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Article

Determining Critical Thresholds of Environmental Flow Restoration Based on Planktonic Index of Biotic Integrity (P−IBI): A Case Study in the Typical Tributaries of Poyang Lake

by
Zhuowei Wang
1,2,
Wei Huang
1,2,*,
Dayu Zhu
1,2,
Qi Huang
3,4,
Leixiang Wu
1,2 and
Xingchen Liu
1,2
1
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
2
Department of Water Ecology and Environment, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
3
Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Nanchang 330022, China
4
School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(1), 169; https://doi.org/10.3390/ijerph20010169
Submission received: 18 November 2022 / Revised: 16 December 2022 / Accepted: 18 December 2022 / Published: 22 December 2022
(This article belongs to the Section Environmental Ecology)
Browse Figures
Review Reports Versions Notes

Abstract

:
Hydropower construction and climate change have aggravated river hydrological changes, which have reduced the water flow regime in the Ruhe River Basin. The reduced flow of the river seriously affected the water supply of nearby residents and the operation of the river ecosystem. Therefore, in order to alleviate the contradiction between water use for hydropower facilities and environmental water use, the urgent need is to explore the ecological flow-threshold of rivers. This study took the Fuhe River Basin as the research object, and summarized the monitoring data of eight hydrological stations from recent decades. Based on this, we explored the response law of P−IBI and flow, a tool to quickly measure the health of the ecosystem. Through the response relationship between alterations in environmental factors of the river and phytoplankton index of biotic integrity (P−IBI), it was determined that environmental flow was the dominant influencing factor of P−IBI. According to P−IBI, the threshold of environmental discharge in the Fuhe River was limited to 273~826.8 m3/s. This study established a regulatory framework for the river flow of large rivers by constructing P−IBI and determining the critical thresholds of environmental flow by constraining the constitution. These results provide a theoretical basis for better planning and improvement of river ecosystem restoration and river utilization.

1. Introduction

The Fuhe River, one of the main rivers in the Poyang Lake water system, plays an important role in sustaining the public water supply and aquatic ecosystem [1]. Unfortunately, various human activities and global climate change have reduced seriously the flow regime in the Fuhe River. As a consequence, there have even been several persistent large-scale dry-water events. Specifically, the sharp reduction of environmental flow has attributed to the construction of hydropower in the Fuhe River Basin. Therefore, the environmental flow is plagued by flow reduction in the Fuhe River [2,3], which has further exacerbated the severity of the water-level drop in Poyang Lake. Furthermore, from July to October, when rainfall decreases and evaporation increases, the surrounding farmland needs so much water irrigation that it squeezes the ecological water of the river. This happens to be the time when the Fuhe River Basin has the least water flow. This leads to the contradiction between ecological water use and public water supply. Inadequate management of water projects may affect the water use of residents and the normal operation of ecosystems, such as the destruction of animal habitats, water-quality degradation, and soil erosion [4,5,6]. Therefore, determining the threshold value for the requirement of the environmental flow is an urgent problem to be solved at present.
Environmental flow refers to the flow magnitude, frequency, timing, duration, and velocity of alteration that are retained in rivers to maintain the operation of river ecosystems. After realizing the existence and impact of these threats, humanity has conducted extensive research to restore rivers [7,8]. The research results show that the community diversity of river microorganisms, plants, and animals can be effectively enhanced by regulating the environmental flow of the river [9,10,11,12]. Maintaining the environmental flow of the river will help to continuously improve the water-environment quality of the river and help to restore the health of the river and the surrounding ecosystems [13,14]. However, constructing the threshold of environmental flow has always been a difficult problem, because of the variable requirements of the components of the ecosystem. Therefore, constructing an evaluation system is a vital process which can comprehensively evaluate the operation and health of a large river basin.
The index of biotic integrity (IBI) is widely regarded as a tool to evaluate the health of river ecosystems. Karr [15] first devised the IBI to measure biological integrity in streams, mainly using fish as an indicator species. Moreover, the Karr index has been adopted by many management agencies and is even used to assess the integrity of estuarine ecosystems [16,17,18]. However, it is difficult to use fish as indicator species when the flow of a river is reduced or even stopped. All components of the water ecosystem function are primarily affected by phytoplankton and zooplankton [19,20,21]. Phytoplankton are the main source of energy driving large aquatic ecosystems such as mussels and clams [22,23], and they also respond quickly to environmental change. The downstream decline in phytoplankton is attributed to low water flow, which also leads to impaired water quality [24]. Kim et al. [25] found that there was a certain dynamic mechanism of phytoplankton changes with the water flow. Therefore, this study developed the plankton integrity index (P−IBI) to indicate the health of the river ecosystem. Therefore, this paper developed the relationship between the P−IBI and the flow to obtain the flow threshold that can maintain river health. The study aims are (1) to establish a quantitative relationship between the P−IBI and the flow regime of Fuhe River, (2) to explore the range of environmental flow thresholds, and (3) to meet the environmental-flow-guarantee requirements of the Fuhe River.

2. Materials and Methods

2.1. Study Region and Sampling Sites

The Fuhe River Basin (115°35′~117°09′ E, 26°30′~28°50′ N) is located in the southeast of Jiangxi Province and is one of the five major tributaries of the Poyang Lake, the largest freshwater lake. It originates from Guangchang County, Fuzhou City, Jiangxi Province, at the western foot of the Wuyi Mountains. It is the second largest river in Jiangxi Province, with a total length of 349 km and a drainage area of 171.86 million square kilometers. Parameters such as phytoplankton biomass, species number, the class of phytoplankton, flow regime, etc. were used to develop the quantitative relationship between the P−IBI and flow regime. The locations of sampling points and hydrological stations are shown in Figure 1.

2.2. Hydrological Alteration in Fuhe River Basin

To identify the year when the hydrological mutation abruptly occurred, and to analyze the hydrological alteration degree before and after the mutation, the MK test [26,27,28] and the sliding t-test were used in this study to analyze the Shaziling (SZL), Liaojiawan (LIA), and Lijiadu (LID) parts of the mainstem of the Fuhe River. The hydrological mutation of the flow at the hydrological stations of Shuangtian (ST), Xinxie (XX), and Gongxi (GX) the main part of the hydrological station and tributaries, as well as the Taopi (TP) and Loujiacun (LOU) hydrological stations in the downstream, were identified over the year.
To evaluate the hydrological alteration, this study is based on the RVA method proposed by Richard et al. [26,29,30]. The RVA method divides the discharge time-series into two parts, before and after hydrological mutation. According to the 33 IHA indicators calculated from the flow time-series before the hydrological variation, the 33 indicators are divided into three intervals, in accordance with the ranking percentage of each indicator: L1 = [0, 25%], L2 = [25%, 75%], L3 = [75%, 100%], where the target interval is L2, and the alteration IAi of each IHA index after mutation is calculated. The calculation formula of IAi is:
IAi = |Ni − Nε|/Nε × 100%
IA = 1 / 33 ∑ i = 0 33 IA i
Among these, i is the serial number of the index, Nε the number of years falling into the target range before the impact, Ni is the number of years falling into the target range after the impact, which is 50%, and IA is the overall degree of hydrological alteration. When IA is in the range of [0, 0.33], it is a low disturbance state, [0.33, 0.67] is a medium disturbance state, and [0.67, 1] is a high disturbance state [27,28,31].
When the calculated and analyzed hydrological-data-event sequence is more than 20 years, climate change can reduce or eliminate the impact on the results of hydrological indicator calculations [32,33,34]. Therefore, the time series of the hydrological data calculated and analyzed using the IHA method should generally not be less than 20 years (Table S1).
The geographic location of the hydrological station is shown in Figure 1. The IHA and RVA methods were used to calculate the hydrological mutation of the hydrological stations in the Fuhe River Basin before and after the hydrological mutation.

