Hydrological model data joint assimilation method and device, storage medium and electronic equipment

Abstract

The invention provides a hydrological model data joint assimilation method and device, a storage medium and electronic equipment, and the method comprises the steps: carrying out the configuration of an ith hydrological model according to an ith group of model parameters corresponding to a current detection period when a first type of updating conditions are met; inputting the target data in the current detection period into the ith hydrological model after configuration is completed; controlling the ith hydrological model to carry out data assimilation on the ith group of model parameters and target data according to a pre-configured assimilation radius and a measurement error so as to determine ith first-order assimilation data of a non-test point location in the research area; and determining target assimilation data of the non-test point location according to the N pieces of first-order assimilation data corresponding to the non-test point location. In combination with the physical property difference between the unsaturated region data of the soil moisture and the saturated region data of the underground water level, assimilation updating is performed on the soil moisture data and the underground water level data respectively, so that the accuracy of data assimilation is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method, device, storage medium and electronic device for joint assimilation of hydrological model data. Background Art

[0002] The Ensemble Kalman Filter (EnKF) technology, which is widely used in the industry, plays an important role in the joint data assimilation of groundwater and soil water. EnKF integrates observation data and model simulation results to gradually update the system state, thereby improving the accuracy of the simulation.

[0003] However, the complexity of groundwater and soil water systems poses a challenge to the effective application of EnKF. Overall, although EnKF has achieved some success in the joint assimilation of groundwater and soil water in experiments using synthetic data, it still needs further development and improvement to better cope with the nonlinear dynamic relationships and complex error structures in actual cases, thereby improving the accuracy and reliability of data assimilation results. Summary of the invention

[0004] The object of the present invention is to provide a method, device, storage medium and electronic device for joint assimilation of hydrological model data to improve the above-mentioned problems.

[0005] In order to achieve the above purpose, the technical solution adopted by the embodiment of the present invention is as follows:

[0006] In a first aspect, an embodiment of the present invention provides a method for joint assimilation of hydrological model data, the method comprising:

[0007] When the first type of update condition is met, the i-th hydrological model is configured according to the i-th group of model parameters corresponding to the current detection period;

[0008] Wherein, the first type of update condition indicates that the time length from the last assimilation of the target data is greater than a first preset length, the model parameters include meteorological driving parameters and soil hydraulic parameters, 1≤i≤N, N is the total number of groups of model parameters;

[0009] Input the target data in the current detection cycle into the configured i-th hydrological model;

[0010] Wherein, the target data includes soil moisture observation data corresponding to the soil moisture observation points in the study area or groundwater level observation data corresponding to the groundwater level observation points in the study area;

[0011] Controlling the i-th hydrological model to perform data assimilation on the i-th group of model parameters and the target data according to a pre-configured assimilation radius and measurement error, so as to determine the i-th preliminary assimilated data of the non-test point in the study area;

[0012] Determining target assimilated data of the non-test point according to the N preliminary assimilated data corresponding to the non-test point;

[0013] Among them, when the target data includes soil moisture observation data corresponding to the soil moisture observation points in the study area, the target assimilated data includes soil moisture assimilated data corresponding to non-test points in the study area; when the target data includes groundwater level observation data corresponding to groundwater level observation points in the study area, the target assimilated data includes groundwater level assimilated data corresponding to non-test points in the study area.

[0014] In a second aspect, an embodiment of the present invention provides a hydrological model data joint assimilation device, the device comprising:

[0015] A first processing unit, configured to configure the i-th hydrological model according to the i-th group of model parameters corresponding to the current detection period when the first type of update condition is met;

[0016] Wherein, the first type of update condition indicates that the time length from the last assimilation of the target data is greater than a first preset length, the model parameters include meteorological driving parameters and soil hydraulic parameters, 1≤i≤N, N is the total number of groups of model parameters;

[0017] The first processing unit is also used to input the target data in the current detection cycle into the configured i-th hydrological model;

[0018] Wherein, the target data includes soil moisture observation data corresponding to the soil moisture observation points in the study area or groundwater level observation data corresponding to the groundwater level observation points in the study area;

[0019] The first processing unit is also used to control the i-th hydrological model to perform data assimilation on the i-th group of model parameters and the target data according to a pre-configured assimilation radius and measurement error, so as to determine the i-th preliminary assimilated data of the non-test point in the study area;

[0020] A second processing unit, configured to determine target assimilated data of the non-test point according to the N preliminary assimilated data corresponding to the non-test point;

[0021] Among them, when the target data includes soil moisture observation data corresponding to the soil moisture observation points in the study area, the target assimilated data includes soil moisture assimilated data corresponding to non-test points in the study area; when the target data includes groundwater level observation data corresponding to groundwater level observation points in the study area, the target assimilated data includes groundwater level assimilated data corresponding to non-test points in the study area.

