Method, device and equipment for zoning of reservoir over-standard flood inundation risk

Abstract

Embodiments of the present application disclose a reservoir over-standard flood inundation risk zoning method, device and equipment, by determining the reservoir siltation influence probability factor and the reservoir dam break influence probability factor under the siltation condition, the reservoir siltation influence probability factor and the reservoir dam break influence probability factor are used to calibrate the reservoir inundation comprehensive risk degree of siltation-dam break double influence probability under the over-standard flood condition, and the K-means clustering algorithm is used to cluster and analyze the reservoir inundation comprehensive risk degree of each grid calculation unit, and finally the flood risk grade is determined. The technical problem of low accuracy of prediction and zoning of flood inundation risk in the prior art for the reservoir area with high sediment concentration and weak dredging capacity is solved, and a scientific basis and certain technical support are provided for reservoir flood inundation risk assessment and disaster prevention and mitigation planning in high sediment concentration basin areas.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of information processing technology, and in particular to a method, apparatus and equipment for zoning the risk of flooding in reservoirs exceeding the standard. Background Technology

[0002] When rivers with high sediment content flow into reservoirs, if the reservoirs lack sufficient dredging capacity, the sediment they carry will accumulate in the reservoir area, leading to siltation. Siltation reduces the effective storage capacity of the reservoir, decreasing its flood control capabilities. Especially during floods exceeding standard levels, siltation can cause a rapid rise in reservoir water levels and increased outflow, thereby increasing the risk of downstream inundation.

[0003] Currently, the methods for zoning reservoir flood inundation risk are relatively mature both domestically and internationally. However, for reservoir areas with high sediment content and weak dredging capacity, the impact of reservoir siltation and probabilistic dam failure on reservoir inundation risk is rarely considered. Summary of the Invention

[0004] This invention provides a method, apparatus, and equipment for zoning the risk of flooding in reservoirs exceeding standard flood levels, solving the technical problem of low accuracy in predicting the risk of flooding in reservoir areas with high sediment content and weak dredging capacity in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a zoning method for assessing the risk of flooding exceeding standard levels in a reservoir, which divides the downstream area of ​​the target reservoir into multiple grid computing units. The zoning method includes:

[0006] The flood risk level of the corresponding grid computing unit is determined based on the basic elements of flood risk of each grid computing unit, wherein the basic elements include at least the maximum flood depth, maximum flow velocity and maximum flood duration of the corresponding grid computing unit.

[0007] Based on the relationship between the degree of reservoir siltation in each of the grid computing units and the risk of flooding exceeding the standard, the probability factor of reservoir siltation impact for the corresponding grid computing unit is determined.

[0008] Based on the impact of the reservoir siltation degree and the frequency of floods exceeding the standard on the probability of dam failure of each grid computing unit, and the relationship between the above impact and the risk of flooding exceeding the standard, the probability factor of reservoir dam failure impact of the corresponding grid computing unit under siltation conditions is determined.

[0009] The comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure is calibrated based on the reservoir siltation impact probability factor and the reservoir dam failure impact probability factor.

[0010] The K-means clustering algorithm is used to perform cluster analysis on the comprehensive risk of reservoir inundation of each grid computing unit to determine the flood risk level.

[0011] Furthermore, before determining the super-standard flood inundation risk level of the corresponding grid computing unit based on the basic elements of inundation risk of each grid computing unit, the zoning method further includes:

[0012] The relationship between reservoir water level and reservoir capacity is determined based on reservoir siltation, wherein the reservoir siltation is determined based on historical measured reservoir capacity curves or predicted by an auxiliary curve derived from the reservoir capacity and area calculation curves to determine siltation depth.

[0013] The inflow process of floods exceeding the standard is calculated based on the hydrological information of the upstream basin of the target reservoir;

[0014] The reservoir flood control discharge process is calculated based on the reservoir scheduling and operation mode of the target reservoir and the reservoir capacity curve after siltation changes.

[0015] Based on the calculation of the inflow process of the super-standard flood and the calculation of the outflow process of the reservoir flood control scheduling, a flood dynamic evolution model is constructed based on the multi-source topographic data of the target reservoir. The flood dynamic evolution model is used to simulate the dynamic change process of flood risk information of different grid computing units at different times.

[0016] Furthermore, determining the super-standard flood inundation risk level of the corresponding grid computing unit based on the basic elements of inundation risk of each grid computing unit includes:

[0017] The equivalent water depth of each grid computing unit is determined based on the formula H = α1α2h, where H is the equivalent water depth of the grid computing unit, H is used to characterize the risk of flooding exceeding the standard, α1 is the correction coefficient for the maximum traveling velocity, α2 is the correction coefficient for the maximum flood duration, and h is the maximum flood depth.

[0018] Furthermore, the probability factor for the impact of reservoir siltation, determined based on the relationship between the degree of reservoir siltation and the risk of flooding exceeding the standard, includes:

[0019] The degree of reservoir siltation is determined based on the amount of siltation at the normal water level and the original reservoir capacity.

[0020] The probability factor of reservoir siltation impact is determined based on the relationship between the degree of reservoir siltation and the risk of flooding exceeding the standard.

[0021] Furthermore, based on the impact of the reservoir siltation level and the frequency of floods exceeding standard levels on the probability of dam failure, and the relationship between the above impacts and the risk of flooding exceeding standard levels, the probability factor for dam failure under siltation conditions is determined as follows:

[0022] Establish a functional relationship between the reservoir siltation degree, the frequency of floods exceeding standard levels, and the probability coefficient of dam failure impact;

[0023] The probability factor of reservoir dam failure under siltation conditions is determined by using the established functional relationship.

[0024] Furthermore, the comprehensive risk level of reservoir inundation under conditions of super-standard floods, calibrated based on the reservoir sedimentation impact probability factor and the reservoir dam failure impact probability factor, includes:

[0025] The comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure in the case of floods exceeding the standard is calculated based on the formula R = a * b * H, where R is the comprehensive risk of reservoir inundation, a is the probability factor of reservoir siltation, b is the probability factor of reservoir dam failure, and H is the equivalent water depth of the corresponding grid calculation unit.

[0026] Secondly, embodiments of the present invention also provide a prediction device for the risk of reservoir flooding exceeding standard levels, which divides the downstream area of ​​the target reservoir into multiple grid computing units. The prediction device includes:

[0027] A basic risk determination unit is used to determine the flood risk level of the corresponding grid computing unit based on the basic flood risk elements of each grid computing unit, wherein the basic elements include at least the maximum flood depth, maximum flow velocity and maximum flood duration of the corresponding grid computing unit.

