Stoch Environ Res Risk Assess (2013) 27:337–351
DOI 10.1007/s00477-012-0607-8
ORIGINAL PAPER
Spatial and temporal variations in rainfall erosivity
during 1960–2005 in the Yangtze River basin
Jin Huang • Jinchi Zhang • Zengxin Zhang
Chong-Yu Xu
•
Published online: 22 June 2012
Springer-Verlag 2012
Abstract Water resources and soil erosion are the most
important environmental concerns in the Yangtze River
basin, where soil erosion and sediment yield are closely
related to rainfall erosivity. The present study explores
the spatial and temporal changing patterns of the rainfall
erosivity in the Yangtze River basin of China during
1960–2005 at annual, seasonal and monthly scales. The
Mann–Kendall test is employed to detect the trends during
1960–2005, and the T test is applied to investigate possible
changes between 1991–2005 and 1960–1990. Meanwhile
the Rescaled Range Analysis is used for exploring future
trend of rainfall erosivity. Moreover the continuous wavelet
transform technique is using studying the periodicity of the
rainfall erosivity. The results show that: (1) The Yangtze
River basin is an area characterized by uneven spatial
distribution of rainfall erosivity in China, with the
annual average rainfall erosivity range from 131.21 to
16842 MJ mm ha-1 h-1. (2) Although the directions of
trends in annual rainfall erosivity at most stations are
J. Huang
Jiangsu Key Laboratory of Atmospheric Environment
Monitoring and Pollution Control, School of Environmental
Science and Engineering, Nanjing University of Information
Science & Technology, Nanjing 210044, China
J. Zhang (&) Z. Zhang
Jiangsu Key Laboratory of Forestry Ecological Engineering,
Nanjing Forestry University, Long pan Road 159,
Nanjing 210037, China
e-mail: zjcforest@yahoo.com.cn
C.-Y. Xu
School of Geographic and Oceanographic Sciences,
Nanjing University, Nanjing 210093, China
C.-Y. Xu
Department of Geosciences, University of Oslo, Oslo, Norway
upward, only 22 stations have significant trends at the 90 %
confidence level, and these stations are mainly located in
the Jinshajiang River basin and Boyang Lake basin. Winter
and summer are the seasons showing strong upward trends.
For the monthly series, significant increasing trends are
mainly found during January, June and July. (3) Generally
speaking, the results detected by the T test are quite consistent with those detected by the Mann–Kendall test. (4)
The rainfall erosivity of Yangtze River basin during winter
and summer will maintain a detected significant increasing
trend in the near future, which may bring greater risks to soil
erosion. (5) The annual and seasonal erosivity of Yangtze
River basin all have one significant periodicity of 2–4 years.
Keywords Rainfall erosivity Yangtze River basin China Trends Changes
1 Introduction
Rainfall is the main external factor contributing to soil
erosion caused by water, the impact of raindrops detaches
soil particles, and subsequent runoff of the water causes
erosion. Soil erosion rate may be expected to change in
response to change in climate for variety reasons, the most
of direct which was the change in the erosive power of
rainfall (Nearing et al. 2004). Global warming, characterized by increasing temperature, has the potential to cause
higher evaporation rates and transport larger amounts of
water vapor into the atmosphere, probably having accelerated the global hydrological cycle (Zhang et al. 2009). One
of the most significant consequences of global warming
would be an increase in the magnitude and frequency of
precipitation maxima brought about by increased atmospheric moisture levels and/or large-scale storm activities.
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338
Soil and water losses is recognized as one of the most
serious global environment problems. Global warming
might give rise to increase and intensification of extreme
events, significantly decreasing number of rainy days and
significantly increasing precipitation intensity were identified in many places of the world, which would brought the
change of soil erosion rate. Therefore, responses of soil
water erosion to the changes of precipitation have become
one important research content of soil and water conservation in the world.
The rainfall erosivity, R, a factor in the universal soil loss
equation (USLE) and revised universal soil loss equation
(RUSLE) models, is the potential ability of the rain to cause
erosion, which have been the best research object for the
responses of soil water erosion to the changes of precipitation
(Nearing et al. 2004; Leek and Olsen 2000; Sauerborn et al.
1999). Wischmeier and Smith (1978) defined R as the scalar
product of rainfall energy and the maximum 30-min rainfall
intensity. This classic expression of R has been widely tested
and used in many countries and regions. However, in practice, this method that considers rainfall erosivity requires
temporally continuous rainfall data, but access to such data is
difficult in many countries and regions. Therefore, regular
rainfall statistics from hydrological or meteorological stations have been used as a substitute to estimate rainfall erosivity. Subsequently, a number of studies have established a
statistical regression equation between R and precipitation
variables, such as average annual precipitation (Renard and
Freimund 1994), average monthly precipitation (Posch and
Seppo 2003; Wu 1994; Oduro-Afriyie 1996; Yu 1998;
Loureiro and Coutinho 2001), average daily precipitation
(Richardson et al. 1983; Yu and Rosewell 1996; Qi et al.
2000; Zhang et al. 2002), and storm events (Sadeghi et al.
2011). These simple calculation methods of rainfall erosivity
have played key role in the studies of soil erosion.
As the source power of soil water erosion, the temporal and
spatial distribution of precipitation can certainly affect the
characteristics of erosion in the different time and region.
