Estuary salt tide invasion business set numerical forecasting method and system

Abstract

The invention discloses an estuary salt tide invasion business set numerical forecasting method, which comprises the following steps: constructing an estuary salt tide invasion numerical model, and setting related parameters of the estuary salt tide invasion numerical model; performing calculation processing on bottom friction parameters of the estuary area, and performing calibration processing on the estuary salt tide invasion numerical model according to a calculation result; performing data assimilation processing on the estuary salt tide invasion numerical model by adopting an optimal interpolation data assimilation technology; constructing a runoff set sample on the basis of the actually measured runoff of the estuary area; and inputting the runoff set sample into the estuary salt tide invasion numerical model for calculation processing to obtain a numerical forecasting result. The method can predict the intensity and duration of the salt tide invasion, is high in prediction accuracy, can guide the operation of the reservoir, guarantees the normal water supply during the salt tide invasion, and guarantees the water taking safety of fresh water resources.

Description

Technical Field

[0001] The invention relates to the technical field of estuary data calculation methods, and in particular to an estuary saltwater intrusion operational ensemble numerical prediction method and a system thereof. Background Art

[0002] Saltwater intrusion in estuaries is a common phenomenon in estuaries. It is affected by the combined effects of tides, runoff, wind stress, mixing, topography and extra-estuary circulation. The dynamic process is complex and is closely related to estuary circulation, sediment flocculation, maximum turbidity zone and ecological environment.

[0003] For example, the Yangtze River estuary is a super-large multi-level branch estuary. Saltwater intrusion is affected by multiple dynamic factors, and there is a special phenomenon of saltwater backflow in the northern branch. The Yangtze River estuary area has a developed economy and a dense population, and has a large demand for freshwater resources. Before 2010, Shanghai's water was mainly taken from the Huangpu River, accounting for about 80% of the total water use, but the water quality was poor and the water volume was insufficient. After 2010, the Qingcaosha Reservoir was built, realizing the strategic transfer of Shanghai's water source. At present, the Qingcaosha Reservoir, Chenhang Reservoir and Dongfeng Xisha Reservoir in the Yangtze River estuary water source area supply more than 80% of the total water volume in Shanghai. Taking water from the Yangtze River estuary will encounter the problem of saltwater intrusion in the estuary, so the development of numerical forecasts of saltwater intrusion has important application significance for the operation of water source reservoirs and the guarantee of water safety.

[0004] The methods of studying saltwater intrusion in estuaries mainly include physical models, field observations, theoretical analysis and numerical simulation. The earliest foreign research on saltwater intrusion began with field observations at the American Waterway Experiment Station (WES) in 1929. At the same time, the Delft Hydraulics Institute in the Netherlands has done a lot of work in the analysis of measured data and experimental research. Systematic research on saltwater intrusion began in the 1950s, mainly focusing on the basic theory of saltwater intrusion in estuaries. In the early days, Pritchard (1952) analyzed the saltwater intrusion observation data of Chesapeak Bay and the James River estuary, and comprehensively explained the dynamic mechanism of the formation of longitudinal circulation in estuaries, and obtained the classic equilibrium relationship between estuary circulation and salinity. Schijf and Schonfeld (1953) derived an analytical solution for the length of saltwater intrusion in estuaries based on the saltwater wedge theory. Bowden (1966) studied the longitudinal velocity distribution characteristics and salinity mixing process of estuaries through measured data, and analyzed the longitudinal circulation of the Mersey estuary. Saltwater intrusion in estuaries is closely related to vertical mixing, and mixing is related to estuary circulation and type. Hansen and Rattray (1966) derived an empirical formula for the vertical mixing coefficient based on measured data, and used dimensionless parameters to describe the estuary, obtaining the classification of stratified-circulation estuaries. Afterwards, many scholars used different standards to classify the types of saltwater intrusion. Pritchard (1967) divided estuaries into three types: highly stratified, partially mixed, and fully mixed, based on the vertical mixing characteristics of fresh and salt water in estuaries. Simmons and Brown (1969) proposed the concept of mixing index, dividing estuaries into three types: highly stratified, partially mixed, and vertically uniform. Officer (1975) divided estuaries into weakly mixed, partially mixed, and strongly mixed types. MacCready (2004) derived the equilibrium relationship between salinity advection and diffusion in an ideal estuary, and pointed out that the vertical circulation of the bottom layer flowing into the surface layer and outflowing in the vertical direction can increase the vertical difference in salinity, while the vertical mixing effect tends to reduce this difference. Prandle (2004) conducted a detailed study on the saltwater upstream and tidal intrusion peaks in some mixed estuaries. The increase in vertical mixing will cause the weakening of vertical circulation, thereby causing the weakening of stratification; at the same time, the weakening of vertical circulation reduces the salt flux entering the estuary, thereby reducing the length of saltwater intrusion (Park and Kuo, 1996; MacCready, 2004). In multi-branched estuaries, lateral exchange between channels can easily affect the longitudinal saltwater intrusion (Conroy et al., 2019). Seasonal changes in wind and the passage of storms affect the saltwater intrusion in the estuary through a combination of barotropic and barotropic processes.

[0005] With the development of computer technology and numerical calculation methods, models related to saltwater intrusion began to develop in the 1980s, and a large number of scholars have studied the problem of saltwater intrusion through numerical simulation methods. At present, the most widely used ocean and estuary numerical models in the world include POM, ECOM, FVCOM, ROMS and UNTRIM, as well as commercial software such as Delft-3D and MIKE models, which have played an important role in estuary hydrodynamics and material transport and diffusion. With the improvement of observation technology and the widespread application of numerical models, the study of saltwater intrusion in estuaries has achieved many results in the understanding of dynamic processes and mechanisms.

[0006] Research on saltwater intrusion at estuaries in my country began in the 1980s, mainly focusing on the Yangtze River Estuary and the Pearl River Estuary. In terms of runoff, the Yangtze River and the Pearl River are the first and second largest rivers in my country. There are megacities at their estuaries, with dense populations and huge water demands for life and production. Large reservoirs have been built. Previous research results have shown that tidal strength and runoff are the two most important dynamic factors affecting saltwater intrusion at the Yangtze River Estuary, and many research results have been achieved. The biggest feature of saltwater intrusion at the Yangtze River Estuary is the backflow of saltwater from the northern branch into the southern branch. The special terrain of the trumpet mouth of the northern branch, the small diversion ratio, the upwelling of the tide level of Qinglonggang in the upper section and the tidal residual flow, as well as the combined effect are the main dynamic mechanisms for its formation. The backflow of saltwater is transported downstream under the action of runoff, and gradually affects the Dongfeng Xisha Reservoir, Chenxingshui Reservoir and Qingcaosha Reservoir in the downstream of the southern branch. Wu et al. (2006) studied the quantitative relationship between the backflow intensity of the North Branch and the upstream runoff and the tidal range of Qinglonggang through numerical models, and obtained the relationship between the backflow volume of the North Branch on a single day and the backflow intensity, as well as the relationship between the backflow volume and the runoff volume in a half month. Zhu Jianrong et al. (2011) gave the salt flux of the North Branch saltwater backflow under different runoff volumes. Lyu and Zhu (2018) proposed that the bottom friction drag coefficient based on the quadratic law of flow velocity in the bottom boundary layer is not suitable for the ultra-shallow river channel of the North Branch, and gave the bottom friction drag coefficient based on the Xie Cai-Manning coefficient, which significantly improved the calculation accuracy of the North Branch saltwater backflow.

