
GEV and high risk is detected in the upper reaches and the
eastern partof the Huai River, parts of Hebei province, and the
city of Zhengzhou which would exceed 250 mm/day for
annual maximum consecutive 5-day precipitation.
Some characteristics of precipitation extremes are obtained
based on statistical analysis. The results of this study may
provide some valuable information of regional precipitation
change in the study area. However, the uncertainty of spatial
distribution may be induced by many factors including sys-
tematic errors in the interpolation and areal average method,
stochastic error due to the random nature of precipitation and
the density of the meteorological stations. The uncertainty of
temporal distribution may be induced by climate changes and
human activities. There is indeed mounting evidence that
hydroclimatic extreme series are not stationary, owing to
natural climate variability or anthropogenic climate change
(Coles et al. 2003). The series of RX5DAY, which were fitted
by generalized extreme-value distribution, could be nonsta-
tionary as shown in Fig. 8. Therefore, there will be uncertainty
in the estimation of extreme precipitation for different return
periods as we use stationary GEV model. Further work should
be done to build a nonstationary GEV model for better under-
standing the distributions of the extreme precipitation. For the
above-discussed reasons, this study confirmed the complexity
and uncertainty of the spatial and temporal variability of
extreme precipitation and the importance of gathering local
information to obtain a reliable and detailed description of
characteristics and dynamics of extreme precipitation at the
regional level. Furthermore, in the context of accelerating
warming and water circulation, relationships between changes
in temperature extremes and attendant changes in precipitation
extremes should be considered, since the two types of ex-
tremes are not necessarily independent.
Acknowledgments This study is jointly funded by the National Basic
Research Program of China (Grant No. 2010CB951102 and 2013CB036406)
and the Innovation Research Group Foundation Program of Natural Science
Foundation of China (Grant No. 51109224). We are also very grateful to the
National Climate Center of China Meteorological Administration for provid-
ing all the data used in the study. Last but not the least, many thanks are given
to two anonymous reviewers for their valuable comments.
References
Akritas MG, Murphy SA, Lavalley MP (1995) The Theil-Sen estimator
with doubly censored data and applications to astronomy. J Am Stat
Assoc 90:170–177
Bartholy J, Pongrácz R (2007) Regional analysis of extreme temperature
and precipitation indices for the Carpathian Basin from 1946 to
2001. Glob Planet Chang 57:83–95
Bartholy J, Pongrácz R (2010) Analysis of precipitation conditions for the
Carpathian Basin based on extreme indices in the 20th century and
climate simulations for 2050 and 2100. Phys Chem Earth A/B/C 35:
43–51
Bielec Z (2001) Long-term variability of thunderstorms and thunderstorm
precipitation occurrence in Cracow, Poland, in the period 1896–
1995. Atmos Res 56:161–170
Böhm R, Auer I, Brunetti M, Maugeri M, Nanni T, Schöner W (2001)
Regional temperature variability in the European Alps: 1760–1998
from homogenized instrumental time series. Int J Climatol 21:1779–
1801
Cao L-J, Yan Z-W (2012) Progress in research on homogenization of
climate data. Adv Clim Chang Res 3:59–67
Cavalcanti IFA (2012) Large scale and synoptic features associated with
extreme precipitation over South America: a review and case studies
for the first decade of the 21st century. Atmos Res 118:27–40
Climate Change (2007) IPCC Fourth Assessment Report. The Physical
Science Basis
Coles S, Casson E (1998) Extreme value modelling of hurricane wind
speeds. Struct Saf 20:283–296
Coles S, Pericchi LR, Sisson S (2003) A fully probabilistic approach to
extreme rainfall modeling. J Hydrol 273:35–50
Changes in extreme precipitation in the Huang-Huai-Hai River
Dash SK, Sharma N, Pattnayak KC, Gao XJ, Shi Y (2012) Temperature
and precipitation changes in the north-east India and their future
projections. Glob Planet Chang 98–99:31–44
Dodla VBR, Ratna SB (2010) Mesoscale characteristics and prediction of
an unusual extreme heavy precipitation event over India using a high
resolution mesoscale model. Atmos Res 95:255–269
Du H, Xia J, Zeng S (2014) Regional frequency analysis of extreme
precipitation and its spatio-temporal characteristics in the Huai River
Basin, China. Nat Hazards 70:195–215
Easterling DR, Evans JL, Groisman PY et al (2000) Observed variability
and trends in extreme climate events. Bull Am Meteorol Soc 72:
1507–1520
Frank J, Masse J (1951) The Kolmogorov–Smirnov Test for goodness of
fit. J Am Stat Assoc 46:68–78
Fu G, Yu J, Yu X, Ouyang R, Zhang Y, Wang P, Liu W, Min L (2013)
Temporal variation of extreme rainfall events in China, 1961–2009.
