A thorough understanding of the ecological impacts behind the hydrologic alteration is still insufficient and hinders the watershed management. Here, we used eco-flow indicators, multiple hydrological indicators, and fluvial biodiversity to investigate the ecological flow in different temporal scales. The case study in the Han River shows a decrease in high flows contributed to the decrease in eco-surplus and increase in eco-deficit in summer and autumn, while the decrease in eco-deficit can be attributed to the change of low flow in spring. An integrated hydrologic alteration was over 48% degree and was under moderate ecological risk degree in impact period I, while DHRAM scores showed the Huangzhuang station faced a high ecological risk degree in impact period II. The decrease (increase) in total seasonal eco-surplus (eco-deficit) was identified after alteration with the change in seasonal eco-flow indicators contributions. Shannon index showed a decreasing trend, indicating the degradation of fluvial biodiversity in the Han River basin. Eco-flow indicators such as eco-surplus and eco-deficit are in strong relationships with 32 hydrological indicators and can be accepted for ecohydrological alterations at multiple temporal scales. This study deepens the understanding of ecological responses to hydrologic alteration, which may provide references for water resources management and ecological security maintenance.

  • Quantitative evaluation of ecological flows considering precipitation and reservoir factors.

  • Unveil effects of reservoirs on flow regimes in the Han River.

  • Comprehensive evaluation of ecological responses to hydrological alterations.

  • Scientific foundation for water resources management in the Han River.

As one of the most fundamental elements in diverse ecosystems, rivers play essential roles and support mankind's survival. A flow regime that reflects the hydrological process has been of critical concern in the integrity and biodiversity of riverine ecosystems (Tonkin et al. 2018). However, warming climates and intensifying anthropogenic activities have caused significant alterations in flow regimes and exerted inevitable impacts on riverine ecosystem security (Li et al. 2020; Deng et al. 2022; Hatamkhani et al. 2022). Therefore, studies regarding the impacts of altered hydrologic regimes on biodiversity have captured considerable attention, intending to improve water resources management and ecological security maintenance (Palmer & Ruhi 2019; Mezger et al. 2021).

The variation in the hydrological regime counts mainly on climate change and anthropogenic activities. In general, instream flow is mainly controlled by precipitation in the natural periods. Climate change, characterized by rising temperatures, lifted sea levels, and shifted precipitation patterns can play a role in the well-being and resilience of the riverine ecosystem (Yang et al. 2021). Moreover, intensifying anthropogenic activities exert pervasive impacts on the ecosystem. Hydraulic engineering represented by reservoirs becomes incredible during human-induced time. The construction and operation of reservoirs alter their nature attributes and translate to copious eco-environmental problems (Yousefi & Moridi 2022). The impacts of dams can be mainly classified into four categories: (1) economic interest can be generated by hydropower generation but it leads to declining downstream flow, and some wetlands downstream even face drying up (Spanoudaki et al. 2022; Yang et al. 2023); (2) the dams separated the river channels and the water body with a low flow rate lacks high self-purification capacity, then water quality deterioration and eutrophication emerge (Zhu et al. 2023); (3) the water stored in the reservoir features in layer temperature and the natural temperature pattern of discharge can be altered and have a negative impact on fish spawning (Benjamin et al. 2020); (4) the upstream sediment is mostly deposited in the reservoir and the clear discharge has a strong scouring ability, often causing the downstream riverbed to be deeply eroded and cut down (Gierszewski et al. 2020). The coarsening of the riverbed affects the survival of insects, which are the bait for certain fish species. Therefore, we need to gain a deeper understanding of their impacts on river ecosystems and biodiversity (Pal & Sarda 2020; Siala et al. 2021).

Ecologists have identified flow regime variations as the principal drivers of key ecological processes in riverine ecosystems (Palmer & Ruhi 2019; Hatamkhani & Moridi 2023). The generally accepted approach for assessing hydrologic regimes focuses on developing comprehensive indicator frameworks to quantify their variability (Gao et al. 2009; Jumani et al. 2020). Richter et al. (1996) proposed Indictors of Hydrologic Alteration (IHA) to evaluate the impacts of anthropogenic activities on hydrologic alteration. IHA contains five categories of flow variability in terms of magnitude, frequency, duration, timing and rate of change (Li et al. 2018). The Range of Variability Approach (RVA) based on IHA employs 75 and 25% percentiles as the threshold of each indicator before disturbance for the natural flow regime. This approach can not only delineate flow regime alteration but also forge correlations between diverse hydrologic indicators and ecological attributes of habitats, including physical and chemical environments. It remains the most used technique for quantitatively evaluating hydrologic alteration and the potential ecological impacts of altered flow regimes. However, the data redundancy existing across IHA indicators with some indicators being highly intercorrelated hampered the water resources management in practice (Clausen & Biggs 2000; Cheng et al. 2019). It presents an obstacle to the effective application of ecologically centered river flow regimes, especially those pertaining to reservoirs. The integrated degree of hydrological variation (D0) and Dundee Hydrological Regime Alteration Method (DHRAM) emerged for the assessment of hydrologic alteration (Black et al. 2005; Shiau & Wu 2007). These evaluation approaches, however, are incapable of dealing with the specific ecological flow variability. Vogel et al. (2007) suggested ecological flow indicators (eco-surplus and eco-deficit) based on the flow duration curve (FDC) to address this issue. It can demonstrate the instream flow surplus and deficit at different temporal scales (annual or seasonal scales) and shed new light on the examination of hydrologic alteration. However, research in this area remains limited despite the potential benefits.

The construction of cascade reservoirs and water transfer projects in the Han River poses a great challenge to the river ecosystem and the surrounding ecological environment (Li et al. 2020; Yan et al. 2022; Zhu et al. 2023). The previous studies mainly focused on the overall trend of runoff under the dual impacts of climate change and anthropogenic activities, showing a deficiency in the hydrologic alteration with associated ecological risks due to reservoir disturbance. The lack of deep exploration of seasonal variations also inhibits potential practical implementations. Furthermore, the Han River undergoes several key eco-environmental issues related to hydrologic alteration and the water resources system becomes more complex. Higher frequency of algal bloom outbreaks and less biodiversity urge to conduct research in this field (Xia et al. 2020; Xin et al. 2020). Given the mentioned research gaps and requirements, more comprehensive and thorough studies on how hydrologic alteration affects ecosystems need investigation.

Therefore, the main objective of this study is (1) to employ eco-surplus and eco-deficit to systematically evaluate instream ecological flow alteration and analyze driving factors at multiple time scales; (2) to utilize IHA framework to determine D0 and DHRAM for assessing the degree of alteration and the level of risk; (3) to assess and investigate the ecological response to ecohydrological variability resulting from reservoir operation. We expect that this study will provide useful information on the evolutionary features of river ecohydrological circumstances, as well as the effects of climate change and anthropogenic activities on the basin at different time scales.

