CN-121998808-A - Evaluation method and system for influence of extreme climate on ecological toughness of wetland

CN121998808ACN 121998808 ACN121998808 ACN 121998808ACN-121998808-A

Abstract

The invention discloses a method and a system for evaluating influence of extreme climate on wetland ecological toughness, which comprise the steps of obtaining multi-source-end climate data for scale alignment, inputting a Bayesian layered fusion model to obtain extreme climate fusion data, calculating comprehensive pressure indexes of different time and space, dynamically simulating and updating wetland state vectors, calculating time-varying toughness indexes, carrying out hierarchical ecological early warning according to the time-varying toughness indexes, calculating the relevance of the time-varying toughness indexes and the wetland state indexes, carrying out causal association, and determining ecological regulation measures according to causal association results and hierarchical ecological early warning results. The method can improve the efficiency and accuracy of the influence evaluation of the extreme climate on the ecological toughness of the wetland, has better interpretability, and can be directly applied to an influence evaluation system of the extreme climate on the ecological toughness of the wetland.

Inventors

SONG YOUTAO
Zhao Zhaokai
ZHANG PENG
LIU XINGJING
WANG LI
XIAO FEIFEI
LI JIAAO

Assignees

天津科技大学
天津环科环境咨询有限公司

Dates

Publication Date: 20260508
Application Date: 20260206

Claims (7)

