Hydrometric station flow simulation method based on nonlinear reservoir equation parameter evaluation

Abstract

The invention belongs to the technical field of hydrology and water resources, and relates to a hydrological station flow simulation method based on nonlinear reservoir equation parameter evaluation, which comprises the following steps: deducing the existence uniqueness condition of a nonlinear reservoir equation solution through a functional analysis theory, establishing a parameter constraint inequality, and performing forced execution in calibration; and a complete simulation framework suitable for the urbanized small watershed is constructed by combining an improved SCS runoff production, nonlinear slope confluence and river network lag calculation module. According to the method, the compression mapping theorem in functional analysis is creatively applied to hydrological nonlinear system modeling, so that a solid operable engineering constraint is provided for parameter determination of a nonlinear reservoir equation on the premise of not sacrificing physical authenticity; the method effectively solves the fundamental contradiction between the nonlinear potential of hydrological data and the solution reliability, enables the nonlinear groundwater confluence model to be stably and reliably integrated into a business hydrological forecasting system, is suitable for an urbanized drainage basin with strong groundwater response nonlinearity, and obtains an accurate simulation effect.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hydrology and water resources, and particularly relates to a hydrological station flow simulation method based on parameter evaluation of a nonlinear reservoir equation. BACKGROUND

[0002] As a core technology support for water resources management, flood forecasting and urban waterlogging prevention, the accuracy and reliability of hydrological simulation are directly related to the scientificity and timeliness of disaster prevention and mitigation decisions. At the basin scale, the confluence process is essentially a nonlinear dynamic system coupled by multiple physical mechanisms. Especially under the background of rapid urbanization, the dramatic change in underlying surface conditions further exacerbates the complexity of runoff and confluence response. For a long time, the hydrology community has had a deep understanding of the nonlinear characteristics of surface runoff: since M. Chell in 1960 revealed the nonlinear relationship between rainfall intensity and unit line form based on small watershed measured data, subsequent studies have continuously verified and expanded this understanding. For example, the Southwest Institute of the China Academy of Railway Sciences reproduced the phenomenon that the increase of rainfall intensity leads to the advance of flood peak and the process of sharp and thin in artificial rainfall test; through analysis of the data of the semi-humid-semi-arid region basin, Xia Jun pointed out that the runoff gain factor, which is assumed to be constant in the traditional linear system theory, actually varies significantly with the soil antecedent moisture, showing a clear nonlinear dependence. On this basis, modern urban rainwater models such as SWMM effectively build a nonlinear solution path for surface slope confluence by coupling water balance equations and Manning formula, which significantly improves the simulation capability of urban short-duration heavy rainfall events.

[0003] The existing technical difficulties are rooted in a deep technical contradiction: on the one hand, the nonlinear reservoir equation is more consistent with the physical nature of the groundwater system in theory, and has the potential to improve simulation accuracy; on the other hand, the equation lacks strict existence and uniqueness guarantee of the solution under general inflow conditions, making the parameter calibration process prone to multiple solutions or divergence risk, thereby shaking the reliability foundation of model application. Existing research has failed to establish a set of operational parameter reasonableness criteria without destroying the original structure of the equation or imposing unrealistic boundary conditions, resulting in that the nonlinear groundwater confluence model has been stuck in the theoretical discussion stage for a long time and is difficult to integrate into the operational hydrological forecasting system. Further, in highly urbanized basins, the proportion of impervious area is high, and the groundwater flow path is strongly disturbed by artificial structures, the sensitivity and nonlinearity of groundwater response to rainfall events are further enhanced. At this time, if the linear assumption or approximate solution strategy is still used, it will be difficult to accurately capture the timing and magnitude of the contribution of groundwater runoff to the total outlet flow, especially under the scenario of continuous rainfall or long-duration heavy rain, which may lead to underestimation of base flow and distortion of recession process, thereby affecting the simulation accuracy of the overall flood process.

