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

Abstract

The present invention proposes a method, device, storage medium, and electronic device for joint assimilation of hydrological model data. When a first-category update condition is met, the i-th hydrological model is configured based on the i-th set of model parameters corresponding to the current detection period. The target data within the current detection period is input into the configured i-th hydrological model. The i-th hydrological model is controlled to assimilate the i-th set of model parameters and the target data based on a preconfigured assimilation radius and measurement error to determine the i-th preliminary assimilated data for non-test points within the study area. The target assimilated data for the non-test points is then determined based on the N preliminary assimilated data corresponding to the non-test points. By taking into account 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 separately assimilated and updated, improving the accuracy of data assimilation.

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 equipment for joint assimilation of hydrological model data. Background Art

[0002] The Ensemble Kalman Filter (EnKF) technology, widely used in the industry, plays a key role in the joint data assimilation of groundwater and soil water. EnKF integrates observational data with model simulation results, gradually updating the system state and improving simulation accuracy.

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

[0004] The purpose 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 objectives, the technical solutions adopted in the embodiments of the present invention are 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] 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, where 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] The target data include soil moisture observation data corresponding to soil moisture observation points within the study area or groundwater level observation data corresponding to groundwater level observation points within 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 is 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] 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, where N is the total number of groups of model parameters;

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

[0018] The target data include soil moisture observation data corresponding to soil moisture observation points within the study area or groundwater level observation data corresponding to groundwater level observation points within the study area;

[0019] The first processing unit is further configured 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 is configured to determine target assimilated data of the non-test point based on 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, which 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, the 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 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 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 method uses the i-th hydrological model to assimilate the i-th set of model parameters and target data based on a preconfigured assimilation radius and measurement error to determine the i-th preliminary assimilated data for non-test points within the study area. The method also uses the N preliminary assimilated data corresponding to non-test points to determine the target assimilated data for non-test points. When the target data include soil moisture observation data corresponding to soil moisture observation points within the study area, the target assimilated data include the soil moisture assimilated data for non-test points within the study area. When the target data include groundwater level observation data corresponding to groundwater level observation points within the study area, the target assimilated data include the groundwater level assimilated data for non-test points within the study area. By assimilating and updating the soil moisture data and groundwater level data separately, the method overcomes the problem of insufficient handling of nonlinear relationships in the traditional Localized 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 following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

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

[0028] Figure 2 This is a flow chart of a method for joint assimilation of hydrological model data provided in an embodiment of the present invention.

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

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

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

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

[0033] Figure 7 A schematic diagram of the units of the 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] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only 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 herein 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 as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall 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. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are used only to distinguish the description and should not be understood as indicating or implying relative importance.

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

[0039] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the inventive product 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 orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limiting the present invention.

[0040] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed" and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; mechanical connections, or electrical connections; direct connections, indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0041] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0042] The interactions between surface and groundwater are complex and exhibit significant nonlinear characteristics. Traditional EnKF methods often struggle to handle these nonlinear relationships, potentially causing assimilation results to deviate from the true reality. Furthermore, groundwater and soil water observations are often unevenly distributed in space and time, leading to uncontrollable error propagation during the EnKF assimilation process. Existing EnKF methods typically assume that errors are Gaussian and time-invariant. However, in reality, errors often exhibit complex spatial structure and temporal variability, compromising the reliability of assimilation results.

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

[0044] (1) Insufficient 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 lead to increased sensitivity of the assimilation results to 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 a local range, 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] The difficulty of 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 address this issue, embodiments of the present invention provide a method for joint assimilation of hydrological model data to address the aforementioned issues. The significance of addressing the aforementioned technical issues is that overcoming these difficulties will significantly improve the accuracy and efficiency of joint assimilation of soil moisture and groundwater levels, providing more reliable data support for water resource management, drought monitoring, and climate change research, and contributing to more accurate hydrological forecasting 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. The processor 10 is used to execute executable modules stored in the memory 11, such as computer programs.

[0049] The processor 10 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the hydrological model data joint assimilation method can be completed by the hardware integrated logic circuit in the processor 10 or by software instructions. The above-mentioned processor 10 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can 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 gate 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 Only one bidirectional arrow is used in the figure, but it does not mean that there is only one bus 12 or one type of bus 12.

