CN109359772B - River time-by-time water temperature forecasting method - Google Patents

River time-by-time water temperature forecasting method Download PDF

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CN109359772B
CN109359772B CN201811187721.2A CN201811187721A CN109359772B CN 109359772 B CN109359772 B CN 109359772B CN 201811187721 A CN201811187721 A CN 201811187721A CN 109359772 B CN109359772 B CN 109359772B
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陈钢
翟月
李小宁
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Abstract

The invention discloses a river hourly water temperature forecasting method, which comprises the following steps: fitting historical water temperature and air temperature data of the river by adopting a composite sine function, and calculating the highest and lowest water temperatures and air temperature forecast initial values of the day to be forecasted; constructing a corresponding relation between the water temperature change and the air temperature change of the river; calculating the credible interval of the weather forecast values of the highest and the lowest air temperature of the day to be forecasted; correcting the forecast initial values of the highest water temperature and the lowest water temperature of the day; and (4) adopting a composite cosine function to forecast the water temperature of the river time by time. The method realizes the time-by-time probability forecast of the river water temperature, improves the precision of the water temperature forecast, and fills the blank of the time-by-time water temperature data requirement in the data-free area.

Description

River time-by-time water temperature forecasting method
Technical Field
The invention relates to a water temperature prediction method, in particular to a river time-by-time water temperature prediction method based on composite sine and cosine.
Background
The method has important significance in the fields of river ecological environment evaluation, industrial water taking and draining scheme design and the like by mastering the dynamic change of the water temperature of the canal. On the one hand, the river water quality is very susceptible to human activities such as pollution or water conservancy engineering, and once the river water quality in a natural state is damaged, a great deal of changes such as reduction of water environment quality, loss of biological diversity and the like are generated, and the change of water temperature is one of the changes. The water temperature is one of important indexes of water environment factors, the change of the water temperature directly influences the change of the water environment quality and has close relation with water ecology, and for example, the growth of aquatic plants, fish spawning and the like are very sensitive to the change of the water temperature. In order to correctly evaluate the environment ecology and the change of the watershed environment, it is very critical to accurately estimate the change of the water temperature before and after the influence of human activities. On the other hand, in various coastal water taking industries (such as fire/nuclear power plants), cooling water is directly taken from rivers or open channels, the dynamic change of the water taking temperature affects the cooling efficiency of a unit, and power generation equipment such as a condenser can be unstable in operation, so that the initial design stage often needs to quickly judge the change condition of the water temperature of the channel in order to optimize the arrangement of a water taking project and determine a safe operation strategy.
The current prediction method for the water temperature mainly comprises an empirical formula method and a mathematical model method. The empirical formula is relatively simple to use, but has low universality and is severely limited by regions and is only limited to the prediction of daily scale water temperature (maximum, minimum or average); the mathematical model starts from the physical process of water heat exchange, establishes a hydrodynamic force numerical model, but needs a large amount of actual measurement data, and has higher data acquisition difficulty. How to use a scientific method to quickly and reasonably estimate the water temperature of the future river channel becomes the primary problem of river ecological environment evaluation and industrial water taking and draining scheme design.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides a river time-by-time water temperature forecasting method which can quickly simulate and forecast the change of river water temperature along with time in a river basin range.
The technical scheme is as follows: in order to realize the purpose of the invention, the technical scheme adopted by the invention is as follows: a river time-by-time water temperature forecasting method comprises the following steps:
(1) acquiring historical water temperature data and historical air temperature data of a river forecast site in at least one year by using a sensor, respectively fitting the historical water temperature data and the historical air temperature data by using a composite sine function to obtain a fitting curve, and calculating to obtain daily simulation values of the highest water temperature, the lowest water temperature, the highest air temperature and the lowest air temperature in a time period covered by the historical data through the curve; expanding the time definition domain of the fitted composite sine function curve, and calculating to obtain the highest water temperature and the lowest water temperature of the day to be forecasted and forecast initial values of the highest air temperature and the lowest air temperature;
