CN113157772A - Lake proper ecological water level determination method based on ancient lake and marsh method - Google Patents
Lake proper ecological water level determination method based on ancient lake and marsh method Download PDFInfo
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Abstract
The invention discloses a lake proper ecological water level determination method based on an ancient lake and marsh method, belonging to the technical field of environmental protection; firstly, selecting an area with less man-made interference in a lake to collect a sediment columnar sample, and slicing the sediment columnar sample at equal intervals and layering the sediment columnar sample for pretreatment; secondly, use210Pb and137determining the age of each sample layer by a Cs year measuring technology; then, identifying and calculating the variety and the quantity of sporopollen of each sample layer to represent the aquatic plant information of an ecological system in a time sequence, collecting and measuring corresponding hydrometeorology and physical and chemical indexes, and constructing an environment factor data set; finally, establishing a generalized additive model according to the key environmental factors suggested by the classification regression tree model, quantifying the response relation between the environmental factors and the spore powder concentration, and determining the appropriate lake ecological water level; book (I)The invention effectively solves the problem that the existing design is difficult to obtain stable long-time sequence biological data, and provides a quantitative method for exploring the response relation between water level fluctuation and local ecological indexes.
Description
Technical Field
The invention relates to the technical field of environmental protection, in particular to a lake proper ecological water level determination method based on an ancient lake and marsh method.
Background
The water level fluctuation is one of the core factors affecting the biomass, diversity, composition and structure of vegetation, and the shallow lake is particularly susceptible to the water level fluctuation in various aquatic ecosystems by causing changes in environmental factors affecting the growth and germination of plants, such as light, oxygen, temperature, nutrients, etc. In recent years, lakes in many regions have corresponding water level fluctuations due to the influence of global climate changes (changes in precipitation amount, precipitation frequency and single precipitation intensity) and human activities (large hydraulic engineering construction, reclamation, etc.). The lake plants are degraded due to the abnormal fluctuation of the water level, the self-purification capacity of rivers and lakes is reduced, submerged vegetation in open water areas disappears, and a lot of shallow lakes are converted from a grass type clear water state into an eutrophication state of an algae type turbid water state, so that water is abused and the water quality is finally reduced. Although the response of the aquatic ecosystem of shallow lakes due to climate change and water circulation change has been studied in recent years, the influence of water level fluctuation on the lake science characteristics and mixing of lakes and fresh water reservoirs has not been paid much attention. In view of the above, water level fluctuation management has become necessary in order to maintain the stability and development of the lake ecosystem.
Traditionally, water level fluctuation management strategies are mainly determined according to two types of methods: (1) taking natural water level fluctuation as a template, and maintaining the water level fluctuation to reach the level of the natural water level fluctuation; (2) the appropriate water level is designed based on the response of the target species to several water level parameters. The first strategy is based on the following assumptions: the lake ecosystem is already adapted to natural water level fluctuation, so that the function integrity of the lake ecosystem can be effectively protected by maintaining the natural water level fluctuation. Scientifically, it is reasonable to minimize the gap between natural and current water level fluctuations, the importance of which has been recognized by more and more research. However, there are some problems in practice when it is used as a management strategy, the main problem being that it is not possible to explore the direct link between the fluctuation of the water level and the local ecological indicators, and therefore we are unable to answer more specific questions, such as: before the aquatic ecosystem begins to degrade, how much the water level conditions should be changed to accommodate the changes, or how the annual and annual patterns of water level conditions should be managed to achieve the desired ecological effect. The second method is based on the response of the target species to the water level change, and is considered to be the most scientific method for determining the water level fluctuation management strategy. To date, macrophytes are the most studied target species of water level fluctuation for the influence of organisms in lake ecosystems, because 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, which can have significant effects even if dropped in small amounts. Recent studies have determined various water level fluctuation management strategies such as restricting the growth of excessive macrophytes, maintaining a stable community structure, or regulating succession of ecosystems, according to the purpose of protection of local macrophytes. However, most lakes around the world lack macrophyte historical data, which hinders the effectiveness of water level management strategies. The historical data is generally the only information that can be used to define the lake's natural state, and it can set a realistic goal and provide a benchmark for evaluating strategy repeatability. Furthermore, variations in data statistics, irregular, incomparable time series data, or sampling technique differences can all contribute to historical data. Retrieving data from the literature may be a viable method to obtain sufficient data without spending a significant amount of time on field investigations, but there is also a problem of data comparability due to the lack of consistency of the method between different studies.
