A method, system, apparatus and storage medium for simulating runoff error correction

Abstract

This invention discloses a method, system, device, and storage medium for correcting simulated runoff errors. The method includes: acquiring simulated runoff data from a target data-free station and measured runoff data from a data-available station upstream or downstream of the target station; performing a first scaling process on the simulated runoff data based on the catchment areas of the data-available and target data-free stations to obtain scaled simulated runoff data; performing error correction processing on the scaled simulated runoff data using a quantile mapping method to obtain corrected simulated runoff data; and performing a second scaling process on the corrected simulated runoff data based on the catchment areas of the target data-free and data-available stations to obtain the target simulated runoff data. This invention can eliminate mean bias and variability bias in simulated runoff, improve the accuracy of simulated runoff correction, and can be widely applied in the field of hydrological simulation technology.

Description

Technical Field

[0001] This invention relates to the field of hydrological simulation technology, and in particular to a method, system, device, and storage medium for correcting simulation runoff errors. Background Technology

[0002] Long-term hydrological and meteorological data, including precipitation, evaporation, and runoff, form the foundation for hydrological analysis, hydrological modeling, and water resources management. Due to extreme weather conditions, monitoring equipment malfunctions, and other factors, hydrological records worldwide often contain missing values. Furthermore, the lack of hydrological stations in some regions results in a lack of hydrological data, hindering hydrological research. Currently, various methods exist for imputation and extension of missing data, including data scaling from neighboring stations, hydrological model simulations, and machine learning simulations. Among these, using hydrological models to simulate data is the most common method, capable of simulating long-term runoff data within a watershed. Simultaneously, numerous global runoff datasets generated by global hydrological models are available for flow estimation, facilitating hydrological research in data-deficient areas. However, simulated runoff is often biased and unreliable, hindering the direct application of raw simulated runoff data. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a highly accurate method, system, device, and storage medium for correcting errors in simulated runoff.

[0004] On one hand, embodiments of the present invention provide a method for correcting simulated runoff errors, including:

[0005] Acquire simulated runoff data for the target station without data, and measured runoff data for the upstream or downstream stations with data for the target station.

[0006] Based on the catchment area of ​​the data-available stations and the target data-unavailable stations, the simulated runoff data is subjected to a first scaling process to obtain scaled simulated runoff data.

[0007] The scaled simulated runoff data is corrected by performing error correction processing on the quantile mapping method to obtain corrected simulated runoff data.

[0008] Based on the catchment area of ​​the target data-free station and the data-available station, the corrected simulated runoff data is subjected to a second scaling process to obtain the target simulated runoff data.

[0009] Optionally, acquiring simulated runoff data from the target station without data, and measured runoff data from upstream or downstream stations with data for the target station, includes:

[0010] Based on the latitude and longitude of the target data-free station, extract the simulated runoff data of the grid where the target data-free station is located;

[0011] Based on the target station without data, determine the measured runoff data of the upstream or downstream stations with data for the target station.

[0012] Optionally, the step of performing a first scaling process on the simulated runoff data based on the catchment area of ​​the data-rich stations and the target dataless stations to obtain scaled simulated runoff data includes:

[0013] Based on the catchment area of ​​the data-available stations and the target data-unavailable stations, the simulated runoff data is scaled using a first scaling formula to obtain scaled simulated runoff data.

[0014] The first scaling formula is:

[0015]

[0016] In the formula, s′ t This indicates scaling of the simulated runoff data, s t This represents simulated runoff data, where F′ represents the catchment area of ​​the data-bearing station and F represents the catchment area of ​​the target data-free station.

[0017] Optionally, the step of correcting the scaled simulated runoff data using the quantile mapping method to obtain corrected simulated runoff data includes:

[0018] The mapping relationship between the scaled simulated runoff data and the measured runoff data is constructed using the quantile mapping method based on the cumulative distribution function.

