A quantitative identification method for the impact of water storage in hydropower development on terrestrial ecosystems

Abstract

The present application relates to the technical field of ecological environment monitoring, and discloses a quantitative identification method for the influence of water storage in water and electricity development on terrestrial ecological system, comprising the following steps: S1, arranging a plurality of sampling points along the reservoir boundary towards the land; S2, collecting the ecological response variable, the environmental factor variable and the horizontal distance from the sampling point to the reservoir boundary as the water storage influence variable for each sampling point respectively, and constructing the collected data into data matrices respectively; S3, taking the data matrices as the response variable; S4, introducing the environmental factor variable as the control variable based on the sorting analysis result; S5, performing variance decomposition analysis based on the partial sorting analysis result, and calculating the independent explained variance and the total explained variance of the water storage influence variable. Through the setting of land space gradient sampling points based on the water storage boundary, the spatial propagation characteristics of the water storage influence on the land are collected in a covering manner, so that the representativeness of the sampling points is enhanced, and the integrity of the ecological gradient is ensured.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring technology, specifically a method for quantitatively identifying the impact of hydropower development and water storage on terrestrial ecosystems. Background Technology

[0002] Hydropower development is an important means of sustainable energy production, but reservoir impoundment alters the hydrothermal conditions of the surrounding environment, thereby affecting soil nutrient cycling and terrestrial ecosystem services. Current technologies primarily employ two methods: historical comparison (comparing before and after impoundment) and spatial comparison (comparing the impounded area with adjacent non-impounded areas). However, the former relies on accumulated monitoring data from previous periods, while the latter cannot effectively identify the impact of reservoir impoundment on the surrounding ecosystem, making it difficult to support a scientific assessment of the ecological and environmental impacts of hydropower projects. The surrounding terrestrial ecosystems, local climate, vegetation conditions, and soil properties are typically influenced by multiple factors, including climate, season, and topography, making it difficult to accurately quantify the effects of reservoir impoundment. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a quantitative identification method for the impact of hydropower development and water storage on terrestrial ecosystems. This method solves the problem that existing technologies have not yet systematically established a gradient sampling point deployment mechanism based on water storage boundaries, and lack an efficient spatial sampling method that can simultaneously ensure the representativeness of sampling points and the integrity of ecological gradients. This results in a limited systematic understanding of the spatial propagation characteristics of water storage disturbances in terrestrial ecosystems, and is prone to misjudgment or weak interpretability in the ordination and variance analysis stages.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for quantitatively identifying the impacts of hydropower development and water storage on terrestrial ecosystems, comprising the following steps:

[0005] S1, Multiple sampling points are set up along the reservoir boundary towards the land. The sampling points are set up in a range coverage manner, with a gradient range from the reservoir boundary.

[0006] S2, collect the ecological response variables, environmental factor variables and the horizontal distance from the sampling point to the reservoir boundary as water storage impact variables for each sampling point, and construct data matrices from the collected data respectively;

[0007] S3. Based on the data matrix as the response variable and the matrices of water storage impact variables and environmental factor variables as explanatory variables, a ranking analysis is performed to establish the correlation between variables.

[0008] S4. Based on the ordination analysis results, environmental factor variables are introduced as control variables, and partial ordination analysis is performed to remove the interference of environmental factors and identify the independent effects of water storage impact variables.

[0009] S5. Based on the partial ordination analysis results, variance decomposition analysis is performed to calculate the independent explained variance and total explained variance of the water storage impact variables, and the contribution rate of water storage impact is judged based on the ratio of the two, which is used to quantitatively identify the impact of hydropower development water storage on terrestrial ecosystems.

[0010] Preferably, in step S1, sampling points are arranged vertically along the reservoir shoreline, and the sampling points are set at equal intervals or in a layered random manner to cover a gradient range from 10 meters to 5000 meters from the reservoir boundary, so as to cover areas with different water storage gradients.

