Method and system for diagnosing and optimizing the simulation sequence of subprocesses in hydrological models

Abstract

The present invention discloses a method and system for diagnosing and optimizing the sub-process simulation sequence of a hydrological model, the method comprising: selecting a hydrological model as a benchmark model, obtaining basic hydrological and meteorological data of a target basin and processing the data to obtain data required for simulation input; separating the runoff and evaporation simulation methods of the hydrological model HM, and constructing two sub-process simulation sequence schemes of runoff priority and evaporation priority; calibrating and testing the parameters of the hydrological model and calculating the simulation accuracy of the two schemes; comparing the simulation accuracy of the two schemes, diagnosing the influence of the sub-process simulation sequence of the hydrological model on the water balance simulation of the target basin; based on the multi-year average meteorological and hydrological conditions of the target basin, using the Budyko framework to achieve the optimization of the scheme. The present invention solves the problem that a hydrological model with a fixed sub-process simulation sequence cannot be fairly applied to a variety of target basins, clarifies the influence of the evaporation-runoff sub-process simulation sequence on the hydrological and water balance simulation, and greatly improves the authenticity and accuracy of the model simulation.

Description

Technical Field

[0001] The present invention belongs to the technical field of hydrological process simulation, and in particular relates to a hydrological model sub-process simulation sequence diagnosis and optimization method and system. Background Art

[0002] Hydrological process simulation is the basis for scientific evaluation and rational allocation of water resources, and is particularly important for water resources planning, management and utilization in a changing environment. As an important tool for describing natural hydrological processes, it is crucial to improve the simulation capabilities of river basin hydrological models.

[0003] All hydrological models are constructed based on the mass balance principle, that is, within a given time step, precipitation (P) is equal to the sum of runoff (Q), evaporation (E), and water storage change (ΔS). Although hydrological models are based on the same mass balance equation, they differ in the mathematical representation of individual processes and the simulation order of the two processes of "runoff-evaporation", thus forming different hydrological models. Currently, there is no method to diagnose and optimize the simulation order of subprocesses in hydrological models. Summary of the invention

[0004] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method and system for diagnosing and optimizing the sub-process simulation sequence of a hydrological model, which solves the problem that a hydrological model with a fixed sub-process simulation sequence cannot be fairly applied to multiple target watersheds, clarifies the influence of the evaporation-runoff sub-process simulation sequence on the hydrological and water balance simulation, and greatly improves the authenticity and accuracy of the model simulation.

[0005] According to one aspect of the present invention, a method for diagnosing and optimizing a hydrological model subprocess simulation sequence is provided, comprising:

[0006] Obtaining basic hydrological and meteorological data of the target basin and processing the data to obtain data required for simulation input, wherein the data required for simulation input includes daily-scale meteorological and hydrological conditions of the target basin and multi-year average-scale meteorological and hydrological conditions of the target basin;

[0007] Separating the runoff-evaporation simulation method of the selected hydrological simulation benchmark model according to the daily-scale meteorological and hydrological conditions of the target watershed to form a runoff priority scheme and an evaporation priority scheme;

[0008] According to the objective function, the optimization algorithm is used to obtain the model parameters of the two schemes at regular intervals, and the simulation accuracy of the two schemes is calculated;

[0009] Based on the calculated simulation accuracy of the two schemes, diagnose the impact of the simulation order of the hydrological model subprocesses on the water balance simulation of the target basin, and determine the scheme optimization criteria based on the diagnosed impact;

[0010] According to the determined scheme optimization criteria and combined with the multi-year average scale meteorological and hydrological conditions of the target basin, the Budyko framework is used to optimize the hydrological model sub-process simulation sequence scheme.

[0011] As a further technical solution, parameter calibration is performed on any of the two constructed solutions, including:

[0012] According to the daily-scale meteorological and hydrological conditions of the target watershed, under a given parameter set, the first simulated runoff sequence is obtained, and the objective function value between the measured sequence and the simulated sequence is calculated;

[0013] Starting from n=2, the optimization algorithm selects a parameter set that is better than the previous one within the parameter range, obtains the runoff sequence of the nth simulation, calculates the objective function value between the measured sequence and the simulated sequence until convergence, and retains the model parameters at this time as the model parameters of the sub-process simulation sequence scheme.

[0014] As a further technical solution, determining the simulation accuracy of the solution also includes: using the Kling-Gupta efficiency coefficient, Nash efficiency coefficient, relative error, determination coefficient and fitting line slope to determine the simulation accuracy of the sub-process simulation sequence solution, and the closer the Kling-Gupta efficiency coefficient, Nash efficiency coefficient, relative error and determination coefficient are to 1 and the closer the slope of the fitting line is to 0, the higher the model simulation accuracy is.

