A river freezing state prediction method under multiple flow conditions

Abstract

The present application relates to river ice disaster prevention technical field, disclose a kind of river freeze state prediction method under multiple flow conditions, comprising: the flat flow of river course before freeze is calculated;The frozen river flow of river course initial freeze is calculated;Obtain the cooling intensity of river course in cooling process;After correlation analysis of the cumulative negative air temperature in the flow ice to frozen river time period with flat flow, frozen river flow, cooling intensity, the cumulative negative air temperature prediction model in the flow ice to frozen river time period is established, solve using nonlinear least square method, obtain optimal fitting coefficient, generate optimal cumulative negative air temperature prediction model;The cumulative negative air temperature of current year, predicted cumulative negative air temperature are obtained, by comparison, obtain the freeze state of current year river course;The present application can accurately obtain the cumulative negative air temperature of river freeze by establishing the nonlinear relationship between river course condition (flat flow), frozen river flow, cooling intensity and cumulative negative air temperature, to accurately determine the freeze state of river section.

Description

Technical Field

[0001] This invention relates to the field of river ice disaster prevention technology, specifically to a method for predicting river freezing status under multiple flow conditions. Background Technology

[0002] The existing indicators used for predicting river freezing status are simple and have poor representativeness. They do not comprehensively consider the differences and changes in thermal, dynamic and river conditions that affect river freezing under different flow conditions. As a result, the forecast accuracy is not high and it is not suitable for the increasingly refined ice control scheduling needs. Summary of the Invention

[0003] To address the aforementioned shortcomings in existing technologies, this invention provides a method for predicting river freezing conditions under multiple flow conditions. This method solves the problem that existing index-based prediction methods for river freezing conditions do not adequately consider factors such as river conditions, freezing flow, and temperature drop intensity, leading to inaccurate prediction results.

[0004] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for predicting river freezing status under multiple flow conditions includes the following steps: S1. Collect the measured water level and corresponding flow rate of the river section before freezing in historical years, and calculate the flat flow rate before freezing by plotting the relationship curve between river water level and flow rate. S2. Obtain the water level values ​​of the characteristic times in the first three days of the initial stage of river freezing in historical years, and calculate the freezing flow in the initial stage of river freezing by combining the relationship curve between river water level and flow. S3. Using four time-averaged methods, calculate the daily average temperature of the stations and use it as the daily average temperature. Select the daily average temperature of the five days before the river freezes in historical years, and calculate the moving average of the daily average temperature of the two adjacent days within the five days before the river freezes. Take the smallest moving average as the cooling intensity to obtain the cooling intensity of the river during the cooling process. S4. After conducting a correlation analysis between the cumulative negative temperature during the period from ice floes to river freezing and the flow rate of the flat beach, the flow rate of the frozen river, and the cooling intensity, a prediction model for the cumulative negative temperature during the period from ice floes to river freezing is established. The nonlinear least squares method is used to solve the model, obtain the optimal fitting coefficient, and generate the optimal prediction model for the cumulative negative temperature. S5. Calculate the cumulative daily average temperature from the start of the free-flowing ice day in the current year to obtain the cumulative negative temperature for the current year; S6. Obtain the current year's flat-shore flow, frozen-river flow, and cooling intensity, and substitute them into the optimal cumulative negative temperature prediction model to obtain the predicted cumulative negative temperature for the current year. S7. Compare the current year's cumulative negative temperature with the predicted cumulative negative temperature to obtain the current year's river freezing status.

[0005] The present invention has the following beneficial effects: The present invention proposes a method for predicting river freezing status under multiple flow conditions. By analyzing the influencing factors of cumulative negative air temperature, it establishes a nonlinear relationship between factors such as river conditions (plain flow), freezing flow, and cooling intensity and cumulative negative air temperature, thereby accurately obtaining the cumulative negative air temperature of river freezing and accurately judging the freezing status of river sections. It can also predict the occurrence time of river freezing.

[0006] Attached image description.

