We analyzed the variation trends in precipitation according to the observed data in the Shaying River catchment, upstream of the Huai River from 1951 to 2010, using the linear regression method and Mann-Kendall test. Further study was made by introducing
Climate variability and human activities have become major concerns among societies and governments, as global warming arising from the anthropogenic-driven emissions of greenhouse gases has emerged in the last two decades and other water-related issues rendering the necessity of studying changes in hydrological processes [
So far, many studies have focused on the analysis of precipitation variability at different temporal scales (from daily to annual) and in different parts of the world. Mosmann et al. [
Besides, there are a number of ongoing techniques to measure trends in hydrological and climatological time series. The Mann-Kendall (M-K) test has been widely used as an effective method to evaluate a presence of a statistically significant trend [
Another concern for precipitation change is the detection of the possible presence of long memory in the data, for example, if the change has occurred, whether and how the change persists. Hurst [
In Huai River basin, the majority of the previous studies in precipitation changes were focused on the whole Huai River based on sparsely observed climate data [
Shaying River, originated in the western mountainous area of Henan province, is the first tributary in the upstream of Huai River. It flows into the western of Anhui province through the central part of the Henan from northwest to southeast and falls into Huai River in the Mo River mouth of Fuyang city in Anhui province. Shaying River is located between 111.95° and 11
Study area and location of the gauge stations.
Daily precipitation data from 38 rainfall stations in the study area were obtained to analyze the trend in annual, seasonal, and monthly scales, and the arithmetic average method was used to get catchment average precipitation. Considering the integrity and reliability of the source material, combined with observation of actual situation in the catchment, different time series in each station were selected, the longest series is 60 years (1951–2010), and the shortest series is 45 years. Thus, we have based our analysis on the period 1951–2010. The geographical location of the stations is shown in Figure
The method of linear regression is widely used in detecting and analyzing the variation trends in time series, which has an advantage that it provides a measure of significance based on the hypothesis test on the slope and also gives the magnitude of the rate of change [
The Mann-Kendall test is a nonparametric test, which was highly recommended for general use by the World Meteorological Organization [
Kendall [
Positive values of
Another useful indicator is the Kendall slope
The basic idea of the
For the time series of a certain physical quantity
Hurst described
Figure
Trends for annual and seasonal precipitation during the period 1951–2010 by linear regression method.
Annual
Spring
Summer
Autumn
winter
The linear regression analysis was further applied to detect the seasonal trends of precipitation for the whole catchment (Figures
The Mann-Kendall test was applied for testing the trends of the precipitation time series in different time scales of the catchment average precipitation (Table
