CN113128871B - Cooperative estimation method for larch distribution change and productivity under climate change condition - Google Patents
Cooperative estimation method for larch distribution change and productivity under climate change condition Download PDFInfo
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Abstract
The invention discloses a cooperative estimation method for larch distribution change and productivity under a climate change condition, which comprises the following steps: determining parameters of artificial larch tree species of a forest farm level 3PG model and climate data of the forest farm level; and inputting parameters and climate data of the larch artificial forest tree species. The coordinated estimation method for the distribution change and the productivity of larch under the climate change condition can simultaneously predict the distribution and the productivity change of the larch artificial forest in a forest farm, can evaluate the influence of the climate change on the larch artificial forest, can evaluate the main climate limiting factors for growth of the larch under the current and future climate change conditions in detail and the distribution and the productivity change of the larch, are mutually influenced under a unified frame, have biological interpretation significance, provide the main productivity variable of interest of forest managers, and can cooperatively evaluate the distribution and the growth potential of the larch artificial forest in the future in space and time.
Description
Technical Field
The invention relates to the fields of forestry and forest manager science, in particular to a coordinated estimation method for larch distribution change and productivity under a climate change condition.
Background
Larch is one of important conifer species in China, is arbor species with fourth important value ranking in China, and occupies 5.86% of 398.55 ten thousand hectares in area of larch in wood according to Chinese forest resource report (2014-2018), and about 80.83 hundred million plants are accumulated 93630 ten thousand cubic meters. Larch plays an important role in the environment, wood supply and human society. Climate factors such as temperature and precipitation strongly influence the physiological characteristics of larch, and climate changes may change the future distribution and growth of the species. China is experiencing climate changes in terms of temperature, precipitation, etc. and corresponding seasonal and regional changes. In such changing climatic conditions, it is not clear whether the historical distribution and productivity of the larch artificial forest across the land will remain as it is.
The traditional statistical growth harvest equation is usually used for predicting the future stand growth of the larch artificial forest, but the model variable does not have climate factors such as temperature, precipitation and the like, and the prediction of the future growth is based on the past climate and growth relationship, so the capability of estimating the stand growth under more variable future climate conditions is limited.
The generalization of the linear regression model constructed by adding the biological geography and the climate factors into the traditional growth harvest equation is poor, the nonlinear regression model is easy to overfit, the popularization in the regional scale is poor, the biological interpretation is also not high, and the methods cannot be used for simulating the change of the adaptive geographic distribution of the larch artificial forest in the future climate change at the same time.
Since future climate changes may change the geographical distribution structure of larch, the change of the geographical distribution of larch in future climate changes is conventionally predicted by establishing a species distribution model and a maximum entropy model, but this method usually requires complex algorithms and detailed parameterization, and cannot simultaneously predict the change of the growth of artificial forest of larch in climate change conditions, for which we propose a coordinated estimation method of the larch distribution change and productivity in climate change conditions.
Disclosure of Invention
The invention mainly aims to provide a coordinated estimation method for larch distribution change and productivity under a climate change condition, which can effectively solve the problems in the background technology.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a method for synergistically estimating larch distribution change and productivity under a climate change condition comprises the following steps:
(1) Determining parameters of artificial larch tree species of a forest farm level 3PG model and climate data of the forest farm level;
(2) Inputting parameters and climate data of the larch artificial forest tree species, and operating a 3PG model to obtain values of 4 climate adjusting factors of a forest farm;
(3) Inputting the values of 4 climate control factors and the larch distribution data of a forest farm into a decision classification tree model to obtain a decision tree with larch existence and climate variables and a larch distribution threshold range of 4 climate control factors;
(4) Inputting a larch distribution threshold range of 4 climate regulating factors and climate data under future climate change situations, and operating a decision classification tree model to obtain the distribution of larch under the future climate change situations under two carbon emission situations;
(5) Inputting parameters of the tree species of the larch artificial forest, climate data under the future climate change situation and distribution of larch under the future climate change situation, and operating a 3PG model to obtain the larch productivity at the future time.
