CN113157772B - Lake proper ecological water level determining method based on ancient lake and marsh gas learning method - Google Patents
Lake proper ecological water level determining method based on ancient lake and marsh gas learning method Download PDFInfo
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Abstract
The invention discloses a lake proper ecological water level determining method based on an ancient lake and marsh gas learning method, belonging to the technical field of environmental protection; firstly, selecting a region with less artificial interference of a lake to collect sediment columnar samples, and slicing the sediment columnar samples equidistantly and layering the sediment columnar samples for pretreatment; next, use is made of 210 Pb and 137 the Cs yearly technique determines the age of each sample layer; then, identifying and calculating the types and the quantity of the sporopollen of each sample layer to represent the aquatic plant information of an ecosystem in a time sequence, collecting and measuring corresponding hydrological and physicochemical indexes, and constructing an environmental factor data set; finally, establishing a generalized additivity model by using key environmental factors suggested by the classification regression tree model, quantifying the response relation between the environmental factors and the sporopollen concentration, and determining the proper ecological water level of the lake; the invention effectively solves the problem that the existing design is difficult to acquire stable long-time sequence biological dataThe problem and provides a quantification method for exploring the response relationship between the water level fluctuation and the local ecological index.
Description
Technical Field
The invention relates to the technical field of environmental protection, in particular to a method for determining a suitable ecological water level of a lake based on an ancient lake-marsh method.
Background
The water level fluctuation is one of core factors affecting vegetation biomass, diversity, composition and structure, and it is particularly susceptible to water level fluctuation in various aquatic ecosystems by causing changes in environmental factors affecting plant growth and germination, such as light, oxygen, temperature, nutrients, etc. In recent years, due to the influence of global climate change (change in precipitation amount, precipitation frequency and single precipitation intensity) and human activities (large hydraulic engineering construction, reclamation and the like), corresponding water level fluctuation occurs in lakes in many areas. Abnormal fluctuation of water level can cause degradation of lake plants, self-cleaning capability of rivers and lakes is reduced, submerged vegetation in open water areas disappears, and a plurality of shallow lakes are converted from grass-type clean water state into eutrophication state of algae-type turbid water state, and water bloom is abused, so that water quality is finally reduced. Although responses of aquatic ecosystems in shallow lakes due to climate and water circulation have been studied in recent years, the influence of water level fluctuations on the lake's and freshwater reservoirs' lake characteristics and mixing has not received attention. In view of this, it has become necessary to maintain stable and evolving water level fluctuation management of the lake ecosystem.
Traditionally, water level fluctuation management strategies are mainly determined according to two types of methods: (1) The natural water level fluctuation is used as a template, and the water level fluctuation is maintained to reach the level of the natural water level fluctuation; (2) The appropriate water level is designed based on the target species' response to several water level parameters. The first strategy is based on the following assumptions: the lake ecosystem has been adapted to natural water level fluctuations, so maintaining natural water level fluctuations can effectively preserve the functional integrity of the lake ecosystem. Scientifically, it is reasonable to minimize the gap between natural water level fluctuation and current water level fluctuation, and the importance of the gap is accepted by more and more researches. However, in practice there are also problems when it is used as a management strategy, the main problem being that the direct link between the water level fluctuation and the local ecological index cannot be explored, so we cannot answer more specific problems, such as: before the aquatic ecosystem begins to degenerate, we should change how much the water level conditions to accommodate its changes, or how we should manage the annual and interpupillary patterns of water level conditions to achieve the desired ecological effect. The second method is considered to be the most scientific method of determining the water level fluctuation management strategy based on the response of the target species to the water level change. Up to now, macrophytes are the target species for which water level fluctuations have been the most studied for biological impact in the lake ecosystem, since macrophytes play a key role in nutrient circulation, sediment stability and community composition at various nutrient levels. In addition, macrophytes are sensitive to the effects of water level fluctuations, and even small drops can have significant effects. Recent studies have determined various water level fluctuation management strategies, such as limiting the growth of excessive macrophytes, maintaining stable colony structures or regulating succession of ecosystems, according to the protection purpose of the local macrophytes. However, most lakes worldwide lack macrophyte historical data, which hampers the effectiveness of water level management strategies. Historical data is typically the only information available to define the natural state of a lake, which can set a realistic goal and provide a benchmark for assessing policy repeatability. Furthermore, deviations in data statistics, irregularities, incomparable time series data or sampling technique differences can all have an impact on historical data. Retrieving data from the literature may be a viable method of obtaining sufficient data without taking a significant amount of time to conduct a field investigation, but there is also a problem of data comparability due to the lack of consistency in the methods between different studies.
