Stoch Environ Res Risk Assess (2010) 24:9–18 DOI 10.1007/s00477-008-0294-7 ORIGINAL PAPER Hydrologic alteration along the Middle and Upper East River (Dongjiang) basin, South China: a visually enhanced mining on the results of RVA method Yongqin David Chen Æ Tao Yang Æ Chong-Yu Xu Æ Qiang Zhang Æ Xi Chen Æ Zhen-Chun Hao Published online: 28 November 2008 Ó Springer-Verlag 2008 Abstract This paper presents a visually enhanced evaluation of the spatio-temporal patterns of the dam-induced hydrologic alteration in the middle and upper East River, south China over 1952–2002, using the range of variability approach (RVA) and visualization package XmdvTool. The impacts of climate variability on hydrological processes have been removed for wet and dry periods, respectively, so that we focus on the impacts of human activities (i.e., dam construction). The results indicate that: (1) along the East River, dams have greatly altered the natural flow regime, range condition and spatial variability; (2) six most remarkable indicators of hydrologic alteration induced by dam-construction are rise rate (1.16), 3-day maximum (0.91), low pulse duration (0.88), January (0.80), July (0.80) and February (0.79) mean flow of the East River during 1952–2002; and (3) spatiotemporal hydrologic alterations are different among three stations along Easter River. Under the influence of dam construction in the upstream, the degree of hydrologic changes from Lingxia, Heyuan to Longchuan Y. D. Chen Q. Zhang Department of Geography and Resource Management and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China T. Yang (&) X. Chen Z.-C. Hao State Key Laboratory of Hydrology-Water Resources and Hydraulics Engineering, Hohai University, 210098 Nanjing, People’s Republic of China e-mail: enigama2000@hhu.edu.cn; tfrank.yang@gmail.com T. Yang The Institute of Hydraulic Engineering of Yellow River, 450003 Zhengzhou, China C.-Y. Xu Department of Geosciences, University of Oslo, Oslo, Norway station increases. This study reveals that visualization techniques for high-dimensional hydrological datasets together with RVA are beneficial for detecting spatio-temporal hydrologic changes. Keywords Range of variability approach (RVA) Indicators of hydrologic alteration (IHA) Visual mining Dam construction Middle and Upper East River 1 Introduction Human activities have directly altered the flow of large rivers for thousands of years (Petts 1980). River systems have been modified worldwide for flood control, navigation, water supply, power generation, and recreation. Modification of both river and riparian habitats can range from relatively localized effects of small-scale grazing to broader effects of impoundment. As a result, many originally defining physical and ecological features of these systems have been profoundly altered (Petts 1979, 1980; Poff et al. 1997; Choi et al. 2005; Timme et al. 2005; Li and Zhang 2008). Altered flows have been one of the primary consequences of impoundments. An impoundment designed primarily for flood control, navigation and water supply tends to dampen natural flow variations by storing large amounts of water for later, controlled release (Kojiri et al. 1989; Bravard and Petts 1996; Mathlouthi and Lebdi 2008). Conversely, dams built for power generation tend to accentuate natural variability by creating daily high and low flow periods to meet electrical demands. Many researchers have developed indices to quantify flow characteristics that are believed to be sensitive to these human perturbations. Improved quantitative evaluations of 123 10 123 2 Study area and data The East River (Dongjiang), originating from Jiangxi province and situated mainly in Guangdong province, is one of the largest tributaries of the Pearl River (Zhujiang River, 97°390 –117°180 E, 3°410 –29°150 N), which is the third largest river basin in China (Fig. 1). The East River is 562 km long and has a drainage area of 27,040 km2, accounting for about 5.96% of the Zhujiang River basin (PRWRC 2006; Shi et al. 2005). Water resources in the East River basin have been highly developed and heavily committed for a variety of demands in flood control, water supply, hydropower, navigation, irrigation, and suppression of saltwater intrusion. The increasing municipal, industrial and agricultural demands of water resources has created substantial needs for further understanding of impacts of hydraulic structures, such as impoundments, dams and reservoirs, during the past 40 years. The construction of large dams along the mainstream and major tributaries has greatly altered the natural flow regime of the river. Presently, East River accounts for about 80% of Hong Kong’s annual water supply (PRWRC 2006); therefore, spatial patterns of hydrologic alterations of the East River water resources systems will be greatly important for sustainable social and economic development in the Zhujiang Delta that is one of the economically developed regions in China. A number of dams and reservoirs were built in the East River basin during 1958–1974 for multiple purposes, such as flood control, water supply and hydropower. Major dams and Riv er The East River Basin Fengshuba Reservior wu Li Riv er Longchuan g R fen Xin Xun human-induced hydrologic changes have been widely used to describe eco-environmental implications of hydrologic alterations in supporting ecosystem management and restoration plans. Richter et al. (1996) developed a method for assessing the degree of hydrologic alteration attributable to human disturbance within an ecosystem. This method, referred to as the ‘‘Indicators of Hydrologic Alteration’’ (IHA), is based either upon hydrologic data available within an ecosystem or upon model-generated data. Results of extensive study have indicated that the range of streamflow regime is a primary driving force in river ecosystems (Stanford and Ward 1996; Poff et al. 1997) and is an essential factor in sustaining aquatic environments (NRC 1992). Richter et al. (1997) proposed the range of variability approach (RVA) as the river management eco-targets, in which 33 hydrologic parameters (Richter et al. 1996, 1997; Richter and Richter 2000) were used to assess hydrologic alterations in terms of streamflow magnitude, timing, frequency, duration and rate of change. Application of RVA for assessing and mapping hydrologic alteration on a river basin scale was demonstrated by evaluating the impacts of dam construction on the hydrologic variability of two major rivers in the upper Colorado River Basin in Colorado and Utah, USA (Richter et al. 1998). RVA is regarded as a practical approach facilitating river restoration planning, yet the published literature still lacks in high-dimensional multivariate visualization in a large variety of spatio-temporal IHA (Yang et al. 2008). Thus, it is not convenient enough to detect spatio-temporal variations, extremes, range, frequency, and patterns of hydrologic alteration. In this regard, we present a visual alternative method as a complement to RVA to assess hydrologic alterations for the East River to tackle the limitations mentioned above. The distinctive benefits of this approach compared with those of traditional approaches (e.g., correlation and spectrum analysis, Mann– Kendall trend test, regression technique, etc.) are that it can offer interactive visualization of spatial and temporal information for high-dimensional samples with limited figures. From these figures more important spatio-temporal behavior (e.g., extreme, variation, range, frequency, trend, etc.) can be observed through the visually interactive method. The objectives of this paper therefore are: (1) to present a visually enhanced hydro-alteration assessment method for streamflow of East River with data series encompassing pre- and post-alteration periods; (2) to determine the spatial behavior of 33 IHA factors which features the hydrologic alteration in East River using parallel coordinates and glyph display techniques; and (3) to evaluate the impact of dams on the hydrological alteration along the East River, south China, using this visually enhanced approach. Stoch Environ Res Risk Assess (2010) 24:9–18 g an R. ji Xinfengjiang Xingfengjiang Reservoir Reservior ng Do Heyuan ng xia Qiu R. Streamflow gauges Lingxia Reservior Xizhi R. 80 E 100 E 120 E 140 E 100 E 120 E 40 N 30 N Basin outlet The Pearl River Basin 20 N 500 km Fig. 1 Location map of the Dongjiang basin and hydrological stations Stoch Environ Res Risk Assess (2010) 24:9–18 11 reservoirs here are the Fengshuba and Xinfengjiang dams and reservoirs in the middle- and up-stream (Fig. 1). The Fengshuba dam, located in the up-East River with a storage capacity of 1,940 million m3 and a drainage area of 5,150 km2, was built in 1970 to reduce floods downstream and provide water supply and hydropower as well (PRWRC 2006). The Xinfengjiang dam, the largest multi-functional hydro-reservoir in Guangdong province with a storage capacity of 13,980 million m3, was constructed during 1958–1962 (PRWRC 2006). Hence, to evaluate the potential hydrologic alterations caused by large dam-construction, this paper targets the study region in the middle and upper East River basin to conduct the evaluation of dam-induced hydrologic alteration, where other human activities (e.g., water diversion and sediment dredging) can be ignored. Daily streamflow data from three gauging stations