Human activities and climatic changes have altered the hydrological ecosystem of the Min River Basin and affected in-river biodiversity. In this paper, the year of abrupt hydrological change was identified using multiple tests, and the drivers of ecohydrological change were quantified in conjunction with the Budyko coupled hydrothermal equilibrium theory. Combined with ecological flow indicators (ecological surplus (ES) and ecological deficit (ED)) calculated based on discharge hydrographs and multiple hydrological indicators (ES/ED), the degree of river hydrological alteration in the Min River Basin was comprehensively evaluated, and its impacts on in-river biodiversity analyzed. The results of the study showed that: (1) according to the Budyko theory, the influence of human activities on the runoff changes in the Min River Basin reached 56.80%, which was the main influencing factor, followed by climatic factors (41.56% for precipitation and 1.64% for evapotranspiration); (2) dam construction has generally resulted in an increase in seasonal ES and a decrease in seasonal ED; (3) the combination of the ecological flow indexes with the ecologically relevant hydrological indicators not only reduces the redundancy between the parameters, but also reflects the essential hydrological information and ecological connotations, and is an effective method for evaluating ecohydrological mechanisms.

  • A comprehensive assessment of flow variability was carried out using eco-flow metrics based on discharge hydrographs (DHs) and a variety of hydrological evaluation metrics.

  • The impact of reservoir-induced changes in basin hydrology on the biodiversity of river ecosystems is explored.

  • There is a good correlation between the eco-flow indicators based on the DH and the 32 IHA indicators.

The river system is known as the main artery of the Earth and plays a crucial role in the global water cycle, climate change, and ecological balance (Barnett et al. 2008; De Castro-Català et al. 2020). However, in recent years, climate change and intensified human activities have led to the deterioration of river ecosystems (Abbas et al. 2022b, 2023; Elahi et al. 2022, 2024). Climate change has led to changes in precipitation distribution and water flow patterns, affecting river levels and flows. In the meantime, a large number of anthropogenic activities, such as dam construction and river diversions, have also disturbed natural hydrological processes. These interventions not only directly alter the flow dynamics of rivers but may also lead to a decline in the quality of water bodies, loss of ecological habitat, and even pose an existential threat to aquatic organisms, making them one of the main factors affecting river ecology (Han et al. 2019; Cui et al. 2020; Zhang & Shang 2023). The construction of these reservoirs not only changes the flow of rivers but also affects the water ecosystems in downstream areas. Moreover, the construction of reservoirs often requires the inundation of large areas of water, resulting in the loss of original ecological habitats and posing a threat to local biodiversity and ecological balance (Zhang et al. 2015; Liu et al. 2019; Ekka et al. 2022). Changes in hydrological processes due to dam construction and their ecological impacts have attracted increasing attention from ecologists, hydrologists, and governments.

Currently, research on the evolution of hydrological processes in watersheds focuses on two levels: the variability of hydrological patterns in watersheds and the quantitative attribution of the drivers of runoff evolution. In particular, changes in the hydrological situation of rivers are analyzed by evaluating the extent of change through indicators. Olden & Poff (2003) summarized more than 170 hydrological indicators based on published literature to show changes in flow conditions, but there are significant correlations between them. Richter et al. (1997) proposed 33 indicators of hydrologic alteration (IHA) to analyze intra- and inter-annual flow changes in terms of five aspects: flow, frequency, duration, timing, and rate of change. Flow, frequency, duration, timing, and rate of change were analyzed in terms of intra- and inter-annual flow variations. On this basis, a number of other indicators have been derived, such as the degree of hydrologic alteration (D0) (Shiau & Wu 2007) and the Dundee hydrologic condition alteration method (DHRAM) (Black et al. 2005). Although the 33 IHA indicators possess a significant simplification relative to the 170 indicators, there are still problems of redundancy and correlation between the indicators that still need to be well addressed. Vogel et al. (2007) introduced ecological flow indicators based on flow duration curves (FDCs) to address this issue. These indicators include ecological surpluses (ESs) and ecological deficits (EDs) and can reveal river flow deficits or surpluses on multiple time scales. To further enhance the usefulness of these indicators, Gao et al. (2012) optimized them by setting the 25 and 75% quartiles of the FDC as the lower and upper bounds for easier application in the assessment of river health management. However, recently, Guo et al. (2022b) pointed out that there are still some limitations of ecological flow indicators calculated based on FDC. For example, it is possible that the monthly ES/ED is zero, while the quarterly or annual ES/ED is not zero. These limitations affect the practical application of ecological flow indicators. In order to overcome these limitations and promote the wide application of eco-flow indicators, Guo et al. proposed a method for calculating eco-flow indicators based on the discharge hydrograph (DH). This approach aims to provide a more accurate assessment of ecological flow by considering the time-varying and dynamic nature of flow changes more comprehensively, which can better address the limitations of the existing methods and enable ecological flow indicators to reflect the impacts of hydrological changes on ecosystems more accurately in practice.

Hydrological models and statistical analyses are two commonly used approaches in quantitative attribution analyses of runoff change drivers (Huang et al. 2016b; Hou et al. 2022). The most commonly used hydrological models are the Variable Infiltration Capacity (VIC) model (Yang et al. 2020), the SWAT model (Polong et al. 2023), the Geomorphology-Based Hydrological Model (GBHM) model (Qiao et al. 2023), etc. Although these traditional physical hydrological models can take into account the complex interactions of surface runoff, evapotranspiration, soil moisture, etc., the need for high spatial and temporal resolution data and the complexity of numerous parameter adjustments make it difficult for these hydrological models to accurately predict runoff change drivers in some cases, which largely limits their application in research. However, these hydrological models may not be able to accurately predict the drivers of runoff changes in some cases due to the need for high-resolution data and complex parameter adjustments, which largely limit their application in research (Zheng et al. 2021); On the contrary, the Budyko hypothesis, which is based on the theory of water-heat balance, has been widely used in runoff attribution analyses in different watersheds in recent years because of its integrated consideration of water balance, energy balance, river substrate conditions, and the interactions between various factors (Zhang et al. 2021; Zheng et al. 2021; Ni et al. 2022).