Thus, in the absence of long-term monitoring data, the residues of aquatic plants held in the sediment may record the time-variation of the lake macrophytes and provide a sufficiently high quality data base for designs supporting ecologically acceptable water level fluctuation strategies. So far, the ancient ecological technology has been widely applied to the reconstruction of historical ecological conditions, the detection of climate change trend or the evaluation of eutrophication by various ecological indexes such as sporopollen, diatom, ratchet, scale insect and the like. In particular, the concentration of macrophyte spore powder can well represent the growth state of the aquatic plant and reflect the historical water level condition. For example, it has been found that the abundance of Artemisia and Chenopodiaceae sporopollen is related to the distance to the edge of the lake, and can be used to predict atrophy in waters. While studies have shown that macrophyte spore powder exhibits zonal responses to water level conditions, they concluded that different water levels may result in different combinations of macrophyte spore powder. In addition, some studies have successfully combined ancient and lake-going records with long-term observations. During the overlap, both data may support and complement each other. Nevertheless, in shallow lakes, huge aquatic plant spore powder is rarely adopted to guide water level management, and in view of the fact, we propose a method for determining the suitable ecological water level of lakes based on the ancient lake biogas method.
Disclosure of Invention
The invention aims at providing a method for determining the proper ecological water level of a lake based on an ancient lake learning method, which is based on the framework of the ancient lake learning method to acquire ecological data of a long-time sequence and determine the ecological water level with a certain ecological basis.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a method for determining the proper ecological water level of a lake based on an ancient lake-marsh method comprises the following steps:
s1, sediment sampling: collecting a deposition column sample at a selected sampling place by using a gravity sampler, and preprocessing the sample;
s2, analyzing the definite year: by means of 210 Pb and 137 the Cs year testing technology carries out the year-by-year analysis work on the soil sample in the deposition column;
s3, identifying sporopollen and processing data: calculating the concentration of the spore powder by adopting an externally-added spore powder method, and estimating the abundance of the macrophytes according to the concentration percentage content of the spore powder;
s4, determining an environment data set: collecting and measuring important environmental factors as an environmental data set for modeling;
s5, screening key environmental factors: establishing a classification and regression tree model, and screening environmental factors for generating the classification and regression tree model as interpretation variables of subsequent modeling;
s6, establishing a generalized additive model: establishing a generalized additive model between specific sporopollen concentration and environmental factors by utilizing interpretation variables determined by classification and regression tree models, and fitting a curve to analyze the relationship between the variables;
s7, obtaining a result: and (3) according to the results obtained in the steps S1-S6, combining the generalized additive model graph to determine the proper ecological water level condition of the lake.
Preferably, the sampling of the deposition column mentioned in S1 specifically includes the following steps:
a1, determining a plurality of lake areas without obvious artificial interference through local recording, village investigation, satellite map and topography map, and selecting the lake areas as candidate sampling sites;
A2. collecting deposition columns with the length of about 1m at each candidate sampling site by using a gravity sampler, selecting a site with a deposition column with a better hierarchical structure as a final sampling site, and collecting deposition columns with the length of about 1m at the sampling site;
A3. the deposition columns were sectioned and layered at 1cm intervals, numbered, freeze-dried, homogenized, and stored in the shade ready for further processing.
Preferably, the year analysis mentioned in S2 specifically includes the following steps:
b1, using Ortec HPGe GWL series detector, direct gamma spectrometry was used to conduct soil sample 210 Pb， 226 Ra and 137 analysis of the radioactivity intensity of Cs;
b2 by slave total 210 Subtracting from Pb radioactivity intensity 226 Ra radioactivity intensity acquisition 210 Pb exc The calculation formula is as follows:
210 Pb exc ＝ 210 Pb tot - 210 Pb supp
wherein: 210 Pb exc indicating the remainder 210 The radioactivity of Pb; 210 Pb tot to measureObtained by the method 210 Total Pb strength; 210 Pb supp to be in a sample 226 Ra decay to form 210 The radioactivity of Pb;
b3, according to the respective sample layers 210 Pb exc Correspondingly obtain each depth 210 Pb x Further, the age of each sample layer is calculated through a formula, and the formula is specifically as follows:
210 Pb x ＝ 210 Pb 0 ·e (-λt)
wherein: 210 Pb x indicating a depth x 210 Pb radioactivity intensity; 210 Pb 0 is a surface layer 210 Pb radioactivity intensity; lambda is 210 Pb half-life constant; t is the age of depth x;
b4, according to the age and nuclide of the nuclear test peak period 137 The distribution of Cs in the sediment corresponds to the time of peak position, so as to obtain the deposition rate and age of the modern sediment, and verify 210 Accuracy of Pb year measurement.