Thus, the study of spatial and temporal distribution characteristics of changes in the rainfall erosivity is of great significance to discover the formation mechanism and evolution
process of soil water erosion. Presently the study of spatiotemporal variation of rainfall erosivity has received increasing
attention from many scholars in various regions (Leek and
Olsen 2000; da Silva 2004; Capolongo et al. 2008; Bonilla and
Vidal 2011; Meusburger et al. 2012). China is one of countries
with most serious soil erosion in the world, the study of rainfall
erosivity seems even more important. Zhang et al. (2002)
developed and adopted a new simple method to calculate
rainfall erosivity base on daily rainfall data, which brought
about great advancement in the study of rainfall erosivity.
Subsequently, the change features of rainfall erosivity have
been explored in several important region (Liu et al. 2010a, b;
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Stoch Environ Res Risk Assess (2013) 27:337–351
Luo et al. 2010; Wei et al. 2011; Xin et al. 2011), but such
attempt was few in the Yangtze River basin. The Yangtze
River, being the longest river in China and the third longest
river in the world, plays a vital important role in the socialeconomic development of China. Being affected by the
abundant rainfall, man-made destructions of natural environment, complex characteristics of landform, and other factors,
the Yangtze River basin is the key national regions of the water
and soil conservation. The second Remote Sensing Investigation and Analysis of Water and Soil Loss in the China
emphatically pointed out that the area of the soil and water
losses in the Yangtze River basin had been up to 637,400 km2,
and the area of water erosion made up 82.2 % of the total area.
According to the strength of the erosion, the area of medium
and serious level made up 55 % of the area of water erosion.
More worryingly, previous studies showed that both the mean
and extreme precipitation had a significant increasing trend in
the Yangtze River basin (Zhang et al. 2005, 2008), which has
potential to result in higher water erosion risk in the region.
Understanding the changing features of rainfall erosivity is
very important because it have been closely correlated with
soil erosion and sediment yield in the Yangtze River basin over
the past five decades. However, compared to the studies
greatly enhanced the understanding of spatiotemporal variations in precipitation and extreme precipitation (Zhang et al.
2005, 2007, 2008; Su et al. 2006), there is still little information on the spatiotemporal variations in rainfall erosivity.
Although Zhang et al. (2003) constructed the spatial distribution of rainfall erosivity in China, but the analysis about
Yangtze River basin in their study were too weak to answer
some key scientific questions, such as: (a) the spatial and
temporal distribution characteristics of rainfall erosivity in the
study river basin; and (b) the changing patterns of rainfall
erosivity across the study river basin. So there are two objectives of this paper: (1) to understand the spatiotemporal distribution of rainfall erosivity in the Yangtze River basin based
on the daily precipitation datasets available; and (2) to explore
the trends and changes of rainfall erosivity in the region. Such
a study has not been reported at least for such a large river
basin, and it may provide valuable database for prevention and
control of soil erosion, soil and water conservation planning,
management and planning of water resources under the
background of globe warming in the Yangtze River basin.
2 Study area, data and methods
2.1 Study region
The Yangtze River basin, located between 91E and 122E
and 25N and 35N, and has a drainage area of 1,808,500 km2.
Because the Yangtze River basin is characterized by different
climate systems, the present studies divides the Yangtze into
Stoch Environ Res Risk Assess (2013) 27:337–351
339
Table 1 The upper, middle and lower Yangtze River basin
Longitude
The upper Yangtze River basin
The middle and lower Yangtze River basin
The entire Yangtze River basin
Latitude
91–110
25–35
111–120
25–35
91–120
25–35
two parts, the upper Yangtze reaches and the middle and lower
Yangtze reaches (Su et al. 2006; Zhang et al. 2010a, b). The
summer monsoon dominated upper reaches is mainly characterized by a southwest current and the middle and lower
reaches is characterized by a southeast current (Su et al. 2006).
The whole basin is divided into two parts based on longitude
(Table 1): (i) the upper Yangtze River (average altitude of
about 2,250 m) with 73 stations and (ii) the middle and lower
Yangtze River (average altitude of about 270 m) with 73
stations (Fig. 1). The climate of the Yangtze River basin is of
the subtropical monsoon type and the rain zone is closely
related to monsoon activities.
2.2 Data
The observed daily precipitation data covering 1960–2005
from 146 national meteorological observatory (NMO) stations were used in this study. The data were provided by the
national climatic centre (NCC) of the China meteorological
administration (CMA). The missing data of 1 or 2 days were
replaced by the average precipitation values of the neighboring stations. If consecutive days had the missing data, the
missing values were replaced with long term averages of the
same days. The location of the weather stations can be
referred to Fig. 1, which was more or less uniformly distributed and could cover the whole river basin well.
2.3 Methodology
In this study, the annual, seasonal and monthly rainfall
erosivity of Yangtze River basin during 1960–2005 were
calculated and analyzed at the scale of individual stations
and region, and several statistical methods were used to
clarify the spatial variations and temporal trends of these
time series.
2.3.1 Rainfall erosivity model
The average annual rainfall and runoff erosivity factor R
is the average of calculated annual EI30 values and is
defined as:
"
#
n
m
X
1X
R¼
ðEI30 Þk
ð1Þ
n j¼1 k¼1
j
Fig. 1 The location of Yangtze
River basin and the location of
146 weather stations
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Stoch Environ Res Risk Assess (2013) 27:337–351
where E is the total kinetic energy of single storm, I30 the
maximum 30 min rainfall intensity, k the number of storms
in each year j, n is the number of years used to obtain the
average R (Renard and Freimund 1994). These data, however, are not always available. The general approach used to
estimate the R-factor, when detailed rainfall data are not
available, is to consider areas with similar climatic conditions and with available detailed data, to develop a regression formula between the R-factor and less detailed rainfall
data and to apply this formula in the area under investigation. This approach was followed by several authors, in
many regions and with different available precipitation data.
