Medium and long term runoff forecasting method and system based on composite air-sea circulation index and elastic coefficient

Abstract

The invention relates to the technical field of runoff forecasting, in particular to a medium-and-long-term runoff forecasting method and system based on a composite air-sea circulation index and an elastic coefficient, and the method comprises the steps: collecting sea temperature horizontal data, sub-high ridge latitude data, drainage basin data and potential evapotranspiration data; a space-time inverse distance weighted interpolation method is adopted to carry out missing value filling and standardization processing, and then a standardized data set is output; constructing a key index reflecting air-sea coupling and land runoff generation characteristics, wherein the key index comprises a composite air-sea circulation index and a rainfall-wet storage elastic coefficient; establishing a rainfall prediction model, and outputting the predicted monthly rainfall of the target month; and an evaporation-runoff production model is constructed, the monthly runoff of the target month is calculated in combination with a water balance equation, an evapotranspiration formula and a runoff production equation, and model parameters are optimized. According to the method, runoff forecasting from monthly scale to seasonal scale can be stably realized, and short-term fluctuation interference is avoided.

Description

Background Art

[0002] Medium- and long-term runoff forecasting is an important basis for water resources management, flood control and drought relief, and ecological protection. Traditional methods mostly rely on historical runoff statistical models or single meteorological factors (such as rainfall) for prediction. However, due to problems such as single data source and excessive model simplification, it is difficult to accurately reflect the complex mechanisms of air-sea coupling and land surface runoff generation. Existing technologies often ignore the lag effect of ocean circulation (such as the Kuroshio Extension and El Niño phenomenon) on regional climate, or do not fully consider the dynamic interaction between soil moisture and rainfall, resulting in insufficient forecasting accuracy. Especially in the context of frequent extreme climate events, its limitations are becoming more and more obvious.

[0003] Traditional methods mostly rely on a single climate factor (such as the ENSO index) and fail to fully quantify the air-sea interaction (such as the combined influence of the Kuroshio Extension and the Niño3.4 region), resulting in inaccurate characterization of the dynamic response of the atmospheric circulation; existing models often ignore the lag effect of soil moisture on runoff, or use fixed parameters to describe the evapotranspiration and runoff generation processes, resulting in poor adaptability to the dynamic changes of soil water storage in long-term forecasting; historical data often has spatio-temporal discontinuity problems, and traditional interpolation methods (such as linear interpolation) do not consider spatio-temporal correlation, affecting the quality of data standardization and the reliability of subsequent modeling; model parameters mostly rely on empirical values or local optimization, and are prone to overfitting or underfitting. Especially in a high-dimensional parameter space, there is a lack of regularization constraints and insufficient generalization ability. Summary of the Invention

[0004] The main purpose of the present invention is to provide a medium- and long-term runoff forecasting method based on a composite air-sea circulation index and an elastic coefficient, and to propose a system for implementing the above forecasting method. By calculating the sea surface temperature anomaly difference between the Kuroshio Extension and the Niño3.4 region and combining it with the standardized value of the latitude of the subtropical high ridge line, the driving effect of air-sea interaction on rainfall is dynamically quantified, and the ability to capture abnormal events of the atmospheric circulation is improved; using the sea surface temperature anomaly difference in the 3 months before the target month and the data of the subtropical high ridge line in the previous month to enhance the modeling of the lag effect of climate factors; introducing an attenuation time constant and historical rainfall weighting to reflect the cumulative response of soil moisture to previous rainfall, and solving the problem of insufficient characterization of the dynamic changes of soil water storage in traditional methods; combining the Zhang-Yang formula with the soil moisture-rainfall dual-response parameter to dynamically correct the actual evapotranspiration and avoid systematic biases caused by fixed coefficients; through the joint optimization of the least squares method and the regularization term, the fitting accuracy and parameter stability are balanced, overfitting is avoided, and the above problems mentioned in the background art are effectively solved.

