CN114818543A - Shallow lake typical recurrence period characteristic wind wave calculation method - Google Patents
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Abstract
The invention discloses a method for calculating characteristic wind waves of a typical reproduction period of a shallow lake, which relates to the technical field of lake monitoring, and comprises the following steps of S1: determining a calculation range: s2: model configuration: s3: selecting a wind field: s4: model verification and coefficient calibration: based on the collected time-by-time wind field observation data around the lake, verifying and checking the model calculation result by using the actually measured long-time series wind wave actual measurement values under the current terrain, S5: calculating the annual waves of the lake waves: s6: calculating the annual maximum cumulative frequency of the waves of the lake: s7: wave calculation of the lake wave recurrence period S8: calculating the extreme value wave in the typical reproduction period in different directions: and repeating the step S6 and the step S7 in different directions to obtain the wave intensities in different recurrence periods in different directions. The invention provides a method for calculating the wave intensity of shallow water area, which is a method for calculating the characteristic wave of shallow lake in typical reproduction period.
Description
Technical Field
The invention relates to the technical field of lake monitoring, in particular to a method for calculating characteristic wind waves of a typical reproduction period of a shallow lake.
Background
In the lake storm monitoring process, when wind blows on the surface of a water body, wind energy is transmitted through the interaction of water and gas to generate storm, and the storm is the most important factor for representing the hydrodynamic process of the lake. The damages and the elutriation of the engineering revetment of the lake and the dam caused by the wind waves are key factors influencing the safety of the lake breakwater, the flood control and the safety operation on water.
The waves in the lake mainly comprise stormy waves, which are limited by instruments and observation environments, the long-term and large-range real-time monitoring of the stormy waves in the lake is difficult, and the meteorological data is relatively convenient to obtain, so that the actual stormy wave strength of the lake can be calculated according to meteorological elements. After the wind field is determined, the change of the wind wave elements along with time or position can be determined according to the relation between the wind field elements and the wind wave elements.
The wind wave model based on the dynamic spectrum balance equation has been developed to the third generation, the current common large-range wind wave model is the third generation wind wave model developed based on the dynamic spectrum balance equation, various physical processes such as wind energy input, shallow water wind wave breaking, white wave dissipation, bottom friction resistance and the like are well considered, and the wind wave growth in different areas in a large range can be well simulated and predicted. The wind WAVE spectrum in the third-generation wind WAVE model is calculated through an integral spectrum balance equation without the need of limiting a given spectrum type, and through the development of the last thirty years, a series of advanced third-generation wind WAVE models emerge, wherein the WAM model, the WAVE-WATCH III model, the TOMAWAC model and the SWAN model are represented by comparison. The SWAN model is improved from a WAM model, the generation, dissipation and four-wave interaction of deep water storms in the WAM model are included, the stormy wave breaking item, bottom friction resistance influence and three-wave interaction of shallow water storms caused by shallow water depth are increased, and a third-generation stormy wave model based on high-precision simulation is provided.
Disclosure of Invention
The invention mainly aims to provide a method for calculating characteristic wind waves of a typical recurrence period of a shallow lake, which can effectively solve the problems in the background art.