[0028] The sedimentation impact factor determination unit is used to determine the reservoir sedimentation impact probability factor of the corresponding grid computing unit based on the relationship between the degree of reservoir sedimentation of each grid computing unit and the risk of flooding exceeding the standard.

[0029] The dam failure impact factor determination unit is used to determine the dam failure impact probability factor of the corresponding grid computing unit under siltation conditions based on the impact of the reservoir siltation degree and the frequency of floods exceeding the standard on the dam failure probability of each grid computing unit, and the relationship between the above impact and the flood inundation risk of the flood exceeding the standard.

[0030] The comprehensive risk determination unit is used to calibrate the comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure in the case of floods exceeding the standard, based on the reservoir siltation impact probability factor and the reservoir dam failure impact probability factor.

[0031] The risk level determination unit is used to perform cluster analysis on the comprehensive reservoir inundation risk of each grid computing unit using the K-means clustering algorithm to determine the flood risk level.

[0032] Thirdly, embodiments of the present invention also provide an electronic device, comprising:

[0033] A processor, and a memory communicatively connected to the processor;

[0034] The memory stores computer-executed instructions;

[0035] The processor executes computer execution instructions stored in the memory to implement the zoning method for reservoir flooding risk exceeding standard as described in the first aspect embodiment above.

[0036] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the zoning method for the risk of reservoir flooding exceeding standards as described in the first aspect embodiment above.

[0037] Fifthly, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the zoning method for the risk of reservoir flooding exceeding standards as described in the first aspect embodiment above.

[0038] This invention discloses a method, apparatus, and equipment for zoning the risk of reservoir inundation under super-standard flood conditions. By determining the probability factors of reservoir sedimentation and dam failure under sedimentation conditions, the comprehensive risk of reservoir inundation under super-standard flood conditions is calibrated based on these factors. Furthermore, the comprehensive risk of reservoir inundation under super-standard flood conditions is analyzed using a K-means clustering algorithm to cluster the comprehensive risk of reservoir inundation in each grid calculation unit, ultimately determining the flood risk level. This invention solves the technical problem of low accuracy in predicting flood inundation risk in reservoir areas with high sediment content and weak dredging capacity in existing technologies, providing a scientific basis and technical support for reservoir flood inundation risk assessment and disaster prevention and mitigation planning in high-sediment-content watersheds. Attached Figure Description

[0039] Figure 1 This is a flowchart of a method for zoning the risk of reservoir flooding exceeding the standard provided by an embodiment of the present invention;

[0040] Figure 2 This is a structural diagram of a reservoir flood inundation risk prediction device provided in an embodiment of the present invention;

[0041] Figure 3The graph shows the original reservoir capacity curve of Reservoir A and the reservoir capacity curve measured in 2017 after siltation changes.

[0042] Figure 4 A schematic diagram of the surface runoff flow process curve under a 24-hour rainfall event with a 100-year return period in Reservoir A.

[0043] Figure 5 This is a schematic diagram illustrating the relationship between the discharge flow and water level changes of Reservoir A during a 100-year flood event.

[0044] Figure 6 This is a schematic diagram of the structure of an electronic device for implementing the zoning method for the risk of reservoir flooding exceeding the standard according to embodiments of the present invention. Detailed Implementation

[0045] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0046] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this invention are used to distinguish different objects, not to limit a specific order. The various embodiments of this invention described below can be performed individually or in combination with each other; the embodiments of this invention do not impose specific limitations in this regard.

[0047] Figure 1 This is a flowchart of a method for zoning the risk of reservoir flooding exceeding the standard, provided by an embodiment of the present invention.

[0048] The downstream area of ​​the target reservoir is divided into multiple grid computing units. For example... Figure 1 As shown, the zoning method for the risk of flooding exceeding the standard inundation of this reservoir specifically includes the following steps:

[0049] S101. Determine the flood risk level of the corresponding grid computing unit based on the basic elements of flood risk of each grid computing unit. The basic elements include at least the maximum flood depth, maximum flow velocity and maximum flood duration of the corresponding grid computing unit.

[0050] Specifically, the basic factors affecting the flood risk of the calculation unit are considered, including the maximum flood depth h, the maximum traveling velocity v, and the maximum flood duration t. Taking the maximum flood depth h as the main factor, and comprehensively considering the influence of the risk factors of the maximum traveling velocity v and the maximum flood duration t, the equivalent water depth H index is used to reflect the basic flood risk level of different grid calculation units under a specific frequency of super-standard flood. The H index is calculated according to the following formula: H = α1α2h (Formula 3), where: the equivalent water depth of the grid calculation unit is in meters; α1 is the correction coefficient for the maximum traveling velocity; α2 is the correction coefficient for the maximum flood duration; and h is the maximum flood depth.

[0051] S102, Based on the relationship between the degree of reservoir siltation and the risk of flooding exceeding the standard in each grid computing unit, determine the probability factor of reservoir siltation impact for the corresponding grid computing unit.

[0052] Specifically, the reservoir siltation volume S t There is a certain positive correlation between the risk of flooding downstream of the reservoir and the reservoir's operation and management. When there are no significant changes in the watershed's ecological environment and the reservoir's operation and management, the reservoir's sedimentation volume S... t The amount of sediment deposition in a reservoir gradually increases with its operating time, but the change in sediment deposition over this period is uncertain. Therefore, using the degree of sediment deposition (L) to reflect the amount of sediment deposition (S) is not appropriate. t While considering the changes in reservoir operation time, the amount of reservoir siltation S is also taken into account. t To ensure accuracy and reliability, a probability coefficient 'a' of siltation impact is defined as a probability factor for reservoir siltation impact, to comprehensively reflect the amount of siltation S in the reservoir. t The probabilistic impact on the risk of flooding.

[0053] S103, based on the impact of reservoir siltation degree and frequency of floods exceeding standard levels on the probability of dam failure in each grid computing unit, and the relationship between the above impacts and the risk of flooding exceeding standard levels, determine the reservoir dam failure impact probability factor of the corresponding grid computing unit under siltation conditions.

