CN115953085A - 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 standard precipitation index SPI and a standard temperature index STI index of a research area, and determining a composite dry heat event; s2, gridding spatial distribution data of drought, high-temperature events and vegetation growth changes in a research area to obtain spatial correlation data, and extracting grid data; s3, calculating the vegetation growth change speed under the condition of a composite dry-heat event; and S4, calculating the influence of the composite dry heat event on the vegetation by utilizing 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 response of the vegetation to the composite dry heat event.
Description
Technical Field
The invention relates to the field of evaluation of influence of extreme composite climatic events, in particular to a method for evaluating influence of composite dry heat events on vegetation growth.
Background
Vegetation is a key component of the land system and may affect climatic conditions, carbon balance and water circulation. The vegetation growth is influenced and restricted by climatic conditions, and the proper hydrothermal combination is an important influence condition for the vegetation growth. Drought and high temperature may affect different processes of vegetation growth, including photosynthesis, respiration, and carbon utilization, resulting in biomass reduction and death. Such extreme weather events are expected to become more frequent and more spacious in the future. Vegetation, as a sensitive indicator of climate change, may be severely threatened.
Drought and high temperature events rarely occur separately, and their co-occurrence, known as a composite dry heat event, may have a more severe impact on vegetation growth than an extreme weather event alone. Currently, the most widely used method for evaluating the influence of composite dry and hot events on vegetation is a copula function method, namely, according to time sequence data of drought, high temperature and NDVI loss values, a three-dimensional copula function is utilized, and the NDVI loss probability is evaluated by taking the drought and high temperature events as conditions. Although the method can be used for evaluating the influence of the composite dry heat event on the vegetation, the method can only evaluate the probability of vegetation loss under the situation of the specific composite dry heat event, and the actual influence of the composite dry heat event actually occurring in the history on the vegetation growth is difficult to describe; at the same time, the spatial heterogeneity of vegetation response to complex dry thermal events cannot be explained.
Indeed, assessing the effect of the composite dry heat event on vegetation growth is essentially accounting for the spatial correlation of the composite dry heat event and the vegetation growth variation. The geographic detector can evaluate the influence degree of the independent variable on the dependent variable according to the similarity degree of the spatial distribution 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 that incorporates a geo-detector approach into the study of the impact of complex dry heat events on vegetation growth.
Disclosure of Invention
Aiming at the defects 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 technical scheme adopted by the invention is as follows: a method of assessing the effect of a composite dry heat event on vegetation growth, comprising the steps of:
s1, calculating a standard precipitation index SPI and a standard temperature index STI in a research area, and determining a composite dry-heat event in a vegetation growing season;
s2, gridding spatial distribution data of drought events, high-temperature events and vegetation growth changes in a research area to obtain spatial correlation data, and extracting raster data;
s3, calculating the vegetation growth change speed under the condition of a composite dry-heat event;
and S4, calculating the influence of the composite dry heat event on the vegetation by utilizing 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 the 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 the vegetation growing season according to the time consistency of the dry event and the high-temperature event.
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 to obtain spatial correlation data, and grid data are extracted; the method specifically comprises the following steps: generating a plurality of effective sampling points in a research area, spatially overlapping the effective sampling points with drought events, high-temperature events and normalized vegetation index NDVI data in the research area to obtain spatial correlation data of the drought events, the high-temperature events and the normalized vegetation index NDVI, and extracting grid point information of the spatial correlation data.
Further, the vegetation growth change speed under the composite dry heat event condition is calculated in the step S3, specifically:
calculating the vegetation growth change speed under the condition of the composite dry heat event by using the normalized vegetation index NDVI data; as shown in formula (1);
in the formula, V NDVI Expressed as the rate of vegetation growth change under the conditions of the composite dry heat event, j refers to the month of occurrence of the composite dry heat event, NDVI j Indicating normalized vegetation index NDVI at month j, NDVI j-1 Represents the normalized vegetation index NDVI at month j-1;
the response of the vegetation in the research area to the composite dry heat event has a hysteresis effect, so the vegetation growth change speed with different hysteresis times under the condition of the composite dry heat event is calculated; as shown in equation (2);
wherein j refers to the month of occurrence of the composite dry heat event, n is the lag month, V NDVI(n) Expressed as the growth rate of vegetation change, NDVI, with a lag of n months under composite dry heat events j Indicating normalized vegetation index NDVI at month j, NDVI j+n Normalized vegetation index NDVI at month j + n.