2.3. Metric Selection

For constructing the P−IBI evaluation system, twenty-six phytoplankton indicators of the types of population, nutrient-structure, algae bloom characteristics, diversity, and evenness were used as candidate indicators (Table 1). All these data were obtained from the field survey and used in laboratory quantitative analysis. After the candidate indicators were counted, using distribution range analysis, discrimination ability analysis, normal distribution test, correlation analysis, etc., the indicators with too small or too large a distribution range, insignificant significance, poor discriminative ability, and highly overlapping information, were eliminated. Finally, the indicators that passed the screening were used as the core indicators.
The distribution-range screening was performed by eliminating the parameter in which the value of 95% of the sample points was 0. The discrimination ability analysis was determined by the discrepancy between reference points and damaged points, referring to the method of Barbour et al. [35] The Pearson correlation analysis was performed on the selected indicators. For indicators with significant correlation, one indicator in a group was selected and left. Finally, 14 indicators, including M1, M2, M9, M10, M11, M12, M19, M20, M21, M22, M23, M24, and M26 were left to construct the P−IBI evaluation-index system.
The P−IBI values were calculated using the ratio method: first, calculate the value of the 95% or 5% quantile of each core index for all sample points. For the index whose value decreases with the increase of interference, the index value of the 95% quantile is the best-expected value. Indicator score = measured value/best expected value; for indicators whose value increases with the increase of interference, the indicator value of the 5% quantile is the best expected value, and indicator score = (maximum value − measured value)/(maximum value − best expected value). Finally, the core index-scores are added to obtain the total P−IBI score for each sample point.

2.4. Phytoplankton Collection and Analysis

The phytoplankton was collected with a no. 25 plankton collection net. Before sampling, the piston was opened to clean the plankton net, and 5 L water samples were collected with a quantitative water collector and poured into the plankton net for filtration and concentration. Two drops of Lugol’s solution were added to the collected samples, and thoroughly mixed. The water needed to be added into the samples up to 50 mL before the laboratory analyses, and 0.1 mL of this solution was put into a 0.1 mL counter chamber. The whole chamber was counted under a 10× 40× microscope, and each sample was counted 2–3 times. Assuming a specific gravity of 1.0, the phytoplankton abundance was converted to biomass. The biomass was calculated by multiplying the phytoplankton abundance by the average wet weight of the corresponding volume.

2.5. Statistical Analysis

The IHA of each hydrological station in the Fuhe River Basin was calculated using IHA software (The nature conservancy, London, UK). The IHA information and the alteration components in the hydrological years of 8 hydrological stations were summarized. Based on the measured hydrological data of the watershed and the physical and chemical properties of the river, the Spearman correlation coefficients between the phytoplankton biomass and flow, temperature, nutrients, electrical conductivity, pH, etc. were analyzed, mainly based on SPSS 19.0 (SPSS, Chicago, IL, USA). Origin 2021 was used to fit and evaluate the fitting of P−IBI and environmental flow. Origin 2021 was used for regression analysis, and the confidence interval of 95% confidence was calculated.

3. Results and Discussion

3.1. Hydrological Alteration Identification in Fuhe River Basin

According to the mutation test analysis, due to the completion of the Qingtong Reservoir and the Chemoling Reservoir, the mutation points of the SZL and ST located in the upper reaches of Fuhe River appeared in 1987 and 1979, respectively. Due to the alteration of water consumption in TP irrigation area, TP and XX in the middle reaches of Fuhe River both mutated in 1991. The hydrological mutation of GX was in 1979, which was mainly related to the construction and operation of the power stations in the middle reaches of the Fuhe River. LOU, LID, and LIA are located near the entrance of Poyang Lake in the lower reaches of the Fuhe River. The mutation time may be significantly affected by the construction of the hydropower project in the lower reaches of the Fuhe River in the 1970s and 1980s. The construction of dams has resulted in reduced runoff and reduced water flow into rivers, causing severe damage to aquatic ecosystems and the surrounding soil ecosystems [36,37,38]. In addition, the Fuhe River Basin often has a great demand for irrigation with massive water withdrawal, which has various impacts on the surrounding environment, such as habitat destruction, water-quality degradation, soil erosion, etc. [39,40,41].

