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Assessment of the flow regime alterations in the Lower Yellow River, China
Ecological Informatics 10 (2012) 56–64
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Assessment of the flow regime alterations in the Lower Yellow River, China Zhifeng Yang ⁎, Yan Yan, Qiang Liu State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
a r t i c l e
i n f o
Article history: Received 2 June 2011 Received in revised form 9 October 2011 Accepted 25 October 2011 Available online 2 November 2011 Keywords: Flow regime Hydrologic alteration Mann–Kendall method Indicators of hydrologic alteration The Yellow River
a b s t r a c t Flow regimes have become a fundamental part of ecological informatics to reveal the complex interactional mechanism lying between flow regimes and ecological system. In this study, the changes of flow regimes were investigated to obtain the suitable flow regimes for maintaining the ecological integrity in the Lower Yellow River, China. The temporal abrupt for annual streamflow was explored with Mann–Kendall method (M–K method), and alterations of flow regimes at daily scale were described in accordance with Indicators of Hydrologic Alteration (IHA) and Histogram Matching Approach (HMA). Results showed that: (i) the annual flow presented a downward abrupt in 1984, and after that year, the flow magnitude was smaller in general, and the frequency of low flow was much higher during all the twelve months; (ii) during the post-impact period, both of the maximum and minimum flow magnitudes for 1-day, 3-day, 7-day, 30-day and 90-day declined, and frequency distribution distances were larger than 70% except for 1-, 3-, 7- and 30-day minimum flows; (iii) the number and duration of low pulse extended for the post-impact period, whereas the number and duration of high pulse decreased; and (iv) suitable ranges of monthly magnitude as well as number and duration days for high/low pulses were obtained (e.g., 743 to 3979 m 3/s for monthly flow magnitude in July, August, September, October and November with similar target ranges, from 94 to 1075 m3/s for the rest seven months). The results indicate that the flow magnitude of the Yellow River has a decreasing trend, and some critical hydrologic characteristics should be taken into account due to their importance for ecosystems health in the downstream Yellow River Basin. © 2011 Published by Elsevier B.V.
1. Introduction River flow regimes are considered to be primary drivers of riverine ecosystems, and have become a fundamental part of ecological informatics for riverine ecosystems. Some ecological consequences of altered natural flow regimes have been reviewed (Lytle and Poff, 2004; Ye et al., 2010), and the impacts on river biota have been documented in detail as well (McVicar et al., 2007). For example, aquatic biological adaptations to flow regime are adequately recorded, ranging from behaviors that result in the avoidance of individual floods or droughts to life history strategies that are synchronized with long-term flow patterns. Another example is that the modification in timing, frequency and duration of floods can eliminate spawning or migratory cues for fish, or reduce access to spawning or nursery areas (Lytle and Poff, 2004). There is a growing need to predict the biological impacts associated with river flow regime alterations and set suitable targets to maintain riverine biota and ecosystems. Ecologically relevant hydrologic indicators have been proposed to describe or assess the alterations of natural river flow regimes in relation to the magnitude of flows; timing of extreme flows; the frequency, predictability and duration
⁎ Corresponding author. Tel.: + 86 1058807596. E-mail address: [email protected] (Z.F. Yang). 1574-9541/$ – see front matter © 2011 Published by Elsevier B.V. doi:10.1016/j.ecoinf.2011.10.002
of floods, droughts, and intermittent flows; daily, seasonal and annual flow variability; and rate of change (Lytle and Poff, 2004). During the past decade, more than 170 hydrologic metrics have been developed in attempting to describe different components of flow regime and capture the ecologically relevant streamflow attributes (Olden and Poff, 2003). The early studies mainly focus on the maintenance of minimum or suitable flows, daily variation, flow predictability, flood frequency, seasonal disturbance of monthly flow and annual series analysis. Virtually these hydrologic variations only describe some characteristic. However, the full range of natural flows other than certain flows is essential for ecosystem health. As a set of the proposed hydrologic indices, Indicators of Hydrologic Alteration (IHA) has been commonly used worldwide (Hu et al., 2008; Magilligan and Nislow, 2005; Shiau and Wu, 2004a, b; Yang et al., 2008b). It considered a full range of natural flow variability, including magnitude, frequency, timing, duration and rate of change (Richter et al., 1997). Thirty-two IHA parameters are categorized into five groups of hydrologic features, including: (i) magnitude of monthly streamflow; (ii) magnitude and duration of annual extreme streamflow; (iii) timing of annual extreme streamflow; (iv) frequency and duration of high and low pulses; and (v) rate and frequency of streamflow changes. Based on IHA, a number of methods have been developed. Black et al. (2005) proposed Dundee Hydrological Regime Alteration Method (DHRAM) to assess the degree and severity of hydrologic alteration by using IHA to classify the risk of damage to instream ecology.
Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
Yang et al. (2008b) used the fish community index as a target and employed Genetic Programming (GP) to identify the most ecologically relevant hydrologic indicators from 32 indicators of hydrologic alteration. Olden and Poff (2003) provided a comprehensive review of 171 available hydrologic indices (including IHA), and found that IHA can adequately characterize flow regimes with ecological knowledge. Based on IHA, Suen (2005) established Taiwan Ecohydrologic Indicator System (TEIS). All of these studies indicated that IHA, addressing full range of flow regimes indicators, can be applied in assessing the variation of hydrologic alteration and will be helpful in understanding the interaction between flow regimes and riverine ecosystem. In order to determine the flow regime targets by using IHA, the Range of Variability Approach (RVA) was established accordingly (Richter et al., 1996). RVA incorporates natural flow regimes to optimize the water release strategies, and presumes the natural (or preimpact) flow series to be the ideal condition. When environmental flow schemes attain the target ranges as the natural flow series at a 50% frequency (the interval between 25% and 75%), ecosystem health would be expected. When using RVA targets, the variations of parameter values falling beyond the target range are not taken into account (Shiau and Wu, 2008). Shiau and Wu (2008) revised the RVA and employed a Histogram Matching Approach (HMA). HMA uses the degree of histogram dissimilarity, employing the quadratic-form distance between frequency vectors of the pre- and post-impact histograms based on IHA, and describes the whole variance of hydrologic alterations. Both degrees of IHA variation and HMA distances can be used to assess hydrologic alteration from different perspectives, which can be helpful to understand changes of ecohydrological status and to provide suitable water resources management. Our goals are: (i) to document the type, magnitude, and direction of hydrologic shifts of the Lower Yellow River; and (ii) to identify and evaluate the most influential ecohydrological indicators offlow regimes for further ecological study and accordingly set suitable ranges of these indicators in the Yellow River, China. All of these will be helpful to provide more suitable water regulations to maintain the ecosystem health in the Lower Yellow River, China. 2. Study area and data processing 2.1. Study area The Yellow River is the second longest river in China and the world's second largest river in terms of sediment discharge to the sea. Originating from the Qinghai–Tibet Plateau in the far west of China, the Yellow River has a total of 5464 km before debouching into the Bohai Sea, with a draining area of about 752,000 km 2 (Wang et al., 2006). A huge load of sediment is supplied to the Bohai Sea through the China North Plain, forming a complex delta at the Bohai Sea coast (Saito et al., 2000). The decreasing precipitation in the drainage basin, as a natural result under global changes, and intensifying impact of human activities lead to a reduction of river streamflow during the past 50 years (Wang et al., 2006). The Lower Yellow River Basin is characterized by cascade dams and reservoirs, which were built in the middle and lower basin between 1950 and 2001, with the aim of controlling floods and reducing sediment deposition downstream (Yang et al., 2008a). Consequently, a steady decrease in sediment discharge was observed from the land to the sea, along with a dramatic fall of streamflow (Milliman and Meade, 1983). The decrease of sediment discharge has dramatic physical, ecological and geomorphologic impacts on the lower reaches of this river (Lin et al., 2001). Meanwhile, the reach below Lijin station is one of the most active areas of land–ocean interaction on the earth. These interactions have accumulative effects on hydrologic processes downstream and further alter river flow regimes towards the coastal ocean (Xu, 2003). More seriously, zero flow events have occurred at this area during the past 50 years, mostly in 1990s. Sediment decline
57
and flow regimes alteration further affected the wetlands ecosystem of the Yellow River Delta below Lijin station. 2.2. Data processing The daily streamflow was used to investigate the variation of flow regime at Lijin station (Fig. 1), which is the last station before the Yellow River reaches the Bohai Sea, and located in the Yellow River Delta, approximately 100 km from the sea. The daily observed flow data at Lijin gauge from 1958 to 2006 was provided by the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. Annual observed streamflow were obtained from daily streamflow at Lijin station and used to detect the temporal trend. These daily observed flow data was also used for IHA and HMA calculations. Both the degrees of variation and HMA frequency distribution distances were employed to explore the alterations of hydrologic indicators. The analysis was finally conducted with Matlab 7.0 in this study. 3. Methodology 3.1. Temporal trend of the annual streamflow Usually, the pre- and post-impact periods are divided by a separating year. The separating year is selected based on the point when the river flow regimes are disturbed significantly after huge dams are built or reservoirs are operated (Hu et al., 2008; Magilligan and Nislow, 2005; Richter et al., 1998; Yang et al., 2008b). However, when assessing the combined impact of human activities (e.g. dams operation and soil conservation measures) and climate change, the separating year cannot be selected solely from certain events. Mann–Kendall (M–K) method, which is widely employed in detecting the temporal and spatial trend of hydrologic time series, could reasonably identify the abrupt point (Khaliq et al., 2009; Miao et al., 2010). In this study, M–K method with a nominal rejection rate of 5% is used to reveal the temporal trends for more accurate results of annual streamflow. The year in which the abrupt occurred is chosen as the separating year for IHA calculation. The test statistic aij and dk are defined as: aij ¼
dk ¼
1; xi > xj ð1≤dj ≤di Þ 0; xi ≤xj
k X i−1 X
aij ðk¼ 2; 3; 4;…;nÞ
ð1Þ
ð2Þ
i¼1 j¼1
where the time series are x1, x2, …, xn, and n is the total number of data in the time series. The expected value E(dk) and variance Var(dk) are calculated as follows: Eðdk Þ ¼
kðk−1Þ 4
Varðdk Þ ¼
kðk−1Þð2k þ 5Þ 72
ð3Þ ð4Þ
The null hypothesis of no trend is tested by UF(dk), and if the standard normal probability |UF(dk)| > uα, the null hypothesis of no trend will be rejected. The UF(dk) constitutes curve C1. d −Eðdk Þ ffi UF ðdk Þ ¼ pkffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Varðdk Þ
ð5Þ
The corresponding rank series for retrograde rows are similarly obtained for retrograde samples (xn, xn-1, …, x1). Following the same
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Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
Fig. 1. Location of the Lijin station in the Yellow River, China.
procedure showed in Eqs. (1)–(5), the statistic vector UF'(dk) can be calculated for the retrograde samples. The statistic variable UB(dk) is defined as the following and constitutes curve C2.