[0054] Specifically, considering the indirect impact of reservoir siltation degree L on reservoir dam failure under super-standard floods, and the direct impact of the super-standard flood recurrence period on reservoir dam failure, for the calculation of the comprehensive risk degree of the calculation unit, the dam failure impact probability coefficient b and the reservoir dam failure impact probability factor are defined to comprehensively reflect the risk amplification effect of probabilistic dam failure on inundation under the combined effects of different super-standard floods (represented by the super-standard flood frequency p) and different reservoir siltation degrees L.

[0055] S104, based on the probability factors of reservoir siltation and dam failure, calibrates the comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure in the case of floods exceeding the standard.

[0056] Optionally, S104 specifically includes: calculating the comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure in the case of floods exceeding the standard, based on the formula R = a * b * H, where R is the comprehensive risk of reservoir inundation, a is the probability factor of reservoir siltation, b is the probability factor of reservoir dam failure, and H is the equivalent water depth of the corresponding grid calculation unit.

[0057] Specifically, this invention considers the dual impact probabilities of siltation and dam failure, and innovatively proposes a formula for calculating the comprehensive risk of reservoir inundation under super-standard floods, taking into account the dual impact probabilities of siltation and dam failure: R=a*b*H (Formula 7), where: H is the equivalent water depth H value corresponding to p in the calculation unit; a is the siltation impact probability coefficient under the corresponding siltation degree, and the value is taken according to Table 1 in the embodiment of this invention; b is the dam failure impact probability coefficient corresponding to the corresponding frequency flood under the corresponding siltation degree, and the value is taken according to Table 2 in the embodiment of this invention; R is the comprehensive risk of flood inundation under a specific frequency super-standard flood after considering the dual impact probabilities of siltation and dam failure.

[0058] The above method is applicable to the calculation of the risk level of a certain flood event. It quantifies the probabilistic influencing factors of reservoir siltation and dam failure in the comprehensive risk level of flood inundation in a more intuitive and clear way. In the embodiments of this invention, the coefficients a and b are exemplarily taken in tabular form (a discrete expression of the functional relationship). In specific applications, it can be modified in combination with actual engineering conditions and expert experience, or the detailed expression of the functional relationship can be directly calculated.

[0059] S105 uses the K-means clustering algorithm to perform cluster analysis on the comprehensive risk of reservoir inundation in each grid computing unit to determine the flood risk level.

[0060] Specifically, after obtaining the comprehensive risk level R of the calculation unit through the above steps, based on the R value of each calculation unit in the model, the K-means clustering algorithm is used to divide the comprehensive risk level into four levels, corresponding to low risk, medium risk, high risk, and extremely high risk, respectively. These levels are used to characterize the flood risk level of different areas (blocks) in the zoning analysis model and the flood risk zoning map. The threshold for each risk level needs to be determined comprehensively based on the actual engineering situation, regional flood control requirements, and the characteristics of the clustering results. Specifically:

[0061] (1) Construct a flood inundation risk dataset S = {S1, S2, ..., S...} for different computing units. N} Initialize and define k cluster centers, each corresponding to a cluster, denoted as P = {P1, P2, ..., P}. k}, 1 <k≤N。

[0062] (2) Allocate each data point in the dataset to the cluster with the nearest Euclidean distance. Once the data allocation is complete, recalculate the average value of the data in the k clusters to obtain the corresponding new cluster centers.

[0063] (3) Repeat step (2) iteratively, redistributing the data and continuously updating the cluster centers until the cluster centers remain unchanged, thus obtaining the optimal clustering result. The sample data S of the i-th evaluation unit in the K-means clustering algorithm. i With the j-th cluster center U j The formula for calculating the Euclidean distance between two points is: d(S) i U j )=||S i -U j ||, where 1≤i≤N, 1≤j≤k.

[0064] As can be seen from the above formula, for each cluster center, the smaller the sum of the Euclidean distances of all sample data in the cluster, the better the clustering effect and the higher the similarity between the sample and the cluster center.

[0065] Based on the above clustering results, the flood inundation risk level attributes corresponding to different grid units are obtained, which can more accurately and reasonably simulate the inundation risk zoning of reservoirs at different scales in the short, medium and long term under conditions of floods exceeding the standard. This can provide a scientific basis and important technical support for flood control risk management of reservoirs in high sediment-bearing areas.

[0066] Based on the above technical solutions, prior to S101, the zoning method also includes:

[0067] S1, the relationship between reservoir water level and reservoir capacity is determined based on reservoir siltation. The reservoir siltation is determined based on historical measured reservoir capacity curves or predicted by an auxiliary curve derived from the reservoir capacity and area calculation curves to determine siltation depth.

[0068] Specifically, the first step is to collect basic geographic data (administrative divisions, settlements, river systems, levee projects, digital elevation data, remote sensing imagery, etc.), reservoir design data, siltation change data, and flood disaster data for the study area, ensuring the data's authority, accuracy, and timeliness, and then systematically compiling and processing it. Then, based on the water level-capacity relationship (capacity curve) of the reservoir under siltation changes, calculations are performed to determine the reservoir's water level-capacity relationship under siltation changes, taking into account the collected data.

[0069] Specifically, it mainly falls into the following two categories:

[0070] (1) If the reservoir has a measured reservoir capacity curve in the recent years, then, under the premise of considering the probabilistic changes in the reservoir siltation in the recent years, it can be directly used as a known quantity in this method.

[0071] (2) If there is no recent measured reservoir storage capacity data, the following method is used for prediction calculation (the method is explained in more detail later), so as to derive the reservoir storage capacity curve under sedimentation changes. This calculation method is applicable to the situation where the sedimentation volume, the original reservoir storage capacity curve, and the original reservoir area curve are known or predicted under the normal storage level of the reservoir.

[0072] Among them, the original reservoir storage capacity and the original area curve are basic data necessary for engineering design, and the sedimentation volume below the normal storage level of the reservoir is a known quantity, and the reservoir storage capacity curve under sedimentation changes is obtained. That is, the reservoir water level Z, the reservoir storage capacity V, and the reservoir sedimentation volume S below the normal storage level of the reservoir t There is a certain relationship among the three. Define Z = f(V, S t )(Formula 1) to express the functional relationship V among the three under the above conditions. Among them, Z is the reservoir water level, with the unit of m; is the reservoir storage capacity, with the unit of m 3 ; S t is the reservoir sedimentation volume, and it is the reservoir sedimentation volume below the normal storage level of the reservoir, with the unit of m 3 .

