Scientific ecological hydrological indicators provide constraints that contribute to the healthy operation and restoration of river ecosystems. Daily flow data from three Jing River outlets (SongZiKou (SZK), TaiPingKou (TPK), and OuChiKou (OCK)) spanning 1955–2019 were used. We employed innovative methods, such as IHA–RVA and annual distribution, to establish ecological flow thresholds. Surplus and deficit indicators were used to analyze annual and seasonal runoff dynamics. The PCA/RVA method identified relevant hydrological indicators and assessed hydrological changes influenced by the Three Gorges Reservoir (TGR). Key findings include suitable ecological flow thresholds for the flood season (SZK/TPK/OCK – 218.6/94.5, 51.7/96.0, and 60.9–4,494.5 m3/s, respectively). The TGR impacted the flow duration curve, causing deficits during the flood season (up to 0.99, OCK) and surpluses in non-flood seasons (up to 5.04, OCK). The study assessed the Jing River watershed's response to hydrological changes, notably due to the reservoir's water storage and flow interruption during the dry season, revealing declining low pulse count (SDG) and duration (MTS) and increasing high pulse duration (GJP). This research employs innovative methods and hydrological indicators, enhancing understanding of Jing River watershed ecological hydrology, and offering essential data for water resource management and ecosystem health.

  • The IHA–RVA method was used to study the biological flow thresholds and changes of three outlets of Jingjiang River in the middle of Yangtze River based on 60 years of daily flow data.

  • This study applied PCA and RDA to filter IHA for ERHIs, addressed redundancy issues, and investigated the ERHI's variation during dry period breaks and the Three Gorges Reservoir.

Rivers are the lifelines of the Earth's ecology, and they play an important role in the preservation of natural processes as well as the economic growth of human society (Palmer & Ruhi 2019). Meanwhile, ecologists essentially acknowledge flow variability as a fundamental driving force of river ecosystem function and structure (Hammond & Fleming 2021). Ecohydrological processes connect hydrological and ecological processes, whereas changes in hydrological situations impact the structure and function of watershed ecosystems (Liu et al. 2022; Gou et al. 2023). The evolution of hydrological conditions is primarily influenced by climate and human activities, with precipitation being one of the key factors in maintaining ecological flow (Amiri & Gocic 2021; Gocic & Arab Amiri 2023). Changes in precipitation directly affect river water levels, impacting the magnitude and seasonal variations of ecological flow and indirectly influencing the diversity of aquatic organisms in rivers. Therefore, precipitation variations have a direct impact on changes in ecological flow and the survival conditions of ecosystems (Amiri & Mesgari 2018; Amiri & Gocic 2023). In recent decades, human development and water resource utilization have considerably impacted natural river flows (Guan et al. 2021). Determining ecological flow thresholds and statistically quantifying changes in hydrological situations are critical components of ecohydrology research (Lu et al. 2018).

Ecological flow is a significant guarantee for maintaining the environmental function and health of rivers and lakes. At this point, research on the ecological flow of rivers and lakes has essentially developed a system in which ecological baseflow is the main index for determining the ecological health changes of rivers and lakes. However, the health of the river and lake ecosystems can be influenced by minimal flow and appropriate ecological flow. Therefore, one of the hot topics in hydrological research is how to establish the appropriate eco-flow threshold. Currently, the methods of calculating ecological flow include the RVA (range of variation approach) method, intra-annual spreading method, and Q90 method. Wang et al. (2022) established the Xiangjiang River's ecological flow process utilizing the RVA and intra-annual spreading methods, which serve as a reference for reservoir scheduling in the Xiangjiang River Basin; Zhou & Sun (2023) calculated the primary water demand of the Huma River using the intra-annual spreading method, which is consistent with the ecological function objectives outlined by the river's basic ecological water demand concept; Zhang & Chen (2022) calculated the minimal ecological flow in the Changshan Port watershed using the Q90 method and the intra-annual spreading method, and the reasonableness analysis indicated that it is better acceptable for estimating the ecological flow in this area. However, the above-mentioned studies do not provide sufficient exploration of the constraints on ecological flow and do not consider the limitations in simulating complex hydrological and ecosystem demands.

River hydrological situation is critical to preserving the integrity of watershed ecosystems and biodiversity (Zhang et al. 2020). The indicators of hydrological alteration (IHA) method has 33 ecohydrological indicators, all linked to the diversity of river ecosystems. Therefore, it is widely used to evaluate changes in river hydrological conditions. The IHA is commonly used to assess the variability of river hydrological circumstances. In five dimensions, the indicator illustrates river hydrological variability and ecological effects, including monthly mean flow, extreme flow, frequency, duration, and rate of change. Huang et al. (2020) utilized IHA to build an ecological water level indicator system in East Dongting Lake to calculate the appropriate ecological water level process; Chen et al. (2015) solely used IHA to assess the impact of Jinjiang Reservoir on downstream runoff. However, Yang et al. (2008) discovered a strong link between 33 IHA measures. Since there are as many as 33 IHAs, reducing the redundancy of information among them and effectively representing the ecohydrological information is currently a significant challenge. However, these indicators cannot accurately reflect the specific changes in ecological flow within river ecosystems, which often directly reflect the characteristics of river ecosystems. Therefore, it is crucial to further develop concise and effective ecological indicators for appropriate watershed river hydrological conditions.

In light of this, to provide scientific guidance, it is necessary to investigate past situations and conduct in-depth research on the impact of ecological hydrological indicators on ecosystems. Vogel et al. (2007) introduced ecological flow indicators based on flow duration curves (FDCs), which can reveal the surplus or deficit of inflow volumes in rivers across multiple time scales. Subsequently, Gu et al. (2016) discovered that eco-surplus and eco-deficit could effectively resolve redundancy and association between many hydrological indicators. Ecological flow indicators (ecological surplus (eco-surplus) and ecological deficit (eco-deficit)) can overcome the problem of redundancy between hydrological variables in practice. River water demand varies with the needs of river ecosystems, and the ecological flow indicator value also varies according to the flow process of each year, which can better reflect the process of changing river water demand than calculating a specific ecological flow minimum value (Vogel et al. 2007). Liu et al. (2021) estimated future changes in runoff in the Yellow River Basin using ecological flow indicators (eco-surplus and eco-deficit) and CMIP6-simulated runoff data. Gao et al. (2012) evaluated the effects of the Three Gorges Dam on water flow in the higher sections of the Yangtze River using ecological flow indicators (eco-surplus and eco-deficit). The eco-surplus and eco-deficit can be used to pre-assess the demand for ecological features of river flow and establish a scientific foundation for the future screening of ecohydrological indicators.