[0004] Therefore, how to establish the uniqueness condition of the solution in the premise of preserving the complete physical structure of the nonlinear reservoir equation from the mathematical theory, and build a set of parameter evaluation and constraint mechanism for practical basin application, to ensure that the model can reflect the nonlinear nature of groundwater convergence in complex urban environment, and has the stability of numerical solution and the rationality of parameter determination, has become the key challenge and technical problem to be solved for the technical personnel in the field. SUMMARY

[0005] To achieve the above invention purposes, the application provides a hydrological station flow simulation method based on parameter evaluation of nonlinear reservoir equation. The method aims at the problems of physical mechanism distortion, recession process simulation deviation and insufficient rainstorm response description caused by the linear reservoir generalization in the groundwater convergence process of existing hydrological model, proposes a technical path of establishing the existence and uniqueness condition of the solution through functional analysis theory in the premise of preserving the original structure of the nonlinear reservoir equation, and building the parameter rationality criterion and constraint mechanism accordingly. The application systematically couples the theoretical achievement with the surface runoff yield and concentration module and the river network concentration module, forms a complete runoff yield and concentration simulation framework suitable for highly urbanized small basins, and completes the engineering verification in the basin from Songhuaba Reservoir to Kunming Hydrological Station in Kunming City.

[0006] To achieve the above purposes, the application provides the following technical scheme: A hydrological station flow simulation method based on parameter evaluation of nonlinear reservoir equation, characterized in that it comprises: S1, based on the digital elevation model and land use data of the research basin, extracting the basin boundary and dividing a plurality of sub-basin calculation units; S2, using the improved SCS curve number method combined with the dynamic soil water deficit state variable to calculate the slope runoff of each sub-basin, and inputting to the slope runoff calculation module; S3, based on the nonlinear slope concentration model coupled with the water balance equation and Manning formula, solving the surface water depth and calculating the surface runoff process, and outputting the surface runoff from the nonlinear slope concentration calculation module; S4, using the full storage runoff structure to calculate the groundwater runoff, and inputting to the groundwater concentration module described by the nonlinear reservoir equation as the inflow; S5, for the nonlinear reservoir equation, defining the integral mapping on the continuous function space, deriving the parameter constraint inequality that guarantees the existence and uniqueness of the numerical solution through the compression mapping theorem and Lipschitz condition analysis, and enforcing the constraint in the parameter calibration process; S6, after superimposing the surface runoff and groundwater runoff in space according to the river network topological relationship, inputting to the river network lag simulation concentration module, regulating and storing by the lag simulation algorithm, and outputting the total flow process from the total flow output end of the hydrological station section.

[0007] Furthermore, the nonlinear reservoir equation is defined as follows: (1); (2); In the formula, I g It is the inflow of underground water reservoir; Q g Outflow from underground reservoir; S For underground water storage; dt For time differentiation; a This is the storage and release coefficient; b The parameter uniqueness constraint criterion module is used to dynamically select the corresponding constraint conditions based on the value range of the storage and release index b to ensure the uniqueness of the equation solution. The combined equations (1) and (2) are: (3) when dt When the data is relatively small, depending on the available information, I g Treating it as a constant, we can apply the following to both sides of equation (1). t Integrals, including: (4) According to equation (4), Newton's descent method is used to solve iteratively. S 2. Then, the groundwater runoff can be calculated according to formula (2).

[0008] Furthermore, when the storage-discharge index b ≥ 1, the parameter constraints are: (5); t2 is the end of the calculation period, t1 is the beginning of the calculation period, S(t2) is the underground water storage at the end of the calculation period, and S(t1) is the underground water storage at the beginning of the calculation period. When 0 < b < 1, the parameter constraint is: (6).

[0009] Furthermore, the slope runoff calculation module uses a dynamically updated soil water shortage state variable Se to represent the previous soil moisture. The update rule is: when the infiltration rate f>0, Se decreases with the infiltration amount. During periods of no rain, Se recovers exponentially with a recovery coefficient of kr = 0.06 / Tr, where Tr is the soil moisture recovery time constant. If the consecutive periods of no rain exceed Tr, Se is reset to the maximum water shortage Smax.

