CN114609694B - Method for predicting response of ecological system attribute to climate change in situ - Google Patents

Method for predicting response of ecological system attribute to climate change in situ Download PDF

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CN114609694B
CN114609694B CN202210220024.2A CN202210220024A CN114609694B CN 114609694 B CN114609694 B CN 114609694B CN 202210220024 A CN202210220024 A CN 202210220024A CN 114609694 B CN114609694 B CN 114609694B
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罗忠奎
张帅
王明明
郭晓伟
肖浏骏
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Abstract

The invention discloses a method for predicting response of ecosystem attributes to climate change in situ, which comprises the steps of 1) respectively obtaining certain ecosystem characteristic point location data of a contrast group and a processing group with specific climate difference in space based on spatial point location data and the spatial difference of climate; 2) Respectively calculating the sample amount, the average value and the standard deviation of the ecological system characteristics of the treatment group and the control group; 3) Calculating the weight of each spatial point location data; 4) And carrying out weighted average on spatially homogeneous data according to the point location weight, and finally calculating to obtain a weighted average value. By the method and the system, the response of the process of the state (such as carbon sink potential) of a certain ecosystem to the climate change on a long time scale can be more accurately estimated and predicted.

Description

Method for predicting response of ecological system attribute to climate change in situ
Technical Field
The invention belongs to the field of ecological system process and climate change research, and particularly relates to a method for predicting response of ecological system attributes to climate change in situ.
Background
The ecosystem process is sensitive to climate change, and due to the nonlinearity of climate change and the gradual change of the ecosystem state attribute, the response of the ecosystem to the climate change is difficult to observe continuously in the field, and the matching of a manpower observation time period (at most years or decades), the climate change (from decades to hundreds of years) and the ecosystem response (up to thousands of years) on a time scale cannot be realized; meanwhile, the observation experiment cost is high, and the conclusion universality is poor.
In this context, the "spatial instead of temporal" method arises. The method is based on a basic assumption that the temporal evolution of a particular ecological element or process in a space of relatively homogeneous other environmental conditions can be reflected by the current state of the process or element at different time stages within the space. This method requires that a particular ecological element or process is at least in two different time phases within the homogenous space and that other ecological processes affecting the ecological element or process are substantially the same. By extrapolating the method, the change of a specific ecological process or element along a certain ecological gradient (such as temperature or precipitation) can be researched, for example, how to respond to the change of environmental factors such as air temperature, precipitation and the like in a certain geographic space by selecting ecological elements or processes which are positioned at different spatial positions and have the same age. Although the method is widely applied, the environmental factors have strong spatial heterogeneity, and a certain ecological element is influenced by multiple factors, so that the research result can be greatly uncertain.
Therefore, it is desirable to provide a new method that can reduce the uncertainty of the method.
Disclosure of Invention
It is an object of the present invention to overcome the deficiencies of the prior art and to provide a method for in situ prediction of the response of ecosystem attributes to climate change. The method utilizes the big data of the ecological environment of the in-situ space in different regions of the world, sets different groups according to the research purpose, and accurately quantifies the response of a certain ecological element or process to certain long-term climate change under the steady-state condition by a weighted average method.
The invention adopts the following specific technical scheme:
the invention provides a method for predicting the response of ecosystem attributes to climate change in situ, which comprises the following steps:
s1: constructing or acquiring a space point observation big data set of an ecological system attribute theta to be predicted, and acquiring climate element data and ecological system classification data of each space point;
s2: presetting the variation amplitude n of the reference climate element data by taking the certain climate element data of the space point location obtained in the step S1 as a reference so as to reflect the climate variation; according to different values of the reference climate element data, a plurality of pairs of contrast groups and processing groups are obtained on the premise that the rest climate element data and the ecosystem classified data are not changed; the control group is an original value i of the reference climate element data, and the processing group is a value i + n of the reference climate element data after the change amplitude changes;
s3: respectively calculating the sample size, the average value and the standard deviation of the ecosystem attribute theta of each pair of the control group and the treatment group; the individual response ratio RR is then calculated as:
Figure BDA0003536824270000021
in the formula:
lnRR i the reference climate element data is based on the response ratio of the change of the ecological system attribute theta after the change of the change amplitude n;
Figure BDA0003536824270000022
-processing the average value of the ecosystem property θ in the group;
Figure BDA0003536824270000023
-average value of ecosystem property θ in control group;
s4: calculating the variance (v) in the group corresponding to the single response ratio RR i ) The calculation formula is as follows:
Figure BDA0003536824270000024
in the formula:
v i -the intra-group variance for each response ratio;
Figure BDA0003536824270000025
-processing the standard deviation of the ecosystem property θ in the group;
Figure BDA0003536824270000026
-standard deviation of ecosystem attribute θ in control group;
Figure BDA0003536824270000027
-processing a sample size of the ecosystem property θ in the group;
Figure BDA0003536824270000028
-sample size of ecosystem properties θ in control group;
s5: calculating the weight w corresponding to the single response ratio RR i The calculation formula is as follows:
Figure BDA0003536824270000029
in the formula:
τ 2 -variance between groups;
the intra-group variance and inter-group variance herein refer to the intra-group variance due to the sample size and standard deviation within a single study group (each pair of control and treatment groups) and the inter-group variance between different study groups, respectively.
S6: calculation of the between-group variance τ 2 Between groups variance τ 2 The calculation can be performed by methods such as ML method, DL method, REML method, HO method, etc., and the following explanation will be given by taking ML method as an example, and if ML method is used, the calculation formula is:
Figure BDA0003536824270000031
s7: calculating the weighted average value of all different value reference climate element data
Figure BDA0003536824270000032
The calculation formula is as follows:
Figure BDA0003536824270000033
s8: the calculated weighted average value
Figure BDA0003536824270000034
By the formula
Figure BDA0003536824270000035
And converting into percentage, wherein the percentage is the response of the attribute theta of the ecosystem to be predicted to the variation amplitude n of the datum climate element data.
Preferably, the ecosystem attributes include soil organic carbon, above ground biomass, underground biomass, and soil nitrogen phosphorus content.
Preferably, the climate element data includes an annual average air temperature, an annual rainfall and a seasonality of precipitation, and the ecosystem classification data includes a terrain, a soil type and a land use type.
Further, in the step S2, the reference climate element data is an annual average temperature.
Furthermore, the allowable screening error range of the annual average air temperature is +/-0.5 ℃, and the allowable screening error range of the annual rainfall is +/-50 mm.
Preferably, in step S5, a mixed effect model method is used as the method for calculating the weight.
Preferably, in step S5, the variance (τ) between groups 2 ) The calculation method of (2) adopts an ML method.
Compared with the prior art, the invention has the following beneficial effects:
the method is suitable for any state attribute of the ecological system, such as soil organic carbon, aboveground biomass, underground biomass, soil nitrogen and phosphorus content and the like, can more accurately predict the response of the state attribute of the ecological system on a long-time scale to future climate change, and the wide application of the technology can provide scientific and technical support for the climate change response.
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FIG. 1 is a schematic flow chart of example 1.
Detailed Description
The invention will be further elucidated and described with reference to the drawings and the detailed description. The technical features of the embodiments of the present invention can be combined correspondingly without mutual conflict.
In order to minimize the uncertainty of the conventional "space-time-instead-of-space" method, the present invention combines the Meta analysis method (i.e., weighted analysis) to modify the "space-time-instead-space" method to improve the accuracy of the method. Classical Meta analysis methods construct weights using the sample size and standard deviation of raw data under different processes to accurately predict the impact of the process. The basic principle is that the larger the sample size is, the smaller the standard deviation is, the larger the weight of single data is, the weighted average is performed after the weight is given to the single research data of different time-space points, and therefore the result is more reliable and the uncertainty is smaller. According to the invention, after the spatial data of the ecological system are sorted and classified, the response of a certain long-term ecological process to climate change is researched in a weighted average mode, so that the research precision is higher and the conclusion is more reliable.
The method idea of the invention is as follows: 1) Based on the spatial point location data and the spatial difference of the climate, acquiring the characteristic point location data of a certain ecosystem of a contrast group and a processing group with specific climate difference in space respectively; 2) Respectively calculating the sample amount, the average value and the standard deviation of the ecological system characteristics of the treatment group and the control group; 3) Calculating the weight of each spatial point location data; 4) And carrying out weighted average on spatially homogeneous data according to the point location weight, and finally calculating to obtain a weighted average value. By the method and the device, the response of a certain ecosystem state (such as carbon sink potential) process to climate change on a long time scale can be more accurately estimated and predicted, and the method and the device are specifically explained by embodiments.
