Dammed lake hydrological emergency forecasting method and system based on coupling meteorological values

Abstract

The invention discloses a barrier lake hydrological emergency forecasting method and system based on coupling meteorological values, and the method comprises the steps: collecting the site information of a barrier lake, and constructing a barrier lake storage capacity curve; measuring the water level and area data of the barrier lake, calculating the water volume of the barrier lake, constructing a static reservoir capacity flood regulation calculation model and a dynamic reservoir capacity flood regulation calculation model, and determining the maximum flow of the breach; flood real-time calculation is carried out, real-time alarm information is collected to optimize and correct parameters, and a final flood forecasting scheme is obtained; massive scenes are constructed based on a water system river network, landforms, actual water and rain conditions and on-dam water levels of a site where the barrier lake occurs, and based on barrier lake hydrological emergency forecasting scheme samples under different scenes, regularity information of optimal emergency forecasting is extracted, and barrier lake hydrological emergency forecasting rules are formulated. Compared with a traditional emergency hydrological forecasting method for the barrier lake, the emergency hydrological forecasting method for the barrier lake has the advantages that the emergency hydrological forecasting scheme can be quickly compiled in a targeted manner, and timely forecasting and early warning are realized.

Description

Technical Field

[0001] The invention relates to a hydrological emergency forecasting method for a barrier lake, in particular to a hydrological emergency forecasting method for a barrier lake based on coupled meteorological values. Background Art

[0002] A barrier lake is a lake formed by the storage of water after a landslide or other body blocks a river valley or riverbed. Its biggest threat mainly comes from the flooding of upstream areas before the barrier lake bursts, and the rapid discharge of lake water to the downstream after the barrier suddenly bursts. Due to the fact that the barrier cuts off the river channel and the water entering the lake continues to accumulate and cannot be discharged, the water level of the barrier lake will rise rapidly, which may cause large-scale serious flooding. Once the barrier lake bursts, it will cause a flood disaster downstream, which is no less destructive than the destructive power of natural floods. Since the occurrence of barrier lake hazards is often sudden, its development process is an abnormal and unpredictable event, and there are often situations such as limited station network layout, incomplete data collection, and difficulty in compiling forecast plans. Therefore, how to quickly and specifically compile hydrological emergency forecast plans and realize timely forecasts and warnings is the key to the hydrological emergency forecast work of barrier lakes. The present invention proposes a hydrological emergency forecast method and system for a barrier lake based on coupled meteorological values ​​to solve the above-mentioned problems that currently exist, provide timely and effective technical support for the formulation of barrier lake hazard removal plans, risk avoidance and transfer, real-time rescue, etc., and reduce the harm caused by the barrier lake. Summary of the invention

[0003] The purpose of the invention is to provide a hydrological emergency forecasting method for a barrier lake based on coupled meteorological values ​​to solve the above-mentioned problems existing in the prior art. On the other hand, a hydrological emergency forecasting system for a barrier lake based on coupled meteorological values ​​is provided.

[0004] The technical solution is a hydrological emergency forecasting method for barrier lakes based on coupled meteorological values, which includes the following steps: Step S1, collecting the river network, topography, actual water and rainfall conditions and water level forecast station information on the dammed lake where the barrier lake occurs, building a hydrological model, calculating the water volume of the interval, using the water level-volume curve derivation method based on DEM to build an initial barrier lake storage capacity curve, collecting hydrological information of the lake inlet station to conduct real-time review and correction on the initial barrier lake storage capacity curve, and obtaining the final barrier lake storage capacity curve; Step S2, measuring the water level and area data of the barrier lake and inputting them into the barrier lake storage capacity curve, calculating the water volume of the barrier lake, constructing a static storage capacity flood regulation calculation model, inputting the water volume of the barrier lake into the static storage capacity flood regulation calculation model, successively calculating the flow process corresponding to the water level fluctuation amplitude of multiple time steps, obtaining the static storage capacity flood regulation calculation result, constructing a dynamic storage capacity flood regulation calculation model, using the river terrain segmentation cut by DEM to calculate the dynamic storage capacity flood regulation calculation result, and determining the maximum flow of the breach based on the dynamic and static storage capacity flood regulation calculation results; Step S3: based on the maximum flow of the breach, three methods, namely, one-dimensional hydrodynamic model, Muskingum segmented calculation and confluence coefficient calculation, are used in turn to perform real-time flood calculation, and the calculation results of the three methods are combined to obtain an initial flood forecasting scheme, and real-time alarm information is collected to optimize and correct the parameters, and the three methods, namely, one-dimensional hydrodynamic model, Muskingum segmented calculation and confluence coefficient calculation, which have been optimized and corrected, are used in turn to perform real-time flood calculation, and the calculation results of the three methods are combined to obtain a final flood forecasting scheme; Step S4, constructing a large number of scenarios based on the river network, topography, actual water and rainfall conditions and water level on the dam where the barrier lake occurs, and calculating the corresponding maximum breach flow and flood forecasting schemes based on the constructed massive scenarios, i.e., the barrier lake hydrological emergency forecasting scheme, and based on samples of the barrier lake hydrological emergency forecasting schemes under different scenarios, extracting the regularity information of the optimal emergency forecast and formulating the barrier lake hydrological emergency forecasting rules.

