Although previous studies have analyzed ecohydrological changes, external pressure sources on the ecohydrological regime have rarely been clarified, and knowledge about the individual impacts of large reservoir regulation is still insufficient. In this study, we reconstructed the natural flow in the middle reaches of the Yangtze River (MRYR) not regulated by the Three Gorges Reservoir (TGR) based on a long–short-term memory model. We then evaluated the dynamics of ecohydrological regimes using environmental flow components and ecohydrological risk indicators and quantified the impacts of the TGR. The results showed that the hydrological stability has increased after TGR construction, with significant decreases in the frequency and duration of extremely low-flows, large floods, and small floods. In addition, there is a high ecohydrological risk in the MRYR, with a higher ecodeficit and showing a continuous upward trend. The impact of the TGR on environmental flow components decreases along the river, averaging 42.1%, with the strongest impact on small floods, accounting for 56.2%. Overall, reservoir regulation has counteracted the increased eco-risk caused by climate change. Considering only the TGR, the ecosurplus in the spring and winter seasons increased, while the ecodeficit increased in the autumn season, with corresponding contribution rates of 81.4, 82.7, and 53.1%, respectively.

  • Reconstructed the natural flow of the river without the influence of reservoirs.

  • Investigated the relationship between environmental flow patterns in response to reservoir construction.

  • Assessed the independent influence of reservoirs on daily flow processes throughout the year.

  • Separated the impacts of reservoir construction and natural factor changes on river ecohydrological components at long–short time scales.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Ecohydrological processes are an important driving force in the evolution of river ecosystems (Li et al. 2019; Bestgen et al. 2020; Wu et al. 2020). Climate change and human activities are two dominant factors causing changes in ecohydrology regimes (Zolfagharpour et al. 2020; Yang et al. 2022). Climate change mainly affects hydrological processes through factors such as temperature, precipitation, and evaporation, while human activities change the underlying surface of the watershed through land use, vegetation cover changes, and water conservancy projects, thereby indirectly changing the runoff mechanism (Sun et al. 2019; Xin et al. 2019; Sadeghi et al. 2020). Under the coupled action of climate change and human activities, the natural ecohydrological conditions of rivers consequently change, the species composition, habitat distribution, and corresponding ecological functions of the ecosystem are altered (Rolls et al. 2018; Thompson et al. 2021). When the change in habitat conditions exceeds the biological self-regulation and recovery capacity, species will face the threat of decline, endangerment, and extinction, resulting in the destruction of river ecosystems (Wang et al. 2015).

Given the critical role of natural hydrological regimes in protecting native species and maintaining ecological integrity, the ecological effects of river flows have received increasing attention (Suen 2011; Taylor et al. 2014; McGregor et al. 2018; van Oorschot et al. 2018; Bestgen et al. 2020). Richter et al. (2006) proposed the ‘environmental flow vocabulary’ that can fully assess flow changes and their ecological significance. Wang et al. (2016) qualitatively analyzed the changes in hydrological conditions of the Yangtze River before and after the construction of the Gezhouba and Three Gorges Reservoirs and the impact of these changes on aquatic biodiversity and fish community structure, particularly on migratory fish. Ma et al. (2019) proposed a river hydrological health assessment method covering different health states of river ecosystems based on different flow thresholds within the year. Therefore, from the perspective of hydrological fluctuations, a comprehensive evaluation of river ecohydrological events and ecohydrological risks is of great significance for adapting to the healthy management of rivers in a changing environment.

Climate change alters the regional water cycle processes. According to the Intergovernmental Panel on Climate Change (IPCC) report, global warming is 1.5 °C above pre-industrial levels and there have been substantial changes in indices related to extreme cold and hot events, as well as the duration of warm periods, as the climate continues to warm (IPCC 2022). Meanwhile, there exists a strong coupling effect between precipitation and temperature, and climate warming not only intensifies heatwaves but also induces drought conditions, aggravating the risk of ecological degradation (Liu et al. 2021; Zhang et al. 2021). In addition, for river ecosystems, the regulation of reservoirs has been proven to be one of the main sources of pressure for many rivers (van Oorschot et al. 2018; Best 2019; Zhang et al. 2022). However, previous research mainly focused on the broad impacts of climate change and human activities, mainly at the macroscopic scale (Xu et al. 2014; Fan et al. 2017; Yang et al. 2022). More reservoir construction and the continued coupling of climate warming will push organisms and ecosystems to the limit of resilience. In this context, the dynamic response mechanisms of key ecological hydrological variables to climate change and reservoir regulation should be further understood in detail.

As the largest hydro project in the world, the operation of the Three Gorges Reservoir (TGR) has changed the natural hydrological regimes of the middle reaches of the Yangtze River (MRYR) (Chen et al. 2016). Many scholars have used a variety of research methods to evaluate the impact of the TGR on river hydrology from different perspectives (Wu et al. 2012; Li et al. 2016; Tao et al. 2020; Xiong et al. 2020; Zhao et al. 2021). These studies mainly attribute the changes in hydrological and ecological indicators before and after reservoir impoundment or between upstream and downstream to the construction of the reservoir (Wang et al. 2016, 2020; Tian et al. 2019; Yu et al. 2019; Li et al. 2021). However, the approach does not take into account changes in natural factors such as climate and upstream inflows. As the main influencing factor of the hydrological cycle, the influence of natural factors on ecological flow is obvious and important (Cao et al. 2021; Wang et al. 2022). Therefore, it is necessary to restore the natural flow process that is not affected by TGR operation in the MRYR by modeling, which is necessary to quantify the driving force of the evolution of ecological hydrology, clarify the mechanism of TGR changing river ecosystems, and evaluate the multiple effects of the TGR on ecological hydrology in a comprehensive timescale.