[0011] Preferably, in step S1, the sampling point sampling adopts the method of setting multiple time periods throughout the year, including spring, summer and autumn. Sample data is repeatedly obtained at the same sampling point in each time period to reflect the seasonal dynamic changes of water storage impact and improve time resolution.

[0012] Preferably, in S2, the ecological response variables include one or more of the normalized vegetation index, leaf area index, net primary productivity, evapotranspiration, water use efficiency, community species diversity index, or soil nutrient index, and the water storage variable is represented by the horizontal projection distance from the sampling point to the reservoir boundary.

[0013] Preferably, in S2, the environmental factors are grouped by nature, including average annual temperature, annual precipitation, light intensity, altitude, slope, aspect, pH value, organic matter content, texture, and land cover type. Each group of variables is organized into a two-dimensional matrix for use as covariate input in subsequent ordination analysis.

[0014] Preferably, in step S3, the ordination analysis method includes redundancy analysis or canonical correspondence analysis. The ordination analysis is selected based on the method for judging the gradient length of ecological response variables, and outputs the variance explained by the ordination axis and the loading values ​​of each variable.

[0015] The preferred method for determining the gradient length of ecological response variables includes the following steps:

[0016] S301, First, perform DCA analysis to obtain the gradient length of the first sorting axis;

[0017] S302. Based on the gradient length obtained in S301, the following judgment is made: when the gradient length is <3SD, it indicates that the ecological response variable has a linear response to the environmental gradient. Therefore, redundancy analysis is selected as the ordination analysis method. When the gradient length is ≥3SD, it indicates that the ecological response variable has a nonlinear response to the environmental gradient. Therefore, canonical correspondence analysis is selected as the ordination analysis method.

[0018] S303, based on the method selected in S302, performs ordination analysis on the ecological response variable matrix, water storage impact variables, and environmental factor variables to obtain the ordination axis explanation, variable loading values, and ordination plot output results.

[0019] Preferably, in S4, the partial ordination analysis is performed on the basis of ordination analysis, using environmental factor variables as control variables, water storage impact variables as explanatory variables, and ecological response variables as response variables to perform redundancy analysis or canonical correspondence analysis.

[0020] Preferably, in step S5, variance decomposition analysis is used to calculate the independent explained variance of the water storage impact variable, the independent explained variance of the environmental factor variable, and the explained variance of their interaction.

[0021] Preferably, in step S5, the formula for calculating the contribution rate of water storage impact is:

[0022] Water storage contribution rate = Independent explained variance of water storage influencing variables ÷ Total explained variance × 100%.

[0023] This invention provides a method for quantitatively identifying the impact of hydropower development and water storage on terrestrial ecosystems.

[0024] It has the following beneficial effects:

[0025] 1. This invention achieves the effect of enhancing the representativeness of the sampling points and ensuring the integrity of ecological gradient capture by setting up land spatial gradient sampling points based on the water storage boundary and collecting the spatial propagation characteristics of the water storage impact on land in a comprehensive manner.

[0026] 2. This invention uses DCA analysis to determine the gradient length of ecological response variables and selects the ranking method accordingly, thereby quantitatively optimizing the matching of the ranking model and achieving the effect of avoiding model misuse and improving the accuracy of variable interpretation.

[0027] 3. This invention quantitatively identifies the independent contribution of water storage variables to ecological changes by performing variance decomposition on the ordination analysis results and constructing a water storage contribution rate index, thereby improving the objectivity of ecological impact analysis and the basis for engineering regulation and decision-making. Attached Figure Description

[0028] Figure 1 This is a flowchart of a method for quantitatively identifying the impact of hydropower development and water storage on terrestrial ecosystems according to the present invention. Detailed Implementation

[0029] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Please see the appendix Figure 1This invention provides a method for quantitatively identifying the impacts of hydropower development and water storage on terrestrial ecosystems, comprising the following steps:

[0031] S1, Multiple sampling points are set up along the reservoir boundary towards the land. The sampling points are set up in a range coverage manner, with a gradient range from the reservoir boundary.