Tang et al. (2021) pointed out that hydrological analyses with a single indicator are one-sided and do not represent the degree of variability of flows in a watershed. Therefore, it is necessary to use multiple indicators to provide a comprehensive evaluation of the ecohydrology of the basin, which is essential for studying hydrological response processes in complex, changing environments. However, the current research on hydrological processes in the Min River Basin is mainly limited to the aspects of trend and mutability. It needs a more comprehensive and integrated evaluation of the Min River Basin's hydrological processes and ecological response. Given this, this study takes the Min River Basin as an example, and the objectives of the study are as follows: (1) the trend of runoff and its abrupt changes were analyzed through the Mann-Kendall, cumulative distance level, and sliding t-test; (2) the evolution of hydrological conditions and their causes in the Min River Basin was analyzed using the Budyko coupled hydrothermal equilibrium theory; (3) a combination of the IHA-RVA indicator, the D0, the DHRAM, and the eco-flow indicator based on the DHs were used to quantify changes in the basin's hydrological situation; and (4) clarify the correlation between ecological flow and IHA indicators.

Study area

The Min River is a major branch on the left bank of the Yangtze River, located at the western end of the Sichuan Basin, and is also a famous freshwater fish resource in China. It is renowned for having abundant freshwater fisheries. With more than half of the Min River covered by water, the Dadu River is said to be the greatest tributary of the Min River (Figure 1). The Min River Basin spans 735 km in total length, the average specific fall in the basin is 4.84‰, whereas the natural fall value is 3,560 m. The basin has a total of 95.30 × 109 m3 of long-term surface water resources, 703.7 mm of equivalent runoff depth, and 24.05 × 109 m3 of groundwater resources. Noticeable inter-annual fluctuations can be seen in the basin's surface water resource distribution, which is unequal all year long. The runoff during the June–September period accounts for 60–75% of the basin's annual surface water resources (Huang et al. 2016a; Chao-nan et al. 2020). With the rapid development of science and technology and the significant improvement of human living standards, the demand for water resources in the Min River Basin has been increasing, which has had a far-reaching impact on the ecosystem of the basin. The problems of water shortage and ecological damage have gradually come to the forefront, threatening the environmental sustainability of the Min River Basin (Guo et al. 2023).
Figure 1

Geographical location of the study area.

Figure 1

Geographical location of the study area.

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Data resource

The data used in this study include meteorological data and runoff data. The meteorological data are the monitoring data of 10 national meteorological stations in the Min River Basin, such as Xiaojin and Seda, from 1961 to 2019, including precipitation, wind speed, relative humidity, etc. (the data of meteorological stations have been processed by the method of summing and averaging), from China Meteorological Data Service Centre (http://data.cma.cn/); the runoff data are the monitoring data of the hydrological stations of Gaochang Station in the Min River Basin from 1961 to 2019, which are derived from the hydrological yearbook of the Yangtze River Basin. The geographic location information of the above stations is shown in Table 1.

Table 1

Station information for hydrological stations and their meteorological stations

StationStation typeAltitude (m)Longitude (E)Latitude (N)
Gaochang Hydrological station 385.20 104.25 28.48 
Xiaojin Meteorological station 2367.00 102.37 31.00 
Maerkang 2669.80 102.48 31.67 
Songpan 2827.70 103.57 32.65 
Wenjiang 538.90 103.83 30.70 
Dujiangyan 706.70 103.67 30.98 
Leshan 424.20 103.75 29.50 
Yuexi 1661.60 102.55 28.62 
Yibin 340.80 104.53 28.82 
Emeishan 3137.00 103.35 29.52 
StationStation typeAltitude (m)Longitude (E)Latitude (N)
Gaochang Hydrological station 385.20 104.25 28.48 
Xiaojin Meteorological station 2367.00 102.37 31.00 
Maerkang 2669.80 102.48 31.67 
Songpan 2827.70 103.57 32.65 
Wenjiang 538.90 103.83 30.70 
Dujiangyan 706.70 103.67 30.98 
Leshan 424.20 103.75 29.50 
Yuexi 1661.60 102.55 28.62 
Yibin 340.80 104.53 28.82 
Emeishan 3137.00 103.35 29.52 

Trend and alteration analysis

To determine the mutation years in the Min River Basin, we used the Mann-Kendall test, the cumulative distance level method, and the sliding t-test method for trend and mutation analyses. Because these three methods are more commonly used, they will not be repeated here, and their detailed calculation process and principle are shown in relevant literature (Guo et al. 2022a; Wang et al. 2022).

Budyko

In hydrological research, the Budyko hypothesis enables the identification and measurement of the factors that influence runoff variability (Zhang et al. 2011). The long-term water balance of a watershed can be expressed as:
(1)
where R is the mean runoff depth (mm); P is the mean precipitation (mm); E is the mean actual evapotranspiration (mm); and ΔS is the change in storage (mm). ΔS is 0 for runoff changes on long time scales.
Based on the Budyko hypothesis, Choudhury (1999) and Yang et al. (2008) used a combination of quantile analysis and mathematical statistics to develop a water balance equation on a multi-year average scale:
(2)
where n is the characteristic parameter of the sub-basin; n can be introduced by knowing R, P, and ET0. The following formula may be used to express how sensitive runoff R is to each element using the elasticity coefficient:
(3)
where x can be expressed as P, ET0, or n; according to the elasticity coefficient of runoff to each influencing factor, the amount of change of each influencing factor to runoff depth can be obtained. Finally, the total amount of change in runoff depth ΔR′ is derived. It is expressed as follows:
(4)
The following formula determines how much each component contributes to the variation in runoff:
(5)

Hydrological alterations

The IHA proposed by Richter et al. (1996) quantified the impact of dam construction on the hydrological status of rivers in five ways (Table 2). The formula is as follows:
(6)
where Di denotes the hydrological variability of the ith indicator; N0,i and Ne denote the actual and expected number of years that the ith indicator will fall within the RVA threshold after the dam is constructed, respectively.
Table 2

