Method and device for simulating and estimating flow and suspended sediment content in rivers in data-free areas

Abstract

The present invention provides a method and device for simulating and estimating the flow and suspended sediment content of rivers in data-free areas. The method integrates analysis, simulation, and estimation, enabling simple, rapid, and accurate derivation of the water-sediment correlation and the establishment of a water-sediment simulation and estimation model, thus solving the problem of calculating water and sediment content for rivers in data-free areas over the years and months. The method comprises: Step 1: constructing a flow simulation model for the target area based on data from a river section A that is in the same river system or an adjacent basin as the target data-free area and has observed data on measured flow Q and suspended sediment content CS; Step 2: constructing a suspended sediment content estimation model based on the flow Q and suspended sediment content CS data for river section A; Step 3: simulating the flow of a river section B in the data-free area based on the flow simulation model constructed in Step 1; and then estimating the suspended sediment content of B based on the suspended sediment content estimation model constructed in Step 2.

Description

Technical Field

[0001] The invention belongs to the technical field of hydrological simulation, and in particular relates to a method and a device for simulating and estimating the flow and suspended sediment content of a river in a data-free area. Technical Background

[0002] Research on river flow and sediment changes, their transport characteristics, and their evolutionary trends is crucial for understanding downstream riverbed erosion and siltation patterns and aquatic habitat changes. Analyzing changes in river discharge and suspended sediment load typically employs traditional methods such as linear regression, trend analysis, mutation testing, cumulative anomaly analysis, and cumulative curve analysis. These methods typically treat water and sediment as independent factors for comparative analysis or simply perform a linear correlation analysis. These methods fail to comprehensively characterize the correlation between the two while simultaneously analyzing their statistical characteristics, thereby exploring their interdependent variations. Traditional methods for analyzing and calculating river discharge and suspended sediment load, particularly suspended sediment load, typically rely on limited mean estimation or calculations based on measured river data using complex hydraulic models and methods. This is inherently difficult to achieve in areas without data, and even more challenging to calculate long-term results on water and sediment load in such areas. Therefore, in order to study the characteristics of water and sediment changes in depth and detail, and to objectively and reasonably reflect the water and sediment changes in each month over the years in areas without data, so as to facilitate engineering construction, flood control scheduling, water and sediment management, etc., it is urgent to study a method model based on water and sediment analysis that integrates simulation and estimation. Summary of the Invention

[0003] The present invention is made to solve the above-mentioned problems. Its purpose is to provide a method and device for simulating and estimating the flow and suspended sediment content of rivers in areas with no data. It integrates analysis, simulation and estimation, and can simply, quickly and accurately deduce the water-sediment correlation relationship, establish a water-sediment simulation estimation model, and solve the problem of calculating the water and sediment content of rivers in areas with no data in each month of the year.

[0004] In order to achieve the above object, the present invention adopts the following scheme:

[0005] <Method>

[0006] The present invention provides a method for simulating and estimating the flow and suspended sediment content of a river in an area without data, which is characterized by comprising the following steps:

[0007] Step 1: Construct a flow simulation model for the target area based on the data of a river section A that is in the same river system or adjacent to the target area with no data and has measured flow Q and suspended sediment concentration CS observation data. The specific steps include the following:

[0008] Step 1-1: Analyze the monthly flow Q over the past years (N years in total) t(t=1,2,…,T;T=12) the multi-year average value, coefficient of variation, skewness coefficient and other statistical parameters (for the convenience of discussion, uniformly recorded as θ Qt ), and then determine the marginal distribution function F of the monthly flow t (·), and the corresponding inverse function Use Copula function to construct the joint distribution function C of the traffic of two consecutive months Qt (·) and its inverse function

[0009] Step 1-2: Randomly generate an initial value u s1,1 ∈(0,1), as the marginal distribution probability of the first month, then the simulated value of Q(t1) in the first year simulation is Randomly generate the joint distribution probability ε of the first and second months 1,1 ∈(0,1), find the marginal distribution probability u of Q(t2) in the second month in the first year simulation s2,1 , and the simulated value q s2,1 ; And so on, deduce the simulated value q of month t+1 in the simulation of year i st+1,i , and then get the 1-year random simulation process of flow Q; repeat the above process N times to get the N-year random simulation process;

[0010] Repeat the above operation M times to obtain a random simulation process of flow Q M times, each time for N years;

[0011] Step 2: Based on the flow rate Q and suspended sediment concentration CS data of river section A, a suspended sediment concentration estimation model is constructed. The specific steps include the following:

[0012] Step 2-1: Based on the actual measured suspended sediment content CS of each month in previous years (N years in total) t , respectively determine the statistical parameters such as the mean value of suspended sediment content in each month (for the convenience of discussion, uniformly recorded as θ CSt ), and then determine the marginal distribution function G of the suspended sediment concentration in each month t (·), and the corresponding inverse function According to the marginal distribution function F of the monthly flow determined in step 1-1 t (·), construct the monthly flow Q respectively t and suspended sediment content CS t The joint distribution function C t (·), and its inverse function Construct the density function c of the Copula function t (·);