Eco-flow indicators

Vogel et al. (2007) put forward two generalized eco-flow indicators, eco-surplus and eco-deficit, to assess the temporal ecological instream flow regimes of rivers. The aforesaid indicators are constructed by flow duration curve (FDC). In general, the daily streamflow data series are arranged in descending order, and the exceedance probability pi can be expressed as
formula
(1)
where n is the sample size of the daily streamflow data series, and i is the rank order.
FDC curves can be transferred into different time scales of interest, such as annual FDC and seasonal FDC based on daily streamflow data series. Taking the annual FDC as an example, the daily discharges within a year are first arranged in descending order. Using the descending daily discharge series and its corresponding pi, an annual FDC is plotted. The schematic illustration of eco-flow indicators is shown in Figure 1.
Figure 1

Definition of eco-surplus and eco-deficit based on FDC.

Figure 1

Definition of eco-surplus and eco-deficit based on FDC.

Close modal

The red and blue dashed line in Figure 1 represents 25th–75th quantiles of FDC, respectively. The black solid line refers to the annual or seasonal FDC of a given year. The annual or seasonal FDC above the 75% quantile is defined as eco-surplus, suggesting that the hydrological element exceeds the demand value. The annual or seasonal FDC below the 25% quantile is defined as eco-deficit, suggesting that the hydrological element is lower than the demand in the river ecosystem.

Hydrologic alteration

The IHA approach is used for quantifying the degree of hydrologic alteration and their influence on river ecosystems since these indicators represent major hydrological features with ecological relevance. These indicators are classified into five groups (see Supplementary material, Table S2): the monthly mean flow, annual extreme flow magnitude and duration, annual extreme flow occurrence time, number and duration of flow pulses, rate, and frequency of flow changes. However, IHA can only calculate the alteration degree of each indicator between the natural condition and disturbed condition, the health degree of the riverine ecosystem posterior to the change is neglected. The RVA method was used to quantify the acceptable threshold of indicators in the IHA for quantitative assessment of the alteration degree. The 25 and 75% percentiles are usually defined as the lower and upper thresholds with the consideration of ecosystem restoration.

The long-term daily flow was involved in statistical analysis and the series prior to the change point was employed for RVA thresholds. The hydrologic alteration can be quantified by:
formula
(2)
where Di refers to the degree of hydrologic alteration of ith indicator; No,i refers to the number of years in which the mutated IHA value falls within the RVA range. Ne represents the expected number of years (Ne = P × NT, P = 50% and NT represents the series length posterior to the change point). The integrated degree of hydrologic alteration () can be attained as (Shiau & Wu 2007):
formula
(3)
where the value of D0 in the ranges of 0–33%, 33–67%, and 67–100% represents a low degree of alteration; a moderate alteration; and a high level of alteration, respectively.

The DHRAM assumes that the structural damage causing danger to the ecosystem is proportional to the cumulative hydrologic alteration and connects IHA indicators to ecological repercussions based on risk. The degree of change in the mean and variance of each category is used to obtain an integrated score. The DHRAM assessment is divided into 5 levels corresponding to different ecological risk degrees: level 1 denotes no risk to the ecological system and level 5 denotes serious hazard to the ecosystem. The greater the value, the higher the degree of variability in the flow regime and the higher the risk of environmental harm. The specific procedures for attaining DHRAM can be referred to Black et al. (2005).

Evaluation of ecological biodiversity

The Shannon index (SI) proposed by Kuo et al. (2001) has been used to evaluate ecological biodiversity. A greater SI indicates a higher ecological diversity. However, field observation data regarding aquatic life are scarce in most rivers. To find a practical approach for rivers without field data, Yang et al. (2008) built an optimal quantitative function between hydrological indicators and SI based on genetic programming as follows,
formula
(4)
where Dmin is the Julian date of minimum flow in a year, Min7 is the annual minimum 7-day flow, Mar and May are the monthly mean flow in March and May, respectively; Min3 and Max3 are the annual minimum and maximum 3-day flow, respectively; RR means positive difference between consecutive daily streamflow.
Han River (Figure 2) is the largest tributary in the middle reaches of the Yangtze River with a total length of 1,577 km. The mainstream runs through Shaanxi and Hubei Province, covering a drainage area of 159,000 km2. It has a high-intensity water resources development and utilization ratio, with many water-division projects as well as large and medium-sized reservoirs. The implementation of the South-North water transfer project and the North-Hubei water transfer project has reduced the total amount of water resources in the middle and lower reaches and the water use contradiction stands out (Kuo et al. 2019). In addition, six large and medium-sized cascade reservoirs were built in the mainstream as shown in Table 1. Taking Hubei Province for example, there are 2,014 reservoirs built in the middle and lower Han River, among which there are 132 large and medium-sized types. The natural river has been greatly affected by human activities with serious fragmentation, leading to considerably altered natural hydrologic regimes. The algal blooms have been reported in the middle and lower reaches many times since 1992. The historical records show that the length of the reach subject to algal blooms with prolonged periods has increased (Xia et al. 2020). Despite extensive studies on the Han River in the past decades, there is still a paucity of concern for ecological flow, particularly studies on the potential linkages between ecological response and hydrologic regimes (Jiang et al. 2019). Furthermore, the impacts of climate change and reservoir operation on the instream ecological flow and hydrologic regimes must be further investigated (Cui et al. 2020).
Table 1

Information of cascade reservoirs in mainstream of the Han River

Reservoir damConstructed periodDrainage area (km2)Regulation abilityTotal capacity (108m3)
Shiquan 1970–1975 24,000 Season 4.4 
Ankang 1982–1992 35,700 Year 25.85 
Danjiangkou 1958–1973 2005–2013 95,200 Year
Multi-year 
174.5
290.5 
Wangfuzhou 1995–2000 95,886 Day 1.495 
Cuijiaying 2005–2010 130,624 Day 2.45 
Reservoir damConstructed periodDrainage area (km2)Regulation abilityTotal capacity (108m3)
Shiquan 1970–1975 24,000 Season 4.4 
Ankang 1982–1992 35,700 Year 25.85 
Danjiangkou 1958–1973 2005–2013 95,200 Year
Multi-year 
174.5
290.5 
Wangfuzhou 1995–2000 95,886 Day 1.495 
Cuijiaying 2005–2010 130,624 Day 2.45 

Note: The Danjiangkou Reservoir dam was constructed during 1958–1973, and heighted for the Middle Route of the South-North Water Transfer Project during 2005–2013.

Figure 2

Sketch map of the Han River and basic information.

Figure 2

Sketch map of the Han River and basic information.

Close modal

In this study, daily streamflow data series of two major hydrological stations (i.e., Huangjiagang and Huangzhuang stations) in the Han River are collected from the Hydrological Bureau of Yangtze River Water Resources Commission. The daily meteorological data series (i.e., precipitation) are obtained from the China National Meteorological Administration, with the missing data filled with the correlation analysis of the nearby meteorological stations and surface-interpolated using the Thiessen polygon method.