[0007] Figure 1 This is a flowchart illustrating a method for predicting river freezing status under multiple flow conditions proposed in this invention. Figure 2 This is a schematic diagram of the relationship between river water level and flow rate in the embodiment; Figure 3 This is a schematic diagram illustrating the relationship between predicted cumulative negative air temperature and measured values ​​during the period from ice floes to river freezing in Inner Mongolia in different historical years, as shown in the example. Detailed Implementation

[0008] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0009] like Figure 1 As shown, a method for predicting river freezing status under multiple flow conditions includes the following steps S1-S7: S1. Collect the measured water level and corresponding flow rate of the river section before freezing in historical years. Calculate the flat-shoal flow rate before freezing by plotting the relationship curve between river water level and flow rate.

[0010] Specifically, the pre-freezing period is the time when the river section has not yet frozen.

[0011] Specifically, step S1 includes S11-S15: S11. Collect the measured water level of the river section before freezing in historical years and the measured flow rate corresponding to the measured water level.

[0012] S12. Establish a two-dimensional rectangular coordinate system with water level as the vertical axis and flow rate as the horizontal axis.

[0013] S13. In a two-dimensional rectangular coordinate system of water level and flow rate, input the measured water level and the corresponding measured flow rate of the river section before freezing in historical years, and mark all measured data points to form a group of measured water level and measured flow rate points of the river section.

[0014] S14. Analyze the distribution pattern of the measured water level and measured flow rate points at the river cross-section, and combine the time relationship between the measured water level and measured flow rate to draw a smooth curve passing through the center of the point group, generating the relationship curve between river water level and flow rate for the corresponding historical years.

[0015] S15. On the relationship curve between river level and flow in the corresponding historical year, find the point on the vertical axis that is equal to the elevation of the cross-section beach by interpolation or extrapolation. Draw a horizontal line from this point to intersect the relationship curve, and take the horizontal axis value corresponding to the intersection point as the beach flow before the river freezes in the historical year.

[0016] In this embodiment, in the early stage of river freezing, a correspondence is established between measured water level and flow data, and then the flow corresponding to the flat beach water level (i.e., beach elevation) is calculated using this relationship, which is the flat beach flow. Since the measured data may not contain a water level and corresponding flow that are exactly equal to the beach elevation, it is necessary to interpolate or extrapolate using the established river water level and flow relationship curve to obtain it. in, Figure 2 The curves showing the relationship between river water level and flow rate in certain historical years include those for 2012, 2018, 2021, and 2022.

[0017] S2. Obtain the water level values ​​of the characteristic times in the first three days of the initial freezing of the river in historical years, and calculate the freezing flow in the initial stage of the river freezing by combining the relationship curve between river water level and flow.

[0018] Specifically, the initial freezing period is the time when the river section begins to freeze.

[0019] Specifically, step S2 includes S21-S23: S21. Obtain the water level values ​​of the first three days of the initial freezing of the river in historical years at characteristic times, and calculate the daily average water level of the first three days of the initial freezing of the river in historical years by arithmetic average.

[0020] S22. Substitute the daily average water level of the first three days after the initial freezing of the river in historical years into the relationship curve between river water level and flow rate to obtain the daily average flow rate of the first three days after the initial freezing of the river in historical years.

[0021] S23. Calculate the arithmetic average of the daily average flow rate of the river in the first three days of the initial freezing period in historical years, and use it as the freezing flow rate of the river in the initial freezing period.

[0022] S3. Using four time-averaged methods, calculate the daily average temperature of the stations and use it as the daily average temperature. Select the daily average temperature of the five days before the river freezes in historical years, and calculate the moving average of the daily average temperature of the two adjacent days within the five days before the river freezes. Take the smallest moving average as the cooling intensity to obtain the cooling intensity of the river during the cooling process.

[0023] Specifically, step S3 includes S31-S34: S31. Using four time-averaged methods, the temperature values ​​of the station at four characteristic times on a given day are obtained. The daily average temperature of the station is calculated by arithmetic average and used as the daily average temperature.

[0024] S32. Select the average daily temperature of the five days before the river freezes in historical years and set it as... , , , , , which represent the average daily temperature for the 1st, 2nd, 3rd, 4th and 5th days, respectively.