Thus, in the absence of long-term monitoring data, the residue of aquatic plants retained in the sediment may record the time-course of lake macrophytes and provide a sufficiently high quality data base for the design to support an ecologically acceptable water level fluctuation strategy. So far, ancient ecological technologies have been widely applied to reconstruct historical ecological conditions, detect climate change trends or evaluate eutrophication through various ecological indexes such as sporopollen, diatom, ratch, coccid and the like. Particularly, the concentration of sporopollen of the macrophyte can well represent the growth state of the hydrophyte 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 lake edge and can be used to predict the atrophy of water areas. Meanwhile, researches show that the macrophyte sporopollen shows response of subareas to water level conditions, and the researches conclude that different water levels can cause different combinations of the macrophyte sporopollen. In addition, some studies have also successfully combined ancient lake documentary records with long-term observations. During the overlap, both data may support and complement each other. Nevertheless, in shallow lakes, ancient ecological studies of sporopollen of macrophytes are rarely used to guide water level management, and in view of this, we propose a lake-adapted ecological water level determination method based on the ancient swamp science method.
Disclosure of Invention
The invention aims to provide a lake proper ecological water level determination method based on an ancient lake and marsh method, which is based on the framework of the ancient lake and marsh method and is used for acquiring ecological data of a long-time sequence and determining an ecological water level with a certain ecological foundation.
In order to achieve the purpose, the invention adopts the following technical scheme:
a lake proper ecological water level determination method based on an ancient lake and marsh method comprises the following steps:
s1, sediment sampling: collecting a sedimentary column sample by using a gravity sampler at a selected sampling site, and preprocessing the sample;
s2, determining the year and analyzing: by using210Pb and137the Cs year measuring technology carries out year-fixed analysis work on the soil sample in the sedimentary column;
s3, sporopollen identification and data processing: calculating the concentration of sporopollen by adopting an external sporopollen adding method, and estimating the abundance of the macrophyte according to the concentration percentage content of the sporopollen;
s4, determining an environment data set: collecting and determining 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 to serve as interpretation variables of subsequent modeling;
s6, establishing a generalized additive model: establishing a generalized addable model between the specific sporopollen concentration and the environmental factor by using the interpretation variables determined by the classification and regression tree model, and fitting a curve to analyze the relation between the variables;
s7, obtaining a result: and determining the appropriate ecological water level conditions of the lake according to the results obtained in the S1-S6 and the graphs of the generalized additive model.
Preferably, the sediment column sampling mentioned in S1 specifically includes the following steps:
a1, determining several lake regions without obvious human interference through local records, village survey, satellite map and topographic map, and selecting the lake regions as candidate sampling sites;
A2. collecting 30-60 cm of sediment columns at each candidate sampling site by using a gravity sampler, selecting the site of the sediment column with a better hierarchical structure as a final sampling site, and collecting the sediment columns with the length of about 1m at the sampling site;
A3. the columns were sectioned at 1cm intervals for stratification, numbered, freeze dried, homogenized, and stored in the shade in preparation for further processing.
Preferably, the annual analysis mentioned in S2 specifically includes the following steps:
b1 direct gamma spectrometry of soil samples using an Ortec HPGe GWL series detector210Pb，226Ra and137analyzing the radioactive intensity of the Cs;
b2, passing through210Subtraction of Pb radioactivity226Ra radioactivity intensity acquisition210PbexcThe calculation formula of (a) is:
210Pbexc＝210Pbtot-210Pbsupp
in the formula:210Pbexcindicates the residue210The radioactive intensity of Pb;210Pbtotobtained for measurement210The total Pb strength;210Pbsuppto be in contact with the sample226Formation of Ra decay210The radioactive intensity of Pb;
b3 according to the respective sample layer210PbexcCorresponding to each depth210PbxAnd then calculating the age of each sample layer through a formula, wherein the formula is as follows:
210Pbx＝210Pb0·e(-λt)
in the formula:210Pbxrepresenting depth x210The radioactive intensity of Pb;210Pb0is a surface layer210The radioactive intensity of Pb; λ is210A Pb half-life constant; t is the age of depth x;
b4, age and nuclides at peak according to Nuclear test137The distribution of Cs in the deposit is corresponded, the age of the peak position is determined, and the deposition rate and age of the deposit in the recent period are obtained to verify210The accuracy of the Pb dating.