Compared with annual and/or monthly rainfall data, the
use of daily rainfall records can provide a better understanding of rainfall erosivity, so many studies have estimated rainfall erosivity using daily rainfall amounts
(Richardson et al. 1983; Yu and Rosewell 1996; Zhang et al.
2002; Angulo-Martinez and Begueria 2009). In this study,
the rainfall erosivity was calculated using the simple
method developed by Zhang et al. (2002), which has been
used most widely in China (Zhang et al. 2003; Men et al.
2008; Cheng et al. 2009; Liu et al. 2010a, b; Luo et al. 2010;
Wei et al. 2011; Xin et al. 2011). This method obtains
annual, seasonal and monthly rainfall erosivity rainfall
erosivity using aggradations of the half-month rainfall
erosivity, which is estimated based on daily rainfall data,
Mi ¼ a
k X
Dj b
ð2Þ
j¼1
where Mi is the half-month rainfall erosivity
(MJ mm ha-1 h-1) and Dj is the effective rainfall for day j
in one half-month. Dj is equal to the actual rainfall if the actual
rainfall is larger than the threshold value of 12 mm, which is
the standard for China’s erosive rainfall. Otherwise, Dj is
equal to zero. The term k is the number of days in the halfmonth. The terms a and b are the undetermined parameters:
18:114 24:455
b ¼ 0:8363 þ þ Pd12
Py12
ð3Þ
a ¼ 21:486b7:1891
ð4Þ
where Pd12 is the average daily rainfall that is larger than
12 mm and Py12 is the yearly average rainfall for days with
rainfall larger than 12 mm.
2.3.2 Statistical tests for trends
In this paper, the Mann–Kendall trend test, which is highly
commended for general use by the World Meteorological
Organization (Mitchell et al. 1966), were used to characterize the trends for the rainfall erosivity and to test their
significance. The rank-based Mann–Kendall method is a
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nonparametric method, commonly used to assess the significance of monotonic trends in of the climate data (Zhang
et al. 2009, 2010a, b). The procedure of MK trend test
adopted in this study is as follows:
First the MK test statistic is calculated as and n is the
sample size. The statistics S is approximately normally
distributed when n C 8, with the mean and the variance as
follows:
S¼
n1 X
n
X
sgn xj xi
where
i¼1 j¼iþ1
8
> þ1; xj [ xi
<
0; xj ¼ xi
sgn xj xi ¼
>
:
1; xj \xi
ð5Þ
EðSÞ ¼ 0
VðSÞ ¼
ð6Þ
nðn 1Þð2n þ 5Þ Pn
i¼1 ti iði
18
1Þð2t þ 5Þ
ð7Þ
where ti is the number of ties of extent i. The standardized
statistics (Z) for one-tailed test is formulated as:
8 s1
pffiffiffiffiffiffiffiffiffiffi ; S [ 0
>
< VarðsÞ
0; S ¼ 0
Z¼
ð8Þ
>
: psþ1
ffiffiffiffiffiffiffiffiffiffi ; S\0
VarðsÞ
A positive value of Z indicates increasing trend, and a
negative value of Z indicates decreasing trend, while a zero
value of Z indicates no trend. At the 1 % significance level,
the null hypothesis of no trend is rejected if |Z| [ 2.576; at
the 5 % significance level, the null hypothesis of no trend
is rejected if |Z| [ 1.96; at the 10 % significance level, the
null hypothesis of no trend is rejected if |Z| [ 1.645. It
should be noted here that the presence of serial correlation
would affect the detection of trends in a series. To
eliminate the effect of serial correlation on M–K results,
von Storch and Navarra (1995) suggested the ‘‘prewhitened’’ technique in the removal of effects of serial
correlation before M–K analysis. The procedure is as
follows: (1) Compute the lag-1 serial correlation p1 of the
time series (x1, x2, x3, …, xn); (2) if p1 \ 0.1, the M–K test
is applied to the time series directly; otherwise, (3) the
M–K test is applied to the ‘‘pre-whitened’’ time series, i.e.,
x2 - p1x1, x3 - p1x2, …, xn - p1 xn-1 (Zhang et al. 2001).
In Mann–Kendall test, another very useful index is the
Kendall slope, which is the magnitude of the monotonic
change (Xu et al. 2003) and is given as:
xj xi
b ¼ Median
; 8i\j
ð9Þ
ji
In which 1 \ i \ j \ n: The estimator b is the median
over all combination of record pairs for the whole data.
Stoch Environ Res Risk Assess (2013) 27:337–351
341
2.3.3 Statistical tests for difference between independent
two-sample
In order to explore changes of rainfall erosivity, this key
index was calculated and compared for two independent
sub-periods (1991–2005 and 1960–1990), then statistical
significance of the changes between the two periods was
assessed using a independent two-sample T test with different significance levels categorized as follows: the 1 %
significance level (|T| [ 2.576); the 5 % significance level
(|Z| [ 1.96); the 10 % significance level (|Z| [ 1.645). A
positive value of T indicates positive change, and a negative value of T indicates negative change, while a zero
value of T indicates no change. The procedure of T test
adopted in this study is as follows:
distributed process. If 0.5 \ H B 1, then a time series is
generated by some kind of persistent process characterized
by long memory effects, it describes a dynamically persistent, or trend reinforcing series. If 0 B H \ 0.5, than a
time series is generated by some kind of anti-persistent
process that reverses itself more frequently than a random
process.
