Identifying Reservoir-Induced Hydrological Alterations in the Upper Yangtze River Basin through Statistical and Modeling Approaches

Abstract

Elucidating the impact of reservoir operation on hydrological signatures is crucial for the effective management of large rivers under the changing climate. This study first revised the reservoir operation scheme in the Soil and Water Assessment Tool (SWAT) model to improve its description of actual operation laws of reservoirs in the upper Yangtze River basin (UYRB). Then, we identified the reservoir-induced hydrological alteration through a hydrological index method driven by observed and simulated daily streamflow from 1960 to 2017. The results revealed the superiority of the revised reservoir algorithm in the SWAT model in simulating streamflow and floods at Cuntan and Yichang stations with the Nash-Sutcliffe efficiency (NSE) coefficient and the Kling-Gupta efficiency (KGE) coefficient improved from 0.01 to 0.08 and 0.01 to 0.05, respectively. Relative to the baseline period (1960–2002), the hydrological signatures in the impact period (2003–2017) changed substantially after 2003. Reservoirs induced a remarkable increase of 27.76% and 55.97% in streamflow from January to March, accompanied by a notable decrease of 6.95% and 20.92% in streamflow from September to October after 2003 at Cuntan and Yichang stations, respectively. Meanwhile, the annual streamflow range contracted, and the flow became more stable with a reduced variation in daily streamflow, extremely low flow spell duration, and extremely high flow spell duration. Consequently, our results improved the quantitative understanding of reservoir-induced alteration and informed the management and planning of reservoir construction in the UYRB under climate change.

1. Introduction

As an essential measure for humans to manage and regulate rivers, dams provide navigation, power generation, water supply, and irrigation [1,2]. About 2.8 million dams worldwide regulate river flows and impound 10,800 km³ of water on land [3,4], hindering the free flow of about 2/3 of the rivers [5]. Dams exert artificial control over rivers, disrupting the river’ natural hydrological connectivity and causing changes in flow patterns [6,7,8]. They further impact the riverine ecosystems negatively, leading to hydrophysical and biotic disturbances, which, in turn, degrade the floodplain and coastal environments extensively [9,10,11].

Reservoirs impact flow regimes by altering the magnitude, frequency, duration, timing, and rate of change of flow [12,13]. They also modify the water levels, sediment load, and nutrient transport [14,15,16]. Reservoir operation permanently affects the river system, resulting in a new pattern in the water cycle process of the basin [17]. Because the expansion of hydropower capacity is regarded as a sustainable source of electricity to meet global energy demand [18], 3700 new dams are planned or under construction globally [19]. For instance, in the Amazon, more than 100 hydropower dams have been constructed, with more than 30 large and 170 small dams slated for future development, consequently impacting the floodplains, estuary, and sediment plume [10,11]. Likewise, the hydropower reservoir storage in the Mekong River basin is projected to increase its 2% mean annual flow in 2008 to 20% by 2025, attenuating seasonal flow variability downstream of numerous dams [20]. Given the unprecedented scale and pace of planned development, a comprehensive evaluation of the benefits and drawbacks associated with dams and reservoirs is imperative [21].

As the world’s third longest river, more than 50,000 dams have been constructed in the Yangtze River basin [22,23], along with 300 reservoirs, boasting a storage capacity of >100 million m³. The Three Gorges Reservoir (TGR) and 22 large reservoirs in the upper Yangtze River basin (UYRB) are vital to flood control downstream and flow regulation. Notably, the TGR significantly reduced the streamflow during flood peaks and the transition from wet to dry seasons, while simultaneously increasing streamflow during the pre-nonflood season, starting in 2003 [14,24,25,26]. Driven by government initiatives aimed at bolstering energy security, flood control, and economic growth, the Changjiang Water Resources Commission plans to increase the development and utilization of hydropower resources from 36% to 45% in the next decade in the Yangtze River basin [27]. Consequently, elucidating the reservoir-induced hydrological alteration is essential for effectively planning and managing the Yangtze River basin.

At present, the Indicators of Hydrological Alteration (IHA) method is widely used to describe the alteration of the flow regime in different facets [17,28,29,30,31]. If only a historical time series is used to compare the change in flow regimes before and after reservoir construction, other factors (such as climate change) are ignored [32]. Combining the IHA method with hydrological simulation can effectively identify the impact of climate change and human activities on flow regimes [33,34,35,36,37,38]. Compared with most hydrological simulation methods, the hydrological model can describe the hydrophysical process and embed the reservoir operation process. However, despite the research efforts to improve the reservoir algorithm to meet the needs of hydrological simulation, the watershed-scale hydrological models still lack the accuracy to simulate the operational effects of reservoirs [39,40,41,42,43,44].

