Northern canal water temperature rolling forecasting method only depending on air temperature and flow

Abstract

The invention relates to a northern canal water temperature rolling forecasting method only depending on air temperature and flow. The northern canal water temperature rolling forecasting method comprises the steps of collecting data; calculating the average flow of a given time period; calculating a dimensionless flow value; fitting parameters; collecting weather forecast; predicting the water temperature; and drawing a change curve of the predicted water temperature and the air temperature along with time. Data needed for predicting water temperature change and icing are few, related parameters are automatically calibrated through rolling and iterative updating of the water temperature, the calculation efficiency is high, and the method is suitable for water temperature estimation of rivers, lakes, reservoirs and water network systems and large-range long-historical water temperature calculation.

Description

Technical Field

[0001] The present invention relates to a rolling prediction method for the water temperature of northern river channels that only depends on air temperature and flow rate. It is a hydraulic calculation method and a rapid estimation method for the water temperature of open channels for water conveyance in water network projects. Background Art

[0002] The water temperature of long-distance water conveyance channels in the north temperate zone at latitudes 35 - 40° north fluctuates significantly between day and night. In some cases, the diurnal temperature difference even exceeds 0.8°C. This phenomenon of rapid water temperature fluctuation in a short period not only affects the water ecological environment and water quality stability of river channels, but may also directly affect the icing situation of water bodies in long-distance water diversion projects, thereby threatening the safety of the water conveyance system. Especially in winter, if local icing occurs due to water temperature changes during open-channel water conveyance, it may cause problems such as ice jams and flow interruption, and in severe cases, it may even affect the normal operation of the entire water network system.

[0003] Currently, the water temperature mechanism model based on energy conservation is comprehensively affected by multiple factors such as meteorological conditions (such as solar radiation, wind speed, air temperature), hydrodynamic processes (such as flow velocity, turbulent diffusion), and aquatic ecological processes (such as biological metabolism, pollutant exchange). There are still simplifications in some mechanisms (such as ice-water phase change, heat exchange process) in the existing model, which in turn affects the prediction accuracy of the model. Secondly, the mechanism model requires high-resolution meteorological data, hydrological data, and flow field data, but the actual monitoring network often fails to provide sufficiently dense spatio-temporal data, resulting in limited application scenarios for the model. Many water temperature empirical models rely on statistical regression means for heat exchange calculation, but the relevant parameters are often based on specific regions or experimental data, and their adaptability is poor when transplanted to other river basins, with limited application scenarios.

[0004] In summary, the existing water temperature prediction models cannot meet the requirements of rapid prediction of the water temperature of open channels for water conveyance in water network projects, restricting the high-quality and rapid development of water network projects. There is an urgent need to establish a rapid prediction method for the water temperature of open channels for water conveyance in water network systems. Summary of the Invention

[0005] In order to overcome the problems of the prior art, the present invention proposes a rolling prediction method for the water temperature of northern river channels that only depends on air temperature and flow rate. The method only uses several data such as air temperature and flow rate, greatly simplifying the calculation process, so as to be able to quickly predict the icing situation and provide strong support for rapid prediction of disaster prevention and mitigation.

[0006] The object of the present invention is achieved as follows: A rolling prediction method for the water temperature of northern river channels that only depends on air temperature and flow rate, and the steps of the method are as follows:

[0007] Step 1, data collection: For the characteristics of rivers, lakes and reservoirs to be studied, obtain the temperature, flow rate and water temperature data of the calculation sites required in the past month or more than one month.

[0008] Step 2, calculate the average flow rate for a given time period The equation is:

[0009]

[0010] Where: Q k is the flow rate at different times in the flow rate data series, the subscript k is the number of data series, and the value range is from 1 to n, where n is the total number of the flow rate series;

[0011] Step 3, calculate the dimensionless flow rate value θ k , the equation is:

[0012]

[0013] Where: θ k is the dimensionless flow rate value corresponding to the kth flow rate Q k ;

[0014] Step 4, fitting parameters: Based on the dimensionless flow rate, temperature and water temperature data in Equation (2), use the least squares method to fit and solve the eight parameters in Equation (3), and the equation is:

[0015]

[0016] Among them, Δt is the calculated time step; is the water temperature corresponding to the current time; is the water temperature corresponding to the next time; is the air temperature corresponding to the current time; c1, c2, c3, c4, c5, c6, c7, c8 are 8 empirical parameters to be fitted and solved; t k is the current calculated time; t f is the period of water temperature change;

[0017] Step 5, collect weather forecasts: Obtain the temperature data forecasted for the cities near the water bodies of the rivers and canals to be calculated in the next 14 days or more than 14 days through the meteorological observatory.