2.3.5 Continuous wavelet transform analysis
where x1 and x2 are the means of samples; s1 and s2 are the
standard deviation of the samples; n1 and n2 are the sample
size.
The continuous wavelet transform (CWT) technique, as a
tool for analyzing localized variations of power within a
time series, is applied in the current study (Xu et al. 2010).
Through CWT analysis, the hydrological series are
decomposed into time–frequency space to determine both
the dominant modes of variability and how those modes
vary in time, the concept and procedure of the wavelet
method were thoroughly explained and discussed by Torrence and Compo (1998), and the wavelet software can be
found at http://paos.colorado.edu/research/wavelets/. In
this paper, the method was also used in studying the periodicity of the rainfall erosivity of Yangtze River basin.
2.3.4 Rescaled range analysis
2.3.6 Spatial interpolation method
Rescaled range analysis proved to be powerful and as a
general tool for exploring future trend in time series (Li et al.
2008; Xu et al. 2008), which was applied for rainfall erosivity in this paper. The Hurst index, H, is a measure of the
bias in fractional Brownian motion and is very significant for
rescaled range analysis. To obtain the Hurst exponent, the
steps in the R/S analysis are as follows: given a time series{x(s)}, for s = 1, 2… to any positive integer t, define the
range series R(s) and standard deviation series S(s) as:
For understanding the spatial distribution for change features of rainfall erosivity in this paper, the mean values,
trends and changes of rainfall erosivity were interpolated
by inverse distance weighted (IDW) interpolation technology with Arcgis 9.2 software package in the Yangtze
River basin. In this study, the Mann–Kendall test statistics
‘‘Z’’ of rainfall erosivity were interpolated for spatial distribution of trends, and the T test statistics ‘‘T’’ of rainfall
erosivity were interpolated for spatial distribution of
changes.
x1 x2
T ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðn1 1Þs21 þðn2 1Þs22 1
1
n1 þ n2
n1 þn2 2
ð10Þ
RðsÞ ¼ max xðt; sÞ min1 t s xðt; sÞ
1ts
ð11Þ
rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1 Xs
½xðtÞ xs 2
SðsÞ ¼
ð12Þ
1
s
P
P
where xs ¼ 1s tt¼1 xðtÞ is the mean series, xðt; sÞ ¼ ti¼1
½xðiÞ xs , 1 B t B s is the accumulative deviation series
and t is the number of data items. The scaling exponent of
the relationship R(s)/S(s) = (as)H is considered to be as the
Hurst exponent.
The interpretation of Hurst exponent is the following: If
H = 0.5, then it signifies Brownian motion, denotes that a
time series is generated by independent identically
3 Results
3.1 Spatial pattern of rainfall erosivity
Annual rainfall erosivity is closely related to intensive soil
erosion and sediment yield (Wei et al. 2011; Xin et al. 2011),
the Table 2 display summary statistics of annual average
rainfall erosivity for each station in the Yangtze River basin
during 1960–2005. It can be seen form Table 2 that the annual
rainfall erosivity for Yangtze River basin varies between
Table 2 Statistical characteristics of annual average rainfall erosivity (MJ mm ha-1 h-1) for each station in the Yangtze River basin during
1960–2005
Rainfall erosivity
Max
Min
Median
Mean
Std
Kurtosis
Skewness
16,842
131.21
5688.4
5646.5
3217.3
3.29
0.40
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Stoch Environ Res Risk Assess (2013) 27:337–351
131.21 and 16,842 MJ mm ha-1 h-1, and the mean value of
them is 5646.5 MJ mm ha-1 h-1 with a very high standard
deviation (std), thereby indicating that the rainfall erosivity
vary spatially across the region. Figure 2 shows both annual
average rainfall and annual average erosivity maps, and it
allows the comparison between spatial distribution of the
annual rainfall depth and the geographic distribution of annual
rainfall erosivity. It can be seen from Fig. 2b that the precipitation decreases from the southeast to the northwest. In the
lower reaches of the Yangtze River basin, especially in the
Poyang Lake Basin, the annual average precipitation is higher
than 2,000 mm, while, in the most of Jinshajiang River basin,
it is less than 800 mm. Being very similar to the distribution of
the annual average rainfall, the spatial pattern of rainfall
erosivity shows a significant increasing trend from the
northwest to the southeast of Yangtze River basin (Fig. 2a).
According to the research results of Zhang et al. (2003) in
China, we define the region with annual average rainfall
erosivity B40,000 MJ mm ha-1 h-1 as the region characterized by low rainfall erosivity and the region with annual
average rainfall erosivity C10,000 MJ mm ha-1 h-1 as the
region dominated by high rainfall erosivity. Annual average
rainfall erosivity ranging between 4,000 and 10,000 MJ
mm ha-1 h-1 are classified as medium rainfall erosivity.
Based on this classification, Fig. 2a shows that low rainfall
erosivity are mainly detected in the Jinshajiang River basin
and Mintuojiang River basin, and the high rainfall erosivity
are mainly detected in the Dongting Lake basin and Poyang
Lake basin.
