Dynamic simulation method for drought risk of ecological groundwater

Abstract

The invention discloses an ecological groundwater drought risk dynamic simulation method, which comprises the following steps of: constructing a short-term driving force layer of a groundwater drought influence ecological effect, and fusing a hysteresis effect and a cumulative effect to obtain a calculation model of a short-term driving force coefficient; constructing a long-term driving force layer of the underground water drought influencing the ecological effect, and obtaining an optimal calculation model of a long-term driving force coefficient; performing weight fusion on the calculation model of the short-term driving force coefficient and the optimal calculation model of the long-term driving force coefficient, and calculating a comprehensive driving force coefficient to obtain a hierarchical memory-threshold response ecological groundwater drought risk model; and realizing dynamic process simulation of the ecological drought risk by using the hierarchical memory-threshold response ecological groundwater drought risk model. According to the method, three types of ecological response mechanisms of lagging, accumulation and leaving are integrated, and conversion of ecological drought risk assessment from a static index to dynamic process simulation is realized.

Description

Technical Field

[0001] This invention relates to the field of ecological risk analysis, specifically to a dynamic simulation method for ecological groundwater drought risk. Background Technology

[0002] Currently, most research on ecological drought indicators focuses on the fusion of different types of drought indicators. For example, atmospheric drought, agricultural drought, and hydrological drought are combined using the Copula model, the Gringorten nonparametric joint distribution method, and non-steady-state models to construct a comprehensive drought indicator. However, these static indicator constructions neglect the three effects of drought on ecosystems (lag, accumulation, and legacy), failing to accurately characterize the dynamic response processes of ecosystems. Therefore, when constructing ecological drought indicators, the temporal response characteristics of drought processes should be taken into account, integrating the three types of ecological response mechanisms—lag, accumulation, and legacy—to achieve a shift in ecological drought risk assessment from static indicators to dynamic process simulation. Summary of the Invention

[0003] To address the aforementioned shortcomings of existing technologies, this invention provides a method for dynamic simulation of ecological groundwater drought risk.

[0004] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for dynamic simulation of ecological groundwater drought risk is provided, which includes the following steps: S1: Construct a short-term driving force layer for the ecological effects of groundwater drought, integrating the impacts of groundwater drought on vegetation. The lag effect of being affected and cumulative effect The short-term driving force coefficient is obtained. The computational model; S2: Construct a long-term driving force layer for the ecological effects of groundwater drought, and construct long-term driving force coefficients. The calculation model obtains the groundwater drought index on a historical annual scale. The driving force coefficient for the construction period The calculation model was fitted to obtain the long-term driving force coefficient. The optimal computational model; S3: Construct a comprehensive driving force layer for the ecological effects of groundwater drought, including short-term driving force coefficients. The calculation model and long-term driving force coefficient The optimal calculation model is used for weight fusion to calculate the comprehensive driving force coefficient. The driving force coefficient As input to the ecological threshold Logistic-Sigmoid response, a hierarchical memory-threshold response ecological groundwater drought risk model is obtained; S4: Utilize the hierarchical memory-threshold response ecological groundwater drought risk model to simulate the dynamic process of ecological drought risk.

[0005] Furthermore, the short-term driving force coefficient The calculation model is as follows: ; in, t The unit for the monthly sliding window of short-term driven research. i For time lag index, n To adjust the window size, The normalized vegetation index outlier for the current month Historical Normalized Variation in Vegetation Index average deviation, The lag strength coefficient of the impact of groundwater drought on the normalized vegetation index (NDVI) is given. This refers to the groundwater drought index for last month. This represents the cumulative intensity coefficient of the impact of groundwater drought on the Normalized Difference Vegetation Index (NDVI). This represents the groundwater drought index for the i-th month in history. These are the short-term driving force weights for the lag effect and the short-term driving force weights for the cumulative effect, respectively. Short-term driving force weights of lag effects The calculation method is as follows: ; Short-term driving force weights of cumulative effects The calculation method is as follows: .