Example 1
As shown in fig. 1, this embodiment researches the change of soil organic carbon in different soil layers of a global full-section area caused by climate warming under field in-situ conditions (i.e. taking the response research of soil organic carbon to global warming as an example), and the specific steps are as follows:
the method comprises the following steps: acquiring global Soil Profile organic carbon content data, soil volume weight and gravel content data based on a global Soil Profile shared Database (WoSIS Soil Profile Database), and acquiring organic carbon reserve data of standard Soil layers (0-0.3, 0.3-1 and 1-2 m) by a Soil Profile data smoothing technology. And simultaneously acquiring data such as the annual average temperature, precipitation, terrain, soil type and seasonality of precipitation of the point position where the soil profile is located.
Step two: all soil profile locations were classified by annual mean temperature, terrain, soil type and seasonal rainfall into a "control group" (annual mean temperature i ℃) and a "treatment group" (annual mean temperature i + n ℃). The annual rainfall, terrain, soil type and seasonal rainfall of the control and treatment soil profiles remained consistent. For this example, the temperature increase range for the treatment group was set to 2 ℃. Meanwhile, the allowable screening error range of the annual average air temperature is set to be +/-0.5 ℃ (namely, the numerical values in the temperature range of i-0.5-i +0.5 can be used as a data set with the annual average temperature being i ℃), and the allowable screening error range of the annual rainfall is set to be +/-50 mm.
Step three: for each soil layer, respectively calculating the sample quantity (N) and the average value of the organic carbon content of the soil of the control group and the soil of the treatment group
Figure BDA0003536824270000051
And standard deviation (S). Calculating a single response ratio according to the average value of the organic carbon content of the soil of the control group and the soil of the treatment group, wherein the calculation formula of the single response ratio is as follows:
Figure BDA0003536824270000052
in the formula:
Figure BDA0003536824270000053
-warming n ℃ to change shadow of organic carbon in soilSingle response ratio of sound;
Figure BDA0003536824270000054
-the organic carbon content of the soil per gram C kg at the temperature of n ℃ is increased by the treatment group –1 Soil or Mg C ha –1 ；
Figure BDA0003536824270000055
-the control group does not increase the content of Wen Xiatu soil organic carbon/g C kg –1 Soil or Mg C ha –1 ；
Step four: calculating the intra-group variance corresponding to the single response ratio, wherein the intra-group variance is calculated according to the formula:
Figure BDA0003536824270000056
in the formula:
v i -the intra-group variance for each response ratio;
Figure BDA0003536824270000057
-processing the standard deviation of the ecosystem property θ in the group;
Figure BDA0003536824270000058
-standard deviation of ecosystem attribute θ in control group;
Figure BDA0003536824270000059
-processing a sample size of the ecosystem property θ in the group;
Figure BDA00035368242700000510
-sample size of ecosystem properties θ in control group; step five: calculating the weight (w) corresponding to the single response ratio i ) Equation for calculating weight corresponding to single response ratio：
Figure BDA00035368242700000511
In the formula:
τ 2 -variance between groups;
step six: computing the variance between groups (τ) 2 ) The formula for calculating the variance between groups (ML method):
Figure BDA00035368242700000512
Figure BDA00035368242700000513
step seven: calculating a weighted average
Figure BDA0003536824270000061
Weighted average calculation formula:
Figure BDA0003536824270000062
for this example, the weighted average ln for a soil profile of 0-0.3, 0.3-1, 1-2m at 1 ℃ of temperature increase
Figure BDA0003536824270000064
R is respectively: 0.068, 0.042 and 0.015.
Step six: passing the calculated weighted average value through a formula
Figure BDA0003536824270000063
And converting into percentage, namely the response of the soil organic carbon with the ecological system state attribute to the temperature rise by n ℃. For this example, the weighted average conversion results for 0-0.3, 0.3-1, 1-2m soil profiles at 1 deg.C of temperature increase were-6.8, -4.3, and-1.5, respectively, i.e., a global 1 deg.C increase would result in a global profile of 0-0.3, 0.3-1And the organic carbon loss of the soil with the thickness of 1-2m is 6.8 percent, 4.3 percent and 1.5 percent respectively.
In the embodiment, global soil profile data is obtained, a 'comparison group' and a 'processing group' are screened according to the temperature increase amplitude and relevant standard settings, and the soil carbon change dynamics under the future climate warming background is calculated by means of weighted average, so that the soil carbon change under the equilibrium condition of long-term climate warming and an ecological system can be ascertained. The invention creatively adopts the method of adding weight to calculate the warming effect, can reduce the system error, improve the data accuracy and the reliability, and is beneficial to understanding and predicting the influence of the future climate change on the ecological system.
The above-described embodiments are merely preferred embodiments of the present invention, and are not intended to limit the present invention. Various changes and modifications may be made by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present invention. Therefore, the technical scheme obtained by adopting the mode of equivalent replacement or equivalent transformation is within the protection scope of the invention.