[0015] As a further technical solution, based on the simulation accuracy of the two schemes, the influence of the simulation order of the hydrological model subprocesses on the water balance simulation of the target basin is diagnosed, and the scheme optimization criteria are determined based on the diagnosed influence, including:

[0016] Determine the factors that affect the water balance of the target watershed, including total evaporation, total runoff, and the evaporation and runoff components that may be output by the model;

[0017] According to the above factors and combined with the simulation accuracy of the sub-process simulation sequence scheme, the thresholds of comprehensive parameters and drought index of the target watershed are determined as the scheme optimization criteria.

[0018] As a further technical solution, according to the determined scheme optimization criteria, combined with the multi-year average scale meteorological and hydrological conditions of the target basin, the Budyko framework is used to optimize the hydrological model sub-process simulation sequence scheme, including:

[0019] Calculate the average annual precipitation, average annual potential evaporation and average annual runoff of the target basin based on the multi-year average scale meteorological and hydrological conditions of the target basin;

[0020] The multi-year scale watershed water balance formula is used to calculate the equilibrium evaporation as the actual evaporation of the target basin, combining the average annual precipitation and the average annual runoff;

[0021] The comprehensive parameters are calculated by using the Budyko framework formula, combining the average annual precipitation, average annual potential evaporation and actual evaporation of the target basin;

[0022] Calculate the drought index of the target watershed;

[0023] Based on the given climatic conditions, combined with the comprehensive parameters and the target basin drought index, the Budyko framework is used to optimize the hydrological model sub-process simulation sequence scheme.

[0024] As a further technical solution, the method also includes: when the target basin drought index is ≤1, both the runoff priority scheme and the evaporation priority scheme are applicable; when the target basin drought index is >1, if the comprehensive parameter is >2.6, the evaporation priority scheme is adopted, otherwise the runoff priority scheme is adopted.

[0025] According to one aspect of the present invention, a hydrological model sub-process simulation sequence diagnosis and optimization system is provided, comprising:

[0026] The first main module is used to obtain basic hydrological and meteorological data of the target basin and process it to obtain data required for simulation input, wherein the data required for simulation input includes daily-scale meteorological and hydrological conditions of the target basin and multi-year average-scale meteorological and hydrological conditions of the target basin;

[0027] The second main module is used to separate the runoff-evaporation simulation method of the selected hydrological simulation benchmark model according to the daily-scale meteorological and hydrological conditions of the target basin to form a runoff priority scheme and an evaporation priority scheme;

[0028] The third main module is used to obtain the model parameters of the two schemes respectively at regular intervals using an optimization algorithm according to the objective function, and calculate the simulation accuracy of the two schemes;

[0029] The fourth main module is used to diagnose the influence of the simulation sequence of the hydrological model subprocesses on the water balance simulation of the target basin according to the simulation accuracy of the two schemes calculated, and determine the scheme optimization criteria according to the diagnosed influence;

[0030] The fifth main module is used to optimize the hydrological model sub-process simulation sequence scheme using the Budyko framework according to the determined scheme optimization criteria and in combination with the multi-year average scale meteorological and hydrological conditions of the target basin.

[0031] According to one aspect of the present specification, there is provided an electronic device, comprising: at least one processor, at least one memory and a communication interface; wherein the processor, memory and communication interface communicate with each other; the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the described method.

[0032] According to one aspect of the present specification, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute the method described above.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention proposes two simulation schemes: runoff priority and evaporation priority, diagnoses the impact of the simulation order of the hydrological model subprocesses on the water balance simulation of the target basin, and develops a scheme optimization method based on the Budyko framework using the long-term meteorological conditions of the target basin. The present invention can improve the structural uncertainty assessment of the hydrological model, improve the general applicability of the model to multiple basins through scheme optimization, and greatly improve the authenticity and accuracy of hydrological and water balance simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A schematic flow chart of a method for diagnosing and optimizing a hydrological model subprocess simulation sequence provided by an embodiment of the present invention;

[0037] Figure 2 A schematic diagram of the structure of the hydrological model GR4J provided in an embodiment of the present invention, wherein (a) is a runoff priority scheme, and (b) is an evaporation priority scheme;

[0038] Figure 3 Schematic diagram of water balance simulation performance of two schemes provided in the embodiments of the present invention, wherein (a) is runoff Q, and (b) is evaporation E;

[0039] Figure 4 A schematic diagram of a preferred solution based on the Budyko framework provided in an embodiment of the present invention;

[0040] Figure 5 A schematic diagram of the structure of a hydrological model sub-process simulation sequential diagnosis and optimization system provided in an embodiment of the present invention.