[0007] The saltwater intrusion at the Yangtze River Estuary is also affected by wind stress. The northerly wind in winter generates Ekman transport to the shore, which aggravates the saltwater intrusion. Climate change causes changes in precipitation in the basin and sea level rise. The sea level rise increases the depth of the estuary as a whole, leading to the intensification of saltwater intrusion at the Yangtze River Estuary. The evolution of the estuary's river regime, including long-term natural evolution and short-term local engineering, affects the hydrodynamics and diversion ratio, which in turn affects the saltwater intrusion. Human activities such as water conservancy projects in the basin, deep-water channel projects at the estuary, and beach enclosure projects affect the hydrodynamics and material transport at the estuary. Basin engineering affects saltwater intrusion and freshwater resources at the Yangtze River Estuary by changing runoff, and estuary engineering mainly affects local topography.

[0008] With the continuous development of numerical formats, the computational accuracy of numerical models has been continuously improved, playing an important role in the research and numerical prediction of saltwater intrusion in estuaries. Wu and Zhu (2010) used the HSIMT numerical calculation method with third-order accuracy to improve the numerical format of the advection term in the salinity transport diffusion equation, eliminating the computational dispersion and significantly reducing the numerical dissipation.

[0009] The applicant has been conducting on-site observations of estuaries, improving and applying three-dimensional numerical models and theoretical analysis of estuaries for a long time, and has achieved many research results in the study of estuary hydrodynamics and saltwater intrusion, playing an important role in reservoir construction (the longest time when water is not suitable for water extraction is given, and the reservoir capacity is designed) and operation (numerical prediction of saltwater intrusion at the estuary). Therefore, in view of the problem of saltwater intrusion at the estuary that will be encountered when taking water from the estuary, it is urgent to carry out numerical prediction of saltwater intrusion, which has important application significance for the operation of water source reservoirs and the guarantee of water safety.

[0010] Therefore, the applicant has found a solution to the above-mentioned problem through beneficial exploration and research, and the technical solution to be introduced below is produced in this context. Summary of the invention

[0011] The technical problem to be solved by the present invention is to provide an operational ensemble numerical prediction method for estuary saltwater intrusion based on data assimilation to guide reservoir operation, ensure normal water supply during saltwater intrusion, and ensure the safety of freshwater resource extraction.

[0012] The second technical problem to be solved by the present invention is to provide a system for realizing the above-mentioned operational ensemble numerical forecasting method for estuary saltwater intrusion.

[0013] As a first aspect of the present invention, a method for operational ensemble numerical prediction of estuary saltwater intrusion comprises:

[0014] Constructing a numerical model of saltwater intrusion at the estuary, and setting relevant parameters of the numerical model of saltwater intrusion at the estuary;

[0015] Calculating and processing the bottom friction parameters of the estuary area, and calibrating the estuary saltwater intrusion numerical model according to the calculation results;

[0016] Using the optimal interpolation data assimilation technology to perform data assimilation processing on the estuary saltwater intrusion numerical model;

[0017] Constructing a runoff aggregate sample based on measured runoff in the estuary area; and

[0018] The runoff collection samples are input into the estuary saltwater intrusion numerical model for calculation and processing to obtain numerical prediction results.

[0019] In a preferred embodiment of the present invention, the step of constructing a numerical model of estuarine saltwater intrusion and setting relevant parameters of the numerical model of estuarine saltwater intrusion includes:

[0020] A numerical model of saltwater intrusion in the estuary was constructed, and dynamic factors such as runoff, tide, wind stress and bottom friction were considered. Non-orthogonal curvilinear grids were used horizontally and σ coordinates were used vertically.

[0021] Set the calculation area and perform local encryption on the grid in key areas;

[0022] Set vertical stratification, time step and critical water depth;

[0023] Set initial field values ​​such as water level field, flow field and salinity field; and

[0024] Set boundary conditions such as tide level, residual water level, runoff and wind field.

[0025] In a preferred embodiment of the present invention, the calculation of the bottom friction parameters of the estuary area and the calibration of the estuary saltwater intrusion numerical model according to the calculation results include:

[0026] The bottom friction parameters in the estuary area include the Manning roughness coefficient and the bottom friction drag coefficient. The Manning roughness coefficient in the estuary area is assigned according to Table 1:

[0027] Table 1 Values ​​of Manning's roughness coefficient

[0028] Water depth D(m) Manning Roughness D<2 0.030 2≤D<3 0.024 3≤D<10 0.014 D≥10 0.010

[0029] Calculate the Xiecai coefficient C according to formula (1):

[0030]

[0031] Where H is the water depth, n is the Manning roughness coefficient;

[0032] The bottom friction drag coefficient C in the estuary area is calculated according to formula (2): d :

[0033]

[0034] Among them, C is the Xie Cai coefficient, g is the acceleration of gravity;

[0035] The numerical model of estuarine saltwater intrusion is calibrated using the Manning roughness coefficient and bottom friction drag coefficient of the estuarine area.

[0036] In a preferred embodiment of the present invention, the optimal interpolation data assimilation technology is used to perform data assimilation processing on the estuary saltwater intrusion numerical model, including:

[0037] Select the measured sites for data assimilation. If the sites in some areas are too sparse, set up virtual sites.

[0038] Conduct quality control on measured data of selected sites;

[0039] The optimal interpolation method shown in formula (3) is used for data assimilation to produce the initial field of the model;

[0040]

[0041] Among them, x a and x b are the analysis field and background field respectively, K is the optimal weight, y is the observation data, H is the linear operator for interpolating the background field value to the observation position, the superscript T represents the transpose of the matrix, B is the background field error covariance matrix, and O is the observation field error covariance matrix.

[0042] In a preferred embodiment of the present invention, the quality control includes format inspection, correlation inspection, missing inspection, range inspection, continuity inspection, visual graphics drawing inspection and comprehensive analysis inspection.