With its enormous regulating capacity, continuous year-round scheduling procedure, unique geographical location, and operation, the Three Gorges Reservoir (TGR), one of the Yangtze River's major human activity projects, has substantially affected the Yangtze River's hydrological pattern (Zeng et al. 2022; Sang et al. 2023; Xiao et al. 2023). The Jingjiang River section is located in the Yangtze River's middle reaches, originating in Zhicheng and finalizing at Chenglingji, the point of entry of Dongting Lake. It is 347.2 km long and 2,000 m wide. The three outlets of the Jingjiang River, SongZiKou, TaiPingKou, and OuChiKou, link the Jingjiang River and the Dongting Lake basin. The hydrological variations at the three Jingjiang River openings significantly impact the Yangtze River–Dongting Lake interaction (Zhang et al. 2022). Ban et al. (2014) utilized the IHA/RVA approach to quantify the hydrological changes in the Yangtze River's middle reaches before and after filling the TGR. He noticed that reservoir supply considerably impacted the amplitude of flow variability and the number of reversals. He also discovered that once the reservoir was complete, the amplitude of extreme flow variations was reduced, the time of minimum flow occurrence was earlier, and the duration of high flow was shorter. Zhu et al. (2016) applied the runoff process reduction approach to quantify the effect of TGR storage on the distribution and process of inflow into the Jingjiang River's three outlets region. They emphasized that the main impact is evident in the redistribution of the per-year flow process in the Yangtze River's middle reaches due to reservoir operation. Zhou et al. (2023) investigated the changes and driving forces in the Yangtze River mainstream and the two lakes before and after the operation of the TGR. He claimed that the primary explanation for the decline in per-year runoff at the Qili Mountain station before and after the procedure of the TGR is a decrease in Jingjiang diversion and precipitation in the Dongting Lake Basin, resulting in a reduction of ‘Four Rivers’ runoff. At present, there is no research that provides a sustainable ecological flow model applicable to the Jingjiang River outlets region, along with the threshold requirements related to its hydrological context. Furthermore, despite being one of the most significant reservoirs in the middle reaches of the Yangtze River, the TGR has not undergone detailed quantitative analysis in previous studies to reveal its impact on the ecological flow of the Jingjiang River outlets. Therefore, there is an urgent need for in-depth research to fill this knowledge gap and provide scientific support for the sustainable development of this region.

Therefore, given the limitations of previous research, the main objectives of this study include the following three aspects: (1) comprehensive computation of in-river ecological flow for different time periods; (2) a comprehensive quantitative assessment of changes in river ecological, hydrological contexts; and (3) in-depth investigation of the evolution of river ecological hydrological contexts by assessing the variations in ecological hydrological indicators under the influence of different driving factors. These objectives aim to fill knowledge gaps in the current research field and provide a scientific basis for the sustainable development of the region. In conclusion, this research suggests a paradigm for integrated evaluation that can measure rivers' hydrological conditions in dynamic situations. The framework consists of four phases specifically: (1) The IHA/RVA method modified by the intra-annual spreading method was used to calculate river ecological flow thresholds, and the Tennant method was used to evaluate the degree of ecological flow satisfaction; (2) Using the eco-surplus and eco-deficit to quantify the features of changes in ecological flow caused by human activities; (3) Combining principal component analysis (PCA) and RDA methods for 33 IHAs to obtain ecological relevant hydrological indicators (ERHIs); and (4) Evaluating the changes in river ecohydrological situation of ERHIs under the dual influence of environmental characteristics and human activities. For the research, the Jingjiang River's three outlets in the middle sections of the Yangtze River were chosen. The region is a vital biological corridor since it is linked to the Yangtze River's main stream on its upper side and to globally significant ecologically protected wetlands on its lower side. The Jingjiang River's three outlets combined flow has considerably decreased in recent years. Additionally, the flow sometimes stops during dry spells and under the impact of intense human activity. Past studies in the Jingjiang River outlets region have either focused solely on river channel geomorphology or considered only the basic impacts of water flow dynamics. Furthermore, research on the temporal characteristics of reservoir disturbance in the ecohydrological contexts has been very limited, and there has been a lack of in-depth investigation into the comprehensive impact of reservoirs on river ecological flows. The results of this study contribute to a better understanding of changes in the ecohydrological contexts of rivers in a changing environment and provide a scientific basis for future water resource management and river ecosystem conservation in the Jingjiang River outlets.

Jingjiang River is the alias for the Yangtze River's mainstream, which extends from Zhicheng City in Hubei Province to Chenglingji Section in Yueyang City in Hunan Province, covering approximately 360 km. The Jingjiang River, originally 404 km long, was later shortened to 331 km, with a width averaging around 2,000 m. The river flows in a northwest-to-southeast direction, conventionally divided into Upper Jingjiang River and Lower Jingjiang River, with the OCK outlet as the boundary. The Lower Jingjiang River, in particular, features meandering and winding characteristics, with a river length of 240 km despite a straight-line distance of only 80 km. In this section, the river forms 16 large bends, earning it the nickname ‘Nine Bends in the Intestines’, and is considered a typical meandering-type river channel. The three Jingjiang River outlets are located on the south bank of the river, which refers to the water network formed by the diversion of Yangtze River water into the northern part of Dongting Lake; the main rivers are Songzi River, Hudu River, Ouchi River, and Huarong River, of which Huarong River is now one of the three Jingjiang River outlets due to the outlet being blocked in the winter of 1958 (Zhou et al. 2016). The Jingjiang River has five representative hydrological stations (Xinjiangkou (XJK), Shadaoguan (SDG), Mituosi (MTS), Guanjiapu (GJP), and Kangjiagang (KJG)), which serve as inflow hydrological stations for Dongting Lake, located at the confluence of the Yangtze River main stem and Dongting Lake.

The three outlets of the Jingjiang River system in the upper part of Dongting Lake (that includes SZK, TPK, and OCK) are examined in this research, and data from five hydrological stations, namely XJK/SDG/MTS/GJP/KJG, are utilized (Figure 1). These five hydrological stations are the primary control hydrological stations in Jingjiang River's three outlets, and the hydrological data from four are the most representative. The measured prototype daily average flow data from 1955 to 2019 from the Jingjiang River's SZK (XJK/SDG), TPK (MTS), and OCK (GJP/KJG) were used in this investigation. The Hubei Provincial Hydrological and Water Resources Center provided the flow series.
Figure 1

Distribution of three outlets system in Jingjiang River area and gauge stations.

Figure 1

Distribution of three outlets system in Jingjiang River area and gauge stations.

Close modal

Flow Data: Daily average flow data from XJK, SDG, MTS, GJP, and KJG stations for the years 1955–2019 were obtained from the Hubei Provincial Hydrological and Water Resources Center.

Geographical Data: Digital Elevation Model (DEM) data with a resolution of 30 m for the Jing River outlets watershed in Dongting Lake was acquired from the National Geospatial Cloud (www.gscloud.cn).

Mutagenicity test

The Mann–Kendall (M-K) trend test compares the standardized variable Z of the time series data to a critical variable at a particular confidence level (taken as 0.05). When Z is positive, it indicates an upward trend; when Z exceeds the critical value, it means a significant upward or downward trend; the same statistic is calculated for the inverse series of the original time series so that UB = −UF, and if the two curves intersect within the 95% confidence level, it indicates a sudden change then. The sample value and distribution type do not affect the M-K non-parametric test, but several mutation sites may arise during the trial and must be validated. The cumulative offset verification accumulates the difference between the yearly average hydrological data and the multi-year annual average hydrological data. The extreme value point where the accumulation exists is chosen as the hydrological abrupt change point. The sliding T-test method calculates the T-statistic and determines whether it surpasses the significant level line; if it does, it indicates that the time point is a hydrological mutation point. Because the three algorithms are extensively utilized and well-known, we will not investigate them in this study (Jiang et al. 2020; Zhang & Wang 2021; Chong et al. 2022). Based on the findings of the three algorithms' calculations and the measured historical hydrological data, the time point of hydrological variability of the three Jingjiang River systems was determined.