[0041] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] It should be noted that:

[0043] Although existing hydrological models are based on the same mass balance equation, they differ in the mathematical representation of a single process and the simulation order of the two processes of "runoff-evaporation", thus forming different hydrological models. However, in view of the empirical nature of the process representation and the model uncertainty associated therewith, the runoff priority and evaporation priority schemes may perform differently in simulating hydrological processes. Therefore, in order to better and more accurately simulate hydrological processes, it is very important to diagnose the impact of the simulation order of the sub-processes of the hydrological model on the model performance. Based on this, the present invention provides a method for diagnosing and optimizing the simulation order of the sub-processes of a hydrological model, which solves the problem that a hydrological model with a fixed sub-process simulation order cannot be fairly applied to a variety of target basins, clarifies the impact of the evaporation-runoff sub-process simulation order on the hydrological and water balance simulation, and greatly improves the authenticity and accuracy of the model simulation.

[0044] The terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0045] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices. The flowcharts shown in the accompanying drawings are merely exemplary and do not necessarily include all the contents and operations / steps, nor do they necessarily have to be executed in the order described. For example, some operations / steps may be further decomposed, while some operations / steps may be combined or partially combined, so the actual execution order may change according to the actual situation.

[0046] 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. Based on the embodiments in 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. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or the structural composition mode, but must be based on the ability of ordinary technicians in this field to achieve. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0047] The present invention provides a method for diagnosing and optimizing the simulation sequence of subprocesses in a hydrological model. Figure 1 As shown, it includes: obtaining basic hydrological and meteorological data of the target basin and processing it to obtain data required for simulation input, wherein the data required for simulation input includes daily-scale meteorological and hydrological conditions of the target basin and multi-year average-scale meteorological and hydrological conditions of the target basin; according to the daily-scale meteorological and hydrological conditions of the target basin, separating the runoff-evaporation simulation method of the selected hydrological simulation benchmark model to form a runoff priority scheme and an evaporation priority scheme; according to the objective function, using an optimization algorithm to obtain model parameters of the two schemes at regular intervals, and calculating the simulation accuracy of the two schemes; according to the calculated simulation accuracy of the two schemes, diagnosing the influence of the simulation sequence of the hydrological model sub-process on the water balance simulation of the target basin, and determining the scheme optimization standard according to the diagnosed influence; according to the determined scheme optimization standard, combined with the multi-year average-scale meteorological and hydrological conditions of the target basin, the Budyko framework of the "water-energy limitation" theory is used to optimize the hydrological model sub-process simulation sequence scheme.

[0048] It should be noted that the hydrological process simulation method in the runoff priority RF and evaporation priority EF schemes of the embodiments of the present invention is the same, and only the simulation order of soil evaporation and runoff generation processes related to watershed water storage is changed.

[0049] The present invention will be further described below in conjunction with specific embodiments, but the present invention is not limited thereto.

[0050] The embodiment of the present invention selects 2061 river basins with different attributes around the world as research examples. The runoff data comes from the Global Runoff Data Center (GRDC) and the CAMELs dataset, and the precipitation and meteorological data come from the CAMELs dataset or the CRU-JRAv2.2 dataset.

[0051] The embodiment of the present invention provides a hydrological model sub-process simulation sequence diagnosis and optimization method based on the evaporation and runoff simulation performance of 2061 river basins around the world. Taking the GR4J model as an example, its structural schematic diagram is shown in the attached figure. Figure 2 As shown, the following steps are included:

[0052] Step 1: Select the hydrological model GR4J as the benchmark model, obtain the basic hydrological and meteorological data of the target basin and process it to obtain the data required for simulation input. Specifically:

[0053] Step 1.1 Collect hydrological and meteorological data of the target basin, including precipitation P (mm), runoff Q (mm), net radiation R n (mm), wind speed u2 (m / s) at 2m near the ground, air temperature T (℃), and saturated water vapor pressure difference D (hPa). The runoff Q is provided by the Global Runoff Data Center (GRDC) and CAMELs dataset, and the precipitation P and other meteorological data are provided by the CAMELs dataset and CRU-JRAv2.2 dataset.

[0054] Step 1.2 Use the meteorological observation data obtained in step 1.1 to solve the model input potential evaporation capacity E pa (mm), which can be realized as follows:

[0055]

[0056] Where: Δ refers to the slope of the saturated vapor pressure curve at a given air temperature (kPa℃ -1 ); γ refers to the psychrometric constant (kPa℃ -1 ); G refers to the ground heat flux (mm), which is negligible on a daily scale.

[0057] Step 1.3 uses the daily meteorological observation data obtained in steps 1.1 and 1.2 to calculate the average annual precipitation, average annual runoff, and average annual potential evaporation.

[0058] Step 2: Separate the runoff-evaporation simulation method of the hydrological model GR4J and construct the sub-process simulation sequence scheme, namely the runoff-first scheme (RF) and the evaporation-first scheme (EF). Specifically:

[0059] Step 2.1 The evaporation simulation method is as follows:

[0060] When P ≥ E, P n =PE,E n =0; otherwise, P n =0,E n =EP

[0061]

[0062] Where: P n is the net rainfall, E n is the net evaporation, P s is the soil water supply, E s is the soil evaporation, S is the soil water storage capacity; x1 is the maximum water storage capacity of the soil reservoir, which varies with the underlying surface conditions and other properties of the basin and is an adjustable parameter.

