CN115953085B - Method for evaluating influence of composite dry-heat event on vegetation growth - Google Patents
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Abstract
The invention discloses a method for evaluating the influence of a composite dry-heat event on vegetation growth, which comprises the following steps: s1, calculating a standardized rainfall index SPI and a standardized temperature index STI index of a research area, and determining a composite dry heat event; s2, meshing spatial distribution data of drought, high-temperature events and vegetation growth changes in a research area to obtain spatial correlation data, and extracting raster data; s3, calculating vegetation growth change speed under the condition of a composite dry heat event; s4, calculating the influence of the composite dry-heat event on vegetation by using an interaction factor module in the geographic detector. Compared with the prior art, the method can better describe the actual influence of the composite dry-heat event actually occurring in the history on the vegetation growth, and can also effectively explain the spatial heterogeneity of the vegetation response to the composite dry-heat event.
Description
Technical Field
The invention relates to the field of influence evaluation of extreme compound climate events, in particular to a method for evaluating influence of compound dry heat events on vegetation growth.
Background
Vegetation is a key component of land systems that can affect climate conditions, carbon balance and water circulation. The vegetation growth is influenced and restricted by climate conditions, and proper hydrothermal combination is an important influence condition for vegetation growth. Drought and high temperatures can affect different processes of vegetation growth, including photosynthesis, respiration, and carbon utilization, leading to reduced biomass and death. Such extreme weather events are expected to become more frequent and more widely spaced in the future. Vegetation as a sensitive indicator of climate change may be severely compromised.
Drought and high temperature events rarely occur alone, and their co-occurrence, known as composite dry heat events, may cause more severe effects on vegetation growth than extreme weather events alone. The most widely used method for evaluating the influence of composite dry heat events on vegetation is a copula function method at present, namely, according to time sequence data of drought and high temperature and NDVI loss values, the NDVI loss probability is evaluated by utilizing a three-dimensional copula function under the condition of drought and high temperature events. Although the method can be used for evaluating the influence of the composite dry heat event on vegetation, only the vegetation loss probability under the situation of a specific composite dry heat event can be evaluated, and the actual influence of the composite dry heat event actually occurring in the history on vegetation growth is difficult to describe; at the same time, the spatial heterogeneity of vegetation response to composite dry-heat events cannot be explained.
In fact, assessing the effect of a composite dry heat event on vegetation growth is essentially accounting for the spatial correlation of the composite dry heat event and vegetation growth changes. The geographic detector can evaluate the influence degree of the independent variable on the dependent variable according to the spatial distribution similarity degree of the independent variable and the dependent variable, and can well describe the spatial correlation of the independent variable and the dependent variable. Currently, there is no literature to introduce a geo-detector approach into the study of the effect of composite dry heat events on vegetation growth.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a method for evaluating the influence of a composite dry heat event on vegetation growth.
In order to solve the technical problems, the invention adopts the following technical scheme: a method of assessing the effect of a composite dry heat event on vegetation growth comprising the steps of:
s1, calculating a standardized rainfall index SPI and a standardized temperature index STI of a research area, and determining a composite dry heat event during a vegetation growing season;
s2, spatial distribution data of drought events, high-temperature events and vegetation growth changes in a gridding research area are obtained, spatial correlation data are obtained, and raster data are extracted;
s3, calculating vegetation growth change speed under the condition of a composite dry heat event;
s4, calculating the influence of the composite dry-heat event on vegetation by using an interaction factor module in the geographic detector.
Further, in the step S1, a normalized precipitation index SPI and a normalized temperature index STI of the research area are calculated, and a composite dry heat event during a vegetation growing season is determined, specifically: and calculating a standardized precipitation index SPI and a standardized temperature index STI of a 1 month time scale, and determining a composite dry heat event during a vegetation growing season according to the consistency of the dry event and the high temperature event in time.
Further, spatial distribution data of drought events, high-temperature events and vegetation growth changes in the gridding research area in the step S2 are obtained, spatial correlation data are obtained, and raster data are extracted; the method comprises the following steps: generating a plurality of effective sampling points in a research area, performing spatial superposition on the plurality of effective sampling points and the drought event, the high-temperature event and the normalized vegetation index NDVI data of the growth season in the research area to obtain spatial correlation data of the drought event, the high-temperature event and the normalized vegetation index NDVI, and extracting grid point information of the spatial correlation data.