Eco-surplus and eco-deficit

Vogel et al. (2007) proposed two broad indicators of eco-surplus and eco-deficit to evaluate the ecological regime of river runoff. The eco-surplus and eco-deficit are based on FDCs, constructed from daily flow data for selected periods and measure the percentage of time over which flows exceed a given value. The probability of exceeding the daily flow data Qi over a while ranked from largest to smallest is:
(1)
where i is the rank order and n is the sample size of daily flow observations Qi. FDC can be expressed as Qi as a function of pi.

Daily flow series can be constructed either on an annual-scale FDC or seasonal-scale FDC. The daily flow data of the Jingjiang River Basin from 1955 to 2019, with the completion time of TGR as the splitting point and the series before the splitting point representing natural mechanism runoff, were constructed as annual FDC and seasonal FDC for each year of the series before the splitting point. Then, the annual FDC and seasonal FDC of the 25 and 75% quantiles were obtained as the river ecosystem protection range. If the annual FDC or seasonal FDC of a given year is higher than the 75th percentile FDC, the area enclosed by the two curves is defined as the eco-surplus; if the annual FDC or seasonal FDC of a given year is lower than the 25th percentile FDC, the area enclosed by the two curves is defined as the eco-deficit (Jiang et al. 2023). Since the Jingjiang River is the focus of this research, the eco-surplus and eco-deficit denote that actual flow falls or exceeds the value of runoff demanded by lake water bodies and river ecosystems, respectively, and both are referred to as eco-flow indicators.

Improved intra-year spreading method based on the IHA/RVA method

The RVA proposed by Richter is used to objectively examine the number of changes in the hydrological conditions of downstream rivers induced by the Three Gorges Project. Based on the IHA, this method evaluates the hydrological state of rivers affected by water conservation initiatives using established ecohydrological indicators (Gierszewski et al. 2020; Guo et al. 2021; Zhou et al. 2023). The IHA method is based on 33 biologically relevant hydrologic metrics grouped into five broad categories to statistically characterize within-year hydrologic variability (Table 1). In this paper, the appropriate ecological flow calculation index system is the average value of the median flow of 12 months in the natural period of the year (average monthly flow), the time of occurrence of high and low flow pulses and their durations, the rate of change of flow, and a total of 32 hydrological indicators. The corresponding threshold value of each indicator is its demand (Sheikh et al. 2022). In addition, the minimum ecological flow is calculated by the intra-annual spreading method, considering the flood and non-flood periods as the ecological base flow. The dynamic calculation method is based on the intra-annual demand for runoff caused by natural runoff and ecohydrological processes. It can compensate for the shortcomings of the traditional hydrological method of calculating ecological flow by employing mean values and runoff-specific assurance rates as hydrological indicators (Yu et al. 2023). The minimum ecological flow function is as follows:
(2)
(3)
(4)
(5)
where and respectively for the natural flow period in the flood and non-flood monthly average flow (m3/s); and respectively for the flood and non-flood flow average; and respectively for the multi-year flood and non-flood minimum flow average.
Table 1

Summary of the IHA indicators and their related ecological characteristics

IHA indicators groupHydrological parameters (No.)Influences on ecosystem
Group 1: Magnitude of monthly water conditions Mean value for each calendar month discharge (1–12) Habitat availability for aquatic organisms. Soil moisture availability for plants. Availability of water and reliability of water supplies. 
Group 2: Magnitude and duration of annual extreme water conditions 1-day maximum discharge (18)
1-day minimum discharge (13)
3-day maximum discharge (19)
3-day minimum discharge (14)
7-day maximum discharge (20)
7-day minimum discharge (15)
30-day maximum discharge (21)
30-day minimum discharge (16)
90-day maximum discharge (22)
90-day minimum discharge (17)
Number of zero-flow days* (33)
Base flow index: annual 7-day minimum discharge divided by annual average flow (23) 
Balance of competitive, ruderal, and stress-tolerant organisms.Creation of sites for plant colonization. Structuring of aquatic ecosystems by abiotic vs. biotic factors. Structuring of river channel morphology and physical habitat conditions. Dehydration in animals. Volume of nutrient exchanges between rivers and floodplains. 
Group 3: Timing of annual extreme water conditions (Julian date) Julian date of each annual 1-day maximum flow (date of maximum) (25)
Julian date of each annual 1-day minimum flow (date of minimum) (24) 
Compatibility with life cycles of organisms. Predictability/avoidability of stress for organisms. Access to special habitats during reproduction or to void predation. Spawning cues for migratory fish. 
Group 4: Frequency and duration of high and low pulses High pulse duration (29)
High pulse count (28)
Low pulse duration (27)
Low pulse count (26) 
Frequency and magnitude of soil moisture stress for plants. Frequency and duration of anaerobic stress for plants. Availability of floodplain habitats for aquatic organisms. Nutrient and organic matter exchanges between river and floodplain. 
Group 5: Rate and frequency of water condition changes Rise rate (30)
Fall rate (31)
Number of reversals (32) 
Drought stress on plants (falling levels). Entrapment of organisms on islands, floodplains (rising levels). Desiccation stress on low-mobility stream edge (varial zone) organisms. 
IHA indicators groupHydrological parameters (No.)Influences on ecosystem
Group 1: Magnitude of monthly water conditions Mean value for each calendar month discharge (1–12) Habitat availability for aquatic organisms. Soil moisture availability for plants. Availability of water and reliability of water supplies. 
Group 2: Magnitude and duration of annual extreme water conditions 1-day maximum discharge (18)
1-day minimum discharge (13)
3-day maximum discharge (19)
3-day minimum discharge (14)
7-day maximum discharge (20)
7-day minimum discharge (15)
30-day maximum discharge (21)
30-day minimum discharge (16)
90-day maximum discharge (22)
90-day minimum discharge (17)
Number of zero-flow days* (33)
Base flow index: annual 7-day minimum discharge divided by annual average flow (23) 
Balance of competitive, ruderal, and stress-tolerant organisms.Creation of sites for plant colonization. Structuring of aquatic ecosystems by abiotic vs. biotic factors. Structuring of river channel morphology and physical habitat conditions. Dehydration in animals. Volume of nutrient exchanges between rivers and floodplains. 
Group 3: Timing of annual extreme water conditions (Julian date) Julian date of each annual 1-day maximum flow (date of maximum) (25)
Julian date of each annual 1-day minimum flow (date of minimum) (24) 
Compatibility with life cycles of organisms. Predictability/avoidability of stress for organisms. Access to special habitats during reproduction or to void predation. Spawning cues for migratory fish. 
Group 4: Frequency and duration of high and low pulses High pulse duration (29)
High pulse count (28)
Low pulse duration (27)
Low pulse count (26) 
Frequency and magnitude of soil moisture stress for plants. Frequency and duration of anaerobic stress for plants. Availability of floodplain habitats for aquatic organisms. Nutrient and organic matter exchanges between river and floodplain. 
Group 5: Rate and frequency of water condition changes Rise rate (30)
Fall rate (31)
Number of reversals (32) 
Drought stress on plants (falling levels). Entrapment of organisms on islands, floodplains (rising levels). Desiccation stress on low-mobility stream edge (varial zone) organisms. 