[0032] S2, collect the ecological response variables, environmental factor variables and the horizontal distance from the sampling point to the reservoir boundary as water storage impact variables for each sampling point, and construct data matrices from the collected data respectively;

[0033] S3. Based on the data matrix as the response variable and the matrices of water storage impact variables and environmental factor variables as explanatory variables, a ranking analysis is performed to establish the correlation between variables.

[0034] S4. Based on the ordination analysis results, environmental factor variables are introduced as control variables, and partial ordination analysis is performed to remove the interference of environmental factors and identify the independent effects of water storage impact variables.

[0035] S5. Based on the partial ordination analysis results, variance decomposition analysis is performed to calculate the independent explained variance and total explained variance of the water storage impact variables, and the contribution rate of water storage impact is judged based on the ratio of the two, which is used to quantitatively identify the impact of hydropower development water storage on terrestrial ecosystems.

[0036] In S1, sampling points are arranged vertically along the reservoir shoreline. The sampling points are set at equal intervals or in a stratified random manner to cover the gradient range from 10 meters to 5000 meters from the reservoir boundary, so as to cover areas with different water storage gradients.

[0037] Specifically, sampling points were selected and extended inland along the reservoir shoreline, arranged perpendicular to the shoreline to form strip-shaped transects. Each transect consisted of multiple sampling points, all with clearly defined distances relative to the reservoir boundary. To balance coverage and data density, the sampling distance was set within a gradient range of 10 meters to 5000 meters. This range includes three functional zones: a nearshore transition zone, an intermediate buffer zone, and a distant background zone, which helps to capture the overall spatial trends of ecological variables.

[0038] Regarding the selection of sampling point spacing, depending on the complexity of the regional terrain and actual operational conditions, either equidistant sampling or stratified random sampling can be used. Equidistant sampling is beneficial for analyzing continuous trends, while stratified random sampling enhances the ability to capture local heterogeneity. In practice, equidistant sampling points can be placed every 50 meters; if a stratified approach is used, several sampling points are randomly selected within three gradient layers: 0–500 meters, 500–1000 meters, and 1000–5000 meters. Each layer should cover at least three points to ensure basic statistical representativeness.

[0039] In S1, sampling points are sampled repeatedly throughout the year in multiple time periods, including spring, summer and autumn. Sample data are repeatedly obtained at the same sampling points in each time period to reflect the seasonal dynamic changes in the impact of water storage and improve the time resolution capability.

[0040] Specifically, the sampling period is divided into three key phases: spring, summer, and autumn. The criteria for this division are based on the synergistic effects of typical seasons on hydrological regulation and vegetation response. Spring typically marks the initial stage of reservoir regulation, with surface moisture beginning to recover and terrestrial vegetation rapidly turning green. Summer is the peak of water storage, leading to increased evapotranspiration and the greatest ecological pressure. Autumn is the final stage of reservoir regulation, a stable adjustment phase before the ecosystem enters dormancy for the following year. The sampling period depends on the sampled object; for example, if leaves are collected, winter is excluded if they are not available, while soil can be collected even in winter. These three phases constitute the most typical window of interaction between hydrological disturbances and terrestrial ecosystem responses. Each round of sampling covers all ecological response variables, and environmental background data, including temperature, humidity, and light, are recorded simultaneously when necessary for subsequent time-level analysis. The location of sampling points or observed factors must not be adjusted between sampling periods to maintain the stability of variable structure and spatial pattern. Data recording can be done in a structured matrix format: let the response variable of the i-th sampling point at time t be... Where t∈{1,2,3} corresponds to spring, summer, and autumn, respectively. This ultimately forms three sets of response variable matrices R. (1) R (2) R (3) Ecological status representation at the same location but at different time periods;

[0041] Ecosystems often exhibit significant lag and asynchronous responses to external hydrological disturbances. Water storage behavior alters not only hydrothermal conditions at a specific point in time, but also represents a comprehensive process involving soil moisture accumulation, vegetation metabolic activity, and microhabitat reconstruction over a period of time. This allows for the reconstruction of the evolutionary trajectory of ecological response variables at different times, thereby more accurately capturing the temporal distribution characteristics of water storage effects.