[0043] In a preferred embodiment of the present invention, constructing a runoff collection sample based on the measured runoff in the estuary area includes:

[0044] To construct a short-term runoff prediction sample, the measured runoff in the estuary area is deducted from the drainage along the way. The specific value is determined according to formula (4) and Table 2:

[0045] y=ax+b (4)

[0046] Among them, y is the runoff input of the model, x is the measured flow in the estuary area, and a and b are adjustment coefficients, which mainly reflect the drainage along the way;

[0047] Table 2 Model runoff input adjustment coefficient table

[0048] Flow value a b <![CDATA[>=20000m 3 / s]]> 1.0 0 <![CDATA[>12000m 3 / s,<20000m 3 / s]]> 1.0 -700 <=12000m3 / s 1.0 -2000

[0049] The runoff prediction samples are constructed based on the forecast information of the authoritative agencies. If there is no forecast information, the runoff prediction samples are constructed based on experience. The prediction value is constructed based on the last measured value or the average value of the most recent period. The specific value is determined according to formula (5) and Table 3:

[0050] y=ax+b (5)

[0051] Among them, y is the runoff input of the model, x is the latest observed value of the measured flow in the estuary area, and a and b are adjustment coefficients, which mainly reflect the change of continuity;

[0052] Table 3 Model runoff input adjustment coefficient table

[0053] Flow value a b >=20000m3 / s 1.0 200 >12000m3 / s,<20000m3 / s 1.0 -700 <=12000m3 / s 1.0 -2200

[0054] If there is no measured data for empirical construction, the climatological state value is used to construct the runoff sample value;

[0055] Construct a runoff aggregate sample, and form a runoff aggregate sample by modifying the measured runoff value and generating a plurality of runoff prediction samples according to the above.

[0056] As a second aspect of the present invention, a system for realizing the above-mentioned operational ensemble numerical prediction method for saltwater intrusion at an estuary comprises:

[0057] A model building module, wherein the model building module is used to build a numerical model of estuarine saltwater intrusion and set relevant parameters of the numerical model of estuarine saltwater intrusion;

[0058] A parameter calibration module, wherein the parameter calibration module is used to calculate the bottom friction parameters of the estuary area and calibrate the estuary saltwater intrusion numerical model according to the calculation results;

[0059] A data assimilation module, wherein the data assimilation module is used to perform data assimilation processing on the estuary saltwater intrusion numerical model by using an optimal interpolation data assimilation technology;

[0060] A sample construction module, the sample construction module is used to construct a runoff collection sample based on the measured runoff in the estuary area; and

[0061] The numerical prediction module is used to input the runoff set samples into the estuary saltwater intrusion numerical model for calculation and processing to obtain numerical prediction results.

[0062] Due to the adoption of the above technical scheme, the beneficial effect of the present invention is that: the present invention generates data forecast results by constructing a set of runoff samples and inputting them into a numerical model of estuary saltwater intrusion based on data assimilation and having completed bottom friction determination for calculation and processing, and predicts the intensity and duration of saltwater intrusion with high prediction accuracy, which can guide reservoir operation, ensure normal water supply during saltwater intrusion, and ensure the safety of freshwater resource extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] 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 or the description of the prior art 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 work.

[0064] Figure 1 The present invention is a flow chart of the operational ensemble numerical forecasting method for estuary saltwater intrusion.

[0065] Figure 2 It is a schematic diagram of the structure of the system of the present invention.

[0066] Figure 3 It is a schematic diagram of the model calculation area, where a is the model calculation area and grid, b is the enlarged grid of the north-south branch bifurcation area, and c is the grid of the north-south trough bifurcation area.

[0067] Figure 4 This is a schematic diagram of the measured water depth distribution of the Yangtze River estuary in 2021 (Yellow Sea 85 datum).

[0068] Figure 5 This is a schematic diagram comparing the salinity simulation results of Chongxi and Nanmen hydrological stations from March 2 to 20, 2017.

[0069] Figure 6 This is a schematic diagram of the location of Wusong Water Plant Station.

[0070] Figure 7 This is a schematic diagram of the location of Pudong Airport Station.

[0071] Figure 8 It is the salinity data quality control process.

[0072] Fig. 9 is a schematic diagram of the runoff collection sample construction. DETAILED DESCRIPTION

[0073] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below with reference to specific diagrams.

[0074] See also Figure 1 , the figure shows an operational ensemble numerical prediction method for estuary saltwater intrusion, which includes the following steps:

[0075] Step S10, constructing a numerical model of estuary saltwater intrusion, and setting relevant parameters of the numerical model of estuary saltwater intrusion.

[0076] Step S20, calculating and processing the bottom friction parameters of the estuary area, and calibrating the estuary saltwater intrusion numerical model according to the calculation results.

[0077] Step S30, using the optimal interpolation data assimilation technology to perform data assimilation processing on the estuary saltwater intrusion numerical model.

[0078] Step S40, constructing a runoff collection sample based on the measured runoff in the estuary area.

[0079] Step S50, inputting the runoff sample set into the estuary saltwater intrusion numerical model for calculation and processing to obtain the numerical prediction result.

[0080] In step S10, a numerical model of estuarine saltwater intrusion is constructed, and relevant parameters of the numerical model of estuarine saltwater intrusion are set, including the following sub-steps:

[0081] Step S11, constructing a numerical model of saltwater intrusion in the estuary, taking into account dynamic factors such as runoff, tide, wind stress and bottom friction, using a non-orthogonal curve grid horizontally and using σ coordinates vertically;

[0082] Step S12, setting the calculation area and performing local encryption processing on the grid of the key area;

[0083] Step S13, setting vertical stratification, time step and critical water depth;

[0084] Step S14, setting initial field values ​​such as water level field, flow field and salinity field;

[0085] Step S15, setting boundary conditions such as tide level, residual water level, runoff and wind field.

[0086] In step S20, the bottom friction parameters of the estuary area are calculated and processed, and the estuary saltwater intrusion numerical model is calibrated according to the calculation results, including the following sub-steps:

[0087] Step S21, the bottom friction parameters of the estuary area include the Manning roughness coefficient and the bottom friction drag coefficient. The Manning roughness coefficient of the estuary area is assigned according to Table 1:

[0088] Table 1 Values ​​of Manning's roughness coefficient

[0089] Water depth D(m) Manning Roughness D<2 0.030 2≤D<3 0.024 3≤D<10 0.014 D≥10 0.010

[0090] Step S22, calculate the coefficient C according to formula (1):

[0091]

[0092] Where H is the water depth, n is the Manning roughness coefficient;

[0093] Step S23, calculate the bottom friction drag coefficient C of the estuary area according to formula (2): d :

[0094]

[0095] Among them, C is the Xie Cai coefficient, g is the acceleration of gravity;

[0096] Step S24, calibrating the estuary saltwater intrusion numerical model using the Manning roughness coefficient and bottom friction drag coefficient of the estuary area.

[0097] In step S30, the optimal interpolation data assimilation technology is used to perform data assimilation processing on the estuary saltwater intrusion numerical model, including the following sub-steps:

[0098] Step S31, selecting the measured sites for data assimilation, if the sites in some areas are too sparse, setting virtual sites;

[0099] Step S32, quality control is performed on the measured data of the selected site, wherein the quality control includes format inspection, correlation inspection, missing inspection, range inspection, continuity inspection, visual graphics drawing inspection and comprehensive analysis inspection, etc.;

[0100] Step S33, using the optimal interpolation method shown in formula (3) to perform data assimilation and produce the model initial field;

[0101]

[0102] Among them, x a and x b are the analysis field and background field respectively, K is the optimal weight, y is the observation data, H is the linear operator for interpolating the background field value to the observation position, the superscript T represents the transpose of the matrix, B is the background field error covariance matrix, and O is the observation field error covariance matrix.