in the East River basin were analyzed in the current study (Fig. 1; Table 1 The streamflow gauging stations in the East River basin Stations Locations Longchuan 115°150 E Drainage area (km2) 24°070 N Series length (year) 7,699 1952–2002 Heyuan 114°42 E 23°440 N 15,750 1951–2002 Lingxia 114°340 E 23°150 N 20,557 1953–2002 0 Table 1). In the basin, Longchuan is situated downstream of the Fengshuba reservoir, and Heyuan and Lingxia are located downstream of the Xinfengjiang reservoir. The streamflow data are divided into pre- and post-dam periods based on the construction period of the reservoirs (TNC 2001). Hence, the length of the daily mean flow record of the pre- and post-dam period varied among gauging stations. Detailed information on the data is given in Table 1. 3 Methodology 3.1 Range of variability approach The RVA uses 33 hydrologic parameters to evaluate potential hydrologic alterations (Richter et al. 1997; TNC 2001). Of these parameters, sixteen hydrologic parameters focus on the magnitude, duration, timing, and frequency of extreme events and geomorphology; the other 16 parameters measure the central tendency of either the magnitude or rate of change of water conditions. Therefore, these 33 IHA parameters provide a detailed representation of the hydrologic regime for assessing hydrologic alterations. According to Richter et al. (1997), these parameters can be categorized into five groups addressing the magnitude, timing, frequency, duration, and rate of change (Table 2). Table 2 Summary of Hydrologic parameters used in the RVA, and their features (Richter et al. 1997) General group Regime features Streamflow parameters used in the RVA Group 1: Magnitude of monthly water conditions Magnitude, timing Mean value for 12 calendar month Group 2: Magnitude and duration of annual extreme conditions Magnitude, duration Annual minimum 1-day means Annual maximum 1-day means Annual minimum 3-day means Annual maximum 3-day means Annual minimum 7-day means Annual maximum 7-day means Annual minimum 30-day means Annual maximum 30-day means Annual minimum 90-day means Group 3: Timing of annual extreme water conditions Timing Group 4: Frequency and duration of high and low pulses Magnitude, frequency duration Annual maximum 90-day means Julian date of each annual 1-day maximum Julian date of each annual 1-day minimum Number of high pulses each year Number of low pulses each year Mean duration of high pulses within each year Mean duration of low pulses within each year Group 5: Rate and frequency of water condition changes Frequency, rate of change Means of all positive differences between consecutive daily values Means of all negative differences between consecutive daily values Number of rises Number of falls 123 12 Stoch Environ Res Risk Assess (2010) 24:9–18 The median, standard deviation, and range of these parameters are computed with pre-dam daily flows. The RVA target ranges of each hydrologic parameter are decided by selected percentile thresholds or a simple multiple of the parameter standard derivations for the natural or pre-dam streamflow regime. The management objective is not to ensure that the river attains the target range every year; rather, it is to attain the range at the same frequency as occurring in the natural or pre-dam flow regime. For example, attainment of RVA target range defined by the 25th and 75th percentile values of a particular parameter would be expected in only 50% of years. The degree to which the RVA target range is not attained is a measure of hydrologic alteration (HA). HA, expressed as a percentage, can be calculated as: Observed frequency Expected frequency HAð%Þ ¼ Expected frequency ð1Þ Hydrologic alteration is equal to zero when the observed frequency of post-dam annual values falling within the RVA target range equals the expected frequency. A positive value indicates that annual parameter values fell inside the RVA target window more often than expected; negative values indicate that annual values fell within the RVA target window less often than expected. 