[0042] In S2, ecological response variables include one or more of the following: normalized vegetation index, leaf area index, net primary productivity, evapotranspiration, water use efficiency, community species diversity index, or soil nutrient indicators. These include ecosystem variables: normalized vegetation index, leaf area index, net primary productivity, evapotranspiration, water use efficiency, community species diversity index, and their derived parameters; plant physiological and ecological indicators: photosynthetic rate, photosynthetically active radiation, stomatal conductance, transpiration rate, specific leaf area, leaf nitrogen content, chlorophyll content, and their derived parameters; and soil properties: soil pH, soil organic matter, nitrogen, phosphorus, and potassium (holistic and available forms), soil microorganisms, soil bulk density, porosity, and water content. The water storage variable is represented by the horizontal projection distance from the sampling point to the reservoir boundary.

[0043] Specifically, the selection of ecological response variables is based on empirical measurability, ecological explanatory power, and data continuity, prioritizing multidimensional indicators obtainable through remote sensing data or field monitoring. Among these, the Normalized Difference Vegetation Index (NDVI), as a standardized indicator reflecting vegetation biomass, has significant advantages in spatial consistency and temporal stability; its calculation formula is as follows:

[0044]

[0045] NIR represents near-infrared reflectance, and RED represents red reflectance. NDVI values ​​typically range from -1 to 1; higher values ​​indicate more abundant vegetation. Leaf area index (LAI) is used to capture changes in vertical canopy structure. This index can be measured using MODIS products or ground-based instruments such as LAI-2000. Higher LAI indicates a larger total leaf area per unit area of ​​soil, and stronger photosynthetic and water use capabilities of the plant community. To further reflect the dynamics of the ecosystem's carbon cycle, net primary productivity (NPP) is included as a core variable. NPP is usually retrieved from remote sensing data or calculated from long-term observations at ecological sites, and its unit is gC / m³. 2 / year, directly describing the net carbon sequestration capacity of plants, shows significant changes under water stress and nutrient limitation. Evapotranspiration (ET) is also a key variable in this step, especially after changes in surface water vapor flux due to hydropower storage, its spatial distribution is easily altered. ET can be inverted using the SEBAL or MOD16 algorithm, with units of mm / day or mm / month, and is mainly controlled by air temperature, soil moisture, and vegetation structure. Water use efficiency (WUE) is used as an integrated variable, defined as ET per unit NPP, i.e.:

[0046]

[0047] In addition to the process and functional variables mentioned above, this embodiment also introduces community species diversity indices, such as the Shannon diversity index, to capture changes in ecosystem structural hierarchy, as defined below:

[0048]

[0049] Where p i Let Y represent the relative abundance of the i-th species, and S represent the total number of species within the sampling point. Diversity indices are highly sensitive to habitat disturbance and can effectively reflect habitat fragmentation or species loss caused by water storage boundary effects. These variables do not necessarily require full measurement at all sampling points; rather, they should be flexibly combined based on regional characteristics and data availability. In the data matrix structure, each sampling point corresponds to a set of ecological response vectors: Y i ={NDVIi LAI i NPP i ...}, different variables form a multidimensional column vector, and the variables affecting water storage are represented by the horizontal projection distance from the sample point to the reservoir boundary. This distance differs from the slope distance or path distance; it only takes the horizontal value of the land surface and is the most representative quantitative indicator of the attenuation effect of water storage space. Its definition is as follows:

[0050]

[0051] Where D i Let x be the shortest horizontal distance from the i-th sample point to the shoreline. i ,y i (x) represents the plane coordinates of the sample point. j ,y j The distance () represents the coordinates of any point in the shoreline point set. A GIS system can quickly calculate this distance in batches and fill it into the explanatory variable matrix in meters. The ecological responses caused by hydropower storage mostly decay exponentially or linearly along the land-water boundary; the farther the sampling point is from the shoreline, the less affected it is. By embedding this distance into the ordination model, the parameterized input of the "spatial disturbance intensity" of water storage can be achieved. Compared to traditional categorical variables including disturbed / undisturbed, this method provides a more continuous and detailed causal modeling path.

