Method for estimating abnormal seepage flow of concrete faced rockfill dam based on parameter transplantation and overflow weir monitoring flow

Abstract

The invention belongs to the field of hydraulic structure safety monitoring and predictive analysis, and particularly relates to a method for estimating abnormal seepage flow of a concrete faced rockfill dam based on parameter transplantation and overflow weir monitoring flow. The scientificity and the precision of seepage flow estimation are improved by combining an upstream watershed hydrological model and dam rear micro-region runoff and confluence simulation; the overflow weir is adopted to monitor flow data, and is combined with the actual reservoir water level change to construct a more reliable water level-seepage flow relationship. The method is suitable for dam seepage flow estimation under the normal working condition, abnormal seepage can be recognized and quantified, and the early warning capacity for the hidden danger of seepage is improved. Dependence of a traditional method on a large number of monitoring devices is avoided, engineering operation and maintenance cost is reduced, and stability and reliability of data are improved.

Description

Technical Field

[0001] The present invention belongs to the field of hydraulic structure safety monitoring and prediction analysis, and more specifically, relates to a method for estimating abnormal seepage of a face rockfill dam based on parameter transplantation and overflow weir flow monitoring. Background Art

[0002] Concrete face rockfill dams, as key hydraulic engineering structures, play a central role in water resource regulation, flood control, power generation, and irrigation. Their safety directly impacts the safety of life and property of residents downstream and the stable development of the regional economy and society. However, seepage has always been a significant threat to the safety of concrete face rockfill dams. Faced with the increasingly severe challenges of climate change, there is an urgent need to strengthen the accurate assessment of dam seepage.

[0003] Currently, methods for calculating dam seepage mainly include groundwater dynamics models, seepage finite element simulation, and traditional equipment measurement. Groundwater dynamics models have poor adaptability to complex geological conditions, require a large number of hydrogeological parameters, and the calculation process is cumbersome, making it difficult to update dynamically in real time. Seepage finite element simulation modeling relies on high-precision geological exploration data, which requires a large amount of calculation and cannot meet the needs of long-term real-time monitoring. Traditional equipment mainly measures seepage through pressure tubes and water measuring weirs. However, this method is easily affected by rainfall and groundwater flow, and may be affected by the accuracy limitations of the equipment itself and improper maintenance, and often cannot accurately reflect the actual seepage volume of the dam. If a rainfall runoff model is constructed based on the area behind the dam to simulate the seepage caused by rainfall, due to the small area of the micro-area behind the dam, the available data is often very limited and cannot support the accurate calibration of the model parameters. Summary of the Invention

[0004] To address these issues, this paper focuses on simulating rainfall runoff in the micro-region behind a concrete face rockfill dam (CFRD) and simulating seepage based on weir monitoring flow. Considering the lack of sufficient data for parameter calibration of rainfall-runoff models in the micro-region behind the dam, this paper proposes using the abundant rainfall and runoff data from the upstream region of the dam for parameter transplantation. Combined with flow monitoring data from the weir behind the dam, this paper constructs a method for estimating abnormal seepage for CFRD. By transplanting parameters and exploring the potential value of weir monitoring flow data, a more efficient method is used to estimate abnormal seepage for the dam, providing data support and reference for dam structural safety monitoring.

[0005] To achieve the above objectives, the first technical solution of the present application provides a method for estimating abnormal seepage of a concrete face rockfill dam based on parameter transplantation and overflow weir flow monitoring, comprising:

[0006] S1. Collect rainfall data from rainfall stations in the upstream rainwater collection area of the target concrete face rockfill dam and runoff data at the dam site, and screen to obtain an initial typical rainfall-runoff process;

[0007] S2. Use the physical drive model to simulate the runoff generation and runoff in the upstream catchment area of the target concrete face rockfill dam for the selected initial typical rainfall runoff process, and calibrate the initial hydrological forecast model parameters;

[0008] S3. Calculate the area of the micro-rainwater collection area behind the target concrete face rockfill dam with the collection pool as the collection point, and distinguish the permeable area and impermeable area of the micro-rainwater collection area;

[0009] S4. Extract monitoring flow data of the overflow weir behind the dam at different reservoir water levels during the flood season with no rainfall or light rainfall, and construct a relationship between reservoir water level and dam seepage flow;

[0010] S5. Extract monitored flow data from the overflow weir during rainfall during the flood season. Calculate the runoff from the micro-collection area behind the dam under different rainfall scenarios based on the relationship between reservoir water level and dam seepage. After correction, extract the corrected runoff process for typical rainfall events.