[0103] In step S40, based on the measured runoff in the estuary area, a runoff aggregate sample is constructed, including the following sub-steps:

[0104] Step S41, construct a short-term runoff prediction sample, subtract the drainage along the way from the measured runoff in the estuary area, and the specific value is determined according to formula (4) and Table 2:

[0105] y=ax+b (4)

[0106] Among them, y is the runoff input of the model, x is the measured flow in the estuary area, and a and b are adjustment coefficients, which mainly reflect the drainage along the way;

[0107] Table 2 Model runoff input adjustment coefficient table

[0108] Flow value a b <![CDATA[>=20000m 3 / s]]> 1.0 0 <![CDATA[>12000m 3 / s,<20000m 3 / s]]> 1.0 -700 <=12000m3 / s 1.0 -2000

[0109] Step S42, constructing runoff prediction samples, preferably based on the forecast information of the authoritative agency, and its construction method is consistent with step S41; if there is no forecast information, the runoff prediction samples are constructed empirically, and the prediction value is constructed based on the last measured value or the average value of the most recent period. The specific value is determined according to formula (5) and Table 3:

[0110] y=ax+b (5)

[0111] Among them, y is the runoff input of the model, x is the latest observed value of the measured flow in the estuary area, and a and b are adjustment coefficients, which mainly reflect the change of continuity;

[0112] Table 3 Model runoff input adjustment coefficient table

[0113] Flow value a b >=20000m3 / s 1.0 200 >12000m3 / s,<20000m3 / s 1.0 -700 <=12000m3 / s 1.0 -2200

[0114] If there is no measured data for empirical construction, the climatological value (multi-year average) is used to construct the runoff sample value;

[0115] Step S43, constructing a runoff aggregate sample, by modifying the measured runoff value, generating a plurality of runoff prediction samples according to the above, and forming a runoff aggregate sample.

[0116] The present invention constructs a set of runoff samples and inputs them into a numerical model of estuary saltwater intrusion based on data assimilation and with bottom friction coefficient determined for calculation and processing, thereby generating data forecast results, predicting the intensity and duration of saltwater intrusion with high prediction accuracy, and guiding reservoir operation to ensure normal water supply during saltwater intrusion and safe water extraction of freshwater resources.

[0117] See also Figure 2 , the figure shows a system for realizing the above-mentioned operational ensemble numerical prediction method for estuary saltwater intrusion, including a model construction module 100, a parameter calibration module 200, a data assimilation module 300, a sample construction module 400 and a numerical prediction module 500.

[0118] The model construction module 100 is used to construct a numerical model of saltwater intrusion in the estuary and set relevant parameters of the numerical model of saltwater intrusion in the estuary. The parameter calibration module 200 is used to calculate the bottom friction parameters of the estuary area and calibrate the numerical model of saltwater intrusion in the estuary according to the calculation results. The data assimilation module 300 is used to use the optimal interpolation data assimilation technology to perform data assimilation on the numerical model of saltwater intrusion in the estuary. The sample construction module 400 is used to construct a runoff collection sample based on the measured runoff in the estuary area. The numerical prediction module 500 is used to input the runoff collection sample into the numerical model of saltwater intrusion in the estuary for calculation and processing to obtain a numerical prediction result.

[0119] Each module in the system of the present invention can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0120] In order to better illustrate the operational ensemble numerical prediction method for estuary saltwater intrusion of the present invention, a more detailed and specific application embodiment is given below, which is implemented based on the Yangtze River Estuary area:

[0121] 1. Establishment of a numerical model for saltwater intrusion in the Yangtze River Estuary

[0122] 1.1 Ocean numerical model

[0123] The numerical model adopts the three-dimensional numerical model of estuarine saltwater intrusion, which is improved and developed on the basis of the estuarine coastal ocean numerical model ECOM-si (Semi-implicit Estuarine, Costal and Ocean Model). It takes into account dynamic factors such as runoff, tide, wind stress and bottom friction, and adopts Arakawa C grid configuration in the horizontal direction. In order to better simulate the complex coastline of the estuary, a non-orthogonal curvilinear grid is developed. The model uses σ coordinates vertically to better describe the changes of the bottom shape with water depth. In order to eliminate the CFL criterion and increase the time step, the positive pressure gradient force term in the momentum equation in the model is solved by implicit method, and the continuity equation is solved by semi-implicit method. The explicit difference method is used for the advection term model that produces slow processes. In order to make the model vertically stable and have a higher resolution, the turbulent diffusion and viscosity terms in the vertical direction are solved by implicit method.

[0124] In order to better simulate the saltwater intrusion at the estuary, many improvements have been made to the model. By introducing the dry-wet discrimination method to deal with the dynamic boundary of the tidal flat, the calculation accuracy of the model in the sandbar shoal at the Yangtze River Estuary is improved. The third-order HSIMT-TVD (high-order spatial interpolation at the middle temporal level) format is used to solve the advection term in the material transport equation. This format greatly reduces numerical dissipation, eliminates numerical dispersion, and improves the calculation accuracy in the material transport process. After these improvements, the model can accurately simulate the hydrodynamic process and saltwater intrusion phenomenon at the Yangtze River Estuary. The improved model has been widely used in the research of hydrodynamic processes and saltwater intrusion at the Yangtze River Estuary, and has achieved a lot of research results.

[0125] 1.2 Grid and water depth

[0126] The computational domain and grid of the model are as follows Figure 3As shown in a, the calculation range mainly covers the entire Yangtze River estuary, Hangzhou Bay and northern Jiangsu waters, with the farthest east to 124.5°E; the river boundary in the west is at Datong Hydrological Station, which monitors the runoff of the Yangtze River in real time; the north is around 33.5°N and the south is around 28°N. The model uses non-orthogonal curve grids to better simulate the complex coastline of the Yangtze River estuary, and the grids in each branch are encrypted, such as the resolution of deep-water channel engineering and north-south branch mouths is as high as 100m ( Figure 3 The model is divided into 10 layers vertically, with a time step of 40 seconds. The wet-dry discrimination method is used to deal with the dynamic boundary of the tidal flat, and the critical water depth is taken as 0.2m.

[0127] The model uses the measured water depth of the Yangtze River estuary in 2021 (such as Figure 4 As shown in the figure, compared with the water depth in 2016, the northern branch continues to silt up. This will lead to the weakening of the saltwater intrusion from the northern branch to the southern branch, reducing the saltwater intrusion from the southern branch and its impact on the water source.

[0128] 1.3 Initial Field

[0129] Since the water level field and flow field adjust quickly, their initial fields are set to 0.

[0130] The initial salinity field outside the Yangtze River estuary was digitized from the Bohai, Yellow Sea and East China Sea Marine Atlas (Hydrology) (Marine Atlas Editorial Committee, 1992), and the initial salinity field inside the Yangtze River estuary was interpolated based on multi-year dry season measured data.

[0131] 1.4 Boundary conditions

[0132] The open boundary of the model offshore is driven by the tide level and residual water level. The tide level considers 16 partial tides (M2, S2, N2, K2, K1, O1, P1, Q1, U2, V2, T2, L2, 2N2, J1, M1, OO1), which are synthesized by the harmonic constants of each partial tide. The data comes from the NAOTIDE database (http: / / www.miz.nao.ac.jp / staffs / nao99 / index_En.html).