[0005] The technical solution of the present invention is as follows:

[0006] In the first aspect, a medium- and long-term runoff forecasting method based on a composite air-sea circulation index and an elastic coefficient is proposed. The method includes the following steps:

[0007] S1. Collect sea surface temperature anomaly data, subtropical high ridge line latitude data, basin data, and potential evapotranspiration data. After filling in missing values using spatio-temporal inverse distance weighted interpolation method and performing standardization processing, output the standardized dataset;

[0008] S2. Construct key indices reflecting air-sea coupling and land surface runoff characteristics, where the key indices include a composite air-sea circulation index and a rainfall-wet storage elasticity coefficient;

[0009] S3. Establish a rainfall prediction model with the input being the composite air-sea circulation index of the target month, the composite air-sea circulation index one month before the target month, and historical monthly rainfall, and the output being the predicted rainfall of the target month. Output the predicted monthly rainfall of the target month;

[0010] S4. Construct an evaporation-runoff model, and calculate the monthly runoff of the target month in combination with the water balance equation, evapotranspiration formula, and runoff generation equation, and optimize the model parameters.

[0011] A further improvement of the present invention is that the S1 includes the following specific steps:

[0012] S11. Collect sea surface temperature anomaly data, subtropical high ridge line latitude data, basin data, and potential evapotranspiration data. The sea surface temperature anomaly data is the monthly average sea surface temperature anomaly in the Kuroshio Extension region and the 3.4 region; the subtropical high ridge line latitude data is the latitude of the subtropical high ridge in the western Pacific Ocean extracted from the 500 hPa geopotential height field; the basin data is monthly rainfall, monthly root zone soil moisture content, and monthly runoff at the control station; the potential evapotranspiration data is the monthly potential evapotranspiration calculated based on the Penman-Monteith formula;

[0013] S12. Use the spatio-temporal inverse distance weighted interpolation method to fill in missing values and perform Z-score standardization processing, and output the standardized dataset.

[0014] A further improvement of the present invention is that the S2 includes the following specific steps:

[0015] S21. Extract the monthly sea surface temperature anomalies in the Kuroshio Extension region and the 3.4 region three months before the target month from the standardized dataset respectively, and calculate the sea surface temperature anomaly difference three months before the target month. The calculation formula is:

[0016] ;

[0017] where, is the sea surface temperature anomaly difference three months before the target month, with the unit of °C, is the sea surface temperature anomaly in the Kuroshio Extension region three months before the target month, with the unit of °C, is three months before the target month The sea surface temperature anomaly in area 3.4, in °C, and t is the target month;

[0018] S22. Calculate the standardized value of the subtropical high ridge line latitude one month before the target month. The calculation formula is:

[0019] ;

[0020] Where, is the standardized value of the subtropical high ridge line latitude one month before the target month, is the subtropical high ridge line latitude one month before the target month, in °N, is the average value of the subtropical high ridge line latitudes in the past five years for the target month, in °N, is the standard deviation of the subtropical high ridge line latitudes in the past five years for the target month, in °N;

[0021] S23. Calculate the composite sea - air circulation index for the target month. The calculation formula is:

[0022] ;

[0023] Where, is the composite sea - air circulation index for the target month, is the historical standard deviation of the difference in sea surface temperature anomalies in the past five years three months before the target month, in °C.

[0024] A further improvement of the present invention is that S2 further includes:

[0025] S24. Extract the monthly rainfall in the six months before the target month and the monthly root - zone soil moisture content in the target month, and calculate the rainfall - wet storage elasticity coefficient for the target month. The calculation formula is:

[0026] ;

[0027] Where, is the rainfall - wet storage elasticity coefficient for the target month, is the monthly rainfall in the k months before the target month, in mm, is the memory decay time constant, with a value of 3, is the monthly root - zone soil moisture content in the target month, in mm, is a constant, with a value of 1 mm.

[0028] A further improvement of the present invention is that S3 includes the following specific steps:

[0029] S31. Extract the composite sea - air circulation index for the target month, the composite sea - air circulation index one month before the target month, and the historical monthly rainfall sequence , where n is the number of historical sample months, and T represents the transpose of the matrix;

[0030] S32. Construct a rainfall prediction model, where the rainfall prediction model is a linear regression model, and the calculation formula is:

[0031] ;

[0032] where, is the predicted monthly rainfall of the target month, is the constant term, , are the regression coefficients;

[0033] S33. Use the least squares method to solve the coefficients , and the formula is:

[0034] ;

[0035] where X is the design matrix, ; n is the number of historical samples;

[0036] S34. Output the predicted monthly rainfall of the target month .

