Tracing Dam Impacts on Braided Riverbank Vegetation: A Spatiotemporal Analysis of Cover Dynamics and Hydrological Drivers

Abstract

Evaluating how dams modify hydrological regimes and their long-term impacts on riverine ecosystems is critical. This study evaluated trends and change points in Fractional Vegetation Cover (FVC) of braided riverbanks downstream of the Xiaolangdi Dam (1990–2020) using time-series decomposition and structural breakpoint analysis. Distinct temporal periods corresponding to different dam construction and operational phases were identified. Partial correlation analysis and linear mixed-effects modeling were employed to elucidate the spatiotemporal linkages between FVC and key driving factors. The results identified 1997 and 2004 as significant change points in FVC, corresponding to the dam’s construction and initial interception in 1997, and its subsequent comprehensive water and sediment regulation from 2004 onwards, respectively. Although dam construction may have initially posed short-term challenges to downstream vegetation, the post-operational phase witnessed a notable increase in significant vegetation growth compared to the pre-dam period, primarily attributed to the altered hydrological conditions. Notably, the dam operation’s contribution to the total FVC increase was 56% in the near-dam Xiaolangdi–Jiahetan reach. The analysis revealed distinct differences in vegetation responses to these hydrological alterations between the upstream Xiaolangdi–Jiahetan and downstream Jiahetan–Gaocun river sections, with the latter demonstrating greater ecological sensitivity to the dam-induced hydrological changes.

1. Introduction

Riverbanks represent transitional zones between aquatic and terrestrial ecosystems and are home to a rich diversity of flora and fauna. As a key component of river ecosystems, riverbank vegetation plays an irreplaceable role in flood control, soil retention, water quality improvement, habitat provision, and biodiversity conservation [1,2,3,4,5]. This unique ecological environment also contributes to primary production and enhances carbon sequestration [6,7].

There is a strong interaction between the morphology of braided rivers and riverbank vegetation [8,9,10,11]. The complex network of multiple anabranches in braided rivers forms numerous sandbars and mid-channel islands, providing diverse habitats for the establishment and growth of vegetation [12]. These riverbank areas are often subjected to dynamic processes such as channel shifting, flood scouring, and changes in hydrological connectivity, which result in a highly variable environment for vegetation communities [13]. Current research has primarily focused on how vegetation influences river morphology within braided channels [14,15]. However, studies on how hydrological conditions and geomorphological features affect vegetation growth during complex human disturbances, such as dam construction, are limited, and quantitative studies are especially scarce.

Human interventions, particularly dam operations, significantly alter river discharge regimes, flood periodicity, hydrological connectivity, and sediment dynamics [16]. These hydrological changes are intrinsically linked to variations in riverbank vegetation dynamics [17,18]. Dams exert direct control over downstream flow, and, from a riparian ecological perspective, water availability is a critical determinant of plant growth. At the same time, upstream dam construction diminishes the frequency of floodplain inundation events, thereby reducing the heterogeneity of riparian species composition and succession processes [19,20]. Furthermore, dam-induced changes extend to downstream channel morphology, indirectly influencing vegetation dynamics. Braided rivers, which are characterized by rapid rates of erosion and deposition, frequent lateral channel migration, and significant cross-sectional changes, are particularly susceptible to such anthropogenic impacts [21,22]. Sediment retention by reservoirs leads to the downstream transport of sediment-deficient water, which scours the riverbed and directly modifies downstream geomorphology. This process, in turn, transforms braided river characteristics, further complicating the response mechanisms of riverbank vegetation [23,24].

A comprehensive understanding of the temporal persistence and spatial extent of dam impacts is crucial. This helps elucidate how variations in driving factors associated with dam operations influence the growth and distribution of riverbank vegetation. Most extant studies delineate the temporal influence of dams based solely on the initiation and completion dates of dam construction [25,26]. However, riverine ecosystems do not exhibit immediate responses to dam construction; substantial lag effects are often observed because the responses of vegetation, biological communities, and channel morphology to hydrological and sediment changes require time to manifest. Furthermore, many investigations into the spatial influence of dams rely heavily on empirical estimates or fixed-distance criteria [26,27,28], which may not adequately capture the true extent of downstream impacts. Given the pronounced spatial heterogeneity in hydrological and geomorphological processes, the influence of dams on downstream ecosystems is likely to vary considerably, according to reach-specific characteristics, tributary confluences, and other contextual factors.

The Xiaolangdi Dam is strategically situated to regulate flow and sediment dynamics in the lower Yellow River. It functions as a multifaceted hydro-junction, fulfilling critical roles such as flood and ice control, agricultural and industrial water supply, and power generation. In addition, it exerts profound effects on downstream riverbank vegetation [29]. The lower Yellow River region, which is characterized by a braided morphology, is also a vital grain-producing area in China [30]. Understanding the spatiotemporal extent of dam-induced impacts, as well as the interactions between braided riverbank vegetation and natural disturbances, is essential for effective ecosystem management. This study integrated multiple driving factors that influence vegetation growth, including temperature and precipitation, which are crucial climatic variables affecting photosynthesis and overall plant development [31,32]; river discharge, which is a key determinant of riverbank vegetation dynamics [12,33]; and hydrological connectivity downstream of the Xiaolangdi Dam, with recent studies providing tools to quantify changes in channel morphology [34,35].

To comprehensively elucidate the relationships between riverbank vegetation coverage and its various driving factors, and to analyze the underlying patterns of these variations, this study focused on the Xiaolangdi to Gaocun sections of the lower Yellow River. The study area was selected to capture the distinct hydrological and geomorphological conditions that influence vegetation dynamics in this region. Remote sensing imagery, combined with meteorological data, was used to evaluate long-term changes in vegetation coverage and the dynamics of key driving factors from 1990 to 2020. Structural breakpoint analysis, partial correlation analysis, and mixed-effects modeling were used to explore the spatiotemporal dimensions of dam-induced impacts and shifts in vegetation response patterns and quantify the influence of driving factors on riverbank vegetation coverage. The findings of this study provide critical insights into the relative importance and underlying mechanisms of various drivers affecting braided riverbank vegetation, thereby guiding riverine management and conservation strategies.