[0013] Step 2-2: The present invention considers that the monthly flow rate Q t and suspended sediment content CSt Various possible joint distribution probability values , so that Q t and CS t The probability value pm of the maximum density function value t,i is the most likely to occur; based on Q t and CS t The inverse function and density function of the joint distribution function are used to estimate the marginal distribution function value of suspended sediment concentration in the i-th year and the t-th month for:

[0014]

[0015] Calculate the marginal distribution function value v of the measured and predicted values ​​of suspended sediment concentration in year i and month t t,i 、 The error between 1t,i , and thus the multi-year average error of month t is obtained Based on this, the marginal distribution function value predicted for month t is The rate is set at On this basis, according to the inverse function of the marginal distribution function of suspended sediment concentration in month t, the suspended sediment concentration in year i and month t is estimated to be: Then, according to the measured suspended sediment content cs in the tth month of the i-th year, t,i Calculate the error e 2t,i , and further deduced to obtain the multi-year average error of month t Then, a secondary calibration is performed to obtain the estimated value of suspended sediment content in the ith year and the tth month.

[0016]

[0017] Repeat the above operation to obtain the estimated value of suspended sediment content in each month for N years;

[0018] Step 3: Based on the flow simulation model constructed in Step 1, simulate the flow of river section B in the data-free area. On this basis, estimate the suspended sediment concentration of section B using the suspended sediment concentration estimation model constructed in Step 2. This includes the following sub-steps:

[0019] Step 3-1: According to the basic information of the drainage area and average precipitation of B and A, the correction coefficient M1 of the flow of B based on the flow of A is obtained. Based on this, the statistical parameter θ Q Revised to M1θ Q ; Then bring it into step 1 to simulate the random simulation process of B's ​​monthly flow;

[0020] Step 3-2: According to the basic information of the watershed area and soil erosion modulus of B and A, the correction coefficient M2 of the suspended sediment content of B based on the suspended sediment content of A is obtained. Based on this, the statistical parameter θ CS Revised to M2θ CS ; The random simulation process Q′ of the monthly flow of B obtained in step 3-1 t , and the revised statistical parameter M2θ CS , use step 2 to deduce the error rate parameter and Substitute the estimated suspended sediment content process corresponding to the simulation process into step 2.

[0021] Preferably, the method for simulating and estimating the flow and suspended sediment content of rivers in data-free areas provided by the present invention may also have the following characteristics: in step 1-1, the marginal distribution function F is constructed t (·), inverse function Joint distribution function C of traffic in adjacent months Qt (·) and its inverse function They are:

[0022]

[0023] Preferably, the method for simulating and estimating the flow and suspended sediment content of rivers in data-free areas provided by the present invention may also have the following characteristics: in step 1-2, the joint distribution probability ε of the first and second months is randomly generated 1,1 ∈(0,1), then

[0024]

[0025] The marginal distribution probability u of Q(t2) in the second month in the first year simulation can be obtained s2,1 , and the simulated value q s2,1 ; Similarly, a recursive formula can be established to derive the simulated value q of the t+1 month in the 2nd year simulation st+1,i ,Right now

[0026]

[0027] We can get q in sequence 4,1 ,q 5,1 ,…,q T,1 , from this, we can get the 1-year random simulation process of Q; repeat the above process N times to get the N-year simulation process of flow Q.

[0028] Preferably, the method for simulating and estimating the flow and suspended sediment content of rivers in data-free areas provided by the present invention may also have the following characteristics: in step 2-1, the constructed monthly flow Q t, suspended sediment content CS t The joint distribution function C t (·), inverse function And the density function c t (·) are:

[0029]

[0030] <Device>

[0031] Furthermore, the present invention also provides a device for simulating and estimating the flow and suspended sediment content of rivers in data-free areas, which automatically implements the above-mentioned <method>, and is characterized by comprising:

[0032] The flow simulation model construction department constructs the flow simulation model of the target area based on the data of the river section A with measured flow Q and suspended sediment concentration CS observation data in the same river system or adjacent river basin as the target data-free area. Specifically, it includes:

[0033] Step 1-1: Analyze the monthly traffic Q over the years t The multi-year average, coefficient of variation, and coefficient of skewness (t=1, 2, …, T;T=12) are all denoted as θ as statistical parameters. Qt , and then determine the marginal distribution function F of each monthly flow t (·), and the corresponding inverse function Use Copula function to construct the joint distribution function C of the traffic of two consecutive months Qt (·) and its inverse function