[0006] Furthermore, the hysteresis strength coefficient The calculation method is as follows: Obtain the driving factors for each month within the monthly sliding window. The driving factors include the precipitation evapotranspiration index. SPEI Soil moisture index SSMI Runoff drought index SRI Groundwater drought index SGI and flood index FE Using driving factors as variables x ; Calculate the lag within the monthly sliding window. i Normalized Difference Vegetation Index for one month With variables x Pearson correlation coefficient Filter within the monthly sliding window n Pearson correlation coefficient corresponding to each lag month The maximum value is used as the hysteresis strength coefficient. ; ; in, For the lagging first i Variables over a month; Cumulative strength coefficient The calculation method is as follows: Get the variables within each lag month in the monthly sliding window x extremum Calculate the extreme values With Normalized Difference Vegetation Index Pearson correlation coefficient Filter within the monthly sliding window n Pearson correlation coefficient corresponding to each lag month The maximum value is used as the cumulative intensity coefficient. ; .

[0007] Further, step S2 includes: S21: Construct a long-term driving force layer for the ecological effects of groundwater drought and obtain the groundwater drought index on historical annual scales. , y For historical years, construct long-term driving force coefficients The computational model; ; in, The residual effect memory decay coefficient; S22: Based on the set legacy effect decay coefficient The range of values Within the range of values, select several residual effect memory decay coefficients, with a step size of 0.05. Input these residual effect memory decay coefficients into the long-term driving force coefficient. In the computational model, the predicted values ​​of the long-term driving force coefficient are calculated under different values ​​of the legacy effect memory decay coefficient. , m This is the numbering of the memory decay coefficient for the legacy effect; S23: Calculated M Predicted values ​​of long-term driving force coefficients Mean square error ,get M Predicted values Corresponding mean square error ; ; in, This represents the true value of the long-term driving force coefficient on a historical year scale. S24: Filtering M Predicted values Corresponding mean square error minimum value , minimum value The corresponding residual effect memory decay coefficient is taken as the optimal value. ; to the optimal value Input long-term driving force coefficient In the calculation model, the long-term driving force coefficient is obtained. The optimal computational model; .

[0008] Further, step S3 includes: S31: Construct a comprehensive driving force layer for the ecological effects of groundwater drought, including short-term driving force coefficients. The calculation model and long-term driving force coefficient The optimal computational model is used for weight fusion; ; in, These are the weights for short-term and long-term driving forces, respectively. For the residual strength coefficient, This is the comprehensive driving force coefficient; Residual strength coefficient The calculation method is as follows: Obtain the extreme values ​​of the driving factors for each year within the annual sliding window as variables. , z Calculate the variable for the year number within the year sliding window. Spring phenological indicators for each year within the annual sliding window SOS Pearson correlation coefficient between The Pearson correlation coefficient for each year within the annual sliding window. The maximum value is used as the residual strength coefficient. ; .

[0009] S32: Integrating the driving force coefficient As input to the ecological threshold Logistic-Sigmoid response, a hierarchical memory-threshold response ecological groundwater drought risk model is constructed. ; in, The ecological threshold Logistic response sensitivity coefficient This represents the annual ecological groundwater drought risk coefficient. This represents the propagation threshold from the atmospheric drought precipitation evapotranspiration index (SPEI) to the groundwater level index (SGI). These represent the combined driving force coefficients. 90% and 10%.

[0010] The beneficial effects of this invention are as follows: Based on short-term and long-term drivers and combined with a hierarchical structure, this invention constructs a hierarchical memory-threshold response ecological groundwater drought risk model that comprehensively considers lag, cumulative and legacy effects and combines drought propagation threshold response quantified by a two-dimensional copula function. It incorporates the time response characteristics of the drought process and integrates the three types of ecological response mechanisms of lag, cumulative and legacy, thus realizing the transformation of ecological drought risk assessment from static indicators to dynamic process simulation. Attached Figure Description

[0011] Picture 1 A flowchart for a dynamic simulation method of ecological groundwater drought risk. Detailed Implementation

[0012] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0013] like Picture 1 As shown, a dynamic simulation method for ecological groundwater drought risk includes the following steps: S1: Construct a short-term driving force layer for the ecological effects of groundwater drought, incorporating the lagged effects of groundwater drought on vegetation. and cumulative effect The short-term driving force coefficient is obtained. The computational model.

