A New Method for Determining the Ecological Flow Regime to Support Sustainable Restoration of Target Fish Habitats in Impaired Rivers

Abstract

Large-scale river degradation constitutes a global challenge, rendering the ecological restoration of impaired rivers ever more crucial. While ecological restoration projects have enhanced the quality of river habitats, given the dynamic nature and complexity of river and lake ecosystems, the achievement of sustainable restoration of fish habitats and the assurance of its effectiveness continue to face numerous challenges. Consequently, this study proposes an improved approach to determine the ecological flow requirements of fish habitats in impaired rivers. In relation to the screening of key species, a bespoke evaluation index system has been developed specifically for impaired rivers lacking rare and endemic fish species. Primary data were collected via field surveys, ecological monitoring, and a review of the literature, while the analytic hierarchy process (AHP) was utilized to quantitatively identify key species. In the development of the assessment framework, three core indicators were integrated: habitat-weighted usable area (WUA), habitat connectivity index (HCI), and microhabitat heterogeneity index (RMH). Incorporating the ecological requirements of key fish species across different life stages, a systematic analysis was undertaken to explore the ecological response effects of different indicator combinations under varying flow regimes. The results revealed that a flow rate of 160 m³/s gives rise to an inflection point in the RMH diversity index at 1.618, whereas a flow rate of 240 m³/s results in a significant inflection point in the HCI at 0.652. At a flow rate of 260 m³/s, the WUA attains 2,007,928 m². The optimal ecological flow range was determined to be 160–240 m³/s for the breeding period (March–June), 240–260 m³/s for the foraging period (July–October), and 120 m³/s for the winter period. These findings provide a theoretical framework for the restoration of target fish populations in similarly degraded rivers.

1. Introduction

It is widely recognized in the academic community that habitat conservation and restoration are prerequisites for the effective protection of species [1]. Faced with ongoing global environmental changes and the intensification of human activities, this consensus is particularly significant [2]. Therefore, ecological restoration and management of degraded rivers have become critical areas of focus. Understanding the relationship between hydrological and geomorphological changes in river habitats and the habitat requirements of aquatic organisms is essential [3]. Ecological flow protection, as the cornerstone of river and lake ecological restoration and management, plays a crucial role in promoting the high-quality development of water resources [4]. Research on ecological flow can be traced back to the 1940s when the U.S. Fish and Wildlife Service conducted a series of streamflow studies and proposed the concept of minimum river flow to prevent ecosystem degradation [5]. With the continuous advancement of ecological flow research, several mature methods have emerged for estimating ecological water requirements. These methods can generally be classified into four main categories: hydrological, habitat, and integrated [6]. Biological communities often respond to environmental changes through the loss of biodiversity and decline of ecologically important species. Therefore, habitat simulation methods based on hydrodynamic models, which can assess quantitative changes in physical habitats as a function of flow, are becoming increasingly effective in river management and conservation projects [7].

As top predators in river ecosystems, changes in fish population biodiversity directly reflect the health status of river and lake ecosystems. They are crucial for maintaining the functionality and stability of food web structures as well as the balance of aquatic ecosystems [8]. Conducting research on suitable ecological flow processes with fish as the focus is currently a popular topic in river and lake ecological restoration [9]. Currently, the selection of target fish species is mainly based on the direct choice of rare and endemic species, such as the Chinese sturgeon, or economically important fish species, such as the Chinook salmon [10]. However, there is a high level of subjectivity in the restoration of target fish populations in degraded rivers that lack rare and endemic fish species, and no scientific evaluation method currently exists [11]. With the development of environmental DNA (eDNA) technology, new methods have emerged that provide a more scientific approach to determining target fish species and exploring the spatial distribution of fish populations [12].

Habitat fragmentation caused by human activities is a major threat to biodiversity and ecosystem integrity [13]. Habitat refers to the spatial areas in which individual organisms, populations, or communities live and reproduce. This is a prerequisite and foundational condition for ensuring and maintaining the ecological functions of rivers [14]. Habitat numerical simulations often use the Weighted Usable Area (WUA) as an assessment metric. However, this metric primarily considers the quantity of fish habitats, overlooking their quality. For example, it does not account for the complexity of river microhabitats and the connectivity between habitat patches. Because of the variations in river channel topography and its spatial heterogeneity, hydrological changes typically have unequal impacts on aquatic habitats within rivers [15]. Moreover, when the total area of suitable habitats in a river section is large but fragmented, information communication, energy transfer, and other ecological processes within populations and between communities remain limited. Therefore, improvements in fish population density and biodiversity may not be significant [16]. Habitat quantity is important; however, relying solely on a single indicator for evaluation can be misleading and may lack consistency. This approach may lead to incorrect conclusions about the ecological health of a river, potentially resulting in misguided decisions regarding the ecological restoration and management of degraded rivers. A more comprehensive assessment should consider both habitat quantity and quality as well as the spatial configuration and connectivity of habitats to provide a more accurate representation of ecological conditions and guide effective management strategies [17].

The Hutuo River Basin is a pioneering demonstration area for ecological water replenishment in North China and was selected as one of the first regions for the Mother River Revitalization Action, making it highly representative and typical. In recent years, river ecosystem functions have been severely degraded by the combined effects of changing environmental conditions and intense human interference. However, with the implementation and advancement of the Hutuo River ecological restoration project, the quality of river habitats improved significantly. Despite these gains, the overall ecological revitalization of rivers still faces challenges due to the dynamic and complex nature of river and lake ecosystems.