*This hydrological parameter is not included in this study.

The specific steps are: input the daily flow data of five representative hydrological stations in the natural period of Jingjiang River's three outlets into the RVA software, calculate the average monthly flow, bring into Equations (2) and (3) to derive the ecological flow in the flood and non-flood periods. The ratio of the calculated and to the multi-year flood and non-flood annual average flow and , respectively, is taken as the mean ratio for the same period, and the monthly average flow process is used as the base for scaling in the same proportion to obtain the minimum ecological flow for each month of the river and lake, i.e., the intra-annual spreading method of the minimum ecological flow:
(6)

Principal component analysis (PCA)

Principal component analysis (PCA) is a multivariate statistical method that uses an orthogonal transformation to turn a collection of correlated variables into a set of orthogonal, uncorrelated variables. The changed set of variables is known as the principal component (PC) (Chang et al. 2022; Jaffres et al. 2022). The goal is to extract the most meaningful information from the dataset, compress it by reducing the number of dimensions, and ensure that no information is lost (Tang et al. 2021; Mahanty et al. 2023). SPSS statistical software was used in this study to optimize the representative indicators that could comprehensively measure changes in hydrological conditions using PCA, and the Kaiser–Guttman criteria, as the eigenvalue more significant than one and the cumulative contribution of at least 80%, was considered in determining the number of principal components (MartinSanz et al. 2022).

Redundancy analysis (RDA)

Redundancy analysis is a ranking method that combines multiple response variable regression analysis with PCA to explicitly investigate and visualize the relationship between the response variable matrix and the explanatory variable matrix in a low-dimensional visual orthogonal ranking axis space. Computationally, the redundancy analysis first performs a multiple regression of each response variable in the centralized response variable matrix (Y) with all explanatory variables to obtain the fitted value vector and residual vector of each response variable and form the fitted value matrix (Y′) and residual matrix (Yres = YY′); these two matrices are then subjected to PCA analysis to obtain the canonical constrained ranking (RDA ranking) and the residual unconstrained ranking (RDA ranking) where the RDA ranking axis is a linear combination of all explanatory variables whose explanation is dependent on the response variable matrix's control (Yi et al. 2023). The RDA ranking axis's explanatory rate reflects the proportion of the total variance of the response variable that it can explain; the PC ranking axis's carrying rate represents the proportion of the total variance of the response variable it carries. This study utilized the generated data from the 32 IHA indicators as the response variable matrix and the explanatory variable matrix for RDA analysis. In this study, RDA analysis based on the correlation matrix was performed due to the uneven magnitudes of the IHA indicators. The first RDA ranking axis and the second RDA ranking axis were chosen to plot the ranking graph. All processes were carried out using R's vegan program package.

Calculation of ecological flow in the three outlets of Jingjiang River

Test for sudden variability of annual average flow in three outlets of Jingjiang River

The annual average flow of the three outlets of the Jingjiang River was tested during the study period, and the theoretical sudden change years of the three outlets of the Jingjiang River were identified using the Mann-Kendall sudden change test, the cumulative distance level method, and the sliding T-test method, as shown in Table 2.

Table 2

Statistical results of annual average flow of Jingjiang River's three outlets

Time seriesOutletsMutation year
Typical year of mutation
M-K sudden change testCumulative distance level methodThe sliding T-test
1955–2019 SZK 1985 1985 1968, 1979, 1985 1985 
TPK 1986 1984 1968, 1984 1984 
OCK 1979, 1981 1971, 1978 1979, 1984 1979 
Time seriesOutletsMutation year
Typical year of mutation
M-K sudden change testCumulative distance level methodThe sliding T-test
1955–2019 SZK 1985 1985 1968, 1979, 1985 1985 
TPK 1986 1984 1968, 1984 1984 
OCK 1979, 1981 1971, 1978 1979, 1984 1979 

The academic years of rapid changes in the three Jingjiang River outlets (SZK, TPK, and OCK) were calculated as 1985, 1984, and 1979, respectively. Thus, the years having the most negligible impact from human activities at the Jingjiang River's three outlets (SZK 1955–1984; TPK 1955–1983; and OCK 1955–1978) were utilized for ecological flow calculations.

Suitable ecological flow thresholds for the three mouths of the Jingjiang River

The average minimum flow in the SZK flood season (April to September) is 1,051.01 m3/s, with a mean ratio of 0.48 for the same period, and the average minimum flow in the multi-year non-flood season (October to March) is 223.88 m3/s, with a minimum mean ratio of 0.47 for the same period; the average minimum flow in the TPK flood season is 359.12 m3/s, with a mean ratio of 0.18; the mean value of the minimum flow in the TPK flood period is 26.83 m3/s, and the minimum mean ratio is 0.10; the mean value of the minimum flow in the OCK flood period is 400.77 m3/s, and the minimum mean ratio is 0.17; and the minimum flow is 26.83 m3/s in the multi-year non-flood period is 0.08. All estimated results will be dispersed throughout the year. Figure 2 depicts the monthly mean flow, minimal ecological flow QE, RVA threshold, post-surge flow, and high and low flows.
Figure 2

Optimal eco-flow threshold of three outlets of Jing River.

Figure 2

Optimal eco-flow threshold of three outlets of Jing River.

Close modal

According to the study's findings, the low flow rates of SZK, TPK, and OCK are 469, 157, and 155 m3/s at 75% guarantee rate, respectively, and their high flow rates are 3,486, 1,319, and 3,031 m3/s at 25% guarantee rate; reasonable eco-flow thresholds should meet the requirements of occurrence time and duration during the high flow period, and should not be higher than the minimum ecological flow. Similarly, during low flow periods, the ecological flow threshold should meet the requirements of occurrence time and duration, and should not fall below the minimum ecological flow. The average fluctuation values of the ecological flow thresholds at SZK, TPK, and OCK are 387.76, 818.6, and 818.7 m3/s. The intra-annual spreading technique flow results at the three mouths of the Jingjiang River show a considerable flow extreme difference from the lower limit of the RVA threshold. This research uses the RVA threshold result as the appropriate ecological flow threshold for Dongting Lake, and the intra-annual spreading technique result is used as the minimal ecological flow. The minimal ecological flow can preserve the health of the river and lake; however, flowing in the minimum ecological flow for an extended period is detrimental to the river and lake's ecological health.

Tennant method to evaluate the ecological flow of three outlets of Jingjiang River

The Tennant approach compared the evaluation criteria to analyze the research outcomes' rationality (Tables 3 and 4). The Tennant method, also known as the Montana method, determines the river's ecological flow by using the average annual flow percentage as the base flow. The analysis results show that 10% of the average multi-year flow is the minimum flow to maintain the health of the river ecosystem, and 40% of the average multi-year flow provides better habitat conditions for most aquatic organisms (Tables 5 and 6).