Values of
Time |
|
|
|
---|---|---|---|
January | −0.74 | −0.036 | 0.452 |
February | 0.48 | 0.037 | 0.556 |
March | 0.13 | 0.015 | 0.689 |
April | −1.32 | −0.269 | 0.754 |
May | 1.41 | 0.489 | 0.634 |
June | 1.51 | 0.546 | 0.534 |
July | 1.21 | 0.760 | 0.562 |
August | −0.17 | −0.131 | 0.512 |
September | 0.82 | 0.205 | 0.687 |
October | −0.68 | −0.168 | 0.623 |
November | −1.26 | −0.240 | 0.546 |
December | −0.34 | −0.026 | 0.641 |
Spring (3–5 month) | 0.13 | 0.077 | 0.595 |
Summer (6–8 month) | 0.49 | 0.637 | 0.596 |
Autumn (9–11 month) | −0.29 | −0.177 | 0.620 |
Winter (12–2 month) | 0.03 | 0.012 | 0.560 |
Flood season (6–9 month) | −0.08 | −0.232 | 0.595 |
Major flood season (7-8 month) | 0.18 | 0.158 | 0.595 |
Nonflood season (10–5 month) | −0.41 | −0.269 | 0.615 |
Annual | 0.08 | 0.050 | 0.447 |
According to positive and negative values of
The results of M-K test using observed time series of individual station were shown in Table
Values of
Station | Spring | Summer | Autumn | Winter | Annual | |||||
---|---|---|---|---|---|---|---|---|---|---|
Z |
|
Z |
|
Z |
|
Z |
|
Z |
| |
Baicaoping (BCP) | −0.06 | −0.031 | 0.08 | 0.104 | −0.72 | −0.441 | −0.25 | −0.038 | −0.46 | −0.909 |
Baiguishan (BGS) | −0.39 | −0.310 | 2.34** | 3.016 | −1.40 | −1.017 | 1.43 | 0.195 | 0.89 | 1.199 |
Baofeng (BF) | −0.63 | −0.525 | 0.95 | 1.331 | −1.33 | −0.931 | 0.18 | 0.049 | −0.01 | −0.008 |
Baohe (BH) | −0.16 | −0.185 | 1.74* | 3.648 | −1.30 | −1.133 | 0.68 | 0.190 | 0.82 | 2.050 |
Dadian (DD) | −0.09 | −0.027 | 0.78 | 1.411 | 0.01 | 0.000 | 0.65 | 0.114 | 0.54 | 1.071 |
Daying (DY) | −0.36 | −0.224 | 0.95 | 1.061 | −1.14 | −0.758 | 0.63 | 0.139 | 0.17 | 0.340 |
Dushu (DS) | −0.21 | −0.144 | 0.51 | 0.587 | −0.36 | −0.278 | −0.19 | −0.019 | 0.11 | 0.114 |
Erlangmiao (ElM) | −0.19 | −0.125 | −0.81 | −1.372 | −0.30 | −0.239 | 0.20 | 0.041 | −1.14 | −2.235 |
Fudian (FD) | 0.00 | 0.000 | −0.89 | −1.152 | −0.07 | −0.037 | 0.91 | 0.133 | −0.99 | −1.605 |
Guaihe (GH) | −0.83 | −0.523 | 1.63 | 2.369 | −0.90 | −0.719 | 0.37 | 0.064 | 0.38 | 0.704 |
Guanzhai (GZ) | −1.12 | −0.686 | 0.78 | 1.278 | −1.15 | −0.941 | −0.15 | −0.054 | 0.23 | 0.355 |
Handian (HD) | −0.59 | −0.477 | 1.82* | 2.242 | −2.17 | −1.239 | 1.05 | 0.203 | 0.99 | 1.575 |
Hekou (HK) | −0.76 | −0.536 | 1.31 | 1.712 | −0.50 | −0.334 | 0.10 | 0.028 | 0.35 | 0.830 |
Huangzhuang (HZ) | −0.80 | −0.456 | −1.26 | −1.355 | 0.27 | 0.089 | 0.30 | 0.052 | −1.48 | −1.863 |
Jizhong (JZ) | 0.96 | 0.745 | 1.55 | 2.512 | −0.83 | −0.683 | 1.14 | 0.245 | 1.69* | 4.332 |
Jiliaojie (JLJ) | −0.85 | −0.700 | −0.58 | −0.717 | −2.14** | −1.689 | 0.34 | 0.076 | −1.28 | −3.251 |
Jiaxian (JX) | 0.03 | 0.026 | −0.44 | −0.528 | −0.79 | −0.428 | 0.49 | 0.070 | −0.47 | −0.519 |
Jintangzhai (JTZ) | −0.06 | −0.034 | 1.35 | 1.721 | −0.56 | −0.423 | −0.53 | −0.089 | 0.52 | 0.885 |
Lianghekou (LHK) | −0.96 | −0.405 | −1.24 | −1.163 | −0.79 | −0.512 | −0.45 | −0.059 | −2.13** | −2.384 |
Linruzhen (LRZ) | −0.62 | −0.268 | −0.67 | −0.624 | −0.12 | −0.059 | 0.83 | 0.117 | −0.70 | −0.856 |
Louzigou (LZG) | −0.78 | −0.376 | −0.88 | −0.774 | −0.41 | −0.290 | 0.24 | 0.031 | −0.93 | −1.523 |
Lushan (LS) | −0.06 | −0.048 | 0.54 | 0.736 | −0.03 | −0.017 | −0.18 | −0.043 | −0.21 | −0.412 |
Luohe (LH) | −0.83 | −0.492 | 0.25 | 0.221 | −0.57 | −0.384 | −1.08 | −0.224 | −0.98 | −1.662 |
Penghe (PH) | −0.28 | −0.307 | 2.53** | 4.911 | −1.19 | −1.342 | 0.31 | 0.068 | 1.03 | 2.532 |