UBðdk Þ ¼ −UF 0 ðdk0 Þ ðk¼ 1; 2;…;nÞ k0 ¼ n þ 1−k
ð6Þ
If the intersection point of C1 and C2 lies between the two confidence lines (P = α), we can reasonably conclude that the abrupt change takes place at that point. 3.2. Indicators of hydrologic alteration IHA is employed to evaluate the hydrologic alteration between pre- and post-impact periods. In this study, 33 parameters are categorized into 5 groups, i.e., the magnitude, timing, frequency, duration, and rate of change (Table 1). The 7-day minimum divided, number of zero flow days and number of flow reversals are included in the calculation instead of number of rises and falls (Richter et al., 1997). The 7-day minimum value is divided by annual mean flow, while number of flow reversals is the sum of the number of rises and falls. In this study, three steps are implemented to calculate IHA. (1) Define hydrologic data series in Lijin station for pre- and postimpact periods in the ecosystem; (2) Calculate the values of hydrologic attributes. We calculate values for each of 33 ecologically relevant hydrologic attributes for each year in each data series, i.e., one set of values for preimpact data series and one for post-impact data series; and (3) Estimate the degree of variation dQV. The degree of variation expressed as a percentage, can be calculated as: dQV ¼
Q Post Q Pre 100% Q Pre
ð7Þ
where QPost and QPre are annual mean values of post-impact and preimpact period of each variable in IHA, respectively. After 33 IHA are obtained for each year, the possible suitable ranges of lower and upper thresholds of IHA are calculated. The lower and upper thresholds are defined as the 1st and 3rd quadrates of each IHA data series.
3.3. Histogram matching approach method In order to assess the flow alteration of post-impact series from natural flow regimes, we employ HMA to evaluate the trend of river flow below Lijin gauge after the separating year, which is based on the quadratic-form distance between frequency vectors of pre- and post-impact histograms weighted by a specified similarity matrix (Shiau and Wu, 2008). nc ¼
rn1=3 2r iq
ð8Þ
in which nc is the number of classes, rrepresents the difference between maximum and minimum, n is the total number of data, and riqis defined as the difference between the third and first quartile values. dQH ðH; K Þ ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðjh−kjÞT Aðjh−kjÞ
ð9Þ
in which h = (h1, h2, …, hnc) T and k = (k1, k2, …, knc) T are the frequency vectors of the histograms H andK, respectively, and |h − k|is the distance vector. A=[aij], where aij is the similarity between classes iandj. aij ¼ 1−
dij dmax
ð10Þ
in which dij is the distance between classes Vi andVj, where Vi and Vjare the mean values of classes iandj, respectively; dmax is the distance between classes Vnc and V1. 4. Results 4.1. Trend analysis Annual streamflow, revealed by the M–K method (Eqs. 1 to 6), presented a downward abrupt change in 1984 (α=0.05) (Fig. 2). By the time of abrupt change, the streamflow from 1958 to 2006 was divided into pre-impacts period (1958–1983) and post-impact period (1984–2006) which represent the streamflow under natural conditions (little change resulting from climate change and human activities) and changeable conditions (obvious change due to climate change and
Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
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Table 1 IHA alteration, HMA distance and degree of variation for Lijin gauge. Hydrologic parameter
Pre-impact period:1958– 1983
Post-impact period:1984– 2006
Groups
Indicators
Means
Coeff. of variation
Means
Coeff. of variation
IHA Group 1
January February March April May June July August September October November December 1-day minimum 1-day maximum 3-day minimum 3-day maximum 7-day minimum 7-day maximum 30-day minimum 30-day maximum 90-day minimum 90-day maximum 7-day minimum divided Zero flow days Julian date of annual minimum Julian date of annual maximum Number of high pulses Number of low pulses Duration of high pulses Duration of low pulses Rise rate Fall rate Number of reversals
499 409 725 736 689 549 1574 2555 2744 2496 1473 763 53 8119 58 5924 68 4961 170 3706 384 2776 0 9 133 237 8 3 23 12 180 135 112
0.42 0.53 0.68 0.72 0.94 1.17 0.65 0.55 0.60 0.57 0.59 0.60 1.50 1.02 1.47 0.50 1.40 0.39 0.87 0.42 0.70 0.47 1.46 2.94 0.56 0.28 0.48 0.7 0.87 0.96 0.41 0.44 0.15
334 258 225 162 213 411 719 1086 964 664 512 345 19 2877 22 2705 26 2453 56 1759 137 1164 0 38 113 224 4 8 10 24 86 71 124
0.58 0.90 1.18 0.85 1.24 1.41 0.67 0.77 0.80 1.29 0.85 0.75 1.90 0.46 1.67 0.47 1.49 0.48 1.31 0.53 1.20 0.56 1.39 1.48 0.65 0.17 0.67 0.74 0.67 0.82 0.39 0.39 0.28
IHA Group 2
IHA Group 3 IHA Group 4
IHA Group 5
dQH
dQV
46.9 60.0 67.8 78.0 44.2 33.1 43.5 67.9 65.4 89.2 48.4 53.3 32.3 88.3 20.7 84.8 19.9 70.7 42.1 71.2 70.5 76.3 20.2 – 29.6 51.3 53.4 67.9 50.5 64.1 78.6 55.5 43.4
− 33.0 − 36.9 − 68.9 − 78.0 − 69.1 − 25.3 − 54.3 − 57.5 − 64.9 − 73.4 − 65.3 − 54.8 − 65.1 − 64.6 − 62.4 − 54.3 − 60.9 − 50.6 − 67.2 − 52.5 − 64.3 − 58.1 19.2 311.7 − 15.2 − 5.5 − 50.0 166.7 − 56.4 106.6 − 52.0 − 47.2 10.8
Note: The unit for monthly flow, 1-, 3-, 7-, 30- and 90-day maximum and minimum is m3/s, unit for zero flow days and duration of high and low pulse is days, and the rest of other IHA parameters are nondimensional. The unit for dQH and dQv is percentage (%).
human activities), respectively. According to the pre- and post-impact periods, streamflow were investigated to address flow regime characteristics by using IHA alteration and HMA calculation. The annual streamflow analysis in Fig. 3 showed a decreasing trend from 1958 to 2006 at Lijin gauge station, and the slope of linear trend was −9.1 × 108 m 3/a. The average annual streamflow was 4.0 × 1010 m 3 before 1984, whereas decreased to 1.6 × 1010 m3 after 1984. The annual streamflow has decreased by 61.1% than that before 1984. In most years after 1984, the streamflow kept below
2.0 × 1010 m 3, whereas before 1984, nearly all annual streamflow maintained above 2.0 × 1010 m3 except for 1960. During the years 1968–1983, the annual streamflow range was 2.0–6.0 × 1010 m 3, and before 1984 the magnitude of the streamflow was much higher, even higher than 6.0 × 10 10 m 3 at times. 4.2. Changes in the magnitude of flows at monthly scales Table 1 summarized results obtained by IHA and HMA, including dQV (degrees of variation calculated using Eq. 7) and dQH (the frequency distribution distance calculated from Eqs. (8)–(10)). If dQV and dQH both exceeded 50%, we would regard it as a signal of significant variation in the flow regimes indicators. Parameters in group 1 described the
Annual runoff (109 m3)
120
Annual runoff Post-1984 average
100
Pre-1984 average Trend
80 60 40 20 0 1958
1968
1978
1988
1998
2008
Year Fig. 2. The abrupt change tested by the Mann–Kendall method for annual flow series in Lijin station from 1958 to 2006.
Fig. 3. The temporal trends of annual streamflow from 1958 to 2006 in the Lijin station of Yellow River, China.
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Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
0.4
January
0.4
Frequency
Frequency
0.5
0.3 0.2 0.1 0
Febuary
0.3 0.2 0.1 0
162
349
536
724
911
120
246
372
Flow (m3/s) March
0.8
Frequency
Frequency
1 0.6 0.4 0.2 0
208
622
1037
1452
1.2 1 0.8 0.6 0.4 0.2 0
1867
240
720
Frequency
Frequency
0.8
May
0.8 0.6 0.4 0.2
1200
1680
June
0.6 0.4 0.2
778
1296
1815
162
2333
485
808 1131 1454 1777 2100 2423
Flow (m3/s) 0.6 0.5 0.4 0.3 0.2 0.1 0
Flow (m3/s) 1
July
Frequency
Frequency
876
0
259
August
0.8 0.6 0.4 0.2 0
464
1153
1841
2530
Flow (m 1.2 1 0.8 0.6 0.4 0.2 0
3218
3907
1160
2219
3/s)
3278
Flow 1
September
Frequency
Frequency
750
Flow (m3/s)
0
4338
5397
4170
5321
(m3/s)
October
0.8 0.6 0.4 0.2 0
1563
3133
4703
6273
715
1867
Flow (m3/s) 1
3018
Flow (m3/s) 0.8
November
0.8
Frequency
Frequency
624
April
Flow (m3/s) 1
498
Flow (m3/s)
0.6 0.4 0.2
December
0.6 0.4 0.2 0
0
801
1432
2063
2694
3325
Flow (m3/s)
296
581
865
1149
1433
1717
2001
Flow (m3/s)
Fig. 4. Frequency distribution of pre- and post-impact series of monthly flows in twelve months.
magnitude of monthly flow. The average of monthly flow throughout the post-impact period showed a decreasing trend, compared with an increasing one in the pre-impact period. Assessed by dQV, degrees of variation were all beyond 50% of monthly streamflow (over half of streamflow was reduced) in March, April, May, July, August, September, October, November and December. The dispersions of variation were
higher than those during the pre-impact period, indicating a higher monthly fluctuation in the post-impact period (Table 1). For HMA analysis, frequency distribution patterns of flow magnitude are illustrated in Fig. 4. Assessed by dQH, hydrologic parameters demonstrated obvious changes. The greatest variations at monthly scales were falling in seven months: February, March, April, August,
Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
4.3. Changes in the extremes and timing of flows
200 150 100 50 0 1958
1968
1978
1998
2008
Fig. 6. The variation of zero flow days in Lijin station from 1958 to 2008.