[0011] S6. Transplant the insensitive parameters of the calibrated initial hydrological forecast model to the micro-area behind the dam, and calibrate the remaining insensitive parameters based on the modified typical rainfall-runoff process;

[0012] S7. Based on the rainfall-runoff generation and confluence model of the micro-area behind the dam and the relationship between reservoir water level and dam seepage, the theoretical value of dam seepage and abnormal seepage with and without rainfall are estimated.

[0013] Furthermore, the physical driving model in S2 is the Xin'an River or water tank model.

[0014] Furthermore, the S3 is specifically:

[0015] Calculate the rear panel area of the face rockfill dam based on the structural dimensions of the dam;

[0016] Based on the location of the water collection pool behind the dam, the area of the rainwater collection areas on the two dam shoulders with the water collection pool as the collection point is calculated. At the same time, the partial rainwater collection area at the bottom of the dam with the water collection pool as the collection point is calculated.

[0017] The area of the panel behind the face rockfill dam is taken as the impervious area, and the area of the rainwater collection area and part of the rainwater collection area is taken as the permeable area.

[0018] Furthermore, the light rainfall during the flood season refers to rainfall of less than 5 mm during the flood season.

[0019] Furthermore, the calculation method of the rainfall flow production described in S5 is: the basic seepage flow under different rainfall conditions in the micro-area behind the dam during the flood season is calculated through the reservoir water level-dam seepage relationship constructed in S4, and the rainfall flow production is obtained by subtracting the basic seepage flow from the observed seepage flow.

[0020] Furthermore, the correction of rainfall flow rate described in S5 is as follows: if the rainfall flow rate has negative values for more than 5 consecutive periods, it is corrected to 0; if it suddenly changes to a negative value, it is eliminated and then linear interpolation is performed.

[0021] Furthermore, S6 is specifically as follows: applying the insensitive parameters in the initial hydrological forecast model parameters in S2 to the area behind the dam, and calibrating only the sensitive parameters based on the corrected typical rainfall runoff process to obtain the optimal model parameters for the area behind the dam.

[0022] Furthermore, the S7 is specifically:

[0023] Based on the optimal model parameters described in S6 and the rainfall P in the micro-area behind the dam, the runoff Q corresponding to the rainfall is obtained. P ,

[0024] According to the relationship between reservoir water level and dam seepage, the normal dam body seepage Q is obtained. Z =f(Z);

[0025] According to the actual monitoring flow Q of the overflow weir behind the dam, the abnormal dam seepage flow Q is obtained. A , Q A =QQ P -Q Z ;

[0026] If Q A ≥10%Q Z , it is considered that there are abnormal seepage points in the dam body; otherwise, it is considered that there are no abnormal seepage points in the dam body.

[0027] Furthermore, it also includes Q P With Q Z The theoretical value of dam seepage Q is obtained by superposition 大坝 , and compared with the actual monitoring flow Q of the overflow weir behind the dam, the RMSE, MRE, and R 2 , QR, and NSE indicators are used to evaluate the model effect and determine the reliability of the model.

[0028] Beneficial effects: (1) The present invention combines the upstream basin hydrological model with the micro-region runoff simulation behind the dam to improve the scientificity and accuracy of seepage estimation; uses overflow weir monitoring flow data and combines it with the actual reservoir water level changes to construct a more reliable water level-seepage relationship.

[0029] (2) This invention is applicable not only to estimating dam seepage under normal operating conditions, but also to identifying and quantifying abnormal seepage, thereby improving the early warning capability for potential leakage hazards. This avoids the traditional method's reliance on a large number of monitoring devices, reduces project operation and maintenance costs, and improves data stability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a method for estimating abnormal seepage of a concrete face rockfill dam provided by the present invention;

[0031] Figure 2 It is the daily rainfall-runoff process after data processing in the upstream area of the dam;

[0032] Figure 3-14 It is the flood simulation process of the selected flooding events in the dam area;

[0033] Figure 15 This is a schematic diagram of the area behind the dam in the Ersai River Basin;

[0034] Figure 16 This is a simplified diagram of the three-dimensional calculation of the right bank behind the dam;

[0035] Figure 17 It is a simplified diagram of the dam body calculation behind the dam;

[0036] Figure 18 This is a simplified diagram for calculating the distance from the rear of the dam to the outer edge of the water measuring weir;

[0037] Figure 19-22 This is a scatter plot of the relationship between the upper reservoir water level and seepage volume under different rainfall scenarios from 2021 to 2024;

[0038] Figure 23-27 It is the flood simulation process of the selected flooding in the area behind the dam;

[0039] Figure 28-31 It is the comparison result between the theoretical value of the dam seepage and the measured seepage from 2021 to 2024. DETAILED DESCRIPTION