Table 3

Results of three typical outlets

Section nameMinimum annual average flow (m3/s)Suitable annual average flow (m3/s)Multi-year average flow (m3/s)Year-over-year average ratio (%)
MinimumAppreciate
Jingjiang River's three outlets SZK 637.4 1,357.1 1,322.5 48.2 102.6 
TPK 193.0 540.1 1,114.7 17.3 48.5 
OCK 213.8 1,218.3 1,352.6 15.8 90.1 
Section nameMinimum annual average flow (m3/s)Suitable annual average flow (m3/s)Multi-year average flow (m3/s)Year-over-year average ratio (%)
MinimumAppreciate
Jingjiang River's three outlets SZK 637.4 1,357.1 1,322.5 48.2 102.6 
TPK 193.0 540.1 1,114.7 17.3 48.5 
OCK 213.8 1,218.3 1,352.6 15.8 90.1 
Table 4

Natural monthly average flow process of three typical stations (m3/s)

Section nameJanFebMarAprMayJuneJulyAugSepOctNovDecMulti-year average
Jingjiang River's three outlets SZK 34 14.1 28.1 195.3 1,003 1,800 3,800 3,240 2,964 1,932 700.3 159.4 1,322.5 
TPK 31.9 426 1,135 3,340 3,900 2,885 1,280 348 30.7 1,114.7 
OCK 0.7 0.2 49.3 600 1,376 4,738 4,335 3,168 1,544 377.3 43.1 1,352.6 
Section nameJanFebMarAprMayJuneJulyAugSepOctNovDecMulti-year average
Jingjiang River's three outlets SZK 34 14.1 28.1 195.3 1,003 1,800 3,800 3,240 2,964 1,932 700.3 159.4 1,322.5 
TPK 31.9 426 1,135 3,340 3,900 2,885 1,280 348 30.7 1,114.7 
OCK 0.7 0.2 49.3 600 1,376 4,738 4,335 3,168 1,544 377.3 43.1 1,352.6 
Table 5

The minimal and optional river ecological flow (m3/s)

Section nameMonthJanFebMarAprMayJuneJulyAugSepOctNovDec
Jingjiang River's three outlets SZK Min. 15.9 6.6 13.1 94.7 486.4 873 1,843 1,571.4 1,437.5 905 328 74.7 
App. 35.3 20.8 49.8 227.3 803.6 1,699.2 3,373.1 2,983 2,006.4 1,766.8 630 144 
TPK Min. 5.9 78.3 208.7 614.2 717.1 530.5 124.2 33.8 
App. 11.8 5.3 20.1 91.2 349.8 720.7 1,341.9 1,200.9 1,089.4 703.7 249.6 52.4 
OCK Min. 0.1 8.3 101.1 231.9 798.6 730.7 534 126.5 30.9 3.5 
App. 7.7 16.5 100.2 461.4 1,533.5 3,622.5 2,957.2 2,635 1,355.2 379.1 61.4 
Section nameMonthJanFebMarAprMayJuneJulyAugSepOctNovDec
Jingjiang River's three outlets SZK Min. 15.9 6.6 13.1 94.7 486.4 873 1,843 1,571.4 1,437.5 905 328 74.7 
App. 35.3 20.8 49.8 227.3 803.6 1,699.2 3,373.1 2,983 2,006.4 1,766.8 630 144 
TPK Min. 5.9 78.3 208.7 614.2 717.1 530.5 124.2 33.8 
App. 11.8 5.3 20.1 91.2 349.8 720.7 1,341.9 1,200.9 1,089.4 703.7 249.6 52.4 
OCK Min. 0.1 8.3 101.1 231.9 798.6 730.7 534 126.5 30.9 3.5 
App. 7.7 16.5 100.2 461.4 1,533.5 3,622.5 2,957.2 2,635 1,355.2 379.1 61.4 
Table 6

Evaluation of river ecological flow by the Tennant method

Section nameCateg.Multi-year average (m3/s)Fish spawning period (April to September)
General water use period (October to March)
Tennant evaluation
eco-flow (m3/s)hk (%)eco-flow (m3/s)hk (%)Fish spawning period (April to September)General water use period (October to March)
Jingjiang River's three outlets SZK Min. 637.4 1,051 164.9 223 35.0 Max Very good 
App. 1,144.9 1,848.8 161.5 372.9 32.6 Max Very good 
TPK Min. 193 359.1 186.1 26.8 13.9 Max Poor 
App. 486.4 799 164.3 173.8 35.7 Max Very good 
OCK Min. 213.8 400.8 187.5 26.8 12.5 Max Bad 
App. 1,094.3 1,884.9 172.2 303.7 27.8 Max Good 
Section nameCateg.Multi-year average (m3/s)Fish spawning period (April to September)
General water use period (October to March)
Tennant evaluation
eco-flow (m3/s)hk (%)eco-flow (m3/s)hk (%)Fish spawning period (April to September)General water use period (October to March)
Jingjiang River's three outlets SZK Min. 637.4 1,051 164.9 223 35.0 Max Very good 
App. 1,144.9 1,848.8 161.5 372.9 32.6 Max Very good 
TPK Min. 193 359.1 186.1 26.8 13.9 Max Poor 
App. 486.4 799 164.3 173.8 35.7 Max Very good 
OCK Min. 213.8 400.8 187.5 26.8 12.5 Max Bad 
App. 1,094.3 1,884.9 172.2 303.7 27.8 Max Good 

According to the calculation results and Tennant method comparison, the minimum ecological flow in the general water use period (October to March of the following year) accounted for 12.5–35.0% of the average annual flow for many years. According to the Tennant method, evaluation criteria are in people experiencing poverty to excellent range, at this time the river runoff conditions to maintain a certain water depth, flow rate, and river width. At this time, river runoff conditions should maintain a specific depth, velocity, and width of the river to ensure fish survival, migration, and general landscape requirements. During the fish spawning and nursery period (April–September), the ecological flow of each control section accounts for 164.9–187.5% of the multi-year average annual runoff, which is within the maximum permissible limit, and three of the mouths are within the maximum permissible limit, which can provide suitable habitat conditions for most aquatic organisms.

According to the Tennant technique, adequate ecological flow accounts for 27.8–35.7% of the multi-year average flow during the general water use period and is rated good to very good. When the water depth and flow velocity are at the maximum permissible limit, and the river ecosystem is very healthy, the ecological flow at each control section accounts for 161.5–172.2% of the multi-year average annual runoff and is within the maximum permissible limit. The river ecosystem can provide a suitable environment for aquatic organisms to inhabit, spawn, raise their young, and maintain biological species diversity (Yu et al. 2021). As a result, the minimum ecological flow calculated using the intra-annual spreading method of ecological flow and the appropriate ecological flow calculated using the IHA–RVA method are consistent with the Tennant method staging and can meet the river's ecological objectives.