[0052] The memory 11 is used to store programs, such as a program for the hydrological model data joint assimilation device. The hydrological model data joint assimilation device includes at least one software functional module that can be stored in the memory 11 in the form of software or firmware, or embedded in the operating system (OS) of the electronic device. Upon receiving an 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 than 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] The embodiment of the present invention provides a method for joint assimilation of hydrological model data, which 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. The model parameters include meteorological driving parameters and soil hydraulic parameters. 1≤i≤N, where 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 the update frequency) of the target data. When the target data includes soil moisture observation data corresponding to soil moisture observation points within the study area, the first preset length can be different from or the same as when the target data includes groundwater level observation data corresponding to groundwater level observation points within the study area, and this is not limited here.

[0059] The update frequency refers to the interval between updates of the state variable (soil moisture or groundwater level) by the assimilation system, which determines the timescale of assimilation. If the observation data is frequent, such as soil moisture, a higher update frequency (such as daily) helps to capture changes in the system state in a timely manner, especially in rapidly changing environments. For more slowly changing variables, such as groundwater level, a lower update frequency (such as weekly or monthly) can reduce the computational burden and mitigate errors 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 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.

[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 non-test points in the study area.

[0063] It should be understood that the assimilation radius is used to indicate non-test points associated with an observation point (soil moisture observation point or groundwater level observation point). Test points within a circle with the assimilation radius, centered around the observation point, are considered non-test points associated with the observation point.

[0064] Measurement error is a key parameter in estimating the uncertainty of observational data during the assimilation process. It influences the weighting of the model and observations during the assimilation process. The smaller the measurement error, the greater the role of the observations, while the smaller the measurement error, the greater the reliance on the model. Instrument error can be used to estimate 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 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 for the non-test points based on 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 can be the average, median, root mean square, etc. of N primary assimilated data.

[0070] The joint hydrological model data assimilation method provided in this embodiment of the present invention integrates and updates soil moisture data (in the unsaturated zone) and groundwater level data (in the saturated zone) based on their physical property differences. This overcomes the traditional LEnKF method's inadequate handling of nonlinear relationships and improves data assimilation accuracy. This improves the accuracy of model predictions, providing more reliable data support for water resources management, climate change research, and drought monitoring, and contributing to the practicality of hydrological forecasts.

[0071] In the embodiment of the present invention, 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 changes. 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. The measurement error of soil moisture and groundwater level is 0.03 cm, respectively. 3 / cm 3 and 0.05 meters. Spatial correlation analysis is performed on the soil moisture and groundwater level observations to be assimilated to determine the assimilation radius for soil moisture and groundwater level, respectively. Optionally, the assimilation radius for soil moisture and groundwater level can be 100 kilometers and 5 kilometers, 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 perturbation coefficient group includes the precipitation perturbation coefficient, the downward shortwave radiation perturbation coefficient, the downward longwave radiation perturbation coefficient and the temperature perturbation coefficient. The perturbation 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] The "Noise" column represents the calculation rules between meteorological driving observations and the corresponding meteorological perturbation coefficients. Precipitation observations are multiplied by the corresponding precipitation perturbation coefficients, downward shortwave radiation observations are multiplied by the corresponding downward shortwave radiation perturbation coefficients, downward longwave radiation observations are added by the corresponding downward longwave radiation perturbation coefficients, and temperature observations are added by the corresponding temperature perturbation coefficients. The standard deviations, temporal correlation scales, and cross-correlations in Table 1 are provided for illustrative purposes only and are not intended to be limiting.

[0078] S320 , combining the meteorological driving observation values ​​corresponding to the study area in the current detection period and the 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 embodiment, 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 embodiment, 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 further includes: S410, S420, S430, S440 and S450, which are specifically described as follows.

[0082] S410 , generating N sets of related soil disturbance coefficients using a random field, 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: Generate N groups of soil texture parameters based on 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 porosities and N groups of soil layer saturated hydraulic conductivities based on 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 described in detail 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 update 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 coordinate value of the target data correlation curve is the distance value between the two observation points, and the vertical coordinate 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 described in detail as follows.

[0100] S111 , obtaining observation points corresponding to target data and combining them to obtain multiple pairs of observation point groups, where each 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: Obtain the distance value between two observation points in each pair of observation point groups.

[0103] Optionally, X ij Indicates A ij The distance 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 a target data correlation curve.

[0107] It should be understood that (X ij , Y ij ) is a horizontal axis 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 the 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 the abscissa corresponding to the assimilation radius of the target data.