[0022] In a third aspect, an embodiment of the present invention provides a storage medium having a computer program stored thereon, and the computer program implements the above method when executed by a processor.

[0023] In a fourth aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory is used to store one or more programs; when the one or more programs are executed by the processor, the above method is implemented.

[0024] Compared with the prior art, a method, device, storage medium and electronic device for joint assimilation of hydrological model data provided by an embodiment of the present invention configures the i-th hydrological model according to the i-th group of model parameters corresponding to the current detection period when the first type of update condition is met; wherein the first type of update condition indicates that the time length from the last assimilation of the target data is greater than the first preset length, the model parameters include meteorological driving parameters and soil hydraulic parameters, 1≤i≤N, N is the total number of groups of model parameters; the target data in the current detection period is input into the configured i-th hydrological model; wherein the target data includes soil moisture observation data corresponding to the soil moisture observation point in the study area or groundwater level observation data corresponding to the groundwater level observation point in the study area. The target assimilation data includes soil moisture observation data corresponding to soil moisture observation points in the study area, and the target assimilation data includes groundwater level observation data corresponding to groundwater level observation points in the study area. The target assimilation data includes groundwater level assimilation data corresponding to groundwater level observation points in the study area. The target assimilation data includes soil moisture observation data corresponding to groundwater level observation points in the study area. The target assimilation data includes groundwater level assimilation data corresponding to groundwater level observation points in the study area. The soil moisture data and groundwater level data are assimilated and updated respectively based on the physical property difference between soil moisture data in the unsaturated zone and groundwater level data in the saturated zone, which overcomes the problem of insufficient processing of nonlinear relationships in the traditional local ensemble Kalman filter (LEnKF) method and improves the accuracy of data assimilation. The improved accuracy of model predictions can provide more reliable data support in water resources management, climate change research and drought monitoring, and help enhance the practicality of hydrological forecasts.

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0028] Figure 2 One of the flow charts of the hydrological model data joint assimilation method provided in an embodiment of the present invention.

[0029] Figure 3 The second flowchart of the hydrological model data joint assimilation method provided in the embodiment of the present invention.

[0030] Figure 4 The third flowchart of the hydrological model data joint assimilation method provided in an embodiment of the present invention.

[0031] Figure 5 The fourth flowchart of the hydrological model data joint assimilation method provided in an embodiment of the present invention.

[0032] Figure 6 The fifth flowchart of the hydrological model data joint assimilation method provided in the embodiment of the present invention.

[0033] Figure 7 A schematic diagram of a unit of a hydrological model data joint assimilation device provided in an embodiment of the present invention.

[0034] In the figure: 10 - processor; 11 - memory; 12 - bus; 13 - communication interface; 701 - first processing unit; 702 - second processing unit. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0036] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0038] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0039] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0040] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "disposed" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0041] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0042] The interaction between surface and groundwater is complex and has significant nonlinear characteristics. Traditional EnKF often seems to be unable to handle these nonlinear relationships, which may cause the assimilation results to deviate from the actual situation. In addition, the observation data of groundwater and soil water are usually unevenly distributed in space and time, which causes EnKF to face uncontrollable error propagation problems during the assimilation process. Existing EnKF methods usually assume that the error is Gaussian distributed and unchanged over time, but in reality, the error often has complex spatial structure and temporal variability, which affects the reliability of the assimilation results.

[0043] From the above content, it can be seen that the problems existing in the prior art are:

[0044] (1) Inadequate processing of nonlinear relationships: The nonlinear dynamic relationship between soil moisture and groundwater level is complex. EnKF has limited performance in processing these nonlinear relationships, which may increase the sensitivity of the assimilation results to the initial conditions and parameter settings.

[0045] (2) Difficulty in error propagation and control: Although the use of the localized ensemble Kalman filter (LEnKF) can effectively reduce errors in the local area, in complex hydrological systems, errors may propagate between different variables and scales, resulting in a decrease in the overall accuracy of the assimilation results.

[0046] Difficulty in solving the above technical problems: In order to address the challenges of LEnKF in error propagation and nonlinear relationship processing, it is necessary to introduce more efficient error control algorithms and improved nonlinear processing methods.

