Distinguishing activities on variation of streamﬂow in the Poyang Lake catchment, China Xuchun
Journal of Hydrology 494 (2013) 83–95 Contents lists available at SciVerse ScienceDirect Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol Distinguishing the relative impacts of climate change and human activities on variation of streamﬂow in the Poyang Lake catchment, China Xuchun Ye a,b, Qi Zhang b,⇑, Jian Liu c, Xianghu Li b, Chong-yu Xu d,e a School of Geographical Sciences, Southwest University, Chongqing 400715, China Nanjing Institute of Geography and Limnology, State Key Laboratory of Lake Science and Environment, Nanjing 210008, China Key Laboratory of Water Resources and Environment, Water Research Institute of Shandong Province, Jinan 250013, China d Department of Geosciences, University of Oslo, Norway e Department of Earth Sciences, Uppsala University, Sweden b c a r t i c l e i n f o Article history: Received 18 January 2013 Received in revised form 8 April 2013 Accepted 20 April 2013 Available online 3 May 2013 This manuscript was handled by Konstantine P. Georgakakos, Editor-in-Chief, with the assistance of Ashish Sharma, Associate Editor Keywords: Climate change Human activities Hydrological response MK test Poyang Lake catchment s u m m a r y Under the background of global climate change and local anthropogenic stresses, many regions of the world have suffered from frequent droughts and ﬂoods in recent decades. Assessing the relative effect of climate change and human activities is essential not only for understanding the mechanism of hydrological response in the catchment, but also for local water resources management as well as ﬂoods and droughts protection. The Poyang Lake catchment in the middle reaches of the Yangtze River has experienced signiﬁcant changes in hydro-climatic variables and human activities during the past decades and therefore provides an excellent site for studying the hydrological impact of climate change and human activities. In this study, the characteristics of hydro-climatic changes of the Poyang Lake catchment were analyzed based on the observed data for the period 1960–2007. The relative effect of climate change and human activities was ﬁrst empirically distinguished by a coupled water and energy budgets analysis, and then the result was further conﬁrmed by a quantitative assessment. A major ﬁnding of this study is that the relative effects of climate change and human activities varied among sub-catchments as well as the whole catchment under different decades. For the whole Poyang Lake catchment, the variations of mean annual streamﬂow in 1970–2007 were primarily affected by climate change with reference to 1960s, while human activities played a complementary role. However, due to the intensiﬁed water utilization, the decrease of streamﬂow in the Fuhe River sub-catchment in 2000s was primarily affected by human activities, rather than climate change. For the catchment average water balance, quantitative assessment revealed that climate change resulted in an increased annual runoff of 75.3–261.7 mm in 1970s–2000s for the Poyang Lake catchment, accounting for 105.0–212.1% of runoff changes relative to 1960s. However, human activities should be responsible for the decreased annual runoff of 5.4–56.3 mm in the other decades, accounting for 5.0% to 112.1% of runoff changes. It is noted that the effects of human activities including soil conservation, water conservancy projects and changes in land cover might accumulate or counteract each other simultaneously, and attempts were not made in this paper to further distinguish them. Ó 2013 Elsevier B.V. All rights reserved. 1. Introduction Climate change and human activities are the two factors that affect the change of catchment hydrology. According to the IPCC (2007), the average global surface temperature increased by 0.74 °C over the last 100 years. One of the most signiﬁcant potential consequences of climate change may be alterations in regional hydrological cycles (e.g., Huntington, 2006). General consensus ⇑ Corresponding author. Tel.: +86 25 86882102; fax: +86 25 57714759. E-mail address: [email protected] (Q. Zhang). 0022-1694/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jhydrol.2013.04.036 have revealed that global warming and related changes to the hydrological cycle are likely to enhance the frequency and severity of extreme climate events, causing more severe ﬂoods and droughts (e.g., Milliman et al., 2008; Bates et al., 2008; Déry et al., 2009; Jung et al., 2012; Thompson, 2012; Li et al., in press; Xiong et al., in press). In addition to global climate change, increases in human activities such as cultivation, irrigation, afforestation, deforestation and urban construction have also introduced changes to ﬂow regime, especially large scale changes of land cover or its management (e.g., Yang et al., 2004; Brown et al., 2005; Jiang et al., 2012; Yang et al., 2012a,b). Depending on the study region, 84 X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 impacts of human activities on streamﬂow may be different. Reduction in streamﬂow has been shown in several arid and semi-arid catchments in north China due to implementation of conservation practices and increased water utilization (e.g., Li et al., 2007; Wang and Meng, 2008; Liu et al., 2009a). However, studies in Iowa’s rivers noted that increasing agricultural intensity may increase stream discharge but reduce its variance due to the decreasing surface runoff and increasing baseﬂow (e.g., Tomer et al., 2005; Schilling, 2004). Moreover, water utilization for agricultural and industrial development can also lead to signiﬁcant change in the water cycle and affect the variation of surface or sub-surface runoff (e.g. Du et al., 2012). Within the last decades, water quantity and quality have become increasingly serious issues for water resources management at catchment and/or regional scale (e.g., Kizza et al., in press; Li et al., in press; Ren et al., 2002; IPCC, 2007; Tomer and Schilliing, 2009; Lakshmi et al., 2012). Therefore, understanding the inﬂuence and relative importance of climate change and human-induced change on hydrology and water resources has recently drawn considerable concerns (e.g. Siriwardena et al., 2006; Ma et al., 2008; St. Jacques et al., 2010; Jin et al., 2012; Carless and Whitehead, in press; Zhan et al., in press). Lahmer et al. (2001) indicated that climate change is the dominant factor that affects the change of streamﬂow in wet regions, while human activities such as some extreme land-use change only resulted in comparatively small impacts on regional water balance. Similar result was also revealed by Legesse et al. (2003) for tropical