3-, 7-, 30- and 90-day maximum flows, and values of dQH were 88.3%, 84.8%, 70.7%, 71.2% and 76.3%, respectively (Table 1). For the annual 1-, 3-, 7-, 30- and 90-day minimum flow, degrees of variation were from − 50.6% to − 65.1%, which meant the minimum flow of post-impact period decreased notably. While frequency distribution distances were not large except for 90-day minimum flow between pre- and post-impact periods. The dQH were 32.3%, 20.7%, 19.9%, 42.1% and 70.5% for the annual 1-, 3-, 7-, 30- and 90-day minimum flow, respectively. The pattern of frequency distributions was much similar with pre-impact periods for 1-, 3-, 7- and 30day minimum flow. Zero flow days increased dramatically between pre- and postimpact periods (Fig. 6). From 1972 to 1984, it ranged from 0 to 26, while it varied from 13 to 202 during 1991–1999. Especially, there was a sharp increase in zero flow days during 1991–1997, reaching up to 202 days at Lijin station in 1997. In the following four years,, the number of zero flow days dropped rapidly and fell to zero in 2000. Zero flow events did not happen from 2000 to 2006 even the days under practical water regulation, e.g., the water sediment regulation implemented every year since 2002. The Julian date of annual 1-day minimum and maximum flows was slightly earlier for post-impact period. Assessed by dQV, the variation between pre- and post-impact periods was not large, with the dQV of −15.2% and − 5.5%, respectively. Nevertheless, the dQH was slightly larger than the dQV, with values of 29.6% and 51.3%, respectively (Table 1). As addressed in Fig. 7-a, b obtained by HMA, 1-day minimum flow and 1-day maximum flow tended to come earlier after 1984.
0.5
12000
0.4
10000 8000 6000 4000
0.3 0.2 0.1
2000
0 47.3
0 1958
1968
1978
1988
1998
2008
117.9
Frequency
1
0.6
0.3 0.2
0.4
0.1
0.2
0 19
0 6660
329.7
0.4
0.8
5204
259.1
(a)
(a)
3748
188.5
Julian date of 1-day minimum flow
Year
Frequency
1988
Year
Frequency
1-day maximum flow (m3/s)
Extreme events were characterized by maximum and minimum flows, zero flow days, Julian date of 1-day maximum and minimum, and high and low pulses. These parameters compromised IHA groups 2, 3 and 4. The annual 1-, 3-, 7-, 30- and 90-day maximum flow differed significantly between two periods. All values in post-impact period were smaller than those in pre-impact period. For example, the annual 1-day maximum flow had a decreasing trend from 1958 to 2006 (Fig. 5-a); the dQV of pre- and postimpact periods was − 64.6%, whereas the dQH was 88.3%; the frequency of flow below 4476 m 3/s was 0.90 for post-impact period but only 0.32 for pre-impact period (Fig. 5-b, obtained using HMA). On average, the annual 1-, 3-, 7-, 30- and 90-day maximum flow have declined over 50% (assessed by dQV), dropped by − 64.6%, –54.3%, − 50.6%, − 52.5% and − 58.1%, respectively. Simultaneously, frequency distribution distances were very large for annual 1-,
250
Zero-flow days
September, October and December (Table 1), and dQH for these five months were 60.0%, 67.8%, 78.0%, 67.9%, 65.4%, 89.2% and 53.3%, respectively. For March, the frequency of flow lower than 515 m 3/s was 0.81, while this figure fell to 0.14 when flow falling between 515 m 3/s and 829 m 3/s during post-impact period. Especially, frequency of flow above 829 m 3/s was only 0.05 during this period. However, during the pre-impact period, the frequency became 0.30 when flow reached 515 m 3/s and grew to 0.33 when flow ranged from 515 m 3/ s to 829 m 3/s. The frequency stayed 0.37 when flow kept higher than 829 m 3/s. For April, the frequency of flows below 480 m 3/s was 0.95, while flow above 960 m3/s was not observed during the post-impact period. However, the frequency of flow below 480 m 3/s was only 0.33 and flow higher than 960 m 3/s was 0.33 during the pre-impact period. The above results indicated that low flow frequencies for March and April during post-impact period were much higher than those in the preimpact period. Similar trends could also be found in February, August, September, October and December. Assessed by both dQV and dQH, the variations concentrated in March, April, August, September, October and December, since values of dQV and dQH both exceeded 50%.
61
8116
9572
1-day maximum flow (m3/s)
(b) Fig. 5. The variation (a) and frequency distribution (b) of annual 1-day maximum flow.
52
85
118 151 185 218
251 284 317
Julian date of 1-day maximum flow
(b) Fig. 7. The frequency distribution of Julian date for 1-day minimum flow (a) and 1-day maximum flow (b).
Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
100
1
80
0.8
Frequency
Mean duration days of high pulse
62
60 40 20
0.6 0.4 0.2
0 1958
1968
1978
1988
1998
0
2008
Year
9
20
32
(a)
54
66
77
88
(b)
100 0.6 80
Frequency
Mean duration days of low pulse
43
Mean duration days of high pulse (days)
60 40 20
0.5 0.4 0.3 0.2 0.1
0 1958
1968
1978
1988
1998
2008
0 4.2
12.6
21.0
29.4
37.8
46.2
54.6
Mean duration days of low pulse (days)
Year
(c)
(d)
Fig. 8. Mean duration days and frequency distribution of mean duration days for the high pulse and low pulse.
The number of high pulses was decreased by 50.0% (dQV = −50.0%) and the dQH was 53.4%, whereas for number of low pulses, the dQH and dQV were 166.7% and 67.9%, respectively. There was a decreasing trend of high pulse duration (Fig. 8-a). It dropped from 23 days to 10, with dQH and dQV of 50.5% and −56.4%, respectively (Table 1). The frequency of duration days shorter than 15 days was only 40% for pre-impact period, while nearly 80% for post-impact period (Fig. 8-b, obtained by HMA). It showed that high pulses had shrunk during the post-impact period. By contrast, the duration of low pulses had risen from 12 days in pre-impact period to 24 days in post-impact period. For mean duration of low flow pulses, there were two obvious peaks from 1993 to 1999 and from 1999 to 2006 (Fig. 8-c). The hydrologic alteration factors dQH and dQV were 64.1% and 106.6%, respectively. The frequency of duration days shorter than 16.8 days was nearly 90% in pre-impact period, while the frequency longer than 16.8 days was almost 60% during post-impact period (Fig. 8-d, obtained by HMA).
Table 2 Suitable ranges of monthly flow and the number and duration of high and low pulses. Hydrologic parameter
January February March April May June July August September October November December Number of high pulses Number of low pulses Duration of high pulses Duration of low pulses
Ecohydrological targets Lower threshold
Upper threshold
315 283 297 243 122 94 743 1508 1034 1426 756 451 4 2 7 8
681 491 1072 1075 1048 661 1851 3381 3605 3797 1906 933 7 9 22 16
Note: The unit for monthly flow and duration of high and low pulses was m3/s and days, respectively.