[0010] Further, the nonlinear slope confluence calculation module solves the surface water depth d in each time step by Newton iterative method, and the control equation is: (7); In the formula, d d is the surface water depth; i is the rainfall intensity; e is the evaporation rate; f is the infiltration rate; q is the runoff depth rate; According to the Manning formula, the outlet flow is calculated Q s is: (8); In the formula, n is the roughness; S is the surface slope; R x is the hydraulic radius; A x is the flow area; Assuming that the flow through the slope is expressed as its width W , height d - d s , d s is the maximum depression depth, and the slope S Uniform flow in a rectangular channel, then: (9); (10); The slope runoff depth rate is calculated according to the following formula q : (11); In the formula, A is the slope area; The variable is constructed, and the aforementioned water balance equation can be changed to: (12); (13); According to formula (12), the d 2 at the end of the time step can be solved by Newton iterative method. d - d s When d - d s ≤0, the following formula is used for calculation: (14); After the water depth of slope area is solved, the slope flow Q is calculated by equation (11) s .

[0011] Further, the groundwater runoff calculation module adopts the full storage runoff model to calculate the groundwater runoff (RG), and the calculation logic is as follows: Constructing variables a : (15); In the formula, WM is the soil tension water storage capacity of the basin, B is the soil tension water storage capacity distribution curve index; When FA + a ≥ WM ( B +1) is: (16); When FA + a < WM ( B +1) is: (17); (18); In the formula, FA is the infiltration amount of the time period; W is the initial soil water storage amount of the time period; RG is the groundwater runoff.

[0012] Further, the river network lagged routing confluence module adopts the following recursive formula for river regulation and storage: (19); In the formula, is the slope flow calculated by the slope confluence method; is the flow after the slope flow is regulated and stored by the river network; CR is the river network water storage recession coefficient; L is the lag time.

[0013] The application belongs to the technical field of hydrology and water resources, and relates to a hydrological station flow simulation method based on parameter evaluation of a nonlinear reservoir equation. Existence and uniqueness conditions of a nonlinear reservoir equation solution are derived through functional analysis theory, parameter constraint inequalities are established, and are enforced in calibration. In combination with improved SCS runoff generation, nonlinear slope confluence and river network lagging calculation modules, a complete simulation framework suitable for urbanized small watersheds is constructed. The application creatively applies the contraction mapping theorem in functional analysis to hydrological nonlinear system modeling, provides solid and operable engineering constraints for parameter determination of the nonlinear reservoir equation without sacrificing physical reality, effectively solves the fundamental contradiction between nonlinear potential of hydrological data and solution reliability, enables the nonlinear groundwater confluence model to be stably and reliably integrated into a business hydrological prediction system, is suitable for urbanized watersheds with strong nonlinear groundwater response, and obtains precise simulation effect. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 A work flow diagram of a hydrological station flow simulation method based on parameter evaluation of a nonlinear reservoir equation claimed by the embodiments of the application. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the application will be clearly and completely described in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0016] The terms "first", "second", "third" in the application are only used for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", "third" can explicitly or implicitly include at least one feature. In the description of the application, the meaning of "multiple" is at least two, for example, two, three, etc., unless otherwise explicitly and specifically limited. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the application are only used to explain the relative position relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the directional indications also change accordingly. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0017] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily all directed to the same embodiment, or to one or more group of alternative embodiments. It is explicitly contemplated that embodiments described herein can be combined with other embodiments.

[0018] Compared with the systematic study of the nonlinear mechanism of surface runoff, the nonlinear characteristics of groundwater confluence process have long been insufficiently valued. Although previous studies have shown that groundwater runoff is influenced by multiple factors such as rainfall infiltration, soil moisture, topographic gradient, previous water storage state of rock-soil body and pore medium structure, and its response process also has significant nonlinear characteristics, in actual operational hydrological models, especially in models for real-time flood forecasting, the groundwater module is still generally approximated by the simple and efficient linear reservoir method. Although this method can obtain "acceptable" simulation results under certain conditions, its core assumption that the outflow is linearly proportional to the storage essentially ignores the inherent nonlinear storage and discharge mechanism of the groundwater system, and it is difficult to accurately depict the dual dynamic characteristics of base flow attenuation in dry season and rapid response of groundwater runoff after heavy rain. To overcome this limitation, a few studies have attempted to introduce nonlinear reservoir equations to describe groundwater confluence, but their solving strategies often rely on idealized assumptions: either assuming no external inflow to simplify the structure of the differential equation, or approximating the nonlinear term as a quadratic function to obtain analytical feasibility. Although such processing reduces the mathematical difficulty of solving, it inevitably sacrifices the fidelity of the model to the real physical process, leading to ambiguous physical meaning of parameters, insufficient stability of solutions, and thus weakening the applicability and generalization ability of the model in complex urban basins.