The period division might induce sensitivity in the eco-flow metrics calculation. Our study focused on the temporal pattern to investigate the impacts of dams on the hydrological regime and potential ecological environment, rather than the exact values. Here, we set the flow series during 1954–1973 as the baseline period since there is no operation of cascade reservoirs. In 2014, the South-North water transfer project started supplying water to north China, which brought new changes to the ecological environment in the downstream Han River. Therefore, we further attained two periods, i.e., impacted period I (1974–2014) and impacted period II (2015–2020), to consider the impact of cascade reservoir regulation and water transfer on the downstream ecological environment.

Variations of eco-flow indicators

Figure 3 depicts the annual and seasonal FDCs in the baseline period and two impacted periods. The blue, orange, and green points correspond to the FDC in the baseline period, impacted period I and impacted period II, respectively. The purple and black lines refer to the 75 and 25% percentile FDCs, respectively. The high- and low-flow components at each station exhibited different degrees of alteration on an annual basis, and the curves shifted toward the origin. There is a significant increase in low flow with an occurrence probability greater than 0.75 and a significant decrease in the value of high flow with an occurrence probability lower than 0.25 during the impacted period. The decrease in the high-flow component may cause a decrease in the ecological surplus or even an increase in the ecological deficit, while the increase in the low-flow component also pulls up the ecological surplus to some extent, but its effect may be slightly smaller. In the impacted period II, the magnitude of the high-flow component appears to decline more significantly, even below the 25% FDC curve, and the ecological deficit is more severe. From the perspective of seasonal variations, they can be categorized into three cases, i.e., summer and autumn, spring, and winter. Summer and autumn witnessed a general decrease in the magnitude of flow. The high-flow component with an exceedance probability of less than 0.25 dropped below 75% FDC. This might result in a reduction in eco-surplus and an increase in eco-deficit. In spring, the occurrence of the high-flow component above 75% FDC decreased significantly while that of the low-flow component rose compared to the baseline period, which led to the reduction in eco-surplus due to the high-flow component and the reduction in eco-deficit due to low-flow component. In winter, the discernible difference could be identified in the FDC in three periods. The increase in the low-flow component might trigger the increase in the eco-surplus and decrease in eco-deficit. The above-mentioned results imply that FDC can help detect the influence of the changing environment on annual and seasonal eco-flow indicators.
Figure 3

Annual and seasonal FDCs before and after alterations at the Huangjiagang station.

Figure 3

Annual and seasonal FDCs before and after alterations at the Huangjiagang station.

Close modal
Based on annual and seasonal FDC, the temporal variation of annual and seasonal ecological indicators together with precipitation anomalies covering 1954–2020 at the Huangjiagang and Huangzhuang stations are shown in Figure 4, where the columns represent precipitation anomaly; blue and red curves represent eco-surplus and eco-deficit, respectively. Each row of the image corresponds to the annual or seasonal scale and two columns correspond to two stations.
Figure 4

Temporal variations of annual and seasonal eco-flow metrics and precipitation anomaly. Columns represent precipitation anomaly; blue and red curves represent eco-surplus and eco-deficit, respectively.

Figure 4

Temporal variations of annual and seasonal eco-flow metrics and precipitation anomaly. Columns represent precipitation anomaly; blue and red curves represent eco-surplus and eco-deficit, respectively.

Close modal

On an annual scale, the variation of eco-surplus and eco-deficit at the Huangjiagang and Huangzhuang stations is in line with that of precipitation. In general, the precipitation-streamflow relationship signified that heavier precipitation caused higher streamflow so the streamflow area above the 75% FDC could be enlarged with increasing eco-surplus. The increasing annual FDC component under the 25% FDC in dry years triggers the increase in eco-deficit. However, eco-surplus and eco-deficit were the same in a higher frequency even with less precipitation, which is a result of the low-flow components regulated by the reservoir. It should also be noted that a significant decrease in eco-surplus is observed in both stations with positive precipitation anomalies. Meanwhile, the eco-deficit became more severe when precipitation was not abundant. This situation is in close relation to the South-North Water Transfer Project implemented in the Danjiangkou Reservoir. Large quantities of water resources supplied to the north cause the decreasing flow downstream. The relevant studies also demonstrate that the operation of the South-North Water Transfer Project could leave an impact on the security of water resources and ecology conservation. In comparison with annual eco-flow indicators, the variation of seasonal ones was subjected to larger variability and less stability. A similarity could be identified between eco-flow indicators and precipitation anomalies in the baseline period. However, it was not apparent in the impacted period under the influence of human intervention. The spring eco-surplus resembled the precipitation anomalies in both stations. The difference between the spring eco-deficit and precipitation became more evident in the impacted periods. For summer, the eco-surplus showed an obvious declining trend. In the impacted periods, the eco-surplus of the two stations nearly kept at 0 whatever precipitation changed. Meanwhile, greater eco-deficit could be identified for both stations. The obvious eco-deficit could be observed even in a wet year, which exhibited the high-flow component regulation under the significant impact of the large reservoirs represented by the Ankang and Danjiangkou reservoirs. The lower variation of autumn eco-surplus was identified and the eco-deficit tended to be 0 in the second phase, which was triggered by the reduced high-flow component and the elevated low-flow component. In winter, the eco-deficit at the Huangjiagang and Huangzhuang stations approached 0 in the impacted period, which indicated that the area below 25% FDC curve decreased under the reservoir regulation in dry seasons.

We divided the period into several decades and organized the eco-flow metrics for investigating the temporal variation at decadal scales. Figure 5 shows the decadal variation of annual and seasonal ecological indicators. The annual and seasonal eco-surplus and eco-deficit were represented by pink box and blue box, respectively. Each box was plotted with a corresponding ecological indicator in the decade. In terms of annual scale, the eco-surplus at the Huangjiagang and Huangzhuang stations showed a ‘low-high-low’ changing trend. The eco-surplus of the Huangjiagang station increased from 1954 to 1983, followed by a decrease from 1984 to 1993. Then it fell to a much lower level in a recent decade after a rebound, which also demonstrated the impact of Danjiangkou Reservoir and the South-North Water Transfer Project. The median annual eco-surplus at the Huangzhuang station showed a similar trend compared to that at the Huangjiagang station. The summer eco-deficit in recent three decades was significantly higher than the level in the early stage, but the eco-surplus maintained around 0. The change of autumn ecological indicators was limited with most values close to 0 in the last three decades. The winter eco-surplus was greater than the eco-deficit in the recent four decades. Generally, the eco-surplus occurs in the wet season (summer in this study) and eco-deficit occurs in the dry seasons such as winter. However, Figure 5 reveals almost the opposite phenomenon, indicating that the hydrological system has been severely impacted by reservoir regulation, and the altered hydrological process threatening the riverine health should arouse wide concern with respect to integrated water management.
Figure 5

Decade variations of annual and seasonal eco-surplus (pink box) and eco-deficit (blue box) at the Huangjiagang and Huangzhuang stations.