[0047] To this end, an embodiment of the present invention provides a method for joint assimilation of hydrological model data to solve the above problems. The significance of solving the above technical problems: Overcoming these difficulties will significantly improve the accuracy and efficiency of joint assimilation of soil moisture and groundwater level, provide more reliable data support for water resources management, drought monitoring and climate change research, and help achieve more accurate hydrological forecasts and management decisions.

[0048] The embodiment of the present invention provides an electronic device, which may be a mobile phone, a computer, a server, etc. Figure 1 , a schematic diagram of the structure of an electronic device. The electronic device includes a processor 10, a memory 11, and a bus 12. The processor 10 and the memory 11 are connected via the bus 12, and the processor 10 is used to execute an executable module stored in the memory 11, such as a computer program.

[0049] The processor 10 may be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the hydrological model data joint assimilation method may be completed by an integrated logic circuit of hardware in the processor 10 or by instructions in the form of software. The above-mentioned processor 10 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, or discrete hardware components.

[0050] The memory 11 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0051] The bus 12 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. Figure 1 Although only one bidirectional arrow is used in the figure, it does not mean that there is only one bus 12 or only one type of bus 12 .

[0052] The memory 11 is used to store programs, such as programs corresponding to the hydrological model data joint assimilation device. The hydrological model data joint assimilation device includes at least one software function module that can be stored in the memory 11 in the form of software or firmware or fixed in the operating system (OS) of the electronic device. After receiving the execution instruction, the processor 10 executes the program to implement the hydrological model data joint assimilation method.

[0053] Possibly, the electronic device provided by the embodiment of the present invention further includes a communication interface 13. The communication interface 13 is connected to the processor 10 via a bus.

[0054] It should be understood that Figure 1The structure shown is only a schematic diagram of a portion of the electronic device. The electronic device may also include Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown. Figure 1 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0055] A hydrological model data joint assimilation method provided by an embodiment of the present invention can be applied to, but not limited to, Figure 1 For detailed procedures, please refer to the electronic equipment shown in Figure 2 ,The hydrological model data joint assimilation method includes: S610, S620, S630 and S640, which are described in detail as follows.

[0056] S610: When the first type of update condition is met, the i-th hydrological model is configured according to the i-th group of model parameters corresponding to the current detection period.

[0057] Among them, the first type of update condition indicates that the time length from the last assimilation of the target data is greater than the first preset length, and the model parameters include meteorological driving parameters and soil hydraulic parameters, 1≤i≤N, and N is the total number of groups of model parameters.

[0058] It should be noted that the first preset length is used to limit the assimilation frequency (also called update frequency) of the target data. When the target data includes soil moisture observation data corresponding to the soil moisture observation point in the study area, the first preset length and when the target data includes groundwater level observation data corresponding to the groundwater level observation point in the study area, the first preset length may be different or the same, and are not limited here.

[0059] The update frequency refers to the time interval at which the assimilation system updates the state variable (soil moisture or groundwater level), which determines the time scale of assimilation. If the observation data is frequent, such as soil moisture, choosing a higher update frequency (such as daily update) helps to capture the changes in the system state in a timely manner, especially in a rapidly changing environment. For variables that change more slowly, such as groundwater level, a lower update frequency (such as weekly or monthly update) can reduce the computational burden and reduce the error caused by observation noise.

[0060] S620: Input the target data in the current detection cycle into the configured i-th hydrological model.

[0061] Among them, the target data include soil moisture observation data corresponding to the soil moisture observation points in the study area or groundwater level observation data corresponding to the groundwater level observation points in the study area.

[0062] S630, controlling the i-th hydrological model to perform data assimilation on the i-th group of model parameters and target data according to a pre-configured assimilation radius and measurement error, so as to determine the i-th preliminary assimilated data of the non-test point in the study area.

[0063] It should be understood that the assimilation radius is used to indicate the non-test points related to the observation point (soil moisture observation point or groundwater level observation point). The test points within the circle with the observation point as the circle and the radius as the assimilation radius are the non-test points related to the observation point.

[0064] Measurement error is a key parameter for estimating the uncertainty of observational data during the assimilation process. It affects the weight of the model and observational data during the assimilation process. The smaller the measurement error, the greater the role of the observational data, and vice versa. The more dependent the model is. The instrument error can be used for estimation to determine the measurement error.