[0063] Secondly, the runoff simulation method only considers the runoff generation process, which is implemented as follows:

[0064]

[0065] P r =Prec+(P n -P s )

[0066] Where: Prec is the flow rate of the soil reservoir, P r is the total flow of the basin.

[0067] Step 2.2: Construct the model runoff priority scheme and evaporation priority scheme. Specifically:

[0068] In the runoff priority scheme, the overall water balance formula is: E = PQ, and the specific implementation method is as follows:

[0069] S1=S0+P s

[0070] S1=S1-Prec

[0071] S1=S1-E s

[0072] Where: S0 is the initial soil water storage capacity for a given step length, S1 is the updated soil water storage capacity after precipitation recharge, soil runoff, and evaporation dissipation; s As a function of S0, the flow rate Prec and evaporation E s They are all functions of S1 in the previous step.

[0073] Secondly, for the evaporation priority scheme, the overall water balance formula is expressed as: Q = PE, and the specific implementation method is as follows:

[0074] S1=S0+P s -E s

[0075] S1=S1-Prec

[0076] Among them, precipitation P s and evaporation E sis a function of S0, and the flow Prec is a function of S1 after the first update.

[0077] Step 3: According to the objective function, the optimization algorithm is used to obtain the model parameters of the two schemes in the rate period, and the simulation accuracy of the two schemes in the test period is calculated using the model optimization parameter set. Specifically:

[0078] In this embodiment, the objective function of the optimization algorithm is:

[0079]

[0080] Where: Y obs,i and Y sim,i are the observed value and simulated value at the i-th moment respectively; is the average value of the observed sequence and the simulated sequence; σ obs , σ sim are the standard deviations of the observed and simulated sequences respectively; N is the length of the observed data sequence.

[0081] The parameters of the two schemes of the model are calibrated by genetic algorithm (GA), including:

[0082] Step 3.1 Initialization phase: Input the hydrological and meteorological observation data sequence. Under the given parameter set, the first simulated runoff sequence {Q sim,1 (i)} and the evaporation sequence {E sim,1 (i)}, calculate the objective function value F1 between the measured sequence and the simulated sequence.

[0083] Step 3.2 Iteration phase: Starting from n = 2, the genetic algorithm selects a parameter set that is better than the previous one within the parameter range, and obtains the runoff sequence {Q sim,n (i)} and the evaporation sequence {E sim,n (i)}, calculate the objective function value F between the measured sequence and the simulated sequence n , until F n The value tends to converge. Experience shows that when the calibration times n reaches 500 times, the parameters of the hydrological model have approached the optimal value. If n ≥ 500 is satisfied, the hydrological model parameters at this time are retained for hydrological simulation.

[0084] Step 4: Compare the simulation accuracy of the two schemes (RF for runoff priority and EF for evaporation priority) to diagnose the impact of the simulation order of the hydrological model subprocesses on the water balance simulation of the target basin. The water balance elements mainly include total evaporation, total runoff, and the evaporation and runoff components that may be output by the model.

[0085] In this embodiment, Kling-Gupta efficiency coefficient KGE, Nash efficiency coefficient NSE, relative error RE, and determination coefficient R are selected. 2, and the slope of the fitted line k index are used to evaluate the performance of the two schemes in evaporation simulation and runoff simulation. Several hydrological evaluation indicators consider the consistency and deviation between the simulated sequence and the measured sequence. Among them, KGE, NSE, R 2 The closer k is to 1 and RE is to 0, the higher the model simulation accuracy is. The calculation formulas are as follows:

[0086]

[0087] Step 5: Based on the multi-year average meteorological and hydrological conditions of the target basin, the Budyko framework of the "water-energy limitation" theory is used to optimize the hydrological model sub-process simulation sequence scheme. The implementation method is as follows:

[0088] Step 5.1 Based on the hydrological and meteorological observation data of the target basin obtained in step 1, calculate its average annual precipitation P and average annual potential evaporation E pa , average annual runoff Q;

[0089] Step 5.2: Use the multi-year basin water balance formula (P = Q + E) to calculate the equilibrium evaporation E wb (=PQ), regarded as the actual evaporation E in the target basin;

[0090] Step 5.3: Using the Budyko framework formula and the annual average variables of the target basin (i.e., P, E pa , E) Back-calculate the comprehensive parameter ω, where the Budyko framework formula is:

[0091]

[0092] Step 5.4 calculates the target basin drought index AI, the specific formula is as follows:

[0093]

[0094] Step 5.5 uses the Budyko framework to optimize the simulation sequence of the hydrological model subprocesses based on the given climate conditions (represented by the drought index AI). Specifically:

[0095] When the basin drought index AI≤1, both the runoff priority RF and evaporation priority EF schemes are applicable and can be freely selected;

[0096] When the basin drought index AI>1 and ω>2.6, the evaporation priority scheme is adopted, otherwise the runoff priority scheme is adopted.

