Xinanjiang model parameter calibration method based on rainfall data

Abstract

The invention relates to the technical field of hydrology and water conservancy, in particular to a Xinanjiang model parameter calibration method based on rainfall data. The drainage basin is divided into a plurality of block-shaped unit drainage basins, a plurality of monitoring points are arranged in each unit drainage basin, the rainfall data difference of the monitoring points is analyzed, and a rainfall influence deviation coefficient is determined; analyzing the correlation condition of the section flow of the unit drainage basin and the rainfall influence deviation coefficient so as to determine the rainfall influence significance degree value of the unit drainage basin under different hierarchical structures; the cumulative effect of rainfall influence among different hierarchical structures is considered, and an objective function based on multi-level error measurement is constructed by adjusting errors so as to capture the influence of rainfall on the model; and finally, parameter calibration is carried out based on the objective functions of all unit drainage basins and the ant colony algorithm, so that the credibility of parameter calibration and the accuracy of an optimal solution are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydrology and water conservancy, and particularly relates to a Xin'anjiang model parameter calibration method based on rainfall data. BACKGROUND

[0002] As a typical distributed hydrological model, the Xin'anjiang model has a wide range of applications in the fields of water resources management, flood forecasting, and watershed hydrological simulation. In order to consider the influence of uneven distribution of precipitation and watershed underlying surface, the structure of the Xin'anjiang model is designed to be decentralized, and its core advantage lies in depicting the physical mechanism of the rainfall-runoff process through multi-level structure (scattered evaporation calculation, runoff calculation, water source calculation, and confluence calculation). The accuracy of parameter calibration directly affects the reliability of runoff simulation.

[0003] The prior art usually directly calibrates the parameters of the Xin'anjiang model based on the ant colony algorithm, but in actual application, the watershed is divided into multiple block unit watersheds, and the influence of rainfall data on different unit watersheds and even different monitoring points will be different, and this difference will be accumulated between different levels of the Xin'anjiang model. Therefore, if these influences are ignored and the parameter calibration is directly based on the ant colony algorithm, it is difficult to obtain the optimal solution, and the credibility of parameter calibration will also be affected. SUMMARY

[0004] In order to solve the technical problem that the influence of rainfall data on different unit watersheds and even different monitoring points will be different, and this difference will be accumulated between different levels of the Xin'anjiang model, and if these influences are ignored and the parameter calibration is directly based on the ant colony algorithm, it is difficult to obtain the optimal solution, and the credibility of parameter calibration will also be affected, the purpose of the present application is to provide a Xin'anjiang model parameter calibration method based on rainfall data, and the technical solution adopted is as follows:

[0005] Obtain the rainfall data set at each monitoring point in each unit watershed, wherein the rainfall data set includes multiple rainfall time series data, and each level of the Xin'anjiang model corresponds to a rainfall time series data;

[0006] Under each level, in each unit watershed, based on the difference between the rainfall time series data at the monitoring points, determine the rainfall influence bias coefficient at each monitoring point; analyze the correlation between the rainfall influence bias coefficient at the monitoring point and the cross-section flow in the unit watershed where the monitoring point is located, and determine the rainfall influence significant degree value of each unit watershed under each level;

[0007] Use the difference in rainfall influence degree value between different levels of each unit watershed to adjust the error between the simulation data and the observed data of each unit watershed under the level, thereby constructing a target function;

[0008] Based on the objective function of all unit catchments and the ant colony algorithm, the Xin'anjiang model is parameterized.

[0009] Further, the method for obtaining the rainfall influence bias coefficient comprises:

[0010] Under each hierarchy, in each unit catchment, data values of the rainfall time series data at all monitoring points are fused to determine rainfall characteristic values at each sampling time;

[0011] Under each hierarchy, in each unit catchment, absolute values of differences between data values of the rainfall time series data at each monitoring point at each sampling time and corresponding rainfall characteristic values are taken as bias factors at each sampling time;

[0012] A normalized value of a mean of the bias factors at all sampling times is taken as the rainfall influence bias coefficient at each monitoring point.