In summary, the objective of this study is to investigate the impacts of large reservoir regulation on ecohydrological conditions. To achieve this, we (1) reconstructed the natural flow of the MRYR without TGR regulation using a long–short-term memory model, (2) evaluated the ecohydrological conditions of the MRYR using environmental flow components and ecohydrological risk indicators, and (3) quantified the impacts of the TGR and climate change on the ecohydrological conditions. The findings of this study improve our understanding of the evolving features and driving mechanisms of ecohydrological conditions under changing environments, and provide scientific evidence for guiding water resources management and ecosystem protection.

Study area and data

The Yangtze River is 6,379 km long, with a basin area of 1.8 × 106 km2, accounting for one-fifth of China's total land area and a total water resource of 9.6 × 1012 m3, making it the largest river basin in China and the third longest river in the world. The main stream of the Yangtze River is the midstream section from Yichang to Hukou, with a length of 955 km and a basin area of 6.8 × 105 km2 (Figure 1). The climate of the basin is subtropical monsoon climate, and the main flood season usually occurs from June to August every year (Liu et al. 2018).

The TGR is the largest hydropower hub project in the world. The dam height is 181 m and the total reservoir capacity reaches 3.9 × 1010m3. Its operation has had a profound impact on runoff, sediment, and water temperature conditions downstream of the reservoir. Yichang Station is located 37 km downstream of the TGR, which is the water control station in the upper reaches of the Yangtze River and the water inflow control station in the middle reaches of the Yangtze River (MRYR). Luoshan Station is located in Baiji Dolphin National Nature Reserve in Xinluo Section of Yangtze River, which controls the ecological and hydrological situation in the reserve (Ban et al. 2014). Hankou Station is located at the confluence of the Han River and the Yangtze River, which is the main hydrological control station in the MRYR. Chenglingji and Xiantao are outlet control stations of Dongting Lake and Han River, respectively.

Flow data: The daily flow data of seven hydrological stations from 1965 to 2019, including Cuntan, Wulong, Yichang, Chenglingji, Luoshan, Xiantao, and Hankou, provided by the Yangtze River Water Conservancy Committee were selected.

Meteorological data: Daily meteorological data of Yichang, Luoshan and Hankou Stations from 1965 to 2019, including daily average temperature, daily rainfall, relative humidity, sunshine duration, and evaporation, the data come from the National Meteorological Science Data Center (https://data.cma.cn/).

The reconstruction method of the natural flow series without TGR regulation

Long–short-term memory model

The long–short-term memory model (LSTM) is a kind of deep learning. It is improved on the basis of the recurrent neural network (RNN) and has the same chain structure as the RNN. Its uniqueness lies in the cell state and three ‘gate’ structures (forgetting gate, input gate, and output gate, which have the following equations), which enables LSTM to learn the long-term and short-term dependence relations. The neuron structure is shown in Figure 2 (Ma et al. 2015; Cho & Kim 2022). Based on an LSTM deep learning neural network, this study divides the time series of the study period into a pre-TGR period (1965–2002) and a post-TGR period (2003–2019) to construct a flow reconstruction model of the MRYR without the influence of the TGR.
(1)
(2)
(3)
(4)
(5)
where σ denotes the Sigmoid function, Ht−1 is the output of the previous cell, xt is the input of the current cell. WF, WI, WC and WO are the weight matrix of 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.
Figure 1

Overview of the Yangtze River Basin.

Figure 1

Overview of the Yangtze River Basin.

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Figure 2

The structure of the LSTM neural network cell.

Figure 2

The structure of the LSTM neural network cell.

Close modal

Model architecture, evaluation parameters, and reliability verification

In order to quantitatively separate the independent effects of the TGR on the ecohydrological regime in the MRYR, different influencing factors need to be considered in the reconstruction of the flow at Yichang, Luoshan, and Hankou Stations. Therefore, the independent variable data used to drive the operation of the model are shown in Table 1.

Table 1

The architecture of the flow model

TargetDriven data
Yichang Flow data for Cuntan and Wulong Stations, meteorological data for Yichang 
Luoshan Flow data for Cuntan, Wulong and Chenglingji Stations, meteorological data for Luoshan 
Hankou Flow data for Cuntan, Wulong, Chenglingji and Xiantao Stations, meteorological data for Hankou 
TargetDriven data
Yichang Flow data for Cuntan and Wulong Stations, meteorological data for Yichang 
Luoshan Flow data for Cuntan, Wulong and Chenglingji Stations, meteorological data for Luoshan 
Hankou Flow data for Cuntan, Wulong, Chenglingji and Xiantao Stations, meteorological data for Hankou 

In this study, the performance of the LSTM model is comprehensively evaluated based on the three indicators of root mean square error (RMSE), mean absolute percentage error (MAPE) and determination coefficient (R2). The specific significance and calculation formula of each indicator are shown in the literature (Graf et al. 2019; Pan et al. 2020; Van et al. 2020). Meanwhile, the 9-year flow data (1965–1974) are selected to verify the reliability of the flow process without the TGR in the reconstructed MRYR under different data drivers.

