Quantitative Assessment of the Impact of Climate Variability and Human Activities on Runoff Changes: a Case Study in Four Catchments of the Haihe River Basin, China

HYDROLOGICAL PROCESSES Hydrol. Process. 27, 1158–1174 (2013) Published online 13 April 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/hyp.9299

Quantitative assessment of the impact of climate variability and human activities on runoff changes: a case study in four catchments of the Haihe River basin, China

Weiguang Wang,1*,† Quanxi Shao,2 Tao Yang,1 Shizhang Peng,1 Wanqiu Xing,1 Fengchao Sun1 and Yufeng Luo1 1 State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China 2 CSIRO Mathematics, Informatics and Statistics, Floreat Park, WA 4016, Australia

Abstract: Quantitative evaluation of the effect of climate variability and human activities on runoff is of great importance for water resources planning and management in terms of maintaining the ecosystem integrity and sustaining the society development. In this paper, hydro-climatic data from four catchments (i.e. Luanhe River catchment, Chaohe River catchment, Hutuo River catchment and Zhanghe River catchment) in the Haihe River basin from 1957 to 2000 were used to quantitatively attribute the hydrological response (i.e. runoff) to climate change and human activities separately. To separate the attributes, the temporal trends of annual precipitation, potential evapotranspiration (PET) and runoff during 1957–2000 were first explored by the Mann–Kendall test. Despite that only Hutuo River catchment was dominated by a significant negative trend in annual precipitation, all four catchments presented significant negative trend in annual runoff varying from 0.859 (Chaohe River) to 1.996 mm a1 (Zhanghe River). Change points in 1977 and 1979 are detected by precipitation–runoff double cumulative curves method and Pettitt’s test for Zhanghe River and the other three rivers, respectively, and are adopted to divide data set into two study periods as the pre-change period and post-change period. Three methods including hydrological model method, hydrological sensitivity analysis method and climate elasticity method were calibrated with the hydro-climatic data during the pre-change period. Then, hydrological runoff response to climate variability and human activities was quantitatively evaluated with the help of the three methods and based on the assumption that climate and human activities are the only drivers for streamflow and are independent of each other. Similar estimates of anthropogenic and climatic effects on runoff for catchments considered can be obtained from the three methods. We found that human activities were the main driving factors for the decline in annual runoff in Luanhe River catchment, Chaohe River catchment and Zhanghe River catchment, accounting for over 50% of runoff reduction. However, climate variability should be responsible for the decrease in annual runoff in the Hutuo River catchment. Copyright © 2012 John Wiley & Sons, Ltd.

KEY WORDS climate change; human activities; Haihe River basin; hydrological response Received 1 November 2011; Accepted 2 March 2012

INTRODUCTION regime and subsequent changes to the amount of water available, in terms of both spatial and temporal distributions According to the latest report of the Intergovernmental Panel (Yang et al., 2004; Brown et al., 2005). For example, on Climate Change (IPCC), the Fourth Assessment Report afforestation causes the decline in streamflow (Brown et al., (IPCC, 2007), the average global surface temperature has 2005; Lane et al., 2005; Shao et al., 2009). Moreover, other increased by 0.74 C over the last 100 years. Concurrently, land use changes such as terraces and irrigation can also lead rainfall has decreased by 0.3% per decade over much of the to change in the water cycle and affect runoff (Li et al., 2007; Northern Hemisphere’s sub-tropical regions during the 20th Scanlon et al., 2007; Ma et al., 2008; Yang and Tian, 2009). century (Chen et al., 2007). One of the most significant Water resource managers and planners are eagerly seeking potential consequences of climate change may be alterations to support sustainable natural resources development, in regional hydrological cycles (Huntington, 2006). In especially at catchment scale (Wei and Zhang, 2010). That addition to global climate change, increases in human is particularly evident when climate change and anthropo- activities due to the sharp growth in population such as genic activities dramatically and extensively alter the cultivation, irrigation, afforestation, deforestation and urban hydrological processes and functions of catchment construction, have also introduced variability in the flow (Schindler, 2001). Therefore, investigations on the impacts of climate variability or climate change and human-induced *Correspondence to: Weiguang Wang, State Key Laboratory of Hydrology- land use change on hydrology and water resources have Water Resources and Hydraulic Engineering, Hohai University, Nanjing recently drawn considerate concerns (e.g. Lane et al., 2005; 210098, China. Siriwardena et al., 2006; Tuteja et al., 2007; St. Jacques E-mail: [email protected] †Present address: College of Water Resources and Hydrology, Hohai et al., 2010). Particularly in China, due to the strong University, Nanjing 210098, China. warming and the significant regional precipitation variation

Copyright © 2012 John Wiley & Sons, Ltd. EFFECTS OF CLIMATE VARIABILITY AND HUMAN ACTIVITIES ON RUNOFF CHANGES 1159 coupled with drastic agricultural and industrial development analysis) and graphical methods (e.g. double mass curves, during the past decades (Piao et al., 2010), more attention has single mass curves and flow duration curves) are always been paid to understand the influence and relative importance used to determine the spurious change points and the of climate variation and human activities on hydrological baseline period (Wei and Zhang, 2010; Jiang et al., 2011). cycle and water resources (e.g. Li et al., 2007; Huo et al., The Haihe River basin is an important economic center 2008; Ma et al., 2008; Zhang et al., 2008; Cong et al., 2009; of China with an important role in the sustainable Liu et al., 2009; Wang et al., 2009; Yang and Tian, 2009; development of the economy and ecology. However, in Zheng et al., 2009; Liu et al., 2010; Ma et al., 2010; Wang recent years, sharply increasing water shortages in this et al., 2010; Wei and Zhang, 2010; Zhao et al., 2010; Jiang basin hindered its economy development and resulted in et al., 2011). Nonetheless, quantifying the individual effects severe environmental and ecological problems (Liu and of climate variability and human activities on hydrological Xia, 2004). To date, a number of studies had reported that regime is a challenge. Furthermore, regional impacts of the streamflow of the Haihe River basin or its tributary climate change and human activities on hydrology vary from has dramatically decreased during recent years (e.g. Yao place to place and need to be investigated for a local scale. et al., 2003; Zhang and Yuan, 2004; Cui and Cui, 2007; Traditionally, paired catchment and physically based Ren, 2007). Human activities including construction of hydrological model are used to measure the effect of reservoir and dam, irrigation with river water or natural and human factors on the water cycle. However, groundwater, man-made land use change and climate paired catchments are not always available for it is change that happened in recent years have been difficult to locate suitable controls and the investigations believed to be responsible for the decline in runoff in are expensive to conduct (Fohrer et al., 2005; Wei and the Haihe River basin. Yang and Tian (2009) identified Zhang, 2010; Zhao et al., 2010). In addition, physically that human activities rather than climatic change are the based hydrological models, such as distributed hydrology main driving factor of runoff decline in eight soils and vegetation model, soil and water assessment tool catchments of the Haihe River basin. However, to the and the variable infiltration capacity model, are always best of our knowledge, systematically quantifying the limited due to the time-consuming calibration and relative contributions of climatic variability and human validation process, requirement of large data sets (e.g. activities to runoff change in the Haihe River basin has topography, vegetation and hydro-climatic data) and not been reported. complexity and uncertainty in model structure and The objectives of our study are (1) to detect statistically parameter estimation. Therefore, simple water balance significant trends and change points in annual streamflow hydrological model provides alternative choice. More- with statistical and graphic method and (2) to estimate the over, some new methods have been developed and widely proportion of streamflow change attributing to climatic used in many regions to estimate the effect of climate and human activities influence with three different variability or human activities, such as regression analysis methods. This paper is organized as follows. The study method, hydrological sensitivity analysis method and area and data are given in the next section, followed by elasticity method. For example, Huo et al. (2008) and the description of methods used. The results and Zhang and Lu (2009) employed regression analysis to discussion, including change points determination, estimate the impact of human activities on streamflow in quantification of climate variability and human activities, the Shiyang River basin and the lower Xijiang, comparison among the results of these three methods, are respectively. The hydrological sensitivity method, devel- presented before the major conclusions. oped by Dooge et al. (1999) and Milly and Dunne (2002) to describe first-order effect of changes in precipitation and potential evaporation on streamflow, was also STUDY AREA, BACKGROUND AND DATA adopted to study hydrologic responses to changes in PROCESSING climate and hence separate the effect of climate change on streamflow from that of human activities (e.g. Jones et al., The Haihe River is located in the eastern part of northern 2006; Zhang et al., 2008; Liu et al., 2009; Zhao et al., China, between 112E–120E and 35N–43N (Figure 1). 2010). Furthermore, climate elasticity method proposed The Haihe River basin, the largest water system in by Schaake (1990), which is similar to the hydrological northern China, has a total drainage area of 318 000 km2 sensitivity method, has been continually extended and and covers Beijing, Tianjin, most parts of Hebei and parts improved (e.g. Fu et al., 2007; Zheng et al., 2009). This of Shandong, Henan, Shanxi and Inner Mongolia. The climate elasticity method has also been widely used to elevation within the basin ranges from 0.4 to 3047 m assess the impact of climate variability (e.g. Zheng et al., above the sea level, and the topography descends 2009; Ma et al., 2010). A general approach can be significantly from northwest to southeast. Among the summarized for these methods as that one effect, usually entire area, plateaus and mountains comprise from climate variability, was estimated first and the 189 000 km2, accounting for nearly 60% of the area, remaining effect was attributed to other factors such as and plains cover 129 000 km2, accounting for about 40%. human activities (Zhao et al., 2010). For a given river The climate of the Haihe River basin is of the temperate basin, before application of these methods, advanced continental monsoon type. The annual precipitation statistical methods (e.g. non-parametric tests and time series ranges from 379.2 to 583.3 mm and annual mean

