CN-121998402-A - Assessment method for vegetation loss risk under drought and waterlogging sharp rotation stress

CN121998402ACN 121998402 ACN121998402 ACN 121998402ACN-121998402-A

Abstract

The application belongs to the field of ecological disaster risk assessment, and particularly discloses an assessment method of vegetation loss risk under drought and waterlogging sharp turn stress, which comprises the steps of processing month-by-month rainfall data and NDVI data of a target area to obtain a rainfall data and NDVI data quaternary scale sequence; the method comprises the steps of calculating the occurrence probability of a dry-wet composite event between adjacent seasons under different situations by using a Copula function based on a precipitation data quaternary scale sequence, calculating the loss probability of vegetation under the stress of the adjacent Ji Jiejian dry-wet composite event under different situations by using a Bayesian framework and the Copula function, calculating the exposure of a vegetation system to the dry-wet composite event by using a quaternary average normalized vegetation index based on an NDVI data quaternary scale sequence, and calculating the vegetation loss risk under the stress of the dry-wet composite event according to the occurrence probability, the loss probability and the exposure. The application can evaluate vegetation loss risk more accurately, and provides scientific basis for biological protection and disaster management.

Inventors

HUANG SHENGZHI
CAO QIANQIAN
ZHANG LIN
GUO HUI
LIU JUNGUO
YU MEIXIU
LIU YI
ZHANG LEI
JIANG YUNZHONG

Assignees

华北水利水电大学

Dates

Publication Date: 20260508
Application Date: 20250819

Claims (10)