Further, in the step S3, the vegetation growth change speed under the condition of the composite dry heat event is calculated, which specifically includes:
calculating vegetation growth change speed under the condition of a composite dry heat event by using normalized vegetation index NDVI data; as shown in formula (1);
wherein V is NDVI Expressed as the vegetation growth change speed under the condition of composite dry heat event, jRefers to the month, NDVI, of the occurrence of the composite dry heat event j Represents the normalized vegetation index NDVI at month j, NDVI j-1 Represents the normalized vegetation index NDVI at month j-1;
the vegetation in the research area has hysteresis effect on the response of the composite dry heat event, so that vegetation growth change speeds of different hysteresis times under the condition of the composite dry heat event are calculated; as shown in formula (2);
wherein j refers to the month of the composite dry heat event, n is the lagging month, V NDVI(n) Expressed as vegetation growth change rate, NDVI, lagging for n months under the condition of composite dry heat event j Represents the normalized vegetation index NDVI at month j, NDVI j+n Is the normalized vegetation index NDVI at month j+n.
Further, in the step S4, the interaction factor module in the geographic detector is used to calculate the influence of the composite dry-heat event on the vegetation, specifically:
dividing drought events and high-temperature events into a plurality of categories according to the intensity degree, and calculating interaction influence on vegetation growth under the condition of simultaneous occurrence of drought and high temperature by using an interaction factor module in a geographic detector under the condition of the drought events and the high-temperature events and taking vegetation growth change speeds of different influence hysteresis events under the condition of the composite dry-heat events as results, namely, influence of the composite dry-heat events on vegetation;
the influence of the composite dry heat event on the vegetation comprises the influence degree of the composite dry heat event on the vegetation and the lag time of the influence of the composite dry heat event on the vegetation;
the influence degree of the composite dry-heat event on the vegetation is the interaction degree of the drought event and the high-temperature event on the vegetation, and the algorithm is shown in a formula (3);
wherein q represents a composite dry heat eventThe influence degree of the parts on vegetation is in the range of [0,1]The method comprises the steps of carrying out a first treatment on the surface of the L is a partition formed by overlapping and combining classification intervals of drought events and high-temperature events with different degrees in space of a research area, and N is i And N is the number of units in the ith zone and the full area of the study zone respectively,
and->
The variances of vegetation growth change speeds of the ith zone and the whole area of the research zone are respectively represented;
the lag time of the composite dry heat event on vegetation is shown in a formula (4);
wherein T is the lag time of the composite dry heat event on vegetation;
for the extent of effect of compound dry heat events on vegetation lag of 0 months, +.>
For the extent of the effect of the compound dry heat event on vegetation lag of 1 month, +.>
To the extent of 2 months of vegetation lag of the composite dry heat event, q n The influence degree of the compound dry heat event on vegetation lags for n months; />
To calculate the sign, i.e. obtain q n Is set, the lag time n of (2).
According to the invention, vegetation growth change speed V under the composite dry-heat condition is calculated by extracting the drought event, the high-temperature event and the normalized vegetation index NDVI space grid data information NDVI On the basis, the influence degree and the lag time of the composite dry heat event on vegetation growth are evaluated by using a geographic detector.
Compared with the prior art, the invention has the beneficial effects that: the invention quantitatively gives the influence degree and the lag time of the composite dry heat event on the vegetation growth based on the space statistics method, and compared with the prior art, the invention can better describe the actual influence of the composite dry heat event actually occurring in the history on the vegetation growth and can effectively explain the space heterogeneity of the vegetation on the response of the composite dry heat event.
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FIG. 1 is a flow chart of the present invention.
FIG. 2 is a schematic illustration of the effect of a composite dry heat event on vegetation growth based on a geographic detector.
Detailed Description
The present invention is described in detail below with reference to examples, but the present invention is not limited to these examples.
As shown in fig. 1, a method for evaluating the effect of a composite dry heat event on vegetation growth comprises the following steps:
s1, calculating a standardized rainfall index SPI and a standardized temperature index STI of a research area, and determining a composite dry heat event during a vegetation growing season.
The indexes capable of reflecting the vegetation growth state include normalized vegetation index NDVI, enhanced vegetation index EVI, leaf area index LAI, and the like, and in this example, the normalized vegetation index NDVI is selected to describe the vegetation growth state.