Preferably, the step of identifying and processing the spore powder mentioned in the step S3 specifically includes the following steps:
c1, weighing: determining the weight of an analysis sample according to lithology, taking 2-5 g of lake sediment soil sample and taking 5-20 g of sample with heavy sandiness;
c2, adding indicative pollen: placing the sample into a plastic beaker, and adding quantitative pinus lycopodium spores as indicative pollen;
carrying out experimental treatment on the sample, and tabletting and preserving;
c4, identifying sporopollen: the sporopollen identification and statistics are completed under a microscope, and generally, only the genus level can be identified, some can only the science level, and the species level can be rarely identified;
and C5, calculating the concentration of the sporopollen: the calculation formula is as follows:
wherein: c is the particle number of the spore powder in the unit volume or the unit weight sample; a is the number of the spore powder particles counted by identification; e is the counted particle number of the lycopodium clavatum; f is the total grain number of the externally added lycopodium clavatum spores; v is the volume or weight of the sample.
And C6, dividing the spore powder combination belt by adopting a CONISS program designed in Tilia Graph software.
Preferably, the screening of the key environmental factors mentioned in S5 specifically includes the following steps:
d1, taking the concentration of the sporopollen as a response variable and the environmental factor as an explanation variable, and establishing a classification and regression tree model;
d2, pruning: pruning the generated tree to obtain a tree with proper size and capable of explaining most variances;
and D3, checking the graph of the final tree, and taking the variable of the spanning tree as the interpretation variable of the subsequent modeling.
Preferably, the establishing the generalized additive model mentioned in S6 specifically includes the following steps:
e1, randomly dividing the sample into 5 groups;
e2, taking the concentration of the sporopollen as a response variable, taking an environmental factor as an interpretation variable, establishing a generalized additivable model by using 4 sets of sample formulas, verifying other 1 sets of sample formulas as prediction data, carrying out 5 times of modeling alternately, and observing the change rule;
e3, the generalized additive model cross modeling formula mentioned in the E2 specifically comprises the following steps:
E(y)＝S(x 1 )+S(x 2 )+...+S(x i )
wherein E () is a response variable; y is the concentration of the spore powder of the aquatic plant; s () is a smooth function; x is an explanatory variable;
e4, checking each parameter of the generalized additive model, and determining the significance of the response relationship between the variables;
and E5, checking a response relation graph between the interpretation variable and the response variable with stronger significance.
Compared with the prior art, the invention provides a lake proper ecological water level determining method based on an ancient lake-marsh method, which has the following beneficial effects:
the invention is firstly used 210 Pb and 137 the Cs year measurement technology determines the years of deposition layers with different depths, and associates monitoring data with deposit records; secondly, identifying the species of the macrophyte spore powder from the sediment layers in each year, dividing the species into different combinations according to different characteristics of the species or own research weight points, and taking the species as biological indexes; thirdly, summarizing the environmental factor data and sediment powder biological index data of the corresponding year into a data set required by modeling; then, screening out key environment variables by using classification and regression tree models; finally, a generalized additivity model is established through the screened variables, and a proper lake ecological water level condition is determined according to an analysis result and specific management preference; compared with the existing design, the method for determining the suitable ecological water level of the lake based on the ancient lake and marsh method provided by the invention acquires ecological data of a long time sequence on the basis of the frame of the ancient lake and marsh method, so as to determine the ecological water level with a certain ecological basis, and effectively solve the problems that the existing design cannot explore direct connection between water level fluctuation and local ecological indexes, data acquisition is insufficient, methods between different researches lack consistency, and data comparability exists.
Drawings
FIG. 1 is a flow chart of a method for determining the proper ecological water level of a lake based on an ancient lake-marsh method;
FIG. 2 is a schematic diagram showing the results of the white lake sediment column in the example 2 of the method for determining the suitable ecological water level of a lake based on the ancient lake or marsh method;
FIG. 3 is a schematic diagram showing the combination of core spore powder of a white lake and partition based on macrophyte spore powder in example 2 of a method for determining the suitable ecological water level of a lake based on an ancient lake and marsh science method;
fig. 4 is a schematic diagram of classification and regression tree results between the concentration of plant spore powder of the wet plant in the lake of the white lake and environmental variables in the embodiment 2 of the method for determining the suitable ecological water level of the lake based on the ancient lake and marsh science method.