2. Study Area and Data Overview

2.1. Study Area

The Xiaolangdi Dam, completed in 2001, plays a pivotal role in regulating water and sediment dynamics in the lower Yellow River. By controlling water discharge and sediment transport, the dam mitigates sediment accumulation and stabilizes downstream braided river channels. Over years of operation, the Xiaolangdi Dam has profoundly altered the characteristics of riverbank vegetation, rendering the downstream area an exemplar for examining the impacts of anthropogenic activities on complex riverine ecosystems. This research centers on the downstream region of the Xiaolangdi Dam, specifically focusing on the braided channels and adjacent floodplain areas between Xiaolangdi and Gaocun. The study area is characterized by abundant water resources, an average annual temperature of approximately 14 °C, and approximately 80 days of rainfall annually, with the flood season spanning June to September. Between 2002 and 2021, 19 water and sediment regulation events were conducted. Before the construction of the Xiaolangdi Reservoir, the lower Yellow River experienced frequent course shifts and pronounced morphological changes during the flood season, resulting in significant channel silting and reduced sediment transport [36,37]. Following the reservoir’s implementation, flood season water flow has remained stable compared with pre-dam conditions, while non-flood season flows have seen a slight increase. Sediment loads during both flood and non-flood seasons have markedly decreased, significantly enhancing channel stability. Figure 1 depicts the study area, with blue regions representing the Yellow River channel and green regions indicating riverbank areas that have undergone substantial vegetation coverage changes over different years.

2.2. Data Overview

To quantify vegetation coverage in the study area, this research utilized the 8-Day NDVI Composite dataset derived from the Landsat 5, Landsat 7, and Landsat 8 satellites. The dataset provides a temporal resolution of 8 days and a spatial resolution of 30 m, covering the period from 1 January 1990 to 31 December 2020. The data has been processed for surface reflectance, ensuring high geometric and radiometric accuracy across the entire time series. Imagery preprocessing was conducted via the Google Earth Engine platform, incorporating steps such as data filtering, cropping, cloud removal, and monthly average calculation.

To analyze hydrological connectivity in the region, this study utilized remote sensing data from the Landsat TM/OLI satellites provided by the United States Geological Survey, covering the period from 1990 to 2020.

Flow rate and sediment transport data were obtained from hydrological stations spanning the period from 1990 to 2020. Temperature data were sourced from the National Centers for Environmental Information under the National Oceanic and Atmospheric Administration [38]. Precipitation data were obtained from the China 1km Resolution Monthly Precipitation Dataset, provided by the National Tibetan Plateau Data Center [39].

3. Methodology

3.1. Fractional Vegetation Coverage

Fractional vegetation coverage (FVC) is defined as the proportion of the vertical projection area of vegetation to the total area of the study region [40]. It reflects the vigor of vegetation growth and, to a certain extent, the area of photosynthetically active vegetation. It serves as a comprehensive quantitative indicator of vegetation health and is an important factor in assessing the ecological quality of the study area.

The Normalized Difference Vegetation Index (NDVI) is a fundamental parameter for quantitatively assessing the growth status of vegetation within the study area and serves as a key indicator for vegetation monitoring. The formula for the NDVI is as follows:

$$NDVI={\displaystyle \frac{NIR-RED}{NIR+RED}}$$

(1)

where NIR represents the reflectance value of the near-infrared band, and RED represents the reflectance value of the red band. The NDVI derived from band calculations of the original imagery helps to mitigate the influence of varying conditions such as solar elevation angle, surface morphology, and cloud cover on vegetation coverage calculations.

The dimidiate pixel model represents remote-sensing pixel information comprising vegetation and soil components. It is used to express the correlation between satellite sensor-monitored remote sensing data and surface vegetation coverage. By applying a dimidiate pixel model, the vegetation coverage in the study area can be quantitatively calculated using a normalized index. The formula is as follows:

$$FVC={\displaystyle \frac{NDVI-NDV{I}_{\mathrm{soil}}}{NDV{I}_{\mathrm{veg}}-NDV{I}_{\mathrm{soil}}}}$$

(2)

In the formula, FVC represents the fractional vegetation coverage; NDVI_veg denotes the NDVI value for fully vegetated areas; NDVI_soil represents the NDVI value for bare soil. Considering errors introduced by atmospheric and other environmental impacts on remote sensing imagery, NDVI_veg and NDVI_soil are taken as the maximum and minimum values within the 95% confidence interval of the raster data, respectively.

3.2. Trend and Structural Breakpoint Analysis Methods

To evaluate the impact of dam construction on regional vegetation cover, the characteristic features of vegetation cover were derived by analyzing the time-series data before and after dam construction. Specific characteristic periods were then identified to analyze the spatial extent of influence exerted by driving factors, as well as the magnitude and temporal dynamics of these factors on vegetation cover during different periods.

In this study, the Seasonal and Trend decomposition using Loess (STL) method [41] was used to investigate the temporal dynamics of monthly average FVC values in the downstream floodplain region of the Xiaolangdi Dam. STL decomposition facilitates the disaggregation of time series data into constituent components, including trend, seasonal variation, and stochastic noise. The trend component delineates the temporal evolution of FVC from 1990 to 2020, while the seasonal component captures the intra-annual periodicity of FVC, which is typically influenced by factors such as vegetative growth cycles, seasonal climatic variability, and anthropogenic interventions. The main advantage of the STL decomposition method is that it can iteratively refine and enhance the estimated trend, period and seasonal components compared with traditional methods; compared with BFAST and other methods, STL is more efficient under the premise of ensuring accuracy [42].

$$Y_i=T_i+S_i+R_i,i=1,\dots ,n$$

(3)

In the formula, $Y_i$ denotes the observed value of the time series at point $i$; $T_i$ represents the value of the trend component at point $i$; $S_i$ represents the seasonal component at point $i$; $R_i$ is the residual component at point $i$. In this study, the STL decomposition parameters were selected as seasonal = 13 and trend = 31.

The utilization of Seasonal Amplitude and Seasonally Adjusted FVC to identify abrupt change points in FVC time series is grounded in their complementary capacities to disentangle distinct components of vegetation dynamics. Seasonal Amplitude refers to the magnitude of seasonal variation within a time series. It quantifies the difference between the maximum and minimum values of the seasonal component over a complete seasonal cycle. Seasonally Adjusted FVC is the time series obtained by removing the seasonal component from the original data. This adjustment isolates the long-term trend and residual variations.

