CN110287624B - Method for building salinity forecasting model of aquaculture bay during typhoon influence period - Google Patents

Method for building salinity forecasting model of aquaculture bay during typhoon influence period Download PDF

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CN110287624B
CN110287624B CN201910580264.1A CN201910580264A CN110287624B CN 110287624 B CN110287624 B CN 110287624B CN 201910580264 A CN201910580264 A CN 201910580264A CN 110287624 B CN110287624 B CN 110287624B
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金靓
钱燕珍
王立超
赵昶昱
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Ning Boshiqixiangtai
Xiangshan County Meteorological Bureau
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Ning Boshiqixiangtai
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Abstract

The invention provides a method for establishing a salinity forecasting model of a culture bay during typhoon influence, which is used for solving the problem that salinity values in the process of accurately forecasting the influence of different types of typhoons cannot be accurately forecasted. By adopting the method, the forecast value of salinity change under the influence of two types of typhoons can be constructed under the condition of routine changes including rainfall, tide, evaporation and the like, so that technical support is provided for marine products sensitive to salinity change by adopting targeted measures.

Description

Method for building salinity forecasting model of aquaculture bay during typhoon influence period
Technical Field
The invention relates to the technical field of meteorological services, in particular to a method for building a salinity forecasting model of a culture bay during a typhoon influence period.
Background
Mariculture is one of the important industrial economies in coastal areas and is part of the sustainable development of marine economy. The yield and quality of marine products are closely related to the salinity and the like of aquaculture water, the refined forecast of the salinity and the like is an important content of weather service for mariculture, and particularly, when the influence of disastrous weather such as typhoon and the like is large, the influence on the water is large, and the disease attack and even death of the marine products can be caused. Related experts have already conducted some researches before, Guowenton and the like preliminarily explore the change characteristics of salinity of seawater on the surface layers of bay of the mansion during the typhoon storm, and find that the runoff is sharply increased by the huge concentrated rainfall during the typhoon, so that the salinity is sharply reduced; zhao conggong and the like utilize ocean monitoring buoy data in the hong Kong to analyze and research salinity change characteristics of the sea anemone during the typhoon period, and find that the typhoon storm water increase and peripheral mountain runoff are converged into the hong Kong together, so that the water quantity in the hong Kong is increased, and the salinity is reduced to the minimum 5 hours after the typhoon landing; research on Argo data in warm pond areas of the western pacific ocean, such as the east West mountain and the like shows that when typhoon passes through the sea, sea surface salinity changes depend on 4 effect-time final results of rainfall, evaporation increase, mixing enhancement in a mixed layer and saltating surge, wherein the typhoon rainfall can inhibit the mixing enhancement effect caused by strong wind, and most of typhoons can cause salinity reduction when passing through the mixed effect. These studies analyzed the characteristics of salinity changes during typhoon effects from different perspectives and also presented some methods of study.
However, the influence of different typhoons on salinity is obviously different, for example, the salinity is suddenly reduced from 28 per thousand to less than 20 per thousand in the stage of the influence of the febrile typhoon in 2013, and the salinity is maintained at about 28 per thousand in the stage of the influence of typhoons for more than 7-8 months in 2018. Therefore, different methods are needed for different types of typhoons, and the salinity change characteristics in the typhoon influence process need to be analyzed and simulated. Currently, analytical methods are mainly classified into physical methods and statistical methods. The physical method has strong mechanization, but the used forecasting factor is single, so that the model error is large; the statistical method is relatively simple in calculation, but the forecasting precision of the established model is influenced by multiple aspects such as mode resolution, initial field conditions and the like.
Disclosure of Invention
The invention aims to solve the technical problems that the forecasting factors used by the existing analysis method for the influence of different typhoons on salinity are single, and the model error is large; the method is relatively simple in calculation, the forecasting precision of the built model is influenced by multiple aspects such as mode resolution, initial field conditions and the like, and the method for building the salinity forecasting model of the aquaculture bay during the typhoon influence period is provided, wherein the salinity forecasting model is built by adopting multiple parameters respectively through typical correlation analysis, an optimal subset regression method and the like aiming at typhoons of different types on the basis of simulating the salinity variation characteristics without the typhoon influence by utilizing a harmonic analysis and linear regression method and researching factors influencing the salinity variation, and a practical tool is added for the marine aquaculture meteorological service.
