A multi-factor interaction-based multiple correction and prediction method for tidal river reaches

Abstract

The present invention provides a multi-factor interaction tidal river section multiple correction and forecasting method, including: obtaining model construction data; superimposing background wind pressure meteorological data and gradient wind field according to weight coefficients to reconstruct model wind speed and pressure field; calculating the incoming flow propagation time according to upstream runoff influencing parameters, and estimating the model inlet flow under future conditions; obtaining the model open boundary tide level sequence based on the harmonic analysis method; constructing a two-dimensional hydrodynamic mathematical model according to various parameters; coupling and nesting the ensemble Kalman filter algorithm based on the correlation coefficient with the mathematical model, dynamically correcting the initial state of the mathematical model according to the measured tide level data of the tidal river section, and obtaining a forecast model after a correction; using the autoregressive method to correct the tide level forecast results of key stations in the tidal river section, and reconstructing the forecast tide level field of the forecast model, and secondarily correcting the model forecast results. The present invention can provide relatively accurate forecast results.

Description

Technical Field

[0001] The present invention relates to the technical field of tidal level forecasting in tidal reaches, and particularly to a multiple correction and forecasting method for tidal reaches with multi-factor interaction. Background Art

[0002] The water level changes in tidal reaches are jointly affected by various factors such as upstream inflow, downstream astronomical tides, storm surges, etc. During the flood season, it often receives floods formed by heavy rains upstream. The coincidence of flood peaks, typhoon storm surges, and astronomical spring tides causes the water levels along the line to soar rapidly, exacerbating the disaster risk caused by typhoon storm surges and posing a serious threat to the flood control safety of all regions along the line. The tidal level forecasting in tidal reaches under the combined influence of extreme factors is of great significance for flood control decision-making, reference for the design of water conservancy projects, etc.

[0003] With the economic development along the river, the demand for tidal level forecasting in tidal reaches is no longer limited to key stations with long-term observation data, and the demand for forecasting accuracy is also continuously increasing. Traditional harmonic analysis forecasting methods usually can only provide astronomical tide forecasts for key stations at the estuary. Unsteady harmonic analysis can provide tidal level forecasts for key stations affected by upstream runoff and offshore astronomical tides. Hydrodynamic mathematical models can provide tidal level forecasts for the entire tidal reach under the combined influence of runoff, astronomical tides, and storm surges, expanding the scope of tidal level forecasting from key stations to the entire tidal reach, and better meeting the demand for the coverage range of tidal level forecasting.

[0004] Due to the strong non-linear effect of the coupled dynamics in tidal reaches and the complex terrain conditions, there are certain generalizations in model parameters and boundary conditions, and there will always be a certain error between the calculation results of the tidal level forecasting model and the measured data. The initial state correction method of the tidal forecasting model based on the ensemble Kalman filter can minimize the error by comparing the measured data with the simulation results and then continuously adjusting the model parameters. At the same time, the secondary correction of the tidal level forecasting results based on the autoregressive algorithm can further improve the forecasting accuracy and better meet the requirements for the accuracy of tidal forecasting. Summary of the Invention

[0005] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a multiple correction and forecasting method for tidal reaches with multi-factor interaction.

[0006] To achieve the above purpose, the present invention provides the following solutions:

[0007] A multiple correction and forecasting method for tidal reaches with multi-factor interaction, comprising:

[0008] Obtaining model construction data; the model construction data includes: upstream runoff influence parameters, open boundary astronomical tide influence parameters, typhoon influence parameters, model terrain data, and tidal reach water level data;

[0009] Superimpose the background wind pressure meteorological data and the gradient wind field according to the typhoon influence parameters with weight coefficients to reconstruct the model wind speed and air pressure field;

[0010] Estimate the flow propagation time according to the upstream runoff influence parameters to predict the model inlet flow under future conditions;

[0011] Based on the harmonic analysis method, determine the model open boundary tide level sequence according to the astronomical tide influence parameters;

[0012] Construct a two-dimensional hydrodynamic mathematical model for tidal level forecasting in a tidal river section affected by upstream runoff, storm surge, and astronomical tide according to the model wind speed, air pressure field, the model inlet flow, the model open boundary tide level sequence, and the model terrain data;

[0013] Couple and nest the ensemble Kalman filter algorithm based on the correlation coefficient with the two-dimensional hydrodynamic mathematical model for tidal level forecasting in the tidal river section, and dynamically correct the initial state of the two-dimensional hydrodynamic mathematical model for tidal level forecasting in the tidal river section according to the measured tidal level data of the tidal river section to obtain a once-corrected forecasting model;

[0014] Adopt an autoregressive method to correct the tidal level forecasting results of key stations in the tidal river section, and reconstruct the predicted tidal level field of the forecasting model to secondarily correct the forecasting results of the model.