Further, in the step S4, the influence of the composite dry heat event on the vegetation is calculated by using an interaction factor module in the geographic detector, specifically:
dividing the drought event and the high-temperature event into a plurality of categories according to the strength, calculating the interaction influence on vegetation growth under the condition of drought and high temperature simultaneously, namely the influence of the composite dry-heat event on the vegetation by using an interaction factor module in a geographic detector, taking the drought event and the high-temperature event as conditions and the vegetation growth change speed of different influence hysteresis events under the composite dry-heat event as a result;
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 composite dry heat event on the vegetation;
the degree of the influence of the composite dry and hot events on the vegetation is the degree of the interaction of the dry events and the high-temperature events on the vegetation, and the algorithm is shown in a formula (3);
wherein q represents the degree of the composite dry heat event on the vegetation, and the range is [0,1 ]](ii) a L is a subarea formed by overlapping and combining the classification intervals of different degrees of drought events and high-temperature events on the space of a research area, and N is i And N is the number of cells in the ith partition and the entire area of interest,
and &>
Respectively representing the variance of vegetation growth change speed of the ith subarea and the whole area of the research area;
the lag time of the composite dry heat event on the vegetation is influenced, and the algorithm is shown in formula (4);
wherein T is the lag time of the composite dry heat event affecting the vegetation;
to the extent that a composite dry heat event has a 0-month lag on vegetation>
To the extent that a compound dry heat event has an effect on vegetation that lags behind by 1 month `>
The extent of the effect of the composite dry heat event on vegetation lag of 2 months, q n The influence degree of the composite dry heat event on vegetation lag by n months; />
For computing symbols, i.e. obtaining q n The lag time n of (d).
The method calculates the vegetation growth change speed V under the composite dry heat condition by extracting the space grid data information of the drought event, the high temperature event and the normalized vegetation index NDVI NDVI And on the basis, the influence degree and the lag time of the composite dry heat event on the vegetation growth are evaluated by using a geographic detector.
Compared with the prior art, the invention has the beneficial effects that: the method quantitatively gives the influence degree and the lag time of the composite dry heat event on the vegetation growth based on a space statistical method, can better describe the actual influence of the composite dry heat event which actually occurs in the history on the vegetation growth compared with the prior art, and can also effectively explain the spatial heterogeneity of the vegetation response to the composite dry heat event.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a schematic diagram of the effect of a composite dry heat event based on a geo-detector on vegetation growth.
Detailed Description
The present invention will be described in detail with reference to examples, but the present invention is not limited to these examples.
As shown in fig. 1, a method for evaluating the influence 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 in a research area, and determining a composite dry-heat event in the vegetation growing season.
The indexes capable of reflecting the vegetation growth state include a normalized vegetation index NDVI, an enhanced vegetation index EVI, a leaf area index LAI and the like, and the normalized vegetation index NDVI is selected to describe the vegetation growth state in the example.
Taking a Poyang lake basin as an example, selecting monthly data of rainfall, air temperature and normalized vegetation index NDVI in the basin between 1998 and 2019, calculating a standardized rainfall index SPI and a standardized temperature index STI of a one-month time scale, and determining a composite dry-heat event in a research period by using the monthly standardized rainfall index SPI less than or equal to-0.5 and the standardized temperature index STI greater than or equal to 0.5; and (4) screening out the watershed composite dry heat events in the growing season (4-10 months) of the vegetation, and screening out 15 composite dry heat events in total.
