Method for quantitatively evaluating dynamic hydrological effect of vegetation

Abstract

The invention discloses a method for quantitatively evaluating the dynamic hydrological effect of vegetation, and belongs to the technical field of climate change evaluation. Aiming at the defect that vegetation and hydrological effects cannot be independently and quantitatively separated due to the fact that underlying surface parameters confuse vegetation and non-vegetation factors in an existing Budyko hydrothermal balance attribution model, meteorological, hydrological, vegetation and environmental data are collected and processed; inverting underlying surface parameters based on a Budyko framework; calculating elastic coefficients of runoff to rainfall, potential evapotranspiration and parameters; constructing a linear model taking the drought index and the soil water-holding capacity as variables to obtain vegetation sensitivity parameters; finally, the runoff component caused by vegetation change is quantitatively separated in combination with the elastic coefficient and the vegetation sensitivity parameter. According to the method, the influence of vegetation dynamics on the hydrological effect is independently and accurately quantified, and the transparency of runoff attribution and the application value in ecological hydrological management are improved.

Description

Technical Field

[0001] This invention belongs to the field of climate change assessment technology, and in particular relates to a method for quantitatively assessing the dynamic hydrological effects of vegetation. Background Technology

[0002] Vegetation-hydrological effects, as a core link connecting climate, ecology, and hydrological processes, have become a research hotspot in Earth system science and global change science. Vegetation plays a crucial role in regulating regional water cycle patterns by controlling key aspects such as evapotranspiration, precipitation redistribution, and runoff generation. Against the backdrop of climate warming, especially in arid mountainous regions such as the Tianshan Mountains (known as the "water tower of Central Asia"), the highly uneven distribution of water and heat conditions makes vegetation dynamics and its hydrological regulatory functions significantly complex and vulnerable. Therefore, quantitatively assessing the impact of vegetation dynamics on hydrological elements such as runoff is essential for understanding the mechanisms of regional water cycle evolution and developing adaptive management strategies.

[0003] Among existing techniques for quantitatively assessing the dynamic hydrological effects of vegetation, the most closely approximating and widely used is the classical attribution model based on the Budyko hydrothermal balance theory. This model comprehensively characterizes the influence of all underlying surface properties on the hydrothermal coupling relationship within a watershed through a core underlying surface characteristic parameter *n*. However, this approach suffers from a fundamental structural flaw: parameter *n* is a hybrid parameter that couples together the complex effects of vegetation change, soil properties, topography, and all human activities on the underlying surface, making it impossible to independently identify and quantitatively separate the dynamic impacts of vegetation from the model's structure. This ambiguity in the attribution structure means that much of the research based on this model can only describe the overall trend of hydrological change, failing to delve into the differential response mechanisms and high sensitivity thresholds of the internal "climate-vegetation-hydrology" coupling relationship at different altitudinal gradients in complex environments such as mountainous areas with significant vertical differentiation.

[0004] The inability of existing classical models to perform refined and mechanistic quantitative attribution significantly limits the practical application value of this technology. For example, in the formulation of refined water resource management and ecological restoration strategies in arid mountainous areas, decision-makers often need to clearly understand the extent to which vegetation restoration or degradation affects water yield in order to balance ecological benefits with water resource security. Existing technologies, unable to isolate the individual contribution of vegetation change, struggle to provide this crucial information, thus greatly diminishing their guiding role. The main difficulty in solving this problem lies in how to innovate its internal structure while preserving the physical foundation and practicality of the Budyko framework, effectively decomposing the originally complex parameter 'n' to independently extract the contribution component of vegetation dynamics. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a method for quantitatively assessing the hydrological effects of vegetation dynamics. This method enables independent and accurate quantification of the impact of vegetation dynamics on hydrological effects, thereby enhancing the transparency of runoff attribution and its application value in eco-hydrological management.

[0006] To achieve the above objectives, the present invention provides a method for quantitatively assessing the dynamic hydrological effects of vegetation, comprising:

[0007] Meteorological data, hydrological data, vegetation remote sensing data, and environmental attribute data of the study area were collected and preprocessed.

[0008] Based on the hydrothermal coupling equilibrium theory, the underlying surface characteristic parameters of the watershed are retrieved using the meteorological and hydrological data.

[0009] Based on the differential relationship between runoff variation and driving factors, the elasticity coefficients of runoff to changes in precipitation, potential evapotranspiration, and underlying surface characteristic parameters are calculated.