[0015] Preferably, the upstream runoff influence parameters include: upstream multi-source stations, the history of the model upstream boundary, and the measured flow sequence; the open boundary astronomical tide influence parameters include the harmonic constants near the open boundary and the historical tide level sequence; the typhoon influence parameters include the large-scale wind speed, air pressure field data, and the measured and predicted typhoon central pressure and maximum wind speed data; the model terrain data includes the shoreline and underwater terrain data covering the model range; the tidal level data of the tidal river section includes the historical and real-time tide level sequences of key stations in the tidal river section.

[0016] Preferably, superimposing the background wind pressure meteorological data and the gradient wind field according to the typhoon influence parameters with weight coefficients to reconstruct the model wind speed and air pressure field includes:

[0017] Superimpose and synthesize the typhoon circular gradient wind field and the moving wind field to form a typhoon model wind field;

[0018] Synthesize the typhoon model wind field with the background wind speed and air pressure field according to the weight coefficients;

[0019] The expression of the typhoon model wind field is: Among them, V g is the gradient wind field; V t is the moving wind field; c1 and c2 are correction coefficients; θ is the angle between the line connecting the calculation point and the typhoon center and the due east direction; β is the inflow angle correction;

[0020] The expression for synthesizing the typhoon model wind field with the background wind speed and pressure field according to the weight coefficient is: V c =(1 - e)V M + eV Q ;

[0021] Among them, V Q is the background wind field; V M is the typhoon model wind field; e is the weight coefficient.

[0022] Preferably, the expression of the harmonic analysis method is:

[0023]

[0024] Among them, is the tidal level forecast value at time t; A0 is the average sea level height measured from a certain reference plane; f i is the intersection factor of the i-th partial tide; H i is the amplitude of the i-th partial tide; σ i is the angular velocity of the partial tide; (v0 + u) i is the astronomical initial phase angle of the partial tide; g i is the lag angle of the partial tide; i is the partial tide number; N is the total number of partial tides.

[0025] Preferably, the equations of the two-dimensional hydrodynamic mathematical model for tidal level forecasting in the tidal river section affected by upstream runoff, storm surge, and astronomical tide are as follows:

[0026]

[0027] The momentum equation is:

[0028]

[0029]

[0030] In the formula, t represents time, x and y represent the coordinates in two mutually perpendicular directions in the horizontal plane, ζ is the water level height measured from the average sea level, U and V respectively represent the vertically integrated average flow velocities in the x and y directions, H is the total water depth, f is the Coriolis force coefficient, ρ0 is the density of water, D x and D y represent the components of the turbulent diffusion term in the x and y directions, p s is the atmospheric pressure term, τ sx and τ sy represent the x and y components of the surface wind stress, and the calculation formula for the wind stress is:

[0031] τ = ρ a C d |U 10 |U10 ;

[0032]

[0033] In the formula, ρ a is the density of the atmosphere, C d is the wind drag coefficient, and U 10 is the 10m wind speed at the sea surface.

[0034] Preferably, the formula for the correlation coefficient is: where the subscript j is the time series number; the superscripts s and ob represent the simulated value and the observed value respectively, and are the average values of C s and C ob respectively.

[0035] Preferably, the autoregressive method is used to correct the tidal level prediction results at key stations in the tidal reach and reconstruct the predicted tidal level field of the prediction model, and the tidal level prediction results in the tidal reach are corrected twice, including:

[0036] Assume that the model error sequence of the water level simulated by the hydrodynamic model relative to the measured water level is Δη(t), and an autoregressive model is used to model the model error sequence Δη(t); the expression of the model error sequence is: where ε t is white noise perturbation; p is the model order; φ i is the autoregressive coefficient;

[0037] After obtaining the corrected error sequence of key stations along the tidal reach, interpolate it to the whole field according to the distance from the stations to obtain the tidal level prediction result field under multi-factor conditions after the second correction.