Change point detection in time series can be approached through various statistical methodologies, among which the Mann–Kendall test and the moving t-test are commonly used to identify structural changes or turning points. The Mann–Kendall test is a non-parametric statistical approach extensively used to assess trends within time series data, and it is suitable for identifying trends without the assumption of any specific data distribution. This test is robust to outliers and excels in detecting monotonic trends over extended periods. Conversely, the moving t-test aims to detect significant temporal shifts by comparing mean differences between two adjacent windows within the time series, thereby effectively identifying change points. To enhance the robustness and accuracy of change point detection, both statistical methods were used in this analysis [43].

This study leveraged the Mann–Kendall trend analysis, in conjunction with Sen’s slope estimator, to rigorously assess vegetation dynamics. The Mann–Kendall trend analysis, being non-parametric in nature, is recognized for its utility in detecting trends in hydrological and meteorological time series, where the underlying data may not conform to normal distribution assumptions. The Mann–Kendall test was used to identify statistically significant trends in FVC at a 95% confidence level. Sen’s slope estimator, often applied in tandem with the Mann–Kendall trend test, provides an estimate of the rate of change, thereby quantifying the magnitude of observed temporal shifts [43]. The calculation method is as follows:

$$Zs=\left\{\begin{array}{ll}{\displaystyle \frac{S}{\sqrt{Var(S)}}}\hfill & (S>0)\hfill \\ 0\hfill & (S=0)\hfill \\ {\displaystyle \frac{S+1}{\sqrt{Var(S)}}}\hfill & (S<0)\hfill \end{array}\right.$$

(4)

$$S={\displaystyle \sum _{i=1}^{n-1}{\displaystyle \sum _{j=i+1}^{n}sign}}(x_j-x_i)$$

(5)

$$sign(\theta )=\left\{\begin{array}{ll}1\hfill & (\theta >0)\hfill \\ 0\hfill & (\theta =0)\hfill \\ -1\hfill & (\theta <0)\hfill \end{array}\right.$$

(6)

$$E(S)=0$$

(7)

$$Var(S)={\displaystyle \frac{n(n-1)(2n+5)}{18}}$$

(8)

In the formula, $x_i$ and $x_j$ are time series data, and $n$ is the number of data; a positive value of $Zs$ indicates an upward trend, and a negative value of $Zs$ indicates a downward trend. The trend test was performed at a specific α = 0.05 level. When $\left|Zs\right|>1.96$, it means that the trend has passed the significance test.

3.3. Driving Factors Analysis Methods

This study selected candidate driving factors from four perspectives: streamflow, hydrologic connectivity, air temperature, and precipitation. To mitigate multicollinearity and enhance model parsimony, variables with weak statistical associations (Spearman’s p > 0.1) were excluded from the candidate indicators through correlation analysis with $FVC$.

Based on the Indicators of Hydrologic Alteration (IHA) framework [44], we selected the following representative parameters to comprehensively characterize streamflow dynamics: annual mean discharge, monthly mean discharge, annual 1/3/7/30/90-day minimum and maximum discharges, baseflow index (BIF), annual frequency and average duration of high/low pulses, annual rates of flow increase/decrease, and flow reversal count.

The hydrologic connectivity (HC) quantification method adopted in this study builds upon the landscape-hydrology coupled model proposed by Zhang et al. [35], which systematically improves the traditional barrier coefficient method. The longitudinal hydrologic connectivity formula is defined as follows:

$$C_j={\displaystyle \frac{{\displaystyle \sum {\omega }_i\ast I_i}}{B_j}}$$

(9)

In the formula, $C_j$ represents the longitudinal hydrologic connectivity of the $j$-th river reach, $I_i$ denotes the normalized value of landscape parameters for the $i$-th grade mid-channel bars, ${\omega }_i$ indicates the weight of landscape parameters for the $i$-th grade mid-channel bars, and $B_j$ represents the barrier index of the $j$-th river reach. The barrier index $B_j$ is calculated as:

$$B_j={\displaystyle \frac{{\displaystyle \sum 100\ast N_i\ast {\alpha }_i}}{S_j}}$$

(10)

In the formula, $S_j$ is the area of the $j$-th river segment, $N_i$ refers to the quantity of the $i$-th grade mid-channel bars, and ${\alpha }_i$ corresponds to the barrier coefficient assigned to the $i$-th grade mid-channel bars. The weights used for HC are shown in

Table 1. Weight of the Hydrological Connectivity Index.

Landscape Pattern Indices	${\mathit{\omega }}_{\mathit{i}}$	Grading Range of Mid-Channel Bar Area (m2)	${\mathit{\alpha }}_{\mathit{i}}$
Shape index	0.14	0–1.7 × 105	0.006
Contagion index	0.18	1.7 × 105–6.0 × 105	0.022
Connectance index	0.24	6.0 × 105–1.3 × 106	0.049
Splitting index	0.17	1.3 × 106–2.9 × 106	0.106
Shannon’s diversity index	0.27	2.9 × 106–1 × 107	0.200

.

The meteorological indicators in this study encompass temperature and precipitation. For temperature, the annual mean temperature and monthly mean temperature were selected to characterize long-term climatic trends and seasonal variations. Precipitation metrics include annual precipitation and monthly mean precipitation.

This study analyzed the relationship between vegetation and driving factors through partial correlation analysis. Partial correlation is a statistical measure that assesses the direction and strength of the linear relationship between two variables while eliminating the influence of control covariates. Partial correlation analysis is commonly used to explore the interaction between vegetation and driving factors [45]. The partial correlation coefficient is defined as follows:

$$R_{XY.Z}={\displaystyle \frac{R_{XY}-R_{XZ}\times R_{YZ}}{\sqrt{(1-R_{XY}^2)(1-R_{YZ}^2)}}}$$

(11)

In the formula, $R_{XY.Z}$ is the partial correlation coefficient between variables $X$ and $Y$. With $Z$ as the control variable, $R_{XY}$, $R_{XZ}$, and $R_{YZ}$ represent the Pearson correlation coefficients between $X$ and $Y$, $X$ and $Z$, $Y$ and $Z$, respectively. To facilitate comparison between different periods, the Fisher Z transformation was applied to the partial correlation coefficients. This transformation ensures that the coefficients are on a comparable scale, allowing for the computation of average values across periods for robust analysis.