Africa. However, the study by Raymond et al. (2008) suggested that land use change and management were more important than climate change for explaining the increasing water export from the Mississippi River. In northern China, increasing water shortage is very common in recent years due to signiﬁcant regional precipitation variation as well as rapid development of local economy (e.g., Piao et al., 2010). A quantitative assessment revealed that local human activities since the 1970s led to a decrease of the water diverted into the main stream of the Tarim River catchment, which has been aggravated in the 2000s (Tao et al., 2011). In Haihe River catchment, an important economic center of China, human activities were estimated to be responsible for the decline in annual water discharge, which accounts for over 50% of runoff reduction, while the contribution of climate change is relatively small (Wang et al., 2012). In a recent study conducted by Zhang et al. (2011a), the trends of the annual streamﬂow and precipitation and the relationship between them were analyzed in nine large river basins of China during 1956– 2005. The results indicated that annual runoff has the same changing trend as precipitation in humid regions revealing a stationary rainfall–runoff relationship is still held. However, in arid and semi-arid regions of north China the decline in streamﬂow is faster than the decreases of precipitation since 1970s, indicating that the relationship between the annual precipitation and streamﬂow presents a non-stationary state. This non-stationary relationship is strongly inﬂuenced by human activities, especially by the increase of irrigation water use. Poyang Lake, the largest fresh water lake in China, is located in the middle reaches of the Yangtze River with a catchment area of 162,225 km2. The Lake and its surrounding catchments have suffered from frequent droughts and ﬂoods in recent decades, especially in 1990s and 2000s (e.g., Wang et al., 2008; Min et al., 2011). These severe drought and ﬂood events have raised concerns for the lake ecology and local water resources management. Studies on hydrological response suggested that the changes of annual streamﬂow in the catchment were primarily caused by climate anomalies in the Yangtze River catchment, while human activities such as land-use change and modiﬁcations to river systems including the Yangtze River also exerted some impacts (e.g., Min, 2002; Guo et al., 2008, 2011; Zhang et al., 2012). Several studies showed that the variations of streamﬂow is much more strongly related to regional climate especially precipitation, but this is insufﬁcient to explain all the changes (e.g., Guo et al., 2007; Zhao et al., 2009; Ye et al., 2009). For example, the increased vulnerability of the lake to ﬂoods is further elevated by deforestation and change of landscape in the basin. In addition, the construction of large-scale water conservancy facilities (reservoir and irrigation system) in the catchment is another important factor altering the annual hydrograph and increasing the water utilization (e.g., Zhang et al., 2011b; Liu et al., 2009b). Large amount of water demand severely decreased the catchment discharges to Poyang Lake and elevated the drought in the lake area, especially in dry years. Effects of climate change and human activities on runoff variation are signiﬁcantly sensitive, especially in arid and semi-arid regions, and these effects have resulted in severe environmental degradation and water crises. However, previous studies revealed that the relative importance of the inﬂuence of climate variability/change and human activities varies from region to region. To our knowledge, the relative contribution of climate change and human activities to runoff change in the Poyang Lake catchment has not been well investigated. Further studies are needed in order to provide a generalized and conclusive interpretation of the changes observed. The answer to this is essential not only for an improved understanding of the mechanism of hydrological response in the catchment, but also for local water resources management as well as ﬂoods and droughts protection and mitigation in the Poyang Lake catchment and the lower reaches of the Yangtze River. The purposes of this study are: (1) to investigate the variability of long-term historical records of climate and hydrological data in the Poyang Lake catchment; and (2) to evaluate the relative impacts of climate change and human activities on catchment-scale streamﬂow response under different spatial and temporal scales. 1.1. Overview of the Poyang Lake catchment The Poyang Lake, connected to the Yangtze River, lies on the northern border of the Jiangxi Province, China. The lake receives water ﬂows mainly from ﬁve rivers: Ganjiang, Fuhe, Xinjiang, Raohe and Xiushui, and discharges into the Yangtze River from a narrow outlet in the north (see Fig. 1). Among the ﬁve major rivers, the Ganjiang is the largest river in the region extending 750 km and contributes almost 55% of the total discharge into the Poyang Lake (Shankman et al., 2006). The topography of the Poyang Lake catchment varies from highly mountainous regions (maximum elevation of about 2200 m above sea-level) to alluvial plains in the lower reaches of the primary watercourses. Headwater of these rivers are located in boundaries of the east, south and west of the Jiangxi Province that surrounded by high mountains. Stream gradient decreases as these rivers ﬂow onto the relatively ﬂat region surrounding the Poyang Lake. The wide alluvial plains surrounding Poyang Lake and the broad alluvial valleys of the tributary streams are important rice growing regions in Jiangxi Province as well as in China; most notably the lower reaches of Ganjiang and Fuhe subcatchments have large irrigation areas over 10,000 ha (see in Fig. 1). The Poyang Lake catchment belongs to a subtropical wet climate zone with an annual mean precipitation of 1680 mm and annual mean temperature of 17.5 °C. Annual precipitation in the catchment shows a wet and a dry season and a short transition period in between (see Fig. 2). Water inputs from the ﬁve subcatchments are particularly important during the wet season from April through June when heavy rainfall produces large surface ﬂows from the sub-catchments to the lake (Shankman et al., 2006). Rainfall decreases sharply from July to September, while evapotranspiration is still very strong in these months (Fig. 2). After September, the dry season sets in and lasts through X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 85 Fig. 1. Topography and river networks of the Poyang Lake catchment, with stream gauging stations (‘‘Hydrostation’’) and meteorological stations (‘‘Meterostation’’) are marked. Fig. 2. Mean monthly precipitation and evapotranspiration of the Poyang Lake catchment for 1960–2007. December, and surface ﬂow of the catchment is very low during this period. In response to annual cycle of precipitation, about 59.1% of the annual discharge arrives from March to June, but only 13.7% arrives from October to next January. In normal years, the Poyang Lake can expand to a large water surface of 4000 km2 with a volume of 320 108 m3 in the wet season, but shrinks to little more than a river during the dry season (Xu et al., 2001). As a typical agriculture catchment, about 13% of the land area is being irrigated. Although water resources in the Poyang Lake catchment are pretty abundant, the difﬁculty of water utilization is affected by the strong variability of annual and seasonal precipitation. Furthermore, with the rapid economic development and population explosion in the catchment, inﬂuence of human activities on water resources has become increasingly important. Direct effects of human activities mainly include the soil conservation and water conservancy projects. The statistical data indicated that a total amount of 9530 reservoirs were built across the catchment until 2007, and most of these projects were constructed before 1980 (Min et al., 2011). Among which, 13 big reservoirs have a vol- ume larger than 1.0 108 m3, including Zhelin Reservoir (50.17 108 m3) on the Xiushui River, Wan’an Reservoir (11.16 108 m3) on the Ganjiang River, and Hongmen Reservoir (5.24 108 m3) on the Fuhe River (see Fig. 1). Most water conservancy projects have special functions for ﬂood protection, water supply, agriculture irrigation, power generation and navigation, which dramatically increase water utilization and change the temporal and spatial distribution of the streamﬂow, but they do not transfer water out of the Poyang Lake catchment. The recent water resources bulletins from Water Conservancy Bureau of Jiangxi Province indicated that annual water consumption is about 94.38 108–126.95 108 m3 during 1997–2007, of which 73% for agriculture irrigation and 21% for domestic and industrial utilization. During the past decades, land use/land cover of the catchment has changed dramatically, which may have affected the change of catchment hydrology indirectly. There is an indication that the forest coverage of the catchment was reduced from over 60% in 1950s to only 32.7% in 1970s. The Grain for Green (returning farmland to forest) policy was launched by the government in earlier 1980s in order to restore the ecological environment, and as a result, the forest coverage recovered to nearly 60% at the end of 1990s (Liu et al., 2009b). Human activities have collectively changed the natural condition of catchment hydrology, and introduced additional challenges for local water resources management. 2. Data and method 2.1. Available data Observed discharges (daily streamﬂow) from ﬁve gauging stations on the lower reaches of the ﬁve rivers were obtained from Management Bureau of the Yangtze River (MBYR) catchment, accounting for 75.4% of the lake’s inﬂow from the catchment area. Among the ﬁve gauging stations, Waizhou, Lijiadu and Meigang are 86 X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 Table 1 List of hydrological stations and their features for 1960–2007. Gauging station Location Drainage area (km2) Mean annual discharge (108 m3/year) Waizhou Gangjiang River (Lower) (28°370 N, 115°490 E) Fuhe River (28°120 N, 116°090 E) Xinjiang River (28°250 N, 116°480 E) Raohe River (Le’anjiang branch) (28°540 N, 117°180 E) Xiushui River (Liaohe branch) (28°510 N, 115°380 E) 80,948 685 15,811 15,535 Lijiadu Meigang Hushan Wanjiabu Coefﬁcient of variation (Cv) Ratio of annual extreme streamﬂow (Qmax/Qmin) 846 0.28 4.85 123 178 780 1144 0.33 0.36 4.31 6.18 6474 74 1137 0.32 3.93 3548 35 990 0.35 4.69 located at the lower reaches of Ganjiang, Fuhe and Xinjiang rivers, which contribute more than 90% of the total inﬂow of the ﬁve rivers. While Hushan and Wanjiabu are located at the branches of Raohe and Xiushui rivers with relative small drainage areas, contributing less than 10% of the total inﬂow of the ﬁve rivers. The basic features of these gauging stations are listed in Table 1. The meteorological data from 19 weather stations inside the catchment (see Fig. 1) were obtained from National Climate Centre of China Meteorological Administration (CMA). They provide daily observations of precipitation, temperature, relative humidity, sunshine duration, actual vapour pressure, and wind speed, among others. The period of record of most weather stations used in this study is 1960–2007, except for Nanxiong, Lichuan, Linchuan and Shangrao stations where the available data period is 1980–2007. Climate variables of these four weather stations are further interpolated for the period 1960–1979 using the nearest weather station. Based on the meteorological datasets, potential evapotranspiration (PET) of the weather stations was estimated by applying the Penman–Monteith equation (Allen et al., 1998). The daily records of streamﬂow and climate variables provided by MBYR and CMA had gone through a standard quality control process before delivery, and with no missing data on the variables used in this study. Before applying the data, all the hydro-meteorological variables were aggregated from daily to monthly and to yearly. In consideration of the large degree of variation in topography and the uneven distribution of weather stations across the catchment, an areabased weighting method was used to calculate the average precipitation, potential evapotranspiration for the whole catchment as well as the individual sub-catchments. The weight coefﬁcient, expressed by the percentage of the area represented by each meteorological station, was calculated using the Thiessen Polygon method. Similarly, annual streamﬂow for the whole catchment was calculated from the ﬁve individual hydro-stations by using the same method. Speciﬁc discharge (mm/year) In which, unused To detect the existence of any step change points in the hydro-climatic data Xt = (x1, x2, x3 . . . xn), sequential Mann–Kendall test was used, in which, the accumulative number ni of samples that xi > xj (1 6 j 6 i) should be ﬁrst calculated. The normally distributed statistic dk can be calculated via the following formula: dk ¼ k X ni ð2 6 k 6 nÞ ð1Þ i¼1 Mean and variance of the normally distributed statistic dk are given by Eðdk Þ ¼ kðk 1Þ=4 ð2Þ varðdk Þ ¼ kðk 1Þð2k þ 5Þ=72 ð3Þ The normalized variable statistic UF(dk) is estimated as follows: ½dk Eðdk Þ UFðdk Þ ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ varðdk Þ ðk ¼ 1; 2; 3 . . . ; nÞ ð4Þ where UF(dk) is the forward sequence, and the backward sequence UB(dk) is calculated using the same equation but with a reversed series of data. The null hypothesis (no step change point) is rejected if any of the points in the forward sequence (UF(dk)) are outside the conﬁdence interval. The sequential Mann–Kendall test was often used to determine the approximate time of occurrence of the change point by locating the intersection of the forward and backward curves of the test statistic. An intersection point of UF(dk) and UB(dk) located within the conﬁdence interval indicates the beginning of a step change point (Moraes et al., 1998; Zhang et al., 2011c). 