[0025] S33. Based on the average daily temperature of the five days before the river freezes in historical years, calculate the moving average of the average daily temperatures of the two consecutive days within the five days before the river freezes, i.e.:

[0026]

[0027]

[0028]

[0029] in, , , , These represent the moving averages of the daily average temperatures for the 1st and 2nd, 2nd and 3rd, 3rd and 4th, and 4th and 5th days within the five days prior to the river freezing over.

[0030] S34. Based on the moving average of the daily average temperature of two consecutive days within five days before the river freezes, the minimum moving average is taken as the cooling intensity to obtain the cooling intensity of the river during the cooling process.

[0031] S4. After conducting a correlation analysis between the cumulative negative temperature during the period from ice floes to river freezing and the flow rate on the flat beach, the flow rate during river freezing, and the cooling intensity, a prediction model for the cumulative negative temperature during the period from ice floes to river freezing is established. The nonlinear least squares method is used to solve the model to obtain the optimal fitting coefficient and generate the optimal prediction model for the cumulative negative temperature.

[0032] Specifically, step S4 includes S41-S43: S41. Using the Pearson correlation coefficient method, calculate the correlation coefficients between the cumulative negative air temperature and the plain flow, the frozen river flow, and the cooling intensity during the period from ice floe to river freezing.

[0033] In this embodiment, "ice flow" refers to the appearance of flowing ice flowers in the river channel.

[0034] S42. Based on the correlation coefficients between the cumulative negative temperature during the period from ice floes to river freezing and the flow rate on the plain, the flow rate on the river freezing and the cooling intensity, obtain the correlation between the cumulative negative temperature during the period from ice floes to river freezing and the flow rate on the plain, the flow rate on the river freezing and the cooling intensity, and establish a prediction model for the cumulative negative temperature during the period from ice floes to river freezing.

[0035] S43. The nonlinear least squares method is used to solve the cumulative negative temperature prediction model for the period from ice floes to river freezing, obtain the optimal fitting coefficient, and generate the optimal cumulative negative temperature prediction model.

[0036] In this embodiment, the cumulative negative temperature prediction model for the period from ice floes to river freezing is as follows:

[0037] in, This represents the predicted cumulative negative temperature during the period from ice floes to river freezing, expressed in °C. The flow rate of the frozen river is expressed in cubic meters (m³). 3 / s, This represents the flow rate at the flat beach, in cubic meters per second (m³). 3 / s, Indicates the cooling intensity, in degrees Celsius (°C). , , , All represent the fitting coefficients, and based on historical data, after performing multivariate regression fitting using the nonlinear least squares method, their optimal values ​​are as follows: , , , Therefore, the optimal formula for the cumulative negative temperature prediction model is: .

[0038] Furthermore, in practical applications, all physical quantities in the model formula are uniformly treated as having a dimension of 1, and the cumulative negative temperature prediction model is a nonlinear model.

[0039] In summary, this invention starts from the formation mechanism of river freezing, studies the mechanism and influencing factors of ice development, and screens hydrological factors (freezing flow), thermodynamic factors (cumulative negative air temperature and cooling intensity during the period from ice floe to freezing), and river conditions (shoal flow) that cause river freezing. Then, using the Pearson correlation coefficient method, the correlation coefficients between each factor are calculated to analyze the correlation between the factors. The analysis shows that the negative value of the cumulative negative air temperature during the period from ice floe to freezing is significantly positively correlated with the freezing flow, and the negative value of the cumulative negative air temperature during the period from ice floe to freezing is significantly positively correlated with the shoal flow. Relatedly, the negative coefficient of cumulative negative temperature during the period from ice floes to river freezing is significantly negatively correlated with the negative coefficient of cooling intensity, and they exhibit an exponential relationship. Therefore, an exponential equation, i.e., a cumulative negative temperature prediction model, can be constructed. Subsequently, this cumulative negative temperature prediction model can be used to predict the cumulative negative temperature under the current river conditions, cooling intensity, and flow rate, thereby determining the river freezing status. Compared with existing technologies, this method clarifies the complex relationship between cumulative negative temperature and river freezing flow, cooling intensity during the freezing period, river conditions, etc., and determines the cumulative negative temperature index required for river freezing under different flow rates and cooling intensities under existing river conditions.