Therefore, this study proposes a new method for determining the ecological flow regime to support the sustainable restoration of target fish habitats in impaired rivers. (1) eDNA technology was utilized to characterize the fish community composition in impaired rivers. For impaired rivers where rare and endemic fish species are absent, a tailored evaluation index system was established, and the Analytic Hierarchy Process (AHP) was integrated to quantitatively screen key indicator species, thereby defining the core targets of the assessment. (2) Three core evaluation indicators—habitat-weighted usable area (WUA), habitat connectivity index (HCI), and microhabitat heterogeneity index (RMH)—were developed to establish a multi-dimensional comprehensive assessment framework for habitat quality. (3) By coupling the ecological requirements of key fish species across different life stages, the ecological response patterns of various indicator combinations under different flow regimes were systematically analyzed. Ultimately, the optimal ecological flow thresholds for key species throughout their life cycles were determined. This method is expected to provide more scientific technical support for fish population restoration and ecological remediation in impaired rivers.

2. Study Content and Methods

2.1. Study Area

The Hutuo River is located between longitudes 113.83° E and 116.09° E and latitudes 39.28° N and 38.20° N and flows through the provinces of Shanxi and Hebei. In Hebei, it passes through the Huangbizhuang Reservoir in Pinghan County and flows into Xianxian County, where it merges with the Fuyangxin and Fuyang rivers to form the Ziya River. The river has a total length of 587 km and a basin area of 27,300 square kilometers. This study focuses on the typical Lingzhou section of the Hutuo River, located in the Huangbizhuang Reservoir area, with a length of approximately 10.6 km. The river segment includes a river island in the center, covering an area of 1.295 km². With the initiation of the Hutuo River ecological restoration project, the area achieved full compliance with flood control safety standards, significant improvements in the ecological environment, and a dramatic enhancement of its landscape, as shown in Figure 1.

2.2. Environmental DNA Technology

In the field of river fish surveys, traditional identification and assessment of key fish species rely on biological monitoring coupled with historical data. These methods document species distribution through direct observation and on-site fishing, and infer species diversity and community structure. However, when confronting fish population decline in rivers with severe ecological degradation, these approaches have inherent limitations and randomness, making it difficult to comprehensively and accurately reflect the actual state of river ecosystems. Environmental DNA (eDNA) refers to total DNA fragments from various species extracted from habitats such as soil, water, and air. After enrichment, extraction, amplification, sequencing, and bioinformatics analysis, it can effectively reveal critical information about species composition, distribution, and genetic diversity in the environment. Compared with traditional fishery resource survey methods, eDNA technology offers advantages such as high sensitivity, high efficiency, and noninvasive sampling. It can overcome the limitations of traditional methods in fishing and sampling processes and is gradually becoming an important tool for aquatic biological resource surveys [18].

During river ecological restoration efforts, fish populations in degraded rivers often undergo significant changes following the implementation of ecological recovery measures [19]. The use of eDNA technology to study the recovery process of target fish populations in degraded rivers enables the precise monitoring and assessment of fish population recovery, thereby providing theoretical support for subsequent comprehensive river ecosystem revitalization (Figure 2). This technology offers a powerful tool for river ecological restoration, particularly in the investigation of fish population distribution and diversity assessment, providing more comprehensive and accurate data and driving the scientific implementation and optimization of ecological restoration efforts. Five typical sampling sites were established along the study river (Figure 1), and the sampling protocol was conducted as follows: 1 L of surface water was collected at each site, followed by filtration through sterile filter membranes to enrich fish environmental DNA (eDNA) in the water samples. The filter membrane samples were then transported to a professional laboratory for DNA extraction and sequencing. Homologous alignment analysis of the sequencing data against a standard fish DNA database was performed to ultimately achieve qualitative identification of fish species in the study area. The DNA sequence reference database employed in this study is GenBank, which is hosted by the National Center for Biotechnology Information (NCBI).

2.3. Target Fish Selection

To accurately identify representative species for restoring target fish populations in impaired rivers, this study systematically integrates field survey data, historical literature records, and academic research findings, conducting a comprehensive evaluation of potential target fish based on a multi-dimensional framework [20]. The evaluation system prioritizes core indicators including fish reproductive cycles, specific spawning habitat requirements, trophic niche characteristics, environmental adaptability, adult morphological parameters, economic utilization value, and population spatiotemporal dynamics [21], thereby establishing targeted screening criteria tailored to the ecological characteristics of the study river (

Table 1. Target fish selection criteria for the Hu Tuo River.

Screening Conditions	Illustration
reproductive age	The preferred age for selective maturation is 1–2 years
Egg-laying site requirements	Priority should be given to fish with flow requirements at spawning grounds
feeding habits	Omnivorous is superior to herbivorous and carnivorous
Lifestyle characteristics	Fish that live in the upper and middle layers of water are preferred over those that live in the bottom and silt
distribution	Fish with wide distribution are preferred over those with narrow distribution
commercial value	Priority should be given to fish with high regional and economic value

). Furthermore, the Analytic Hierarchy Process (AHP) was introduced for quantitative analysis. AHP is a method that analyzes the relative importance of various factors by constructing a judgment matrix. This method can effectively present the hierarchical relationship among constituent parameters and derive the weight value of each parameter through mathematical calculations. Based on the fish species composition, combined with the actual conditions of the Hutao River and research findings from relevant scholars, this study applied the AHP method to screen target fish species [22]. During this process, SPSS v20.0 software was used to conduct qualitative analysis on the judgment matrix.