[0005] According to one aspect of the present application, step S1 further comprises: Step S11, collecting information on the location where the barrier lake occurs, including: the river network, topography, actual water and rainfall conditions, and water level forecast station information on the dam where the barrier lake occurs; Step S12, extract the water level station information on the upstream dam, obtain the upstream water volume, build a hydrological model, extract the river network, topography and real-time water and rainfall information of the place where the barrier lake occurs and input it into the hydrological model, and calculate the interval water volume based on the similarity of the sub-basins; Step S13, extracting upstream water volume and interval water volume data, and constructing an initial barrier lake storage capacity curve using a DEM-based water level-volume curve derivation method; Step S14: Collect the hydrological information of the lake inlet station to conduct real-time review and correction on the initial reservoir capacity curve of the barrier lake to obtain the final reservoir capacity curve of the barrier lake.

[0006] According to one aspect of the present application, step S12 further comprises: Step S12a, extracting the water level station information on the upstream dam to obtain the upstream water volume; Step S12b, constructing the Xin'an River hydrological model; Step S12c, extract the river network, topography and actual water and rainfall information of the place where the barrier lake occurs, input the Xin'anjiang hydrological model to calculate the water volume of the area around the blockage point of the barrier lake, and based on the sub-basin similarity, couple the flow of the area around the blockage point of the barrier lake to calculate the water volume of the area where the barrier lake is located, that is, the interval water volume.

[0007] According to one aspect of the present application, step S14 is further: Step S14a, calculating the water storage capacity of the river channel when the barrier body blocks the river channel to form a barrier lake; Step S14b: after the barrier lake is formed, the water level in front of the barrier lake dam is recorded in real time; Step S14c, calculate the cumulative amount of water entering the lake based on the hydrological information of the lake entry station, plus the water storage in the river channel at the time of the formation of the barrier lake, to obtain the water storage capacity of the barrier lake; Step S14d, plotting the calculated water level-volume data onto the initial reservoir capacity curve; Step S14e, using the least squares method to fit the data and the storage capacity curve to obtain a new storage capacity curve, and reasonably adjusting the nodes of the new storage capacity curve to finally obtain a verified and corrected storage capacity curve.

[0008] According to one aspect of the present application, step S2 further comprises: Step S21, collecting the water level and area information of the barrier lake on site, inputting the barrier lake storage capacity curve, and calculating the water storage capacity of the barrier lake; Step S22, constructing a static reservoir capacity flood regulation calculation model and a dynamic reservoir capacity flood regulation calculation model; Step S23, input the water storage capacity of the barrier lake into the static reservoir capacity flood regulation calculation model, calculate the flow rate process corresponding to the water level fluctuation of multiple time steps in sequence, and obtain the static reservoir capacity flood regulation calculation result; Step S24, using the river terrain segments cut by DEM to calculate the dynamic reservoir capacity flood control calculation model to obtain the dynamic reservoir capacity flood control calculation results, and determining the maximum flow of the breach based on the dynamic and static reservoir capacity flood control calculation results.

[0009] According to one aspect of the present application, step S3 further comprises: Step S31, based on the maximum flow of the breach, three methods, namely, one-dimensional hydrodynamic model, Muskingum segmented calculation and confluence coefficient calculation, are used in turn to perform real-time flood calculation, and the calculation results of the three methods are combined to obtain an initial flood forecasting scheme; Step S32, collecting real-time information to optimize and correct parameters; Step S33, sequentially adopting the one-dimensional hydrodynamic model after parameter optimization and correction, Muskingum segmented calculation and confluence coefficient calculation to perform real-time flood calculation, and combining the calculation results of the three methods to obtain the final flood forecasting plan.

[0010] According to one aspect of the present application, step S4 is further: Step S41, fitting the river network, topography, actual water and rainfall conditions and probability distribution of water level above the dam where the barrier lake occurs, and randomly sampling to obtain a large number of scenarios; Step S42, sequentially calculating the maximum breach flow and flood forecasting scheme corresponding to the massive scenario, i.e., the hydrological emergency forecasting scheme for the barrier lake; Step S43: Based on samples of hydrological emergency forecasting schemes for barrier lakes under different scenarios, regular information of optimal emergency forecasting is extracted and rules for hydrological emergency forecasting for barrier lakes are formulated.