[0097] This example uses 2061 river basins covering multiple climate types around the world as the research object, and diagnoses the two sub-process simulation sequence schemes of the river basin hydrological model (GR4J) in this example, namely, the runoff priority scheme RF and the evaporation priority scheme EF. The results are shown in the simulation results of evaporation and runoff. Figure 3In the figure, Q sim represents the simulated runoff, Q obs represents the observed runoff, E sim represents the simulated evaporation, E wb Represents the evaporation obtained from the water balance equation (regarded as observed evaporation).

[0098] This implementation also proposes a solution optimization method based on the Budyko framework, and the results are shown in Figure 4 In the figure, the blue solid line represents the Budyko standard curve when w = 2.6, and the dotted line represents the watershed energy and water limit line, △KGE = KGE rf —KGE ef , which is the difference in KGE between the two schemes. The results show that the RF and EF schemes have differentiated simulation performances, especially for evaporation simulation. The hydrological model with a fixed simulation order cannot be fairly applied to a wide range of basins. Given the empirical nature of process representation and the model uncertainty associated with it, it is important to diagnose the impact of the simulation order of the subprocesses in the hydrological model and establish a preferred method, especially for studying the authenticity of the water balance simulation and division at the daily scale of the basin. This example answers this question well.

[0099] The implementation basis of each embodiment of the present invention is to implement programmed processing through a device with a processor function. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this reality, on the basis of the above embodiments, an embodiment of the present invention provides a hydrological model sub-process simulation sequence diagnosis and optimization system, which is used to execute the hydrological model sub-process simulation sequence diagnosis and optimization method in the above method embodiment.

[0100] See also Figure 5 The system includes: a first main module, which is used to obtain the basic hydrological and meteorological data of the target basin and process it to obtain the data required for simulation input, wherein the data required for simulation input includes the daily-scale meteorological and hydrological conditions of the target basin and the multi-year average-scale meteorological and hydrological conditions of the target basin; a second main module, which is used to separate the runoff-evaporation simulation method of the selected hydrological simulation benchmark model according to the daily-scale meteorological and hydrological conditions of the target basin, and form a runoff priority scheme and an evaporation priority scheme; a third main module, which is used to obtain the model parameters of the two schemes respectively at a rate period according to the objective function using an optimization algorithm, and calculate the simulation accuracy of the two schemes; a fourth main module, which is used to diagnose the influence of the simulation sequence of the hydrological model sub-process on the water balance simulation of the target basin according to the calculated simulation accuracy of the two schemes, and determine the scheme optimization criteria according to the diagnosed influence; a fifth main module, which is used to optimize the hydrological model sub-process simulation sequence scheme using the Budyko framework according to the determined scheme optimization criteria and in combination with the multi-year average-scale meteorological and hydrological conditions of the target basin.

[0101] The hydrological model sub-process simulation sequence diagnosis and optimization system provided by the embodiment of the present invention aims at the problem that the hydrological model with a fixed sub-process simulation sequence cannot be fairly applied to a variety of target basins. Figure 5 Several modules in the system were used to diagnose the impact of the simulation order of hydrological model sub-processes on the water balance simulation of the target basin through two simulation schemes: runoff priority and evaporation priority. A scheme optimization method using the long-term meteorological conditions of the target basin was developed based on the Budyko framework.

[0102] It should be noted that the system embodiment provided by the present invention is used to implement the methods in the above method embodiment as well as the methods in other method embodiments provided by the present invention. The only difference is that the corresponding functional modules are set. The principle is basically the same as the principle of the above system embodiment provided by the present invention. As long as the technical personnel in this field refer to the specific technical solutions in other method embodiments on the basis of the above system embodiment, obtain the corresponding technical means and the technical solutions composed of these technical means by combining the technical features, and on the premise of ensuring the practicality of the technical solutions, improve the modules in the above system embodiment to obtain the corresponding system class embodiments, which are used to implement the methods in other method class embodiments. For example:

[0103] Based on the contents of the above system embodiment, as a preferred embodiment, the hydrological model sub-process simulation sequential diagnosis and optimization system provided in the embodiment of the present invention, in the third main module, parameter calibration is performed on any of the two constructed schemes, and further includes:

[0104] According to the daily-scale meteorological and hydrological conditions of the target watershed, under a given parameter set, the first simulated runoff sequence is obtained, and the objective function value between the measured sequence and the simulated sequence is calculated;

[0105] Starting from n=2, the optimization algorithm selects a parameter set that is better than the previous one within the parameter range, obtains the runoff sequence of the nth simulation, calculates the objective function value between the measured sequence and the simulated sequence until convergence, and retains the model parameters at this time as the model parameters of the sub-process simulation sequence scheme.