[0073] Specifically, the relationship between the volume and water depth of a general reservoir can be expressed by V = N*h m (Formula 8), where the water depth h is obtained from h = Z i -Z0 (Formula 9). In the formula, V represents the reservoir storage capacity, with the unit of m 3 ; h represents the water depth, with the unit of m; Z i represents a certain water level elevation, with the unit of m; Z0 represents the elevation of the zero point of the reservoir storage capacity, with the unit of m; N is a coefficient; m is an exponent.

[0074] According to the exponent m of the power function of the reservoir depth - storage capacity curve in Formula 8, the reservoir is divided into 4 types, as shown in Table 1.

[0075] Table 1. Reservoir type table

[0076] Reservoir Types Classification m Ⅰ Lake type ＞3.5 Ⅱ Floodplain-Pierre type 2.5～3.5 Ⅲ hilly type 1.5～2.5 Ⅳ Canyon type ＜1.5

[0077] The total reservoir sedimentation volume S t can be calculated by . In the formula, S t is the total reservoir sedimentation volume; h0 represents the pre-dam sedimentation depth at the sedimentation starting point, that is, the original water depth in front of the dam at the sedimentation starting point; A is the sedimentation area at the reservoir depth h. When h < h0, the sedimentation area is equal to the original reservoir area; dh is the depth increment; H0 is the reservoir depth at the normal storage level; Kf is the reservoir sedimentation area corresponding to the reservoir depth h when h > h0, f is the relative sedimentation area, and K is a proportionality constant for converting its relative sedimentation area into the actual sedimentation area. For a known reservoir:

[0078] Equation 10 is obtained by integration. Among them, V r V represents the reservoir sedimentation volume below the sedimentation starting point; h denoted as h, where h is the volume of sediment accumulation below the reservoir depth; a0 is the relative sediment accumulation area corresponding to the starting point of sediment accumulation when h = h0; and A0 is the reservoir area corresponding to the starting point of sediment accumulation when h = h0.

[0079] Introducing relative depth P, where P represents the ratio of water depth h at a certain water level in front of the dam to water depth H0 at the normal storage level, i.e., P = h / H0. The relationship between Pf for the four reservoir types is shown in Table 2 below.

[0080] Table 2. Relationship between relative depth and relative siltation area

[0081] Reservoir Types Pf expression Ⅰ <![CDATA[f=5.074P 1.85 (1-P) 0.35 ]]> Ⅱ <![CDATA[f=2.487P 0.57 (1-P) 0.41 ]]> Ⅲ <![CDATA[f=16.967P 1.15 (1-P) 2.32 ]]> Ⅳ <![CDATA[f=1.486P -0.25 (1-P) 1.34 ]]>

[0082] Auxiliary curves for determining siltation depth for four types of reservoirs are derived from the reservoir capacity and area calculation curves (these auxiliary curves do not have mathematical expressions; for ease of use, relevant data are listed in Table 3 and can be used for interpolation calculations). Then, given the total siltation volume and total water depth of the reservoir, the S values ​​for multiple different siltation depths or relative depths P in front of the dam are calculated. t -V0 (remaining sediment volume above the sedimentation surface in front of the dam, where V0 is the sedimentation volume below h = h0 corresponding to a certain assumed relative depth P) and (S t -V0) / H0A0.

[0083] (S) t -V0) / H0A0 and auxiliary lines are drawn on a table, (S t The intersection of the -V0) / H0A0 curve and the type curve determined by the exponent m value is the relative depth P of siltation in front of the dam, from which the siltation distribution (reservoir capacity curve) of the reservoir can be determined.

[0084] Table 3. Auxiliary curves used to determine the depth of the starting point of sediment deposition.

[0085]

[0086]

[0087] S2 calculates the inflow process of floods exceeding the standard based on hydrological information of the upstream basin of the target reservoir.

[0088] Specifically, by combining hydrological information from the upstream basin of the reservoir and calculating the inflow flood flow process using relevant model methods, the outflow process of the reservoir is then calculated. It should be noted that, in this embodiment of the invention, the SCS (Soil Conservation Service Model) model is preferred, but not limited to, being used.

[0089] The SCS runoff model is used for small watershed hydrological forecasting. It calculates runoff depth (cumulative rainwater volume) under given rainfall amounts (extreme rainfall amounts), and is a mathematical model for estimating surface runoff. The model calculation process is simple and requires few parameters. The SCS model can be used to effectively study the influence of other factors on rainwater runoff and calculate surface rainwater runoff volume in the catchment area.

[0090] The SCS model, through two fundamental assumptions—the water balance equation and the assumption of proportionality, and the assumption of initial loss (maximum potential retention) relationship—can determine the magnitude of surface runoff during rainfall. Its basic principle is: during rainfall, if the precipitation amount does not reach the soil's initial absorption value I... a No runoff will occur if the precipitation reaches level I; a Subsequently, the direct surface runoff Q is equal to the total rainfall minus the actual infiltration F and the initial absorption value of the soil I. a The remaining items after that.

[0091] First: (1) Based on the assumption of equal proportions, that is, the ratio of the actual infiltration volume F to the maximum possible retention volume S at that time is equal to the ratio of the actual direct surface runoff Q to the maximum possible runoff PI. a The ratio, its expression is: Where P is the total rainfall, in mm; Q is the direct surface runoff, in mm; I a Initial loss (the initial absorption value of the soil), in mm, includes interception and surface water storage; F excludes I. a The cumulative infiltration amount (actual infiltration amount) is in mm; S is the maximum possible retention amount at that time (maximum potential retention amount) in mm.

[0092] (2) According to the watershed water balance equation, the total rainfall P equals the initial loss (initial soil absorption) I in the catchment area of ​​the watershed. a The sum of actual infiltration F and direct surface runoff Q is expressed as P = I a +F+Q (Formula 14).

[0093] (3) Based on the relationship between the initial loss and the maximum possible retention at that time, the initial loss I is assumed to be... a It is proportional to the maximum possible retention S at that time, and its expression is I a =λS (Formula 15), where: λ is the initial loss coefficient, usually taken as the empirical value λ = 0.2.