[0011] According to one aspect of the present application, step S41 is further: Step S41a, fitting the marginal distribution of the river network, topography, actual water and rainfall conditions and water level above the dam where the barrier lake occurs, cross-checking the fitting effect, selecting the distribution with the highest fitting degree, and determining the parameters of the selected distribution by using the maximum likelihood estimation method; Step S41b, using multiple correlation coefficients to cross-validate the river network, topography, actual water and rainfall conditions, and water level on the dam to analyze the correlation between edge functions, and analyze and establish the confidence level of the joint probability distribution function; Step S41c, constructing the optimal Copula function of the river network, topography, actual water and rainfall conditions and water level above the dam; Step S41d, using the D-DREAM algorithm to take the joint probability distribution of the river network, topography, actual water and rainfall conditions and the water level on the dam as the prior distribution, randomly sample the combination of the river network, topography, actual water and rainfall conditions and the water level on the dam to obtain a large number of scenarios.

[0012] According to one aspect of the present application, step S43 is further: Step S43a, setting the upstream dam front water level, actual water and rainfall conditions, interval water inflow, barrier lake water storage capacity, and barrier lake water level as state variables, and setting the barrier lake breach time, barrier lake breach flow, and downstream dam front water level as decision variables; Step S43b, constructing a BP artificial neural network model for hydrological emergency forecasting of the barrier lake, and extracting hydrological emergency forecasting rules for the barrier lake based on samples of hydrological emergency forecasting schemes for the barrier lake under different scenarios; Step S43c, correct the extracted barrier lake hydrological emergency forecast rules to obtain the final emergency forecast rules.

[0013] According to another aspect of the present application, a barrier lake hydrological emergency forecasting system based on coupled meteorological values ​​is provided, comprising: at least one processor; and a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the method for hydrological emergency forecasting of a barrier lake based on coupled meteorological values ​​as described in any of the above technical solutions.

[0014] Beneficial effect: The hydrological emergency forecast method of the barrier lake based on coupled meteorological numerical values ​​can accurately obtain the water storage volume of the barrier lake, better carry out emergency disposal, take reasonable and effective prevention and control measures to eliminate the risk of the barrier lake, and reduce or avoid the disaster losses caused by the barrier lake. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the present invention.

[0016] Figure 2 It is a flow chart of step S1 of the present invention.

[0017] Figure 3 It is a flow chart of step S2 of the present invention.

[0018] Figure 4 It is a flow chart of step S3 of the present invention.

[0019] Figure 5 It is a flow chart of step S4 of the present invention. DETAILED DESCRIPTION

[0020] like Figure 1 As shown, the following technical solution is proposed. According to one aspect of the present application, a hydrological emergency forecasting method for a barrier lake based on coupled meteorological values ​​is provided, comprising the following steps: Step S1, collecting the river network, topography, actual water and rainfall conditions and water level forecast station information on the dammed lake where the barrier lake occurs, building a hydrological model, calculating the water volume of the interval, using the water level-volume curve derivation method based on DEM to build an initial barrier lake storage capacity curve, collecting hydrological information of the lake inlet station to conduct real-time review and correction on the initial barrier lake storage capacity curve, and obtaining the final barrier lake storage capacity curve; Step S2, measuring the water level and area data of the barrier lake and inputting them into the barrier lake storage capacity curve, calculating the water volume of the barrier lake, constructing a static storage capacity flood regulation calculation model, inputting the water volume of the barrier lake into the static storage capacity flood regulation calculation model, successively calculating the flow process corresponding to the water level fluctuation amplitude of multiple time steps, obtaining the static storage capacity flood regulation calculation result, constructing a dynamic storage capacity flood regulation calculation model, using the river terrain segmentation cut by DEM to calculate the dynamic storage capacity flood regulation calculation result, and determining the maximum flow of the breach based on the dynamic and static storage capacity flood regulation calculation results; Step S3: based on the maximum flow of the breach, three methods, namely, one-dimensional hydrodynamic model, Muskingum segmented calculation and confluence coefficient calculation, are used in turn to perform real-time flood calculation, and the calculation results of the three methods are combined to obtain an initial flood forecasting scheme, and real-time alarm information is collected to optimize and correct the parameters, and the three methods, namely, one-dimensional hydrodynamic model, Muskingum segmented calculation and confluence coefficient calculation, which have been optimized and corrected, are used in turn to perform real-time flood calculation, and the calculation results of the three methods are combined to obtain a final flood forecasting scheme; Step S4, constructing a large number of scenarios based on the river network, topography, actual water and rainfall conditions and water level on the dam where the barrier lake occurs, and calculating the corresponding maximum breach flow and flood forecasting schemes based on the constructed massive scenarios, i.e., the barrier lake hydrological emergency forecasting scheme, and based on samples of the barrier lake hydrological emergency forecasting schemes under different scenarios, extracting the regularity information of the optimal emergency forecast and formulating the barrier lake hydrological emergency forecasting rules.

