Tidal reach flow prediction method based on astronomical tide level and hydrological mode simulation flow

Abstract

The invention discloses a tidal reach flow prediction method based on astronomical tide level and hydrological mode simulation flow. The method comprises the following steps: (1) preparing river flow of a hydrological station and historical observation data of the astronomical tide level; (2) arranging observation data according to a time sequence, and removing discontinuous points and abnormal points; (3) performing 25-hour moving averaging on the observation flow at each time to obtain average observation flow, and further obtaining the component of the observation flow affected by the moisture level; (4) solving the time differential of the astronomical tide level at each time; (5) constructing intercept-free linear regression by using a least square method; (6) constructing a time-weighted astronomical tide differential; (7) observing the flow-astronomical tide response characteristics of the hydrological station, constructing an astronomical tide differential function, and completing regression analysis by using the regression equation in the step (5); and (8) obtaining predicted flow. According to the method, the application effect of a traditional hydrological mode in flow forecasting of the tidal reach is effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of hydrological forecasting, and in particular to a method for predicting flow in a tidal river section based on astronomical tide levels and hydrological model simulated flow. Background Art

[0002] The simulation of river water level and flow in traditional hydrological models (such as WRF-Hydro) is usually determined by hydrodynamic equations. However, the water level and flow of rivers in tidal river sections in many coastal basins are affected by astronomical tides and show periodic fluctuations. Since traditional hydrological models cannot simulate this feature, the water level or flow output by them can only be used as a moving average of the actual value and cannot be used directly for forecasting. Previous studies have shown that astronomical tide levels can be obtained by harmonic analysis of historical meteorological and tidal observation data or by calculation using ocean dynamics models. The forecast value of the actual water level can be obtained by superimposing the water level simulated by the hydrological model with the astronomical tide level. However, since there are often significant differences in the waveform and phase of river flow and astronomical tide level, there is currently no simple method to use known astronomical tide levels to infer river flow affected by astronomical tides, which limits the application of hydrological models in flow prediction in tidal river sections. Summary of the invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a method for predicting flow in a tidal river section based on hydrological model simulated flow and known astronomical tide level. The method establishes a regression formula for predicting actual flow through linear regression analysis based on historical astronomical tide level and observed flow, thereby simplifying the application of hydrological model simulated flow in predicting actual flow in tidal river sections and improving the accuracy and convenience of the forecast.

[0004] Technical solution: The method for predicting flow in a tidal river section based on astronomical tide level and hydrological model simulated flow of the present invention comprises the following steps: (1) Prepare historical observation data of river flow and astronomical tide level at hydrological stations; (2) The observation data are arranged in chronological order, and discontinuous points and abnormal points are removed; (3) Observed flow at each time Perform a 25-hour moving average to obtain the average observed flow , and then the component of the observed flow affected by the tidal level is obtained ; (4) Calculate the time differential of the astronomical tide level at each time ; (5) Use the least squares method to construct a linear regression without an intercept ; (6) Constructing time-weighted astronomical tidal differentials ; (7) Observe the flow-astronomical tide response characteristics of the hydrological station and construct the astronomical tide differential function ; and use the regression equation in step (5) to complete the regression analysis; (8) Get the predicted flow.

[0005] Furthermore, in step (1), the data includes: annual, medium- to long-term, or short-term data on hydrological events; wherein the medium- to long-term data is on a monthly to seasonal scale; and the short-term data is on a daily scale.

[0006] Furthermore, in step (2), if the discontinuity point is when a certain moment is missing, the current moment is removed.

[0007] Furthermore, in step (3), the formula for the component of the observed flow affected by the tidal level is as follows: ; in, and Not included The start and end time is 12 hours.

[0008] Furthermore, the differential equation formula of step (4) is as follows: ; Where, T is the astronomical tide level in meters; t is the predicted time in hours or minutes; each time does not include the start and end times.

[0009] Furthermore, the linear regression equation formula of step (5) is as follows: ; in, is the regression coefficient, To take into account the time differential of the astronomical tide level with respect to the flow response time, for function.

[0010] Furthermore, step (6) is as follows: Case a: If the data time interval is hourly, first observe the average time difference between the flow and the astronomical tidal peak or trough to roughly determine the flow response time of the basin, and then construct the time-weighted astronomical tidal differential : ; Among them, a is the weight coefficient, the response time is between n and n+1 hours, ; Then use the regression formula in step (5) to perform regression testing, , constantly adjust the weight a so that Reaching the maximum value, this is the optimal weight; Case b: If the data time interval is in minutes, it is constructed in the following form : ; in, is the data time interval, is the estimated flow response time, ; Then use the regression formula in step (5) to test, , constantly adjust the weight n so that Reached maximum value.