2.4. Hydrodynamic Model Validation

Based on field survey data, a two-dimensional hydrodynamic model was used for the simulation analysis with a riverbed roughness coefficient of 0.035, as determined from the field investigation. According to the field research and historical flow data for the study area, input values for the model boundary were set at 20 m³/s, 40 m³/s, 80 m³/s, 120 m³/s, 160 m³/s, 200 m³/s, 240 m³/s, 260 m³/s, 280 m³/s, 300 m³/s, 320 m³/s, 340 m³/s, 360 m³/s, 380 m³/s, 400 m³/s, 420 m³/s, 440 m³/s, 460 m³/s, 480 m³/s, and 500 m³/s, with a downstream water level boundary set at 82.5 m. The cross-sections S3 (38°27′06″ N, 114°36′32″ E) and S5 (38°26′95″ N, 114°38′55″ E) were selected as calibration sections. A comparative analysis of the simulated and measured water levels at a flow rate of 40 m³/s showed relative errors of 0.016% and 0.028%, respectively, within 0.05%.

2.5. Indicator Calculation Method

2.5.1. Weighted Usable Area (WUA) Calculation

The weighted usable area (WUA) was used to represent the quality of suitable fish habitats. The specific calculation process is as follows:

$$WUA={\displaystyle \sum _{i=1}^n{CSF}_i\times }{A}_i$$

(1)

$${CSF}_i=V_i\times D_i\times C_i$$

(2)

where CSF_i represents the combined adaptation factor at grid i; n is the table number of grids; V_i is the velocity index; D_i is the depth index; and C_i is the base index. The matrix distribution in the study area was relatively uniform; therefore, only the effects of speed and depth were considered in subsequent simulations.

2.5.2. Habitat Connectivity (HCI)

The HCI is a key quantitative metric in ecology for assessing the degree of spatial connectivity of habitats; a higher HCI value indicates stronger connectivity, which provides fish with broader spaces for foraging, reproduction, and migration, thereby supporting population stability and expansion [23]. As the core basis for HCI calculation, Habitat Patches (HPAs) are defined as spatial areas where the CSF exceeds a specific threshold, with their fundamental data derived from reach-scale CSF values under different flow scenarios in WUA simulations. Combining the fluvial geomorphic characteristics of the study area and the results of previous hydro-habitat simulations, this study set the CSF threshold at 0.6 to define the scope of valid HPAs [24]. The Minimum Spanning Tree (MST) serves as a core tool for quantifying the connectivity relationships between patches; a smaller MST value means a shorter shortest path connecting all patches and smaller distances between patches, which can improve the efficiency of material exchange and biological migration, ultimately enhancing the overall connectivity of habitats. To accurately calculate the HCI values under different flow scenarios, this study, based on the Prim algorithm from graph theory, utilized programming software to explore the changes in habitat patch connectivity under different flow conditions. The specific steps are as follows: first, each HPA was defined as an independent node, with the weight of edges between nodes representing the spatial distance between corresponding patches; Python 3.8.0 was then used to calculate the total length of the shortest path connecting all HPAs under different flows, while ArcGIS 10.2 was employed to statistics the area of HPAs under varying flows. Finally, the parameters obtained from the above calculations were substituted into the HCI calculation formula to derive the HCI values under different flow conditions.

The formulas used to calculate HPA, MST, and HCI are as follows:

$$HPA={\displaystyle \sum _{i=1}^n{P}_i}$$

(3)

$$MST=\min {\displaystyle \sum _{i=1}^{n=1}{\displaystyle \sum _{j=i+1}^n{w}_{ij}{x}_{ij}}}$$

(4)

$$HCI={\displaystyle \sum _{j=1}^n\frac{P_j}{A}}/MST$$

(5)

In this context, P_j represents the area (m²) of the j habitat patch and n is the total number of habitat patches. w_ij denotes the weight of the edge connecting node i to node j, whereas x_ij is either 0 or 1; it is 1 if the connection between i and j is chosen, and 0 otherwise. where A is the total area of the river segment. The MST refers to the shortest distance between all habitat patches.

2.6. Microhabitat Heterogeneity (RMH) Index

River Microhabitat Heterogeneity (RMH) specifically refers to the spatial differentiation characteristics of resource distribution within river systems. This differentiation directly determines the distribution patterns and survival status of species or higher-level biological communities in microhabitats, and essentially reflects the spatial heterogeneity of various structural components (e.g., riverbed morphology, substrate type) and ecological factors (e.g., water quality, hydrological processes) in rivers [25]. As the core indicator characterizing RMH levels, the value of the River Microhabitat Heterogeneity Index is mainly driven by river characteristic variables, among which flow velocity and water depth are the core factors regulating index changes [26]. The diversity of flow conditions is a key component of RMH; by regulating the formation process of physical structures of aquatic habitats (e.g., flow regime zones, water depth gradients), it exerts a profound impact on core functions of river ecosystems such as material cycling and biological interaction. Specifically, more significant heterogeneity in flow conditions can form more diverse microhabitat mosaics, providing aquatic organisms with more stable environments for foraging, reproduction and predator avoidance, thereby reducing the interference of environmental fluctuations on biological survival [27].