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

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

[0113] The observation depth of the cosmic ray neutron meter is not fixed; it changes with soil moisture. Higher humidity reduces the observation depth, while lower humidity increases the depth. The observation depth of the cosmic ray neutron meter can be calculated using empirical formulas. This dynamic daily measurement depth is calculated based on daily soil moisture data.

[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: Assign 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 includes the soil moisture corresponding to each soil layer of the non-test points.

[0117] In an optional embodiment, 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 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;

[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 model parameter groups;

[0122] The first processing unit 701 is further configured 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 soil moisture observation points within the study area or groundwater level observation data corresponding to groundwater level observation points within the study area;

[0124] The first processing unit 701 is further configured to control the i-th hydrological model to perform data assimilation on the i-th set 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 points within the study area;

[0125] The second processing unit 702 is configured to determine target assimilated data of the non-test points based on the N preliminary assimilated data corresponding to the non-test points;

[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 effects. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above embodiment.

[0129] Embodiments of the present invention further provide a storage medium storing computer instructions or programs that, when read and executed, execute the hydrological model data joint assimilation method described above. The storage medium may include memory, flash memory, registers, or a combination thereof.

[0130] The following provides an electronic device, which can be a machine, a computer, a server, etc. Figure 1 As shown, the aforementioned method for joint assimilation of hydrological model data can be implemented. Specifically, the electronic device includes: a processor 10, a memory 11, and a bus 12. The processor 10 may 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 method for joint assimilation of hydrological model data described in the aforementioned embodiment is executed.

[0131] In summary, the 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 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 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 set of model parameters and target data according to the pre-configured assimilation radius and measurement error to determine the ith preliminary assimilated data for the non-test points within the study area. The target assimilated data for the non-test points are determined based on the N preliminary assimilated data corresponding to the non-test points. When the target data include soil moisture observation data corresponding to soil moisture observation points within the study area, the target assimilated data include the soil moisture assimilated data corresponding to the non-test points within the study area. When the target data include groundwater level observation data corresponding to groundwater level observation points within the study area, the target assimilated data include the groundwater level assimilated data corresponding to the non-test points within the study area. By taking into account 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 separately, overcoming the problem of insufficient handling of nonlinear relationships in the traditional LEnKF method and improving 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 foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0133] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed 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; 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, where 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; The target data include soil moisture observation data corresponding to soil moisture observation points within the study area or groundwater level observation data corresponding to groundwater level observation points within 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 perturbation coefficient groups by combining the cross-correlations between various types of meteorological driving parameters, wherein each meteorological perturbation coefficient group includes a precipitation perturbation coefficient, a downward shortwave radiation perturbation coefficient, a downward longwave radiation perturbation coefficient, and a temperature perturbation coefficient, and the perturbation coefficient corresponding to each type of meteorological driving parameter satisfies a normal distribution with a corresponding standard deviation; Combined with the meteorological driving observation values ​​corresponding to the study area in the current detection period and N meteorological disturbance coefficient groups, N groups of meteorological driving parameters are generated, wherein the meteorological driving 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, wherein: The soil hydraulic parameters include soil layer porosity, soil layer saturated hydraulic conductivity, bedrock layer porosity, and bedrock layer saturated hydraulic conductivity, wherein the bedrock layer porosity is a preset fixed porosity. 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 sets of related soil disturbance coefficients using a random field, wherein the soil disturbance coefficients include a sand disturbance coefficient and a clay disturbance coefficient; Generate N groups of soil texture parameters based on the observed sand content, the observed clay content, and N groups of soil disturbance coefficients in the study area, wherein the soil texture parameters include a sand content parameter, a clay content parameter, and a silt content parameter, and the sum of the sand content parameter, the clay content parameter, and the silt content parameter 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 is generated.

4. The method for joint assimilation of hydrological model data according to claim 3, wherein: 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 based on 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, wherein: The method further comprises: Obtain a target data correlation curve, wherein the horizontal coordinate value of the target data correlation curve is the distance value between the two observation points, and the vertical coordinate 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 the 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 multiple 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 is 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; 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, where N is the total number of groups of model parameters; The first processing unit is further configured to input the target data in the current detection cycle into the configured i-th hydrological model; The target data include soil moisture observation data corresponding to soil moisture observation points within the study area or groundwater level observation data corresponding to groundwater level observation points within the study area; The first processing unit is further configured 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 is configured to determine target assimilated data of the non-test point based on 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 configured 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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