[0011] Furthermore, in step (8), the regression equation after analysis is transformed to obtain the predicted flow, which is as follows: ; in, Simulate flow for hydrological models.

[0012] An electronic device described in the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, it implements any one of the methods for predicting flow in a tidal river section based on astronomical tides and hydrological model simulation flow.

[0013] A storage medium described in the present invention stores a computer program, and when the computer program is executed by a processor, it implements any one of the methods for predicting flow in a tidal river section based on astronomical tides and hydrological model simulated flow.

[0014] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: by performing linear regression analysis on the differential variation of the astronomical tide level and the tidal component of the historically observed flow, and using the obtained regression formula, in actual forecasting, only the hydrological model simulated flow and the predicted astronomical tide level need to be provided, so that the actual flow of the tidal river section can be simply and accurately forecasted. This method effectively improves the application effect of traditional hydrological models in the flow forecast of tidal river sections, and provides reliable technical support for flood prevention and disaster reduction work. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the present invention; Figure 2 It is the sample scatter distribution and fitting trend line of the linear regression analysis in the present invention.

[0016] Figure 3 It is the observed flow rate, 25-hour moving average observed flow rate, and predicted flow rate of the linear regression analysis in the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0018] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting flow in a tidal river section based on astronomical tide level and hydrological model simulated flow, and performs river flow regression analysis and prediction based on astronomical tide level on the Chenglangyan hydrological station (located in a tidal river section) of Fenghua River in Ningbo City, Zhejiang Province, including the following steps: S1 takes the seasonal scale (typhoon season from July to September) as the forecast requirement and prepares observational data of hourly flow and astronomical tide level from July to September 2020-2022.

[0019] S2 arranges the observation data in chronological order and removes discontinuous points (if a certain moment is missing, then that moment is removed). Then, the abnormal points in the observation data are removed.

[0020] Observed flow for each hour (except the first and last 12 hours) The average observed flow is obtained by moving average for 25 hours. , and then the component of the observed flow affected by the tidal level is obtained : ; in, and Not included The start and end time is 12 hours.

[0021] S4 calculates the time differential of the astronomical tide level at each time (except the start and end time): ; Where, T is the astronomical tide level in meters; t is the predicted time in hours or minutes; each time does not include the start and end times.

[0022] S5 uses the least squares method to and Perform linear regression without intercept. The regression equation formula is as follows: ; in, is the regression coefficient, To take into account the time differential of the astronomical tide level with respect to the flow response time, for function.

[0023] Since the data time interval of S6 is 1 hour, we first observe the average time difference between the flow and the peak (valley) of the astronomical tidal wave, and find that the flow response time of Chenglangyan Station is between 1 and 2 hours. Constructing time-weighted astronomical tidal differentials : ; Among them, a is the weight coefficient, the response time is between n and n+1 hours, ; Then use the regression formula in step 5 to test (here let ) Constantly adjust the weight a so that The maximum value is reached, which is the optimal weight, and the Chenglangyan station is 0.75.

[0024] S7 for construction , we found that Chenglangyan Station has the following two characteristics: (1) Amplitude fluctuation ratio The amplitude fluctuation of is small. Taking a power between 0 and 1 (0.77, from regression testing) and multiplying it by its sign makes its amplitude fluctuations smaller.

[0025] (2) Higher than a certain benchmark value (take 200m 3 / s), the higher the flow rate, the smaller the impact of astronomical tide on the flow rate. Therefore, the whole formula is multiplied by an exponent of 2, which is the coefficient -0.0035 (derived from the regression test) multiplied by and 0, which can reduce the average flow rate above 200m 3 / s after the impact of astronomical tide on flow.

[0026] From this we get:

[0027] According to the regression equation of S5, linear regression is performed to obtain .

[0028] In the actual prediction of S8, it is necessary to obtain the flow simulated by the hydrological model and the astronomical tide level prediction data, and the time scale should be similar to the regression analysis. The regression equation obtained in S7 is deformed to predict the flow This is derived from the following equation: ; ; in, Simulated flow for hydrological model (m 3 / s), for The symbol of , T is the astronomical tide level (m), and t is the predicted time (h).