[0094] (4) Combining formulas 13 to 15, we can obtain the formula for calculating the direct surface runoff Q in the SCS model:

[0095] (5) To determine the maximum possible retention capacity S in the watershed at that time, the model introduces the parameter CN. As shown in Equation 16, Q is determined by P and S, and S is related to factors such as land use, soil type, and soil moisture before precipitation within the watershed. S varies in different watersheds, making it difficult to determine its value. Therefore, the SCS model introduces a comprehensive parameter CN that reflects the characteristics of the watershed to obtain the value of S. The relationship is as follows:

[0096] In the formula: CN is a parameter that describes the strength of the land's ability to retain rainwater and reflects the runoff generation capacity of the underlying surface unit in the region. Its influencing factors include the previous soil moisture level, soil type, land use type, slope, vegetation and other underlying surface factors. The CN value is determined by using the CN value calculation table provided by the U.S. Soil and Water Conservation Bureau based on the information of the influencing factors. Its range is 0 to 100. The smaller the CN, the greater the infiltration.

[0097] Second: The SCS model considers the impact of anterior rainfall on runoff and introduces the anterior rainfall index (AMC), which is calculated as follows: In the formula: P i This represents the daily rainfall over the past five days, in mm.

[0098] (1) Based on the AMC (Advance Precipitation Index), the soil moisture level is divided into three types: I (dry), II (moderate), and III (wet), as shown in Table 4.

[0099] Table 4. Relationship between previous soil moisture conditions and rainfall index

[0100]

[0101] The CN values ​​under conditions I and III are calculated using the following formulas:

[0102]

[0103] (2) The formula for calculating the amount of rainfall affected in the early stages is:

[0104]

[0105] In the formula: P a,t P represents the preceding rainfall on day t, in mm. t Represents the rainfall on day t, in mm; parameter K is the daily decline coefficient of soil moisture content; E m Represents the daily evapotranspiration capacity of the watershed, measured in mm; W m This refers to the tensile water storage capacity, expressed in mm.

[0106] S3, Calculate the reservoir flood control discharge process based on the reservoir scheduling and operation mode of the target reservoir and the reservoir capacity curve after siltation changes;

[0107] Specifically, based on the reservoir's operation mode and considering the reservoir capacity curve after siltation changes, the process of flood control discharge from the reservoir is calculated. This embodiment of the invention uses a typical gated weir flow as an example for illustration.

[0108] The basic principle for calculating the flood discharge flow from a reservoir is water balance, and the equation is as follows:

[0109]

[0110] Where Δt is the length of the calculation period, in seconds; Q t Q t+1 This refers to the inflow volume at the beginning and end of the time period, expressed in cubic meters (m³). 3 / s;q t q t+1 This refers to the outflow volume at the beginning and end of the time period, in cubic meters (m³). 3 / s;V t V t+1 This refers to the initial and final reservoir water storage volumes for the period, expressed in cubic meters (m³). 3 By combining the reservoir inflow process, the reservoir capacity curve under siltation changes, and the reservoir operation mode, the reservoir flood control discharge process can be iteratively calculated. During the operation calculation, the possibility of dam failure must be considered, mainly in the following two scenarios:

[0111] (1) In the process of flood control calculation, the reservoir water level will change with the inflow of floodwater. When the highest water level of the reservoir is less than the check water level, normal scheduling will be carried out according to the pre-scheduled scheduling method of the reservoir to obtain the outflow process of the reservoir.

[0112] (2) In the flood control calculation process, if the highest water level of the reservoir reaches or exceeds the reservoir check water level, the risk and probability of reservoir dam failure are considered. When considering the probability of reservoir dam failure, an appropriate failure theory is selected to calculate the dam failure flow process according to the specific reservoir type, engineering structure, hydrogeological and other factors.

[0113] Based on the above basic route, the outflow process of the reservoir under different siltation conditions and different inflow flood conditions can be obtained.

[0114] The principle of the instantaneous dam failure model is explained in detail below.

[0115] Instantaneous total failure is a major form of dam failure and the simplest and most direct type of dam failure problem. It forms the basis for studying the gradual or partial failure of dams and dikes. Furthermore, some small and medium-sized dams typically fail, erode, and collapse within minutes or even less, with floodwaters rushing down rapidly. Therefore, instantaneous total failure is highly representative. Calculating the breach flood is crucial for understanding dam failure problems. Once the maximum breach flow is determined, the breach flood hydrograph can be derived.

[0116] The maximum flow rate during a sudden dam failure can be calculated using the following empirical formula:

[0117]

[0118] In formula 22: Q M The maximum flow rate at the breach is expressed in cubic meters per second (m³). 3 / s; B is the dam length, in meters. 3 / s; b is the average width of the ulcer, in meters; H s ρ represents the upstream water depth before dam failure, in meters (m); h represents the effective water depth, also in meters. Based on experience, the value of b, the average width of the breach, directly affects the breach discharge; in the event of a complete breach, it equals the dam length. When the reservoir capacity V ≥ 1 million m³... 3 At that time, according to It is estimated that (K1 is called the material coefficient of the dam body; for clay, clay core walls or inclined walls, as well as soil, stone, concrete, etc., K1 = 1.19; for homogeneous loam, K1 = 1.98), when V < 1 million m³ 3 At that time, b = K2(VH0) 1 / 4 The estimated value is 6.6 for dam construction and management quality and 9.1 for poor quality.

[0119] The instantaneous dam-break flow rate hydrograph is derived using the generalized typical flow rate hydrograph method. The instantaneous dam-break flow rate hydrograph is related to the maximum flow rate Q. M The discharge flow Q0 before dam failure and the reservoir capacity W that can be released after dam failure (which can be determined by the final residual height of the dam and the reservoir capacity curve) are related. Its shape can be generalized as a fourth-order parabola or a second- and fifth-order parabola, that is, at the moment of dam failure, the flow rate increases sharply to Q0. M The flow rate then rapidly decreases, forming a concave curve, eventually approaching the original discharge flow rate Q0. The fourth and 2.5th order parabolas that generalize typical flow process curves are shown in Table 5 below, where T is the time for the reservoir to empty due to dam failure, and t is any time. Currently, the fourth order parabola is mostly used to generalize the flow process curve.

[0120] Table 5. Generalized Typical Flow Process Line Table

[0121]

[0122] When Q MWhen Q0 and the dam-break reservoir capacity W are known, the flow process curve can be determined by trial calculation. The steps are as follows:

[0123] 1) According to Q M The initial venting time T is determined by W. T is calculated as T = K(W / Q). M (Equation 23) is used to calculate K; in Equation 23, K is a coefficient. For a fourth-order parabola, K is generally 4 to 5, and for a second-order parabola, K = 3.5.

[0124] 2) Based on T and Q M The flow process line is initially determined from Table 5 for Q0.