[0019] By using the error correction formula, the cumulative distribution function of the scaled simulated runoff data is mapped to the cumulative distribution function of the measured runoff data to obtain the corrected simulated runoff data;

[0020] The error correction formula is as follows:

[0021]

[0022] In the formula, This indicates a correction to the simulated runoff data. GDF represents the inverse function of the cumulative distribution function of measured runoff data. s′ (·) represents the cumulative distribution function for scaling simulated runoff data, s t ′ indicates scaling the simulated runoff data.

[0023] Optionally, the second scaling process performed on the corrected simulated runoff data based on the catchment area of ​​the target data-free station and the data-available station to obtain the target simulated runoff data includes:

[0024] Based on the catchment area of ​​the target data-free station and the data-available station, the corrected simulated runoff data is subjected to a second scaling process using a second scaling formula to obtain the target simulated runoff data.

[0025] The second scaling formula is:

[0026]

[0027] In the formula, This represents the target simulated runoff data. This indicates the correction of simulated runoff data, where F represents the catchment area of ​​the target station without data, and F′ represents the catchment area of ​​the station with data.

[0028] Optionally, the method further includes:

[0029] The simulated runoff data of the target are subjected to data analysis and visualization.

[0030] Optionally, the data analysis and visualization processing of the target simulated runoff data includes:

[0031] The target simulated runoff data are analyzed and processed using test indicators, including the Kling-Gupta efficiency coefficient, the Nash efficiency coefficient, and the root mean square error.

[0032] The target simulated runoff data are visualized using the pyplot function in Matplotlib of the Python language.

[0033] Secondly, embodiments of the present invention provide a simulated runoff error correction system, comprising:

[0034] The first module is used to acquire simulated runoff data of the target station without data, and measured runoff data of the upstream or downstream stations with data of the target station.

[0035] The second module is used to perform a first scaling process on the simulated runoff data based on the catchment area of ​​the data-available stations and the target dataless stations to obtain scaled simulated runoff data.

[0036] The third module is used to perform error correction processing on the scaled simulated runoff data using the quantile mapping method to obtain corrected simulated runoff data.

[0037] The fourth module is used to perform a second scaling process on the corrected simulated runoff data based on the catchment area of ​​the target dataless station and the data-available station to obtain the target simulated runoff data.

[0038] On the other hand, embodiments of the present invention provide a simulated runoff error correction device, including a processor and a memory;

[0039] The memory is used to store programs;

[0040] The processor executes the program to implement the method described above.

[0041] On the other hand, embodiments of the present invention provide a computer-readable storage medium storing a program that is executed by a processor to implement the methods described above.

[0042] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0043] The embodiments of the present invention first acquire simulated runoff data from a target data-free station and measured runoff data from a data-available station upstream or downstream of the target station. Based on the catchment areas of the data-available and target data-free stations, the simulated runoff data undergoes a first scaling process to obtain scaled simulated runoff data. The scaled simulated runoff data is then corrected using a quantile mapping method to obtain corrected simulated runoff data. Finally, based on the catchment areas of the target data-free and data-available stations, the corrected simulated runoff data undergoes a second scaling process to obtain the target simulated runoff data. This invention, based on measured and simulated runoff data with upstream and downstream relationships, considers the differences in catchment areas between upstream and downstream stations, uses catchment area for scaling, and combines this with quantile mapping for correction, thereby eliminating certain mean and variability biases and improving the accuracy of simulated runoff correction. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1This is a schematic diagram of the overall steps of the simulated runoff error correction method provided in the embodiments of the present invention;

[0046] Figure 2 This is a schematic diagram illustrating the implementation process of the simulated runoff error correction method provided in this embodiment of the invention;

[0047] Figure 3 A schematic diagram of measured runoff data from the Near Livingston hydrological station provided in an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of the simulated runoff sequence after error correction at the Corwin Springs hydrological station, provided in an embodiment of the present invention.