Preferably, the sporopollen identification and data processing 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 sand;
c2, adding indicative pollen: putting the sample into a plastic beaker, and adding quantitative lycopodium clavatum spores as indicative pollen;
c3, performing experimental treatment on the sample, and performing slice storage;
c4, sporopollen identification: spore powder identification and statistics are completed under a microscope, and generally only genus level, some scientific level and few species level can be identified;
c5, calculating spore powder concentration: the calculation formula is as follows:
in the formula: c is the number of particles of sporopollen in unit volume or unit weight of the sample; a is the number of spore powder particles identified and counted; e is the counted number of the spore particles of the lycopodium clavatum; f is the total number of the added lycopodium clavatum spores; v is the volume or weight of the sample.
And C6, dividing the sporopollen combined bands by adopting a CONISS program designed in the Tilia Graph software.
Preferably, the screening of the key environmental factors mentioned in S5 specifically includes the following steps:
d1, establishing a classification and regression tree model by taking the spore powder concentration as a response variable and taking the environmental factor as an explanation variable;
d2, pruning: pruning the generated tree to obtain a tree with proper size and capable of explaining most variances;
d3, viewing the graph of the final tree, and taking the variable of the spanning tree as an explanation variable of the subsequent modeling.
Preferably, the establishing of the generalized additive model mentioned in S6 specifically includes the following steps:
e1, randomly dividing the sample prescription into 5 groups;
e2, taking the spore powder concentration as a response variable and the environmental factor as an explanatory variable, establishing a generalized additive model by using 4 groups of sample recipes, verifying other 1 groups of sample recipes as prediction data, modeling for 5 times in a crossed manner, and observing the change rule of the sample recipes;
e3, the generalized additive model cross-modeling formula mentioned in E2 is specifically:
E(y)＝S(x1)+S(x2)+...+S(xi)
wherein E () is a response variable; y is the concentration of aquatic plant sporopollen; s () is a smoothing function; x is an explanatory variable;
e4, checking each parameter of the generalized additive model, and determining the significance of the response relation between the variables;
e5, checking a response relation graph between the explanation variable with stronger significance and the response variable.
Compared with the prior art, the invention provides a lake proper ecological water level determination method based on an ancient lake and marsh method, which has the following beneficial effects:
the invention is used firstly210Pb and137the Cs year measuring technology determines the ages of settled layers with different depths, and the monitoring data is related to the sediment recordsConnecting; secondly, identifying the species of the sporopollen of the macrophyte from sedimentary layers of each year, dividing the sporopollen into different combinations according to different characteristics of the sporopollen or research focus of the macrophyte, and taking the sporopollen as a biological index; thirdly, summarizing the environmental factor data and the sediment spore powder biological index data of the corresponding year into a data set required by modeling; then, screening out key environment variables by using a classification and regression tree model; finally, establishing a generalized additive model through the screened variables, and determining a proper lake ecological water level condition according to the analysis result and the specific management preference; compared with the existing design, the lake proper ecological water level determination method based on the ancient lake and marsh method provided by the invention obtains long-time sequence ecological data 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 foundation, and effectively solves the problems that the existing design cannot explore direct connection between water level fluctuation and local ecological indexes, data acquisition is insufficient, methods in different researches lack consistency, and data comparability exists.
Drawings
FIG. 1 is a flow chart of a method for determining a suitable ecological water level of a lake based on a method for determining a suitable ecological water level of a lake in ancient lakes and marshes, which is provided by the invention;
FIG. 2 is a schematic diagram showing the dating result of a sedimentation column of the lake Baiyan lake in example 2 of a method for determining a suitable ecological water level of a lake based on the palustrine method according to the present invention;
FIG. 3 is a schematic sectional view of a combination of Podospora albuginea lake core sporopollen and a macrophyte sporopollen-based lake in example 2 of a lake proper ecological water level determination method based on the ancient lake and marsh method according to the present invention;
FIG. 4 is a schematic diagram showing the classification and regression tree results between the concentrations of the sporopollen of the humics of the lake of the Baiyang lake and environmental variables in example 2 of the method for determining the suitable ecological water level of the lake based on the paludiology method of the ancient lake.