[0040] also, Figure 3 This demonstrates the relationship between the predicted cumulative negative air temperature (calculated cumulative negative) and the measured value (actual cumulative negative) during the period from ice floe to river freeze in Inner Mongolian river sections in different historical years. Figure 3 As can be seen, there is a good correlation between the predicted cumulative negative temperature during the period from ice floes to river freezing, calculated by the cumulative negative temperature prediction model (nonlinear model) and the measured cumulative negative temperature during the same period. Therefore, the established cumulative negative temperature prediction model can be used to predict the cumulative negative temperature during the period from ice floes to river freezing.

[0041] S5. Calculate the cumulative daily average temperature from the start of the free-flowing ice day in the current year to obtain the cumulative negative temperature for the current year.

[0042] S6. Obtain the current year's flat-shore flow, frozen-river flow, and cooling intensity, and substitute them into the optimal cumulative negative temperature prediction model to obtain the predicted cumulative negative temperature for the current year.

[0043] S7. Compare the current year's cumulative negative temperature with the predicted cumulative negative temperature to obtain the current year's river freezing status.

[0044] Specifically, step S7 includes: Determine whether the current year's cumulative negative temperature is higher than the predicted cumulative negative temperature. If so, the river channel is not frozen; otherwise, it is frozen. In this embodiment, the cumulative negative temperature of the year is calculated daily based on the actual temperature of the year and the temperature forecast by the meteorological department. When the cumulative temperature exceeds the predicted cumulative negative temperature calculated by the prediction model, the freezing state and the time of freezing state can be determined.

[0045] In summary, the river freezing state prediction method proposed in this invention, under multiple flow conditions, establishes a nonlinear relationship between factors such as river conditions (plain flow), freezing flow, and cooling intensity and cumulative negative temperature by analyzing the influencing factors of cumulative negative temperature. This allows for accurate acquisition of the cumulative negative temperature for river freezing and accurate determination of the freezing state of river sections. Furthermore, it can predict the timing of river freezing, specifically by calculating the cumulative negative temperature from the current year's free-flowing ice day on the daily average temperature (including actual and future temperature forecasts from meteorological departments). The date on which the cumulative temperature reaches the predicted value of the prediction model is the date of freezing.

[0046] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0047] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for predicting river freezing status under multiple flow conditions, characterized in that, Includes the following steps: S1. Collect measured water levels and corresponding flow rates of river sections before freezing in historical years. Calculate the flood discharge at the riverbank before freezing by plotting the relationship between water level and flow rate. Specifically: S11. Collect the measured water level of the river section before freezing in historical years and the measured flow rate corresponding to the measured water level; S12. Establish a two-dimensional rectangular coordinate system with water level as the vertical axis and flow rate as the horizontal axis; S13. In the two-dimensional rectangular coordinate system of water level and flow, input the measured water level and the measured flow corresponding to the measured water level of the river section before freezing in historical years, and mark all measured data points to form a group of measured water level and measured flow points of the river section. S14. Analyze the distribution pattern of the measured water level and measured flow rate points at the river cross-section, and combine the time relationship between the measured water level and measured flow rate to draw a smooth curve passing through the center of the point group, generating the relationship curve between the river water level and flow rate in the corresponding historical years. S15. On the relationship curve between river level and flow in the corresponding historical year, find the point on the vertical axis that is equal to the elevation of the cross-section beach by interpolation or extrapolation. Draw a horizontal line from this point to intersect the relationship curve, and take the horizontal axis value corresponding to the intersection point as the beach flow before the river freezes in the historical year. S2. Obtain the water level values ​​of the characteristic times in the first three days of the initial stage of river freezing in historical years, and calculate the freezing flow in the initial stage of river freezing by combining the relationship curve between river water level and flow. S3. Using four time-averaged methods, calculate the daily average temperature of the stations and use it as the daily average temperature. Select the daily average temperature of the five days before the river freezes in historical years, and calculate the moving average of the daily average temperature of the two adjacent days within the five days before the river freezes. Take the smallest moving average as the cooling intensity to obtain the cooling intensity of the river during the cooling process. S4. After conducting a correlation analysis with the cumulative negative air temperature during the period from ice floes to river freezing, the discharge at the flat beach, the discharge at the river freezing, and the cooling intensity, a prediction model for the cumulative negative air temperature during this period is established. The model is then solved using the nonlinear least squares method to obtain the optimal fitting coefficients, thus generating the optimal prediction model for the cumulative negative air temperature. Specifically: S41. Using the Pearson correlation coefficient method, calculate the correlation coefficients between the cumulative negative air temperature and the plain flow, the frozen river flow, and the cooling intensity during the period from ice floe to river freezing. S42. Based on the correlation coefficients between the cumulative negative air temperature and the flow rate at the flat beach, the flow rate at the frozen river, and the cooling intensity during the period from ice floes to river freezing, obtain the correlation between the cumulative negative air temperature and the flow rate at the flat beach, the flow rate at the frozen river, and the cooling intensity during this period, and establish a prediction model for the cumulative negative air temperature during this period, expressed as follows: in, This indicates the predicted cumulative negative temperature during the period from ice floes to river freezing. Indicates the flow rate of the frozen river. Indicates the flow rate at the flat beach. Indicates the intensity of cooling. , , , All represent the fitting coefficients; S43. The nonlinear least squares method is used to solve the cumulative negative temperature prediction model during the period from ice floes to river freezing, obtain the optimal fitting coefficient, and generate the optimal cumulative negative temperature prediction model. S5. Calculate the cumulative daily average temperature from the start of the free-flowing ice day in the current year to obtain the cumulative negative temperature for the current year; S6. Obtain the current year's flat-shore flow, frozen-river flow, and cooling intensity, and substitute them into the optimal cumulative negative temperature prediction model to obtain the predicted cumulative negative temperature for the current year. S7. Compare the current year's cumulative negative temperature with the predicted cumulative negative temperature to obtain the current year's river freezing status.