Figure 3 and Table 2 demonstrate that the LSTM model architecture can achieve good overall simulation performance for the reconstruction of target flows at the three hydrological stations, and can capture extreme flow conditions well. During both the train and test periods, the measured and reconstructed flow series at the three hydrological stations exhibited good fit, with MAPE values ranging from 2.09 to 8.70% and from 5.82 to 10.20%, RMSE values ranging from 1,206.7 to 1,308.8 m3/s and from 1,561.8 to 1,739.3 m3/s, and R2 values ranging from 0.9870 to 0.9907 and from 0.9788 to 0.9852, respectively. Furthermore, the small differences in evaluation metrics between the training and testing periods indicate that the partitioning of the dataset did not affect the transmission of model uncertainty. Therefore, the LSTM model can be considered highly reliable and can be used to reconstruct natural flow regimes without the influence of the TGR.
Table 2

Validation results at Yichang, Luoshan, and Hankou stations

StationTrain
Test
MAPE (%)RMSER2MAPE (%)RMSER2
Yichang 8.70 1,206.7 0.9870 10.20 1590.9 0.9788 
Luoshan 5.65 1,308.8 0.9890 6.98 1,739.3 0.9812 
Hankou 5.09 1,288.3 0.9907 5.82 1,561.8 0.9852 
StationTrain
Test
MAPE (%)RMSER2MAPE (%)RMSER2
Yichang 8.70 1,206.7 0.9870 10.20 1590.9 0.9788 
Luoshan 5.65 1,308.8 0.9890 6.98 1,739.3 0.9812 
Hankou 5.09 1,288.3 0.9907 5.82 1,561.8 0.9852 
Figure 3

Verification of the LSTM model.

Figure 3

Verification of the LSTM model.

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Assessing the ecohydrological regime alteration

Environmental flow components

Environmental flow components (EFC) are the flow and its processes required to maintain the ecological environment of rivers, including low-flow, extreme low-flow, high-flow pulses, small floods, and large floods, a total of 34 ecologically significant indicators in five groups (Table 3). The inter-annual fluctuation of river flow patterns can be described in detail by EFC, and the threshold division of flow events related to EFC can be found in Richter & Thomas (2007).

Table 3

Indicators used in EFC and ecological function

GroupsIndicatorsEcological function
Low-flow (1–12) Monthly low-flow Maintain groundwater levels in floodplains, provide drinking water for terrestrial animals, etc. 
Extreme low-flow (13–16) Peak, duration, timing, frequency Expand the species of floodplain plants and prevent the invasion of alien species, etc. 
High-flow pulse (17–22) Peak, duration, timing, frequency, rise rate, fall rate Shape physical characteristics of river course and maintain normal water quality conditions, etc. 
Small flood (23–28) Peak, duration, timing, frequency, rise rate, fall rate Provide clues to fish migration and spawning, recharge water levels in flooding areas, control the population structure and distribution of floodplain plants, etc. 
Large flood (29–34) Peak, duration, timing, frequency, rise rate, fall rate Maintain the balance of species in aquatic and riparian communities, and promote material exchange in channels and floodplains, etc. 
GroupsIndicatorsEcological function
Low-flow (1–12) Monthly low-flow Maintain groundwater levels in floodplains, provide drinking water for terrestrial animals, etc. 
Extreme low-flow (13–16) Peak, duration, timing, frequency Expand the species of floodplain plants and prevent the invasion of alien species, etc. 
High-flow pulse (17–22) Peak, duration, timing, frequency, rise rate, fall rate Shape physical characteristics of river course and maintain normal water quality conditions, etc. 
Small flood (23–28) Peak, duration, timing, frequency, rise rate, fall rate Provide clues to fish migration and spawning, recharge water levels in flooding areas, control the population structure and distribution of floodplain plants, etc. 
Large flood (29–34) Peak, duration, timing, frequency, rise rate, fall rate Maintain the balance of species in aquatic and riparian communities, and promote material exchange in channels and floodplains, etc. 

Note: Numbers 1–34 represent various indicators.

The deviation factor FD represents the deviation of each index value before and after man-made interference relative to the reference period, and the FD of each index is calculated as in the following equation:

(6)
where Dpre and Dpost are the values of various EFC indicators before and after the TGR construction, respectively.

Risk of ecohydrological condition

Based on the flow duration curve (FDC), Vogel et al. (2007) proposed two indicators: ecosurplus and ecodeficit, which can measure the degree of annual flow change meeting the water demand of river ecosystems. The fluctuations in the intra-annual ecohydrological regime can be described by these two indicators. The FDC is constructed from daily flow data and represents the probability Pi of the river exceeds the target flow during the study period. After sorting the daily traffic Qi during the study period in descending order, Pi can be given by the following equation:
(7)
where i is the rank of Qi and n is the length of the research period.

