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

Abstract

The present invention relates to the field of runoff forecasting technology, and in particular to a medium- and long-term runoff forecasting method and system based on a composite ocean-air circulation index and elasticity coefficient. The method comprises: collecting sea temperature anomaly data, subtropical high ridgeline latitude data, watershed data, and potential evapotranspiration data; using the spatiotemporal inverse distance weighted interpolation method to fill missing values, standardize the data, and output a standardized dataset; constructing a key index reflecting the characteristics of ocean-air coupling and land surface runoff generation, the key index comprising the composite ocean-air circulation index and the rainfall-wet storage elasticity coefficient; establishing a rainfall prediction model to output the predicted monthly rainfall for the target month; constructing an evaporation-runoff model to calculate the monthly runoff for the target month by combining the water balance equation, the evapotranspiration formula, and the runoff generation equation, and optimizing the model parameters. This method can stably achieve runoff forecasts from monthly to seasonal scales, avoiding interference from short-term fluctuations.

Description

Technical Field

[0001] The present invention relates to the technical field of runoff forecasting, and in particular to a medium- and long-term runoff forecasting method and system based on a composite air-sea circulation index and elastic coefficient. Background Art

[0002] Medium- and long-term runoff forecasts are an important foundation for water resource management, flood and drought prevention, and ecological protection. Traditional methods often rely on historical runoff statistical models or single meteorological factors (such as rainfall) for prediction. However, due to problems such as a single data source and overly simplified models, they cannot accurately reflect the complex mechanisms of ocean-atmosphere coupling and land-surface runoff generation. Existing technologies often ignore the lagged impact of ocean circulation (such as the Kuroshio Extension and El Niño) on regional climate, or fail to fully consider the dynamic interaction between soil moisture and rainfall, resulting in insufficient forecast accuracy. These limitations are particularly prominent in the context of frequent extreme climate events.

[0003] Traditional methods often rely on a single climate factor (such as the ENSO index) and fail to fully quantify ocean-air interactions (such as the synergistic influence of the Kuroshio Extension area and the Niño3.4 area), resulting in inaccurate characterization of the dynamic response of atmospheric circulation; existing models often ignore the lag effect of soil moisture on runoff, or use fixed parameters to describe evapotranspiration and runoff production processes, resulting in poor adaptability to dynamic changes in soil water storage in long-term forecasts; historical data often have spatiotemporal discontinuities, and traditional interpolation methods (such as linear interpolation) do not consider spatiotemporal correlations, affecting the quality of data standardization and the reliability of subsequent modeling; model parameters often rely on empirical values or local optimization, which is prone to overfitting or underfitting, especially in the lack of regularization constraints in high-dimensional parameter space, 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 sea-air circulation index and elastic coefficient, and propose a system for implementing the above-mentioned forecasting method. By combining the sea temperature anomaly difference between the Kuroshio Extension Zone and the Niño 3.4 Zone with the latitude normalized value of the subtropical high ridge line, the driving effect of sea-air interaction on rainfall is dynamically quantified, thereby improving the ability to capture abnormal atmospheric circulation events; the sea temperature anomaly difference of the three months before the target month and the subtropical high ridge line data of the previous month are used to enhance the modeling of the lag effect of climate factors; the decay time constant and historical rainfall weighting are introduced to reflect the cumulative response of soil moisture to previous rainfall, thereby solving the problem of insufficient description of the dynamic changes of soil water storage in traditional methods; the Zhang-Yang formula and the soil moisture-rainfall dual response parameter are combined to dynamically correct the actual evapotranspiration to avoid systematic deviations 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 to avoid overfitting, effectively solving the above-mentioned problems mentioned in the background technology.

[0005] The technical solutions of the present invention are as follows:

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

[0007] S1. Collect sea temperature anomaly data, subtropical high ridgeline latitude data, watershed data, and potential evapotranspiration data, use the spatiotemporal inverse distance weighted interpolation method to fill missing values, and then standardize the data to output the standardized dataset;

[0008] S2. Constructing key indices reflecting the characteristics of sea-air coupling and land surface runoff generation, wherein the key indices include a composite sea-air circulation index and a rainfall-humidity storage elasticity coefficient;

[0009] S3. Establish a rainfall prediction model whose inputs are 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 whose output is the predicted rainfall for the target month, and output the predicted monthly rainfall for the target month;

[0010] S4. Construct an evaporation-runoff model, combine the water balance equation, evapotranspiration formula, and runoff output equation to calculate the monthly runoff in the target month, and optimize the model parameters.