1. The method for evaluating the influence of extreme climate on the ecological toughness of the wetland is characterized by comprising the following steps of: S1, acquiring multi-source-end climate data for scale alignment, and inputting a Bayesian layered fusion model to perform space-time fusion with a wetland state to acquire extreme climate fusion data; S2, calculating comprehensive pressure indexes of different time and space through three-dimensional features of extreme climate fusion data, and carrying out wetland state dynamic simulation on the comprehensive pressure indexes of different time and space to update a wetland state vector; s3, calculating a time-varying toughness index according to the updated wetland state vector, and carrying out hierarchical ecological early warning according to the time-varying toughness index, wherein the time-varying toughness index reflects the sensitivity of wetland ecology to extreme climate and comprises a hydrological time-varying toughness index, a biological time-varying toughness index and a system function time-varying toughness index; S4, performing causal correlation on the correlation between the calculated time-varying toughness index and the wetland state index, and determining ecological regulation measures according to the causal correlation result and the grading ecological early warning result.
2. The method for evaluating the influence of extreme climate on the ecological toughness of the wetland according to claim 1, wherein the method for obtaining the extreme climate fusion data comprises the following steps: Acquiring multi-source terminal climate data to perform scale alignment, wherein the multi-source terminal climate data comprises global climate mode data, satellite remote sensing data, ground fixed-point monitoring data and social sensing data; Driving a high-resolution regional climate mode to increase the resolution of global climate mode data to be consistent with satellite remote sensing data, adopting a multi-element statistical relationship and a geographic weighted regression model to reduce the scale of the global climate mode data and the satellite remote sensing data to be consistent with ground fixed-point monitoring data, and uniformly resampling all the data to the same space-time grid frame; inputting the multi-source end climate data with the aligned scales and the wetland priori knowledge into a Bayesian hierarchical fusion model for space-time fusion to obtain extreme climate fusion data, wherein the wetland priori knowledge comprises initial estimation and uncertainty of a wetland state; The Bayesian hierarchical fusion model comprises an input layer, a fusion layer and an output layer; the fusion layer quantifies uncertainty of each data source based on a Bayesian statistical theory, and dynamically weighting and fusing to generate optimal estimation of a real wetland state, wherein the optimal estimation comprises an observation model unit, a process model unit and a parameter model unit; the joint likelihood function expression is: ; ; ; ; Wherein the method comprises the steps of Is in a real wetland state All observations below Is a function of the joint likelihood of (a) and (b), For the position index to be used, For the time index of the time index, Is that Extreme climate observations of the data source, For the number of data sources, For the space-time monitoring point set of the area to be fused, 、 Labeling the local expected values and estimated uncertainty for extreme climate observations is poor, Is that The dynamic weight of the data source is determined, Is that The inherent trustworthiness parameters of the data source, 、 Is a smoothing coefficient.
3. The method for evaluating the influence of extreme climates on the ecological toughness of the wetland according to claim 1, wherein the method for calculating the comprehensive pressure index of different time and space comprises the following steps: three-dimensional characteristics of the extreme climate fusion data are counted, and comprehensive pressure indexes of different time and space are calculated, wherein the expression is as follows: ; Wherein the method comprises the steps of Is that Time wetland The comprehensive pressure index of the position is calculated, For the number of categories of extreme events, For extreme event type weights, In order for the attenuation factor to be a factor, In order to be able to count the number of events in the time window, In order to accumulate the coefficient of effect, 、、 Is the first The real-time intensity, frequency, duration of extreme-like events, 、 As the mean of the reference period frequency and duration, Is the first Extreme event-like wetland spatial heterogeneous weighting fields, Is a random disturbance term and obeys space-time heteroscedastic distribution.
4. The method for evaluating the influence of extreme climate on the ecological toughness of the wetland according to claim 1, wherein the dynamic simulation of the state of the wetland is randomly and dynamically evolved through a random differential equation set; The system of random differential equations describes the random dynamics of the wetland state vector using the following form of the Earthway process: ; Wherein the method comprises the steps of As a wetland state vector, the water-saving type water-saving, For the time index of the time index, Is a deterministic ecological process function, belongs to a normal differential equation, Is a pressure-driven random disturbance intensity function, belongs to a random differential equation diffusion term, For a spatially heterogeneous weighting field of the wetland, Is a hysteresis response function, belongs to a time-lag differential equation, In order to integrate the pressure vectors, Is a time lag parameter; the wetland state vector Comprises water level, vegetation coverage, soil organic carbon and biological diversity index, wherein the comprehensive pressure vector Including flood pressure, drought pressure, and high temperature pressure; The deterministic ecological process function is based on a logic substance growth term, and is combined with a pressure inhibition term and an ecological coupling feedback term in a Hill function form to update the state of the wetland, wherein the expression is as follows: ; Wherein the method comprises the steps of Is the intrinsic growth rate of the Chinese herbal medicine, Is an index of the state of the wetland, Is a set of wetland state indexes, In order to accommodate the amount of the environment, In order to stress the coefficient of sensitivity, In order to make the coefficient of the conversion, As a pressure-resistant vector, Is a wetland state index And (3) with Is a coupled feedback term of (1); the expression of the pressure-driven random disturbance intensity function is as follows: ; Wherein the method comprises the steps of The inherent randomness inside the ecological system is characterized by the basic fluctuation intensity when no extreme climate exists, In order to provide a pressure amplification factor, As the reference value of the pressure, Is a state dependency index; the hysteresis response function expression is: ; Wherein the method comprises the steps of For the duration of the maximum memory period, In order to fix the time of the lag, Is a memory decay time constant.
5. The method for evaluating the influence of extreme climate on the ecological toughness of the wetland according to claim 1, wherein the method for carrying out hierarchical ecological early warning comprises the following steps: calculating a time-varying toughness index according to the updated wetland state vector, wherein the expression is as follows: ; Wherein the method comprises the steps of In order to provide a time-varying toughness index, Is the expected vector of the wetland state variables, Is the standard deviation of the wetland state variable, Is the comprehensive pressure And wet land state Is used for the correlation coefficient of (a), Is a critical correlation coefficient threshold; The time-varying toughness index comprises a hydrologic time-varying toughness index, a biological time-varying toughness index and a system function time-varying toughness index, wherein the hydrologic time-varying toughness index is determined by water level update and vegetation coverage update, the biological time-varying toughness index is determined by biodiversity index update, and the system function time-varying toughness index is determined by vegetation coverage update, soil organic carbon update and biodiversity index update; determining a primary toughness index and a secondary toughness index, and carrying out graded ecological early warning according to the time-varying toughness index.
6. The method for evaluating the influence of extreme weather on the ecological toughness of a wetland according to claim 1, wherein said method for determining ecological regulation measures comprises: Calculating mutual information entropy of each time-varying toughness index and corresponding wetland state indexes as data association degree, selecting two groups of wetland state indexes with highest data association degree as corresponding time-varying toughness index influence factors, and determining ecological regulation measures according to hierarchical ecological early warning results and influence factors.
7. A system for evaluating the effect of extreme weather on the ecological toughness of a wetland for performing the method of any one of claims 1-6, comprising: The fusion module is used for carrying out scale alignment on the multi-source terminal climate data, inputting a Bayesian layered fusion model and carrying out space-time fusion on the multi-source terminal climate data and the wetland state to obtain extreme climate fusion data; The system comprises a state updating module, a state vector generation module and a state vector generation module, wherein the state updating module is used for calculating the comprehensive pressure indexes of different time and space through three-dimensional features of extreme climate fusion data, and carrying out wetland state dynamic simulation on the comprehensive pressure indexes of different time and space to update a wetland state vector; The grading early warning module is used for calculating a time-varying toughness index according to the updated wetland state vector and carrying out grading ecological early warning according to the time-varying toughness index, wherein the time-varying toughness index reflects the sensitivity of the wetland ecology to the polar climate and comprises a hydrological time-varying toughness index, a biological time-varying toughness index and a system function time-varying toughness index; And the regulation and control module calculates the relevance between the time-varying toughness index and the wetland state index to carry out causal relation, and determines ecological regulation and control measures according to the causal relation result and the grading ecological early warning result.