Referring to Gao et al. (2012), the flow series in the MRYR from 1965 to 2002 was used as the base period flow that was not affected by the impoundment of the TGR. The threshold FDC was constructed according to the rearranged 25% quantile and 75% quantile of daily flow in the base period, as the flow target for river ecological protection. The ecosurplus in the target year is defined as the portion above 75% FDC, and the ecodeficit is defined as the portion below 25% FDC. In order to eliminate the large magnitude difference of observed flow at different research stations, the ecosurplus and ecodeficit are divided by the areas below 75 and 25% FDC, respectively, for normalization (Wang et al. 2017).

Ecohydrological regimes transition caused by TGR regulation

Hydrological scenarios setting

According to the flow reconstruction method, the natural flow change process of the MRYR without the influence of the TGR is obtained. Then, combined with 34 environmental flow parameters and ecological flow indicators, a variety of flow reconstruction scenarios (Table 4) were set up, and the comparison of each group of scenarios was used to quantitatively evaluate the impacts of the TGR operation and changes in other natural factors on the ecological and hydrological situation.

Table 4

Different scenarios of flow in the MRYR

GroupsScenariosFlow process in the MRYR
Group 1 Q1 Measured flow process from 1965 to 2002 (Obspre-TGR
Q2 Measured flow process from 2003 to 2019 (Obspost-TGR
Q3 Reconstruction flow process 1965–2002 (Simpre-TGR
Q4 Reconstruction flow process 2003–2019 (Simpost-TGR
Group 2 S1 Measured EFC changes 
S2 Variation of EFC only under the influence of changes in natural factors 
S3 EFC changes under TGR influence 
Group 3 E1 Measured changes in ecosurplus and ecodeficit 
E2 Variation of ecosurplus and ecodeficit only under the influence of changes in natural factors 
E3 Ecosurplus and ecodeficit changes under TGR influence 
GroupsScenariosFlow process in the MRYR
Group 1 Q1 Measured flow process from 1965 to 2002 (Obspre-TGR
Q2 Measured flow process from 2003 to 2019 (Obspost-TGR
Q3 Reconstruction flow process 1965–2002 (Simpre-TGR
Q4 Reconstruction flow process 2003–2019 (Simpost-TGR
Group 2 S1 Measured EFC changes 
S2 Variation of EFC only under the influence of changes in natural factors 
S3 EFC changes under TGR influence 
Group 3 E1 Measured changes in ecosurplus and ecodeficit 
E2 Variation of ecosurplus and ecodeficit only under the influence of changes in natural factors 
E3 Ecosurplus and ecodeficit changes under TGR influence 

Group 1: Comparing Q1 and Q3, the reliability of the LSTM model to reconstruct the natural flow process in the MRYR that is not affected by the TGR is evaluated. Comparing Q2 and Q4, the flow changes caused by the operation of the TGR are evaluated.

Group 2: Define the thresholds of each EFC indicator according to the base period Q1. The deviation factors S1 and S2 of the 34 EFC indicators under the Q2 and Q4 scenarios were obtained, respectively. The effect of the TGR was then quantitatively differentiated by comparing S1 and S2.

Group 3: The 25 and 75% FDCs were determined according to the base period Q1 and the ecosurplus and ecodeficit at the annual and seasonal scales were calculated from the FDC quantile curves of the base period in Q2 and Q4. By comparing Q1 and Q2, the changes in the measured ecosurplus and ecodeficit in the MRYR can be analyzed (E1). Comparing Q1 and Q4, quantify the ecohydrological regime under the influence of changes in natural factors (E2). By comparing E1 and E2, the effect of the TGR operation can be quantitatively distinguished.

Attribution analysis

This study assumes that changes in natural factors such as the TGR and climate have independent impacts on the ecohydrological regime in the MRYR. Therefore, the actual change ΔR of each index can be expressed as in Equation (8):
(8)
Then, the effects of the TGR (ΔCTGR) and change in natural factors (ΔCnat) can be calculated as in the following equations:
(9)
(10)
The relative contributions of the TGR and natural factors can be quantitatively evaluated as in Equation (11):
(11)

Effects of the TGR on the intra-annual flow process

As shown in Figure 4, before the TGR was put into operation, the flow of the reconstructed Yichang, Luoshan, and Hankou Stations was basically consistent with the measured flow, and the peak flow was well captured. The MAPE during the training period was all lower than 10%. It shows that the model performs well (Moriasi et al. 2007). It can be considered that this model can accurately reflect the natural flow process in the MRYR without considering the reservoir impoundment. By comparing the measured and reconstructed values during the post-TGR period, the impact of the TGR on rivers during the non-flood season is more pronounced. If there was no TGR, the peak flow rate would be larger, and the magnitude of flow during the dry season would be smaller. The Yichang Station is located downstream of the TGR, and its flow process is most severely affected by the TGR. In terms of the fit between simulated and measured values, the MAPE and RMSE at Yichang Station increased from 13.33% and 1,522 m3/s during the pre-TGR period to 18.18% and 3,083 m3/s during the post-TGR period, and R2 decreased from 0.980 to 0.870 (Table 5).
Table 5