To accurately quantify RMH, this study divided the flow parameters in the target area into grades using ArcGIS 10.2 software based on the research conclusions on the adaptive flow velocity and water depth thresholds of Hypophthalmichthys molitrix (Silver Carp): flow velocity was classified into 4 intervals (0–0.25, 0.25–0.5, 0.5–0.75, >0.75 m/s), and water depth was divided into 4 intervals (0–0.5, 0.5–1, 1–1.5, >1.5 m). On this basis, the quantity and proportion of each flow velocity/water depth interval under different flow scenarios were statistically analyzed, respectively, providing basic data support for the calculation of Shannon’s Diversity Index. In the process of RMH quantification, Shannon’s Diversity Index, as a mature heterogeneity quantification tool in the field of ecology, can effectively characterize the complexity and heterogeneity level of microhabitats. Its core logic is to realize the quantitative expression of heterogeneity through the weighted calculation of interval type richness and the proportion of each type. A higher index value indicates richer microhabitat types, stronger heterogeneity, and higher stability of living conditions for organisms in the habitat. This study applied this index to the quantitative analysis of RMH under different flow scenarios, and constructed the flow-microhabitat heterogeneity response curve by calculating the index value corresponding to each flow rate.

$$H=-{\displaystyle \sum _{i=1}^m(P_x/P_T)}\log {}_2(P_x/P_T)$$

(6)

$$H_S=H_v\ast H_d\ast H_s$$

(7)

where m is the total number of microhabitats in the study area, P_x is the number of microhabitat units of type x, and P_T is the total number of microhabitat units. where H_s is the microhabitat heterogeneity diversity index, H_v is the flow velocity diversity index, H_d is the water depth diversity index, and H_s is the substrate diversity index; the substrate distribution in the study area is relatively uniform, and H_s is 1.

3. Interpretation of Results

3.1. Fish Species Composition

Through database comparison and references to historical literature, 18 common fish species belonging to three orders, six families, and 18 genera were identified, providing an important scientific basis for the selection of target fish species in subsequent studies (see

Table 2. Fish Resource Survey of the Hutuo River Based on eDNA Technology.

Family	Section	Genus	Species	Sequences
				S1	S2	S3	S4	S5
CypriniformeS	Cyprinida	Acheilognathus	Acheilognathus macropterus	13,979	1	0	0	0
		Muntiacus	Hemiculter leucisculus	16,765	7999	999	1	1
		Pseudorasbora	Pseudorasbora parva	1512	51	1108	56,397	16,426
		Hypophthalmichthys	Hypophthalmichthys molitrix	1233	4011	16	8	25
		Abbottina	Abbottina rivularis	0	3718	432	1	1
		Carassius	Carassius auratus	20,270	160,543	96,118	42,878	34,874
		Cyprinus	Cyprinus carpio	8	3352	1	2	7
		Opsariichthys bidens	Opsariichthys uncirostris bidens	3056	16,944	2	17	22,895
		Ctenopharyngodon	Ctenopharyngodon idella	15	5626	1	0	4
		Sarcocheilichthys	Sarcocheilichthys nigripinnis	56,777	4	1	0	1
		Squalidus chankaensis	Squalidus chankaensis chankaensis	1072	4	0	1	0
		Hemibarbus	Hemibarbus maculatus	912	4	2	3584	3878
Tetraodontiformes	Gobiidae	Rhinogobius	Rhinogobius Brunneus	46,901	0	0	0	0
	Gobiidae	Rhinogobius	Rhinogobius giurinus	4147	13,542	0	5	29,882
	Scombrida	Channa	Channa argus	4402	9	0	2	0
Siluriformes	Bagridae	Pelteobagrus	Pelteobagrus fulvidraco	1	1	0	8649	0
	Siluridae	Silurus	Silurus asotus	48,234	6	3942	35,584	18

). Among these species, the DNA sequences showed spatial distribution differences, suggesting that populations in different regions may possess distinct adaptive traits. This finding offers valuable insights for further research on fish responses to environmental changes and provides a foundation for selecting representative target fish species and assessing habitat quality.

3.2. Target Fish Determined

Software calculations yielded a maximum eigenvalue of 15.375 and a Consistency Ratio (CR) of 0.067, which is significantly lower than 0.1. This indicates high consistency of the calculation results, thereby verifying the rationality of the data (

Table 3. Target fish partial judgment matrix of the Hutuo River.

Class	Hemiculter leucisculus	Acheilognathus macropterus	Sarcocheilichthys nigripinnis	Pseudorasbora parva	Carassius auratus subsp	Cyprinus carpio	Hypophthalmichthys molitrix	Ctenopharyngodon idella	Opsariichthys bidens	Mylopharyngodon piceus	Pelteobagrus fulvidraco	Silurus asotus	Channa argus
Hemiculter leucisculus	1.00	3.00	1.00	1.00	0.33	0.25	0.33	0.25	0.50	0.20	0.50	0.25	0.33
Acheilognathus macropterus	0.33	1.00	0.33	0.33	0.25	0.25	0.25	0.25	0.33	0.25	0.25	0.20	0.20
Sarcocheilichthys nigripinnis	1.00	3.00	1.00	1.00	0.33	0.25	0.33	0.25	0.50	0.20	0.50	0.25	0.33
Pseudorasbora parva	1.00	3.00	1.00	1.00	0.33	0.25	0.33	0.25	0.50	0.20	0.50	0.25	0.33
Carassius auratus subsp	3.00	4.00	3.00	3.00	1.00	1.00	0.50	0.50	3.00	1.00	2.00	2.00	2.00
Cyprinus carpio	4.00	4.00	4.00	4.00	0.50	1.00	0.50	1.00	1.00	2.00	1.00	0.25	0.33
Hypophthalmichthys molitrix	3.00	4.00	3.00	3.00	2.00	2.00	1.00	3.00	4.00	5.00	3.00	2.00	2.00
Ctenopharyngodon idella	4.00	4.00	4.00	4.00	0.50	1.00	0.33	1.00	4.00	2.00	3.00	0.50	0.50
Opsariichthys bidens	2.00	3.00	2.00	2.00	0.33	1.00	0.25	0.25	1.00	1.00	0.50	0.25	0.33
Mylopharyngodon piceus	5.00	4.00	5.00	5.00	1.00	0.50	0.20	0.50	1.00	1.00	0.50	0.33	0.33
Pelteobagrus fulvidraco	2.00	4.00	2.00	2.00	0.50	1.00	0.33	0.33	2.00	2.00	1.00	0.33	0.33
Silurus asotus	4.00	5.00	4.00	4.00	0.50	4.00	0.50	2.00	4.00	3.00	3.00	1.00	1.00
Channa argus	3.00	5.00	3.00	3.00	0.50	3.00	0.50	2.00	3.00	3.00	3.00	1.00	1.00