[0052] In S2, environmental factors are grouped by nature, including annual average temperature, annual precipitation, light intensity, altitude, slope, aspect, pH value, organic matter content, texture, and land cover type. These include meteorological factors: annual average temperature, annual precipitation, and light intensity; topographic factors: altitude, slope, and aspect; and soil or site condition factors: pH value, organic matter content, texture, and land cover type. Each group of variables is organized into a two-dimensional matrix for use as covariate input in subsequent ordination analysis.

[0053] Specifically, in terms of data structure construction, this invention organizes each group of environmental factors into a two-dimensional matrix structure. Using the sample point number as the row dimension and the variable factors as the column dimension, a unified covariate input matrix and climate group matrix are constructed. Where n is the number of sample points, and the columns represent AMT, AP, and Radiation values, respectively; terrain group matrix. These are Elevation, Slope, Aspect, and so on.

[0054] These environmental factor matrices will be used as covariate inputs in subsequent ordination analyses, together with the water storage variable, to explain the changing trends of the ecological response variable. The ordination model will automatically identify the independent effects of the water storage distance variable on the ecological response, while controlling for differences in various environmental backgrounds, thus achieving a net effect estimate of the spatial disturbance gradient.

[0055] The innovation of this approach lies in its three-step method of grouping, matrixing, and control, which explicitly organizes environmental factors into a multidimensional covariate system, avoiding the spurious causal problems caused by background bias in traditional ecological analysis. Furthermore, the grouping matrix for each environmental factor can be flexibly inserted and removed, allowing for grouped regression, main effect, and interaction effect comparison analyses in different ranking models, demonstrating excellent modeling scalability.

[0056] In S3, the ordination analysis method includes redundancy analysis or canonical correspondence analysis. The ordination analysis method is selected based on the gradient length of the ecological response variable, and the output is the variance explained by the ordination axis and the loading values ​​of each variable.

[0057] Specifically, a constrained ordination method is employed, which explicitly incorporates water storage distance and environmental variables as explanatory factors into the model while analyzing ecological response patterns. The main analytical tools include:

[0058] Redundancy analysis: Applicable to situations where the relationship between ecological response variables is relatively linear;

[0059] Canonical correspondence analysis (CCE) is suitable for situations where ecological variables exhibit nonlinear, categorical, or widely distributed changes. The specific choice of ordination method depends on the gradient length of the ecological response variable. Gradient length can be understood as "the degree of change of the variable in the ecological space." If the change is small and relatively stable, redundancy analysis is suitable; however, when the variable changes drastically and unevenly in space, it indicates that the ecological response is sensitive to environmental changes and exhibits complex patterns, making CCE more suitable.

[0060] In the analysis, multiple ecological response indicators, including NDVI, NPP, and biodiversity, were constructed into a matrix, with each row corresponding to a sample point and each column corresponding to an ecological variable. Simultaneously, another explanatory matrix was created by combining water storage distance with a series of environmental factors, including temperature, precipitation, slope, and soil properties.

[0061] The core task of ordination analysis is to find a set of principal axes from the ecological response matrix, that is, a set of comprehensive factors that can explain the trend of ecological variable changes to the greatest extent, and to determine whether these principal axes are significantly related to the water storage distance. Each principal axis has a value of explained variance, which represents the proportion of ecological response changes that the axis can explain. If the first two principal axes can explain more than 50% of the total variation, it indicates that the ordination model is performing well.

