Lan-lan YU*, Zi-qiang XIA, Jing-ku LI, Tao CAI

1. River Basin Authority of Liaoning Province, Shenyang 110003, P. R. China

2. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, P. R. China

3. Institute of International Rivers Research Academy, Hohai University, Nanjing 210098, P. R. China

4. Hydrological Bureau of Liaoning Province, Shenyang 110003, P. R. China

Climate change characteristics of Amur River

Lan-lan YU*1, Zi-qiang XIA2,3, Jing-ku LI1, Tao CAI4

1. River Basin Authority of Liaoning Province, Shenyang 110003, P. R. China

2. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210098, P. R. China

3. Institute of International Rivers Research Academy, Hohai University, Nanjing 210098, P. R. China

4. Hydrological Bureau of Liaoning Province, Shenyang 110003, P. R. China

Unusually severe weather is occurring more frequently due to global climate change. Heat waves, rainstorms, snowstorms, and droughts are becoming increasingly common all over the world, threatening human lives and property. Both temperature and precipitation are representative variables usually used to directly reflect and forecast the influences of climate change. In this study, daily data (from 1953 to 1995) and monthly data (from 1950 to 2010) of temperature and precipitation in five regions of the Amur River were examined. The significance of changes in temperature and precipitation was tested using the Mann-Kendall test method. The amplitudes were computed using the linear least-squares regression model, and the extreme temperature and precipitation were analyzed using hydrological statistical methods. The results show the following: the mean annual temperature increased significantly from 1950 to 2010 in the five regions, mainly due to the warming in spring and winter; the annual precipitation changed significantly from 1950 to 2010 only in the lower mainstream of the Amur River; the frequency of extremely low temperature events decreased from 1953 to 1995 in the mainstream of the Amur River; the frequency of high temperature events increased from 1953 to 1995 in the mainstream of the Amur River; and the frequency of extreme precipitation events did not change significantly from 1953 to 1995 in the mainstream of the Amur River. This study provides a valuable theoretical basis for settling disputes between China and Russia on sustainable development and utilization of water resources of the Amur River.

climate change; temperature; precipitation; extreme weather events; Mann-Kendall test method; linear least-squares regression model; Amur River

1 Introduction

Climate change has become a hot topic for researchers, attracting the attention of experts and scholars, as well as the governments of many countries. Climate change can bring about glacial retreats, permafrost melts, rises in sea levels, frequent catastrophic weather events,increasing desertification, and threats to rare and endangered species (Houghton et al. 2001; Shi and Liu 2005; Nelson 2003; Lzrael and Anokhin 2002; Osterkamp et al. 2000; Shen et al. 2012; Dai and Zhang 2012). The frequency of central pacific (CP) El Ni?o at the end of the 21st century will increase by five times from what it is now (Dai et al. 2010). Climate change also threatens society’s sustainable development. It directly and indirectly affects the ecological environment and natural resources.

Temperature and precipitation, both of which are vital meteorological factors, are usually selected as representative variables to directly reflect and predict global climate change. Reports from the Intergovernmental Panel on Climate Change (IPCC) show that the global mean annual temperature rose by 0.4℃ to 0.8℃ in the 20th century (Solomon et al. 2007). At the same time, precipitation has also significantly changed all over the world (Li et al. 2012). Under these conditions, extreme weather events, including heat waves, rainstorms, floods, snowstorms, and droughts, are now more likely to occur than they previously were (Douglas et al. 2000; Zhang et al. 2008). Extreme weather events have recently become an important point of analysis for climate change. Thus, scholars began to focus on studying extreme weather events, resulting in significant findings. Jones et al. (1998) adopted extreme climate indices to simulate variations of extreme weather events in the world. Lzrael and Anokhin (2002) performed research on the influence of extreme weather events on the social economy and ecological environment. Zhai et al. (2005) conducted research on the change trends of extreme temperature and precipitation in North China. Studies show that the extremely low and high temperatures were found to be increasing in China (Zhang et al. 2008; Zhao et al. 2012), while the extreme precipitation changed in different ways (Min and Qian 2008; Yang et al. 2008; Wang et al. 2012).