[0037] A further improvement of the present invention is that the S4 includes the following specific steps:

[0038] S41. Describe the change of soil moisture content through the water balance equation, and the specific formula is:

[0039] ;

[0040] where, is the monthly root zone soil moisture content of the target month, is the monthly root zone soil moisture content one month before the target month, is the predicted monthly rainfall of the target month, is the monthly actual evapotranspiration of the target month, is the runoff of the target month;

[0041] S42. The calculation formula of the monthly actual evapotranspiration of the target month is:

[0042] ;

[0043] where, is the evapotranspiration correction coefficient, is the monthly potential evapotranspiration of the target month, is the soil moisture response parameter, is the rainfall response parameter;

[0044] S43. Calculate the runoff of the target month in combination with the rainfall-wet storage elasticity coefficient , and the calculation formula is:

[0045] ;

[0046] Among them, is the runoff of the target month, is the runoff coefficient, is the soil water storage conversion index, is the PMSI sensitivity coefficient, .

[0047] A further improvement of the present invention is that the S4 further includes:

[0048] S44. Optimize the model parameter set ; construct a parameter optimization objective function;

[0049] S45. The parameter optimization objective function is:

[0050] ;

[0051] Among them, is the monthly runoff of the control station in the jth historical sample month, is the runoff in the jth historical sample month, is the regularization coefficient, is the value of the ith parameter in the model parameter set, is the prior mean of the ith parameter in the model parameter set, is the prior standard deviation of the ith parameter in the model parameter set.

[0052] In the second aspect of the present invention, a medium- and long-term runoff forecasting system is proposed, which can automatically execute the medium- and long-term runoff forecasting method based on the composite sea-air circulation index and elasticity coefficient described in the first aspect.

[0053] The system includes four components: a data acquisition module, a first processing module, a second processing module, and an output module.

[0054] The data acquisition module is used to collect sea surface temperature anomaly data, subtropical high ridge line latitude data, basin data, and potential evapotranspiration data, and perform missing value filling and standardization processing using the spatio-temporal inverse distance weighted interpolation method, and then output the standardized data set;

[0055] The first processing module is used to construct key indices reflecting sea-air coupling and land surface runoff characteristics, and the key indices include the composite sea-air circulation index and the rainfall-wet storage elasticity coefficient;

[0056] The second processing module is used to establish a rainfall prediction model with the input being the composite air-sea circulation index of the target month, the composite air-sea circulation index one month before the target month, and the historical monthly rainfall, and the output being the predicted rainfall of the target month, and output the predicted monthly rainfall of the target month;

[0057] The output module is used to construct an evaporation-runoff generation model, and calculate the monthly runoff of the target month in combination with the water balance equation, the evapotranspiration formula, and the runoff generation equation, and optimize the model parameters.

[0058] In the third aspect of the present invention, an electronic device is proposed. The electronic device includes a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the medium- and long-term runoff prediction method based on the composite air-sea circulation index and the elastic coefficient as described in the first aspect is implemented.

[0059] In the fourth aspect of the present invention, a computer-readable storage medium is proposed. At least one executable instruction is stored in the storage medium. When the executable instruction runs on an electronic device, the electronic device is enabled to execute the medium- and long-term runoff prediction method based on the composite air-sea circulation index and the elastic coefficient as described in the first aspect.