Preferably, the parameters of the larch artificial forest tree species in the step (1) are obtained by searching related documents, fitting parameters, repeatedly verifying a model and a default method for system parameters.
Preferably, the forest farm level climate data in step (1) includes climate data for specific areas under current and future climate change, and the ClimateAP is used to extract and downscale the month normal data (2.5x2.5 arc minutes, 4 x 4 km) of PRISM and worldtlim for a plurality of reference periods 1961-1990 to produce location specific spatial resolution adaptive seasonal and annual climate variables based on latitude, longitude and altitude;
the data are raster data of month average minimum temperature, month maximum temperature and month average rainfall of each month of 20 x 20m of the forest farm;
climate data for future periods including 2020 (2010-2039), 2050 (2040-2069) and 2080 (2070-2100), coupling patterns from IPCC fifth assessment report (IPCC 2014) compare atmospheric flow models of items to each other;
two greenhouse gas emission scenarios (RCPs 4.5 and RCPs 8.5), providing a month predicted time series of RCPs4.5 and RCPs 8.5 during 2011-2100 years for three global climate models;
the RCP8.5 path simulates a scenario of 5 ℃ rise to 2100 years as a high-end path; the RCP4.5 scenario is to take a series of techniques and measures to control greenhouse gas emissions not to exceed target levels and force the total radiation to reach a stable intermediate stable path before 2100 years;
the reference or current climate conditions are represented using the climate mean (30 years mean) of month data during the period 1981 to 2010.
Preferably, the data entered in step (2) are climate data provided in monthly time steps including total short wave radiation, average precipitation, frost days and minimum and maximum temperatures, species parameters and site variables including soil depth, available soil moisture (ASW), stand initial density and stand age.
Preferably, in the step (2), the model is operated for 20 years after the data are input into the 3PG model, so that the forest stand growth and the forest stand productivity of the larch artificial forest range under the current climate conditions are obtained, and 4 climate regulating factors, namely the effective water, the temperature, the frost and the atmospheric Vapor Pressure Difference (VPD) of soil are extracted every year so as to limit the photosynthesis degree.
Preferably, the specific steps of step (3) are as follows:
(1) applying decision tree analysis to evaluate the range of 3PG climate regulator factor predicted larch distribution;
(2) decision tree analysis employs a 10-fold cross-validation technique, beginning with the use of all available data;
(3) only larch presence data is used for training a decision tree model, the larch distribution is predicted, and evaluation method accuracy evaluation is performed according to actual larch presence/absence data.
Preferably, when the decision classification tree model is operated in the step (4), the values of four climate adjustment factors each month, namely effective water, temperature, frost and atmospheric water vapor pressure difference of the soil are calculated by using the climate data of a future predicted year generated under the climate change scenes of RCPs4.5 and RCPs 8.5 respectively. Then, inputting the decision tree model established in the step (3), and applying the threshold rules of the four climate adjustment factors established in the step (3) to obtain the existence points of the larch artificial forest in the climate change scenes of RCPs4.5 and RCPs 8.5, namely the future potential distribution of the larch artificial forest.
Preferably, the larch distribution data is used as a mask in step (5) to predict the yield of larch at a future time, and is cut to a position where larch is predicted to exist;
taking the larch as the larch site input data, simultaneously inputting physiological parameters of larch, climate data under the conditions of RCPs4.5 and RCPs 8.5, running 3PG for the second time and the third time to a plurality of years in the future, and stopping simulation after the maximum LAI and canopy closure are reached, thus obtaining the artificial forest productivity of larch in the corresponding year.
Preferably, the artificial forest productivity of larch in step (5) may be output in annual or monthly time steps, including variables such as stand density, leaf area index, mean annual growth (MAI), mean breast Diameter (DBH), stand accumulation and cross-sectional area.