Taking a Poyang lake basin as an example, selecting rainfall, air temperature and normalized vegetation index NDVI month-by-month data in the basin 1998-2019, calculating a normalized rainfall index SPI and a normalized temperature index STI of a month time scale, and determining a research period composite dry heat event by using the month normalized rainfall index SPI which is less than or equal to-0.5 and the normalized temperature index STI which is more than or equal to 0.5; and screening out drainage basin type composite dry heat events during vegetation growing season (4-10 months), and selecting out 15 composite dry heat events.
S2, spatial distribution data of drought events, high-temperature events and vegetation growth changes in the grid research area are obtained, spatial correlation data are obtained, and grid data are extracted.
And (3) screening out drought event, high-temperature event and normalized vegetation index NDVI spatial distribution data corresponding to the composite dry-heat event, removing vegetation bare or non-vegetation grid points with the normalized vegetation index NDVI less than 0.1, generating 12344 effective sampling points in a research area, carrying out spatial superposition on 12344 sampling points and normalized vegetation index NDVI, normalized precipitation index SPI and normalized temperature index STI data in the 1998-2019 year growth season of the research area to obtain spatial correlation data of the drought event, the high-temperature event and the normalized vegetation index NDVI, and extracting grid data.
S3, calculating the vegetation growth change speed under the condition of the composite dry heat event.
And calculating vegetation growth change speed without response lag of vegetation to the composite dry heat event.
Wherein V is NDVI Expressed as vegetation growth change speed under the condition of a composite dry heat event, j refers to month of occurrence of the composite dry heat event, and NDVI j Represents the normalized vegetation index NDVI at month j, NDVI j-1 Represents the normalized vegetation index NDVI at month j-1.
Considering the response lag time of the Poyang lake basin vegetation to precipitation and air temperature of 1-2 months, respectively calculating the response change of vegetation growth lag of 1 month and 2 months under the condition of a composite dry heat event, wherein the response change comprises the following formula:
wherein:
、/>
vegetation growth change speeds lagging for 1 month and lagging for 2 months under the condition of respectively compounding dry heat events, and j is time (month) Refers to the month in which the composite dry heat event occurred.
S4, calculating the influence of the composite dry-heat event on vegetation by using an interaction factor module in the geographic detector.
At present, classification modes include a natural break point method, an equidistant method, a quantile method, a geometric interval method and the like.
In the example, the standardized rainfall index SPI and the standardized temperature index STI are divided into 9 types by using a natural break point method, and the interaction factor modules based on the geographic detectors are V with different lag times under the condition of drought events and high-temperature events NDVI As a result, the interactive effect on vegetation growth under conditions where drought and high temperature occur simultaneously is calculated.
For the composite dry heat event occurring in 7 months in 2003, the standardized rainfall index SPI and the standardized temperature index STI are respectively-1.36 and 1.34, the influence of the composite dry heat event on vegetation is 0.31 when no hysteresis exists, the influence of the composite dry heat event on vegetation is 0.29 when one month of hysteresis exists, and the influence of the composite dry heat event on vegetation is 0.31 when two months of hysteresis exists and the influence of the composite dry heat event on vegetation is 0.15.
As shown in fig. 2, a schematic diagram of the effect of the composite dry heat event on vegetation growth based on a geographic detector is shown, the normalized precipitation index SPI and the normalized temperature index STI data of the composite dry heat event in the research area are spatially overlapped and combined to form a partition, and the evaluation of how much the composite dry heat event explains V NDVI Spatial heterogeneity of (a);
representing V in the whole region of the research region NDVI Total variance of->
And->
V in the second and third partitions respectively NDVI Is a variance of (2); />
And->
Respectively representing +.>
Mean value of V NDVI Representing V in the whole region of the research region NDVI Is a mean value of (c).
In conclusion, the invention calculates the vegetation growth change speed V under the condition of the composite dry heat event by determining the vegetation growth season composite dry heat event NDVI The extent of influence and the lag time of the composite dry heat event on vegetation growth are evaluated based on a geographic detector method. The invention quantitatively gives the influence degree and the lag time of the composite dry heat event on the vegetation growth based on the space statistics method, and compared with the prior art, the invention can better describe the actual influence of the composite dry heat event actually occurring in the history on the vegetation growth and can effectively explain the space heterogeneity of the vegetation on the response of the composite dry heat event.