[0019] According to the first embodiment of the application, the application claims a hydrological station flow simulation method based on nonlinear reservoir equation parameter evaluation, referring to Figure 1 , comprising: S1, based on the digital elevation model and land use data of the study basin, extracting the basin boundary and dividing a plurality of sub-basin calculation units; S2, using the improved SCS curve number method combined with the dynamic soil water deficit state variable to calculate the slope runoff of each sub-basin and inputting it into the slope runoff calculation module; S3, based on the nonlinear slope confluence model coupled with the water balance equation and the Manning formula, solving the surface water depth and calculating the surface runoff process, and outputting the surface runoff from the nonlinear slope confluence calculation module; S4, using the storage-full runoff structure to calculate the groundwater runoff, and inputting it as inflow into the groundwater confluence module described by the nonlinear reservoir equation; S5, on the nonlinear reservoir equation, define the integral mapping on the continuous function space, through the compression mapping theorem and Lipschitz condition analysis, derive the parameter constraint inequality to ensure the existence and uniqueness of the numerical solution, and enforce the constraint in the parameter calibration process; S6, after superimposing the surface runoff and groundwater runoff in space according to the river network topological relationship, inputting into the river network lag calculation confluence module, and after regulation by the lag calculation method, outputting the total flow process from the total flow output end of the hydrological station section.

[0020] In this embodiment, SRTM90mDEM is used to extract the basin boundary of the basin from Songhuaba Reservoir to Kunming Station. The precipitation data of Youguoqiao, Jindian and Xueshan from 00:00:00 on July 20, 2012 to 23:00:00 on September 21, 2013 (Lanlongtan rainfall station was built in 2014), the evaporation data and the flow data of Kunming hydrological station during the same period are used. The data are from the hydrological yearbook. According to the investigation, Songhuaba Reservoir did not discharge during the research period, that is, the flow process monitored by Kunming Station is the rainfall runoff process of the basin from Songhuaba Reservoir to Kunming Station.

[0021] Further, the nonlinear reservoir equation is defined as: (1); (2); In the formula, I g is the inflow of the underground reservoir; Q g is the outflow of the underground reservoir; S is the storage capacity of the underground reservoir; dt is the time differential; a is the storage and discharge coefficient; b is the storage and discharge index, and the parameter uniqueness constraint criterion module is used to dynamically select the corresponding constraint condition according to the value range of the storage and discharge index b to ensure the uniqueness of the equation solution; By combining equations (1) and (2), we have: (3); When dt is small, according to the data conditions, I g is regarded as a constant, and the integral of equation (1) with respect to t is: (4); According to equation (4), the Newton down-hill method is used to iteratively solve S 2, and according to equation (2), the groundwater runoff can be calculated.

[0022] In this embodiment, the unique solution condition analysis method for the nonlinear reservoir equation adopts the compression mapping theorem; set up X For distance space, T : X → X It is a mapping if there exists 0≤ <1, such that: d ( Tx , Ty )≤ d ( x , y ), ; Then T is a compressed mapping. If X Furthermore, for a complete distance space, the mapping... T exist X There exists a unique fixed point on the surface, that is, there exists a unique fixed point. , so that: Tx 0= x 0; Lipschitz conditional analysis The following equation takes the form of a differential equation: ; in f ( x , t In space C[ t 1, t 2] (Easy proof C[ t 1, t [2] is continuous in a complete distance space, and for variables x The following conditions must be met: ; Then the equation is valid for any t0∈[ t 1, t There exists a unique solution on [2].