[0021] The greatest threat to mankind from the barrier lake formed by the blocking of river valleys by landslides, collapses, mud-rock flows, etc. mainly comes from the inundation of upstream areas before the barrier lake bursts, and the rapid discharge of lake water to the downstream after the sudden collapse of the barrier lake dam, as the occurrence of barrier lake hazards is often sudden, its development process is an abnormal and unpredictable event, often with limited station network layout, incomplete data collection, and difficulty in compiling forecast plans. Therefore, how to quickly and specifically compile hydrological emergency forecast plans and realize timely forecast and warning is the key to the hydrological emergency forecast work of barrier lakes. In this embodiment, the measurement accuracy of the water storage capacity of the barrier lake is improved by coupling meteorological values, and the rationality of the hydrological emergency measures of the barrier lake is improved. At the same time, the hydrological emergency forecast speed of the barrier lake is improved by constructing the hydrological emergency forecast rules of the barrier lake under massive scenarios to cope with the suddenness of the barrier lake hazards.

[0022] like Figure 2 As shown, according to one aspect of the present application, the step S1 is further: Step S11, collecting information on the location where the barrier lake occurs, including: the river network, topography, actual water and rainfall conditions, and water level forecast station information on the dam where the barrier lake occurs; Step S12, extract the water level station information on the upstream dam, obtain the upstream water volume, build a hydrological model, extract the river network, topography and real-time water and rainfall information of the place where the barrier lake occurs and input it into the hydrological model, and calculate the interval water volume based on the similarity of the sub-basins; Step S13, extracting upstream water volume and interval water volume data, and constructing an initial barrier lake storage capacity curve using a DEM-based water level-volume curve derivation method; The rapid formulation of the water level and storage capacity curve of the barrier lake is the basis for predicting and analyzing the water storage capacity, water level, breach time, breach flow and other factors of the barrier lake. The maximum water storage capacity is usually estimated according to the barrier lake storage capacity curve using the elevation of the barrier body pass or the height of the barrier body, so as to analyze the flood and the corresponding risks under different breach modes, as well as the necessity and feasibility of taking artificial intervention measures. However, due to the randomness of the time and location of the barrier lake, and the lack of relevant data on the barrier lake, especially in the early stage of the formation of the barrier lake, the data that can be collected may only be the observation information of the upstream water station and the water level station in front of the dam, and the satellite remote sensing images or DEM terrain data with relatively rough accuracy. In addition, due to the urgent time for emergency disposal of the barrier lake, there is no condition for field measurement. Therefore, in this embodiment, the water level-volume curve derivation method based on DEM is used to formulate the barrier lake storage capacity curve. In order to obtain a higher-precision storage capacity curve, the corresponding barrier lake water storage capacity of different water levels is calculated in real time according to the hydrological information of the lake inlet station, and the storage capacity curve is corrected in real time accordingly.

[0023] The reservoir capacity curve of the barrier lake, that is, the relationship between the water level and volume of the barrier lake, can be obtained by calculating and analyzing DEM terrain data and remote sensing image data. In this embodiment, two methods, a 3D analysis method based on ArcGIS and a 2D analysis method based on ArcGIS, are used.

[0024] The 3D analysis method based on ArcGIS takes the dam of the barrier lake as the outlet, calculates the watershed area of ​​the area, cuts out the DEM data using the watershed area, and sets a highest water storage elevation in the area. The volume between the water storage elevation and the DEM terrain is the volume of the barrier lake at this elevation. By setting a set of water levels, the corresponding volume can be obtained, and the water level-volume curve of the barrier lake can be deduced. The 2D analysis method based on ArcGIS extracts contour clusters through DEM and measures the area corresponding to each elevation, calculates the volume of adjacent elevations, and finally establishes the water level-area-volume relationship. The derivation steps are as follows: Use DEM to generate topographic map contours, extract area from contour lines one by one, and establish a water level-area relationship; Use the volume formula to calculate the water level~volume relationship.

[0025] This method is mainly suitable for areas with obvious elevation differences such as hills and valleys.

[0026] In one embodiment, First, a 30-meter resolution digital elevation model was obtained, covering the barrier lake and surrounding areas; Through remote sensing impact identification, the water surface boundary of the barrier lake is determined, and based on the upstream water volume and interval water volume data, the change data of the reservoir capacity of the barrier lake as the water level rises is obtained; Import DEM and lake boundary data into ArcGIS; In ArcGIS, use the Create TIN tool in the 3D Analyst tool to create a 3D terrain model based on the DEM; Use the constant water level tool to generate corresponding water body models for DEM based on the set water level, and save each water level model; Use the volume calculation tool to input the model of different water levels, measure the reservoir capacity under different water levels, and obtain the reservoir capacity corresponding to each water level; In ArcGIS, open the table and use the chart tool to draw a water level-reservoir capacity curve, setting the X-axis to water level and the Y-axis to reservoir capacity; In ArcGIS, set up an appropriate map layout, add lake boundaries and DEM, add a legend, scale bar, and north arrow, and export the map; Obtain the reservoir capacity curve of the barrier lake.

[0027] Step S14: Collect the hydrological information of the lake inlet station to conduct real-time review and correction on the initial reservoir capacity curve of the barrier lake to obtain the final reservoir capacity curve of the barrier lake.

