Using a combination of the long short-term memory model and hydrological change indicators, this study proposes an assessment framework at inter-annual and intra-annual scales to quantify the hydrological regime changes and ecological event responses caused by the regulation of the Three Gorges Dam (TGD) in the upper reaches of the Yangtze River. The results indicate that during the post-TGD period (2004–2019), 2 indicators of the natural flow regime undergo a high degree of alteration at the inter-annual scale, which increase to 12 when regulated flows are considered. Furthermore, we find that while climate and incoming water change significantly reduces the annual flow and monthly flow during the flood season, it increases the complexity (79%) and ecodeficit at the seasonal scale (94%). Among the 32 indicators of hydrologic alteration, TGD is the dominant factor influencing changes in 20 indicators, increasing the magnitude of low-flow events, decreasing the frequency of high-flow pulses, and advancing the timing of 1-day minimum flow (43 Julian date). From the hydrological perspective, the altered rising water conditions due to TGD regulation may cause an average decrease of 19.5% in the fry abundance for the Four Famous Major Carps.

  • The multiscale changes in the flow regime were evaluated and the impact of dam construction by introducing entropy theory was quantified.

  • After 2003, the flow regime tended to be more complex, while the singularity of hydrological events increased, with the Three Gorges Dam being the dominant factor.

  • The dam advanced the timing of minimum flow, reduced the concentration of flow regimes, and increased the ecosurplus.

The natural variability of flow regime in rivers plays a vital ecological role in maintaining river morphology, preserving biodiversity, and shaping natural habitats (Shiau & Wu 2004; Wang et al. 2012; Gabiri et al. 2018). However, under changing environmental conditions, the hydrological regimes of many rivers worldwide have experienced significant alterations (Rolls et al. 2018; Wang et al. 2022; Battin et al. 2023). Human activities primarily manifest as flow regulation through dam operations, which directly influence the flow regime of rivers and are considered the most prominent factor. In addition, climate change, characterized by changes in precipitation and extreme weather events, further drives alterations in flow regimes (Li et al. 2015; Stagl & Hattermann 2016; Thompson et al. 2021).

In recent years, under the increasing influence of external drivers, the flow regime of rivers has exhibited complex patterns of change, attracting the attention of numerous scholars (Suen 2011; Taylor et al. 2014; McGregor et al. 2018; van Oorschot et al. 2018; Bestgen et al. 2020). Richter et al. (1996) proposed a method to assess the degree of hydrologic alteration caused by human impacts within ecosystems, known as the ‘Indicators of Hydrologic Alteration’ (IHA). Masikini et al. (2018) introduced a river health assessment approach that encompasses different states of aquatic ecosystems, with flow as a crucial environmental variable. From the perspective of hydrological variability, the flow regime directly influences the vertical and horizontal structures of aquatic ecosystems, and the intra-annual flow processes serve as important stimuli for aquatic life activities (Wu et al. 2023). Therefore, investigations into the flow regime of rivers should not only focus on inter-annual changes but also comprehensively understand the characteristics of intra-annual flow processes and hydrological events, which are of significant importance for adaptive management of rivers in changing environments. Furthermore, climate change accelerates hydrological cycles and increases water-related risks. The IPCC report indicates that global average temperature has exceeded pre-industrial levels by 1.5 °C, further impacting the physical attributes of water security and increasing water resource vulnerability (IPCC 2022). Meanwhile, strong coupling exists between precipitation and temperature, with climate warming leading to more frequent and intense extreme weather events. In addition, dam regulation has been identified as an important stressor on rivers. These factors collectively exacerbate the degradation risks of river health (Zhang et al. 2021; Yang et al. 2024). The coupling of increased dam construction and sustained climate warming pushes river systems to the limits of their resilience. In this context, it is crucial to gain a detailed understanding of the multiscale changes in river hydrological regimes and quantitatively assess the responses of flow regimes and hydrological events to different driving forces.

Like other random time series, streamflow sequences exhibit multi-timescale structures, indicating that the characteristics of hydrological systems change over the study period and contain periodic features and local fluctuations at different scales within the same timeframe (Chen et al. 2023). Entropy is a measure of the disorder or unpredictability of system changes, and entropy theory encompasses diversity. Based on multi-timescale entropy, Zhang et al. (2019) analyzed the complex fluctuation characteristics of water and sediment load at different timescales and qualitatively assessed the impact of reservoir operations. Huang et al. (2019) designed a streamflow diversity index based on Shannon entropy and analyzed the relationship between streamflow diversity and the average aggregation rate of fish, demonstrating that entropy theory is an effective indicator with general ecological significance. Therefore, entropy theory is an effective approach that can be utilized to assess the complexity of hydrological regime changes. In addition, considering the proven significant impact of dams on flow regimes at intra-annual scales, entropy theory can also be applied to reveal the characteristics of dam influences.

The Three Gorges Dam (TGD), located in the upper reaches of the Yangtze River (URYR), is the largest hydropower project in the world. Its impoundment operation has significantly changed the natural flow and hydrological regimes in the downstream of the dam (Wu et al. 2012; Li et al. 2016; Tao et al. 2020; Xiong et al. 2020; Zhao et al. 2021). However, previous studies have mainly focused on the inter-annual variations of river hydrological regimes and assessed the impact of dam construction based on the difference in hydrological conditions before and after disturbance (Chen et al. 2016; Wang et al. 2016; Peng et al. 2022). The flow regime changes and potential mechanisms between natural flow and observed flow affected by dams have not been fully understood. For highly regulated rivers like the Yangtze River, it is essential to separate the effects of dam construction and other driving forces on the hydrological regime to facilitate water resource management. Therefore, utilizing modeling approaches to reconstruct the natural flow regime without dam regulation and conducting a difference analysis between the reconstructed and observed flow regime is crucial for separating and quantifying the impact of the dam at comprehensive timescales.

There are various methods for reconstructing the natural flow, primarily divided into distributed hydrological models and data-driven models (Valeh et al. 2021; Ren et al. 2024). Distributed hydrological models based on physical principles have strict data requirements and are limited by boundary conditions of the hydrological system (Ma et al. 2016; Su et al. 2020). Data-driven models have also been proven feasible for flow reconstruction, where the principle of these models is to map independent variables to dependent variables (Gao et al. 2020). However, traditional machine learning methods like artificial neural networks are not specifically designed to handle time series data and cannot directly capture temporal dependencies between time series. In contrast, the long short-term memory (LSTM) model addresses the issue of long-term and short-term dependencies in time series, significantly improving the accuracy of time series prediction (Fan et al. 2021; Cho & Kim 2022). Moreover, it has been shown that streamflow in the Yangtze River basin is closely related to precipitation (You et al. 2023), indicating that the LSTM models are feasible in the URYR.

This study proposes an assessment framework aimed at analyzing the multiscale variations of flow regime and their driving factors, as well as investigating the response of ecological events, with the upstream Yangtze River basin as a case study. The range of variation approach is employed to analyze the variations in environmental flow components (EFCs), inter- and intra-annual flow regimes, and the natural flow regime is reconstructed using the LSTM model. Subsequently, we quantitatively assess the impacts of dam construction, climate and incoming water change on flow regimes, and hydrological events and analyze the responses of fish fry abundance to hydrological process changes under dam regulation. This study provides valuable insights into the mechanisms of river hydrological conditions and their responses to driving factors and offers a scientific basis for dam operation strategies oriented toward ecological restoration.