In order to reveal the spatial characteristics of rainfall
erosivity in the Yangtze River basin, the relationship
among annual average rainfall erosivity, annual average
rainfall, longitude, latitude and elevation were explored,
the data from 1960 to 2005 were plotted along with the
averaged data for 146 stations (Fig. 3). Regression analyses show a more significant linear relationship (R2 =
0.903) between annual average rainfall erosivity and
annual average rainfall, which suggest that the rainfall
Fig. 2 Spatial distribution for annual average rainfall erosivity
(MJ mm ha-1 h-1) and precipitation (mm) during 1960–2005
Fig. 3 Relationships among annual rainfall erosivity, annual rainfall,
longitude and latitude
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Table 3 Statistical characteristics of monthly rainfall erosivity of two sub regions (MJ mm ha-1 h-1)
The upper Yangtze river reaches
Max
Min
Median
The mid-lower Yangtze reaches
Mean
Kurtosis
Skewness
Max
Jan.
41.96
0
9.57
7.18
5.59
1.54
508.33
Feb.
59.78
0
19.67
17.66
3.19
0.81
461.19
56
54.55
3.38
0.87
10.64
1258.7
Min
0
Median
Mean
Kurtosis
Skewness
122.04
96.91
6.63
1.56
13.07
205.27
199.83
2.41
0.24
166.78
417.36
363.08
8.75
1.99
Mar.
142.2
Apr.
331.11
58.06
181.33
170.93
2.65
0.36
1388.7
271.37
2.79
0.5
May
617.8
205.14
392.07
367.96
2.44
0.46
1788.8
379.35
1066.5
1030.2
3.29
0.27
Jun.
1030.5
327.09
691.56
696.45
2.69
-0.09
2655.3
681.37
1508.7
1475.9
3.71
0.7
Jul.
Aug.
1381.5
1361.3
545.61
315.85
967.11
814.48
954.63
819.5
2.78
3.11
0.07
0.02
2664.1
2107.5
337.58
152.04
1208.9
871.02
1082.4
829.73
3.68
4.56
1.05
0.99
1134.3
36.27
516.62
484.23
3.15
0.77
77.8
280.84
265.73
4.53
0.93
232.58
197.59
2.57
0.64
84.31
49.13
4.96
1.39
Sep.
958.72
250.26
529.87
511.66
2.83
0.53
Oct.
338.82
54.61
201.76
199.32
1.85
0.1
725.35
Nov.
174.16
5.36
56.85
52.15
4.66
1.09
626.71
3.59
Dec.
62.69
9.77
6.23
10.47
2.48
385.26
0
0
erosivity will increase more dramatically with increases in
annual rainfall. Accordingly, it also implies that rainfall
erosivity will significantly decrease with decreases in
annual rainfall. It is concluded from Fig. 3a that the
geographic distribution of the annual erosivity is closely
related to annual rainfall, the spatial variations of rainfall
erosivity are mainly caused by the uneven spatial distribution of precipitation, and the similar phenomena were
also reported for Brazil by da Silva (2004) and for the
Chinese Loess Plateau by Xin et al. (2011). Moreover, the
linear correlation found between annual average erosivity
and longitude is R2 = 0.455, and the linear correlation
between annual average erosivity and latitude is R2 =
0.074 (Fig. 3b, c). Obviously, the main axis of spatial
variation of the annual average erosivity values is
longitudinal.
The statistical properties monthly rainfall erosivity in
the upper and mid-lower Yangtze reaches are given in
the Table 3, the distribution of monthly rainfall erosivity
indicates that rainfall erosivity increases from January to
June, and decreases thereafter at the two sub regions. It
can be seen from Table 3 that higher monthly rainfall
erosivity is observed mainly during May–August and
lower monthly rainfall erosivity in January, February and
December. Most parts of the Yangtze River basin are
dominated by a subtropical monsoon climate, except for
some areas located in the Tibetan plateau. There are
three types of monsoon in a year, the Siberian northwest
monsoon in winter, the Asian summer monsoon in the
middle and lower Yangtze reaches (and Indian southwest
summer monsoon in the upper Yangtze reaches). Normally, the summer monsoon starts to influence the
Yangtze River Basin in April and retreats in October.
The precipitation is mostly concentrated in the summer
season, from June to August, accounting for nearly half
769.72
737.23
Fig. 4 Number of stations showing increasing/decreasing trends in
rainfall erosivity during 1960–2005
of the annual total. This may be the cause of the higher
rainfall erosivity in June and July when compared with
those of other months.
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344
Fig. 5 Spatial distribution for trends of rainfall erosivity during 1960–2005
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Stoch Environ Res Risk Assess (2013) 27:337–351
Stoch Environ Res Risk Assess (2013) 27:337–351
3.2 Trends of rainfall erosivity during 1960–2005
The results of the Mann–Kendall test for the annual rainfall
erosivity series at each station in the Yangtze River basin
during 1960–2005 are given in Fig. 4. Figure 5 shows the
spatial distribution of trends of annual erosivity at different
significance levels. It can be seen from Fig. 4 that most parts
of Yangtze River basin is dominated by increasing trends in
annual erosivity. However, the increasing trends are statistically significant in only 22 out of 146 stations, of which
only 4 stations show significant decreasing trends at the 1 %
significance level and 12 stations at the 5 % significance
level. From a spatial perspective, those stations showing
significant decreasing trends are mainly located in the Jinshajiang River basin and Boyang Lake basin (Fig. 5a).