4.4. The possible suitable ranges of the IHA According to the results addressed in Table 1, we can conclude that: (i) the most differential parameters fell in March, April, August, September, October and December at monthly scales; (ii) for magnitude and duration of annual extreme streamflow, 1-, 3-, 7-, 30- and 90-day maximum flow as well as 90-day minimum flow had large variation; and (iii) the number and duration of high pulses and low pulses changed clearly between pre- and post-impact periods. All of these indicated that the possible suitable ranges should be guaranteed, especially for monthly flow in March, April, August, September, October and December with obvious alterations, which should be helpful in maintaining the ecological health of the Yellow River. Furthermore, high and low pulses with high ecological importance also should be maintained both for the number and the duration days. In practical regulation, targets should be kept from 743 to 3979 m 3/s for monthly flow magnitude in July, August, September, October and November with similar target ranges, and from 94 to 1075 m 3/s for the rest seven months (Table 2). When considering the suitable ranges of high/low pulses, we ranked the streamflow series of pre-impact period. The low pulses were those below 349 m 3/s (25%) and high pulses (75%) were those above 1690 m 3/s for preimpact period. Using these two values, the ranges of high/low pulses were recalculated (Table 2). The number of high pulses should range from 4 to 7 and duration days should range from 7 to 22 days for each year. Similarly, the number and duration days of low pulses should be controlled from 2 to 9 and from 8 to 16, respectively. 5. Discussion 5.1. The ecological response to the changes of the hydrologic variations Hydrological variations such as high pulse and magnitude can directly influence hydrologic process and riverine ecosystems (Lytle and Poff, 2004). High pulses, carrying huge of sediment, connect the river and adjacent floodplain wetlands, especially during the flooding period. Meanwhile, the frequency and durations of high pulses can change soil moisture gradients and create distinctive microhabitats, such as different vegetation patches inside the floodplain wetlands (Riis et al., 2008). Currently, in the Lower Yellow River, the number and duration days of high pulse have decreased by 50.0% and 50.5%,
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respectively. Consequently, the flooding area has shrunk and the area of bottomland expanded in contrast. The relationship between the river and its floodplain were seriously destroyed (Liu et al., 2010). Furthermore, it has dramatically changed the pattern of landscape in the Yellow River Delta. For example, the area of Phragmites australis kept shrinking before the restoration project implemented in 2002 (Cui et al., 2010). At the same time, altered high pulses in floodplain wetlands often favored exotic species while prevented the growth and survival of native aquatic macrophytes (Hu et al., 2008). Also, the timing of high pulse is very important for life history patterns of many stream and river animals (Bunn and Arthington, 2002). The lateral expansion of floodplain habitats during flooding period serves favorable spawning, nursery and foraging areas for a variety of fish species and other vertebrates (Koel and Sparks, 2002). Decreasing flow magnitude can increase the salinity of estuarine areas, resulting in many concomitant biological changes (Pringle, 2001). For example, the high salinity and low water content can impact seedlings and thus influence the growth rate of vegetation (Hu et al., 2008). Due to the increasing salinity, reverse succession of the vegetation populations occurred, with a decreasing coverage in the Yellow River Delta (Li et al., 2009). Since the salinity area expanded 50 years ago, Tamarix chinensis had spread widely in the Yellow River Delta (Cui et al., 2010). The sediment flux of the Yellow River showed a marked tendency to decline, owing to the low transport capability and trapping effect of its dams. During the period of 1986–2000, the sediment was only 31.2% of that in the period of 1950–1969, resulting in shoreline retreat, coastal erosion and delta degradation in the Yellow River Delta (Bianchi and Allison, 2009; Fan et al., 2010). On the contrary, after the water sediment regulation was carried out in 2002, high pulses carried away large loads of sediment with a reducing aggradations of the channel, and therefore the flow capacity was increased (Xu et al., 2005). 5.2. Precipitation and anthropogenic impacts on hydrologic alterations Precipitation is regarded as one of the key climatic factors in influencing river flow regime. The average annual precipitation in the Yellow River Basin from 1985 to 2000 was only 427.8 mm, approximately 10% less than that before 1960s (Wang et al., 2006). A similar decreasing trend was popular in most of the precipitation stations in the Yellow River during the past 50 years (Liu et al., 2008). On a large scale, the global climate change tremendously affected streamflow by shifting the pattern of precipitation, which was the major source for the Yellow River Basin. In 1997, the ENSO events led to the lowest precipitation in the drainage basin (350.3 mm) among the 50-year record, and consequently resulted in the lowest natural river discharge (3.1 × 10 10 m 3) and the lowest streamflow to the sea (Wang et al., 2006). This effect was of great concern because the Lower Yellow River had no streamflow to the sea for a surprising period of 202 days. However, from 2000 to 2006, zero flow events did not occur anymore, mainly due to water regulations such as water sediment regulation implemented in 2002. Other reasons for changes in flow regimes are human activities, such as construction of dams and reservoirs, soil conservation measures,
Table 3 Summary information of 4 major reservoirs in the mainstream Yellow River (Miao et al., 2010). Reservoir
Longyangxia
Launch time 1986 Storage capacity 24.70 9 3 (10 m ) Location Upper reaches
Liujiaxia
Sanmenxia
Xiaolangdi
1969 5.70
1960 9.7
2000 12.65
Upper reaches
Middle reaches
Middle reaches
63
deforestation and afforestation. The observed streamflow at Lijin gauge from 1984 to 2006 decreased to 38.9% compared with that from 1958 to 1983, whereas the natural discharge only dropped by 30.5% compared with that in 1950s. According to the statistics, more than 3147 reservoirs have been built in the Yellow River Basin, with a total storage capacity of 5.7 × 1010 m 3 in 2001 (Zhang et al., 2001), and 24 reservoirs scattered widely in the river basin with storage capacities exceeding 1.0 × 10 8 m 3 (Wang et al., 2006). A decreasing trend in monthly average streamflow in Lijin gauge has been observed from 1958 to 2006 due to the operation of the Longyangxia and Xiaolangdi reservoirs, started in 1986 and 2001, respectively (Table 3). The commencement time of large reservoirs is almost consistent with the abrupt year 1984. Using records at Huayuankou gauge, the measured streamflow in flood seasons accounted for more than 60% of annual water discharge in the 1950s, but it decreased to 43% in the 1990s, and the number of high pulse days was reduced sharply in response to the operation of dams and reservoirs (Wang et al., 2006), similar with the results revealed in this study. However, since the water sediment regulation began in 2002, duration of high pulses has increased, ranging from 11 to 20 days (Fig. 8-a), which is close to the target range. Meanwhile, soil conservation measure, deforestation and afforestation also have contributed to hydrological variations in the Yellow River (Li et al., 2007; Liu et al., 2009). For example, in order to reduce the soil erosion in the Loess Plateau, the Sloping Lands Conversion Program has resulted in a decrease of streamflow in the Yellow River (McVicar et al., 2007). Changes of vegetation may lead to a series of changes in hydrologic processes, e.g. vegetation intercepted precipitation, enhanced evaporation, improved soil structure, increased infiltration and then, reduce the amount of streamflow (McVicar et al., 2007; Miao et al., 2010). All of these indicated that further study should be conducted to reveal the interaction between the climate, vegetation and hydrologic processes, which should be helpful to provide a suitable water management and maintain the health of aquatic ecosystems, e.g. riparian ecosystems and wetlands. 6. Conclusion Flow regime is a primary determinant of structure and function of an aquatic and riparian ecosystem. By using M–K, IHA and HAM methods, the ecohydrological status of flow regimes in the Yellow River has been investigated. The conclusion can be drawn as follows. The downward trend has been detected for annual streamflow in 1984, which is used to divide pre- and post-impact periods during 1958–2006, probably resulted from climate change as well as human activities (e.g. dam construction and soil conversation measures). Comparing with that in the pre-impact period, hydrologic features demonstrated obvious changes during the post-impact period. The flow magnitude was smaller and the frequency of low flow events increased for all the twelve months; both maximum flows and minimum flows for 1-day, 3-day, 7-day, 30-day and 90-day reduced; the number and duration days of high pulse events presented a decreasing trend, while those of low pulse events addressed an increasing trend. In order to protect the ecosystem health in the Lower Yellow River, monthly flow magnitude, number and duration days of high pulses and low pulses should be applied as critical indicators for water management. The possible suitable range is from 743 to 3979 m 3/s for monthly flow in July, August, September, October and November, and it was from 94 to 1075 m 3/s for the rest seven months. For the number and duration days of high pulses, the possible suitable ranges were from 4 to 7 and from 7 to 22 days, respectively; but for low pulses, the ranges of the number and duration days were 2 to 9, and 8 to 16 days, respectively. Furthermore, the interaction between flow regime and ecological processes should be studied further, which would be helpful to provide more suitable water regulation measures.
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Acknowledgements This study was funded by the National Natural Science Foundation of China (No. 50939001) and National Basic Research Program of China (No. 2010CB951104). We would like to thank the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences for providing daily flow data of Lijin gauge from 1958 to 2006. References Bianchi, T.S., Allison, M.A., 2009. Large-river delta-front estuaries as natural “recorders” of global environmental change. Proceedings of the National academy of Sciences of the United States of America 106, 8085–8092. Black, A., Rowan, J., Duck, R., Bragg, O., Clelland, B., 2005. DHRAM: a method for classifying river flow regime alterations for the EC Water Framework Directive. Aquatic Conservation 15, 427–446. Bunn, S.E., Arthington, A.H., 2002. Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity. Environmental Management 30, 492–507. Cui, B.S., Yang, Q.C., Zhang, K.J., Zhao, X.S., You, Z.Y., 2010. Responses of saltcedar (Tamarix chinensis) to water table depth and soil salinity in the Yellow River Delta, China. Plant Ecology 209, 279–290. Fan, H., Huang, H., Zeng, T., 2010. Impacts of anthropogenic activity on the recent evolution of the Huanghe (Yellow) River Delta. Journal of Coastal Research 22, 919–929. Hu, W., Wang, G., Deng, W., Li, S., 2008. The influence of dams on ecohydrological conditions in the Huaihe River basin, China. Ecological Engineering 33, 233–241. Khaliq, M.N., Ouarda, T.B.M.J., Gachon, P., Sushama, L., St-Hilaire, A., 2009. Identification of hydrological trends in the presence of serial and cross correlations: a review of selected methods and their application to annual flow regimes of Canadian rivers. Journal of Hydrology 368, 117–130. Koel, T., Sparks, R., 2002. Historical patterns of river stage and fish communities as criteria for operations of dams on the Illinois River. River Research and Applications 18, 3–19. Li, L.J., Zhang, L., Wang, H., Wang, J., Yang, J.W., Jiang, D.J., Li, J.Y., Qin, D.Y., 2007. Assessing the impact of climate variability and human activities on streamflow from the Wuding River basin in China. Hydrological Processes 21, 3485–3491. Li, S.N., Wang, G.X., Deng, W., Hu, Y.M., 2009. Effects of runoff and sediment variation on landscape pattern in the Yellow River Delta of China (in Chinese). Advances in Water Science 20, 325–331. Lin, C., Su, J., Xu, B., Tang, Q., 2001. Longterm variations of temperature and salinity of the Bohai Sea and their influence on its ecosystem. Progress in Oceanography 49, 7–19. Liu, Q., Yang, Z., Cui, B., 2008. Spatial and temporal variability of annual precipitation during 1961–2006 in Yellow River Basin, China. Journal of Hydrology 361, 330–338. Liu, Q., Yang, Z.F., Cui, B.S., Sun, T., 2009. Temporal trends of hydro-climatic variables and runoff response to climatic variability and vegetation changes in the Yiluo River basin, China. Hydrological Processes 23, 3030–3039. Liu, Y.F., Zhang, J., Ma, Y., Shan, K., jin, X.H., Wang, J.H., 2010. Changes of wetland landscape pattern in Eastern Yellow River Delta Nature Reserve from 1995 to 1999 (in Chinese). Chinese Journal of Applied Ecology 21, 2904–2911. Lytle, D.A., Poff, N.L., 2004. Adaptation to natural flow regimes. Trends in Ecology & Evolution 19, 94–100. Magilligan, F.J., Nislow, K.H., 2005. Changes in hydrologic regime by dams. Geomorphology 71, 61–78.