[0013] Further, the method for obtaining the rainfall characteristic value comprises:

[0014] Under each hierarchy, for any sampling time, a mean of data values of the rainfall time series data at all monitoring points in each unit catchment at the sampling time is taken as the rainfall characteristic value at the sampling time.

[0015] Further, the method for obtaining the rainfall influence significant degree value comprises:

[0016] The cross-section flow comprises a simulated flow at a catchment outlet cross-section and an actual flow at the catchment outlet cross-section;

[0017] In each unit catchment under each hierarchy, a cross-section flow difference factor is determined based on a difference between the simulated flow at the catchment outlet cross-section and the actual flow at the catchment outlet cross-section;

[0018] In each unit catchment, a rainfall influence significant factor at each monitoring point is determined based on a correlation between the cross-section flow difference factor of the unit catchment and the rainfall influence bias coefficient at each monitoring point;

[0019] A normalized value of a mean of the rainfall influence significant factors at all monitoring points in each unit catchment is taken as a rainfall influence significant degree value of each unit catchment under each hierarchy.

[0020] Further, the method for obtaining the flow cross-section difference factor comprises:

[0021] In each unit watershed, the absolute value of the difference between the simulated flow at the watershed outlet section and the actual flow at the watershed outlet section is normalized to obtain a section flow difference factor.

[0022] Further, the method for obtaining the rainfall influence significant factor comprises:

[0023] In each unit watershed, the absolute value of the difference between the section flow difference factor of the unit watershed and the ratio of the rainfall influence bias coefficient at each monitoring point to the preset constant is normalized to obtain a rainfall influence significant factor at each monitoring point.

[0024] Further, the method for obtaining the target function comprises:

[0025] The difference between the rainfall influence degree values of each unit watershed between different hierarchical structures is analyzed to determine the watershed fluctuation adjustment coefficient corresponding to each hierarchical structure.

[0026] The simulated data is adjusted by using the watershed fluctuation adjustment coefficient of each unit watershed under each hierarchical structure to obtain adjusted simulated data.

[0027] In each unit watershed, the root mean square error between the adjusted simulated data and the observation data under all hierarchical structures is calculated to obtain a target function.

[0028] Further, the method for obtaining the watershed fluctuation adjustment coefficient comprises:

[0029] For any unit watershed, the difference between the rainfall influence significant degree value of the next hierarchical structure and the previous rainfall influence significant degree value is taken as the cumulative influence value of the unit watershed under the next hierarchical structure, wherein the cumulative influence value of the unit watershed under the first hierarchical structure in all hierarchical structures is a preset value.

[0030] In any hierarchical structure, the mean value of the cumulative influence values of each unit watershed under the hierarchical structure and the hierarchical structure before the hierarchical structure is normalized to obtain the watershed fluctuation adjustment coefficient of each unit watershed under the hierarchical structure.

[0031] Further, the method for obtaining the adjusted simulated data comprises:

[0032] The ratio of the simulated data of each unit watershed under each hierarchical structure to the watershed fluctuation adjustment coefficient is taken as the adjusted simulated data.

[0033] Further, the preset value is the rainfall influence significant degree value of the unit watershed under the first hierarchical structure.

[0034] The present application has the following advantages:

[0035] The three water source hierarchical structure of the Xin'anjiang model is dynamically associated with rainfall time series data, so that different hierarchical structures are matched with different rainfall inputs, so that the model can dynamically identify the differentiated contribution of different rainfall data to the basin runoff and infiltration process, and provide more accurate data driving for parameter calibration. There are regional divisions in the actual basin river and lake area, the entire basin is divided into a plurality of block unit basins, a plurality of monitoring points are arranged in the same block unit basin, and the monitoring rainfall data of different monitoring points will change according to the different rainfall conditions of the specific position, so the difference of the rainfall time series data at the monitoring point is analyzed, and the rainfall influence bias coefficient at each monitoring point is determined; if there is a certain degree of correlation between the cross section flow of the block unit basin and the rainfall influence bias coefficient, it is considered that the rainfall data has an influence on the unit basin, so the rainfall influence significant degree value of each unit basin under different hierarchical structures can be obtained. Further, the influence of rainfall data on the unit basin will exist between different hierarchical structures of the model, so if the error between the simulation data and the observation data of the unit basin under the hierarchical structure is directly used to construct the objective function, a large deviation will be generated, so in the present application, the difference of the rainfall influence significant degree value between different hierarchical structures is used to adjust the error, so as to obtain the objective function, at this time, the objective function is based on the error measurement of multiple levels, and can better capture the influence of rainfall data on the model. Finally, based on the objective function of all unit basins and the ant colony algorithm, the Xin'anjiang model is calibrated, and the optimal solution can be obtained more accurately and effectively, and the credibility of parameter calibration is improved. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0037] Figure 1 A method flowchart of a Xin'anjiang model parameter calibration method based on rainfall data provided by an embodiment of the present application;

[0038] Figure 2 A unit division schematic diagram of a certain basin provided by an embodiment of the present application;

[0039] Figure 3 A method flowchart of a rainfall influence bias coefficient acquisition method provided by an embodiment of the present application;

[0040] Figure 4A method flowchart of a method for obtaining a rainfall influence significant degree value provided by one embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined inventive purpose, the following describes in detail the specific implementation, structure, characteristics and effects of a new loss method based on rainfall data of Xin'anjiang model parameter calibration according to the present application, combined with the preferred embodiments and the drawings. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0043] The following describes in detail the specific scheme of a new loss method based on rainfall data of Xin'anjiang model parameter calibration provided by the present application.

[0044] Please refer to Figure 1 , which shows a method flowchart of a new loss method based on rainfall data of Xin'anjiang model parameter calibration provided by one embodiment of the present application, which includes the following steps:

[0045] Step S1: Obtain rainfall data set at each monitoring point in each unit catchment, wherein the rainfall data set contains multiple rainfall time series data, and each hierarchical structure of the Xin'anjiang model corresponds to a rainfall time series data.

[0046] Xin'anjiang model is a widely used tool for hydrological simulation, especially in the fields of watershed water resources management, flood forecasting and ecological research. The model provides an effective understanding of the water cycle in the catchment by comprehensively considering rainfall, evaporation, runoff and soil moisture dynamics and other hydrological factors. The core of Xin'anjiang model is the full storage runoff theory, which is a generalization of runoff mechanism. Its basic assumption is that at any location, before the soil water content reaches full storage (i.e. reaches field water capacity), rainfall fully replenishes soil water content and does not produce runoff; when the soil is full, the subsequent rainfall produces runoff. From the basic assumption of full storage runoff, it can be seen that this model is mainly applicable to humid and semi-humid areas with little soil water deficit, and the quality, type and spatial distribution of rainfall data have a significant impact on the calibration of model parameters, especially in complex terrain and rainfall intensity significantly changed basins, rainfall data will affect the hydrological process.

[0047] Reasonable parameter calibration is essential for the accuracy of the model, mainly because different rainfall patterns and intensities will lead to changes in the hydrological processes of the basin, affecting the setting of key parameters such as soil moisture and runoff coefficient, so analyzing the dynamic influence of rainfall data on the parameters of the Xin'anjiang model in practical application is the key to ensuring the reliability and accuracy of the parameter calibration of the model.

[0048] The Xin'anjiang model program is used to simulate the runoff process of a closed basin, and the basic calculation unit is the calculation unit of the natural watershed ridge division. Please refer to Figure 2 , which shows the unit division of a certain watershed, which contains 17 unit basins, and each unit basin is connected to the outlet section of the basin by a river channel.

[0049] Since the rainfall conditions at different locations may be different, multiple monitoring points are usually set in each unit basin, and at each monitoring point, rainfall data sets are obtained based on monitoring stations (weather stations, rainfall stations) (synchronously collected), which contain multiple rainfall time series data. In this embodiment of the present application, the types of rainfall data may include rainfall, rainfall intensity, etc.; and each hierarchical structure of the Xin'anjiang model corresponds to a type of rainfall time series data, for example: the first hierarchical structure corresponds to rainfall, and the second hierarchical structure corresponds to rainfall intensity.