[0034] Step 1-2: Randomly generate an initial value u s1,1 ∈(0,1), as the marginal distribution probability of the first month, then the simulated value of Q(t1) in the first year simulation is Randomly generate the joint distribution probability ε of the first and second months 1,1 ∈(0,1), find the marginal distribution probability u of Q(t2) in the second month in the first year simulation s2,1 , and the simulated value q s2,1 ; And so on, deduce the simulated value q of month t+1 in the simulation of year i st+1,i , and then get the 1-year random simulation process of flow Q; repeat the above process N times to get the N-year random simulation process;

[0035] Repeat the above operation M times to obtain a random simulation process of flow Q M times, each time for N years;

[0036] The sediment load model construction department builds a suspended sediment load estimation model based on the flow Q and suspended sediment load CS data of river section A. Specifically, it includes:

[0037] Step 2-1: Based on the measured suspended sediment concentration CS of river section A in each month over N years t , respectively determine the mean value of suspended sediment concentration in each month, and use them as statistical parameters and record them as θ CSt , and then determine the marginal distribution function G of suspended sediment concentration in each month t (·), and the corresponding inverse function According to the marginal distribution function F of the monthly flow determined in step 1-1 t (·), construct the monthly flow Q respectively t and suspended sediment content CS t The joint distribution function C t (·), and its inverse function Construct the density function c of the Copula function t (·);

[0038] Step 2-2: Monthly traffic Q t and suspended sediment content CS t Various possible joint distribution probability values , so that Q t and CS t The probability value pm of the maximum density function value t,i is the most likely to occur; based on Q t and CS t The inverse function and density function of the joint distribution function are used to estimate the marginal distribution function value of suspended sediment concentration in the i-th year and the t-th month for:

[0039]

[0040] Calculate the marginal distribution function value v of the measured and predicted values ​​of suspended sediment concentration in year i and month t t,i 、 The error between 1t,i , and thus the multi-year average error of month t is obtained Based on this, the marginal distribution function value predicted for month t is The rate is set at On this basis, according to the inverse function of the marginal distribution function of suspended sediment concentration in month t, the suspended sediment concentration in year i and month t is estimated to be: Then, according to the measured suspended sediment content cs in the tth month of the i-th year, t,i Calculate the error e 2t,i , and further deduced to obtain the multi-year average error of month t Then, a secondary calibration is performed to obtain the estimated value of suspended sediment content in the ith year and the tth month.

[0041]

[0042] Repeat the above operation to obtain the estimated value of suspended sediment content in each month for N years;

[0043] The simulation calculation department simulates the flow of river section B in the data-free area based on the flow simulation model constructed by the flow simulation model construction department. On this basis, the suspended sediment content of B is obtained based on the sediment content estimation model constructed by the sediment content model construction department. Specifically,

[0044] Step 3-1: According to the basic information of the drainage area and average precipitation of B and A, the correction coefficient M1 of the flow of B based on the flow of A is obtained. Based on this, the statistical parameter θ Q Revised to M1θ Q ; Then bring it into the flow simulation model construction part to simulate the random simulation process of the flow of B in each month;

[0045] Step 3-2: According to the basic information of the watershed area and soil erosion modulus of B and A, the correction coefficient M2 of the suspended sediment content of B based on the suspended sediment content of A is obtained. Based on this, the statistical parameter θ CS Revised to M2θ CS ; The revised statistical parameter M2θ CS and a random simulation process Q′ of the monthly flow of B obtained in step 3-1 t , brought into the sediment content model construction part to estimate the suspended sediment content process corresponding to the simulation process;

[0046] The control unit is connected to the flow simulation model construction unit, the sediment content model construction unit and the simulation calculation unit to control their operations.

[0047] Preferably, the device for simulating and estimating the flow and suspended sediment content of rivers in data-free areas provided by the present invention may also include: a scheduling unit, which is communicatively connected to the control unit and the regulation modules of the reservoir and the dam, determines the scheduling plan based on the suspended sediment content process simulated by the simulation calculation unit, and performs corresponding regulation on the scheduling process of the reservoir and the dam.

[0048] Preferably, the device for simulating and estimating the flow and suspended sediment content of rivers in data-free areas provided by the present invention may further include: an input display unit, which is communicatively connected to the control unit and can perform corresponding display according to control instructions input by the user.

[0049] Preferably, the flow and suspended sediment content simulation and estimation device for rivers in data-free areas provided by the present invention may also include: an input display unit that can display the flow Q, suspended sediment content CS data, geographical location information and the basin relationship with the target data-free area of ​​the river section A obtained by the flow simulation model construction unit according to corresponding control instructions, display the constructed flow simulation model and simulation data, display the suspended sediment content estimation model and simulation data constructed by the sediment content model construction unit, display the random simulation process of the flow and suspended sediment content process of each month obtained by the simulation calculation unit, and the simulation results can be displayed in the form of distribution maps, contour maps, evolution process maps, and comparison maps.