A B

Elevation(m) 0 - 500 # Meteorological station 500 -1000 Hydrological station 1000 - 1500 Tributary 1500 - 2000 Main stream 2000 - 2500 Catchment C 2500 - 3000 D

Figure 1. Map showing the location of the four catchments (A) Luanhe River catchment, (B) Chao River catchment, (C) Hutuo River catchment and (D) Zhanghe River catchment in the Haihe River basin and the meteorological stations and hydrological stations used in this study temperatures are between 4.9 and 15 C (Yang and Accelerated development of food production, GDP, Tian, 2009). Summer (June–August) is the main flooding urbanization and agriculture during 1980–2000 in the season for the Haihe River basin. Haihe River basin can be easily seen by their high The basin has a tremendous conflict between water increase rates presented in Table I. A huge amount of supply and demand, with the water volume per capita of water was thus consumed by agricultural irrigation, 305 m3 from 1956 to 1998, merely 1/7 of the national industrial production, daily life and municipal engineer- average and 1/24 of the world average (Xia et al., 2006). ing after 1980. There are several forms of human activity The rapid economic development and the urban popula- with direct or indirect influences on runoff change in the tion explosion in this region, especially after China’s Haihe River basin. Direct human activities mainly include reform and opening-up policy occurred in 1979, intensi- the soil conservation and water control works, increasing fied the issue of water shortages and resulted in many water demand, whereas indirect influences refer to land severe environmental and ecological problems (Yuan cover and land use changes over the Haihe River basin. et al., 2005). Table I shows that the gross domestic Mini-dams have been constructed during 1958–1975 in product (GDP), town population, food production and the almost all the valleys to control the soil erosion (Gao level of urbanization after 1980 in the Haihe River basin. et al., 2002). The construction of mega-reservoirs was largely made after 1960s for flood control and water storage for agricultural and domestic water demands Table I. Social and economic development in the Haihe River basin during the period of 1980–2000 (Zhang, 2003). For example, in Ziya River, Huangbiz- huang reservoir with 1.21 billion m3 of storage was built Town GDP Food Level of in 1963. In Luanhe River, Panjiakou reservoir with 4 population (billion (10 urbanization 2.63 billion m3 of storage was built in 1985 (Ren, 2007). Year (million) yuan) ton) (%) Mini-dams have been proved to have the capacity to 1980 22.89 159.2 2655 24 affect runoff (Wang et al., 2009). Mega-reservoirs can 1985 28.15 265 3440 27 result in enhancing water-withdrawing capacity of the 1990 33.64 382.1 4202 29 local population and an increase in total evaporation and 1995 38.35 705.2 4816 32 leakage from the reservoirs. In addition, in this basin, 2000 45.12 1163.3 4576 36 rainfall often does not meet crop water demand; Average annual 3.45% 10.46% 2.76% 2.05% growth rate irrigation, which is increasingly reliant on groundwater pumping, has thus become an indispensable agronomic