[0010] A sensitivity model of the underlying surface feature parameters to vegetation change is constructed to obtain vegetation sensitivity parameters;

[0011] By combining the elastic coefficient of the runoff to the underlying surface characteristic parameters and the vegetation sensitivity parameters, the runoff change component caused by the dynamic changes in vegetation is separated from the total runoff change and calculated.

[0012] Optionally, the data collection and preprocessing include: preparing normalized vegetation index, annual runoff, annual measured precipitation, annual actual evapotranspiration and annual potential evapotranspiration to characterize vegetation dynamics; preparing soil water holding capacity data; and calculating the drought index using the annual potential evapotranspiration and annual measured precipitation.

[0013] Optionally, the underlying surface characteristic parameters of the inverted watershed include: based on the Budyko hydrothermal coupling equilibrium theoretical framework, the underlying surface parameter n, which characterizes the comprehensive characteristics of the watershed underlying surface, is calculated year by year using measured precipitation, potential evapotranspiration, and actual evapotranspiration.

[0014] Optionally, calculating the runoff elasticity coefficient includes: performing total differential decomposition on the runoff variation equation established based on the hydrothermal coupling equilibrium theory to obtain the elasticity coefficients of runoff to changes in precipitation, changes in potential evapotranspiration, and changes in the underlying surface parameter n.

[0015] Optionally, constructing a sensitivity model to obtain vegetation sensitivity parameters includes:

[0016] Establish a linear response model of the underlying surface parameter n to changes in the vegetation index;

[0017] The drought index and the soil water holding capacity were selected as the key explanatory variables for the linear response model.

[0018] The vegetation sensitivity parameters are calculated using the linear response model to quantitatively assess the impact of vegetation changes on underlying surface characteristics.

[0019] Optionally, separating and calculating the runoff change component caused by vegetation change includes multiplying the elasticity coefficient of the runoff with respect to the underlying surface parameter n, the relative change rate of the vegetation sensitivity parameter and the vegetation index, and the product result is the relative change in runoff caused by vegetation dynamic change alone.

[0020] An electronic device, the electronic device comprising: a processor and a memory storing computer program instructions;

[0021] When the processor executes the computer program instructions, it implements the method for quantitatively assessing the dynamic hydrological effects of vegetation.

[0022] A computer storage medium storing computer program instructions, which, when executed by a processor, implement the method for quantitatively assessing the dynamic hydrological effects of vegetation.

[0023] Technical Advantages of this Invention: This invention discloses a method for quantitatively assessing the dynamic hydrological effects of vegetation. While preserving the physical interpretability of the Budyko hydrothermal balance framework, it successfully resolves the attribution confounding problem of underlying surface parameters in classical attribution models by introducing key structural improvements such as vegetation sensitivity parameters. This allows for the independent and quantitative separation of the impact of vegetation dynamics from the influences of soil, topography, and other human activities. This improvement significantly enhances the transparency and reliability of global runoff change attribution, while achieving accurate attribution and dynamic diagnosis of vegetation hydrological effects without increasing the complexity of traditional frameworks. Therefore, this invention significantly enhances the mechanistic explanatory power of scientific discoveries and increases its application value in the fields of hydrology and ecological engineering in arid regions. Attached Figure Description

[0024] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0025] Figure 1 This is a flowchart illustrating a method for quantitatively assessing the dynamic hydrological effects of vegetation, according to an embodiment of the present invention. Detailed Implementation

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0027] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0028] like Figure 1 As shown, this embodiment provides a method for quantitatively assessing the dynamic hydrological effects of vegetation, including: collecting meteorological data, hydrological data, vegetation remote sensing data and environmental attribute data of the study area, and performing preprocessing.

[0029] Based on the hydrothermal coupling equilibrium theory, the underlying surface characteristic parameters of the watershed are retrieved using the meteorological and hydrological data.

[0030] Based on the differential relationship between runoff variation and driving factors, the elasticity coefficients of runoff to changes in precipitation, potential evapotranspiration, and underlying surface characteristic parameters are calculated.

[0031] A sensitivity model of the underlying surface feature parameters to vegetation change is constructed to obtain vegetation sensitivity parameters;

[0032] By combining the elastic coefficient of the runoff to the underlying surface characteristic parameters and the vegetation sensitivity parameters, the runoff change component caused by the dynamic changes in vegetation is separated from the total runoff change and calculated.