Study area

The Yangtze River is the largest river in China in terms of water resources. It originates from the Qinghai-Tibet Plateau, spanning a length of 6,379 km; the total amount of water resources is 9.6 × 1012 m3, with a basin area of 1.8 × 106 km2, accounting for one-fifth of the total land area of China. The mainstream of the Yangtze River is divided into the upper reaches, stretching from the source to Yichang city, covering a length of 4,511 km and controlling a basin area of 1 × 106 km2. This section exhibits distinct characteristics of the East Asian subtropical climate, with distinct flood seasons and dry winters. The flood season typically occurs from May to October each year, with the main flood season occurring from July to August (Liu et al. 2018). Due to the abundant water resources, a number of large dams have been constructed in the URYR, and the river flow regime is seriously affected.

The TGD has a dam height of 181 m and a normal water level of 175 m. The flow regulation began in 2003. The operation of the TGD has had profound impacts on hydrological elements such as runoff, sediment, and water temperature in the downstream reservoir area. The Yichang station is located 37 km downstream of the TGD, which serves as the outlet control station for the URYR. The measured flow regime at the Yichang station is significantly influenced by the TGD.

Data sources

Flow data: Daily flow series data of Cuntan, Wulong, and Yichang hydrological stations from 1965 to 2019 were collected and provided by the Changjiang Water Resources Commission (Figure 1). The study period was divided into pre-TGD (1965–2003) and post-TGD (2004–2019) periods based on the year of the TGD regulation. The flow measurements at Cuntan and Wulong stations are unaffected by the TGD, enabling them to reflect the upstream water inflow conditions influenced by the TGD.
Figure 1

Overview of the URYR basin.

Figure 1

Overview of the URYR basin.

Close modal

Meteorological data: Daily meteorological data, including daily average temperature and daily precipitation, from 21 meteorological stations in the URYR were collected for the period 1965–2019. The data were obtained from the National Meteorological Science Data Center (https://data.cma.cn/).

Riverine biological data: Early-stage fishery resource data of the Four Famous Major Carps (FFMC) in the Yichang section of the mainstream of the Yangtze River during the spawning period (May–July) were collected. The dataset includes 37 monitoring records from 1997 to 2003 before the construction of the TGD, 31 monitoring records from 2004 to 2009, and 10 monitoring records from 2014 to 2019 (the TGD has been conducting 14 ecological regulation experiments to promote fish reproduction continuously for 10 years since 2011). The data were obtained from the Yangtze River Fisheries Research Institute (http://www.cjyzbgs.moa.gov.cn/).

Climate change, such as variations in precipitation and increasing temperatures that accelerate the water cycle, significantly affect the flow process (Cui et al. 2020). Therefore, when quantifying the impact of dams on flow regimes, it is important to consider the influence of these meteorological factors. Here, we propose a framework for quantitatively assessing the independent effects of dam construction, climate, and incoming water change, involving several consecutive components (Figure 2).
Figure 2

The general framework for quantifying the impacts of dam construction and climate change on river hydrological regime.

Figure 2

The general framework for quantifying the impacts of dam construction and climate change on river hydrological regime.

Close modal

Step 1: The daily hydrological and meteorological data are collected. The study period is divided into pre-TGD and post-TGD periods based on the construction of the TGD.

Step 2: Reconstruction of the natural flow regime unaffected by the TGD. Climate change and variations in upstream water alter the inflow regime of the TGD, coupled with further regulation by the TGD, resulting in changes in the flow regime at the Yichang station. In this study, the LSTM model is employed to simulate the natural flow regime.

Step 3: Evaluation of multiscale changes in flow regime and ecohydrological events. This study integrates the IHA and five additional indicators that reflect intra-annual hydrological processes. From a hydrological perspective, five ecologically significant flow events are identified.

Step 4: Quantification of the impact of dam construction on flow regime and ecohydrological events. After the construction of the TGD, changes in the hydrological regimes at the Yichang station reflect the combined effects of dam regulation, climate change, and variations in upstream inflow. The specific impact of the TGD is quantified by analyzing the differences between observed and modeled conditions.

Reconstruction method of river natural flow without dam influence

This study divided the time series into pre-dam (1965–2003) and post-dam (2004–2019) periods. Using meteorological data and measured upstream flow data from the dam, the daily flow data for the post-dam period were reconstructed to analyze the changes in flow regime caused by climate change and dam construction. The LSTM is a type of deep learning model that is an improvement upon the recurrent neural network (RNN). It has a similar chain-like structure to RNN but with the addition of a cell state and three ‘gates’ (forget gate, input gate, and output gate). The neuron structure is shown in Figure 3 (Ma et al. 2015; Cho & Kim 2022).
Figure 3

The structure of LSTM neural network cell.

Figure 3

The structure of LSTM neural network cell.

Close modal

The LSTM network exhibits robustness and the ability to learn and remember information, enabling it to capture long-term and short-term dependencies between input and output data. It effectively solves the vanishing gradient problem. The framework based on the LSTM network can identify the flow process under the condition without dam regulation, providing a prerequisite for quantifying the impact of the TGD. By combining indices that characterize the flow regime at inter- and intra-annual timescales, as well as hydrological event indices, the multiscale effects of the dam on the flow regime can be comprehensively revealed.

Forget gate (Ft), input gate (It), output gate (Ot), hidden state (Ht), and cell state (Ct) can be given by the following formulas:
formula
(1)
formula
(2)
formula
(3)
formula
(4)
formula
(5)
where, σ denotes the Sigmoid function, Ht−1 is the output of the previous cell, and xt is the input of the current cell. WF, WI, WC, and WO are the weight matrices of the LSTM network, and the number of elements is equal to the multiplication of input dimension and output dimension. Similarly, bF, bI, bC, and bO are bias vectors, and the number of elements equals the dimension of the output.

The pre-TGD period is divided into calibration period (1965–1990) and verification period (1991–2003) to train the LSTM and then reconstruct the natural flow from 2004 to 2019. The root mean square error (RMSE) and Nash–Sutcliffe efficiency coefficient (NSE) are used to evaluate the performance of the LSTM model. The specific principle is referred to in the study by Graf et al. (2019).

For constructing our model, we utilized meteorological data between the Wulong and Yichang stations, as well as discharge data from the Cuntan and Wulong stations, as the driving forces. The discharge data from the Yichang station during the calibration period (1965–1990) and the validation period (1991–2003) served as the training and validation datasets for developing the LSTM model. Finally, the model was used to output the runoff data for the post-TGD period at the Yichang station. Figure 4 demonstrates the performance of the LSTM model in flow reconstruction, showing its effectiveness in capturing extreme flow variations. The fitting of observed and reconstructed flow during the calibration and validation periods is as follows: RMSE = 1,780 m3/s and 1,865 m3/s; NSE = 0.972 and 0.970, respectively. The insignificant changes in both evaluation indicators indicate the good performance of the model, reflecting that the partitioning of the dataset does not affect the transfer of model uncertainty (Moriasi et al. 2007). It can be concluded that the model accurately reflects the natural flow process at the Yichang station without considering the TGD regulation subsequently.
Figure 4

Verification of the reliability of the LSTM model.