IHA indicators and their ecological impact

IHA parameter groupHydrological parametersInfluences on ecosystem
Group 1: Magnitude of monthly water conditions Average monthly flow Meet the habitat needs of aquatic organisms, and the water quantity needs of terrestrial animals 
Group 2: Magnitude and duration of annual extreme water conditions Annual average 1, 3, 7, 30, and 90-day minimum and maximum flow
Baseflow index (7-day minimum flow/mean flow for year) 
Meet plant site needs, river topography, and plant community distribution 
Group 3: Timing of annual extreme water conditions Julian date of each annual 1-day maximum
Julian date of each annual 1-day minimum 
Meeting habitat conditions for fish spawning and rearing, species evolutionary needs 
Group 4: Frequency and duration of high and low pulses The average number of high and low pulses per year and pulse duration Provide for animal resting needs, meet sediment transport, streambed structural needs 
Group 5: Flow change rate and frequency Annual average rates of increase and decrease and the number of reversals Impacts on plant drought and causes retention of diffuse organic matter 
IHA parameter groupHydrological parametersInfluences on ecosystem
Group 1: Magnitude of monthly water conditions Average monthly flow Meet the habitat needs of aquatic organisms, and the water quantity needs of terrestrial animals 
Group 2: Magnitude and duration of annual extreme water conditions Annual average 1, 3, 7, 30, and 90-day minimum and maximum flow
Baseflow index (7-day minimum flow/mean flow for year) 
Meet plant site needs, river topography, and plant community distribution 
Group 3: Timing of annual extreme water conditions Julian date of each annual 1-day maximum
Julian date of each annual 1-day minimum 
Meeting habitat conditions for fish spawning and rearing, species evolutionary needs 
Group 4: Frequency and duration of high and low pulses The average number of high and low pulses per year and pulse duration Provide for animal resting needs, meet sediment transport, streambed structural needs 
Group 5: Flow change rate and frequency Annual average rates of increase and decrease and the number of reversals Impacts on plant drought and causes retention of diffuse organic matter 
In addition, the change in the composite indicator was further calculated using the following formula:
(7)
where D0 is the degree of hydrological alteration of the basin as a whole. The general definition of Di, D0 value between 0 and 33% for low change, 33–67% for medium change, and 67–100% for high change.

Ecological flow indicators

Guo et al. (2021) showed that there are some limitations in the ecological flow indicators (ES and ED) calculated based on FDC, such as the situation where the monthly ES/ED is zero but the seasonal ES/ED or the annual ES/ED is not; or there are the phenomena that the monthly ES/ED is larger than the seasonal ES/ED, and the seasonal ES/ED is larger than the annual ES/ED. These limit the ecological runoff indicators calculated based on FDC. Therefore, this study calculates ecological flow indicators based on the DH proposed by Guo. The principle is as follows: 25 and 75% percentile daily flows are obtained using one year's daily flow calculations, from which 25 and 75% DHs are then made up, respectively (Figure 2(a)), and the area between the two lines is considered to be the river ecosystem control management target. The maximum water demand (Maxrunoff) (Figure 2(b)) and the minimum water demand (Minrunoff) (Figure 2(c)) were defined. For example, for 1 January–31 January, the area below the 75th percentile flow process line portion is the maximum water demand for January and the area below the 25th percentile flow process line portion is the minimum water demand for January. Calculations for other months are similar. The ES and ED formulas are calculated as follows:
(8)
(9)
where m represents the month, m = 1, 2, … , 12; n is the number of days in a month, e.g., n = 31 when m = 1; D is 86,400, s; Qi,25% is the 25% percentile daily flow, m3/s; Qi,75% is the 75% percentile daily flow, m3/s; Maxrunoffm is the maximum runoff of the adaptation range in month m, m3; Minrunoffm is the minimum runoff of the adaptation range in month m, m3; EDm is the ecological deficit in month m, non-positive; ESm is the ES in month m, non-negative. Seasonal ecological flow indicators (ES and ED) are the sum of three monthly values for the local climate. The annual ecological flow indicator (ES and ED) is the sum of 12 monthly values.
Figure 2

(a) River ecosystem control management target (magenta region). (b) Maximum water demand (light green region). (c) Minimum water demand (light orange region).

Figure 2

(a) River ecosystem control management target (magenta region). (b) Maximum water demand (light green region). (c) Minimum water demand (light orange region).

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Evaluation of fluvial biodiversity

The Shannon Index (SI) is a commonly used indicator for evaluating biological richness (Kuo et al. 2001). Among them, the more immense the SI, the richer the biodiversity of the river section in the study area. However, due to the need for quantitative relationships between riverine biomes and populations in most of the watersheds in China, it is impossible to calculate an accurate assessment of them. Yang et al. (2008) addressed this problem by establishing a relationship between IHA and SI with the following equation:
(10)

Basin runoff and climate change characteristics

The results of the trend and mutation tests for the Min River Basin are shown in Table 3. The mutation points all passed the 0.01 confidence level test. The results show that 1993 is the year of mutation detected by the three methods together, so 1993 is considered to be the year of mutation in the Min River Basin. Therefore, the Min River Basin was divided into pre-mutation (1961–1993) and post-mutation (1994–2019). This corresponds to the construction and operation of large and medium-sized reservoirs in the Min River since the 1990s.