3.2 Removal of potential impacts of climate variability and change Various potential impacts of climate variability and change which had been mixed in the initial hydrological time series in the study region must be removed in advance of the RVA calculation. Generally, wet and dry years, which serve as the indicator of climate variability and change and lead to highand low-flow years, respectively, can be considered for separating the water years of hydrological time series to retain the same sources of impacts, i.e., dam construction on hydrologic regime. Yoo (2006) recommends that a proper period in which annual basin precipitation more than Pmean?0.75stdv (P C Pmean?0.75stdv) can be decided as a wet year, whereas that whose annual basin precipitation less than Pmean-0.75stdv is decided as a dry year (P C Pmean-0.75stdv). The years with annual basin precipitation more than Pmean-0.75stdv and less than Pmean?0.75stdv are considered as normal years (Pmean-0.75stdv B P B Pmean?0.75stdv). Thus, only streamflow records in normal-flow years are applied in the RVA hydrological alteration assessment after excluding the records in wet and dry years. The results of water year (wet, normal and dry year) separation of the streamflow timeseries for the middle and upper East River are shown in Fig. 2 and Table 3. The middle-flow years (normal years) and the 123 Fig. 2 Water year separation of the streamflow time-series for the middle and upper East River (The threshold of wet/dry year is accordingly referred to the results and recommendation by Yoo 2006. The shaded area represents the hydrological time-series containing in normal year which will be used in the RVA analysis, the records in wet year and dry year will be excluded out) Table 3 The middle-flow years in the middle and upper East River (Threshold: Pmean?0.75stdv = 1,471 mm, Pmean-0.75stdv = 1,891 mm) No. Year Mean precipitation (mm) No. Year Mean precipitation (mm) 1. 1952 1,518 17. 1979 1,736 2. 1953 1,739 18. 1980 1,717 3. 4. 1954 1955 1,821 1,498 19. 20. 1981 1982 1,849 1,664 5. 1957 1,703 21. 1984 1,634 6. 1960 1,790 22. 1985 1,784 7. 1961 1,883 23. 1986 1,592 8. 1964 1,832 24. 1987 1,747 9. 1965 1,623 25. 1988 1,557 10. 1966 1,862 26. 1989 1,531 11. 1968 1,865 27. 1990 1,475 12. 1970 1,702 28. 1994 1,735 13. 1972 1,714 29. 1995 1,574 14. 1974 1,733 30. 1996 1,526 15. 1976 1,796 31. 1998 1,672 16. 1978 1,781 32. 2000 1,690 time of dam construction used in the current study are listed in Tables 3 and 4, respectively. 3.3 Visual approach on hydrologic alteration evaluation In many cases, visualization on the spatiotemporal environmental data can offer much more information than a Stoch Environ Res Risk Assess (2010) 24:9–18 13 Table 4 The middle-flow water year (Table 3) of streamflow records which are excluded the high-flow year (P B Pmean-0.75stdv, 1,471 mm) and the low-flow year (P C Pmean ? 0.75stdv, 1,891 mm) to remove the impact of climate variability and changes. Crosshatched bars represent the pre-dam period, shades bars represent the No. Dam or reservoir River 1. Fengshuba Mainstem of East River 2. Xinfengjiang Mainstem of East River Pre-dam period 1952-69 1952-57 post-dam period. The total number of years of record available for these pre- and post-periods are in parentheses within each bar. The construction dates for each reservoir are identified within each interlude between pre- and post-dam periods Construction period 11 years 5 years 1970-74 1958-62 Post-dam period 1975-2002 1963-2006 (18 years) (25 years) (1) The hydrological dataset of Longchuan station will be separated into pre- and post-alternation periods in terms of the construction period (1970–1974) of Fengshuba dam which is the neighborhood dam in upper stream to Longchuan station (2) The hydrological dataset of Heyuan and Lingxia station will be separated into pre- and post-alternation periods with the total construction period (1958–1974) containing Fengshuba and Xingfengjiang dam which influenced the natural hydrological regime of downstream collectively table of numbers, which often leads to new insights and decisions based on more information. Haan (2002) states that because of the larger amounts of data available to hydrologists, graphical display of the information is a useful initial step in data analysis. Keim and Kriegel (1996) note that visual data mining techniques have proven to be of high value in exploratory data analysis and those techniques have a high potential for mining large datasets. Reimann et al. (2001) report that multi-element and multimedium regional geochemical mapping has been found to be a tool for understanding the cycling of elements in the environment. Consecutive element maps are still the usual way of visualizing spatial and temporal trends in the distribution of an element (Katrin 2005). A freeware visualization tool for high-dimensional data XmdvTool (1993) was chosen to display and explore datasets for evaluating hydrologic alteration. In a scatterplot matrix, which can be opened as an auxiliary display window, twodimensional scatterplots of all pairs of variables are plotted (Fig. 3a). On the other side, each dimension corresponds to an axis, and in a parallel coordinate display, the N axes are organized as uniformly spaced vertical lines. A data element in N-dimensional space manifests itself as a