Copyright © 2012 John Wiley & Sons, Ltd. Hydrol. Process. 27, 1158–1174 (2013) EFFECTS OF CLIMATE VARIABILITY AND HUMAN ACTIVITIES ON RUNOFF CHANGES 1161 practice as it significantly enhances harvest (Yang and accumulated values of the another related variable for a Tian, 2009). During the past decades, land use/cover has concurrent period (Searcy and Hardison, 1960). It is a changed dramatically. Before 1985, large expanses of straight line if two variables are proportional, and the natural forest/grassland were transformed into agricultural slope of this line will present the ratio between the two lands. However, the reverse trend has been encouraged by variables. A change in the gradient of the curve may the government since 1990 due to the huge impact of the indicate that the original relationship between variables land transformation on the environment (Wang and was broken. DMC can be used to check the consistency Wang, 2007). It can be seen that most of activities of hydrological records and has recently become an occurred at different socio-economic periods. Therefore, effective tool for detecting the changes of hydrological determining the abrupt change point based on the regime due to anthropogenic disturbances (e.g. Huo et al., associated human activities could be conductive to 2008; Zhang and Lu, 2009; Zhang et al., 2009). In this understanding the driving factors of runoff decline study, DMC between precipitation and runoff was (Gerstengarbe and Werner, 1999). employed as an auxiliary confirmation of the change Four catchments (i.e. Luanhe, Chaohe, Hutuo and points when human activities imposed the influences on Zhanghe) in the Haihe River basin were selected as the the river. study area. The data used in this study include runoff data from four hydrological stations and meteorological data Pettitt’s test. The Pettitt’s test (Pettitt, 1979) is a non- from seven meteorological stations distributed in each parametric approach to determine the occurrence of a catchment for the period of 1957–2000 (Figure 1). The change point. This approach is a rank-based and runoff data were obtained from the ‘Hydrological Year distribution-free test for detecting a significant change Book’ published by the Hydrological Bureau of the in the mean of a time series when the exact time of the Ministry of Water Resources. The hydrological stations change is unknown. Pettitt’s test has been widely used to include Luanxian station (with the drainage area of detect changes in the hydrological as well as climatic 44 100 km2 in the Luanhe River catchment), Daiying records (e.g. Verstraeten et al., 2006; Zhang and Lu, station (with the drainage area of 4701 km2 in the Chaohe 2009; Zhang et al., 2009). We will use the Pettitt’s test to River catchment), Xiaojue station (with the drainage area identify the change point of the runoff series as a of 15 580 km2 in the Hutuo River catchment) and Guantai reconfirmation of the change points detected by DMC. station (with the drainage area of 17 800 km2 in the Zhanghe River catchment). The meteorological data with General framework for separating effects of climate precipitation, temperature, pan evaporation, wind speed, variability and human activities on streamflow sunshine duration and relative humidity were obtained For a given catchment, the streamflow can be modeled from the China Meteorological Administration. In as a function of climate variables and human activities by addition, potential evapotranspiration is estimated using (Zhang et al., 2008; Zheng et al., 2009) the Penman–Monteith equation recommended by the Food and Agriculture Organization of the United Nations Q ¼ fCðÞ; H (1) (Allen et al., 1998). where Q is streamflow, C represents climate factors on hydrological cycle, and H is a factor that represents the integrated effects of human activities on streamflow. METHODOLOGY Following Equation (1), changes in streamflow due to Trend test and break point analysis method climate variability and human activities can be approxi- Mann–Kendall test. The rank-based Mann–Kendall mated as test (Mann, 1945; Kendall, 1975) was used to detect ′ ′ ΔQ ¼ f ΔC þ f ΔH (2) trends in the hydro-climatic series in this study. The C H method was highly recommended by the World where ΔQ, ΔCandΔH are changes in streamflow, climate ′ @ =@ Meteorological Organization and widely used (e.g. Zhang and human activities, respectively, with f C ¼ Q C and et al et al ′ @ =@ ., 2008; Yang and Tian, 2009; Wang ., 2011a,b) f H ¼ Q H. to assess the significance of monotonic trends in As a first-order approximation, Equation (2) can be hydrological series, for it has an advantage of not written as (Zhang et al., 2008) assuming any distributional form for the data and has the Δ Δ Δ same power as its parametric competitors. Meanwhile, Q ¼ QC þ QH (3) Sen’s slope method (Sen, 1968; Hirsch et al., 1982) was used to estimate the trends magnitudes (slopes) based on where ΔQC and ΔQH represent changes in streamflow due Kendall’stau. to climate variability and human activities, respectively. Although climate variability and human activities interact Double mass curve method. The double mass curve with each other, for a small basin, climate variability is (DMC), frequently used in paired studies to detect mainly controlled by external forces. Therefore, these two hydrological changes caused by human activities, is the factors were regarded as independent variables at basin plot of the accumulated values of one variable against the scale (Wang et al., 2009; Jiang et al., 2011). ΔQ can be

Copyright © 2012 John Wiley & Sons, Ltd. Hydrol. Process. 27, 1158–1174 (2013) 1162 W. WANG ET AL estimated by subtracting the observed average streamflow EP(t), because of feedback mechanism of soil-plant- obs during the baseline period Q1 from the observed average atmosphere system, although the term of soil moisture fl obs annual stream ow during the changed period Q2 . content does not appear in Equation (5). Consequently, separating the effect of climate change from The monthly runoff is closely related to the soil water that of human activities can be achieved by this framework if content via ΔQC or ΔQH is known. QtðÞ¼StðÞtan hSt½ðÞ=SC (6) Estimating the impact of climate variability or human where Q(t) is the monthly runoff, S(t)is the water content fl activities on stream ow in soil, and the second model parameter SC represents the Description of different methods. According to the field capacity of basins, with the unit of millimeter. The aforementioned framework, the first step of separating actual monthly evapotranspiration E(t) can be determined the effects is to estimate the impact of climate change or through Equation (5) by the monthly rainfall P(t) and the human activities on streamflow. The physical-based monthly pan evaporation EP(t). The quantity of the hydrological model is usually a good choice to measure remaining water in the soil at the beginning of the tth the effect of natural and human factors on streamflow. month is [S(t 1) + P(t) E(t)], after loss through evapo- However, in this study, the limited information and the transpiration E(t), with S(t 1) being the water content at available data sets fail to meet the minimal requirements the end of the (t 1)th month and at the beginning of the tth for physical-based hydrological model. Instead, a simple month. Equation (6) is then used to calculate the tth monthly hydrological water balance model and statistical methods runoff Q(t)as are used to estimate the effects of climate variability or human activities on annual streamflow. QtðÞ¼½StðÞþ 1 PtðÞEtðÞ (7) tanhfg½StðÞþ 1 PtðÞEtðÞ=SC Hydrological model method. For the effect of human activities on runoff to be estimated, the hydrological Finally, the water content at the end of the tth month model was first calibrated based on observed runoff in the was calculated according to the water conservation law: baseline period, and the natural runoff without human activities was reconstructed for the human-induced StðÞ¼StðÞþ 1 PtðÞEtðÞQtðÞ (8) period. Consequently, the effect of human activities on runoff can be calculated as follows: Three statistics, namely the correlation coefficient (R), Δ obs obs – fi QH ¼ Q2 Q2 (4) the Nash Sutcliffe coef cient (NSCE) and Relative Bias obs (BIAS), were used to evaluate the performance of the where Q2 indicates the reconstructed runoff for the regression model and are defined as changed period, and ΔQ and Qobs are defined as before. H 2 PN The two-parameter monthly hydrological model proposed ðÞQobsðÞi Qobs ðÞQsimðÞi Qsim by Xiong and Guo (1999) was used in this study. The i¼1 R ¼ = = model has only two parameters to be calibrated and PN 1 2 PN 1 2 2 2 requires only the monthly areal precipitation, pan ðÞQobsðÞi Qobs ðÞQsimðÞi Qsim evaporation and air temperature. The model outputs i¼1 i¼1 include monthly streamflow, actual evapotranspiration (9) (AET) and soil moisture content index. PN 2 The model has been tested in more than 100 small-size ðÞQobsðÞi QsimðÞi and medium-size basins with different types of climate in NSCE ¼ 1 i¼1 (10) PN China and has proved to be quite efficient in simulating 2 ðÞQobsðÞi Qobs monthly runoff with its simple structure (Xiong and Guo, i¼1 1999; Guo et al., 2002; Guo et al., 2005). Because of its PN simplicity and good performance, the two-parameter ðÞQ ðÞi Q ðÞi monthly water balance model can be easily and efficiently obs sim BIAS ¼ i¼1 (11) incorporated in the water resources planning program and PN study of climate change impacts. The actual monthly QobsðÞi evapotranspiration can be calculated as (Xiong and Guo, i¼1

1999) where N is the number of data points, Qobs(i) is the observed runoff (millimeter per month) at time step i, Q (i)isthe = sim EtðÞ¼C EPðÞ t tan hPt½ðÞ EPðÞ t (5) simulated runoff (millimeter per month) at time step i,and Qobs and Qsim are the means of the observed and simulated where E(t), EP(t) and P(t) represent monthly AET, values (millimeter per month), respectively. monthly pan evaporation and monthly rainfall, respectively, and C is the ﬁrst model parameter. Soil moisture Hydrological sensitivity analysis method. Hydrological content can be reﬂected indirectly by rainfall and potential sensitivity can be described as the percentage change in