[0028] According to one aspect of the present application, step S12 further comprises: Step S12a, extracting the water level station information on the upstream dam to obtain the upstream water volume; Step S12b, constructing the Xin'an River hydrological model; Step S12c, extract the river network, topography and actual water and rainfall information of the place where the barrier lake occurs, input the Xin'anjiang hydrological model to calculate the water volume of the area around the blockage point of the barrier lake, and based on the sub-basin similarity, couple the flow of the area around the blockage point of the barrier lake to calculate the water volume of the area where the barrier lake is located, that is, the interval water volume.

[0029] According to one aspect of the present application, step S14 is further: Step S14a, calculating the water storage capacity of the river channel when the barrier body blocks the river channel to form a barrier lake; Step S14b: after the barrier lake is formed, the water level in front of the barrier lake dam is recorded in real time; Step S14c, calculate the cumulative amount of water entering the lake based on the hydrological information of the lake entry station, plus the water storage in the river channel at the time of the formation of the barrier lake, to obtain the water storage capacity of the barrier lake; Step S14d, plotting the calculated water level-volume data onto the initial reservoir capacity curve; Step S14e, using the least squares method to fit the data and the storage capacity curve to obtain a new storage capacity curve, and reasonably adjusting the nodes of the new storage capacity curve to finally obtain a verified and corrected storage capacity curve.

[0030] like Figure 3As shown, according to one aspect of the present application, the step S2 is further: Step S21, collecting the water level and area information of the barrier lake on site, inputting the barrier lake storage capacity curve, and calculating the water storage capacity of the barrier lake; Step S22, constructing a static reservoir capacity flood regulation calculation model and a dynamic reservoir capacity flood regulation calculation model; Step S23, input the water storage capacity of the barrier lake into the static reservoir capacity flood regulation calculation model, calculate the flow rate process corresponding to the water level fluctuation of multiple time steps in sequence, and obtain the static reservoir capacity flood regulation calculation result; Step S24, using the river terrain segments cut by DEM to calculate the dynamic reservoir capacity flood control calculation model to obtain the dynamic reservoir capacity flood control calculation results, and determining the maximum flow of the breach based on the dynamic and static reservoir capacity flood control calculation results.

[0031] The real-time flow prediction process of the breach flow mainly uses the static reservoir capacity flood control calculation model with different time steps as the basic analysis tool. For the barrier lake with long river channel, it is also necessary to fully consider the wedge-shaped dynamic reservoir capacity that starts from the end near the dam and goes up to the water surface line below the farthest backwater end. In this embodiment, the water level fluctuation amplitude with different time steps of 10min, 15min, 30min and 60min is used as input to calculate the flow process of the corresponding time step, and the final flow process is obtained after integration. At the same time, a dynamic reservoir capacity flood control calculation model is built, and the river channel terrain cut by DEM is used to calculate the reservoir capacity in sections. Taking the farthest backwater end into consideration, the static reservoir capacity flood control calculation results are compared to finally determine the maximum flow of the breach.

[0032] like Figure 4 As shown, according to one aspect of the present application, step S3 is further: Step S31, based on the maximum flow of the breach, three methods, namely, one-dimensional hydrodynamic model, Muskingum segmented calculation and confluence coefficient calculation, are used in turn to perform real-time flood calculation, and the calculation results of the three methods are combined to obtain an initial flood forecasting scheme; Muskingum segmented continuous routing is to divide the routed river section into N units, and perform Muskingum routing N times to obtain the flow process; The hydrodynamic model is mainly based on the simultaneous solution of the Saint-Venant equations. The MIKE11 model can realize flood routing of one-dimensional rivers and can simulate continuous rapid flow well. The MIKE11 model is based on the one-dimensional unsteady flow Saint-Venant equations to simulate the flow state of rivers or estuaries. The continuity equation and momentum equation in the Saint-Venant equations are discretized by the finite difference method. The calculation grid consists of flow points and water point points, where the flow points and water point points are calculated separately at the same time step. The calculation grid is automatically generated by the model. The water point point is the location of the cross section. The distances between adjacent water point points may be different. The flow point is located between two adjacent water point points. The distribution of the calculation grid points follows the following rules: 1. The downstream endpoint is the calculated water point; 2. The inflow point is used to calculate the water level; 3. The cross-section data point is used to calculate the water level point; 4. The point automatically inserted according to the maximum value is used to calculate the water point; 5. The object point is the calculated water point; 6. There is only one flow point between water points.

[0033] The basic principle of the confluence coefficient algorithm is consistent with the Muskingum method, both of which jointly solve the water balance equation and the channel storage equation. The confluence coefficient method has different number of model confluence coefficients according to different confluence generalization conditions. By dividing the confluence units, calibrating the flood samples, determining the confluence coefficient of each unit, and finally summing the confluence calculation results of each unit to form the flood process of the calculation section.

[0034] In this embodiment, three methods are simultaneously used to calculate the flood in real time, which can reduce the error caused by a single method and improve the accuracy of the calculation.