Pinggou (PG) | −1.58 | −2.410 | 0.59 | 1.218 | −1.48 | −1.490 | 0.17 | 0.069 | −0.37 | −1.023 |
Ruzhou (RZ) | −0.33 | −0.167 | −0.17 | −0.135 | −0.07 | −0.046 | −0.30 | −0.039 | −0.25 | −0.213 |
Shenhou (SH) | 0.16 | 0.125 | −0.84 | −1.083 | −0.22 | −0.111 | 0.57 | 0.090 | −0.82 | −1.253 |
Silidian (SLD) | −0.32 | −0.151 | 1.90* | 3.167 | 0.18 | 0.095 | 1.24 | 0.254 | 1.14 | 2.539 |
Wawu (WW) | 0.57 | 0.315 | −0.64 | −0.868 | −0.09 | −0.032 | 0.94 | 0.166 | −0.97 | −1.436 |
Xiagushan (XGS) | 0.03 | 0.024 | 1.82* | 3.033 | −1.39 | −1.175 | 0.15 | 0.022 | 0.90 | 1.635 |
Xiatang (XT) | 0.19 | 0.139 | 0.76 | 1.307 | 0.01 | 0.003 | 0.03 | 0.004 | 0.27 | 0.521 |
Xiaoshidian (XSD) | −0.55 | −0.257 | 0.26 | 0.466 | −1.02 | −0.700 | −0.13 | −0.027 | −0.27 | −0.549 |
Xiangcheng (XC) | −0.25 | −0.098 | −0.67 | −0.768 | −0.73 | −0.475 | −0.41 | −0.065 | −0.96 | −1.116 |
Zhaopingtai (ZPT) | 0.42 | 0.317 | 3.01*** | 4.444 | −0.82 | −0.726 | 0.61 | 0.153 | 1.62 | 3.347 |
Zhiping (ZP) | 2.34** | 2.087 | 2.20** | 4.200 | −0.44 | −0.347 | 4.82*** | 1.217 | 2.98*** | 7.476 |
Zhiyang (ZY) | 0.12 | 0.090 | −0.11 | −0.101 | −0.23 | −0.197 | 1.68* | 0.292 | 0.04 | 0.027 |
Zhongtang (ZT) | 0.00 | 0.000 | 0.75 | 1.123 | −1.35 | −1.100 | 0.00 | 0.000 | 0.01 | 0.000 |
Ziluoshan (ZLS) | 0.32 | 0.114 | 0.00 | 0.000 | −0.15 | −0.105 | 1.18 | 0.165 | −0.22 | −0.289 |
On the seasonal scale, the trends detected significantly at the 90%, 95%, and 99% confidence levels were mostly positive and these significant positive trends occurred in spring, summer, and winter. Among all 38 stations in Table
Further analysis was executed in three subcatchments of the Beiru River, Sha River, and Li River in the south, middle, and north of the study catchment, respectively (Figure
Values of
Subcatchments | Spring | Summer | Autumn | Winter | Annual | |||||
---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|
|
|
|
|
|
|
|
Beiru River | −0.22 | −0.150 | −0.62 | −0.602 | −0.35 | −0.202 | 0.30 | 0.046 | −0.90 | −1.004 |
Sha River | 0.47 | 0.268 | 0.52 | 0.579 | −0.06 | −0.029 | 0.21 | 0.045 | 0.21 | 0.297 |
Li River | −0.16 | −0.078 | 0.86 | 1.364 | −0.59 | −0.349 | −0.08 | −0.025 | 0.17 | 0.274 |
The Hurst Exponent (
Values of
Station |
|
||||
---|---|---|---|---|---|
Spring | Summer | Autumn | Winter | Annual | |
BCP | 0.560 | 0.640 | 0.636 | 0.668 | 0.532 |
BGS | 0.513 | 0.572 | 0.633 | 0.658 | 0.399 |
BF | 0.488 | 0.612 | 0.662 | 0.714 | 0.478 |
BH | 0.401 | 0.529 | 0.664 | 0.558 | 0.418 |
DD | 0.659 | 0.672 | 0.673 | 0.542 | 0.338 |
DY | 0.547 | 0.648 | 0.643 | 0.666 | 0.468 |
DS | 0.499 | 0.617 | 0.610 | 0.515 | 0.485 |
ELM | 0.491 | 0.614 | 0.672 | 0.624 | 0.487 |
FD | 0.511 | 0.748 | 0.659 | 0.569 | 0.669 |
GH | 0.616 | 0.655 | 0.655 | 0.550 | 0.461 |
GZ | 0.511 | 0.530 | 0.588 | 0.607 | 0.401 |
HD | 0.503 | 0.651 | 0.712 | 0.547 | 0.542 |
HK | 0.461 | 0.595 | 0.678 | 0.536 | 0.430 |
HZ | 0.560 | 0.691 | 0.629 | 0.637 | 0.611 |
JZ | 0.744 | 0.629 | 0.581 | 0.653 | 0.740 |
JLJ | 0.541 | 0.736 | 0.722 | 0.690 | 0.586 |
JX | 0.572 | 0.762 | 0.609 | 0.658 | 0.609 |
JTZ | 0.501 | 0.661 | 0.554 | 0.633 | 0.540 |
LHK | 0.430 | 0.698 | 0.679 | 0.587 | 0.649 |
LRZ | 0.551 | 0.675 | 0.717 | 0.622 | 0.506 |
LZG | 0.575 | 0.609 | 0.611 | 0.576 | 0.596 |
LS | 0.614 | 0.710 | 0.712 | 0.640 | 0.587 |
LH | 0.445 | 0.557 | 0.670 | 0.678 | 0.637 |