A large number of studies have focused on analyzing variations of temperature and precipitation in different places. However, research on the effects of climate change on international rivers is limited. In the Erqisi River Basin, both temperature and precipitation show significant increasing trends (Li et al. 2008). The Amur River has been the official boundary between China and Russia since 2004. Since the Amur River is located at the middle and high latitudes, it is very sensitive to global climate change. Although studies on climate change in this river basin are important to both China and Russia, few studies have been conducted. Also, few studies have discussed the extreme temperature and extreme precipitation in the Amur River. Furthermore, issues of sustainable water resources utilization and management between China and Russia, which may be affected by variations of temperature and precipitation, are the most important factors that affect the relationship between the two countries. As it is an international river, further research on the Amur River must be performed. Thus, the variations of climate characteristics and extreme weather events in the Amur River are analyzed in this paper.

2 Methodology

The methods presented in this paper include the Mann-Kendall test method, the linearleast-squares regression model, and the hydrological statistical method. These methods are widely used, highly credible, and can directly reflect the changes in the variables.

2.1 Mann-Kendall test method

The Mann-Kendall test method was used to test the change trends of temperature and precipitation. A stationary series has a constant mean, variance, and autocorrelation. Both parametric and non-parametric tests are commonly used to test the change trends. Although parametric trend tests are more powerful, they require the data to be independent and normally distributed, while non-parametric trend tests only require that the data are independent and that outliers can be tolerated. The Mann-Kendall test method (Mann 1945; Kendall 1948), as a kind of non-parametric test, is highly recommended by the World Meteorological Organization in assessing the significance of monotonic trends in hydrological series.

The rank correlation test (Kendall 1948) for two sets of observations,x1,x2,…,xnandy1,y2,…,yn, is formulated, and the statisticSis expressed as

andbijis similarly defined for observationsy1,y2,…,yn.

Under the null hypothesis thatx1,x2,…,xnandy1,y2,…,ynare independent and randomly ordered, the statisticStends to be normally distributed for a largen. The mean and variance are given by

If the values ofy1,y2,…,ynare replaced with the time order of time seriesx1,x2,…,xn, that is, 1,2,…,n, the test can be used as a trend test (Mann 1945). In this case, the statisticSis simplified as

2.2 Linear least-squares regression model

In this study, the amplitudes were calculated using the linear least-squares regressionmodel. The most common method of linear regression is the least squares of the residuals, which is generally accepted as a useful calculation method for the change rate of a climate series. The primary equation of the linear least-squares regression model isy=bx+a. This method determines the coefficientsaandbof the fit line by minimizing the sum of the squares of the residuals (vertical offsets) of the dependent variable in a set of points (xi,yi), wherenis the number of points. The sum of the squares of the residualsR2is given by

Finally, 10bis the amplitude of the variation we want to obtain.

2.3Hydrological statistical method for defining indices of extreme weather events

The standards of extreme weather events are quite different all over the world, so the methods for defining them differ as well. The gamma distribution density function has been recommended to describe the distribution of daily temperature and precipitation (Jones et al. 1998). Bonsal et al. (2000) arranged the meteorological data series in ascending order, as inx1,x2,…,xm,…,xn, and set the probability of a value less than or equal toxm:

In this study, the indices of extreme weather events consisted of the threshold and duration. Extreme temperature events included extremely low temperature and extremely high temperature events, determined by the daily minimum (maximum) temperature series. Then, within each year, the daily minimum (maximum) temperature series was arranged in ascending order. Based on the study of Pan (2002), in order to avoid the influences of errors,the mean value of the 5th (95th) daily minimum (maximum) temperature in each year was selected as the extremely low (high) threshold. Extremely low temperature events were considered to have occurred if the daily minimum temperature was below the threshold, and extremely high temperature events were considered to have occurred if the daily maximum temperature was above the threshold. The duration of extremely low (high) temperature events were considered to be the days when extremely low (high) temperature events took place.