[0038] According to the specific embodiments provided by the present invention, the following technical effects of the present invention are disclosed:

[0039] The present invention provides a tidal river section multiple correction and forecasting method with multi-factor interaction, comprising: acquiring model construction data; the model construction data comprising: upstream runoff influence parameters, open boundary astronomical tide influence parameters, typhoon influence parameters, model terrain data and tidal river section tide level data; according to the typhoon influence parameters, background wind pressure meteorological data and gradient wind field are superimposed according to weight coefficients to reconstruct model wind speed and pressure field; according to the upstream runoff influence parameters, the incoming flow propagation time is calculated to estimate the model inlet flow under future conditions; based on the harmonic analysis method, the model open boundary tide level sequence is determined according to the astronomical tide influence parameters; according to the model wind speed , pressure field, the model inlet flow, the model open boundary tide sequence and model terrain data are used to construct a two-dimensional hydrodynamic mathematical model for tidal river section tide level forecast under the influence of upstream runoff, storm surge and astronomical tide; the ensemble Kalman filter algorithm based on correlation coefficient is coupled and nested with the two-dimensional hydrodynamic mathematical model for tidal river section tide level forecast, and the initial state of the two-dimensional hydrodynamic mathematical model for tidal river section tide level forecast is dynamically corrected according to the measured tide level data of the tidal river section to obtain a forecast model after one correction; the tidal level forecast results of key stations of the tidal river section are corrected by the autoregressive method, the forecast tide level field of the forecast model is reconstructed, and the forecast results of the whole field of the tidal river section are corrected twice. The present invention can provide relatively accurate forecast results. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0041] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a technical route provided for an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of a model grid provided by an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of the main forecast sites provided by an embodiment of the present invention;

[0045] Figure 5 A comparison diagram of the wind speed of the reconstructed model obtained by superimposing the background wind pressure meteorological data and the gradient wind field according to the weight coefficient and the measured data provided in the embodiment of the present invention;

[0046] Figure 6Comparison chart of the reconstructed model wind direction obtained by superimposing background wind pressure meteorological data and gradient wind field according to the weight coefficient and the measured data provided by the embodiments of the present invention

[0047] Figure 7 Analysis chart of the correlation between the upstream flow and the target model boundary flow obtained by calculating the runoff propagation time based on the upstream station of the river section and the runoff data of the upstream flow boundary of the model provided by the embodiments of the present invention

[0048] Figure 8 Comparison chart of the predicted tide level and the measured tide level with a 24-hour prediction period in the dry season provided by the embodiments of the present invention; where Figure 8 (a) is Wuhu tide gauge station; Figure 8 (b) is Nanjing tide gauge station; Figure 8 (c) is Zhenjiang tide gauge station; Figure 8 (d) is Jiangyin tide gauge station;

[0049] Figure 9 Comparison chart of the predicted tide level and the measured tide level with a 24-hour prediction period in the flood season provided by the embodiments of the present invention; where Figure 9 (a) is Wuhu tide gauge station; Figure 9 (b) is Nanjing tide gauge station; Figure 9 (c) is Zhenjiang tide gauge station; Figure 9 (d) is Jiangyin tide gauge station;

[0050] Figure 10 Comparison chart of the predicted tide level and the measured tide level with a 24-hour prediction period during three typical typhoons provided by the embodiments of the present invention; where Figure 10 (a) is the westward landing typhoon No. 9711, Figure 10 (b) is the northward moving typhoon No. 0012 in the open sea, Figure 10 (c) is the northward landing typhoon No. 2004. Detailed implementation manners

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] The purpose of the present invention is to provide a multi-factor interaction-based multiple correction and prediction method for tidal river sections, which can provide relatively accurate prediction results.