Therefore, this study revised the reservoir operation scheme in the Soil and Water Assessment Tool (SWAT) model to improve the accuracy of streamflow simulation. Then, we combined the revised SWAT model and a hydrological indicator method to analyze the reservoir-induced hydrological alteration in the UYRB and their causes before and after reservoir construction. Section 2 describes the study area and data. The methodology used is outlined in Section 3. The results and discussion are presented in Section 4 and Section 5. Finally, our conclusions are presented in Section 6.

2. Study Area and Data

2.1. Study Area

The upper Yangtze River basin, situated in southwest China, spans an extensive length of ≈4500 km (Figure 1a). The drainage area is ≈1 × 10⁶ km², accounting for 55.56% of the total area of the river. The precipitation is concentrated from June to September, strongly influenced by the southwest Indian Ocean monsoon and the southeast Pacific monsoon. The annual average streamflow of the upper Yangtze River is 13,500 m³/s (1960–2017). Currently, 21 reservoirs operate officially in the UYRB. By 2002, the reservoir storage capacity accounted for only 172 × 10⁸ m³ (4.05% of the mean annual streamflow), while it increased rapidly after the TGR began operating in 2003, reaching 1184 × 10⁸ m³ (27.81% of the mean annual streamflow) (Figure 1b).

2.2. Data

We applied daily precipitation, temperature, wind speed, relative humidity, and sunshine duration data (1960–2017) obtained from 92 meteorological stations, serving as input data for the SWAT model. Additionally, the digital elevation model (DEM), soil types, and land-use data (spatial resolution of 1 km × 1 km) were also applied. For calibration and validation purposes, we used daily streamflow data collected from Cuntan (1960–1986 and 1993–2017) and Yichang (1960–2017) hydrological stations along the trunk of the river. Cuntan station is situated at the entrance of the TGR; it receives contributions from the Jinsha, Yalong, Min, and Jialing Rivers. Yichang station, as a control station upstream, is located 44 km downstream of the TGR. In addition, we chose the daily streamflow data of 37 adjacent hydrological stations to set the operating parameters of the reservoir (Table 1).

The daily discharge data were obtained from the Hydrological Yearbooks of China. The meteorological data were obtained from meteorological stations associated with the China Meteorological Data Service Center (CMDC) (http://data.cma.cn/en; accessed on 6 March 2023). The DEM and 1980 land-use data were downloaded from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn/; accessed on 6 March 2023). Soil data were obtained from the Harmonized World Soil Database (HWSD) developed by the Food and Agriculture Organization (FAO).

3. Method

3.1. Mann–Kendall Test

The Mann–Kendall (MK) test [45,46] detected monotonic patterns in the streamflow and precipitation time series at annual and monthly scales. As the TGR operated in 2003, we took 2003 as the demarcation point and divided the time series into the baseline (1960–2002) and impact (2003–2017) periods to study the influence of reservoirs on streamflow [25,36]. Furthermore, we compared the precipitation and streamflow in the baseline and impact periods on the monthly scale to determine the influence of climate change and reservoirs on intra-annual streamflow.

3.2. IHA Method

The IHA method evaluated the magnitude, variation and frequency, duration, timing, and rates of change in the flow regime. To comprehensively describe the long-term characteristics of flow regimes, we selected 21 hydrological signatures to effectively describe these five groups of components [47] (Table 2).

The reservoir-induced changes in the flow regime were quantified by comparing 21 hydrological signatures calculated from daily streamflow between the baseline and impact periods. At each station, we calculated the mean values of each hydrological signature at two periods and their changes according to the following Equation (1):

(1)$HA=\frac{I_{imp}-I_{bas}}{I_{bas}}\times 100\%$

where $HA$ is the alteration rate of each hydrological signature, $I_{imp}$ and $I_{bas}$ are the average values of hydrological signatures for the impact and baseline periods, respectively. The HA significance was detected by the Wilcoxon rank sum test [48,49].

3.3. Revised Reservoir Operation Scheme

In SWAT, the operation of the reservoir conformed to the water balance contained by inflow, outflow, precipitation, evaporation, seepage from the reservoir bottom, and diversion. The change in reservoir volume can be expressed as Equation (2):

(2)$V_{i+1}=V_i+Q_{in}+P-Q_i-E-S$

where $V_{i+1}$ is the storage volume of the reservoir on day i + 1 (m³); $V_i$ is the storage volume of the reservoir on day i (m³); $Q_{in}$ is the volume of water entering the reservoir on day i (m³); $P$ is the precipitation falling on the reservoir on day i (m³); $Q_i$ is the volume of water the reservoir releases on day i (m³); $E$ is the volume of evaporation from the reservoir on day i (m³); $S$ is the volume of water lost by seepage on day i (m³).