(2) respectively calculating the errors between each analog value and the current day measured value in historical data according to the daily maximum water temperature, minimum water temperature, maximum air temperature and minimum air temperature analog values obtained by the last step, and establishing a linear regression relationship between the maximum water temperature error and the maximum air temperature error and a linear regression relationship between the minimum water temperature error and the minimum air temperature error;
(3) calculating a credible interval of a weather forecast value of the highest air temperature and the lowest air temperature of a day to be forecasted by adopting a Bayesian probability forecasting method, wherein the weather forecast value is derived from real-time weather forecast of a weather bureau;
(4) substituting the difference value between the weather forecast value of the highest air temperature with the credible interval and the forecast initial value of the highest air temperature calculated in the step 1 into the linear regression relation obtained in the step 2, calculating to obtain the highest water temperature error of the day to be forecasted, correcting the highest water temperature of the day to be forecasted according to the highest water temperature error of the day to be forecasted, and obtaining the highest water temperature T of the day to be forecastedmaxThe same method obtains the minimum daily water temperature T to be forecastedmin；
(5) The river time-by-time water temperature is forecasted by adopting a composite cosine function, and when H is more than or equal to 0 and less than (RISE +2) and (NOON +4) and less than or equal to H and less than or equal to 24, the forecasting function is
Figure RE-GDA0001900829900000021
When (RISE +2) is less than or equal to H less than or equal to (NOON +4), the prediction function is
Figure RE-GDA0001900829900000022
Wherein H is the time to be forecasted on the day, Tw(H) Water temperature corresponding to H, RISE time, NOON time, TWAVEIs the average temperature of the day, TWAVE＝(Tmax+Tmin) AMP is temperature amplitude, AMP ═ T (T)max-Tmin) H 'is an intermediate variable, when H < (RISE +2), H' ═ H +8,
when H > (NOON +4), H' ═ H-16.
Calculating to obtain river hourly water temperature forecast Tw(H) And is used for guiding the actual production work.
Further, the historical data fitting in step 1 adopts a compound sine function of
Figure RE-GDA0001900829900000023
Wherein the content of the first and second substances,
Figure RE-GDA0001900829900000024
is the annual average temperature or water temperature, TamThe amplitude and the phase shift of the sine function are determined by fitting historical data by a least square method, wherein t is time, theta is the phase shift of the sine function, and omega is a fixed value of 2 pi/365. The fitting process needs to be performed on the highest water temperature, the lowest water temperature, the highest air temperature and the lowest air temperature in the historical data respectively to obtain four fitted composite sine functions.
Further, the calculation of the trusted interval in step 3 specifically includes: the method comprises the steps of adopting a Bayes probability forecasting method, giving a confidence level, generally setting a value range to be 80% -100%, determining posterior density function distribution through a Bayes assumption, determining parameters of the posterior density function distribution through a least square method, and solving a maximum posterior density confidence interval through iteration.
Further, the method for calculating sunrise time in step 5 includes: calculating a yellow-red intersection angle true value, a geometric mean yellow longitude of the sun, the earth orbit eccentricity, a mean near point angle of the sun, a difference value between two solar times (a mean solar time and a true solar time), a solar declination angle and a time angle according to the specific date of the day to be predicted and the longitude and latitude coordinates of the river, calculating and correcting the Greenwich mean solar time and converting the Greenwich mean solar time to local standard time to obtain the sunrise time of the place where the river is located; the calculation method of noon time comprises the following steps: and calculating the time difference of the two types of sun, calculating the Greenwich mean time and converting the Greenwich mean time to the local standard time to obtain the mean time of the place where the river is located.
Has the advantages that: compared with the prior art, the invention has the following advantages: the forecasting method firstly considers the seasonal variation of the temperature by using the compound sine function, and then forecasts the time-by-time variation of the temperature by using the compound cosine function, and the forecasting is accurate to the hour. The forecasting method simultaneously considers the uncertainty of the air temperature forecast value, gives a credible interval of the forecast value, and is more reasonable compared with the deterministic forecasting result in the prior art. The forecasting method needs less basic data, improves the applicability of the model in the data-free areas, ensures the stability and the long-term property of water temperature calculation, and improves the accuracy of river water temperature time-by-time forecasting.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of a sine function of water temperature fitting using historical water temperature data for many years according to the present invention;