[0023] The proof is as follows: Space C[ t 1, t Consider the following mapping on 2]: ; It can be known T From C[ t 1, t 2] to C[ t 1, t 2] Its own mapping.

[0024] Based on distance analysis, we have: ; According to the compression mapping theorem, the equation holds true for any t0∈[ t 1, t On [2], there exists a unique solution: ; Substituting the nonlinear reservoir equations into the Lipschitz condition analysis, we get: ; Based on the reservoir storage and release relationship, it is easy to know the outflow of groundwater reservoirs. Q With storage S If they are directly proportional, then it can be determined from a physical perspective. a and b All are greater than 0.

[0025] The following inequality of the absolute value of the difference of power functions can be proven:

[0026] Furthermore, when the storage-discharge index b ≥ 1, the parameter constraints are: (5); t2 is the end of the calculation period, t1 is the beginning of the calculation period, S(t2) is the underground water storage at the end of the calculation period, and S(t1) is the underground water storage at the beginning of the calculation period. When 0 < b < 1, the parameter constraint is: (6).

[0027] Furthermore, the slope runoff calculation module uses a dynamically updated soil water shortage state variable Se to represent the previous soil moisture. The update rule is: when the infiltration rate f>0, Se decreases with the infiltration amount. During periods of no rain, Se recovers exponentially with a recovery coefficient of kr = 0.06 / Tr, where Tr is the soil moisture recovery time constant. If the consecutive periods of no rain exceed Tr, Se is reset to the maximum water shortage Smax.

[0028] In this embodiment, the slope runoff generation method is employed using the SCS curve number method. The basic equation for the SCS curve number method is as follows: ; In the formula, P This refers to cumulative rainfall. R For cumulative runoff depth. S max This represents the maximum water shortage in the soil.

[0029] Experiential S maxThe curve number can be based on the land use type CN The calculation is as follows: ; The SCS curve number method considers that the slope runoff loss is only infiltration, and the cumulative infiltration amount F The calculation formula is as follows: ; Let P 1 and F 1 be the time step The cumulative rainfall and infiltration amount at the beginning are P 2 and F 2, respectively, and the cumulative rainfall and infiltration amount at the end of the period are ; ; In the formula, i is the rainfall intensity; S e is the soil water deficit at the beginning of the time step.

[0030] Further, the slope infiltration rate f can be calculated as follows: ; In actual application, the S e at the end of the period (i.e., at the beginning of the next period) can be updated according to the following rules:

[0031] In the formula, Tr is the recovery interval; kr is the water storage capacity recovery coefficient, kr = 0.06 / Tr .

[0032] The slope runoff amount RS can be calculated as follows: .

[0033] Further, the non-linear slope confluence calculation module iteratively solves the surface water depth d at each time step by the Newton iterative method, and the control equation is as follows: (7); In the formula, d is the surface water depth; i is the rainfall intensity; e is the evaporation rate; f is the infiltration rate; q is the runoff depth rate; According to Manning formula, the outlet flow is calculated Q s is: (8); In the formula, n is the roughness; S is the ground slope; R x is the hydraulic radius; A x is the flow area; Suppose the flow through the slope surface is expressed as its width W , height d - d s , d s is the maximum depression depth, and the slope S For uniform flow in a rectangular channel, we have: (9); (10); The slope runoff depth rate is calculated by the following formula q : (11); In the formula, A is the slope area; The construction variable , the aforementioned water balance equation can be changed to: (12); (13); According to formula (12), the time step end d 2 can be solved by Newton's iterative method. Formula (12) is only applicable to d - d s Greater than 0, when d - d s ≤0, the following formula is used for calculation: (14); After calculating the slope area water depth, the slope flow Q is calculated by formula (11) s .

[0034] Further, the groundwater runoff calculation module uses the full storage runoff model to calculate the groundwater runoff (RG), and its calculation logic is: The construction variable a : (15); wherein, WM is the soil tension water storage capacity of the catchment, B is the soil tension water storage capacity distribution curve index; when FA + a ≥ WM ( B +1) is: (16); when FA + a < WM ( B +1) is: (17); (18); wherein, FA is the infiltration amount of the period; W is the initial soil water storage amount of the period; RG is the groundwater yield.