[0065] It should be understood that the target data in the current detection cycle is used for prediction by the hydrological model to determine the prediction data corresponding to the current detection cycle for the non-test points in the study area.

[0066] Then, the i-th hydrological model is controlled based on the prediction data corresponding to the non-test point in the current detection period and the observation data of the relevant observation points of the non-test point (soil moisture observation data or groundwater level observation data), and then combined with the measurement error to determine the i-th preliminary assimilated data of the non-test point in the study area.

[0067] S640: Determine target assimilated data of the non-test points according to the N preliminary assimilated data corresponding to the non-test points.

[0068] Among them, when the target data includes soil moisture observation data corresponding to soil moisture observation points in the study area, the target assimilated data includes soil moisture assimilated data corresponding to non-test points in the study area; when the target data includes groundwater level observation data corresponding to groundwater level observation points in the study area, the target assimilated data includes groundwater level assimilated data corresponding to non-test points in the study area.

[0069] It should be noted that the target assimilated data may be the average, median, root mean square, etc. of N primary assimilated data.

[0070] In the method for joint assimilation of hydrological model data provided in the embodiment of the present invention, the soil moisture data and groundwater level data are assimilated and updated respectively in combination with the physical property difference between the soil moisture data in the unsaturated zone and the groundwater level data in the saturated zone, thereby overcoming the problem of insufficient processing of nonlinear relationships in the traditional LEnKF method and improving the accuracy of data assimilation. The accuracy of model prediction is improved, and more reliable data support can be provided in water resources management, climate change research and drought monitoring, which helps to enhance the practicality of hydrological forecasting.

[0071] In the embodiment of the present invention, the assimilation parameters include update frequency, assimilation radius, measurement error, etc. The update frequency should match the physical time scale of groundwater level or soil moisture change. Groundwater level usually changes slowly and can be set to update weekly or monthly, while soil moisture changes quickly and can be updated daily. The measurement error is usually determined according to the technical specifications of the observation equipment, where the measurement errors of soil moisture and groundwater level are 0.03cm and 0.1cm, respectively. 3 / cm 3 and 0.05 m. Spatial correlation analysis is performed on the soil moisture and groundwater level observation data to be assimilated to determine the assimilation radius used for soil moisture and groundwater level respectively when performing data assimilation. Optionally, the assimilation radius of soil moisture and groundwater level is 100 km and 5 km respectively.

[0072] In an optional implementation, the meteorological driving parameters include precipitation parameters, downward shortwave radiation parameters, downward longwave radiation parameters, and temperature parameters. On this basis, regarding how to obtain meteorological driving parameters, the embodiment of the present invention also provides an optional implementation, please refer to Figure 3 Before configuring the i-th hydrological model according to the i-th group of model parameters corresponding to the current detection period, the hydrological model data joint assimilation method further includes: S310 and S320, which are specifically described as follows.

[0073] S310 , generating N meteorological disturbance coefficient groups by combining cross-correlations between meteorological driving parameters of various types.

[0074] Among them, each meteorological disturbance coefficient group includes precipitation disturbance coefficient, downward shortwave radiation disturbance coefficient, downward longwave radiation disturbance coefficient and temperature disturbance coefficient. The disturbance coefficient corresponding to each type of meteorological driving parameter satisfies the normal distribution with the corresponding standard deviation.

[0075] Table 1. Statistics of the meteorological driving parameters of the disturbance, where the last column shows the cross-correlations between the meteorological driving parameters.

[0076]

[0077] Among them, the noise column represents the operation rules between the meteorological driving observations and the corresponding meteorological disturbance coefficients. The precipitation observations are multiplied by the corresponding precipitation disturbance coefficients, the downward shortwave radiation observations are multiplied by the corresponding downward shortwave radiation disturbance coefficients, the downward longwave radiation observations are added by the corresponding downward longwave radiation disturbance coefficients, and the temperature observations are added by the corresponding temperature disturbance coefficients. The standard deviations, time-related scales, and cross-correlations in Table 1 are used for illustration purposes only and are not specifically limited.

[0078] S320, combining the meteorological driving observation values ​​corresponding to the study area in the current detection period and N meteorological disturbance coefficient groups to generate N groups of meteorological driving parameters.

[0079] Among them, meteorological driven observations include precipitation observations, downward shortwave radiation observations, downward longwave radiation observations and temperature observations.

[0080] It should be noted that by perturbing the observed values, N groups of meteorological driving parameters are generated to reflect the uncertainty in the system and ensure the accuracy of the assimilation results.