In order to achieve the above object, the technical solution adopted by the present invention is a method for establishing a salinity forecast model of a culture bay during a typhoon-affected period, comprising the following steps:
s1: since the period of the phase change of the moon is 29.5 days, the period of the lunar calendar month is approximately defined as 30 days in the present embodiment. Acquiring the average value of the daily average/maximum/minimum salinity of each day in the lunar calendar month without the influence of the typhoon for many years, and simulating the acquired daily average/maximum/minimum salinity by adopting a harmonic method to obtain a forecasting model of the daily average/maximum/minimum salinity of the lunar calendar month without the influence of the typhoon:
Figure GDA0002556243430000021
Figure GDA0002556243430000022
Figure GDA0002556243430000023
wherein:
save is the daily average salinity of the lunar calendar month during periods without typhoon effects;
smax is the daily maximum salinity of an lunar calendar month during periods without typhoon effects;
smin is the daily minimum salinity of the lunar calendar month during periods without typhoon effects;
a is the average salinity value of the climate in lunar calendar months during the period without typhoon influence;
b is the highest salinity value of the climate month in the lunar calendar month during the period without typhoon influence;
c is the minimum salinity value of the lunar climate in the lunar calendar month during the period without typhoon influence;
t is the first day, the second day … … thirty-th day of lunar calendar month;
s2: defining the difference between the precipitation (P) and the evaporation (E) in a certain period as the effective precipitation. Obtaining the sum (P-E) of the effective precipitation of the first 2 months of the preset year and the lunar calendar month, and obtaining a forecasting model of the average/maximum/minimum salinity of the preset year and the lunar calendar month according to the sum (P-E) of the effective precipitation of the first 2 months of the preset year and the preset lunar calendar month:
Figure GDA0002556243430000031
Figure GDA0002556243430000032
Figure GDA0002556243430000033
wherein:
Figure GDA0002556243430000034
the average salinity is the average salinity of the month in the preset year and lunar calendar month;
Figure GDA0002556243430000035
the maximum salinity of the month in a preset year and lunar calendar month;
Figure GDA0002556243430000036
the minimum monthly salinity is the preset year and lunar calendar month;
p is the sum of the precipitation of the first 2 months of the preset year and lunar calendar month;
e is the sum of the evaporation capacity of the preset year and the first 2 months of the lunar calendar month;
e is the average monthly salinity value of the preset year and lunar calendar month in the area without considering early precipitation and evaporation;
f is the monthly highest salinity value of the preset year and lunar calendar month in the area without considering early precipitation and evaporation;
g is the monthly minimum salinity value for the preset year and lunar calendar months in the area without considering early precipitation and evaporation.
S3: replacing the average salinity/the maximum salinity/the minimum salinity of climate months of lunar calendar months without typhoon influence with the average salinity/the maximum salinity/the minimum salinity of climate months of lunar calendar months of preset year and lunar calendar months, namely substituting the average salinity/the maximum salinity/the minimum salinity of moon months of preset year and lunar calendar months into the forecast model of daily average/daily maximum/daily minimum salinity of lunar calendar months without typhoon influence, and obtaining the forecast model of daily average/daily maximum/daily minimum salinity of lunar calendar months without typhoon influence:
Figure GDA0002556243430000037
Figure GDA0002556243430000038
Figure GDA0002556243430000039
Syavea forecasting model of daily average salinity of lunar calendar months during periods without typhoon influence;
Symaxa forecasting model of daily maximum salinity of lunar calendar months during the period without typhoon influence;
Syminforecasting models of the daily minimum salinity of lunar calendar months during periods without typhoon influence;
t is the first day, the second day … … thirty-th day of lunar calendar month;
the preset year and lunar calendar month are preset lunar calendar months of each year in the preset year, such as the lunar calendar august of 1999, wherein the date of the lunar calendar august is 30 days, and t is 1, 2, 3 … … 30.