2.3. Estimating the relative impact of climate change and human activities on streamﬂow For a natural catchment, annual water balance can be quantiﬁed as: 2.2. Statistical analysis In order to analyze the temporal variation of the streamﬂow data, the Partial Mann–Kendall test (hereafter we call it MK test), which is known as Kendall’s statistic (Kahya and Partal, 2007), was applied in this study. The MK test is a rank-based non-parametric method that has been widely applied for trend detecting in hydro-climatic time series due to its robustness against the inﬂuence of abnormal data and especially its reliability for biased variables (e.g., Burn and Hag Elnur, 2002; Chen et al., 2007; Zhang and Lu, 2009). Before the trend analysis was performed, autocorrelation (serialcorrelation) was examined through the autocorrelation and partial autocorrelation function for all the hydrological data, and results revealed (not shown here) that no signiﬁcant autocorrelation existed in the data. DS=Dt ¼ P ET Q ð5Þ where P is the precipitation, ET is the actual evapotranspiration, Q is the streamﬂow, DS is the change in water storage and Dt is the time step. Over a long period of time (i.e., 10 years or more), DS can be reasonably assumed as zero, and then ET can be estimated as the difference between P and Q. Among the water balance components, ET is mainly controlled by available water (P) and available energy (or evaporative demand – PET) of a catchment, and of course by the human activities such as the landuse change and farmland irrigation. Based on the P, PET and ET values in a catchment, a coupled water and energy budgets analysis is available to be used to evaluate the efﬁciency of water and energy use by an ecosystem (Milne et al., 2002; Tomer and Schilliing, 2009). In which, unused water (P–ET) and unused X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 energy (PET–ET) available in the environment can be used to calculate the proportions of available water and energy, i.e. Pex and Eex, that are unused (i.e., in excess) as: Pex ¼ ðP ETÞ=P ð6Þ Eex ¼ ðPET ETÞ=PET ð7Þ where Pex and Eex take values from 0 to 1. Based on the analysis of Pex and Eex, Tomer and Schilliing (2009) developed a conceptual model to simply distinguish land-use and climate change effects on watershed hydrology (see Fig. 2 in Tomer and Schilliing, 2009). In which, climate change inﬂuences P and PET, and causes the increase of excess water (Pex) and decrease of excess energy (Eex), or vice versa. However, changes in vegetation or its management will directly affect ET, but not P or PET, which result in a regime shift in excess water and energy with either increase or decrease, depending on the effect of the change on ET. The plots of excess water (Pex) versus excess energy (Eex) provide an effective way to empirically distinguish the relative effects of climate change and human activities on watershed hydrology for any two different periods. It should be noted that the applicability of the conceptual model is based on the assumptions that human activities are independent of climate change, and the landuse change affects only ET. However, the effects of human activities and climate are commonly interrelated with each other indirectly at broad scales. For example, in large catchments, climate change effects on hydrology have coincided with land cover changes (Zheng et al., 2009; Tomer and Schilliing, 2009), and construction of river regulation system or big reservoirs will affect regional precipitation and temperature and consequently results in changes in the hydrological regime (Wu et al., 2006; Li et al., 2002). Despite that human activities and climate system may interact with each other, it is commonly not considered in most quantitative assessment studies (e.g. Li et al., 2007; Fan et al., 2010; Jiang et al., 2011; Wang et al., 2012). In the Poyang Lake catchment, human activities are complex which include a wide range of landuse change, river regulation and rapid development of local society and economy. Due to the fact that speciﬁc human activities (e.g. land use change, farmland irrigation and river regulation) may exist simultaneously in a catchment and their effects may accumulate or counteract each other, we extended this assumption that all anthropogenic stresses in the Poyang Lake catchment will result in a change of ET. Then, changes associated with local climate and human impacts are likely to result in a shift in excess water and energy, and the rela- 87 tionship can be illustrated in Fig. 3. The directions of these hydrological shifts may indicate the relative impacts of climate change and human activities on catchment hydrology. Here we applied this empirical method aimed to analyze the hydrological shifts in the Lake catchment associated with climate change and human activities as an auxiliary analysis for demonstration of the relative importance of climate change and human activities, and the result is afterwards inspected from a quantitative assessment. Under the assumption that water balance is controlled by water availability and atmospheric demand, Zhang et al. (2001) noted that long-term mean annual evapotranspiration has the following relationship with local precipitation and potential evapotranspiration: ET 1 þ wðPET=PÞ ¼ P 1 þ wðPET=PÞ þ ðPET=PÞ1 ð8Þ where w is a model parameter of available water coefﬁcient related to vegetation type (Zhang et al., 2001), and can be calibrated using annual hydro-climatic data. Hydrologic sensitivity can be described as the percentage change in mean annual runoff in response to changes in mean annual precipitation and potential evapotranspiration (Jones et al., 2006; Li et al., 2007). Variation in both precipitation and potential evapotranspiration can lead to changes in water balance. It can be assumed that a change in mean annual runoff due to climate change can be approximated as follows (Koster and Suarez, 1999; Milly and Dunne, 2002): DQ c lim ¼ a DP þ b DPET ð9Þ where DQclim, DP, DPET are the changes in streamﬂow, precipitation and potential evapotranspiration