[0081] In an optional implementation, the soil hydraulic parameters include soil layer porosity, soil layer saturated hydraulic conductivity, bedrock layer porosity and bedrock layer saturated hydraulic conductivity, and the bedrock layer porosity is a preset fixed porosity. On this basis, regarding how to obtain soil hydraulic parameters, the embodiment of the present invention also provides an optional implementation, please refer to Figure 4 Before configuring the i-th hydrological model according to the i-th group of model parameters corresponding to the current detection period, the hydrological model data joint assimilation method also includes: S410, S420, S430, S440 and S450, which are specifically described as follows.

[0082] S410, using a random field to generate N groups of related soil disturbance coefficients, wherein the soil disturbance coefficients include sand disturbance coefficients and clay disturbance coefficients.

[0083] Optionally, the mean of the soil disturbance coefficient is 0, the correlation length of the soil disturbance coefficient is a preset length (12.5 kilometers), and the variance of the soil disturbance coefficient is (50%) 2 , the random field adopts the spherical variation graph model.

[0084] S420, generating N groups of soil texture parameters according to the observed sand content, the observed clay content and the N groups of soil disturbance coefficients in the study area.

[0085] The soil texture parameters include sand content parameters, clay content parameters and silt content parameters, and the sum of the sand content parameters, clay content parameters and silt content parameters in the same group is 100%.

[0086] The rationality of soil texture parameters is ensured by disturbing the observed sand content and clay content.

[0087] S430, generating N groups of soil layer porosity and N groups of soil layer saturated hydraulic conductivity according to the soil transfer function and the N groups of soil texture parameters.

[0088] S440, generating N bedrock layer disturbance coefficients using a uniform distribution model.

[0089] The bedrock layer may be a stratum with a depth of more than 3 meters. The logarithmic perturbation coefficients of the N bedrock layers may be between -0.5 and 0.5.

[0090] S450: Generate N saturated hydraulic conductivities of the bedrock layers according to the original saturated hydraulic conductivities and the N bedrock layer disturbance coefficients.

[0091] Alternatively, the original saturated hydraulic conductivity value is taken from a hydrogeological map. In addition, the porosity of the underlying bedrock layer (fixed porosity) may be a constant value of 0.15.

[0092] Please continue to refer to Figure 4 In an optional implementation, the hydrological model data joint assimilation method further includes: S510, which is specifically described as follows.

[0093] S510, when the second type of update conditions are met, the saturated hydraulic conductivity of the soil layer and the saturated hydraulic conductivity of the bedrock layer are updated according to the soil moisture observation data corresponding to the soil moisture observation points in the study area and the groundwater level observation data corresponding to the groundwater level observation points in the study area.

[0094] The second type of updating condition indicates that the time length from the last assimilation of the saturated hydraulic conductivity is greater than a second preset length.

[0095] By updating the saturated hydraulic conductivity of the soil layer and the saturated hydraulic conductivity of the bedrock layer, data distortion can be avoided and the assimilation effect can be guaranteed.

[0096] Based on the above, regarding how to determine the assimilation radius, the embodiment of the present invention also provides an optional implementation method, please refer to Figure 5 ,The hydrological model data joint assimilation method also includes: S110 and S120 , which are described in detail as follows.

[0097] S110, obtaining a target data correlation curve.

[0098] The horizontal axis value of the target data correlation curve is the distance value between the two observation points, and the vertical axis value of the target data correlation curve is the correlation coefficient of the observation data corresponding to the two observation points.

[0099] Optionally, S110, the step of obtaining a target data correlation curve includes: S111, S112, S113 and S114, which are specifically described as follows.

[0100] S111, obtaining observation points corresponding to target data and combining them to obtain multiple pairs of observation point groups, where an observation point group includes any two observation points.

[0101] Among them, the expression A of the observation point group ij Represents the combination of the i-th observation point and the j-th observation point.

[0102] S112, obtaining the distance value between two observation points in each pair of observation point groups.

[0103] Optionally, X ij Indicates A ij The distance value between the i-th observation point and the j-th observation point in the combination.

[0104] S113, obtaining the correlation value between the observation data corresponding to each pair of observation point groups.

[0105] Optionally, Y ij Indicates A ij The correlation value between the observation data of the i-th observation point and the observation data of the j-th observation point in the combination.

[0106] S114, performing curve fitting according to the distance value between two observation points in the observation point group and the correlation value between the observation data corresponding to the observation point group, so as to determine the target data correlation curve.

