CN102567622A - Method for evaluating proper water level for aquatic plants in lakes during historic period - Google Patents

Abstract

The invention discloses a method for evaluating proper water level for aquatic plants in lakes during historic period, which aims to solve the problem of deficiency of growing condition information of species in the lakes during historic period. The method is the technology including utilizing Kendall rank correlation coefficient, correspondence analysis, gray prediction and mathematic methods of non-linear fitting to build relationship among sporopollen, diatom and water level of lakes on the basis of sporopollen and diatom species in lake sediments, computing threshold value of proper water level for aquatic plants, and finally acquiring species growing states and hydrological conditions of lake ecosystems during historic period, and accordingly, the method has significant guidance action for maintaining diversity of species of lake ecosystems.

Description

The method of the suitable water level of a kind of evaluation history lake aquatic in period plant

Technical field

The invention belongs to aquatic ecosystem monitoring field, relating to a kind of is foundation with cryptogam diatom assemblage zone in the lake sediment, utilizes the method for the suitable water level of mathematical modeling evaluation history lake aquatic in period plant.

Background technology

The cryptogam diatom as phytolite can better preserve in lake sediment, reflection period of history vegetation growth situation is had very high reference value.Different plants are different to the accommodation of envirment factor, and the freshwater lake plant is suitable for being grown under the condition of low salinity, and sea-plant is then mostly to be the salt tolerant species.In like manner, different plants can pass through reactions such as biomass, many degree, structure of community to the responsiveness of lake level difference to some extent.The short time fluctuation of interior lake level is not remarkable to the influence of vegetation growth state, but the water level information of long-time sequence helps to find the Changing Pattern of plant quantity.The suitable water level of computation history lake aquatic in period plant can be assessed lake ecosystem and be in the required hydrologic condition of health status; Understand the upgrowth situation of species through monitoring modern lake level; In time the allotment lake water yield plays the species diversity of keeping lake ecosystem

Vital role.

Chinese scholars has been carried out more deep research to the relation of the cryptogam diatom and the lake hydrology factor in recent ten years.Domestic, there is the scholar to utilize palynogram to calculate the ancient precipitation index of ancient temperature exponential sum, the flora general picture can reflect temperature and the precipitation condition (Lauren moral, 1996) that has this flora growth in the environment.There is the scholar to point out that lake diatom oxygen isotope can reflect palaeotemperature, weather drought event and atmospheric precipitation source; Simultaneously; The lake variation of diatom oxygen isotope on sedimentary sequence reflected the effect of hydrologic factor more, rather than temperature variation (Chinese Jingtai etc., 2006).There is the scholar to utilize typical correspondence analysis effectively to disclose the relation of lake superficial deposit diatom population and water environment, in the water environment index of choosing, the depth of water, conductivity, Cl ^-, Mg ²⁺, K ⁺, the pH value can effectively explain the diatom data, simultaneously the analytical table open fire dark with salinity be two the important environmental element gradients (sheep is waited eastwards, 2001) that influence the diatom distribution.Abroad, Kangur finds cryptogam through the sediment of analyzing the lake zones of different rate of sedimentation is along with the increase of water level increases, and the distribution of cryptogam simultaneously receives the influence of florescence and lake terrain factor; Utilization weighted means such as Duthie and regression model have been set up the relation of Lake Ontario surface deposit diatom group and lake level, and its related coefficient reaches 0.9; Brugam etc. utilize weighted average model to analyze Lake Michigan diatom group to form the situation with SEA LEVEL VARIATION, and research shows kind of the indication high water stage that swims in the diatom group, and the end kind of the indication low-water level of dwelling.

Retrieval finds to calculate the patent of invention of modern lake ecological moisturizing, and application number is 201010182787.X, and name is called the ecological water supplement technology for lakes based on multi-scale wavelet transform; Publication number is CN101824808A; It not appearance well extracted the information of water level on different scale through the excellent properties of multiresolution analysis of utilization wavelet transformation, and the wavelet coefficient of main yardstick is rebuild; In conjunction with other hydrology and ecological data; Can draw the division foundation in hydrology stage, confirm to comprise the suitable hydrology stage in rich low water season, propose a lake ecological moisturizing scheme of considering the water-level fluctuation characteristic of minimum ecological water level and Nian Nei simultaneously.