Changes in ecological flow indicators

Figure 3 depicts the interannual and seasonal calendar year FDC scatter dispersion characteristics along the three Jingjiang River outlets (SZK/TPK/OCK) before and after the impoundment of the TGR. The interannual and spring FDC reservoir FDC distribution ranges are more consistent before and after impoundment, as shown in Figure 3. The high and low flows after impoundment can better cover the areas where high and low flows occur before impoundment. There is a significant difference in the range of high and low flows before and after impoundment in spring, autumn, and winter, particularly in spring and autumn. High flow levels and times increase significantly after spring, autumn, and winter water storage. In contrast, the number of low flow levels and times decreases significantly, increasing ecological surplus and decreasing the ecological deficit caused by high flows in spring and winter. The effects of the TGR impoundment on annual-scale and seasonal-scale ecological indicators can be provisionally determined before and after impoundment due to the changing characteristics of FDC.
Figure 3

Annual flow duration curves on before and after construction of the Three Gorges Dam (TGD).

Figure 3

Annual flow duration curves on before and after construction of the Three Gorges Dam (TGD).

Close modal
Figure 4 depicts the temporal variation characteristics of the annual and seasonal ecological flow indicators (eco-surplus and eco-deficit) calculated using annual FDC and seasonal FDC, respectively. The per-year eco-surplus changes more consistently and is less impacted by the reservoir annually. The high annual flow rises, the fraction of annual FDC above the 75% interquartile line increases, the eco-surplus rises, and the ecological water demand of the Jingjiang River's three outlets rises. From the mid-1950s to the 1970s, the eco-surplus was more evident throughout the Jingjiang River's three outlets, and following the 1970s, the eco-surplus essentially stabilized around 0. The reservoir has a higher impact on the annual eco-deficit than the annual eco-surplus. The low annual flow is decreasing, the fraction of annual FDC below the 25% quantile line is increasing, the eco-deficit is increasing, and the water demand at the Jingjiang River's three outlets is intense. The annual eco-deficit increased considerably between 1985 and 2002 and 2003 and 2019. However, because of the storage transfer impact of the TGR, the drop in annual low flow for SZK halted between 2003 and 2019.
Figure 4

Changes in the annual and season eco-flow indicators in the period from 1955 to 2019.

Figure 4

Changes in the annual and season eco-flow indicators in the period from 1955 to 2019.

Close modal

Change pattern of ERHI indicators

Selection of ERHI indicators (PCA/RDA)

  • (1)

    The PCA method was used to choose ecologically most relevant indicators (ERHIs): statistical IHA indicators based on long-series data on daily runoff from 1955 to 2019 from the three mouths of the Yangtze River's Jingjiang River. The eigenvalues and cumulative contribution rates of the 32 IHA indicators determined by SPSS software using the PCA approach are shown in Figure 5. The eigenvalues of the first seven principal components of the Jingjiang River's three outlets (SZK/TPK/OCK) are all greater than one, as shown in Figure 5, and the cumulative contribution rates of its three outlets (SZK/TPK/OCK) are 83.42, 88.08, and 87.51%, respectively. The recommended principal components of the three outlets of the Jingjiang River were identified as PC1–PC7 using the PCA method.

Figure 5

Diagram of eigenvalues and cumulative contribution rates of principal component analysis.

Figure 5

Diagram of eigenvalues and cumulative contribution rates of principal component analysis.

Close modal
Figure 5 depicts the eigenvalues of the first seven principal components, and the indicators with the highest eigenvalues are identified as representative indicators in each PC. As shown in Figure 6, the seven typical indicators preferred for each of the three Jingjiang River outlets are: (1) SZK: mean flow in March, 90-day maximum, base flow index, date of maximum, low pulse duration, high pulse duration, and fall rate; (2) TPK: mean flow in March, 90-day maximum, base flow index, date of maximum, low pulse duration, high pulse duration, and fall rate; and (3) OCK: April mean flow, 3-day minimum, 7-day minimum, 90-day maximum, date of maximum, high pulse duration (heavy factor), and rising rate.
  • (2)

    The redundancy analysis approach was used to choose the ecologically most relevant indicators (ERHIs). Each IHA indicator was counted based on long-series daily discharge data from 1955 to 2019 at the three Yangtze River outlets of the Jingjiang River. The RDA ranking diagram of 32 IHA indicators generated by the R program using the RDA approach is shown in Figure 7. According to Figure 7, the typical indicators preferentially chosen for each of the three Jingjiang River outlets are (1) SZK: The following nine indicative indicators are preferred: mean flow in February, mean flow in March, 1-day minimum, 7-day minimum, 30-day minimum, base flow index, minimum date, low pulse count, and the number of reversals. (2) TPK: three representative indicators, namely base flow index, low pulse count, and low pulse duration; (3) OCK: four representative indicators, namely 90-day maximum, base flow index, low pulse count, and low pulse duration.

Figure 6

Normalized load values of the first seven principal components.

Figure 6

Normalized load values of the first seven principal components.

Close modal
Figure 7

RDA sequencing map of Jingjiang Estuary based on IHA indicators. (a) SongZiKou, (b) TaiPingKou, and (c) OuChiKou.

Figure 7

RDA sequencing map of Jingjiang Estuary based on IHA indicators. (a) SongZiKou, (b) TaiPingKou, and (c) OuChiKou.

Close modal

Comparison of ERHI indicators and their rationality analysis

As shown in Table 7, the ERHIs of the Jingjiang River's three outlets can be optimized using the PCA and RDA methods, respectively. Finally, a comparison of the IHA indicators adjusted by the two approaches can yield the following ERHIs (ecologically most important indicators) for each of the three Jingjiang River outlets: (1) SZK: base flow index, date of minimum, and low pulse count; (2) TPK: 90-day maximum, date of minimum, and low pulse duration; and (3) OCK: 90-day maximum, date of maximum, and high pulse duration.

Table 7

ERHIs of the three mouths of Jingjiang River were selected based on IHA parameters

MethodsERHIs
SongZiKouTaiPingKouOuChiKou
PCA x22 x23 x24 x26 x29 x31 x3 x22 x23 x25 x27 x28 x31 x4 x14 x15 x22 x25 x29 x30 
RDA x2 x3 x13 x15 x16 x23 x24 x26 x32 x22 x23 x26 x27 x22 x23 x25 x26 x27 x29 
Results x23 x24 x26 x22 x23 x27 x22 x25 x29 
MethodsERHIs
SongZiKouTaiPingKouOuChiKou
PCA x22 x23 x24 x26 x29 x31 x3 x22 x23 x25 x27 x28 x31 x4 x14 x15 x22 x25 x29 x30 
RDA x2 x3 x13 x15 x16 x23 x24 x26 x32 x22 x23 x26 x27 x22 x23 x25 x26 x27 x29 
Results x23 x24 x26 x22 x23 x27 x22 x25 x29 

Figure 8 depicts the correlations among the ERHIs of the preferred outlets of the Jingjiang River to analyze further the rationality of the final preferred ERHIs for each of the three outlets of the Jingjiang River, and it is clear that the correlations among the three ERHIs of each outlet are significantly reduced. The most vital relationships, as shown in Figure 2, are: (1) TPK: 90-day maximum and base flow index, but their correlation values are only 0.54; and (2) OCK: 90-day maximum and high pulse duration, but their correlation coefficients are only 0.51. Most ERHIs have correlation coefficients that do not surpass 0.30, and more than half of the variables have correlation values that are less than 0.10.
Figure 8

Correlation among the three ERHIs in the JingJiang three outlets.