The target release method is a reservoir operation algorithm in the SWAT model that can simulate a small reservoir with flood control function. The reservoir outflow is calculated using Equation (3) [50]:

(3)$Q_i=\frac{V_i-V_{targ}}{N{D}_{targ}}$

where $V_{targ}$ (m³) is the target reservoir storage on a given day, and $N{D}_{targ}$ is the number of days required for the reservoir to reach the target storage.

Generally, the $V_{targ}$ can be set according to the storage observation or the reservoir management. If the annual variation in reservoir storage cannot be obtained, then, $V_{targ}$ can be calculated by Equations (4) and (5):

(4)$V_{targ}=V_{em}\hspace{1em}\hspace{1em}mo{n}_{fld,beg}<mon<mo{n}_{fld,end}$

(5)$V_{targ}=V_{pr}+\frac{\left(1-min\left[\frac{SW}{FC},1\right]\right)}{2}·(V_{em}-V_{pr})\hspace{1em}\hspace{1em}mon\le mo{n}_{fld,beg} \mathrm{or} mon\ge mo{n}_{fld,end}$

where $V_{em}$ (m³) is the volume of water held in the reservoir when filled to the emergency spillway, $V_{pr}$ (m³) is the volume of water held in the reservoir when filled to the principal spillway, $SW$ (mm) is the average soil water content in the subbasin, $FC$ (mm) is the water content of the subbasin soil at field capacity, $mon$ is the month of the year. $mo{n}_{fld,beg}$ is the beginning month of the flood season, and $mo{n}_{fld,end}$ is the ending month of the flood season.

The original reservoir module in the SWAT model is too simple and does not always simulate the reservoir release accurately [39]. Here, we proposed a revised reservoir operation scheme to improve the simulation accuracy of streamflow in the flood season. To satisfy the water balance of the reservoir, the reservoir is accorded the inflow and reservoir storage capacity to adjust outflow divided into non-flood and flood seasons. First, we set outflow and reservoir storage limits (a) and (b) as follows:

• (a). Limitation of water storage capacity:

  $V_{min}\le V_i\le V_{max}$

  where $V_{min}$ (m³) is the minimum limit of reservoir storage on day i; $V_{max}$ (m³) is the maximum limit of reservoir storage on day i; $V_{min}$ and $V_{max}$ can be obtained from the reservoir regulation scheme, but the daily water level of the reservoir is not detailed. The monthly target storage was simply averaged linearly to the daily scale and set at 5–20% above and below the monthly target storage value as the maximum and minimum values, respectively. The percentage is determined by the storage of the reservoir.

• (b). Limitation of outflow:

  $Q_{min}\le Q_i\le Q_{max}$

  where $Q_{min}$ (m³) is the minimum limit of outflow on day i; $Q_{max}$ (m³) is the maximum limit of outflow on day i; $Q_{min}$ and $Q_{max}$ can be obtained from the observation.

In the non-flood season, we added a coefficient α in Equation (6) to calculate the outflow using Equations (6)–(8):

(6)$Q_i=Q_{in}+P-E-S+\frac{V_{targ}^t-V_{targ}^{t+1}}{D}\times \alpha$

(7)$r=F_{sim}^i/F_{mean}$

(8)$\alpha ,\beta =e·r^3+f·r^2+g·r+h$

where $V_{targ}^{t+1}$ (m³) and $V_{targ}^t$ (m³) are the target reservoir storage in the month t + 1 and t, respectively. $D$ is the number of days in the month where day i resides; $F_{sim}^i$ (m³/s) is the observed streamflow on day i; $F_{mean}$ (m³/s) is the observed mean monthly streamflow on day i; $r$ is the ratio of $F_{sim}^i$ to $F_{mean}$; $\alpha$, $\beta$, e, f, g, and h are coefficients (dimensionless).

In the flood season, we also added a coefficient β in Equation (9) to derive the outflow via Equations (7)–(9):

(9)$Q_i=\left(Q_{in}+P-E-S\right)\times \beta$

We adjusted the outflow based on the inflow. If the inflow exceeds the monthly mean of observation, the daily release of stored water will be reduced, and a portion of the flow will be stored in the reservoir. Here, we implemented this process by applying coefficients $\alpha$ and $\beta$, whose values differ slightly in each month for each reservoir. Here, we took a multiple linear regression equation in the flood season and non-flood season, respectively. By comparing the simulation with the observation, multiple linear relationships $\alpha$ and $\beta$ were obtained (Table 3).