3.2. Hydrological Alteration Assessment in Fuhe River Basin

Based on the divided hydrological-sequence-change nodes, the IHA software was used to calculate the IHA degree of each hydrological station in the Fuhe River Basin (Table 2 and Table 3). An analysis of the change characteristics of 33 indicators in 5 groups shows that the alterations of SZL, ST, and downstream LOU, LIA, and LID located in the upstream source section are mainly low-level disturbances, with an alteration degree of less than 33%. There are 22 indicators with low alterations in SZL, 22 in Loujia Village, 18 in LIA, and 20 in Lijiadu. This presents the characteristics of low-level disturbance, which is mainly the result of the combined effect of the presence of tributaries in the upstream and downstream reaches and the coordinated regulation of gates and dams. [42,43,44]. ST in the upper reaches and XX, GX, and TP in the middle reaches of the river are dominated by medium-to-high disturbances. There are 18 indicators with moderate alterations in ST, 20 in XX, 16 in GX, and 15 in TP. In comparison, the hydrological variability of the tributaries is higher than that of the mainstem. The hydrological variation of tributaries is mainly reflected in the monthly average flow and extreme flow from March to June, which shows a significant increase in variation characteristics. The mainstem in the upper and middle reaches of SZL and LIA does not show high variability indicators, while the LID has a significant decrease in the indicators of minimum flow at different time scales, which may affect the overwintering conditions of species in the marsh or reduce the ability of organisms to cope with extreme weather [45,46,47]. In addition, the reduced-flow river can easily cause algal blooms [48,49], which exacerbates the risk of algal blooms in the Fu River Basin. In addition, LID is the last hydrological station that flows into Poyang Lake. Its hydrological indicators and mutations are related to the stable operation of the Poyang Lake ecosystem. Therefore, LID was chosen as the threshold evaluation of the P−IBI environmental flow.
The monthly average flow is shown in Table S2, and the difference in Figure 2. In the monthly average of the SZL, LOU, LID, and LIA on the mainstem of the Fuhe River from June to September and December, the flow showed a decrease; the decrease in June was the most obvious, and the decrease in September was the weakest. In the 12 months, the SZL and the LIA all experienced moderate-to-low alterations, among which the monthly average-flow alterations of the SZL in May and September were a minimum of 4%. The monthly average-flow alteration of LIA in February was at least 2%. The hydrological alteration of the monthly average flow at LOU in April showed a high alteration of 73%. The hydrological alteration of the monthly average flow of LID in September showed a high alteration of 88%. The rest of the months showed low to moderate alterations. LOU, LID and LIA are located at the exit of the lower reaches of the Fuhe River. There are many hydropower projects which impact the hydrological alteration of the monthly average flow. The monthly average flow of ST, XX, and TP on the tributaries of the Fuhe River decreased from June to December, and the monthly average flow of GX from June to September, November, and December showed a decrease. In particular, the decrease in June was the most obvious, and the decrease in November was the weakest. In the 12 months, ST, XX, GX, and TP accounted for half of the low-to-medium alteration and high alteration; the high alteration of ST in April and June was 83%, and the minimum alteration in January was 8%. The hydrological alteration of XX in March, May, and June was 72% in height, and the hydrological alteration in September and December was at least 3%. The high alteration of TP in March and May was 128% and 68%, respectively. The monthly average-flow alteration in January and October was the lowest, at 8%. The monthly average flow of GX in April and October showed a high degree of alteration. The largest alteration was the monthly average flow in October, which reached 92%. The monthly average-flow alteration in February was at least 4%. In short, since the SZL is located at the confluence point of the tributaries in the upper reaches of the watershed and downstream of the LIA, the monthly average-flow alterations of the two were mainly low to moderate. However, the height variation of other hydrological stations is attributed to the regulation and operation of hydropower projects and dam building in the Fuhe River Basin [50].
After the disturbance, the hydrological alterations of the annual extreme flow at the SZL, LOU, and LIA on the mainstem were all low-to-moderate alterations. The hydrological alterations of the 1-day minimum flow and the 3-day maximum flow were at least 4%. The hydrological alteration of the 90-day maximum flow was the highest, reaching 46%. The hydrological alteration of the 1-day minimum flow at LOU was the lowest, at 2%, and the hydrological alteration of the 30-day minimum flow was the largest, reaching 61%. LIA had the lowest alteration in 30-day maximum flow, which was also 2%. The hydrological alteration of the 7-day and 30-day minimum flow was the largest, which was the same as the hydrological alteration of the 30-day minimum flow at LOU, which was 61%. This shows that the construction of hydropower projects has the same impact on the extreme value of the daily flow at LOU and LIA. The hydrological alterations of the extreme flow at the ST on the tributaries are all low-to-moderate, and the maximum alteration of the daily maximum flow is the largest, reaching 58%. The hydrological alterations of the 7-day minimum flow, 7-day maximum flow, and 90-day maximum flow were the lowest, and were both 8%. Among the hydrological alterations of the extreme flow at XX, GX, and TP, the mid-altitude alteration accounted for half of the hydrological alterations, and the 90-day maximum flow at XX and TP both showed high alterations. The hydrological variation of the TP daily minimum discharge was a maximum of 100% hydrological variation of the extreme discharge in the tributaries.
After perturbation, the upstream SZL extreme-flow date has a tendency to delay in the upstream SZL, contrary to ST. The date of the extreme flow of each hydrological station in the midstream shows a tendency to be delayed. The dates of extreme flow at the downstream LID and LIA tend to be delayed, but the extreme flow period of LOU tends to be advanced.
From the pulse times of high and low flow and the hydrological alteration over time, it can be seen that the hydrological alterations of extreme flow at SZL, ST, LOU, and LIA are all low-to-moderate alterations after disturbance. The number and duration of high- and low-flow pulses at LID, XX, GX, and TP have altered in height, but only once or twice, such as twice in TP. The occurrence time of the maximum traffic of TP and GX varies greatly, especially where TP reaches 104%. The hydrological alterations of the average flow rise/fall rate, and the reversal times of the hydrological stations on the mainstem after disturbance were mainly low to moderate. The average flow fall rate of the SZL had a high alteration degree of 68%, and the number of reversals also increased slightly. The hydrological alteration degree of the reversal times of the ST, XX, and TP on the tributaries all showed a high degree of alteration, reaching more than 70%.
We summarized the IHA information and the altered components of the hydrological indicators per year for eight stations, and the results of the overall hydrological alterations of each section are shown in Figure 3. XX, TP, GX, LOU, ST, LID, and LIA are all in a moderate disturbance state. From upstream to midstream, and then from midstream to downstream, the hydrological disturbance is weak, from strong. The fluctuation range from upstream to midstream is 0.31~0.55, and the fluctuation range from midstream to downstream is 0.36~0.55. The TP and XX of the tributaries have the strongest disturbance, and the SZL upstream of the mainstem has the weakest disturbance. The mutation rate and degree of the mainstem hydrological regime in the Fuhe River Basin were relatively low. However, the increased demand for domestic water from hydropower projects and surrounding residents has resulted in a decrease in tributary flows [51,52]. River populations (such as phytoplankton and zooplankton) respond to alterations in flow [53,54]. In recent years, the construction of dams in the Fuhe River Basin and extreme drought conditions have occurred from time to time. Inappropriate flow regulation in the lower reaches of the river basin will cause the ecosystem to operate in a disorderly way. Therefore, it is a requisite to maintain the flow alteration in an appropriate range, called the environmental flow, to preserve the health of the ecosystem of the river. This study develops an approach to evaluate the environmental-flow-threshold range based on the response of the P−IBI to the flow of the Fuhe River.

3.3. Spatial and Temporal Distribution of Phytoplankton

A total of 128 species of phytoplankton were detected in Fuhe River Basin, including 51 phyla of Bacillariophyta, accounting for 39.8% of the total phyla, 51 species of Chlorophyta, accounting for 39.8% of the total phyla, and 15 species of Cyanobacteria, accounting for 11.7% of the total phyla. Among them, Anabaena pseudobaena and Anabaena granulosa are the dominant phyla in the Fuhe River Basin.
The phyla and quantity of phytoplankton in Fuhe River show significant differences in different-flow river reaches from upstream to downstream; the number of phytoplankton species is upward, the number of Chlorophyta is gradually increasing, and the number of Bacillariophyta is gradually decreasing (Figure 4). In summer (July), with the appropriate water temperature and sufficient light, the phytoplankton species in the Fuhe River Basin is at a peak. From upstream to downstream, the dominant species is not significantly different in the wet season, while in the dry season and in the normal flow season, the phytoplankton species decreased significantly at each sampling point. The biomass of phytoplankton in the wet season is much higher than in the other two periods. The proportion of biomass of the dominant phytoplankton species (cyanobacteria and chlorophyta) is also much higher than that in normal and reduced-flow river periods. The sensitivity of phytoplankton to river flow and the feasibility of constructing P−IBI were verified.

3.4. Response of Phytoplankton to Environmental Indicators

Phytoplankton are affected by multiple factors, such as temperature, light, nutrients, flow rate, etc. To evaluate the impact of various major environmental factors on phytoplankton, based on the measured hydrological data and the basic indicators of the river, the Spearman correlation coefficients between phytoplankton biomass and flow, temperature, nutrients, electrical conductivity, pH, etc. were analyzed. Since biological growth has a cycle, there may be a certain hysteresis in response to environmental alterations, in this study; in addition to paying attention to the current flow during the ecological survey, the average flow of the previous month and the previous two months was also considered. There was a significant linear correlation between phytoplankton and the 1-month, 2-month, and 3-month average flow (p < 0.05), and the correlation with the 2-month average flow was the most significant (p < 0.01); however, there was no significant correlation between other environmental factors and phytoplankton biomass (Figure 5). The results showed that flow was the main factor affecting phytoplankton biomass. With the increase in flow, phytoplankton biomass showed a positive ecological response, showing an overall upward trend [55,56,57,58]. In the early stage of river restoration, flow restoration will promote the growth and metabolism of primary producers, thereby promoting the ecological restoration of the entire ecosystem [59,60].