Fig. 5 is a schematic diagram of 5-time cross modeling response of a generalized additive model between the concentration of plant spore powder of a wet plant in a white lake and an environmental variable in an embodiment 2 of a method for determining a suitable ecological water level of a lake based on an ancient lake and marsh science method.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1:
referring to fig. 1, a method for determining a suitable ecological water level of a lake based on an ancient lake-marsh method comprises the following steps:
s1, sediment sampling: collecting a deposition column sample at a selected sampling place by using a gravity sampler, and preprocessing the sample;
the deposition column sampling specifically comprises the following steps:
a1, determining a plurality of lake areas without obvious artificial interference through local recording, village investigation, satellite map and topography map, and selecting the lake areas as candidate sampling sites;
a2, collecting 30-60 cm of sedimentation columns at each candidate sampling site by using a gravity sampler, selecting a site of the sedimentation column with a better hierarchical structure as a final sampling site, and collecting the sedimentation column with the length of about 1m at the sampling site;
a3, slicing and layering the deposition columns at intervals of 1cm, numbering, freeze-drying, homogenizing, storing in a shade place, and preparing for the next treatment;
s2, analyzing the definite year: by means of 210 Pb and 137 the Cs year testing technology carries out the year-by-year analysis work on the soil sample in the deposition column;
the year analysis specifically comprises the following steps:
b1, using Ortec HPGe GWL series detector, direct gamma spectrometry was used to conduct soil sample 210 Pb， 226 Ra and 137 analysis of the radioactivity intensity of Cs;
b2 by slave total 210 Subtracting from Pb radioactivity intensity 226 Ra radioactivity intensity acquisition 210 Pb exc The calculation formula is as follows:
210 Pb exc ＝ 210 Pb tot - 210 Pb supp
wherein: 210 Pb exc indicating the remainder 210 The radioactivity of Pb; 210 Pb tot for measurement of 210 Total Pb strength; 210 Pb supp to be in a sample 226 Ra decay to form 210 The radioactivity of Pb;
b3, according to the respective sample layers 210 Pb exc Correspondingly obtain each depth 210 Pb x Further, the age of each sample layer is calculated through a formula, and the formula is specifically as follows:
210 Pb x ＝ 210 Pb 0 ·e (-λt)
wherein: 210 Pb x indicating a depth x 210 Pb radioactivity intensity; 210 Pb 0 is a surface layer 210 Pb radioactivity intensity; lambda is 210 Pb half-life constant; t is the age of depth x;
b4, according to the age and nuclide of the nuclear test peak period 137 The distribution of Cs in the sediment corresponds to the time of peak position, so as to obtain the deposition rate and age of the modern sediment, and verify 210 The accuracy of Pb year measurement;
s3, identifying sporopollen and processing data: calculating the concentration of the spore powder by adopting an externally-added spore powder method, and estimating the abundance of the macrophytes according to the concentration percentage content of the spore powder;
the sporopollen identification and data processing specifically comprises the following steps:
c1, weighing: determining the weight of an analysis sample according to lithology, taking 2-5 g of lake sediment soil sample and taking 5-20 g of sample with heavy sandiness;
c2, adding indicative pollen: placing the sample into a plastic beaker, and adding quantitative pinus lycopodium spores as indicative pollen;
carrying out experimental treatment on the sample, and tabletting and preserving;
c4, identifying sporopollen: the sporopollen identification and statistics are completed under a microscope, and generally, only the genus level can be identified, some can only the science level, and the species level can be rarely identified;
and C5, calculating the concentration of the sporopollen: the calculation formula is as follows:
wherein: c is the particle number of the spore powder in the unit volume or the unit weight sample; a is the number of the spore powder particles counted by identification; e is the counted particle number of the lycopodium clavatum; f is the total grain number of the externally added lycopodium clavatum spores; v is the volume or weight of the sample.