Fig. 5 is a schematic diagram of 5 times of cross modeling responses of a generalized additive model between the concentration of sporopollen of hygrophytes in the lake of the white ocean and environmental variables in example 2 of the lake proper ecological water level determination method based on the ancient lake and marsh method provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example 1:
referring to fig. 1, a method for determining a suitable ecological water level of a lake based on the ancient lake and marsh method includes the following steps:
s1, sediment sampling: collecting a sedimentary column sample by using a gravity sampler at a selected sampling site, and preprocessing the sample;
the sedimentary column sampling specifically comprises the following steps:
a1, determining several lake regions without obvious human interference through local records, village survey, satellite map and topographic map, and selecting the lake regions as candidate sampling sites;
a2, collecting 30-60 cm sediment columns at each candidate sampling site by using a gravity sampler, selecting the site of the sediment column with a better hierarchical structure as a final sampling site, and collecting the sediment column with the length of about 1m at the sampling site;
a3, slicing the deposition columns at intervals of 1cm, numbering, freeze-drying, homogenizing, storing in the shade, and preparing for the next step;
s2, determining the year and analyzing: by using210Pb and137the Cs year measuring technology carries out year-fixed analysis work on the soil sample in the sedimentary column;
the chronologic analysis specifically comprises the following steps:
b1 direct gamma spectrometry of soil samples using an Ortec HPGe GWL series detector210Pb，226Ra and137analyzing the radioactive intensity of the Cs;
b2, passing through210Subtraction of Pb radioactivity226Ra radioactivity intensity acquisition210PbexcThe calculation formula of (a) is:
210Pbexc＝210Pbtot-210Pbsupp
in the formula:210Pbexcindicates the residue210The radioactive intensity of Pb;210Pbtotobtained for measurement210The total Pb strength;210Pbsuppto be in contact with the sample226Formation of Ra decay210The radioactive intensity of Pb;
b3 according to the respective sample layer210PbexcCorresponding to each depth210PbxAnd then calculating the age of each sample layer through a formula, wherein the formula is as follows:
210Pbx＝210Pb0·e(-λt)
in the formula:210Pbxrepresenting depth x210The radioactive intensity of Pb;210Pb0is a surface layer210The radioactive intensity of Pb; λ is210A Pb half-life constant; t is the age of depth x;
b4, age and nuclides at peak according to Nuclear test137The distribution of Cs in the deposit is corresponded, the age of the peak position is determined, and the deposition rate and age of the deposit in the recent period are obtained to verify210The accuracy of the year measurement of Pb;
s3, sporopollen identification and data processing: calculating the concentration of sporopollen by adopting an external sporopollen adding method, and estimating the abundance of the macrophyte according to the concentration percentage content of the sporopollen;
the sporopollen identification and data processing method 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 sand;
c2, adding indicative pollen: putting the sample into a plastic beaker, and adding quantitative lycopodium clavatum spores as indicative pollen;
c3, performing experimental treatment on the sample, and performing slice storage;
c4, sporopollen identification: spore powder identification and statistics are completed under a microscope, and generally only genus level, some scientific level and few species level can be identified;
c5, calculating spore powder concentration: the calculation formula is as follows:
in the formula: c is the number of particles of sporopollen in unit volume or unit weight of the sample; a is the number of spore powder particles identified and counted; e is the counted number of the spore particles of the lycopodium clavatum; f is the total number of the added lycopodium clavatum spores; v is the volume or weight of the sample.