[0040] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0041] In the description of the present application, it should be noted that for directional words, such as the terms "center", "transverse", "longitudinal", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like, indicating directions and positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and cannot be understood as limiting the specific scope of protection of the present application. The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0042] like Figure 1 As shown, the first embodiment of the present application discloses a method for estimating abnormal seepage of a concrete face rockfill dam based on parameter transplantation and overflow weir flow monitoring, comprising:

[0043] S1. Collect rainfall data from rainfall stations in the upstream rainwater collection area of the target concrete face rockfill dam and runoff data at the dam site, and screen to obtain an initial typical rainfall-runoff process;

[0044] S2. Use the physical drive model to simulate the runoff generation and runoff in the upstream catchment area of the target concrete face rockfill dam for the selected initial typical rainfall runoff process, and calibrate the initial hydrological forecast model parameters;

[0045] S3. Calculate the area of the micro-rainwater collection area behind the target concrete face rockfill dam with the collection pool as the collection point, and distinguish the permeable area and impermeable area of the micro-rainwater collection area;

[0046] S4. Extract monitoring flow data of the overflow weir behind the dam at different reservoir water levels during the flood season with no rainfall or light rainfall, and construct a relationship between reservoir water level and dam seepage flow;

[0047] S5. Extract monitored flow data from the overflow weir during rainfall during the flood season. Calculate the runoff from the micro-collection area behind the dam under different rainfall scenarios based on the relationship between reservoir water level and dam seepage. After correction, extract the corrected runoff process for typical rainfall events.

[0048] S6. Transplant the insensitive parameters of the calibrated initial hydrological forecast model to the micro-area behind the dam, and calibrate the remaining insensitive parameters based on the modified typical rainfall-runoff process;

[0049] S7. Based on the rainfall-runoff generation and confluence model of the micro-area behind the dam and the relationship between reservoir water level and dam seepage, estimate the theoretical value of dam seepage and abnormal seepage with and without rainfall.

[0050] In this implementation, the rainfall data from regional rainfall stations upstream of the dam in S1 includes daily rainfall and evaporation data from relevant rainfall stations. The runoff data at the dam site is the inflow runoff data from the dam site section. Based on data completeness, the flood season within the past 10 years can be selected as the analysis period. This data is then screened for outliers and missing data is filled, eliminating non-numeric records and negative values. Correlation interpolation is then performed based on the rainfall data. Finally, 10-15 initial typical rainfall-runoff events are selected from this data.

[0051] The typical rainfall runoff process is based on base flow, combined with the time of the rainfall process, to observe the rising and receding points of the flood, and the entire process from the rising to the peak to the receding of the flood.

[0052] In this embodiment, the physical driving model described in S2 is a hydrological physical driving model, which is based on the laws of physics and directly describes the movement of water (such as precipitation, infiltration, evaporation, runoff, etc.) based on physical principles such as fluid mechanics, thermodynamics, Darcy's law, and the mass conservation equation; in this embodiment, the Xin'anjiang model or the water tank model is preferably used, and the above model is used to simulate the runoff generation and confluence of typical rainfall runoff processes.

[0053] In the above-mentioned embodiment, the hydrological forecast model parameters are calibrated using an intelligent optimization algorithm. Based on mathematical foundations, intelligent optimization algorithms seek a global optimal solution by establishing a relationship between parameters and simulation value errors. In this embodiment, a particle swarm optimization algorithm is used for parameter calibration. The specific process includes initializing a particle swarm, calculating fitness values, updating particle positions and velocities, and ultimately determining the optimal parameter combination through multiple iterative optimizations, thereby improving the model's simulation accuracy and applicability.

[0054] In the above implementation, model accuracy is determined according to the "Specifications for Hydrological Information Forecasting" (GB / T22482-2008). The coefficient of certainty (DC) and the pass rate (QR) are used as evaluation indicators for runoff process simulation. The allowable error for the measured peak flow and flood volume is 20%. The allowable error for the predicted peak time is 30% of the interval between the predicted time and the measured peak time, with a lower limit of 1 hour. If the error is within the allowable error range, the model is considered qualified. The pass rate is the percentage of qualified events to the total number of events.

[0055] In the above embodiment, S3 describes the measurement of the area of the micro rainwater collection area behind the target panel rockfill dam with the water collection tank as the collection point as follows: according to the structural dimensions of the dam, the area of the panel behind the panel rockfill dam is calculated; according to the position of the water collection tank behind the dam, the area of the two dam shoulder rainwater collection areas behind the dam with the water collection tank as the collection point is calculated, and at the same time, the partial rainwater collection area at the bottom of the dam with the water collection tank as the collection point is calculated; the area of the panel behind the panel rockfill dam is used as the impermeable area, and the area of the rainwater collection area and the partial rainwater collection area is used as the permeable area.