Table 3

Statistical results of mutation tests

Hydrological stationYear of hydrological variation for different tests
Mutation year
Mann-Kendall testSliding t-testCumulative distance level
Gaochang station 1993, 1995, 2000 1991, 1993 1968, 1973, 1993 1993 
Hydrological stationYear of hydrological variation for different tests
Mutation year
Mann-Kendall testSliding t-testCumulative distance level
Gaochang station 1993, 1995, 2000 1991, 1993 1968, 1973, 1993 1993 

The inter-annual trends of precipitation, runoff, and potential evapotranspiration in the Min River Basin before and after the mutation are given based on the identified year of the runoff mutation (1993) (Figure 3 and Table 4). The rate of change of potential evapotranspiration was −0.28 before the mutation and 1.41 after the mutation, with a mean value of 111.14 mm higher than that before the mutation; the rate of change of precipitation was −1.34 before the mutation and 5.43 after the mutation, with a mean value of 43.27 mm lower; the rate of change of runoff depth changed from 0.11 before the mutation to 1.72 after the mutation, with a mean value of 52.56 mm lower.
Table 4

Change in mean annual precipitation, evapotranspiration, and runoff

PeriodRunoff depth
Precipitation
Potential evapotranspiration
Mean (mm)Rate of change (%)Mean (mm)Rate of change (%)Mean (mm)Rate of change (%)
1961–1993 643.60 0.11 1029.68 −1.34 545.25 −0.28 
1994–2019 591.05 0.172 986.40 5.43 656.39 1.41 
PeriodRunoff depth
Precipitation
Potential evapotranspiration
Mean (mm)Rate of change (%)Mean (mm)Rate of change (%)Mean (mm)Rate of change (%)
1961–1993 643.60 0.11 1029.68 −1.34 545.25 −0.28 
1994–2019 591.05 0.172 986.40 5.43 656.39 1.41 
Figure 3

Characteristics of temporal changes in climate (precipitation, evapotranspiration) and runoff in the Min River Basin.

Figure 3

Characteristics of temporal changes in climate (precipitation, evapotranspiration) and runoff in the Min River Basin.

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Increased precipitation generally favors increased runoff recharge, while increased evapotranspiration depletes runoff. In terms of trends in climatic factors, an increase in potential evapotranspiration leads to a gradual decrease in runoff. In contrast, increased precipitation leads to a gradual increase in runoff. However, in terms of the mean values of the meteorological factors, the mean value of precipitation is lower in the post-mutation period than in the pre-mutation period, indicating that precipitation recharge decreases and runoff decreases; the mean value of evapotranspiration becomes larger, indicating that evaporation intensifies and runoff decreases. This indicates that the effect of climate on runoff is complex, and the response of runoff to climate change cannot be judged only from the amount of change in runoff and climate factors. Therefore, this paper will quantify the effects of climate change and human activities on runoff through the water balance principle based on the Budyko hypothesis.

Budyko coupled hydrothermal equilibrium theory

According to the Budyko coupled hydrothermal equilibrium theory (Table 5), the elasticity coefficient of precipitation is positively related to runoff. In contrast, the elasticity coefficients of potential evapotranspiration and subsurface are negatively related to runoff. The elasticity coefficients of precipitation were positive, indicating that runoff increased with the increase of the elasticity coefficient of precipitation. In contrast, the elasticity coefficients of potential evapotranspiration and subsurface were negative, indicating that runoff decreased with the increase of both. That is, for every 1% increase in the value of precipitation, runoff will increase by 1.59–1.56%; for every 1% increase in potential evapotranspiration, runoff will decrease by 0.42–0.43%; and for every 1% increase in the subsurface parameter, runoff will decrease by 0.31–0.34%.

Table 5

Characteristic values of meteorological and hydrological variables in the Min River Basin

PeriodP (mm)R (mm)ET0 (mm)nElasticity coefficients
εPεET0εn
1961–1993 1029.68 643.60 545.25 1.33 1.59 −0.42 −0.31 
1994–2019 986.40 591.05 656.39 1.96 1.56 −0.43 −0.34 
PeriodP (mm)R (mm)ET0 (mm)nElasticity coefficients
εPεET0εn
1961–1993 1029.68 643.60 545.25 1.33 1.59 −0.42 −0.31 
1994–2019 986.40 591.05 656.39 1.96 1.56 −0.43 −0.34 

The data shown in Table 6 indicate that the variation in the runoff of Gaochang station due to rainfall is −80.45 mm. Additionally, the runoff of Gaochang station is increased by 3.25 mm by potential evapotranspiration and decreased by 106.42 mm by underlying subsurface factors. Changes in the subsurface contributed the most to the change in runoff at the Gaochang station with 54.20%. The second-largest contributor is rainfall, at 41.56%, while potential evapotranspiration has the lowest impact, at 1.64%.

Table 6

Characterization of runoff changes in the Min River Basin

ΔRP (mm)ΔRET0 (mm)ΔRn (mm)ΔR (mm)ηP (%)ηET0 (%)ηn (%)
−80.45 3.25 −106.42 −49.85 41.56 1.64 56.8 
ΔRP (mm)ΔRET0 (mm)ΔRn (mm)ΔR (mm)ηP (%)ηET0 (%)ηn (%)
−80.45 3.25 −106.42 −49.85 41.56 1.64 56.8 

Quantitative assessment of hydrological regime change

Changes in the 32 hydrological elements of the IHA prior to the abrupt change were analyzed using daily runoff data from 1961 to 2019 at the Gaochang hydrological station (Figure 4). For the first group of indicators, the change in mean flow was more evident in months 2, 5, 7, 8, and 11, all of which had a moderate degree of change, while the other months had less change. After 1993, the number of reservoirs and water storage increased significantly, which increased the runoff after the construction of the dams and caused a certain degree of disruption of aquatic organisms' reproduction. For the second group of indicators, 1, 3, 7, and 90-day minimum flow increased compared with the pre-mutation period, and 1, 3, 7, and 90-day maximum flow decreased compared with the pre-mutation period. Analysis of its causes, mainly due to the Min River reservoir ‘string flow’ mode, the construction and operation of the reservoir to change the original flow direction of the river, so that the Min River downstream storage has the role of regulating the flood season storage can reduce the amount of water discharged, and the dry season release of water can be avoided downstream of the water shortage. Low pulse count in the fourth group of indicators decreased from 4 to 2 times, high pulse count increased from 8 to 10 times, the degree of change of both groups reached medium change (54 and 51%), and the rest of the change degree is lower, which belongs to low change. The fifth group of indicators, the fall rate, changed from −110 to −152. The number of reversals increased from 153 to 185, and both groups' degrees of change reached high (100 and 88%). The construction of reservoirs in the post-impact period greatly influenced the lower basin of the Min River, and plants on both sides of the river were threatened by droughts, which affected the transport of sediment to produce specific changes in the riverbed structure. The ecosystem has a specific limit to external changes. Once exceeding this limit, it will threaten the stability of the river ecosystem and bring some unfavorable factors.
Figure 4

Hydrological change schematic (L is low change, M is moderate change, H is high change, and all three units are in %).