[0050] It should be noted that, taking rainfall time series data as an example, in this embodiment of the present application, the rainfall in every 15 minutes is divided into 15-minute sampling time points, and the average rainfall in every 15 minutes is taken as the rainfall at a sampling time point; other types of rainfall time series data are the same. The type, length, etc. of the rainfall time series data can be divided according to the implementation scene, which is not limited here, and the spatial distribution and number of monitoring points can also be adjusted according to the implementation scene, which is not limited here.

[0051] Step S2: In each hierarchical structure, in each unit basin, based on the difference between the rainfall time series data at the monitoring points, the rainfall influence bias coefficient at each monitoring point is determined; the correlation between the rainfall influence bias coefficient at the monitoring point and the section flow in the unit basin is analyzed, and the rainfall influence significant degree value of each unit basin under each hierarchical structure is determined.

[0052] Since multiple rainfall monitoring points will be set in a region, the rainfall data of different monitoring points will vary according to the rainfall conditions at specific locations, so first, the difference between the rainfall time series data at different monitoring point locations can be analyzed in each unit basin under each hierarchy, the differentiated contribution of rainfall data at different locations to a specific hierarchy is quantified, and the rainfall influence bias coefficient at each monitoring point is obtained. In each unit basin, the variation of rainfall data between monitoring points will affect the flow at the outlet section of the basin, and to some extent, there is a change correlation, so the correlation between the rainfall influence bias coefficient at the monitoring point and the section flow in the unit basin can be further analyzed, thereby determining the rainfall influence significance value of each unit basin under each hierarchy.

[0053] Preferably, in an embodiment of the present application, the method for obtaining the rainfall influence bias coefficient comprises:

[0054] Please refer to Figure 3 which shows a method flowchart of the method for obtaining the rainfall influence bias coefficient in an embodiment of the present application, and the method comprises the following steps:

[0055] Step S201: In each unit basin under each hierarchy, the data values of all rainfall time series data at the monitoring points are fused to determine the rainfall characteristic value at each sampling time.

[0056] Under each hierarchy, for any sampling time, the mean value of the data values of the rainfall time series data at all monitoring points in each unit basin at the sampling time is taken as the rainfall characteristic value at the sampling time. The rainfall characteristic value reflects the average level of the rainfall conditions of all monitoring points in the unit basin at the same sampling time. At this time, each sampling time corresponds to a rainfall characteristic value for the unit basin, which is used to represent the overall rainfall condition of the unit basin at each sampling time.

[0057] Step S202: In each unit basin under each hierarchy, the deviation between the data values in the rainfall time series data at each monitoring point and the rainfall characteristic value is analyzed to determine the rainfall influence bias coefficient at each monitoring point.

[0058] Under each hierarchy, in each unit basin, the absolute value of the difference between the data value of the rainfall time series data at each monitoring point at each sampling time and the corresponding rainfall characteristic value is taken as the deviation factor at each sampling time. The larger the deviation factor, the greater the degree to which the rainfall data at the monitoring point deviates from the overall rainfall condition at the sampling time, and the greater the influence of the rainfall data at the monitoring point on the model at the sampling time.

[0059] Finally, the normalized value of the mean of the bias factors at all sampling times is taken as the rainfall influence bias coefficient at each monitoring point. Based on the foregoing analysis, the greater the rainfall influence bias coefficient, the greater the influence of the rainfall data at the monitoring point on the model. The normalization is a technique well known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited herein.

[0060] At this point, at each hierarchical structure, the rainfall influence bias coefficient at each monitoring point can be obtained in each unit watershed, for reflecting the influence of the change in the rainfall data on the model.