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

[0012] S11. Collect sea temperature anomaly data, subtropical high ridgeline latitude data, river basin data and potential evapotranspiration data. The sea temperature anomaly data is for the Kuroshio extension area and 3.4 Monthly mean sea temperature anomalies in the region; the subtropical high ridgeline latitude data is the latitude of the western Pacific subtropical high ridgeline extracted from the 500hPa geopotential height field; the watershed data are monthly rainfall, monthly root zone soil moisture content, and monthly runoff at the control station; the potential evapotranspiration data are monthly potential evapotranspiration calculated based on the Penman-Monteith formula;

[0013] S12. Use the spatiotemporal inverse distance weighted interpolation method to fill missing values and perform Z-score standardization, and output the standardized data set.

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

[0015] S21, extract the Kuroshio extension area and the 3.4 Monthly sea temperature anomaly in the region, calculate the sea temperature anomaly difference three months before the target month, the calculation formula is:

[0016] ;

[0017] in, is the sea temperature anomaly three months before the target month, in °C. is the sea temperature anomaly in the Kuroshio extension area three months before the target month, in degrees Celsius. 3 months before the target month 3.4 SST anomalies in region, in °C, with t being the target month;

[0018] S22. Calculate the normalized latitude of the subtropical high ridgeline one month before the target month using the following formula:

[0019] ;

[0020] in, is the normalized value of the subtropical high ridgeline latitude one month before the target month, is the latitude of the subtropical high ridgeline one month before the target month, in degrees N. is the average latitude of the subtropical high ridgeline in the target month over the past five years, in degrees N. is the standard deviation of the subtropical high ridgeline latitude in the target month over the past five years, in degrees North;

[0021] S23. Calculate the composite air-sea circulation index for the target month using the following formula:

[0022] ;

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

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

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

[0026] ;

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

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

[0029] S31. Extract the composite air-sea circulation index for the target month , Composite Sea-Air Circulation Index one month before the target month , historical monthly rainfall series , n is the number of historical sample months, T represents the transpose of the matrix;

[0030] S32, constructing a rainfall prediction model, wherein the rainfall prediction model is a linear regression model, and the calculation formula is:

[0031] ;

[0032] in, is the predicted monthly rainfall for the target month, is a constant term, 、 is the regression coefficient;

[0033] S33, use the least squares method to solve the coefficient , 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 for the target month .

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

[0038] S41. The change of soil moisture content is described by the water balance equation. The specific formula is:

[0039] ;

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

[0041] S42. Actual monthly evapotranspiration in the target month The calculation formula is:

[0042] ;

[0043] in, 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 volume for the target month by combining the rainfall-wet storage elasticity coefficient , the calculation formula is:

[0045] ;

[0046] in, is the runoff volume in 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 step S4 further comprises:

[0048] S44. Optimize model parameter set ;Construct parameter optimization objective function;

[0049] S45, the parameter optimization objective function is:

[0050] ;

[0051] in, is the monthly runoff of the control station in the jth sample month in history, is the runoff volume of the jth sample month in history, 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.

[0052] The second aspect of the present invention provides a medium- and long-term runoff forecasting system, which can automatically execute the medium- and long-term runoff forecasting method based on the composite sea-air circulation index and elastic 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 temperature anomaly data, subtropical high ridgeline latitude data, watershed data, and potential evapotranspiration data. It uses the spatiotemporal inverse distance weighted interpolation method to fill missing values and standardize the data before outputting the standardized data set.