[0106] Based on the content of the above system embodiment, as a preferred embodiment, the hydrological model sub-process simulation sequence diagnosis and optimization system provided in the embodiment of the present invention, in the third main module, the simulation accuracy of the scheme is determined, and further includes:

[0107] The Kling-Gupta efficiency coefficient, Nash efficiency coefficient, relative error, determination coefficient and fitting line slope are used to determine the simulation accuracy of the sub-process simulation sequence scheme. The closer the Kling-Gupta efficiency coefficient, Nash efficiency coefficient, relative error and determination coefficient are to 1 and the closer the slope of the fitting line is to 0, the higher the model simulation accuracy is.

[0108] Based on the content of the above system embodiment, as a preferred embodiment, the hydrological model sub-process simulation sequence diagnosis and optimization system provided in the embodiment of the present invention, the fourth main module is also used to execute the following instructions:

[0109] Determine the factors that affect the water balance of the target watershed, including total evaporation, total runoff, and the evaporation and runoff components that may be output by the model;

[0110] According to the above factors and combined with the simulation accuracy of the sub-process simulation sequence scheme, the thresholds of comprehensive parameters and drought index of the target watershed are determined as the scheme optimization criteria.

[0111] Based on the content of the above system embodiment, as a preferred embodiment, the hydrological model sub-process simulation sequence diagnosis and optimization system provided in the embodiment of the present invention, the fifth main module is also used to execute the following instructions:

[0112] Calculate the average annual precipitation, average annual potential evaporation and average annual runoff of the target basin based on the multi-year average scale meteorological and hydrological conditions of the target basin;

[0113] The multi-year scale watershed water balance formula is used to calculate the equilibrium evaporation as the actual evaporation of the target basin, combining the average annual precipitation and the average annual runoff;

[0114] The comprehensive parameters are calculated by using the Budyko framework formula, combining the average annual precipitation, average annual potential evaporation and actual evaporation of the target basin;

[0115] Calculate the drought index of the target watershed;

[0116] Based on the given climatic conditions, combined with the comprehensive parameters and the target basin drought index, the Budyko framework is used to optimize the hydrological model sub-process simulation sequence scheme.

[0117] Based on the content of the above system embodiment, as a preferred embodiment, the hydrological model sub-process simulation sequence diagnosis and optimization system provided in the embodiment of the present invention, the fifth main module is also used to execute the following instructions:

[0118] When the target basin drought index is ≤1, both the runoff priority scheme and the evaporation priority scheme are applicable; when the target basin drought index is >1, if the comprehensive parameter is >2.6, the evaporation priority scheme is adopted, otherwise the runoff priority scheme is adopted.

[0119] The method of the embodiment of the present invention is implemented by relying on electronic devices, so it is necessary to introduce the relevant electronic devices. Based on this purpose, the embodiment of the present invention provides an electronic device, such as Figure 6 As shown, the electronic device includes: at least one processor, a communication interface, at least one memory, and a communication bus, wherein at least one processor, the communication interface, and at least one memory communicate with each other through the communication bus. At least one processor calls the logic instructions in at least one memory to execute all or part of the steps of the method provided by the aforementioned various method embodiments.

[0120] In addition, when the logic instructions in the above-mentioned at least one memory are implemented in the form of a software functional unit and sold or used as an independent product, they are stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly in other words, the part that contributes to the prior art or part of the technical solution is embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions for enabling a computer device (a personal computer, a server, or a network device) to perform all or part of the steps of the method described in each method embodiment of the present invention. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and various media for storing program codes.

[0121] The system embodiments described above are merely illustrative, wherein the units described as separate components are or are not physically separated, and the components shown as units are or are not physical units, located in one place, or distributed on multiple network units. Some or all of the modules are selected according to actual conditions to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement without creative effort.