connected set of points (one on each axis) forming a polyline (Fig. 3b). Brushing is a selection process in which the user can highlight (select) or mask (hide) a subset of data being graphically displayed by pointing at the data elements, and brushing is associated with linking, which means brushing data elements in one view affects the same data in all other views (Fig. 3). The shape of the brush is an N-dimensional hyperbox, and the user specifies N brush dimensions using N slider bars. Brushes are displayed as shaded regions, where data points which fall within the brush are highlighted in a different color. Fig. 3 Visualization of twodimensional point data as a scatterplot (a) and in parallel coordinates (b). A brush defined in two dimensions (shaded area) selects the highest four values of variables A (ranging from -0.50 to 10.50 in the vertical coordinate) and B (ranging from -1.00 to 21.00 in the vertical coordinate) (black points/polylines) 123 14 Stoch Environ Res Risk Assess (2010) 24:9–18 4 Results (A) 4.1 Evaluation of hydrologic alteration caused by dam construction based on RVA approach The subsequent assessment of hydrologic alteration from three important aspects can be made: (1) (2) (3) Hydrologic alteration of mean monthly streamflow: it can be seen from Fig. 4 that mean monthly streamflows along the river are altered differently by the construction of dams. The results hereby demonstrate the flood cutting-off adjustment of the Xinfengjiang dam (Fig. 4b, c, Heyuan and Lingxia), the largest multi-functional hydro-reservoir in Guangdong province with a remarkable storage capacity, exerted considerable impacts on the mean July flow than the remaining months of the year. Whereas, these effects of Fengshuba dam with a smaller storage capacity are less (Fig. 4a, Longchuan). Besides, the release of runoff storage for irrigation and hydro-power in nonflood seasons obviously increases the mean monthly flow of the East River. With help of RVA approach, we further analyze the comprehensive hydrologic changes of the River. The results (Table 5) show that hydrologic changes of mean monthly flow in the middle of flood period (July) and in winter (January, February and March) are more obvious than other months in the transition periods. Among the 12 months, a low change (0.18) of mean monthly flow in August is observed, which could be due to August is a transition month from flood period to dry period in the region. Hydrologic alteration of extreme values: The alteration of multi-day maximum (or minimum) is represented by the change of the highest (or lowest) multi-day average value of the year. High hydrologic alterations of 7-, 30-, 90-day minimum, 1-, 3-, 7-day maximum (Table 5; Fig. 5) suggest substantial environmental stress and disturbance on the East River caused by dams. Hydrologic alteration of frequency and duration of high and low pulses: The pulsing behavior of the East River has been severely affected, because both high and low pulses occur with different frequencies, where low pulses at Heyuan and Lingxia occur with higher frequencies. The average duration of pulses, on the other hand, is much shorter in the post-dam period (Table 5; Fig. 5). This is a byproduct of hydropower generation, wherein water is stored in the reservoir until sufficient head is attained to generate power efficiently, at which time the flow is rapidly released through the dam turbines (Richter 123 (B) (C) Fig. 4 Hydrologic alterations of 12 monthly flow in the pre- and post-dam period, the specific pre- and post period for each station can be referred to Table 4 et al. 1996). The impacts of hydro-project on the hydrologic regimes are elucidated by the greater frequency of high and low pulses of lesser duration and also the increase in the number of hydrograph rises and falls. Stoch Environ Res Risk Assess (2010) 24:9–18 Table 5 Summary of absolute 33 indicators of hydrologic alternation for three stream stations along the East River (1) The dashes represent that there are no observed events falling inside the target range (from Median -25% to Median ?25%), thus they have not been employed in the evaluation of hydrologic alternation (2) Some mean values of 3 stream stations have not been offered herein considering that there are two dashes or above existing for the specific item (3) ‘–’ means the value exceeds the valid ranges 15 IHA factors Longchuan Heyuan Lingxia Mean value (3 stations) 1. January 0.72 0.92 0.77 0.80 2. February 0.91 0.69 0.77 0.79 3. March 0.81 0.84 0.66 0.77 4. April 0.72 – – – 5. May 0.81 – – – 6. June 0.44 0.77 0.71 0.64 7. July 0.63 0.87 0.89 0.80 8. August 0.34 0.07 0.14 0.18 9. September 0.68 0.84 0.02 0.51 10. October 0.16 0.38 0.66 0.40 11. November 0.44 0.53 0.77 0.58 12. December 0.03 0.61 0.77 0.47 13. 