[0056] In the above steps S4-S5, the relationship between reservoir water level and dam seepage is first constructed by using the monitoring flow data of the overflow weir behind the dam at different reservoir water levels when there is no rainfall or little rainfall during the flood season. Then, the monitoring flow data of the overflow weir when there is rainfall during the flood season is extracted, and the rainfall runoff of the micro-rainwater collection area behind the dam under different rainfall conditions is estimated based on the reservoir water level-dam seepage relationship. After correction, the corrected typical rainfall runoff process is extracted.

[0057] In this implementation, when there is no rainfall, the seepage behind the dam is mainly related to the reservoir water level. Therefore, when there is no rainfall or light rainfall, the reservoir water level-dam seepage relationship constructed by monitoring the flow data of the dam overflow weir under different reservoir water levels is the basic seepage of the dam Q. Z (Normal dam seepage).

[0058] The screening basis of the light rainfall in the flood season is to perform a causal test on different rainfall data in the flood season and the corresponding leakage sequence, and determine that the maximum rainfall without obvious causal relationship is the light rainfall in the flood season.

[0059] In this embodiment, the data are all selected from the flood season because there is no rainfall in the dry season. The regularity of the curve in different years varies greatly, thus causing a large error.

[0060] In a further embodiment, after the reservoir water level-dam seepage relationship is constructed, during the flood season, the actual observed seepage (Q) is subtracted from the base seepage (Q Z ), that is, the rainfall flow (Q P0 );then Q P0 Correction is performed. If there are negative values for more than 5 consecutive periods, they are corrected to 0; if there are sudden changes to negative values, they are eliminated and linear interpolation is performed to extract the corrected typical rainfall runoff process.

[0061] In this embodiment, S6 transplants the parameters of the initial hydrological forecast model described in S2. Parameter transplantation refers to the process of applying the parameters of a model calibrated in one basin or region to another basin or region. Its purpose is to reduce the workload of calibrating the model parameters in the new region by utilizing the hydrological characteristic information of the calibrated region, thereby improving efficiency. The specific basis for transplantation is: the insensitive parameters in the initial hydrological forecast model parameters are applied to the area behind the dam, and only the sensitive parameters are calibrated. At the same time, the calibration range of the parameters related to the impervious area is adjusted, and the runoff decay coefficient parameter CSS of the impervious area is increased to reflect the significant difference in the convergence time between the impervious and permeable areas. Based on the corrected typical rainfall runoff process, the optimal model parameters for the area behind the dam are calibrated.

[0062] In this embodiment, the S7 is specifically:

[0063] Based on the optimal model parameters described in S6 and the rainfall P in the micro-area behind the dam, the runoff Q corresponding to the rainfall is obtained. P ;

[0064] According to the relationship between reservoir water level and dam seepage, the normal dam body seepage Q is obtained. Z =f(Z);

[0065] According to the actual monitoring flow Q of the overflow weir behind the dam, the abnormal dam seepage flow Q is obtained. A , Q A =QQ P -Q Z ;

[0066] If Q A ≥10%Q Z , it is considered that there are abnormal seepage points in the dam body; otherwise, it is considered that there are no abnormal seepage points in the dam body.

[0067] Taking the seepage analysis of a concrete face rockfill dam in a certain river basin as an example, the seepage of the dam is estimated according to the method of the present invention.

[0068] The case study object is the upper station of the Lower Ersai Hydropower Station. The catchment area of the upstream basin of the dam site is 1481km 2 , with a total storage capacity of 417.7 million m 3 The normal water level of the dam is 263m, and the designed average flow rate of the dam site is 75.8m over many years. 3 / s, and the catchment area behind the dam is 61149m 2The historical seepage monitoring data spans 1,197 days. The dam's stable seepage rate is 270 L / s, with a peak value of 499.91 L / s. Dam monitoring data includes five types: seepage rate monitored by a weir, reservoir water level, previous rainfall at the dam site, piezometers around the dam, and piezometers behind the dam. The weir is located in the seepage collection channel at the downstream foot of the dam and monitors the amount of seepage collected by the dam body. Piezometers monitor seepage within the dam body and foundation, and are installed upstream and downstream along the dam foundation and in micro-areas behind the dam.

[0069] The present invention provides a method for estimating abnormal seepage of a concrete face rockfill dam, comprising:

[0070] S1. Collect rainfall data from rainfall stations in the upstream rainwater collection area of the target concrete face rockfill dam and runoff data at the dam site, and screen out the initial typical rainfall-runoff process.