3.5. Quantitative Response Relationship between P−IBI Index and Flow

In accordance with to correlation analysis in 3.2, the sites with hydrological monitoring data points were selected, and the response relationship between the P−IBI score and the 2-month average flow was constructed. Since the catchment area of each hydrological station in the basin was different, the flow itself had a certain difference. To eliminate the flow difference caused by the catchment area of the river basin, the method of flow/catchment area was used in this study to construct the P−IBI index and the unit set. The quantitative response relationship between the water area and the river flow regime is shown in Figure 6a. The response relationship between the P−IBI score and flow rate is y = (5.94 ± 0.33) × x + (−3743.35 ± 335.40) × x2 (R2 = 0.84).
This response relationship shows that the health of the watershed ecosystem increases with the increase of flow, and when it reaches a certain peak it decreases with the increase of flow. This is consistent with our prediction. When the flow rate is too low, the hydrodynamic conditions are insufficient, and the minerals and nutrients in the water body accumulate easily, which easily causes algae outbreaks, which in turn affects the health of the river ecosystem; when the flow rate is too high, minerals and nutrients in the water body are easily washed away, phytoplankton lacks the substances needed for growth, and the flow rate is too fast; algae are also easily washed away with the water flow, so it is not conducive to the growth of algae, causing the food of benthic animals, zooplankton and fish sources to be reduced, thereby affecting the health of river ecosystems.
The 95th percentile of all P−IBI scores in the watershed was chosen as the criterion for watershed health to assess the range of flow thresholds when the P−IBI score was at a healthy level (Figure 6b). The value of the 95% quantile of the watershed is 9.896. Solving the one-dimensional quadratic equation of the correlation, the flow range per unit catchment area is 0.0175–0.0530 m3/s. For the catchment area of LID, the flow-threshold range is 273–827 m3/s. Many studies have shown that damming and hydropower projects in the upper reaches of the lake will lead to a substantial reduction (more than 80%) or even drying up in some river sections, and flow-reduced river reaches downstream will be formed [61,62]. As a result, the downstream hydrological conditions have undergone significant alterations, resulting in serious degradation of the structure and function of the river ecosystem [63,64]. The reduction of flow has a significant negative impact on the aquatic ecosystem and the microbial, plant, and animal-community structures in the flow-reduced reaches. Through the regulation and restoration of downstream flow, the structure and function of the biological community in the downstream ecosystem can be significantly restored [65]. Therefore, in this study, the environmental flow-threshold of the lower reaches of the Fuhe River Basin was set as 312–908 m3/s through the ecological response of the LID P−IBI index to the flow. When the dry and wet seasons in the basin alternate, dams and hydropower projects have a feasible flow-threshold range for downstream environmental-flow-regulation.

4. Conclusions

Based on the data from eight hydrological monitoring stations in the Fuhe River Basin, this paper summarizes the hydrological situation of the Fuhe River Basin under the influence of human and climatic conditions in recent decades. LID is a hydrological station entering Poyang Lake, and its environmental flow regulation is related to the health and operation of the Fuhe River Basin and Poyang Lake. Therefore, in our study, through constructing the relationship between the phytoplankton index of biotic integrity and the watershed environmental factors, we discovered a rapid tool for evaluating the biological water quality of the ecosystem. The results showed that river flow was the main influencing factor for phytoplankton. In the early stage of ecological restoration, P−IBI showed a positive ecological response with the restoration of flow, showing an overall upward trend. However, with the flow regime increasing, P−IBI showed a hump-shaped downward trend. Based on this, the ecological response relationship between P−IBI and river flow in a statistical sense was constructed: y = (5.94 ± 0.33) × x + (−3743.35 ± 335.40) × x2 (R2 = 0.84). The 95% quantile of all P−IBI scores in the watershed was selected as the standard of watershed health. The environmental flow-threshold range of LID was 273–827 m3/s. This has an important reference significance for regulating environmental flow and improving the health and operation of the river ecosystem.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph20010169/s1, Table S1: Hydrological station data duration. Table S2: Monthly average flow difference.

Author Contributions

Z.W.: Writing—original draft, visualization; W.H.: conceptualization; D.Z.: writing—review and editing; Q.H.: idea suggestion; L.W.: project administration, funding acquisition; X.L.: formal analysis. All authors have read and agreed to the published version of the manuscript.

Funding

This work was jointly supported by the National Key Project R & D of China [grant number 2018YFE0206400], the Natural Science Foundation of China [grant number 51879278 and 51879279], and the IWHR Research & Development Support Program [grant number WE0145B052021].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all participants involved in the study.

Data Availability Statement

The dataset analyzed during this study is available from the corresponding author upon reasonable request.