[0018] Step 6: Predict the water temperature: Use the change equation (3) to roll and predict the future water temperature process The specific calculation method is shown in Equation (4):

[0019]

[0020] Where the water temperature air temperature Adopt the data of the previous moment; dimensionless flow rate θ k It is constantly taken as the dimensionless flow rate value corresponding to the currently measured flow rate;

[0021] Step 7, plot the curves of the predicted water temperature and air temperature changing with time: Plot the curves of the water temperature and air temperature changing with time with the temperature as the vertical coordinate and the time as the horizontal coordinate.

[0022] The advantages and beneficial effects of the present invention are: The present invention requires less data for predicting water temperature changes and icing. With the rolling and iterative update of the water temperature, relevant parameters are automatically calibrated, and the calculation efficiency is high. It is applicable to the water temperature estimation of rivers, lakes, reservoirs and water network systems and the calculation of water temperature over a large range and long history. Brief Description of the Drawings

[0023] The present invention will be further described below in conjunction with the drawings and embodiments.

[0024] Figure 1 is the flow chart of the method described in the embodiment of the present invention;

[0025] Figure 2 is the measured air temperature and water temperature data of the Beijuma River from December 8, 2024 to February 10, 2025, which is an application example of the embodiment of the present invention;

[0026] Figure 3 is the measured flow rate data of the Beijuma River from December 8, 2024 to February 10, 2025, which is an application example of the embodiment of the present invention;

[0027] Figure 4 is the dimensionless flow rate data of the Beijuma River from December 8, 2024 to February 10, 2025, which is an application example of the embodiment of the present invention;

[0028] Figure 5 is the predicted air temperature and water temperature of the Beijuma River from February 10, 2024 to February 24, 2025, which is an application example of the embodiment of the present invention. Detailed Embodiment

[0029] Embodiment:

[0030] This embodiment is a rolling forecasting method for the water temperature of northern river channels that only depends on air temperature and flow rate. The following describes the specific implementation manner of this embodiment in conjunction with the application example of the water temperature forecasting in front of the regulating sluice of the Beijuma River (abbreviated as the Beijuma River). The specific implementation steps are as follows, and the flow chart is as Figure 1 shown:

[0031] Step 1, data collection: For the characteristics of rivers, lakes and reservoirs to be studied, obtain the air temperature, flow rate and water temperature data of the calculation site for the past month or more.

[0032] In the application example, in this step, the measured air temperature, flow rate, and water temperature data of the Beiju Mahe River from December 8, 2024, to February 10, 2025, are collected and sorted. The air temperature and water temperature data are as shown in Figure 2 the coordinate diagram. Among them, the vertical coordinate is the temperature change in degrees Celsius, and the horizontal coordinate is the time in "days". The flow rate data is as shown in Figure 3 the coordinate diagram. Among them, the vertical coordinate is the measured flow rate in cubic meters per second, and the horizontal coordinate is the time in "days".

[0033] Step 2: Calculate the average flow rate for a given time period The equation is:

[0034]

[0035] Where: Q k is the flow rate at different times in the flow rate data sequence. The subscript k is the number of data sequences, and its value range is from 1 to n, where n is the total number of the flow rate sequence;

[0036] In the application example, through the calculation of Equation (1), the average flow rate of the Beiju Mahe River is 23.42 m 3 / s.

[0037] Step 3: Calculate the dimensionless flow rate value θ k , and the equation is:

[0038]

[0039] Where: θ k is the dimensionless flow rate value corresponding to the k-th flow rate Q k .

[0040] In the application example, the dimensionless flow rate data of the Beiju Mahe River from December 8, 2024, to February 10, 2025, are as shown in Figure 4 the figure. Among them, the vertical coordinate is the measured flow rate in cubic meters per second, and the horizontal coordinate is the time in "days".