[0107] It should be understood that (X ij , Y ij ) is a horizontal axis with X ij , the vertical coordinate is Y ij Point. ij , Y ij ) to perform curve fitting to determine the target data correlation curve.

[0108] Among them, i and j are both smaller than the total number of observation points, and the two are not equal.

[0109] S120: Determine an assimilation radius corresponding to the target data based on a preset correlation threshold.

[0110] Optionally, the point whose ordinate is equal to the correlation threshold has a corresponding abscissa that is the assimilation radius corresponding to the target data.

[0111] In an optional implementation, soil moisture can be measured by a cosmic ray neutron meter. In this case, the embodiment of the present invention also provides an optional implementation, please refer to Figure 6 , the hydrological model data joint assimilation method also includes: S210, S220 and S230, which are specifically described as follows.

[0112] S210, performing reverse calculation based on the soil moisture observation data to determine the measurement depth corresponding to the soil moisture observation point.

[0113] Because the observation depth of the cosmic ray neutron instrument is not fixed, it will change with the change of soil moisture. The higher the humidity, the smaller the observation depth; the lower the humidity, the greater the observation depth. The observation depth of the cosmic ray neutron instrument can be calculated by empirical formulas, etc. The dynamic daily measurement depth is calculated based on the actual soil moisture data on a daily scale.

[0114] S220: Divide the soil moisture observation point into a plurality of soil layers according to the measurement depth corresponding to the soil moisture observation point and a preset soil layer division rule.

[0115] S230, assigning soil moisture observation data to each soil layer corresponding to the soil moisture observation point.

[0116] It should be understood that the soil moisture assimilation data corresponding to the non-test points include the soil moisture corresponding to each soil layer of the non-test points.

[0117] In an optional implementation, the groundwater level observation data needs to be converted into the pressure head of the saturated zone in the underground aquifer system, and the calculated pressure head is designated as the saturated zone data, and the pressure head is input into the hydrological model.

[0118] See also Figure 7 , Figure 7 An embodiment of the present invention provides a hydrological model data joint assimilation device. Optionally, the hydrological model data joint assimilation device is applied to the electronic device described above.

[0119] The hydrological model data joint assimilation device includes: a first processing unit 701 and a second processing unit 702 .

[0120] The first processing unit 701 is used to configure the i-th hydrological model according to the i-th group of model parameters corresponding to the current detection period when the first type of update condition is met;

[0121] Among them, the first type of update condition indicates that the time length from the last assimilation of the target data is greater than the first preset length, the model parameters include meteorological driving parameters and soil hydraulic parameters, 1≤i≤N, N is the total number of groups of model parameters;

[0122] The first processing unit 701 is also used to input the target data in the current detection cycle into the configured i-th hydrological model;

[0123] The target data include soil moisture observation data corresponding to the soil moisture observation points in the study area or groundwater level observation data corresponding to the groundwater level observation points in the study area;

[0124] The first processing unit 701 is also used to control the i-th hydrological model to perform data assimilation on the i-th group of model parameters and target data according to the pre-configured assimilation radius and measurement error, so as to determine the i-th preliminary assimilated data of the non-test point in the study area;

[0125] The second processing unit 702 is used to determine the target assimilated data of the non-test point according to the N preliminary assimilated data corresponding to the non-test point;

[0126] Among them, when the target data includes soil moisture observation data corresponding to soil moisture observation points in the study area, the target assimilated data includes soil moisture assimilated data corresponding to non-test points in the study area; when the target data includes groundwater level observation data corresponding to groundwater level observation points in the study area, the target assimilated data includes groundwater level assimilated data corresponding to non-test points in the study area.

[0127] Optionally, the second processing unit 702 may execute the above S640, and the first processing unit 701 may execute the above Figures 2 to 6 Other steps in .

[0128] It should be noted that the hydrological model data joint assimilation device provided in this embodiment can execute the method flow shown in the above method flow embodiment to achieve the corresponding technical effect. For the sake of brief description, for parts not mentioned in this embodiment, reference can be made to the corresponding contents in the above embodiment.

[0129] The embodiment of the present invention further provides a storage medium, which stores computer instructions and programs, which, when read and executed, execute the hydrological model data joint assimilation method of the above embodiment. The storage medium may include a memory, a flash memory, a register, or a combination thereof.