[0062] The method for determining the gradient length of ecological response variables includes the following steps:

[0063] S301, First, perform DCA analysis to obtain the gradient length of the first sorting axis;

[0064] S302. Based on the gradient length obtained in S301, the following judgment is made: when the gradient length is <3SD, it indicates that the ecological response variable has a linear response to the environmental gradient. Therefore, redundancy analysis is selected as the ordination analysis method. When the gradient length is ≥3SD, it indicates that the ecological response variable has a nonlinear response to the environmental gradient. Therefore, canonical correspondence analysis is selected as the ordination analysis method.

[0065] S303, based on the method selected in S302, performs ordination analysis on the ecological response variable matrix, water storage impact variables, and environmental factor variables to obtain the ordination axis explanation, variable loading values, and ordination plot output results.

[0066] Specifically, firstly, detrended correspondence analysis is performed on the ecological response variable matrix. Detrended correspondence analysis is a ranking method used to detect whether ecological variables exhibit a non-linear distribution. The length of the first ranking axis in its output is the gradient length. After obtaining the gradient length of the first axis, the judgment stage is entered. If the gradient length is less than 3 standard deviations (<3SD), it indicates that the relationship between the ecological response variables and the environmental gradient tends to be linear. In this case, the redundancy analysis ranking method is used. If the gradient length is greater than or equal to 3 standard deviations (≥3SD), it indicates that the ecological response has non-linear response characteristics to environmental factors. Canonical correspondence analysis should be selected for ranking modeling.

[0067] The sorting analysis will output the following core results:

[0068] The percentage of total variation in the ecological response variable explained by each ordination axis is used to assess the model fit. For example, if ordination axis 1 explains 35% of the variation and ordination axis 2 explains 18%, then the two axes together explain 53% of the response pattern. The graph shows the contribution and direction of each ecological indicator on each ordination axis. The magnitude of the loading values ​​indicates the degree of influence of the variable on the ordination structure; consistent directions indicate similar trends among variables, while opposite directions indicate different response mechanisms. The graph displays the distribution of sample points along the ordination axes and can be overlaid with environmental factor arrows to show the direction of variable gradients. The graph visually reveals whether water storage disturbance has triggered significant ecological distribution patterns, such as whether affected sample points cluster in specific areas of the ordination map.

[0069] In S4, partial ordination analysis is based on ordination analysis, and performs redundancy analysis or canonical correspondence analysis with environmental factor variables as control variables, water storage impact variables as explanatory variables, and ecological response variables as response variables.

[0070] Specifically, first, three types of data matrices are prepared, and their roles are clearly defined. Second, the ordination model type is determined based on the gradient characteristics of the ecological response variables. When the DCA analysis results show that the variation of ecological variables on the ordination axis is small and less than 3 standard deviations, redundancy analysis (RDA) is selected; if the variation is large and greater than or equal to 3 standard deviations, canonical correspondence analysis (CCA) is used. In the modeling process, water storage variables directly participate in the modeling as the main explanatory factors, while environmental factors enter the model through "condition terms," ​​that is, they are specified as variables that need to be "controlled." As an example:

[0071] Y~X+Condition(Z);

[0072] Here, Y represents the ecological response variable matrix, X represents the water storage impact variable, and Z represents the environmental control variable. "Condition" means first removing the parts related to Z from Y, then using X to explain the remaining variance. Finally, a partial ordination analysis is performed, outputting the net explanatory power on the ordination axis, variable loadings, and the ordination plot structure. The net explanatory power reflects the actual explanatory power of the water storage variable after controlling for environmental factors; a higher value indicates a stronger interference effect. The loadings reveal the degree and direction of contribution of each ecological indicator to the ordination axis, facilitating the identification of variables most sensitive to water storage. The ordination plot shows the distribution structure of sample points in the ordination space; if the water storage gradient has a significant impact, it will be manifested as a significant difference in the coordinate clustering direction between sample points near and far from the reservoir.