[0055] The first processing module is used to construct a key index reflecting the characteristics of sea-air coupling and land surface runoff generation, wherein the key index includes a composite sea-air circulation index and a rainfall-humidity storage elasticity coefficient;

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

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

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

[0059] In a fourth aspect of the present invention, a computer-readable storage medium is proposed, which stores at least one executable instruction. When the executable instruction is run on an electronic device, the electronic device executes the medium- and long-term runoff forecasting method based on the composite sea-air circulation index and elastic coefficient as described in the first aspect.

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

[0061] This paper constructs a medium- and long-term runoff forecast method based on a composite air-sea circulation index and elasticity coefficient. By combining the sea temperature anomalies between the Kuroshio Extension and Niño 3.4 regions with the latitude-normalized value of the subtropical high ridgeline, the method dynamically quantifies the driving effect of air-sea interactions on rainfall, improving the ability to capture anomalous atmospheric circulation events. The method also utilizes sea temperature anomalies from the three months preceding the target month and subtropical high ridgeline data from the previous month to enhance the modeling of the lag effect of climate factors. The method also introduces a decay time constant and historical rainfall weighting to reflect the cumulative response of soil moisture to previous rainfall, addressing the inadequate depiction of dynamic changes in soil water storage in traditional methods. The Zhang-Yang formula is combined with a soil moisture-rainfall dual-response parameter to dynamically correct actual evapotranspiration to avoid systematic biases caused by fixed coefficients. Finally, a joint optimization approach using the least squares method and regularization terms is employed to balance fitting accuracy with parameter stability to avoid overfitting. Relying on the 1-3 month forward signal of SAI and the 0-6 month rainfall memory of PMSI, it can stably achieve runoff forecasts from monthly scale (1-3 months) to seasonal scale (1 quarter), avoiding the interference of short-term fluctuations, capturing medium- and long-term trends, and improving time period adaptability. It only requires conventional meteorological observations (rainfall, potential evapotranspiration) and land surface model output (soil moisture content), and is suitable for medium and large basins (area > 1000 km²). The data acquisition cost is low, and there is no need to deploy high-precision sensor networks, which improves basin universality. For the first time, it directly links the Western Pacific-Eastern Pacific SST Dipole with the swing of the subtropical high ridgeline, quantifying the remote regulation of rainfall by "ocean thermal-atmospheric circulation", which is particularly applicable to the East Asian monsoon-affected area. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0064] Figure 2 This is the trial report result of the monthly flow process of Lishi Station in the embodiment.

[0065] Figure 3 This is the trial report result of the monthly flow process of Changba Station in the embodiment.

[0066] Figure 4 This is the trial report result of the monthly flow process of Gaodao Station in the embodiment.

[0067] Figure 5 This is the trial report result of the monthly flow process of Changhu Dam Station in the embodiment.

[0068] Figure 6 This is the trial report result of the monthly flow process of Shijiao Station in the embodiment.

[0069] Figure 7 This is the trial report result of the monthly flow process of Shigou Station in the embodiment. DETAILED DESCRIPTION

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

[0071] S1. Collect sea temperature anomaly data, subtropical high ridgeline latitude data, watershed data, and potential evapotranspiration data, use the spatiotemporal inverse distance weighted interpolation method to fill missing values, and then standardize the data to output the standardized dataset;

[0072] S2. Construct key indices reflecting the characteristics of air-sea coupling and land surface runoff generation, including the composite air-sea circulation index and the rainfall-humidity storage elasticity coefficient;

[0073] S3. Establish a rainfall prediction model whose inputs are 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 whose output is the predicted rainfall for the target month, and output the predicted monthly rainfall for the target month;

[0074] S4. Construct an evaporation-runoff model, combine the water balance equation, evapotranspiration formula, and runoff output equation to calculate the monthly runoff in the target month, and optimize the model parameters.