[0060] The technical effects of the present invention are as follows:

[0061] The present invention constructs a medium- and long-term runoff forecasting method based on a composite sea-air circulation index and an elastic coefficient. By calculating the sea surface temperature anomaly difference between the Kuroshio Extension region and the Niño3.4 region, and combining it with the standardized value of the latitude of the subtropical high ridge line, the driving effect of sea-air interaction on rainfall is dynamically quantified, enhancing the ability to capture abnormal atmospheric circulation events. Using the sea surface temperature anomaly difference in the three months before the target month and the subtropical high ridge line data in the previous month, the lag effect modeling of climate factors is enhanced. An attenuation time constant and historical rainfall weighting are introduced to reflect the cumulative response of soil moisture to previous rainfall, addressing the problem of insufficient characterization of the dynamic changes in soil water storage in traditional methods. Combining the Zhang-Yang formula with the soil moisture-rainfall dual-response parameter to dynamically correct the actual evapotranspiration, avoiding systematic biases caused by fixed coefficients. Through the joint optimization of the least squares method and the regularization term, the fitting accuracy and parameter stability are balanced, avoiding overfitting. Relying on the 1-3-month leading signal of SAI and the 0-6-month rainfall memory of PMSI, runoff forecasting at the monthly scale (1-3 months) to the seasonal scale (1 quarter) can be stably achieved, avoiding interference from short-term fluctuations, capturing medium- and long-term trends, and improving the adaptability to time periods. Only requiring conventional meteorological observations (rainfall, potential evapotranspiration) and land surface model outputs (soil water content), it is applicable to medium and large basins (area > 1000 km²), with low data acquisition costs and no need to deploy a high-precision sensor network, improving the universality of the basin. For the first time, the western Pacific-eastern Pacific sea surface temperature dipole is directly associated with the swing of the subtropical high ridge line, quantifying the long-distance regulation of "ocean thermodynamics-atmospheric circulation" on rainfall, especially applicable to the East Asian monsoon influence region. Description of the Drawings

[0062] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0063] Figure 1 It is a schematic flow diagram of the medium- and long-term runoff forecasting method based on the composite sea-air circulation index and the elastic coefficient in the embodiment.

[0064] Figure 2 It is the trial forecast result of the monthly flow process at the Lishi Station in the embodiment.

[0065] Figure 3 It is the trial forecast result of the monthly flow process at the Changba Station in the embodiment.

[0066] Figure 4 It is the trial forecast result of the monthly flow process at the Gaodao Station in the embodiment.

[0067] Figure 5 It is the trial forecast result of the monthly flow process at the Changhu Dam Station in the embodiment.

[0068] Figure 6 It is the trial forecast result of the monthly flow process at the Shijiao Station in the embodiment.

[0069] Figure 7 Monthly flow process trial forecast results of Shigou Station in the embodiment. Specific implementation mode

[0070] As Figure 1 shown, this embodiment discloses a medium- and long-term runoff forecasting method based on a composite sea-air circulation index and an elastic coefficient, and the steps are as follows:

[0071] S1. Collect sea surface temperature anomaly data, subtropical high ridge line latitude data, basin data, and potential evapotranspiration data, and use the spatio-temporal inverse distance weighted interpolation method to fill in missing values and perform standardization processing, and then output the standardized data set;

[0072] S2. Construct key indices reflecting sea-air coupling and land surface runoff generation characteristics, where the key indices include a composite sea-air circulation index and a rainfall-wet storage elastic coefficient;

[0073] S3. Establish a rainfall prediction model with the composite sea-air circulation index of the target month, the composite sea-air circulation index one month before the target month, and historical monthly rainfall as inputs, and the predicted rainfall of the target month as the output, and output the predicted monthly rainfall of the target month;

[0074] S4. Construct an evaporation-runoff generation model, and calculate the monthly runoff of the target month in combination with the water balance equation, the evapotranspiration formula, and the runoff generation equation, and optimize the model parameters.

[0075] As a preferred embodiment, in step S1:

[0076] The sea surface temperature anomaly data is the monthly average sea surface temperature anomaly in the Kuroshio Extension area and area 3.4;

[0077] The subtropical high ridge line latitude data is the subtropical high ridge line latitude in the western Pacific extracted from the 500 hPa geopotential height field;

[0078] The basin data is monthly rainfall, monthly root zone soil moisture content, and monthly runoff at the control station;

[0079] The potential evapotranspiration data is the monthly potential evapotranspiration calculated based on the Penman-Monteith formula.