Copyright © 2012 John Wiley & Sons, Ltd. Hydrol. Process. 27, 1158–1174 (2013) EFFECTS OF CLIMATE VARIABILITY AND HUMAN ACTIVITIES ON RUNOFF CHANGES 1163 mean annual runoff in response to the change in mean change in runoff divided by the proportional change in a annual precipitation and potential evapotranspiration. The climatic variable such as precipitation. The precipitation water balance for a basin can be described as elasticity of runoff was thus expressed as P ¼ E þ Q þ ΔS (12) dQ=Q e ¼ (17) P dP=P where P is precipitation, E is AET, Q is streamflow, and ΔS is the change in soil water storage. On the mean where P and Q are precipitation and runoff, respectively. annual time scale, the change ΔS can reasonably be Sankarasubramanian et al. (2001) proposed a non- neglected, that is, ΔS can be assumed as zero for a long parametric approach, the median descriptive statistics, to period (i.e. 10 years or more). estimate the climate elasticity directly from observed data. Budyko (1974) considered that the available energy Although the approach was tested and found to be robust and water are the primary factors determining the rate of via Monte Carlo experiments for three basins in the evapotranspiration and developed a framework for United States, it is weak when the sample size is small. estimating AET based on dryness index. Following a Zheng et al. (2009) proposed an alternative non- similar assumption to Budyko (1974), a simple model parametric estimator of climate elasticity to overcome (called Zhang’s curve) developed by Zhang et al. (2001) the problem associated with small sample size. The to estimate the AET is consistent with previous theoretical elasticity (e) of runoff (Q) to climatic variable (X) can be work and shows good agreement over the world (Zhang expressed as the product of the correlation coefficient et al., 2001, 2004; Li et al., 2007; Liu et al., 2009; Shao rX, Q and the ratio between coefficients of variation of et al., 2012): runoff CQ and climatic variable CX. Following Zheng et al. (2009), the estimator of precipitation elasticity of E 1 þ wðÞ PET=P ¼ (13) runoff can be expressed as P 1 þ wðÞþ PET=P ðÞPET=P 1 ΔQi=Q ¼ ePΔPi=P (18) where PET is the potential evapotranspiration and w is the Δ Δ plant-available water coefficient related to vegetation type where Qi and Pi are changes of the annual runoff and the precipitation with respect to long-term average of (Zhang et al., 2001). The details of the relationship can be found in Zhang et al. (2001). In this study, the parameter runoff Q and precipitation P, respectively. The values of eP w is calibrated by comparing the long-term annual AET were calculated using observed precipitation as well as from Equation (12). runoff data before the year of change points detected by Perturbations in both precipitation and PET can lead to statistical method. Changes in annual runoff after the changes of water balance. It can be assumed that a change in change points could be estimated from the annual mean annual runoff is determined by the following precipitation data during the post-change period using expression (Koster and Suarez, 1999; Milly and Dunne, Equation (18). 2002): Calibration and validation for different methods. Accord- ΔQC ¼ bΔP þ gΔPET (14) ing to change points detected in runoff, the study period Δ Δ Δ for all the catchments can be divided into two periods, where QC, Pand PETdenote changes in runoff, pre-change period and post-change period. Pre-change precipitation and PET, respectively, and b and g are the fi period, when few human activities affecting runoff in the sensitivity coef cients of runoff to precipitation and PET, four catchments, was treated as a baseline period to expressed as (Li et al., 2007) calibrate and validate the parameters for different 1 þ 2x þ 3wx methods. b ¼ (15) The baseline period was divided into two phases to ðÞ1 þ x þ wx2 2 determine the two-parameter hydrological model parameter. The model parameters were estimated by minimizing 1 þ 2wx the absolute values of BIAS and ensuring the NSCE and g ¼ (16) fi ðÞ1 þ x þ wx2 2 correlation coef cient close to 1. w is the key model parameter for hydrological sensitivity analysis method. where x is the mean annual index of dryness (equal to PET/P). Most of the catchments are located in the mountainous area of the Haihe River basin. Before the 1980s, the forest The climatic elasticity model. In addition to the hydro- coverage rate in these regions is over 50% (Ren, 2007). logical sensitivity analysis method, the climate elasticity Zhang et al. (2001) considered that the plant-available model was widely employed to assess the climate coefficient (w) was set to 2.0 for forest and 0.5 for variability impact (e.g. Ma et al., 2010). The concept of pasture. It is difficult to assign the parameter w for the climate elasticity was introduced by Schaake (1990) and mixed vegetation, which always exists in a real then improved constantly (e.g. Sankarasubramanian et al., catchment, because of the difficulty to accurately separate 2001; Fu et al., 2007; Zheng et al., 2009). The climate herbaceous and forest cover for individual catchments. In elasticity of runoff can be defined by the proportional this study, different with the way to set w value (e.g., Li

Copyright © 2012 John Wiley & Sons, Ltd. Hydrol. Process. 27, 1158–1174 (2013) 1164 W. WANG ET AL et al., 2007; Huo et al., 2008; Liu et al., 2010), the RESULTS parameter w is calibrated by comparing long-term annual Trends and change points analysis for hydro-climatic series AET calculated by using the Zhang’s curve and the water balance equation (WBE) for the pre-change period. Observed annual precipitation, runoff and calculated According to non-parametric estimator of climate elasti- PET series for the four catchments during the period of city proposed by Zheng et al. (2009), the precipitation 1957–2000 are presented in Figure 2. All the catchments elasticity eP can be estimated by annual runoff and annual exhibited a large variation in their runoff, whereas the precipitation data during the pre-change period. With the changes in precipitation and PET appear to be more calibrated eP, climate elasticity model can be used to stationary. Table II further showed the statistical results of simulate the runoff change due to climate variability trends in precipitation, runoff and PET based on the Mann– during the post-change period. Kendall test. Although annual precipitation in all the According to Equation (4), runoff change contributed catchments decreased, the statistically significant trends by local human activities, which represented as the can only be identified in the Hutuo River catchment. additional runoff change, can be obtained from the Similarly, only the Luanhe River catchment was dominated difference between reconstructed natural runoff and by significant trends in PET. Different from precipitation observed runoff. Consequently, with the human-induced and PET, which have no clear trends, annual runoff in all runoff change determined by hydrological model method four catchments presented significant negative trends with and the climate-induced runoff change calculated by even remarkable negative trends (with a confidence level of hydrological sensitivity analysis method and climate 99%) in three catchments, at the rates from 0.859 (Chaohe 1 elasticity method, the effect of climate variability and River) to 1.996 mm a (Zhanghe River). human activities can be quantitatively evaluated on the The DMC method was employed to further investigate basis of the general framework. the change points of runoff series. Figure 3 shows the

1200 1200 Luanhe River Chaohe River 1000 1000

800 800

600 600

400 400

200 200 Annual P,R and PET(mm) Annual P,R and PET(mm)

0 0 1957 1962 1967 1972 1977 1982 1987 1992 1997 1957 1962 1967 1972 1977 1982 1987 1992 1997 Year Year

1200 1200 Hutuo River Zhanghe River 1000 1000

800 800

600 600

400 400

200 200 Annual P,R and PET(mm) Annual P,R and PET(mm)

0 0 1957 1962 1967 1972 1977 1982 1987 1992 1997 1957 1962 1967 1972 1977 1982 1987 1992 1997 Year Year

Precipitation Runoff Potential evapotranspiration

Figure 2. Long term variations of precipitation, potential evapotranspiration (PET) and runoff series in the four catchments of the Haihe River basin

Table II. Summary of trend analysis for annual streamﬂow, precipitation, and potential evapotranspiration by Mann–Kendall test

Annual streamﬂow Annual precipitation Annual PET

Slope (b) Slope (b) Slope (b) Catchments z-test Significance (mm a1)z-test Significance (mm a1)z-test Significance (mm a1)