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

[0076] The sea temperature anomaly data are for the Kuroshio extension area and Monthly mean sea temperature anomalies in Region 3.4;

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

[0078] The watershed data are 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, the construction of a key index reflecting the characteristics of air-sea coupling and land surface runoff generation can be achieved by using the following specific process:

[0081] S21, extract the Kuroshio extension area and the 3.4 Monthly sea temperature anomaly in the region, calculate the sea temperature anomaly difference three months before the target month, the calculation formula is:

[0082] ;

[0083] in, is the sea temperature anomaly three months before the target month, in °C. is the sea temperature anomaly in the Kuroshio extension area three months before the target month, in degrees Celsius. 3 months before the target month 3.4 SST anomalies in region, in °C, with t being the target month;

[0084] S22. Calculate the normalized latitude of the subtropical high ridgeline one month before the target month using the following formula:

[0085] ;

[0086] in, is the normalized value of the subtropical high ridgeline latitude one month before the target month, is the latitude of the subtropical high ridgeline one month before the target month, in degrees N. is the average latitude of the subtropical high ridgeline in the target month over the past five years, in degrees N. is the standard deviation of the subtropical high ridgeline latitude in the target month over the past five years, in degrees North;

[0087] S23. Calculate the composite air-sea circulation index for the target month using the following formula:

[0088] ;

[0089] in, is the composite air-sea circulation index of the target month, is the historical standard deviation of the sea temperature anomaly three months prior to the target month for the past five years, in °C;

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

[0091] ;

[0092] in, is the rainfall-humidity storage elasticity coefficient of the target month, is the monthly rainfall in the k months before the target month, in mm. is the memory decay time constant, which is 3. is the soil moisture content in the root zone of the target month, in mm. is a constant, and its value is 1mm. When the soil water storage is close to saturation, rainfall is easily converted into runoff; At this time, the soil layer still has stagnant storage space.

[0093] As a preferred embodiment, step S3 may be implemented as follows:

[0094] S31. Extract the composite air-sea circulation index for the target month , Composite Sea-Air Circulation Index one month before the target month , historical monthly rainfall series , n is the number of historical sample months, T represents the transpose of the matrix;

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

[0096] ;

[0097] in, is the predicted monthly rainfall for the target month, is a constant term, 、 is the regression coefficient;

[0098] S33, use the least squares method to solve the coefficient , 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 for the target month .

[0102] As a preferred embodiment, step S4 may be implemented as follows:

[0103] S41. The change of soil moisture content is described by the water balance equation. The specific formula is:

[0104] ;

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

[0106] S42. Actual monthly evapotranspiration in the target month The calculation formula is:

[0107] ;

[0108] in, is the evapotranspiration correction factor, 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 volume for the target month by combining the rainfall-wet storage elasticity coefficient , the calculation formula is:

[0110] ;

[0111] in, is the runoff volume in the target month, is the runoff coefficient, is the soil water storage conversion index, is the PMSI sensitivity coefficient, ;

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

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

[0114] ;

[0115] in, is the monthly runoff of the control station in the jth sample month in history, is the runoff volume of the jth sample month in history, 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.

[0116] This example reports the monthly natural runoff process of the outlet section of each control station in the Beijiang River Basin (Lishi Station, Changba Station, Gaodao Station, Changhu Dam Station, Shijiao Station, and Shigou Station) from 2005 to 2010. The results of the report are shown in Figures 2 to 7 .

[0117] This example uses the coefficient of certainty (DC) and the relative error (RE) of runoff volume to analyze the accuracy of monthly runoff simulations and the accuracy of monthly runoff predictions for each sub-region of the Beijiang River Basin during the simulation period. The accuracy statistics for simulations and predictions are shown in Table 1.

[0118] Table 1 Statistics of simulation and trial prediction accuracy for each sub-region of the Beijiang River Basin

[0119]