[0080] As a preferred embodiment, to construct the key indices reflecting sea-air coupling and land surface runoff generation characteristics, the following specific process can be adopted to implement:

[0081] S21. Respectively extract the monthly sea surface temperature anomalies in the Kuroshio Extension area and area 3.4 three months before the target month from the standardized data set, and calculate the sea surface temperature anomaly difference three months before the target month. The calculation formula is:

[0082] ;

[0083] Among them, is the sea surface temperature anomaly difference three months before the target month, with the unit of °C, is the sea surface temperature anomaly in the Kuroshio Extension area three months before the target month, with the unit of °C, is three months before the target month The sea surface temperature anomaly in the 3.4 area, with the unit of °C, and t is the target month;

[0084] S22. Calculate the standardized value of the subtropical high ridge line latitude one month before the target month. The calculation formula is:

[0085] ;

[0086] Among them, is the standardized value of the subtropical high ridge line latitude one month before the target month, is the subtropical high ridge line latitude one month before the target month, with the unit of °N, is the average value of the subtropical high ridge line latitude in the past five years of the target month, with the unit of °N, is the standard deviation of the subtropical high ridge line latitude in the past five years of the target month, with the unit of °N;

[0087] S23. Calculate the composite sea-air circulation index of the target month. The calculation formula is:

[0088] ;

[0089] Among them, is the composite sea-air circulation index of the target month, is the historical standard deviation of the sea surface temperature anomaly difference in the past five years three months before the target month, with the unit of °C;

[0090] S24. Extract the monthly rainfall in the six months before the target month and the monthly root zone soil moisture content of the target month, and calculate the rainfall-wet storage elasticity coefficient of the target month. The calculation formula is:

[0091] ;

[0092] Among them, is the rainfall-wet storage elasticity coefficient of the target month, is the monthly rainfall in the k months before the target month, with the unit of mm, is the memory decay time constant, with a value of 3, is the monthly root zone soil moisture content of the target month, with the unit of mm, is a constant, with a value of 1 mm. When, the soil water storage is close to saturation, and rainfall is easily converted into runoff; When, there is still storage space in the soil layer.

[0093] As a preferred embodiment, step S3 may adopt the following implementation steps:

[0094] S31. Extract the composite air-sea circulation index of the target month , the composite air-sea circulation index one month before the target month , and the historical monthly rainfall sequence , where n is the number of historical sample months, and T represents the transpose of the matrix;

[0095] S32. Construct a rainfall prediction model, where the rainfall prediction model is a linear regression model, and the calculation formula is:

[0096] ;

[0097] where is the predicted monthly rainfall of the target month, is the constant term, , are the regression coefficients;

[0098] S33. Solve the coefficients using the least squares method , and the formula is:

[0099] ;

[0100] where X is the design matrix, ; n is the number of historical samples;

[0101] S34. Output the predicted monthly rainfall of the target month .

[0102] As a preferred embodiment, step S4 may adopt the following implementation steps:

[0103] S41. Describe the change in soil moisture content through the water balance equation, and the specific formula is:

[0104] ;

[0105] where is the monthly root zone soil moisture content of the target month, is the monthly root zone soil moisture content one month before the target month, is the predicted monthly rainfall of the target month, is the monthly actual evapotranspiration of the target month, is the runoff of the target month;

[0106] S42. The calculation formula for the monthly actual evapotranspiration of the target month is:

[0107] ;

[0108] Among them, is the evapotranspiration correction coefficient, is the monthly potential evapotranspiration of the target month, is the soil moisture response parameter, is the rainfall response parameter;

[0109] S43. Calculate the runoff of the target month in combination with the rainfall-wet storage elasticity coefficient , and the calculation formula is:

[0110] ;

[0111] Among them, is the runoff of the target month, is the runoff coefficient, is the soil water storage conversion index, is the PMSI sensitivity coefficient, ;

[0112] S44. Optimize the model parameter set ; Construct the parameter optimization objective function;

[0113] S45. The parameter optimization objective function is:

[0114] ;

[0115] Among them, is the monthly runoff of the control station in the jth historical sample month, is the runoff in the jth historical sample month, is the regularization coefficient, is the value of the ith parameter in the model parameter set, is the prior mean of the ith parameter in the model parameter set, is the prior standard deviation of the ith parameter in the model parameter set.