Figure 4

Verification of the reliability of the LSTM model.

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Indicators of river flow regime change

Inter-annual variations in flow regime

Richter et al. (1996) proposed the IHA index system, which characterizes the inter-annual variations of flow regime from five perspectives: magnitude, timing, duration, frequency, and variability. The framework consists of a total of 33 indicators grouped into five categories. Since zero-flow events were not observed at the Yichang station, the analysis in this study includes only 32 of these indicators (Table 1).

Table 1

IHA parameter groups and their ecological effects on rivers

IHA groupsRegime characteristicsHydrologic parametersEcological effects
Monthly conditions (1–12) Magnitude, timing Mean value for each calendar month Availability of habitat; mammalian food acquisition; dissolved oxygen levels, water temperature, and chemical reactions 
Extreme conditions (13–23) Magnitude, duration Annual 1, 3, 7, 30, 90 days minimum and maximum; base flow index Formation of plant growing sites; balancing food chain; shaping river channel structure and habitat conditions 
Timing of extreme conditions (24–25) Timing Julian date of each annual 1 day minimum and maximum Nutrient exchange between rivers and floodplains; stimulatory signals for aquatic organism life cycles; behavior of aquatic organisms 
High and low pulses (26–29) Duration, frequency No. and duration of low/high pulses Water stress conditions for plant water requirements; habitat conditions in floodplain areas; organic matter exchange between rivers and floodplains 
Water condition changes (30–32) Frequency, variability No. of rises, falls, and reversals Retention of biota in floodplain areas; survival and reproduction of organisms in riverbanks 
IHA groupsRegime characteristicsHydrologic parametersEcological effects
Monthly conditions (1–12) Magnitude, timing Mean value for each calendar month Availability of habitat; mammalian food acquisition; dissolved oxygen levels, water temperature, and chemical reactions 
Extreme conditions (13–23) Magnitude, duration Annual 1, 3, 7, 30, 90 days minimum and maximum; base flow index Formation of plant growing sites; balancing food chain; shaping river channel structure and habitat conditions 
Timing of extreme conditions (24–25) Timing Julian date of each annual 1 day minimum and maximum Nutrient exchange between rivers and floodplains; stimulatory signals for aquatic organism life cycles; behavior of aquatic organisms 
High and low pulses (26–29) Duration, frequency No. and duration of low/high pulses Water stress conditions for plant water requirements; habitat conditions in floodplain areas; organic matter exchange between rivers and floodplains 
Water condition changes (30–32) Frequency, variability No. of rises, falls, and reversals Retention of biota in floodplain areas; survival and reproduction of organisms in riverbanks 

Intra-annual variations in flow regime

Sample entropy
Sample entropy (SampEn) is used to describe the irregularity of complex systems, where higher entropy values correspond to more irregular systems, indicating a higher probability of generating new patterns and thus higher complexity of the samples. For a given finite N-dimensional time series, the phase space dimension m (mN − 2) is defined as
formula
(6)
The Euclidean distance between vectors X(i) and X(j) is calculated, and the similarity tolerance limit r is introduced to count the number of in the range of r.
formula
(7)
formula
(8)
Therefore, the complexity of intra-annual flow series can be calculated as
formula
(9)
formula
(10)
Distribution characteristics
To capture the distribution characteristics of intra-annual flow, this study examined the concentration degree (Cd) and coefficient of variation (Cv) as defined by the following formulas:
formula
(11)
formula
(12)
formula
(13)
where Q(t) is the flow on day t and n represents the number of days (365 or 366). Qx and Qy represent the components of each day flow vector in x and y directions, respectively. Qxy represents the module of the vector, where θ(t) = 2πt/n.
Ecosurplus and ecodeficit
Vogel et al. (2007) proposed indicators, ecosurplus and ecodeficit, based on the flow duration curve (FDC) to assess the extent to which intra-annual flow variations meet the water requirements of riverine ecosystems. The FDC is constructed using daily flow data, where the flow values Qi for each day in the study period are arranged in descending order. The exceedance probability Pi, representing the probability of flow being exceeded, can be calculated as follows:
formula
(14)
where i is the rank of Qi and n is the length of the period.

Based on the exceedance probabilities of 25 and 75% during the reference period, the threshold range is reconstructed. Ecosurplus and ecodeficit are defined as the portions above the 75% FDC and below the 25% FDC, respectively. They are then normalized using the method proposed by Wang et al. (2017).

EFCs of rivers

EFCs are defined as the required flow and associated processes necessary to maintain ecological stability in a river system. They encompass five flow events: low flow, extreme low flow, high flow pulse, small flood, and large flood (Table 2). The threshold delineation for these flow events associated with EFC can be found in the study by Richter & Thomas (2007).

Table 2

EFCs and their ecological roles

Ecohydrological eventsEcological roles
Low flow Maintain groundwater levels in flood plains and provide drinking water for terrestrial animals 
Extreme low flow Expand species of floodplain plants to prevent invasive alien species 
High flow pulse Shaping the physical characteristics of the river, maintaining normal water quality conditions, ensuring the appropriate salinity conditions of the estuary 
Small flood Provide clues to fish migration and spawning, recharge water levels in flooding areas, and control the population structure and distribution of floodplain plants 
Large flood Maintain species balance in aquatic and riparian communities; promoting material exchange between river and floodplains 
Ecohydrological eventsEcological roles
Low flow Maintain groundwater levels in flood plains and provide drinking water for terrestrial animals 
Extreme low flow Expand species of floodplain plants to prevent invasive alien species 
High flow pulse Shaping the physical characteristics of the river, maintaining normal water quality conditions, ensuring the appropriate salinity conditions of the estuary 
Small flood Provide clues to fish migration and spawning, recharge water levels in flooding areas, and control the population structure and distribution of floodplain plants 
Large flood Maintain species balance in aquatic and riparian communities; promoting material exchange between river and floodplains 

Ecological response assessment of the river

The FFMC are economically important fish species in the Yangtze River and China. This study focuses on the FFMC as representative fish species to investigate the impact of hydrological process changes caused by dam regulation on riverine ecological events. The spawning period of FFMC generally occurs from May to July, with a lower limit water temperature of 18 °C for reproduction. Under suitable water temperatures, river flooding becomes an important factor inducing FFMC reproductive activities (Wang et al., 2020a, 2020b). Previous studies have shown that FFMC typically start spawning approximately 0.5–2 days, or even 3 days, after the river begins to rise, and spawning ceases when the rising stops, and the fry abundance of FFMC increases with the magnitude of rise (Li & Xia 2011; Yu et al. 2019). Suitable flood conditions are crucial for FFMC reproduction. From the flow perspective, this study focuses on the maximum rising duration, the rising count (2 days and above), and the changes in the maximum and minimum daily average rising rates from 1965 to 2019 during May to July. Furthermore, the fry abundance, as a biological factor reflecting the scale of FFMC reproduction, is correlated with the rising indicators associated with each spawning event, including the rising duration (days), daily average rising rate (m3s−1day−1), and magnitude of rise (m3s−1).