The patent of invention of calculating lake level of historical period is found in retrieval; Application number is 201010271505.3; Name is called a kind of technology of calculating lake level of historical period; Publication number is CN101908104A, and it is a foundation with sporopollen assemblage band, typical physical and chemical index in the lake sediment, and the mathematical method of utilization principal component analysis (PCA), grey correlation, recurrence has been set up the relation between cryptogam, environmental index and the lake level.

This shows; We can set up the relation of cryptogam diatom species and lake level by mathematical model; Understand the lake in the vegetation growth situation of period of history and the response that hydrologic condition is changed, have very important significance for the species diversity of protecting lake ecosystem.

Summary of the invention

The assessment of the suitable water level of period of history water-based plant comprises three parts: analysis part, construction part and match part.Analysis part is the relation that utilization Kendall rank correlation analysis and model's correspondence analysis are understood cryptogam diatom species and water level; Construction partly is the responsive cryptogam diatom kind of water level is the basis, the anti-lake level that pushes away the period of history of utilization ash bag prediction; Match partly is based on the principle of least square, utilizes the piecewise fitting curve that cryptogam diatom and lake level are organically combined; The suitable water level of confirming each cryptogam diatom species according to fitting result is interval, with each interval stack, obtains the suitable watermark threshold of period of history hydrophyte.

Description of drawings

The lake level Fig. 2 species of Fig. 1 period of history and the matched curve of lake level

Embodiment

1) relation of plant cryptogam and water level

The utilization trace method is confirmed the corresponding age of each sedimentary section layer; Differentiate each section layer cryptogam and diatom kind; Cryptogam quantity and distribution can reflect period of history vegetation growth situation, use the relation of Kendall rank correlation coefficient analysis of history known time in period sequence water level and aquatic hygrophyte cryptogam.

A, setting observation data are to (X _i, Y _i) and (X _j, Y _j), if X _j-X _iWith Y _j-Y _iSame-sign is arranged, think that then change direction is consistent, the point of unanimity is designated as M to number, if X _j-X _iWith Y _j-Y _iContrary sign is arranged, think that then change direction is inconsistent, number is designated as wherein X of N inconsistent _iAnd X _jRepresent the quantity of certain cryptogam, Y at i layer and j layer _iAnd Y _jRepresent that i layer and j layer corresponding historical write down water level period

B, introducing relationship strength variable S _t, S _t=M-N

C, Kendall rank correlation coefficient τ=2S _t/ (n (n-1)), wherein n representes the sedimentary section number of plies observed

2) relation of diatom and water level

Diatom as the important phytoplankton of lake ecosystem, has response more by force to the fluctuation of lake level, and utilization model correspondence analysis is inquired into the relation of diatom and water level, and this process is through the realization of Canoco software.

A, confirm the ranking value of each sedimentary section layer

B, the ranking value and the lake level of section layer combined with regression analysis, its regression equation is:

Z _j＝b ₀+bU _j

Z _jRepresent the ranking value of j section layer, b ₀Be intercept, b is the regression coefficient between section layer and the water level, U _jRepresentation level is in the observed reading of j section layer

The ranking value weighted mean of C, utilization section layer is asked the ranking value of diatom kind

${\mathrm{Sp}}_k=\frac{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Z}_j\×{\mathrm{Abund}}_{\mathrm{kj}}}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Abund}}_{\mathrm{kj}}}$

Sp _kRepresent the ranking value of k kind diatom, Abund _KjRepresent the richness of k kind diatom, the number of n representative profile layer at j section layer

D, set up the corresponding relation of diatom kind and lake level

3) the anti-lake level that pushes away the period of history

Based on cryptogam-water level correlation analysis and diatom-water level correspondence analysis, select cryptogam and the diatom species responsive to water level, the utilization gray method of prediction is calculated the lake level of period of history.