Reconstruction results of flow process

StationPre-TGR
Post-TGR
MAPE (%)RMSER2MAPE (%)RMSER2
Yichang 13.33 1,522.1 0.9796 18.18 3,083.4 0.8698 
Luoshan 8.96 2,106.3 0.9721 11.44 2,423.1 0.9451 
Hankou 9.53 1,986.6 0.9781 13.37 2,660.0 0.9406 
StationPre-TGR
Post-TGR
MAPE (%)RMSER2MAPE (%)RMSER2
Yichang 13.33 1,522.1 0.9796 18.18 3,083.4 0.8698 
Luoshan 8.96 2,106.3 0.9721 11.44 2,423.1 0.9451 
Hankou 9.53 1,986.6 0.9781 13.37 2,660.0 0.9406 
Figure 4

Comparison of measured and reconstructed flows in Yichang, Luoshan and Hankou before and after the construction of the TGR.

Figure 4

Comparison of measured and reconstructed flows in Yichang, Luoshan and Hankou before and after the construction of the TGR.

Close modal

For the Luoshan Station, the influence of the TGR is mainly significant from January to June and September to October due to the regulation of lake. For the downstream Hankou station, the TGR's impact is mainly significant in September and October, and the impact is alleviated by climate changes and the convergence of tributaries during other months. Table 4 shows that the changes in the MAPE and RMSE for the measured and reconstructed values at the Luoshan and Hankou Stations were smaller than those at the Yichang Station, with the MAPE increasing from 8.96% and 9.53% during the pre-TGR period to between 11.44 and 13.37% during the post-TGR period, and the RMSE increasing from 2,106 and 1,987 m3/s to 2,423 and 2,660 m3/s, respectively. R2 also decreased from 0.9721 and 0.9781 to 0.9451 and 0.9406, respectively. These changes indicate that the impact of the TGR on downstream flow decreases with distance.

The potential impacts of the TGR on the EFCs

Multi-scenario alteration of EFCs

For the measured EFC scenario S1, it can be found that after the operation of the TGR, the singleness of the environmental flow structure in the MRYR is enhanced, which is mainly composed of high-flow pulses and low-flow events. Extreme low-flow and large flood events are basically no longer occurred, and the occurrence frequency of small flood events has decreased significantly, especially at Yichang Station close to the TGR (Figure 5). At the same time, the closer to the downstream, the frequency of small flood events gradually increases, which reflects the characteristics of numerous flood sources, frequent small flood events, and the rare occurrence of large flows due to lake regulation and storage.
Figure 5

Comparison of measured and simulated environmental flows in the MRYR (a: Yichang; b: Luoshan and c: Hankou).

Figure 5

Comparison of measured and simulated environmental flows in the MRYR (a: Yichang; b: Luoshan and c: Hankou).

Close modal

Comparing the environmental flow structure of the measured S1 and the non-TGR-affected S2, the impact of the TGR operation on the environmental flow was quantitatively distinguished. The results show that the reservoir has a strong simplification impact on the annual flow events in the downstream. Compared with S1, S2 has more abundant flow events, more small flood events, and frequent extreme low-flow events, especially the difference in Yichang Station. The above phenomena reflect the great flood control function and ecological water supplement function of the TGR, and the weakening influence on the ecological hydrological situation along the MRYR.

For the 12-month low-flow variability, the degree of variability is generally higher at Yichang than at Luoshan and Hankou stations, with average FD values of 15.86, 8.69, and 9.51%, respectively, with the most significant changes occurring in February (34.7%), October (22.2%), and March (22.5%), respectively (Figure 6). The duration and frequency of the extreme low-flow events in the MRYR decrease, with changes in duration corresponding to 64.1–77.8% and changes in frequency corresponding to 78.1–90.1%, with the maximum values of the changes occurring at Hankou Station. High-flow pulse events spatially vary most at Luoshan Station and least at Yichang Station, with FD values corresponding to 31.8 and 11.1%, respectively, while the change in high-flow pulse duration is strongest throughout the MRYR, with an average of 41.8%. In addition, the mean deviation coefficient for small flood events is spatially the largest at Yichang Station and the smallest at Luoshan Station. In contrast, the rate of small flood rise increases significantly at Yichang Station, with a deviation coefficient of 162%, the most significant change among the 34 indicators. For the entire MRYR, the variability of large flood events is consistent across the three catchments, with all but 100% of the FD for frequency of occurrence being 0. This evolutionary feature results from the combined effects of upstream water, climate change, natural variability of tributary catchments, and TGR regulation.
Figure 6

The deviation factor of environmental flow in the MRYR after the operation of the TGR (1–34 correspond to 34 environmental flow parameters).

Figure 6

The deviation factor of environmental flow in the MRYR after the operation of the TGR (1–34 correspond to 34 environmental flow parameters).