Quantifying the impacts of climate change and dam construction

To quantify the degree of alteration in each indicator after disturbances, the range of variability approach (RVA) is used for evaluation. The change degree in the indicators can be calculated as follows:
formula
(15)
formula
(16)
where Di represents the degree of alteration for the ith indicator and Do represents the overall magnitude of change for n indicators. Noi denotes the number of years in which the ith indicator falls within the RVA threshold after a mutation occurs. The RVA threshold is determined based on the 0.25 and 0.75 exceedance probabilities of each indicator during the pre-TGD period. Ne represents the expected number of years in which the indicator falls within the RVA threshold after a mutation. r indicates the proportion of years for each indicator during the pre-TGD period that fall within the RVA threshold. Nt represents the number of years in the post-TGD period, and n represents the number of indicators. The levels of change degree are defined as follows: 0–33% as low-level change (L), 33–67% as moderate-level change (M), and 67–100% as high-level change (H).
The hydrological regime changes at the Yichang station reflect the combined effects of dam regulation, climate, and incoming water changes, while the modeled flow variations can be regarded as the state unaffected by TGD regulation. By comparing the observed and modeled values, the driving factors can be categorized as the impact of TGD regulation (ΔCTGD), the impact of climate and incoming water changes (ΔCcli&iw). The changes in each indicator, ΔQ, can be represented as follows:
formula
(17)
Among them, the impacts of TGD regulation (ΔCTGD), climate, and incoming water changes (ΔCcli&iw) can be calculated as
formula
(18)
formula
(19)
Then, the impact weight of TGD regulation (ηTGD) and the impact weight of climate and incoming water changes (ηcli&iw) can be quantitatively assessed as follows:
formula
(20)

Temporal variation characteristics

Due to the impact of climate change on the upstream reservoir area of the TGD, as well as the fact that 56% of TGD's storage capacity is used for water regulation, significant changes in monthly flow are observed at the Yichang station. Compared to the pre-TGD period, the monthly average flow at the Cuntan station decreases from May to November, while it increases from December to the following April (Figure 5(a)). TGD reduces the peak flow of the river from May to October for downstream flood control purposes, and increases the peak flow from October to the following April to meet the water demand for agriculture, industry, and navigation downstream, resulting in a smoothing of the annual flow pattern downstream of the reservoir (Figure 5(b)). It can also be observed that during the main flood season (July–August), where the reduction in peak flow is most pronounced. The monthly average flow increases from December to the following May and decreases from June to November, with the largest decrease occurring in October.
Figure 5

Monthly average flow changes in the pre-TGD and post-TGD periods.

Figure 5

Monthly average flow changes in the pre-TGD and post-TGD periods.

Close modal

Performance of the LSTM model

As shown in Figure 6, the annual distribution of the reconstructed flow results obtained from the LSTM model is presented. As expected, the impact of the TGD on the flow regime is more pronounced during the non-flood season, leading to significant differences between observed and reconstructed flow during the post-TGD period. However, the impact is smaller during the flood season. Based on the reconstructed flow results, it can be inferred that without the TGD, the annual peak flow would be larger, and the flow during the dry season would be smaller. The R2 value, indicating the goodness of fit between reconstructed and observed flow, decreased from 0.980 before dam construction to 0.870 after dam construction.
Figure 6

Comparison of measured and modeled flow in Yichang station before and after the regulation of TGD.

Figure 6

Comparison of measured and modeled flow in Yichang station before and after the regulation of TGD.

Close modal

Impact of TGD regulation, climate, and incoming water changes on flow regimes

Inter-annual patterns

Based on the analysis of IHA-RVA, the degree of alteration in natural flow unaffected by TGD regulation was 38.1% after 2003, with 10 indicators exhibiting moderate and high levels of change (Figure 7(b)). When considering the regulatory effect of TGD, the regulated flow showed significant changes at the inter-annual scale, with an overall change degree of 59.9%, approaching a high level. In this case, 16 indicators experienced moderate to high levels of change (Figure 7(a)).
Figure 7

The change degree of the flow regime ((a) the measured flow regime; (b) the flow regime only under the impact of climate and incoming water change).

Figure 7

The change degree of the flow regime ((a) the measured flow regime; (b) the flow regime only under the impact of climate and incoming water change).

Close modal

For monthly flow, the change degree is 47.4%, indicating a moderate level. The non-flood season months exhibit higher variability than the flood season, with change degrees of 61.2 and 27.3%, respectively, which align with the regulation of TGD. Furthermore, the decrease in the annual average flow is primarily attributed to the influence of climate and incoming water changes, which accounts for 85% of the overall change. Regarding monthly flow, the dam demonstrates a downstream inhibitory effect from July to November, while it exhibits a rising effect in other months. The most significant impacts are observed in May (rising effect) and November (inhibitory effect). However, climate and incoming water changes lead to reduced flow at the Yichang station from May to October, with notable impacts in April (rising effect) and August (inhibitory effect) (Table 3).

Regarding extreme flow conditions, after the initiation of TGD regulation, minimum and maximum flows tend to increase and decrease, respectively, with a rise in base flow index. The change in minimum flow is significant, with a change degree exceeding 67%, while there is no significant change observed in maximum flow. Climate and incoming water change primarily drives the decrease in 7, 30, and 90-day maximum flows, while the reduction in 1- and 3-day maximum flows, as well as the increase in minimum flow, can be primarily attributed to the regulation of TGD. The occurrence of the 1-day minimum flow has shifted from the pre-TGD period of the 57 Julian date to the post-TGD period of the 4 Julian date, with an advance of 53 days and a change degree of 74.8%. This may be related to the early discharge from the dam (−43). There is no significant change in the timing of the 1-day maximum flow, with an average delay of 2 Julian dates.