In this study, a Linear Mixed Model (LMM) was used to quantify the relative importance of the driving factors. An LMM is an extension of a general linear model, and it is applied to clustered, longitudinal, or repeated-measures data. An LMM can quantify the relationship between a continuous response variable and multiple predictor variables. It can include fixed effect parameters related to several continuous or categorical covariates, as well as random effects related to random factors, thus relaxing some fundamental assumptions of the general linear model, such as the independence of explanatory variables, the independence of residuals, and the homogeneity of variance. The general form of the LMM is:

$$Y_i=\alpha +{\displaystyle \sum X_i}\times \beta +Z_i\times {\mu }_i+{\epsilon }_i$$

(12)

In the formula, $Y_i$ represents the response variable; $\alpha$ denotes the model intercept; $X_i\times \beta$ corresponds to the fixed effect; $Z_i\times {\mu }_i$ represents the random effect; and ${\epsilon }_i$ is the error term. This study used the ”lme4“ package in R to fit the LMM [46]. We assumed that there were random effects of influencing factors across different spatial coordinates. To analyze the impact of the Xiaolangdi Dam, a systematic sampling approach was conducted using a 300 m × 300 m grid. This grid size was chosen to ensure robust spatial representation by aggregating the native 30 m resolution FVC data, which helps to reduce inherent noise and local pixel-level variability from the satellite imagery, providing more stable FVC values for each analytical unit. The scale also allows for capturing key landform-vegetation relationships, while also maintaining computational feasibility for the subsequent LMM analysis. Model assessment was performed using Bayesian Information Criterion (BIC) and conditional R² [47]. BIC is particularly suitable for large sample sizes as it imposes a stronger penalty for model complexity compared to AIC, helping to identify more parsimonious models and reduce overfitting risk in datasets with numerous observations [48]. On the basis of hierarchical partitioning theory, the R² was averaged to calculate the relative importance of different explanatory variables [49].

4. Results

4.1. Characteristics of Vegetation Coverage Mutation Based on STL

To clearly demonstrate the temporal change trend of FVC, the original monthly FVC data were analyzed using a time series decomposition approach. Figure 2a shows the monthly FVC of the study area from 1990 to 2020, indicating that the overall FVC exhibited a fluctuating upward trend. To identify inflection points in the long-term trend of FVC and elucidate the driving mechanisms and underlying factors more accurately, the seasonal component was extracted from the original FVC data, yielding seasonally adjusted data that captured the interannual trend of FVC (Figure 2b). The seasonal component of the time series decomposition is presented in Figure 2c, indicating a FVC change cycle of 12 months. During the periods of 1990 to 1997 and 2005 to 2016, the seasonal fluctuations progressively diminished, whereas from 1998 to 2004, the fluctuations intensified before stabilizing between 2017 and 2020. The pronounced variation in seasonal intensity during 1998–2004 suggested potentially significant climatic or environmental changes, or substantial anthropogenic influences during that timeframe.

The Xiaolangdi Dam commenced flow interception in October 1997 and was completed by the end of 2001, leading to the initiation of water regulation and sediment management. To pinpoint the specific periods during which the dam influenced downstream vegetation, a Mann–Kendall test was conducted to detect trends and mutation points in FVC from 1990 to 2020 (Figure 3). Figure 3a shows that an abrupt mutation in FVC occurred in 2004, followed by a marked increase in FVC. Figure 3c demonstrates multiple abrupt points in the seasonal amplitude during 1997, with several exceeding the confidence interval, indicating that mutation point detection requires triangulation with other methodologies. This study used a moving t-test to further investigate mutation points in the FVC time series (Figure 3b,d). The combined application of the Mann–Kendall test and the moving t-test identified mutations in FVC in both 1997 and 2004, indicating that these were critical periods influenced by the Xiaolangdi Dam. Thus, the response of FVC to driving factors may exhibit distinct patterns before and after the dam’s impact.

On the basis of the comprehensive FVC time series analysis, we concluded that 1997 and 2004 were critical time points influenced by dam construction, with the period from 1998 to 2004 being the main impact phase of the dam. The river was closed at the end of 1997 due to the construction of the Xiaolangdi Dam, and between 2002 and 2004, there was an experimental period for water and sediment regulation. The construction of the dam and subsequent regulation of water and sediment probably had a significant impact on hydrological conditions, thereby altering the natural balance between vegetation and water flow, which led to changes in vegetation trends. Temporal FVC trends will first be analyzed across three distinct periods: 1990–1997, 1998–2004, and 2005–2020, as defined by breakpoints in 1997 and 2004. For the subsequent driver attribution analysis, these will be re-grouped into two periods: 1990–2004 and 2005–2020. This re-grouping is intended not only to ensure robust correlation detection by meeting minimum sample requirements but also to achieve comparable sample sizes between these two periods for a balanced driver analysis.

4.2. Spatiotemporal Trends of Vegetation Coverage

Investigating the spatiotemporal evolution of FVC is crucial for understanding regional environmental change. This section details these trends by first employing segmented linear regression to capture overall FVC dynamics across three key periods, as determined by breakpoint analysis. Following this, we map and analyze the pixel-level FVC trends within each period to highlight spatial heterogeneities in vegetation response, potentially influenced by varying. As delineated in Figure 4, the segmented regression analysis revealed three distinct FVC phases: (1) a stable baseline period (1990–1997, slope = 0.001/yr), (2) an abrupt 1998 surge followed by progressive decline through 2004 (slope = −0.011/yr), and (3) a gradual recovery phase (2005–2020, slope = +0.003/yr).

Figure 5a shows the annual mean FVC per pixel for key years, with a clear visualization of a river flowing through the region. In general, the vegetation coverage in the middle and lower reaches of the river exhibited a high level, while areas further from both sides of the river also showed relatively high vegetation coverage. By contrast, vegetation coverage in river channels where water flowed was only between 0 and 0.1. A comparison between 1990 and 2004 revealed a marked increase in FVC within the riparian zones adjacent to the river channel, particularly pronounced in near-channel areas, suggesting potential linkages to localized hydrological modifications. Using 30 m-resolution annual mean FVC raster data, the spatial trend of FVC in the study area from 1990 to 2020 was analyzed using the Sen + Mann–Kendall trend test. The results were classified into five types of changes, and the spatial distribution of FVC change trends is shown in Figure 5b. The figure indicates that areas with improved FVC were mainly distributed near the riverbanks. The statistical results of the FVC change trends are presented in Figure 5c, showing that during 1990–2020, significant increases in vegetation covered 81.2% of the total area; areas without significant change accounted for 5.3%; and areas experiencing vegetation degradation accounted for only 13.5%. During the pre-dam operation period (1990–2004) and post-dam operation period (2005–2020), the overall proportions of vegetation change trends were similar. Specifically, across these two periods, an average of about 4% of the area showed significant decrease, 22% showed slight decrease, 5% remained stable, 49% showed slight increase, and 20% showed significant increase. However, there were differences in spatial distribution patterns: during 1990–2004, areas with significant increase were mainly concentrated near the riverbanks in the relatively upstream sections of the study reach; while during 2005–2020, areas with significant increase were primarily distributed in the relatively downstream sections of the study reach, with a more uniform distribution. These results indicated that spatial differences existed in vegetation dynamics before and after dam operation.