Close modal

Quantifying the impacts of the TGR along the river reach

Comparing the FD of the EFC indicators of S1 and S2, the influence of the reservoir on the EFC parameters under S3 was quantitatively distinguished. The results show that the environmental flow in the MRYR is affected by the TGR as a whole by 42.1%. Yichang Station is most significantly affected by the reservoir, the relative contribution of the TGR is 52.6%, which exceeds the impact of natural factors (Table 6). Meanwhile, the TGR significantly changed the small flood events in the MRYR with a relative contribution of 56.2%, resulting in an increase in the peak, rise, and fall rates of small floods, significantly reduced duration, frequency, and slightly delayed timing, especially at Yichang Station (Figure 7).
Table 6

The relative contribution of the TGR to EFCs

EFCYichangLuoshanHankouMean
Low-flow (%) 62.7 48.6 42.7 51.3 
Extreme low-flow (%) 44.2 45.3 37.6 42.4 
High-flow pulse (%) 56.8 41.7 56.2 51.6 
Small flood (%) 86.3 34.9 47.3 56.2 
Large flood (%) 0.00 0.00 0.00 0.00 
ALL (%) 52.6 36.0 37.7 42.1 
EFCYichangLuoshanHankouMean
Low-flow (%) 62.7 48.6 42.7 51.3 
Extreme low-flow (%) 44.2 45.3 37.6 42.4 
High-flow pulse (%) 56.8 41.7 56.2 51.6 
Small flood (%) 86.3 34.9 47.3 56.2 
Large flood (%) 0.00 0.00 0.00 0.00 
ALL (%) 52.6 36.0 37.7 42.1 
Figure 7

Contributions of TGR and changes in natural factors to the variation of 34 environmental flow indicators in the MRYR.

Figure 7

Contributions of TGR and changes in natural factors to the variation of 34 environmental flow indicators in the MRYR.

Close modal

On the numerical side, the FD of low-flow from September to November decreased under TGR regulation, especially in October (−10.29%), with the highest increase of 19.88% in January. Under the impact of the TGR, the frequency of extreme low-flow was significantly reduced (−244.34%), which far exceeded the 159.60% influence of natural factors change. The TGR also showed a positive effect on high-flow pulses, in which the FD of the rise rate changed by 49.01% under the influence of the reservoir, while the impact of natural factors change was basically 0. In addition, since the occurrence of large flood events has not been observed in the statistical years (2003–2019) in the post period of the TGR construction, the contribution of the TGR to its changes is still counted as 0.

The potential impacts of the TGR on the ecohydrological risk

Changes in the ecosurplus and ecodeficit

On the annual scale, the distribution of high-flow and low-flow in the MRYR is obviously different pre- and post-TGR. The magnitude and frequency of high-flow decreased significantly in the post-TGR period, while the magnitude and frequency of low-flow increased (Figure 8). Compared with before the TGR operation, the occurrence range of high-flow and low-flow during the year is quite different after the construction of the database, especially in autumn and winter. In autumn, the low-flow part below 25% FDC increased significantly, indicating that the ecohydrological regime at this time was mainly deteriorating. In winter, under the regulation of the TGR, the frequency of low-flow rates below 25% FDC is significantly reduced, and it basically appears as a high-flow scenario with more than 75% FDC.
Figure 8

Distribution characteristics of FDC in the MRYR at different time scales (a: Yichang; b: Luoshan and c: Hankou).

Figure 8

Distribution characteristics of FDC in the MRYR at different time scales (a: Yichang; b: Luoshan and c: Hankou).

Close modal
According to the calculation results of E1 and the regression fitting curve, the evolution characteristics of the ecohydrological regime in the MRYR was evaluated (Figure 9(E1)). On the annual scale, the ecodeficit of the three control sections are larger than those of the ecosurplus, and the contradiction of ecological water demand is obvious. Both indicators show a slow upward trend and the upward trend of Yichang station is more obvious. In addition, the seasonal scale, the ecological indicators have obvious inter-annual changes, periodic changes of synchronization and spatial differences. Before 2000, the changes of ecosurplus and ecodeficit in spring and winter were relatively stable. After 2000, the ecosurplus continued to increase, with the most significant changes in Yichang, and the change range along the route was reduced. Both indicators remained largely unchanged during the summer, with the largest ecodeficit in 2006. The overall decrease in ecological surplus in autumn may be due to the significant increase in ecological deficit caused by the retention of upstream water by the TGR to meet the demand for power generation. The annual average ecodeficit of Yichang Station reached 0.137 in autumn, higher than 0.127 in Luoshan and 0.119 in Hankou. The influence of the TGR on the annual and seasonal ecohydrological regime can be preliminary judged by the changes of FDC and ecological indicators. However, the changes of ecological indicators in the MRYR are also affected by natural factors such as climate change, upstream inflows, and tributary confluence.
Figure 9

Evolution characteristics of ecosurplus and ecodeficit at different time scales in the MRYR.

Figure 9

Evolution characteristics of ecosurplus and ecodeficit at different time scales in the MRYR.