Table 3

The results of IHA indicators calculation through the proposed framework

IHA indicatorsPre-TGDPost-TGDChange (relative %)ΔCcli&iw (ηcli&iw %)ΔCTGD (ηTGD %)MeasuredModeled
Di %Di %
Group 1 
 January 4,285 5,994 1,709(39.9) 622 (36.40) 1,087 (63.60) 69.53 19.72 
 February 3,872 5,847 1,975 (51.0) 582 (29.47) 1,393 (70.53) 100 45.83 
 March 4,351 6,433 2,082 (47.9) 973 (46.73) 1,109 (53.27) 79.69 73.88 
 April 6,720 8,439 1,719 (25.6) 911 (53.00) 808 (47.00) 29.23 10.63 
 May 11,460 13,120 1,660 (14.5) −110 (5.85) 1,770 (94.15) 6.25 21.88 
 June 18,140 17,370 −770 ( − 4.2) −2,230 (60.43) 1,460 (39.57) 17.36 12.5 
 July 29,740 26,390 −3,350 (25.0) −3,060 (91.34) −290 (8.66) 25.0 31.75 
 August 26,080 23,140 −2,940 ( − 11.3) −2,810 (95.58) −130 (4.42) 7.54 9.27 
 September 25,390 19,360 −6,030 ( − 11.3) −3,350 (55.56) −2,680 (44.44) 25.0 17.0 
 October 17,880 12,690 −5,190 ( − 23.7) −2,110 (40.66) −3,080 (59.34) 53.13 0.69 
 November 9,745 9,270 −475 ( − 29.0) 111 (15.93) −586 (84.07) 18.75 11.72 
 December 5,803 6,203 400 (6.9) 66 (16.50) 334 (83.50) 8.59 4.46 
Group 2 
 1 day minimum 3,381 5,139 1,758 (52.0) 453 (25.77) 1,305 (74.23) 90.97 51.25 
 3 days minimum 3,413 5,246 1,833 (53.7) 411 (22.42) 1,422 (77.58) 90.63 39.06 
 7 days minimum 3,470 5,299 1,859 (52.7) 465 (24.49) 1,434 (75.51) 100 41.5 
 30 days minimum 3,679 5,548 1,869 (50.8) 598 (31.11) 1,324 (68.89) 100 51.25 
 90 days minimum 4,146 6,090 1,944 (46.9) 632 (32.51) 1,312 (67.49) 90.97 63.89 
 1 day maximum 48,770 39,370 −9,400 ( − 19.3) −4,630 (49.26) −4,770 (50.74) 43.75 25.83 
 3 days maximum 47,110 38,550 −8,560 ( − 18.2) −4,210 (49.18) −4,350 (50.82) 36.81 57.77 
 7 days maximum 42,630 36,450 −6,180 ( − 14.5) −3,776 (61.10) −2,404 (38.90) 21.65 18.75 
 30 days maximum 34,090 29,800 −4,290 ( − 12.6) −3,566 (83.12) −724 (16.88) 15.95 18.75 
 90 days maximum 27,830 23,920 −3,910 ( − 14.0) −3,620 (92.58) −290 (7.42) 13.51 12.95 
 Base flow index 0.26 0.41 0.15 (61.9) 0.04 (26.67) 0.11 (73.33) 90.63 51.25 
Group 3 
 Data of 1 day minimum 57 − 53 (− 92.5) −9 (16.98) −44 (83.02) 74.78 47.77 
 Data of 1 day maximum 215 217 2 (0.7) 8 (44.44) −10 (55.56) 4.62 21.88 
Group 4 
 Low pulse count 0.23 0.06 −0.17 ( − 74.0) −1.49 (53.02) 1.32 (46.98) 18.18 2.5 
 Low pulse duration 4.08 0.92 (22.5) −0.07 (6.60) 0.99 (93.40) 100 18.18 
 High pulse count 5.18 3.94 −1.24 ( − 24.0) −0.36 (29.03) −0.88 (70.97) 13.17 88.92 
 High pulse duration 14 13.25 −0.75 ( − 5.4) −3.02 (57.09) 2.27 (42.91) 18.75 26.94 
Group 5 
 Rise rate 455 297 −158 ( − 34.57) −49 (31.01) −109 (68.99) 63.89 15.95 
 Fall rate −330 −340 −10 (3.03) 86 (47.25) −96 (52.75) 18.75 15.63 
 No. of reversals 97 146 49 (50.6) 9 (0.1837) 40 (81.63) 100 9.72 
IHA indicatorsPre-TGDPost-TGDChange (relative %)ΔCcli&iw (ηcli&iw %)ΔCTGD (ηTGD %)MeasuredModeled
Di %Di %
Group 1 
 January 4,285 5,994 1,709(39.9) 622 (36.40) 1,087 (63.60) 69.53 19.72 
 February 3,872 5,847 1,975 (51.0) 582 (29.47) 1,393 (70.53) 100 45.83 
 March 4,351 6,433 2,082 (47.9) 973 (46.73) 1,109 (53.27) 79.69 73.88 
 April 6,720 8,439 1,719 (25.6) 911 (53.00) 808 (47.00) 29.23 10.63 
 May 11,460 13,120 1,660 (14.5) −110 (5.85) 1,770 (94.15) 6.25 21.88 
 June 18,140 17,370 −770 ( − 4.2) −2,230 (60.43) 1,460 (39.57) 17.36 12.5 
 July 29,740 26,390 −3,350 (25.0) −3,060 (91.34) −290 (8.66) 25.0 31.75 
 August 26,080 23,140 −2,940 ( − 11.3) −2,810 (95.58) −130 (4.42) 7.54 9.27 
 September 25,390 19,360 −6,030 ( − 11.3) −3,350 (55.56) −2,680 (44.44) 25.0 17.0 
 October 17,880 12,690 −5,190 ( − 23.7) −2,110 (40.66) −3,080 (59.34) 53.13 0.69 
 November 9,745 9,270 −475 ( − 29.0) 111 (15.93) −586 (84.07) 18.75 11.72 
 December 5,803 6,203 400 (6.9) 66 (16.50) 334 (83.50) 8.59 4.46 
Group 2 
 1 day minimum 3,381 5,139 1,758 (52.0) 453 (25.77) 1,305 (74.23) 90.97 51.25 
 3 days minimum 3,413 5,246 1,833 (53.7) 411 (22.42) 1,422 (77.58) 90.63 39.06 
 7 days minimum 3,470 5,299 1,859 (52.7) 465 (24.49) 1,434 (75.51) 100 41.5 
 30 days minimum 3,679 5,548 1,869 (50.8) 598 (31.11) 1,324 (68.89) 100 51.25 
 90 days minimum 4,146 6,090 1,944 (46.9) 632 (32.51) 1,312 (67.49) 90.97 63.89 
 1 day maximum 48,770 39,370 −9,400 ( − 19.3) −4,630 (49.26) −4,770 (50.74) 43.75 25.83 
 3 days maximum 47,110 38,550 −8,560 ( − 18.2) −4,210 (49.18) −4,350 (50.82) 36.81 57.77 
 7 days maximum 42,630 36,450 −6,180 ( − 14.5) −3,776 (61.10) −2,404 (38.90) 21.65 18.75 
 30 days maximum 34,090 29,800 −4,290 ( − 12.6) −3,566 (83.12) −724 (16.88) 15.95 18.75 
 90 days maximum 27,830 23,920 −3,910 ( − 14.0) −3,620 (92.58) −290 (7.42) 13.51 12.95 
 Base flow index 0.26 0.41 0.15 (61.9) 0.04 (26.67) 0.11 (73.33) 90.63 51.25 
Group 3 
 Data of 1 day minimum 57 − 53 (− 92.5) −9 (16.98) −44 (83.02) 74.78 47.77 
 Data of 1 day maximum 215 217 2 (0.7) 8 (44.44) −10 (55.56) 4.62 21.88 
Group 4 
 Low pulse count 0.23 0.06 −0.17 ( − 74.0) −1.49 (53.02) 1.32 (46.98) 18.18 2.5 
 Low pulse duration 4.08 0.92 (22.5) −0.07 (6.60) 0.99 (93.40) 100 18.18 
 High pulse count 5.18 3.94 −1.24 ( − 24.0) −0.36 (29.03) −0.88 (70.97) 13.17 88.92 
 High pulse duration 14 13.25 −0.75 ( − 5.4) −3.02 (57.09) 2.27 (42.91) 18.75 26.94 
Group 5 
 Rise rate 455 297 −158 ( − 34.57) −49 (31.01) −109 (68.99) 63.89 15.95 
 Fall rate −330 −340 −10 (3.03) 86 (47.25) −96 (52.75) 18.75 15.63 
 No. of reversals 97 146 49 (50.6) 9 (0.1837) 40 (81.63) 100 9.72 

Note: Bold values in the table indicate a high degree of change.