McVicar, T., Li, L., Van Niel, T., Zhang, L., Li, R., Yang, Q., Zhang, X., Mu, X., Wen, Z., Liu, W., 2007. Developing a decision support tool for China's re-vegetation program: simulating regional impacts of afforestation on average annual streamflow in the Loess Plateau. Forest Ecological Management 251, 65–81. Miao, C., Ni, J., Borthwick, A., 2010. Recent changes of water discharge and sediment load in the Yellow River basin, China. Progress in Physics Geographical 34, 541–561. Milliman, J.D., Meade, R.H., 1983. World-wide delivery of river sediment to the oceans. Journal of Geology 91, 1–21. Olden, J., Poff, N., 2003. Redundancy and the choice of hydrologic indices for characterizing streamflow regimes. River Research and Applications 19, 101–121. Pringle, C.M., 2001. Hydrologic connectivity and the management of biological reserves: a global perspective. Ecological Applications 11, 981–998. Richter, B.D., Baumgartner, J.V., Powell, J., Braun, D.P., 1996. A method for assessing hydrologic alteration within ecosystems. Conservation Biology 10, 1163–1174. Richter, B.D., Baumgartner, J., Wigington, R., Braun, D., 1997. How much water does a river need? Freshwater Biology 37, 231–249. Richter, B., Baumgartner, J., Braun, D., Powell, J., 1998. A spatial assessment of hydrologic alteration within a river network. Regulatory River 14, 329–340. Riis, T., Suren, A.M., Clausen, B., Sand-Jensen, K., 2008. Vegetation and flow regime in lowland streams. Freshwater Biology 53, 1531–1543. Saito, Y., Wei, H., Zhou, Y., Nishimura, A., Sato, Y., Yokota, S., 2000. Delta progradation and chenier formation in the Huanghe (Yellow River) delta, China. Journal of Asian Earth Sciences 18, 489–497. Shiau, J., Wu, F., 2004a. Assessment of hydrologic alterations caused by Chi-Chi diversion weir in Chou-Shui Creek, Taiwan: opportunities for restoring natural flow conditions. River Research and Applications 20, 401–412. Shiau, J., Wu, F., 2004b. Feasible diversion and instream flow release using range of variability approach. Journal of Water Resources Planning and Management 130, 395–404. Shiau, J.T., Wu, F.C., 2008. A histogram matching approach for assessment of flow regime alteration: application to environmental flow optimization. River Research and Applications 24, 914–928. Suen, J.P., 2005. Ecologically based methods for multi-objective water resources management in Taiwan (Ph.D dissertation). Univ. of Illinois at Urbana-Champaign, Urbana. Wang, H., Yang, Z., Saito, Y., Liu, J.P., Sun, X., 2006. Interannual and seasonal variation of the Huanghe (Yellow River) water discharge over the past 50 years: connections to impacts from ENSO events and dams. Global and Planetary Change 50, 212–225. Xu, J.X., 2003. Sediment flux to the sea as influenced by changing human activities and precipitation: example of the Yellow River, China. Environmental Management 31, 328–341. Xu, G.B., Zhang, J.L., lian, J.J., 2005. Effect of water sediment regulation of the Yellow River on the lower reach (in Chinese). Advances in Water Science 16, 518–523. Yang, T., Zhang, Q., Chen, Y., Tao, X., Xu, C., Chen, X., 2008a. A spatial assessment of hydrologic alteration caused by dam construction in the middle and lower Yellow River, China. Hydrological Processes 22, 3829–3843. Yang, Y.C.E., Cai, X., Herricks, E.E., 2008b. Identification of hydrologic indicators related to fish diversity and abundance: a data mining approach for fish community analysis. Water Resources Research 44. doi:10.1029/2006WR005764. Ye, F., Chen, Q., Li, R., 2010. Modelling the riparian vegetation evolution due to flow regulation of Lijiang River by unstructured cellular automata. Ecological Information 5, 108–114. Zhang, X., Wang, L., Si, F., 2001. Prediction water consumption in the Huanghe river basin (in Chinese with English abstract). Water Resources and Hydropower Technology 6, 8–13.
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Ecological Informatics journal homepage: www.elsevier.com/locate/ecolinf
Assessment of the flow regime alterations in the Lower Yellow River, China Zhifeng Yang ⁎, Yan Yan, Qiang Liu State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China
a r t i c l e
i n f o
Article history: Received 2 June 2011 Received in revised form 9 October 2011 Accepted 25 October 2011 Available online 2 November 2011 Keywords: Flow regime Hydrologic alteration Mann–Kendall method Indicators of hydrologic alteration The Yellow River
a b s t r a c t Flow regimes have become a fundamental part of ecological informatics to reveal the complex interactional mechanism lying between flow regimes and ecological system. In this study, the changes of flow regimes were investigated to obtain the suitable flow regimes for maintaining the ecological integrity in the Lower Yellow River, China. The temporal abrupt for annual streamflow was explored with Mann–Kendall method (M–K method), and alterations of flow regimes at daily scale were described in accordance with Indicators of Hydrologic Alteration (IHA) and Histogram Matching Approach (HMA). Results showed that: (i) the annual flow presented a downward abrupt in 1984, and after that year, the flow magnitude was smaller in general, and the frequency of low flow was much higher during all the twelve months; (ii) during the post-impact period, both of the maximum and minimum flow magnitudes for 1-day, 3-day, 7-day, 30-day and 90-day declined, and frequency distribution distances were larger than 70% except for 1-, 3-, 7- and 30-day minimum flows; (iii) the number and duration of low pulse extended for the post-impact period, whereas the number and duration of high pulse decreased; and (iv) suitable ranges of monthly magnitude as well as number and duration days for high/low pulses were obtained (e.g., 743 to 3979 m 3/s for monthly flow magnitude in July, August, September, October and November with similar target ranges, from 94 to 1075 m3/s for the rest seven months). The results indicate that the flow magnitude of the Yellow River has a decreasing trend, and some critical hydrologic characteristics should be taken into account due to their importance for ecosystems health in the downstream Yellow River Basin. © 2011 Published by Elsevier B.V.
1. Introduction River flow regimes are considered to be primary drivers of riverine ecosystems, and have become a fundamental part of ecological informatics for riverine ecosystems. Some ecological consequences of altered natural flow regimes have been reviewed (Lytle and Poff, 2004; Ye et al., 2010), and the impacts on river biota have been documented in detail as well (McVicar et al., 2007). For example, aquatic biological adaptations to flow regime are adequately recorded, ranging from behaviors that result in the avoidance of individual floods or droughts to life history strategies that are synchronized with long-term flow patterns. Another example is that the modification in timing, frequency and duration of floods can eliminate spawning or migratory cues for fish, or reduce access to spawning or nursery areas (Lytle and Poff, 2004). There is a growing need to predict the biological impacts associated with river flow regime alterations and set suitable targets to maintain riverine biota and ecosystems. Ecologically relevant hydrologic indicators have been proposed to describe or assess the alterations of natural river flow regimes in relation to the magnitude of flows; timing of extreme flows; the frequency, predictability and duration
⁎ Corresponding author. Tel.: + 86 1058807596. E-mail address: [email protected] (Z.F. Yang). 1574-9541/$ – see front matter © 2011 Published by Elsevier B.V. doi:10.1016/j.ecoinf.2011.10.002
of floods, droughts, and intermittent flows; daily, seasonal and annual flow variability; and rate of change (Lytle and Poff, 2004). During the past decade, more than 170 hydrologic metrics have been developed in attempting to describe different components of flow regime and capture the ecologically relevant streamflow attributes (Olden and Poff, 2003). The early studies mainly focus on the maintenance of minimum or suitable flows, daily variation, flow predictability, flood frequency, seasonal disturbance of monthly flow and annual series analysis. Virtually these hydrologic variations only describe some characteristic. However, the full range of natural flows other than certain flows is essential for ecosystem health. As a set of the proposed hydrologic indices, Indicators of Hydrologic Alteration (IHA) has been commonly used worldwide (Hu et al., 2008; Magilligan and Nislow, 2005; Shiau and Wu, 2004a, b; Yang et al., 2008b). It considered a full range of natural flow variability, including magnitude, frequency, timing, duration and rate of change (Richter et al., 1997). Thirty-two IHA parameters are categorized into five groups of hydrologic features, including: (i) magnitude of monthly streamflow; (ii) magnitude and duration of annual extreme streamflow; (iii) timing of annual extreme streamflow; (iv) frequency and duration of high and low pulses; and (v) rate and frequency of streamflow changes. Based on IHA, a number of methods have been developed. Black et al. (2005) proposed Dundee Hydrological Regime Alteration Method (DHRAM) to assess the degree and severity of hydrologic alteration by using IHA to classify the risk of damage to instream ecology.
Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
Yang et al. (2008b) used the fish community index as a target and employed Genetic Programming (GP) to identify the most ecologically relevant hydrologic indicators from 32 indicators of hydrologic alteration. Olden and Poff (2003) provided a comprehensive review of 171 available hydrologic indices (including IHA), and found that IHA can adequately characterize flow regimes with ecological knowledge. Based on IHA, Suen (2005) established Taiwan Ecohydrologic Indicator System (TEIS). All of these studies indicated that IHA, addressing full range of flow regimes indicators, can be applied in assessing the variation of hydrologic alteration and will be helpful in understanding the interaction between flow regimes and riverine ecosystem. In order to determine the flow regime targets by using IHA, the Range of Variability Approach (RVA) was established accordingly (Richter et al., 1996). RVA incorporates natural flow regimes to optimize the water release strategies, and presumes the natural (or preimpact) flow series to be the ideal condition. When environmental flow schemes attain the target ranges as the natural flow series at a 50% frequency (the interval between 25% and 75%), ecosystem health would be expected. When using RVA targets, the variations of parameter values falling beyond the target range are not taken into account (Shiau and Wu, 2008). Shiau and Wu (2008) revised the RVA and employed a Histogram Matching Approach (HMA). HMA uses the degree of histogram dissimilarity, employing the quadratic-form distance between frequency vectors of the pre- and post-impact histograms based on IHA, and describes the whole variance of hydrologic alterations. Both degrees of IHA variation and HMA distances can be used to assess hydrologic alteration from different perspectives, which can be helpful to understand changes of ecohydrological status and to provide suitable water resources management. Our goals are: (i) to document the type, magnitude, and direction of hydrologic shifts of the Lower Yellow River; and (ii) to identify and evaluate the most influential ecohydrological indicators offlow regimes for further ecological study and accordingly set suitable ranges of these indicators in the Yellow River, China. All of these will be helpful to provide more suitable water regulations to maintain the ecosystem health in the Lower Yellow River, China. 2. Study area and data processing 2.1. Study area The Yellow River is the second longest river in China and the world's second largest river in terms of sediment discharge to the sea. Originating from the Qinghai–Tibet Plateau in the far west of China, the Yellow River has a total of 5464 km before debouching into the Bohai Sea, with a draining area of about 752,000 km 2 (Wang et al., 2006). A huge load of sediment is supplied to the Bohai Sea through the China North Plain, forming a complex delta at the Bohai Sea coast (Saito et al., 2000). The decreasing precipitation in the drainage basin, as a natural result under global changes, and intensifying impact of human activities lead to a reduction of river streamflow during the past 50 years (Wang et al., 2006). The Lower Yellow River Basin is characterized by cascade dams and reservoirs, which were built in the middle and lower basin between 1950 and 2001, with the aim of controlling floods and reducing sediment deposition downstream (Yang et al., 2008a). Consequently, a steady decrease in sediment discharge was observed from the land to the sea, along with a dramatic fall of streamflow (Milliman and Meade, 1983). The decrease of sediment discharge has dramatic physical, ecological and geomorphologic impacts on the lower reaches of this river (Lin et al., 2001). Meanwhile, the reach below Lijin station is one of the most active areas of land–ocean interaction on the earth. These interactions have accumulative effects on hydrologic processes downstream and further alter river flow regimes towards the coastal ocean (Xu, 2003). More seriously, zero flow events have occurred at this area during the past 50 years, mostly in 1990s. Sediment decline
57
and flow regimes alteration further affected the wetlands ecosystem of the Yellow River Delta below Lijin station. 2.2. Data processing The daily streamflow was used to investigate the variation of flow regime at Lijin station (Fig. 1), which is the last station before the Yellow River reaches the Bohai Sea, and located in the Yellow River Delta, approximately 100 km from the sea. The daily observed flow data at Lijin gauge from 1958 to 2006 was provided by the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. Annual observed streamflow were obtained from daily streamflow at Lijin station and used to detect the temporal trend. These daily observed flow data was also used for IHA and HMA calculations. Both the degrees of variation and HMA frequency distribution distances were employed to explore the alterations of hydrologic indicators. The analysis was finally conducted with Matlab 7.0 in this study. 3. Methodology 3.1. Temporal trend of the annual streamflow Usually, the pre- and post-impact periods are divided by a separating year. The separating year is selected based on the point when the river flow regimes are disturbed significantly after huge dams are built or reservoirs are operated (Hu et al., 2008; Magilligan and Nislow, 2005; Richter et al., 1998; Yang et al., 2008b). However, when assessing the combined impact of human activities (e.g. dams operation and soil conservation measures) and climate change, the separating year cannot be selected solely from certain events. Mann–Kendall (M–K) method, which is widely employed in detecting the temporal and spatial trend of hydrologic time series, could reasonably identify the abrupt point (Khaliq et al., 2009; Miao et al., 2010). In this study, M–K method with a nominal rejection rate of 5% is used to reveal the temporal trends for more accurate results of annual streamflow. The year in which the abrupt occurred is chosen as the separating year for IHA calculation. The test statistic aij and dk are defined as: aij ¼
dk ¼
1; xi > xj ð1≤dj ≤di Þ 0; xi ≤xj
k X i−1 X
aij ðk¼ 2; 3; 4;…;nÞ
ð1Þ
ð2Þ
i¼1 j¼1
where the time series are x1, x2, …, xn, and n is the total number of data in the time series. The expected value E(dk) and variance Var(dk) are calculated as follows: Eðdk Þ ¼
kðk−1Þ 4
Varðdk Þ ¼
kðk−1Þð2k þ 5Þ 72
ð3Þ ð4Þ
The null hypothesis of no trend is tested by UF(dk), and if the standard normal probability |UF(dk)| > uα, the null hypothesis of no trend will be rejected. The UF(dk) constitutes curve C1. d −Eðdk Þ ffi UF ðdk Þ ¼ pkffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Varðdk Þ
ð5Þ
The corresponding rank series for retrograde rows are similarly obtained for retrograde samples (xn, xn-1, …, x1). Following the same
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Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
Fig. 1. Location of the Lijin station in the Yellow River, China.
procedure showed in Eqs. (1)–(5), the statistic vector UF'(dk) can be calculated for the retrograde samples. The statistic variable UB(dk) is defined as the following and constitutes curve C2.
UBðdk Þ ¼ −UF 0 ðdk0 Þ ðk¼ 1; 2;…;nÞ k0 ¼ n þ 1−k
ð6Þ
If the intersection point of C1 and C2 lies between the two confidence lines (P = α), we can reasonably conclude that the abrupt change takes place at that point. 3.2. Indicators of hydrologic alteration IHA is employed to evaluate the hydrologic alteration between pre- and post-impact periods. In this study, 33 parameters are categorized into 5 groups, i.e., the magnitude, timing, frequency, duration, and rate of change (Table 1). The 7-day minimum divided, number of zero flow days and number of flow reversals are included in the calculation instead of number of rises and falls (Richter et al., 1997). The 7-day minimum value is divided by annual mean flow, while number of flow reversals is the sum of the number of rises and falls. In this study, three steps are implemented to calculate IHA. (1) Define hydrologic data series in Lijin station for pre- and postimpact periods in the ecosystem; (2) Calculate the values of hydrologic attributes. We calculate values for each of 33 ecologically relevant hydrologic attributes for each year in each data series, i.e., one set of values for preimpact data series and one for post-impact data series; and (3) Estimate the degree of variation dQV. The degree of variation expressed as a percentage, can be calculated as: dQV ¼
Q Post Q Pre 100% Q Pre
ð7Þ
where QPost and QPre are annual mean values of post-impact and preimpact period of each variable in IHA, respectively. After 33 IHA are obtained for each year, the possible suitable ranges of lower and upper thresholds of IHA are calculated. The lower and upper thresholds are defined as the 1st and 3rd quadrates of each IHA data series.
3.3. Histogram matching approach method In order to assess the flow alteration of post-impact series from natural flow regimes, we employ HMA to evaluate the trend of river flow below Lijin gauge after the separating year, which is based on the quadratic-form distance between frequency vectors of pre- and post-impact histograms weighted by a specified similarity matrix (Shiau and Wu, 2008). nc ¼
rn1=3 2r iq
ð8Þ
in which nc is the number of classes, rrepresents the difference between maximum and minimum, n is the total number of data, and riqis defined as the difference between the third and first quartile values. dQH ðH; K Þ ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðjh−kjÞT Aðjh−kjÞ
ð9Þ
in which h = (h1, h2, …, hnc) T and k = (k1, k2, …, knc) T are the frequency vectors of the histograms H andK, respectively, and |h − k|is the distance vector. A=[aij], where aij is the similarity between classes iandj. aij ¼ 1−
dij dmax
ð10Þ
in which dij is the distance between classes Vi andVj, where Vi and Vjare the mean values of classes iandj, respectively; dmax is the distance between classes Vnc and V1. 4. Results 4.1. Trend analysis Annual streamflow, revealed by the M–K method (Eqs. 1 to 6), presented a downward abrupt change in 1984 (α=0.05) (Fig. 2). By the time of abrupt change, the streamflow from 1958 to 2006 was divided into pre-impacts period (1958–1983) and post-impact period (1984–2006) which represent the streamflow under natural conditions (little change resulting from climate change and human activities) and changeable conditions (obvious change due to climate change and
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Table 1 IHA alteration, HMA distance and degree of variation for Lijin gauge. Hydrologic parameter
Pre-impact period:1958– 1983
Post-impact period:1984– 2006
Groups
Indicators
Means
Coeff. of variation
Means
Coeff. of variation
IHA Group 1
January February March April May June July August September October November December 1-day minimum 1-day maximum 3-day minimum 3-day maximum 7-day minimum 7-day maximum 30-day minimum 30-day maximum 90-day minimum 90-day maximum 7-day minimum divided Zero flow days Julian date of annual minimum Julian date of annual maximum Number of high pulses Number of low pulses Duration of high pulses Duration of low pulses Rise rate Fall rate Number of reversals
499 409 725 736 689 549 1574 2555 2744 2496 1473 763 53 8119 58 5924 68 4961 170 3706 384 2776 0 9 133 237 8 3 23 12 180 135 112
0.42 0.53 0.68 0.72 0.94 1.17 0.65 0.55 0.60 0.57 0.59 0.60 1.50 1.02 1.47 0.50 1.40 0.39 0.87 0.42 0.70 0.47 1.46 2.94 0.56 0.28 0.48 0.7 0.87 0.96 0.41 0.44 0.15
334 258 225 162 213 411 719 1086 964 664 512 345 19 2877 22 2705 26 2453 56 1759 137 1164 0 38 113 224 4 8 10 24 86 71 124
0.58 0.90 1.18 0.85 1.24 1.41 0.67 0.77 0.80 1.29 0.85 0.75 1.90 0.46 1.67 0.47 1.49 0.48 1.31 0.53 1.20 0.56 1.39 1.48 0.65 0.17 0.67 0.74 0.67 0.82 0.39 0.39 0.28
IHA Group 2
IHA Group 3 IHA Group 4
IHA Group 5
dQH
dQV
46.9 60.0 67.8 78.0 44.2 33.1 43.5 67.9 65.4 89.2 48.4 53.3 32.3 88.3 20.7 84.8 19.9 70.7 42.1 71.2 70.5 76.3 20.2 – 29.6 51.3 53.4 67.9 50.5 64.1 78.6 55.5 43.4
− 33.0 − 36.9 − 68.9 − 78.0 − 69.1 − 25.3 − 54.3 − 57.5 − 64.9 − 73.4 − 65.3 − 54.8 − 65.1 − 64.6 − 62.4 − 54.3 − 60.9 − 50.6 − 67.2 − 52.5 − 64.3 − 58.1 19.2 311.7 − 15.2 − 5.5 − 50.0 166.7 − 56.4 106.6 − 52.0 − 47.2 10.8
Note: The unit for monthly flow, 1-, 3-, 7-, 30- and 90-day maximum and minimum is m3/s, unit for zero flow days and duration of high and low pulse is days, and the rest of other IHA parameters are nondimensional. The unit for dQH and dQv is percentage (%).