respectively; a and b are the sensitivity parameters and can be further expressed as (Li et al., 2007): a¼ b¼ 1 þ 2x þ 3wx ð1 þ x þ wx2 Þ2 1 þ 2wx ð1 þ x þ wx2 Þ2 ð10Þ ð11Þ where x is the dryness index (equal to PET/P), w is same as in Eq. (8). A change in mean annual streamﬂow can be calculated as follows: DQ obs ¼ Q obs2 Q obs1 ð12Þ where DQobs indicates the observed change in mean annual stream ﬂow between two different periods, Q obs1 is the average annual streamﬂow during the reference period, and Q obs2 is the average annual streamﬂow during the other period. The change of catchment hydrology is mainly affected by the two driving factors of climate change and human activities. As a ﬁrst-order approximation, the change in mean annual runoff can be estimated as follows: DQ obs ¼ DQ c lim þ DQ hum ð13Þ where DQclim and DQhum are the changes in the mean annual streamﬂow due to climate change and human activities, respectively. The relative contributions of climate change and human activities on streamﬂow can be further expressed as: Fig. 3. Conceptual model of hydrological shifts associated with climate change and human activities (modiﬁed from Tomer and Schilliing, 2009). gc lim ¼ DQ c lim 100% jDQ obs j ð14Þ ghum ¼ DQ hum 100% jDQ obs j ð15Þ where = clim and = hum are the percentages of the impact of climate change and human activities on streamﬂow, respectively. 88 X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 Table 2 Results of MK test for seven variables on seasonal and annual basis 1960–2007. Parameter Annual mean Cv (%) T P RH VP W SD PET 17.7 1656.6 78.5 9.9 2.1 4.8 1050.6 3 15 2 0 13 10 5 Trends Spring (MAM) Summer (JJA) Autumn (SON) Winter (DJF) Annual 2.30* 0.44 2.21* 2.21* 7.54** 0.63 0.67 0.84 1.31 0.12 0.12 4.92** 4.24** 2.82** 1.47 1.02 1.31 1.15 6.66** 1.48 2.04* 2.78* 1.68 0.42 0.40 6.82** 2.85** 1.52 3.40** 1.11 0.56 1.59 7.83** 4.47** 2.59** Note: Delineates negative trends based on the MK test. Cv: coefﬁcient of variation (%); P: precipitation (mm); T: temperature (°C); VP: vapour pressure (kPa); RH: relative humidity (%); SD: sunshine duration (h); W: wind speed (m/s); PET: potential evapotranspiration (mm/y). * Delineate signiﬁcance at 0.05 signiﬁcance level. ** Delineate signiﬁcance at 0.01 signiﬁcance level. 3. Results 3.1. Changes of catchment climate In order to investigate the variation of regional climate in the Poyang Lake catchment, basin-scale averaged time-series 1960– 2007 of six variables, i.e., precipitation, temperature, relative humidity, sunshine duration, vapour pressure and wind speed were examined by the MK test. The potential evapotranspiration (PET) estimated by Penmam–Monteith method was also analyzed. Results of the trend test for the seven variables are displayed in Table 2. It is seen that on annual basis, temperature and precipitation show positive trends; relative humidity, vapour pressure, wind speed, sunshine duration and PET show negative trends. Among which, four variables, i.e., temperature, wind speed, sunshine duration and PET have undergone signiﬁcant trends at 0.01 signiﬁcance level. Precipitation and wind speed show the highest coefﬁcient of variation with 15% and 13%, respectively, showing the highest variability. On seasonal basis, statistically signiﬁcant (0.01) positive trends of temperature were detected for spring and winter; however, the positive trends in summer and autumn are not signiﬁcant. Although precipitation shows an upward trend for summer and winter, and downward trend for spring and autumn, no signiﬁcant trend was detected through four seasons. Both relative humidity and vapour pressure decreased signiﬁcantly in spring, but trends in other seasons are not signiﬁcant. Among the seven variables, wind speed is the only one which decreased signiﬁcantly in all seasons. In summer and winter, sunshine duration decreased signiﬁcantly during 1960–2007, but the negative trends in spring and autumn are not signiﬁcant. PET decreased through all seasons except spring, and the negative trends in summer and autumn are statistically signiﬁcant at 0.01 signiﬁcance level, respectively. It can be summarized that most climate variables have experienced signiﬁcant trends during the past decades. For illustrative purposes, the variation of annual precipitation and calculated potential evapotranspiration (PET) series and their corresponding MK sequential test in 1960–2007 are shown in Fig. 4a, c and b, d, respectively. As depicted in Fig. 4a and c, catchment annual precipitation and PET show a long-term increase and decrease linear trend, respectively. Fig. 4b and d shows that an abrupt change of the two variables occurred in 1969 at 0.05 signiﬁcance level as the intersection point of the two curves located within the conﬁdence interval. Catchment annual precipitation showed a decreasing trend from 1960 to 1969, while opposite trend was found for the other periods. There is an obvious increasing tendency since 1990, which is signiﬁcant during the period 1999–2002 as the values of UF are above the critical limit. On the contrary, the values of Fig. 4. Time series of catchment annual precipitation (a) and PET (c) and corresponding MK trend test (b and d). The long dashed line in the left ﬁgures means linear trend for this period, and the horizontal dashed lines in the right ﬁgures represent the critical value of 0.05 signiﬁcance level. X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 UF for catchment annual PET are below zero for almost the whole period especially after 1969, and the long term decrease trend becomes signiﬁcant after 1982. 3.2. Variation of annual and monthly streamﬂows Fig. 5 shows the MK test of the annual and monthly streamﬂows of each station in the ﬁve sub-catchments. As shown in Fig. 5a, the UF curve of Waizhou station indicates a decreasing trend of streamﬂow from 1960 to 1972, while an increasing trend is found 89 for the other periods. The variation in annual streamﬂow is small before 1990, and after that streamﬂow increased obviously. For Meigang station, the UF curve exceeds zero for almost the whole period and it intersects with the critical value lines for a short period, which conﬁrmed an increasing trend especially in the end of 1990s. No obvious trend was detected for the streamﬂow at Lijiadu station with UF ﬂuctuates between the two critical value lines. However, streamﬂow trend for Lijiadu station shows a slight decrease during 1987–1996 with UF < 0, which is obviously different from that of the other stations. Annual streamﬂow of Wanjiabu and Fig. 5. (a) Trends variation and change point test for annual streamﬂow; the horizontal dashed lines represent the critical value of 0.05 signiﬁcance level; (b) trends of monthly streamﬂow, the horizontal solid lines and dashed lines represent the critical values of 0.01 and 0.05 signiﬁcance level, respectively. 