The numbers of stations with significant decreasing and
increasing trends detected by the Mann–Kendall test for
monthly and seasonal rainfall erosivity series during the
study period 1960–2005 are also summarized in Fig. 4. For
monthly trends, January has the largest number of stations
showing significant increasing trends with 41 stations at
significance level of 5 % and 24 stations at significance level
of 1 %. June ranks the second largest in the number of stations showing significant increasing trends, with 17 stations
at significance level of 5 % and 3 stations at significance
level of 1 %. July ranks third largest, with 11 stations and 5
stations, respectively. September has the largest number of
stations showing a significant decreasing tendency, with 14
stations at significance level of 10 % and 7 stations at significance level of 5 %, followed by April with 8 stations and
3 stations, respectively. Among the four seasons, winter
shows the largest number of stations with significant trends,
with 34 out of 146 stations show significant increasing trends
at significance level of 10 %, accounting for 23.3 % of all
stations in the Yangtze River basin. Figure 5 also gives the
spatial distribution of trends in monthly and seasonal rainfall
erosivity detected by the Mann–Kendall test in the region.
Strong trends in monthly rainfall erosivity mainly happened in January, June, July and September. Among these
4 months, January, June and July exhibit significant
increasing trends, while September displays significant
decreasing trends. For January, the significant upward trends
are observed in the Taihu Lake basin, Boyang Lake basin and
Dongting Lake basin. The significant increasing trends for
June are mainly located in the Jinshajiang River basin and the
middle area of Yangtze River basin. For July, the significant
increase in rainfall erosivity mostly occurs in the Dongting
River basin. The significant decreasing trends for September
are mainly located in Jialingjiang River basin and Hanjiang
River basin. In other months, most stations in the Yangtze
River basin do not show significant trends of rainfall erosivity. As for the seasonal patterns, winter and summer are
the seasons presenting strong trends. Significant increases in
345
rainfall erosivity in winter can be found mainly in the Taihu
Lake basin, Boyang Lake basin and Dongting Lake basin,
and the significant increases in summer can be found mainly
in the Jinshajiang River basin and the middle area of Yangtze
River basin. The Mann–Kendall test for monthly precipitation in the Yangtze River basin reveals that the statistically
significant upward trends were mainly observed in January,
June and July, and the statistically significant downward
trends were mainly found in April, September and December
(Jiang et al. 2007, 2008). These phenomena are in good
agreement with monthly rainfall erosivity trends in this
paper, which indicate that the temporal variation of monthly
precipitation may be the proximate cause of the changes in
monthly rainfall erosivity.
3.3 Changes of rainfall erosivity between 1991–2005
and 1960–1990
The results of T test for the annual rainfall erosivity series
at each station in the Yangtze River basin between two
Fig. 6 Number of stations showing positive/negative changes in
rainfall erosivity between 1991–2005 and 1960–1990
123
346
Stoch Environ Res Risk Assess (2013) 27:337–351
Fig. 7 Spatial distribution for changes of rainfall erosivity between 1991–2005 and 1960–1990
independent sub-periods (1991–2005 and 1960–1990) are
given in Fig. 6. Figure 7 shows the spatial distribution of
changes of annual erosivity at different significance levels.
123
It can be seen from Fig. 6 that Yangtze River basin is
dominated by positive changes in annual erosivity, except
for the middle area. However, the positive changes are
Stoch Environ Res Risk Assess (2013) 27:337–351
Table 4 The Mann–Kendall
test, T test and rescaled range
analysis for the monthly,
seasonal and annual rainfall
erosivity of upper Yangtze
reaches
Mann–Kendall test
Z_values
Trends
T test
Kendall slope
T_values
Rescaled range analysis
Changes
Hurst exponents
0.56
Jan.
2.74
Upward**
0.20
3.37
Positive**
Feb.
0.15
Upward
0.04
1.02
Positive
0.58
Mar.
-0.25
Downward
-0.09
1.08
Positive
0.54
Apr.
-0.54
Downward
-0.38
-0.31
Negative
0.69
May
0.47
Upward
0.36
-0.21
Negative
0.51
Jun.
1.57
Upward
2.93
1.98
Positive*
0.60
Jul.
Aug.
Note Results in bold type denote
statistically significant at the
10 % significance level, the
‘‘*’’ mean significant at the 5 %
significance level, and the ‘‘**’’
mean significant at the 1 %
significance level
347
1.04
-0.40
Upward
Downward
2.29
0.20
Positive
0.70
-0.64
0.00
No
0.61
Sep.
-2.76
Downward**
-3.51
-2.63
Negative**
0.70
Oct.
-0.59
Downward
-0.56
-0.60
Negative
0.49
Nov.
Dec.
-0.62
-1.07
Downward
Downward
-0.28
-0.06
0.57
-1.13
Positive
Negative
0.56
0.55
Spring
-0.03
Downward
-0.06
-0.08
Summer
3.25
Upward**
4.59
0.95
Autumn
-1.95
Downward
-4.31
-2.40
Winter
0.78
Annual
-0.09
Upward
Downward
statistically significant in only 20 out of 146 stations, of
which only 12 stations show significant positive changes at
the 5 % significance level. From a spatial perspective,
those stations showing significant decreasing changes are
mainly located in the Jinshajiang River basin and Boyang
Lake basin (Fig. 7a).
The numbers of stations with significant negative and
positive changes detected by the T test for monthly and
seasonal rainfall erosivity series are also summarized in
Fig. 6. For monthly changes, January has the largest
number of stations showing significant positive changes
with 47 stations at significance level of 5 % and 31 stations
at significance level of 1 %. March ranks the second largest
in the number of stations showing significant positive
changes, with about 20 stations at significance level of 5 %
and 2 stations at significance level of 1 %. July ranks third
largest, with 21 stations and 7 stations, respectively. September has the largest number of stations showing a significant negative change, with 36 stations at significance
level of 10 % and 21 stations at significance level of 5 %,
followed by April with 15 stations and 7 stations, respectively. Among the four seasons, winter shows the largest
number of stations with significant changes, with 42 out of
146 stations show significant positive changes at significance level of 10 %, accounting for 28.8 % of all stations
in the Yangtze River basin. Figure 7 also gives the spatial
distribution of changes in monthly and seasonal rainfall
erosivity detected by the T test in the Yangtze River basin.