Luanhe River 2.64 *** 1.766 0.56 ns 1.133 4.52 *** 2.167 Chaohe River 1.73 * 0.859 1.15 ns 1.343 0.62 ns 0.222 Hutuo River 3.57 *** 1.201 2.42 ** 5.170 0.46 ns 0.785 Zhanghe River 3.83 *** 1.996 1.55 ns 2.725 1.32 ns 0.577

***, ** and * indicate conﬁdence levels of 99%, 95% and 90%, respectively; ns indicates conﬁdence level under 90%.

2500 3500 Luanhe River Chaohe River y = 0.1042x + 358.66 3000 R = 0.9928 2000

2500 y = 0.1062x + 957.44 R = 0.9793 1500 2000 y = 0.1664x + 177.95 y = 0.13x + 73.264 R = 0.9888 R = 0.9903 1500 1000 1957-1979 1957-1979 1000 1980-2000 1980-2000 500 Cumulative annual runoff (mm) Cumulative annual runoff (mm) 500 Trend Line (1957-1979) Trend Line (1957-1979) Trend Line (1980-2000) Trend Line (1980-2000) 0 0 0 5000 10000 15000 20000 25000 0 5000 10000 15000 Cumulative annual precipitation (mm) Cumulative annual precipitation (mm)

2500 2500 Hutuo River y = 0.0589x + 403.34 Zhanghe River R = 0.9824 2000 2000 y = 0.0605x + 924.53 1500 y = 0.1436x + 28.184 1500 R = 0.9863 y = 0.0856x + 46.673 R = 0.9823 R = 0.9933 1000 1000 1957-1979 1957-1977 1980-2000 1978-2000 500 500 Cumulative annual runoff (mm) Cumulative annual runoff (mm) Trend Line (1957-1979) Trend Line (1957-1977) Trend Line (1980-2000) Trend Line (1978-2000) 0 0 0 5000 10000 15000 20000 25000 0 5000 10000 15000 20000 25000 Cumulative annual precipitation (mm) Cumulative annual precipitation (mm)

Figure 3. Double mass curve of annual precipitation and runoff for four catchments of the Haihe River basin double cumulative curve of annual precipitation–runoff Chaohe River catchment, break point at 1979 for the over the four catchments. The relationships between runoff record has also been found by order clustering precipitation and runoff can be presented nearly two analysis method in the literature (Xie et al., 2005; Wang straight lines with different slopes before and after 1979 et al., 2009). Overall, the change points identified for for the Luanhe River catchment, the Chaohe River annual runoff occurred intensively between late 1970s catchment and the Hutuo River catchment, and before and early 1980s (listed in Table III). Accordingly, the and after 1977 for the Zhanghe River catchment, study period for all the catchments can be divided into indicating that there may be an abrupt change in the two periods (i.e. pre-change period and post-change runoff series. Furthermore, we use Pettitt’s test to detect period) by the change points. the change points in runoff series. The results by Pettitt’s The means and standard deviations of the annual runoff test (Figure 4) present the significant change points in in the pre-change period and post-change period are 1979 for the Luanhe River catchment and the Hutuo analyzed to better understand the characteristics of the River catchment and in 1977 for the Zhanghe River runoff change. The ratio of the mean annual runoff for the catchment and marginally significant change point in post-change period relative to that for the pre-change 1979 for the Chaohe River catchment, further confirming period varied from 36.27% to 66.27%, indicating large the change points detected by DMC method. For the reductions in the annual runoff during the post-change

350 350 Luanhe River 1979 Chaohe River 300 99% confidence level 300 99% confidence level

250 250

200 95% confidence level 200 95% confidence level U U 150 150 1979 100 100

50 50

0 0 1957 1962 1967 1972 1977 1982 1987 1992 1997 1957 1962 1967 1972 1977 1982 1987 1992 1997 Year Year

350 450 Hutuo River Zhanghe River 400 1977 300 99% confidence level 350 250 1979 300 99% confidence level 95% confidence level 200 250 U U 150 200 95% confidence level 150 100 100 50 50

0 0 1957 1962 1967 1972 1977 1982 1987 1992 1997 1957 1962 1967 1972 1977 1982 1987 1992 1997 Year Year

Figure 4. Change points of runoff series during the period 1957–2007 detected by Pettitt’s test for four catchments of the Haihe River basin

Table III. Summary for the annual runoff change points analysis

Change point Pre-change period Post-change period Changes Catchments Year Signiﬁcance Mean (mm) SD (mm) CV Mean (mm) SD (mm) CV (%)

Luanhe River 1979 ** 106.5 57.5 0.54 51.6** 33.9** 0.66 51.55 Chaohe River 1979 ns 69.2 40.3 0.58 44.1* 22.0* 0.50 36.27 Hutuo River 1979 ** 58.5 27.9 0.48 28.7** 21.7** 0.76 50.94 Zhanghe River 1977 ** 84.5 49.0 0.58 28.5** 21.8** 0.76 66.27

Pre-change and post-change periods were defined by the change points listed in the table. ** and * indicate confidence levels of 95% and 90%, respectively. ns indicates confidence level under 90%. CV, coefficient of variation; SD, standard deviation. period. The significant differences between the means of runoff during the two periods. For all the catchments, the the two samples during the pre-change and post-change correlations between precipitation and runoff for the pre- periods can be found at 90% confidence level for all change period are stronger than that for the post-change catchments using the t-test. Concurrently, using F-test, period. there are statistically significant differences between the standard deviations of the two samples at 95% confidence The results of calibration and validation level for all the catchments except the Chaohe River Hydrological model method. The model was calibrated catchment. In addition, the increasing coefficient of using the historical data from January 1961 to December variation can be found in three catchments, indicating 1975 for Luanhe River catchment, Chaohe River that the runoffs in these catchments vary more during the catchment and Hutuo River catchment, and from January post-change period. These changes are consistent with the 1961 to December 1970 for Zhanghe River catchment. fact that the factors influencing runoff become more Correspondingly, the validation periods for the Luanhe complex during the post-change periods. In addition, the River catchment, Chaohe River catchment and Hutuo differences in the correlation of precipitation and runoff River catchment are from January 1976 to December for the two periods (Figure 5) were investigated to further 1980 and for the Zhanghe River catchment is from understand the effects of climate and other factors on January 1971 to December 1977. Figure 6 presents the

200 160 Luanhe River 180 140 Chaohe River 160 120 140 100 120 y = 0.30 x - 91.05 y = 0.33 x - 93.93 R = 0.66 100 80 R = 0.57 80 y = 0.20 x - 63.00 60 Annual runoff (mm) Annual runoff (mm) 60 R = 0.55 y = 0.16 x - 28.03 40 40 R = 0.52 20 20

0 0 300 450 600 750 900 200 350 500 650 800 Annual precipitation (mm) Annual precipitation (mm)

120 160

Hutuo River 140 Zhanghe River 100 120 80 100 y = 0.28 x - 87.25 y = 0.14 x - 36.68 R = 0.54 60 R = 0.85 80

60 40 y = 0.14 x - 48.10 Annual runoff (mm) R = 0.46 Annual runoff (mm) 40 y = 0.10 x - 25.70 20 20 R = 0.19

0 0 300 450 600 750 900 1050 1200 200 350 500 650 800 950 Annual precipitation (mm) Annual precipitation (mm)

Pre-change period Post-change period

Trend line (pre-change period) Trend line (post-change period)