Compared with the prior art, the method for synergistically estimating the distribution change and the productivity of larch under the climate change condition has the following beneficial effects:
1. the method can simultaneously predict the distribution and productivity change of the larch artificial forest in the forest farm, and can evaluate the influence of climate change on the larch artificial forest;
2. the invention can evaluate the main climate limiting factors of larch growing under the current and future climate change conditions and the variation of larch distribution and productivity in detail, and the variation is mutually influenced under a unified frame and has biological interpretation significance;
3. the invention provides main productivity variables of interest to forest managers, including forest stand density, leaf area index, average annual growth quantity (MAI), average breast Diameter (DBH), forest stand accumulation and cross-sectional area, and can be used for cooperatively evaluating the distribution and growth potential of future larch artificial forests in space and time.
Drawings
FIG. 1 is a block flow diagram of a method for collaborative estimation of larch distribution variation and productivity under climate change conditions according to the present invention.
Detailed Description
The invention is further described in connection with the following detailed description, in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
Examples
A method for synergistically estimating larch distribution change and productivity under a climate change condition comprises the following steps:
(1) Determining parameters of artificial larch tree species of a forest farm level 3PG model and climate data of the forest farm level;
the 3PG model is a physiological model driven by key physiological parameters; the 3PG model is a simplified single-species forest stand growth model based on a process; the 3PG model calculates the total primary productivity (GPP) using available, absorbed photosynthetically active radiation and the cap quantum efficiency. The 3PG model is relatively simplified, a mature physiological relationship and a proven constant are applied, and the 3PG model does not need to calculate respiration; instead, the model uses the ratio of net primary production total (NPP/GPP). 3PG also produces an equation for the distribution of tree biomass to stems, leaves and roots, and the 3PG model uses climate control factors ranging from 0 to 1 in dimensionless numbers, representing the extent to which climate factors limit photosynthesis by soil effective water, temperature, frost and atmospheric Vapor Pressure Differential (VPD). The 3PG model estimates on the stand level the on-site productivity and climate control factors for a given tree species, expressed in annual or monthly time steps, including stand density, leaf area index, mean annual growth (MAI), mean chest diameter (DBH, also known as DOB 1.3), stand accumulation and cross-sectional area.
The parameters of the larch artificial forest tree species do not need to be fitted empirically each time, and are used in the later calculation, but the parameters of the larch artificial forest tree species must be determined when using the model.
The parameters of the larch artificial forest tree species are obtained by searching related documents, fitting parameters, repeatedly verifying a model and a default method for system parameters, the physiological parameters of the larch artificial forest tree species are required to be objectively and systematically subjected to parameter optimization adjustment within the allowable range of biological parameters, and the prediction effect of the model is verified by utilizing independent samples, wherein the key physiological parameters are shown in the following table:
the farm-level climate data includes climate data for specific areas under current and future climate change, and the clineateeap is used to extract and downscale the month normal data (2.5 x 2.5 arc minutes, 4 x 4 km) of PRISM and worldtlim for multiple reference periods 1961-1990 to produce location-specific spatial resolution-adaptive seasonal and annual climate variables based on latitude, longitude and altitude;
climate data for future periods including 2020 (2010-2039), 2050 (2040-2069) and 2080 (2070-2100), coupling patterns from IPCC fifth assessment report (IPCC 2014) compare atmospheric flow models of items to each other;
two greenhouse gas emission scenarios (RCPs 4.5 and RCPs 8.5), providing a month predicted time series of RCPs4.5 and RCPs 8.5 during 2011-2100 years for three global climate models;
the RCP8.5 path simulates a scenario of 5 ℃ rise to 2100 years as a high-end path; the RCP4.5 scenario is to take a series of techniques and measures to control greenhouse gas emissions not to exceed target levels and force the total radiation to reach a stable intermediate stable path before 2100 years;
the data are raster data of month average minimum temperature, month maximum temperature and month average rainfall of each month of 20 x 20m of the forest farm;
the climate mean (30-year mean) of month data during the period 1981 to 2010 was used to represent the reference or current climate conditions (i.e. baseline).
(2) Inputting parameters and climate data of the larch artificial forest tree species, and operating a 3PG model to obtain values of 4 climate adjusting factors of a forest farm;
the data entered by the 3PG model are climate data provided in month time steps, including total short wave radiation, average precipitation, frost days, and minimum and maximum temperatures, species parameters, and site variables, including soil depth, available soil moisture (ASW), stand initial density, and stand age.