[0029] In this example, the goodness of fit of the linear regression is 0.9002 (by Figure 2 It can be seen that the root mean square error ( ) is 104.62, and the normalized root mean square error ( , defined as and Range 1569.20 (or The ratio of the interquartile range (interquartile range) is 0.067 (or 0.178). Among them, for the data with the absolute value of the residual in the first 90%, is 76.12, is 0.049 (or 0.129). Figure 3 As shown in the figure (data from July 1 to August 1, 2021) it can be seen that this model has a high prediction accuracy at most time points. At the same time, the prediction accuracy in the normal water period is generally higher than that in the flood period.

[0030] An embodiment of the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is loaded into the processor, the computer program implements any one of the methods for predicting flow in a tidal river section based on astronomical tides and hydrological model simulation flow.

[0031] An embodiment of the present invention further provides a storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any one of the methods for predicting flow in a tidal river section based on astronomical tides and hydrological model simulated flow.

Claims

1. A method for predicting flow in a tidal river section based on astronomical tide level and hydrological model simulated flow, characterized in that: The following steps are involved: (1) Prepare historical observation data of river flow and astronomical tide level at hydrological stations; (2) The observation data are arranged in chronological order, and discontinuous points and abnormal points are removed; (3) Observed flow at each time Perform a 25-hour moving average to obtain the average observed flow , and then the component of the observed flow affected by the tidal level is obtained ; (4) Calculate the time differential of the astronomical tide level at each time ; (5) Use the least squares method to construct a linear regression without an intercept ; (6) Constructing time-weighted astronomical tidal differentials ; (7) Observe the flow-astronomical tide response characteristics of the hydrological station and construct the astronomical tide differential function ; and use the regression equation in step (5) to complete the regression analysis; (8) Get the predicted flow.

2. The method for predicting flow in a tidal river section based on astronomical tide level and hydrological model simulated flow according to claim 1, characterized in that: In step (1), the data include: annual, medium-term or short-term data on hydrological events; medium-term data are on a monthly to seasonal scale; short-term data are on a daily scale.

3. The method for predicting flow in a tidal river section based on astronomical tide level and hydrological model simulated flow according to claim 1, characterized in that: In step (2), the discontinuity point is when a certain moment is missing, so the current moment is removed.

4. The method for predicting flow in a tidal river section based on astronomical tide level and hydrological model simulated flow according to claim 1, characterized in that: In step (3), the formula for the component of the observed flow affected by the tidal level is as follows: ； in, and Not included The start and end time is 12 hours.

5. The method for predicting flow in a tidal river section based on astronomical tide level and hydrological model simulated flow according to claim 1, characterized in that: Step (4) The differential equation formula is as follows: ； Wherein, T is the astronomical tide level in meters; t is the predicted time in hours or minutes; each time does not include the start and end times.

6. The method for predicting flow in a tidal river section based on astronomical tide level and hydrological model simulated flow according to claim 1, characterized in that: Step (5) The linear regression equation formula is as follows: ； in, is the regression coefficient, To take into account the time differential of the astronomical tide level with respect to the flow response time, for function.

7. The method for predicting flow in a tidal river section based on astronomical tide level and hydrological model simulated flow according to claim 1, characterized in that: Step (6) is as follows: Case a: If the data time interval is hours, first observe the average time difference between the flow and the astronomical tidal peak or trough to preliminarily determine the flow response time of the basin, and then construct the time-weighted astronomical tidal differential : ； Among them, a is the weight coefficient, the response time is between n and n+1 hours, ; Then use the regression formula in step (5) to perform regression testing, , constantly adjust the weight a so that Reaching the maximum value, this is the optimal weight; Case b: If the data time interval is in minutes, it is constructed in the following form : ； in, is the data time interval, is the estimated flow response time, ; Then use the regression formula in step (5) to test, , constantly adjust the weight n so that Reached maximum value.

8. The method for predicting flow in a tidal river section based on astronomical tide level and hydrological model simulated flow according to claim 1, characterized in that: In step (8), the regression equation after analysis is transformed to obtain the predicted flow, which is as follows: ； in, Simulate flow for hydrological models.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into a processor, the method for predicting flow in a tidal river section based on astronomical tide levels and hydrological model simulated flow according to any one of claims 1 to 8 is implemented.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a method for predicting flow in a tidal river section based on astronomical tide levels and hydrological model simulated flow is implemented according to any one of claims 1 to 8.

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