JHydrol487:48–59
Gaffin SR, Rosenzweig C, Xing X, Yetman G (2004) Downscaling and
geo-spatial gridding of socio-economic projections from the IPCC
Special Report on Emissions Scenarios (SRES). Glob Environ
Chang 14:105–123
Gellens D (2002) Combining regional approach and data extension
procedure for assessing GEV distribution of extreme precipitation
in Belgium. J Hydrol 268:113–126
Goudie AS (2006) Global warming and fluvial geomorphology.
Geomorphology 79:384–394
Griffiths G, Salinger M, Leleu I (2003) Trends in extreme daily rainfall
across the South Pacific and relationship to the South Pacific
Convergence Zone. Int J Climatol 23:847–869
Groisman PY, Knight RW, Zolina OG (2013) 5.03 - Recent trends in
regional and global intense precipitation patterns. In: Pielke RA (ed)
Climate vulnerability. Academic Press, Oxford, pp 25–55
Hamed K (2009) Exact distribution of the Mann–Kendall trend test
statistic for persistent data. J Hydrol 365:86–94
Haylock M, Nicholls N (2000) Trends in extreme rainfall indices for an
updated high quality data set for Australia, 1910–1998. Int J
Climatol 20:1533–1541
Hidalgo-Muñoz JM, Argüeso D, Gámiz-Fortis SR, Esteban-Parra MJ,
Castro-Díez Y (2011) Trends of extreme precipitation and associat-
ed synoptic patterns over the southern Iberian Peninsula. J Hydrol
409:497–511
Hirsch RM, Slack JR, Smith RA (1982) Techniques of trend analysis for
monthly water quality data. Water Resour Res 18:107–121
Jenkinson AF (1955) The frequency distribution of the annual maximum
(or minimum) values of meteorological elements. Q J R Meteorol
Soc 81:158–171
Jhajharia D, Yadav BK, Maske S, Chattopadhyay S, Kar AK (2012)
Identification of trends in rainfall, rainy days and 24h maximum
rainfall over subtropical Assam in Northeast India. Compt Rendus
Geosci 344:1–13
Khaliq M, Ouarda T, Ondo J-C, Gachon P, Bobée B (2006) Frequency
analysis of a sequence of dependent and/or non-stationary hydro-
meteorological observations: a review. J Hydrol 329:534–552
Kunkel KE, Andsager K, Easterling DR (1999) Long-term trends in
extreme precipitation events over the conterminous United States
and Canada. J Clim 12(8):2515–2527
Li ZX, He YQ, Theakstone WH, Wang XF, Zhang W, Cao WH, Du JK,
Xin HJ, Chang L (2011) Altitude dependency of trends of daily
climate extremes in southwestern China, 1961e2008. Acta
Geograph Sin 22(3):416–430
Liu Q, Yang Z, Cui B (2008) Spatial and temporal variability of annual
precipitation during 1961–2006 in Yellow River Basin, China. J
Hydrol 361:330–338
Lu F, Wang H, Yan D, Zhang D, Xiao W (2013) Application of profile
likelihood function to the uncertainty analysis of hydrometeorolog-
ical extreme inference. Sci China Technol Sci 56:3151–3160
Mann HB (1945) Non-parametric test against trend. Econometrika 13:
245–259
Manton M, Della-Marta P, Haylock M, Hennessy K, Nicholls N,
Chambers L, Collins D, Daw G, Finet A, Gunawan D (2001)
Trends in extreme daily rainfall and temperature in Southeast Asia
and the South Pacific: 1961–1998. Int J Climatol 21:269–284
Martinez CJ, Maleski JJ, Miller MF (2012) Trends in precipitation and
temperature in Florida, USA. J Hydrol 452:259–281
Nogaj M, Parey S, Dacunha-Castelle D (2007) Non-stationary extreme
models and a climatic application. Nonlinear Process Geophys 14:
305–316
Pittock B, Abbs D, Suppiah R, Jones R (2006) Climatic background to
past and future floods inAustralia. Floods in an arid continent:13–39
Salinger M, Griffiths G (2001) Trends in New Zealand daily temperature