Intra-annual patterns

Figure 8 and Table 4 demonstrate that, in terms of hydrological complexity, the SampEn remains stable before 2003 but exhibits a significant increase afterward, with a change degree of 70.8%, reaching a high level. The flow regime downstream of the dam tends to become more irregular, with similar impacts observed from both climate and incoming water change and dam construction, although the contribution of climate change is greater. Regarding the intra-annual distribution of flow, both Cd and Cv exhibit synchronous changes. After the regulation of TGD, there is a noticeable decrease in both measures, with relative changes of −20.7 and −18.0%, respectively, indicating a moderate alteration. The TGD plays an important role in regulating the upstream inflow, leading to a more uniform and smoothed flow pattern throughout the year.
Table 4

Change and attribution analysis of intra-annual flow regime at Yichang station

IndicatorsPre-TGDPost-TGDChange (relative %)ΔCcli&iw (ηcli&iw %)ΔCTGD (ηTGD %)Di (%)
SampEn 0.112 0.159 0.044 (39.3) 0.033 (75.0) 0.011 (25.0) 70.8 
Cd 0.460 0.366 −0.095 ( − 20.7) −0.042 (44.8) −0.052 (55.2) 47.1 
Cv 75.63 62.04 −13.59 ( − 18.0) −6.25 (46.2) −7.34 (53.8) 47.2 
Ecological surplus 0.021 0.035 0.014 (66.7) −0.021 (37.5) 0.035 (62.5) 21.9 
Ecological deficit 0.02 0.065 0.045 (225) 0.049 (92.4) −0.004 (7.6) 2.5 
IndicatorsPre-TGDPost-TGDChange (relative %)ΔCcli&iw (ηcli&iw %)ΔCTGD (ηTGD %)Di (%)
SampEn 0.112 0.159 0.044 (39.3) 0.033 (75.0) 0.011 (25.0) 70.8 
Cd 0.460 0.366 −0.095 ( − 20.7) −0.042 (44.8) −0.052 (55.2) 47.1 
Cv 75.63 62.04 −13.59 ( − 18.0) −6.25 (46.2) −7.34 (53.8) 47.2 
Ecological surplus 0.021 0.035 0.014 (66.7) −0.021 (37.5) 0.035 (62.5) 21.9 
Ecological deficit 0.02 0.065 0.045 (225) 0.049 (92.4) −0.004 (7.6) 2.5 
Figure 8

Variation of annual flow complexity and distribution characteristics in rivers.

Figure 8

Variation of annual flow complexity and distribution characteristics in rivers.

Close modal
Before and after the construction of TGD, there are noticeable differences in the high- and low-flow conditions at the Yichang station. The magnitude and frequency of high flows have significantly decreased, while the magnitude and frequency of low flows have increased (Figure 9). Consequently, there is a tendency for ecosurplus and ecodeficit to increase, with the growth rate of ecodeficit surpassing that of ecosurplus. The conflict of ecological water demand is severe. In terms of inter-annual variations, ecosurplus and ecodeficit exhibit a weak negative correlation, with the correlation coefficient weakening from −0.28 in the pre-TGD period to −0.15 in the post-TGD period. Before 2003, the average of ecosurplus was slightly higher than that of ecodeficit. However, after the regulation of TGD, the average of ecosurplus is significantly lower than that of ecodeficit, highlighting the substantial conflict of ecological water demand. The largest ecosurplus and ecodeficit occurred in 1998 and 2006, respectively. The reason is that the increase in ecosurplus is more significantly influenced by the TGD regulation (62.5%), while climate and incoming water change is the main driving factor for the increase in ecodeficit (92.4%). In addition, TGD has led to a reduction in ecodeficit, which may be attributed to increased dam releases during the dry season.
Figure 9

Changes in the ecosurplus and ecodeficit of the Yichang station during the pre-TGD and post-TGD periods.

Figure 9

Changes in the ecosurplus and ecodeficit of the Yichang station during the pre-TGD and post-TGD periods.

Close modal

Changes in hydrological events

For the observed flow condition, it can be seen that after the construction of TGD, the environmental flow events become more singular, mainly composed of flow high- and low-flow pulse events, while the occurrence of extreme low-flow and large flood events is essentially eliminated, and the frequency of small flood events has significantly decreased (Figure 10). This reflects the characteristics of the dam's regulation effect, which lead to the rarity of extreme low-flow and high-flow events. Comparing the observed conditions with the EFC unaffected by the TGD, the results indicate that the TGD regulation has an obvious homogenizing effect on the downstream hydrological events. In comparison to the observed conditions, the reconstructed river flow exhibits more EFCs, including frequent small flood and extreme low-flow events. This highlights the substantial flood control role and ecological water replenishment function of the TGD.
Figure 10

Impact of climate and incoming water change, as well as construction of TGD on environmental flow events at Yichang station.

Figure 10

Impact of climate and incoming water change, as well as construction of TGD on environmental flow events at Yichang station.

Close modal
According to the detection results shown in Figure 11, the ecohydrological events at the Yichang station are predominantly composed of extreme low-flow, low-flow, and high-flow pulse events. Without the influence of the TGD, the composition of river hydrological events is diverse. However, under observed conditions, the frequency of hydrological events at the Yichang station has changed moderately, with a value of 61.7% according to the RVA method. Apart from the decreased frequency of high-flow pulses and large flood events primarily influenced by climate and incoming water change, significant changes have occurred in extreme low-flow, low-flow, and small flood events due to the regulation of the TGD, with contributions of 86.3, 59.4, and 70.6%, respectively (Table 5). The regulation of TGD in the dry season may result in a decrease in the frequency of extreme low-flow events, while the reduced occurrence of small flood events may be directly related to the flood control operation. It is worth noting that due to the absence of observed large flood events from 2004 to 2019, the impact of the TGD on their occurrence remains unknown, and thus this study attributes the frequency reduction to climate and incoming water change.
Table 5

Frequencies of EFCs in the pre-TGD and post-TGD periods

Hydrological componentsPre-TGDPost-TGDChange (relative %)ΔCcli&iw (ηcli&iw %)ΔCTGD (ηTGD %)Di (%)
Extreme low flow 0.103 0.004 −0.099 ( − 96) −0.015 (15.2) −0.084 (84.8) 79.7 
Low flow 0.650 0.824 0.175 (26.9) 0.071 (40.6) 0.104 (59.4) 26.9 
High flow pulse 0.179 0.169 −0.01 ( − 5.6) −0.023 (63.9) 0.013 (36.1) 8.3 
Small flood 0.054 0.003 −0.051 ( − 94.9) −0.015 (29.4) −0.036 (70.6) 79.7 
Large flood 0.014 −0.014 ( − 100) −0.014 (100) 0 (0) 79.7 
Hydrological componentsPre-TGDPost-TGDChange (relative %)ΔCcli&iw (ηcli&iw %)ΔCTGD (ηTGD %)Di (%)
Extreme low flow 0.103 0.004 −0.099 ( − 96) −0.015 (15.2) −0.084 (84.8) 79.7 
Low flow 0.650 0.824 0.175 (26.9) 0.071 (40.6) 0.104 (59.4) 26.9 
High flow pulse 0.179 0.169 −0.01 ( − 5.6) −0.023 (63.9) 0.013 (36.1) 8.3 
Small flood 0.054 0.003 −0.051 ( − 94.9) −0.015 (29.4) −0.036 (70.6) 79.7 
Large flood 0.014 −0.014 ( − 100) −0.014 (100) 0 (0) 79.7 
Figure 11

Environmental flow events of unregulated flow from 1965 to 2019. Observed flows are used in 1965–2003, while naturalized flow modelling is used in 2004–2019 ((1): extreme low flow; (2): low flow; (3): high flow pulse; (4): small flood; (5): large flood).