[0033] Furthermore, the data collection and preprocessing include: preparing normalized vegetation index, annual runoff, annual measured precipitation, annual actual evapotranspiration and annual potential evapotranspiration to characterize vegetation dynamics; preparing soil water holding capacity data; and calculating the drought index using the annual potential evapotranspiration and annual measured precipitation.

[0034] Specifically, the implementation process of this embodiment includes:

[0035] Prepare remote sensing data for Normalized Difference Vegetation Index (NDVI, dimensionless), runoff (Q, mm), measured precipitation (P, mm), actual evapotranspiration (E, mm), potential evapotranspiration (E0, mm), and drought index. ) and soil water holding capacity ( Data such as ) are used. Substituting the above data into formula (1), the underlying surface parameters (n, dimensionless) are obtained:

[0036] (1).

[0037] Furthermore, the underlying surface characteristic parameters of the inverted watershed include: based on the Budyko hydrothermal coupling equilibrium theoretical framework, the underlying surface parameter n, which characterizes the comprehensive characteristics of the watershed underlying surface, is calculated year by year using measured precipitation, potential evapotranspiration, and actual evapotranspiration.

[0038] Specifically, the implementation process of this embodiment includes:

[0039] The variation components of runoff (Q) are calculated. In formula (1), E is the input annual average actual evaporation, P is the annual average precipitation, and E0 is the annual average potential evaporation. The parameter n represents the characteristics of the watershed underlying surface, which is mainly related to soil, topography, and vegetation attributes. This parameter will be associated with vegetation (represented by Veg in the formula; in specific calculations, the vegetation index NDVI can be used to characterize vegetation changes). Therefore, the variation of runoff Q is expressed as:

[0040] (2).

[0041] Where d represents the differential. Representing partial differentials, further, the calculation of runoff elastic coefficients includes: performing total differential decomposition on the runoff variation equation established based on the hydrothermal coupling equilibrium theory to obtain the elastic coefficients of runoff in response to changes in precipitation, changes in potential evapotranspiration, and changes in the underlying surface parameter n.

[0042] Specifically, the implementation process of this embodiment includes:

[0043] The change in runoff consists of three parts (S represents the sensitivity coefficient of Q to each component (P, n, and E0, etc.), each part is expressed as:

[0044] (3);

[0045] (4);

[0046] (5).

[0047] Furthermore, the sensitivity model is constructed to obtain vegetation sensitivity parameters, including:

[0048] Establish a linear response model of the underlying surface parameter n to changes in the vegetation index;

[0049] The drought index and the soil water holding capacity were selected as the key explanatory variables for the linear response model.

[0050] The vegetation sensitivity parameters are calculated using the linear response model to quantitatively assess the impact of vegetation changes on underlying surface characteristics.

[0051] Specifically, the implementation process of this embodiment includes:

[0052] By adding vegetation change (Veg) to the model, the resulting runoff change can be further derived, as shown in the following formula:

[0053] (6);

[0054] in, This is the sensitivity of parameter n to vegetation changes. Dividing both sides of equation (2), we obtain the expression for Q:

[0055] (7);

[0056] The final relative change in runoff Q is summarized into three variables. , and The change in this is also known as the relative sensitivity coefficient or elasticity.

[0057] Furthermore, separating and calculating the runoff change component caused by vegetation change includes multiplying the elasticity coefficient of the runoff with respect to the underlying surface parameter n, the relative change rate of the vegetation sensitivity parameter and the vegetation index, and the product result is the relative change in runoff caused by vegetation dynamic change alone.

[0058] Specifically, the implementation process of this embodiment includes:

[0059] The elasticity relationship between runoff Q and vegetation Veg is expressed as:

[0060] (8);

[0061] The estimated expression for the relative change in runoff Q caused by vegetation Veg is as follows:

[0062] (9);

[0063] The calculations are mainly obtained through linear models and empirical models. The model was evaluated, and an importance analysis was performed on eight environmental variables across 663 watersheds globally, with five cross-validations. The linear and empirical model formulas are as follows:

[0064] (10);

[0065] in, (Drought Index) ) and soil water holding capacity ( Both environmental variables were statistically significant. and c are the fitting coefficients; when the model is optimal, The values ​​of c are 1.4, 95.9, and -0.73, respectively (Luo et al., 2020). Here (drought index) ) and soil water holding capacity ( The coordinates are given based on latitude and longitude and are used as known parameters.