[0041] Step 4: Fitting parameters: Based on the dimensionless flow rate, air temperature, and water temperature data in Equation (2), use the least squares method to fit and solve the eight parameters in Equation (3). The equation is:

[0042]

[0043] Among them, Δt is the calculated time step; is the water temperature corresponding to the current time; is the water temperature corresponding to the next time; is the air temperature corresponding to the current time; c1, c2, c3, c4, c5, c6, c7, c8 are 8 empirical parameters to be fitted and solved; t k is the currently calculated time; t f is the period of water temperature change, generally taken as 1 year, i.e., 31536000 seconds.

[0044] In the application example, the eight fitted parameters of the Beiju Mahe River are: c1 = -3.228813, c2 = 0.164335, c3 = -0.135589, c4 = 0.568472, c5 = 5.011084, c6 = 0.118451, c7 = 0.080008, c8 = 0.472740.

[0045] Step 5, collect weather forecasts: Obtain the air temperature data of the future 14 days or more than 14 days of the cities near the river channel water body to be calculated through the meteorological observatory.

[0046] In the application example, in this step, collect the air temperature data forecasted by the adjacent meteorological stations of the authoritative meteorological observatory from February 10, 2024 to February 24, 2025 in the nearby city of Zhuozhou. See the Figure 5 predicted air temperature curve for details;

[0047] Step 6: Predict the water temperature: Use the change equation (3) to roll and predict the future water temperature process The specific calculation method is shown in equation (4):

[0048]

[0049] where the water temperature air temperature adopts the data of the previous moment; the dimensionless flow rate θ k is constantly taken as the dimensionless flow rate value corresponding to the currently measured flow rate.

[0050] The change equation (3) can roll and predict the future water temperature process, where the θ k is taken as the dimensionless flow rate value corresponding to the currently measured flow rate, and the water temperature value of the next moment is predicted by rolling using equation (4) in combination with the forecasted air temperature.

[0051] Step 7, draw the change curves of the predicted water temperature and air temperature with time: Draw the change curves of the water temperature and air temperature with time with temperature as the vertical coordinate and time as the horizontal coordinate.

[0052] In the application example, the change curves of the air temperature and water temperature of the Beiju Mahe River from February 10, 2024 to February 24, 2025 are as shown in Figure 5 shown.

[0053] Finally, it should be noted that the above is only used to illustrate the technical solution of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred arrangement, those of ordinary skill in the art should understand that the technical solution of the present invention (such as the composition of the river channel, the application of various formulas, the sequence of steps, etc.) can be modified or equivalently replaced without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A rolling forecast method for water temperature in northern rivers and canals that relies only on air temperature and flow, characterized in that: The steps of the method are as follows: Step 1, data collection: according to the characteristics of rivers, lakes and reservoirs to be studied, obtain the air temperature, flow and water temperature data of the required calculation station for the past month or more; Step 2: Calculate the average flow rate for a given time period The equation is: Where: Q k is the flow rate at different times in the flow data sequence, the subscript k is the number of data sequences, and the value range is 1 to n, where n is the total number of flow sequences; Step 3: Calculate the dimensionless flow value θ k , the equation is: Where: θ k is the kth flow Q k The corresponding dimensionless flow value; Step 4, fitting parameters: Based on the dimensionless flow, air temperature and water temperature data in equation (2), the eight parameters in equation (3) are fitted using the least squares method. The equation is: Where Δt is the time step of the calculation; is the water temperature corresponding to the current time; is the water temperature corresponding to the next time; is the temperature corresponding to the current time; c1, c2, c3, c4, c5, c6, c7, c8 are the eight empirical parameters to be fitted and solved; t k is the current calculation time; t f is the period of water temperature change; Step 5, collect weather forecast: obtain the temperature data of the city near the river water body for the next 14 days or more through the meteorological station; Step 6: Predict water temperature: Use the variation equation (3) to make a rolling forecast of the future water temperature process The specific calculation method is shown in equation (4): The water temperature Temperature Use the data from the last moment; dimensionless flow θ k The constant is taken as the dimensionless flow value corresponding to the current measured flow; Step 7, draw the predicted water temperature and air temperature change curve over time: draw the water temperature and air temperature change curve over time with temperature as the vertical axis and time as the horizontal axis.

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