[0050] Preferably, the device for simulating and estimating the flow and suspended sediment content of a river in a data-free area provided by the present invention may further include: an input display unit capable of displaying the evolution of simulation results in the form of dynamic video.

[0051] Preferably, the device for simulating and estimating the flow and suspended sediment content of rivers in data-free areas provided by the present invention may also include: an input display unit that can also display the scheduling plan and control conditions determined by the scheduling unit and the operating conditions of reservoirs and dams.

[0052] Functions and effects of the invention

[0053] (1) The present invention integrates analysis, simulation, and estimation. Based on existing data, the statistical characteristics of flow and suspended sediment content are correlated and analyzed, and the spatial correlation structure of the two is analyzed. On this basis, a flow simulation model is constructed, and a flow series that conforms to regional statistical characteristics in areas without data is simulated according to revised parameters. A suspended sediment content estimation model is further developed, which can estimate the suspended sediment content of river sections in areas without data. It has been verified that the estimation results obtained by the present invention are basically consistent with the measured values.

[0054] (2) The method model proposed in this invention can solve the problem of calculating flow and suspended sediment content in areas without data, and can provide a long series of results over the years and months.

[0055] (3) The method model proposed in the present invention does not rely on the measured data of the river section, thus avoiding the problem of river channel testing.

[0056] (4) The device of the present invention can display the result data in the form of contour maps, evolution process maps, etc. through the input display part, which is intuitive and clear.

[0057] (5) The simulation calculation results of the present invention objectively and reasonably reflect the changes in water and sand in each month over the years in areas without data, which is of great significance to the scientific and reasonable formulation and implementation of engineering plans such as water conservancy and hydropower project construction, flood control scheduling, and water and sand control. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 The Gumbel Copula function calculation results for the flow rates of two consecutive months involved in the embodiment of the present invention (taking January and February as examples), where (a) is the spatial distribution diagram of the joint distribution function, and (b) is the contour diagram of the joint distribution function;

[0059] Figure 2 Statistical parameter comparison diagram of the simulated flow and the measured flow at hydrological station A involved in an embodiment of the present invention, where (a) is the average flow, (b) is the flow Cv value, and (c) is the flow Cs value;

[0060] Figure 3 The Gumbel Copula function calculation results for discharge and suspended sediment concentration at hydrological station A involved in the embodiment of the present invention (taking July as an example), where (a) is the spatial distribution map of the joint distribution function, (b) is the contour map of the joint distribution function, (c) is the spatial distribution map of the probability density function, and (d) is the contour map of the probability density function.

[0061] Figure 4 A comparison chart of the measured and estimated suspended sediment content of hydrological station A according to an embodiment of the present invention;

[0062] Figure 5 This is a comparison diagram of the simulated flow and the measured flow process of the B hydrological station involved in the embodiment of the present invention;

[0063] Figure 6 This is a comparison chart of the estimated value and measured value of suspended sediment content at hydrological station B involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following describes in detail the method and device for simulating and estimating the flow and suspended sediment content of a river in a data-free area according to the present invention in conjunction with the accompanying drawings.

[0065] <Example>

[0066] Hydrological Station A, located on the main stream of a river, has 65 years of measured flow and suspended sediment data from 1955 to 2019. Hydrological Station B, located on a tributary of the same river, has 22 years of measured flow and suspended sediment data from 1966 to 1987. In this example, it is assumed that hydrological station B is in an area without data. Flow simulation and suspended sediment estimation are performed for station B according to the method of the present invention. The results are compared with the existing data for station B to demonstrate the feasibility of the method of the present invention.

[0067] Step 1: Construct a flow simulation model for the target area based on the data of a river section A that is in the same river system or adjacent to the target data-free area and has measured flow Q and suspended sediment concentration CS observation data.

[0068] Step 1-1: Analyze the monthly discharge Q of hydrological station A over 65 years t Statistical parameters θ such as mean, coefficient of variation Cv, skewness coefficient Cs, etc. (t=1,2,…,T;T=12) Qt The results are shown in Table 1 below.

[0069] Table 1 Statistical parameters of monthly flow series at hydrological station A over the years

[0070]

[0071] Using the P-Ⅲ distribution to deduce the monthly flow series Q of hydrological station A over the years t The marginal distribution function F t (·), and the corresponding inverse function After comprehensive analysis, the Gumbel Copula function in the Copula function is selected to construct the joint distribution function C of the traffic in the adjacent two months. Qt (·) and its inverse function Taking January and February as an example, Figure 1 The spatial distribution and contour distribution results of the joint distribution function for these two months are given.