Figure 11

Environmental flow events of unregulated flow from 1965 to 2019. Observed flows are used in 1965–2003, while naturalized flow modelling is used in 2004–2019 ((1): extreme low flow; (2): low flow; (3): high flow pulse; (4): small flood; (5): large flood).

Close modal

Changes in river ecological events and their response to hydrological processes

The rising process of rivers plays a crucial role in promoting gonadal development of fish and serves as an important hydrological signal inducing spawning behavior. For the flooding process during the spawning period of the FFMC from May to July, this study focuses on the max rising duration, the rising count (2 days and above), and the changes in the max and min daily average rising rates, as shown in Figure 12. During the study period, both the max rising duration and the min daily average rising rate exhibited a decreasing trend. The mean value of the post-TGD period is 0.71 days and 245.03 m3 s−1day−1 lower than that of the pre-TGD period, respectively. The rising count (2 days and above) showed an increasing trend, while the max daily average rising rate exhibited a pattern of initial increase followed by decrease. After TGD regulation, the rising count (2 days and above) increased by 1.13, and the max daily average rising rate decreased by 467.85 m3s−1d−1. These results indicate that the regulation of TGD has led to more frequent flooding activities during the spawning period of the FFMC, but with reduced flooding intensity, thereby weakening the hydrological signal that stimulates spawning behavior. Further investigation is needed to explore the potential impacts of changes in hydrological processes on fish events.
Figure 12

Changes in the characteristics of the rising process during the spawning period (May to July) of the FFMC from 1965 to 2019.

Figure 12

Changes in the characteristics of the rising process during the spawning period (May to July) of the FFMC from 1965 to 2019.

Close modal
Based on the observed fish fry abundance during each spawning event of the FFMC and their corresponding rising characteristics, including rising duration, daily average rising rate, and magnitude of rise, it was found that in the absence of the TGD regulation, there existed a positive correlation between fry abundance and rising duration, with a correlation coefficient of 0.78. The correlation coefficients for fish fry abundance with daily average rising rate and magnitude of rise were 0.22 and 0.51, respectively. Fish fry abundance increased with an increase in rising duration and daily average rising rate. However, after the TGD construction, the correlation coefficients changed to −0.16, 0.27, and 0.12, respectively. The anthropogenic interference attenuated the connection between rising duration and fish reproductive events while strengthening the influence of daily average rising rate. From Figure 13, it can be observed that during the 2004–2009, there was a noticeable decrease in the occurrence probability of flooding events with favorable daily average rising rates and longer durations compared to the pre-TGD period. For example, during 2004–2009, the longest rising duration during the spawning period of the FFMC was only 6 days, whereas during 1997–2003, there were eight instances of flooding events with durations exceeding 6 days and four instances with durations of 7–8 days.
Figure 13

Changes in the rising process corresponding to the occurrence period of fish fry, observed fry abundance, and expected fry abundance reduced based on LSTM model and rising conditions (rising conditions: rising duration, daily average rising rate and magnitude of rise. The TGD carried out ecological operation for FFMC from 2011 to 2019).

Figure 13

Changes in the rising process corresponding to the occurrence period of fish fry, observed fry abundance, and expected fry abundance reduced based on LSTM model and rising conditions (rising conditions: rising duration, daily average rising rate and magnitude of rise. The TGD carried out ecological operation for FFMC from 2011 to 2019).

Close modal

In order to further investigate the impact of changes in rising processes caused by dam regulation on fish fry abundance, this study utilized an LSTM model to establish a quantitative relationship between key habitat factors and the fry abundance of the FFMC. The feasibility of establishing the LSTM model was supported by the potential connection between fish fry abundance and habitat factors in the natural state without TGD regulation. The calibration dataset consisted of 26 spawning events from 1997 to 2001, including environmental factors and fry abundance, while the validation dataset included 11 spawning events from 2002 to 2003. The LSTM model was trained using 500 hidden neurons, 2,000 epochs, an initial learning rate of 0.35, a descent period of 220, a descent factor of 0.1, and a batch size of 7. The model's fitting accuracy was examined by comparing the expected values obtained from the LSTM model with the observed values. The results showed an NSE of 0.967 and an RMSE of 1.003 × 108, indicating a good performance. Subsequently, based on the key habitat factors, the expected fry abundance of the FFMC in 2004–2009 and 2014–2019 was obtained (Figure 13).

After the construction of the TGD, an obvious decrease in fry abundance of the FFMC was observed. During 2004–2009, the average fry abundance per spawning event was 3.19 × 107, lower than the 4.18 × 108 observed during 1997–2003. However, during the ecological operation period, the average fry abundance was 9.24 × 108, reaching a peak value of 26.39 × 108 in 2019. From a hydrological perspective, the fit between the expected fry abundance obtained from the deep learning model and the observed values was good in the absence of TGD regulation, with an average expected abundance of 4.16 × 108. However, during the post-TGD period, the expected fry abundance of FFMC spawning events in 2004–2009 was significantly higher than the observed abundance, with an average expected abundance of 3.35 × 108, lower than the average abundance in the pre-TGD period. These results indicate that the reduction in flooding events with durations exceeding 6 days and the occurrence of excessive daily average flooding rates in some events are important factors contributing to the decrease in fry abundance. When considering only the influence of flooding processes, the TGD may have caused an average decrease of 19.5% in fry abundance. During the ecological operation period, the average expected larval abundance was 6.03 × 108, lower than the observed values but higher than the expected values for the periods 1997–2003 and 2004–2009. This indicates that the rising water condition changes caused by the ecological release conducted by TGD effectively stimulated the natural reproductive activity of the FFMC. From the hydrological perspective, the ecological operation by TGD resulted in an average increase of 53.2% in fry abundance.

Hydrological impact of the TGD

This study revealed that the inter-annual flow regime variability at the Yichang station was 59.9% according to the RVA method. The concentration of intra-annual flow regime changes decreased, and entropy value, ecosurplus, and ecodeficit showed an increase, with ecodeficit experiencing a higher growth rate than ecosurplus. Compared to the pre-TGD period (1965–2003), the post-TGD period (2004–2019) experienced enhanced hydrological stability of the river, with a trend toward a more uniform distribution of EFCs. Furthermore, we quantified the potential impacts of climate and incoming water changes, as well as the effects of dam construction. The TGD emerged as the primary driver of monthly flow variations at the Yichang station during the non-flood season months (from November to the following April), with an average impact weight of 67.46%. The effects of climate and incoming water changes on flow regime alterations were not as significant as those caused by TGD, making TGD the dominant factor influencing changes observed in 20 IHA indicators. Qualitative analysis by Wang et al. (2016) indicated that the variability and seasonal distribution of flow are significantly regulated by the TGD. Wang et al. (2023) found that the flow regulation by the TGD during June to August is the main factor driving changes in high- and low-flow events. Qian et al. (2022) studied the impacts of climate and human factors on streamflow in the Yangtze River Basin and found that human activities were the dominant factor influencing streamflow changes from 2001 to 2020, with impact weights ranging from 0.48 to 0.61. The conclusions drawn in this study are consistent with previous research. We further discovered that the TGD has led to an advance in the date of the 1-day minimum flow (43 Julian date), resulting in increased complexity and smoothing of the intra-annual flow regime. The decrease in frequency of extreme low-flow and small flood events is also primarily attributed to the regulation by the TGD. However, the increase in maximum flow conditions, the increase in complexity of the intra-annual flow regime, and the increase in ecodeficit are mainly associated with climate change.