Figure 5. Correlation between precipitation and runoff for pre-change period and post-change period

160 0

140 100 120 200 100 Calibration Validation 80 Luanhe River 300 Precipitation Runoff (mm) 60 400 Measured runoff Precipitation (mm) 40 Simulated runoff 500 20

0 600 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 Time series (Jan.1961-Dec.1975) Time series (Jan.1976-Dec.1979)

120 0

100 100

80 200

Validation 60 Chaohe River Calibration 300

Precipitation Runoff (mm) 40 400 Measured runoff Precipitation (mm) Simulated runoff 20 500

0 600 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 Time series (Jan.1961-Dec.1975) Time series (Jan.1976-Dec.1979)

120 0

100 100

80 200

Validation 60 Calibration 300 Hutuo River

Runoff(mm) Precipitation 40 400 Measured runoff Precipitation (mm) Simulated runoff 20 500

0 600 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 Time series(Jan.1961-Dec.1975) Time series(Jan.1976-Dec.1979)

120 0

100 100

80 200

Calibration Validation 60 Precipitation 300 Zhanghe River Measured runoff Runoff (mm) 40 400 Simulated runoff Precipitation (mm)

20 500

0 600 1 16 31 46 61 76 91 106121 136 151 166 181 196 Time series(Jan.1961-Dec.1970) Time series(Jan.1971-Dec.1977)

Figure 6. Two parameter model calibration and validation for the pre-change period simulated and measured runoffs for the calibration and Although the model performance during the validation validation period. It can be seen that the model was period was not as good as that in calibration period, with calibrated generally well to the measured runoff depth, the larger BIAS and smaller R and NSCE, the model and the calibrated model performed well for the validation results presented in Figure 6 and Table IV are overall data, even though there are over or under estimates in acceptable, suggesting also that two-parameter model was some peak runoff. The optimized parameter values and applicable to estimate the effect of climate variability and statistics for evaluating the performance model are human activities in the four catchments of the Haihe summarized in Table IV. During the calibration period, River basin. After the two-parameter model is bench- the correlation coefficient (R) ranges from 0.89 to 0.93, marked with the hydro-meteorological conditions of the the NSCE coefficient ranges from 0.83 to 0.86, and all the baseline period (i.e. the pre-change period), meteoro- absolute values of BIAS coefficients are lower than 3%. logical data including precipitation and pan evaporation

Table IV. Model parameters and performance assessment of the two-parameter model

Parameter values BIAS Catchments CSC Period R NSCE (%)

Luanhe River 0.972 650 Simulation period (1961–1975) 0.91 0.84 2.96 Validation period (1976–1979) 0.85 0.75 3.07 Chao River 0.936 950 Simulation period (1961–1975) 0.92 0.85 1.12 Validation period (1976–1979) 0.83 0.73 4.26 Hutuo River 1.015 400 Simulation period (1961–1975) 0.93 0.86 0.10 Validation period (1976–1979) 0.79 0.62 4.20 Zhanghe River 0.895 550 Simulation period (1961–1970) 0.89 0.83 2.19 Validation period (1971–1977) 0.78 0.66 4.69 data for the human-induced period (i.e. the post-change Climate elasticity method. Taking the Luanhe River period) are used as input to simulate natural runoff catchment as an example, regarded as the slope in the without consideration of catchment characteristics change linear regression model of the change of runoff against or artificial water intake based on the validated model the change of precipitation in Equation (18), the parameter. The natural runoff series for the catchments precipitation elasticity eP is estimated to be 1.81 during the post-change period can thus be reconstructed (Figure 8), indicating that a 10% change in precipitation and the effects of climate variability and human activities will cause an 18.1% change in runoff in the Luanhe River can be quantitatively estimated on the basis of the general catchment. The precipitation elasticity of four catchments framework. was calculated and listed in Table V. The values of eP for the Luanhe River catchment, Chaohe River catchment, Hydrological sensitivity analysis method. The calibrated Hutuo River catchment and Zhanghe River catchment w value and the runoff sensitivity coefficients to precipitation were obtained as 1.81, 2.21, 1.68 and 1.52, respectively. b and PET g are shown in Table V. The comparison of AET The results indicate that the strongest and weakest calculated by Zhang’s curve and WBE for the four response of runoff to precipitation change can be found catchments is shown in Figure 7. All the R2 values are in the Chaohe River catchment and the Zhanghe River greater than 0.87, especially for the Hutuo River catchment catchment, respectively. with R2 values reaching 0.98. Concurrently, the simulated results for all the catchments present small mean absolute Effects of climate variability and human activities on runoff error. Generally, all the scatter points calculated by WBE and simulated by Zhang’s curve are concentrated around the 1 : 1 Figure 9 presents the comparison between simulated line (Figure 7) and all the w values are between 0.5 and 2 runoff based on the two-parameter model and observed (Table V), indicating that the results simulated by Zhang’s runoff for the post-change period. Compared with that curve are realistic and acceptable. For all the catchments, the during the pre-change period (Figure 6), the more obvious absolute value of runoff sensitivity coefficients to precipita- and larger difference between reconstructed and observed tion is larger than that to PET (Table V), revealing that the runoffs in the monthly runoff processes were found change in runoff was more sensitive to change in precipitation during the post-change period (Figure 9), indicating than to change in PET in this region. With the runoff runoff during the post-change periods should be driven by sensitivity coefficients to precipitation and PET calibrated, increased human activities in these regions. The quanti- runoff change caused by climate variability can be estimated tative effects derived from three different methods are by Equation (14), and quantitativelyestimatingtheeffectsof shown in Table VI. climate variability and human activities on runoff can be thus For all the catchments, the hydrological model method, achieved by Equation (3) hydrological sensitivity analysis method and climate elasticity method provided similar estimates of the changes in runoff for post-change period due to climate variability and human activities. Human activities were Table V. Model parameter for hydrological sensitivity analysis dominant factors for runoff change across all the method and precipitation elasticity for climate elasticity method catchments except the Hutuo River catchment. Taking the Luanhe River catchment as an example, human Hydrological sensitivity activity should be responsible for 61%, 67% and 57% analysis Climate elasticity runoff change computed by hydrological model, hydro- Catchments bg logical sensitivity analysis and climate elasticity method, respectively, whereas the percentages due to the climate Luanhe River 1.32 0.32 0.16 1.81 variability were 39%, 33% and 43%, respectively. The Chaohe River 1.07 0.23 0.11 2.21 Hutuo River catchment is the only catchment where Hutuo River 1.13 0.29 0.14 1.68 Zhanghe River 1.15 0.28 0.13 1.52 runoff reduction during the post-change period should be mainly attributed to climate variability (70%, 72% and

700 550 Luanhe River Chaohe River

650 500

600

450 550

500 400

450

R =0.92 350 R =0.87 Annual AET calculated from WBE (mm) Annual AET calculated from WBE (mm) 400 MAE=38.1 mm MAE=21.5 mm

350 300 350 400 450 500 550 600 650 700 300 350 400 450 500 550 Annual AET simulated by Zhang`s Curve (mm) Annual AET simulated by Zhang`s Curve (mm)

1000 700 Hutuo River Zhanghe River

900 650

800 600

700 550 600

500 500 R =0.98 R =0.96 450 Annual AET calculated from WBE (mm) Annual AET calculated from WBE (mm) 400 MAE=22.1 mm MAE=26.9 mm

300 400 300 400 500 600 700 800 900 1000 400 450 500 550 600 650 700 Annual AET simulated by Zhang`s Curve (mm) Annual AET simulated by Zhang`s Curve (mm)

Figure 7. Correlation relationships between annual actual evapotranspiration (AET) calculated directly from water balance equation (WBE) and simulated by Zhang’s curve for pre-change periods

2.0 ΔQC present a similar simulation for all the catchments. The phenomenon has been found that the effect of climate y = 1.81x 1.5 R2 = 0.67 variability is sensitive to the annual precipitation. When the precipitation is high, a positive effect of climate 1.0 variability on runoff can be easily found. Similar 0.5 conclusions were also found in some earlier studies in Q/Q Δ other area of China (e.g. Laohahe River catchment; Jiang 0.0 et al., 2011).