FIG. 3 is a schematic diagram showing the relationship between water temperature change and climate change according to the present invention;
FIG. 4 is a schematic diagram of river hourly water temperature forecast in the present invention.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Fig. 1 is a flow chart of a river hourly water temperature forecasting method according to the present invention, which specifically includes the following steps:
(1) acquiring historical water temperature data and historical air temperature data of a river forecast site in at least one year by using a sensor, respectively fitting the historical water temperature data and the historical air temperature data by using a composite sine function to obtain a fitting curve, and calculating to obtain daily simulation values of the highest water temperature, the lowest water temperature, the highest air temperature and the lowest air temperature in a time period covered by the historical data through the curve; and expanding the time definition domain of the fitted composite sine function curve, and calculating to obtain the maximum water temperature, the minimum water temperature and the forecast initial values of the maximum air temperature and the minimum air temperature on the day to be forecasted.
Specifically, according to historical highest and lowest water temperature and air temperature data, each parameter in a sine function is solved by using a least square method, four sine functions are totally and respectively used as 'highest water temperature', 'highest air temperature', 'lowest water temperature' and 'lowest air temperature', and the highest water temperature, the lowest water temperature and the air temperature initial value of the day to be forecasted are obtained according to the parameters. Fig. 2 shows an example of a fitted sine function t (t) of the maximum water temperature, which is 20.59+9.03sin (0.17t-1.80), and the initial value of the maximum water temperature for each day can be calculated according to the time variation. Similarly, the forecast initial values of the lowest water temperature, the highest water temperature, the lowest air temperature and the highest air temperature of each day can be calculated by using other three fitted sine functions, and the simulation values of the highest water temperature, the lowest water temperature, the highest air temperature and the lowest air temperature of each day in the time period covered by the historical data are calculated.
(2) And respectively calculating the errors between each analog value and the current day measured value in historical data according to the daily analog values of the highest water temperature, the lowest water temperature, the highest air temperature and the lowest air temperature obtained by the last step of calculation, and establishing a linear regression relationship between the highest water temperature error and the highest air temperature error and a linear regression relationship between the lowest water temperature error and the lowest air temperature error.
Comparing the simulated values of the highest water temperature, the lowest water temperature and the air temperature of each day simulated in the step 1 with the measured values of the same day in historical data respectively, and calculating the errors of the highest water temperature, the lowest water temperature and the air temperature, and the errors of the highest water temperature, the highest air temperature, the lowest water temperature and the lowest air temperature corresponding to the four sine functions.
Establishing a linear regression relationship between the water temperature error and the air temperature error, and calculating by adopting the following linear regression relationship:
DTw＝A·DTA+B
wherein A, B is regression coefficient, DTwAnd DTARespectively representing the water temperature error and the air temperature error;
as shown in fig. 3, a set of correspondence relationships between the maximum water temperature change and the air temperature change is constructed using the "maximum water temperature error" and the "maximum air temperature error": DTw＝0.42·DTA+0.05，DTwAnd DTARespectively the water temperature error and the air temperature error. Similarly, a corresponding relation between the lowest water temperature change and the air temperature change can be constructed by utilizing the lowest water temperature error and the lowest air temperature error.
(3) And calculating a credible interval of a weather forecast value of the highest air temperature and the lowest air temperature of the day to be forecasted by adopting a Bayesian probability forecasting method, wherein the weather forecast value is derived from real-time weather forecast of a weather bureau.
And (3) adopting a Bayesian probability forecasting method, giving the determined confidence level, and calculating the confidence interval of the maximum posterior density. Specifically, the confidence level is given to be 90%, the posterior density function distribution of the model is determined through Bayesian assumption, the parameters of the model are determined through a least square method, and the maximum posterior density confidence interval when the confidence level is 90% is solved through iteration.