Claims (8)

1. A method for predicting the response of the attribute of an ecosystem to climate change in situ is characterized by comprising the following steps:
s1: constructing or acquiring a space point observation big data set of an ecosystem attribute theta to be predicted, and simultaneously acquiring climate element data and ecosystem classification data of each space point;
s2: presetting the variation amplitude n of the reference climate element data by taking the certain climate element data of the space point location obtained in the step S1 as a reference so as to reflect the climate variation; according to different values of the reference climate element data, a plurality of pairs of contrast groups and processing groups are obtained on the premise that the rest climate element data and the ecosystem classification data are not changed; the control group is an original value i of the reference climate element data, and the processing group is a value i + n of the reference climate element data after the change amplitude changes;
s3: respectively calculating the sample size, the average value and the standard deviation of the ecosystem attribute theta of each pair of the control group and the treatment group; the individual response ratio RR is then calculated as:
Figure FDA0003536824260000011
in the formula:
lnRR i the reference climate element data is based on the response ratio of the change of the ecological system attribute theta after the change of the change amplitude n;
Figure FDA0003536824260000012
-processing the average value of the ecosystem property θ in the group;
Figure FDA0003536824260000013
-average of ecosystem property θ in control group;
s4: calculating the intra-group variance v corresponding to the single response ratio RR i The calculation formula is as follows:
Figure FDA0003536824260000014
in the formula:
v i -the intra-group variance for each response ratio;
Figure FDA0003536824260000015
-processing the standard deviation of the ecosystem property θ in the group;
Figure FDA0003536824260000016
-standard deviation of ecosystem attributes θ in the control group;
Figure FDA0003536824260000017
-processing a sample size of the ecosystem property θ in the group;
Figure FDA0003536824260000018
-sample size of ecosystem properties θ in control group;
s5: calculating the weight w corresponding to the single response ratio RR i The calculation formula is as follows:
Figure FDA0003536824260000021
in the formula:
τ 2 -variance between groups;
s6: calculating the weighted average value of all different value reference climate element data
Figure FDA0003536824260000022
The calculation formula is as follows:
Figure FDA0003536824260000023
s7: the calculated weighted average value
Figure FDA0003536824260000024
By the formula
Figure FDA0003536824260000025
And converting into percentage, wherein the percentage is the response of the attribute theta of the ecosystem to be predicted to the variation amplitude n of the datum climate element data.
2. The method of claim 1, wherein the ecosystem attributes comprise soil organic carbon, above-ground biomass, underground biomass, and soil nitrogen and phosphorus content.
3. The method of claim 1, wherein the climate element data comprises annual average air temperature, annual rainfall and seasonality of precipitation, and the ecosystem classification data comprises terrain, soil type and land use type.
4. The method as claimed in claim 3, wherein the reference climate element data in step S2 is an annual average temperature.
5. The method of claim 4, wherein the allowable screening error range of the annual average air temperature is ± 0.5 ℃ and the allowable screening error range of the annual rainfall is ± 50mm.
6. The method for in-situ predicting the response of the ecosystem property to the climate change according to claim 1, wherein in the step S5, the weight is calculated by a mixed effect model method.
7. The method of claim 1, wherein in step S5, the interclass variance τ is calculated by using the inter-group variance τ 2 The ML method is adopted for calculation, and the calculation formula is as follows:
Figure FDA0003536824260000026
Figure FDA0003536824260000027
8. the in situ prediction ecosystem response to climate change of claim 1In step S5, the inter-group variance τ is 2 And calculating by adopting a DL method, an ML method, an REML method or an HO method.
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