-0.5 DISCUSSION -1.0 -0.4 -0.2 0.0 0.2 0.4 0.6 Remarkable downward trends in runoff can be found in ΔP/P four catchments of the Haihe River basin despite the fi Figure 8. Relationship between proportional changes of annual precipi- precipitation presents signi cant decreasing trends only in tation and runoff of the Luanhe River catchment one catchment. This implies that runoff in most catchments might be affected by other factors (human 69% detected by hydrological model, hydrological activities) besides the precipitation. Concurrently, the sensitivity analysis and climate elasticity method, fact that the regression lines (Figure 5) for the post- respectively), which directly corresponds to the fact that change period always lie below that for the pre-change only the Hutuo River catchment is dominated by the period means that in post-change period, the same annual significant decreasing trends in precipitation. The gener- precipitation in the pre-change period produces less ally consistent results in quantifying the effects provided runoff, also suggesting that runoff should be driven by confidence in the methods used to separate the effects of increased human activities in the study area. Overall, climate change and human activities. Furthermore, to despite that no clear and consistent trends in precipitation present the temporal patterns of the yearly climate and PET were identified, the runoff presents statistically variability effect, the annual time series of ΔQC for the significant negative trends in annual runoff for all four post-change period were calculated by the hydrological catchments, indicating that there are other factors for model method, hydrological sensitivity analysis method explaining the abrupt change in the annual runoff. Water- and climate elasticity method (Figure 10). Although there related human activities including agricultural irrigation, are some differences in annual runoff change simulated dam construction and industry development are usually by the three methods for some years, especially for the considered to be responsible for the sharp decline in times when precipitation is high, the overall processes of runoff. The change points of runoff in the four catchments

80 0

70 100 60 200 50 Luanhe Rive r 40 300

Runoff (mm) 30 Precipitation 400 Precipitation (mm) 20 Measured runoff Simulated runoff 500 10

0 600 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 Time series (Jan.1980-Dec.2000)

40 0

35 100 30 200 25 Precipitation Chaohe River 20 Measured runoff 300 Simulated runoff Runoff (mm) 15 400 Precipitation (mm) 10 500 5

0 600 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 Time series (Jan.1980-Dec.2000)

50 0

45 100 40

35 200 30 Precipitation Hutuo River 25 Measured runoff 300 20 Simulated runoff Runoff (mm) 400 15 Precipitation (mm)

10 500 5

0 600 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 Time series (Jan.1980-Dec.2000)

40 0

35 100 30 200 25 Precipitation Zhanghe River 20 Measured runoff 300 Simulated runoff Runoff (mm) 15 400 Precipitation (mm) 10 500 5

0 600 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 Time series (Jan.1978-Dec.2000)

Figure 9. Comparison between monthly measured runoff and simulated runoff by the two parameter model for the post-change period detected by DMC and Pettitt’s test happened in the late hydrological prediction. Certainly, distributed physically 1970s, which corresponds to the start of China’slandreform based hydrological model may be preferred and even the when the absolute responsibility for productively managing most optimal choice for hydrological effect study the lands were returned to farmers. Therefore, increasing (Legesse et al., 2003). However, as a matter of fact, agricultural land and related agricultural water use as well as there are always limitations in practice for distributed water control works may be the main driving factors hydrological model to be applied at basin scale because of of runoff decline. This ﬁnding agrees with previous studies its complexity in model setup, time consumption in (e.g. Gao et al., 2002; Yang and Tian, 2009). calibration and validation, as well as data set requirements In this study, a simple two-parameter water balance involving topography, vegetation and soil hydraulic model was selected as the hydrological model for making properties and so on (Liu et al., 2009; Wei and Zhang,

Table VI. Effects of climate variability and human activity on mean annual runoff for post-change period using different estimation method

Proportional change in average annual runoff due to (%)

Two-parameter model method Hydrological sensitivity analysis method Climate elasticity method

Catchments Climate variability Human activity Climate variability Human activity Climate variability Human activity

Luanhe River 39 61 33 67 43 57 Chaohe River 46 54 34 66 35 65 Hutuo River 70 30 72 28 69 31 Zhanghe River 31 69 35 65 36 64

150 0 0 150 120 400 120 400 90 800 90 800 60 1200 60 1200 30 1600 30 0 1600 Q (mm) Q (mm) 2000 0 Δ Δ -30 2000 Precipitation (mm) 2400 Precipitation (mm) -30 -60 2400 Precipitation 2800 Precipitation -90 -60 Hydrological model method Hydrological model method 2800 -120 Hydrological sensitivity analysis method 3200 -90 Hydrological sensitivity analysis method Luanhe River Climate elasticity method Chaohe River Climate elasticity method -150 3600 -120 3200 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 Year Year

0 120 0 150 90 120 400 400

90 60 800 800 60 30 1200 1200 30 0

0 1600 (mm) 1600 Q Q (mm) -30 Δ Δ -30 2000 2000 Precipitation (mm) Precipitation (mm) -60 -60 Precipitation 2400 Precipitation 2400 -90 -90 Hydrological model method Hydrological model method Hydrological sensitivity analysis method Hydrological sensitivity analysis method 2800 -120 2800 -120 Hutuo River Climate elasticity method Zhanghe River Climate elasticity method -150 3200 -150 3200 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 Year Year

Figure 10. Climate variability effects on runoff (ΔQC) determined by hydrological model method, hydrological sensitivity analysis and climate elasticity method for post-change period, respectively

2010). The simple water balance model needs only small approach and provides independent assessment of climate data quantity, which usually includes only basic me- and anthropogenic effects on runoff. The climate teorological data such as precipitation and temperature elasticity method is similar to the hydrological sensitivity and normal hydrological data such as runoff series. analysis method (Dooge et al., 1999). However, the Normally, the simple water balance model is not expected difference between the hydrological sensitivity analysis to provide enough information compared with distributed method and climate elasticity employed in this study physically based model and even to work as good as mainly represents that (1) climate elasticity needs less physically based distributed model. Nevertheless, our data compared with the hydrological sensitivity method results show that it did not affect the performance of and (2) climate elasticity parameter can be directly simple water balance model in terms of quantifying the estimated by hydro-climatic data without parameter climate and anthropogenic effects on runoff. The adjustment based on non-parametric estimator. We only hydrological sensitivity analysis method has been use precipitation elasticity to estimate the effect on runoff considered and proved as the more ﬂexible method for in the present study because runoff responds directly to the advantage of being applicable to large basins where precipitation. The elasticities of other climate variables paired catchments are not practical (Zhao et al., 2010). (such as PET) estimated using different hydrological The consistent results with other methods in this study models are more likely to be different for the different and literature (e.g. Jiang et al., 2011) justify again that methods used by different models to simulate PET hydrological sensitivity analysis can be an alternative (Chiew, 2006). Therefore, precipitation elasticity eP is