[0035] Further, the river network lag calculation confluence module adopts the following recursive formula for river regulation and storage: (19); wherein, is the slope flow calculated by the slope confluence method; is the flow after the slope flow is regulated by the river network; CR is the river network storage recession coefficient; L is the lag time.

[0036] In this embodiment, the current coupling SCS slope runoff, nonlinear slope confluence, full storage groundwater runoff, river network confluence, and the nonlinear reservoir groundwater confluence calculation method developed in this research are used to construct the urban small watershed runoff (USWR) model. Based on the water and rainfall data of the research basin, the model is used in combination with the setting of the possible maximum outflow of the research basin groundwater 1000 m³ / s and the minimum outflow 0.001 m³ / s, and the condition relationship between the outflow and the parameters a 、 b in equations (36) and (37), the constraint conditions in the improved time encoding bee colony algorithm IR-BCO are configured.

[0037] On this basis, the Nash efficiency coefficient is taken as the optimization objective, and the model parameters are optimized for the hourly flow process of the research period (2012-07-02 00:00:00~2013-09-21 23:00:00) of Kunming hydrological station. The optimized model parameters are shown in Table 1. The constructed model can better simulate the hourly flow process of Kunming station. According to statistics, the Nash efficiency coefficient is 0.812 during the research period, and the water balance coefficient is 0.918.

[0038] As shown in Table 1, the parameters of the underground reservoir equation obtained by this rating a are 0.00001, b 0.67987. Since b<1, formula (37) is used for calculation and evaluation. According to the analysis, even if the minimum outflow of the research basin is 0.0001m³ / s (which is actually impossible), the left value of formula (37) is 0.23, and the right value is 26.81, which also meets the requirements of the inequality condition. It can be seen that the parameters a , b obtained by this rating are applicable to the change range of the outflow of the research basin, which can guarantee the convergence of the nonlinear reservoir equation and the uniqueness of the solution of the outflow of the underground water.

[0039] Further analysis is made on the 20130718-20130721 flood. The urban small watershed runoff and infiltration (USWR) model constructed this time can better simulate the process of the flood. From the indicators, the water balance coefficient of the simulation of this flood is 0.990, the Nash efficiency coefficient is 0.970, the relative error of the flood peak is-2.19%, and the peak time difference is-1h, which has high simulation accuracy.

[0040] Table 1 Parameters of urban small watershed runoff and infiltration model of research basin

[0041] In the several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, and the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0042] In addition, the various functional units in the embodiments of the present application can be integrated in one processing unit, or each can exist as an independent physical unit, or two or more than two of them can be integrated in one physical unit. The above-mentioned integrated unit can be implemented in the form of hardware, or in the form of a software functional unit. The above is only an embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0043] The specific embodiments of the application are described above, but it is only as an example, and the present application is not limited to the specific embodiments described above. Any equivalent modification or replacement of the present application for those skilled in the art is also within the scope of the present application, therefore, any equivalent transformation and modification, improvement, etc. made without departing from the spirit and principle range of the present application should be covered in the scope of the present application.

Claims

1. A hydrological station flow simulation method based on nonlinear reservoir equation parameter evaluation, characterized by, The method comprises the following steps: S1, based on the digital elevation model and land use data of the study basin, extracting the basin boundary and dividing a plurality of sub-basin calculation units; S2, using the improved SCS curve number method combined with the dynamic soil water deficit state variable to calculate the slope runoff of each sub-basin, and inputting into the slope runoff calculation module; S3, based on the nonlinear slope confluence model coupled by the water balance equation and the Manning formula, solving the surface water depth and calculating the surface runoff process, and outputting the surface runoff from the nonlinear slope confluence calculation module; S4, using the full storage runoff structure to calculate the groundwater runoff, and inputting into the groundwater confluence module described by the nonlinear reservoir equation as an inflow; S5, for the nonlinear reservoir equation, defining an integral mapping on a continuous function space, deriving a parameter constraint inequality that guarantees the existence and uniqueness of the numerical solution through the compression mapping theorem and Lipschitz condition analysis, and enforcing the constraint in the parameter calibration process; S6, after superimposing the surface runoff and the groundwater runoff in space according to the river network topological relationship, inputting into the river network lagged routing confluence module, and outputting the total flow process from the total flow output end of the hydrological station section after regulation by the lagged routing algorithm.