The reservoir storage capacity was divided into four parts (Figure 2), as guidelines for reservoir operation in the flood season: (1) Dead storage: the minimum allowable water level in the reservoir. If the water level falls below the dead level, the outflow of the reservoir is reduced to replenish the storage capacity above the dead level. (2) Storage capacity between the dead and the critical levels. Within this range, the outflow is equal to the inflow below the set maximum, and the flood peak will be completely stored in the reservoir during flood season. (3) Storage capacity between the critical and the flood control levels. When the water level is within this range, the outflow is calculated using Equation (9). (4) Storage capacity higher than the flood control level. When the reservoir storage capacity is within this range, all inflow will be released.

To evaluate the performance of the SWAT model, we used three metrics: the Nash–Sutcliffe efficiency coefficient (NSE) [51], the Kling–Gupta efficiency coefficient (KGE) [52,53], and the Percent Bias (PBIAS). The NSE compares the residual variance of the simulation with the variance of the observation [51]. The KGE represents the minimum Euclidean distance between the simulation and the observation, while the PBIAS is the mean difference between the simulation and the observation. The closer the NSE and KGE values are to 1 and the PBIAS values are to 0, the better the simulations.

4. Results

4.1. Performance of the Revised Reservoir Operation Scheme in the SWAT Model

Using the SWAT model for the revised reservoir operation scheme, we simulated the daily and monthly streamflow of Cuntan and Yichang in the UYRB (Figure 3 and Table 4). We chose the warm-up period from 1960 to 1961, the calibration period from 1962 to 1980, and the validation period from 2003 to 2017. Compared with the original one, the revised SWAT model performed better in the validation period, with the NSE and KGE between the simulated and observed daily streamflow improved by 0.01 and 0.01 at Cuntan, and by 0.08 and 0.05 at Yichang, respectively. On the whole, the improved reservoir algorithm can satisfy the streamflow simulation at daily (NSE ≥ 0.78 and KGE ≥ 0.89) and monthly (NSE ≥ 0.85 and KGE ≥ 0.92) scales in the UYRB.

To verify the model performance in simulating flood characteristics, we compared the 95th (Q95) and 90th (Q90) quantiles of daily streamflow at Cuntan and Yichang stations simulated by the SWAT model with revised and original reservoir algorithms (Figure 4). The revised SWAT model performed better with the KGE (PBIAS) between simulated and observed Q95, and Q90 increased by 0.1–0.19 (5–10%).

4.2. Alteration of Flow Regimes

We compared the daily streamflow of Cuntan and Yichang stations in the impact (2003–2017) and baseline (1960–2002) periods (Figure 5). The overall intra-annual streamflow distribution became flat in the impact period compared to the baseline period. For example, the daily streamflow increased from December to March at both stations, especially Yichang. Since the TGR began operations in 2003, and the storage gradually increased, it became imperative to analyze the influence of the reservoir during its various operation stages. By dividing the 15 years (2003–2017) equally into three (2003–2007, 2008–2012 and 2013–2017) periods, we aimed to capture the evolving impact of the reservoirs on streamflow. Compared to the baseline period streamflow of 4585 m³/s, the daily average streamflow from December to March exhibited a gradual increase to 5007, 5951, and 7094 m³/s during the respective periods, coinciding with the reservoir operations. From May to November, the range of daily streamflow narrowed at both stations, whereas in the impact period, the minimum daily streamflow decreased at Yichang from September to November.

The M-K test of annual precipitation and streamflow at two hydrological stations showed that the precipitation trend was opposite the streamflow annually. The annual precipitation at Cuntan and Yichang stations did not increase significantly (i.e., by 0.15 and 0.03 mm, respectively). Conversely, the respective annual mean streamflows at Cuntan and Yichang stations decreased by 13 and 18 m³/s (10% level) yearly. We further analyzed the monthly changes in precipitation and streamflow (Figure 6). The precipitation increased (decreased) from January to July (August to December), with significant changes at a 5% level in January and March at both stations. The precipitation (streamflow) at Yichang in January and February decreased (increased) by 0.7% and 2.6% (34.4% and 46.4%), respectively, compared with those before 2003. The streamflow significantly increased (decreased) from January to March (October and November) at the 5% level at both stations. The streamflow at Yichang (Cuntan) increased by 40.40% (28.74%) from January to March and decreased by 15.02% (9.36%) from June to October. These changes were consistent with the reservoir regulation. For example, the TGR replenished streamflow from January to April and impounded streamflow during September and October. From June to August, the TGR impounded part of the flood water in the reservoir, gradually releasing it after the flood, so the monthly streamflow did not change significantly in this period. Generally, the streamflow varied similarly with precipitation (the main source of streamflow). The opposite trends of precipitation and streamflow in several months (e.g., June, July, and December) and the trend of monthly streamflow revealed the hydrological effects of reservoir operation in the UYRB.