S4: taking a preset data sample to obtain daily average/daily maximum/daily minimum salinity variation Y during typhoon influence period1/Y2/Y3Daily cumulative precipitation/daily evaporation/daily average tidal level variation X during typhoon-affected period1/X2/X3Acquiring the day of the typhoon influence period by an optimal subset methodAverage/daily maximum/daily minimum salinity variation Y1/Y2/Y3Cumulative daily precipitation/daily evaporation/average daily tidal level Change X during typhoon-influenced period1/X2/X3The model relationship between:
Y1＝-0.2-0.01X1-0.115X2+2.855X3(10)
Y2＝-0.21-0.007X1+1.316X3(11)
Y3＝-0.81-0.014X1+0.069X2+3.814X3(12)
the daily average/daily maximum/daily minimum salinity variation Y in the typhoon influence period1/Y2/Y3Respectively adding to the forecast model S of daily average/daily maximum/daily minimum salinity of lunar calendar month during the period without typhoon influenceyave/Symax/SyminAnd (3) obtaining a forecasting model of daily average/daily maximum/daily minimum salinity of lunar calendar in a typhoon-influenced period:
Figure GDA0002556243430000041
Figure GDA0002556243430000042
Figure GDA0002556243430000043
further, step S1 includes:
acquiring salinity A of the previous day during the typhoon influence period and salinity B during the typhoon influence period;
obtaining the difference (A-B) between the salinity A of the previous day during the typhoon influence period and the lowest salinity B during the typhoon influence period;
judging whether the difference (A-B) between the salinity of the day before the typhoon influence period and the lowest salinity of the typhoon influence period is less than 0;
if (A-B) is less than 0, determining that the typhoon is a first type typhoon; and if the (A-B) is not less than 0, determining that the typhoon is a second typhoon.
Further, step S2 includes:
analyzing the sum (P-E) of the effective precipitation of the previous 2 months of the preset year and the lunar calendar month and the average/maximum/minimum salinity of the preset year and the lunar calendar month respectively according to a cross correlation analysis method to obtain a correlation coefficient of the sum of the salinity of the preset year and the lunar calendar month and the effective precipitation of the previous 2 months;
carrying out significance test on a correlation coefficient of the salinity of the preset year and the lunar calendar month and the sum of the effective precipitation of the preset year and 2 months before the lunar calendar month to obtain a significance test level value, and judging whether the significance test of the alpha-0.01 level is passed or not;
if yes, forecasting the salinity of the preset year and the lunar calendar month by the sum of the effective precipitation of the preset year and 2 months before the lunar calendar month.
Further, step S4 further includes the steps of:
the difference between the average tidal level of the typhoon on first day and the lowest daily average tidal level of the typhoon influence period is defined as the tidal level variation, and the difference between the average/maximum/minimum salinity of the typhoon first day and the lowest daily average/maximum/minimum salinity of the typhoon influence period is defined as the average/maximum/minimum salinity variation respectively.
Carrying out significance test on a correlation coefficient of daily average/highest/lowest salinity variation and effective precipitation during the first type of typhoon influence period and a correlation coefficient of daily average/highest/lowest salinity variation and effective tide level variation during the first type of typhoon influence period to obtain a significance test level value;
acquiring a correlation coefficient of daily average/highest/lowest salinity variation during the second type of typhoon influence period and effective precipitation during the second type of typhoon influence period and a correlation coefficient of daily average/highest/lowest salinity variation during the second type of typhoon influence period and effective tide level variation during the second type of typhoon influence period;
carrying out significance test on a correlation coefficient of daily average/highest/lowest salinity variation and effective precipitation during the second type of typhoon influence period and a correlation coefficient of daily average/highest/lowest salinity variation and effective tide level variation during the second type of typhoon influence period to obtain a significance test level value;
and confirming the influence factors influencing the salinity change during the influence of the first type of typhoon and the influence factors influencing the salinity change during the influence of the second type of typhoon according to the acquired significance check level values of the first type of typhoon and the second type of typhoon.