Figure 5

Decade variations of annual and seasonal eco-surplus (pink box) and eco-deficit (blue box) at the Huangjiagang and Huangzhuang stations.

Close modal

Integrated evaluation of hydrologic alterations

The percentages of changes of 32 individual IHA indicators are illustrated in Figure 6. The changes in the monthly mean flow can be generally identified as two types: the increasing trend from December to March and the decreasing trend from April to November. This is consistent with the common reservoir regulation of impoundment in the wet seasons and replenishment in the dry seasons. Furthermore, Figure 6 also shows that most of the monthly mean flow declined in the impacted period II compared to the impacted period I, which was attributed to the joint effect of the negative precipitation anomalies and water diversion project. The increase in 1-day, 3-day, 7-day, and 30-day minimum flow was observed at the Huangjiagang and Huangzhuang stations, and the increase in the minimum flow components could be attributed to the regulation of large reservoirs. Figure 6 demonstrates that all the high-flow components, including 1-day, 3-day, 7-day, 30-day, and 90-day maximum flow, have experienced a remarkable decrease with absolute change percentages varying between 13.44 and 67.10%. The Huangjiagang station exhibited particularly distinct decreasing degrees varying from 26.34% to 67.10%, implying the significant operation effect of the Danjiangkou reservoir. Moreover, the decreasing degrees became higher in the impacted period II. The decrease in high-flow components in flood season contributed to the falling peak of FDCs, thus lower level or even zero eco-surplus in summer. It also resulted in a significant increase in eco-deficit (Figures 3 and 4). The time of occurrence of minimum flow tended to be delayed generally with the largest change percentage of 103.21% in the Huangzhuang station in impacted period II. The time of extremely high flow showed a relatively moderate change. The time of occurrence of extreme flow was closely related to the life cycle of aquatic organisms and exerted inevitable impacts on the proliferation and living activities.
Figure 6

Percentage of changes for the mean values of IHA parameters. Y-axis represents the abbreviation of IHA parameters shown in Supplementary material, Table S1.

Figure 6

Percentage of changes for the mean values of IHA parameters. Y-axis represents the abbreviation of IHA parameters shown in Supplementary material, Table S1.

Close modal

Figure 6 also shows that the duration of high pulses tended to be increasing while the numbers exhibited comparatively moderate changes. Meanwhile, an increase in the count of low pulses could be detected while the duration showed a decreasing trend. The decreasing precipitation and water transfer combined for the increase in the count of low pulses. The mean rise rate and fall rate decreased by 73.01 and 57.94% in the impacted period II at Huangjiagang station, respectively. The frequency as well as peak-load control of power grids also contributed to the increasing numbers of hydrologic reversals.

Equation (3) and DHRAM are employed for the integrated hydrologic alteration evaluation. Based on the 32 indicators in five groups of IHA, DHRAM can provide the quantitative evaluation of the alteration degrees. The assessment results are listed in Table 2. From the perspectives of D0, both stations in both periods experienced a changing degree of over 48%. The Huangzhuang station exhibits the largest alteration degree, reaching 68.97% in the impact period II, and the alteration degree is 65.35% at the Huangjiagang station. Huangzhuang station faced a high level of alteration in impacted period II. In general, the higher hydrologic alteration degree can be found in the impacted period II relative to that in the impacted period I, which manifests that the implementation of water diversion projects and cascade hydropower engineering took a toll on ecohydrological regimes. The greatly altered regimes could exert adverse effects on the fluvial eco-environment and should arouse extensive attention. DHRAM shows that the total points at the Huangjiagang station are 9 and 10 in two periods, while those of the Huangzhuang station are 10 and 13, respectively. The hydrologic alteration degrees at the Huangjiagang station were both level 3, in two impacted periods, while those at the Huangzhuang station were level 3 and level 4 respectively. The results manifest that both stations are facing moderate ecological risk in impact period I, while the Huangzhuang station is under a high ecological risk degree in the impact period II. The DHRAM evaluation method illustrates similar evaluation results when compared to those by the D0 evaluations, i.e., the higher hydrological alteration degree can be found in the impact period II.

Table 2

Integrated hydrologic alteration indicators, DHRAM scores and D0 at two hydrological stations

Hydrological stationsTime periodIHAMean changing percentage (%)
Impact points
Total pointsD0 (%)
GroupMeanCVMeanCV
Huangjiagang 1974–2014 39.37 25.12 9(3) 52.23 
  68.02 31.80   
  19.87 31.42   
  54.55 115.09   
  74.56 52.39   
 2015–2020 39.18 41.65 10(3) 65.35 
  76.94 42.01   
  10.42 15.91   
  55.73 97.82   
  74.72 86.82   
Huangzhuang 1974–2014 28.98 27.59 10(3) 48.96 
  44.65 35.71   
  32.12 20.46   
  42.05 97.25   
  50.56 59.04   
 2015–2020 32.08 48.03 13(4) 68.97 
  51.64 62.49   
  58.72 34.67   
  56.36 52.18   
  57.97 61.61   
Hydrological stationsTime periodIHAMean changing percentage (%)
Impact points
Total pointsD0 (%)
GroupMeanCVMeanCV
Huangjiagang 1974–2014 39.37 25.12 9(3) 52.23 
  68.02 31.80   
  19.87 31.42   
  54.55 115.09   
  74.56 52.39   
 2015–2020 39.18 41.65 10(3) 65.35 
  76.94 42.01   
  10.42 15.91   
  55.73 97.82   
  74.72 86.82   
Huangzhuang 1974–2014 28.98 27.59 10(3) 48.96 
  44.65 35.71   
  32.12 20.46   
  42.05 97.25   
  50.56 59.04   
 2015–2020 32.08 48.03 13(4) 68.97 
  51.64 62.49   
  58.72 34.67   
  56.36 52.18   
  57.97 61.61   

Impacts of ecological instream flow alteration on biodiversity

The total seasonal eco-surplus and eco-deficit are the sum of eco-surplus and eco-deficit of four seasons respectively, as shown in Figure 7. The total seasonal eco-surplus and eco-deficit can reflect the variation of seasonal ecological indicators on the annual scale, corresponding to the annual ecological diversity indicator. The total seasonal eco-surplus and eco-deficit are fitted by the loess function with a 95% confidence interval to quantify trends.
Figure 7

Temporal variations of the total seasonal ecological surplus and deficit. The shadowy regions denote the 95% confidence intervals; pink curves denote eco-surplus; and cyan curves denote curves eco-deficit.