). Through calculating and analyzing the weight values of various fish species, the results showed that Hypophthalmichthys molitrix ranked first with the highest weight value of 1.97. According to fish DNA detection results and species selection principles, Hypophthalmichthys molitrix has characteristics such as specific spawning flow velocity requirements, high economic value, participation in water purification, and sensitivity to hydrological changes. Therefore, it was identified as the most suitable target species for ecological restoration in the Linshui section of the Hutao River. Based on the research results of relevant scholars [28] and the current situation of the Hutao River, this study further integrated fish habitat suitability assessment and constructed a Habitat Suitability Index curve for Hypophthalmichthys molitrix (Figure 3). This study provides important theoretical support for ecological restoration and fish habitat protection.

3.3. Weighted Usable Area (WUA)

According to the analysis of Figure 4, with the gradual increase in flow, the habitat suitability index for Hypophthalmichthys molitrix (Silver Carp) shows a trend of increasing spatial diversity, while the proportion of high-suitability areas significantly increases. This suggests that changes in water flow have a positive regulatory effect on the suitability of Hypophthalmichthys molitrix habitats, significantly improving habitat quality. The increase in flow not only expands the total area of suitable habitats, but also leads to the dynamic evolution of habitat patches in both spatial and temporal dimensions, demonstrating the role of flow changes in driving the optimization of habitat suitability. This trend reflects the adaptive adjustments made by Hypophthalmichthys molitrix habitats in response to flow variation [29].

As shown in Figure 5, the weighted usable area (WUA) of the Hypophthalmichthys molitrix habitats increased and then decreased with changes in flow. Specifically, when the flow is in the range of 20–240 m³/s, the WUA increases rapidly, with the area expanding significantly from 139,177 m² to 1,957,468 m². This indicates that, within this flow range, the total area of suitable habitats for Hypophthalmichthys molitrix increased substantially. However, as the flow increased to 260–320 m³/s, particularly when the flow reached 260 m³/s, the growth of the WUA slowed, and the habitat area fluctuated between 2,007,928 m² and 2,047,609 m². This suggests that within this flow range, the suitability of the habitat is near saturation, and further increases in flow have a diminishing effect on the expansion of the habitat area. The maximum WUA was achieved when the flow rate was 320 m³/s. In the range of 340–500 m³/s, WUA shows a distinct decreasing trend, with the habitat area shrinking from 2,044,953 m² to 1,755,219 m². This indicates that when the flow becomes excessively high, the increased flow velocity may negatively affect Hypophthalmichthys molitrix habitats, leading to a reduction in the area of suitable habitats [30].

3.4. Habitat Connectivity (HCI)

Results from the flow-HCI relationship curve (Figure 6) showed that when the flow rate was 200 m³/s, the HCI reached a significant inflection point at 0.652. As the flow rate increased to 240 m³/s, the HCI attained its maximum value of 0.672. However, with a further increase in flow rate, the HCI gradually decreased, reflecting the weakened connectivity between habitat patches under high-flow conditions. As an effective tool for evaluating habitat patch connectivity, the HCI simplifies complex ecological networks and provides researchers with an intuitive characterization of changes in habitat connectivity. Variations in connectivity under different flow conditions reveal the dynamic relationship between flow and habitat patches, further deepening our understanding of the link between habitat connectivity and ecological health [31].

3.5. RMH Diversity Indices

Based on a two-dimensional hydrodynamic model, this study simulates the spatial distribution of flow velocity and water depth under 20 different flow boundary conditions (ranging from 20 m³/s to 500 m³/s) for the selected river section (Figure 7 and Figure 8). Overall, as the flow increased, the water depth, flow velocity, and water surface area exhibited gradual upward trends. Spatially, flow velocity and water depth show strong spatial heterogeneity and dynamic variability under different flow conditions, reflecting the sensitivity of hydrodynamic processes to flow changes and the complexity of their spatial distribution [32]. Statistical analysis was performed, and the Shannon diversity index was used to calculate the results, as shown in Figure 9.

The results of the trend analysis of the H in Figure 9 show that with the increase in Q, H exhibits a trend of first increasing and then decreasing. When the Q reached 80 m³/s, the diversity index H reached 1.583, indicating a significant increase. As the flow continued to increase, when Q increased to 160 m³/s, a clear inflection point appeared at H = 1.972, indicating a notable deceleration in the growth rate. when Q = 200 m³/s, H reached a peak value of 1.988. After this point, H gradually decreased as the flow continued to increase. This phenomenon suggests that within a certain range of flow, the increase in H tends to saturate, and further increases in flow have a diminishing effect on the diversity index, reflecting the nonlinear characteristics of the impact of flow on river microhabitat heterogeneity [33].