Further, step S4 includes:
the method comprises the steps of obtaining preset data samples, wherein the daily average/daily maximum/daily minimum salinity variation (difference value between daily salinity and previous daily salinity) during a first typhoon influence period is Y1/Y2/Y3, the daily accumulated precipitation (difference value between typhoon influence first day to the current day)/daily evaporation/daily average tidal level variation (difference value between daily average tidal level and previous daily average tidal level) during the first typhoon influence period is X1/X2/X3, carrying out typical correlation analysis on the Y data and the X data, obtaining correlation coefficients between the Y data and the X data, carrying out significance test on the Y data and the X data, obtaining significance test level values, and confirming the correlation between the salinity variation during the first typhoon influence period and the daily accumulated precipitation, daily evaporation and daily average tidal level variation.
Calculating all subset regressions in daily average/daily maximum/daily minimum salinity variation Y1/Y2/Y3 during the first type typhoon influence period of the preset data sample, daily accumulated precipitation/daily evaporation/daily average tidal level variation X1/X2/X3 during the first type typhoon influence period, and determining 1 optimal subset regression according to CSC criteria.
The invention at least comprises the following beneficial effects:
(1) the method can be used for classifying the current typhoon into a first type of typhoon and a second type of typhoon according to calculation and analysis of the influence of the current typhoon on the bay and the offshore salinity, so that bay culture salinity forecasting models during different typhoon influence periods can be established according to the influence of different types of typhoons on the bay and the offshore salinity;
(2) the method adopts harmonic analysis and linear regression methods to simulate salinity change characteristics when no typhoon is affected, obtains influence factors of salinity change, obtains the influence of effective precipitation, tidal level variation and evaporation on the salinity of a bay during the typhoon affected period through typical correlation analysis and an optimal subset regression method aiming at different types of typhoons, establishes a corresponding salinity forecasting model, and increases a practical tool for mariculture meteorological services.
Drawings
FIG. 1 is a block diagram of a method for establishing a salinity forecast model for a culture bay during typhoon exposure;
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
Examples
The embodiment provides a method for establishing a salinity forecast model of a culture bay during a typhoon influence period, as shown in fig. 1, wherein the typhoon influence is divided into two categories, the first category is that daily average/maximum/minimum salinity variation during the typhoon period is less than 0, the difference (A-B) between the previous daily salinity A during the typhoon influence period and the lowest salinity B during the typhoon influence period is less than 0, and the salinity is obviously reduced due to the typhoon. The typhoons are first typhoons, and the others are second typhoons.
The method for establishing the salinity forecasting model of the aquaculture bay during the typhoon influence period comprises the steps of firstly extracting and analyzing the salinity change characteristics in the aspect of climate, and specifically comprises the steps of extracting and analyzing the lunar calendar month change characteristics of the multi-year average salinity without the typhoon influence and extracting and analyzing the salinity change characteristics of the preset year and the lunar calendar month.
Wherein, the extraction and analysis of the lunar calendar month change characteristics of the annual average salinity without typhoon influence is to simulate the annual average value of daily average/highest/lowest salinity in the lunar calendar month without typhoon influence by a harmonic method and analyze the period of salinity change; the harmonic method is to decompose the time sequence of the elements into superposition of different harmonics and judge whether the elements have harmonic period characteristics:
Figure GDA0002556243430000071
k is a harmonic serial number, since the period of the lunar phase change is 29.5d, we define approximately that the basic period T is 30d, the kth harmonic period is T/k, and k has a value range of 1, 2 and 3 … 15.
a0Is a monthly average value, akAnd bkIs the Fourier coefficient, omegak2 pi K/T is the kth harmonic frequency.