[0071] ① Daily rainfall data, evaporation data, and dam-site runoff data were collected from relevant rainfall stations upstream of the Ersai dam site (the middle reaches of the Ersai River, Pursat, and Wodai River). Based on the completeness of the data, the flood season (June to October) from 2017 to 2024 was selected as the analysis period.

[0072] ② Eliminate non-numeric records and negative data from the collected data, and perform correlation interpolation on the runoff data based on the rainfall data. The rainfall data of the first-level stations of the middle reaches of Ersai, Pusa, and Wodai are taken as the cumulative average rainfall in the upper reservoir basin. The processed rainfall runoff process diagram is shown in Figure 2 Twelve typical rainfall-runoff processes were selected for calibration, and the selected simulated flood periods are detailed in Table 1.

[0073] Table 1 Selected periods for simulated floods

[0074]

[0075] S2. Use the physical drive model to simulate the runoff generation and runoff in the upstream rainwater collection area of the target concrete face rockfill dam for the selected initial typical rainfall runoff processes, and calibrate the initial hydrological forecast model parameters.

[0076] ① Based on the actual situation of the Ersai River Basin, the Xin'an River model was selected as the runoff model. The PSO algorithm was used to calibrate the parameters of the Xin'an River model. The parameters that need to be calibrated and their initial ranges are shown in Table 2.

[0077] Table 2 Calibration parameters and optimization range of Xinanjiang model

[0078]

[0079] The PSO algorithm calculates the fitness value of each particle based on the objective function to evaluate the quality of its parameter combination, the selection of which will affect the simulation of different characteristics of the hydrological process. In order to fully reflect the overall water balance and flow process, the following objective function is selected for evaluation:

[0080]

[0081] Where: Q obs,i is the measured flow sequence at regular intervals, Q sim,i is the simulated flow sequence at the rate period, N is the total number of measured and simulated flow data, is the average value of the measured flow rate, is the average value of the simulated flow rate.

[0082] ② According to the "Specifications for Hydrological Information Forecasting" (GB / T22482-2008), the coefficient of certainty (DC) and the pass rate (QR) were selected as evaluation indicators for runoff process simulation. The measured peak discharge value and 20% of the flood volume were taken as their corresponding allowable errors. The allowable error for the predicted peak time was 30% of the interval between the predicted time and the measured peak time, with 1 hour as the lower limit. If the error was within the allowable error range, the simulation was considered qualified. The pass rate was calculated as the percentage of qualified events to the total number of events.

[0083] ③ Commonly used PSO algorithm parameter settings are not universally applicable to specific optimization problems. If a comprehensive traversal approach were used to test all possible parameter value combinations, the experimental scale would be extremely large. Orthogonal experiments are a highly efficient, scientific, and economical experimental design method that can be used to study the impact of multiple factors on experimental indicators.

[0084] The five parameters pop, w, c1, c2 and m are set as experimental factors. Based on experience, four levels are set for each factor, as shown in Table 3. 16 (45) Orthogonal table design orthogonal experiment, with the help of this method, only 16 experiments are needed to analyze the optimal value level of each factor, which greatly improves the experimental efficiency.

[0085] Table 3 Orthogonal test factor level table

[0086]

[0087] The orthogonal test scheme and results are shown in Table 4. The parameter combinations in the table are substituted into the PSO algorithm. In order to avoid the influence of random value selection in the model on the parameter optimization effect, the model is repeated 20 times in each test, and the mean of the objective function obtained by the 20 simulations is recorded as the experimental calculation result Y i .

[0088] Table 4 Orthogonal test scheme and results

[0089]

[0090]

[0091] The sum of the test results at different levels can be used to analyze the optimal level of a parameter. Table 5 is an analysis table of the orthogonal test calculation results, showing the mean values of the test results at different levels.

[0092] Table 5 Test results analysis

[0093]

[0094] k in the table ij The level corresponding to the minimum value is the optimal level of factor i; R j The larger the value, the greater the impact of the level change of the PSO algorithm parameters on the simulation effect of the Xin'an River model. Combined with the analysis in Table 4, it can be seen that the impact ranking is: population size pop (196.32) > learning factor c1 (121.33) > speed-position correlation coefficient m (101.89) > inertia weight w (96.55) > learning factor c2 (121.33). The optimal combination is pop = 1000, w = 0.9-0.4 linearly decreasing, c1 = 1.44945, c2 = 1.44945, and m = 0.03.

[0095] The comparison of various indicators between the optimal and worst combinations is shown in Table 6, and the optimal calibration parameters are shown in Table 7. Table 6 shows that the optimized parameters can effectively improve the accuracy of the model. The PSO parameters of this scheme will be used in the future to further test the generalization ability of the model.