C6, dividing sporopollen combined zone by using CONISS program designed in Tilia Graph software
S4, determining an environment data set: collecting and determining 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 to serve as interpretation variables of subsequent modeling;
the screening of the key environmental factors specifically comprises the following steps:
d1, establishing a classification and regression tree model by taking the spore powder concentration as a response variable and taking the environmental factor as an explanation variable;
d2, pruning: pruning the generated tree to obtain a tree with proper size and capable of explaining most variances;
d3, viewing the graph of the final tree, and taking the variable of the spanning tree as an explanatory variable of subsequent modeling;
s6, establishing a generalized additive model: establishing a generalized addable model between the specific sporopollen concentration and the environmental factor by using the interpretation variables determined by the classification and regression tree model, and fitting a curve to analyze the relation between the variables;
the establishment of the generalized additive model specifically comprises the following steps:
e1, randomly dividing the sample prescription into 5 groups;
e2, taking the spore powder concentration as a response variable and the environmental factor as an explanatory variable, establishing a generalized additive model by using 4 groups of sample recipes, verifying other 1 groups of sample recipes as prediction data, modeling for 5 times in a crossed manner, and observing the change rule of the sample recipes;
e3, the generalized additive model cross-modeling formula mentioned in E2 is specifically:
E(y)＝S(x1)+S(x2)+...+S(xi)
wherein E () is a response variable; y is the concentration of aquatic plant sporopollen; s () is a smoothing function; x is an explanatory variable;
e4, checking each parameter of the generalized additive model, and determining the significance of the response relation between the variables;
e5, checking a response relation graph between the explanation variable with stronger significance and the response variable;
s7, obtaining a result: and determining the appropriate ecological water level conditions of the lake according to the results obtained in the S1-S6 and the graphs of the generalized additive model.
The invention is used firstly210Pb and137the Cs year measuring technology determines the ages of settled layers with different depths, and the monitoring data is related to the sediment records; secondly, identifying the species of the sporopollen of the macrophyte from sedimentary layers of each year, dividing the sporopollen into different combinations according to different characteristics of the sporopollen or research focus of the macrophyte, and taking the sporopollen as a biological index; thirdly, summarizing the environmental factor data and the sediment spore powder biological index data of the corresponding year into a data set required by modeling; then, screening out key environment variables by using a classification and regression tree model; finally, establishing a generalized additive model through the screened variables, and determining a proper lake ecological water level condition according to the analysis result and the specific management preference; compared with the existing design, the lake proper ecological water level determination method based on the ancient lake and marsh method provided by the invention obtains long-time sequence ecological data 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 foundation, and effectively solves the problem that the existing design cannot explore direct connection between water level fluctuation and local ecological indexes and the problem that the data are directly relatedInsufficient collection, lack of consistency of methods among different researches, and data comparability problem.
Example 2:
referring to fig. 1 to 5, based on embodiment 1 but with a difference,
taking the white lake as an example for analysis, taking the hygrophyte as a research object, and determining the appropriate ecological water level.
(1) The sediment sampling comprises the following specific operation steps and method principles:
determining several areas without obvious human interference in the lake of the white ocean through local records, village survey, a satellite map and a bottom topographic map, and selecting the areas as candidate sampling sites;
collecting 30-60 cm of deposition columns from each sampling site of the lake of the white ocean 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 sites with the suitable deposition columns as sites for final sampling research;
thirdly, collecting a sedimentary column with the length of 1m at a sampling place, wherein the water depth is 1.5 m;
fourthly, cutting the sedimentary columns into small cylindrical slice soil samples at intervals of 1cm, numbering each sample in sequence, putting the samples into an aluminum foil, freeze-drying, homogenizing, and storing the samples in a shade place for further processing.
(2) And (4) determining the year by depositing a column: fixed year use of soil sample in sedimentary column210Pb and137cs year-measuring technology.
The principle is as follows: dissolved in a lake210Pb, adsorbed and combined by the particles of the sediments in the lake and continuously accumulated on the bottom of the lake, carried into the lake by the basin sediments210Pb also accumulates on the surface of the deposit. Incorporated into lake sediments in the manner described above210Pb with higher activity ratio than the parent226Activity resulting from Ra decay.
Of lake sediments210The Pb dating technology is based on the residue210Decay of Pb, usually in the form of210PbexcCan be represented byFrom measurement of formula210Calculation of total radioactive intensity of Pb, i.e.
210Pbexc＝210Pbtot-210Pbsupp
In the formula:210Pbtotis composed of210Total strength from Pb measurement;210Pbsuppto be in contact with the sample226Formation of Ra decay210The radioactive intensity of Pb.