(4) Substituting the difference value between the weather forecast value of the highest air temperature with the credible interval and the forecast initial value of the highest air temperature calculated in the step 1 into the linear regression relation obtained in the step 2, calculating to obtain the highest water temperature error of the day to be forecasted, correcting the highest water temperature of the day to be forecasted according to the highest water temperature error of the day to be forecasted, and obtaining the highest water temperature T of the day to be forecastedmaxThe same method obtains the minimum daily water temperature T to be forecastedmin；
(5) Forecasting the river time-by-time water temperature by adopting a composite cosine function, which comprises the following specific processes:
when 0 & ltH & lt (RISE +2) and (NOON +4) & lt H & ltH & lt 24 are satisfied,
Figure RE-GDA0001900829900000041
when (RISE +2) H ≦ (NOON +4) is satisfied,
Figure RE-GDA0001900829900000042
wherein H is a time of day, the value range is 0-24 (the time is expressed by decimal point approximation, for example, 0.5 is obtained at 00: 30am of day), Tw(H) Water temperature at H, RISE time, NOON time, TWAVEAMP is the temperature amplitude, H' is the intermediate variable;
h' is determined using the following formula:
when H < (RISE +2),
H'＝H+8
when H > (NOON +4),
H'＝H-16
average temperature T in the dayWAVEThe following formula is used for calculation:
Figure RE-GDA0001900829900000051
the temperature amplitude AMP is calculated using the following formula:
Figure RE-GDA0001900829900000052
wherein, TmaxAnd TminThe highest water temperature and the lowest water temperature of the day obtained by the correction in the step 4 are respectively.
Calculating the sunrise time RISE and NOON time NOON of the place where the river is located:
and calculating a true value of a yellow-red intersection angle, a geometric mean yellow longitude of the sun, the earth orbit eccentricity, a mean near point angle of the sun, a difference value between two solar times (a mean solar time and a true solar time), a solar declination angle and a time angle according to the specific date of the day to be predicted and the longitude and latitude coordinates of the river, calculating and correcting the mean solar time of Greenwich mean and converting to the local standard time, and obtaining the sunrise time of the place of the river.
And calculating the time difference of the two types of sun, calculating the Greenwich mean time and converting the Greenwich mean time to the local standard time to obtain the mean time of the place where the river is located.
Fig. 4 shows the 48-hour forecast result of a certain river with a water temperature confidence level of 90%. The result shows the forecasting of the water temperature within 48 hours, and the measured values of the water temperature are all within the forecasting interval due to the adoption of probability forecasting, so that the effect is good. The method realizes the accurate forecasting of the river water temperature time by time, considers the uncertainty of real-time forecasting of the air temperature, and improves the accuracy and feasibility of forecasting the water temperature time by introducing a probability forecasting method; meanwhile, the blank of the time-by-time water temperature data requirement in the data-free area is filled.
The prediction of river time-by-time water temperature can be applied to: (1) the dynamic change of the water temperature of the canal is mastered, and the method plays an important role in evaluating the ecological environment of the river. On one hand, the river water quality is very easily affected by human activities such as pollution or hydraulic engineering, once the river water quality in a natural state is damaged, a plurality of changes such as reduction of water environment quality, loss of biological diversity and the like can be generated, and the change of water temperature is one of the changes; on the other hand, the water temperature is one of important indexes of water environment factors, the change of the water temperature directly influences the change of the water environment quality and has close relation with water ecology, and for example, the growth of aquatic plants, the spawning of fishes and the like are very sensitive to the change of the water temperature. In order to correctly evaluate the environment ecology of the drainage basin and the change of the environment ecology, the change of the water temperature before and after the influence of human activities needs to be accurately estimated. (2) In the field of industrial water taking and discharging scheme design, cooling water is directly taken from rivers or by adopting open channels in various coastal water taking industries such as thermal power plants, nuclear power plants, paper mills and the like, and the dynamic change of the water taking temperature influences the cooling efficiency of a unit and can cause unstable operation of power generation equipment such as a condenser and the like, so that the change condition of the water temperature of the canal is usually required to be predicted and judged in the engineering design stage, and help is provided for optimizing water taking engineering arrangement and determining safe operation strategies.