C6, dividing the spore powder combination belt by adopting CONISS program designed in Tilia Graph software
S4, determining an environment data set: collecting and measuring important environmental factors as an environmental data set for modeling;
s5, screening key environmental factors: establishing a classification and regression tree model, and screening environmental factors for generating the classification and regression tree model as interpretation variables of subsequent modeling;
the method for screening the key environmental factors specifically comprises the following steps:
d1, taking the concentration of the sporopollen as a response variable and the environmental factor as an explanation variable, and establishing a classification and regression tree model;
d2, pruning: pruning the generated tree to obtain a tree with proper size and capable of explaining most variances;
d3, checking the graph of the final tree, and taking the variable of the spanning tree as an interpretation variable of the subsequent modeling;
s6, establishing a generalized additive model: establishing a generalized additive model between specific sporopollen concentration and environmental factors by utilizing interpretation variables determined by classification and regression tree models, and fitting a curve to analyze the relationship between the variables;
the generalized additive model building method specifically comprises the following steps:
e1, randomly dividing the sample into 5 groups;
e2, taking the concentration of the sporopollen as a response variable, taking an environmental factor as an interpretation variable, establishing a generalized additivable model by using 4 sets of sample formulas, verifying other 1 sets of sample formulas as prediction data, carrying out 5 times of modeling alternately, and observing the change rule;
e3, the generalized additive model cross modeling formula mentioned in the E2 specifically comprises the following steps:
E(y)＝S(x 1 )+S(x 2 )+...+S(x i )
wherein E () is a response variable; y is the concentration of the spore powder of the aquatic plant; s () is a smooth function; x is an explanatory variable;
e4, checking each parameter of the generalized additive model, and determining the significance of the response relationship between the variables;
e5, checking a response relation graph between the interpretation variable and the response variable with stronger significance;
s7, obtaining a result: and (3) according to the results obtained in the steps S1-S6, combining the generalized additive model graph to determine the proper ecological water level condition of the lake.
The invention is firstly used 210 Pb and 137 the Cs year measurement technology determines the years of deposition layers with different depths, and associates monitoring data with deposit records; secondly, identifying the species of the macrophyte spore powder from the sediment layers in each year, dividing the species into different combinations according to different characteristics of the species or own research weight points, and taking the species as biological indexes; thirdly, summarizing the environmental factor data and sediment powder biological index data of the corresponding year into a data set required by modeling; then, screening out key environment variables by using classification and regression tree models; finally, a generalized additivity model is established through the screened variables, and a proper lake ecological water level condition is determined according to an analysis result and specific management preference; compared with the prior design, the method for determining the proper ecological water level of the lake based on the ancient lake and marsh science method provided by the invention obtains a long-time sequence on the basis of the framework of the ancient lake and marsh science methodEcological data is used for determining the ecological water level with a certain ecological basis, and the problems that the existing design cannot explore direct connection between water level fluctuation and local ecological indexes, data acquisition is insufficient, methods between different researches lack of consistency, and data comparability exists are effectively solved.
Example 2:
referring to fig. 1-5, based on embodiment 1 but with the difference that,
the method takes the white lake as an example for analysis, takes the wet plant as a research object, and determines the proper ecological water level.
(1) Sediment sampling, the specific operation steps and the method principle are as follows:
(1) determining several areas without obvious artificial interference in the white lake through local recording, village investigation, satellite map and bottom topography map, and selecting the areas as candidate sampling sites;
(2) collecting 30-60 cm deposition columns from each sampling site of the lake by using a gravity sampler, comparing lithology (color and grain size) of each layer in the columns, wherein the deposition columns with obvious laminated structures and good compatibility are suitable, and taking the site with the suitable deposition column as the site of the final sampling study;
(3) collecting a deposition column with the length of 1m at a sampling site, wherein the water depth is 1.5m;
(4) the deposition column was cut into small cylindrical sheet soil samples at 1cm intervals and each sample was sequentially numbered into aluminum foil, freeze-dried, homogenized and stored in a cool place for further processing.
(2) Setting years by a deposition column: years of use of soil samples in sedimentation columns 210 Pb and 137 cs year measurement technique.
Principle of: into lakes to dissolve 210 Pb, adsorbed and bound by sediment particles in the lake and deposited on the bottom of the lake without interruption, is carried into the lake by the river basin sediment 210 Pb was also deposited on the surface of the deposit. Incorporated into lake sediments by the above-described means 210 Pb, having a higher activity ratio than the parent 226 Activity resulting from Ra decay.
Lake sediments 210 The Pb year measuring technology is based on the rest 210 The decay of Pb, in general 210 Pb exc Expressed by the following formula from the measured value 210 Calculation of Pb total radioactivity, i.e
210 Pb exc ＝ 210 Pb tot - 210 Pb supp
Wherein: 210 Pb tot is that 210 Total intensity obtained by Pb measurement; 210 Pb supp to be in a sample 226 Ra decay to form 210 The radioactivity of Pb.