4. Discussion

4.1. Determination of the Ecological Flow Rate

Ecological flow plays a critical supporting role in enhancing fish biodiversity in degraded rivers, maintaining the structural and functional stability of river ecosystems, and sustaining their sustainability. Previous studies have primarily focused on determining a single minimum ecological flow value [34]. Although this approach provides some reference, it fails to adequately consider the specific ecological water requirements of different aquatic species at various life stages [35]; thus, it does not fully capture the complexity and diverse needs of river ecosystems. During the key phases of the fish life cycle, such as spawning, foraging, and overwintering, water flow requirements can vary significantly [36]. A single-flow standard is often insufficient for satisfying these differentiated ecological demands. Therefore, this study focuses on Hypophthalmichthys molitrix (Silver Carp) as the target species, using H, HCI, and WUA as primary indicators, to explore the ecological effects of different flow combinations on the restoration of fish populations in degraded rivers. The ecological needs of Hypophthalmichthys molitrix (Silver Carp) during the period from March to June are influenced by two major life cycle events: foraging and spawning. In addition, the spawning process of Hypophthalmichthys molitrix requires specific flow conditions, making this period a critical window for ecological management. During this period, a higher RMH provided more diversified flow conditions for fish to lay eggs and promoted silver carp spawning [37]. Moreover, the HCI guaranteed the migration and population communication of silver carp in the key cycle stage, which helped improve the silver carp bait to provide an energy guarantee for subsequent spawning [38]. Therefore, H and HCI were the key factors during this period.

During the growth and development period from July to October, the core ecological demand of Hypophthalmichthys molitrix focuses on food ingestion, a process that directly supports its energy accumulation, growth and development [39]. In this period, the spatial distribution range of suitable habitats and the connectivity characteristics between patches jointly determine the foraging activity range and comprehensive habitat quality of Hypophthalmichthys molitrix, serving as key ecological factors regulating its survival strategy. Specifically, the expansion of the effective area of suitable habitats can increase the spatial distribution density of food resources, while the improvement of connectivity between patches can reduce the migration energy consumption and predation risk of Hypophthalmichthys molitrix during foraging. The synergistic effect of these two factors can significantly optimize its foraging efficiency and improve the stability of the habitat environment. Based on the analysis of the above ecological processes, the WUA and HCI were clearly defined as the core indicators for evaluating the satisfaction degree of the ecological demands of Hypophthalmichthys molitrix from July to October. Among them, WUA characterizes the effective carrying capacity of suitable habitats, and HCI quantifies the ecological connectivity level between habitat patches [40].

During the overwintering period (November to February), the habits of silver carp and most fish were significantly different from the previous two life cycle stages [41]. Silver carp tend to live in areas with deep water and slow flow rates (deep pools). The water depth in these areas is deep and slow, which can provide a relatively stable water temperature suitable for fish to perch and winter during the low-temperature season. Therefore, based on the determination of deep pools in the relevant literature (V < 0.5, D > 1), ArcGIS was used to analyze the areas of deep pools under different flow conditions. According to the results of Figure 10, the deep pool area is in the maximum value when the flow rate is 120 m³/s. Therefore, based on the above three indexes and the relationship curve of deep pool, this paper determines the recovery flow process of target fish populations in damaged rivers, defines 200–240 m³/s as the suitable ecological flow interval during the breeding period (March–June), 240–260 m³/s as the feeding period (July–October), and 120 m³/s as the appropriate ecological flow during the overwintering period, to provide a theoretical basis for the recovery of target fish populations in similar damaged rivers [42].

4.2. Habitat Assessment Models Based on Improved Ecological Restoration of Damaged Rivers

An ecosystem is a whole. Once a system is formed, each ecological element cannot be decomposed into independent elements and exists in isolation. At the same time, habitat elements cannot act in isolation, but produce multiple comprehensive effects and form a coupling relationship with various biological factors [43]. Similarly, fish habitat assessments should not be limited to suitable habitat. Most previous studies have relied on the area of suitable habitat to measure habitat quality, but this approach often overlooks the biological habits of fish and the ecological issues caused by habitat fragmentation [44]. Especially in the ecological recovery process of damaged rivers, their more complex and dynamic ecological characteristics require more intensive research than conventional rivers. The more prominent the heterogeneity of river microhabitats, the more stable spaces they provide for aquatic organisms to reproduce and thrive, effectively buffering the interference of environmental fluctuations on the survival of these organisms. Therefore, the construction of river habitats with high heterogeneity plays a crucial supporting role in accelerating the ecological restoration process of impaired rivers [45]. Meanwhile, higher habitat connectivity can facilitate the efficient migration of biological populations between different habitat patches, thereby enhancing their resource acquisition capacity, which is essential for improving the stability and species diversity of impaired river ecosystems [46].

Based on this, this study proposes a novel method for calculating the ecological flow of impaired rivers. This method selects three indicators that can characterize the quality of fish habitats, couples the ecological requirements of key fish species across different life cycles, and ultimately establishes the appropriate range of ecological flow for each period. Compared with existing domestic and international methods for calculating ecological flow, this method avoids the randomness of a single indicator through the coupling of multiple indicators and comprehensively considers the ecological needs of key species at different life stages, aiming to better adapt to the high complexity and systematic nature of ecological restoration in impaired rivers [47]. Its core objective is to more accurately reflect the demands of fish populations for habitat water quality and flow conditions, thereby providing theoretical support for the determination of ecological flow thresholds [48]. However, the ecological restoration of impaired rivers is a highly complex and systematic process. This process not only involves the restoration of fish populations but also requires the simultaneous promotion of water purification, sediment and riparian restoration, as well as integrated management at the watershed scale [49]. Therefore, future ecological restoration practices must fully consider the synergistic effects among the aforementioned factors to achieve the comprehensive restoration goal of impaired river ecosystems.