[0049] Figure 5 A schematic diagram comparing simulated and measured runoff at the Corwin Springs hydrological station before error correction, provided in an embodiment of the present invention.

[0050] Figure 6 This is a schematic diagram comparing simulated and measured runoff after error correction at the Corwin Springs hydrological station, provided in an embodiment of the present invention.

[0051] Figure 7 This is a schematic diagram of a simulated runoff error correction system provided in an embodiment of the present invention;

[0052] Figure 8 This is a schematic diagram of a simulated runoff error correction device provided in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] To address the problems existing in the prior art, this invention provides a method for correcting simulation runoff errors, such as... Figure 1 As shown, the method includes the following steps:

[0055] S100. Obtain simulated runoff data for the target station without data, and measured runoff data for the upstream or downstream stations with data for the target station.

[0056] It should be noted that in some embodiments, simulated runoff data of the grid where the target data-free station is located is extracted based on the latitude and longitude of the target data-free station; and measured runoff data of the data-rich stations upstream or downstream of the target station are determined based on the target data-free station.

[0057] Specifically, this invention uses simulated runoff grid data and two stations with upstream and downstream relationships as a basis. Based on the latitude and longitude of the target station without data, it extracts the simulated runoff data of the grid where the target station without data is located. Let S represent the simulated runoff data extracted from the target station without data, and O′ represent the measured runoff data of a downstream (or upstream) station with data. Let x, y, and t represent longitude, latitude, and time, respectively. Then, the measured runoff and simulated runoff can be expressed as:

[0058]

[0059] In the formula, O′ x,y This represents measured runoff data, i.e., the set of measured runoff data from available stations; o′ t S represents the measured runoff value at time t, that is, a single measured runoff value at a data station downstream (or upstream) of the target area where there is no data station; x,y This represents simulated runoff data, specifically the collection of simulated runoff from stations without data available; s t This represents the simulated runoff value at time t, i.e., a single value of the simulated runoff data for the target dataless station.

[0060] S200. Based on the catchment area of ​​data stations and target data-free stations, the simulated runoff data is first scaled to obtain scaled simulated runoff data.

[0061] It should be noted that in some embodiments, based on the catchment areas of data-rich stations and target data-unrich stations, the simulated runoff data is scaled using a first scaling formula to obtain scaled simulated runoff data; wherein, the first scaling formula is:

[0062]

[0063] In the formula, s′ t This indicates scaling of the simulated runoff data, s t This represents simulated runoff data, where F′ represents the catchment area of ​​the data-bearing station and F represents the catchment area of ​​the target data-free station.

[0064] Specifically, the catchment area, or runoff area, can be calculated using relevant records or with the help of a digital elevation model (DEM).

[0065] S300. Error correction processing is performed on the scaled simulated runoff data using the quantile mapping method to obtain corrected simulated runoff data.

[0066] It should be noted that in some embodiments, a mapping relationship between scaled simulated runoff data and measured runoff data is constructed based on the cumulative distribution function using the quantile mapping method; the cumulative distribution function of the scaled simulated runoff data is mapped to the cumulative distribution function of the measured runoff data using an error correction formula, thereby obtaining corrected simulated runoff data; wherein, the error correction formula is:

[0067]

[0068] In the formula, This indicates a correction to the simulated runoff data. GDF represents the inverse function of the cumulative distribution function of measured runoff data. s′ (·) represents the cumulative distribution function for scaling simulated runoff data, s t ′ indicates scaling the simulated runoff data.

[0069] Specifically, in the obtained scaled simulated runoff s t Building upon this foundation, quantile mapping (QM) is used within the framework of leave-one-out cross-validation to correct simulated runoff in the absence of data, resulting in corrected simulated runoff. Leave-one-out cross-validation corrects the simulated runoff error year by year, excluding simulated and measured runoff samples from the target year from the training samples. Quantile mapping constructs a mapping relationship between simulated and measured runoff based on the cumulative distribution function (CDF), mapping the CDF of simulated runoff to the CDF of measured runoff, thus obtaining the corrected simulated runoff.