Once it is 210 Pb exc The water entering the lake and the sediment carried by the lake water are deposited at the bottom of the lake together, and automatically attenuate according to the half life of 22.3 years under the condition of external isolation, and the depth distribution can be calculated by the following formula
210 Pb x ＝ 210 Pb 0 ·e (-λt)
Wherein: 210 Pb x for a certain depth 210 Pb strength; 210 Pb 0 is a surface layer 210 Pb strength; lambda is 210 Pb half-life constant; t is the age of depth x.
According to the formula, the ages of the soil samples with different depths, namely the definite year, can be calculated. 137 Cs annual survey results for use in conjunction with 210 The results obtained by Pb year measurement are mutually complemented, and the annual sequence of lake sediments in the past 200 years can be well rebuilt.
The steps are as follows: soil samples were subjected to direct gamma spectroscopy using an Ortec HPGe GWL series detector 210 Pb， 226 Ra and 137 radioactivity intensity analysis of Cs by measuring the total 210 Subtraction from Pb radioactivity 226 Ra radioactivity intensity acquisition 210 Pb exc According to the intensity of (3) 210 Pb and 137 model date of Cs the age of each sample was determined and the results of the post-deposition years for the area of the white lake are shown in fig. 2.
(3) And (3) sporopollen identification and data processing: the abundance of the macrophytes is estimated according to the concentration percentage content of the sporopollen, and the concentration of the sporopollen is generally calculated by adopting an externally-added sporopollen method.
The steps are as follows:
1) Weighing: according to the weight of the lithology determination analysis sample, the lake sediment soil sample is generally taken to be 2-5 g, and the sample with heavy sandiness is generally required to be 5-20 g;
2) Adding indicative pollen: placing the sample into a plastic beaker, and adding quantitative pinus lycopodium spores as indicative pollen;
3) Adding 10% dilute hydrochloric acid to remove carbonate until the reaction is complete, and washing with water to neutrality;
4) Adding hydrofluoric acid: adding 40% concentrated hydrofluoric acid to a fume hood to remove silicate from the sample;
5) Adding 10% -15% of dilute hydrochloric acid, heating until the solution becomes clear, and then washing with water to be neutral;
6) Adding a mixed solution of 9 parts of acetic anhydride and 1 part of concentrated sulfuric acid into a test tube, removing protoplasm on spore powder after water bath for 5 minutes, and then cleaning to neutrality;
7) Sieving: in an ultrasonic generator, passing the washed neutral sample through a screen with the aperture of 6 mu m, and collecting residues on the screen;
8) Preserving and tabletting: the collected samples were centrifuged into a fingertube, stored with glycerol, and tableted.
9) Identifying sporopollen: the sporopollen identification and statistics are completed under a microscope, and generally, only the genus level can be identified, some can only the science level, and the species level can be rarely identified;
10 Spore powder concentration calculation: the calculation formula is as follows:
wherein: c is the particle number of the spore powder in the unit volume or the unit weight sample; a is the number of the spore powder particles counted by identification; e is the counted particle number of the lycopodium clavatum; f is the total grain number of the externally added lycopodium clavatum spores; v is the volume or weight of the sample.
11 Finally, the CONISS program designed in Tilia Graph software is adopted to divide the spore powder combination belt, and the result is shown in figure 3.
(4) Environmental dataset determination: since water level management cannot take into account all water level conditions, a series of key parameters are used to represent the water level conditions. The invention selects parameters which have proved ecological importance for the macrophytes, including water level factors (such as average water level before flood, average water level after flood, etc.), meteorological factors (such as average annual temperature, precipitation before flood, precipitation after flood, etc.) and nutritional factors (such as total nitrogen, total phosphorus, total organic carbon, etc.), and takes the environmental factors as a modeled environmental data set.
(5) Screening key environmental factors: the classification and regression tree (CART) model is a binary recursive decomposition method that can produce a tree-based model. This approach is attractive for many exploratory environmental and ecological studies because it has the ability to process both continuous and discrete variables, it can model interactions between predicted variables, and has hierarchical features. The use of CART to identify environmental factors that contribute significantly to biological abundance is a more scientific statistical approach.
And (3) establishing a classification and regression tree model, and screening environmental factors for generating the classification and regression tree model as interpretation variables for subsequent modeling.