Figure 7

Temporal variations of the total seasonal ecological surplus and deficit. The shadowy regions denote the 95% confidence intervals; pink curves denote eco-surplus; and cyan curves denote curves eco-deficit.

Close modal

It can be seen from Figure 7 that the total seasonal eco-surplus increased from 1954 to 1980, followed by a steady decline trend. However, the total seasonal eco-deficit showed a relatively increased trend in these years. The gaps between the total seasonal eco-surplus and eco-deficit became more and more narrow. Particularly, the total seasonal eco-deficit surpassed eco-surplus in recent years.

The variation of the ecological biodiversity indicator (SI) is illustrated in Figure 8, which could be segmented into two categories. The fitted curves for the Huangjiangang and Huangzhuang stations showed a persistent downward trend, implying decreasing ecological biodiversity. The construction and operation of reservoirs such as the Ankang and Danjiangkou reservoirs and the over-exploited water resources greatly altered the hydrologic regimes, exerting a massive influence on the riverine ecosystem. Comparing Figures 7 and 8, the period posterior to the change point witnessed a higher total seasonal eco-deficit and a lower SI, implying that the imbalance of fluvial eco-flow regimes had an impact on the balance of biota diversity. With the cascade hydropower construction along the mainstream and the implementation of water diversion projects, the water quantity of the middle and lower reaches has been reduced with destroyed river continuity and fragmented habitats. The delayed temperature effect of the reservoirs has led to significant changes in the water temperature of the downstream river channels, and the reproduction of the fish species has been severely impacted, resulting in the substantial reduction of fish resources. The literature demonstrated that in the middle and lower reaches, the spawning of fish with pelagic eggs dropped from 4.7 billion (tails) in 1977–1978 to 0.62 billion (tails) in 2012. Among them, the spawning of the four major Chinese carps declined from nearly 500 million grains (tails) in the late 1970s to 93 million grains (tails) in 2004, and to only 69 million grains (tails) in 2018 (Qin et al. 2014; Li et al. 2016; Lei et al. 2022).
Figure 8

Temporal variations of the SI. The solid curves denote the loess-based fitting curves and the shadowy regions denote the loess-based 95% confidence intervals.

Figure 8

Temporal variations of the SI. The solid curves denote the loess-based fitting curves and the shadowy regions denote the loess-based 95% confidence intervals.

Close modal

To investigate the role of influencing ecological flow in the Han River, the correlations between seasonal ecological indicators and seasonal precipitation are analyzed in Table 3.

Table 3

Correlation coefficients between precipitation and eco-flow indicators

Hydrological stationsPrecipitationEco-flow indicatorsBaseline periodImpacted period
Huangjiagang Spring S1 (D1) 0.82**( − 0.33) 0.08( − 0.23) 
 Summer S2 (D2) 0.49( − 0.74**) 0.02( − 0.06) 
 Autumn S3 (D3) 0.81**( − 0.46) 0.71**( − 0.45**) 
 Winter S4 (D4) 0.11( − 0.24) 0.12( − 0.07) 
Huangzhuang Spring S1 (D1) 0.77**(−0.59**) 0.04( − 0.25) 
 Summer S2 (D2) 0.52( − 0.78**) 0.01( − 0.03) 
 Autumn S3 (D3) 0.25( − 0.40) 0.71**( − 0.49**) 
 Winter S4 (D4) 0.10( − 0.23) 0.11( − 0.01) 
Hydrological stationsPrecipitationEco-flow indicatorsBaseline periodImpacted period
Huangjiagang Spring S1 (D1) 0.82**( − 0.33) 0.08( − 0.23) 
 Summer S2 (D2) 0.49( − 0.74**) 0.02( − 0.06) 
 Autumn S3 (D3) 0.81**( − 0.46) 0.71**( − 0.45**) 
 Winter S4 (D4) 0.11( − 0.24) 0.12( − 0.07) 
Huangzhuang Spring S1 (D1) 0.77**(−0.59**) 0.04( − 0.25) 
 Summer S2 (D2) 0.52( − 0.78**) 0.01( − 0.03) 
 Autumn S3 (D3) 0.25( − 0.40) 0.71**( − 0.49**) 
 Winter S4 (D4) 0.10( − 0.23) 0.11( − 0.01) 

**Correlation is significant at the 0.05 level of significance.

In the baseline period and the impacted period, the seasonal eco-surplus always showed a positive correlation with the seasonal precipitation (with the largest correlation of 0.82 at the Huangjiagang station in the baseline period) and the negative correlation could be identified between eco-deficit and precipitation (with the largest correlation of −0.78 at the Huangzhuang station in the baseline period). Comparatively, the correlations exhibited a distinct decline in the impacted period, indicating that the direct impact of precipitation had been weakened after the construction of dams.

Figure 9 demonstrates that the seasonal precipitation showed only subtle change, but the seasonal eco-flow indicators contribution changed a lot. The eco-surplus in autumn and winter played a critical role in the accumulation of the eco-surplus compared to the baseline period. The summer eco-deficit made the greatest contribution to the total eco-deficit in the altered period in all stations. The significant difference in the performance of the seasonal contribution could be identified, which unveiled the impact of dams on the ecological flow regimes.
Figure 9

Percentage of seasonal precipitation, eco-surplus, and eco-deficit in two hydrological stations. Note: Pre-P and Post-P denote the precipitation in the baseline and the impacted period, respectively. Pre-ES and Post-ES denote the eco-surplus in the baseline and the impacted period, respectively; Pre-ED and Post-ED denote the eco-deficit in the baseline and the impacted period, respectively.

Figure 9

Percentage of seasonal precipitation, eco-surplus, and eco-deficit in two hydrological stations. Note: Pre-P and Post-P denote the precipitation in the baseline and the impacted period, respectively. Pre-ES and Post-ES denote the eco-surplus in the baseline and the impacted period, respectively; Pre-ED and Post-ED denote the eco-deficit in the baseline and the impacted period, respectively.

Close modal

Comparison between eco-flow and IHA indicators

To understand the relationships between IHA and eco-flow indicators and explore an efficient approach for analyzing the eco-hydrologic regimes, the correlations between IHA and eco-flow indicators are summarized in Figure 10. The spring flow (March to May) was in a strong positive correlation with the spring eco-surplus. The positive correlation between summer and autumn runoff and the annual eco-surplus could be identified while a negative correlation for annual eco-deficit instead. Meanwhile, the 1-day, 3-day, 7-day, 30-day, and 90-day maximum flow show a negative correlation with the annual and total seasonal eco-deficit while staying positive with eco-surplus in different periods, particularly the annual, summer and autumn eco-surplus. The low-flow components had negative correlativity with the winter and total seasonal eco-deficit. The date of minimum flow was in negative correlation with the autumn and winter eco-surplus and in positive correlation with the eco-deficit, respectively. Furthermore, the negative correlativity could be identified between the total seasonal eco-deficit and the indicators from the 1st and 2nd IHA groups while the weak correlativity could be identified with other groups. Therefore, the eco-flow indicators could reflect the ecological implications of IHA indicators. The calculation of eco-flow indicators and IHA indicators is independent and the eco-flow indicators could be used for dealing with the redundancy of multiple hydrological indicators and the evaluation of ecological instream flow variations.
Figure 10

Correlations between the ecological instream flow indicators and IHA32-based indicators. Note: SA, S1, S2, S3, S4, and SS denote annual, spring, summer, autumn, winter, and total seasonal eco-surplus, respectively. DA, D1, D2, D3, D4, and DS denote annual, spring, summer, winter, and total seasonal eco-deficit, respectively.