[0125] 3) Verify whether the water volume between the process line and the Q=Q0 line is equal to the dam-break reservoir capacity (the reservoir capacity above the bottom of the dam). If they are not equal, the initially determined T value needs to be adjusted until they are equal.

[0126] It should be noted that there are many calculation formulas for dam failure scenarios, and the appropriate calculation formula should be determined according to the specific conditions.

[0127] S4. Based on the calculation of the inflow process of floods exceeding the standard and the outflow process of flood control scheduling of the reservoir, a flood dynamic evolution model is constructed based on the multi-source topographic data of the target reservoir. The flood dynamic evolution model is used to simulate the dynamic change process of flood risk information of different grid computing units at different times.

[0128] Specifically, based on multi-source data such as the downstream topography and elevation, river system, dike engineering, and remote sensing images, a two-dimensional hydrodynamic model of the downstream river channel and floodplain based on hydrodynamic theory is constructed. The discharge process of the reservoir during flood control scheduling is used as the flow constraint condition of its inflow boundary to simulate the dynamic evolution and inundation process of the downstream flood. The dynamic changes of flood risk information in different grid units at different times are calculated to obtain data such as flood inundation depth, flood velocity, and inundation duration.

[0129] Based on the above principles and steps, and combined with the aforementioned relevant data, the dynamic evolution of reservoir floodwaters downstream under different return periods is calculated and simulated, and the downstream inundation results of floods with different return periods are obtained.

[0130] The principles and construction methods of the flood dynamic evolution model are as follows:

[0131] 1) Model Principle: The simulation model for flood evolution and inundation risk of the downstream river channel of the reservoir adopts a two-dimensional planar hydrodynamic model. The basic equations for calculating two-dimensional unsteady flow include the continuity equation and the momentum equation, as follows:

[0132] ① Continuity equation:

[0133]

[0134] ② Momentum equation:

[0135]

[0136] In the formula: The following values ​​represent the flow velocity based on water depth, in m / s; t represents time, in s; x, y, and z are Cartesian coordinates; η is the channel-floodplain surface elevation, in m; d is the still water depth, in m; h is the total head, in m; and S is the point source discharge, in m³ / s. 3 / s; u and v are the velocity components in the x and y directions, respectively, in m / s; g is the acceleration due to gravity, in m. 3 / s; ρ is the density of water, in kg / m³. 3 ; sxx, sxy, syx, and syy are the components of radiation stress, in N; pa is atmospheric pressure, in kPa; ρ0 is the relative density of water; us and vs are the flow velocities of the source and sink water flows, in m / s.

[0137] Lateral stress term Tij: includes viscous friction, turbulent friction, and differential advection, and its value is estimated by the eddy viscosity formula based on the average velocity gradient of the water depth.

[0138]

[0139] 2) Model building:

[0140] ① Determining the modeling scope: Based on the CAD elevation points of the downstream river channel and the area outside the river channel of the reservoir under study, along the downstream river channel of the reservoir, considering the maximum possible inundation range of the river channel flood overflow, a "closed boundary between the river channel and the protected area" is established.

[0141] ② Mesh subdivision: Extract the model calculation boundary line and CAD elevation point data, perform unstructured triangular mesh subdivision on the model calculation area, assign terrain elevation attributes to the mesh units, and process the maximum area, minimum angle, etc. of the calculation unit mesh in important areas.

[0142] ③ Set time series conditions: Considering the "flow-time" process of reservoir scheduling and the need for flood inundation simulation downstream of the reservoir, set the flood duration and simulation calculation time step. Convert the reservoir flood control scheduling calculation output (hourly scheduling and discharge flow process of the reservoir under different frequency floods) into a time series file, which serves as the flow constraint condition for the upstream inflow boundary of the overall two-dimensional hydrodynamic model of the downstream river channel-protected area.

[0143] ④ Parameter settings: Based on the actual conditions of the underlying surface in the study area and combined with empirical coefficient settings, set data such as eddy viscosity coefficient, roughness, wet and dry water depth, and initial submerged water depth.

[0144] Based on the above technical solutions, S101 specifically includes:

[0145] The equivalent water depth of each grid calculation unit is determined based on the formula H = α1α2h, where H is the equivalent water depth of the grid calculation unit, H is used to characterize the risk of flooding beyond the standard, α1 is the correction coefficient for the maximum traveling velocity, α2 is the correction coefficient for the maximum flood duration, and h is the maximum flood depth.

[0146] For example, when v ≥ 3.0 m / s, α1 = 1.5; when 3.0 m / s > v ≥ 1.5 m / s, α1 = 1.2; when v < 1.5 m / s, α1 = 1.0; when t ≥ 7d, α2 = 1.5; when 7 > t ≥ 3d, α2 = 1.2; when t < 3d, α2 = 1.

[0147] Based on the above technical solutions, S102 specifically includes:

[0148] The degree of reservoir siltation is determined based on the amount of siltation at the normal water level and the original reservoir capacity; the probability factor of reservoir siltation impact is determined based on the relationship between the degree of reservoir siltation and the risk of flooding exceeding the standard.

[0149] Specifically, firstly through the formula The degree of reservoir siltation, L, is calculated, and then an innovative formula, a = f(L) (Formula 5), ​​is established to characterize the functional relationship between the probability coefficient of siltation impact, a, and the degree of reservoir siltation, L. For example, Table 6 illustrates this functional relationship, but it is not limited to the correspondence between the degree of reservoir siltation, L, and the probability coefficient of siltation impact, a, in Table 6. For specific reservoirs, the correspondence in Table 6 can be adjusted according to the actual engineering situation and expert experience, or the detailed expression of Formula 5 can be directly derived.

[0150] Table 6. Relationship between the probability coefficient of siltation impact and the degree of reservoir siltation

[0151] reservoir siltation level L The probability coefficient of siltation impact a 0-10％ 1.0 10％-20％ 1.1 20％-30％ 1.2 30％-40％ 1.3 40％-50％ 1.4 50％-60％ 1.5 60％-70％ 1.6 70％-80％ 1.7 More than 80% 1.8

[0152] Where L represents the degree of reservoir siltation, which is related to the reservoir's operating time and generally increases with the increase of the reservoir's operating time; S t S represents the amount of sediment deposited below the normal water level of the reservoir, where V0 is the original reservoir capacity, i.e., the capacity below the normal water level before sedimentation. When the degree of sedimentation L is 0-10%, the probability coefficient of sedimentation impact a = 1.0, representing the amount of sedimentation S in the reservoir. t The aforementioned equivalent water depth H has no effect; the probability coefficient 'a' of the siltation effect gradually increases with the increase of the reservoir siltation degree L, representing the amount of siltation S in the reservoir. t The risk of flooding is amplified.