Selection of the optimum order (m) for harmonic simulation is based on the cumulative variance contribution (F) of each order of harmonicm) Carrying out harmonic simulation on the salinity sample, and calculating the subharmonic variance (S) of each wavek) Sorting from big to small, accumulating the variance of m different wave subharmonics before ranking, and if the accumulated variance contribution rate F is obtainedmAnd if the variance contribution rate exceeds 85% and the variance of the m +1 th wave subharmonic is not more than 5%, the m wave is the best harmonic.
By utilizing a harmonic wave method, simulating the lunar month salinity change characteristics of the daily average/maximum/minimum salinity without typhoon influence, and acquiring a forecasting model of the current salinity, namely:
Figure GDA0002556243430000072
Figure GDA0002556243430000073
Figure GDA0002556243430000074
Save、Smax、Sminthe daily average, maximum and minimum salinity respectively, and t is the date.
Wherein 23.66, 23.91 and 23.36 of the model (1) are the average salinity value of the climate months of the lunar calendar months when t is not affected by typhoon for 30 days, the maximum salinity value of the climate months of the lunar calendar months when no typhoon is affected and the minimum salinity value of the climate months of the lunar calendar months when no typhoon is affected in the embodiment; wherein
Figure GDA0002556243430000075
For 30 daysA periodic 1 st harmonic model with a variance of 10.59, tested for significance at the level of α ═ 0.01,
Figure GDA0002556243430000076
the variance of the model 2 is 10.93, and the significance test of α ═ 0.01 level is passed, and the 1 st harmonic and the 2 nd harmonic constitute the 2 nd harmonic, i.e. the best harmonic, as described above.
The extraction and analysis of salinity change characteristics of preset years and lunar calendar months show that the ocean surface salinity distribution rule has better consistency with the distribution rule of the difference (P-E) between the precipitation amount and the evaporation amount, and the salinity change has certain hysteresis relative to the evaporation and precipitation processes.
Similarly there are climatically similar situations: the salinity of the seawater in the months after precipitation is different due to the obvious difference of precipitation and evaporation in different local years and months. Defining the difference (P-E) between the precipitation and the evaporation as effective precipitation, respectively calculating the average value of the average/maximum/minimum salinity of different years and the effective precipitation of the months before the preset year and lunar calendar month, and performing cross correlation analysis on the salinity and the effective precipitation to obtain the salt with the strongest correlation with the sum of the effective precipitation of the previous 2 months, wherein the correlation coefficient is more than 0.5.
Therefore, the sum (P-E) of the effective precipitation amounts of the first 2 months of the predetermined year and the lunar calendar month can be used to obtain the average/maximum/minimum salinity of the predetermined year and the lunar calendar month
Figure GDA0002556243430000081
The forecasting model is as follows:
Figure GDA0002556243430000082
Figure GDA0002556243430000083
Figure GDA0002556243430000084
26.3, 26.6 and 25.9 in the models (4), (5) and (6) are respectively the monthly average/monthly maximum/monthly minimum salinity values of the preset year and the lunar calendar month, which are obtained without considering early precipitation and evaporation; substituting the models (4) to (6) into the harmonic models (1) to (3), and predicting the daily average/maximum/minimum salinity model of the preset year and the lunar calendar month without typhoon by combining a month salinity prediction model obtained by using the effective precipitation amount of the preset year and 2 months before the lunar calendar month with the lunar calendar day:
Figure GDA0002556243430000085
Figure GDA0002556243430000086
Figure GDA0002556243430000087
t is the date of the lunar calendar, and the value is 1 and 2 … … 30;
the obtained model is a daily average/maximum/minimum salinity model without typhoon influence;
when the typhoon water reducing amplitude is large or the typhoon water increasing amplitude is small and the typhoon precipitation is large, the salinity can be remarkably reduced.
The difference between the average tidal level of the typhoon on first day and the lowest daily average tidal level of the typhoon influence period is defined as the tidal level variation, and the difference between the average/maximum/minimum salinity of the typhoon first day and the lowest daily average/maximum/minimum salinity of the typhoon influence period is defined as the average/maximum/minimum salinity variation respectively.