4.3. Ecological Recovery Process of Damaged Rivers

Reviving the ecological environment of rivers and lakes is essential to promote high-quality water resource management in the new era. With rapid economic development and the increasing use of water resources, river and lake ecosystems have been subjected to varying degrees of damage, particularly in terms of water volume, ecological flow, riverbed morphology, and water quality [50]. This has made the restoration of river and lake ecosystems an urgent issue that must be addressed. To achieve a balance between sustainable water resource development and ecological and environmental protection, it is crucial to focus on the comprehensive restoration of river and lake ecosystems [51].

Basic ecological water requirements, restoration of fundamental riverbed morphology, and water quality standards represent the initial stages of ecosystem recovery. The restoration of river corridor functions and the recovery of key indicator species mark advanced stages of ecological restoration. Based on these foundations, it is necessary to integrate the climatic characteristics of North China and incorporate key variables such as dam regulation, human water abstraction activities, and climate change to precisely regulate ecological flows [52]. Among these factors, dam regulation tends to alter the natural hydrological regime of rivers by impounding flood-season runoff and controlling discharge rhythms. If the discharge pattern mismatches the needs of aquatic organisms (e.g., insufficient pulsed flow in the flood season, excessive or insufficient discharge in the dry season), it will directly disrupt the spatiotemporal distribution pattern of ecological flows and affect the stability of hydrological conditions in habitats. Meanwhile, human water abstraction (for agricultural irrigation, industrial water use, etc.) will encroach on the ecological base flow of rivers. Particularly against the backdrop of tight water supply-demand balance in North China, excessive water abstraction is likely to cause the river flow in the dry season to remain consistently below the ecological threshold, undermining the minimum guarantee pattern of ecological flows. Furthermore, climate change (e.g., increased precipitation variability, more frequent droughts) will intensify the fluctuation range of natural runoff, invalidating the original spatiotemporal allocation logic of ecological flows. For instance, extreme precipitation in the flood season may lead to a sudden surge in flow that exceeds the ecological carrying capacity, while reduced precipitation in the dry season may result in insufficient supply of ecological flows, further restructuring the overall pattern of river ecological flows [53]. This study proposes an ecological restoration plan based on the suitable ecological flow for key indicator species and oriented towards the sustainable restoration of river ecosystems. This process, with the core goal of achieving river ecological sustainability, usually requires a long-term and gradual approach. Particularly in severely impaired rivers, it is difficult to observe significant restoration effects that are both effective and sustainable in the short term. For example, as a core indicator reflecting the sustainable state of river ecosystems, the recovery of fish population size and diversity may be relatively slow, and some species may even experience temporary disappearance in the short term. However, through the long-term and continuous implementation of ecological flow management measures, aquatic biodiversity will gradually recover, the overall functions of river ecosystems will also be systematically improved, and ultimately the evolution of impaired river ecosystems towards a sustainable and stable state will be promoted [54].

4.4. Comparative Analysis of Traditional Biological Monitoring and DNA Monitoring

Unlike traditional methods, environmental DNA (eDNA) technology leverages high-throughput gene sequencing technology to efficiently identify and quantify species DNA fragments in environmental samples such as water samples and sediments, thereby enabling scientific inference of the presence or absence of species and their relative abundance [55]. This technology possesses significant advantages in multiple aspects: (1) High sensitivity, allowing the detection of species with low biomass. (2) Broad species coverage, enabling simultaneous identification of multiple taxa. (3) Non-invasiveness, avoiding interference or damage to target species and their habitats (4) Minimal environmental interference, reducing secondary impacts on river ecology during the monitoring process. (5) High spatiotemporal monitoring efficiency, enabling rapid coverage of large-scale areas or long-term time series. (6) Excellent cost-effectiveness, with lower long-term monitoring costs compared to traditional methods. These characteristics endow eDNA technology with unique value in river ecosystem assessment and fish species monitoring, especially suitable for scenarios where traditional monitoring is more challenging, such as degraded rivers [56].

However, uncertainties in the environmental DNA (eDNA) detection process can compromise the reliability of experimental results, and such issues primarily stem from three aspects: first, exogenous DNA interference—in addition to the introduction of exogenous species DNA from other regions via ecological water replenishment, cross-contamination of sampling tools and exogenous biological residues in water bodies may also lead to false-positive biases; second, database matching bias—general eDNA databases lack localized species gene banks for the study area, which easily results in low matching rates for region-specific species or sequence confusion with closely related species; third, experimental operation errors—reagent contamination during eDNA extraction, fluctuations in sample storage temperature, and the omission of gene fragments caused by low-depth sequencing in the sequencing stage can all lead to result distortion [57]. To address these issues, this study established a “full-process quality control system” to control errors: during the sampling and preprocessing stage, sterile kits were used and blank controls were set up, and samples were stored at −20 °C within 1 h after field sampling; during the eDNA extraction and sequencing stage, standardized procedures were followed, samples with abnormal OD values (outside the range of 1.8–2.0) were re-extracted, and a sequencing depth of ≥30× was adopted to ensure coverage [58]; during the result interpretation and verification stage, a threefold comparison mechanism of “historical data—on-site monitoring—laboratory verification” was established by integrating the fish survey data of the study area over the past 10 years, and for newly emerged species detected unexpectedly, the original sequencing data were rechecked and a 1-month field survey was supplemented for confirmation. This scheme not only reduces the uncertainties of eDNA detection but also provides a reusable technical framework for eDNA monitoring in disturbed rivers (e.g., rivers with ecological water replenishment or inter-basin water transfer), further enhancing the application reliability of eDNA technology in the ecological assessment of impaired rivers.