[0073] In S5, variance decomposition analysis is used to calculate the independent explained variance of the variables affecting water storage, the independent explained variance of environmental factor variables, and the explained variance of their interaction.

[0074] Specifically, firstly, a three-set variable input system is established to ensure the logical rigor of the variance decomposition structure. Ecological response variables remain the objects of explanation, including indicators characterizing ecosystem function and structure such as NDVI and NPP. Explanatory variables are divided into two main categories: firstly, water storage impact variables, typically using the distance from the sampling point to the reservoir boundary as a quantitative indicator, representing the dominant human disturbance factor; and secondly, environmental factor variables, including temperature, slope, and soil properties, used to express natural background control factors. These three together constitute the basic data unit for variance decomposition analysis.

[0075] Three models were used to perform ordination analysis to obtain the necessary explanatory structure: an ordination model was constructed using only the water storage variable to obtain its explained total variance; a model was constructed using only environmental factors; and a joint model was constructed by incorporating both the water storage variable and environmental factors. By comparing the total variance explained by the outputs of these three models, the independent and shared effects of each group of variables can be decomposed. The structure of the variance decomposition can be expressed as follows:

[0076] [a]: Independently explained variance of water storage variable;

[0077] [b]: Shared explanatory variance of water storage variables and environmental factors;

[0078] [c]: Independently explained variance of environmental factors;

[0079] [d]: Unexplained residual variance;

[0080] The above algebra satisfies the following relationship:

[0081] Total explanatory power = [a] + [b] + [c], Total variation = [a] + [b] + [c] + [d];

[0082] In this table, [a] represents the independent contribution of water storage behavior to the ecological response after removing environmental factors; [c] represents the explanatory power of environmental factors after removing the influence of water storage; and [b] reflects the synergistic and overlapping effects of the two factors. Finally, the variance decomposition results are numerically summarized and visualized. The proportion of each variance component in the total explained variance is usually presented as a percentage. For example, the independent explanation of water storage is 28%, environmental factors are 42%, the interaction between the two is 15%, and the remainder is unexplained variance.

[0083] In S5, the formula for calculating the contribution rate of water storage impact is:

[0084] Water storage contribution rate = Independent explained variance of water storage influencing variables ÷ Total explained variance × 100%.

[0085] Specifically, the analysis steps logically follow the results of the preceding modeling and play important roles in refining mechanisms, attribution weights, and assessing the level of impact. In terms of data processing, three types of ordination models are first constructed: First, a model using the horizontal distance from the sampling point to the reservoir boundary as the sole explanatory variable for water storage impact variables, obtaining the variance of the ecological response it can explain; second, a model using environmental factor variables, including temperature, slope, and soil pH, as the sole explanatory variables; and third, a model using both as combined explanatory variables to obtain the overall explanatory power. Through cross-comparison of the explanatory power among these three sets of models, the total explanatory variance in the ordination model can be divided into three parts: the independent explanatory part of water storage variables, the independent explanatory part of environmental factors, and the shared explanatory part resulting from the overlap between the two.

[0086] To accurately measure the independent impact of water storage on ecosystem change, the water storage contribution rate index is introduced, and its calculation formula is as follows:

[0087]