[0120] It can be seen from Table 1 that 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 of the simulated and measured flow process lines, it can be seen that for different spatial scales and different geographical locations of the Beijiang River Basin, the model can well depict the monthly runoff process, and the peak values of the simulation and the measurement have a good correspondence. The regular deterministic coefficients of each partition rate are all above 0.9, and the total amount of simulated runoff is basically balanced with the total amount of measured runoff; except for the Changba Station, which is 0.707, the deterministic coefficients of the other stations during the trial reporting period are all above 0.8, and the relative error of the average total amount of runoff in the trial report 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 through 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 elasticity coefficient disclosed in the above embodiment can be automatically implemented by a medium- and long-term runoff forecasting system. The system can include four components: a data acquisition module, a first processing module, a second processing module, and an output module. The data acquisition module is used to collect sea temperature anomaly data, subtropical high ridge latitude data, watershed data, and potential evapotranspiration data, and uses the spatiotemporal inverse distance weighted interpolation method to fill missing values and standardize the data, and then output a standardized data set. The first processing module is used to construct a key index reflecting the characteristics of sea-air coupling and land surface runoff generation, and the key index includes a composite sea-air circulation index and a rainfall-humidity storage elasticity coefficient. The second processing module is used to establish a rainfall prediction model whose input is the composite sea-air circulation index of the target month, the composite sea-air circulation index one month before the target month, and the historical monthly rainfall, and the output is the predicted rainfall of the target month, and outputs 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 output equation to calculate the monthly runoff in the target month, and optimize the model parameters.

[0123] When implemented using hardware, the above embodiments can also be run on an electronic device by compiling all or part of the operating logic and computational processes into software. The electronic device includes a processor, a memory, a communication interface, and a communication bus. The processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to execute the technical process of the medium- and long-term runoff forecasting method disclosed in the above embodiments, which will not be described in detail here.

[0124] The electronic device may vary significantly due to different configurations or performance, and may include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the medium- and long-term runoff forecasting method based on the composite ocean-air circulation index and elasticity coefficient provided in the above-mentioned method embodiment. The electronic device may also include other components for implementing the device functions. For example, the electronic device may also have components such as wired or wireless network interfaces and input and output interfaces for data input and output. This embodiment will not be described in detail here.

[0125] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented as a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0126] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with 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 thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or device.

[0127] The present invention is described with reference to flowcharts and block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process or block in the flowcharts and block diagrams, as well as combinations of processes and blocks in the flowcharts or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts. Figure 1 A process or multiple processes and boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 A process or multiple processes and boxes Figure 1 A step that specifies a function in one or more boxes.

[0129] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A medium- and long-term runoff forecast method based on a composite sea-air circulation index and elastic coefficient, characterized by: The following steps are involved: S1. Collect sea temperature anomaly data, subtropical high ridgeline latitude data, watershed data, and potential evapotranspiration data, use the spatiotemporal inverse distance weighted interpolation method to fill missing values, and then standardize the data to output the standardized dataset; S11. Collect sea temperature anomaly data, subtropical high ridgeline latitude data, river basin data and potential evapotranspiration data. The sea temperature anomaly data is for the Kuroshio extension area and 3.4 Monthly mean sea temperature anomalies in the region; the subtropical high ridgeline latitude data is the latitude of the western Pacific subtropical high ridgeline extracted from the 500hPa geopotential height field; the watershed data are monthly rainfall, monthly root zone soil moisture content, and monthly runoff at the control station; the potential evapotranspiration data are monthly potential evapotranspiration calculated based on the Penman-Monteith formula; S12, using the spatiotemporal inverse distance weighted interpolation method to fill missing values and perform Z-score normalization, and output the normalized data set; S2. Constructing key indices reflecting the characteristics of air-sea coupling and land surface runoff generation, wherein the key indices include a composite air-sea circulation index and a rainfall-humidity storage elasticity coefficient; S21, extract the Kuroshio extension area and the 3.4 Monthly sea temperature anomaly in the region, calculate the sea temperature anomaly difference three months before the target month, the calculation formula is: ； in, is the sea temperature anomaly three months before the target month, in °C. is the sea temperature anomaly in the Kuroshio extension area three months before the target month, in degrees Celsius. 3 months before the target month 3.4 SST anomalies in region, in °C, with t being the target month; S22. Calculate the normalized latitude of the subtropical high ridgeline one month before the target month using the following formula: ； in, is the normalized value of the subtropical high ridgeline latitude one month before the target month, is the latitude of the subtropical high ridgeline one month before the target month, in degrees N. is the average latitude of the subtropical high ridgeline in the target month over the past five years, in degrees N. is the standard deviation of the subtropical high ridgeline latitude in the target month over the past five years, in degrees North; S23. Calculate the composite air-sea circulation index for the target month using the following formula: ； in, is the composite air-sea circulation index of the target month, is the historical standard deviation of the sea temperature anomaly three months prior to the target month for the past five years, in °C; S3. Establish a rainfall prediction model whose inputs are 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 whose output is the predicted rainfall for the target month, and output the predicted monthly rainfall for the target month; S4. Construct an evaporation-runoff model, combine the water balance equation, evapotranspiration formula, and runoff output equation to calculate the monthly runoff in 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 is characterized in that: Said S2 further comprises: S24. Extract the monthly rainfall for the six months preceding the target month and the soil moisture content in the root zone of the target month, and calculate the rainfall-wet storage elasticity coefficient for the target month. The calculation formula is: ； in, is the rainfall-humidity storage elasticity coefficient of the target month, is the monthly rainfall in the k months before the target month, in mm. is the memory decay time constant, which is 3. is the soil moisture content in the root zone of the target month, in mm. is a constant, and its value is 1mm.