[0070]

[0071] In the formula, s t ′ represents the scaled simulated runoff value at time t, CDF s′ (·) represents the cumulative distribution function for scaling simulated runoff data. This represents the inverse function of the cumulative distribution function of measured runoff data. To correct for the simulated runoff at time t, the cumulative distribution function here uses the empirical distribution function.

[0072] S400. Based on the catchment area of ​​the target data-free and data-available stations, the corrected simulated runoff data is subjected to a second scaling process to obtain the target simulated runoff data.

[0073] It should be noted that, in some embodiments, based on the catchment areas of the target data-free and data-available stations, the corrected simulated runoff data is scaled using a second scaling formula to obtain the target simulated runoff data; wherein, the second scaling formula is:

[0074]

[0075] In the formula, This represents the target simulated runoff data. This indicates the correction of simulated runoff data, where F represents the catchment area of ​​the target station without data, and F′ represents the catchment area of ​​the station with data.

[0076] It should also be noted that some embodiments further include: data analysis and visualization of the target simulated runoff data.

[0077] Specifically, the target simulated runoff data is analyzed and processed using test indicators, including the Kling-Gupta efficiency coefficient, Nash efficiency coefficient, and root mean square error; and the target simulated runoff data is visualized using the pyplot function in Matplotlib.

[0078] Specifically, to verify the error correction effect of the simulated runoff data, two hydrological stations with available data can be used for verification. Assuming one station has no data, the simulated runoff corrected for the lack of data is calculated using this method and compared with the measured runoff. The corresponding verification indicators are then calculated, and the corresponding graphs are plotted.

[0079] The test indicators include Kling-Gupta efficiency (KGE), Nash efficiency (NSE), and root mean square error (RMSE). Let O represent measured runoff data from downstream (upstream) stations without data. Then we can obtain:

[0080]

[0081] In the formula, r is the Pearson correlation coefficient, β is the mean deviation, γ is the variability deviation, μ is the mean, and σ is the standard deviation. and 'o' represent the final target simulated runoff data and the measured runoff data without data, respectively. The value of KGE ranges from -∞ to 1, with an optimal value of 1.

[0082]

[0083] In the formula, T represents the total number of all times. The value of NSE ranges from -∞ to 1, with an optimal value of 1.

[0084]

[0085] In the formula, T represents the total number of all times. The RMSE ranges from 0 to +∞, with an optimal value of 0.

[0086] The corrected simulated runoff is visualized using the pyp lot function in Matp lot lib.

[0087] It should also be noted that in some embodiments, the calculation and visualization processes in the above method flow are encapsulated into class functions by defining a function `def()`, and the code is saved as a `.py` file to complete the encapsulation of the class functions. When it is necessary to call the class function, it can be used by calling the `import` function.

[0088] The following describes in detail the implementation principle of the simulated runoff error correction of the present invention. The following is an explanation of the technical solution of the present invention, not a limitation thereof:

[0089] This invention primarily targets measured runoff process sequences and simulated runoff raster data with upstream and downstream relationships. The measured runoff process sequences are text data in CSV or XLSX format, while the simulated runoff raster data are raster data in netCDF or TIFF format. Using this data as input, the specific implementation steps are as follows: Figure 1 :

[0090] S1. Data Input: The measured runoff process sequence (i.e., measured runoff data) is read using the Pandas package in Python, and the raster data is read using the netCDF4 package. Simulated runoff (i.e., simulated runoff data) is extracted based on the coordinates of stations without data.

[0091] S2. Data Correction: Based on the Python language platform, the simulated runoff without data is scaled according to the catchment area of ​​data-available and data-free stations. Further, the empirical frequencies of the measured runoff with data and the scaled simulated runoff without data are calculated. The quantile mapping model is constructed using the interp1d function in the third-party library scipy.interpolate in Python to correct the error of the simulated runoff. Finally, the corrected simulated runoff is scaled according to the watershed area to obtain the final corrected simulated runoff.