Close modal

Quantifying the impacts of the TGR

Comparing E1 and E2, the contribution of the TGR and natural factors to the changes of ecosurplus and ecodeficit indicators in the MRYR in the temporal and spatial patterns were quantified. According to the fitting results of Figure 9(E2), in the scenario without the TGR operation, upstream inflow and climate change mainly caused the increase of ecodeficit in Yichang, reflecting the decrease of low-flow hydrological magnitude. For the other two river sections affected by tributary confluence, the ecosurplus in spring and winter had similar periodic changes. Before 2000, the fluctuation of ecosurplus decreased and then increased. In summer and autumn, the ecodeficit generally showed an increasing trend, especially in the autumn with the largest change.

Combined with the assessment results of the contribution degree, the changes in natural factors have increased the ecosurplus and ecodeficit in the MRYR, and the ecodeficit has been more affected. On the seasonal scale, natural factors basically lead to a decrease in ecosurplus and an increase in ecodeficit in the MRYR (Figure 10). The impact was greatest in the autumn, when the ecodeficit increased by an average of 0.045, while the ecosurplus decreased by 0.024. Among the three river sections, the annual ecosurplus in Yichang decreased by 0.02, while the ecodeficit increased by 0.065. The ecological contradiction caused by changes in natural factors was the most serious.
Figure 10

Effects of natural factor changes and TGR on ecohydrological risk indicators in Yichang, Luoshan and Hankou Stations.

Figure 10

Effects of natural factor changes and TGR on ecohydrological risk indicators in Yichang, Luoshan and Hankou Stations.

Close modal

Affected by the reservoir, the changes of ecological indicators in Yichang were significantly higher than those in the downstream stations, and the impact of the TGR on the MRYR generally showed a decrease in space along the route (Figure 10). At the same time, the changes of ecological surplus and ecological deficit of the three monitoring sections in the MRYR are synchronized in the time domain, and the peak-to-valley changes of ecological surplus and ecological deficit also have a significant negative correlation. The operation of the TGR to a certain extent leads to an average increase of 0.015 in annual ecosurplus and an average decrease of 0.002 in ecodeficit, which are generally beneficial to the ecological hydrological regime in the MRYR (Table 7). Because the application of the TGR was mainly carried out in spring and winter, the ecological surplus of downstream reservoir increased significantly, which increased by 0.057 and 0.11, respectively. In the summer flood season, the TGR flood control task requires its peak flow. When the inflow is lower than the flood control target, the water level of the reservoir area is reduced to 145 m by increasing the discharge, and then the water is equal to the discharge. Therefore, the TGR has little influence on the downstream ecological indicators in this period, and it mainly contributes to the reduction of the ecological deficit by 0.01. In autumn, in order to meet the requirements of power generation, the reservoir began to store water, and the discharge was much lower than the inflow, resulting in a decrease in the downstream ecological surplus and a substantial increase in the ecological deficit, and the growth rate reached the annual maximum (0.051).

Table 7

Quantification of changes in ecohydrological risk indicators in the middle reach of the Yangtze River

Time scaleActual change
Contribution of TGR
Contribution of natural factors
EcosurplusEcodeficitEcosurplusEcodeficitEcosurplusEcodeficit
Annual 0.000 0.032 0.015 (50%) − 0.002 (5.6%) − 0.015 (50.0%) 0.034 (94.4%) 
Spring 0.044 − 0.013 0.057 (81.4%) − 0.013 (92.9%) − 0.013 (18.6%) − 0.001 (7.1%) 
Summer − 0.018 0.028 0.004 (16.0%) − 0.010 (20.8%) − 0.021 (84.0%) 0.038 (79.2%) 
Autumn − 0.025 0.096 − 0.001 (4.0%) 0.051 (53.1%) − 0.024 (96.0%) 0.045 (46.9%) 
Winter 0.132 − 0.014 0.110 (82.7%) − 0.011 (78.6%) 0.023 (17.3%) − 0.003 (21.4%) 
Time scaleActual change
Contribution of TGR
Contribution of natural factors
EcosurplusEcodeficitEcosurplusEcodeficitEcosurplusEcodeficit
Annual 0.000 0.032 0.015 (50%) − 0.002 (5.6%) − 0.015 (50.0%) 0.034 (94.4%) 
Spring 0.044 − 0.013 0.057 (81.4%) − 0.013 (92.9%) − 0.013 (18.6%) − 0.001 (7.1%) 
Summer − 0.018 0.028 0.004 (16.0%) − 0.010 (20.8%) − 0.021 (84.0%) 0.038 (79.2%) 
Autumn − 0.025 0.096 − 0.001 (4.0%) 0.051 (53.1%) − 0.024 (96.0%) 0.045 (46.9%) 
Winter 0.132 − 0.014 0.110 (82.7%) − 0.011 (78.6%) 0.023 (17.3%) − 0.003 (21.4%) 

The natural fluctuation of flow determines the material cycle, energy transfer, and the interaction between habitats and organisms of the river ecosystems (Hart & Finelli 1999; Zhang et al. 2015; Wang et al. 2022). This study found that the TGR led to different degrees of frankness in the annual flow process of each river section in the MRYR, and its influence effect decreased along the course, which was in line with the existing research results (Chen et al. 2016; Liu et al. 2018). According to our assessment of the ecological response of the MRYR, after the TGR impoundment, the hydrological stability of the MRYR is enhanced, and no extreme low-flow and large flood events occur, and the duration and frequency of small floods are significantly reduced (Figure 5). Meanwhile, the ecodeficit in the MRYR shows an increasing trend on an annual scale. In spring and winter, the magnitude of low-flow increases, which reduces the ecodeficit and increases the ecosurplus. In autumn, the magnitude of the low-flow decreases, resulting in a significant increase in the ecodeficit (Figure 9(E2)).