1-day minimum 0.63 0.46 – 0.55 14. 3-day minimum 0.34 0.77 – 0.56 15. 7-day minimum 16. 30-day minimum 0.63 0.72 0.77 0.69 – – 0.70 0.71 17. 90-day minimum 0.63 0.92 – 0.78 18. 1-day maximum 0.53 0.92 – 0.73 19. 3-day maximum 0.91 0.92 0.89 0.91 20. 7-day maximum 0.16 0.92 0.71 0.60 21. 30-day maximum 0.16 0.77 0.89 0.61 22. 90-day maximum 0.22 0.53 0.20 0.32 23. Number of zero days 0 0 0 0 24. Base flow index 0.53 – 0.94 0.74 25. Date of minimum 0.91 0.92 0.37 0.73 26. Date of maximum 0.53 0.61 0.66 0.60 27. Low pulse count 0.58 0.77 0.77 0.71 28. Low pulse duration 0.93 – 0.83 0.88 29. High pulse count 0.53 0.81 0.66 0.67 30. High pulse duration 0.64 0.48 0.77 0.63 31. Rise rate 32. Fall rate 1.53 – 1.33 – 0.61 1.16 – 33. Number of reversals – – – – Mean value 0.57 0.71 0.64 0.64 In summary, the six most remarkable dam-induced impacts factors are the rise rate (1.16), 3-day maximum (0.91), low pulse duration (0.88), January (0.80), July (0.80), February (0.79), whereas the seven most slight daminduced impact factors are August (0.18), 90-day maximum (0.32), October (0.40), December (0.47), September (0.51), 1- and 3-day minimum(0.55 and 0.56) in the middle and upper East River, South China, during 1952–2002. The variability of monthly mean flow, extremely low water conditions, timing of annual high and lows, high and low pulse durations, and frequency and rate of hydrograph rises and falls in summer and winter, has been reduced along the middle and upper East River. On the other hand, increased coefficients of variation and flow magnitude in dry season can be observed. 4.2 A visual explanation of extreme indicators of hydrologic alteration (1) Visualization of hydrologic alteration through parallel-coordinates display Within this study, a parallel-coordinate display of the combined indicators of hydrologic alteration dataset for the middle and upper East River is shown in Fig. 6. The degree of hydrologic alteration of each station is plotted on the 123 1.5 1.0 Longchuan Heyuan 75%Percentlie=0.77 Lingxia Meanvalue(3 stations) 25%Percentlie=0.56 0.5 ru a M ry ar ch A p ri M l a Ju y n e J A u S u ly ep gu te st m O b e N cto r o ve be r -d De mb a c e 3- y m em r d a in be im r 7- y d m u 30 ay in m -d m im 90 ay in um -d m im u 1- ay ini m d m m a u 3- y ini m d m m a a u 7- y xi m d m m 30 ay ax um -d m im a N 90 ay ximum u m -da ma u m b er y m xim a u o B f z xi m a e m D se ro um at fl d o D e o w ay s at f e m ind L of in ex L ow m im o w pu ax um p ls im H uls e um H igh e co ig d u h pu ur nt a p u lse tio ls e co n d u un R ra t is tio e n F ra al te lr at e 0.0 F eb Fig. 5 Degrees of Indicators of Hydrologic Alteration for three streamgauges on the middle and upper East River Stoch Environ Res Risk Assess (2010) 24:9–18 Degrees of Indicators of Hydologic Alternation 16 Fig. 6 Parallel coordinate display for 6 highest indicators of hydrologic alteration at three stream stations of the East River (1952–2002). Each item is represented by a polyline connecting vertical axes (in light grey), black lines represent indicators of hydrologic alternation at the selected Heyuan station, the other unselected stations were drawn in light grey. The shaded area consists of 50 highlighted polylines, each of them represents a set of 6 IHA degrees of Heyuan gauge in specific year (1952–2002). There are 150 polylines to describe the features of 6 IHA degrees for three sites in the East River vertical axes and each in the six most remarkable indicators of hydrologic alteration (January, February, July, 3-day maximum, Rise rate and 90-day minimum) forms a line (polyline). In Fig. 6 hydrologic alterations for Heyuan station are selected (black polyline) to visualize the variability of 6 hydrologic alteration items at the Heyuan station. It can be inferred that the black polyline shows the lowest value of flow in February and 3-day maximum and highest value in January. The simplicity of interactive brushing promotes visual queries, as different indictors, and sites can be brushed and localized in both space and time. 123 (2) Spatial patterns of hydrologic alteration implied by glyph display Figure 7 illustrates different spatial patterns of degrees of hydrologic alteration at 3 stations along the middle and upper East River induced by dam operations from 1952 to 2002. Each direction with a different length of a specific glyph represents an IHA factor with a different value (1– Stoch Environ Res Risk Assess (2010) 24:9–18 Fig. 7 Glyph display for six highest indicators of hydrologic c alteration at three streamflow stations of the East River (1952– 2002). a Longchuan station, b Heyuan station, c Lingxia station. Each number (1–33) on the edge of the circle corresponds to a IHA factor as is shown in Table 5. 