In order to achieve the purpose, the invention adopts the technical scheme that: a shallow lake typical recurrence period characteristic wind wave calculation method comprises the following steps:
s1: determining a calculation range:
selecting a specific lake, determining a shoreline range and a topographic condition, counting historical shorelines according to the historical long-time evolution condition of the lakeshore, wherein the shoreline generally has large change, and selecting the shoreline in a stable state as a reference shoreline;
s2: model configuration:
selecting an SWAN storm model based on a dynamic spectrum balance equation as a calculation model, and performing calculation grid subdivision based on landform and a shoreline, wherein the grid range completely comprises a lake range, the grid interval is not more than 0.01L x 0.01H, L and H are respectively the length and the width of the calculation range, the frequency range of the model is 0.1-2.0 Hz, and the model is divided into 40 frequency sections. The direction is divided equally into 36 parts in the entire circumferential direction, i.e. the directional resolution is 10 °. In the physical process of the model, the wind input takes linear growth and exponential growth into consideration. Consumption caused by bottom friction was modeled as JONSWAP with a coefficient of 0.067. The mean value of the shallow water wind wave breaking coefficient is 0.73 during calculation, the initial value of the model is 0, a cold start mode is adopted during calculation, the interaction of three waves in shallow water is considered in the model, and default values are adopted for the others;
s3: selecting a wind field:
collecting and arranging hourly wind field observation data positioned around the appointed lake for model verification; meanwhile, collecting and arranging previous day-by-day observation data of a weather station around a designated lake, wherein the data comprises longitude, latitude, year, month, day, maximum wind speed and wind direction of the maximum wind speed, and is used for calculating the wave of the lake recurrence period and calculating the wave of the lake recurrence period;
s4: model verification and coefficient calibration:
based on collected hourly wind field observation data of national weather stations around the lake, the wind field is interpolated on a specified lake calculation grid by adopting an inverse distance interpolation method to serve as a model calculation wind field. The method comprises the steps of utilizing long-time series wind wave data actually measured under the current terrain, wherein the time is not less than 10 days, so that a calculated wind field can cover different wind speeds and wind directions, the actually measured wind wave data comprise effective wave heights and average wave periods, verifying and checking a model calculation result, adjusting a wind field input coefficient according to a verification result, and repeating the calculation until the calculation result is well matched with the actually measured result;
s5: calculating the waves in the lake wave year:
based on collected previous day-by-day observation data of the wind field around the designated lake, obtaining a model calculation wind field by a reverse distance interpolation method, inputting a well-verified lake wind wave SWAN model, carrying out calculation, segmenting the wind field by year, and respectively calculating the daily effective wave height values in different years;
s6: calculating the annual maximum cumulative frequency of the waves of the lake:
selecting a maximum value from the effective wave heights on different grid points calculated every day by taking the year as a time axis to form a maximum value series, arranging the obtained extreme values of the years (n) from small to large in sequence to obtain the cumulative frequency of the wave heights in the sequence of m, calculating the empirical cumulative frequency of the effective wave heights on each grid point,
s7: lake wave recurrence period wave calculation
Extending the obtained experience accumulated frequency curve according to collected wind field day-by-day observation data around the previous appointed lake, carrying out recurrence period calculation on the extension of the experience accumulated frequency curve by adopting Gunn-Bell distribution, Pearson type III curves and the like, and obtaining wave intensities at different grid points in different recurrence periods according to the fitted PIII curve;
because the collected meteorological data at present only comprises day-by-day data since 1951, the data length is limited, the design wave elements of one hundred years or even longer recurrence periods cannot be directly obtained, and the obtained experience cumulative frequency curve needs to be extended. Can adoptPerforming recurrence period calculation on Gunbel (Gumble) distribution, Pearson III (PIII) type curves and the like; wherein the probability density function of the Gunble distribution curve is as follows:
y＝α(H-α)，
in the formula
Wherein the Pearson type III curve probability density function is:
in the formula: coefficient of dispersion
Coefficient of skewness
Usually take C v Multiple of (2), modulus ratio coefficient
The probability distribution function is such that,
according to the fitted PIII curve, the wave intensities at different grid points in different recurrence periods can be obtained, for example, the stormy waves meeting at 5 years, 10 years, 20 years, 50 years and 100 years respectively correspond to the stormy wave intensity values corresponding to 20%, 10%, 5%, 2% and 1% on the PIII fitted cumulative frequency curve.
S8: calculating extreme waves of typical reproduction period in different directions
Further, the annual daily effective wave height distribution calculated in step S5 is classified into eight directions, the annual maximum wave height values in different directions of each year are extracted, and then step S6 and step S7 are repeated to finally obtain the wind wave intensities in different directions, such as 5 years, 10 years, 20 years, 50 years and 100 years.