[0092] S3. Data Analysis and Visualization: Analyze and visualize the corrected simulated runoff as needed, using the pyplot function in Matplotlib to visualize the corrected simulated runoff.

[0093] S4. Function encapsulation and calling: In Python, functions are encapsulated using class() and def() so that they can be called in other programs.

[0094] To more clearly illustrate the purpose and content of this invention, the invention will be further described below with reference to specific embodiments, but the implementation of this invention is not limited thereto.

[0095] In some specific embodiments, the Near Livingston and Corwin Springs hydrological stations in the Yelllowstone River basin of the United States are considered as having data and Corwin Springs not having data. The error correction steps for simulated runoff without data based on the present invention are analyzed as follows:

[0096] (1) Prepare measured runoff data from the Near Livingston hydrological station, such as Figure 3 As shown, the first column is the date index corresponding to the measured value, and the second column is the measured flow rate. The data in the data file is read using the `read_csv` function from the Pandas package in Python. The parameter `parse_date = True` is set in the `read_csv` function to recognize the first column as a datetime object; this prepares the raster data for simulated runoff. Simulated runoff from the Corwin Springs hydrological station is extracted from the raster data.

[0097] (2) Data-free simulated runoff correction:

[0098] The catchment area of ​​the S1.Near Livingston hydrological station is 9197.1 km2, and the catchment area of ​​the Corwin Springs hydrological station is 6783.2 km2. Based on the catchment areas of the two hydrological stations, the simulated runoff of the Corwin Springs hydrological station is scaled.

[0099] S2. Error correction is performed on the scaled simulated runoff at the Corwin Springs hydrological station. Specifically, the empirical frequencies of the measured runoff at the Near Livingston hydrological station and the scaled simulated runoff at the Corwin Springs hydrological station are calculated. A quantile mapping model is constructed using the interp1d function in scipy.interpolate to correct the errors in the simulated runoff.

[0100] S3. The Corwin Springs hydrological station simulated runoff corrected in S2 is scaled again based on the catchment area of ​​the two hydrological stations to obtain the final Corwin Springs hydrological station corrected simulated runoff (i.e., the target simulated runoff).

[0101] (3) Based on the numerical results calculated in (2), in order to verify the effect, the measured runoff of the Corwin Springs hydrological station was input. The KGE before correction was calculated to be -0.14, and the KGE after correction was 0.82. The numerical results were visualized using the Python third-party library Matplotlib. Based on the numerical results calculated in (2) and the visualized image results, different storage methods were used for storage. The specific methods are as follows:

[0102] ① The `pyp lot.p lot` function is used to visualize the corrected simulated runoff at the Corwin Springs hydrological station, such as... Figure 4 As shown;

[0103] ② Use the `pyp lot.p lot` function to plot a comparison diagram of simulated and measured runoff at the Corwin Springs hydrological station before correction, such as... Figure 5 As shown;

[0104] ③ Use the `pyp lot.p lot` function to plot a comparison graph of the corrected simulated runoff and measured runoff at the Corwin Springs hydrological station, such as... Figure 6 As shown;

[0105] ④ Numerical result storage: Use the to_csv() function to store the pandas.Servlets object in CSV format to the specified location.

[0106] ⑤ Image result storage: Use the savefig function in MATLAB to store the image result in the specified location.

[0107] (4) Define a class using the class statement, and define functions such as quantile mapping model and related parameter calculation in the class using def(). Implement the mathematical process and plotting process related to the function through programming, encapsulate them into class functions, and save them as .py files. You can realize the correction of simulated runoff error without data stations by simply calling the encapsulated class functions.