[0072] Step 1-2: Randomly assign an initial value u 1,1 ∈(0,1), calculate the simulation value of the first month Generate independent random numbers ε that follow a uniform distribution (0,1) 1,1 , ε 2,1 ,…,ε T-1,1 (T=12), u 1,1 and ε 1,1 Substituting into (3), we can solve for u 2,1 ,q 2,1 ; And so on, find q 4,1 ,q 5,1 ,…,q T,1 , we get a one-year flow simulation process. Repeat the above calculation 65 times to get a 65-year flow process with the same sample size as the measured one.

[0073] Repeat the above operation 300 times, and a total of 300 flow processes of 65 years each are simulated. For the simulated flow process, the linear moment method is used to estimate the mean and coefficient of variation of each month, and the simulated mean value and sampling standard deviation of each parameter can be obtained. Figure 2As can be seen from the figure, the mean and coefficient of variation of the measured samples are similar to the average of the simulated values, and both fall within the upper and lower limits of the simulated mean ± standard deviation. This indicates that the simulated flow population can be used as an inference population and that the simulation results have good applicability. In summary, the flow simulation model constructed based on the measured flow data from Hydrological Station A is feasible.

[0074] Step 2: Based on the flow Q and suspended sediment concentration CS data of river section A, a suspended sediment concentration estimation model is constructed.

[0075] Step 2-1: Analyze the monthly suspended sediment concentration CS of hydrological station A for 65 years t Statistical parameters such as the multi-year average value, coefficient of variation Cv, and skewness coefficient Cs θ CSt The results are shown in Table 2 below.

[0076] Table 2 Statistical parameters of suspended sediment concentration in each month of hydrological station A over the years

[0077]

[0078] Using the P-Ⅲ distribution to deduce the measured suspended sediment concentration series CS of hydrological station A in each month over the years t The marginal distribution function G t (·), and the corresponding inverse function Select Gumbel Copula function to construct monthly flow Q t and suspended sediment content CS t The joint distribution function C t (·), and its inverse function Construct the density function c of the Gumbel Copula function t (·). The results can be drawn into spatial distribution maps and contour maps. Taking July as an example, Figure 3 As shown, it represents the possibility of a certain combination of flow Q7 and suspended sediment concentration CS7 in July.

[0079] Step 2-2: Based on the monthly flow Q from 1955 to 2019 t , estimate the corresponding suspended sediment content CS according to formula (5) t , and then deduce the error rate parameter and The results are shown in Table 3.

[0080] Table 3 Error rate determination parameter results

[0081]

[0082] The suspended sediment content CS of each month t The estimated results are compared with the measured results. Figure 4 As can be seen from the figure, the distribution trends and changes in the predicted suspended sediment concentration are generally consistent with the measured results, with no significant differences. The relative error between the estimated and measured annual average suspended sediment concentrations from 1955 to 2019 ranged from -6.50% to 4.67%, a relatively small error. This demonstrates the feasibility of the suspended sediment concentration model constructed based on the measured water and sediment data from Hydrological Station A.

[0083] Step 3: Based on the flow simulation model constructed in step 1, simulate the flow of river section B in the data-free area; on this basis, estimate the suspended sediment content of B based on the suspended sediment content estimation model constructed in step 2.

[0084] Step 3-1: According to the drainage area and average precipitation of B and A, the correction coefficient M1 of the flow of B based on the flow of A is obtained. Based on this, the statistical parameter θ of the flow of A in Table 1 is converted to Q Revised to M1θ Q , the results are shown in Table 4.

[0085] Table 4 Correction coefficient M1 of flow statistics parameters B based on flow statistics parameters A

[0086]

[0087] By using the flow simulation model constructed based on the measured flow data of hydrological station A, the revised statistical parameters are applied and steps 1 and 2 are repeated to simulate the flow process of each month of B from 1955 to 2019.

[0088] In order to verify the reliability of the flow simulation of B, the measured flow processes of B in each month from 1966 to 1987 were compared with the simulated processes of the corresponding years and months. Figure 5 As can be seen from the figure, the simulation process is consistent with the measured process, the extreme values ​​correspond, and there is no outstanding data, which shows that the simulated flow process can reflect the actual situation of B.

[0089] Step 3-2: Based on the watershed area, soil erosion modulus, etc. of B and A, the correction coefficient M2 of the suspended sediment content of B based on the suspended sediment content of A is analyzed. The results are shown in Table 5.

[0090] Table 5 Revised statistical parameters of monthly flow series over the years

[0091]

[0092] According to the above simulation, the monthly flow process Q′ of B from 1955 to 2019 t , combined with the revised statistical parameter M2θ CS, by bringing in the suspended sediment content model constructed based on the measured water and sediment data of hydrological station A, the suspended sediment content process of B can be estimated.