In step S2, the wave model is a spectrum model, and the wave spectrum not only indicates which component waves are inside the wave, but also provides external characteristics of the wave, for example, theoretically, various characteristic wave heights and average periods can be calculated from the spectrum, and by using these characteristic quantities together with the probability density distribution of the wave heights and periods, it can be calculated which waves with different heights and lengths are formed in the wave appearance.
In the step S2, wave fields in the constant wind field and the typhoon time-varying wind field are calculated in the SWAN wave model, different wind speeds and wind zone lengths are assumed in the constant wind field, an application range of SWAN model wave simulation is obtained, a correction method for a wind energy input item is provided by analyzing factors such as reduction of input wind speed and saturation of drag coefficient caused by reference wave speed for the case of large calculation wave height under large wind speed, and calculation results before and after correction are compared and verified by using synchronously observed wind and wave data.
Compared with the prior art, the invention has the following beneficial effects:
in the invention, firstly, the calculation range is determined: selecting a specific lake, determining a shoreline range and a topographic condition, and selecting a shoreline in a relatively stable state as a reference shoreline according to the historical long-time evolution condition of the lakeshore; secondly, model configuration: selecting a SWAN wind wave model based on a dynamic spectrum balance equation as a calculation model, and performing calculation grid subdivision based on landform and a shoreline, wherein in the physical process of the model, wind input considers two parts of linear growth and exponential growth, consumption caused by bottom friction adopts a JONSWAP model, and initial parameters and initial coefficients are configured according to the initial model; then, selecting a wind field: collecting and sorting hourly wind field observation data of national weather stations around the designated lake for model verification; meanwhile, collecting and arranging previous day-by-day observation data of wind fields of weather stations around the designated lake for calculating waves in the lake recurrence period; then, model validation and coefficient calibration: based on collected time-by-time wind field observation data around the lake, interpolating the wind field on a designated lake calculation grid by adopting an inverse distance interpolation method to serve as a model calculation wind field, verifying and checking a model calculation result by utilizing a long-time series wind wave actual measurement value actually measured under the current terrain, adjusting a wind field input coefficient according to the verification result, and repeating the calculation until the calculation result is well matched with the actual measurement result; then, calculating the waves in the lake wave year: based on collected previous day-by-day observation data of the wind field around the designated lake, obtaining a model calculation wind field by a reverse distance interpolation method, inputting a well-verified lake wind wave SWAN model, carrying out calculation, segmenting the wind field by year, and respectively calculating the daily effective wave height values in different years; then, calculating the cumulative frequency of annual maximum values of the waves of the lake: selecting a maximum value from the effective wave heights on different grid points calculated every day by taking the year as a time axis to form a maximum value series, arranging the obtained extreme values of the years (n) from small to large in sequence, obtaining the cumulative frequency of the wave heights in the sequence of m, and calculating the empirical cumulative frequency of the effective wave heights on each grid point; then, calculating the waves at the wave reproduction period of the lake, extending the obtained experience accumulated frequency curve according to collected wind field day-by-day observation data around the previous appointed lake, carrying out reproduction period calculation on the extension by adopting Gunn-Bell distribution and Pearson III type curves, and obtaining the wave intensities at different grid points at different reproduction periods according to the fitted PIII curve; and finally, calculating the extreme waves in the typical reproduction period in different directions: and further classifying the annual effective wave height distribution calculated in the step according to eight directions, extracting annual maximum values in different directions every year, and repeating the steps to obtain the storm wave intensities in different recurrence periods in different directions, thereby providing important parameters for shallow lake engineering design, lake water building safety and ship navigation safety design.