[0096] Table 6 Simulation indicators of different parameter combinations

[0097]

[0098] Table 7 Calibration results of parameters in the upstream area of the dam

[0099]

[0100] The indicators of each flood are shown in Table 8. The simulation process of the 12 floods is detailed in Figures 3 to 14 Analysis of the flow hydrographs and related indicators for various flood scenarios reveals a certain degree of consistency between the overall trends of measured and predicted flows for most flood events, demonstrating good stability and reliability. However, significant differences were observed for some events, such as the lower simulated peak for flood No. 4 and the significant differences between the flow hydrographs for floods No. 8 and No. 11. This may be due to the large upstream basin and uneven spatial distribution of rainfall, which leads to quantitative deviations in the model's simulation of rainfall-generated runoff mechanisms.

[0101] Table 8 Flood simulation indicators in the upstream area of the dam

[0102]

[0103]

[0104] S3. Calculate the area of the micro-rainwater collection area behind the target panel rockfill dam with the collection pool as the collection point, and distinguish the permeable area and impermeable area of the micro-rainwater collection area.

[0105] ① The area from the dam back to the water measuring weir can be divided into the following four parts: the right bank trapezoidal slope area abcd, the dam back trapezoidal area bcef, the dam bottom to the outer edge of the trapezoidal area cdfg, and the left bank polygonal area efghijk (see Figure 15 The slopes on both banks are covered in bamboo forests and grasslands, and the distance from the dam bottom to the outer edge of the water-measuring weir is covered in grass and gravel. A small channel at the bottom of the left bank's slope diverts surface runoff from the left bank and prevents it from entering the water-measuring weir. Therefore, the left bank can be ignored when calculating the total area to eliminate the impact of runoff from both banks and the bottom.

[0106] ② Extract the right bank area and simplify it into a trapezoid (see Figure 16 In the figure, ' represents estimated data. az is the height of the dam behind the dam, calculated from the difference between the dam crest elevation and the ground level elevation. cd is the distance from the dam base to the outer edge of the weir, with the unknown length set as x. ab is the top length, estimated from the dam length and the number of black and yellow grids in the dam divider.

[0107] The dam body behind the dam is extracted and simplified into a trapezoidal shape (see Figure 17 In the figure, be is the length of the crest, cf is the length of the lower base, which is obtained by the ratio of the lengths of line segment cf and line segment be, and l is the height of trapezoid becf.

[0108] The calculation diagram of the area from the dam back to the outer edge of the water measuring weir is as follows: Figure 18 As shown, dg is the length of the outer edge of the measuring weir, which is obtained by the ratio of the lengths of line segment dg and line segment cf.

[0109] ③ Preliminarily set x to 41.43 m, and exclude the left bank area from the calculation. A summary of the area and land type of each region is shown in Table 9. The face of the concrete face rockfill dam is considered the impermeable portion, and the remaining rainwater collection area is considered the permeable portion.

[0110] Table 9 Summary of area and land type in each region

[0111]

[0112] S4. Extract the monitoring flow data of the overflow weir behind the dam at different reservoir water levels when there is no rainfall or little rainfall during the flood season, and construct the relationship between reservoir water level and dam seepage flow.

[0113] ① The leakage observation data behind the upper dam of the Ersai Hydropower Station from 2014 to 2024 were collected. Due to the poor continuity of the data from 2014 to 2021, 2021 to 2024 were selected as the research period, and linear interpolation was performed for the missing data values.

[0114] ② The rainfall data with rainfall below 20 mm, 10 mm, and 5 mm during the flood season were screened and the corresponding seepage rate series were tested for causality (see Table 10 for details). It was found that when the rainfall was 5 mm, there was no obvious causal relationship between the rainfall in front of the dam in the 48 hours and the change in seepage rate. It can be preliminarily concluded that rainfall below 5 mm during the flood season has no significant effect on the change in seepage rate.

[0115] Table 10 Causal relationship test between rainfall data and seepage data

[0116]

[0117] ③ Filter out data under different conditions: no rainfall in the dry season, rainfall in the flood season (>5mm), and no rainfall in the flood season (<5mm), and fit the relationship curve between reservoir water level and dam seepage in different years. Figures 19 to 22 As can be seen from the figure, the relationship curve of no rainfall in the dry season varies greatly in different years, which will cause certain errors, so the situation of no rainfall in the flood season is selected.

[0118] S5. Extract the monitored flow data from the overflow weir during rainfall during the flood season. Based on the reservoir water level-dam seepage relationship, estimate the rainfall runoff in the micro-collection area behind the dam under different rainfall scenarios. After correction, extract the corrected typical rainfall runoff process. Based on the monitoring data of the overflow weir behind the dam, extract the monitored flow data from the overflow weir under rainfall scenarios. Based on the constructed reservoir water level-dam seepage relationship, estimate the rainfall runoff in the micro-area behind the dam under different rainfall scenarios. The corresponding rainfall can be based on the rainfall monitoring point data on the dam, or the average rainfall of multiple rainfall monitoring points near the dam area. Based on the rainfall and runoff in the micro-area behind the dam under different rainfall scenarios, extract 5-10 typical rainfall runoff processes.