PH | 0.602 | 0.649 | 0.579 | 0.502 | 0.511 |
PG | 0.522 | 0.642 | 0.710 | 0.533 | 0.453 |
RZ | 0.595 | 0.633 | 0.758 | 0.626 | 0.463 |
SH | 0.565 | 0.629 | 0.756 | 0.544 | 0.541 |
SLD | 0.566 | 0.669 | 0.663 | 0.601 | 0.503 |
WW | 0.618 | 0.682 | 0.716 | 0.567 | 0.465 |
XGS | 0.543 | 0.620 | 0.708 | 0.636 | 0.564 |
XT | 0.564 | 0.651 | 0.668 | 0.540 | 0.465 |
XSD | 0.428 | 0.647 | 0.678 | 0.635 | 0.593 |
XC | 0.547 | 0.716 | 0.625 | 0.624 | 0.643 |
ZPT | 0.508 | 0.651 | 0.673 | 0.596 | 0.364 |
ZP | 0.611 | 0.629 | 0.696 | 0.673 | 0.851 |
ZY | 0.605 | 0.669 | 0.653 | 0.662 | 0.427 |
ZT | 0.549 | 0.612 | 0.682 | 0.606 | 0.569 |
ZLS | 0.564 | 0.607 | 0.582 | 0.603 | 0.463 |
Table
Figures
In this paper, trends of precipitation were investigated using the linear regression method, the Mann-Kendall test, and the
Trends for annual and seasonal precipitation in the 38 stations during the period 1951–2010.
Annual
Spring
Summer
Autumn
Winter
Values of
annual
Spring
Summer
Autumn
Winter
Values of
The relationship analysis of
Z > 0
Z < 0
The results show that the trends in precipitation were relatively not significant in catchment average even though significant trends in some observation stations were found. The annual precipitation in the whole catchment represented a slightly increase trend in the past 60 years, with the rate of 0.042 mm/year. Precipitation series in spring and autumn presented decrease trend in a rate of −0.011 and −0.365 mm/year, respectively, and presented increase trend in a rate of 0.241 and 0.051 mm/year, respectively.
Increase trend in summer is relatively significant. Significant trends in summer account for 21% of the stations. Meanwhile, most of stations with significant increase trends were located in the downstream of the catchment. This increase trends in summer, particularly in the plain area of downstream station, would result in increase of flood occurrences in the study region.
According to calculated Hurst Exponent values, the future precipitation in January and annual series was anticipated to be antipersistent with the past, the rest time series showed persistent trends in the future, and the future precipitation trends were enhanced in all of the 38 rainfall stations in summer, autumn, and winter. The majority of the stations with an antipersistent nature in annual series were concentrated in the central area of the catchment, which probably related to the three large reservoirs (Zhaopingtai, Baiguishan, and Gushitan) in the central region. The three reservoirs were all started to construct in the year 1958 and completed in the 1960s.
The relationships of
However, as the data series are not long enough, the question of whether we are facing long-term climatic trends or whether the observed trends are only a part of long-term variability remains unanswered, and the understanding of influence of the reservoirs is not thorough clearly. In addition, it is also suggested to explore other hydrological and meteorological variables available in the study area. Further studies would be interesting to investigate the interrelationship between different variables.
The first author thanks the following financial support: Major Program of National Natural Science Foundation of China (51190091); the National 973 Project under Grant no. 2010CB951102; and the National Natural Science Foundation of China (no. 41001011/40901015/41101018).