[0122] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0126] In summary, the present invention discloses a method for diagnosing and optimizing the simulation sequence of a hydrological model subprocess, which can optimize the submodule sequence that accurately reflects the hydrological physical process of the basin, thereby achieving more accurate water balance simulation and forecasting. The method for diagnosing and optimizing the subprocess sequence of a hydrological model includes: selecting a hydrological model (HM) as a benchmark model, obtaining the basic hydrological and meteorological data of the target basin and processing it to obtain the data required for the simulation input; separating the runoff and evaporation simulation methods of the hydrological model HM, and constructing two subprocess simulation sequence schemes of runoff priority and evaporation priority; calibrating and testing the parameters of the hydrological model and calculating the simulation accuracy of the two schemes; comparing the simulation accuracy of the two schemes, diagnosing the influence of the simulation sequence of the hydrological model subprocess on the water balance simulation of the target basin; based on the multi-year average meteorological and hydrological conditions of the target basin, using the Budyko framework to achieve the optimization of the scheme. The present invention solves the problem that a hydrological model with a fixed subprocess simulation sequence cannot be fairly applied to a variety of target basins, clarifies the influence of the evaporation-runoff subprocess simulation sequence on the hydrological and water balance simulation, and greatly improves the authenticity and accuracy of the model simulation.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for diagnosing and optimizing the simulation sequence of subprocesses in a hydrological model, characterized in that: include: Obtaining basic hydrological and meteorological data of the target basin and processing the data to obtain data required for simulation input, wherein the data required for simulation input includes daily-scale meteorological and hydrological conditions of the target basin and multi-year average-scale meteorological and hydrological conditions of the target basin; According to the selected hydrological model, the simulation method of basin runoff and evaporation process is separated, and further based on the basin water balance formula P = E + Q The runoff priority scheme and evaporation priority scheme are formed, where P is precipitation, E is evaporation, and Q is runoff; Constructing the runoff priority scheme and the evaporation priority scheme includes: constructing the runoff priority scheme and the evaporation priority scheme according to the watershed water balance formula, wherein in the runoff priority scheme, within a given calculation step and spatial unit, the runoff is simulated first and then the evaporation is simulated, and the water balance formula is: E=PQ; in the evaporation priority scheme, the evaporation is simulated first and then the runoff is simulated, and the water balance formula is: Q=PE; According to the objective function, the optimization algorithm is used to calibrate the model parameters of the two schemes. In the calibration process, the Kling-Gupta efficiency coefficient KGE maximization is selected as the objective function of the simulation sequence. F ; The evaporation-runoff simulation accuracy of the two schemes of the hydrological model during the calibration period is evaluated according to the evaluation indicators. The influence of the simulation sequence of the sub-processes of the hydrological model on the hydrological simulation of the target basin is further diagnosed based on the simulation accuracy. The optimization criteria of the scheme are determined according to the diagnosed influence, which further includes: Select the Kling-Gupta efficiency coefficient KGE to characterize the model simulation accuracy, and calculate the difference in simulation accuracy between the two schemes: According to the simulation accuracy of the two schemes, the influence of the simulation sequence of the sub-processes of the hydrological model on the water balance simulation of the target watershed is diagnosed, and the scheme optimization criteria are determined according to the diagnosed influence, including: determining the factors affecting the water balance of the target watershed, the factors include total evaporation, total runoff, and evaporation and runoff components output by the model; according to the factors, combined with the simulation accuracy of the sub-process simulation sequence scheme, the comprehensive parameters of the Budyko framework and the threshold value of the drought index of the target watershed are determined as the scheme optimization criteria; The optimization criteria are determined according to the Budyko framework, where the Budyko formula is as follows: ， Where, P is the average annual rainfall in the basin, mm; E is the average annual evaporation in the basin, mm, calculated by the water balance formula; Epa is the average annual potential evaporation of the basin, mm; ω is the comprehensive parameter of the Budyko formula; The optimization criteria for the scheme are as follows: when the dryness index is ≤1, both the runoff priority scheme and the evaporation priority scheme are applicable; when the dryness index is >1, if ω>2.6, the evaporation priority scheme is adopted, otherwise the runoff priority scheme is adopted; According to the determined scheme optimization criteria, combined with the multi-year average scale meteorological and hydrological conditions of the target basin, the Budyko framework is used to optimize the hydrological model sub-process simulation sequence scheme suitable for the target basin, including: calculating the average annual precipitation of the target basin according to the multi-year average scale meteorological and hydrological conditions of the target basin P , annual potential evaporation E pa and annual average runoff Q ; Use the multi-year scale basin water balance formula, combined with the target basin's average annual precipitation P and annual runoff Q , calculate the equilibrium evaporation as the actual evaporation of the target basin, that is E = P - Q ; Using the Budyko framework formula, combined with the average annual precipitation of the target basin P , annual potential evaporation E pa and actual evaporation E , back-calculate comprehensive parameters ω ; Using the average annual precipitation in the basin P and annual potential evaporation E pa Calculate the target watershed dryness index AI, the calculation formula is as follows: ， Based on the given climatic conditions, combined with the comprehensive parameters ω and the target watershed dryness index AI, determine the optimal hydrological model sub-process simulation sequence scheme.