Extreme precipitation events included extreme precipitation and the duration of extreme precipitation events. Extreme precipitation was determined by the daily precipitation series, also first arranged in the same manner as the daily temperature, and the mean value of the 99th daily precipitation series in each year was selected as the threshold of extreme precipitation. Extreme precipitation events were considered to have occurred if the daily precipitation exceeded this threshold, and the duration covered the period during which extreme precipitation events took place.

3 Data

In this study, 18 meteorological stations in the Amur River Basin were selected as representative stations. The Amur River was divided into five research regions: the source regions, including the left source region (Ulan-ude Station, Chita Station, Mogoca Station, and Sretensk Station) and the right source region (Xinbaerhuyouqi Station, Manzhouli Station, and Ergun Station); and the entire mainstream of the Amur River, including the upper mainstream (Mohe Station, Skovorodino Station, Tahe Station, Huma Station, Norsk Station, and Blagovescensk Station), the middle mainstream (Sunwu Station, Yichun Station, and Ekaterino-Nikol’skoe Station), and the lower mainstream (Habarovsk Station and Nikolaevsk Station). The five research regions are shown in Fig. 1.

Fig. 1 Location of Amur River

Basic characteristics of the river basin are shown in Table 1. The monthly temperature and precipitation from 1950 to 2010 in these five regions was obtained to analyze the annualand seasonal variations. The daily minimum and maximum temperatures, as well as daily precipitation from 1953 to 1995 in the mainstream of the Amur River, were obtained to analyze the frequency of extreme events.

Table 1Basic characteristic of different regions of Amur River

4 Results and discussion

4.1 Variations of mean annual temperature and annual precipitation

As far as mean annual temperature was concerned, positive trends were observed from 1950 to 2010 in the five regions of the Amur River. Table 2 clearly shows the increase of temperature in all regions. As a whole, the temperature anomaly changed from negative values to positive values around the 1980s. For these five regions, the values of the mean interannual temperature anomaly increased from the 1950s to 2000s: in the left source region, the values changed from ?0.58℃ in the 1950s to 0.83℃ in the 2000s; in the right source region, the values changed from ?0.95℃ to 0.84℃; in the upper mainstream, the values changed from?0.37℃ to 0.72℃; in the middle mainstream, the values changed from ?1.52℃ to 0.86℃; and in the lower mainstream, the values changed from ?1.14℃ to 0.60℃.

Table 2Values of mean interannual temperature anomaly

As far as annual precipitation was concerned, as a whole, the trends of change cannot be perceived clearly in the five regions of the Amur River. From the values of the interannual precipitation anomaly shown in Table 3, the precipitation increased significantly only in the lower mainstream from the 1950s to the 2000s, with values ranging from ?121.2 mm in the 1950s to 32.4 mm in the 2000s. For the other four regions, the positive values and negative values of the precipitation anomaly did not show regular trends from the 1950s to 2000s as shown in Table 3, so the trends of precipitation variation were not distinct.

Table 3Values of interannual precipitation anomaly

In order to analyze the specific change trends of mean annual temperature and annual precipitation in the five regions of the Amur River, the variation processes are described in Fig. 2. The change trends were calculated using the linear least-squares regression model, and the significance of change trends was tested with the Mann-Kendall test method.

Fig. 2Variations of mean annual temperature and precipitation in five research regions

The mean annual temperature shows a significant increase in the five regions of the Amur River from 1950 to 2010 (Fig. 2), with the increasing rates of 0.32℃ per ten years in the left source region, 0.42℃ per ten years in the right source region, 0.29℃ per ten years in the uppermainstream, 0.48℃ per ten years in the middle mainstream, and 0.32℃ per ten years in the lower mainstream, and all the increasing trends passed the 0.99 significance test. From the values of these increasing rates, the fastest warming appeared in the right source region (0.42℃ per ten years) and the middle mainstream (0.48℃ per ten years), and this may be because these two regions are located on a plain, where the carbon emissions are higher due to a relatively lager population and more intensive human activities; the warming increase was largely the same in the left source region (0.32℃ per ten years) and the lower mainstream (0.32℃ per ten years), both of which are located in Russia with a relatively smaller population and fewer human activities; and the warming was slow in the upper mainstream (0.29℃ per ten years), which is located in a mountainous area.