Description

Evaluation method and system for influence of extreme climate on ecological toughness of wetland Technical Field The invention relates to the technical field of ecological monitoring and disaster early warning, in particular to a method and a system for evaluating influence of extreme climate on wetland ecological toughness. Background The wetland ecosystem is a key ecological barrier of the earth, plays an irreplaceable role in regulating climate, conserving water sources and protecting biodiversity, and in recent years, under the global climate change background, extreme climate events are frequent and intensified, and the stability and restoring force (namely ecological toughness) of the wetland ecosystem are seriously threatened. Therefore, scientifically and accurately evaluating the influence of extreme climate on the ecological toughness of the wetland and realizing prospective early warning and regulation, and the method has become a key scientific problem to be solved urgently for ecological protection and adaptability management. The current wetland ecological toughness evaluation technology mainly has three defects that firstly, an evaluation paradigm is static, nonlinear response, critical threshold effect and recovery hysteresis of an ecological variable to extreme pressure cannot be described, secondly, data fusion and process simulation are disjointed, the wetland underlying surface heterogeneity and ecological state feedback are not taken as constraint conditions, the fusion result and an ecological process mechanism are not causally related, finally, a decision support system is lagged, a real-time causal inference and measure effect prediction mechanism of pressure-state-toughness is not established, and a closed loop of monitoring-early warning-regulation is difficult to realize. Therefore, the invention provides an evaluation method and system for influence of extreme climate on the ecological toughness of the wetland, which realizes alignment and bidirectional constraint of climate-ecological space-time scale through Bayesian layered fusion, constructs a random differential equation set containing pressure driving heteroscedasticity and multiscale hysteresis, designs a three-dimensional time-varying toughness index of hydrologic-biological-function, carries out intelligent regulation and matching based on causal relation of information entropy, and selects response measures from experience to make a probability optimal decision, and the technology breaks through upgrading the evaluation of the ecological toughness of the wetland from static post-evaluation to dynamic pre-early warning, thereby providing core technical support for improving the adaptability of the wetland to the climate change and the accurate management level. Disclosure of Invention The invention aims to provide a method and a system for evaluating influence of extreme climate on ecological toughness of a wetland. In order to achieve the above purpose, the invention is implemented according to the following technical scheme: The invention comprises the following steps: Acquiring multi-source-end climate data for scale alignment, and inputting a Bayesian layered fusion model to perform space-time fusion with the wetland state to acquire extreme climate fusion data; Calculating comprehensive pressure indexes of different time and space through three-dimensional features of extreme climate fusion data, and carrying out wetland state dynamic simulation on the comprehensive pressure indexes of different time and space to update a wetland state vector; Calculating a time-varying toughness index according to the updated wetland state vector, and carrying out hierarchical ecological early warning according to the time-varying toughness index, wherein the time-varying toughness index reflects the sensitivity of the wetland ecology to the extreme climate and comprises a hydrological time-varying toughness index, a biological time-varying toughness index and a system function time-varying toughness index; And carrying out causal association on the association of the calculated time-varying toughness index and the wetland state index, and determining ecological regulation measures according to the causal association result and the grading ecological early warning result. Further, the method for obtaining extreme climate fusion data comprises the following steps: Acquiring multi-source terminal climate data to perform scale alignment, wherein the multi-source terminal climate data comprises global climate mode data, satellite remote sensing data, ground fixed-point monitoring data and social sensing data; Driving a high-resolution regional climate mode to increase the resolution of global climate mode data to be consistent with satellite remote sensing data, adopting a multi-element statistical relationship and a geographic weighted regression model to reduce the scale of the global climate mode data and the satellite remote sensin