Acknowledgments

The authors would like to thank all monitoring stations in the Fuhe River Basin for their monitoring data and all participants for their continued support and commitment to this project.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, J.; Sheng, Y.; Gleason, C.J.; Wada, Y. Downstream Yangtze River levels impacted by Three Gorges Dam. Environ. Res. Lett. 2013, 8, 44012. [Google Scholar] [CrossRef]
  2. Watchorn, K.E.; Goldsborough, G.; Hudon, C.; Taranu, Z.E. Emergent vegetation in Netley-Libau Marsh: Temporal changes (1990–2013) in cover in relation to Lake Winnipeg level and Red River flow. J. Great Lakes Res. 2021, 47, 690–702. [Google Scholar] [CrossRef]
  3. Liu, C.; Hu, R.; Wang, Y.; Lin, H.; Zeng, H.; Wu, D.; Liu, Z.; Dai, Y.; Song, X.; Shao, C. Monitoring water level and volume changes of lakes and reservoirs in the Yellow River Basin using ICESat-2 laser altimetry and Google Earth Engine. J. Hydro-Environ. Res. 2022, 44, 53–64. [Google Scholar] [CrossRef]
  4. Deng, X.; Xu, H.; Ye, M.; Li, B.; Fu, J.; Yang, Z. Impact of long-term zero-flow and ecological water conveyance on the radial increment of Populus euphratica in the lower reaches of the Tarim River, Xinjiang, China. Reg. Environ. Chang. 2015, 15, 13–23. [Google Scholar] [CrossRef]
  5. Zhang, H.; Singh, V.P.; Zhang, Q.; Gu, L.; Sun, W. Variation in ecological flow regimes and their response to dams in the upper Yellow River basin. Environ. Earth Sci. 2016, 75, 938. [Google Scholar] [CrossRef]
  6. Cui, T.; Yang, T.; Xu, C.Y.; Shao, Q.; Wang, X.; Li, Z. Assessment of the impact of climate change on flow regime at multiple temporal scales and potential ecological implications in an alpine river. Stoch. Environ. Res. Risk Assess. 2018, 32, 1849–1866. [Google Scholar] [CrossRef]
  7. Grizzetti, B.; Liquete, C.; Pistocchi, A.; Vigiak, O.; Zulian, G.; Bouraoui, F.; De Roo, A.; Cardoso, A.C. Relationship between ecological condition and ecosystem services in European rivers, lakes and coastal waters. Sci. Total Environ. 2019, 671, 452–465. [Google Scholar] [CrossRef]
  8. Xu, Z.; Wei, H.; Fan, W.; Wang, X.; Zhang, P.; Ren, J.; Lu, N.; Gao, Z.; Dong, X.; Kong, W. Relationships between ecosystem services and human well-being changes based on carbon flow—A case study of the Manas River Basin, Xinjiang, China. Ecosyst. Serv. 2019, 37, 100934. [Google Scholar] [CrossRef]
  9. Fan, X.; Njeri, H.K.; Pu, Y.; La, Q.; Li, W.; Li, X.; Chen, Y. Contrasting relationships between genetic diversity and species diversity in conserved and disturbed submerged macrophyte communities of Honghu Lake, a typical freshwater lake of Yangtze River Basin. Glob. Ecol. Conserv. 2021, 31, e1873. [Google Scholar] [CrossRef]
  10. Fu, W.; Cao, Y.; Li, X.; Sun, J.; Liu, F.; Li, W. The responses of riparian plant communities to environmental and spatial factors in the upper Han River basin, China. Glob. Ecol. Conserv. 2022, 36, e2118. [Google Scholar] [CrossRef]
  11. Lin, C.; Cao, L.; Xu, Y.; Zhang, Z.; Yang, H.; Xiang, H.; Li, K.; Gao, Y.; Bernhardt, T.M. Spatio-temporal variability (up- and down-stream and three seasons) of response patterns of macroinvertebrates to commonly applied low-head concrete weirs under reclaimed water intervention in urban river restoration. J. Clean. Prod. 2022, 370, 133544. [Google Scholar] [CrossRef]
  12. Stoffers, T.; Buijse, A.D.; Geerling, G.W.; Jans, L.H.; Schoor, M.M.; Poos, J.J.; Verreth, J.A.J.; Nagelkerke, L.A.J. Freshwater fish biodiversity restoration in floodplain rivers requires connectivity and habitat heterogeneity at multiple spatial scales. Sci. Total Environ. 2022, 838, 156509. [Google Scholar] [CrossRef] [PubMed]
  13. Bark, R.H.; Robinson, C.J.; Flessa, K.W. Tracking cultural ecosystem services: Water chasing the Colorado River restoration pulse flow. Ecol. Econ. 2016, 127, 165–172. [Google Scholar] [CrossRef]
  14. Huang, W.; Liu, X.; Peng, W.; Wu, L.; Yano, S.; Zhang, J.; Zhao, F. Periphyton and ecosystem metabolism as indicators of river ecosystem response to environmental flow restoration in a flow-reduced river. Ecol. Indic. 2018, 92, 394–401. [Google Scholar] [CrossRef]
  15. Karr, J.R. Assessment of Biotic Integrity Using Fish Communities. Fisheries 1981, 6, 21–27. [Google Scholar] [CrossRef]
  16. Roth, N.; Southerland, M.; Chaillou, J.; Klauda, R.; Kazyak, P.; Stranko, S.; Weisberg, S.; Hall, L.; Morgan Ii, R. Maryland Biological Stream Survey: Development of a Fish Index of Biotic Integrity. Environ. Monit. Assess. 1998, 51, 89–106. [Google Scholar] [CrossRef]
  17. Llansó, R.J.; Scott, L.C.; Hyland, J.L.; Dauer, D.M.; Russell, D.E.; Kutz, F.W. An estuarine benthic index of biotic integrity for the Mid-Atlantic region of the United States. II. Index development. Estuaries 2002, 25, 1231–1242. [Google Scholar] [CrossRef]
  18. Llansó, R.J.; Scott, L.C.; Dauer, D.M.; Hyland, J.L.; Russell, D.E. An estuarine benthic index of biotic integrity for the Mid-Atlantic region of the United States. I. Classification of assemblages and habitat definition. Estuaries 2002, 25, 1219–1230. [Google Scholar] [CrossRef]
  19. Shchur, L.-A. Phytoplankton as an indicator of ecosystem state of water-cooling reservoir at Berezovskaya SDPP-1 (Krasnoyarsk Territory). Water Resour. 2009, 36, 568–576. [Google Scholar] [CrossRef]
  20. Nor, S.M.M.; Jaafar, M.; Jaafar, N.M.S.N.; Redzuan, N.S.; Omar, W.B.W.; Deraman, M.Y.; Azli, S.N.S.N.; Shehrom, N.A.; Mahyudin, A.; Bahari, N.A.; et al. Dataset of physico-chemical water parameters, phytoplankton, flora and fauna in mangrove ecosystem at Sungai Kertih, Terengganu, Malaysia. Data Brief 2022, 42, 108096. [Google Scholar] [CrossRef]
  21. Zhao, K.; Wang, L.; You, Q.; Zhang, J.; Pang, W.; Wang, Q. Impact of cyanobacterial bloom intensity on plankton ecosystem functioning measured by eukaryotic phytoplankton and zooplankton indicators. Ecol. Indic. 2022, 140, 109028. [Google Scholar] [CrossRef]
  22. Ding, Y.; Pan, B.; Zhao, X.; Zhao, G.; Han, X.; Li, M. Will a heavy sediment load affect responses of phytoplankton functional groups to aquatic environmental changes in different water body types? Sci. Total Environ. 2022, 837, 155863. [Google Scholar] [CrossRef] [PubMed]
  23. Hu, X.; Hu, M.; Zhu, Y.; Wang, G.; Xue, B.; Shrestha, S. Phytoplankton community variation and ecological health assessment for impounded lakes along the eastern route of China’s South-to-North Water Diversion Project. J. Environ. Manag. 2022, 318, 115561. [Google Scholar] [CrossRef] [PubMed]
  24. Wang, J.; Zhang, Z.; Johnson, B. Low flows and downstream decline in phytoplankton contribute to impaired water quality in the lower Minnesota River. Water Res. 2019, 161, 262–273. [Google Scholar] [CrossRef] [PubMed]
  25. Kim, H.G.; Recknagel, F.; Kim, H.W.; Joo, G.J. Implications of flow regulation for habitat conditions and phytoplankton populations of the Nakdong River, South Korea. Water Res. 2021, 207, 117807. [Google Scholar] [CrossRef] [PubMed]
  26. Güçlü, Y.S. Improved visualization for trend analysis by comparing with classical Mann-Kendall test and ITA. J. Hydrol. 2020, 584, 124674. [Google Scholar] [CrossRef]
  27. Yu, C.; Yang, Z. A revised range of variability approach for the comprehensive assessment of the alteration of flow regime. Ecol. Eng. 2016, 96, 200–207. [Google Scholar] [CrossRef] [Green Version]
  28. Oliva, A.; Llabres, M. Limitations of the quality range approach in analytical similarity assessment: Effect of mean shift and relative variability. J. Pharm. Biomed. Anal. 2021, 198, 114017. [Google Scholar] [CrossRef]
  29. Hamed, K.-H. Exact distribution of the Mann–Kendall trend test statistic for persistent data. J. Hydrol. 2009, 365, 86–94. [Google Scholar] [CrossRef]
  30. Güçlü, Y.S. Multiple Şen-innovative trend analyses and partial Mann-Kendall test. J. Hydrol. 2018, 566, 685–704. [Google Scholar] [CrossRef]
  31. Pal, S.; Sarda, R. Measuring the degree of hydrological variability of riparian wetland using hydrological attributes integration (HAI) histogram comparison approach (HCA) and range of variability approach (RVA). Ecol. Indic. 2021, 120, 106966. [Google Scholar] [CrossRef]
  32. García-Martínez, B.; Rinaldi, M. Changes in meander geometry over the last 250 years along the lower Guadalquivir River (southern Spain) in response to hydrological and human factors. Geomorphology 2022, 410, 108284. [Google Scholar] [CrossRef]