The annual precipitation shows different variations in the five regions of the Amur River from 1950 to 2010 (Fig. 2). The precipitation increased in the left region and the lower mainstream, and only the increasing trend in the lower mainstream passed the 0.99 significance test, with an increasing rate of 24.88 mm per ten years. The precipitation decreased in the other three research regions, and all the decreasing trends did not pass the significance test.

4.2 Variations of seasonal temperature and precipitation

Based on the foregoing analysis, the mean annual temperature and annual precipitation changed in different way from 1950 to 2010 in all research regions of the Amur River. In order to determine the season whose change was the main cause of the annual variations, the variations of seasonal temperature and seasonal precipitation were analyzed.

The change rates of seasonal temperature were calculated by the linear least-squares regression model. The results are shown in Table 4. The variations of seasonal temperature were different in different regions. In the left source region, the temperature increased significantly in all four seasons, and relatively fast warming occurred in spring and winter, with increasing rates of 0.44℃ per ten years and 0.38℃ per ten years, respectively. Thus, the warming in these two seasons contributed greatly to the annual warming (0.32℃ per ten years) in this region. In the right source region, the temperature increased significantly in the three seasons other than winter, and relatively fast warming occurred in spring and summer, with the increasing rates of 0.54℃ per ten years and 0.52℃ per ten years, respectively. Thus, the warming in these two seasons contributed greatly to the annual warming (0.42℃ per ten years) in this region. In the upper mainstream, the temperature increased significantly in spring and winter, with the respective increasing rates of 0.40℃ per ten years and 0.56℃ per ten years. Therefore, the warming in these two seasons contributed greatly to the annual warming (0.29℃ per ten years) in this region. In the middle mainstream, the temperature increased significantly in all four seasons, and relatively fast warming occurred in spring and winter, with the respective increasing rates of 0.65℃ per ten years and 0.80℃ per ten years. Therefore, the warming in these two seasons contributed greatly to the annual warming (0.48℃ per tenyears) in this research region. In the lower mainstream, the temperature increased significantly in all four seasons, and relatively fast warming appeared in spring and winter, with the increasing rates of 0.41℃ per ten years and 0.46℃ per ten years, respectively. Hence, the warming in these two seasons contributed greatly to the annual warming (0.32℃ per ten years) in this region. In conclusion, the mean annual temperature increased significantly in the five regions of the Amur River mainly because of the fast warming in spring and winter.