Figure 6 details the spatial FVC change trends around the 1997 breakpoint, which aligns with distinct dam construction phases. The trends before dam construction are shown in Figure 6a, while those during the construction period are presented in Figure 6b. Figure 6c provides a statistical summary of these FVC change trends for the respective periods. During 1990–1997, 68.3% of the vegetation showed an increasing trend, while during the dam construction period from 1998 to 2004, 58.4% of the area showed a decreasing trend. From 2005 to 2020, the increasing areas recovered to nearly 65.5% of the pre-dam level, but the areas with significant increases were 14.8% higher than before the dam construction. These results indicated that dam construction had a short-term negative impact on FVC, with the main area of FVC reduction located in the downstream section of the study area.

4.3. Spatial Characteristics of Vegetation Cover Responses to Drivers

The Spearman correlation coefficients among all candidate indicators are presented in Supplementary Figure S1. Figure 7 specifically illustrates the correlations between FVC and those indicators that showed a significant correlation with FVC. The results demonstrate that precipitation exhibits a negative correlation with FVC, whereas discharge, hydrological connectivity, and temperature indicators all show positive correlations with FVC. Notably, June average discharge (Jun_AvgQ) and HC demonstrated the strongest positive correlations with FVC.

Prior to further analysis, to avoid multicollinearity among variables, we calculated the variance inflation factor (VIF) for each variable. Results showed that 3dMA_MinQ and 7dMA_MinQ exhibited VIF values exceeding the threshold of 5, with values of 7.7 and 9.7, respectively, indicating substantial collinearity. Based on the highest VIF principle, 7dMA_MinQ was subsequently removed from the analysis. The VIF values of all variables are shown in Supplementary Tables S1 and S2. The filtered indicators and their characteristics are presented in

Table 2. Definitions and descriptions of hydrological and meteorological driving variables.

Variables	Number	Definition	Unit	Description
Jun_AvgQ	X1	June average discharge	104 m3/s	Mean discharge in June, representing typical hydrological conditions for the month.
3dMA_MinQ	X2	3-day moving average minimum discharge	104 m3/s	Minimum discharge averaged over a 3-day sliding window, reflecting short-term low-flow extremes.
BFI	X3	Baseflow index	-	Ratio of the annual minimum 7-day discharge to the median annual discharge.
Rev_Count	X4	Reversal count	102	Number of daily flow direction reversals per year.
HC	X5	Hydrologic connectivity	-	Connectivity index calculated using landscape-hydrology model
Ann_Temp	X6	Annual mean temperature	°C	Long-term average temperature, reflecting regional thermal conditions.
Jun_Temp	X7	June mean temperature	°C	Average temperature in June, reflecting thermal conditions during the early growing season.
Ann_Precip	X8	Annual mean of monthly cumulative precipitation	102 mm	Average of monthly cumulative precipitation over a year, reflecting annual moisture availability.
Jun_Precip	X9	June cumulative precipitation	102 mm	Total precipitation in June, representing moisture supply during the critical growth period.

.

To eliminate interference from other factors and focus on the effect of individual environmental factors on FVC, partial correlation coefficients between all variables in Table 2 and FVC were calculated (Figure 8). Figure 8a–i displays the pixel-by-pixel partial correlation coefficients between various environmental factors and FVC, revealing evident spatial heterogeneity in the relationships between vegetation and these driving factors. A visual inspection of the spatial distribution of partial correlation coefficients showed significant spatial variations, with Jiahetan acting as a distinct boundary for these differences.

The statistical results for the partial correlation coefficients upstream and downstream of Jiahetan are displayed in Figure 9. By applying Fisher Z transformations to each correlation coefficient, calculating the average Z value, and then performing an inverse Fisher Z transformation, the average partial correlation coefficient was determined, and then the statistical significance proportion (SP) was checked. In the Xiaolangdi–Jiahetan region, BFI, Rev_Count, HC, and Jun_Temp exhibited positive correlations with FVC, with Fisher Z means of 0.10, 0.28, 0.18, and 0.29, respectively. Conversely, Jun_AvgQ, 3dMA_MinQ, Ann_Temp, and Ann_Precip demonstrated weaker correlations with FVC, with Fisher Z means of −0.04, −0.09, −0.02, and −0.05, respectively. Jun_Precip showed a negative correlation with FVC, with a mean of −0.13. The Jiahetan-Gaocun region manifested distinct response patterns: BFI, Rev_Count, Ann_Temp, Jun_Temp, and Ann_Precip exhibited positive correlations with FVC, with means of 0.18, 0.16, 0.24, 0.23, and 0.12, respectively. Conversely, Jun_AvgQ, 3dMA_MinQ, and Jun_Precip demonstrated negative correlations, with means of −0.21, −0.13, and −0.35, respectively. The correlation of HC in this area was relatively weak, with an average value of 0.01.

Interregional comparison revealed substantial differences in partial correlation means for 3dMA_MinQ, HC, Ann_Temp, Ann_Precip, and Jun_Precip. Rev_Count and Jun_Temp, two indicators with close mean values, also exhibited significant differences in their SSP. This spatial heterogeneity suggests that vegetation response mechanisms to environmental factors differ between these regions, illuminating the complexity of ecological processes within riverine systems. These findings underscore the necessity of region-specific assessments of environmental influences on vegetation in subsequent analyses.