Strong changes in monthly rainfall erosivity mainly happened in January, March, July and September. Among
these 4 months, January, March and July exhibit significant
0.20
1.42
-0.27
-0.22
Negative
0.63
Positive
0.72
Negative*
0.69
Positive
0.56
Negative
0.52
positive changes, while September displays significant
negative changes. For January, the significant positive
changes are observed in the more than half of mid-lower
Yangtze reaches. The significant positive changes for
March are mainly located in the southern costal of Yangtze
River basin. For July, the significant positive change in
rainfall erosivity mostly occurs in the Dongting River
basin. The significant negative changes for September are
mainly located in the middle area of Yangtze River basin
and Taihu Lake basin. In other months, most stations in the
Yangtze River basin do not show significant changes of
rainfall erosivity. As for the seasonal patterns, winter and
summer are the seasons presenting strong changes. Significant positive changes in rainfall erosivity in winter can
be found mainly in the Taihu Lake basin and Dongting
Lake basin, and the significant positive changes in summer
can be found mainly in the Jinshajiang River basin,
Dongting Lake basin and Boyang Lake basin.
3.4 Change features of regional rainfall erosivity
For further understanding of the statistical characteristics
of rainfall erosivity, the annual, seasonal and monthly
rainfall erosivity on the scale of river basin are constructed
by the average values of all stations, which are thoroughly
studied in this paper. The Mann–Kendall test, T test and
Rescaled Range Analysis are used to explore the change
features in the regional rainfall erosivity.
It can be seen from Table 4 that the rainfall erosivity of
upper Yangtze reaches display significant trends for January, September, summer and autumn during 1960–2005,
123
348
Fig. 8 Wavelet transform of areal seasonal and annual rainfall
erosivity during 1960–2005 in the Yangtze river basin. a–e the upper
Yangtze reaches; f–j the mid-lower Yangtze reaches. The thick black
123
Stoch Environ Res Risk Assess (2013) 27:337–351
contour designates the 95 % confidence level against red noise and
the cone of influence (COI) where edge effects might distort the
picture is shown as a U-shaped line
Stoch Environ Res Risk Assess (2013) 27:337–351
Table 5 The Mann–Kendall
test, T test and rescaled range
analysis for monthly, seasonal
and annual rainfall erosivity of
mid-lower Yangtze reaches
Note Results in bold type denote
statistically significant at the
10 % significance level, the
‘‘*’’ mean significant at the 5 %
significance level, and the ‘‘**’’
mean significant at the 1 %
significance level
349
Mann–Kendall test
Z_values
Trends
T test
Kendall slope
T_values
Rescaled range analysis
Changes
Hurst exponents
0.63
Jan.
3.16
Upward**
2.90
3.59
Positive**
Feb.
1.84
Upward
1.92
0.27
Positive
0.59
Mar.
-0.13
Downward
-0.37
1.33
Positive
0.54
Apr.
-0.51
Downward
-1.68
-0.36
Negative
0.55
May
-0.02
Upward
-0.28
-0.10
Negative
0.64
Jun.
2.07
Upward*
7.33
2.21
Positive*
0.58
Jul.
2.06
Upward*
10.16
2.21
Positive*
0.54
Aug.
0.95
Upward
5.34
0.93
Positive
0.61
Sep.
-1.38
Downward
-3.17
-2.69
Negative**
0.54
Oct.
-0.02
Downward
-0.21
-0.70
Negative
0.45
Nov.
Dec.
0.31
-0.05
Upward
Downward
0.62
-0.04
0.11
0.95
Positive
Positive
0.53
0.58
Spring
-0.66
Summer
2.27
Autumn
-1.37
Downward
-3.24
0.26
Positive
0.57
Upward*
18.45
2.82
Positive**
0.57
Downward
-3.69
-2.02
Negative*
0.58
5.53
2.87
Positive**
0.72
23.30
1.87
Positive
0.58
Winter
2.48
Upward*
Annual
1.63
Upward
and the Kendall slope of them can be up to 0.20, -3.51,
4.59 and -4.31 MJ mm ha-1 h-1/year respectively.
Comparing the rainfall erosivity of upper Yangtze reaches
between 1991–2005 and 1960–1990, the significant positive changes can be found during January and June, while
the significant negative changes can be observed during
September and autumn. The Rescaled Range Analysis for
the time series of annual, seasonal and monthly rainfall
erosivity of upper Yangtze reaches during 1960–2005 are
also given in Table 4. Only the Hurst exponent of October
is smaller than 0.5, implying that the future tendency in
rainfall erosivity during October might appear to be an
anti-persistent pattern that differs from the present situation, that is, there could be a very slight increasing trend in
October, a reverse tendency of the present trend according
the positive values of the Mann–Kendall statistic Z. For the
other time series, the Hurst exponents are all greater than
0.5, indicating that the future tendency of these time series
is consistent with those of the past years. Moreover, the
continuous wavelet transform (CWT) technique was used
to detect the characteristics of cycles of rainfall erosivity
for upper Yangtze reaches, it can be seen from Fig. 8 that
the annual and seasonal rainfall erosivity of upper Yangtze
reaches all have one significant periodicity of 2–4 years.