Figure 4

Hydrological change schematic (L is low change, M is moderate change, H is high change, and all three units are in %).

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According to Table 7, the change in the third group of indicators after the mutation is 16%, which is a low change; the change in the first, second, and fourth groups of indicators was 39, 39, and 42%, respectively, which is a moderate change. The change in the fifth group of indicators was 79%, which is a high change. The change in the overall hydrological alteration of the Min River Basin was 44%. Five categories are used by DHRAM to categorize the degree of change, with 1 denoting no risk to the environment and 5 denoting serious harm. Based on the DHRAM scoring data, Gaochang station has a total score of 3, and its change level is 2, indicating a low degree of alteration. The hydrological changes in the Min River Basin were comprehensively judged by combining two different methods of assessing the degree of change, and the degree of change was considered moderate. This change reflects a significant change in the natural hydrological conditions in the basin.

Table 7

Overall hydrological variability in the Min River Basin

Hydrological stationHydrological degree of change in each group of indicators (%)
Total pointsD0 (%)
Group 1Group 2Group 3Group 4Group 5
Gaochang station 39 (M) 49 (M) 16 (L) 42 (M) 79 (H) 3 (2) 44 (M) 
Hydrological stationHydrological degree of change in each group of indicators (%)
Total pointsD0 (%)
Group 1Group 2Group 3Group 4Group 5
Gaochang station 39 (M) 49 (M) 16 (L) 42 (M) 79 (H) 3 (2) 44 (M) 

Variations of ecological flow indicators

The bar charts in Figure 5 show the variability of seasonal ecological flows at the high-field stations. Generally, the maximum ES value for each season occurs in the post-impact period. For seasonal ED, the maximum absolute ED value in winter occurs in the post-impact period. In contrast, the maximum absolute ED value in the other three seasons occurs in the pre-impact period, which is a temporal feature suggesting that the construction of the dam generally reduces the seasonal flow. Another noteworthy point is the occurrence of consecutive non-zero winter ES and zero winter ED values in the post-impact period, which implies that reservoir scheduling has a significant positive effect in winter.
Figure 5

1961–2019 Seasonal ES and ED and their cumulative values. (a) Spring; (b) summer; (c) autumn; and (d) winter.

Figure 5

1961–2019 Seasonal ES and ED and their cumulative values. (a) Spring; (b) summer; (c) autumn; and (d) winter.

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Cumulative ecological runoff maps (Figure 5, dotted line graph) were developed to better assess the overall characteristics of changes in ES and ED before and after dam construction.

Cumulative ecological runoff maps provide a good indication of inter-annual trends within a given period. In the graph, a small line segment that rises steeply means that the seasonal ES of the year corresponding to its right endpoint is significantly larger than that of the previous year; for a small line segment that stays horizontal, it means that the seasonal ES of the year corresponding to the right endpoint is zero; for any two small line segments, the seasonal ES of the year corresponding to the right endpoint of the one with a larger slope is larger than that of the one with a smaller slope. The seasonal ED maintains the same pattern. The seasonal ED also maintains this pattern. As an example, in Figure 5(a), the black line between 1993 and 1994 has a large slope (at the red arrow), which indicates that the ES in 1994 (6.30) is much larger than that in 1993 (5.51). In addition to the inter-annual trends, the slopes of the curves at each stage also reveal, to some extent, the dam's impact. The slopes of the curves in the periods before and after the mutation are significantly different. For example, in Figure 5(a), the seasonal ES gradually increases in the pre-impact period and rapidly increases in the years of the post-impact period, which means that the ES becomes larger in the spring. This change suggests that the dam's construction has had a positive impact. In contrast, the seasonal ED decreases in the post-impact period, and the decreasing curve gradually becomes smooth, which means that spring ED gradually becomes 0, implying a negative impact on the dam construction. For the autumn ED, the cumulative curve declines faster in the post-impact period than in the pre-impact period, implying a positive impact on dam construction.

Analysis of riverine biodiversity

From Figure 6, it can be seen that there is a significant change in the river SI indicators of the Min River Basin in the post-impact period. Biodiversity decreased significantly. The SI index was tested for significance using the Trend-Free Pre-Whitening Mann-Kendall (TRPW-MK) test and the result was Z = −2.13 (|Z| > 1.96), indicating a decreasing tendency in the flow biodiversity index from 1961 to 2019 and passed the significance test. The mean value of SI was 534 before the dam's construction (1961–1993), with the implementation of the hydraulic project and the operation of the tandem reservoir after 1993, the mean value of SI decreased rapidly to 411, indicating that the biodiversity decreased significantly in the post-impact period.
Figure 6

SI time-varying characteristics. The red curve indicates the loess function fit curve and the shaded area indicates the 95% confidence interval of the loess fit curve.

Figure 6

SI time-varying characteristics. The red curve indicates the loess function fit curve and the shaded area indicates the 95% confidence interval of the loess fit curve.

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Changes in ecologically relevant hydrological indicators

Figure 7 shows the correlation between the IHA indicators, from which it can be seen that some of the hydrological indicators have strong correlations with each other, and their correlation coefficients even reach 0.94. The correlation between the minimum 1, 3, 7, 30, and 90-day flows is stronger, with an average correlation of 0.76, and the correlation between the maximum 1, 3, 7, 30, and 90-day flows, and the baseflow index is even stronger, with an average correlation of 0.82. In addition, the correlation between the flow in the dry season (December–March) is also vital; the flow in the abundant season (July–September) has a strong correlation with the flow on the 1st, 3rd, 7th, 30th, and 90th day of the year, and the baseflow index; the flow on the 1st, 3rd, 7th, 30th, and 90th day of the year has a strong correlation; the flow on the 1st, 3rd, 7th, 30th, and 90th day of the year has a strong correlation with the baseflow index; the flow on the 1st, 3rd, 7th, 30th, and 90th day of the year has a strong correlation with the baseflow index. 1-, 7-, 30-, and 90-day flows have strong correlations with parameters such as baseflow index and number of reversals, in addition to solid correlations between themselves. As a result, there is a high degree of autocorrelation among the 32 IHA variables, and redundancy among the indicators is more prominent.
Figure 7

Correlation between 32 IHA parameters.