Figure 8

Correlation among the three ERHIs in the JingJiang three outlets.

Close modal

Time-varying characteristics of ERHI indicators

Although the IHA parameters are very comprehensive in describing the hydrological situation characteristics of each Jingjiang River outlet, using ERHIs reduces the autocorrelation and redundancy problems of IHAs and allows for further screening of the most relevant hydrological indicators for ecohydrology (Cheng et al. 2018). According to Figure 9, each of the three ERHIs at each of the three outlets of the Jingjiang River exhibits the following temporal fluctuation characteristics: (1) As demonstrated in Figure 9(a), the base flow index, date of minimum, and low pulse count of the three ERHIs at SZK all exhibit an upward trend; (2) As shown in Figure 9(b), the three ERHIs at TPK – annual 90-day maximum, base flow index, and low pulse duration – all show a tendency toward decreasing values; (3) From Figure 9(c), it is clear that while date of maximum has a very slow increasing tendency between 1955 and 2019, 90-day maximum and high pulse duration of OCK have a declining trend. Date of minimum, base flow index, and low pulses count in Figure 9(a) all decline at rates of 1.50 × 10−3, 5.49 days, and 0.15 times annually, respectively; Figure 9(b) shows that the base flow index and 90-day maximum decline at rates of 0.10 m3/s and 0.10 × 10−3 per year, respectively, while the low pulse duration grows at a rate of 3.34 days per year. Figure 9(c) shows that while the date of maximum is earlier, the 90-day maximum and high pulse duration drop at rates of 0.12 m3/s and 1.78 days per year, respectively. This changing feature suggests that the flood flow at the Jingjiang River's three outlets has been significantly reduced while the low flow has increased. The primary cause is that water storage at the Three Gorges Dam and the following Jingjiang River cut bends were initiated due to human activity in the Jingjiang River Basin.
Figure 9

Temporal variations of ERHIs (1955–2019). (The red lines denote the trend of each ERHIs, the regions between two blue dashed lines denote the 95% confidence interval.)

Figure 9

Temporal variations of ERHIs (1955–2019). (The red lines denote the trend of each ERHIs, the regions between two blue dashed lines denote the 95% confidence interval.)

Close modal

Analysis of ecological indicators of the three outlets of Jingjiang River

Jingjiang River's three outlets ecological indicators

The minimum monthly and suitable eco-flow at each were calculated to identify the most ERHIs for each of the three Jingjiang River outlets, and 33 IHA indicators were chosen using the PCA and RDA methods. The Jingjiang River Ecological Indicators (JREIs) employed in this study were the lowest ecological flow, suitable ecological flow on a monthly scale at each of the three outlets of the Jingjiang River, and the chosen ERHIs, as indicated in Table 8.

Table 8

The Jingjiang River's ecological indicators

JREI groupHydrological parametersInfluences on ecosystem
Group 1: Magnitude of monthly minimum ecological flow January July Meet the minimum demand for ecological water to maintain regional rivers 
February August 
March September 
April October 
May November 
June December 
Group 2: Magnitude of monthly appropriate ecological flow January July Allow flows that meet the conditions of river ecosystem stability and health 
February August 
March September 
April October 
May November 
June December 
Group 3: Ecological relevant hydrologic indicators (ERHIs) SongZiKou Base flow index
Date of minimum
Low pulse count 
Reducing information redundancy becomes the key to accurately assessing river hydrological characteristics and constructing ecohydrological links 
TaiPingKou 90-day maximum
Base flow index
Low pulse duration 
OuChiKou 90-day maximum
Date of maximum
High pulse duration 
JREI groupHydrological parametersInfluences on ecosystem
Group 1: Magnitude of monthly minimum ecological flow January July Meet the minimum demand for ecological water to maintain regional rivers 
February August 
March September 
April October 
May November 
June December 
Group 2: Magnitude of monthly appropriate ecological flow January July Allow flows that meet the conditions of river ecosystem stability and health 
February August 
March September 
April October 
May November 
June December 
Group 3: Ecological relevant hydrologic indicators (ERHIs) SongZiKou Base flow index
Date of minimum
Low pulse count 
Reducing information redundancy becomes the key to accurately assessing river hydrological characteristics and constructing ecohydrological links 
TaiPingKou 90-day maximum
Base flow index
Low pulse duration 
OuChiKou 90-day maximum
Date of maximum
High pulse duration 

Time-scale analysis of ERHI under the variation of natural incoming water

Table 8 shows that there are three categories of indicators for the Jingjiang River, with a total of 27 indicators in each group: (1) minimal eco-flow; (2) suitable eco-flow; and (3) ERHIs of each outlet. Each river system of the Jingjiang River's ERHI indicator change pattern was examined annually.

Figure 10 displays the outcomes of our analysis of each outlet in this paper's focus on ERHIs. Base flow index is 142.4 × 10−4, and the three ERHI indicators of SZK are in an average declining trend from 1955 to 1984. The date of minimum is in a very gradual growing trend, although in 1971. With a notable turnaround in 1968 and 1974 with 5,686 and 4,057 m3/s, respectively, the three ERHI indicators of TPK reveal an overall significant declining trend of 90-day maximum in 1955–1983. Low pulse duration is in a clear upward trend, with a lengthy low pulse duration in 1975–1978 and a maximum of 162 days in 1977. The base flow index is in a clear downward trend and essentially hovers around 0. However, there are significant values of 672.5 × 10−5 and 487.1 × 10−5 in 1957 and 1964. Although there are two substantial oscillations (1968 and 1974) with values of 6,259 and 4,352 m3/s from 1966 to 1978, the three ERHI indicators of OCK show a marked declining tendency in the 90-day maximum from 1955 to 1978. However, the date of maximum is in an up-and-down trend with slight changes. The maximum date is essentially in the trend of modest variation, although in 1975, the maximum date was more than 280 days. High pulse duration shows a falling tendency and high pulse duration is getting longer every year; it is 111 days in 1960 and 116 days in 1968, respectively.
Figure 10

Temporal change pattern of ERHIs (pre-altered).

Figure 10

Temporal change pattern of ERHIs (pre-altered).