And (3) inputting the data into a 3PG model, and then running the model for 20 years to obtain the forest stand growth and the forest stand productivity of the larch artificial forest under the current climate conditions, wherein 4 climate regulating factors, namely the photosynthesis degree of the soil is limited by effective water, temperature, frost and atmospheric Vapor Pressure Difference (VPD), are extracted every month.
The environmental constraint of obtaining larch artificial forest range by the 3PG model is to utilize a climate control factor ranging from 0 to 1 in dimensionless number, which represents the extent of photosynthesis of climate factors through the pressure difference (VPD) of the atmosphere and water vapor in the daytime, the soil moisture deficiency and the extreme minimum/maximum temperature limitation.
The four seasons are determined according to the specific distribution of the larch artificial forest, for example, winter (11 months to 4 months), spring (5 months to 6 months), summer (7 months to 8 months) and autumn (9 months to 10 months) are determined in northeast regions.
(3) Inputting the values of 4 climate control factors and the larch distribution data of a forest farm into a decision classification tree model to obtain a decision tree with larch existence and climate variables and a larch distribution threshold range of 4 climate control factors;
the specific steps are as follows:
(1) applying decision tree analysis to evaluate the range of 3PG climate regulator factor predicted larch distribution;
the decision tree method takes the data of the existence or non-existence of larch as a dependent variable, takes the 4 climate control factor values of the existence or non-existence point of each larch as independent variables, and divides the method into a series of choices, wherein the choices not only determine the importance of each climate control factor, namely a constraint variable, but also establish the threshold value of 4 climate control factors (constraint variables) of the existence of larch.
(2) Decision tree analysis employs a 10-fold cross-validation technique, starting with the use of all available data (reference tree);
the entire dataset is randomly divided into 10 equally sized groups (or folds), one set is kept, and the other nine are grouped together and a model is generated, the accuracy of the model is assessed using the remaining 10% of the data that is not used in model development, this process is repeated ten times, producing ten different test trees and ten different accuracy assessments, then the decision rules of the ten models are combined to produce the final decision tree, the overall accuracy of which is assessed by averaging the independent results of the ten simulations, the overall accuracy of the model should not be less than 70%.
(3) Only using larch presence data to train a decision tree model, predicting larch distribution, and carrying out evaluation method precision evaluation according to actual larch presence/absence data;
random "pseudo-nonexistent" larch data is first generated that matches the existing quantity of larch. The points are randomly distributed throughout the area, but they must be at least 1 km away from the existing. The 4 climate control factors generated using the 3PG model were then used to predict possible "no existing" larches using an initial decision tree. Random "pseudo-none" larches having a "true" "none" larch probability of less than or equal to 30% are deleted from the "none" larch list. And finally, establishing a decision tree model by using filtered 'fake' larch-free larch and larch-free larch data to obtain the current larch distribution.
(4) Inputting a larch distribution threshold range of 4 climate regulating factors and climate data under future climate change situations, and operating a decision classification tree model to obtain the distribution of larch under the future climate change situations under two carbon emission situations;
when the decision classification tree model is operated, the values of four climate regulating factors per month, namely effective water, temperature, frost and atmospheric water vapor pressure difference of soil are calculated by using climate data of future predicted years generated under the climate change scenes of RCPs4.5 and RCPs 8.5 respectively. Then, inputting the decision tree model established in the step (3), and applying the threshold rules of the four climate adjustment factors established in the step (3) to obtain the existence points of the larch artificial forest in the climate change scenes of RCPs4.5 and RCPs 8.5, namely the future potential distribution of the larch artificial forest.