4.4. Variation in the Impact of FVC Driving Factors Across Periods

On the basis of the results of Section 4.2 and Section 4.3, temperature, precipitation, streamflow, and hydrologic connectivity exhibited varying partial correlations in different periods, while the FVC levels also differed among pixels within the Xiaolangdi to Gaocun region. Therefore, a linear mixed model was applied, with temperature, precipitation, streamflow, and HC set as fixed effects, and spatial location treated as a random effect to model FVC. By selecting the lowest BIC value, we established models for the Xiaolangdi–Jiahetan and Jiahetan–Gaocun regions across three periods: 1990–2020, 1990–2004, and 2005–2020, with the screening results shown in the

Table 3. Optimal models for fractional vegetation cover prediction by region and time period.

Reach	Period	Model	Fixed Effects Variable	Random Slopes Variable	R2c
Xiaolangdi–Jiahetan	1990–2020	1	X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9	X2 + X3 + X5	0.746
	1990–2004	2	X1 + X3 + X4 + X5 + X7 + X8 + X9	X4	0.696
	2005–2020	3	X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8	X1 + X3 + X5	0.809
Jiahetan–Gaocun	1990–2020	4	X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9	-	0.549
	1990–2004	5	X1 + X3 + X4 + X5 + X7 + X8 + X9	-	0.615
	2005–2020	6	X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8	X5	0.714

R²c: variance explained by fixed and random effects combined.

. Notably, the optimal models were identical within each of the three periods, this makes the models in the same period highly comparable. However, differences in model structure emerged among the periods, reflecting temporal variations in the influence mechanisms of environmental factors on vegetation coverage.

Figure 10 presents scatter density plots of predicted versus measured values for the target variable across six different models. The goodness-of-fit for each model was assessed using three key evaluation metrics: R², MSE, and RMSE. Model 1, Model 3, and Model 6 demonstrated good predictive accuracy. The fitted lines indicate a general tendency for the models to underestimate high values and overestimate low values.

The fixed-effect coefficients and relative importance (RI) of variables in the models are summarized in

Table 4. Regression coefficients and relative importance.

Predictors	Unit	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
X1	104 m3/s	0.02 **(7.21)	1.71 **(11.75)	−0.19 **(4.80)	−0.23 **(3.29)	1.24 **(9.69)	−0.77 **(40.87)
X2	104 m3/s	−0.27 **(3.85)	-	−0.32 **(9.83)	−0.35 **(5.47)	-	−0.33 **(11.36)
X3	-	0.13 **(6.42)	−0.51 **(8.92)	0.19 **(7.21)	0.17 **(4.41)	−1.15 **(35.15)	0.21 **(10.08)
X4	102	0.17 **(27.77)	0.06 **(21.89)	0.18 **(4.15)	0.15 **(19.62)	0.16 **(12.32)	−0.13 **(2.65)
X5	-	0.07 **(14.03)	−0.08 **(2.97)	0.20 **(32.53)	0.05 **(11.19)	−0.1 **(1.95)	−0.08 **(2.44)
X6	°C	0.01 **(4.45)	-	−0.02 **(4.80)	0.04 **(14.55)	-	0.04 **(10.83)
X7	°C	0.03 **(17.49)	0.11 **(27.54)	0.02 **(15.28)	0.01 **(5.40)	0.12 **(12.83)	0.03 **(7.86)
X8	102 mm	−0.05 **(4.45)	0.32 **(6.94)	−0.62 **(21.40)	−0.1 **(12.57)	0.12 **(12.44)	−0.51 **(13.91)
X9	102 mm	−0.11 **(14.33)	−0.39 **(19.98)	-	−0.18 **(23.50)	−0.24 **(15.62)	-

** p < 0.01; values in parentheses = relative importance (%).

. The most critical drivers varied across regions: For the 1990–2020 model, upstream of the Jiahetan section, the top four drivers were Rev_Count, Jun_Temp, Jun_Precip, and HC; downstream, the key drivers shifted to Jun_Precip, Rev_Count, Ann_Temp, and Ann_Precip. For the 1990–2004 model, the top four driving factors upstream of the Jiahetan Section were Jun_Temp, Rev_Count, Jun_Precip, and Jun_AvgQ; the top four driving factors downstream were BFI, Jun_Precip, Jun_Temp, and Ann_Precip. For the 2005–2020 model, the top four driving factors upstream of the Jiahetan Section were HC, Ann_Precip, Jun_Temp, and 3dMA_MinQ; the top four driving factors downstream were Jun_AvgQ, Ann_Precip, 3dMA_MinQ, and Ann_Temp. The observed reconfigured driver hierarchies between epochs underscore evolving environmental constraints. Meteorological factors, encompassing variables such as annual/June temperature and annual/June precipitation, frequently demonstrated considerable relative importance in the various models. Such consistent influence across different model specifications underscores the appropriateness of their inclusion as control variables, ensuring their potential confounding effects were accounted for.

5. Discussion

5.1. Characteristics of Vegetation Cover Changes Before and After Dam Construction

This study used time-series decomposition and trend change detection methods to analyze the spatiotemporal variation of vegetation cover along the Xiaolangdi–Gaocun section of the lower Yellow River from 1990 to 2020, revealing dynamic changes observed around the period of dam construction. Overall, vegetation cover near the river channel remained relatively low, especially in areas close to the river. This may result from dynamic landforms, excessive moisture, or seasonal water level fluctuations [50,51] Between 1990 and 1997, 68.3% of the vegetation in the study area exhibited an increasing trend, reflecting a natural recovery of riverbank vegetation in the absence of major disturbances. During the dam construction period from 1998 to 2004, 58.4% of the area showed a decline in vegetation cover, a change potentially linked to the combined effects of human activities such as construction and water impoundment, along with climate change in the North China Plain during the 1990s [52,53]. However, from 2005 to 2020, vegetation coverage significantly recovered, with 65.5% of the area showing growth, close to pre-construction levels. The areas with significant vegetation increase rose by 14.8% compared with pre-dam conditions, especially along the mid-to-lower reaches of the riverbanks. This recovery appeared to be associated with a notable rise in temperature and the stabilization of hydrological conditions following the completion of the dam, with our subsequent modeling analyses designed to quantify the statistical strength of such potential attributions.