The hydrological signatures of Cuntan and Yichang stations have undergone complex changes with climate change and human activities in the UYRB (Figure 7). For example, the MDFN2 (MDFN1) in the flow magnitude group significantly decreased (increased) by 17.62% (40.24%) at Yichang from the baseline to the impact period. Most hydrological signatures in the flow frequency (duration) group, except for FDF10 (DDF75), increased (decreased) at both stations. The DDF90 and DDF10 at Yichang decreased by 40.86% and 30.79%, respectively. The TMxDF in the flow timing group showed no significant change. The TMnDF was delayed at Yichang but was advanced at Cuntan. Both hydrological signatures (RR and RF) in the flow rating group decreased steadily at Cuntan and Yichang stations, especially the RR (25.96%and 27.94%). Overall, most hydrological signatures decreased in flow magnitude, flow variability, flow duration and flow rating groups in the UYRB.

4.3. Reservoir-Induced Hydrological Alteration

Figure 8 shows the average and relative changes in the simulated monthly streamflow with reservoir operation (wRes) or without reservoir operation (woRes) in the impact period compared with the baseline period at Cuntan and Yichang stations. In the woRes scenario, the mean streamflow of the pre-nonflood season (January–March) decreased by 13 m³/s (0.67%) and 50 m³/s (1.79%) at Cuntan and Yichang stations, respectively. However, the mean streamflow increased by 805 m³/s (27.76%) and 2015 m³/s (55.97%) from January to March at Cuntan and Yichang stations, respectively, induced by the reservoir operations. Reservoirs are essential to replenishing streamflow from January to June. In July and August, the streamflow decreased slightly by <10% at Cuntan and Yichang stations. The monthly mean streamflow from September to October was reduced by 1118 m³/s (6.95%) and 4448 m³/s (20.92%), induced by reservoirs, but by 833 m³/s (5.18%) and 1106 m³/s (5.76%), due to climate change. The water level of the reservoir reached the normal limit in November and December, and the streamflow remained stable.

The hydrological signatures altered by climate change (woRes) and reservoir operation (wRes) at Cuntan and Yichang stations in the impact period (2003–2017) compared with the baseline period (1960–2002) are shown in Figure 9. For the flow magnitude, the MDFN1 significantly increased by 55.09% (36.94%), while MDNF2 significantly decreased by 11.99% (7.43%) by reservoirs at Yichang (Cuntan). For the variability, reservoirs reduced all signatures at the two stations, including CVDFN1, which significantly increased due to climate change. For the frequency, reservoirs increased most hydrological signatures, while climate change induced a 26% increase in the FDF10, with relatively minor impacts on other hydrological signatures. Reservoirs reduced all hydrological signatures in the flow frequency group, especially DDF90, which decreased by 14.42% and 25.72% at Cuntan and Yichang stations, respectively. The TMnDF in the flow timing group decreased (increased) by 55.56% (117.29%) at Yichang (Cuntan) due to reservoir impacts. Compared with the limited impacts of climate change, reservoirs were crucial to altering flow regimes.

5. Discussion

The mean daily streamflow increased (decreased) in the pre-nonflood (post-nonflood) season due to reservoir replenishment (impoundment) after 2003. Meanwhile, the flood control of reservoirs reduced the magnitude, frequency and duration of extremely high flow in the flood season. These reservoir-induced hydrological alterations conformed with previous studies [15,25,54]. Climate change increased the CV of mean daily streamflow, while reservoir operation smoothed the flow process and improved flow stability in the pre-nonflood season. During the flood season, the TGR effectively mitigated against extreme flood events. These reservoir-induced hydrological alterations agreed with van Oorschot et al. [55] and Mei et al. [56].

Reservoir operation affected the magnitude of minimum and maximum daily streamflow, the duration and frequency of extreme flow, and the streamflow in non-flood season. It might influence floodplains and river ecosystems downstream, hindering the habitat and food supply for aquatic animals [11,38,57]. The Dongting and Poyang Lake ecosystems were affected by water level reduction induced by the decrease in streamflow in the post-nonflood season [58,59]. Some aquatic mammals (such as finless porpoises) were near extinction due to hydrological alteration [60].