The steps are as follows:
1) Using the sporopollen concentration as a response variable and the environmental factor as an interpretation variable, and establishing a classification and regression tree model;
2) Pruning: pruning the generated tree to obtain a tree with proper size and capable of explaining most variances;
3) Looking at the graph of the final tree, the variables of the spanning tree are taken as the interpretation variables of the subsequent modeling, as shown in fig. 4, and suggested key variables can be obtained according to fig. 4.
(6) Building Generalized Additive Model (GAM): the Generalized Additive Model (GAM) is a combination of a generalized linear model and an additive model, integrates the advantages of the generalized linear model and the additive model, promotes the distribution form of response variables, can be suitable for exponential distribution family data, simultaneously eliminates the limitation on the form of predicted variables, and has more flexible model form, so that the nonlinear relationship and the implicit ecological relationship of the response variables and the explanatory variables, such as double peaks and asymmetric phenomena, can be well disclosed. The mathematical expression of GAM is as follows:
g(μ)＝α+f(X1)+…+f(XP)
where g () is a join function, α is an intercept, μ is an expected value of a response variable, i.e., μ=e (Y/X1, …, xi), and f (Xi) is a univariate function of a single interpretation variable Xi, which may take the form of a parameter or a nonparametric form depending on the actual situation.
And establishing a generalized additive model between the specific sporopollen concentration and the environmental factor by utilizing the interpretation variables determined by the classification and regression tree models, and fitting a curve to analyze the relationship between the variables.
The steps are as follows:
1) Randomly dividing the sample into 5 groups;
2) Taking the sporopollen concentration as a response variable, taking an environmental factor as an interpretation variable, establishing a generalized additivable model by taking 4 sets of sample formulas, verifying the other 1 sets of sample formulas as prediction data, carrying out 5 times of modeling alternately, and observing the change rule;
3) The generalized additive model cross modeling formula mentioned in E2 specifically comprises the following steps:
E(y)＝S(x 1 )+S(x 2 )+...+S(x i )
wherein E () is a response variable; y is the concentration of the spore powder of the aquatic plant; s () is a smooth function; x is an explanatory variable;
4) Checking each parameter of the generalized additive model, and determining the significance of the response relationship between the variables;
5) And (5) looking up a response relation graph between the interpretation variable and the response variable with strong significance, wherein the graph is shown in fig. 5.
(7) According to the result of the above procedure, in combination with the GAM image (fig. 5), the appropriate ecological water level condition of the lake is determined.
In summary, as shown in fig. 4 and 5, variables having significant influence on the wet plant are Total Nitrogen (TN) and pre-flood mean water level (pre), and the generalized additive model of 5 times of modeling of the cross reveals that the change rule is: the concentration of the wet plant spore powder increases with the increase of the total nitrogen concentration and decreases with the increase of the average water level before flood. This suggests that the growth of the wet plant is dependent on the total nitrogen concentration, which is at least greater than 0.25% to facilitate the growth of the wet plant; the average water level before flood is inversely related to the growth of the wet plant, and the average water level before flood should be regulated to be not higher than 2.8 meters to be used as the proper ecological water level of the wet plant.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (4)
1. A method for determining the proper ecological water level of a lake based on an ancient lake and marsh gas learning method is characterized by comprising the following steps:
s1, sampling a deposition column: collecting a deposition column sample at a selected sampling place by using a gravity sampler, and preprocessing the sample;
s2, analyzing the definite year: by means of 210 Pb and 137 the Cs year testing technology carries out the year-by-year analysis work on the soil sample in the deposition column;
s3, identifying sporopollen and processing data: calculating the concentration of the spore powder by adopting an externally-added spore powder method, and estimating the abundance of the macrophytes according to the concentration percentage content of the spore powder;
s4, determining an environment data set: collecting and measuring important environmental factors as an environmental data set for modeling;
s5, screening key environmental factors: establishing a classification and regression tree model, and screening environmental factors for generating the classification and regression tree model as interpretation variables of subsequent modeling; the screening key environmental factors mentioned in S5 specifically include the following steps:
d1, taking the concentration of the sporopollen as a response variable and the environmental factor as an explanation variable, and establishing a classification and regression tree model;
d2, pruning: pruning the generated tree to obtain a tree with proper size and capable of explaining most variances;
d3, checking the graph of the final tree, and taking the variable of the spanning tree as an interpretation variable of the subsequent modeling;
s6, establishing a generalized additive model: establishing a generalized additive model between specific sporopollen concentration and environmental factors by utilizing interpretation variables determined by classification and regression tree models, and fitting a curve to analyze the relationship between the variables;
the generalized additive model establishment mentioned in the step S6 specifically comprises the following steps:
e1, randomly dividing the sample into 5 groups;
e2, taking the concentration of the sporopollen as a response variable, taking an environmental factor as an interpretation variable, establishing a generalized additivable model by using 4 sets of sample formulas, verifying other 1 sets of sample formulas as prediction data, carrying out 5 times of modeling alternately, and observing the change rule;
e3, the generalized additive model cross modeling formula mentioned in the E2 specifically comprises the following steps:
E(y)＝S(x 1 )+S(x 2 )+...+S(x i )
wherein E () is a response variable; y is the concentration of the spore powder of the aquatic plant; s () is a smooth function; x is an explanatory variable;
e4, checking each parameter of the generalized additive model, and determining the significance of the response relationship between the variables;
e5, checking a response relation graph between the interpretation variable and the response variable with stronger significance;
s7, obtaining a result: and (3) according to the results obtained in the steps S1-S6, combining the generalized additive model graph to determine the proper ecological water level condition of the lake.