A, be provided with q variable x ₁, x ₂, x ₃..., x _q, to q original ordered series of numbers should be arranged

L≤i≤q, the number of n representative profile layer wherein, x ₁Representation level, x ₂, x ₃..., x _qRepresent each cryptogam and diatom species

First number of B, new ordered series of numbers equals first number of original ordered series of numbers; First number that second number equals original ordered series of numbers adds second number; The 3rd number equal original ordered series of numbers first number, second number and the 3rd number and, take turns doing accumulation calculating, obtain new ordered series of numbers:

$x_i^1=[x_i^1\left(1\right),x_i^1\left(2\right),...,x_i^1\left(n\right)]=[\underset{j=1}{\overset{1}{\mathrm{\Σ}}}{x}_i^0\left(j\right),\underset{j=1}{\overset{2}{\mathrm{\Σ}}}{x}_i^0\left(j\right),...,\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{x}_i^0\left(j\right)]$

C, x wherein ₁Receive x ₂, x ₃..., x _qInfluence, x ₂, x ₃..., x _qIn each variable be mutually independent, receive x again ₁Influence, set up the following differential equation

${\mathrm{dx}}_1^1/\mathrm{dt}+a_{11}{x}_1^1=a_{12}{x}_2^1+a_{13}{x}_3^1+...+a_{1q}{x}_q^1$

${\mathrm{dx}}_2^1/\mathrm{dt}+a_{21}{x}_2^1=a_{22}{x}_1^1,$ ${\mathrm{dx}}_3^1/\mathrm{dt}+a_{31}{x}_3^1=a_{32}{x}_1^1,...,$ ${\mathrm{dx}}_q^1/\mathrm{dt}+a_{q1}{x}_q^1=a_{q2}{x}_1^1$

D, ordered series of numbers

is predicted according to ordered series of numbers ; Do the contrary generation that adds up then to predicting the outcome, just obtain predicting the outcome data row

4) set up the relation of cryptogam diatom species and lake level

Therefore different historical diatom cryptogam can better reflect the changing condition that diatom cryptogam species are driven by water level with the sectional curve match to the poor to some extent boundary of the responsiveness of lake level, and match is based on the principle of least square.

A, one group of data (x _Ik, y _i), be divided into m section, wherein x by its distribution _IkRepresent i section layer, the quantity of k species, y _iThe corresponding water level of representing i section layer

B, meet the linear fit of using of linear relationship, i.e. Y=kx+b does not meet the nonlinear fitting of using of linear relationship, i.e. Y '=c ₁X+c ₂x ²+ ...+c _px ^p+ c ₀, k wherein, c ₁, c ₂..., c _pBe regression coefficient, b, c ₀Be intercept

C, will guarantee at the frontier point place smoothly continuously, the functional value of the both sides curve that then frontier point is corresponding is equal with derivative value

D, introducing least squares estimator S, Wherein j representes section, and l≤j≤m, l represent the section number of plies of j section, f (x _Ik) matched curve of j section of expression, make that S is zero to the partial derivative of the regression coefficient of each section matched curve, obtain the regression coefficient of each section plan platform curve afterwards.

5) the suitable watermark threshold of computation history hydrophyte in period

The suitable water level of confirming each cryptogam and diatom species based on matched curve is interval, then with each interval stack, calculates the suitable watermark threshold of lake period of history water plant.

Embodiment

The suitable water level computing method of period of history hydrophyte of this paper are applied to certain lake, the China north.

1. use the Kendall rank correlation coefficient, analyze that aquatic hygrophyte cryptogam reed in the sediment, hair are good, the relation of watermifoil, nutgrass flatsedge, cattail and corresponding lake level.