We revised the reservoir module in the SWAT hydrological model to better simulate the impact of reservoir operation on streamflow. By dividing the simulation period into flood and non-flood seasons and refining the target release method, the simulation accuracy at the daily scale was improved, particularly in the flood season. This revised reservoir algorithm in the SWAT model can also be applied in other hydrological models that incorporate similar modules. The revised reservoir module in the SWAT model helped to understand reservoir simulation in the Yangtze River basin.

The impact of climate change and reservoir operation on flow regimes was analyzed, but the influence of other human activities (such as land use and cover change, water withdrawal and irrigation) was not considered. Previous studies have shown that these factors also altered hydrological processes in the UYRB [61,62,63,64]. Moreover, many small reservoirs in the UYRB were not included in this study. More reservoirs should be included in the analysis to fully consider the reservoir-induced hydrological alteration comprehensively in future research. The performance of the revised SWAT model in simulating streamflow was crucial to evaluating the impact of reservoirs on flow regimes. However, the various forcing datasets and the accuracy of the hydrological model caused several unavoidable uncertainties in simulating the alteration of hydrological signatures (i.e., FDF10, TMnDF, and RR in this study) arising from reservoir operations [65,66]. Hence, it remained a challenge to describe the dynamical and joint operation schemes in hydrological models with the continuous growth of multiple reservoirs [67].

6. Conclusions

We utilized observed and simulated data to analyze the effects of climate change and reservoir operation on flow regimes in the UYRB. Our analysis indicated that reservoirs significantly impacted the flow regime, as demonstrated by the synthesis of hydrological changes across the UYRB. We improved the accuracy of streamflow simulation using the SWAT model with a revised reservoir algorithm, resulting in improved NSE (0.78–0.83) and KGE (0.89–0.91) values between observed and simulated daily streamflow at Cuntan and Yichang stations. Our findings suggested that the magnitude, frequency, variation, duration, timing, and rate of change of streamflow were altered to varying degrees in the impact period. Reservoirs induced a notable increase in streamflow from January to March (27.76% and 55.97%), accompanied by a decrease from September to October (6.95% and 20.92%) after 2003 at Cuntan and Yichang stations, respectively. Moreover, the annual streamflow range contracted, and the flow became more stable with a reduced variation in daily streamflow. The duration of extremely low and high flow spells also decreased significantly at Cuntan and Yichang stations due to reservoir operations. These results provided valuable insights into the complex interactions between flow regimes and reservoirs, evincing significant implications for managing and planning reservoir construction in the UYRB as climate change continues.

Author Contributions

Conceptualization, H.L. and W.L.; methodology, H.L.; software, H.L. and N.W.; validation, H.L.; formal analysis, H.L.; investigation, H.L.; resources, H.L. and H.W.; data curation, H.L.; writing—original draft preparation, H.L.; writing—review and editing, W.L., H.W., T.W., Y.F. and F.L.; visualization, H.L.; supervision, F.S. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Please contact the first author for the data referenced in this publication.

Acknowledgments

The authors would like to thank the anonymous reviewers for helpful comments and suggestions on this article.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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Figures and Tables

Figure 1. The upper Yangtze River basin: (a) the reservoirs and mainstream hydrological stations analyzed in this study. (b) Changes in the number and total storage capacity of reservoirs from 1976 to 2017. The arrows indicate flow directions.

Figure 2. The storage water levels of reservoirs.

Figure 3. The daily streamflow simulated by the SWAT model with original and revised reservoir algorithms at Cuntan and Yichang stations for the calibration (1962–1980) (a,b) and validation (2003–2017) (c,d) periods.

Figure 4. The Q95 and Q90 were simulated by the SWAT model with revised and original reservoir algorithms at Cuntan and Yichang stations in the validation period.

Figure 5. Intra-annual variations in streamflow in the baseline and impact periods at Cuntan (a) and Yichang (b). The dark grey (light blue line) and slate grey (black line) areas represent the range of streamflow variability (mean streamflow) in the baseline and impact periods, respectively. The intra-annual variations in the five-year average streamflow of 2003–2007, 2008–2012 and 2013–2017 are shown with colored lines.

Figure 6. (a,c) Changes in monthly precipitation and streamflow at Cuntan and Yichang stations in the baseline (1960–2002) and impact (2003–2017) periods. (b,d) The MK trend test of monthly precipitation and streamflow at Cuntan and Yichang from 1960 to 2017.