[0093] In order to verify the reliability of the estimated results of suspended sediment concentration of B, the measured suspended sediment concentration of B in each month from 1966 to 1987 was compared with the estimated values ​​of the corresponding years and months (Table 6, Figure 6 ), it can be seen that the annual and monthly variations in suspended sediment concentration are generally consistent with those in the measured results, with no significant discrepancies. The relative error between the estimated and measured monthly average suspended sediment concentrations from 1966 to 1987 ranges from -6.25% to 5.00%, indicating a relatively small error. This indicates that the estimated suspended sediment concentration for B is feasible.

[0094] Table 6 Comparison of statistical parameters of suspended sediment content in B

[0095]

[0096] Furthermore, this embodiment also provides a device for simulating and estimating the flow and suspended sediment content of rivers in data-free areas, which can automatically implement the above method. The device includes a flow simulation model construction unit, a sediment content model construction unit, a simulation calculation unit, a scheduling unit, an input display unit, and a control unit.

[0097] The flow simulation model construction unit executes the contents described in step 1 above and constructs a flow simulation model for the target area based on the data of the river section A that is in the same water system or adjacent watershed as the target data-free area and has measured flow Q and suspended sediment concentration CS observation data.

[0098] The sediment content model construction part executes the contents described in step 2 above, and constructs a suspended sediment content estimation model based on the flow Q and suspended sediment content CS data of the river section A.

[0099] The simulation calculation unit executes the contents described in step 3 above, and simulates the flow of river section B in the data-free area according to the flow simulation model constructed by the flow simulation model construction unit; on this basis, the suspended sediment content of B is obtained according to the sediment content estimation model constructed by the sediment content model construction unit.

[0100] The dispatching department is connected to the control modules of the reservoir and dam in communication. According to the suspended sediment content process simulated by the simulation calculation department, the dispatching plan is determined and the dispatching process of the reservoir and dam is regulated accordingly.

[0101] The input display unit is connected to the control unit in communication and can display the corresponding information according to the control instructions input by the user. Specifically, the input display unit can display the flow Q, suspended sediment content CS data, geographical location information and the basin relationship with the target data-free area of ​​the river section A obtained by the flow simulation model construction unit according to the corresponding control instructions, display the constructed flow simulation model and simulation data, display the suspended sediment content estimation model and simulation data constructed by the sediment content model construction unit, and display the random simulation process of each month of flow and suspended sediment content process simulated by the simulation calculation unit, and the simulation results can be displayed through the following methods: Figures 1 to 6 The input display unit can also display the evolution of simulation results through dynamic video, corresponding to the dispatching plan and control status determined by the dispatching unit, as well as the operation status of reservoirs and dams.

[0102] The control unit is communicatively connected with the flow simulation model construction unit, the sediment content model construction unit, the simulation calculation unit, the scheduling unit, and the input display unit to control their operations.

[0103] The above embodiments are merely illustrative of the technical solutions of the present invention. The method and apparatus for simulating and estimating flow and suspended sediment content in rivers in data-free areas, as described herein, are not limited solely to the methods and apparatus described in the above embodiments but are subject to the scope defined by the claims. Any modifications, supplements, or equivalent substitutions made by persons skilled in the art based on these embodiments are considered within the scope of protection claimed in the claims.