And S2, gridding spatial distribution data of drought events, high-temperature events and vegetation growth changes in the research area to obtain spatial correlation data, and extracting grid data.
Spatial distribution data of drought events, high-temperature events and normalized vegetation indexes NDVI corresponding to the screened composite dry-heat events are eliminated, vegetation bare or non-vegetation grid points with the normalized vegetation indexes NDVI <0.1 are removed, 12344 effective sampling points are generated in a research area, the 12344 sampling points are spatially overlapped with the normalized vegetation indexes NDVI, the normalized rainfall indexes SPI and the normalized temperature indexes STI data in the research area in 1998-2019, spatial correlation data of the normalized vegetation indexes NDVI, the normalized rainfall indexes SPI and the normalized temperature indexes STI data are obtained, and grid data are extracted.
And S3, calculating the vegetation growth change speed under the condition of the composite dry heat event.
And calculating the vegetation growth change speed of the vegetation without response delay to the composite dry heat event.
In the formula, V NDVI Expressed as the rate of vegetation growth change under the conditions of the composite dry heat event, j refers to the month of occurrence of the composite dry heat event, NDVI j Indicating normalized vegetation index NDVI at month j, NDVI j-1 Indicating the normalized vegetation index NDVI at month j-1.
Considering the response lag time of the Poyang lake basin vegetation to the rainfall and the air temperature for 1 to 2 months, the changes of the vegetation growth lag time for 1 month and 2 months under the composite dry-heat event condition are calculated respectively, and the following formula is shown:
in the formula:
、/>
the vegetation growth change speed delayed by 1 month and 2 months respectively under the condition of the composite dry heat event, and j is time (month) and refers to the month of the composite dry heat event.
And S4, calculating the influence of the composite dry heat event on the vegetation by utilizing an interaction factor module in the geographic detector.
At present, the classification method includes a natural break point method, an equal interval method, a quantile method, a geometric interval method and the like.
In the example, the standard precipitation index SPI and the standard temperature index STI are divided into 9 types by using a natural breakpoint method, and the interaction factor module based on the geographic detector takes drought events and high-temperature events as conditions and has V with different lag times NDVI As a result, the interactive effect on vegetation growth under conditions of drought and high temperature co-occurrence was calculated.
For the composite dry heat event which occurs in 7 months in 2003, the standardized precipitation 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 delay exists, the influence of the composite dry heat event after one month is 0.29, and the influence of the composite dry heat event after two months is 0.15, so that the influence degree of the composite dry heat event on vegetation growth is 0.31, and the influence does not have delay.
As shown in FIG. 2, vegetation is grown for a geo-detector based composite dry heat eventThe standardized data of the precipitation index SPI and the standardized data of the temperature index STI of the composite dry heat event in the research area are overlapped and combined in space to form a subarea, and the evaluation of the degree of the composite dry heat event explains V NDVI Spatial heterogeneity of (a);
indicates the V in the whole region of the study NDVI Is greater than or equal to>
And &>
V in the second and third partitions respectively NDVI The variance of (a); />
And &>
Respectively denotes a second and a third sub-zone>
Mean value of (V) NDVI Indicates the V in the whole region of the study NDVI Of the average value of (a).
In conclusion, the vegetation growth change speed V under the condition of the composite dry heat event is calculated by determining the composite dry heat event of the vegetation growth season NDVI And evaluating the influence degree and lag time of the composite dry heat event on vegetation growth based on a geographic detector method. The method quantitatively gives the influence degree and the lag time of the composite dry heat event on the vegetation growth based on a space statistical method, can better describe the actual influence of the composite dry heat event which actually occurs in the history on the vegetation growth compared with the prior art, and can also effectively explain the spatial heterogeneity of the vegetation response to the composite dry heat event.