[0053] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0054] Figure 1 A flow chart of a method provided by an embodiment of the present invention, such as Figure 1 As shown, the present invention provides a tidal river section multiple correction and prediction method with multi-factor interaction, comprising:

[0055] Step 100: Acquire model building data; the model building data includes: upstream runoff influence parameters, open boundary astronomical tide influence parameters, typhoon influence parameters, model terrain data and tidal river section tide level data;

[0056] Step 200: Based on the typhoon impact parameters, the background wind pressure meteorological data and the gradient wind field are superimposed according to the weight coefficient to reconstruct the model wind speed and pressure field;

[0057] Step 300: Calculate the incoming flow propagation time according to the upstream runoff influence parameter, and estimate the model inlet flow under future conditions;

[0058] Step 400: Based on the harmonic analysis method, determine the model open boundary tide level sequence according to the astronomical tide influence parameters;

[0059] Step 500: constructing a two-dimensional hydrodynamic mathematical model for tidal river section tide level prediction under the influence of upstream runoff, storm surge, and astronomical tide according to model wind speed, pressure field, model inlet flow, model open boundary tide level sequence, and model terrain data;

[0060] Step 600: coupling and nesting the ensemble Kalman filter algorithm based on the correlation coefficient with the two-dimensional hydrodynamic mathematical model for tidal river section tide level prediction, and dynamically correcting the initial state of the two-dimensional hydrodynamic mathematical model for tidal river section tide level prediction according to the measured tidal river section tide level data to obtain a corrected prediction model;

[0061] Step 700: Using the autoregressive method to correct the tidal river section parameters, reconstruct the tide level forecast field, and perform secondary correction on the model forecast results.

[0062] like Figure 2 As shown, the technical route in this embodiment includes the following steps:

[0063] S1 collects the terrain, tide level, flow, and meteorological data required to build a forecast model under multi-factor conditions;

[0064] S2 reconstructs the model wind and pressure fields by superimposing the background wind pressure meteorological data and the gradient wind field according to the weight coefficient based on the measured and predicted typhoon path and intensity data, and the large-scale wind speed and air pressure data;

[0065] S3 estimates the model inlet flow under future conditions based on the runoff data from upstream stations in the river section and the model upstream flow boundary according to the propagation time of the incoming flow;

[0066] S4 Obtain the tidal level sequence at the open boundary of the model by using the harmonic analysis method based on the open boundary tidal component parameters or tidal level data.

[0067] S5 Establish a two-dimensional hydrodynamic mathematical model for tidal level prediction in the tidal reach under the influence of upstream runoff, storm surge, and astronomical tide.

[0068] S6 Adopt the ensemble Kalman filter algorithm based on the correlation coefficient, couple and nest this algorithm with the tidal prediction model, and dynamically correct the initial state of the prediction model according to the measured tidal level data in the study area.

[0069] S7 Correct the error sequence between the prediction results of the stations along the study area and the measured tidal levels by using the autoregressive method, reconstruct the predicted tidal level field of the tidal reach model, and secondarily correct the prediction model.

[0070] Furthermore, in step S1, the collection of the topographic, flow, tidal level, and meteorological data required for constructing the prediction model under multi-factor conditions refers to the collection of shoreline and underwater topographic data covering the model scope; upstream multi-source station, historical and measured flow sequences at the upstream boundary of the model; harmonic constants and historical tidal level sequences near the open boundary; historical and real-time tidal level sequences in the tidal reach; large-scale wind speed and air pressure field data, as well as measured and predicted typhoon central pressure and maximum wind speed data.

[0071] Furthermore, in step S2, the method for reconstructing the model wind speed and air pressure field synthesizes the typhoon model wind field by superimposing the typhoon circular gradient wind field and the moving wind field, and then synthesizes the typhoon model wind field with the background wind speed and air pressure field according to the weight coefficient. The expression for the superposition of the circular gradient wind field and the moving wind field to form the model wind field is as follows:

[0072]

[0073] In the formula: V g is the gradient wind field; V t is the moving wind field; c1 and c2 are correction coefficients; θ is the angle between the line connecting the calculation point and the typhoon center and the due east direction; β is the correction of the inflow angle.

[0074] The expression for the superposition of the background wind field and the typhoon model according to the weight coefficient is as follows:

[0075] V c =(1 - e)V M +eV Q (2)

[0076] In the formula: V Q is the background wind field; V M is the model wind field V M ; e is the weight coefficient to ensure the smooth transition between the background wind field and the model wind field, and e = c 4 / (1 + c 4), c = r / (10×R max ).

[0077] Further, in step S3, according to the location of the upstream site, the correlation between the distance to the target site and the flow time series, the flow prediction series of the boundary site is calculated using the flow series of the upstream site.