Ecological impact of the TGD

The construction of TGD has altered flow events in terms of magnitude, timing, frequency, and other aspects, leading to changes in hydrological processes, which can pose a threat to riverine biodiversity and ecological structure (Sun et al. 2024). From the hydrological perspective, intra-annual flow processes are vital ecological events in river systems. Increased flow complexity implies higher water fluctuation, which can weaken stratification and facilitate nutrient exchange between different water layers (Wang et al., 2020a, 2020b). In addition, rising water temperatures due to increasing air temperatures lead to elevated nutrient loads in water, promoting changes in algal populations and becoming one of the driving factors behind shifts in water environmental conditions. The decrease in the frequency of extreme low-flow events alleviates survival pressures for macrophytes, algae, and fish (Zeng et al. 2018). However, significant changes in small flood events can reduce the river's self-purification capacity, decrease lateral connectivity between rivers and floodplains, result in fragmented habitats on a large scale, and impact the natural reproduction of drift-spawning fish species (Yi et al. 2010; Zhao et al. 2021). Previous studies have demonstrated that the construction of the TGD has significantly reduced the biodiversity of aquatic organisms in the middle reaches of the Yangtze River, while sediment load has decreased by nearly 90%, leading to a sharp decline in total phosphorus concentration and further affecting the survival of planktonic organisms (Li et al. 2013; Zhou et al. 2013; Wang et al. 2016). In summary, the flow responses triggered by dam construction disrupt the ecological balance of rivers and facilitate new population structure dynamics. Adaptive management policies need to be formulated based on conservation goals to address these impacts.

For fish spawning, the rising process in rivers serves as an important stimulus signal, particularly under suitable water temperature conditions (Shen et al. 2018). Previous studies have indicated that after the regulation of the TGD, the majority of FFMC spawning grounds in the reservoir area disappeared, and FFMC shifted their spawning to the upstream reach above the Cuntan station. This is the main reason for the drastic decline in FFMC abundance in the Yichang section after 2003 (Zhang et al. 2012; Ding et al. 2020). In this study, we found that the altered rising process caused by the TGD regulation, including a decrease in the duration of rising and an increase in the daily average rising rate, disrupted hydrological signals for fish behavior. It is another important factor contributing to the reduction in fish fry abundance. When considering only the impact of the rising process, the TGD may lead to an average decrease of 19.5% in FFMC fry abundance. Moreover, the increased monthly average flow during the spawning period caused by the TGD is unfavorable for the safe drift of fish eggs, thereby affecting the hatching success rate (Hung et al. 2022). However, the ecological operation by TGD has been proven to partially meet the hydrological requirements for fish reproduction, promoting the formation of reproductive peaks, and mitigating the adverse impacts of dam construction on fish resources in the Yangtze River. This study found that, from a hydrological perspective, the rising water condition changes caused by TGD's ecological releases led to an average increase of 53.2% in larval fish abundance.

Limitations and applicability

There are some limitations to this study. While we considered the influence of climate and incoming water changes on flow regimes, we did not separate the effects of incoming water changes caused by the construction of cascade reservoirs on the Jinsha River. Due to the considerable distance from the Yichang station, the influence of these reservoirs weakens gradually due to factors such as the merging of tributaries (You et al. 2023). Consequently, their impact on the Yichang station is not significant. This perspective is supported by the study conducted by Wu et al. (2023).

On the one hand, while this study focused primarily on the impact of climate and incoming water changes on river flow patterns, it did not account for other potential factors such as riverbed morphology and land use that could also affect the flow response at the Yichang station. This is because the Yichang station is located approximately 43 km downstream of the Three Gorges Reservoir. From the TGD to Yichang, there are no other major tributaries converging, meaning that the Yichang station is primarily influenced by upstream water inflow and the TGD. Consequently, the influence of other factors such as riverbed morphology and land use within this stretch of the river is relatively minor (Lai et al. 2023). Since the input data for the LSTM model consisted of upstream water inflow and meteorological data, we interpreted the influence of external factors as minor model errors through a comparative analysis of flow rates between the variant and baseline periods from the LSTM simulation results (Yang et al. 2023). Therefore, the attribution results obtained in this study for the hydrological conditions at the Yichang station can be primarily attributed to the regulation of the TGD, as well as upstream water inflow and climate change impacts. On the other hand, the attribution analysis of flow regime changes based on the LSTM model and model parameters are a significant source of uncertainty. We trained the hyperparameters to minimize model fluctuations while maximizing accuracy, and we used the mean method to process the output results to reduce the impact of uncertainty. Under these constraints, the reconstructed flow results at the Yichang station during the reference period show good fit with the measured values (Figure 4). These results indicate that the model performance can effectively evaluate the impact of dam regulation on river flow regimes and the response of ecological events.

A framework is proposed to quantitatively assess the multiscale impacts of climate and incoming water changes, as well as dam construction, on flow regimes and ecological events in regulated rivers, with the URYR as a case study. The framework utilizes an LSTM model to reconstruct natural flows unaffected by TGD, synthesizing hydrological indicators that characterize inter-annual and intra-annual flow regime changes. It examines the multiscale evolution characteristics of the river and quantitatively assesses the responses of fish abundance events to hydrological process changes.

The study results indicate that after the regulation of the TGD, the flow regime of the river has undergone moderate changes on an inter-annual scale, with a value of 0.60 according to the RVA method, where 16 indicators have experienced moderate or higher levels of change. Furthermore, the homogenization of environmental flow events has resulted in enhanced hydrological stability of the river, where the frequency of low-flow events has increased while the frequency of extreme low-flow and small flood events has significantly decreased, indicating a high degree of change.

From a hydrological perspective, the reduction in flood events lasting for more than 6 days, as well as the excessive daily flood rate during some flood events caused by TGD regulation, played a crucial role in the decline of larval fish abundance. When considering only the impact of flood events, TGD resulted in an average decrease of 19.5% in larval fish abundance. However, the changes in water level conditions caused by TGD's ecological releases led to an average increase of 53.2% in observed larval fish abundance compared to the expected values. Climate and incoming water change was identified as the primary driver behind the decrease in annual flow and monthly flow during the flood season, contributing significantly to the increased complexity of the flow regime and ecodeficit, with contribution rates of 70.6 and 86.3%, respectively. The findings contribute to the understanding of the driving mechanisms of flow regimes and the ecological response characteristics under changing environments, providing valuable insights for water resource management and ecosystem protection.

This study was supported by the National Natural Science Foundation of China (51779094), Science and Technology Program of Guizhou Province (KT202008), and Henan Province Science and Technology Innovation 676 Talent Program (16HASTIT024).

HW and XB planned the research, designed the experiments, and revised the manuscript. HY performed the experiments. LH and XJ analyzed the data. XB and HY wrote the draft. HW and WG checked and improved the manuscript. All authors read, revised, and approved the final manuscript.

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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