To better distinguish the application differences between traditional fish monitoring methods and environmental DNA (eDNA) technology, this study compared the traditionally collected seine survey data and eDNA detection results obtained simultaneously by the research team in the Hutuo River study area in 2022 (

Table 4. Detection of traditional fish species.

Sampling Point	Species	Overall Length/cm	Body Length/cm	Weight/g
Huang Zhuang (114.7168, 38.0843)	Carassius auratus	19.4	15.4	108.3
		11.2	9	18.8
		10.5	8.2	17.3
		8	6.2	6.1
		5.1	4.2	1.4
		10.8	8.8	18.2
		5	4	1.1
		4.3	3.5	0.8
		5	3.8	1
		3.6	2.5	0.5
		8.5	6.8	8.3
		4.8	3.8	1.4
		5.5	4.3	1.8
		4.6	3.6	0.9
	Cultrichthys erythropterus	17.3	14.2	32.9
		17.6	14	33.8
		16.3	13	30
		17	13.8	34.1
		15.4	12.4	21.5
		16.6	13.5	27.3
		13.3	11	14.9
		14	11.3	16.8
		15.3	12.6	23.4
		13.4	11	13.3
		16.4	13.5	27.6
		16.4	13.5	29.5
		16	13	26.8
	Hypophthalmichthys molitrix	13	10.5	12.6
	Pseudorasbora parva	8.6	7	5.9
		7.8	6.5	3.6
	Abbottina rivularis	7.3	5.9	3.4
	Rhinogobius giurinus	6.8	5.8	2.8
		5.4	4.5	1.3
		5.2	4.3	1.1
		5.7	4.8	1.9
		5.6	4.6	2
		5.4	4.5	1.5
		5.5	4.5	1.5
		5.5	4.5	1.5
		4.7	3.8	0.9
	Misgurnus anguillicaudatus	9.4	8.2	2.8

).The results showed that eDNA technology can achieve quantitative characterization of the population density of various fish species to a certain extent by analyzing the DNA read counts of target species in water samples. It also outperforms traditional methods in the number of detected species, enabling the capture of species that are easily missed by traditional approaches, such as Hemibarbus maculatus, Pelteobagrus fulvidraco, Sarcocheilichthys nigripinnis, Squalidus chankaensis chankaensis, Silurus asotus, Rhinogobius Brunneus, and Acheilognathus macropterus, et al. In contrast, traditional fish monitoring methods have unique advantages in reflecting the individual growth status of fish. Through on-site fishing, they can directly obtain morphological parameters such as body length and weight of fish in the study area, providing critical fundamental data for evaluating the age structure and growth status of fish populations [59].

5. Conclusions

Given the complexity and systematic nature of ecological restoration in impaired rivers, as well as the core goal of achieving the sustainability of such restoration, this study proposes a new sustainability-oriented method for determining ecological flow regimes, with a view to supporting the transition of target fish habitats in impaired rivers from short-term restoration to a long-term sustainable state. This study identified the spatial distribution characteristics of fish populations in impaired rivers through the application of environmental DNA (eDNA) technology; in contexts where rare or endemic fish species are absent, it further developed principles for the selection of target species, defined the target fish species for ecological flow calculation, and concurrently completed the simulation of hydrological conditions through the integration of a two-dimensional (2D) hydrodynamic model. In the process of calculating habitat indicators with ArcGIS software, this study took full account of the ecological requirements of key fish species across their different life stages. Through an analysis of the ecological impacts of different flow combinations on the population restoration of target fish species, this study ultimately identified the optimal flow regime for achieving species restoration. This paper will elaborate in detail on the specific research findings of this study from three aspects:

• This study used environmental DNA (eDNA) technology to analyze and identify fish species in the Lingxiu section of the Hutuo River. Based on the principles for screening fish species diversity in degraded rivers, and using the Analytic Hierarchy Process (AHP) combined with expert ratings, the weight values for each fish species were calculated. The results indicated that Hypophthalmichthys molitrix had the highest weight value in this river section. Therefore, Hypophthalmichthys molitrix is an ideal target species for ecological flow in this river segment.
• Habitat indicators were calculated using ArcGIS. When the flow rate was 160 m³/s, the RMH diversity index showed an inflection point at 1.618. When the flow is 240 m³/s, the HCI reaches a significant inflection point at 0.652. When the flow is 260 m³/s, the WUA reaches an inflection point at 2,007,928 m².
• Considering the ecological needs of key fish species at different life cycle stages, we explored the ecological effects of various flow combinations on the recovery of target fish populations. The suitable ecological flow range for the breeding period (March to June) is set between 160 and 240 m³/s. For the feeding period (July to October), the suitable flow range is between 240 and 260 m³/s. The suitable flow rate for the overwintering period was set at 120 m³/s. The aim of this study was to provide theoretical support for the recovery of target fish populations from similarly degraded rivers.