Claims (5)

1. A river time-by-time water temperature forecasting method comprises the following steps:
(1) acquiring historical water temperature data and historical air temperature data of a river forecast site in at least one year by using a sensor, respectively fitting the historical water temperature data and the historical air temperature data by using a composite sine function to obtain a fitting curve, and calculating to obtain daily simulation values of the highest water temperature, the lowest water temperature, the highest air temperature and the lowest air temperature in a time period covered by the historical data through the curve; expanding the time definition domain of the fitted composite sine function curve, and calculating to obtain the highest water temperature and the lowest water temperature of the day to be forecasted and forecast initial values of the highest air temperature and the lowest air temperature;
(2) respectively calculating the errors between each analog value and the current day measured value in historical data according to the daily maximum water temperature, minimum water temperature, maximum air temperature and minimum air temperature analog values obtained by the last step, and establishing a linear regression relationship between the maximum water temperature error and the maximum air temperature error and a linear regression relationship between the minimum water temperature error and the minimum air temperature error;
(3) calculating a credible interval of a weather forecast value of the highest air temperature and the lowest air temperature of a day to be forecasted by adopting a Bayesian probability forecasting method, wherein the weather forecast value is derived from real-time weather forecast of a weather bureau;
(4) substituting the difference value between the weather forecast value of the highest air temperature with the credible interval and the forecast initial value of the highest air temperature calculated in the step (1) into the linear regression relationship obtained in the step (2), calculating to obtain the highest water temperature error of the day to be forecasted, correcting the highest water temperature of the day to be forecasted according to the highest water temperature error, and obtaining the highest water temperature T of the day to be forecastedmaxThe same method obtains the minimum daily water temperature T to be forecastedmin；
(5) The river time-by-time water temperature is forecasted by adopting a composite cosine function, and when H is more than or equal to 0 and less than (RISE +2) and (NOON +4) and less than or equal to H and less than or equal to 24, the forecasting function is
Figure FDA0002953711040000011
When (RISE +2) is less than or equal to H less than or equal to (NOON +4), the prediction function is
Figure FDA0002953711040000012
Wherein H is the time to be forecasted on the day, Tw(H) Water temperature corresponding to H, RISE time, NOON time, TWAVEIs the average temperature of the day, TWAVE＝(Tmax+Tmin) AMP is temperature amplitude, AMP ═ T (T)max-Tmin) H ' is an intermediate variable, H ' ═ H +8 when H < (RISE +2) and H ' ═ H-16 when H > (NOON + 4).
2. The river time-by-time water temperature forecasting method according to claim 1, characterized in that: the fitting curve in the step (1) adopts a compound sine function of
Figure FDA0002953711040000013
Wherein the content of the first and second substances,
Figure FDA0002953711040000014
is the annual average temperature or water temperature, TamThe amplitude and the phase shift of the sine function are determined by fitting historical data by a least square method, wherein t is time, theta is the phase shift of the sine function, and omega is a fixed value of 2 pi/365.
3. The river time-by-time water temperature forecasting method according to claim 2, characterized in that: and (2) respectively fitting the historical water temperature data and the historical air temperature data by adopting the composite sine functions in the step (1), wherein the fitting process is respectively carried out on the highest water temperature, the lowest water temperature, the highest air temperature and the lowest air temperature in the historical data to obtain four fitted composite sine functions.
4. The river time-by-time water temperature forecasting method according to claim 1, characterized in that: the specific calculation process of the trusted interval in the step (3) is as follows: the method comprises the steps of adopting a Bayes probability forecasting method, giving a confidence level, determining posterior density function distribution of the Bayes probability forecasting method within a value range of 80% -100%, adopting a Bayes hypothesis to determine posterior density function distribution parameters, and solving a maximum posterior density confidence interval through iteration.
5. The river time-by-time water temperature forecasting method according to claim 1, characterized in that: the sunrise time calculation method in the step (5) comprises the following steps: calculating a true value of a yellow-red intersection angle, a geometric mean yellow longitude of the sun, the earth orbit eccentricity, a mean near point angle of the sun, a difference value between mean solar time and true solar time, solar declination and a time angle according to a specific date of a day to be predicted and longitude and latitude coordinates of a river, calculating and correcting Greenwich mean solar time and converting the Greenwich mean solar time to local standard time to obtain sunrise time of the place of the river; the calculation method of noon time comprises the following steps: and calculating the difference between the average solar time and the real solar time, calculating the Greenwich mean noon time and converting the Greenwich mean noon time to the local standard time to obtain the noon time of the place where the river is located.
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Citations (1)

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CN105160162A (en) * 2015-08-18 2015-12-16 华中科技大学 Lake three-dimensional water power-water temperature-water quality simulation and prediction method based on splitting algorithm

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Publication number Priority date Publication date Assignee Title
CN105160162A (en) * 2015-08-18 2015-12-16 华中科技大学 Lake three-dimensional water power-water temperature-water quality simulation and prediction method based on splitting algorithm

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River Temperature Forecasting: A Coupled-Modeling Framework for Management of River Habitat;Eric M. Danner等;《IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING》;20121231;第5卷(第6期);第1752-1760页 *
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