[0116] In this embodiment, the monthly natural runoff processes of the outlet section stations of each control station (Lishi Station, Changba Station, Gaodao Station, Changhu Dam Station, Shijiao Station, Shigou Station) in the Beijiang River Basin from 2005 to 2010 were tried and reported. The trial and report results are shown in Figures 2 to 7 .

[0117] In this embodiment, the monthly runoff simulation accuracy in the simulation period and the monthly runoff trial and report accuracy in the prediction period of each sub-region in the Beijiang River Basin were analyzed by using the coefficient of determination (DC) and the relative error of runoff total amount (RE). The simulation and trial and report accuracy statistics are shown in Table 1.

[0118] Table 1 Statistics of simulation and trial and report accuracies of each sub-region in the Beijiang River Basin

[0119]

[0120] As can be seen from Table 1, the evaporation-runoff model proposed in this embodiment has strong applicability for simulating the monthly runoff process in the Beijiang River Basin, and the simulation effect is good. From the relevant accuracy indicators and the comparison between the simulated and measured flow process lines, it can be seen that for different spatial scales and different geographical location sub-regions or sub-basins in the Beijiang River Basin, the model can well depict the monthly runoff process, and the corresponding relationship between the simulated and measured peaks is good. The deterministic coefficients of each sub-region during the calibration period are all above 0.9, and the total simulated runoff is basically balanced with the total measured runoff; except for the Changba Station with a deterministic coefficient of 0.707 during the verification period, the deterministic coefficients of the other stations are all above 0.8, and the relative error of the average total runoff during the verification is basically controlled within 20%.

[0121] The technical process of the medium- and long-term runoff forecasting method disclosed in the above embodiment can be implemented in whole or in part by software, hardware, firmware, or any other combination.

[0122] When implemented using hardware, the logic of the medium- and long-term runoff forecasting method based on the composite sea-air circulation index and elastic coefficient disclosed in the above embodiment can be automatically run and implemented by a set of medium- and long-term runoff forecasting systems. The system can include four components: a data collection module, a first processing module, a second processing module, and an output module. The data collection module is used to collect sea surface temperature anomaly data, subtropical high ridge line latitude data, basin data, and potential evapotranspiration data, and perform missing value filling and standardization processing using the spatio-temporal inverse distance weighted interpolation method, and then output the standardized data set. The first processing module is used to construct key indices reflecting sea-air coupling and land surface runoff generation characteristics, and the key indices include the composite sea-air circulation index and the rainfall-wet storage elastic coefficient. The second processing module is used to establish a rainfall prediction model with the composite sea-air circulation index of the target month, the composite sea-air circulation index one month before the target month, and historical monthly rainfall as inputs, and the predicted rainfall of the target month as the output, and output the predicted monthly rainfall of the target month. The output module is used to construct an evaporation-runoff model, combine the water balance equation, evapotranspiration formula, and runoff generation equation to calculate the monthly runoff of the target month, and optimize the model parameters.

[0123] When implemented using hardware, the above embodiment can also run the working logic and calculation process on an electronic device after being compiled by software in whole or in part. The electronic device includes a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete communication with each other through the communication bus. The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the technical process of the medium- and long-term runoff forecasting method disclosed in the above embodiment, which will not be elaborated here.

[0124] The electronic device may vary greatly due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) and one or more memories. Among them, at least one computer program is stored in the memory, and this computer program is loaded and executed by the processor to implement the medium- and long-term runoff forecasting method based on the composite ocean-atmosphere circulation index and elastic coefficient provided by the above method embodiments. The electronic device can also include other components for implementing the functions of the device. For example, the electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for inputting and outputting data. This embodiment will not be elaborated here.

[0125] Those skilled in the art of the present technology know that the present invention can be implemented as a system, a method, or a computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, that is: it can be completely hardware, can also be completely software (including firmware, resident software, microcode, etc.), or can also be in the form of a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" in this article. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable media contains computer-readable program code.