  33. James, N.; Bondell, H. Temporal and spectral governing dynamics of Australian hydrological streamflow time series. J. Comput. Sci. 2022, 63, 101767. [Google Scholar] [CrossRef]
  34. Xie, Y.; Liu, S.; Huang, S.; Fang, H.; Ding, M.; Huang, C.; Shen, T. Local trend analysis method of hydrological time series based on piecewise linear representation and hypothesis test. J. Clean. Prod. 2022, 339, 130695. [Google Scholar] [CrossRef]
  35. United-States-Environmental-Protection Water. Rapid Bioassessment Protocols for Use in Wadeable Streams and Rivers: Periphyton, Benthic Macroinvertebrates, and Fish; United States Environmental Protection Agency: Washington, DC, USA, 1999; EPA 841-B-99-002. [Google Scholar]
  36. Sanyal, J.; Lauer, J.W.; Kanae, S. Examining the downstream geomorphic impact of a large dam under climate change. CATENA 2021, 196, 104850. [Google Scholar] [CrossRef]
  37. Swanson, A.C.; Kaplan, D.; Toh, K.B.; Marques, E.E.; Bohlman, S.A. Changes in floodplain hydrology following serial damming of the Tocantins River in the eastern Amazon. Sci. Total Environ. 2021, 800, 149494. [Google Scholar] [CrossRef]
  38. Yuan, S.; Li, Z.; Chen, L.; Li, P.; Zhang, Z. Influence of check dams on flood hydrology across varying stages of their lifespan in a highly erodible Catchment, Loess Plateau of China. CATENA 2022, 210, 105864. [Google Scholar] [CrossRef]
  39. Xue, B.; Wang, G.; Xiao, J.; Helman, D.; Sun, W.; Wang, J.; Liu, T. Global convergence but regional disparity in the hydrological resilience of ecosystems and watersheds to drought. J. Hydrol. 2020, 591, 125589. [Google Scholar] [CrossRef]
  40. Fooladi, M.; Golmohammadi, M.H.; Safavi, H.R.; Singh, V.P. Application of meteorological drought for assessing watershed health using fuzzy-based reliability, resilience, and vulnerability. Int. J. Disaster Risk Reduct. 2021, 66, 102616. [Google Scholar] [CrossRef]
  41. Villani, L.; Castelli, G.; Piemontese, L.; Penna, D.; Bresci, E. Drought risk assessment in Mediterranean agricultural watersheds: A case study in Central Italy. Agric. Water Manag. 2022, 271, 107748. [Google Scholar] [CrossRef]
  42. Li, L.; Jiang, E.; Yin, H.; Wu, K.; Dong, G. Ultrashort-term responses of riparian vegetation restoration to adjacent cycles of ecological water conveyance scheduling in a hyperarid endorheic river basin. J. Environ. Manag. 2022, 320, 115803. [Google Scholar] [CrossRef] [PubMed]
  43. Zhai, L.; Cheng, S.; Sang, H.; Xie, W.; Gan, L.; Wang, T. Remote sensing evaluation of ecological restoration engineering effect: A case study of the Yongding River Watershed, China. Ecol. Eng. 2022, 182, 106724. [Google Scholar] [CrossRef]
  44. Consoli, G.; Haller, R.M.; Doering, M.; Hashemi, S.; Robinson, C.T. Tributary effects on the ecological responses of a regulated river to experimental floods. J. Environ. Manag. 2022, 303, 114122. [Google Scholar] [CrossRef] [PubMed]
  45. Li, Y.; Zhong, Y.; Shao, R.; Yan, C.; Jin, J.; Shan, J.; Li, F.; Ji, W.; Bin, L.; Zhang, X.; et al. Modified hydrological regime from the Three Gorges Dam increases the risk of food shortages for wintering waterbirds in Poyang Lake. Glob. Ecol. Conserv. 2020, 24, e1286. [Google Scholar] [CrossRef]
  46. Vizzo, J.I.; Cabrerizo, M.J.; Helbling, E.W.; Villafañe, V.E. Extreme and gradual rainfall effects on winter and summer estuarine phytoplankton communities from Patagonia (Argentina). Mar. Environ. Res. 2021, 163, 105235. [Google Scholar] [CrossRef]
  47. Yao, K.S.; He, Y.J.; Van den Brink, P.J.; Dai, Y.N.; Yang, Y.; Cui, Y.D. Multivariate approaches to assess the drivers of benthic macroinvertebrate communities and biotic indices in a highly urbanized fluvial-estuarine system. Ecol. Indic. 2022, 139, 108956. [Google Scholar] [CrossRef]
  48. Sin, Y.; Lee, H. Changes in hydrology, water quality, and algal blooms in a freshwater system impounded with engineered structures in a temperate monsoon river estuary. J. Hydrol. Reg. Stud. 2020, 32, 100744. [Google Scholar] [CrossRef]
  49. Tan, B.; Hu, P.; Niu, X.; Zhang, X.; Liu, J.; Frenken, T.; Hamilton, P.B.; Haffner, G.D.; Chaganti, S.R.; Nwankwegu, A.S.; et al. Microbial community day-to-day dynamics during a spring algal bloom event in a tributary of Three Gorges Reservoir. Sci. Total Environ. 2022, 839, 156183. [Google Scholar] [CrossRef]
  50. Arif, M.; Jie, Z.; Wokadala, C.; Songlin, Z.; Zhongxun, Y.; Zhangting, C.; Zhi, D.; Xinrui, H.; Changxiao, L. Assessing riparian zone changes under the influence of stress factors in higher-order streams and tributaries: Implications for the management of massive dams and reservoirs. Sci. Total Environ. 2021, 776, 146011. [Google Scholar] [CrossRef]
  51. Gao, Y.; Chen, L.; Zhang, W.; Li, X.; Xu, Q. Spatiotemporal variations in characteristic discharge in the Yangtze River downstream of the Three Gorges Dam. Sci. Total Environ. 2021, 785, 147343. [Google Scholar] [CrossRef]
  52. Guo, S.; Xiong, L.; Zha, X.; Zeng, L.; Cheng, L. Impacts of the Three Gorges Dam on the streamflow fluctuations in the downstream region. J. Hydrol. 2021, 598, 126480. [Google Scholar] [CrossRef]
  53. Man, Y.B.; Chow, K.L.; Zhang, F.; Lei, K.M.; Leung, A.O.W.; Mo, W.Y.; Wong, M.H. Protecting water birds of wetlands: Using toxicological tests and ecological risk assessment, based on metal/loid (s) of water, sediment and biota samples. Sci. Total Environ. 2021, 778, 146317. [Google Scholar] [CrossRef] [PubMed]
  54. Zhang, P.; Li, K.; Liu, Q.; Liu, R.; Qin, L.; Wang, H.; Zhang, Z.; Wang, K.; Wang, Y.; Liang, R.; et al. Linking bait and feeding opportunities to fish foraging habitat for the assessment of environmental flows and river restoration. Sci. Total Environ. 2021, 768, 144580. [Google Scholar] [CrossRef] [PubMed]
  55. Ding, Y.; Pan, B.; Zhao, G.; Sun, C.; Han, X.; Li, M. Geo-climatic factors weaken the effectiveness of phytoplankton diversity as a water quality indicator in a large sediment-laden river. Sci. Total Environ. 2021, 792, 148346. [Google Scholar] [CrossRef] [PubMed]
  56. Tian, Y.; Jiang, Y.; Liu, Q.; Xu, D.; Liu, Y.; Song, J. The impacts of local and regional factors on the phytoplankton community dynamics in a temperate river, northern China. Ecol. Indic. 2021, 123, 107352. [Google Scholar] [CrossRef]
  57. Zhang, J.; Li, F.; Lv, Q.; Wang, Y.; Yu, J.; Gao, Y.; Ren, Z.; Zhang, X.; Lv, Z. Impact of the Water–Sediment Regulation Scheme on the phytoplankton community in the Yellow River estuary. J. Clean. Prod. 2021, 294, 126291. [Google Scholar] [CrossRef]
  58. Chao, C.; Lv, T.; Wang, L.; Li, Y.; Han, C.; Yu, W.; Yan, Z.; Ma, X.; Zhao, H.; Zuo, Z.; et al. The spatiotemporal characteristics of water quality and phytoplankton community in a shallow eutrophic lake: Implications for submerged vegetation restoration. Sci. Total Environ. 2022, 821, 153460. [Google Scholar] [CrossRef]
  59. Zhang, J.; Ma, J.; Zhang, Z.; He, B.; Zhang, Y.; Su, L.; Wang, B.; Shao, J.; Tai, Y.; Zhang, X.; et al. Initial ecological restoration assessment of an urban river in the subtropical region in China. Sci. Total Environ. 2022, 838, 156156. [Google Scholar] [CrossRef]
  60. Mittal, N.; Mishra, A.; Singh, R.; Bhave, A.G.; van der Valk, M. Flow regime alteration due to anthropogenic and climatic changes in the Kangsabati River, India. Ecohydrol. Hydrobiol. 2014, 14, 182–191. [Google Scholar] [CrossRef]
  61. Hackenberger, B.K.; Velki, M.; Lončarić, Ž.; Hackenberger, D.K.; Ečimović, S. Effect of different river flow rates on biomarker responses in common carp (Cyprinus carpio). Ecotoxicol. Environ. Saf. 2015, 112, 153–160. [Google Scholar] [CrossRef]
  62. Freitas, C.E.; de Almeida Mereles, M.; Pereira, D.V.; Siqueira-Souza, F.; Hurd, L.; Kahn, J.; Morais, G.; Sousa, R.G.C. Death by a thousand cuts: Small local dams can produce large regional impacts in the Brazilian Legal Amazon. Environ. Sci. Policy 2022, 136, 447–452. [Google Scholar] [CrossRef]
  63. Peluso, L.M.; Mateus, L.; Penha, J.; Bailly, D.; Cassemiro, F.; Suárez, Y.; Fantin-Cruz, I.; Kashiwaqui, E.; Lemes, P. Climate change negative effects on the Neotropical fishery resources may be exacerbated by hydroelectric dams. Sci. Total Environ. 2022, 828, 154485. [Google Scholar] [CrossRef] [PubMed]
  64. Shen, J.; Qin, G.; Gu, X.; Liu, Y.; An, S.; Liu, R.; Leng, X.; Wan, Y. Effects of seasonal hydrological regulation of cascade dams on the functional diversity of zooplankton: Implications for the management of massive reservoirs and dams. J. Hydrol. 2022, 610, 127825. [Google Scholar] [CrossRef]