Table 1 Kendall rank correlation coefficient result of calculation

According to Kendall rank correlation coefficient distribution table, when comparable data was 11 groups, the related coefficient absolute value represented that greater than 0.45 both have correlativity, can know that according to table 1 reed, watermifoil and lake level are proportionate, and nutgrass flatsedge and lake level are inverse correlation.

2. use model's correspondence analysis to inquire into the relation of diatom and lake level.At first calculate the number percent that different diatom kinds in each section layer account for total diatom quantity, rare species (number percent in the sedimentary section layer all less than 1%) are removed, choose the diatom kind stronger water level response through calculating.According to analysis result, U.S. wall algae, rhombus algae, melosira, two eyebrow algae, boat type algae, shank algae are arranged with the stronger diatom kind of lake level response.

3. make up the lake level of period of history

Gray system theory is the method for a kind of research " minority certificate, poor information " uncertain problem.Lake level changes all influential to each species quantity, but relation is complicated.Utilize the responsive kind of 9 kinds of water levels, the anti-lake level that pushes away the period of history of utilization grey method the results are shown in Figure 1.

4. the match of species and water level

According to the distribution of different plant species and water level, based on the principle of least square, piecewise fitting; Get higher limit and the lower limit of the highs and lows of matched curve as the suitable water level of these species; Fig. 2 has listed the match relation of one of them species and the water level of 9 kinds of responsive kinds of water level, and as shown in the figure, the suitable water level of these species is [7.2; 8.8], unit is a rice.

5. the calculating of the suitable water level of hydrophyte

According to matched curve, confirm the suitable water level of each diatom and cryptogam species, the interval stack of the water level that will suit (getting interval the common factor) obtains the suitable water level of period of history lake aquatic plant and is [7.5,8.1], and unit is a rice.

Claims (1)

1. method of utilizing lake sediment cryptogam, diatom assemblage zone to calculate period of history hydrophyte suitable growth water level; It is characterized in that: utilization Kendall rank correlation coefficient is analyzed the relation of aquatic hygrophyte cryptogam and water level; Inquire into the relation of diatom and water level by model's correspondence analysis; Select the responsive kind of water level based on above two kinds of analyses,, utilize nonlinear fitting to set up the relation between cryptogam diatom assemblage zone and lake level through the anti-water level that pushes away the period of history of grey method; Thereby computation history hydrophyte in period suitable growth watermark threshold, concrete steps are following:

1) relation of plant cryptogam and water level

The utilization trace method is confirmed the corresponding age of each sedimentary section layer; Differentiate each section layer cryptogam and diatom kind; Cryptogam quantity and distribution can reflect period of history vegetation growth situation, use the relation of Kendall rank correlation coefficient analysis of history known time in period sequence water level and aquatic hygrophyte cryptogam;

A, setting observation data are to (X _i, Y _i) and (X _j, Y _j), if X _j-X _iWith Y _j-Y _iSame-sign is arranged, think that then change direction is consistent, the point of unanimity is designated as M to number, if X _j-X _iWith Y _j-Y _iContrary sign is arranged, think that then change direction is inconsistent, number is designated as wherein X of N inconsistent _iAnd X _jRepresent the quantity of certain cryptogam, Y at i layer and j layer _iAnd Y _jRepresent that i layer and j layer corresponding historical write down water level period

B, introducing relationship strength variable S _t, S _t=M-N

C, Kendall rank correlation coefficient τ=2S _t/ (n (n-1)), wherein n representes the sedimentary section number of plies observed

2) relation of diatom and water level

The arsenic algae as the important phytoplankton of lake ecosystem, has response more by force to the fluctuation of lake level, and utilization model correspondence analysis is inquired into the relation of diatom and water level

A, confirm the ranking value of each sedimentary section layer

B, the ranking value and the lake level of section layer combined with regression analysis, its regression equation is:

Z _j＝b ₀+bU _j

Z _jRepresent the ranking value of j section layer, b ₀Be intercept, b is the regression coefficient between section layer and the water level, U _jRepresentation level is in the observed reading of j section layer