1. The method for evaluating the vegetation loss risk under drought and waterlogging sharp turning stress is characterized by comprising the following steps of: s10, acquiring and processing month-by-month precipitation data and NDVI data of a target area to obtain a quaternary scale sequence of the grid precipitation data and the NDVI data; s20, identifying dry-wet composite events between adjacent seasons based on a precipitation data quaternary scale sequence, dividing moderate and severe scenes, calculating the occurrence probability of the dry-wet composite events between the adjacent seasons under different scenes by using a Copula function, and calculating the loss probability of vegetation under the stress of the adjacent Ji Jiejian dry-wet composite events under different scenes by using a Bayesian framework and the Copula function in combination; s30, calculating the exposure of the vegetation system to the wet-dry composite event by adopting a quaternary average normalized vegetation index based on the NDVI data quaternary scale sequence; And S40, calculating vegetation loss risk under the stress of the wet and dry composite event according to the occurrence probability and the loss probability calculated in the step S20 and the exposure degree calculated in the step S30.
2. The method for evaluating vegetation loss risk under drought/water logging and tight turning stress according to claim 1, wherein in step S20, based on the rainfall data quaternary scale sequence, the steps of identifying the dry-wet composite event between adjacent seasons and dividing the moderate and severe situations are specifically as follows: calculating SPI index according to the rainfall data quaternary scale sequence; extracting drought events and wetting events by using the SPI index according to drought grade division standards, and dividing moderate and severe scenes; Identifying a dry-wet composite event between adjacent seasons according to seasons in which drought events and wet events occur, wherein the composite event comprises four of dry-to-wet transition from spring to summer, wet-to-dry transition from spring to summer, continuous drought in spring to summer, dry-to-wet transition from summer to autumn, continuous drought in summer and autumn, continuous wet transition from autumn to winter, dry-to-dry transition from autumn to winter, continuous dry-to-winter, continuous wet transition from dry to wet in winter, dry-to-wet transition from winter to spring, continuous dry-to-dry transition from winter to spring, and continuous wet transition from winter to spring.
3. The method for evaluating vegetation loss risk under drought and water logging sharp turn stress according to claim 2, wherein the drought grade classification standard is that-2 < SPI is less than or equal to-1.5 and is heavy drought, -1.5< SPI is less than or equal to-1 and is moderate drought, 1< SPI is less than or equal to 1.5 and is moderate flood, and 1.5< SPI is less than or equal to 2 and is heavy flood.
4. The method for evaluating vegetation loss risk under drought and water logging and tight turning stress according to claim 1, wherein in step S20, the probability of occurrence of the dry and wet composite event between adjacent seasons in different situations is calculated by using Copula function, specifically comprising the steps of: Fitting the edge distribution of the SPI indexes in adjacent seasons by adopting normal distribution and generalized extremum distribution, and screening out the optimal edge distribution by utilizing a red pool information criterion; Selecting an optimal joint distribution function of adjacent Ji Jiejian SPI sequences from Clayton-Copula, frank-Copula, gumbel-Copula, gaussian-Copula and t-Copula functions by using root mean square error and AIC criteria; and calculating the occurrence probability of the dry-wet composite event between adjacent seasons under different scenes according to the optimal joint distribution function.
5. The method for evaluating vegetation loss risk under drought and water logging and sharp turn stress according to claim 1 or 4, wherein the occurrence probability calculation formulas of adjacent Ji Jiejian from dry to wet, from wet to dry, continuous drought and continuous wetting in moderate situations are respectively as follows: The occurrence probability calculation of the adjacent Ji Jiejian from dry to wet, from wet to dry, continuous drought and continuous wetting under severe conditions is respectively as follows: in the formula, ( ) X and Y respectively represent SPI values of two events, X represents the precipitation condition of the current season, and Y represents the precipitation condition of the next season; 、 Respectively representing the occurrence probability of the adjacent Ji Jiejian from dry to wet in moderate and severe situations; 、 Respectively representing the occurrence probability of wet-to-dry transition between adjacent internodes in moderate and severe situations; 、 respectively representing the occurrence probability of adjacent Ji Jiejian continuous drought under moderate and severe situations; 、 the probability of occurrence of continuous wetting of adjacent Ji Jiejian in moderate and severe scenarios, respectively, is shown.
6. The method for evaluating vegetation loss risk under drought and flood emergency stress according to claim 1, wherein in step S20, a bayesian framework is combined with Copula function based on SPI index of a rainfall data quaternary scale sequence and month-by-month NDVI data, and vegetation loss probability under the stress of adjacent Ji Jiejian dry-wet composite events under different situations is calculated.
7. The method for evaluating vegetation loss risk under drought and flood emergency stress according to claim 1, wherein in step S20, the step of calculating the loss probability of vegetation under the stress of adjacent Ji Jiejian dry and wet composite events under different situations by using a bayesian framework and a Copula function is specifically as follows: The alternative edge distribution is normal distribution, generalized extremum distribution and Geng Beier distribution respectively, the alternative joint distribution functions are Clayton-Copula, frank-Copula, gumbel-Copula, gaussian-Copula and t-Copula, and the optimal distribution of NDVI is optimized through K-S test and red pool information criterion; the root mean square error and AIC criteria are used to optimize the joint distribution function; And then calculating the loss probability of vegetation under the stress of adjacent Ji Jiejian wet and dry composite events under different scenes according to the joint distribution function.
8. The method for assessing the risk of vegetation loss under drought/water logging emergency stress according to claim 1, wherein in step S20, the adjacent Ji Jiejian under moderate conditions is represented by the loss probability expression of vegetation under the stress of a dry-wet event: The loss probability expression of vegetation under the stress of wet-to-dry event between adjacent seasons in a moderate scene is as follows: a loss probability expression of vegetation under the stress of continuous drought events between adjacent seasons in a moderate scene: The loss probability expression of vegetation under the stress of continuous wetting event between adjacent seasons in moderate scene: adjacent Ji Jiejian under severe conditions is represented by the loss probability of vegetation under the stress of a dry-wet event: loss probability expression of vegetation under the stress of wet-to-dry event between adjacent season nodes in severe situations: the loss probability expression of vegetation under the stress of continuous drought events between adjacent seasons in severe situations is as follows: Loss probability expression of vegetation under stress of adjacent Ji Jiejian continuous wetting events in severe scenario: in the formula, Represents an NDVI sequence; Representing an SPI sequence and an NDVI sequence; an edge distribution function representing the NDVI data sequence; Representing NDVI < NDVI 40th , vegetation loss scenario; And An edge distribution function representing two seasonal SPI sequences; a joint distribution function representing two seasonal SPI sequences; representing a joint distribution function of the two seasonal SPI sequences and the NDVI sequences; 、 Respectively representing the loss probability of vegetation under the condition of the adjacent Ji Jiejian under the condition of moderate and severe stress from dry to wet; 、 respectively representing the loss probability of vegetation under the stress of wet-to-dry between adjacent seasons under moderate and severe conditions; 、 Respectively representing the loss probability of vegetation under the continuous drought stress of adjacent Ji Jiejian under moderate and severe situations; 、 the loss probability of vegetation under continuous wetting stress of adjacent Ji Jiejian under moderate and severe scenarios is shown, respectively.
9. The method for evaluating vegetation loss risk under drought/water logging and tight turning stress according to claim 1, wherein in step S40, the vegetation loss risk under the stress of the compound event of drought/humidity is calculated by multiplying the occurrence probability and the loss probability in step S20 and the exposure degree in step S30.
10. The vegetation loss risk assessment device under the stress of a wet and dry composite event is characterized by comprising a processor and a storage medium, wherein the processor loads and executes instructions and data in the storage medium to realize the vegetation loss risk assessment method under the stress of drought and flood emergency rotation according to any one of claims 1-9.