Figure 10

Correlations between the ecological instream flow indicators and IHA32-based indicators. Note: SA, S1, S2, S3, S4, and SS denote annual, spring, summer, autumn, winter, and total seasonal eco-surplus, respectively. DA, D1, D2, D3, D4, and DS denote annual, spring, summer, winter, and total seasonal eco-deficit, respectively.

Close modal

Implication, limitation and future work

The intensifying anthropogenic activities have caused great hydrologic alteration and exercised inevitable influence on the riverine ecosystem. This study employed eco-flow indicators (eco-deficit and eco-surplus), SI, IHA, D0 and DHRAM to evaluate ecological flow in multiple time scales while considering ecological responses to evolutionary characteristics of flow regime. The findings can offer valuable implications for water resources management in the Han River. They can facilitate the strictest water resources management in this basin and promote the safety of the South-North water transfer project.

This study found that the fluvial biodiversity closely related to the flow regime showed a declining trend due to reservoir disturbance. For example, the IHA indicators in Group 4 have experienced a high alteration; they can change the frequency and magnitude of soil moisture stress for plants, the availability of floodplain habitats for aquatic organisms and organic matter exchanges between river and floodplain (Cui et al. 2020). Another example is that the monthly flows are related to the habitat availability for aquatic organisms. They can influence water temperature, oxygen levels, and photosynthesis in the water column (Luo et al. 2023). The monthly flows are beyond the thresholds after reservoir operation and the significant change imposed a negative impact on the migration and spawning of fish. The issue of eutrophication in the Han River has attracted widespread attention. Reservoir operation shifted the pattern of the discharge and water velocity, which are reported as the key factors to restrict water bloom in the Han River (Xiao et al. 2024). They all urge the governors to take response measures to prevent deterioration of the ecological environment. The hydrologic alteration should be considered in reservoir operation. Some studies have integrated IHA parameters into the objectives in optimal reservoirs to make the released flow maintain a more natural flow regime (Li et al. 2018; Yan et al. 2021). The governors can set strict water resources management rules such as reducing the hydrologic alteration or optimizing reservoir operation to control key factors to control key factors influencing environmental flow. In recent years, Danjiangkou Reservoir carried out emergency ecological scheduling to control the overgrowth of Elodea Nuttallii or to suppress algal blooms (Xin et al. 2020). Although they can play a role in preventing or limiting the impacts of emergencies, longstanding problems such as the shifting pattern of month flow or extreme water conditions still need further investigation. Governors should balance the hydropower and water supply benefits, as well as the ecological environmental conservation.

On the other hand, this study has some limitations and needs more improvements in future research. First, the lack of comprehensive data pertaining to the long-term variations in the quantity of biological species and the composition of the community in the Han River precludes the direct computation of SI. Hence, conducting a preliminary assessment of the potential effects on river biodiversity after reservoir construction through Equation (4) is acceptable. Further investigations may profitably focus on cross-validating biodiversity indices with actual biological communities and species data. Next, the sensitivity induced by the period division or the length of the record can also be explored in the further step. In this study, we adopted the maximum length of accessible data and mainly divided the study period into two pieces according to the operation of the Danjiangkou reservoir and the south-north water transfer project. However, the flow regime in the natural period was used as the reference and IHA values change with the length of the data records in the impacted period. The application of IHA also requires at least 15 years for different periods according to previous studies (Kennard et al. 2010; Fantin-Cruz et al. 2015). It also influences the correlation analysis between seasonal ecological indicators and seasonal precipitation. Therefore, the sensitivity induced by data length and division can also be explored in the further step. Moreover, quantifying hydrologic alteration and potential ecological responses under future climate warming and complex human activities is also a challenge for protecting the ecological environment. This exploration can integrate climate models, hydrological simulation, and the influence of reservoir operation. The findings will offer valuable guidance for initiatives aimed at ecological preservation and water resource management.

Han River, one of the research hotspots in China, is subject to climate change and human activities with altered hydrologic regimes. To explore the ecological response under the influence of hydrologic alteration, daily streamflow data of Huangjiagang and Huangzhuang stations and daily precipitation data covering 1954–2020 were collected and eco-surplus and eco-deficit at multiple scales, multiple hydrological indicators, and the fluvial biodiversity index were examined. The main conclusions are as follows:

  • (1)

    Ecological flow was mainly controlled by precipitation in the baseline period and dams significantly altered instream ecological flow in the Han River basin. The annual magnitude and frequency of high (low) flow are decreased (increased) under the impacts of reservoir regulation. The seasonal effect of variation of eco-flow indicators subject to FDC is different. The decline of high flow contributed to the increase in eco-deficit in summer. Spring exhibited a decrease in eco-surplus and eco-deficit due to high flow and low flow, respectively.

  • (2)

    Flow regimes in the Han River have been altered greatly. An integrated hydrologic alteration was over 48% degrees in both impacted periods and was under moderate ecological risk in the impact period I (ecological hazard class at level 3), while the Huangzhuang station faced high ecological risk with 68.97% degree in the impact period II (ecological hazard class at level 4).

  • (3)

    The variation of the SI is closely related to instream ecological flow which is influenced by precipitation and dams. The SI showed a decreasing trend in recent decades although eco-surplus exceeded eco-deficit with a narrow gap. Dams and water transfer projects have extremely changed seasonal contributions of eco-surplus and eco-deficit that caused the decline of fluvial biodiversity.

  • (4)

    Good correlation relationships existed between the eco-flow indicators (eco-surplus and eco-deficit) and IHA indicators, implying that they can capture the critical ecological implications identified by IHA indicators, making them a valuable tool for evaluating ecohydrological systems. Overall, the use of eco-surplus and eco-deficit is a practical and efficient approach for assessing river systems.