[0078] Further, in step S4, the predicted tidal level series at the open boundary of the model is obtained by using the harmonic analysis method based on the open boundary tidal constituent parameters or historical tidal level data. The expression of the harmonic analysis method is as follows:

[0079]

[0080] In the formula: is the predicted tidal level value at time t, m; A0 is the average sea level height measured from a certain reference surface, m; f i is the intersection factor of the i-th tidal constituent; H i is the amplitude of the i-th tidal constituent, m; σ i is the angular velocity of the tidal constituent; (v0 + u) i is the astronomical initial phase angle of the tidal constituent; g i is the tidal constituent lag angle; i is the tidal constituent number; N is the total number of tidal constituents; H i and g i are also called harmonic constants and are solved by the least squares method.

[0081] Further, in step S5, a two-dimensional hydrodynamic mathematical model for tidal level prediction in the tidal river section under the influence of upstream runoff, storm surge, and astronomical tide is established. The equations are as follows:

[0082]

[0083] Momentum equation:

[0084]

[0085]

[0086] In the formula, t represents time, x and y represent the coordinates in two mutually perpendicular directions in the horizontal plane, ζ is the water level height measured from the average sea level, U and V respectively represent the vertically integrated average velocities in the x and y directions, H is the total water depth, f is the Coriolis force coefficient, ρ0 is the density of water, D x and D y represent the components of the turbulent diffusion term in the x and y directions. To consider the influence of storm surge, p s is the atmospheric pressure term, τ sx and τ sy represent the x and y components of the surface wind stress, and the calculation formula of the wind stress is:

[0087] τ = ρ a C d |U 10 |U 10 (7)

[0088]

[0089] where ρ a is the density of the atmosphere, C d is the wind drag coefficient, which is closely related to the 10m wind speed U 10 at the sea surface.

[0090] Furthermore, in step S6, when using the correlation coefficient to replace the tidal level as the state variable in the ensemble Kalman filter algorithm, the correlation coefficient can be expressed by the following formula:

[0091]

[0092] where: the subscript j is the time series number; the superscripts s and ob represent the simulated value and the observed value respectively, and are the average values of C s and C ob respectively.

[0093] Taking into account the existing measured data set in the past and based on the changing trend of these observed data, the parameters involved in the model are filtered and adjusted, avoiding the wrong filtering caused by single - observation error. The corrected tidal prediction model parameter is the initial tidal level field, and the optimized model parameters are obtained for tidal prediction.

[0094] Furthermore, in step S7, the error sequence between the prediction result of the stations along the research area and the measured tidal level is corrected by the autoregressive method. Assuming that the error between the water - level simulated by the hydrodynamic model and the measured water - level is Δη(t), when using the AR model to model the model error sequence Δη(t), its expression is:

[0095]

[0096] where ε t is the white - noise perturbation; p is the model order; φ i (i = 1~p) are the autoregressive coefficients. After determining the model order p, the autoregressive coefficients φ i can be solved by the least - squares method.

[0097] After obtaining the error sequence of the key stations along the tidal river section after correction, it is interpolated to the whole field according to the distance from the stations, and the tidal - level prediction field of the tidal river section under multi - factor conditions is corrected twice.

[0098] In the specific implementation process, the steps of this embodiment are as follows:

[0099] (1)Taking the tidal reach from Datong to the Yangtze River Estuary as the research reach, Wuhu, Nanjing, Zhenjiang, and Jiangyin along the line are the key control stations, and the tidal level field along the Yangtze River is predicted.

[0100] (2)According to the steps in S1, collect the topographic, flow, tidal level, and meteorological data required to construct the prediction model under multi-factor conditions. In the example, collect the large-scale shoreline and underwater topographic data from Datong in the upper reaches of the Yangtze River to the outside of the Yangtze River Estuary; the flow sequences of Yichang, Songzikou, Chenglingji, Hukou, and Datong Station (the upper boundary of the model) on the Yangtze River from 2011 to 2020; the tidal harmonic constants near the open boundary; the tidal level sequences of stations such as Wuhu, Nanjing, Zhenjiang, and Jiangyin in the tidal reach of the Yangtze River from 2018 to 2020 and during multiple typical typhoon storm surges; the paths of Typhoon 9711, Typhoon 0012, and recent Typhoon 2004, the background wind field, and the measured wind speed data of coastal meteorological stations. The model grid of the prediction model and the main prediction stations are as shown in Figure 3 and Figure 4 .