[0153] Based on the above technical solutions, S103 specifically includes:

[0154] Establish a functional relationship between the degree of reservoir siltation, the frequency of floods exceeding the standard, and the probability coefficient of dam failure; use the established functional relationship to determine the probability factor of dam failure under siltation conditions.

[0155] Specifically, an innovative formula, b = g(L, p) (Formula 6), is established to characterize the functional relationship between the dam failure probability coefficient b, the reservoir siltation degree L, and the frequency of floods exceeding the standard. Here, the dam failure probability coefficient b and the reservoir siltation degree L represent a conditional probability relationship.

[0156] In this embodiment of the invention, the functional relationship of Formula 6 is exemplarily expressed in Table 7, where the standard flood frequency p is represented by the highest water level during the reservoir flood discharge process under a flood exceeding the standard frequency, as shown in Table 7. It should be noted that the data in Table 7 may deviate slightly in specific reservoirs and can be adjusted based on actual engineering conditions and expert experience, rather than being limited to the data set in the table or directly deriving the detailed expression of Formula 6.

[0157] Table 7. Relationship between the probability coefficient of dam failure impact and reservoir siltation level and flood exceeding standard levels.

[0158]

[0159] Where b is the probability coefficient of dam failure, starting from 1, indicating that the probability of dam failure has no effect on the aforementioned equivalent water depth H; b increases with the increase of reservoir siltation degree L, representing the amount of reservoir siltation S. t The amplification effect on the risk of flooding and dam failure. Meanwhile, under super-standard floods with frequency p, the value of b also considers whether the reservoir water level exceeds the check water level during the reservoir discharge process, reflecting the influence of p on b.

[0160] In this embodiment of the invention, a method for risk zoning of reservoirs under super-standard floods is proposed, considering the practical engineering problems of reservoir siltation changes and dam failure probabilities in high-sediment-laden areas. Firstly, considering the probabilistic impact of reservoir siltation changes over time, a siltation impact probability coefficient is innovatively proposed. Secondly, considering the amplification effect of siltation changes on dam failure risk under super-standard floods, a dam failure impact probability coefficient is innovatively proposed. Finally, an innovative method for calculating the comprehensive risk of reservoir inundation under super-standard floods, considering the dual impact probabilities of siltation and dam failure, is proposed.

[0161] This invention provides a new perspective on flood inundation risk zoning in reservoir flood control, effectively enhancing the ability to address diverse and complex issues in watershed flood control and offering new ideas for reservoir flood control. Furthermore, using this method for flood risk zoning helps provide guidance for watershed flood control and disaster reduction by considering siltation and dam failure scenarios. It can also further support existing flood risk zoning schemes from a new perspective, making reservoir flood control schemes more multi-dimensional and scientific, and providing a scientific basis and technical support for flood risk management in reservoirs in high sediment content areas.

[0162] Figure 2 This is a structural diagram of a reservoir flood risk prediction device provided in an embodiment of the present invention.

[0163] This invention also provides a device for predicting the risk of reservoir flooding exceeding standard levels, such as... Figure 2 As shown, the prediction device specifically includes:

[0164] The basic risk determination unit 21 is used to determine the flood risk level of the corresponding grid computing unit based on the basic elements of flood risk of each grid computing unit. The basic elements include at least the maximum flood depth, maximum flow velocity and maximum flood duration of the corresponding grid computing unit.

[0165] The sedimentation impact factor determination unit 22 is used to determine the reservoir sedimentation impact probability factor of the corresponding grid computing unit based on the relationship between the degree of reservoir sedimentation and the risk of flooding exceeding the standard in each grid computing unit.

[0166] The dam failure impact factor determination unit 23 is used to determine the dam failure impact probability factor of the corresponding grid computing unit under siltation conditions based on the impact of the reservoir siltation degree and the frequency of floods exceeding the standard on the dam failure probability of each grid computing unit, and the relationship between the above impact and the flood inundation risk of the above standard.

[0167] The comprehensive risk determination unit 24 is used to calibrate the comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure in the case of floods exceeding the standard, based on the probability factors of reservoir siltation impact and reservoir dam failure impact.

[0168] Risk level determination unit 25 is used to perform cluster analysis on the comprehensive risk of reservoir inundation of each grid computing unit using the K-means clustering algorithm to determine the flood risk level.

[0169] Optionally, before the basic risk determination unit 21 determines the super-standard flood inundation risk level of the corresponding grid computing unit based on the basic elements of inundation risk of each grid computing unit, the prediction device further includes:

[0170] The first calculation unit is used to determine the relationship between reservoir water level and reservoir capacity based on reservoir siltation. The reservoir siltation is determined based on historical measured reservoir capacity curves or predicted by deriving an auxiliary curve for determining siltation depth based on reservoir capacity and area calculation curves.

[0171] The second calculation unit is used to calculate the inflow process of floods exceeding the standard based on the hydrological information of the upstream basin of the target reservoir;

[0172] The third calculation unit is used to calculate the reservoir flood control scheduling and discharge process based on the reservoir scheduling and operation mode of the target reservoir and the reservoir capacity curve after siltation changes.

[0173] The model building unit is used to calculate the inflow process of floods exceeding the standard and the outflow process of flood control scheduling of the reservoir. Based on the multi-source topographic data of the target reservoir, a flood dynamic evolution model is constructed. The flood dynamic evolution model is used to simulate the dynamic change process of flood risk information obtained from different grid computing units at different times.

[0174] Optionally, the basic risk determination unit 21 is specifically used for:

[0175] The equivalent water depth of each grid calculation unit is determined based on the formula H = α1α2h, where H is the equivalent water depth of the grid calculation unit, H is used to characterize the risk of flooding beyond the standard, α1 is the correction coefficient for the maximum traveling velocity, α2 is the correction coefficient for the maximum flood duration, and h is the maximum flood depth.

[0176] Optionally, the sedimentation impact factor determination unit 22 is specifically used for:

[0177] The degree of reservoir siltation is determined based on the amount of siltation at the normal water level and the original reservoir capacity.

[0178] The probability factor of reservoir siltation impact is determined based on the relationship between the degree of reservoir siltation and the risk of flood inundation.