Figure 7. Changes in hydrological signatures at Cuntan and Yichang stations in the impact period compared with the baseline period. Purple and green colors indicate positive and negative changes (%), respectively. ‘*’ p < 0.05.

Figure 8. Average and relative changes in simulated monthly streamflow (wRes and woRes) at Cuntan (a,b) and Yichang (c,d) stations in the impact period compared with the baseline period.

Figure 9. Changes in hydrological signatures induced by climate change (woRes) and reservoirs (wRes) at Cuntan and Yichang stations in the impact period compared with the baseline period. Purple and green colors indicate positive and negative changes (%), respectively. ‘*’ p < 0.05.

Table 1

Detailed information on all hydrological stations in the upper Yangtze River basin.

No.	Station Name	Longitude (°)	Latitude (°)	Period	Time Step
1	Yichang	111.28	30.7	1960–2017	Daily
2	Zhutuo	105.85	29.02	1971–1986, 2007–2017	Daily
3	Cuntan	106.6	29.62	1960–1986, 1993–2017	Daily
4	Huadan	102.88	26.92	1977–1987, 2007–2014	Daily
5	Pingshan	104.17	28.63	1977–1987, 2007–2011	Daily
6	Wudongde	102.6	26.28	2012–2017	Daily
7	Xiangjiaba	104.4	28.63	2012–2017	Daily
8	Baihetan	102.88	27.27	2015–2017	Daily
9	Shigu	99.93	26.9	2007–2015	Daily
10	Jinjiangjie	100.55	26.23	1987, 2010	Daily
11	Panzhihua	101.72	26.58	2006–2015	Daily
12	Ahai	100.5	27.35	2012–2015	Daily
13	Jin’anqiao	100.43	26.8	2012–2015	Daily
14	Zhongjiang	100.42	26.5	2012–2015	Daily
15	Tingzikou	105.82	31.85	1969–1983, 2007–2012	Daily
16	Langzhong	105.97	31.57	2007–2017	Daily
17	Wusheng	106.27	30.27	1965–1983, 2007–2017	Daily
18	Beibei	106.47	29.82	1975–1983, 2007–2017	Daily
19	Wudu	104.92	33.38	1965–1983, 2007–2017	Daily
20	Bikou	105.25	32.75	1965–1983, 2007–2017	Daily
21	Sanleiba	105.65	32.42	1975–1983, 2007–2017	Daily
22	Zhenjiangguan	103.73	32.3	1960–1983, 2007–2017	Daily
23	Pengshan	103.88	30.2	1960–1983, 2007–2017	Daily
24	Gaochang	104.42	28.8	1960–1983, 2007–2017	Daily
25	Jiangsheba	103.58	31.48	1960–1983, 2012–2017	Daily
26	Zipingpu	103.57	30.98	1976–1983	Daily
27	Shimian	102.37	29.25	1960–1976, 2008–2017	Daily
28	Shawan	103.55	29.4	2010–2017	Daily
29	Huning	101.87	28.45	1967–1987, 2007–2017	Daily
30	Tongzilin	101.85	26.68	2007–2017	Daily
31	Wali	101.57	28.1	1972–1987	Daily
32	Xiaodeshi	101.83	26.75	1967–1987	Daily
33	Wujiangdu	106.78	27.3	1965–1983, 2006–2017	Daily
34	Goupitan	107.68	27.4	2006–2017	Daily
35	Sinan	108.25	27.95	1965–1983, 2006–2017	Daily
36	Yanhe	108.47	28.55	2006–2017	Daily
37	Longtan	108.35	28.92	1965–1983, 2006–2007	Daily
38	Pengshui	108.17	29.28	2006–2017	Daily
39	Wulong	107.75	29.32	1965–1983, 2006–2017	Daily

Table 2

Summary of hydrological signatures used in the IHA.