90 X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 Hushan stations has similar changing trends which increased for almost the whole period. Furthermore, streamﬂow of these two stations increased signiﬁcantly in 1970s and 1990s with UF curves intersect with the critical value lines during these two periods. Change point analysis for annual streamﬂow indicates that except for the Wanjiabu station where the intersection of UF and UB curves occurred in 1969 (at 0.05 signiﬁcance level), no clear step change can be identiﬁed for the other stations. Estimated results for the long-term trends of annual streamﬂow indicate that most gauging stations show increasing trends except for Lijiadu station, but monthly variations are obviously different. As shown in Fig. 5b, most stations present long-term decrease but not signiﬁcant trends during the wet season from April to June. However, increasing trends are detected for the other months. Except for Hushan station, streamﬂow of most stations increases obviously at 0.05 or 0.01 signiﬁcance level for August and September. Also, streamﬂow of Wanjiabu and Hushan stations increases signiﬁcantly in January at 0.01 signiﬁcance level. The monthly variability of streamﬂow may indicate the change of seasonal climate regime and catchment management practices. 3.3. Hydrological response associated with changes in climate and human activities The spatial and temporal variation of streamﬂow in Fig. 5 reﬂects the combined effects of climate change and human activities, which provides valuable information in water resources planning and management in the catchment. Within the study period, we cannot distinguish a natural period during which the effect of human activities on streamﬂow was less recognized like in most previous studies (e.g. Li et al., 2007; Wang et al., 2012). In this study, attempts, therefore, are made to investigate the inﬂuence of anthropogenic activities and climate change on the streamﬂow of the sub-catchments as well as the whole catchment under different decades. The changes of precipitation, PET, ET and streamﬂow relative to baseline period 1960s for the whole Poyang Lake catchment and the three largest sub-catchments are analysed in details and the results are plotted in Fig. 6. Compared to 1960s, precipitation increased during all the other decades, especially in 1990s an increase of 168.7–316.1 mm was detected for the three sub-catchments. The range of decrease in PET is 63.9–86.9 mm for Ganjiang, 42.3–130.8 mm for Xinjiang and 48.0–83.4 mm for Fuhe. Change of streamﬂow is consistent with precipitation except for a decrease of 67.1 mm in Fuhe sub-catchment during 2000s. Calculated ET increases obviously in Fuhe especially in 1990s and 2000s with 69.1 mm and 83.2 mm, respectively, but decreases in Xinjiang in recent decades. Based on the water-energy budget analysis, excess water (Pex) and excess energy (Eex) of the three sub-catchments as well as the whole catchment were calculated under different decades. Using the background of regional climate condition and anthropogenic stresses of 1960s as the reference baseline period (benchmark), the shifts of Pex and Eex relative to 1960s are illustrated in Fig. 7. In the ﬁgure, an increasing trend for Pex and a decreasing trend for Eex are detected in the other decades for Ganjiang and Xinjiang sub-catchments, as well as the whole catchment. These results indicate that compared to the baseline period 1960s, changes of streamﬂow in 1970s, 1980s, 1990s and 2000s were primarily affected by climate change, while human activities played a complementary role. However, there is some difference in the Fuhe River sub-catchment with decreasing Pex and Eex in 2000s, implying that relative changes of streamﬂow in 2000s were primarily affected by human activities rather than climate change. 3.4. Quantitative assessment of relative impacts of climate change and human activities on streamﬂow trends The effect of climate change on streamﬂow can be estimated using the hydrologic sensitivity analysis method. In this method, w is the main model parameter that needs to be calibrated. For each river catchment, the calibration was conducted by comparing the observed with simulated annual streamﬂow by using Eqs. (5) and (8) for the baseline period 1960–1969. Generally, optimized w values are 0.40, 0.20, 1.20 and 0.45 for Ganjiang, Xinjiang, Fuhe and the whole Poyang Lake catchment, respectively. As w represents available water stress coefﬁcient that related to vegetation type (Zhang et al., 2001), optimized w values for each sub-catchment also indicate the inhomogeneous distribution of vegetation in the Poyang Lake catchment. Fig. 8 shows the correlation between observed and simulated annual streamﬂow, in which, scatter points are concentrated around the 1:1 line. All the R2 values are greater than 0.82 with small mean absolute error (MAE), indicating that the simulated results are acceptable. With the optimized w values of Ganjiang, Xinjiang, Fuhe and the whole Poyang Fig. 6. Change of precipitation, PET, ET and streamﬂow compared to the baseline period 1960s. X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 91 Fig. 7. Variation of excess water (Pex) and excess evaporative demand (Eex) relative to 1960s. Fig. 8. Correlation between observed and simulated annual streamﬂow for the baseline period 1960s. Lake catchment, the calculated values for the sensitivity coefﬁcient a were 0.86, 0.88, 0.96, 0.90 and b were 0.63, 0.75, 0.47, 0.64, respectively. In addition, the absolute values of sensitivity coefﬁcient also reveal that the change in streamﬂow was more sensitive to precipitation (P) than to potential evapotranspiration (PET) in this region. Based on the method of hydrological sensitivity analysis, all the calculated parameters were then used to estimate the impacts of climate change and human activities on variation of streamﬂow reference to 1960s. As presented in Table 3, impacts of climate change across the catchment were generally positive for the streamﬂow compared to the reference period of 1960s, i.e. climate change resulted in an increased streamﬂow. Take the whole catchment as an example, changes in regional climate (precipitation and potential evapotranspiration) in 1970–2007 were the main factors that increased runoff with a contribution of 150.7% relative to 1960s, while the reduction percentages due to human activities were only 50.7%. Speciﬁc impacts of climate change on the increase of annual streamﬂow were estimated to be 105.0 mm, 118.6 mm, 261.7 mm and 75.3 mm for 1970s, 1980s, 1990s and 2000s, respectively (Table 3). On the contrary, the impacts of human activities were always negative on the water volume, leading to a decrease of annual streamﬂow of 5.4 mm, 56.3 mm, 47.6 mm and 39.8 mm during these periods. The proportional change in annual 92 X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 Table 3 Contribution of climate change and human activities on the change of annual streamﬂow compared to the baseline period 1960–1969. Catchments Periods Q (mm) DQ (mm) Contribution of climate change Contribution of human activities DQclim (mm) gclim (%) Ganjiang 1960–1969 1970–1979 1980–1989 1990–1999 2000–2007 760.7 876.3 813.0 954.8 822.9 – 115.7 52.3 180.3 62.2 – 119.3 108.2 231.4 116.1 – 103.2 206.6 128.4 186.5 – 3.6 55.8 51.2 53.9 – 3.2 106.6 28.4 86.5 Xinjiang 1960–1969 1970–1979 1980–1989 1990–1999 2000–2007 1017.3 1158.8 1100.4 1372.2 1046.9 – 141.4 83.1 354.8 29.5 – 154.1 120.7 378.4 64.8 – 109.0 145.3 106.6 219.6 – 12.7 37.6 23.6 35.3 9.0 45.3 6.6 119.6 Fuhe 1960–1969 1970–1979 1980–1989 1990–1999 2000–2007 749.4 803.4 796.3 849.1 682.3 – 54.0 46.9 99.7 67.1 – 69.8 102.6 201.8 38.2 – 129.2 218.8 202.5 56.9 – 15.8 55.7 102.2 105.3 – 29.2 118.8 102.5 156.9 The whole Poyang Lake Catchment 1960–1969 1970–1979 1980–1989 1990–1999 2000–2007 1970–2007 801.9 911.4 864.2 1029.6 837.4 914.5 – 109.5 62.3 214.1 35.5 112.6 – 115.0 118.6 261.7 75.3 169.7 105.0 190.3 122.3 212.1 150.7 – 5.4 56.3 47.6 39.8 57.1 – 5.0 90.3 22.3 112.1 50.7 DQhum (mm) ghum (%) Fig. 9. (a) The sensitivity of the potential evapotranspiration to the four major meteorological variables; (b) trends analysis by MK test. The horizontal dashed lines in the right ﬁgure represent the critical value of 0.01 signiﬁcance level. streamﬂow due to climate change (gclim) ranges from 105.0– 212.1%, while the proportional change due to human activities (ghum) ranges from 5.0% to 112.1%. Relative effect of climate change is greater than that of human activities, which is consistent with the results from the coupled water-energy budgets analysis. Due to the discrepancy in regional climate and the intensive human activities in the catchment, the contribution of individual impacts varied under different spatial and temporal scales. According to Table 3, increased streamﬂows in Ganjiang, Xinjiang and Fuhe sub-catchments due to climate change (DQclim) were estimated to 108.2–231.4 mm, 64.8–378.4 mm and 38.2–201.8 mm, while the speciﬁc reduction impacts of human activities (DQhum) were 3.6– 55.8 mm, 12.7–37.6 mm and 15.8–105.3 mm, respectively. Relative to 1960s, climate change has the largest impact on the increase in streamﬂow in 1990s, while the impact is the least in 2000s, which is consistent with the increased frequency and severity of ﬂoods and droughts during these time periods (Min et al., 2011; Guo et al., 2007). However, percentage of the impact is bigger in 1980s in Ganjiang River sub-catchment and in 2000s in Xinjiang River sub-catchment. The Fuhe River sub-catchment is the only catchment where the reduction in streamﬂow during 2000s should be mainly attributed to the intensive human activities. As shown in Table 3, the contribution of climate change accounted for 56.9% of the changes in streamﬂow, while human activities were responsible for 156.9% of the change. 4. Discussions The empirical estimation and hydrological sensitivity analysis performed in this study are mainly for studying the effect of mean annual climate change on the variations in annual streamﬂow. Although the long-term trends of annual streamﬂow of the ﬁve gauging stations are not signiﬁcant, monthly variations were obvious (as shown in Fig. 5b). Previous studies indicate that the increase of precipitation, especially the increased frequency of summer storm is the main reason for the increase of streamﬂow and for the cause of ﬂoods in 1990s (e.g., Guo et al., 2007). Additionally, in both methods, effect of climate change on streamﬂow is mainly through the changes in precipitation and potential evapotranspiration. Because of the calculated potential evapotranspiration represents the integrated effect of climate variables, further analysis indicated that in the Poyang Lake catchment, the most important predictor for the decreasing trend in potential evapotranspiration is the net radiation and wind speed, since they are not only sensitive variables in determining PET, but also decrease signiﬁcantly in the catchment (see Fig. 9). Comparable result was given in Xu et al. (2006), who concluded that the decreasing trend of reference evapotranspiration in the Yangtze River catchment is most attributed to the changes in net radiation and wind speed. Like many regions in China, Poyang Lake catchment has undergone intensive human activities since 1950s, including X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 afforestation and deforestation, land reclamation, river regulation, agriculture intensiﬁcation and extensive infrastructure construction, which may have exerted considerable impacts on catchment hydrology. A modeling study in Xinjiang River sub-catchment indicated that when agriculture land changed into forest, which accounts for up to 23.3% of the catchment area, it would result in a decrease of annual discharge by up to 3.2% (Guo et al., 2008). The decreases of streamﬂow caused by increased forest cover are particularly strong in the wet season through the increase of evapotranspiration, but streamﬂow increase in the dry season has been primarily resulted from the increased groundwater contribution (Guo et al., 2007; Ye et al., 2011). Fig. 10 shows the changes of forest coverage and the corresponding area of soil erosion in the Poyang Lake catchment. The increase of forest coverage since 1980s may play an important role in reducing streamﬂow during ﬂood season. In addition, the variation of annual streamﬂow has been further elevated by the extensive water utilization according to the local social and economic development. Due to the lack of information of long-term water consumption, we here present the changes of reservoir construction and the storage volume in the catchment, which may well indicate the variation of water utilization. As shown in Fig. 11, although there is litter change in the total amount of reservoirs in the catchment since 1980s, the volume of water storage increased steadily for the same period, especially after 1990s. The reasons for this are the rapid changes in society and social life, population, economic forces and technological development, which increased the demand and the ability of water supply for industrial and domestic consumptions. The different changing patterns in annual streamﬂow of Lijiadu station in Fuhe River sub-catchment since 1990s (Fig. 5a