In the first type of typhoon process, average/highest/lowest salinity variation (the difference between the lowest value of the typhoon influence period and the first day is defined as salinity variation) is negatively correlated with effective precipitation, correlation coefficients are respectively 0.8, 0.63 and 0.83, and positive correlation is formed with tide level variation (the difference between the typhoon influence period and the lowest daily average tide level is defined as tide level variation, the daily average tide level is obtained by averaging 24h real-time tide levels), the correlation coefficients are respectively 0.88, 0.86 and 0.85, and the average salinity variation passes the level significance test of alpha being 0.01;
in the second typhoon process, the correlation between salinity and each element is not obvious.
Therefore, when daily average/maximum/minimum salinity during typhoon influence is actually forecasted, the models (7) to (9) are just substituted for the second typhoon, and the daily salinity change is calculated according to the precipitation amount, the evaporation amount and the tidal level change for the first typhoon.
Furthermore, the first variable Y is the daily average, maximum and minimum salinity variation (difference between the daily salinity and the previous daily salinity), and is recorded as Y1、Y2、Y3The second variable X is the cumulative daily precipitation (the precipitation from the first day to the current day influenced by typhoon), the daily evaporation and the daily average tidal level variation (the difference between the current average tidal level and the previous average tidal level), and is recorded as X1、X2、X3The number n of data samples in each group is 28. Correlation analysis by a typical correlation method can obtain: the salinity variation is obviously negatively correlated with the accumulated precipitation, is obviously positively correlated with the tidal level variation, and is negatively correlated with the evaporation capacity.
Thus dependent variable Y1、Y2、Y3Respectively taking daily average, highest and lowest salinity reduction amplitudes, and respectively selecting 3 independent variables: cumulative daily precipitation, evaporation and daily average tidal level Change (X)1、X2、X3) All possible subset regressions are calculated and 1 optimal subset regression is determined from them according to the CSC criterion. To obtain Y1、Y3The optimal subset is X1、X2、X3，Y2The optimal subset is X1And X3。
Y1＝-0.2-0.01X1-0.115X2+2.855X3(10)
Y2＝-0.21-0.007X1+1.316X3(11)
Y3＝-0.81-0.014X1+0.069X2+3.814X3(12)
Substituting the models (10) to (12) into the models (7) to (9) to obtain a forecasting model of the salinity of the typhoon influence period of the first type:
Figure GDA0002556243430000101
Figure GDA0002556243430000102
Figure GDA0002556243430000103
wherein t is lunar calendar date and takes values of 1 and 2 … … 30, P is sum of precipitation of the preset year and the first 2 months of lunar calendar month, E is sum of evaporation of the preset year and the first 2 months of lunar calendar month, and X is1、X2、X3Daily cumulative precipitation, evaporation and daily average tidal level change were respectively.
On the basis of salinity data of a preset multi-year gulf culture area and tide and meteorological data near the salinity data, salinity change characteristics without typhoon influence are simulated by utilizing a harmonic analysis and linear regression method, factors influencing salinity change are explored, on the basis, corresponding forecasting models are respectively established by adopting various parameters through typical correlation analysis, an optimal subset regression method and the like aiming at different types of typhoons, salinity change during the typhoon influence period is tried to be forecasted, and a practical tool is added for mariculture meteorological service.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.