human activities), respectively. According to the pre- and post-impact periods, streamflow were investigated to address flow regime characteristics by using IHA alteration and HMA calculation. The annual streamflow analysis in Fig. 3 showed a decreasing trend from 1958 to 2006 at Lijin gauge station, and the slope of linear trend was −9.1 × 108 m 3/a. The average annual streamflow was 4.0 × 1010 m 3 before 1984, whereas decreased to 1.6 × 1010 m3 after 1984. The annual streamflow has decreased by 61.1% than that before 1984. In most years after 1984, the streamflow kept below
2.0 × 1010 m 3, whereas before 1984, nearly all annual streamflow maintained above 2.0 × 1010 m3 except for 1960. During the years 1968–1983, the annual streamflow range was 2.0–6.0 × 1010 m 3, and before 1984 the magnitude of the streamflow was much higher, even higher than 6.0 × 10 10 m 3 at times. 4.2. Changes in the magnitude of flows at monthly scales Table 1 summarized results obtained by IHA and HMA, including dQV (degrees of variation calculated using Eq. 7) and dQH (the frequency distribution distance calculated from Eqs. (8)–(10)). If dQV and dQH both exceeded 50%, we would regard it as a signal of significant variation in the flow regimes indicators. Parameters in group 1 described the
Annual runoff (109 m3)
120
Annual runoff Post-1984 average
100
Pre-1984 average Trend
80 60 40 20 0 1958
1968
1978
1988
1998
2008
Year Fig. 2. The abrupt change tested by the Mann–Kendall method for annual flow series in Lijin station from 1958 to 2006.
Fig. 3. The temporal trends of annual streamflow from 1958 to 2006 in the Lijin station of Yellow River, China.
60
Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
0.4
January
0.4
Frequency
Frequency
0.5
0.3 0.2 0.1 0
Febuary
0.3 0.2 0.1 0
162
349
536
724
911
120
246
372
Flow (m3/s) March
0.8
Frequency
Frequency
1 0.6 0.4 0.2 0
208
622
1037
1452
1.2 1 0.8 0.6 0.4 0.2 0
1867
240
720
Frequency
Frequency
0.8
May
0.8 0.6 0.4 0.2
1200
1680
June
0.6 0.4 0.2
778
1296
1815
162
2333
485
808 1131 1454 1777 2100 2423
Flow (m3/s) 0.6 0.5 0.4 0.3 0.2 0.1 0
Flow (m3/s) 1
July
Frequency
Frequency
876
0
259
August
0.8 0.6 0.4 0.2 0
464
1153
1841
2530
Flow (m 1.2 1 0.8 0.6 0.4 0.2 0
3218
3907
1160
2219
3/s)
3278
Flow 1
September
Frequency
Frequency
750
Flow (m3/s)
0
4338
5397
4170
5321
(m3/s)
October
0.8 0.6 0.4 0.2 0
1563
3133
4703
6273
715
1867
Flow (m3/s) 1
3018
Flow (m3/s) 0.8
November
0.8
Frequency
Frequency
624
April
Flow (m3/s) 1
498
Flow (m3/s)
0.6 0.4 0.2
December
0.6 0.4 0.2 0
0
801
1432
2063
2694
3325
Flow (m3/s)
296
581
865
1149
1433
1717
2001
Flow (m3/s)
Fig. 4. Frequency distribution of pre- and post-impact series of monthly flows in twelve months.
magnitude of monthly flow. The average of monthly flow throughout the post-impact period showed a decreasing trend, compared with an increasing one in the pre-impact period. Assessed by dQV, degrees of variation were all beyond 50% of monthly streamflow (over half of streamflow was reduced) in March, April, May, July, August, September, October, November and December. The dispersions of variation were
higher than those during the pre-impact period, indicating a higher monthly fluctuation in the post-impact period (Table 1). For HMA analysis, frequency distribution patterns of flow magnitude are illustrated in Fig. 4. Assessed by dQH, hydrologic parameters demonstrated obvious changes. The greatest variations at monthly scales were falling in seven months: February, March, April, August,
Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
4.3. Changes in the extremes and timing of flows
200 150 100 50 0 1958
1968
1978
1998
2008
Fig. 6. The variation of zero flow days in Lijin station from 1958 to 2008.
3-, 7-, 30- and 90-day maximum flows, and values of dQH were 88.3%, 84.8%, 70.7%, 71.2% and 76.3%, respectively (Table 1). For the annual 1-, 3-, 7-, 30- and 90-day minimum flow, degrees of variation were from − 50.6% to − 65.1%, which meant the minimum flow of post-impact period decreased notably. While frequency distribution distances were not large except for 90-day minimum flow between pre- and post-impact periods. The dQH were 32.3%, 20.7%, 19.9%, 42.1% and 70.5% for the annual 1-, 3-, 7-, 30- and 90-day minimum flow, respectively. The pattern of frequency distributions was much similar with pre-impact periods for 1-, 3-, 7- and 30day minimum flow. Zero flow days increased dramatically between pre- and postimpact periods (Fig. 6). From 1972 to 1984, it ranged from 0 to 26, while it varied from 13 to 202 during 1991–1999. Especially, there was a sharp increase in zero flow days during 1991–1997, reaching up to 202 days at Lijin station in 1997. In the following four years,, the number of zero flow days dropped rapidly and fell to zero in 2000. Zero flow events did not happen from 2000 to 2006 even the days under practical water regulation, e.g., the water sediment regulation implemented every year since 2002. The Julian date of annual 1-day minimum and maximum flows was slightly earlier for post-impact period. Assessed by dQV, the variation between pre- and post-impact periods was not large, with the dQV of −15.2% and − 5.5%, respectively. Nevertheless, the dQH was slightly larger than the dQV, with values of 29.6% and 51.3%, respectively (Table 1). As addressed in Fig. 7-a, b obtained by HMA, 1-day minimum flow and 1-day maximum flow tended to come earlier after 1984.
0.5
12000
0.4
10000 8000 6000 4000
0.3 0.2 0.1
2000
0 47.3
0 1958
1968
1978
1988
1998
2008
117.9
Frequency
1
0.6
0.3 0.2
0.4
0.1
0.2
0 19
0 6660
329.7
0.4
0.8
5204
259.1
(a)
(a)
3748
188.5
Julian date of 1-day minimum flow
Year
Frequency
1988
Year
Frequency
1-day maximum flow (m3/s)
Extreme events were characterized by maximum and minimum flows, zero flow days, Julian date of 1-day maximum and minimum, and high and low pulses. These parameters compromised IHA groups 2, 3 and 4. The annual 1-, 3-, 7-, 30- and 90-day maximum flow differed significantly between two periods. All values in post-impact period were smaller than those in pre-impact period. For example, the annual 1-day maximum flow had a decreasing trend from 1958 to 2006 (Fig. 5-a); the dQV of pre- and postimpact periods was − 64.6%, whereas the dQH was 88.3%; the frequency of flow below 4476 m 3/s was 0.90 for post-impact period but only 0.32 for pre-impact period (Fig. 5-b, obtained using HMA). On average, the annual 1-, 3-, 7-, 30- and 90-day maximum flow have declined over 50% (assessed by dQV), dropped by − 64.6%, –54.3%, − 50.6%, − 52.5% and − 58.1%, respectively. Simultaneously, frequency distribution distances were very large for annual 1-,
250
Zero-flow days
September, October and December (Table 1), and dQH for these five months were 60.0%, 67.8%, 78.0%, 67.9%, 65.4%, 89.2% and 53.3%, respectively. For March, the frequency of flow lower than 515 m 3/s was 0.81, while this figure fell to 0.14 when flow falling between 515 m 3/s and 829 m 3/s during post-impact period. Especially, frequency of flow above 829 m 3/s was only 0.05 during this period. However, during the pre-impact period, the frequency became 0.30 when flow reached 515 m 3/s and grew to 0.33 when flow ranged from 515 m 3/ s to 829 m 3/s. The frequency stayed 0.37 when flow kept higher than 829 m 3/s. For April, the frequency of flows below 480 m 3/s was 0.95, while flow above 960 m3/s was not observed during the post-impact period. However, the frequency of flow below 480 m 3/s was only 0.33 and flow higher than 960 m 3/s was 0.33 during the pre-impact period. The above results indicated that low flow frequencies for March and April during post-impact period were much higher than those in the preimpact period. Similar trends could also be found in February, August, September, October and December. Assessed by both dQV and dQH, the variations concentrated in March, April, August, September, October and December, since values of dQV and dQH both exceeded 50%.
61
8116
9572
1-day maximum flow (m3/s)
(b) Fig. 5. The variation (a) and frequency distribution (b) of annual 1-day maximum flow.
52
85
118 151 185 218
251 284 317
Julian date of 1-day maximum flow
(b) Fig. 7. The frequency distribution of Julian date for 1-day minimum flow (a) and 1-day maximum flow (b).
Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
100
1
80
0.8
Frequency
Mean duration days of high pulse
62
60 40 20
0.6 0.4 0.2
0 1958
1968
1978
1988
1998
0
2008
Year
9
20
32
(a)
54
66
77
88
(b)
100 0.6 80
Frequency
Mean duration days of low pulse
43
Mean duration days of high pulse (days)
60 40 20
0.5 0.4 0.3 0.2 0.1
0 1958
1968
1978
1988
1998
2008
0 4.2
12.6
21.0
29.4
37.8
46.2
54.6
Mean duration days of low pulse (days)
Year
(c)
(d)
Fig. 8. Mean duration days and frequency distribution of mean duration days for the high pulse and low pulse.