2. The method for predicting river freezing status under multiple flow conditions according to claim 1, characterized in that, The pre-freezing period is the time before the river section freezes.

3. The method for predicting river freezing status under multiple flow conditions according to claim 1, characterized in that, The initial freezing period refers to the time when the river section begins to freeze.

4. The method for predicting river freezing status under multiple flow conditions according to claim 1, characterized in that, Step S2 specifically includes: S21. Obtain the water level values ​​of the first three days of the initial freezing of the river in historical years at characteristic times, and calculate the daily average water level of the first three days of the initial freezing of the river in historical years by arithmetic average. S22. Substitute the daily average water level of the first three days after the initial freezing of the river in historical years into the relationship curve between river water level and flow rate to obtain the daily average flow rate of the first three days after the initial freezing of the river in historical years. S23. Calculate the arithmetic average of the daily average flow rate of the river in the first three days of the initial freezing period in historical years, and use it as the freezing flow rate of the river in the initial freezing period.

5. The method for predicting river freezing status under multiple flow conditions according to claim 1, characterized in that, Step S3 specifically includes: S31. Using four time-averaged methods, the temperature values ​​of the station at four characteristic times on a given day are obtained. The daily average temperature of the station is calculated by arithmetic average and used as the daily average temperature. S32. Select the average daily temperature of the five days before the river freezes in historical years and set it as... , , , , These represent the average daily temperature for the 1st, 2nd, 3rd, 4th, and 5th days, respectively. S33. Based on the average daily temperature of the five days before the river freezes in historical years, calculate the moving average of the average daily temperatures of the two consecutive days within the five days before the river freezes, i.e.: in, , , , These represent the moving averages of the daily average temperatures on days 1 and 2, days 2 and 3, days 3 and 4, and days 4 and 5, respectively, within the five days before the river freezes over. S34. Based on the moving average of the daily average temperature of two consecutive days within five days before the river freezes, the minimum moving average is taken as the cooling intensity to obtain the cooling intensity of the river during the cooling process.

6. The method for predicting river freezing status under multiple flow conditions according to claim 1, characterized in that, Step S7 specifically includes: Determine whether the cumulative negative temperature of the current year is higher than the predicted cumulative negative temperature. If so, the river channel is not frozen in the current year; otherwise, it is frozen.

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