Figure 7

Correlation between 32 IHA parameters.

Close modal
Utilizing principal component analysis (PCA) as a tool, we refined the selection of ecologically relevant hydrological indicators (ERHIs), aiming to alleviate the issues of redundancy and autocorrelation among the IHA indicators. As illustrated in Figure 8, seven primary components with eigenvalues surpassing unity were identified, jointly explaining 78.43% of the variability. To gain a more nuanced understanding of each principal component and to pinpoint its most influential variable, we compared the loading strengths of each variable within the components, as outlined in Table 8. From each of these components, we selected a representative IHA indicator based on its maximum absolute loading value: May flow, 7-day minimum flow (Min-7), 7-day maximum flow (Max-7), date of peak flow (DMAX), high pulse count (HPC), rate of flow increase (RR), and the count of flow reversals (NRV). This approach guaranteed that at least one indicator from each IHA parameter category was represented in the ERHIs, thereby effectively resolving the issue of indicator redundancy.
Table 8

PCA screening results and blackbody values in each group are selected ERHIs

IHA parametersPC1PC2PC3PC4PC5PC6PC7
January 0.437 0.367 0.237 0.225 −0.091 −0.136 −0.147 
February 0.479 0.251 0.244 −0.201 −0.087 −0.045 −0.012 
March 0.456 0.347 −0.219 −0.156 0.068 0.046 −0.123 
April 0.526 0.345 −0.156 −0.279 0.243 0.082 −0.096 
May 0.206 0.312 0.491 −0.469 0.403 0.203 0.426 
June −0.262 −0.214 −0.409 0.303 0.379 0.039 −0.245 
July −0.292 0.451 −0.345 −0.304 0.054 −0.125 −0.124 
August −0.362 0.434 −0.206 0.308 −0.107 −0.109 −0.182 
September −0.345 0.355 −0.533 0.409 0.059 0.253 0.123 
October −0.289 0.362 0.476 0.434 0.207 0.073 −0.283 
November −0.238 0.422 0.489 0.423 0.263 0.062 −0.176 
December 0.367 0.532 0.381 0.422 0.032 −0.107 −0.145 
1-day minimum 0.437 0.561 0.015 −0.225 −0.131 0.366 −0.204 
3-day minimum 0.546 0.578 0.025 −0.094 −0.119 0.096 0.121 
7-day minimum 0.563 0.679 0.016 −0.042 −0.087 0.055 0.104 
30-day minimum 0.584 0.548 0.021 −0.031 −0.075 −0.012 0.038 
90-day minimum 0.576 0.612 −0.016 −0.056 −0.015 −0.024 −0.009 
1-day maximum −0.375 0.394 −0.216 0.309 −0.198 0.066 −0.012 
3-day maximum −0.396 0.345 −0.167 0.362 −0.164 0.005 0.043 
7-day maximum −0.497 0.467 −0.129 0.485 −0.158 0.022 0.009 
30-day maximum −0.372 0.502 −0.174 0.345 −0.078 −0.042 0.008 
90-day maximum −0.402 0.498 −0.046 0.401 −0.033 0.0.31 0.069 
Baseflow index 0.489 −0.152 0.016 −0.091 −0.162 0.226 0.026 
Date of minimum 0.279 0.218 0.108 0.211 −0.163 0.226 0.397 
Date of maximum 0.304 −0.256 0.315 0.223 −0.052 − 0.543 0.352 
Low pulse count 0.207 −0.352 −0.148 0.205 −0.088 0.271 0.105 
Low pulse duration −0.294 0.231 0.271 −0.382 0.142 −0.159 0.032 
High pulse count 0.335 −0.282 −0.018 0.297 0.471 −0.093 −0.026 
High pulse duration −0.291 0.289 0.317 −0.232 −0.041 0.344 0.211 
Rise rate 0.365 0.451 0.568 −0.121 0.278 −0.205 0.048 
Fall rate −0.428 −0.294 0.166 −0.297 −0.172 −0.131 0.101 
Number of reversals 0.895 0.349 −0.216 0.332 −0.117 0.175 −0.027 
IHA parametersPC1PC2PC3PC4PC5PC6PC7
January 0.437 0.367 0.237 0.225 −0.091 −0.136 −0.147 
February 0.479 0.251 0.244 −0.201 −0.087 −0.045 −0.012 
March 0.456 0.347 −0.219 −0.156 0.068 0.046 −0.123 
April 0.526 0.345 −0.156 −0.279 0.243 0.082 −0.096 
May 0.206 0.312 0.491 −0.469 0.403 0.203 0.426 
June −0.262 −0.214 −0.409 0.303 0.379 0.039 −0.245 
July −0.292 0.451 −0.345 −0.304 0.054 −0.125 −0.124 
August −0.362 0.434 −0.206 0.308 −0.107 −0.109 −0.182 
September −0.345 0.355 −0.533 0.409 0.059 0.253 0.123 
October −0.289 0.362 0.476 0.434 0.207 0.073 −0.283 
November −0.238 0.422 0.489 0.423 0.263 0.062 −0.176 
December 0.367 0.532 0.381 0.422 0.032 −0.107 −0.145 
1-day minimum 0.437 0.561 0.015 −0.225 −0.131 0.366 −0.204 
3-day minimum 0.546 0.578 0.025 −0.094 −0.119 0.096 0.121 
7-day minimum 0.563 0.679 0.016 −0.042 −0.087 0.055 0.104 
30-day minimum 0.584 0.548 0.021 −0.031 −0.075 −0.012 0.038 
90-day minimum 0.576 0.612 −0.016 −0.056 −0.015 −0.024 −0.009 
1-day maximum −0.375 0.394 −0.216 0.309 −0.198 0.066 −0.012 
3-day maximum −0.396 0.345 −0.167 0.362 −0.164 0.005 0.043 
7-day maximum −0.497 0.467 −0.129 0.485 −0.158 0.022 0.009 
30-day maximum −0.372 0.502 −0.174 0.345 −0.078 −0.042 0.008 
90-day maximum −0.402 0.498 −0.046 0.401 −0.033 0.0.31 0.069 
Baseflow index 0.489 −0.152 0.016 −0.091 −0.162 0.226 0.026 
Date of minimum 0.279 0.218 0.108 0.211 −0.163 0.226 0.397 
Date of maximum 0.304 −0.256 0.315 0.223 −0.052 − 0.543 0.352 
Low pulse count 0.207 −0.352 −0.148 0.205 −0.088 0.271 0.105 
Low pulse duration −0.294 0.231 0.271 −0.382 0.142 −0.159 0.032 
High pulse count 0.335 −0.282 −0.018 0.297 0.471 −0.093 −0.026 
High pulse duration −0.291 0.289 0.317 −0.232 −0.041 0.344 0.211 
Rise rate 0.365 0.451 0.568 −0.121 0.278 −0.205 0.048 
Fall rate −0.428 −0.294 0.166 −0.297 −0.172 −0.131 0.101 
Number of reversals 0.895 0.349 −0.216 0.332 −0.117 0.175 −0.027 