2. The method for determining the proper ecological water level of a lake based on the paleo-lake biogas method according to claim 1, wherein the sediment column sampling mentioned in S1 specifically comprises the following steps:
a1, determining a plurality of lake areas without obvious artificial interference through local recording, village investigation, satellite map and topography map, and selecting the lake areas as candidate sampling sites;
A2. collecting deposition columns with the length of about 1m at each candidate sampling site by using a gravity sampler, selecting a site with a deposition column with a better hierarchical structure as a final sampling site, and collecting deposition columns with the length of about 1m at the sampling site;
A3. the deposition columns were sectioned and layered at 1cm intervals, numbered, freeze-dried, homogenized, and stored in the shade ready for further processing.
3. The method for determining the proper ecological water level of the lake based on the paleo-lake biogas method according to claim 1, wherein the definite year analysis mentioned in S2 specifically comprises the following steps:
b1, using Ortec HPGe GWL series detector, direct gamma spectrometry was used to conduct soil sample 210 Pb， 226 Ra and 137 analysis of the radioactivity intensity of Cs;
b2 by slave total 210 Subtracting from Pb radioactivity intensity 226 Ra radioactivity intensity acquisition 210 Pb exc The calculation formula is as follows:
210 Pb exc ＝ 210 Pb tot - 210 Pb supp
wherein: 210 Pb exc indicating the remainder 210 The radioactivity of Pb; 210 Pb tot for measurement of 210 Total Pb strength; 210 Pb supp to be in a sample 226 Ra decay to form 210 The radioactivity of Pb;
b3, according to the respective sample layers 210 Pb exc Correspondingly obtain each depth 210 Pb x Further, the age of each sample layer is calculated through a formula, and the formula is specifically as follows:
210 Pb x ＝ 210 Pb 0 ×e (-λt)
wherein: 210 Pb x indicating a depth x 210 Pb radioactivity intensity; 210 Pb 0 is a surface layer 210 Pb radioactivity intensity; lambda is 210 Pb half-life constant; t is the age of depth x;
b4, according to the age and nuclide of the nuclear test peak period 137 The distribution of Cs in the sediment corresponds to the time of peak position, so as to obtain the deposition rate and age of the modern sediment, and verify 210 Accuracy of Pb year measurement.
4. The method for determining the proper ecological water level of the lake based on the ancient lake or marsh science method according to claim 1, wherein the identifying and data processing of the sporopollen mentioned in S3 specifically comprises the following steps:
c1, weighing: determining the weight of an analysis sample according to lithology, taking 2-5 g of lake sediment soil sample and taking 5-20 g of sample with heavy sandiness;
c2, adding indicative pollen: placing the sample into a plastic beaker, and adding quantitative pinus lycopodium spores as indicative pollen;
carrying out experimental treatment on the sample, and tabletting and preserving;
c4, identifying sporopollen: the sporopollen identification and statistics are completed under a microscope, and generally, only the genus level can be identified, some can only the science level, and the species level can be rarely identified;
and C5, calculating the concentration of the sporopollen: the calculation formula is as follows:
wherein: c is the particle number of the spore powder in the unit volume or the unit weight sample; a is the number of the spore powder particles counted by identification; e is the counted particle number of the lycopodium clavatum; f is the total grain number of the externally added lycopodium clavatum spores; v is the volume or weight of the sample;
and C6, dividing the spore powder combination belt by adopting a CONISS program designed in Tilia Graph software.
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