It can be seen from Table 5 that the rainfall erosivity of
mid-lower Yangtze reaches display significant trends for
January, June, July, summer and winter during 1960–2005,
and the Kendall slope of them can be up to 2.90, 7.33,
10.16, 18.45 and 5.53 MJ mm ha-1 h-1/year respectively.
Comparing the rainfall erosivity of mid-lower Yangtze
reaches between 1991–2005 and 1960–1990, the significant positive changes can be found for January, June, July,
summer, winter and annual, while the significant negative
changes can be observed during September and autumn.
The rescaled range analysis for the time series of annual,
seasonal and monthly rainfall erosivity of mid-lower
Yangtze reaches during 1960–2008 are also given in
Table 4. Only the Hurst exponent of October is smaller
than 0.5, implying that the future tendency in rainfall
erosivity during October might appear to be an anti-persistent pattern that differs from the present situation, that is,
there could be a very slight increasing trend in October, a
reverse tendency of the present trend according the positive
values of the Mann–Kendall statistic Z. For the other time
series, the Hurst exponents are all greater than 0.5, indicating that the future tendency of these time series is
consistent with those of the past years. Moreover, the
continuous wavelet transform (CWT) technique was used
to detect the characteristics of cycles of rainfall erosivity
for upper Yangtze reaches, it can be seen from Fig. 8 that
the annual and seasonal rainfall erosivity of mid-lower
Yangtze reaches all have one significant periodicity of
2–4 years.
4 Conclusions
Yangtze River basin is a humid region with serious soil
erosion. The statistical analysis of rainfall erosivity has
scientific and practical value not only in climate change
123
350
Stoch Environ Res Risk Assess (2013) 27:337–351
assessment but also in ecological restoration, water
resource management, agricultural planning and irrigation
in the Yangtze River basin. In this study, the spatial and
temporal variations in rainfall erosivity during 1960–2005
in the Yangtze River basin were explored at the annual,
seasonal and monthly scales by Mann–Kendall test, T test,
rescaled range analysis and continuous wavelet transform
technique, using data for both individual stations and
region. Main findings are summarized as follows.
(1)
(2)
(3)
(4)
The annual average rainfall erosivity in the Yangtze
River basin was distributed unevenly, with a minimum 131.21 MJ mm ha-1 h-1 at Wudaoliang station
and a maximum of 16842 MJ mm ha-1 h-1 at the
Huangshan station. Due to the spatial distribution of
annual average rainfall, the spatial pattern of annual
average rainfall erosivity showed a significant
increasing trend from the northwest to the southeast
of Yangtze River basin. The annual average rainfall
erosivity in the northwest of Yangtze River basin was
smaller than 4,000 MJ mm ha-1 h-1, and that in the
southeast of Yangtze River basin was larger than
10000 MJ mm ha-1 h-1.
From the Mann–Kendall test, the annual rainfall
erosivity presented upward trends in the most of
stations. However, only 22 out of 146 stations were
significant at the 90 % confidence level, and these
stations were mainly located in the Jinshajiang River
basin and Boyang Lake basin. As for monthly rainfall
erosivity, significant increasing trends were mainly
found during January, June and July, while significant
decreasing trends were mainly found during September. When it comes to seasonal rainfall erosivity,
winter and summer were the seasons showing strong
upward trends.
From the T test, the positive changes in annual
rainfall erosivity between 1991–2005 and 1960–1990
occurred in more than half of stations. However, only
20 out of 146 stations were significant at the 90 %
confidence level, and these stations were mainly
located in the Jinshajiang River basin and Boyang
Lake basin. As for monthly rainfall erosivity, significant positive changes were mainly found during
January, March and July, while significant negative
changes were mainly found during September. When
it comes to seasonal rainfall erosivity, winter and
summer were the seasons showing strong positive
changes.
From the rescaled range analysis, the annual rainfall
erosivity of upper Yangtze River reaches would
maintain a slight decreasing trend in the near future,
while the annual rainfall erosivity of mid-lower
Yangtze River reaches would maintain a strong
123
increasing trend in the near future. As for monthly
rainfall erosivity, the rainfall erosivity would present
a very slight increasing trend during October in the
near future, which was opposite to the current trend;
for the rest months, future tendency of rainfall
erosivity would be consistent with the current
patterns. When it comes to seasonal rainfall erosivity,
future tendency of each season would be consistent
with those of the past years. Worryingly, a long-range
dependence characteristic existed in the time series of
rainfall erosivity indicated that rainfall erosivity
during winter and summer would maintain a detected
significant increasing trend in the near future, which
would cause more serious soil erosion in the Yangtze
River basin. In addition, the annual and seasonal
erosivity of Yangtze River basin all had one significant periodicity of 2-4 years base on the continuous
wavelet transform technique.
Acknowledgments This paper was financially supported by Forestry
Industry Research special funds for Public Welfare Projects ‘‘Study of
water resource control function of typical forest vegetation in the
region of Yangtze river delta’’ (No: 201104005-04), fully supported by
Key Project of National Science and Technology during the 11th FiveYear Plan (No. 2006BAD03A16), National Key Technology Research
and Development Program of the Ministry of Science and Technology
of China (2012BAC23B01, 2012BAD16B0305), National 973 Program (2006CB705809). We would like to thank the National Climate
Centre (NCC) in Beijing for providing valuable climate datasets.
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