[0014] ; in, t This embodiment uses the monthly sliding window as the unit for short-term driving research. t Take the 12 months of the current year, with the monthly sliding window unit being 1 month. t ), i For a time-lag index, it shifts backwards from the current month to each previous historical month (e.g., i =0 represents the current month ( t ), i =1 represents last month ( t− 1), and so on; n The sliding window size is for the past 12 months (in this example, it represents the past 12 months). The normalized vegetation index outlier for the current month Historical Normalized Variation in Vegetation Index average deviation, The average normalized vegetation index for a given month across multiple years, representing long-term historical data or a sliding window average across different months. NDVI Values ​​(for example, you can calculate each month over the past 10 or 20 years) NDVI The mean, for each month from 2003 to 2022 in this study. NDVI (mean) The impact of groundwater drought on the normalized vegetation index NDVI The hysteresis coefficient of the influence ranges from 0 to 1. This refers to the groundwater drought index for last month. The impact of groundwater drought on the normalized vegetation index NDVI The cumulative intensity coefficient of the influence ranges from 0 to 1. For the first time in history i Monthly groundwater drought index i =0-11 months, These are the short-term driving force weights for the lag effect and the short-term driving force weights for the cumulative effect, respectively. At that time, groundwater drought NDVI The impact has not diminished; it is continuous. The impact will diminish rapidly, with only the current month's groundwater drought affecting the area. NDVI The impact at other times is almost zero; The impact will gradually diminish.

[0015] Fusion lag effect and cumulative effect This represents the ecosystem's rapid response to environmental stresses during the current growing season.

[0016] Short-term driving force weights of lag effects The calculation method is as follows: ; Short-term driving force weights of cumulative effects The calculation method is as follows: ; Hysteresis strength coefficient The calculation method is as follows: Obtain the driving factors for each month within the monthly sliding window. The driving factors include the precipitation evapotranspiration index. SPEI Soil moisture index SSMI Runoff drought index SRI Groundwater drought index (SGI) and flood index FEUsing driving factors as variables x ; Calculate the lag within the monthly sliding window. i Normalized Difference Vegetation Index for one month With variables x Pearson correlation coefficient Filter within the monthly sliding window n Pearson correlation coefficient corresponding to each lag month The maximum value is used as the hysteresis strength coefficient. ; ; in, For the lagging first i Variables over a month; Cumulative strength coefficient The calculation method is as follows: Get the variables within each lag month in the monthly sliding window x extremum Calculate the extreme values With Normalized Difference Vegetation Index Pearson correlation coefficient Filter within the monthly sliding window n Pearson correlation coefficient corresponding to each lag month The maximum value is used as the cumulative intensity coefficient. ; .

[0017] S2: Construct a long-term driving force layer for the ecological effects of groundwater drought, and construct long-term driving force coefficients. The calculation model obtains the groundwater drought index on a historical annual scale. The driving force coefficient for the construction period The calculation model was fitted to obtain the long-term driving force coefficient. The optimal computational model.

[0018] The legacy effect reflects the slow response and memory of an ecosystem to long-term historical groundwater conditions.

[0019] Step S2 specifically includes: S21: Construct a long-term driving force layer for the ecological effects of groundwater drought and obtain the groundwater drought index on historical annual scales. , y For historical years, construct long-term driving force coefficients The computational model; ; in, The residual effect memory decay coefficient; The larger the value, the better the system's understanding of past states. The stronger the memory, the better for the present. The weaker the state response, the stronger the memory and the weaker the response, mainly represented by forest ecosystems; The smaller the value, the better the system's response to the current situation. The more sensitive the response to the state, the shorter the "memory" of history, exhibiting weak memory and strong response, mainly represented by grassland ecosystems.

[0020] S22: Based on the set legacy effect decay coefficient The range of values Within the range of values, select several residual effect memory decay coefficients, with a step size of 0.05. Input these residual effect memory decay coefficients into the long-term driving force coefficient. In the computational model, the predicted values ​​of the long-term driving force coefficient are calculated under different values ​​of the legacy effect memory decay coefficient. , m This is the numbering of the memory decay coefficient for the legacy effect; S23: Calculated M Predicted values ​​of long-term driving force coefficients Mean square error ,get M Predicted values Corresponding mean square error ; ; in, This represents the true value of the long-term driving force coefficient on a historical year scale. S24: Filtering M Predicted values Corresponding mean square error minimum value , minimum value The corresponding residual effect memory decay coefficient is taken as the optimal value. ; to the optimal value Input long-term driving force coefficient In the calculation model, the long-term driving force coefficient is obtained. The optimal computational model; .