The findings can enhance our comprehension of the ecological responses prompted by hydrologic alterations. Particularly, it reveals that flow is presently subjected to the influences of reservoir construction and operation while it is primarily influenced by precipitation during natural periods. The insights may provide references for water resources management and ecological security maintenance. However, some limitations in this study are still worth emphasizing. Future research may profitably focus on cross-validating biodiversity indices with proper biological data. The sensitivity induced by the period division or the length of the record can also be explored in the further step. Quantifying hydrologic alteration and potential ecological responses under future climate warming and complex human activities is also a challenge for protecting the ecological environment. This exploration can integrate climate models, hydrological simulation, and the influence of reservoir operation. The findings will offer valuable guidance for initiatives aimed at ecological preservation and water resource management.

This study is financially supported by the National Natural Science Foundation of China (No. U20A20317).

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

Benjamin
J. R.
,
Vidergar
D. T.
&
Dunham
J. B.
2020
Thermal heterogeneity, migration, and consequences for spawning potential of female bull trout in a river–reservoir system
.
Ecology and Evolution
10
(
9
),
4128
4142
.
https://doi.org/10.1002/ece3.6184
.
Black
A. R.
,
Rowan
J. S.
,
Duck
R. W.
,
Bragg
O. M.
&
Clelland
B. E.
2005
DHRAM: A method for classifying river flow regime alterations for the EC Water Framework Directive
.
Aquatic Conservation: Marine and Freshwater Ecosystems
15
(
5
),
427
446
.
https://doi.org/10.1002/aqc.707
.
Cheng
J.
,
Xu
L.
,
Fan
H.
&
Jiang
J.
2019
Changes in the flow regimes associated with climate change and human activities in the Yangtze River
.
River Research and Applications
35
(
9
),
1415
1427
.
https://doi.org/10.1002/rra.3518
.
Clausen
B.
&
Biggs
B. J. F.
2000
Flow variables for ecological studies in temperate streams: Groupings based on covariance
.
Journal of Hydrology
237
(
3
),
184
197
.
https://doi.org/10.1016/S0022-1694(00)00306-1
.
Cui
T.
,
Tian
F.
,
Yang
T.
,
Wen
J.
&
Khan
M. Y. A.
2020
Development of a comprehensive framework for assessing the impacts of climate change and dam construction on flow regimes
.
Journal of Hydrology
590
,
125358
.
https://doi.org/10.1016/j.jhydrol.2020.125358
.
Deng
L.
,
Guo
S.
,
Yin
J.
,
Zeng
Y.
&
Chen
K.
2022
Multi-objective optimization of water resources allocation in Han River basin (China) integrating efficiency, equity and sustainability
.
Scientific Reports
12
(
1
),
Article 1
.
https://doi.org/10.1038/s41598-021-04734-2
.
Fantin-Cruz
I.
,
Pedrollo
O.
,
Girard
P.
,
Zeilhofer
P.
&
Hamilton
S. K.
2015
Effects of a diversion hydropower facility on the hydrological regime of the Correntes River, a tributary to the Pantanal floodplain, Brazil
.
Journal of Hydrology
531
,
810
820
.
https://doi.org/10.1016/j.jhydrol.2015.10.045
.
Gao
Y.
,
Vogel
R. M.
,
Kroll
C. N.
,
Poff
N. L.
&
Olden
J. D.
2009
Development of representative indicators of hydrologic alteration
.
Journal of Hydrology
374
(
1
),
136
147
.
https://doi.org/10.1016/j.jhydrol.2009.06.009
.
Gierszewski
P. J.
,
Habel
M.
,
Szmańda
J.
&
Luc
M.
2020
Evaluating effects of dam operation on flow regimes and riverbed adaptation to those changes
.
Science of The Total Environment
710
,
136202
.
https://doi.org/10.1016/j.scitotenv.2019.136202
.
Hatamkhani
A.
&
Moridi
A.
2023
A simulation optimization approach for wetland conservation and management in an agricultural basin
.
Sustainability
15
(
18
),
Article 18
.
https://doi.org/10.3390/su151813926
.
Hatamkhani
A.
,
Moridi
A.
&
Asadzadeh
M.
2022
Water allocation using ecological and agricultural value of water
.
Sustainable Production and Consumption
33
,
49
62
.
https://doi.org/10.1016/j.spc.2022.06.017
.
Jiang
C.
,
Wang
J.
,
Li
C.
,
Wang
X.
&
Wang
D.
2019
Understanding the hydropower exploitation's hydrological impacts through a len of change in flow-sediment relationship: A case study in the Han River basin, China
.
Ecological Engineering
129
,
82
96
.
https://doi.org/10.1016/j.ecoleng.2019.01.011
.
Jumani
S.
,
Deitch
M. J.
,
Kaplan
D.
,
Anderson
E. P.
,
Krishnaswamy
J.
,
Lecours
V.
&
Whiles
M. R.
2020
River fragmentation and flow alteration metrics: A review of methods and directions for future research
.
Environmental Research Letters
15
(
12
),
123009
.
https://doi.org/10.1088/1748-9326/abcb37
.
Kennard
M. J.
,
Mackay
S. J.
,
Pusey
B. J.
,
Olden
J. D.
&
Marsh
N.
2010
Quantifying uncertainty in estimation of hydrologic metrics for ecohydrological studies
.
River Research and Applications
26
(
2
),
137
156
.
https://doi.org/10.1002/rra.1249
.
Kuo
S.
,
Lin
H.-J.
&
Shao
K.
2001
Seasonal changes in abundance and composition of the fish assemblage in Chiku lagoon, southwestern Taiwan
.
Bulletin of Marine Science
.
https://doi. org/10.1515/BOT.2001.012
.
Kuo
Y.-M.
,
Liu
W.
,
Zhao
E.
,
Li
R.
&
Muñoz-Carpena
R.
2019
Water quality variability in the middle and down streams of Han River under the influence of the middle route of south-north water diversion project, China
.
Journal of Hydrology
569
,
218
229
.
https://doi.org/10.1016/j.jhydrol.2018.12.001
.
Lei
H.
,
Chen
F.
&
Xie
W.
2022
Effects of the first ecological operation of cascade reservoirs in the middle and lower reaches of Hanjiang River on the natural reproduction of pelagic fish
.
Journal of Lake Sciences
34
(
4
),
1219
1233
.
https://doi.org/10.18307/2022.0415
.
Li
X.
,
Huang
D.
&
Xie
W.
2016
Current status of spawning grounds of fish with pelagic eggs in the middle reaches of Hanjiang River