17 (A) 32 1 33 2 3 31 4 30 5 29 6 28 7 27 33), which indicates the specific IHA factor listed in Table 5. Thus we can easily examine the degree of hydrologic changes from the direction and length of glyph. The slimmest glyph (Fig. 7a) implies that the impact on natural flow regime of Longchuan station imposed by the Fengshuba dam is the slightest among the three stations. Therefore, the overall degree of hydrologic change at Longchuan station is the lowest in the middle and upper East River. The most full-grown glyph suggests the remarkable degree of hydrologic alteration at Heyuan station and Lingxia station (Figs. 7b, c), perturbed by the largest dam—Xinfengjiang and Fengshuba. Comparison of Fig. 7b with c reveals that the impact of dam construction of Xinfengjiang on Lingxia is smaller than that on Heyuan for extreme values (e.g., number 15–18, Fig. 7b, c). This is because Lingxia station, with a long distance from Xinfengjiang (Fig. 7c), receives the natural confluence from the Qiuxiang River and district area (Heyuan to Lingxia). 8 6 9 5 1 24 11 23 12 22 13 21 14 20 19 18 17 16 15 (B) 5 Conclusion and discussion The influence of dam construction on hydrological regimes of the East River was systematically studied with the help of visual RVA-based method. Some interesting conclusions can be summarized as follows: 1) 2) 3) (C) Along the East River, dams have greatly altered the natural flow regime, range condition, and spatial variability. Six most remarkable indicators of hydrologic alteration induced by dam-construction are the rise rate (1.16), 3-day maximum (0.91), low pulse duration (0.88), January (0.80), July (0.80) and February mean flow (0.79) of the middle and upstream East River during non-flood seasons (1952–2002); whereas other 27 IHA factors remain moderate hydro-changes (percentiles \75%). The spatio-temporal hydrologic alterations are different among the three stations. It is implied that the overall degree of hydrologic change at Longchuan station is the lowest in the East River. The highest degree of hydrologic alteration is at Heyuan station, 123 18 Stoch Environ Res Risk Assess (2010) 24:9–18 perturbed by the largest dam—Xinfengjiang in Guangdong province and Fengshuba reservoir upstream as well. Lingxia station, receiving flow from the natural confluence of the Qiuxiang River and district area (Heyuan to Lingxia) exhibits a moderate degree of hydrologic alteration. Construction and operation of the reservoirs, with the aim to reduce flood disaster and sediment deposition, have inevitably caused significant hydrologic alterations, which severely changed the balance of natural eco-flow regime with substantial threat to wild species. With the alternative visual enhanced RVA method, the spatial variation and extremes of hydrologic alterations caused by dam construction in the East River over recent five decades was visually assessed and mined, which can reveal more behavior through spatial and temporal trends of hydrologic alterations than the traditional approach and can make results more convincing and effective to recognize the hydrological patterns and trends. The results of this study will be greatly helpful for the future management of water resources and will be greatly important for further understanding of the human impacts (e.g., hydro-dam projects) on hydrological regimes in the East River. Furthermore, it is significant for sustainable social and economic development in the Pearl River (Zhujiang) Delta which is one of the economically developed regions in China, south China. Acknowledgments The work described in this paper was supported by a key grant from the Chinese Ministry of Education (308012), key grant from the National Natural Science Foundation of China (40830639), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (CUHK4627/05H), an open research grant from the Key Sediment Lab of the Ministry for Water Resources (2008001), a National Key Technology R&D Program (2007BAC03A060301), and the Program of Introducing Talents of Discipline to Universities—the 111 Project of Hohai University (B08048). Cordial thanks should be extended to the Nature Conservancy, USA for the ‘Indicators of Hydrologic Alteration’ (IHA) software used in RVA computation, the XmdvTool for visualization of high-dimensional hydro-data developed by The University of California, Davis, and the Department of Water Resources and Environment, Sun Yat-sen University for providing hydrologic data of study area. Prof. V. P. 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