Claims

1. A method for simulating and estimating the flow and suspended sediment load of rivers in data-free areas, characterized by: The following steps are involved: Step 1: Construct a flow simulation model for the target area based on the data of a river section A that is in the same river system or adjacent to the target area with no data and has measured flow Q and suspended sediment concentration CS observation data. The specific steps include the following: Step 1-1: Analyze the monthly traffic Q over the years t The multi-year average, coefficient of variation, and coefficient of skewness are all recorded as θ as statistical parameters. Qt , and then determine the marginal distribution function F of each monthly flow t (·), and the corresponding inverse function t=1,2,…,T;T=12;Use Copula function to construct the joint distribution function C of the traffic of two consecutive months Qt (·) and its inverse function Step 1-2: Randomly generate an initial value u s1,1 ∈(0,1), as the marginal distribution probability of the first month, then the simulated value of Q(t1) in the first year simulation is Randomly generate the joint distribution probability ε of the first and second months 1,1 ∈(0,1), find the marginal distribution probability u of Q(t2) in the second month in the first year simulation s2,1 , and the simulated value q s2,1 ; And so on, deduce the simulated value q of month t+1 in the simulation of year i st+1,i , and then the 1-year random simulation process of flow Q is obtained; Repeat the above process N times to obtain a random simulation process of N years; Repeat the above operation M times to obtain a random simulation process of flow Q M times, each time for N years; Step 2: Based on the flow rate Q and suspended sediment concentration CS data of river section A, a suspended sediment concentration estimation model is constructed. The specific steps include the following: Step 2-1: Based on the actual measured suspended sediment content CS of each month in N years t , respectively determine the mean value of suspended sediment concentration in each month, and use them as statistical parameters and record them as θ CSt , and then determine the marginal distribution function G of suspended sediment concentration in each month t (·), and the corresponding inverse function According to the marginal distribution function F of the monthly flow determined in step 1-1 t (·), construct the monthly flow Q t and suspended sediment content CS t The joint distribution function C t (·), and its inverse function Construct the density function c of the Copula function t (·); Step 2-2: Monthly traffic Q t and suspended sediment content CS t Among the various possible joint distribution probability values ​​p, Make Q t and CS t The probability value pm of the maximum density function value t,i is the most likely to occur; based on Q t and CS t The inverse function and density function of the joint distribution function are used to estimate the marginal distribution function value of suspended sediment concentration in the i-th year and the t-th month for: Calculate the marginal distribution function value v of the measured and predicted values ​​of suspended sediment concentration in year i and month t t,i 、 The error between 1t,i , and thus the multi-year average error of month t is obtained Based on this, the marginal distribution function value predicted for month t is The rate is set at On this basis, according to the inverse function of the marginal distribution function of suspended sediment concentration in month t, the suspended sediment concentration in year i and month t is estimated to be: Then, according to the measured suspended sediment content cs in the tth month of the i-th year, t,i Calculate the error e 2t,i , and further deduced to obtain the multi-year average error of month t Then, a secondary calibration is performed to obtain the estimated value of suspended sediment content in the ith year and the tth month. Repeat the above operation to obtain the estimated value of suspended sediment content in each month for N years; Step 3: Based on the flow simulation model constructed in Step 1, simulate the flow of river section B in the data-free area. On this basis, estimate the suspended sediment concentration of section B using the suspended sediment concentration estimation model constructed in Step 2. This includes the following sub-steps: Step 3-1: According to the basic information of the drainage area and average precipitation of B and A, the correction coefficient M1 of the flow of B based on the flow of A is obtained. Based on this, the statistical parameter θ Q Revised to M1θ Q ; Then bring it into step 1 to simulate the random simulation process of B's ​​monthly flow; Step 3-2: According to the basic information of the watershed area and soil erosion modulus of B and A, the correction coefficient M2 of the suspended sediment content of B based on the suspended sediment content of A is obtained. Based on this, the statistical parameter θ CS Revised to M2θ CS ; A random simulation process Q of the monthly flow of B obtained in step 3-1 t ' , and the revised statistical parameter M2θ CS , use step 2 to deduce the error rate parameter and Substitute the estimated suspended sediment content process corresponding to the simulation process into step 2.

2. The method for simulating and estimating the flow and suspended sediment content of a river in a data-free area according to claim 1, characterized in that: in, In step 1-1, the marginal distribution function F is constructed t (·), inverse function Joint distribution function C of traffic in adjacent months Qt (·) and its inverse function They are:

3. The method for simulating and estimating the flow and suspended sediment content of a river in a data-free area according to claim 1, characterized in that: in, In step 1-2, the joint distribution probability ε of the first and second months is randomly generated 1,1 ∈(0,1), then The marginal distribution probability u of Q(t2) in the second month in the first year simulation can be obtained s2,1 , and the simulated value q s2,1 ; Similarly, a recursive formula can be established to derive the simulated value q of the t+1 month in the 2nd year simulation st+1,i ,Right now We can get q in sequence 4,1 ,q 5,1 ,…,q T,1 , from this, we can get the 1-year random simulation process of Q; repeat the above process N times to get the N-year simulation process of flow Q.

4. The method for simulating and estimating the flow and suspended sediment content of a river in a data-free area according to claim 1 is characterized by: in, In step 2-1, the monthly flow Q is constructed t , suspended sediment content CS t The joint distribution function C t (·), inverse function C - t 1 (·) and the density function c t (·) are:

5. A device for simulating and estimating the flow and suspended sediment content of rivers in data-free areas, characterized by: include: The flow simulation model construction department constructs the flow simulation model of the target area based on the data of the river section A with measured flow Q and suspended sediment concentration CS observation data in the same river system or adjacent river basin as the target data-free area. Specifically, it includes: Step 1-1: Analyze the monthly traffic Q over the years t The multi-year average, coefficient of variation, and coefficient of skewness are all recorded as θ as statistical parameters. Qt , and then determine the marginal distribution function F of each monthly flow t (·), and the corresponding inverse function t=1,2,…,T;T=12;Use Copula function to construct the joint distribution function C of the traffic of two consecutive months Qt (·) and its inverse function Step 1-2: Randomly generate an initial value u s1,1 ∈(0,1), as the marginal distribution probability of the first month, then the simulated value of Q(t1) in the first year simulation is Randomly generate the joint distribution probability ε of the first and second months 1,1 ∈(0,1), find the marginal distribution probability u of Q(t2) in the second month in the first year simulation s2,1 , and the simulated value q s2,1 ; And so on, deduce the simulated value q of month t+1 in the simulation of year i st+1,i , and then get the 1-year random simulation process of flow Q; repeat the above process N times to get the N-year random simulation process; Repeat the above operation M times to obtain a random simulation process of flow Q M times, each time for N years; The sediment load model construction department builds a suspended sediment load estimation model based on the flow Q and suspended sediment load CS data of river section A. Specifically, it includes: Step 2-1: Based on the measured suspended sediment concentration CS of river section A in each month over N years t , respectively determine the mean value of suspended sediment concentration in each month, and use them as statistical parameters and record them as θ CSt , and then determine the marginal distribution function G of suspended sediment concentration in each month t (·), and the corresponding inverse function According to the marginal distribution function F of the monthly flow determined in step 1-1 t (·), construct the monthly flow Q t and suspended sediment content CS t The joint distribution function C t (·), and its inverse function Construct the density function c of the Copula function t (·); Step 2-2: Monthly traffic Q t and suspended sediment content CS t Among the various possible joint distribution probability values ​​p, Make Q t and CS t The probability value pm of the maximum density function value t,i is the most likely to occur; based on Q t and CS t The inverse function and density function of the joint distribution function are used to estimate the marginal distribution function value of suspended sediment concentration in the i-th year and the t-th month for: Calculate the marginal distribution function value v of the measured and predicted values ​​of suspended sediment concentration in year i and month t t,i 、 The error between 1t,i , and thus the multi-year average error of month t is obtained Based on this, the marginal distribution function value predicted for month t is The rate is set at On this basis, according to the inverse function of the marginal distribution function of suspended sediment concentration in month t, the suspended sediment concentration in year i and month t is estimated to be: Then, according to the measured suspended sediment content cs in the tth month of the i-th year, t,i Calculate the error e 2t,i , and further deduced to obtain the multi-year average error of month t Then, a secondary calibration is performed to obtain the estimated value of suspended sediment content in the ith year and the tth month. Repeat the above operation to obtain the estimated value of suspended sediment content in each month for N years; The simulation calculation unit simulates the flow of the river section B in the data-free area based on the flow simulation model constructed by the flow simulation model construction unit; on this basis, the suspended sediment content of B is obtained based on the sediment content estimation model constructed by the sediment content model construction unit; specifically, the simulation calculation unit includes: Step 3-1: According to the basic information of the drainage area and average precipitation of B and A, the correction coefficient M1 of the flow of B based on the flow of A is obtained. Based on this, the statistical parameter θ Q Revised to M1θ Q ; Then bring it into the flow simulation model construction part to simulate the random simulation process of the monthly flow of B; Step 3-2: According to the basic information of the watershed area and soil erosion modulus of B and A, the correction coefficient M2 of the suspended sediment content of B based on the suspended sediment content of A is obtained. Based on this, the statistical parameter θ CS Revised to M2θ CS ; The revised statistical parameter M2θ CS and a random simulation process Q of the monthly flow of B obtained in step 3-1 t ' , brought into the sediment content model construction part to estimate the suspended sediment content process corresponding to the simulation process; The control unit is connected to the flow simulation model construction unit, the sediment content model construction unit and the simulation calculation unit to control their operations.

6. The device for simulating and estimating the flow and suspended sediment content of a river in a data-free area according to claim 5, characterized in that: Also includes: The dispatching unit is in communication with the control unit and the regulation modules of the reservoir and the dam, determines the dispatching plan according to the suspended sediment content process simulated by the simulation calculation unit, and performs corresponding regulation on the dispatching process of the reservoir and the dam.

7. The device for simulating and estimating the flow and suspended sediment content of a river in a data-free area according to claim 6, characterized in that: Also includes: The input display unit is connected to the control unit for communication and can display corresponding information according to the control instructions input by the user.

8. The device for simulating and estimating the flow and suspended sediment content of a river in a data-free area according to claim 7, characterized in that: in, The input display unit can display the flow Q, suspended sediment content CS data, geographical location information and basin relationship with the target data-free area of ​​the river section A obtained by the flow simulation model construction unit according to corresponding control instructions, display the constructed flow simulation model and simulation data, display the suspended sediment content estimation model and simulation data constructed by the sediment content model construction unit, and display the random simulation process of flow and suspended sediment content process of each month obtained by the simulation calculation unit, and the simulation results can be displayed in the form of distribution maps, contour maps, evolution process maps, and comparison maps.

9. The device for simulating and estimating the flow and suspended sediment content of a river in a data-free area according to claim 8, characterized in that: in, The input display unit can also display the evolution process of the simulation results in the form of dynamic video.

10. The device for simulating and estimating the flow and suspended sediment content of a river in a data-free area according to claim 8, characterized in that: in, The input display unit can also display the scheduling plan and control status determined by the scheduling unit and the operating conditions of the reservoir and dam.

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