[0021] S3: Construct a comprehensive driving force layer for the ecological effects of groundwater drought, including short-term driving force coefficients. The calculation model and long-term driving force coefficient The optimal calculation model is used for weight fusion to calculate the comprehensive driving force coefficient. The driving force coefficient As input to the ecological threshold Logistic-Sigmoid response, a hierarchical memory-threshold response ecological groundwater drought risk model is obtained.

[0022] Step S3 specifically includes: S31: Construct a comprehensive driving force layer for the ecological effects of groundwater drought, including short-term driving force coefficients. The calculation model and long-term driving force coefficient The optimal computational model is used for weight fusion; ; in, These are the weights for short-term and long-term driving forces, respectively. This is the residual strength coefficient, with a value ranging from 0 to 1. This is the comprehensive driving force coefficient; Residual strength coefficient The calculation method is as follows: Obtain the extreme values ​​of the driving factors for each year within the annual sliding window as variables. , z Calculate the variable for the year number within the year sliding window. Spring phenological indicators for each year within the annual sliding window SOS Pearson correlation coefficient between The Pearson correlation coefficient for each year within the annual sliding window. The maximum value is used as the residual strength coefficient. ; .

[0023] S32: Integrating the driving force coefficient As input to the ecological threshold Logistic-Sigmoid response, a hierarchical memory-threshold response ecological groundwater drought risk model is constructed. ; in, The ecological threshold Logistic response sensitivity coefficient This represents the annual ecological groundwater drought risk coefficient. This represents the propagation threshold from the atmospheric drought precipitation evapotranspiration index (SPEI) to the groundwater level index (SGI). These represent the combined driving force coefficients. 90% and 10%.

[0024] S4: Utilize the hierarchical memory-threshold response ecological groundwater drought risk model to simulate the dynamic process of ecological drought risk.

[0025] By calculating the comprehensive driving force coefficient of groundwater drought on an annual scale to determine the ecological groundwater drought risk coefficient at different annual scales, the risk of groundwater drought on the ecology under different lag, cumulative and residual effects can be studied and simulated, thus realizing the dynamic process simulation of ecological drought risk.

[0026] This invention constructs a hierarchical memory-threshold response ecological groundwater drought risk model based on short-term and long-term drivers and a hierarchical structure. This model comprehensively considers lag, cumulative, and legacy effects, combined with a two-dimensional copula function to quantify the drought propagation threshold response. It incorporates the time response characteristics of the drought process and integrates the three types of ecological response mechanisms—lag, cumulative, and legacy—to realize the transformation of ecological drought risk assessment from static indicators to dynamic process simulation.

Claims

1. A dynamic simulation method for ecological groundwater drought risk, characterized in that, Includes the following steps: S1: Construct a short-term driving force layer for the ecological effects of groundwater drought, integrating the impacts of groundwater drought on vegetation. The lag effect of being affected and cumulative effect The short-term driving force coefficient is obtained. The computational model; S2: Construct a long-term driving force layer for the ecological effects of groundwater drought, and construct long-term driving force coefficients. The calculation model obtains the groundwater drought index on a historical annual scale. The driving force coefficient for the construction period The calculation model was fitted to obtain the long-term driving force coefficient. The optimal computational model; S3: Construct a comprehensive driving force layer for the ecological effects of groundwater drought, including short-term driving force coefficients. The calculation model and long-term driving force coefficient The optimal calculation model is used for weight fusion to calculate the comprehensive driving force coefficient. The driving force coefficient As input to the ecological threshold Logistic-Sigmoid response, a hierarchical memory-threshold response ecological groundwater drought risk model is obtained; S4: Utilize the hierarchical memory-threshold response ecological groundwater drought risk model to simulate the dynamic process of ecological drought risk.