[0108] On the other hand, refer to Figure 7This invention provides a simulated runoff error correction system 500, comprising a first module 510, a second module 520, a third module 530, and a fourth module 540. The first module 510 is used to acquire simulated runoff data from a target data-free station and measured runoff data from data-available stations upstream or downstream of the target station. The second module 520 is used to perform a first scaling process on the simulated runoff data based on the catchment areas of the data-available and target data-free stations to obtain scaled simulated runoff data. The third module 530 is used to perform error correction processing on the scaled simulated runoff data using a quantile mapping method to obtain corrected simulated runoff data. The fourth module 540 is used to perform a second scaling process on the corrected simulated runoff data based on the catchment areas of the target data-free and data-available stations to obtain target simulated runoff data.

[0109] The content of the method embodiments of the present invention is applicable to the system embodiments. The specific functions implemented in the system embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0110] Reference Figure 8 Another aspect of the present invention provides a simulated runoff error correction device 600, including a processor 610 and a memory 620; the memory 610 is used to store a program; the processor 620 executes the program to implement the method described above.

[0111] The content of the method embodiments of the present invention is applicable to the device embodiments. The specific functions implemented by the device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0112] Another aspect of this invention provides a computer-readable storage medium storing a program that is executed by a processor to implement the methods described above.

[0113] The content of the method embodiments of the present invention is applicable to the computer-readable storage medium embodiments. The specific functions implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0114] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0115] In summary, this invention achieves idempotency through a combination of server-side and client-side mechanisms: the client handles retries, and the server ensures idempotency. For uncertain states, the client performs a delayed secondary verification. The interface implemented using this invention effectively guarantees request idempotency and ensures no request loss even in abnormal scenarios.

[0116] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0117] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the experimental implementation of each module is unnecessary for understanding the invention. Rather, considering the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the experimental implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0118] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0119] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0120] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0121] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0122] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0123] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0124] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method of analog runoff error correction, characterized by, The method comprises the following steps: obtaining simulated runoff data of a target ungauged site and measured runoff data of a gauged site upstream or downstream of the target ungauged site; performing first scaling processing on the simulated runoff data based on catchment areas of the gauged site and the target ungauged site to obtain scaled simulated runoff data; wherein the first scaling processing on the simulated runoff data based on the catchment areas of the gauged site and the target ungauged site to obtain the scaled simulated runoff data comprises: performing first scaling processing on the simulated runoff data based on the catchment areas of the gauged site and the target ungauged site by a first scaling formula to obtain the scaled simulated runoff data; wherein the first scaling formula is: wherein, denotes scaled simulated runoff data, denotes simulated runoff data, denotes the catchment area of the gauging station, denotes the catchment area of the target ungauged site; performing error correction processing on the scaled simulated runoff data by a quantile mapping method to obtain corrected simulated runoff data; wherein the error correction processing on the scaled simulated runoff data by the quantile mapping method to obtain the corrected simulated runoff data comprises: constructing a mapping relationship between the scaled simulated runoff data and the measured runoff data according to a cumulative distribution function by the quantile mapping method; mapping a cumulative distribution function of the scaled simulated runoff data to a cumulative distribution function of the measured runoff data by an error correction formula to obtain the corrected simulated runoff data; wherein the error correction formula is: wherein denotes the revised simulated runoff data, denotes the inverse function of the cumulative distribution function of the measured runoff data, denotes the cumulative distribution function of the scaled simulated runoff data, denotes the scaled simulated runoff data; performing second scaling processing on the corrected simulated runoff data based on the catchment areas of the target ungauged site and the gauged site to obtain target simulated runoff data; wherein the second scaling processing on the corrected simulated runoff data based on the catchment areas of the target ungauged site and the gauged site to obtain the target simulated runoff data comprises: performing second scaling processing on the corrected simulated runoff data based on the catchment areas of the target ungauged site and the gauged site by a second scaling formula to obtain the target simulated runoff data; wherein the second scaling formula is: In the formula, denotes the target simulated runoff data, denotes the revised simulated runoff data, denotes the catchment area of the target ungauged site, denotes the catchment area of the gauged site.