Claims (5)
1. A method for evaluating the influence of a composite dry heat event on vegetation growth is characterized by comprising the following steps: the method comprises the following steps:
s1, calculating a standard precipitation index SPI and a standard temperature index STI in a research area, and determining a composite dry-heat event in a vegetation growing season;
s2, gridding spatial distribution data of drought events, high-temperature events and vegetation growth changes in a research area to obtain spatial correlation data, and extracting raster data;
s3, calculating the vegetation growth change speed under the condition of a composite dry-heat event;
and S4, calculating the influence of the composite dry heat event on the vegetation by utilizing an interaction factor module in the geographic detector.
2. The method of claim 1, wherein the method comprises the steps of: in the step S1, a standardized precipitation index SPI and a standardized temperature index STI of the research area are calculated, and a composite dry-heat event during the 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 the vegetation growing season according to the time consistency of the dry event and the high-temperature event.
3. The method of claim 2, wherein the method comprises the steps of: spatial distribution data of drought events, high-temperature events and vegetation growth changes in the gridding research area in the step S2 are obtained to obtain spatial correlation data, and grid data are extracted; the method comprises the following specific steps: generating a plurality of effective sampling points in a research area, carrying out spatial superposition on the effective sampling points and drought events, high-temperature events and normalized vegetation index NDVI data in the research area to obtain spatial correlation data of the drought events, the high-temperature events and the normalized vegetation index NDVI, and extracting grid point information of the spatial correlation data.
4. The method of claim 3, wherein the method comprises the steps of: in the step S3, the vegetation growth change speed under the composite dry heat event condition is calculated, specifically:
calculating the vegetation growth change speed under the condition of the composite dry heat event by using the normalized vegetation index NDVI data; as shown in formula (1);
in the formula, V NDVI Expressed as the growth change speed of vegetation in the current month under the condition of the composite dry heat event, j refers to the month of the composite dry heat event, NDVI j Means normalized vegetation index at month j, NDVI j-1 Represents the normalized vegetation index NDVI at month j-1;
the response of the vegetation in the research area to the composite dry heat event has a lag effect, so that the vegetation growth change speed with different lag times under the condition of the composite dry heat event is calculated; as shown in equation (2);
wherein j refers to the month in which the composite dry heat event occurs, n is the month of hysteresis, V NDVI(n) Expressed as the growth rate of vegetation change, NDVI, with a lag of n months under composite dry heat events j Means normalized vegetation index at month j, NDVI j+n Normalized vegetation index NDVI at month j + n.
5. The method of claim 4, wherein the method comprises the steps of: and S4, calculating the influence of the composite dry heat event on the vegetation by utilizing an interaction factor module in the geographic detector, specifically:
dividing the drought event and the high-temperature event into a plurality of categories according to the strength, utilizing an interaction factor module in a geographic detector, taking the drought event and the high-temperature event as conditions, taking the vegetation growth change speeds of different influence hysteresis events under the condition of the composite dry-heat event as results, and calculating the interaction influence on vegetation growth under the condition of the simultaneous occurrence of the drought and the high temperature, namely the influence of the composite dry-heat event on the 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 composite dry heat event on the vegetation;
the degree of the influence of the composite dry and hot events on the vegetation is the degree of the interaction of the dry events and the high-temperature events on the vegetation, and the algorithm is shown in a formula (3);
wherein q represents the degree of the composite dry heat event on the vegetation, and the range is [0,1 ]](ii) a L is a subarea formed by overlapping and combining the classification intervals of different degrees of drought events and high-temperature events on the space of a research area, and N is i And N is the number of cells in the ith partition and the entire area of interest,
and &>
Respectively representing the variance of vegetation growth change speed of the ith subarea and the whole area of the research area;
the lag time of the composite dry heat event on the vegetation is influenced, and the algorithm is shown in formula (4);
wherein T is the lag time of the composite dry heat event on the vegetation;
to the extent that a composite dry heat event has an effect on vegetation that lags behind by 0 months, a->
To the extent that a compound dry heat event has an effect on vegetation that lags behind by 1 month `>
The extent of the effect of the composite dry heat event on vegetation lag of 2 months, q n The influence degree of the composite dry heat event on vegetation lag by n months; />
For computing symbols, i.e. obtaining q n The lag time n of (d). />
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