The disappearance of the extreme low-flow event means that the survival pressure of organisms such as macrophytes, algae and fish is relieved (Zeng et al. 2018). However, the disappearance of large floods and the obvious indigenous changes of small floods will lead to the decline of the self-purification capacity of rivers, reduce the lateral connectivity between rivers and flood areas, cause the fragmentation of large-scale habitats, and affect the natural reproduction of fish with drifting eggs (Yi et al. 2010; Zhao et al. 2021). The ecosurplus reflects the change of water regime of high-level flow, and its increase means the increase of the flow rate and the rise of the water level, which is conducive to the growth of submerged vegetation and the reproduction of viscous spawning fish (Baumgartner et al. 2018; Lei et al. 2022). But high-water levels can also crowd out living space for terrestrial creatures. The increase of ecological deficit reflects the change of water regime with low-flow rate. In dry season, this may be manifested as the continuous occurrence of ultra-low water level in the MRYR for many years. Although it is beneficial for some birds feeding on the rhizomes of submerged plants to obtain food, it will threaten the survival of benthic organisms (Gao et al. 2012). For vegetation, the original hygrophyte community will gradually evolve into the xerophyte community type (Han et al. 2017).

Under the strong interference of upstream inflow, regional climate, tributary catchment, and TGR, the ecological response process in the MRYR fluctuates greatly, the stability of river ecological structure, and the integrity of function are destroyed. The change of 34 inter-annual indicators and two intra-annual indicators also confirmed this conclusion. It is found that in terms of inter-annual indexes, the influence of the TGR is basically the same as that of natural factors (Table 6), and the relative contribution of the TGR to low-flow, high-flow pulse, and small flood events has exceeded the influence of natural factors. This feature is most obvious in Yichang, which clarifies the specific mechanism of reservoir regulation of river ecological response processes (Figure 7). In terms of intra-annual indexes, changes in natural factors in most cases lead to the decrease of ecosurplus and the increase of ecodeficit. The operation of the TGR increased the ecosurplus and decreased the ecodeficit at the annual scale in the MRYR, and its influence weakened along the way. On the seasonal scale, TGR mainly increases the ecosurplus in spring and winter in the MRYR by water supplement, and reduces the discharge in autumn to meet the power generation, resulting in a substantial increase in the ecological deficit.

However, there are still defects in this study. Both the EFC index and the ecosurplus and ecodeficit index cannot reflect the change process of the appropriate ecological flow in the year, so they cannot give specific protection objectives from the perspective of management. In the future, it is necessary to improve the accuracy of research methods according to the specific requirements of river ecological protection, and further deepen the research framework to give the flow process of river ecological protection.

This study comprehensively evaluated the temporal characteristics of the ecohydrological regime in the MRYR using the environmental flow components and ecohydrological risk indicators. Then, the natural flow regime of the MRYR without TGR was reconstructed based on the LSTM model. Combining the contribution evaluation method, the multiple effects of large-scale reservoir regulation on the transformation of ecohydrological regimes were examined. The results showed that after the TGR was constructed, the ecohydrological events in the MRYR were mainly composed of high-flow pulses and low-flow events. The frequency of small flood events has significantly decreased, especially at the Yichang Station. On an annual basis, the ecodeficit and ecosurplus of the three hydrological stations showed an increasing trend, with the former showing a stronger growth trend and higher average level, indicating an increase in potential ecohydrological risks.

TGR not only enhanced the singularity of ecohydrological events but also significantly reduced the frequency and duration of extremely low-flows and small flood events, showing a decreasing trend along the river. TGR had the greatest impact on small flood events, accounting for 12.4% higher than climate change, leading to the increase of the peak, rise and fall rate, and the significant decrease of duration and frequency, especially at the Yichang Station. Overall, 42.1% of the changes in the EFC coefficient were attributed to the reservoir. On an annual scale, TGR increased ecosurplus and slightly decreased ecodeficit, with contributions of 50.0 and 5.6%, respectively. On a seasonal scale, only considering the regulation of TGR, the ecosurplus in spring and winter in the MRYR increased, while the ecodeficit in autumn increased, with contributions of 81.4, 82.7, and 53.1%, respectively. Reservoir regulation resisted the increase of ecological risks caused by climate change, but climate change is still the dominant factor.

The study results demonstrate that the modeling method is an effective and reliable means to evaluate the impact of reservoir regulation on the hydrological regimes and quantify the specific effects of large-scale reservoirs, which were not fully considered in previous studies. In future work, more comprehensive ecohydrological indicators will be incorporated to analyze the ecohydrological effects of the basin under future climate scenarios. Combining with key ecological protection objectives, the ecohydrological response model of the basin will be established to ultimately determine multi-objective eco-flow management thresholds, providing solutions to maintain the health of river ecosystems and ease basin water use conflicts.

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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