[0119] ① Based on the S4 fitting relationship curve, the basic seepage volume under different rainfall conditions in the micro-area behind the dam during the flood season was calculated, and the rainfall flow yield was obtained by subtracting the basic seepage volume from the observed seepage volume.

[0120] ② Due to the limitations of the fitting curve, some rainfall runoff is negative. If the negative value exists for more than five consecutive periods, it is corrected to 0; if the value suddenly changes to negative, it is eliminated and then linear interpolation is performed.

[0121] ③ Based on the revised rainfall runoff obtained from S5, five typical rainfall runoff processes were selected for calibration. The selected simulated flood periods are shown in Table 11.

[0122] Table 11 Selected periods for simulated flood in the area behind the dam

[0123]

[0124] S6. Calibrate the optimal model parameters based on the modified typical rainfall-runoff process in the micro-area behind the dam.

[0125] Parameters from models such as the Xin'an River or water tank model in the upstream area of the dam were used as the initial parameters for the runoff generation and confluence model for the micro-region behind the dam. As S3 indicates, the impervious area in the area behind the dam accounts for a significant proportion, so the original PSO calibration IMP parameter range was modified to 0.6–0.8. Furthermore, due to the significant difference in confluence time between impervious and permeable areas, a parameter for the impervious area runoff generation and confluence coefficient was added. The remaining parameter ranges remained the same as those in Table 2. The model's simulation accuracy was evaluated using parameters such as the pass rate, relative error, and coefficient of certainty, and the optimal model parameters were calibrated. The parameter calibration results are shown in Table 12.

[0126] Table 12 Calibration results of parameters in the area behind the dam

[0127]

[0128] The indicators of each flood are shown in Table 13, and the simulation process of the five floods is detailed in Figures 23 to 27 The results show that the model has a certain ability to capture the flow change trend of most flood events. The simulation effect of some events, such as Flood No. 2, is relatively good, showing a certain flood simulation ability. However, the model is not stable when dealing with floods with different characteristics. The main reasons may be: ① Various simplification methods in the data processing process may cause errors, such as linear curve fitting fails to effectively represent the nonlinear characteristics of hydrological elements; ② Flood patterns vary greatly from year to year, and the model cannot effectively adapt to the unified parameters or processing methods; ③ The basin area behind the dam is small, and there is a deviation between the rainfall station and the area behind the dam. The rainfall data is difficult to accurately reflect the actual situation, resulting in deviations in the model's simulation of rainfall runoff. ④ Because the simulated runoff accounts for a small proportion of the actual seepage flow and varies significantly.

[0129] Table 13 Flood simulation indicators of various areas behind the dam

[0130]

[0131] S7. Based on the rainfall-runoff generation and confluence model of the micro-area behind the dam and the relationship between reservoir water level and dam seepage, estimate the theoretical value of dam seepage and abnormal seepage with and without rainfall.

[0132] ① Based on the parameters determined by the S7 rate and the rainfall in the micro-area behind the dam, the runoff Q corresponding to the rainfall is calculated. P , the simulation period is shown in the table below.

[0133] Table 14 Selected periods for simulated floods behind the dam

[0134]

[0135] According to the above relationship between reservoir water level and dam seepage, the normal dam body seepage Q is calculated. Z =f(Z), Q P With Q Z The theoretical value of dam seepage Q is obtained by superposition 大坝 , compared with the actual monitoring flow Q of the overflow weir behind the dam, the RMSE, MRE, and R 2 , QR, NSE and other indicators are used to evaluate the model effect. The evaluation indicators for different years are shown in Table 15. The simulation process is detailed in Figures 28 to 31 .

[0136] Table 15 Results of seepage simulation evaluation index

[0137]

[0138] The results show that the model performs well in seepage simulation, and the evaluation indicators are ideal in most years, and the simulation of seepage has high accuracy and reliability.

[0139] ② Based on the actual monitored flow rate Q of the overflow weir behind the dam, estimate the abnormal dam seepage flow rate Q A .

[0140] Q A =QQ P -Q Z ;

[0141] If Q A Greater than the normal dam body seepage Q Z If the seepage rate is 10%, it is considered that there are abnormal seepage points in the dam body, which need further investigation to provide technical support for the safe and stable operation of the dam.