2. The method for diagnosing and optimizing the hydrological model subprocess simulation sequence according to claim 1, characterized in that: The model parameters of the two schemes are calibrated using an optimization algorithm based on the objective function, including: Constructing the objective function F : ， Where: and Respectively i Observed and simulated values ​​at time instant; , is the average value of the observed and simulated series; , are the standard deviations of the observed and simulated series, respectively; According to the daily-scale meteorological and hydrological conditions of the target watershed, under a given parameter set, the first simulated runoff sequence is obtained, and the objective function value between the measured sequence and the simulated sequence is calculated; Starting from n=2, the optimization algorithm selects a parameter set that is better than the previous one within the parameter range, obtains the runoff sequence of the nth simulation, calculates the objective function value between the measured sequence and the simulated sequence until convergence, and retains the model parameters at this time as the model parameters of the sub-process simulation sequence scheme.

3. Hydrological model sub-process simulation sequence diagnosis and optimization system, characterized by: include: The first main module is used to obtain basic hydrological and meteorological data of the target basin and process it to obtain data required for simulation input, wherein the data required for simulation input includes daily-scale meteorological and hydrological conditions of the target basin and multi-year average-scale meteorological and hydrological conditions of the target basin; The second main module is used to separate the simulation methods of basin runoff and evaporation processes according to the selected hydrological model, and further according to the basin water balance formula P = E + Q The runoff priority scheme and evaporation priority scheme are formed, where P is precipitation, E is evaporation, and Q is runoff; Constructing the runoff priority scheme and the evaporation priority scheme includes: constructing the runoff priority scheme and the evaporation priority scheme according to the watershed water balance formula, wherein in the runoff priority scheme, within a given calculation step and spatial unit, the runoff is simulated first and then the evaporation is simulated, and the water balance formula is: E=PQ; in the evaporation priority scheme, the evaporation is simulated first and then the runoff is simulated, and the water balance formula is: Q=PE; The third main module is used to calibrate the model parameters of the two schemes using an optimization algorithm according to the objective function, in which the Kling-Gupta efficiency coefficient KGE maximization is selected as the objective function of the simulation sequence during the calibration process. F ; The fourth main module is used to evaluate the evaporation-runoff simulation accuracy of the two schemes of the hydrological model during the calibration period according to the evaluation indicators, further diagnose the impact of the simulation sequence of the sub-processes of the hydrological model on the hydrological simulation of the target basin based on the simulation accuracy, and determine the scheme optimization criteria according to the diagnosed impact, further including: Select the Kling-Gupta efficiency coefficient KGE to characterize the model simulation accuracy, and calculate the difference in simulation accuracy between the two schemes: According to the simulation accuracy of the two schemes, the influence of the simulation sequence of the sub-processes of the hydrological model on the water balance simulation of the target watershed is diagnosed, and the scheme optimization criteria are determined according to the diagnosed influence, including: determining the factors affecting the water balance of the target watershed, the factors include total evaporation, total runoff, and evaporation and runoff components output by the model; according to the factors, combined with the simulation accuracy of the sub-process simulation sequence scheme, the comprehensive parameters of the Budyko framework and the threshold value of the drought index of the target watershed are determined as the scheme optimization criteria; The optimization criteria are determined according to the Budyko framework, where the Budyko formula is as follows: ， Where, P is the average annual rainfall in the basin, mm; E is the average annual evaporation in the basin, mm, calculated by the water balance formula; Epa is the average annual potential evaporation of the basin, mm; ω is the comprehensive parameter of the Budyko formula; The optimization criteria for the scheme are as follows: when the dryness index is ≤1, both the runoff priority scheme and the evaporation priority scheme are applicable; when the dryness index is >1, if ω>2.6, the evaporation priority scheme is adopted, otherwise the runoff priority scheme is adopted; The fifth main module is used to select the hydrological model sub-process simulation sequence scheme suitable for the target basin based on the determined scheme optimization criteria and the multi-year average scale meteorological and hydrological conditions of the target basin using the Budyko framework, including: calculating the average annual precipitation of the target basin based on the multi-year average scale meteorological and hydrological conditions of the target basin; P , annual potential evaporation E pa and annual average runoff Q ; Use the multi-year scale basin water balance formula, combined with the target basin's average annual precipitation P and annual runoff Q , calculate the equilibrium evaporation as the actual evaporation of the target basin, that is E = P - Q ; Using the Budyko framework formula, combined with the average annual precipitation of the target basin P , annual potential evaporation E pa and actual evaporation E , back-calculate comprehensive parameters ω ; Using the average annual precipitation in the basin P and annual potential evaporation E pa Calculate the target watershed dryness index AI, the calculation formula is as follows: ， Based on the given climatic conditions, combined with the comprehensive parameters ω and the target watershed dryness index AI, determine the optimal hydrological model sub-process simulation sequence scheme.

4. An electronic device, characterized in that: include: At least one processor, at least one memory and a communication interface; wherein the processor, memory and communication interface communicate with each other; The memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the method according to any one of claims 1 to 3.

5. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions cause the computer to execute the method of any one of claims 1 to 3.

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