[0035] In a certain embodiment, specifically: Collect watershed data, including: watershed area, rainfall duration and rainfall intensity; Collect river parameters and initial water level. River parameters include: river width, river bottom elevation, river slope and Manning roughness coefficient; HEC-RAS was used to simulate the one-dimensional hydrodynamic model, and the boundary condition was set as the initial water level; The maximum flow of floods in different time periods is calculated using the formula Q=C*I*A, where Q is the flow, C is the confluence coefficient, I is the rainfall intensity, and A is the basin area. The confluence coefficient is set to 0.5, and the flow in different time periods is calculated; Combined with rainfall characteristics, the formula Q t =C*P t Calculate the flow rate, where Q t is the flow rate at any time, C is the confluence coefficient, P t is the rainfall depth; Set up the river channel in HEC-RAS, create terrain data and input the initial water level, use 6 hours of rainfall data for simulation, and obtain water level change and flow change data; The maximum flow is calculated in Muskingum segmentation algorithm, and the maximum flow data corresponding to each hour segment is obtained; In the confluence routing, hourly rainfall depth changes and flow data are obtained; The calculation results of the three methods are integrated to generate a table. The calculation results of the three methods are weighted, and the weights are set as one-dimensional flow weight: 30%, Muskingum flow weight: 50%, and confluence flow weight: 20%, to obtain a weighted flow forecast.

[0036] Step S32, collecting real-time information to optimize and correct parameters; Step S33, sequentially adopting the one-dimensional hydrodynamic model after parameter optimization and correction, Muskingum segmented calculation and confluence coefficient calculation to perform real-time flood calculation, and combining the calculation results of the three methods to obtain the final flood forecasting plan.

[0037] like Figure 5 As shown, according to one aspect of the present application, step S4 is further: Step S41, fitting the river network, topography, actual water and rainfall conditions and probability distribution of water level above the dam where the barrier lake occurs, and randomly sampling to obtain a large number of scenarios; Step S42, sequentially calculating the maximum breach flow and flood forecasting scheme corresponding to the massive scenario, i.e., the hydrological emergency forecasting scheme for the barrier lake; Step S43: Based on samples of hydrological emergency forecasting schemes for barrier lakes under different scenarios, regular information of optimal emergency forecasting is extracted and rules for hydrological emergency forecasting for barrier lakes are formulated.

[0038] According to one aspect of the present application, step S41 is further: Step S41a, fitting the marginal distribution of the river network, topography, actual water and rainfall conditions and water level above the dam where the barrier lake occurs, cross-checking the fitting effect, selecting the distribution with the highest fitting degree, and determining the parameters of the selected distribution by using the maximum likelihood estimation method; Step S41b, using multiple correlation coefficients to cross-validate the river network, topography, actual water and rainfall conditions, and water level on the dam to analyze the correlation between edge functions, and analyze and establish the confidence level of the joint probability distribution function; Step S41c, constructing the optimal Copula function of the river network, topography, actual water and rainfall conditions and water level above the dam; Step S41d, using the D-DREAM algorithm to take the joint probability distribution of the river network, topography, actual water and rainfall conditions and the water level on the dam as the prior distribution, randomly sample the combination of the river network, topography, actual water and rainfall conditions and the water level on the dam to obtain a large number of scenarios.

[0039] In this embodiment, the marginal function is fitted to the river network, topography, actual water and rainfall conditions and water level on the dam, and a variety of marginal distribution functions are selected to fit them, and finally the optimal marginal distribution function is determined.

[0040] The parameter values ​​of various distribution functions are determined by the maximum likelihood estimation method, and the Kolmogorov-Smirnov test and Andeson Darling test are used to test each distribution function. By comparing the probability values ​​of the null hypothesis, the optimal marginal distribution function is finally determined. By optimizing the fitting of the marginal distribution functions, the function type with the best fitting degree is selected as the distribution function of the variable, which serves the accuracy of the subsequent construction of the joint probability distribution function.

[0041] When testing the marginal distribution function, there is a possibility of misjudgment if one correlation coefficient method is used for calculation. Therefore, in this embodiment, multiple correlation coefficient methods are used simultaneously to respectively determine the marginal distribution functions of the river network, topography, actual water and rainfall conditions, and water level on the dam, and then the correlation between multiple variables is tested and their respective correlation coefficients are calculated.

[0042] According to one aspect of the present application, step S43 is further: Step S43a, setting the upstream dam front water level, actual water and rainfall conditions, interval water inflow, barrier lake water storage capacity, and barrier lake water level as state variables, and setting the barrier lake breach time, barrier lake breach flow, and downstream dam front water level as decision variables; Step S43b, constructing a BP artificial neural network model for hydrological emergency forecasting of the barrier lake, and extracting hydrological emergency forecasting rules for the barrier lake based on samples of hydrological emergency forecasting schemes for the barrier lake under different scenarios; Step S43c, correct the extracted barrier lake hydrological emergency forecast rules to obtain the final emergency forecast rules.