[0061] Further, taking the data input and output of the confluence stage as an example, since the simulated flow at the outlet section of the unit watershed is the output value of the single block-shaped unit watershed, if the difference between the simulated flow at the outlet section of the block-shaped unit watershed and the actually measured flow at the outlet section of the watershed is related to the corresponding change in the influence bias coefficient at the monitoring point, then the significant degree of the rainfall influence in each unit watershed can be determined, thereby obtaining the rainfall influence significant degree value.

[0062] Preferably, in an embodiment of the present application, the method for obtaining the rainfall influence significant degree value comprises:

[0063] Referring to Figure 4 which shows the method flowchart of the method for obtaining the rainfall influence significant degree value in an embodiment of the present application, the method comprises the following steps:

[0064] Step S211: The section flow comprises the simulated flow at the outlet section of the watershed and the actual flow at the outlet section of the watershed; in each unit watershed at each hierarchical structure, a section flow difference factor is determined based on the difference between the simulated flow at the outlet section of the watershed and the actual flow at the outlet section of the watershed.

[0065] In each unit watershed at each hierarchical structure, the absolute value of the difference between the simulated flow at the outlet section of the watershed and the actual flow at the outlet section of the watershed is normalized as the section flow difference factor. The section flow difference factor represents the deviation between the simulated flow at the outlet section of the unit watershed and the actual flow, and the greater the value, the greater the deviation.

[0066] It should be noted that the simulated flow at the outlet section of the watershed and the actual flow at the outlet section of the watershed can be set based on prior knowledge or measured to obtain, and the specific method is not described herein.

[0067] Step S212: In each unit watershed, based on the correlation between the cross-section flow difference factor of the unit watershed and the rainfall influence bias coefficient at each monitoring point, the rainfall influence significant factor at each monitoring point is determined.

[0068] The rainfall influence bias coefficient at each monitoring point reflects the influence of the rainfall data at the monitoring point on the model, and the cross-section flow difference factor of each unit watershed reflects the deviation between the simulated flow and the actual flow of the cross-section. Under normal circumstances, the two should be in a relatively close degree, but when the significant degree of rainfall influence increases, the closeness between the two will be destroyed.

[0069] Therefore, in each unit watershed, the ratio of the cross-section flow difference factor of the unit watershed to the rainfall influence bias coefficient at each monitoring point is calculated. The ratio can reflect the closeness between the cross-section flow difference factor and the rainfall influence bias coefficient to a certain extent. When the ratio is closer to 1, it means that the values of the cross-section flow difference factor and the rainfall influence bias coefficient are closer, and thus the two can be considered to have high synchronicity, and therefore the influence of the rainfall at the monitoring point on the cross-section flow of the unit watershed will be smaller. Conversely, when the ratio is further away from 1, it means that the synchronicity of the two is lower, and therefore the influence of the rainfall at the monitoring point on the cross-section flow of the unit watershed will be greater. Therefore, the absolute value of the difference between the ratio and the preset constant 1 is normalized to obtain the rainfall influence significant factor at each monitoring point. At this time, the greater the rainfall influence significant factor at a certain monitoring point, the lower the synchronicity of the cross-section flow difference factor and the rainfall influence bias coefficient, and the greater the significant influence of the rainfall data. The normalization is a well-known technical means to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited herein.

[0070] Step S213: Under each hierarchical structure, the rainfall influence significant factors at all monitoring points in each unit watershed are fused to determine the rainfall influence significant degree value of each unit watershed under each hierarchical structure.

[0071] Based on the foregoing steps, the rainfall influence significant factor at each monitoring point in each unit watershed under each hierarchical structure can be obtained, and finally the rainfall influence significant factors at all monitoring points in each unit watershed are fused. The normalized value of the mean of the rainfall influence significant factors at all monitoring points in each unit watershed is taken as the rainfall influence significant degree value of each unit watershed under each hierarchical structure. The greater the rainfall influence significant degree value, the greater the influence of the rainfall data on the watershed outlet cross-section flow of the unit watershed, and therefore the greater the influence degree on the parameter calibration of the model. The normalization is a well-known technical means to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited herein.