It should be noted that while the FVC estimates in this study are derived from Landsat NDVI using a widely applied dimidiate pixel model, direct validation against ground-truth vegetation data or a systematic comparison with alternative vegetation indices or published FVC conversion benchmarks specifically for this region and extensive time period was not conducted as part of this particular investigation. This represents a limitation of the current study. The reliance on an NDVI-based FVC, particularly with the dimidiate pixel model, can be prone to saturation in areas of high biomass, potentially leading to an underestimation of FVC changes in the most densely vegetated patches, especially during periods of strong post-dam recovery. While we believe this potential underestimation would primarily affect the absolute magnitude of FVC in highly vegetated areas rather than fundamentally altering the identified temporal trends, key change points, or the relative importance of the drivers investigated, future research could benefit from incorporating such validation or exploring methodologies less susceptible to saturation to further refine FVC estimations.

Comparing changes in vegetation cover between the braided riverbanks and broader agricultural areas in the North China Plain helps to clarify the role of dam operation and associated hydrological alterations in riverbank vegetation dynamics. Along the braided riverbanks, the average FVC was 0.3362 for the period 1990–2004, increasing to 0.4345 for the period 2005–2020. This represents an overall growth rate of approximately 0.066 per decade for the entire study period (1990–2020). In contrast, the long-term average vegetation cover in the wider agricultural areas of the North China Plain was 0.6520, with a significantly lower growth rate of only 0.00024 per decade [52]. The accelerated FVC increase along the braided riverbanks, particularly post-2004, can be attributed to specific hydrological changes induced by the operation of the Xiaolangdi Dam. As quantified by our models (Model 1 and Model 4, detailed in Figure 10, the dam’s operation and subsequent water regulation significantly altered downstream hydrological factors. For instance, June average discharge increased by approximately 1100 m³/s, the 3-day moving average minimum discharge rose by around 900 m³/s, the baseflow index increased by 0.14, reversal count increased by 16, and hydrologic connectivity improved by 0.39. These altered hydrological conditions, characterized by changes such as more stable baseflows, increased minimum flows, and modified connectivity, collectively appear to have provided more favorable conditions for vegetation growth and establishment along the riverbanks. Specifically, our analysis indicates that, according to the model, the dam’s operation was associated with an FVC increase of 0.05 in the Xiaolangdi–Jiahetan reach, with the model attributing a substantial 56% of the total observed mean FVC increase (0.089) in this upstream segment to these dam-related hydrological changes. Further downstream, in the Jiahetan–Gaocun reach, the modeled dam-induced FVC increase was 0.01, with this representing an estimated 17% of the total mean FVC increase (0.073) observed in that area attributable to dam operation as captured by our model (Figure 11).

In assessing the predictive uncertainty of these models, we further conducted model residual analysis. The residual patterns of the models are shown in Supplementary Figure S2, while the normality of the residuals is depicted in Supplementary Figure S3. From the model residual analysis, the residuals of all models did not fully satisfy the normality assumption. Meanwhile, the residual scatter plots exhibited certain patterns, rather than an ideal random distribution, suggesting potential heteroscedasticity. The primary reason for the non-normality and heteroscedasticity of the residuals is that the response variable FVC in this study has a value range between 0 and 1, is inherently bounded, and its distribution typically exhibits skewness, particularly with the models’ tendency to overestimate zero values. We acknowledge that the non-normality and slight heteroscedasticity of the residuals are potential limitations in the application of LMMs in this study. However, previous studies have shown that LMMs, particularly with large sample sizes, exhibit a degree of robustness to deviations from the residual normality assumption, meaning their parameter estimates and inferences can still be reliable to some extent [54,55]. Furthermore, a significant advantage of LMMs is their ability to provide meaningful parameter estimates.

5.2. Dynamic Relationships Among Dam Construction, Braided River Morphology, and Vegetation Response Patterns

This study delves into the dynamic processes by which the operation of the Xiaolangdi Dam, a significant temporal anthropogenic disturbance, interacts with the inherent spatial geographical differences in the braided river morphology of the study reaches to collectively shape riparian vegetation response patterns. Dam operation represents a critical juncture in understanding the hydro-sediment dynamics and ecological responses within the study area. Firstly, dam operation led to a drastic reduction in sediment transport rates, directly altering the formative capacity of the downstream channel and serving as a fundamental driver for channel morphological adjustments. Concurrently, dam regulation modified the intra-annual water allocation, profoundly influencing a suite of key hydrological variables in the downstream channel, such as monthly mean discharge and extreme flow events, all of which are directly linked to the growth and succession of riparian vegetation.

Spatially, prior to dam construction, the Xiaolangdi–Gaocun reach, though classified holistically as braided, exhibited significant internal morphological heterogeneity: the upstream Xiaolangdi–Jiahetan reach displayed more pronounced braided characteristics, with a wide, shallow channel, frequent bifurcations, and highly dynamic changes; conversely, the downstream Jiahetan–Gaocun reach presented as a transitional segment from braided to slightly meandering, with a relatively stable morphology [56]. The initial phase of water-sediment regulation post-dam operation, particularly clearwater releases, induced intense scouring of sediments within the channel, leading to bed incision and morphological adjustments. However, the intensity of this scouring was inconsistent between the upstream and downstream reaches, being more severe upstream. Crucially, this did not fundamentally alter the pre-existing macro-morphological differences between the upstream and downstream reaches. This persistent spatial morphological heterogeneity, superimposed by the temporal impacts of dam operation, provides a critical geomorphological and hydraulic context for understanding the spatial differentiation of vegetation response patterns.

The response patterns of vegetation to these spatiotemporal changes are elucidated through the analysis of meteorological, flow, and hydrological connectivity variables. Meteorological variables acting as regional background environmental conditions, exhibited relatively consistent influence patterns and importance both before and after dam construction and across the upstream and downstream reaches. This indicates that their macro-scale impact on vegetation did not undergo fundamental changes due to the dam or reach-specific differences. In contrast, the response to flow variables clearly reflected the regulatory effects of dam operation and their differentiated impacts across different reaches. Temporally, post-dam, the relationship between June average discharge and FVC in both regions changed from positive to negative. The 3-day moving average minimum discharge was included in post-dam models, and the influence of the baseflow index also generally shifted towards being positive. Conversely, the importance of flow reversal counts significantly decreased post-dam (from 21.89% to 4.15% upstream, and from 12.32% to 2.65% downstream). These changes collectively indicate a stabilization of the flow regime under dam regulation, reducing short-term fluctuations and potentially optimizing water availability during critical growth periods and low-flow baseflow conditions, thereby exerting a more positive or distinctly different influence on vegetation. Spatially, the importance of flow variables also demonstrated upstream-downstream differences. Pre-dam, the baseflow index (Model 5: 35.15%) in the downstream reach was far more important than upstream (Model 2: 8.92%), suggesting a greater dependence of downstream vegetation on stable baseflow. Post-dam, the importance of June average discharge (Model 6: 40.87%) in the downstream reach was significantly higher than upstream (Model 3: 4.80%), indicating that after dam operation, downstream vegetation became more sensitive to water supply during the growing season, and flow factors generally had a greater impact on downstream vegetation compared to upstream.