[0130] The following provides an electronic device, which may be a machine, a computer, a server, etc. Figure 1 As shown, the above-mentioned hydrological model data joint assimilation method can be implemented; specifically, the electronic device includes: a processor 10, a memory 11, and a bus 12. The processor 10 can be a CPU. The memory 11 is used to store one or more programs. When the one or more programs are executed by the processor 10, the hydrological model data joint assimilation method of the above-mentioned embodiment is executed.

[0131] In summary, an embodiment of the present invention provides a method, device, storage medium and electronic device for joint assimilation of hydrological model data. When the first type of update condition is met, the i-th hydrological model is configured according to the i-th group of model parameters corresponding to the current detection period; wherein the first type of update condition indicates that the time length from the last assimilation of the target data is greater than the first preset length, the model parameters include meteorological driving parameters and soil hydraulic parameters, 1≤i≤N, N is the total number of groups of model parameters; the target data in the current detection period is input into the configured i-th hydrological model; wherein the target data includes soil moisture observation data corresponding to the soil moisture observation point in the study area or groundwater level observation data corresponding to the groundwater level observation point in the study area. The ith hydrological model is controlled to perform data assimilation on the ith group of model parameters and target data according to the pre-configured assimilation radius and measurement error to determine the ith preliminary assimilated data of the non-test points in the study area; the target assimilated data of the non-test points are determined according to the N preliminary assimilated data corresponding to the non-test points; when the target data includes the soil moisture observation data corresponding to the soil moisture observation points in the study area, the target assimilated data includes the soil moisture assimilated data corresponding to the non-test points in the study area; when the target data includes the groundwater level observation data corresponding to the groundwater level observation points in the study area, the target assimilated data includes the groundwater level assimilated data corresponding to the non-test points in the study area. In combination with the physical property differences between soil moisture data in the unsaturated zone and groundwater level data in the saturated zone, the soil moisture data and groundwater level data are assimilated and updated respectively, which overcomes the problem of insufficient processing of nonlinear relationships in the traditional LEnKF method and improves the accuracy of data assimilation. The improved accuracy of model predictions can provide more reliable data support in water resources management, climate change research and drought monitoring, and help enhance the practicality of hydrological forecasts.

[0132] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0133] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for joint assimilation of hydrological model data, characterized in that: The method comprises: When the first type of update condition is met, the i-th hydrological model is configured according to the i-th group of model parameters corresponding to the current detection period; Wherein, the first type of update condition indicates that the time length from the last assimilation of the target data is greater than a first preset length, the model parameters include meteorological driving parameters and soil hydraulic parameters, 1≤i≤N, N is the total number of groups of model parameters; Input the target data in the current detection cycle into the configured i-th hydrological model; Wherein, the target data includes soil moisture observation data corresponding to the soil moisture observation points in the study area or groundwater level observation data corresponding to the groundwater level observation points in the study area; Controlling the i-th hydrological model to perform data assimilation on the i-th group of model parameters and the target data according to a pre-configured assimilation radius and measurement error, so as to determine the i-th preliminary assimilated data of the non-test point in the study area; Determining target assimilated data of the non-test point according to the N preliminary assimilated data corresponding to the non-test point; Among them, when the target data includes soil moisture observation data corresponding to the soil moisture observation points in the study area, the target assimilated data includes soil moisture assimilated data corresponding to non-test points in the study area; when the target data includes groundwater level observation data corresponding to groundwater level observation points in the study area, the target assimilated data includes groundwater level assimilated data corresponding to non-test points in the study area.

2. The method for joint assimilation of hydrological model data according to claim 1, characterized in that: The meteorological driving parameters include precipitation parameters, downward shortwave radiation parameters, downward longwave radiation parameters and temperature parameters. Before configuring the i-th hydrological model according to the i-th group of model parameters corresponding to the current detection period, the method further includes: Generate N meteorological disturbance coefficient groups by combining the cross-correlations between various types of meteorological driving parameters, wherein each meteorological disturbance coefficient group includes a precipitation disturbance coefficient, a downward shortwave radiation disturbance coefficient, a downward longwave radiation disturbance coefficient, and a temperature disturbance coefficient, and the disturbance coefficient corresponding to each type of meteorological driving parameter satisfies a normal distribution with a corresponding standard deviation; Combined with the meteorological driven observation values ​​corresponding to the study area in the current detection period and N meteorological disturbance coefficient groups, N groups of meteorological driven parameters are generated, wherein the meteorological driven observation values ​​include precipitation observation values, downward shortwave radiation observation values, downward longwave radiation observation values ​​and temperature observation values.