No.	Groups	Hydrological Signatures	Abbreviation	Unit
1	Group1: Magnitude	Mean daily streamflow	MDF	m3s−1
2		Mean daily streamflow in the flood season (April–September)	NDFF	m3s−1
3		Mean daily streamflow in the pre-nonflood season (January–March)	MDFN1	m3s−1
4		Mean daily streamflow in the post-nonflood season (October–December)	MDFN2	m3s−1
5	Group2: Variability and Frequency	Coefficient of variation (CV) of the mean daily streamflow	CVDF	–
6		CV of mean daily streamflow in the flood season	CVDFF	–
7		CV of mean daily streamflow in the pre-nonflood season	CVDFN1	–
8		CV of mean daily streamflow in the post-nonflood season	CVDFN2	–
9		Low flow spell count (75th percentile of MDF)	FDF75	–
10		Extremely low flow spell count (90th percentile of MDF)	FDF90	–
11		High flow spell count (25th percentile of MDF)	FDF25	–
12		Extremely high flow spell count (10th percentile of MDF)	FDF10	–
13	Group3: Duration	Low flow spell duration	DDF75	days
14		Extremely low flow spell duration	DDF90	days
15		High flow spell duration	DDF25	days
16		Extremely high flow spell duration	DDF10	days
17	Group4: Timing	Colwell’s constancy of the mean daily streamflow	TDFC	–
18		Julian date of the annual minimum daily streamflow	TMnDF	–
19		Julian date of the annual maximum daily streamflow	TMxDF	–
20	Group5: Rates of Change	Mean rate of positive changes in flow from one day to the next	RR	–
21		Mean rate of negative changes in flow from one day to the next	RF	–

Table 3

The values of coefficients α and β for each reservoir.

No.	Name	FloodSeason	FloodPreventionStorage(108 m3)	α				β
				e	f	g	h	e	f	g	h
1	Liyuan	7.1–7.31	1.73	−1.74	5.67	−6.30	3.36	−0.45	1.18	−1.05	1.33
2	Ahai	7.1–7.31	2.15	−0.40	1.02	−0.92	1.30	−0.52	1.44	−1.46	1.54
3	Jinanqiao	7.1–7.31	1.58	−1.43	4.77	−5.42	3.07	−0.49	1.29	−1.19	1.39
4	Longkaikou	7.1–7.31	1.26	−1.49	4.73	−5.19	2.91	−0.44	1.08	−0.94	1.30
5	Ludila	7.1–7.31	5.64	−1.49	5.15	−5.99	3.32	−0.17	0.55	−0.70	1.31
6	Guanyinyan	7.1–7.31	5.42	−2.06	6.80	−7.59	3.85	−0.03	−0.15	0.08	1.09
7	Ertan	7.1–8.31	9	−12.77	39.85	−41.75	15.67	−0.05	0.23	−0.36	1.18
8	Jinpingyiji	7.1–8.31	16	−2.59	7.23	−6.83	3.21	−0.10	0.36	−0.47	1.21
9	Pubugou	7.1–9.30	11/7.27	−0.37	1.29	−1.57	1.65	−0.51	1.53	−1.65	1.63
10	Zipingpu	6.1–9.30	1.67	−0.31	0.99	−1.12	1.44	−0.06	0.25	−0.37	1.17
11	Bikou	5.1–9.30	0.5/0.7	−0.02	0.11	−0.20	1.11	−0.01	0.05	−0.16	1.12
12	Baozhusi	7.1–9.30	2.8	−0.03	0.17	−0.32	1.18	−0.02	0.11	−0.20	1.12
13	Tingzikou	6.21–8.31	14.4	−0.39	1.46	−1.89	1.82	−0.30	0.76	−0.66	1.19
14	Caojie	6.1–8.31	1.99	−1.44	4.20	−4.16	2.40	−0.03	0.20	−0.53	1.38
15	Goupitan	6.1–8.31	4/2	−0.04	0.23	−0.42	1.22	−0.09	0.30	−0.35	1.14
16	Silin	6.1–8.31	1.84	−1.47	4.73	−5.19	2.93	0.02	−0.03	−0.22	1.24
17	Shatuo	6.1–8.31	2.09	−1.27	4.02	−4.27	2.51	−0.08	0.12	−0.20	1.14
18	Pengshui	5.21–8.31	2.32	−0.70	2.24	−2.47	1.93	0.07	−0.25	−0.04	1.23
19	Xiluodu	7.1–9.10	46.5	−35.90	110.49	−113.69	40.11	0.07	−0.28	−0.11	1.20
20	Xiangjiaba	7.1–9.10	9.03	−3.69	12.56	−14.38	6.51	−0.47	0.98	−0.98	1.44
21	Sanxia (TGR)	6.10–8.24	221.5	−3.21	9.60	−9.74	4.35	−1.12	3.65	−4.04	2.52

Table 4

Performance of the simulated monthly streamflow at Cuntan and Yichang stations for the calibration and validation periods.

Station	Calibration		Validation (Original)		Validation (Revised)
	NSE	KGE	NSE	KGE	NSE	KGE
Cuntan	0.95	0.95	0.92	0.96	0.92	0.95
Yichang	0.96	0.97	0.81	0.90	0.85	0.92

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