Once the cover is closed210PbexcThe water body entering the lake and the sediments carried by the lake water are deposited on the bottom of the lake together, and the water body decays automatically according to the half-life period of 22.3 years under the condition of external isolation, and the depth distribution can be calculated by the following formula, namely
210Pbx＝210Pb0·e(-λt)
In the formula:210Pbxat a certain depth210The Pb strength;210Pb0is a surface layer210The Pb strength; λ is210A Pb half-life constant; t is the age of depth x.
According to the formula, the ages, namely the definite years, of the soil samples at different depths can be calculated.137Cs annual survey results in210The results obtained by Pb measurement are mutually complementary, and the chronology of lake sediments in nearly 200 years can be well reconstructed.
The method comprises the following steps: soil samples were subjected to direct gamma spectroscopy using an Ortec HPGe GWL series detector210Pb，226Ra and137analysis of the radioactivity of Cs by210Subtraction of Pb radioactivity226Ra radioactivity intensity acquisition210PbexcIs finally based on210Pb and137the model date of the Cs determines the age of each sample and the results of the dating of the sedimentary columns in the lake region of the Baiyanghu are shown in FIG. 2.
(3) Sporopollen identification and data processing: the abundance of the macrophyte is estimated according to the concentration percentage content of the sporopollen, and the sporopollen concentration is generally calculated by adopting an external sporopollen method.
The method comprises the following steps:
1) weighing: determining the weight of an analysis sample according to lithology, wherein 2-5 g of lake sediment soil sample is generally taken, and 5-20 g of sample with heavy sand is generally required;
2) adding indicative pollen: putting the sample into a plastic beaker, and adding quantitative lycopodium clavatum spores as indicative pollen;
3) adding 10% dilute hydrochloric acid to remove carbonate until the reaction is complete, and washing with water to be neutral;
4) adding hydrofluoric acid: adding 40% concentrated hydrofluoric acid in a fume hood to remove silicates 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 cleaning to be neutral;
7) sieving: in an ultrasonic generator, passing the neutral sample through a screen with the aperture of 6 mu m, and collecting residues on the screen cloth;
8) preserving and preparing slices: the collected samples were centrifuged into a finger tube, stored with glycerol, and pelleted.
9) And (3) identifying sporopollen: spore powder identification and statistics are completed under a microscope, and generally only genus level, some scientific level and few species level can be identified;
10) calculating the concentration of sporopollen: the calculation formula is as follows:
in the formula: c is the number of particles of sporopollen in unit volume or unit weight of the sample; a is the number of spore powder particles identified and counted; e is the counted number of the spore particles of the lycopodium clavatum; f is the total number of the added lycopodium clavatum spores; v is the volume or weight of the sample.
11) And finally, dividing the sporopollen combined zone by adopting a CONISS program designed in the Tilia Graph software, wherein the result is shown in figure 3.
(4) Determining an environment data set: since water level management cannot take into account all water level conditions, a series of key parameters are used to represent water level conditions. The method selects parameters which are proved to have ecological importance on the large aquatic plants, and the parameters comprise water level factors (such as average water level before flood, average water level in flood season, average water level after flood and the like), meteorological factors (such as annual average temperature, precipitation before flood, precipitation after flood and the like) and nutritional factors (such as total nitrogen, total phosphorus, total organic carbon and the like), and the environmental factors are used as an environment data set for modeling.
(5) Screening key environmental factors: the classification and regression tree (CART) model is a binary recursive decomposition method that can produce tree-based models. This approach is attractive for many exploratory environmental and ecological studies because it has the capability to handle both continuous and discrete variables, it can model the interactions between predicted variables, and it has hierarchical features. The CART is a scientific statistical method for identifying environmental factors which have significant contribution to biological abundance.
And establishing a classification and regression tree model, and screening the environment factors generating the classification and regression tree model to serve as the explanation variables of the subsequent modeling.
The method comprises the following steps:
1) establishing a classification and regression tree model by taking the spore powder concentration as a response variable and taking the environmental factor as an explanation variable;
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 used as the explanatory variables of the subsequent modeling, and as shown in FIG. 4, the suggested key variables can be obtained according to FIG. 4.