The ranking value weighted mean of C, utilization section layer is asked the ranking value of diatom kind

${\mathrm{Sp}}_k=\frac{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Z}_j\×{\mathrm{Abund}}_{\mathrm{kj}}}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Abund}}_{\mathrm{kj}}}$

Sp _kRepresent the ranking value of k kind diatom, Abund _KjRepresent the richness of k kind diatom, the number of n representative profile layer at j section layer

D, set up the corresponding relation of diatom kind and lake level

3) the anti-lake level that pushes away the period of history

Based on above two kinds of analyses, select cryptogam and the diatom kind responsive to water level, the utilization gray method of prediction is calculated the lake level of period of history

A, be provided with q variable x ₁, x ₂, x ₃.., x _q, to q original ordered series of numbers should be arranged L≤i≤q, the number of n representative profile layer wherein, x ₁Representation level, x ₂, x ₃..., x _qRepresent each cryptogam and diatom species

First number of B, new ordered series of numbers equals first number of original ordered series of numbers; First number that second number equals original ordered series of numbers adds second number; The 3rd number equal original ordered series of numbers first number, second number and the 3rd number and, take turns doing accumulation calculating, obtain new ordered series of numbers:

$x_i^1=[x_i^1\left(1\right),x_i^1\left(2\right),...,x_i^1\left(n\right)]=[\underset{j=1}{\overset{1}{\mathrm{\Σ}}}{x}_i^0\left(j\right),\underset{j=1}{\overset{2}{\mathrm{\Σ}}}{x}_i^0\left(j\right),...,\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{x}_i^0\left(j\right)]$

C, x wherein ₁Receive x ₂, x ₃..., x _qInfluence, x ₂, x ₃..., x _qIn each variable be mutually independent, receive the influence of x1 again, set up the following differential equation

${\mathrm{dx}}_1^1/\mathrm{dt}+a_{11}{x}_1^1=a_{12}{x}_2^1+a_{13}{x}_3^1+...+a_{1q}{x}_q^1$

${\mathrm{dx}}_2^1/\mathrm{dt}+a_{21}{x}_2^1=a_{22}{x}_1^1,$ ${\mathrm{dx}}_3^1/\mathrm{dt}+a_{31}{x}_3^1=a_{32}{x}_1^1,...,$ ${\mathrm{dx}}_q^1/\mathrm{dt}+a_{q1}{x}_q^1=a_{q2}{x}_1^1$

D, ordered series of numbers is predicted according to ordered series of numbers

; Do the contrary generation that adds up then to predicting the outcome, just obtain predicting the outcome data row

4) set up the relation of cryptogam diatom species and lake level

Therefore different historical diatom cryptogam can better reflect that with the sectional curve match diatom cryptogam species receive the changing condition of water level driving to the responsiveness of lake level difference to some extent, and match is former based on least square

A, one group of data (x _Ik, y _i), according to the distribution of point it is divided into m section, wherein x _IkRepresent i section layer, the quantity of k species, y _iThe corresponding water level of representing i section layer

B, meet the linear fit of using of linear relationship, i.e. Y=kx+b does not meet the nonlinear fitting of using of linear relationship, i.e. Y '=c ₁X+c ₂x ²+ ...+c _px ^p+ c ₀, k wherein, c ₁, c ₂..., c _pBe regression coefficient, b, c ₀Be intercept

C, will guarantee at the frontier point place smoothly continuously, the functional value of the both sides curve that then frontier point is corresponding is equal with derivative value

D, introducing least squares estimator S,

Wherein j representes section, and l≤j≤m, l represent the section number of plies of j section, f (x _Ik) matched curve of j section of expression, make that S is zero to the partial derivative of the regression coefficient of each section matched curve, obtain the regression coefficient of each section matched curve afterwards

5) the suitable watermark threshold of computation history hydrophyte in period

The suitable water level of confirming each cryptogam and diatom species based on matched curve is interval, then with each interval stack, calculates the suitable watermark threshold of lake period of history water plant.

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