Close modal

Since the 1950s, the Jing River outlets region has undergone significant human activities, including the straightening of the Lower Jing River, the diversion of water at the Gezhouba Dam hydraulic complex, and the impoundment of the TGR. These activities have further disrupted the pre-existing water-sediment balance and led to adjustments in the river–lake relationship. In terms of determining FDCs, Wang et al. (2017) analyzed the impact of the Three Gorges Dam on the FDCs of the Yangtze River. The final results indicated a significant influence of the Three Gorges Dam operation on the downstream of the Three Gorges Dam, particularly in terms of an increase in low flow periods and a decrease in high flow periods. The findings of this study are consistent with previous research. Subsequently, we further quantified the impact of the Three Gorges Dam operation on the ecological flow of the Jing River outlets in the middle reaches of the Yangtze River before and after its operation using a combination of multiple methods, primarily based on FDC-based ecological flow indicators (ecological surplus and ecological deficit). Additionally, we employed the PCA/RDA method to optimize 32 IHA indicators, reducing solid correlations between them. In the study of the runoff patterns and influencing factors of the Jingjiang River outlets, Wei et al. (2020) analyzed the annual-scale runoff variations in the Jingjiang River mouths following the impoundment of the TGR using measured data from 2008 to 2017. The study found that the bifurcation ratio of the three mouths continued to experience significant attenuation during this period; Li et al. (2021) examined the characteristics of bifurcation changes in SZK, one of the outlets of the Jing River, during dry periods since 1960. The analysis revealed a significant decline in the intra-annual bifurcation flow at SongZiKou following the impoundment of the TGR; Ju et al. (2022) researched the annual hydrological situation changes in the Wei River Basin using IHA indicators. The study found that the overall hydrological change degree in the Wei River Basin between 1961 and 2015 was only 29%, indicating a low level of change. Therefore, it is necessary to investigate further the runoff variations of the study area under the dual influences of the TGR impoundment and dry-season flow interruption. Additionally, by optimizing 32 IHA indicators using PCA/RDA analysis, the most ecologically hydrologically characteristic indicators (ERHIs) were identified further to quantify the runoff characteristics under the dual influences. This study revealed significant variations in ERHIs in the Jingjiang River outlets under dual effects from 1955 to 2019. Regarding the changes in ERHIs around 2003, the 90-day maximum change degree in MTS/GJP/KJG was −100%. The date of maximum change degree in GJP was 67.65%, whereas in KJG, it was only 2.9%. The high pulse duration change degree in KJG was 35.29%.

The temporal fluctuation characteristics of the flow-related ERHI indicators at the four hydrological stations at the three outlets of the Jingjiang River can be seen from the ERHI indicators in Figure 11 before the construction and operation of the TGR (1981–2002). While the low pulse count (SDG) and low pulse duration (MTS) both demonstrate an increasing tendency, the 90-day maximum (MTS/GJP/KJG) and high pulse duration (GJP) all show a declining trend. Among them, the 90-day maximum (MTS/GJP) are both increasing rapidly at a rate of 140.28 m3/s per year, the 90-day maximum (KJG) is slowly declining at a rate of 13.28 m3/s per year, and the low pulse count (SDG) are both increasing at a rate of 0.18 times and 8.05 days, respectively. This variational feature shows that, before the storage of TGR, the low flow at the three Jingjiang River outlets increased, and the flood flow generally weakened. The cause of this is that, since 1956, there have been outflows in the floodway of the three outlets of the Jingjiang River. Outflows have occurred in all five hydrological stations of the three outlets, except for XJK, due to the influence of siltation in the floodway of the three outlets of the Jingjiang River and fluctuations in the abundance and depletion of the incoming water of the Yangtze River main stream. The third group of ERHI indicators' temporal change characteristics after the TGR's enlargement (2003–2019) shows that low pulse count (SDG) and low pulse duration (MTS) show a declining trend, while high pulse duration (GJP) shows an increasing trend, date of minimum (GJP/KJG) shows signs of advancement, and date of maximum (GJP/KJG) shows signs of delay. Low pulse count (SDG), low pulse duration (MTS), and high pulse duration (GJP) are among them, and they all show a slow decline of 0.53 per year, 12.35 days per year, and 2.68 days per year, respectively. This suggests that the phenomenon of low flow at the three outlets of the Jingjiang River has subsided. This change in characteristic could be explained by the TGR switching from flood control to the storage of abundant water to make up for the dryness after storage, which provides a strong guarantee for water supply, shipping, and power generation in the winter and spring. The dry season conducts a replenishment schedule for the downstream after the TGR achieves capacity each year in October, considerably improving the ecological state of the middle and lower portions of the Yangtze River. Through this research framework, the ERHIs selected by Jingjiang can be seen as follows: this change may be caused by the rise in the river surface following the impounding of the TGR. The TGR changes from flood control to flood storage, which provides a strong guarantee for water supply, shipping, and power generation in the winter and spring. The implementation of water replenishment operations in the lower reaches of the TGR during the dry season after the reservoir fills in October each year is further supported by the low pulse count and low pulse duration, which will help to improve the ecological environment of the middle and lower reaches of the Yangtze River.
Figure 11

Temporal change pattern of ERHIs (cutoff period).

Figure 11

Temporal change pattern of ERHIs (cutoff period).

Close modal

This study presents a comprehensive assessment framework for quantifying hydrological conditions in changing environments. Coupling the IHA/RVA method with the annual distribution method describes the variations in annual ecological flows. It evaluates the ecological flows of the Jingjiang River outlets using the Tennant method. Subsequently, traditional IHA indicators were optimized using the combined PCA/RDA method to obtain ERHIs. These ERHIs, and general ecological flow indicators, were used to quantify the ecohydrological condition changes in the Jingjiang River outlets. The results are as follows:

  • (1)

    Based on the IHA–RVA method, this study calculated the ecological flow thresholds for SZK/TPK/OCK by improving the annual distribution method. The provided threshold ranges are conducive to maintaining biodiversity in the Jing River outlets and the health of the riparian vegetation communities. During the fish spawning period (April to September), the Tennant method assessment results indicated an overall favorable status for the minimum ecological flow throughout the year, meeting habitat requirements.

  • (2)

    The study found that natural inflows primarily influence the variation in near-natural ecological flows. Subsequently, the impact of the TGR significantly intensified, leading to significant changes in the river ecological flows of the Jingjiang River outlets, especially regarding the magnitude and high-flow count periods during the flood season, which significantly decreased below the 25% FDC level. This resulted in high-level ecological deficits (with a maximum of 0.99 occurring in the spring of 2000 in OCK). Low flow periods sharply increased during the non-flood season, exceeding the 75% FDC threshold, resulting in remarkable ecological surpluses (with a maximum of 5.04 occurring in the winter of 1961 in OCK).

  • (3)

    Furthermore, the study identified that ERHIs can effectively mitigate the redundancy issue in IHA indicators while preserving the most critical ecological hydrological information. Using PCA and RDA methods, the original 33 hydrological indicators were reduced to 3, greatly simplifying water resource management objectives.

The study's findings provide insight into the hydrological changes that occur in regions where the ecological flow of rivers is significantly influenced by unique natural incoming water and reservoir operation during the natural incoming water period, as well as serving as a model for research in other contexts. Using the suggested ERHI indicator screening procedure in various industries is possible. To provide a foundation for managing water resources and safeguarding river ecology, future studies can concentrate on the ecological response to anticipated hydrological changes under the dual influence of climate change and human activity. However, this study primarily focuses on the current hydrological situation at the three outlets of the Jing River. Furthermore, this study focused on analyzing changes in runoff itself and did not consider the influence of precipitation on runoff and ecological flow. Future research will delve deeper into the impact of rainfall on runoff variations to gain a more comprehensive understanding of hydrological conditions in the Jing River outlets region.

This study was supported by the Basic Research Project of Key Scientific Research Projects of Colleges and Universities of Henan Province (23ZX012) and the National Natural Science Foundation of China (51779094).

Not required as the study did not involve humans or animals.

Authors have consented to participate in any offer by the journal.

Authors are giving consent to publish the article in the submitted journal.

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

The authors declare there is no conflict.

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