[0101] (3)Adopt the background wind pressure field and typhoon parameter wind field synthesis method shown in step S2 of the present invention to reconstruct the model wind pressure field. The comparison with the measured wind speed and direction at the coastal meteorological station (Lusi Station) in Jiangsu during Typhoon 9711 from August 8 to August 22, 1997, is shown in Figure 5 and Figure 6 . The wind field reconstruction method adopted by the present invention can better reproduce the wind speed changes during typhoons.

[0102] (4)Adopt the method described in step S3 of the present invention. According to the relationship between the distances, flow velocities, and flow sequences of Yichang, Chenglingji, Xiantao, and Hukou stations in the upper reaches of the Yangtze River and Datong (the upper boundary of the model), calculate the time for the flow to propagate to Datong, so as to establish the correlation between the flow in the previous few days at the upstream stations and the flow at Datong on the same day. As shown in Figure 7 , the correlation coefficient between the calculated flow at Datong and the measured flow at Datong is 0.986.

[0103] (5) Figure 8 and Figure 9 show the comparison between the 24-hour prediction results before correction and after two corrections and the measured tidal levels at 4 key stations along the tidal reach of the Yangtze River under the hydrological conditions in the dry season (January 2019) and the flood season (July 2019) calculated according to the prediction method described in this article. As shown in Figure 8 and the figure, the prediction results of the previous hydrodynamic model along the river will show the situation that the predicted values are continuously higher or lower than the measured tidal levels. This kind of error is more obvious at stations such as Wuhu and Nanjing where the flow is larger and the influence of runoff power is stronger. After two corrections, the accuracy is significantly improved, and the prediction results of the tidal level model can be better corrected.

[0104] The root mean square error (RMSE), mean absolute error (MAE), and tide level qualification rate (QR) are used to evaluate the forecasting accuracy under different hydrological conditions, as shown in Table 1. According to the hydrological information forecasting specification, the allowable error of the tide level is ±0.30 m, and the qualification rate within the allowable error range is statistically calculated. The calculation formulas for the three accuracy evaluation indicators are as follows:

[0105]

[0106]

[0107]

[0108] where n is the length of the measured data, m i is the i-th predicted value, v i is the i-th measured value, and m is the number of qualified predictions.

[0109] Table 1

[0110]

[0111] (6) Figure 10 (a-c) are the comparison charts of the tide level forecasts and the measured tide levels along the tidal reach of the Yangtze River under the 24-hour lead time condition during three typical typhoon storm surges calculated by the forecasting method described in this embodiment (Typhoon No. 9711 moving westward at landfall, Typhoon No. 0012 moving northward in the open sea, and Typhoon No. 2004 moving northward at landfall). The error accuracy evaluation of the predicted highest tide level and the measured highest tide level is shown in Table 2.

[0112] The above results show that the tide level forecasting method for tidal reaches under multi-factor conditions described in the present invention can provide relatively accurate forecasting results, and the technical route is feasible.

[0113] Table 2

[0114]

[0115] The beneficial effects of the present invention are as follows:

[0116] The present invention can provide more accurate tide level forecasting data covering the entire tidal reach for flood control decision-making and water conservancy project design in relevant areas. The present invention provides a multiple correction and forecasting method for tidal reaches based on the interaction of runoff, tides, and typhoons in a hydrodynamic model.

[0117] In the present specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts among the various embodiments can be referred to each other.

[0118] Specific examples are used in this article to elaborate on the principles and implementation manners of the present invention. The descriptions of the above embodiments are only for helping to understand the method and its core idea of the present invention. At the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation on the present invention.