Drawings
FIG. 1 is a flow chart of a method for calculating characteristic wind waves of a typical reproduction period of a shallow lake according to the invention;
FIG. 2 is a grid diagram of the topography of a particular lake according to the present invention;
FIG. 3 is a comparison chart of the effective wave height and average wave period calculated by the specific lake waves of the present invention and the measured values.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "connected," and the like are to be construed broadly, such as "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Referring to fig. 1-3, the invention relates to a method for calculating characteristic waves of a typical recurrence period of a shallow lake, comprising the following steps:
s1: determining a calculation range:
selecting a specific lake, determining a shoreline range and a topographic condition, counting historical shorelines according to the historical long-time evolution condition of the lakeshore, wherein the shoreline generally has large change, and selecting the shoreline in a stable state as a reference shoreline;
s2: model configuration:
selecting an SWAN storm model based on a dynamic spectrum balance equation as a calculation model, and performing calculation grid subdivision based on landform and a shoreline, wherein the grid range completely comprises a lake range, the grid interval is not more than 0.01L x 0.01H, L and H are respectively the length and the width of the calculation range, the frequency range of the model is 0.1-2.0 Hz, and the model is divided into 40 frequency sections. The direction is divided equally into 36 parts in the entire circumferential direction, i.e. the directional resolution is 10 °. In the physical process of the model, the wind input takes linear growth and exponential growth into consideration. Consumption caused by bottom friction was modeled as JONSWAP with a coefficient of 0.067. The mean value of the shallow water wind wave breaking coefficient is 0.73 during calculation, the initial value of the model is 0, a cold start mode is adopted during calculation, the interaction of three waves in shallow water is considered in the model, and the other default values are adopted;
s3: selecting a wind field:
collecting and arranging hourly wind field observation data positioned around the appointed lake for model verification; meanwhile, collecting and arranging previous day-by-day observation data of a weather station around a designated lake, wherein the data comprises longitude, latitude, year, month, day, maximum wind speed and wind direction of the maximum wind speed, and is used for calculating the wave of the lake recurrence period and calculating the wave of the lake recurrence period;
s4: model verification and coefficient calibration:
based on collected hourly wind field observation data of national weather stations around the lake, the wind field is interpolated on a specified lake calculation grid by adopting an inverse distance interpolation method to serve as a model calculation wind field. The method comprises the steps of utilizing wind wave actual measurement serial data actually measured under current terrain for a long time, wherein the time is not less than 10 days, so that a calculated wind field can cover different wind speeds and wind directions, the actual wind wave measurement value comprises an effective wave height and an average wave period, verifying and checking a model calculation result, adjusting a wind field input coefficient according to a verification result, and repeating calculation until the calculation result is well matched with the actual measurement result;
s5: calculating the waves in the lake wave year:
based on collected previous day-by-day observation data of the wind field around the designated lake, obtaining a model calculation wind field by a reverse distance interpolation method, inputting a well-verified lake wind wave SWAN model, carrying out calculation, segmenting the wind field by year, and respectively calculating the daily effective wave height values in different years;
s6: calculating the annual maximum cumulative frequency of the waves of the lake:
selecting a maximum value from the effective wave heights on different grid points calculated every day by taking the year as a time axis to form a maximum value series, arranging the obtained extreme values of the years (n) from small to large in sequence to obtain the cumulative frequency of the wave heights in the sequence of m, calculating the empirical cumulative frequency of the effective wave heights on each grid point,
s7: lake wave recurrence period wave calculation
Extending the obtained experience accumulated frequency curve according to collected wind field day-by-day observation data around the previous appointed lake, carrying out recurrence period calculation on the extension of the experience accumulated frequency curve by adopting Gunn-Bell distribution, Pearson type III curves and the like, and obtaining wave intensities at different grid points in different recurrence periods according to the fitted PIII curve;
because the collected meteorological data at present only comprises day-by-day data since 1951, the data length is limited, the design wave elements of one hundred years or even longer recurrence periods cannot be directly obtained, and the obtained experience cumulative frequency curve needs to be extended. The reproduction period can be calculated by using Gunbel (Gumble) distribution, Pearson III (PIII) type curves and the like; wherein the probability density function of the Gunble distribution curve is as follows:
y＝α(H-α)，
in the formula
Wherein the leatherThe probability density function for the elson type III curve is:
in the formula: coefficient of dispersion
Coefficient of skewness
Usually take C v Multiple of (2), modulus ratio coefficient
The function of the probability distribution is such that,
according to the fitted PIII curve, the wave intensities at different grid points in different recurrence periods can be obtained, for example, the stormy waves meeting at 5 years, 10 years, 20 years, 50 years and 100 years respectively correspond to the stormy wave intensity values corresponding to 20%, 10%, 5%, 2% and 1% on the PIII fitted cumulative frequency curve.