Claims (5)

1. A method for establishing a salinity forecast model of a culture bay during typhoon influence period is characterized by comprising the following steps:
s1: acquiring a preset multi-year average value of daily average/maximum/minimum salinity in the lunar calendar month without typhoon influence, and simulating the acquired preset multi-year average value of daily average/maximum/minimum salinity by adopting a harmonic method to obtain a forecasting model of daily average/daily maximum/daily minimum salinity in the lunar calendar month without typhoon influence:
Figure FDA0002574002200000011
Figure FDA0002574002200000012
Figure FDA0002574002200000013
Savethe daily average salinity of lunar calendar months when no typhoon is affected;
Smaxthe highest salinity of lunar calendar months when no typhoon is affected;
Sminthe daily minimum salinity of lunar calendar months when no typhoon is influenced;
a is the average salinity value of the climate month in the lunar calendar month when no typhoon is influenced;
b is the climate month maximum salinity value of the lunar calendar month without typhoon influence;
c is the minimum salinity value of the lunar climate in lunar calendar months when no typhoon is affected;
t is the first day, the second day … … thirty-th day of lunar calendar month;
s2: obtaining the sum (P-E) of effective precipitation of the first 2 months of the preset year and the lunar calendar month, and obtaining a forecasting model of the average/maximum/minimum salinity of the month of the preset year and the lunar calendar month according to the sum (P-E) of the effective precipitation of the first 2 months of the preset year and the lunar calendar month:
Figure FDA0002574002200000014
Figure FDA0002574002200000015
Figure FDA0002574002200000016
wherein:
Figure FDA0002574002200000017
the average salinity is the average salinity of the month in the preset year and lunar calendar month;
Figure FDA0002574002200000018
the maximum salinity of the month in a preset year and lunar calendar month;
Figure FDA0002574002200000019
the minimum monthly salinity is the preset year and lunar calendar month;
p is the sum of the precipitation of the first 2 months of the preset year and lunar calendar month;
e is the sum of the evaporation capacity of the preset year and the first 2 months of the lunar calendar month;
e is the monthly average salinity value of the preset year and lunar calendar month without considering early stage dewatering and evaporation;
f is the monthly highest salinity value of the preset year and lunar calendar month without considering early stage dewatering and evaporation;
g is the monthly minimum salinity value of the preset year and lunar calendar month without considering early stage dewatering and evaporation;
s3: substituting the average monthly salinity/the maximum monthly salinity/the minimum monthly salinity of the preset years and the lunar calendar months into the forecasting model of the average daily salinity/the maximum daily salinity/the minimum daily salinity of the lunar calendar months without typhoon influence to obtain the forecasting model of the average daily salinity/the maximum daily salinity/the minimum daily salinity of the preset years and the lunar calendar months without typhoon influence:
Figure FDA0002574002200000021
Figure FDA0002574002200000022
Figure FDA0002574002200000023
Syavea forecasting model of daily average salinity of lunar calendar months during periods without typhoon influence;
Symaxa forecasting model of daily maximum salinity of lunar calendar months during the period without typhoon influence;
Symina forecasting model of the lowest salinity of the lunar calendar month in the period without typhoon influence;
s4: taking a preset data sample to obtain daily average/daily maximum/daily minimum salinity variation Y during typhoon influence period1/Y2/Y3Daily cumulative precipitation/daily evaporation/daily average tidal level variation X during typhoon-affected period1/X2/X3Acquiring daily average/daily maximum/daily minimum salinity variation Y during typhoon influence period by using optimal subset method1/Y2/Y3Cumulative daily precipitation/daily evaporation/average daily tidal level Change X during typhoon-influenced period1/X2/X3The model relationship between:
Y1＝-0.2-0.01X1-0.115X2+2.855X3(10)
Y2＝-0.21-0.007X1+1.316X3(11)
Y3＝-0.81-0.014X1+0.069X2+3.814X3(12)
the daily average/daily maximum/daily minimum salinity variation Y in the typhoon influence period1/Y2/Y3Respectively adding to the forecast model S of daily average/daily maximum/daily minimum salinity of lunar calendar month during the period without typhoon influenceyave/Symax/SyminObtaining a forecasting model of daily average/daily maximum/daily minimum salinity of lunar calendar in a typhoon-influenced period:
Figure FDA0002574002200000024
Figure FDA0002574002200000031
Figure FDA0002574002200000032
2. the method for modeling the salinity forecast of a culture bay during the typhoon influence period according to claim 1, wherein the step S1 further comprises:
acquiring salinity A of the previous day during the typhoon influence period and salinity B during the typhoon influence period;
obtaining the difference between the salinity A of the previous day in the typhoon influence period and the salinity B of the typhoon influence period: (A-B)
Judging whether the difference (A-B) between the salinity of the day before the typhoon influence period and the lowest salinity of the typhoon influence period is less than 0;
if (A-B) is less than 0, determining that the typhoon is a first type typhoon; and if the (A-B) is not less than 0, determining that the typhoon is a second typhoon.