Table 4Seasonal and annual change rates of temperature in different regions

The change rates of seasonal precipitation were calculated by the linear least-squares regression model, and are shown in Table 5. The variations of seasonal precipitation were different in different regions. In the left source region, the precipitation increased insignificantly in all four seasons, with amplitudes of 1.65 mm per ten years in spring, 1.86 mm per ten years in summer, 1.19 mm per ten years in autumn, and 0.39 mm per ten years in winter. Thus, the precipitation increase in all seasons contributed to the insignificant annual wetting (5.34 mm per ten years) in this region. In the right source region, the precipitation decreased insignificantly in the three seasons other than winter, and the amplitudes of drying were higher in summer (–8.49 mm per ten years) and autumn (?1.74 mm per ten years), so the precipitation decrease in these two seasons contributed to the insignificant annual drying (–10.13 mm per ten years) in this region. In the upper mainstream, the precipitation increased insignificantly in spring, decreased insignificantly in summer and autumn, and increased significantly in winter. Because the amplitudes of drying appeared in summer (?1.33 mm per ten years) and autumn (?4.28 mm per ten years), the precipitation decrease in these two seasons contributed to the insignificant annual drying (–4.12 mm per ten years) in this region. In the middle mainstream, the precipitation increased insignificantly in spring and winter, decreased insignificantly in summer, and decreased significantly in autumn. Because the amplitudes of drying appeared in summer (–2.05 mm per ten years) and autumn (?8.75 mm per ten years), the precipitation decrease in these two seasons contributed to the insignificant annual drying (–7.73 mm per ten years) in this region. In the lower mainstream, the precipitation increased in all four seasons, and increased significantly in winter, with the amplitudes of 5.42 mm per ten years in spring, 9.39 mm per ten years in summer, 3.81 mm perten years in autumn, and 6.25 mm per ten years in winter, so the precipitation increase in all seasons contributed to the insignificant annual wetting (24.88 mm per ten years) in this region. Therefore, in the Amur River, the annual precipitation increased mainly due to the wetting in all seasons in the left source region and the lower mainstream, and the insignificant annual drying was mainly because of the drying in summer and autumn in the other three regions.

Table 5Change rates of seasonal precipitation in different regions

4.3 Frequencies of extreme weather events

In addition to variations of mean annual temperature and annual precipitation, frequent extreme weather events, including extreme temperature and extreme precipitation events, also occurred along the mainstream of the Amur River. Extreme temperature and extreme precipitation have regular variations along the mainstream of the Amur River. Daily data of temperature and precipitation from 1953 to 1995 along the mainstream of the Amur River were selected to calculate the thresholds of extreme events (Table 6). Table 6 shows that the thresholds were highest in the middle mainstream and lowest in the lower mainstream.

Table 6Thresholds of extreme events from 1953 to 1995

Based on the thresholds shown in Table 6, the variations of extreme events were analyzed. The processes of extreme events along the entire mainstream of the Amur River were selected as a representative to describe the variations (Fig. 3). The results show that the annual extremely low temperature increased, whereas the annual duration of extremely low temperature events decreased; both the annual extremely high temperature and the duration increased; and both the annual extreme precipitation and the duration increased.

In order to analyze the specific change trends of annual extreme events along the entire mainstream of the Amur River, the change trends were calculated by the linear least-squaresregression model, and the significant change trends were tested with the Mann-Kendall test method. The results are shown in Table 7. Different extreme events changed in different ways from 1953 to 1995 along the mainstream of the Amur River. For extremely low temperature, the amplitudes increased significantly, while the durations decreased significantly, with the respective change rates of 0.7℃ per ten years and ?3 days per ten years in the mainstream, which means that the frequency of extremely low temperature events was decreasing. Such a phenomenon mainly occurred in the upper and lower mainstreams, and it was not obvious in the middle mainstream. For extremely high temperature, both the extremely high temperature and the duration increased significantly, with the respective change rates of 0.3℃ per ten years and 2 days per ten years over the entire mainstream, which means that the frequency of extreme high temperature events was increasing. Such a phenomenon mainly occurred in the middle and lower mainstreams, and it was not obvious in the upper mainstream. For extreme precipitation, the calculated results showed that it showed insignificant change trends over the research period (1953 to 1995).

Fig. 3Variations of extreme events from 1953 to 1995 along entire mainstream of Amur River

Table 7Amplitudes and durations of extreme events per ten years from 1953 to 1995

5 Conclusions and future research

In summary, this study analyzed the variations of climate change in five research regions of the Amur River (both in China and in Russia), including change processes of temperature and precipitation from 1950 to 2010, as well as the frequency of extreme weather events from 1953 to 1995. The main conclusions are as follows:

(1) Significant increasing trends of mean annual temperature could be observed from 1950 to 2010 in the five research regions of the Amur River. In all research regions, the values of the mean interannual temperature anomaly increased significantly, and the negative values changed to positive values around in the 1980s. The increasing rates also verified that the warming was significant in all regions. Different change rates appeared in the five regions: the warming was fastest in the right source region and the middle mainstream, relatively fast in the left source region and the lower mainstream, and relatively slow in the upper mainstream. The variations of seasonal temperature were analyzed. Although the increasing trends of the four seasons were different in the five different regions, on the whole, the results show that the warming in spring and winter was the main cause of annual warming from 1950 to 2010.