Claims (3)
1. A method of assessing the effect of a composite dry heat event on vegetation growth, characterized by: the method comprises the following steps:
s1, calculating a standardized rainfall index SPI and a standardized temperature index STI of a research area, and determining a composite dry heat event during a vegetation growing season;
s2, spatial distribution data of drought events, high-temperature events and vegetation growth changes in a gridding research area are obtained, spatial correlation data are obtained, and raster data are extracted;
s3, calculating vegetation growth change speed under the condition of a composite dry heat event;
s4, calculating the influence of the composite dry-heat event on vegetation by using an interactive factor module in the geographic detector;
in the step S1, a normalized precipitation index SPI and a normalized temperature index STI of the research area are calculated, and a composite dry heat event during a vegetation growing season is determined, specifically: calculating a standardized rainfall index SPI and a standardized temperature index STI of a 1 month time scale, and determining a composite dry-heat event during a vegetation growing season according to the consistency of the dry event and the high-temperature event in time;
step S4 is to calculate the influence of the composite dry-heat event on vegetation by using an interaction factor module in the geographic detector, and specifically comprises the following steps:
dividing drought events and high-temperature events into a plurality of categories according to the intensity degree, and calculating interaction influence on vegetation growth under the condition of simultaneous occurrence of drought and high temperature by using an interaction factor module in a geographic detector under the condition of the drought events and the high-temperature events and taking vegetation growth change speeds of different influence hysteresis events under the condition of the composite dry-heat events as results, namely, influence of the composite dry-heat events on vegetation;
the influence of the composite dry heat event on the vegetation comprises the influence degree of the composite dry heat event on the vegetation and the lag time of the influence of the composite dry heat event on the vegetation;
the influence degree of the composite dry-heat event on the vegetation is the interaction degree of the drought event and the high-temperature event on the vegetation, and the algorithm is shown in a formula (3);
in the method, in the process of the invention,qindicating the influence degree of the compound dry heat event on vegetation, the range is [0,1]The method comprises the steps of carrying out a first treatment on the surface of the L is a partition formed by overlapping and combining classification intervals of drought events and high-temperature events with different degrees in space of a research area, and N is i And N is respectively the firstiNumber of units in each of the zones and the total area of the investigation region,
and->
Respectively represent the firstiVariance of vegetation growth change rates of individual zones and study zone total zones;
the lag time of the composite dry heat event on vegetation is shown in a formula (4);
in the method, in the process of the invention,t is the lag time of the composite dry heat event on vegetation;
for the extent of effect of compound dry heat events on vegetation lag of 0 months, +.>
For the extent of the effect of the compound dry heat event on vegetation lag of 1 month, +.>
To the extent that the composite dry heat event affects vegetation for 2 months,q n vegetation hysteresis for composite dry heat eventnThe extent of the month-to-month effect; />
To calculate the sign, i.e. obtainq n Is a lag time of (2)n。
2. The method of assessing the effect of a composite dry heat event on vegetation growth of claim 1 wherein: in the step S2, spatial distribution data of drought events, high-temperature events and vegetation growth changes in the gridding research area are obtained, spatial correlation data are obtained, and grid data are extracted; the method comprises the following steps: generating a plurality of effective sampling points in a research area, performing spatial superposition on the plurality of effective sampling points and the drought event, the high-temperature event and the normalized vegetation index NDVI data of the growth season in the research area to obtain spatial correlation data of the drought event, the high-temperature event and the normalized vegetation index NDVI, and extracting grid point information of the spatial correlation data.
3. A method of assessing the effect of a composite dry heat event on vegetation growth as claimed in claim 2 wherein: the vegetation growth change speed under the condition of the composite dry heat event is calculated in the step S3, and specifically comprises the following steps:
calculating vegetation growth change speed under the condition of a composite dry heat event by using normalized vegetation index NDVI data; as shown in formula (1);
wherein V is NDVI Expressed as the growth change speed of vegetation in the current month under the condition of the composite dry heat event,jrefers to the month, NDVI, of the occurrence of the composite dry heat event j Represent the firstjNormalized vegetation index NDVI at month, NDVI j-1 Represent the firstj-normalized vegetation index NDVI at 1 month;
the vegetation in the research area has hysteresis effect on the response of the composite dry heat event, so that vegetation growth change speeds of different hysteresis times under the condition of the composite dry heat event are calculated; as shown in formula (2);
in the method, in the process of the invention,jrefers to the month in which the composite dry heat event occurred,nfor the month of hysteresis, V nNDVI() Expressed as hysteresis under composite dry heat event conditionsnVegetation growth rate, NDVI, over a month j Represent the firstjNormalized vegetation index NDVI at month, NDVI j n+ Is the firstj+nNormalized vegetation index NDVI at month.
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