2. The method for dynamic simulation of ecological groundwater drought risk according to claim 1, characterized in that, The short-term driving force coefficient The calculation model is as follows: ； in, t The unit for the monthly sliding window of short-term driven research. i For time lag index, n To adjust the window size, The normalized vegetation index outlier for the current month Historical Normalized Variation in Vegetation Index average deviation, The lag strength coefficient of the impact of groundwater drought on the normalized vegetation index (NDVI) is given. This refers to the groundwater drought index for last month. This represents the cumulative intensity coefficient of the impact of groundwater drought on the Normalized Difference Vegetation Index (NDVI). For the first time in history i Monthly groundwater drought index These are the short-term driving force weights of the lag effect and the short-term driving force weights of the cumulative effect, respectively. Short-term driving force weights of lag effects The calculation method is as follows: ; Short-term driving force weights of cumulative effects The calculation method is as follows: .

3. The method for dynamic simulation of ecological groundwater drought risk according to claim 2, characterized in that, The hysteresis coefficient The calculation method is as follows: Obtain the driving factors for each month within the monthly sliding window. The driving factors include the precipitation evapotranspiration index. SPEI Soil moisture index SSMI Runoff drought index SRI Groundwater drought index SGI and flood index FE Using driving factors as variables x ; Calculate the lag within the monthly sliding window. i Normalized Difference Vegetation Index for one month With variables x Pearson correlation coefficient Filter within the monthly sliding window n Pearson correlation coefficient corresponding to each lag month The maximum value is used as the hysteresis strength coefficient. ; ； in, For the lagging first i Variables over a month; Cumulative strength coefficient The calculation method is as follows: Get the variables within each lag month in the monthly sliding window x extremum Calculate the extreme values With Normalized Difference Vegetation Index Pearson correlation coefficient Filter within the monthly sliding window n Pearson correlation coefficient corresponding to each lag month The maximum value is used as the cumulative intensity coefficient. ; 。 4. The method for dynamic simulation of ecological groundwater drought risk according to claim 3, characterized in that, Step S2 includes: S21: Construct a long-term driving force layer for the ecological effects of groundwater drought and obtain the groundwater drought index on historical annual scales. , y For historical years, construct long-term driving force coefficients The computational model; ； in, The residual effect memory decay coefficient; S22: Based on the set legacy effect decay coefficient The range of values Within the range of values, select several residual effect memory decay coefficients, with a step size of 0.

05. Input these residual effect memory decay coefficients into the long-term driving force coefficient. In the computational model, the predicted values ​​of the long-term driving force coefficient are calculated under different values ​​of the legacy effect memory decay coefficient. , m This is the numbering of the memory decay coefficient for the legacy effect; S23: Calculated M Predicted values ​​of long-term driving force coefficients mean square error ,get M Predicted values Corresponding mean square error ; ； in, This represents the true value of the long-term driving force coefficient on a historical year scale. S24: Filtering M Predicted values Corresponding mean square error minimum value , minimum value The corresponding residual effect memory decay coefficient is taken as the optimal value. ; to the optimal value Input long-term driving force coefficient In the calculation model, the long-term driving force coefficient is obtained. The optimal computational model; 。 5. The method for dynamic simulation of ecological groundwater drought risk according to claim 4, characterized in that, Step S3 includes: S31: Construct a comprehensive driving force layer for the ecological effects of groundwater drought, including short-term driving force coefficients. The calculation model and long-term driving force coefficient The optimal computational model is used for weight fusion; ； in, These are the weights for short-term and long-term driving forces, respectively. For the residual strength coefficient, This is the comprehensive driving force coefficient; Residual strength coefficient The calculation method is as follows: Obtain the extreme values ​​of the driving factors for each year within the annual sliding window as variables. , z Calculate the variable for the year number within the year sliding window. Spring phenological indicators for each year within the annual sliding window SOS Pearson correlation coefficient between The Pearson correlation coefficient for each year within the annual sliding window. The maximum value is used as the residual strength coefficient. ; 。 S32: Integrating the driving force coefficient As input to the ecological threshold Logistic-Sigmoid response, a hierarchical memory-threshold response ecological groundwater drought risk model is constructed. ； in, The ecological threshold Logistic response sensitivity coefficient This represents the annual ecological groundwater drought risk coefficient. This represents the propagation threshold from the atmospheric drought precipitation evapotranspiration index (SPEI) to the groundwater level index (SGI). These represent the combined driving force coefficients. 90% and 10%.

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