3. The medium- and long-term runoff forecasting method based on the composite air-sea circulation index and elastic coefficient according to claim 2 is characterized in that: The S3 includes the following specific steps: S31. Extract the composite air-sea circulation index for the target month , Composite Sea-Air Circulation Index one month before the target month , historical monthly rainfall series , n is the number of historical sample months, T represents the transpose of the matrix; S32, constructing a rainfall prediction model, wherein the rainfall prediction model is a linear regression model, and the calculation formula is: ； in, is the predicted monthly rainfall for the target month, is a constant term, 、 is the regression coefficient; S33, use the least squares method to solve the coefficient , 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 .

4. The medium- and long-term runoff forecasting method based on the composite air-sea circulation index and elastic coefficient according to claim 3 is characterized in that: The S4 includes the following specific steps: S41. The water balance equation is used to describe the change in soil moisture content. The specific formula is: ； in, is the soil moisture content in the root zone of the target month, is the soil moisture content in the root zone one month before the target month, is the predicted monthly rainfall for the target month, is the actual monthly evapotranspiration of the target month, is the runoff volume in the target month; S42. Actual monthly evapotranspiration in the target month The calculation formula is: ； in, 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 volume for the target month by combining the rainfall-wet storage elasticity coefficient , the calculation formula is: ； in, is the runoff volume in the target month, is the runoff coefficient, is the soil water storage conversion index, is the PMSI sensitivity coefficient, .

5. The medium- and long-term runoff forecasting method based on the composite air-sea circulation index and elastic coefficient according to claim 4 is characterized in that: Said S4 further comprises: S44. Optimize model parameter set ;Construct parameter optimization objective function; S45, the parameter optimization objective function is: ； in, is the monthly runoff of the control station in the jth sample month in history, is the runoff volume of the jth sample month in history, 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.

6. A medium- to long-term runoff forecast system, characterized in that: include: The data acquisition module is used to collect sea temperature anomaly data, subtropical high ridgeline latitude data, watershed data, and potential evapotranspiration data. It uses the spatiotemporal inverse distance weighted interpolation method to fill missing values and standardize the data before outputting the standardized data set. The first processing module is used to construct a key index reflecting the characteristics of sea-air coupling and land surface runoff generation, wherein the key index includes a composite sea-air circulation index and a rainfall-humidity storage elasticity coefficient; The second processing module is used to establish a rainfall prediction model whose input is the composite sea-air circulation index of the target month, the composite sea-air circulation index one month before the target month, and the historical monthly rainfall, and output is the predicted rainfall of the target month, and output the predicted monthly rainfall of the target month; The output module is used to build an evaporation-runoff model, combine the water balance equation, evapotranspiration formula, and runoff output equation to calculate the monthly runoff in 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 elastic coefficient as described in any one of claims 1 to 5.

7. 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, it implements the medium- and long-term runoff forecasting method based on the composite sea-air circulation index and elastic coefficient as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The storage medium stores at least one executable instruction. When the executable instruction is executed on the electronic device, the electronic device executes the medium- and long-term runoff forecasting method based on the composite sea-air circulation index and elastic coefficient according to any one of claims 1 to 5.

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