[0088] The numerator represents the ecological response variation that can be explained solely by the water storage variable after controlling for environmental factors; the denominator represents the total variance explained by the ordination model with both water storage and environmental variables involved. This percentage reflects the net driving force of reservoir water storage behavior on changes in terrestrial ecosystem patterns in the current context. After performing this analysis, the results can be presented in tabular or graphical form, showing the proportion of each variance component.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for quantitatively identifying the impacts of hydropower development and water storage on terrestrial ecosystems, characterized in that, Includes the following steps: S1, Multiple sampling points are set up along the reservoir boundary towards the land. The sampling points are set up in a range coverage manner, with a gradient range from the reservoir boundary. S2, collect the ecological response variables, environmental factor variables and the horizontal distance from the sampling point to the reservoir boundary as water storage impact variables for each sampling point, and construct data matrices from the collected data respectively; Ecological response variables include one or more of the following: normalized vegetation index, leaf area index, net primary productivity, evapotranspiration, water use efficiency, community species diversity index, or soil nutrient index. The water storage impact variables are represented by the horizontal projection distance from the sampling point to the reservoir boundary. Environmental factors were grouped by nature, including annual average temperature, annual precipitation, light intensity, altitude, slope, aspect, pH value, organic matter content, texture, and land cover type. Each group of variables was organized into a two-dimensional matrix for use as covariate input in subsequent ordination analysis. S3. Based on the data matrix as the response variable and the matrices of water storage impact variables and environmental factor variables as explanatory variables, a ranking analysis is performed to establish the correlation between variables. The ordination analysis method includes redundancy analysis or canonical correspondence analysis. The ordination analysis is selected based on the method for judging the gradient length of ecological response variables, and outputs the variance explained by the ordination axis and the loading values ​​of each variable. The method for determining the gradient length of ecological response variables includes the following steps: S301, First, perform DCA analysis to obtain the gradient length of the first sorting axis; S302. Based on the gradient length obtained in S301, the following judgment is made: when the gradient length is <3SD, it indicates that the ecological response variable has a linear response to the environmental gradient. Therefore, redundancy analysis is selected as the ordination analysis method. When the gradient length is ≥3SD, it indicates that the ecological response variable has a nonlinear response to the environmental gradient. Therefore, canonical correspondence analysis is selected as the ordination analysis method. S303, based on the method selected in S302, performs ordination analysis on the ecological response variable matrix, water storage impact variables and environmental factor variables to obtain ordination axis explanation, variable loading values ​​and ordination plot output results; S4. Based on the ordination analysis results, environmental factor variables are introduced as control variables, and partial ordination analysis is performed to remove the interference of environmental factors and identify the independent effects of water storage impact variables. S5. Based on the partial ordination analysis results, variance decomposition analysis is performed to calculate the independent explained variance and total explained variance of the water storage impact variables, and the contribution rate of water storage impact is judged based on the ratio of the two, which is used to quantitatively identify the impact of hydropower development water storage on terrestrial ecosystems.

2. The method for quantitatively identifying the impact of hydropower development and water storage on terrestrial ecosystems according to claim 1, characterized in that: In S1, sampling points are arranged vertically along the reservoir shoreline. The sampling points are set at equal intervals or in a layered random manner to cover a gradient range from 10 meters to 2000 meters from the reservoir boundary, so as to cover areas with different water storage gradients.

3. The method for quantitatively identifying the impact of hydropower development and water storage on terrestrial ecosystems according to claim 1, characterized in that: In S1, the sampling point sampling adopts the method of setting multiple time periods throughout the year, including spring, summer and autumn. Sample data is repeatedly obtained at the same sampling point in each time period to reflect the seasonal dynamic changes of water storage impact and improve time resolution.

4. The method for quantitatively identifying the impact of hydropower development and water storage on terrestrial ecosystems according to claim 1, characterized in that: In S4, partial ordination analysis is performed on the basis of ordination analysis, using environmental factor variables as control variables, water storage impact variables as explanatory variables, and ecological response variables as response variables to perform redundancy analysis or canonical correspondence analysis.

5. The method for quantitatively identifying the impact of hydropower development and water storage on terrestrial ecosystems according to claim 1, characterized in that: In S5, variance decomposition analysis is used to calculate the independent explained variances of the water storage impact variables and the environmental factor variables, as well as the explained variances of their interaction.

6. The method for quantitatively identifying the impact of hydropower development and water storage on terrestrial ecosystems according to claim 1, characterized in that: In S5, the formula for calculating the contribution rate of water storage impact is: Water storage contribution rate = Independent explained variance of water storage influencing variables ÷ Total explained variance × 100%.

Images