Note: Selected principal components are highlighted in bold.

Figure 8

Eigenvalues of PCA and their cumulative contributions.

Figure 8

Eigenvalues of PCA and their cumulative contributions.

Close modal
The rationality of the selection of ERHIs was further analyzed, and the heat map of the correlation between ERHIs is presented in Figure 9. As can be seen from the figure, the correlation between the seven selected ERHIs is significantly reduced, with the strongest correlations being Max-7 and HPC. However, their correlation coefficients are only 0.42 (absolute value). The correlation coefficient (absolute value) between the vast majority of ERHIs does not exceed 0.3, proving the rationality of selecting ERHI parameters.
Figure 9

Correlation between the selected ERHIs.

Figure 9

Correlation between the selected ERHIs.

Close modal
Figure 10 illustrates the temporal variation of ERHI parameters. It can be observed from the figure that the RR started to show a decreasing trend after 1993, but has increased in the last 5 years, reaching a maximum of 230 m3 s−1 day−1, which may directly affect fish reproduction. There are signs of a delay in the emergence of the DMAX, but the changes are gradually stabilizing. Looking at the mean values, HPC values averaged 8 before damming and increased to 10 after damming. This trend may have a series of effects on sediment transport within the riverbed and on the structure of the riverbed, which in turn may have an impact on fish community composition and density. The average flow in May showed a decreasing trend due to the dam construction. However, May is the peak spawning season for the four major Chinese carp. Porcher & Travade (2002) point out that when river flows exceed natural conditions, there is a consequent reduction in the distance and speed at which fish can migrate, which can lead to impeded migration. It is clear that reduced flows in May will adversely affect fish migration and reproduction in the Minnesota River. During the later stages of damming, Max-7 values declined rapidly and remained in a relatively low range, while Min-7 values increased sharply and remained at a high level. This variation in annual flow extremes may affect the demand for in-stream vegetation growth sites and the distribution of plant communities. At the same time, NRV values increased significantly during the later stages of damming, and changes in NRV will affect the environment for aquatic organisms. This change in environment may be more favorable for the survival of exotic species, leading to a decrease in the number of native species.
Figure 10

Characteristics of ERHIs over time. The red curve indicates the loess function fitting curve.

Figure 10

Characteristics of ERHIs over time. The red curve indicates the loess function fitting curve.

Close modal

Comparison of ecological flow indicators with IHA indicators

In order to further understand the relationship between ecological flow indicators (ES and ED) and IHA indicators, we analyzed the correlation between them and IHA indicators (Figure 11). From the figure, we can observe that the vast majority of IHA indicators have strong positive or negative correlations with ecological flow indicators (ES and ED). Specifically, there are strong positive or negative correlations between the monthly means and their corresponding seasonal ecological flow indicators, e.g., there is a strong positive correlation between the March–May monthly mean flow (spring) and the spring ES deficit and a strong positive correlation between the December–February monthly mean flow and the winter ES deficit, as well as the other months. Minimum 1-, 3-, 7-, 30-, and 90-day flows had strong positive correlations with almost every ecological indicator (excluding summer ES deficit and autumn ES and ED). Unlike the minimum flow, the maximum 1-, 3-, 7-, 30-, and 90-day flows had weak correlations with all the ecological flow indicators except the summer ES, which had a strong positive correlation with the summer ES. In summary, the ecological flow indicators can better reflect the information of the IHA32 indicators; for example, the increase of eco-surplus and decrease of eco-deficit in summer can fully reflect the increase of monthly runoff in summer, etc.
Figure 11

Heatmap of correlations between the eco-flow indicators and IHA parameters.

Figure 11

Heatmap of correlations between the eco-flow indicators and IHA parameters.

Close modal

Through the above analyses, the ecological runoff indicators based on DH calculation can well reflect the information of IHA32 indicators, which is a good evaluation criterion to assess the hydrological changes. At the same time, the combination of ecological flow indicators based on DH calculation and ERHIs indicators to assess the degree of ecohydrological situation change will not only reduce the redundancy between the parameters but also reflect the essential hydrological information and related ecological connotations, which is an effective method for evaluating the ecohydrological mechanism.