2. The method of claim 1, wherein, the obtaining of the simulated runoff data of the target ungauged site and the measured runoff data of the gauged site upstream or downstream of the target ungauged site comprises: extracting simulated runoff data of a grid where the target ungauged site is located according to longitude and latitude of the target ungauged site; determining measured runoff data of the gauged site upstream or downstream of the target ungauged site based on the target ungauged site.

3. The method of claim 1, wherein, The method further comprises: performing data analysis processing and visualization processing on the target simulated runoff data.

4. The method of claim 3, wherein, The data analysis processing and visualization processing on the target simulated runoff data comprise: performing data analysis processing on the target simulated runoff data by an inspection index; wherein the inspection index comprises a Kling-Gupta efficiency coefficient, a Nash efficiency coefficient and a root mean square error; and performing visualization processing on the target simulated runoff data by a pyplot function in Matplotlib in Python language.

5. A simulated streamflow error correction system characterized by, The method comprises the following steps: The first module is configured to acquire analog runoff data of a target ungauged site and measured runoff data of a gauged site upstream or downstream of the target ungauged site; The second module is configured to perform first scaling processing on the analog runoff data based on catchment areas of the gauged site and the target ungauged site, to obtain scaled analog runoff data; The second module is configured to perform first scaling processing on the analog runoff data based on catchment areas of the gauged site and the target ungauged site, to obtain scaled analog runoff data; The second module is configured to perform first scaling processing on the analog runoff data based on catchment areas of the gauged site and the target ungauged site, to obtain scaled analog runoff data; The second module is configured to perform first scaling processing on the analog runoff data based on catchment areas of the gauged site and the target ungauged site, to obtain scaled analog runoff data; wherein, represents scaled simulated runoff data, represents simulated runoff data, represents the catchment area of the gauged site, represents the catchment area of the target ungauged site; The third module is configured to perform error correction processing on the scaled analog runoff data by using a quantile mapping method, to obtain corrected analog runoff data; The third module is configured to perform error correction processing on the scaled analog runoff data by using a quantile mapping method, to obtain corrected analog runoff data; The third module is configured to perform error correction processing on the scaled analog runoff data by using a quantile mapping method, to obtain corrected analog runoff data; The third module is configured to perform error correction processing on the scaled analog runoff data by using a quantile mapping method, to obtain corrected analog runoff data; The third module is configured to perform error correction processing on the scaled analog runoff data by using a quantile mapping method, to obtain corrected analog runoff data; wherein denotes the revised simulated runoff data, denotes the inverse function of the cumulative distribution function of the measured runoff data, denotes the cumulative distribution function of the scaled simulated runoff data, denotes the scaled simulated runoff data; The third module is configured to perform error correction processing on the scaled analog runoff data by using a quantile mapping method, to obtain corrected analog runoff data; The fourth module is configured to perform second scaling processing on the corrected analog runoff data based on catchment areas of the target ungauged site and the gauged site, to obtain target analog runoff data; The fourth module is configured to perform second scaling processing on the corrected analog runoff data based on catchment areas of the target ungauged site and the gauged site, to obtain target analog runoff data; The fourth module is configured to perform second scaling processing on the corrected analog runoff data based on catchment areas of the target ungauged site and the gauged site, to obtain target analog runoff data; In the formula, denotes the target simulated runoff data, denotes the revised simulated runoff data, denotes the catchment area of the target ungauged site, denotes the catchment area of the gauged site.

6. A device for simulating runoff error correction, characterized by The fourth module is configured to perform second scaling processing on the corrected analog runoff data based on catchment areas of the target ungauged site and the gauged site, to obtain target analog runoff data; The device comprises a processor and a memory; The memory is configured to store a program; 7. A computer readable storage medium characterized in that, The processor executes the program to implement the method according to any one of claims 1 to 4. The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 4.

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