  65. Zhang, H.; Sun, T.; Zhou, Z.; Cao, H.; Qiu, J.; Huang, X. Increased river flow enhances the resilience of spatially patterned mudflats to erosion. Water Res. 2022, 220, 118660. [Google Scholar] [CrossRef]
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Figure 1. Distribution of hydrological stations in the Fuhe river basin.
Figure 1. Distribution of hydrological stations in the Fuhe river basin.
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Figure 2. Monthly mean-flow deviation and Y−value deviation of each hydrological station. ((a): Monthy average flow difference; (b): Offset Y−value deviation).
Figure 2. Monthly mean-flow deviation and Y−value deviation of each hydrological station. ((a): Monthy average flow difference; (b): Offset Y−value deviation).
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Figure 3. The overall alteration of hydrology before and after sluice construction in the typical control section of the mainstem.
Figure 3. The overall alteration of hydrology before and after sluice construction in the typical control section of the mainstem.
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Figure 4. (a) Temporal and spatial distribution of phytoplankton species in Fuhe River Basin. (b) Spatial and temporal distribution of phytoplankton biomass in Fuhe River Basin (from left to right, each point is in order of high-water period, low-water period, and flat-water period).
Figure 4. (a) Temporal and spatial distribution of phytoplankton species in Fuhe River Basin. (b) Spatial and temporal distribution of phytoplankton biomass in Fuhe River Basin (from left to right, each point is in order of high-water period, low-water period, and flat-water period).
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Figure 5. Spearman correlation heat map of environmental indicators and phytoplankton (* means the significant correlation between the two indicators, p < 0.05).
Figure 5. Spearman correlation heat map of environmental indicators and phytoplankton (* means the significant correlation between the two indicators, p < 0.05).
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Figure 6. (a) Relationship between P−IBI index and flow response in the Fuhe River Basin; (b) flow–threshold range of downstream healthy ecosystems.
Figure 6. (a) Relationship between P−IBI index and flow response in the Fuhe River Basin; (b) flow–threshold range of downstream healthy ecosystems.
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Table
Table 1. Candidate phytoplankton indicators of P−IBI in the Fuhe River Basin and their responses to disturbance.
Table 1. Candidate phytoplankton indicators of P−IBI in the Fuhe River Basin and their responses to disturbance.
NumberBiometricsResponse to Disturbance
M1Number of algal species (total taxa)Decrease
M2Number of diatom speciesDecrease
M3Number of Dinophyta + Cryptophyta speciesIncrease
M4Number of diatom speciesDecrease
M5Number of Dinophyta + Cryptophyta speciesIncrease
M6Number of algal cellsIncrease
M7Dominant algal-cell densityIncrease
M8Bloom algae algal-cell densityIncrease
M9Dinophyta + Cryptophyta cell densityIncrease
M10Diatom algae-cell densityDecrease
M11Dinophyta + Cryptophyta cell densityIncrease
M12Total biomassIncrease
M13Diatom biomassIncrease
M14Dinophyta + Cryptophyta biomassIncrease
M15Cyanobacteria + Chlorophyta biomassIncrease
M16Diatom biomassDecrease
M17Cyanobacteria + Chlorophyta biomassIncrease
M18Dinophyta + Cryptophyta biomassIncrease
M19The cell density of the TOP 3 dominant speciesIncrease
M20Dominant algal-cell densityIncrease
M21Number of bloom-algae speciesIncrease
M22Bloom-algae algal-cell densityIncrease
M23Shannon’s diversity indexDecrease
M24Margalef indexDecrease
M25Simpson indexDecrease
M26Pielou indexDecrease
Table
Table 2. Index of hydrological alteration in the mainstem hydrological station of the Fuhe River. (Note: ↑ means the alteration is increasing; ↓ means the alteration is decreasing).
Table 2. Index of hydrological alteration in the mainstem hydrological station of the Fuhe River. (Note: ↑ means the alteration is increasing; ↓ means the alteration is decreasing).
IHASZLLOULIDLIA
Alteration Rate and SituationAlteration Rate and SituationAlteration Rate and SituationAlteration Rate and Situation
January average flow14% (L) ↑2% (L) ↑49% (M) ↓14% (L) ↑
February average flow46% (M) ↑53% (M) ↑66% (M) ↓45% (M) ↑
March average flow29% (L) ↑6% (L) ↑15% (L) ↑29% (L) ↑
April average flow29% (L) ↑73% (H) ↓62% (M) ↓18% (L) ↓
May average flow4% (L) ↓25% (L) ↓28% (L) ↓6% (L) ↓
June average flow36% (M) ↓18% (L) ↑20% (L) ↑10% (L) ↑
July average flow46% (M) ↑6% (L) ↑23% (L) ↑37% (M) ↑
August average flow46% (M) ↑37% (M) ↑20% (L) ↓10% (L) ↑
September average flow4% (L) ↑22% (L) ↑88% (H) ↑6% (L) ↑
October average flow36% (M) ↑49% (M) ↑23% (L) ↓22% (L) ↓
November average flow46% (M) ↑41% (M) ↑32% (L) ↓10% (L) ↓
December average flow25% (L) ↑29% (L) ↑6% (L) ↑2% (L) ↑
1-day minimum flow4% (L) ↑2% (L) ↑37% (M) ↑37% (M) ↑
3-day minimum flow18% (L) ↓14% (L) ↑71% (H) ↓53% (M) ↑
7-day minimum flow7% (L) ↑22% (L) ↑105% (H) ↓61% (M) ↑
30-day minimum flow4% (L) ↑61% (M) ↑71% (H) ↓61% (M) ↓
90-day minimum flow39% (M) ↑41% (M)2% (L) ↓14% (L) ↓
1-day maximum flow25% (L) ↓18% (L) ↓20% (L) ↓37% (M) ↓
3-day maximum flow4% (L) ↓33% (L) ↓28% (L) ↓53% (M)
7-day maximum flow14% (L) ↓33% (L) ↓45% (M) ↓29% (L) ↓
30-day maximum flow7% (L) ↓25% (L) ↓28% (L) ↓2% (L) ↓
90-day maximum flow46% (M) ↓18% (L) ↓28% (L) ↓22% (L) ↓
Number of zero-flow days0% (L) ↓0% (L) ↓0% (L) ↓0% (L) ↓
Base flow25% (L) ↑2% (L) ↑88% (H) ↓61% (M) ↑
Date of minimal flow25% (L) ↓14% (L) ↓74% (H) ↑45% (M) ↑
Date of maximal flow33% (L) ↑19% (L) ↓11% (L) ↑16% (L) ↑
Low-pulse flow count25% (L) ↓67% (H) ↓22% (L) ↑50% (M) ↑
Low-pulse flow duration23% (L) ↓61% (M) ↓6% (L) ↑12% (L) ↓
High-pulse flow count5% (L) ↓55% (M) ↑16% (L) ↓2% (L) ↑
High-pulse flow duration27% (L) ↑10% (L) ↑45% (M) ↑60% (M) ↓
Rise rate23% (L) ↓29% (L) ↓11% (L) ↑42% (M) ↑
Fall rate68% (H) ↑10% (L) ↓37% (M) ↓37% (M) ↓
Number of reversals52% (M) ↑53% (M) ↑32% (L) ↑6% (L) ↑
Table
Table 3. Index of hydrological alteration in the hydrological stations of the tributaries of the Fuhe River. (Note: ↑ means the alteration is increasing; ↓ means the alteration is decreasing).
Table 3. Index of hydrological alteration in the hydrological stations of the tributaries of the Fuhe River. (Note: ↑ means the alteration is increasing; ↓ means the alteration is decreasing).
IHASTXXGXTP
Alteration Rate and SituationAlteration Rate and SituationAlteration Rate and SituationAlteration Rate and Situation
January average flow8% (L) ↑8% (L) ↓28% (L) ↓8% (L) ↑
February average flow33% (L) ↑8% (L) ↓4% (L) ↑16% (L) ↓
March average flow50% (M) ↑72% (H) ↓44% (M) ↑128% (H) ↓
April average flow83% (H) ↓8% (L) ↓76% (H) ↓56% (M) ↓
May average flow25% (L) ↑72% (H) ↓32% (L) ↓68% (H) ↓
June average flow83% (H) ↓72% (H) ↑44% (M) ↑20% (L) ↑
July average flow33% (L) ↑8% (L) ↑20% (L) ↓32% (L) ↑
August average flow25% (L) ↑66% (M) ↑52% (M) ↑64% (M) ↑
September average flow50% (M) ↑3% (L) ↑8% (L) ↑44% (M) ↑
October average flow58% (M) ↓43% (M) ↑92% (H) ↑8% (L) ↑
November average flow58% (M) ↓66% (M) ↑32% (L) ↑20% (L) ↑
December average flow67% (H) ↑3% (L) ↑20% (L) ↑64% (M) ↑
1-day minimum flow25% (L) ↑49% (M) ↑28% (L) ↑100% (H) ↓
3-day minimum flow50% (M) ↑83% (H) ↑28% (L) ↑64% (M) ↑
7-day minimum flow8% (L) ↑60% (M) ↑40% (M) ↑64% (M) ↑
30-day minimum flow67% (H) ↑26% (L) ↑16% (L) ↑52% (M) ↑
90-day minimum flow50% (M) ↑8% (L) ↑80% (H) ↑8% (L) ↑
1-day maximum flow58% (M) ↑43 (M) ↓56%(M) ↑7% (L) ↑
3-day maximum flow67% (H) ↑26% (L) ↓32% (L) ↑4% (L) ↑
7-day maximum flow8% (L) ↑72% (H)↓80% (H) ↑8% (L) ↑
30-day maximum flow50% (M) ↑49% (M) ↑56% (M) ↑32% (L) ↓
90-day maximum flow8% (L) ↑83% (H) ↑44% (M) ↑68% (H) ↓
Number of zero-flow days0% (L) ↓0% (L) ↓0% (L) ↓0% (L) ↓
Base flow42% (M) ↑31% (L) ↓76% (H) ↑76% (H) ↑
Date of minimal flow67% (H) ↓100% (H) ↑20% (L) ↓20% (L) ↑
Date of maximal flow17% (L) ↓49% (M) ↓68% (H) ↑104% (H) ↑
Low-pulse flow count22% (L) ↓59% (M) ↓23% (L) ↓62% (M) ↓
Low-pulse flow duration63% (M) ↓59% (M) ↑25% (L) ↑71% (H) ↓
High-pulse flow count42% (M) ↑20% (L) ↑44% (M) ↓15% (L) ↑
High-pulse flow duration3% (L) ↑48% (M) ↑4% (L) ↑4% (L) ↓
Rise rate33% (L) ↓49% (M)↑4% (L) ↓4% (L) ↓
Fall rate17% (L) ↑8% (L) ↑56% (M) ↑7% (L) ↓
Number of reversals75% (H) ↑69% (H) ↓4% (L) ↑79% (H) ↑
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MDPI and ACS Style