[0072] Step S3: adjusting the error between the simulation data and the observation data of each unit watershed under the hierarchy by using the difference between the rainfall influence degree values of each unit watershed between different hierarchies, thereby constructing a target function.

[0073] The entire watershed is divided into multiple unit watersheds, and the calculation of four hierarchies is performed on each unit watershed to obtain the outlet flow process of the unit watershed, which is equivalent to solving the uneven distribution of precipitation, soil water deficit and the like in a whole statistical accumulation manner according to the theory of full storage runoff, and the error variation between the four hierarchies will enter the next calculation, for example, the parameter EX reflects the uneven distribution of the surface free water storage condition, which determines the development process of the saturated slope flow runoff area in hill slope hydrology, that is, the error of the rainfall data in the accumulation stage will continue to affect the process of the confluence calculation, therefore, when analyzing the error between the simulation data and the observation data of each unit watershed under the hierarchy, the cumulative relationship of the error of the unit watershed in multiple links needs to be considered, and meanwhile, since the rainfall data also has a cumulative effect on the unit watershed, for the Xin'anjiang model processing process, the abnormal prominent condition of the rainfall influence degree value in the flow sequence needs to be analyzed, that is, the difference between the rainfall influence degree values of each unit watershed between different hierarchies is analyzed, the error between the simulation data and the observation data of each unit watershed under the hierarchy is adjusted, and thereby a target function is constructed.

[0074] Preferably, in an embodiment of the present application, the method for obtaining the target function comprises the following steps:

[0075] Please refer to Figure 3 which shows a method flowchart of the method for obtaining the target function in an embodiment of the present application, and the method comprises the following steps:

[0076] Step S301: analyzing the difference between the rainfall influence degree values of each unit watershed between different hierarchies, and determining the watershed fluctuation adjustment coefficient corresponding to each hierarchy.

[0077] For any unit watershed, the difference between the rainfall influence significant degree value of the next hierarchy and the rainfall influence significant degree value of the previous hierarchy is taken as the cumulative influence value of the unit watershed under the next hierarchy, wherein the cumulative influence value of the unit watershed under the first hierarchy in all hierarchies is a preset value, and specifically, the preset value is set to the rainfall influence significant degree value of the unit watershed under the first hierarchy in this embodiment of the present application. At this time, the unit watershed has a cumulative influence value under each hierarchy, and the greater the value, the greater the cumulative influence degree.

[0078] Then, under any one hierarchy, the mean value of the cumulative influence value of each unit watershed under the hierarchy and the hierarchy before the hierarchy is normalized as the watershed fluctuation adjustment coefficient of each unit watershed under the hierarchy, so that the watershed fluctuation adjustment coefficient of each unit watershed under each hierarchy can be obtained, which is used to reflect the cumulative influence, and the greater the value, the greater the cumulative influence. Wherein the normalization is a technology known to those skilled in the art, and the selection of the normalization function can be linear normalization or standard normalization, and the specific normalization method is not limited here.

[0079] Step S302: Adjusting the simulation data by using the watershed fluctuation adjustment coefficient of each unit watershed under each hierarchy to obtain adjusted simulation data.

[0080] Based on the foregoing analysis, when the watershed fluctuation adjustment coefficient under a certain hierarchy is greater, it means that the cumulative influence of the rainfall data is greater, and then in order to more accurately measure the error between the simulation data and the observation data, the simulation data should be corrected and adjusted.

[0081] When the watershed fluctuation adjustment coefficient is greater, in order to reduce the influence, the simulation data should be reduced, so the ratio of the simulation data of each unit watershed under each hierarchy to the watershed fluctuation adjustment coefficient is taken as the adjusted simulation data.

[0082] Step S303: In each unit watershed, the error between the adjusted simulation data and the observation data under all hierarchies is calculated, so as to construct a target function.

[0083] In each unit watershed, the root mean square error between the adjusted simulation data and the observation data under all hierarchies is calculated, so as to obtain a target function, and the formula model of the target function includes:

[0084]

[0085] Wherein, RMSE(O, S) represents the target function; n represents the number of hierarchies; O j represents the adjusted simulation data of each unit watershed under the jth hierarchy; S j represents the observation data of each unit watershed under the jth hierarchy.