3. The method for joint assimilation of hydrological model data according to claim 1, characterized in that: The soil hydraulic parameters include soil layer porosity, soil layer saturated hydraulic conductivity, bedrock layer porosity and bedrock layer saturated hydraulic conductivity, the bedrock layer porosity is a preset fixed porosity, and before configuring the i-th hydrological model according to the i-th group of model parameters corresponding to the current detection period, the method further includes: Using a random field to generate N groups of related soil disturbance coefficients, wherein the soil disturbance coefficients include sand disturbance coefficients and clay disturbance coefficients; Generate N groups of soil texture parameters according to the observed sand content, the observed clay content and the N groups of soil disturbance coefficients in the study area, wherein the soil texture parameters include sand content parameters, clay content parameters and silt content parameters, and the sum of the sand content parameters, clay content parameters and silt content parameters in the same group is 100%; According to the soil transfer function and N groups of soil texture parameters, the porosity of N groups of soil layers and the saturated hydraulic conductivity of N groups of soil layers are generated; The uniform distribution model is used to generate N bedrock layer disturbance coefficients; According to the original saturated hydraulic conductivity and the N bedrock layer disturbance coefficients, N bedrock layer saturated hydraulic conductivity are generated.

4. The method for joint assimilation of hydrological model data according to claim 3, characterized in that: The method further comprises: When the second type of update conditions are met, the saturated hydraulic conductivity of the soil layer and the saturated hydraulic conductivity of the bedrock layer are updated according to the soil moisture observation data corresponding to the soil moisture observation points in the study area and the groundwater level observation data corresponding to the groundwater level observation points in the study area.

5. The method for joint assimilation of hydrological model data according to claim 1, characterized in that: The method further comprises: Obtain a target data correlation curve, wherein the abscissa value of the target data correlation curve is the distance value between the two observation points, and the ordinate value of the target data correlation curve is the correlation coefficient of the observation data corresponding to the two observation points; Based on a preset correlation threshold, an assimilation radius corresponding to the target data is determined.

6. The method for joint assimilation of hydrological model data according to claim 5, characterized in that: The step of obtaining the target data correlation curve includes: Acquire observation points corresponding to the target data and combine them to obtain multiple pairs of observation point groups, wherein the observation point groups include any two observation points; Get the distance value between two observation points in each pair of observation point groups; Obtain the correlation value between the observation data corresponding to each pair of observation point groups; Curve fitting is performed based on the distance value between two observation points in the observation point group and the correlation value between the observation data corresponding to the observation point group to determine the target data correlation curve.

7. The method for joint assimilation of hydrological model data according to claim 1, characterized in that: The method further comprises: Perform reverse calculation based on the soil moisture observation data to determine the measurement depth corresponding to the soil moisture observation point; Dividing the soil moisture observation point into a plurality of soil layers according to the measurement depth corresponding to the soil moisture observation point and a preset soil layer division rule; The soil moisture observation data is assigned to each soil layer corresponding to the soil moisture observation point.

8. A hydrological model data joint assimilation device, characterized in that: The device comprises: A first processing unit, configured to configure the i-th hydrological model according to the i-th group of model parameters corresponding to the current detection period when the first type of update condition is met; Wherein, the first type of update condition indicates that the time length from the last assimilation of the target data is greater than a first preset length, the model parameters include meteorological driving parameters and soil hydraulic parameters, 1≤i≤N, N is the total number of groups of model parameters; The first processing unit is also used to input the target data in the current detection cycle into the configured i-th hydrological model; Wherein, the target data includes soil moisture observation data corresponding to the soil moisture observation points in the study area or groundwater level observation data corresponding to the groundwater level observation points in the study area; The first processing unit is also used to control the i-th hydrological model to perform data assimilation on the i-th group of model parameters and the target data according to a pre-configured assimilation radius and measurement error, so as to determine the i-th preliminary assimilated data of the non-test point in the study area; A second processing unit, configured to determine target assimilated data of the non-test point according to the N preliminary assimilated data corresponding to the non-test point; Among them, when the target data includes soil moisture observation data corresponding to the soil moisture observation points in the study area, the target assimilated data includes soil moisture assimilated data corresponding to non-test points in the study area; when the target data includes groundwater level observation data corresponding to groundwater level observation points in the study area, the target assimilated data includes groundwater level assimilated data corresponding to non-test points in the study area.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: A processor and a memory, the memory being used to store one or more programs; When the one or more programs are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

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