(2) Compared with the variation of mean annual temperature, the annual precipitation changed insignificantly from 1950 to 2010 as a whole. For the five research regions, an obvious change only appeared in the lower mainstream according to the values of the interannual precipitation anomaly, and the values changed from –121.2 mm in the 1950s to 32.4 mm in the 2000s. The change rates also verified that the wetting was noticeable only in the lower mainstream, with insignificant change in the other four research regions. The variations of the seasonal precipitation from 1950 to 2010 were analyzed. The results showed that the wetting in all four seasons contributed to the annual precipitation change both in the left source region and in the lower mainstream, and the drying in summer and autumn contributed to the insignificant annual precipitation decrease in the other three research regions.

(3) The extreme weather events also showed regular variations from 1953 to 1995 in the research regions along the entire mainstream of the Amur River. First, the thresholds of extreme weather events were calculated, and the results showed that the values were highest in the middle mainstream and lowest in the lower mainstream. Second, according to these thresholds, the frequencies of extreme events were analyzed. The results showed that the frequency of extremely low temperature events decreased from 1953 to 1995 in the upper and lower mainstreams, because of a significant increase of extremely low temperature and a significant decrease of duration; the frequencies of extremely high temperature events increased from 1953 to 1995 in the middle and lower mainstreams, because of insignificant increases of the extremely high temperature and duration; and the frequencies of extreme precipitation events did not change significantly from 1953 to 1995 in the research regions along the mainstream of the Amur River, because the extreme precipitation and duration did not change significantly.

The variations of temperature and precipitation may have many consequences. Climate warming significantly impacts the trend of crop yield in the Amur River Basin. For example, the yield of maize is increasing and the yield of soybeans is falling (Gao et al. 2007). The wetting could increase the flow in the left source region and the lower mainstream, resulting in a decrease of the frequency of the shortage of ecological water demand. On the other hand, the insignificant wetting in summer might increase the frequency of flooding. The insignificant drying in the other three research regions might be an issue for the normal operation of aquatic ecosystems. The decrease in frequency of extremely low temperature events could influence the survival rates of aquatic organisms and plants along the coast, which may have adapted to climate characteristics in colder areas. From another perspective, this phenomenon might benefit people who live on both sides of the Amur River, and it could be favorable because of savings of heating energy. Similarly, the increase in frequency of extremely high temperature events could change the living conditions of aquatic animals and plants.

Though many conclusions were obtained in this study, several limitations may still be found, and the following future research activities can be carried out:

(1) Forecasting the climate change in terms of temperature and precipitation in the whole Amur River Basin.

(2) Analyzing the main causes of the variations of temperature and precipitation in the five research regions, including natural reasons and human activities.

(3) Discussing the impact factors and trend prediction of the extremely low temperature variations, particularly because extremely low temperature increases may affect snow melting, which is a hidden obstacle to flood safety in the spring.

(4) Collecting related materials on the changes in species and amounts of aquatic organisms, as well as plants in the Amur River Basin.

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(Edited by Yun-li YU)

This work was supported by the Innovative Project of Scientific Research for Postgraduates inOrdinary Universities in Jiangsu Province (Grant No. CX09B_161Z), the Cultivation Project for Excellent Doctoral Dissertations in Hohai University, the Fundamental Research Funds for the Central Universities (Grant No. 2010B18714), and Special Funds for Scientific Research on Public Causes of the Ministryof Water Resources of China (Grant No. 201001052).

*Corresponding author (e-mail: qincai_208@163.com)

Received Oct. 28, 2011; accepted May 28, 2012