[0133]

[0134] Among them, ζ is the actual total water level, H i is the amplitude, ω i is the angular frequency, V i +u i represents the correction angle, g i is the retardation angle, f is the node factor, ζ0 is the boundary residual water level, which reflects the shelf circulation and is provided by a larger regional model calculation.

[0135] The upstream open boundary of the model runoff is set at Datong hydrological station and is given in the form of flux (average daily runoff).

[0136] Wind stress is also an important factor in estuary saltwater intrusion, which is calculated by the wind field in the 10m layer of the sea surface according to the quadratic law formula.

[0137] 2. Bottom Friction Parameter Calibration

[0138] Bottom friction is the main factor in tidal wave energy dissipation and has a very important influence on the amplitude and phase distribution of tidal waves. Tidal wave motion is one of the basic forms of seawater motion, which determines material transport and is very important for numerical simulation of salinity. The bottom friction coefficient is proposed based on boundary layer theory and changes with time in periodic motion.

[0139] For estuarine ocean numerical models, the solution of the bottom friction drag coefficient is generally based on the logarithmic rate formula of the bottom friction drag coefficient of the bottom boundary layer (Batchelor, 1953; Dyer, 1980; Soulsby and Dyer, 1981; Lueck, 1988):

[0140] τ b =ρC d |u b |u b (2-1)

[0141]

[0142] Among them, τ b is the bottom friction; C d is the bottom friction drag coefficient; u b is the bottom flow velocity; z b is the bottom thickness; κ is the Karman constant (here taken as 0.4); z0 is the bottom roughness.

[0143] Collins (1998) analyzed 192 sets of velocity data collected from Loughor Estuary, Swansea Bay and Wash Bay tidal flats and found that less than 40% of the data were logarithmically distributed. Therefore, the logarithmic rate formula is not necessarily used in shallow and ultra-shallow estuaries. The northern branch of the Yangtze River estuary is an ultra-shallow river channel, with average water depths of 3.07, 3.44 and 5.07 m (Huanghai 85 datum) in the upper, middle and lower sections, respectively. In addition, the water depths below 2 m in the upper, middle and lower sections of the northern branch account for 39%, 23% and 32% respectively. According to previous studies, for shallow open channels, the Xie Cai-Manning formula is often used to solve the bottom friction drag coefficient (Dronkers, 1964; Ludwick, 1975; Dunbar et al., 1991; Bokhorst, 2003).

[0144]

[0145] In the formula, C is the Chow coefficient, whose value depends on the Manning roughness coefficient n (Chow, 1959).

[0146]

[0147] Where H is the water depth.

[0148] In the above formula, the size of Manning's roughness coefficient needs to be determined. Studies have found that Manning's roughness will change with water depth. Ree (1949) found that the values ​​of Manning's roughness are different at different water depths through a large number of flume experiments. Qian Ning (1959) analyzed the measured data of various hydrological stations in the lower reaches of the Yellow River and concluded that Manning's roughness coefficient will decrease with the increase of water level. Chow (1959) believed that in most open channels, Manning's roughness coefficient will decrease with the increase of water depth. Knight (1981) and Knight (2009) conducted field measurements on Conwy Estuary, Main River, Severn River, Waiwakaiho River and Tomebamba River and found that the values ​​of Manning's roughness coefficient at different water levels are quite different and will decrease with the increase of water level. Cheng (1993) found that when the Manning's roughness coefficient is assigned according to the law that "the water depth increases, the Manning's roughness coefficient decreases", the model calculation results fit the measured data well.

[0149] According to the above, according to previous studies (Cheng et al., 1993; Bokhorst, 2003; Knight et al., 2009), the Manning roughness coefficient n will decrease with the increase of water depth. After a lot of debugging and experiments, the Manning roughness coefficient of the North Branch is assigned according to the rules in Table 2-1.

[0150] Table 2-1 Values ​​of the Manning Roughness Coefficient of the North Branch

[0151] Water depth(m) Manning Roughness D<2 0.030 2≤D<3 0.024 3≤D<10 0.014 D≥10 0.010

[0152] The traditional bottom friction drag coefficient adopts the quadratic law formula and the Xie Cai-Manning coefficient that varies with water depth, and the model calculation results (such as Figure 5 As shown in Figure 2, it can be seen that the calculation results of the bottom friction drag coefficient calculated using the improved Xie Cai-Manning formula (Test 2) can well simulate the backflow phenomenon of the North Branch. If the logarithmic rate formula is used to solve the bottom friction drag coefficient, the backflow of the North Branch calculated (Test 1) is obviously lower. Therefore, the accuracy of the calculation of the bottom friction drag coefficient has a very important influence on the simulation accuracy of the North Branch backflow.

[0153] The source of inland saltwater intrusion in the Yangtze River estuary waters mainly comes from the backflow of the northern branch saltwater tide. Therefore, the calculation of the bottom friction drag coefficient of the northern branch using the Xie Cai-Manning formula can better simulate the backflow of the northern branch saltwater tide and improve the accuracy of saltwater intrusion forecasts.

[0154] 3. Data Assimilation

[0155] Based on the online monitoring network of saltwater intrusion in the Yangtze River Estuary, after data cleaning and quality control, the optimal interpolation data assimilation technology is applied to carry out online salinity observation data assimilation three times a day to improve the initial field accuracy of numerical forecasts, thereby improving the accuracy of forecast results.

[0156] 3.1 Setting of assimilation stations

[0157] 3.1.1 Site selection

[0158] The assimilated data are based on the salinity data observed by the online monitoring network for saltwater intrusion in the Yangtze River Estuary. Some stations are selected from the online monitoring network for data assimilation.

[0159] The selection principles of assimilation stations are: select as many as possible; be sufficiently far away from key forecast points; and not be affected by unconsidered inland river runoff.

[0160] According to the above principles, the current analysis shows that there are 10 stations that cannot be used for data assimilation (Table).

[0161]

[0162]

[0163] For example, see Figure 6 , Wusong Water Plant Station is located at the mouth of the Huangpu River. The model does not consider the flow of the Huangpu River into the river, so this station is not used for assimilation. Figure 7 ,The Pudong Airport observation point is also affected by the nearby river and is not used for assimilation.

[0164] 3.1.2 Virtual site settings

[0165] The observation stations in some areas are too sparse, which is not conducive to the assimilation of the model's initial field. The assimilation of salinity gradient differences can be achieved by setting virtual stations.

[0166] The virtual site location is the midpoint of the line connecting the two reference sites, and its data value is configured in the form of a polynomial. Virtual site salinity value:

[0167] z=ax+by

[0168] Where a and b are coefficients, and x and y are the salinities of the selected sites.

[0169] Virtual site configuration coefficient table

[0170] coefficient value a 0.5 b 0.5

[0171] For example, the observation station in the middle section of the northern branch is missing, and the salinity gradient difference between Sanjiao Port and Qinglong Port is assimilated by setting three virtual stations.