[0086] It should be noted that the simulation data and the observation data in the embodiment of the application can be set based on prior knowledge or measured to obtain, and the specific method is not described here.

[0087] At this point, the target function can be constructed based on the error between the adjusted simulation data and the observation data in each watershed unit, which is used to measure the error.

[0088] Step S4: based on the objective function of all unit catchments and the ant colony algorithm, the Xin'anjiang model is parameterized.

[0089] In the foregoing steps, the error measurement method in each unit catchment is optimized by the embodiments of the present application, so the objective function of each catchment unit obtained in the foregoing steps can be used as the objective function of the ant colony algorithm for parameterizing the Xin'anjiang model, so that the parameterization result can be obtained.

[0090] It should be noted that the parameterization of the Xin'anjiang model based on the ant colony algorithm is a known technology, and the specific process is not described here.

[0091] As described above, the three water source hierarchical structure of the Xin'anjiang model is dynamically associated with the rainfall time series data, so that different hierarchical structures are matched with different rainfall inputs, so that the model can dynamically identify the differentiated contribution of different rainfall data to the catchment runoff process, and provide more accurate data driving for parameterization. There is regional division in the actual river and lake area of the catchment, the entire catchment is divided into a plurality of block unit catchments, a plurality of monitoring points are arranged in the same block unit catchment, and the monitoring rainfall data of different monitoring points will change according to the rainfall conditions of the specific position. Therefore, the difference of the rainfall data at the monitoring points is analyzed, and the rainfall influence bias coefficient at each monitoring point is determined. If there is a certain degree of correlation between the cross section flow of the block unit catchment and the rainfall influence bias coefficient, it is considered that the rainfall data has an influence on the unit catchment, so the rainfall influence significant degree value of each unit catchment under different hierarchical structures can be obtained. Further, the influence of the rainfall data on the unit catchment will be accumulated between different hierarchical structures of the model, so if the error between the simulation data and the observation data of the unit catchment under the hierarchical structure is directly used to construct the objective function, a large deviation will be generated. Therefore, in the embodiments of the present application, the error is adjusted by using the difference of the rainfall influence significant degree value between different hierarchical structures, so that the objective function is obtained. At this time, the objective function is based on multi-level error measurement, and can better capture the influence of rainfall data on the model. Finally, based on the objective function of all unit catchments and the ant colony algorithm, the Xin'anjiang model is parameterized, so that the optimal solution can be more accurately and effectively obtained, and the credibility of parameterization is improved.

[0092] It should be noted that the above-mentioned embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous.

[0093] The various embodiments described in this specification are presented by way of example, and each embodiment is not inherently more important than any other embodiment.

Claims

1. A Xinanjiang model parameter calibration method based on rainfall data, characterized in that, The method comprises: obtaining a rainfall data set at each monitoring point in each unit watershed, wherein the rainfall data set comprises a plurality of rainfall time series data, and each hierarchy of the Xin'anjiang model corresponds to one rainfall time series data; under each hierarchy, determining a rainfall influence bias coefficient at each monitoring point in each unit watershed based on differences between the rainfall time series data at the monitoring points; analyzing the correlation between the rainfall influence bias coefficient at the monitoring point and the cross-section flow in the unit watershed where the monitoring point is located to determine a rainfall influence significance value of each unit watershed under each hierarchy; adjusting the error between the simulation data and the observation data of each unit watershed under each hierarchy by using the difference between the rainfall influence degree values of each unit watershed between different hierarchies to construct a target function; based on the target function of all unit watersheds and the ant colony algorithm, parameterizing the Xin'anjiang model.

2. The Xin'anjiang model parameter calibration method based on rainfall data according to claim 1, characterized in that, The method for obtaining the rainfall influence bias coefficient comprises: under each hierarchy, fusing the data values of all rainfall time series data at the monitoring points to determine a rainfall characteristic value at each sampling time; under each hierarchy, taking the absolute value of the difference between the data value of the rainfall time series data at each monitoring point at each sampling time and the corresponding rainfall characteristic value as a deviation factor at each sampling time; taking the normalized value of the mean value of all deviation factors at all sampling times as the rainfall influence bias coefficient at each monitoring point.