[0126] Any combination of one or more computer-readable media can be adopted. The computer-readable media can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component.

[0127] The present invention will be described with reference to the flowcharts and block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow or block in the flowcharts and block diagrams, as well as the combination of flows and blocks in the flowcharts or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and blocks Figure 1 or one or more of the blocks.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, so that the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and blocks Figure 1 or one or more of the blocks.

[0129] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit and scope of the present invention as protected by the claims. All of these fall within the protection scope of the present invention.

Claims

1. A medium- and long-term runoff forecasting method based on a composite sea-air circulation index and an elasticity coefficient, characterized in that It includes the following steps: S1. Collect sea surface temperature anomaly data, subtropical high ridge line latitude data, basin data, and potential evapotranspiration data, perform missing value filling using spatio-temporal inverse distance weighted interpolation method and standardize the data, and then output the standardized data set; S2. Construct key indices reflecting sea-air coupling and land surface runoff generation characteristics, where the key indices include a composite sea-air circulation index and a rainfall-wet storage elasticity coefficient; S3. Establish a rainfall prediction model with the input being the composite sea-air circulation index of the target month, the composite sea-air circulation index one month before the target month, and historical monthly rainfall, and the output being the predicted rainfall of the target month, and output the predicted monthly rainfall of the target month; S4. Construct an evaporation-runoff generation model, combine the water balance equation, evapotranspiration formula, and runoff generation equation to calculate the monthly runoff of the target month, and optimize the model parameters.

2. The medium- and long-term runoff forecasting method based on the composite air-sea circulation index and elastic coefficient according to claim 1, wherein The S1 includes the following specific steps: S11. Collect sea surface temperature anomaly data, subtropical high ridge line latitude data, basin data, and potential evapotranspiration data. The sea surface temperature anomaly data is the monthly average sea surface temperature anomaly in the Kuroshio Extension region and Area 3.4; the subtropical high ridge line latitude data is the subtropical high ridge line latitude in the western Pacific extracted from the 500 hPa geopotential height field; the basin data is the monthly rainfall, monthly root zone soil moisture content, and monthly runoff at the control station; the potential evapotranspiration data is the monthly potential evapotranspiration calculated based on the Penman-Monteith formula. S12. Perform missing value filling using spatio-temporal inverse distance weighted interpolation method and perform Z-score standardization, and then output the standardized data set.

3. The medium- and long-term runoff forecasting method based on the composite air-sea circulation index and elastic coefficient according to claim 2, characterized in that The S2 includes the following specific steps: S21. Extract the monthly sea surface temperature anomaly of the Kuroshio Extension and Area 3.4 three months before the target month from the standardized dataset, and calculate the sea surface temperature anomaly difference three months before the target month. The calculation formula is as follows: ； Among them, is the sea surface temperature anomaly difference three months before the target month, with the unit of °C, is the sea surface temperature anomaly in the Kuroshio Extension area three months before the target month, with the unit of °C, is three months before the target month the sea surface temperature anomaly in the 3.4 area, with the unit of °C, and t is the target month; S22. Calculate the standardized value of the subtropical high ridge line latitude one month before the target month, and the calculation formula is: ； Among them, is the standardized value of the latitude of the subtropical high ridge line one month before the target month, is the latitude of the subtropical high ridge line one month before the target month, with the unit of °N, is the average value of the latitudes of the subtropical high ridge lines in the past five years of the target month, with the unit of °N, is the standard deviation of the latitudes of the subtropical high ridge lines in the past five years of the target month, with the unit of °N; S23. Calculate the composite sea-air circulation index of the target month, and the calculation formula is: ； Among them, is the composite air-sea circulation index for the target month, is the historical standard deviation of the sea surface temperature anomaly difference in the past five years three months before the target month, with the unit of °C.