and rainfall extremes. Int J Climatol 21:1437–1452
Shao M, Gong Z, Xu X (2013) Risk assessment of rainstorm and flood
disasters in China between 2004 and 2009 based on gray fixed
weight cluster analysis. Natural Hazards:1–28
Sheffield J, Wood EF (2008) Projected changes in drought occurrence
under future global warming from multi-model, multi-scenario,
IPCC AR4 simulations. Clim Dyn 31:79–105
Stock CA, Alexander MA, Bond NA, Brander KM, Cheung WWL,
Curchitser EN, Delworth TL, Dunne JP, Griffies SM, Haltuch
MA, Hare JA, Hollowed AB, Lehodey P, Levin SA, Link JS,
Rose KA, Rykaczewski RR, Sarmiento JL, Stouffer RJ, Schwing
FB, Vecchi GA, Werner FE (2011) On the use of IPCC-class models
to assess the impact of climate on Living Marine Resources. Prog
Oceanogr 88:1–27
Suppiah R, Hennessy KJ (1998) Trends in total rainfall, heavy rain events
and number of dry days in Australia, 1910–1990. Int J Climatol 18:
1141–1164
Tian Y, Xu Y-P, Booij M, Lin S, Zhang Q, Lou Z (2012) Detection of
trends in precipitation extremes in Zhejiang, east China. Theor Appl
Climatol 107:201–210
Tramblay Y, Badi W, Driouech F, El Adlouni S, Neppel L, Servat E
(2012) Climate change impacts on extreme precipitation in
Morocco. Glob Planet Chang 82–83:104–114
Wang W, Chen X, Shi P, Van Gelder P (2008) Detecting changes in
extreme precipitation and extreme streamflow in the Dongjiang
River Basin in southern China. Hydrol Earth Syst Sci 12(1):207–
221
Wang W, Shao Q, Peng S, Zhang Z, Xing W, An G, Yong B (2011)
Spatial and temporal characteristics of changes in precipitation
during 1957–2007 in the Haihe River basin, China. Stoch Env Res
Risk A 25:881–895
Wang B, Zhang M, Wei J, Wang S, Li X, Li S, Zhao A, Li X, Fan J
(2013a) Changes in extreme precipitation over Northeast China,
1960–2011. Quat Int 298:177–186
Wang BL, Zhang MJ, Wei JL, Wang SJ, Li SS, Ma Q, Li XF, Pan SK
(2013b) Change in extreme events of temperature and precipitation
over Xinjiang, Northwest China, during 1960–2009. Quat Int 298:
141–151
Wang W, Shao Q, Yang T, Peng S, Yu Z, Taylor J, Xing W, Zhao C, Sun F
(2013c) Changes in daily temperature and precipitation extremes in
the Yellow River Basin, China. Stoch Env Res Risk A 27:401–421
Xia J, She DX, Zhang YY, Du H (2011) Spatio-temporal trend and
statistical distribution of extreme precipitation events in Huaihe
River Basin during 1960–2009. J Geogr Sci 22(2):195–208
Yan D, Wu D, Huang R, Wang L, Yang G (2013) Drought evolution
characteristics and precipitation intensity changes during alternating
dry-wet changes in the Huang-Huai-Hai River basin. Hydrol Earth
Syst Sci Discuss 10
Yang Z, Yan Y (2006) Research and development of self-organizing
maps algorithm. Comput Eng 32:201–228
You QL, Kang SC, Aguilar E, Pepin N, Flügel WA, Yan YP, Xu YW,
Zhang YJ, Huang J (2011) Changes in daily climate extremes
D.-D. Zhang et al.
in China and its connection to the large scale atmospheric
circulation during 1961–2003. Clim Dyn 36(11–12):2399–
2417
Zhai PM, Zhang XB, Wan H, Pan XH (2005) Trends in total precipitation
and frequency of daily precipitation extremes over China. J Clim 18:
1096–1108
Zhang Q, Xu CY, Chen X, Zhang Z (2011) Statistical behaviours of
precipitation regimes in China and their links with atmospheric
circulation 1960–2005. Int J Climatol 31:1665–1678
Zhang Q, Singh VP, Li J, Jiang F, Bai Y (2012) Spatio-temporal varia-
tions of precipitation extremes in Xinjiang, China. J Hydrol 434–
435:7–18
Changes in extreme precipitation in the Huang-Huai-Hai River