[0172] 3.1.3 Assimilation of stations

[0173] After comprehensive station selection and virtual station configuration, 23 assimilated stations were finally determined, as shown in the following table:

[0174] X Y Z name 1 383280.4508 3509352.869 0 Sanjo Port 2 358694.929 3518422.53 0 Qingsan Middle School 2 3 346401.5 3522957 0 Qingsan Middle School 1 4 370987.705 3513887.7 0 Qingsan Middle School 3 5 334109.4068 3527492.188 0 Qinglong Port 6 326867.0937 3516682.035 0 Chongming Head 7 326816.478 3523781.163 0 Qingniu Hydrological Station 8 316016.0055 3514070.185 0 White sand 9 337259.0007 3496591.105 0 Taicang Petrochemical 10 343749.0145 3488569.359 0 River Center 11 349596.8838 3484046.354 0 Stone cave entrance 12 347271.8027 3500290.002 0 South Gate 13 367446.8023 3490150.977 0 Fort Town 14 375289.9512 3473304.313 0 Changxing Island 15 390470.8284 3463833.758 0 Hengsha Island 16 376581.3635 3481405.629 0 Green Grass Sand B 17 369173.7125 3486527.466 0 Green Grass Sand C 18 388321.6799 3477371.43 0 Hengsha North 19 403804.8783 3471456.455 0 Outside Beigang 20 354384.5553 3489479.692 0 Xiabiantansha 21 413282.9164 3467274.819 0 Hengsha East 22 377074.7479 3487002.321 0 Liuyang 23 390036.8501 3473183.262 0 Gongqingwei

[0175] 3.2 Data Quality Control

[0176] The purpose of data assimilation is to improve the accuracy of the initial field of numerical calculation by assimilating observational data. Therefore, the observational data used must be accurate and of high quality. To establish high-quality marine scientific data, quality control is essential. The purpose of quality control is to strictly control the quality of data and ensure the authority of the data.

[0177] The quality control methods for salinity data include format inspection, correlation inspection, missing inspection, range inspection, continuity inspection, visual graphics inspection and comprehensive analysis inspection. The quality control process is as follows: Figure 8 The data element range test values ​​are shown in Table 3-1. The quality control method is implemented through computer automatic quality control and manual review. The computer is used to implement the unified standardization test and value range, logic and other tests of large quantities of data, and then combined with manual visual drawing and other audits.

[0178] Table 3-1 Salinity element test value parameters

[0179] parameter Range Testing Gradient Testing Peak test salinity [0，41] 5 1

[0180] (1) Format check

[0181] File name verification: Verify whether the data files are named according to standard file naming rules.

[0182] Data record format inspection: Check whether the data record arrangement order, starting position, length, record type identification, data storage type, etc. comply with the corresponding format regulations. If not, errors will occur when reading the data and should be corrected before other quality control inspections.

[0183] (2) Missing test

[0184] Check whether a certain observation data record is missing data. If it is missing data, no further checks will be performed on the record.

[0185] (3) Range test

[0186] According to the characteristics of the observed elements themselves, the normal value range of the elements is determined. If it exceeds this range, the data is considered abnormal. The range test method is divided into the following aspects according to the way the parameters of the normal value range of the elements are determined.

[0187] a) Extreme value range test

[0188] In general, when the observed value of a certain element in a fixed area exceeds the statistical extreme value range of the element in that area for many years (generally not less than 20 years), the data is suspicious. i Formula (3-1) should be satisfied; otherwise, the data is suspicious and further analysis should be performed to determine whether it is correct.

[0189] X min ≤x i ≤X max (3-1)

[0190] Where: X min is the minimum value of the factor in multiple years; x i is the observed value; X max It is the maximum value of the feature's statistics over many years.

[0191] b) Experience range test

[0192] The value range of the factors obtained from personal experience or literature is used as the quality control parameter.

[0193] c) Grubbs Criterion

[0194] When using the Grubbs criterion to perform quality control on element observations, the data should satisfy formula (3-2), otherwise the data is suspicious.

[0195]

[0196] Where: x i is the observed value; is the average value of the observations; σ is the standard deviation of the data series; G is the Grubbs critical value, which is calculated by formula (3-3).

[0197]

[0198] In the formula: n is the number of data sequences; t is the critical value of the single boundary test t distribution with n-2 degrees of freedom and a / n significance level, which is generally obtained by looking up the general function or t distribution critical value table. a is generally taken as 0.05 or 0.01.

[0199] (4) Continuity test

[0200] (a) Gradient test

[0201] The ocean observation elements have continuity within a certain time or space range, and the change values ​​of observation elements that are close in time or location should be within a certain range, otherwise the data is considered abnormal. The specific method is:

[0202] Assume that the current observation value x i , the previous non-missing value x adjacent to it in time or space i-1 , continuity test parameter H g , then formula (3-4) should be satisfied, otherwise x i and x i-1 The gradient of change is large, and both data are suspicious. Further analysis is needed to confirm whether they are abnormal.

[0203] |x i -x i-1 |≤H g (3-4)

[0204] Where: H g It is a gradient test parameter, which is determined according to factors such as feature type, observation time interval, spatial distance, observation time and area.

[0205] (b) Peak test

[0206] The changes of ocean observation elements within a certain space or time range are limited. If an observation value is significantly different from the surrounding observation values ​​and there is a large mutation, it is judged as an outlier.

[0207] Assume that the current observation value is x i , the first correct observation adjacent to it in time or space is x i-1 and x i+1 , then x is required i Satisfy formula (3-5), otherwise it is considered that x i suspicious.

[0208] |x i -(x i-1 +x i+1 ) / 2|≤H j (3-5)

[0209] Where: H j It is the peak test parameter, which is determined according to the factors such as feature type, observation time interval or spatial interval, observation time and area.

[0210] (c) Constant test

[0211] When the sensitivity and accuracy of the observation instrument are sufficient, the ocean observation elements are affected by fluid dynamic factors and will not remain constant within a certain time or space range. If they remain constant, the data is suspicious. The specific method is as follows.

[0212] Find the maximum value V of an element in a certain period of time (or a profile) max and minimum value V min , formula (3-6) should be satisfied, otherwise the data of this section (section) is abnormal.

[0213] V max -V min ≥H h (3-6)

[0214] Where: V max is the maximum value of the observed data sequence or the maximum value of a certain observed section; V min is the maximum value of the observed data sequence or the minimum value of a certain observed section; H h To maintain constant test parameters, the parameters are determined based on factors such as feature type, observation time, data accuracy and region.

[0215] (5) Correlation test

[0216] The data anomalies are checked through the mutual relationship between the elements. For example, the recorded values ​​at fixed or hourly intervals within a day cannot exceed the daily extreme value; the data of the same element at different observation frequencies and at the same observation time should be equal; the maximum wave height should be greater than or equal to the average wave height; the extreme high (low) tide height should not be lower (higher) than the hourly tide height at the adjacent time; the wind speed and wave height level should meet the corresponding relationship, etc.