[0043] Establishing the correspondence between massive scenarios and their corresponding hydrological emergency forecast plans for barrier lakes can greatly improve the speed of generating hydrological emergency forecast plans when used, and can quickly and specifically prepare hydrological emergency forecast plans to achieve timely forecasts and warnings.

[0044] According to another aspect of the present application, a barrier lake hydrological emergency forecasting system based on coupled meteorological values ​​is provided, characterized in that it includes: at least one processor; and a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement any of the above-mentioned methods for hydrological emergency forecasting of barrier lakes based on coupled meteorological values.

[0045] The preferred embodiments of the present invention are described in detail above; however, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. A hydrological emergency forecasting method for barrier lakes based on coupled meteorological values, characterized in that: The steps include: Step S1, collecting the river network, topography, actual water and rainfall conditions and water level forecast station information on the dammed lake where the barrier lake occurs, building a hydrological model, calculating the water volume of the interval, using the water level-volume curve derivation method based on DEM to build an initial barrier lake storage capacity curve, collecting hydrological information of the lake inlet station to conduct real-time review and correction on the initial barrier lake storage capacity curve, and obtaining the final barrier lake storage capacity curve; Step S2, measuring the water level and area data of the barrier lake and inputting them into the barrier lake storage capacity curve, calculating the water volume of the barrier lake, constructing a static storage capacity flood regulation calculation model, inputting the water volume of the barrier lake into the static storage capacity flood regulation calculation model, successively calculating the flow process corresponding to the water level fluctuation amplitude of multiple time steps, obtaining the static storage capacity flood regulation calculation result, constructing a dynamic storage capacity flood regulation calculation model, using the river terrain segmentation cut by DEM to calculate the dynamic storage capacity flood regulation calculation result, and determining the maximum flow of the breach based on the dynamic and static storage capacity flood regulation calculation results; Step S3: based on the maximum flow of the breach, three methods, namely, one-dimensional hydrodynamic model, Muskingum segmented calculation and confluence coefficient calculation, are used in turn to perform real-time flood calculation, and the calculation results of the three methods are combined to obtain an initial flood forecasting scheme, and real-time alarm information is collected to optimize and correct the parameters, and the three methods, namely, one-dimensional hydrodynamic model, Muskingum segmented calculation and confluence coefficient calculation, which have been optimized and corrected, are used in turn to perform real-time flood calculation, and the calculation results of the three methods are combined to obtain a final flood forecasting scheme; Step S4, constructing a large number of scenarios based on the river network, topography, actual water and rainfall conditions and water level on the dam where the barrier lake occurs, and calculating the corresponding maximum breach flow and flood forecasting schemes based on the constructed massive scenarios, i.e., the barrier lake hydrological emergency forecasting scheme, and based on samples of the barrier lake hydrological emergency forecasting schemes under different scenarios, extracting the regularity information of the optimal emergency forecast and formulating the barrier lake hydrological emergency forecasting rules.

2. The method for emergency hydrological forecasting of a barrier lake based on coupled meteorological values ​​according to claim 1, characterized in that: The step S1 is further as follows: Step S11, collecting information on the location where the barrier lake occurs, including: the river network, topography, actual water and rainfall conditions, and water level forecast station information on the dam where the barrier lake occurs; Step S12, extract the water level station information on the upstream dam, obtain the upstream water volume, build a hydrological model, extract the river network, topography and real-time water and rainfall information of the place where the barrier lake occurs and input it into the hydrological model, and calculate the interval water volume based on the similarity of the sub-basins; Step S13, extracting upstream water volume and interval water volume data, and constructing an initial barrier lake storage capacity curve using a DEM-based water level-volume curve derivation method; Step S14: Collect hydrological information from the lake entry station to conduct real-time review and correction on the initial reservoir capacity curve of the barrier lake to obtain the final reservoir capacity curve of the barrier lake.

3. The method for emergency hydrological forecasting of a barrier lake based on coupled meteorological values ​​according to claim 2, characterized in that: The step S12 is further as follows: Step S12a, extracting the water level station information on the upstream dam to obtain the upstream water volume; Step S12b, constructing the Xin'an River hydrological model; Step S12c, extract the river network, topography and actual water and rainfall information of the place where the barrier lake occurs, input the Xin'anjiang hydrological model to calculate the water volume of the area around the blockage point of the barrier lake, and based on the sub-basin similarity, couple the flow of the area around the blockage point of the barrier lake to calculate the water volume of the area where the barrier lake is located, that is, the interval water volume.