3. The Xin'anjiang model parameter calibration method based on rainfall data according to claim 2, characterized in that, The method for obtaining the rainfall characteristic value comprises: under each hierarchy, for any sampling time, taking the mean value of the data values of all rainfall time series data at the monitoring points in each unit watershed at the sampling time as the rainfall characteristic value at the sampling time.

4. The Xin'anjiang model parameter calibration method based on rainfall data according to claim 1, characterized in that, The method for obtaining the rainfall influence significance value comprises: The cross-section flow comprises a simulated flow at the outlet cross-section of the watershed and an actual flow at the outlet cross-section of the watershed; under each hierarchy in each unit watershed, determining a cross-section flow difference factor based on the difference between the simulated flow at the outlet cross-section of the watershed and the actual flow at the outlet cross-section of the watershed; under each unit watershed, determining a rainfall influence significant factor at each monitoring point based on the correlation between the cross-section flow difference factor of the unit watershed and the rainfall influence bias coefficient at each monitoring point; taking the normalized value of the mean value of all rainfall influence significant factors at the monitoring points in each unit watershed as the rainfall influence significance value of each unit watershed under each hierarchy.

5. The Xin'anjiang model parameter calibration method based on rainfall data according to claim 4, characterized in that, The method for obtaining the cross-section flow difference factor comprises: under each hierarchy in each unit watershed, taking the normalized value of the absolute value of the difference between the simulated flow at the outlet cross-section of the watershed and the actual flow at the outlet cross-section of the watershed as the cross-section flow difference factor.

6. The Xin'anjiang model parameter calibration method based on rainfall data according to claim 4, characterized in that, The method for obtaining the rainfall influence significant factor comprises: under each unit watershed, taking the normalized value of the absolute value of the difference between the ratio of the cross-section flow difference factor of the unit watershed and the rainfall influence bias coefficient at each monitoring point and a preset constant as the rainfall influence significant factor at each monitoring point.

7. The Xinanjiang model parameter calibration method based on rainfall data according to claim 1, characterized in that, The target function acquisition method comprises: The difference of the rainfall influence degree value of each unit watershed between different hierarchical structures is analyzed to determine the watershed fluctuation adjustment coefficient corresponding to each hierarchical structure; The simulation data is adjusted by using the watershed fluctuation adjustment coefficient of each unit watershed under each hierarchical structure to obtain adjusted simulation data; In each unit watershed, the root mean square error between the adjusted simulation data and the observation data under all hierarchical structures is calculated to obtain the target function.

8. The Xinanjiang model parameter calibration method based on rainfall data according to claim 7, characterized in that, The watershed fluctuation adjustment coefficient acquisition method comprises: For any one unit watershed, the difference between the rainfall influence significant degree value of the next hierarchical structure and the previous rainfall influence significant degree value is taken as the cumulative influence value of the unit watershed under the next hierarchical structure between the two adjacent hierarchical structures, wherein the cumulative influence value of the unit watershed under the first hierarchical structure in all hierarchical structures is a preset value; In any one hierarchical structure, the normalized value of the average of the cumulative influence values of each unit watershed under the hierarchical structure and the hierarchical structure before the hierarchical structure is taken as the watershed fluctuation adjustment coefficient of each unit watershed under the hierarchical structure.

9. The Xinanjiang model parameter calibration method based on rainfall data according to claim 7, characterized in that, The adjusted simulation data acquisition method comprises: The ratio of the simulation data of each unit watershed under each hierarchical structure to the watershed fluctuation adjustment coefficient is taken as the adjusted simulation data.

10. The Xinanjiang model parameter calibration method based on rainfall data according to claim 8, characterized in that, The preset value is the rainfall influence significant degree value of the unit watershed under the first hierarchical structure.

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