(5) Inputting parameters of tree species of larch artificial forests, climate data under the future climate change situation and distribution of larch under the future climate change situation, and operating a 3PG model to obtain the larch productivity at the future time;
using larch distribution data as a mask when predicting larch productivity at a future time, clipping to a position where larch is predicted to exist;
taking the larch as the larch site input data, simultaneously inputting physiological parameters of larch, climate data under the conditions of RCPs4.5 and RCPs 8.5, running 3PG for the second time and the third time to a plurality of years in the future, and stopping simulation after the maximum LAI and canopy closure are reached, so as to obtain the artificial forest productivity of larch in the corresponding year;
larch artificial forest productivity may be output in annual or monthly time steps, including variables such as stand density, leaf area index, mean annual growth (MAI), mean breast Diameter (DBH), stand accumulation, and cross-sectional area.
The method for collaborative estimation of larch distribution change and productivity under the climate change condition can simultaneously predict the distribution and productivity change of the larch artificial forest in a forest farm and can evaluate the influence of the climate change on the larch artificial forest;
the invention can evaluate the main climate limiting factors of larch growing under the current and future climate change conditions and the variation of larch distribution and productivity in detail, and the variation is mutually influenced under a unified frame and has biological interpretation significance;
the invention provides main productivity variables of interest to forest managers, including forest stand density, leaf area index, average annual growth quantity (MAI), average breast Diameter (DBH), forest stand accumulation and cross-sectional area, and can be used for cooperatively evaluating the distribution and growth potential of future larch artificial forests in space and time.
The invention estimates the influence of climate change on the larch artificial forest based on the physiological model of a process, namely a 3PG model and a decision tree model to jointly predict the productivity and distribution change of the larch artificial forest. Specifically, the method comprises five stages. Firstly, determining tree species parameters of a larch artificial forest based on a physiological model 3PG of a process; the larch artificial forest species parameters and current climate data are then input into a process-based physiological model driven by the monthly climate data to derive environmental constraints for the larch artificial forest range. And thirdly, substituting environmental constraint of the larch artificial forest range and larch distribution data into a decision tree model to obtain a climate factor rule for determining larch distribution under the current climate condition. And fourthly, obtaining the distribution of the larch artificial forest under the future climate change condition by utilizing the decision tree rules and the decision tree models. And fifthly, calculating the productivity of the larch artificial forest under the condition of future climate change by using the 3PG model and the obtained larch artificial forest distribution. The invention is suitable for the simultaneous estimation of the distribution and the productivity of the forest farm-level larch artificial forest under the future climate change condition.
The method uses a physiological principle (3 PG) model for predicting growth, calculates the photosynthesis and growth degree of a species affected by climate variables, and is characterized by the interpretable simplicity and data availability of the model, and draws the distribution and productivity of tree species. Once the distribution model is developed, the extent of current range variation and productivity of larch is assessed. These variables are then used in decision tree models to formulate rules that provide basis for predicting the distribution of species under current climate conditions. Future climate predictions are used, and then future larch productivity increases and distribution changes are simulated. This dual modeling approach makes it possible to quantify the impact of climate change on selected species and to verify that climate prediction is in range and productivity assessment.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. A method for synergistically estimating larch distribution change and productivity under a climate change condition is characterized by comprising the following steps of: the method comprises the following steps:
(1) Determining parameters of artificial larch tree species of a forest farm level 3PG model and climate data of the forest farm level;
(2) Inputting parameters and climate data of the larch artificial forest tree species, and operating a 3PG model to obtain values of 4 climate adjusting factors of a forest farm;
(3) Inputting the values of 4 climate control factors and the larch distribution data of a forest farm into a decision classification tree model to obtain a decision tree with larch existence and climate variables and a larch distribution threshold range of 4 climate control factors;
(4) Inputting a larch distribution threshold range of 4 climate regulating factors and climate data under future climate change situations, and operating a decision classification tree model to obtain the distribution of larch under the future climate change situations under two carbon emission situations;
(5) Inputting parameters of the tree species of the larch artificial forest, climate data under the future climate change situation and distribution of larch under the future climate change situation, and operating a 3PG model to obtain the larch productivity at the future time.
2. The method for collaborative estimation of larch distribution variation and productivity under climate change conditions according to claim 1, wherein: in the step (1), parameters of the larch artificial forest tree species are obtained through searching of related documents, fitting of the parameters, repeated verification of a model and a default method for system parameters.