Evolution of the hydrological situation and its causes

Hydrological change in the natural state is necessary to maintain the ecological diversity and ecosystem health of the river. Analyses and research on the hydrological situation of the Min River have indicated that 1993 was a year of abrupt change in the basin. The hydrological status of the Min River Basin has been evaluated, and the results show that, in general, the hydrological changes in the area are of medium variability, and the potential ecological hazards to the area are of medium level. According to the Budyko coupled hydrothermal equilibrium theory, climate change (precipitation and evapotranspiration) in the Min River Basin contributes 45.8% to runoff, and human activities contribute 56.8% to runoff changes. Zhai et al. (2022) also pointed out in their study that the contribution of human activities, vegetation, and climate change to runoff changes in the Min River Basin was 76.24, 13.62, and 10.14%, respectively. This is also consistent with the results of this paper. Zhao et al. (2023) conducted an attribution analysis of runoff changes in the Danjiang River Basin based on the Budyko theory, and their results showed that human activities, especially land use changes and reservoir construction, were the main causes of runoff reduction in the Danjiang River Basin. The integrated use of multiple hydrological indicators to assess variations in the hydrological situation of a river basin can help to better adapt to differences in the hydrological situation of the basin and better maintain the normal ecological function and ecological base flow of the river (Zhang et al. 2018; Li et al. 2020).

The construction of water conservancy projects in the Min River Basin has changed the downstream hydrological situation. To mitigate the adverse impacts of dam construction and other human activities on the river ecosystem, corresponding measures should be taken to strengthen the management of water resources downstream of the Min River Basin, such as improving the monitoring system for meteorological droughts and droughts in the water level, setting up ecohydrological variability zones, and rationalizing eco-water resource scheduling, etc., to find an entry point to the practice of water resource management and thus ensure the healthy development of the ecosystem (Abbas et al. 2021, 2022a).

Effects of hydrological situations on ecological systems

According to the SI index, the biodiversity index of the Min River is found to be changing smoothly in its natural flow state. However, after a mutation, human activity has caused a notable decline in the biodiversity index; the more severely human activity is disrupted, the faster this decline is occurring. The outcomes of several academic researchers on fish and species richness in the Min River Basin support the findings of this investigation. For example, Lv et al. (2018) surveyed fish resources in the lower reaches of the Min River mainstem in 2014, and a total of 71 fish species were investigated in the study area. Bao et al. (2019) conducted a field survey of the Dadu River, a tributary of the Min River, in 2019, and the number of fish populations declined to 40, while hundreds of small, medium, and large reservoirs had already been constructed in the basin at that time. Jiang et al. (2014) used fish integrity biotic indexes (F-IBI) to analyze the integrity of fish in the Min River, and their findings also showed that the construction of water conservancy projects would lead to the reduction and degradation of fish resources in the Min River Basin. Other studies have shown that changes in flow regimes have resulted in a sustained reduction in the viability of Cyprinus carpio and Carassius auratus and that for migratory spawning fish, which use changes in flow as a survival strategy, reductions in flood flows can weaken or even eliminate this signal, thus affecting the reproduction and fecundity of migratory fish (Yuxian et al. 2022).

The operation of reservoirs destroys the natural state of rivers, which to a certain extent affects the degree of hydrological variability of waterways, which in turn interferes with the ecohydrological rhythms of rivers, affects the spawning and reproduction of fish within downstream rivers, threatens the habitat of living organisms, and leads to a decrease in the stability of ecosystems, a reduction in biodiversity and biomass, and a trend toward degradation of ecosystems. Therefore, in future studies, more emphasis should be placed on long-term monitoring of fish communities before and after dam construction to understand ecosystem responses to hydrological changes so that effective resource management measures can be implemented to manage regulated rivers.

In this research, the quantitative attribution study of runoff evolution in the Min River Basin was carried out based on the daily runoff data from 1961 to 2019, combined with meteorological data. Second, the ecological flow indicators were analyzed based on DH calculations. On this basis, the hydrological processes in the Min River Basin were comprehensively estimated by using several hydrological indicators from IHA-RVA, D0, and DHRAM, and the results of the research showed that:

The elasticity coefficients of runoff with respect to the several contributing elements were computed using the Budyko coupled hydrothermal equilibrium theory. It was discovered that human activity contributed 56.80% of the total effect, which was the main influencing factor, followed by climatic factors (41.56% for precipitation and 1.64% for evapotranspiration). Human activities significantly influence the hydrological situation of the Min River Basin. At the same time, changes in the subsurface n further indicate that the influence of climate change on runoff in the Min River Basin maintains an increasing trend.

Each seasonal ES maximum occurs in the post-impact period. In contrast, for seasonal ED, the absolute maximum ED occurs in the pre-impact period in all three seasons except for winter, when the absolute maximum ED occurs in the post-impact period, a temporal feature that suggests that the construction of the dam has generally led to an increase in the seasonal ES and a decrease in the seasonal ED.

A comprehensive analysis using multiple hydrological indicators from IHA-RVA, D0, and DHRAM shows that the overall degree of hydrological change in the Min River Basin is 44% (moderate alteration); the DHRAM score is 3, and the alteration level is 2 (low alteration). The comprehensive determination of the degree of hydrological alteration in the Min River Basin is moderate alteration. In addition, according to the changes in SI values, it can be concluded that the diversity of riverine organisms in the basin during the post-impact period significantly declined, and the construction of reservoirs harmed the basin's biodiversity.

The ecological flow indicators (ES and ED) based on DH calculation have a sound correlation with the 32 IHA indicators, which can reflect the information of the IHA32 indicators well. On this basis, the ecological flow indicators based on DH and ERHI indicators were constructed to achieve a quantitative assessment of changes in the ecohydrological pattern of the watershed, which can not only reduce the redundancy among the parameters but also embody the critical hydrological information in the watershed, reveal the mechanism of the ecohydrological-cum-hydrological coupling action in the watershed, and provide a scientific basis for the watershed management decision-making.

We would like to express our sincere gratitude to the editor and anonymous referees for their insightful and constructive comments.

The scope of the work does not require any approval of the ethics committee. The ethical approval is not applicable for the present work.

W.G.: Funding acquisition; Project administration; Resources; Investigation; Supervision. L.Y.: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Resources; Software; Validation; Visualization; Writing – original draft, Writing – review and editing. L.H. and B.W.: Investigation; Formal analysis; Methodology; Validation; Visualization. H.W.: Funding acquisition; Project administration.

This study was supported by the Basic Research Project of Key Scientific Research Projects of Colleges and Universities of Henan Province (23ZX012); the National Natural Science Foundation of China (Grant No. 51779094); and the North China University of Water Resources and Electric Power (NCWUYC-202315017).

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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