The number of high pulses was decreased by 50.0% (dQV = −50.0%) and the dQH was 53.4%, whereas for number of low pulses, the dQH and dQV were 166.7% and 67.9%, respectively. There was a decreasing trend of high pulse duration (Fig. 8-a). It dropped from 23 days to 10, with dQH and dQV of 50.5% and −56.4%, respectively (Table 1). The frequency of duration days shorter than 15 days was only 40% for pre-impact period, while nearly 80% for post-impact period (Fig. 8-b, obtained by HMA). It showed that high pulses had shrunk during the post-impact period. By contrast, the duration of low pulses had risen from 12 days in pre-impact period to 24 days in post-impact period. For mean duration of low flow pulses, there were two obvious peaks from 1993 to 1999 and from 1999 to 2006 (Fig. 8-c). The hydrologic alteration factors dQH and dQV were 64.1% and 106.6%, respectively. The frequency of duration days shorter than 16.8 days was nearly 90% in pre-impact period, while the frequency longer than 16.8 days was almost 60% during post-impact period (Fig. 8-d, obtained by HMA).
Table 2 Suitable ranges of monthly flow and the number and duration of high and low pulses. Hydrologic parameter
January February March April May June July August September October November December Number of high pulses Number of low pulses Duration of high pulses Duration of low pulses
Ecohydrological targets Lower threshold
Upper threshold
315 283 297 243 122 94 743 1508 1034 1426 756 451 4 2 7 8
681 491 1072 1075 1048 661 1851 3381 3605 3797 1906 933 7 9 22 16
Note: The unit for monthly flow and duration of high and low pulses was m3/s and days, respectively.
4.4. The possible suitable ranges of the IHA According to the results addressed in Table 1, we can conclude that: (i) the most differential parameters fell in March, April, August, September, October and December at monthly scales; (ii) for magnitude and duration of annual extreme streamflow, 1-, 3-, 7-, 30- and 90-day maximum flow as well as 90-day minimum flow had large variation; and (iii) the number and duration of high pulses and low pulses changed clearly between pre- and post-impact periods. All of these indicated that the possible suitable ranges should be guaranteed, especially for monthly flow in March, April, August, September, October and December with obvious alterations, which should be helpful in maintaining the ecological health of the Yellow River. Furthermore, high and low pulses with high ecological importance also should be maintained both for the number and the duration days. In practical regulation, targets should be kept from 743 to 3979 m 3/s for monthly flow magnitude in July, August, September, October and November with similar target ranges, and from 94 to 1075 m 3/s for the rest seven months (Table 2). When considering the suitable ranges of high/low pulses, we ranked the streamflow series of pre-impact period. The low pulses were those below 349 m 3/s (25%) and high pulses (75%) were those above 1690 m 3/s for preimpact period. Using these two values, the ranges of high/low pulses were recalculated (Table 2). The number of high pulses should range from 4 to 7 and duration days should range from 7 to 22 days for each year. Similarly, the number and duration days of low pulses should be controlled from 2 to 9 and from 8 to 16, respectively. 5. Discussion 5.1. The ecological response to the changes of the hydrologic variations Hydrological variations such as high pulse and magnitude can directly influence hydrologic process and riverine ecosystems (Lytle and Poff, 2004). High pulses, carrying huge of sediment, connect the river and adjacent floodplain wetlands, especially during the flooding period. Meanwhile, the frequency and durations of high pulses can change soil moisture gradients and create distinctive microhabitats, such as different vegetation patches inside the floodplain wetlands (Riis et al., 2008). Currently, in the Lower Yellow River, the number and duration days of high pulse have decreased by 50.0% and 50.5%,
Z. Yang et al. / Ecological Informatics 10 (2012) 56–64
respectively. Consequently, the flooding area has shrunk and the area of bottomland expanded in contrast. The relationship between the river and its floodplain were seriously destroyed (Liu et al., 2010). Furthermore, it has dramatically changed the pattern of landscape in the Yellow River Delta. For example, the area of Phragmites australis kept shrinking before the restoration project implemented in 2002 (Cui et al., 2010). At the same time, altered high pulses in floodplain wetlands often favored exotic species while prevented the growth and survival of native aquatic macrophytes (Hu et al., 2008). Also, the timing of high pulse is very important for life history patterns of many stream and river animals (Bunn and Arthington, 2002). The lateral expansion of floodplain habitats during flooding period serves favorable spawning, nursery and foraging areas for a variety of fish species and other vertebrates (Koel and Sparks, 2002). Decreasing flow magnitude can increase the salinity of estuarine areas, resulting in many concomitant biological changes (Pringle, 2001). For example, the high salinity and low water content can impact seedlings and thus influence the growth rate of vegetation (Hu et al., 2008). Due to the increasing salinity, reverse succession of the vegetation populations occurred, with a decreasing coverage in the Yellow River Delta (Li et al., 2009). Since the salinity area expanded 50 years ago, Tamarix chinensis had spread widely in the Yellow River Delta (Cui et al., 2010). The sediment flux of the Yellow River showed a marked tendency to decline, owing to the low transport capability and trapping effect of its dams. During the period of 1986–2000, the sediment was only 31.2% of that in the period of 1950–1969, resulting in shoreline retreat, coastal erosion and delta degradation in the Yellow River Delta (Bianchi and Allison, 2009; Fan et al., 2010). On the contrary, after the water sediment regulation was carried out in 2002, high pulses carried away large loads of sediment with a reducing aggradations of the channel, and therefore the flow capacity was increased (Xu et al., 2005). 5.2. Precipitation and anthropogenic impacts on hydrologic alterations Precipitation is regarded as one of the key climatic factors in influencing river flow regime. The average annual precipitation in the Yellow River Basin from 1985 to 2000 was only 427.8 mm, approximately 10% less than that before 1960s (Wang et al., 2006). A similar decreasing trend was popular in most of the precipitation stations in the Yellow River during the past 50 years (Liu et al., 2008). On a large scale, the global climate change tremendously affected streamflow by shifting the pattern of precipitation, which was the major source for the Yellow River Basin. In 1997, the ENSO events led to the lowest precipitation in the drainage basin (350.3 mm) among the 50-year record, and consequently resulted in the lowest natural river discharge (3.1 × 10 10 m 3) and the lowest streamflow to the sea (Wang et al., 2006). This effect was of great concern because the Lower Yellow River had no streamflow to the sea for a surprising period of 202 days. However, from 2000 to 2006, zero flow events did not occur anymore, mainly due to water regulations such as water sediment regulation implemented in 2002. Other reasons for changes in flow regimes are human activities, such as construction of dams and reservoirs, soil conservation measures,
Table 3 Summary information of 4 major reservoirs in the mainstream Yellow River (Miao et al., 2010). Reservoir
Longyangxia
Launch time 1986 Storage capacity 24.70 9 3 (10 m ) Location Upper reaches
Liujiaxia
Sanmenxia
Xiaolangdi
1969 5.70
1960 9.7
2000 12.65
Upper reaches
Middle reaches
Middle reaches
63
deforestation and afforestation. The observed streamflow at Lijin gauge from 1984 to 2006 decreased to 38.9% compared with that from 1958 to 1983, whereas the natural discharge only dropped by 30.5% compared with that in 1950s. According to the statistics, more than 3147 reservoirs have been built in the Yellow River Basin, with a total storage capacity of 5.7 × 1010 m 3 in 2001 (Zhang et al., 2001), and 24 reservoirs scattered widely in the river basin with storage capacities exceeding 1.0 × 10 8 m 3 (Wang et al., 2006). A decreasing trend in monthly average streamflow in Lijin gauge has been observed from 1958 to 2006 due to the operation of the Longyangxia and Xiaolangdi reservoirs, started in 1986 and 2001, respectively (Table 3). The commencement time of large reservoirs is almost consistent with the abrupt year 1984. Using records at Huayuankou gauge, the measured streamflow in flood seasons accounted for more than 60% of annual water discharge in the 1950s, but it decreased to 43% in the 1990s, and the number of high pulse days was reduced sharply in response to the operation of dams and reservoirs (Wang et al., 2006), similar with the results revealed in this study. However, since the water sediment regulation began in 2002, duration of high pulses has increased, ranging from 11 to 20 days (Fig. 8-a), which is close to the target range. Meanwhile, soil conservation measure, deforestation and afforestation also have contributed to hydrological variations in the Yellow River (Li et al., 2007; Liu et al., 2009). For example, in order to reduce the soil erosion in the Loess Plateau, the Sloping Lands Conversion Program has resulted in a decrease of streamflow in the Yellow River (McVicar et al., 2007). Changes of vegetation may lead to a series of changes in hydrologic processes, e.g. vegetation intercepted precipitation, enhanced evaporation, improved soil structure, increased infiltration and then, reduce the amount of streamflow (McVicar et al., 2007; Miao et al., 2010). All of these indicated that further study should be conducted to reveal the interaction between the climate, vegetation and hydrologic processes, which should be helpful to provide a suitable water management and maintain the health of aquatic ecosystems, e.g. riparian ecosystems and wetlands. 6. Conclusion Flow regime is a primary determinant of structure and function of an aquatic and riparian ecosystem. By using M–K, IHA and HAM methods, the ecohydrological status of flow regimes in the Yellow River has been investigated. The conclusion can be drawn as follows. The downward trend has been detected for annual streamflow in 1984, which is used to divide pre- and post-impact periods during 1958–2006, probably resulted from climate change as well as human activities (e.g. dam construction and soil conversation measures). Comparing with that in the pre-impact period, hydrologic features demonstrated obvious changes during the post-impact period. The flow magnitude was smaller and the frequency of low flow events increased for all the twelve months; both maximum flows and minimum flows for 1-day, 3-day, 7-day, 30-day and 90-day reduced; the number and duration days of high pulse events presented a decreasing trend, while those of low pulse events addressed an increasing trend. In order to protect the ecosystem health in the Lower Yellow River, monthly flow magnitude, number and duration days of high pulses and low pulses should be applied as critical indicators for water management. The possible suitable range is from 743 to 3979 m 3/s for monthly flow in July, August, September, October and November, and it was from 94 to 1075 m 3/s for the rest seven months. For the number and duration days of high pulses, the possible suitable ranges were from 4 to 7 and from 7 to 22 days, respectively; but for low pulses, the ranges of the number and duration days were 2 to 9, and 8 to 16 days, respectively. Furthermore, the interaction between flow regime and ecological processes should be studied further, which would be helpful to provide more suitable water regulation measures.
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