4. The medium- and long-term runoff forecasting method based on the composite air-sea circulation index and elastic coefficient according to claim 3, wherein The S2 further includes: S24. Extract the monthly rainfall in the six months before the target month and the monthly root zone soil moisture content of the target month, and calculate the rainfall-wet storage elasticity coefficient of the target month, and the calculation formula is: ； Among them, is the rainfall-wet storage elasticity coefficient of the target month, is the monthly rainfall in the k months before the target month, with the unit of mm, is the memory decay time constant, with a value of 3, is the monthly root zone soil water content of the target month, with the unit of mm, is a constant, with a value of 1mm.

5. The medium- and long-term runoff forecasting method based on the composite air-sea circulation index and elastic coefficient according to claim 4, characterized in that, The S3 includes the following specific steps: S31. Extract the composite ocean - atmosphere circulation index for the target month and the composite ocean - atmosphere circulation index one month before the target month and the historical monthly rainfall series , where n is the number of historical sample months, and T represents the transpose of the matrix; S32. Construct a rainfall prediction model, and the rainfall prediction model is a linear regression model, and the calculation formula is: ； Among them, is the predicted monthly rainfall for the target month, is the constant term, , are the regression coefficients; S33. Solve the coefficients using the least squares method , and the formula is: ； where X is the design matrix, ; n is the number of historical samples; S34. Output the predicted monthly rainfall for the target month .

6. The medium- and long-term runoff forecasting method based on the composite air-sea circulation index and elastic coefficient according to claim 5, characterized in that The S4 includes the following specific steps: S41. Describe the change of soil moisture content through the water balance equation, and the specific formula is: ； Among them, is the soil water content in the root zone of the target month, is the soil water content in the root zone one month before the target month, is the predicted monthly rainfall of the target month, is the actual monthly evapotranspiration of the target month, is the runoff of the target month; S42. Monthly actual evapotranspiration of the target month The calculation formula is as follows: ； Among them, is the evapotranspiration correction coefficient, is the monthly potential evapotranspiration of the target month, is the soil moisture response parameter, is the rainfall response parameter; S43. Calculate the runoff of the target month in combination with the rainfall-wet storage elasticity coefficient , and the calculation formula is as follows: ； Among them, is the runoff of the target month, is the runoff coefficient, is the soil water storage conversion index, is the PMSI sensitivity coefficient, .

7. The medium- and long-term runoff forecasting method based on the composite air-sea circulation index and elastic coefficient according to claim 6, wherein The S4 further includes: S44. Optimize the set of model parameters ; Construct the objective function for parameter optimization; S45. The parameter optimization objective function is: ； Among them, is the monthly runoff of the control station in the j-th historical sample month, is the runoff in the j-th historical sample month, is the regularization coefficient, is the value of the i-th parameter in the model parameter set, is the prior mean of the i-th parameter in the model parameter set, is the prior standard deviation of the i-th parameter in the model parameter set.

8. A medium- and long-term runoff forecasting system, characterized in that, It includes: A data acquisition module, which is used to collect sea surface temperature anomaly data, subtropical high ridge line latitude data, basin data, and potential evapotranspiration data, perform missing value filling using spatio-temporal inverse distance weighted interpolation method and standardize the data, and then output the standardized data set; A first processing module, which is used to construct key indices reflecting sea-air coupling and land surface runoff generation characteristics, where the key indices include a composite sea-air circulation index and a rainfall-wet storage elasticity coefficient; A second processing module, which is used to establish a rainfall prediction model with the input being the composite sea-air circulation index of the target month, the composite sea-air circulation index one month before the target month, and historical monthly rainfall, and the output being the predicted rainfall of the target month, and output the predicted monthly rainfall of the target month; An output module, which is used to construct an evaporation-runoff generation model, combine the water balance equation, evapotranspiration formula, and runoff generation equation to calculate the monthly runoff of the target month, and optimize the model parameters; The medium and long-term runoff forecasting system can automatically execute the medium and long-term runoff forecasting method based on the composite sea-air circulation index and elasticity coefficient as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the medium- and long-term runoff forecasting method based on the composite air-sea circulation index and the elastic coefficient as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, At least one executable instruction is stored in the storage medium, and when the executable instruction runs on the electronic device, the electronic device is caused to execute the medium- and long-term runoff forecasting method based on the composite air-sea circulation index and the elastic coefficient as described in any one of claims 1 to 7.

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