.
Journal of Dalian Ocean University
21
(
2
),
105
111
.
https://doi.org/10.3969/j.issn.1000-9957.2006.02.003
.
Li
D.
,
Wan
W.
&
Zhao
J.
2018
Optimizing environmental flow operations based on explicit quantification of IHA parameters
.
Journal of Hydrology
563
,
510
522
.
https://doi.org/10.1016/j.jhydrol.2018.06.031
.
Li
F.
,
Altermatt
F.
,
Yang
J.
,
An
S.
,
Li
A.
&
Zhang
X.
2020
Human activities’ fingerprint on multitrophic biodiversity and ecosystem functions across a major river catchment in China
.
Global Change Biology
26
(
12
),
6867
6879
.
https://doi.org/10.1111/gcb.15357
.
Luo
Z.
,
Zhang
S.
,
Liu
H.
,
Wang
L.
,
Wang
S.
&
Wang
L.
2023
Assessment of multiple dam- and sluice-induced alterations in hydrologic regime and ecological flow
.
Journal of Hydrology
617
,
128960
.
https://doi.org/10.1016/j.jhydrol.2022.128960
.
Mezger
G.
,
Tánago
M. G. d.
&
Stefano
L. D.
2021
Environmental flows and the mitigation of hydrological alteration downstream from dams: The Spanish case
.
Journal of Hydrology
598
,
125732
.
https://doi.org/10.1016/j.jhydrol.2020.125732
.
Pal
S.
&
Sarda
R.
2020
Damming effects on the degree of hydrological alteration and stability of wetland in lower Atreyee River basin
.
Ecological Indicators
116
,
106542
.
https://doi.org/10.1016/j.ecolind.2020.106542
.
Palmer
M.
&
Ruhi
A.
2019
Linkages between flow regime, biota, and ecosystem processes: Implications for river restoration
.
Science
365
(
6459
),
eaaw2087
.
https://doi.org/10.1126/science.aaw2087
.
Qin
X.
,
Chen
J.
&
Xiang
F.
2014
Impact of cascaded hydroelectric on reproduction of fish with pelagic eggs in middle and lower reaches of Hanjiang River
.
Environmental Science & Technology
37
,
501
506
.
Richter
B. D.
,
Baumgartner
J. V.
,
Powell
J.
&
Braun
D. P.
1996
A method for assessing hydrologic alteration within ecosystems
.
Conservation Biology
10
(
4
),
1163
1174
.
https://doi.org/10.1046/j.1523-1739.1996.10041163.x
.
Siala
K.
,
Chowdhury
A. K.
,
Dang
T. D.
&
Galelli
S.
2021
Solar energy and regional coordination as a feasible alternative to large hydropower in Southeast Asia
.
Nature Communications
12
(
1
),
4159
.
https://doi.org/10.1038/s41467-021-24437-6
.
Spanoudaki
K.
,
Dimitriadis
P.
,
Varouchakis
E. A.
&
Perez
G. A. C.
2022
Estimation of hydropower potential using Bayesian and stochastic approaches for streamflow simulation and accounting for the intermediate storage retention
.
Energies
15
(
4
),
Article 4
.
https://doi.org/10.3390/en15041413
.
Tonkin
J. D.
,
Merritt
D. M.
,
Olden
J. D.
,
Reynolds
L. V.
&
Lytle
D. A.
2018
Flow regime alteration degrades ecological networks in riparian ecosystems
.
Nature Ecology & Evolution
2
(
1
),
86
93
.
https://doi.org/10.1038/s41559-017-0379-0
.
Vogel
R. M.
,
Sieber
J.
,
Archfield
S. A.
,
Smith
M. P.
,
Apse
C. D.
&
Huber-Lee
A.
2007
Relations among storage, yield, and instream flow
.
Water Resources Research
43
(
5
).
https://doi.org/10.1029/2006WR005226
.
Xia
R.
,
Wang
G.
,
Zhang
Y.
,
Yang
P.
,
Yang
Z.
,
Ding
S.
,
Jia
X.
,
Yang
C.
,
Liu
C.
,
Ma
S.
,
Lin
J.
,
Wang
X.
,
Hou
X.
,
Zhang
K.
,
Gao
X.
,
Duan
P.
&
Qian
C.
2020
River algal blooms are well predicted by antecedent environmental conditions
.
Water Research
185
,
116221
.
https://doi.org/10.1016/j.watres.2020.116221
.
Xiao
X.
,
Peng
Y.
,
Zhang
W.
,
Yang
X.
,
Zhang
Z.
,
Ren
B.
,
Zhu
G.
&
Zhou
S.
2024
Current status and prospects of algal bloom early warning technologies: A review
.
Journal of Environmental Management
349
,
119510
.
https://doi.org/10.1016/j.jenvman.2023.119510
.
Xin
X.
,
Zhang
H.
,
Lei
P.
,
Tang
W.
,
Yin
W.
,
Li
J.
,
Zhong
H.
&
Li
K.
2020
Algal blooms in the middle and lower Han River: Characteristics, early warning and prevention
.
Science of The Total Environment
706
,
135293
.
https://doi.org/10.1016/j.scitotenv.2019.135293
.
Yan
M.
,
Fang
G.-H.
,
Dai
L.-H.
,
Tan
Q.-F.
&
Huang
X.-F.
2021
Optimizing reservoir operation considering downstream ecological demands of water quantity and fluctuation based on IHA parameters
.
Journal of Hydrology
600
,
126647
.
https://doi.org/10.1016/j.jhydrol.2021.126647
.
Yan
B.
,
Jiang
H.
,
Zou
Y.
,
Liu
Y.
,
Mu
R.
&
Wang
H.
2022
An integrated model for optimal water resources allocation under ‘3 redlines’ water policy of the upper Hanjiang river basin
.
Journal of Hydrology: Regional Studies
42
,
101167
.
https://doi.org/10.1016/j.ejrh.2022.101167
.
Yang
Y.-C. E.
,
Cai
X.
&
Herricks
E. E.
2008
Identification of hydrologic indicators related to fish diversity and abundance: a data mining approach for fish community analysis
.
Water Resources Research
44
(
4
),
W04412
.
https://doi.org/10.1029/2006WR005764
.
Yang
G.
,
Zaitchik
B.
,
Badr
H.
&
Block
P.
2021
A Bayesian adaptive reservoir operation framework incorporating streamflow non-stationarity
.
Journal of Hydrology
594
,
125959
.
https://doi.org/10.1016/j.jhydrol.2021.125959
.
Yang
G.
,
Giuliani
M.
&
Castelletti
A.
2023
Operationalizing equity in multipurpose water systems
.
Hydrology and Earth System Sciences
27
(
1
),
69
81
.
https://doi.org/10.5194/hess-27-69-2023
.
Yousefi
H.
&
Moridi
A.
2022
Multiobjective optimization of agricultural planning considering climate change impacts: Minab Reservoir upstream watershed in Iran
.
Journal of Irrigation and Drainage Engineering
148
(
4
),
04022007
.
https://doi.org/10.1061/(ASCE)IR.1943-4774.0001675
.
Zhu
D.
,
Zhou
Y.
,
Guo
S.
,
Chang
F.-J.
,
Lin
K.
&
Deng
Z.
2023
Exploring a multi-objective optimization operation model of water projects for boosting synergies and water quality improvement in big river systems
.
Journal of Environmental Management
345
,
118673
.
https://doi.org/10.1016/j.jenvman.2023.118673
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).

Supplementary data