[0217] (6) Visual graphics drawing test

[0218] The changes of observed elements are continuous within a certain time and space range. Drawing visual graphics can intuitively display abnormal data that exceeds the range, abnormal data that changes suddenly, peak values, and missing values, which is a very effective auxiliary method for manual review. For example, by drawing the time series process curve of each element, the peak value displayed is the abnormal value; when drawing the data series process curve of the same element with different observation frequencies, the observation values ​​at the same time should overlap in the process curve, otherwise, the data record at that time is abnormal.

[0219] When necessary, perform manual inspection of visual graphics.

[0220] 3.3 Assimilation method

[0221] The improvement of numerical forecasting level depends heavily on the accuracy of initial conditions and the perfection of models. In recent years, with the continuous improvement of numerical models, the requirements for the accuracy of model initial values ​​have become increasingly prominent. Data assimilation technology is an effective way to obtain accurate model initial values. Common data assimilation methods include dynamic approximation, optimal interpolation, Kalman filtering, variational assimilation, etc.

[0222] The optimal interpolation method is a data assimilation method that is widely used and relatively mature internationally. It can take into account the statistical characteristics of the background field and observation errors, that is, it includes the internal relationship between observation, forecast and analysis, and determines the best weight in the sense of least squares, so that the analysis error can be minimized in a statistical sense. The weather forecast system and ocean forecast system of the Japan Meteorological Agency, and the ocean data assimilation system in the ENSO forecast system of the US NCEP all use the optimal interpolation method as one of the assimilation schemes for observation data.

[0223] The assimilation scheme of the numerical forecast system adopts the optimal interpolation assimilation method. The basic algorithm of this method is:

[0224] x a =x b +K(yH[x b ])

[0225] K=BH T (HBH T +O) -1

[0226] Among them, x a and x a are the analysis field and background field respectively, K is the optimal weight, y is the observation data, H is the linear operator for interpolating the background field value to the observation position, the superscript T represents the transpose of the matrix, B is the background field error covariance matrix, and O is the observation field error covariance matrix.

[0227] 3. Construction of runoff aggregate samples

[0228] The boundary conditions that need to be adjusted for the numerical model of saltwater intrusion in the Yangtze River Estuary over time are the runoff boundary conditions and the wind field upper boundary conditions. Considering the uncertainty of upstream water and wind field, it is necessary to use ensemble forecasting technology to improve the coverage of numerical simulation results.

[0229] The magnitude of runoff determines the strength of saltwater intrusion at the estuary, and the correct value of the runoff is extremely important for the numerical forecast of saltwater intrusion. Datong Hydrological Station, 620 km away from Xuliujing at the Yangtze River Estuary, has daily measured runoff data, which provides a basis for the model to give the river boundary of the momentum equation. The upstream boundary of the river in the numerical forecast model of saltwater intrusion at the Yangtze River Estuary is set at Datong Hydrological Station. The runoff there affects the saltwater intrusion at the estuary about a week later, and its impact lag is naturally included in it.

[0230] However, the Datong hydrological station is still 620 km away from Xuliujing at the Yangtze River estuary. The runoff changes during this period will affect the runoff into the sea and the accuracy of saltwater intrusion forecasts. The main factors affecting the runoff are the East Route Project of the South-to-North Water Diversion Project and other drainage projects along the river (Qiu Cheng, 2014). The East Route Project of the South-to-North Water Diversion Project is in Yangzhou, diverting water to the north through the Grand Canal. The first phase of the project is 500m3 / s, the second phase project is 800m 3 / s. In extremely dry years, pumping water along the river below Datong reduces the runoff of the Yangtze River by 500-700m 3 / s. For normal years, the amount of water diversion and drainage along the river has not been given in detail, but the amount of water pumped is much smaller than that in dry years, so it can be generally considered that water diversion and drainage are basically balanced. Therefore, in the numerical prediction model of the Yangtze River Estuary, the runoff boundary condition is based on the measured runoff in the estuary area, minus the water diversion volume of the East Route Project of the South-to-North Water Diversion Project.

[0231] 4.1 Short-term runoff prediction sample

[0232] The model runoff is input from the Datong Station of the Yangtze River. The Datong runoff affects the Yangtze River Estuary for 5-7 days. Therefore, the current Datong runoff is the Yangtze River runoff at the Yangtze River Estuary 5-7 days later, and the Datong runoff before 5-7 days is the current Yangtze River runoff at the Yangtze River Estuary.

[0233] There is still a certain gap between the flow value of Datong and the runoff volume that is finally input to the Yangtze River Estuary. This gap is mainly caused by the fact that there is actually drainage along the way from Datong Station to the Yangtze River Estuary, but the model does not set the drainage along the way. Therefore, it is necessary to directly reflect the drainage along the way in the Datong input of the model. According to the above analysis, it is believed that the reduction in extremely dry years is 500-700m 3 / s. In normal years, water diversion and drainage are basically balanced.

[0234] The large-diameter flow input before the start time of model calculation is mainly given based on the measured flow at the large-diameter station:

[0235] y=ax+b

[0236] Among them, y is the runoff input of the model, x is the measured flow of the pass, and a and b are adjustment coefficients, which mainly reflect the drainage along the way.

[0237] Table of runoff input adjustment factors for the model

[0238] Maximum flow rate a b >=20000m3 / s 1.0 0 >12000m3 / s,<20000m3 / s 1.0 -700 <=12000m3 / s 1.0 -2000

[0239] 4.2 Construction of runoff prediction samples

[0240] The runoff input after the model calculation start time, that is, the input for predicted runoff, needs to be constructed based on other information because there is no measured data.

[0241] 4.2.1 Constructing runoff prediction samples based on forecast information

[0242] The Forecast Center of the Yangtze River Commission's Hydrological Bureau will release forecast information on large-scale runoff flow. When the forecast information is available, the runoff input will be constructed based on this forecast information.

[0243] The large-flow flow information released by the Forecast Center of the Yangtze River Commission's Hydrological Bureau includes short-term forecasts (daily forecasts for the past five days), medium-term forecasts (daily forecasts for every ten days), and long-term forecasts (monthly characteristic forecasts).

[0244]

[0245] The construction method is consistent with the measured part:

[0246] y=ax+b

[0247] Among them, y is the runoff input of the model, x is the measured flow of the pass, and a and b are adjustment coefficients, which mainly reflect the drainage along the way.

[0248] Table of runoff input adjustment factors for the model

[0249] Maximum flow rate a b >=20000m3 / s 1.0 0 >12000m3 / s,<20000m3 / s 1.0 -700 <=12000m3 / s 1.0 -2000

[0250] There are differences in confidence between short-term, medium-term and long-term forecast products. The confidence of short-term forecast is higher than that of medium-term, which is higher than that of long-term. Therefore, the runoff sample construction gives priority to the forecast product with high confidence at a specific moment.

[0251] 4.2.2 Empirical construction of runoff prediction samples

[0252] In the absence of runoff forecast information, the runoff prediction sample can be predicted by empirical construction. Considering that runoff changes are continuous, the runoff prediction value in a short period of time can be estimated based on the latest observation value.

[0253] Construct a forecast based on the last observed value (or the average value over a recent period of time, such as 24 hours).

