Abstract
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.
HIGHLIGHTS
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.
INTRODUCTION
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.
STUDY AREA AND DATA
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.
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).
METHODOLOGY
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
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
IHA indicators group . | Hydrological 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 group . | Hydrological 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.
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 = Y − Y′); 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.
RESULTS
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.
Time series . | Outlets . | Mutation year . | Typical year of mutation . | ||
---|---|---|---|---|---|
M-K sudden change test . | Cumulative distance level method . | The 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 series . | Outlets . | Mutation year . | Typical year of mutation . | ||
---|---|---|---|---|---|
M-K sudden change test . | Cumulative distance level method . | The 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
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).
Section name . | Minimum annual average flow (m3/s) . | Suitable annual average flow (m3/s) . | Multi-year average flow (m3/s) . | Year-over-year average ratio (%) . | ||
---|---|---|---|---|---|---|
Minimum . | Appreciate . | |||||
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 name . | Minimum annual average flow (m3/s) . | Suitable annual average flow (m3/s) . | Multi-year average flow (m3/s) . | Year-over-year average ratio (%) . | ||
---|---|---|---|---|---|---|
Minimum . | Appreciate . | |||||
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 name . | Jan . | Feb . | Mar . | Apr . | May . | June . | July . | Aug . | Sep . | Oct . | Nov . | Dec . | Multi-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 | 0 | 0 | 0 | 31.9 | 426 | 1,135 | 3,340 | 3,900 | 2,885 | 1,280 | 348 | 30.7 | 1,114.7 | |
OCK | 0.7 | 0 | 0.2 | 49.3 | 600 | 1,376 | 4,738 | 4,335 | 3,168 | 1,544 | 377.3 | 43.1 | 1,352.6 |
Section name . | Jan . | Feb . | Mar . | Apr . | May . | June . | July . | Aug . | Sep . | Oct . | Nov . | Dec . | Multi-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 | 0 | 0 | 0 | 31.9 | 426 | 1,135 | 3,340 | 3,900 | 2,885 | 1,280 | 348 | 30.7 | 1,114.7 | |
OCK | 0.7 | 0 | 0.2 | 49.3 | 600 | 1,376 | 4,738 | 4,335 | 3,168 | 1,544 | 377.3 | 43.1 | 1,352.6 |
Section name . | Month . | Jan . | Feb . | Mar . | Apr . | May . | June . | July . | Aug . | Sep . | Oct . | Nov . | Dec . | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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. | 0 | 0 | 0 | 5.9 | 78.3 | 208.7 | 614.2 | 717.1 | 530.5 | 124.2 | 33.8 | 3 | |
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 | 0 | 0 | 8.3 | 101.1 | 231.9 | 798.6 | 730.7 | 534 | 126.5 | 30.9 | 3.5 | |
App. | 7.7 | 2 | 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 name . | Month . | Jan . | Feb . | Mar . | Apr . | May . | June . | July . | Aug . | Sep . | Oct . | Nov . | Dec . | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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. | 0 | 0 | 0 | 5.9 | 78.3 | 208.7 | 614.2 | 717.1 | 530.5 | 124.2 | 33.8 | 3 | |
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 | 0 | 0 | 8.3 | 101.1 | 231.9 | 798.6 | 730.7 | 534 | 126.5 | 30.9 | 3.5 | |
App. | 7.7 | 2 | 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 name . | Categ. . | 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 name . | Categ. . | 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
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.
- (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.
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.
Methods . | ERHIs . | ||
---|---|---|---|
SongZiKou . | TaiPingKou . | OuChiKou . | |
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 |
Methods . | ERHIs . | ||
---|---|---|---|
SongZiKou . | TaiPingKou . | OuChiKou . | |
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 |
Time-varying characteristics of ERHI indicators
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.
JREI group . | Hydrological parameters . | Influences 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 group . | Hydrological parameters . | Influences 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.
DISCUSSION
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%.
CONCLUSION
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.
ACKNOWLEDGEMENTS
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).
ETHICAL APPROVAL
Not required as the study did not involve humans or animals.
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DATA AVAILABILITY STATEMENT
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CONFLICT OF INTEREST
The authors declare there is no conflict.