and Table 3) as compared with other sub-catchments are the results of high water-use efﬁciency. The biggest irrigation farmland as well as the irrigation systems of Jiangxi Province are located in the middle and lower reach of Fuhe, which will notably increase the water utilization and directly decrease streamﬂow, especially in those dry years. The results of this study indicated that the impact of human activities on the change of catchment streamﬂow was negative compared to the baseline period of 1960s. However, the impact of human activities on water resources at local scale is complex and different human activities may accumulate or counteract each other; attempts have not been made in this study to distinguish them from each other. It is worth of noting that the concept of the model by Tomer and Schilliing (2009) is based on long-term observations of landuse change in the catchment. Although, the application of empirical method in the Poyang Lake catchment gives a comparative result in distinguishing the effects of human activities and climate change on catchment hydrology, the applicability and implications need more documentation, as suggested by Tomer and Schilliing (2009), and this study contributes to this call. Fig. 10. Changes of forest coverage and soil erosion in the Poyang Lake catchment. 93 Fig. 11. Changes of total reservoir amount and storage in the Poyang Lake catchment. In this study, the variability of climate indices is consistent with that of the data time series. The 1960s were used as the baseline period to analyze the relative impacts of climate change and human activities in other decades, which is because the intensity of human activities in 1960s is relatively small. In addition, the coupled water-energy budget analysis is an empirical method which heavily depends on the quality of P, PET and ET measurements or estimates, especially the latter two being the most difﬁcult components to estimate. The possible inﬂuence of data error on the results is yet to be investigated in the future study. Meteorological data from 19 weather stations in the study area might not be sufﬁcient coverage for such a large-scale catchment. Inﬂuence of reservoir storage on the estimation of mean annual ET is not considered. Additionally, some uncertainties also exist in the hydrologic sensitivity analysis method which separates the effects of climate change and human activities on streamﬂow. The performance of the hydrologic sensitivity analysis depends on the streamﬂow data of the long-term baseline period, with no effect of human activities, for model calibration. In reality, there was a lack of detailed long-term observation data in the Poyang Lake catchment to distinguish a natural period, and even during the baseline period of 1960s, there were some human disturbances such as farmland irrigation, river regulation and land cover changes. Although calibrated available water coefﬁcient (w) in this study well reﬂects the average vegetation condition of the catchment during 1960s, this could still affect the estimation results to some extent. 5. Conclusions Under the background of global warming, increased regional climate change and human activities were identiﬁed as the main factors causing the ﬂoods and droughts in the Poyang Lake catchment, especially since 1990s. In this study, we performed an assessment of the relative effects of climate change and human activities on the changes of streamﬂow in the catchment during the past ﬁve decades. MK test indicated a long term increase trend of precipitation and a decrease trend of potential evapotranspiration. An increasing trend of annual streamﬂow of the catchment except for Fuhe sub-catchment was detected. The ﬁndings suggest that relative effects of climate change and human activities on the change of streamﬂow varied among sub-catchments as well as the whole catchment under different decades, due to the discrepancies in regional climate and anthropogenic stresses across the Poyang Lake catchment. The increase of annual streamﬂow (52.35– 180.28 mm) was primarily affected by climate change for the whole catchment compared to the reference period of 1960s, while human activities played a complementary role. However, due to the intensiﬁed water utilization for industrial and agricultural 94 X. Ye et al. / Journal of Hydrology 494 (2013) 83–95 development, especially in those dry years, the decrease of streamﬂow (67.13 mm) in the Fuhe sub-catchment in 2000s was primarily affected by human activities. Quantitative assessment revealed that climate change resulted in an increase in runoff of 75.3–261.7 mm in 1970s–2000s for the whole catchment, accounting for 105.0–212.1% of runoff changes relative to 1960s. However, human activities may be responsible for the decrease in runoff of 5.4–56.3 mm in 1970s– 2000s, which accounts for 5.0% to 112.1% of runoff changes. Moreover, human activities such as farmland irrigation, river regulation and deforestation were the main anthropogenic stresses in altering hydrological processes before 1990s. Afterwards, afforestation and rapid local socio-economic development became increasingly apparent. However, the effects of speciﬁc human activities may accumulate or counteract each other, and attempts were not made in this study to distinguish them from each other. As a sub-tropical humid catchment with strong seasonal variations of river discharge, current and future water resource management and planning in the Poyang Lake catchment may take a wide range of afforestation, soil conservation, and construction of water conservancy engineering system into account. Implementation of a possible integrated water regulation system incorporating the Yangtze River is needed in order to reduce ﬂood and drought disasters in the lake region and to maintain a healthy ecosystem of the lake. Acknowledgements This work is supported by the National Basic Research Program of China (2012CB417003 and 2012CB956103-5), National Natural Science Foundation of China (41201026), State Key Laboratory of Lake Science and Environment (2010SKL014), and Science Foundation of Nanjing Institute of Geography and Limnology (NIGLAS2012135001 and NIGLAS2010XK02). The authors extend their thanks to two anonymous reviewers for their constructive comments, which greatly improved the quality of this paper. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper No. 56. FAO, Rome. Bates, B.C., Kundzewicz, Z.W., Wu, S., Palutikof, J.P., 2008. Climate Change and Water. Technical Paper of the Intergovernmental Panel on Climate Change. IPCC Secretariat, Geneva. p. 210. Brown, A., Zhang, L., McMahon, T., Western, A., Vertessy, R., 2005. 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