4. The method for emergency hydrological forecasting of a barrier lake based on coupled meteorological values ​​according to claim 2, characterized in that: The step S14 is further as follows: Step S14a, calculating the water storage capacity of the river channel when the barrier body blocks the river channel to form a barrier lake; Step S14b: after the barrier lake is formed, the water level in front of the barrier lake dam is recorded in real time; Step S14c, calculate the cumulative amount of water entering the lake based on the hydrological information of the lake entry station, plus the water storage in the river channel at the time of the formation of the barrier lake, to obtain the water storage capacity of the barrier lake; Step S14d, plotting the calculated water level-volume data onto the initial reservoir capacity curve; Step S14e, using the least squares method to fit the data and the storage capacity curve to obtain a new storage capacity curve, and reasonably adjusting the nodes of the new storage capacity curve to finally obtain a verified and corrected storage capacity curve.

5. The method for emergency hydrological forecasting of a barrier lake based on coupled meteorological values ​​according to claim 1, characterized in that: The step S2 is further as follows: Step S21, collecting the water level and area information of the barrier lake on site, inputting the barrier lake storage capacity curve, and calculating the water storage capacity of the barrier lake; Step S22, constructing a static reservoir capacity flood regulation calculation model and a dynamic reservoir capacity flood regulation calculation model; Step S23, input the water storage capacity of the barrier lake into the static reservoir capacity flood regulation calculation model, calculate the flow rate process corresponding to the water level fluctuation of multiple time steps in sequence, and obtain the static reservoir capacity flood regulation calculation result; Step S24, using the river terrain segments cut by DEM to calculate the dynamic reservoir capacity flood control calculation model to obtain the dynamic reservoir capacity flood control calculation results, and determining the maximum flow of the breach based on the dynamic and static reservoir capacity flood control calculation results.

6. The method for emergency hydrological forecasting of a barrier lake based on coupled meteorological values ​​according to claim 1, characterized in that: The step S3 is further as follows: Step S31, based on the maximum flow of the breach, three methods, namely, one-dimensional hydrodynamic model, Muskingum segmented calculation and confluence coefficient calculation, are used in turn to perform real-time flood calculation, and the calculation results of the three methods are combined to obtain an initial flood forecasting scheme; Step S32, collecting real-time information to optimize and correct parameters; Step S33, sequentially adopting the one-dimensional hydrodynamic model after parameter optimization and correction, Muskingum segmented calculation and confluence coefficient calculation to perform real-time flood calculation, and combining the calculation results of the three methods to obtain the final flood forecasting plan.

7. The method for emergency hydrological forecasting of a barrier lake based on coupled meteorological values ​​according to claim 1, characterized in that: The step S4 is further as follows: Step S41, fitting the river network, topography, actual water and rainfall conditions and probability distribution of water level above the dam where the barrier lake occurs, and randomly sampling to obtain a large number of scenarios; Step S42, sequentially calculating the maximum breach flow and flood forecasting scheme corresponding to the massive scenario, i.e., the hydrological emergency forecasting scheme for the barrier lake; Step S43: Based on samples of hydrological emergency forecasting schemes for barrier lakes under different scenarios, regular information of optimal emergency forecasting is extracted and rules for hydrological emergency forecasting for barrier lakes are formulated.

8. The method for emergency hydrological forecasting of a barrier lake based on coupled meteorological values ​​according to claim 7, characterized in that: The step S41 is further as follows: Step S41a, fitting the marginal distribution of the river network, topography, actual water and rainfall conditions and water level above the dam where the barrier lake occurs, cross-checking the fitting effect, selecting the distribution with the highest fitting degree, and determining the parameters of the selected distribution by using the maximum likelihood estimation method; Step S41b, using multiple correlation coefficients to cross-validate the river network, topography, actual water and rainfall conditions, and water level on the dam to analyze the correlation between edge functions, and analyze and establish the confidence level of the joint probability distribution function; Step S41c, constructing the optimal Copula function of the river network, topography, actual water and rainfall conditions and water level above the dam; Step S41d, using the D-DREAM algorithm to take the joint probability distribution of the river network, topography, actual water and rainfall conditions and the water level on the dam as the prior distribution, randomly sample the combination of the river network, topography, actual water and rainfall conditions and the water level on the dam to obtain a large number of scenarios.

9. The method for emergency hydrological forecasting of a barrier lake based on coupled meteorological values ​​according to claim 7, characterized in that: The step S43 is further as follows: Step S43a, setting the upstream dam front water level, actual water and rainfall conditions, interval water inflow, barrier lake water storage capacity, and barrier lake water level as state variables, and setting the barrier lake breach time, barrier lake breach flow, and downstream dam front water level as decision variables; Step S43b, constructing a BP artificial neural network model for hydrological emergency forecasting of the barrier lake, and extracting hydrological emergency forecasting rules for the barrier lake based on samples of hydrological emergency forecasting schemes for the barrier lake under different scenarios; Step S43c, correct the extracted barrier lake hydrological emergency forecast rules to obtain the final emergency forecast rules.

10. The hydrological emergency forecast system for barrier lakes based on coupled meteorological values ​​is characterized by: include: at least one processor; as well as a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the method for hydrological emergency forecasting of a barrier lake coupled with meteorological values ​​as described in any one of claims 1 to 9.

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