(6) Establishing a Generalized Additive Model (GAM): the Generalized Additive Model (GAM) is the combination of the generalized linear model and the additive model, integrates the advantages of the generalized linear model and the additive model, not only popularizes the distribution form of the response variable and can be suitable for index distribution family data, but also eliminates the limitation on the form of the predictive variable, and the model form is more flexible, thereby well revealing the nonlinear relation and the implicit ecological relation of the response variable and the explanatory variable, such as bimodal and asymmetric phenomena. The mathematical expression for GAM is as follows:
g(μ)＝α+f(X1)+…+f(XP)
where g () is a concatenation function, α is an intercept, and μ is an expected value of a response variable, i.e., μ ═ E (Y/X1, …, Xi), f (Xi) is a univariate function of a single interpretation variable Xi, which may take parametric or non-parametric forms depending on the actual situation.
And establishing a generalized addable model between the specific sporopollen concentration and the environmental factor by using the classification and the explanatory variable determined by the regression tree model, and fitting a curve to analyze the relation between the variables.
The method comprises the following steps:
1) randomly dividing the sample prescription into 5 groups;
2) the spore powder concentration is used as a response variable, the environmental factor is used as an explanation variable, 4 groups of sample prescriptions are used for establishing a generalized additive model, the other 1 groups of sample prescriptions are used for verifying prediction data, modeling is performed for 5 times in a crossed mode, and the change rule of the sample prescriptions is observed;
3) the generalized additive model cross-modeling formula mentioned in E2 is specifically:
E(y)＝S(x1)+S(x2)+...+S(xi)
wherein E () is a response variable; y is the concentration of aquatic plant sporopollen; s () is a smoothing function; x is an explanatory variable;
4) looking up various parameters of the generalized additive model and determining the significance of the response relation between the variables;
5) and (5) looking up a graph of the response relation between the interpretation variable and the response variable with stronger significance, wherein the graph is shown in FIG. 5.
(7) According to the result of the flow, the GAM image (figure 5) is combined to determine the proper ecological water level condition of the lake.
In summary, as shown in fig. 4 and 5, the variables having significant influence on the hygrophytes are Total Nitrogen (TN) and pre-flood mean water level (prelevel), and the generalized additive model with 5 modeling crossings reveals the change rule: the concentration of the hygrophyte sporopollen is increased along with the increase of the total nitrogen concentration, and is reduced along with the increase of the average water level before flood. This demonstrates that the growth of hygrophytes is dependent on the concentration of total nitrogen, which is at least greater than 0.25% to favor the growth of hygrophytes; the average water level before flood is in negative correlation with the growth of the hygrophytes, and the average water level before flood is not higher than 2.8 meters to serve as the proper ecological water level of the hygrophytes.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (6)
1. A lake proper ecological water level determination method based on an ancient lake and marsh method is characterized by comprising the following steps:
s1, sedimentary column sampling: collecting a sedimentary column sample by using a gravity sampler at a selected sampling site, and preprocessing the sample;
s2, determining the year and analyzing: by using210Pb and137the Cs year measuring technology carries out year-fixed analysis work on the soil sample in the sedimentary column;
s3, sporopollen identification and data processing: calculating the concentration of sporopollen by adopting an external sporopollen adding method, and estimating the abundance of the macrophyte according to the concentration percentage content of the sporopollen;
s4, determining an environment data set: collecting and determining 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 to serve as interpretation variables of subsequent modeling;
s6, establishing a generalized additive model: establishing a generalized addable model between the specific sporopollen concentration and the environmental factor by using the interpretation variables determined by the classification and regression tree model, and fitting a curve to analyze the relation between the variables;
s7, obtaining a result: and determining the appropriate ecological water level conditions of the lake according to the results obtained in the S1-S6 and the graphs of the generalized additive model.
2. The method for determining the suitable ecological water level of the lake based on the ancient lake and marsh method as claimed in claim 1, wherein the sedimentary column sampling mentioned in S1 comprises the following steps:
a1, determining several lake regions without obvious human interference through local records, village survey, satellite map and topographic map, and selecting the lake regions as candidate sampling sites;
A2. collecting 30-60 cm of sediment columns at each candidate sampling site by using a gravity sampler, selecting the site of the sediment column with a better hierarchical structure as a final sampling site, and collecting the sediment columns with the length of about 1m at the sampling site;
A3. the columns were sectioned at 1cm intervals for stratification, numbered, freeze dried, homogenized, and stored in the shade in preparation for further processing.