3. The method for modeling the salinity forecast of a culture bay during the typhoon influence period according to claim 1, wherein the step S2 further comprises:
respectively analyzing the sum (P-E) of the effective precipitation of the previous 2 months of the preset year and the lunar calendar month and the average/maximum/minimum salinity of the preset year and the lunar calendar month according to a cross correlation analysis method to obtain a correlation coefficient of the sum of the salinity of the preset year and the lunar calendar month and the effective precipitation of the previous 2 months;
carrying out significance test on a correlation coefficient of the salinity of the preset year and the lunar calendar month and the sum of the effective precipitation of the preset year and 2 months before the lunar calendar month to obtain a significance test level value, and judging whether the significance test of the alpha-0.01 level is passed or not;
if yes, forecasting the salinity of the preset year and the lunar calendar month by the sum of the effective precipitation of the preset year and the first 2 months of the lunar calendar month.
4. The method for modeling the salinity forecast of a culture bay during the effect of typhoon according to claim 2, wherein the step S4 further comprises the steps of:
acquiring a correlation coefficient of daily average/highest/lowest salinity variation during the first type of typhoon influence period and effective precipitation during the first type of typhoon influence period and a correlation coefficient of daily average/highest/lowest salinity variation during the first type of typhoon influence period and effective tide level variation during the first type of typhoon influence period;
carrying out significance test on a correlation coefficient of daily average/highest/lowest salinity variation and effective precipitation during the first type of typhoon influence period and a correlation coefficient of daily average/highest/lowest salinity variation and effective tide level variation during the first type of typhoon influence period to obtain a significance test level value;
acquiring a correlation coefficient of daily average/highest/lowest salinity variation during the second type of typhoon influence period and effective precipitation during the second type of typhoon influence period and a correlation coefficient of daily average/highest/lowest salinity variation during the second type of typhoon influence period and effective tide level variation during the second type of typhoon influence period;
carrying out significance test on a correlation coefficient of daily average/highest/lowest salinity variation and effective precipitation during the second type of typhoon influence period and a correlation coefficient of daily average/highest/lowest salinity variation and effective tide level variation during the second type of typhoon influence period to obtain a significance test level value;
and confirming the influence factors influencing the salinity change during the influence of the first type of typhoon and the influence factors influencing the salinity change during the influence of the second type of typhoon according to the acquired significance check level values of the first type of typhoon and the second type of typhoon.
5. The method for modeling the salinity forecast of a culture bay during the typhoon influence period according to claim 1, wherein the step S4 further comprises:
acquiring preset data samples, wherein the daily average/daily maximum/daily minimum salinity variation Y1/Y2/Y3 during the first-class typhoon influence period is acquired, the daily accumulated precipitation/daily evaporation/daily average tidal level variation X1/X2/X3 during the first-class typhoon influence period is acquired, typical correlation analysis is carried out on the two groups of data, namely Y and X, correlation coefficients between the Y and X are acquired, significance test is carried out on the correlation coefficients, a significance test level value is acquired, and the correlation between the salinity variation during the first-class typhoon influence period and the daily accumulated precipitation, the daily evaporation and the daily average tidal level variation is confirmed;
calculating all subset regressions in daily average/daily maximum/daily minimum salinity variation Y1/Y2/Y3 during the first type typhoon influence period of the preset data sample, daily accumulated precipitation/daily evaporation/daily average tidal level variation X1/X2/X3 during the first type typhoon influence period, and determining 1 optimal subset regression according to CSC criteria.
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