Claims

1. A multiple correction and prediction method for tidal river sections with multi-factor interaction, characterized in that Including: Obtain model construction data; The model construction data includes: upstream runoff influence parameters, open boundary astronomical tide influence parameters, typhoon influence parameters, model terrain data, and tidal reach water level data; According to the typhoon influence parameters, superimpose the background wind pressure meteorological data and the gradient wind field according to the weight coefficient to reconstruct the model wind speed and air pressure field; Estimate the flow propagation time according to the upstream runoff influence parameters to predict the model inlet flow under future conditions; Based on the harmonic analysis method, determine the model open boundary water level sequence according to the open boundary astronomical tide influence parameters; Construct a two-dimensional hydrodynamic mathematical model for predicting the water level of the tidal reach under the influence of upstream runoff, storm surge, and astronomical tide according to the model wind speed, air pressure field, the model inlet flow, the model open boundary water level sequence, and the model terrain data; Couple and nest the ensemble Kalman filter algorithm based on the correlation coefficient with the two-dimensional hydrodynamic mathematical model for predicting the water level of the tidal reach, and dynamically correct the initial state of the two-dimensional hydrodynamic mathematical model for predicting the water level of the tidal reach according to the measured tidal reach water level data to obtain a once-corrected prediction model; Use the autoregressive method to correct the water level prediction results of key stations in the tidal reach, and reconstruct the predicted water level field of the prediction model to secondarily correct the prediction results of the model; The upstream runoff influence parameters include: upstream multi-source stations, the history of the model upstream boundary, and the measured flow sequence; the open boundary astronomical tide influence parameters include the harmonic constants near the open boundary and the historical water level sequence; the typhoon influence parameters include the large-scale wind speed, air pressure field data, and the measured and predicted typhoon central air pressure and maximum wind speed data; the terrain data includes the shoreline and underwater terrain data covering the model range; the tidal reach water level data includes the historical and real-time water level sequences of key stations in the tidal reach; The formula for the correlation coefficient is as follows: where the subscript j is the time series number; the superscripts s and ob represent the simulated value and the observed value respectively, and are the average values of C s and C ob respectively; Use the autoregressive method to correct the water level prediction results of key stations in the tidal reach, and reconstruct the predicted water level field of the prediction model to secondarily correct the prediction results of the model, including: Suppose the model error sequence of the water level at the key station simulated by the hydrodynamic model relative to the measured water level is Δη(t), and an autoregressive model is used to model the model error sequence Δη(t); the expression of the model error sequence is: where ε t is white noise disturbance; o is the model order; φ i is the autoregressive coefficient; After obtaining the error sequence of key stations along the tidal reach after correction, interpolate it to the whole field in segments according to the distance from the stations to obtain the water level prediction result field of the tidal reach under multi-factor conditions after secondary correction; The expression of the harmonic analysis method is: Among them, is the predicted tide level at time t; A0 is the average sea level height measured from a certain reference level; f i is the intersection factor of the i-th tidal constituent; H i is the amplitude of the i-th tidal constituent; σ i is the angular velocity of the tidal constituent; (v0 + u) i is the astronomical initial phase angle of the tidal constituent; g i is the tidal lag angle; i is the tidal constituent number; N is the total number of tidal constituents; The equation of the two-dimensional hydrodynamic mathematical model for predicting the water level of the tidal reach under the influence of upstream runoff, storm surge, and astronomical tide constructed is as follows: The momentum equation is: where t represents time, x and y represent coordinates in two mutually perpendicular directions in the horizontal plane, ζ is the water level height measured from the mean sea level, U and V respectively represent the vertically integrated mean velocities in the x and y directions, H is the total water depth, f is the Coriolis force coefficient, ρ0 is the density of water, D x and D y are used to represent the components of the turbulent diffusion term in the x and y directions, p s is the atmospheric pressure term, τ sx and τ sy represent the x and y components of the surface wind stress, and the calculation formula for the wind stress is as follows: τ = ρ a C d |U 10 |U 10 ; where ρ a is the density of the atmosphere, C d is the wind drag coefficient, and U 10 is the 10m wind speed at the sea surface.

2. The multi-factor interaction-based multiple correction and prediction method for tidal river sections according to claim 1, characterized in that According to the typhoon influence parameters, superimpose the background wind pressure meteorological data and the gradient wind field according to the weight coefficient to reconstruct the model wind speed and air pressure field, including: Synthesize the typhoon model wind field by superimposing the typhoon circular gradient wind field and the moving wind field; Synthesize the typhoon model wind field with the background wind speed and air pressure field according to the weight coefficient; The expression of the typhoon model wind field is as follows: Among them, V g is the gradient wind field; V t is the translational wind field; c1 and c2 are correction coefficients; θ is the angle between the line connecting the calculation point and the typhoon center and the due east direction; β is the correction of the inflow angle; The expression for synthesizing the typhoon model wind field with the background wind speed and pressure field according to the weight coefficient is: V c =(1 - e)V M + eV Q ; Among them, V Q is the background wind field; V M is the typhoon model wind field; e is the weight coefficient.

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