The influence pattern of the hydrological connectivity variable was particularly noteworthy, with its importance reaching 32.53% only in the post-dam upstream region, significantly higher than in all other spatiotemporal scenarios. This phenomenon is closely related to the more intense scouring and channel incision experienced by the upstream reach post-dam. Such drastic geomorphological adjustments might have rendered the hydraulic linkage between the channel and floodplain a key limiting or driving factor for vegetation re-establishment and growth in this specific region, highlighting the unique role of hydrological connectivity in influencing vegetation during particular stages of geomorphological evolution.

5.3. The Impact of Hydrological Factors on Vegetation Coverage

We analyzed the relative influence of climatic and hydrological factors on vegetation cover dynamics. As shown in Figure 12, distinct spatiotemporal patterns emerge in the dominance of these drivers. Before the construction of the Xiaolangdi Dam, climatic factors were the primary drivers of vegetation change in the upstream Xiaolangdi–Jiahetan reach, with a combined relative importance of 54.46%, while hydrological factors accounted for 45.53%. In contrast, during the same pre-dam period, the downstream Jiahetan–Gaocun reach already showed a dominance of hydrological factors, which contributed 59.11% to vegetation variability, surpassing the climatic influence. This indicates an inherent spatial difference in the primary environmental controls on vegetation even before large-scale anthropogenic intervention. Following the dam’s construction and commencement of operation, a significant shift towards hydrological dominance was observed across both reaches. In the upstream Xiaolangdi–Jiahetan reach, the relative importance of hydrological factors increased substantially from 45.53% to 58.52%. This trend was also evident the downstream Jiahetan–Gaocun reach, where the relative importance of hydrological factors saw their relative importance further increase from 59.11% to 67.4%.

This accentuated importance of hydrological factors post-dam can be attributed to the dam’s significant regulation of downstream hydrological processes. Once operational, the dam artificially controlled the river system’s natural fluctuations, thereby making riparian and floodplain vegetation more acutely sensitive to the altered hydrological regime. For example, the substantial reduction in sediment load after dam construction has led to channel incision and modifications to the channel cross-section. These geomorphological changes alter the flow-water level relationship, significantly influencing soil moisture availability in the riparian zone and making it more directly responsive to variations in runoff [57]. Consequently, the altered predictability, timing of water availability, and changes in flood pulse characteristics exert a significant influence on riparian vegetation establishment and survival. The specific impacts of key hydrological variables are nuanced: regarding Jun_AvgQ, it promoted riparian vegetation growth pre-dam when ample water during the growing season was beneficial, but post-dam, its effect shifted to inhibition as the now more stable riverbanks supported more existing riparian vegetation, and artificially high regulated June flows could inundate the floodplain. The observed inhibitory effect of the 3dMA_MinQ on riparian vegetation cover may be attributed to low water levels during dry periods expanding exposed sediment areas along the riverbanks, potentially facilitating the encroachment of shrubs towards the channel. For the BFI, its pre-dam inhibitory effect might be similar to that of 3dMA_MinQ; however, post-dam, as the channel transitioned from wide and shallow to narrower and deeper, reducing the contact area between surface water and groundwater and thus weakening lateral seepage efficiency, a stable baseflow during the dry season provided a continuous water supply, thereby promoting riparian vegetation growth. Furthermore, Rev_Count generally promotes riparian vegetation by altering flow direction, which enhances the transport capacity for sediments, delivering nutrient-rich silts to the riparian zone. Lastly, HC primarily exhibited a positive influence on riparian vegetation in scoured river sections post-dam, suggesting it enhances vegetation by improving pathways for material migration and energy flow. Given that the impact of dams on downstream vegetation can last for decades [58], the dominance of hydrological factors may continue to grow over time. As downstream channel incision progresses, riverbank collapse and weakened resistance to soil erosion are emerging as ecological challenges [59].

Our findings align with previous research on the Xiaolangdi Dam, which indicated that dam operation can enhance vegetation cover, particularly during non-flood seasons due to stabilized low flows [34]. Many researchers have also found that changes in flow regimes downstream of dams are closely related to increased or altered riverbank vegetation patterns [60,61,62]. The consistently high importance of hydrological factors in explaining vegetation change in regulated river systems, as reported in various studies, strongly supports our conclusion that hydrological factors become increasingly influential in shaping vegetation responses following major dam construction and operation.

6. Conclusions

This study investigated the 1990–2020 spatiotemporal dynamics of vegetation cover along the Xiaolangdi–Gaocun braided river section, identifying key change points related to the Xiaolangdi Dam’s operations and quantifying vegetation responses to meteorological and hydrological drivers. The findings offer insights into the interplay between hydraulic engineering and riparian ecosystems.

Key findings reveal that while dam construction initially induced a short-term negative impact, the subsequent operational phase of the Xiaolangdi Dam significantly promoted an increase in FVC along the downstream braided riverbanks. This positive long-term effect was found to primarily stem from substantial dam-induced alterations in the downstream hydrological regime, which collectively created more favorable conditions for riverbank vegetation growth and establishment. Our LMM analysis suggests that these dam-related hydrological changes were linked to 56% of the total observed FVC increase in the Xiaolangdi–Jiahetan reach and 17% in the Jiahetan–Gaocun reach. Furthermore, the study underscored a shift in the dominant hydrological drivers post-dam operation, with an increased importance of factors like hydrologic connectivity and baseflow index. Notably, a clear spatial heterogeneity was observed in how riparian vegetation in different downstream reaches responded to these hydrological drivers, emphasizing the site-specific nature of ecosystem responses. These findings carry significant implications for the ecological management of regulated rivers, informing strategies for dam operation, riparian habitat restoration, and sustainable watershed management in similar braided river systems.

Future research should aim to further refine the mechanistic understanding of riparian vegetation responses, improve FVC estimation methodologies, and enhance predictive modeling capabilities. These advancements will be crucial for developing more precise assessments and robust strategies for the sustainable management of regulated river ecosystems.