[0142] After calculation, Q in this case A None of them exceeded the normal dam body seepage rate Q Z Based on this, it can be preliminarily determined that there is no abnormal seepage point in the dam body at present.

[0143] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for estimating abnormal seepage of a concrete face rockfill dam based on parameter transplantation and overflow weir monitoring flow, characterized in that: include: S1. Collect rainfall data from rainfall stations in the upstream rainwater collection area of the target concrete face rockfill dam and runoff data at the dam site, and screen to obtain an initial typical rainfall-runoff process; S2. Use the physical drive model to simulate the runoff generation and runoff in the upstream catchment area of the target concrete face rockfill dam for the selected initial typical rainfall runoff process, and calibrate the initial hydrological forecast model parameters; S3. Calculate the area of the micro-rainwater collection area behind the target concrete face rockfill dam with the collection pool as the collection point, and distinguish the permeable area and impermeable area of the micro-rainwater collection area; S4. Extract monitoring flow data of the overflow weir behind the dam at different reservoir water levels during the flood season with no rainfall or light rainfall, and construct a relationship between reservoir water level and dam seepage flow; S5. Extract the monitored flow data of the overflow weir during rainfall during the flood season. Based on the relationship between reservoir water level and dam seepage, estimate the rainfall runoff of the micro-collection area behind the dam under different rainfall scenarios. After correction, extract the corrected runoff process for typical rainfall events. S6. Transplant the insensitive parameters of the calibrated initial hydrological forecast model to the micro-area behind the dam, and calibrate the remaining insensitive parameters based on the modified typical rainfall-runoff process; S7. Based on the rainfall-runoff generation and confluence model of the micro-area behind the dam and the relationship between reservoir water level and dam seepage, the theoretical value of dam seepage and abnormal seepage with and without rainfall are estimated.

2. The estimation method according to claim 1, characterized in that: The physical driving model described in S2 is the Xinan River or water tank model.

3. The estimation method according to claim 1, characterized in that: The S3 is specifically: Calculate the rear panel area of the face rockfill dam based on the structural dimensions of the dam; Based on the location of the water collection pool behind the dam, the area of the rainwater collection areas on the two dam shoulders with the water collection pool as the collection point is calculated. At the same time, the partial rainwater collection area at the bottom of the dam with the water collection pool as the collection point is calculated. The area of the panel behind the face rockfill dam is taken as the impervious area, and the area of the rainwater collection area and part of the rainwater collection area is taken as the permeable area.

4. The estimation method according to claim 1, characterized in that: The light rainfall during the flood season refers to rainfall of less than 5 mm during the flood season.

5. The estimation method according to claim 1, characterized in that: The calculation method of the rainfall flow production described in S5 is: the basic seepage volume under different rainfall conditions in the micro-area behind the dam during the flood season is calculated through the reservoir water level-dam seepage volume relationship constructed in S4, and the rainfall flow production is obtained by subtracting the basic seepage volume from the observed seepage volume.

6. The even number estimation method according to claim 1, characterized in that: S5 describes the correction of rainfall flow as follows: if the rainfall flow has negative values for more than five consecutive periods, it is corrected to 0; if it suddenly changes to a negative value, it is eliminated and then linear interpolation is performed.

7. The estimation method according to claim 1, characterized in that: Specifically, S6 comprises: using the corrected typical runoff rainfall process to calibrate the initial hydrological forecast model parameters in S2, and using the qualified rate, relative error, and certainty coefficient to evaluate the model accuracy, and calibrate to obtain the optimal model parameters.

8. The estimation method according to claim 1, characterized in that: The S7 is specifically: Based on the optimal model parameters described in S6 and the rainfall P in the micro-area behind the dam, the runoff Q corresponding to the rainfall is obtained. P , According to the relationship between reservoir water level and dam seepage, the normal dam body seepage Q is obtained. Z =f(Z); According to the actual monitoring flow Q of the overflow weir behind the dam, the abnormal dam seepage flow Q is obtained. A , Q A =QQ P -Q Z ; If Q A ≥10%Q Z , it is considered that there are abnormal seepage points in the dam body; otherwise, it is considered that there are no abnormal seepage points in the dam body.

9. The estimation method according to claim 8, characterized in that: The calculation method of the micro-area rainfall P behind the dam is: for the impervious area, the net rainfall is determined as the rainfall; for the pervious area, the physical driving model is used to obtain the simulated rainfall, and the two are added together to obtain the micro-area rainfall.

10. The estimation method according to claim 8, further comprising: P With Q Z The theoretical value of dam seepage Q is obtained by superposition 大坝 , and compared with the actual monitoring flow Q of the overflow weir behind the dam, the RMSE, MRE, and R 2 , QR, and NSE indicators are used to evaluate the model effect and determine the reliability of the model.

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