Wang, Z.; Huang, W.; Zhu, D.; Huang, Q.; Wu, L.; Liu, X. Determining Critical Thresholds of Environmental Flow Restoration Based on Planktonic Index of Biotic Integrity (P−IBI): A Case Study in the Typical Tributaries of Poyang Lake. Int. J. Environ. Res. Public Health 2023, 20, 169. https://doi.org/10.3390/ijerph20010169

AMA Style

Wang Z, Huang W, Zhu D, Huang Q, Wu L, Liu X. Determining Critical Thresholds of Environmental Flow Restoration Based on Planktonic Index of Biotic Integrity (P−IBI): A Case Study in the Typical Tributaries of Poyang Lake. International Journal of Environmental Research and Public Health. 2023; 20(1):169. https://doi.org/10.3390/ijerph20010169

Chicago/Turabian Style

Wang, Zhuowei, Wei Huang, Dayu Zhu, Qi Huang, Leixiang Wu, and Xingchen Liu. 2023. "Determining Critical Thresholds of Environmental Flow Restoration Based on Planktonic Index of Biotic Integrity (P−IBI): A Case Study in the Typical Tributaries of Poyang Lake" International Journal of Environmental Research and Public Health 20, no. 1: 169. https://doi.org/10.3390/ijerph20010169

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