S8: calculating extreme value waves of typical reproduction periods in different directions
Further, the annual daily effective wave height distribution calculated in step S5 is classified according to eight directions (east, southeast, south, southwest, west, northwest, north, and northeast), annual maximum values in different directions are extracted, which are called annual extreme values, and then step S6 and step S7 are repeated to finally obtain the wind wave intensities in different directions, such as 5 years, 10 years, 20 years, 50 years, and 100 years.
In step S2, the wave model is a spectrum model, and the wave spectrum not only indicates which component waves inside the wave are formed, but also provides external characteristics of the wave, for example, theoretically, the wave height and average period of various characteristics can be calculated from the spectrum, and by using these characteristic quantities and the probability density distribution of the wave height and period, it can be calculated which waves with different heights and lengths form the wave appearance.
In the step S2, wave fields in the constant wind field and the typhoon time-varying wind field are calculated in the SWAN wave model, different wind speeds and wind zone lengths are assumed in the constant wind field, an application range of SWAN model wave simulation is obtained, a correction method for a wind energy input item is provided by analyzing factors such as reduction of input wind speed and saturation of drag coefficient caused by reference wave speed for the case of large calculation wave height under large wind speed, and calculation results before and after correction are compared and verified by using synchronously observed wind and wave data.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A method for calculating characteristic wind waves of a typical reproduction period of a shallow lake is characterized by comprising the following steps:
s1: determining a calculation range:
selecting a specific lake, determining a shoreline range and a topographic condition, and selecting a shoreline in a relatively stable state as a reference shoreline according to a historical long-time evolution condition of the shoreline of the lake;
s2: model configuration:
selecting a SWAN wind wave model based on a dynamic spectrum balance equation as a calculation model, and performing calculation grid subdivision based on landform and a shoreline, wherein in the physical process of the model, wind input considers two parts of linear growth and exponential growth, consumption caused by bottom friction adopts a JONSWAP model, and initial parameters and initial coefficients are configured according to the initial model;
s3: selecting a wind field:
collecting and sorting hourly wind field observation data of national weather stations around the designated lake for model verification calculation; meanwhile, collecting and arranging previous day-by-day observation data of wind fields of weather stations around the designated lake for calculating the waves of the lake recurrence period;
s4: model verification and coefficient calibration:
based on collected time-by-time wind field observation data around the lake, interpolating the wind field on a designated lake calculation grid by adopting an inverse distance interpolation method to serve as a model calculation wind field, verifying and checking a model calculation result by utilizing a long-time series wind wave actual measurement value actually measured under the current terrain, adjusting a wind field input coefficient according to the verification result, and repeating the calculation until the calculation result is well matched with the actual measurement result;
s5: calculating the waves in the lake wave year:
based on collected historical day-by-day observation data of the wind field around the designated lake, a model calculation wind field is obtained by sampling an inverse distance interpolation method, the model calculation wind field is input into a well-verified lake wind wave SWAN model for calculation, the wind field is segmented by year, and the daily effective wave height values in different years are respectively calculated;
s6: calculating the annual maximum cumulative frequency of the waves of the lake:
selecting a maximum value from the effective wave heights on different grid points calculated every day by taking the year as a time axis to form a maximum value series, arranging the obtained extreme values of the years (n) from small to large in sequence, obtaining the cumulative frequency of the wave heights in the sequence of m, and calculating the empirical cumulative frequency of the effective wave heights on each grid point;
s7: lake wave recurrence period wave calculation
Extending the obtained experience accumulated frequency curve according to collected historical daily observation data of wind fields around the appointed lake, carrying out recurrence period calculation on the extension of the experience accumulated frequency curve by adopting Gunn-Bell distribution and Pearson III type curves, and obtaining wave intensities at different grid points in different recurrence periods according to the fitted PIII curve;
s8: calculating the extreme value wave of the typical recurrence period in different directions:
further, the annual daily effective wave height distribution calculated in step S5 is classified in eight directions, the annual maximum values in different directions are extracted, and then step S6 and step S7 are repeated to obtain the wind wave intensities in different recurrence periods in different directions.