[0254] y=ax+b

[0255] Among them, y is the runoff input of the model, x is the latest observation value of the measured flow in Datong, and a and b are adjustment coefficients, which mainly reflect the changes in continuity.

[0256] Table of runoff input adjustment factors for the model

[0257]

[0258]

[0259] In the absence of long-term forecast information on a monthly scale, climatological values ​​(multi-year averages) can be used to construct runoff sample values ​​over a longer period of time.

[0260] 4.3 Runoff aggregate sample construction scheme

[0261] The historical data of the runoff sample is calculated according to the observation value at the corresponding time as y=a1x+b1 (where y is the historical data value of the sample and x is the observation value at the corresponding time); the configuration items are the historical runoff coefficient a1 and the historical runoff offset b1.

[0262] The prediction data is constructed based on the average observation value of the last 24 hours through y=a2x+b2 (where y is the prediction value and x is the average observation value). The time is 6 hours later and 9999 hours later; the configuration items are the historical runoff coefficient a2 and the historical runoff offset b2.

[0263] Configure the constant value of runoff in the format of time + value. The configured data replaces the calculated data at that time. There may be multiple values.

[0264] The runoff sample set is constructed as follows: Fig. 9 shown.

[0265] 5. Operational Numerical Forecasting

[0266] The runoff ensemble samples are input into the estuary saltwater intrusion numerical model for calculation and processing to obtain the numerical forecast results. The uncertainty of the Yangtze River runoff is the main source of uncertainty in the calculation of the Yangtze River estuary saltwater intrusion model. The research and development of runoff ensemble forecasting technology, using multiple conditions with different occurrence probabilities and multiple cases for parallel forecasting, can more comprehensively cover the possibility of saltwater intrusion, and can better avoid underreporting in actual forecasts.

[0267] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for operational ensemble numerical prediction of estuary saltwater intrusion, characterized in that: include: Constructing a numerical model of saltwater intrusion at the estuary, and setting relevant parameters of the numerical model of saltwater intrusion at the estuary; Calculating and processing the bottom friction parameters of the estuary area, and calibrating the estuary saltwater intrusion numerical model according to the calculation results; Using the optimal interpolation data assimilation technology to perform data assimilation processing on the estuary saltwater intrusion numerical model; Based on the measured runoff in the estuary area, a runoff aggregate sample is constructed; as well as The runoff collection samples are input into the estuary saltwater intrusion numerical model for calculation and processing to obtain numerical prediction results.

2. The operational ensemble numerical prediction method for estuary saltwater intrusion according to claim 1, characterized in that: The method of constructing a numerical model of estuary saltwater intrusion and setting relevant parameters of the numerical model of estuary saltwater intrusion includes: A numerical model of saltwater intrusion in the estuary was constructed, and dynamic factors such as runoff, tide, wind stress and bottom friction were considered. Non-orthogonal curvilinear grids were used horizontally and σ coordinates were used vertically. Set the calculation area and perform local encryption on the grid in key areas; Set vertical stratification, time step and critical water depth; Set initial field values ​​such as water level field, flow field and salinity field; and Set boundary conditions such as tide level, residual water level, runoff and wind field.

3. The operational ensemble numerical prediction method for estuary saltwater intrusion according to claim 1, characterized in that: The method of calculating the bottom friction parameters of the estuary area and calibrating the estuary saltwater intrusion numerical model according to the calculation results includes: The bottom friction parameters in the estuary area include the Manning roughness coefficient and the bottom friction drag coefficient. The Manning roughness coefficient in the estuary area is assigned according to Table 1: Table 1 Values ​​of Manning's roughness coefficient Calculate the Xiecai coefficient C according to formula (1): Where H is the water depth, n is the Manning roughness coefficient; The bottom friction drag coefficient C in the estuary area is calculated according to formula (2): d : Among them, C is the Xie Cai coefficient, g is the acceleration of gravity; The numerical model of estuarine saltwater intrusion is calibrated using the Manning roughness coefficient and bottom friction drag coefficient of the estuarine area.

4. The operational ensemble numerical prediction method for estuary saltwater intrusion according to claim 1, characterized in that: The optimal interpolation data assimilation technology is used to perform data assimilation processing on the estuary saltwater intrusion numerical model, including: Select the measured sites for data assimilation. If the sites in some areas are too sparse, set up virtual sites. Conduct quality control on measured data of selected sites; The optimal interpolation method shown in formula (3) is used for data assimilation to produce the initial field of the model; Among them, x a and x b are the analysis field and background field respectively, K is the optimal weight, y is the observation data, H is the linear operator for interpolating the background field value to the observation position, the superscript T represents the transpose of the matrix, B is the background field error covariance matrix, and O is the observation field error covariance matrix.

5. The operational ensemble numerical prediction method for estuary saltwater intrusion as claimed in claim 4, characterized in that: The quality control includes format inspection, relevance inspection, missing inspection, range inspection, continuity inspection, visual graphics drawing inspection and comprehensive analysis inspection.

6. The operational ensemble numerical prediction method for estuary saltwater intrusion according to claim 1, characterized in that: The runoff aggregate sample is constructed based on the measured runoff in the estuary area, including: To construct a short-term runoff prediction sample, the measured runoff in the estuary area is deducted from the drainage along the way. The specific value is determined according to formula (4) and Table 2: y=ax+b (4) Among them, y is the runoff input of the model, x is the measured flow in the estuary area, and a and b are adjustment coefficients, which mainly reflect the drainage along the way; Table 2 Model runoff input adjustment coefficient table The runoff prediction samples are constructed based on the forecast information of the authoritative agencies. If there is no forecast information, the runoff prediction samples are constructed based on experience. The prediction value is constructed based on the last measured value or the average value of the most recent period. The specific value is determined according to formula (5) and Table 3: y=ax+b (5) Among them, y is the runoff input of the model, x is the latest observed value of the measured flow in the estuary area, and a and b are adjustment coefficients, which mainly reflect the change of continuity; Table 3 Model runoff input adjustment coefficient table If there is no measured data for empirical construction, the climatological state value is used to construct the runoff sample value; Construct a runoff aggregate sample, and form a runoff aggregate sample by modifying the measured runoff value and generating a plurality of runoff prediction samples according to the above.

7. A system for implementing the operational ensemble numerical prediction method for estuary saltwater intrusion according to any one of claims 1 to 6, characterized in that: include: A model building module, wherein the model building module is used to build a numerical model of estuarine saltwater intrusion and set relevant parameters of the numerical model of estuarine saltwater intrusion; A parameter calibration module, wherein the parameter calibration module is used to calculate the bottom friction parameters of the estuary area and calibrate the estuary saltwater intrusion numerical model according to the calculation results; A data assimilation module, wherein the data assimilation module is used to perform data assimilation processing on the estuary saltwater intrusion numerical model by using an optimal interpolation data assimilation technology; A sample construction module, wherein the sample construction module is used to construct a runoff collection sample based on the measured runoff in the estuary area; as well as The numerical prediction module is used to input the runoff set samples into the estuary saltwater intrusion numerical model for calculation and processing to obtain numerical prediction results.

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