Copyright © 2012 John Wiley & Sons, Ltd. Hydrol. Process. 27, 1158–1174 (2013) 1172 W. WANG ET AL commonly used to investigate the impacts of climate identified as the main factors causing significantly change on annual streamflow (e.g. Sankarasubramanian declined runoff in most catchments of the Haihe River et al., 2001; Sankarasubramanian and Vogel, 2003; basin (Yang and Tian, 2009). It is hence useful to separate Niemann and Eltahir, 2005; Chiew, 2006; Novotny and the effects of climate variability from that of human Stefan, 2007). Moreover, non-parametric estimator for eP activities. In this study, we employ three methods (i.e. was regarded as more appropriate with smaller bias and hydrological model method, hydrological sensitivity more robust than modeling approaches (Chiew, 2006). method and climate elasticity method) to quantitatively It should be noted that there are uncertainties associated assess the impacts of climate variability and human with assessing effects of climate variability and human activities on runoff in four catchments (i.e. the Luanhe activities on runoff. Although the estimating effects are River catchment, the Chaohe River catchment, the Hutuo relatively consistent, there is still a certain variation for River catchment and the Zhanghe River catchment) of the catchments with different methods (Table VI). Firstly, Haihe River basin. A statistically identified change point there are still limitations for the methods used in this (derived comprehensively from double cumulative curve study. The two-parameter model is easy to satisfy the data and Pettitt’s test) was first used to determine the baseline requirement but its disadvantages are also apparent. It period. A two-parameter model has been calibrated and provides less information about detailed description of validated based on hydro-climatic data during the pre- hydrological process compared with complex hydrologic- change period. Meanwhile, the parameters w, b and gin al model. Furthermore, the hydrological sensitivity the hydrological sensitivity analysis method are succes- analysis and climate elasticity method are only for the sively calibrated within the pre-change period, and the effect of the changes in runoff with changes in mean precipitation elasticity eP of climate elasticity method is annual precipitation. However, runoff can be influenced also determined with precipitation and runoff data during by changes in other precipitation characteristics, such as the same period. The effects of human activities on runoff seasonality, intensity and concentration. In addition, were evaluated as a difference between measured runoff occurrence of extreme runoff may also affect the accuracy and simulated runoff with no consideration of local of the hydrological sensitivity analysis method (Zhao human activities on the basis of two-parameter model. et al., 2010). Secondly, uncertainty may come from the Concurrently, the effects of climate variability on runoff approximation in the framework to separate the effect. It during the post-change period can be estimated with the should be noted that the framework used to estimate hydrological sensitivity analysis method and climate proportional contribution of climate variability and elasticity with the calibrated parameters. The climate human activities to runoff is based on the assumption and human activity effects can consequently be separated that human activities is independent of climate change by the general framework based on the assumption that (Zheng et al., 2009). However, the effects of human climate and human activities are dominant drivers for activities and climate are inter-related with each other and runoff change. The results show that three methods are not readily separable. Climate change may influence provide generally consistent estimates of the contributions the human activities such as land use and thus change of climate variability and human activities to runoff, runoff (Zheng et al., 2009). Conversely, extensive indicating that the period separation, the selected methods urbanization and expanded population may cause increase and conceptual framework are appropriate. in temperature and consequently result in change in Significant downward trends in runoff can be found in hydrological regime (Wang et al., 2010). Therefore, all the catchments, especially for Luanhe River, Hutuo despite that human activities and climate system interact River and Zhanghe River, which are dominated by with each other, even in baseline period, it is not significant decreasing trends at 99% confidence level, considered as such in the present study. Finally, whereas significant downward trend in precipitation can uncertainties may arise from limited hydro-climatic only be found in the Hutuo River catchment. The change variables observation data and model parameters. Scarce in the gradient of precipitation–runoff double cumulative meteorological station data may limit the simulation curves can be found in 1979 for the Luanhe River accuracy of the hydro-climatic variables such as PET and catchment, Chaohe River catchment and Hutuo River runoff. Moreover, uncertainty in model parameters can catchment and in 1977 for the Zhanghe River catchment, also inevitably affect the simulation results (Li et al., indicating that the relationship between precipitation and 2010; Jiang et al., 2011). Therefore, further studies should runoff has changed abruptly. As a result, the annual be conducted in future to improve the results of runoff during 1957–2000 can be divided into two periods quantification of climate and anthropogenic effects with named pre-change and post-change periods accordingly. consideration of these uncertainties. Human activities should be mainly responsible for the runoff reduction in the Luanhe River catchment (accounting for 61%, 67% and 57% by hydrological model SUMMARY method, hydrological sensitivity method and climate Global and regional climate variability (e.g. increased elasticity method, respectively), Chaohe River catchment temperatures and reduced precipitation) is regarded as an (accounting for 54%, 66% and 65%, respectively) and important factor affecting hydrological processes. Zhanghe River catchment (accounting for 69%, 65% and Concurrently, irrigation-based human activities have been 64%, respectively), whereas 70%, 72% and 69% of runoff

Copyright © 2012 John Wiley & Sons, Ltd. Hydrol. Process. 27, 1158–1174 (2013) EFFECTS OF CLIMATE VARIABILITY AND HUMAN ACTIVITIES ON RUNOFF CHANGES 1173 reduction in the Hutuo River catchment were attributed to Gao YC, Yao ZJ, Liu BQ, Lv AF. 2002. Evolution trend of Miyun reservoir inflow and its motivating factors analysis. Progress in climate variability. Geography 21: 546–553 (in Chinese). This study presents a quantitative assessment of the Gerstengarbe FW, Werner PC. 1999. Estimation of the beginning and effects of climate variability and human activities on end of recurrent events within a climate regime. Climate Research 11: 97–107. runoff in four catchments of the Haihe River basin. In Guo SL, Chen H, Zhang HG, Xiong LH, Liu P, Pang B, Wang GQ, Wang conjunction with other findings of the hydro-climatic YZ. 2005. A semi-distributed monthly water balance model and its variables change (e.g. Yang and Tian, 2009; Wang et al., application in a climate change impact study in the middle and lower Yellow River Basin. Water International 30(2): 250–260. 2011a,b), an ongoing crisis in water supply with Guo SL, Wang JX, Xiong LH, Ying AW. 2002. A macro-scale and semi- decreasing water resource amount and increasing water distributed monthly water balance model to predict climate change demand in the Haihe River basin has been portended by impacts in China. Journal of Hydrology 268:1–15. Hirsch RM, Slack JR, Smith RA. 1982. Techniques of trend analysis for the results of this study. Furthermore, the results of the monthly water quality data. Water Resources Research 18: 107–121, present study can serve as a reference for regional water DOI: 10.1029/WR018i001p00107. resources management and planning, and at the same Huntington TG. 2006. Evidence for intensification of the global water cycle: review and synthesis. Journal of Hydrology 319:83–95. time, also put forward a practically possible proposition Huo Z, Feng S, Kang S, Li W, Chen S. 2008. Effect of climate changes for local administrative managers to reasonably arrange and water-related human activities on annual stream flows of the the local actions and to reduce the negative effect of Shiyang river basin in arid north-west China. Hydrological Processes 22: 3155–3167. climate change to local water resources. IPCC. 2007. Summary for policymakers. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Solomon S, Qin D, Manning M, Chen Z, Marquis M, Avery KB, ACKNOWLEDGEMENTS Tignor M, Miller HL (eds). Gambridge University Press: Gambridge, fi United Kingdom and New York, NY, USE. Webpage: http://www.ipcc. 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