Description

Assessment method for vegetation loss risk under drought and waterlogging sharp rotation stress Technical Field The application belongs to the field of ecological disaster risk assessment, and particularly relates to an assessment method of vegetation loss risk under drought and waterlogging sharp turn stress. Background With the aggravation of global climate change, the occurrence frequency and intensity of dry and wet compound events such as drought and waterlogging and the like are continuously increased, and serious stress and damage are generated on an ecological system, particularly vegetation. However, most of the conventional disaster risk assessment methods focus on single disasters (such as drought or flood), and there are many limitations, such as lack of quantification of synergy of dry and wet composite events, neglecting the superposition effect of hydrologic stress in the dry and wet transformation process, possibly causing underestimation of ecological loss risk, and most of the existing vulnerability assessment is based on static indexes, not considering dynamic response of disaster-bearing bodies under composite events, and single disaster model cannot capture cascade effect among various events. These problems result in the inability of conventional disaster risk assessment methods to accurately assess the risk of manufacturing loss. Therefore, there is a need for a method for evaluating vegetation loss risk under drought and waterlogging and tight turning stress, so as to evaluate vegetation loss risk more accurately, and provide scientific basis for biological protection and disaster management. Disclosure of Invention Aiming at the defects of the prior art, the application aims to provide an assessment method for vegetation loss risk under drought and waterlogging emergency stress, which can more accurately assess vegetation loss risk and provide scientific basis for biological protection and disaster management. In order to achieve the above object, in a first aspect, the present application provides a method for evaluating vegetation loss risk under drought and waterlogging sharp turning stress, comprising the following steps: s10, acquiring and processing month-by-month precipitation data and NDVI data of a target area to obtain a quaternary scale sequence of the grid precipitation data and the NDVI data; s20, identifying dry-wet composite events between adjacent seasons based on a precipitation data quaternary scale sequence, dividing moderate and severe scenes, calculating the occurrence probability of the dry-wet composite events between the adjacent seasons under different scenes by using a Copula function, and calculating the loss probability of vegetation under the stress of the adjacent Ji Jiejian dry-wet composite events under different scenes by using a Bayesian framework and the Copula function in combination; s30, calculating the exposure of the vegetation system to the wet-dry composite event by adopting a quaternary average normalized vegetation index based on the NDVI data quaternary scale sequence; And S40, calculating vegetation loss risk under the stress of the wet and dry composite event according to the occurrence probability and the loss probability calculated in the step S20 and the exposure degree calculated in the step S30. The evaluation method of vegetation loss risk under drought and waterlogging sharp turn stress has the following effects that (1) the combined distribution of composite events is constructed based on the composite event probability modeling of Copula functions, the dependence of the composite events is quantized, the superposition of single disaster probability is more in line with the actual disaster chain characteristics, and (2) compared with the exploration of risks based on human systems (population and GDP), the high-risk vegetation areas (such as wetland-grassland transition zones) can be identified by the natural system exposure priority, so that ecological management is more spatially targeted, and meanwhile, the evaluation method has important reference value for ecological risk prevention and control under climate change. As a further preferable mode, in step S20, based on the precipitation data quaternary scale sequence, the steps of identifying the dry-wet composite event between adjacent seasons and dividing the moderate and severe situations are specifically: calculating SPI index according to the rainfall data quaternary scale sequence; extracting drought events and wetting events by using the SPI index according to drought grade division standards, and dividing moderate and severe scenes; Identifying a dry-wet composite event between adjacent seasons according to seasons in which drought events and wet events occur, wherein the composite event comprises four of dry-to-wet transition from spring to summer, wet-to-dry transition from spring to summer, continuous drought in spring to summer, dry-to-wet transition from summer t