2. The hydrological station flow simulation method of claim 1, wherein, The nonlinear reservoir equation is defined as: （1）; （2）; In the formula, I g is the inflow of the underground reservoir; Q g is the outflow of the underground reservoir; S is the storage of the underground reservoir; dt is the time differential; a is the storage-discharge coefficient; b is the storage-discharge index, and the parameter uniqueness constraint criterion module is configured to dynamically select a corresponding constraint condition according to a value range of the storage-discharge index b to ensure uniqueness of a solution of the equation. The simultaneous equations (1) and (2) are: （3） When dt The smaller, according to the material conditions, I g Considering the constant, to both sides of the equation (1) on t Integration, there: （4） According to the Newton-Raphson method of formula (4), the iterative solution is S 2, and then the groundwater runoff can be calculated according to formula (2).

3. The hydrological station flow simulation method of claim 2, wherein, When the storage and discharge index b is greater than or equal to 1, the parameter constraint condition is: （5）； t2 is the end time of the calculation period, t1 is the initial time of the calculation period, S(t2) is the corresponding groundwater reservoir storage at t2, and S(t1) is the corresponding groundwater reservoir storage at t1; When 0 < b < 1, the parameter constraint condition is: （6）。 4. The hydrological station flow simulation method of claim 1, wherein, The slope runoff calculation module uses a dynamically updated soil water deficit state variable Se to represent the soil moisture in the early stage, and the updating rule is that when the infiltration rate f is greater than 0, Se decreases with the decrease of the infiltration amount; In the rain-free period, Se recovers according to an exponential law, and the recovery coefficient kr is 0.06 / Tr, wherein Tr is a soil moisture recovery time constant; If the continuous rain-free period exceeds Tr, Se is reset to the maximum water deficit Smax.

5. The hydrological station flow simulation method of claim 1, wherein, The nonlinear slope confluence calculation module iteratively solves the surface water depth d in each time step by the Newton down-hill method, and the control equation is: （7）； wherein d is the depth of surface water accumulation; i is the rainfall intensity; e is the evaporation rate; f is the infiltration rate; q is the runoff depth rate; According to the Manning formula, the outlet flow rate is calculated Q s is: （8）; wherein n is the roughness; S is the surface slope; R x is the hydraulic radius; A x is the flow area; Assume that the flow over the slope is expressed as its width W , height d - d s , d s is the maximum depression depth, and slope S uniform flow in a rectangular channel, then we have: （9）; （10）; The slope runoff depth rate is calculated as follows q : （11）; In the formula, A Slope area; Construction variables Then the water balance equation can be modified as follows: （12）; （13）; According to equation (12), the time step end can be solved by Newton's iterative method d 2, equation (12) is only applicable to d - d s greater than 0, when d - d s ≤0, the following formula is used: （14）; After the water depth of the slope area is solved, the slope flow Q is calculated by equation (11) s .

6. The hydrological station flow simulation method of claim 1, wherein, The groundwater runoff calculation module uses the full storage runoff model to calculate the groundwater runoff (RG), and the calculation logic is: Constructing variables a : （15）; wherein WM is the soil tension water storage capacity of the watershed, B is the soil tension water storage capacity distribution curve exponent; When FA + a ≥ WM ( B +1) times: （16）; When FA + a < WM ( B +1) （17）; （18）; wherein FA is the infiltration amount for the time period; W is the initial soil water storage for the time period; RG is the groundwater production flow.

7. The hydrological station flow simulation method of claim 1, wherein, The river network lagged routing confluence module uses the following recursive formula for river regulation: （19）； In the formula, the slope flow calculated by the slope confluence method; the flow after the slope flow is regulated by the river network. CR is the water storage recession coefficient for the river network; L is the time lag.

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