3. The method for determining the suitable ecological water level of the lake based on the ancient lake and marsh method as claimed in claim 1, wherein the annual analysis mentioned in S2 specifically comprises the following steps:
b1 direct gamma spectrometry of soil samples using an Ortec HPGe GWL series detector210Pb，226Ra and137analyzing the radioactive intensity of the Cs;
b2, passing through210Subtraction of Pb radioactivity226Ra radioactivity intensity acquisition210PbexcThe calculation formula of (a) is:
210Pbexc＝210Pbtot-210Pbsupp
in the formula:210Pbexcindicates the residue210The radioactive intensity of Pb;210Pbtotobtained for measurement210The total Pb strength;210Pbsuppto be in contact with the sample226Formation of Ra decay210The radioactive intensity of Pb;
b3 according to the respective sample layer210PbexcCorresponding to each depth210PbxAnd then calculating the age of each sample layer through a formula, wherein the formula is as follows:
210Pbx＝210Pb0×e(-λt)
in the formula:210Pbxrepresenting depth x210The radioactive intensity of Pb;210Pb0is a surface layer210The radioactive intensity of Pb; λ is210A Pb half-life constant; t is the age of depth x;
b4, age and nuclides at peak according to Nuclear test137The distribution of Cs in the deposit is corresponded, the age of the peak position is determined, and the deposition rate and age of the deposit in the recent period are obtained to verify210The accuracy of the Pb dating.
4. The method for determining the appropriate ecological water level of the lake based on the ancient lake marsh method as claimed in claim 1, wherein the sporopollen identification and data processing 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 sand;
c2, adding indicative pollen: putting the sample into a plastic beaker, and adding quantitative lycopodium clavatum spores as indicative pollen;
c3, performing experimental treatment on the sample, and performing slice storage;
c4, sporopollen identification: spore powder identification and statistics are completed under a microscope, and generally only genus level, some scientific level and few species level can be identified;
c5, calculating spore powder concentration: the calculation formula is as follows:
in the formula: c is the number of particles of sporopollen in unit volume or unit weight of the sample; a is the number of spore powder particles identified and counted; e is the counted number of the spore particles of the lycopodium clavatum; f is the total number of the added lycopodium clavatum spores; v is the volume or weight of the sample;
and C6, dividing the sporopollen combined bands by adopting a CONISS program designed in the Tilia Graph software.
5. The method for determining the suitable ecological water level of the lake based on the ancient lake and marsh method as claimed in claim 1, wherein the step of screening the key environmental factors mentioned in S5 specifically comprises the steps of:
d1, establishing a classification and regression tree model by taking the spore powder concentration as a response variable and taking the environmental factor as an explanation variable;
d2, pruning: pruning the generated tree to obtain a tree with proper size and capable of explaining most variances;
d3, viewing the graph of the final tree, and taking the variable of the spanning tree as an explanation variable of the subsequent modeling.
6. The method for determining the suitable ecological water level of the lake based on the ancient lake and marsh method as claimed in claim 1, wherein the establishing of the generalized additive model mentioned in S6 comprises the following steps:
e1, randomly dividing the sample prescription into 5 groups;
e2, taking the spore powder concentration as a response variable and the environmental factor as an explanatory variable, establishing a generalized additive model by using 4 groups of sample recipes, verifying other 1 groups of sample recipes as prediction data, modeling for 5 times in a crossed manner, and observing the change rule of the sample recipes;
e3, the generalized additive model cross-modeling formula mentioned in E2 is specifically:
E(y)＝S(x1)+S(x2)+...+S(xi)
wherein E () is a response variable; y is the concentration of aquatic plant sporopollen; s () is a smoothing function; x is an explanatory variable;
e4, checking each parameter of the generalized additive model, and determining the significance of the response relation between the variables;
e5, checking a response relation graph between the explanation variable with stronger significance and the response variable.
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