[0066] An electronic device, the electronic device comprising: a processor and a memory storing computer program instructions;

[0067] When the processor executes the computer program instructions, it implements the method for quantitatively assessing the dynamic hydrological effects of vegetation.

[0068] A computer storage medium storing computer program instructions, which, when executed by a processor, implement the method for quantitatively assessing the dynamic hydrological effects of vegetation.

[0069] This invention discloses a method for quantitatively assessing the dynamic hydrological effects of vegetation. While preserving the physical interpretability of the Budyko hydrothermal balance framework, it successfully resolves the attribution confounding problem of underlying surface parameters in classical attribution models by introducing key structural improvements such as vegetation sensitivity parameters. This allows for the independent and quantitative separation of the impact of vegetation dynamics from the influences of soil, topography, and other human activities. This improvement significantly enhances the transparency and reliability of global runoff change attribution, while achieving accurate attribution and dynamic diagnosis of vegetation hydrological effects without increasing the complexity of traditional frameworks. Therefore, this invention significantly enhances the mechanistic explanatory power of scientific discoveries and increases its application value in the fields of hydrology and ecological engineering in arid regions.

[0070] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for quantitatively assessing the dynamic hydrological effects of vegetation, characterized in that, include: Meteorological data, hydrological data, vegetation remote sensing data, and environmental attribute data of the study area were collected and preprocessed. Based on the hydrothermal coupling equilibrium theory, the underlying surface characteristic parameters of the watershed are retrieved using the meteorological and hydrological data. Based on the differential relationship between runoff variation and driving factors, the elasticity coefficients of runoff to changes in precipitation, potential evapotranspiration, and underlying surface characteristic parameters are calculated. A sensitivity model of the underlying surface feature parameters to vegetation change is constructed to obtain vegetation sensitivity parameters; By combining the elastic coefficient of the runoff to the underlying surface characteristic parameters and the vegetation sensitivity parameters, the runoff change component caused by the dynamic changes in vegetation is separated from the total runoff change and calculated.

2. The method for quantitatively assessing the dynamic hydrological effects of vegetation as described in claim 1, characterized in that, The data collection and preprocessing include: preparing normalized vegetation index, annual runoff, annual measured precipitation, annual actual evapotranspiration and annual potential evapotranspiration to characterize vegetation dynamics; preparing soil water holding capacity data; and calculating the drought index using the annual potential evapotranspiration and annual measured precipitation.

3. The method for quantitatively assessing the dynamic hydrological effects of vegetation as described in claim 1, characterized in that, The underlying surface characteristic parameters of the inverted watershed include: based on the Budyko hydrothermal coupling equilibrium theoretical framework, the underlying surface parameter n, which characterizes the comprehensive characteristics of the watershed underlying surface, is calculated year by year using measured precipitation, potential evapotranspiration, and actual evapotranspiration.

4. The method for quantitatively assessing the dynamic hydrological effects of vegetation as described in claim 1, characterized in that, The calculation of runoff elasticity coefficients includes: performing total differential decomposition on the runoff variation equation established based on the hydrothermal coupling equilibrium theory to obtain the elasticity coefficients of runoff to changes in precipitation, changes in potential evapotranspiration, and changes in the underlying surface parameter n.

5. The method for quantitatively assessing the dynamic hydrological effects of vegetation as described in claim 2, characterized in that, Constructing a sensitivity model to obtain vegetation sensitivity parameters includes: Establish a linear response model of the underlying surface parameter n to changes in the vegetation index; The drought index and the soil water holding capacity were selected as the key explanatory variables for the linear response model. The vegetation sensitivity parameters are calculated using the linear response model to quantitatively assess the impact of vegetation changes on underlying surface characteristics.

6. The method for quantitatively assessing the dynamic hydrological effects of vegetation as described in claim 1, characterized in that, Separating and calculating the runoff change component caused by vegetation change includes multiplying the elasticity coefficient of the runoff with respect to the underlying surface parameter n, the relative change rate of the vegetation sensitivity parameter and the vegetation index, and the product result is the relative change in runoff caused by vegetation dynamic change alone.

7. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements a method for quantitatively assessing the dynamic hydrological effects of vegetation as described in any one of claims 1-6.

8. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, implement a method for quantitatively assessing the dynamic hydrological effects of vegetation as described in any one of claims 1-6.

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