3. A method for collaborative estimation of larch distribution and productivity under climate change conditions according to claim 2 wherein: the forest farm-level climate data in step (1) comprises climate data of specific areas under current and future climate change, and the scimateap is adopted to extract and downscale month normal data of PRISM and Worldcalim in multiple reference periods 1961-1990 so as to generate season and annual climate variables which are adaptive based on the spatial resolution of specific positions of latitude, longitude and altitude;
the data are raster data of month average minimum temperature, month maximum temperature and month average rainfall of each month of 20 x 20m of the forest farm;
climate data for future time periods including 2010-2039, 2040-2069 and 2070-2100, atmospheric flow models from the coupling patterns in the IPCC fifth assessment report comparing items to each other;
two greenhouse gas emission scenarios, providing a month predicted time series of RCPs4.5 and RCPs 8.5 during 2011-2100 years for three global climate models;
the RCP8.5 path simulates a scenario of 5 ℃ rise to 2100 years as a high-end path; the RCP4.5 scenario is to take a series of techniques and measures to control greenhouse gas emissions not to exceed target levels and force the total radiation to reach a stable intermediate stable path before 2100 years;
the reference or current climate conditions are represented using the climate mean of month data during the period 1981 to 2010.
4. A method for collaborative estimation of larch distribution and productivity under climate change conditions according to claim 2 wherein: the data entered in step (2) are climate data provided in month time steps including total short wave radiation, average precipitation, frost days and minimum and maximum temperatures, species parameters and site variables including soil depth, available soil moisture (ASW), stand initial density and stand age.
5. A method for collaborative estimation of larch distribution and productivity under climate change conditions according to claim 2 wherein: and (3) inputting the data in the step (2) into a 3PG model, then running the model for 20 years, obtaining the forest stand growth and the forest stand productivity of the larch artificial forest in the current climate condition, and extracting 4 climate regulating factors, namely the degree of photosynthesis limitation of soil effective water, temperature, frost and atmospheric Vapor Pressure Difference (VPD) each year.
6. The method for collaborative estimation of larch distribution variation and productivity under climate change conditions according to claim 1, wherein: the specific steps of the step (3) are as follows:
(1) applying decision tree analysis to evaluate the range of 3PG climate regulator factor predicted larch distribution;
(2) decision tree analysis employs a 10-fold cross-validation technique, beginning with the use of all available data;
(3) only larch presence data is used for training a decision tree model, the larch distribution is predicted, and evaluation method accuracy evaluation is performed according to actual larch presence/absence data.
7. The method for collaborative estimation of larch distribution variation and productivity under climate change conditions according to claim 1, wherein: when the decision classification tree model is operated in the step (4), calculating the values of four climate regulating factors each year, namely effective water, temperature, frost and atmospheric water vapor pressure difference of the soil by using climate data of future predicted years generated under the climate change scenes of RCPs4.5 and RCPs 8.5 respectively; then, inputting the decision tree model established in the step (3), and applying the threshold rules of the four climate adjustment factors established in the step (3) to obtain the existence points of the larch artificial forest in the climate change scenes of RCPs4.5 and RCPs 8.5, namely the future potential distribution of the larch artificial forest.
8. The method for collaborative estimation of larch distribution variation and productivity under climate change conditions according to claim 1, wherein: using larch distribution data as a mask when predicting larch productivity at a future time in step (5), and clipping to a position where larch is predicted to exist;
taking the larch as the larch site input data, simultaneously inputting physiological parameters of larch, climate data under the conditions of RCPs4.5 and RCPs 8.5, running 3PG for the second time and the third time to a plurality of years in the future, and stopping simulation after the maximum LAI and canopy closure are reached, thus obtaining the artificial forest productivity of larch in the corresponding year.
9. The method for collaborative estimation of larch distribution variation and productivity under climate change conditions according to claim 1, wherein: the artificial forest productivity of larch in step (5) may be output in annual or monthly time steps including variables such as stand density, leaf area index, mean annual growth amount (MAI), mean breast Diameter (DBH), stand accumulation and cross-sectional area.
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