2. The method for calculating the characteristic wind waves of the typical reproduction period of the shallow lake according to claim 1, which is characterized in that: in step S2, based on the process of dividing the landform and shoreline grids, the grid range should completely include the lake range, the grid spacing is not greater than 0.01L 0.01H, where L and H are the length and width of the calculation range, respectively, the model frequency range is 0.1 to 2.0Hz, and is divided into 40 frequency segments, the direction is divided into 36 parts along the entire circumference direction, i.e., the direction resolution is 10 °.
3. The method for calculating the characteristic wind waves of the typical reproduction period of the shallow lake according to claim 1, which is characterized in that: in the step S2, the JONSWAP model coefficient is 0.067, the average value of the shallow water wave breaking coefficient is 0.73 during calculation, the initial value of the model is 0, a cold start mode is adopted during calculation, the interaction of three waves in shallow water is considered in the model, and other default values are adopted.
4. The method for calculating the characteristic wind waves of the typical reproduction period of the shallow lake according to claim 1, which is characterized in that: in step S4, a long time series of wind wave processes actually measured by the wind waves under the current terrain are utilized, the time of the long time series is not less than 10 days, so that the calculated wind field can cover different wind speeds and wind directions, and the actually measured wind wave values include the effective wave height and the average wave period.
5. The method for calculating the characteristic wind waves of the typical reproduction period of the shallow lake as claimed in claim 1, characterized in that: in step S3, the observation data includes longitude, latitude, year, month, day, maximum wind speed value, and wind direction of maximum wind speed.
6. The method for calculating the characteristic wind waves of the typical reproduction period of the shallow lake according to claim 1, which is characterized in that: in step S2, the SWAN storm model is a storm spectrum model, the storm spectrum not only indicates which component waves constitute the interior of the storm, but also provides the external characteristics of the storm, for example, theoretically, the heights and average periods of various characteristic waves can be calculated from the spectrum, and by using the characteristic quantities and the probability density distribution of the heights and periods, the waves with different heights and lengths can be calculated from the appearance of the storm.
7. The method for calculating the characteristic wind waves of the typical reproduction period of the shallow lake according to claim 1, which is characterized in that: in the SWAN wave model in step S2, wave fields under a constant wind field and a typhoon time-varying wind field are calculated respectively, different wind speeds and wind zone lengths are assumed in the constant wind field to obtain an application range of SWAN model wave simulation, and for the case of a large calculated wave height under a large wind speed, a correction method of a wind energy input item is provided by analyzing factors such as reduction of input wind speed and saturation of drag coefficient caused by reference wave speed, and calculation results before and after correction are compared and verified by adopting synchronously observed wind and wave data.
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|---|---|---|---|---|
| CN116702656A (en) * | 2023-08-03 | 2023-09-05 | 深圳市广汇源环境水务有限公司 | Construction method of seawall wave mathematical model and wave elimination system |
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| CN117077558A (en) * | 2023-07-18 | 2023-11-17 | 西南林业大学 | Space-time refined wind speed field construction method |
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| CN117195775A (en) * | 2023-09-20 | 2023-12-08 | 上海勘测设计研究院有限公司 | Method, system, medium and device for calculating extreme mixed wave elements of offshore water area |
| CN117195775B (en) * | 2023-09-20 | 2024-04-05 | 上海勘测设计研究院有限公司 | Method, system, medium and device for calculating extreme mixed wave elements of offshore water area |
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