[0179] Optionally, the dam failure impact factor determination unit 23 is specifically used for:

[0180] Establish a functional relationship between reservoir siltation degree, frequency of floods exceeding standard levels, and probability coefficient of dam failure impact;

[0181] The probability factor of reservoir dam failure under siltation conditions is determined by using the established functional relationship.

[0182] Optionally, the comprehensive risk determination unit 24 is specifically used for:

[0183] The comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure in the case of floods exceeding the standard is calculated based on the formula R = a * b * H, where R is the comprehensive risk of reservoir inundation, a is the probability factor of reservoir siltation, b is the probability factor of reservoir dam failure, and H is the equivalent water depth of the corresponding grid calculation unit.

[0184] The reservoir flood inundation risk prediction device provided in this embodiment of the invention can execute the reservoir flood inundation risk zoning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0185] The following is a specific embodiment to illustrate the zoning method for reservoir flooding risk exceeding standard provided by the above embodiments of the present invention.

[0186] For example, taking the A reservoir area as the research object, basic geographic data (administrative divisions, settlements, river systems, levee projects, digital elevation, remote sensing images, etc.), A reservoir design data, siltation change data, and flood disaster data of the A reservoir basin are collected and systematically compiled.

[0187] According to the data, Reservoir A was built in 1972. The reservoir underwent two reinforcement projects in 2003 and 2022. The dead water level of Reservoir A is 66m, and the normal storage water level is 72.9m. The reservoir is severely silted up. The latest actual measurement was in 2017, and the measured storage capacity below the normal storage water level was 6.4458 million cubic meters. The relevant data of the two measured storage capacity curves of the reservoir are shown in Table 8.

[0188] Table 8. Comparison and Analysis of Two Measured Reservoir Capacity Curves at Diaoyutai Reservoir

[0189]

[0190] Considering that reservoir siltation typically increases parabolically over time (i.e., the siltation rate gradually decreases over time) and the normal and reasonable operation of reservoirs in recent years, this embodiment of the invention selects the siltation status in 2017 as a case study for illustration, and conducts subsequent calculations based on the measured data in 2017, which is reasonable and has reference value.

[0191] Furthermore, to illustrate the effectiveness and rationality of this invention, parallel calculations and analyses were performed using the reservoir capacity curve from its original, un-silted state. For example, the original reservoir capacity curve and the reservoir capacity curve from 2017 after siltation changes were compared using data. Figure 3 As shown.

[0192] (1) The process of flood inflow into Reservoir A exceeding the standard.

[0193] Because Reservoir A lacks measured runoff and rainfall data, relevant hydrological models are used to calculate the upstream inflow flood process. Based on the 24-hour design areal rainfall for a 100-year return period in the Reservoir A area, the hourly rainfall process of the design storm is obtained according to the storm time history distribution. Combining the SCS model and the instantaneous unit hydrograph model, the surface runoff flow process curve at p=1% is derived, such as... Figure 4 As shown, in the design flood, surface runoff, due to its rapid convergence, large volume, and direct impact on the reservoir, becomes the main component of the inflow flood, while groundwater runoff is slow and complex, and its short-term impact is relatively weak. Therefore, considering only surface runoff here is reasonable. Thus, the calculated surface runoff flow rate is taken as the super-standard flood process of Reservoir A.

[0194] Table 9. Design Aspect Rainfall Results for Reservoir A

[0195]

[0196] (2) The discharge flow process of Reservoir A.

[0197] The scheduling rules for Reservoir A are as follows: "Before the main flood season, the reservoir water level will be lowered to 69.0m through beneficial scheduling measures; after the arrival of floods during the main flood season, causing the reservoir water level to exceed the flood limit level of 70.0m, all spillway gates will be opened to allow for open discharge, strictly controlling the reservoir water level below the flood limit level of 70.0m. The discharge flow from the reservoir will not take into account the flood discharge capacity of the downstream main river channel. At the same time, the hydropower station will generate electricity at full capacity, and the discharge flow from the power generation tunnel will be kept at maximum."

[0198] This invention takes a 100-year flood exceeding the standard as an example. Considering the flood risk prevention and control needs downstream of Reservoir A, when a 100-year flood exceeding the standard occurs, the reservoir water level will be lowered to the dead water level (66m). After the reservoir stores water and regulates the flood, when the water level exceeds the flood limit level (70.0m), the water will start to flow out. At the same time, all the spillway gates will be kept open, and the discharge flow of the hydropower station's power generation tunnel will be kept at maximum capacity.

[0199] Therefore, combining the different reservoir capacity-water level relationships before and after siltation, and the combined discharge methods of Reservoir A's spillway (open discharge and power generation / irrigation tunnel discharge) (where relevant discharge functions and coefficients were obtained based on relevant data and standards), the hourly discharge flow and reservoir water level changes under different siltation conditions are derived, such as... Figure 5 As shown in Table 10, the characteristics of the reservoir's discharge flow and water level changes under siltation changes are as follows.

[0200] Table 10. Characteristic parameters of reservoir discharge and reservoir water level change process under different siltation conditions

[0201]

[0202] In this embodiment of the invention, the highest water level did not exceed the check water level, so the discharge flow process was calculated according to the normal scheduling process. Meanwhile, Table 10 shows the impact of siltation on the reservoir discharge process under the same flood standard: as the degree of siltation increases, both the peak discharge flow and peak discharge water level increase, and the start time and peak discharge time both advance, indicating that siltation weakens the reservoir's water storage and flood control capacity to some extent.

[0203] (3) Simulation of the dynamic evolution of floods and inundation risks downstream of the reservoir.

[0204] Based on multi-source data including downstream topography, river system, levee engineering, and remote sensing imagery, a two-dimensional planar free surface flow model based on hydrodynamic theory was used to construct a two-dimensional channel-floodplain flood risk analysis model for the downstream area of ​​the reservoir. The unstructured grid finite volume method was employed to solve the two-dimensional planar shallow water equations. Considering the relatively flat inundation zone and neglecting vertical flow acceleration, the vertically averaged flow factor was used as the research object to simulate the dynamic evolution of the channel-floodplain flood and the dynamic changes in water level. The reservoir's flood control scheduling calculations were used as the flow constraint condition for its inflow boundary to simulate the dynamic evolution of the downstream channel-floodplain flood and the distribution characteristics of inundation risk under a 100-year flood exceeding the standard discharge condition. The downstream inundation results under different siltation conditions for a 100-year flood exceeding the standard were obtained, and the inundation risk characteristics are shown in Table 11.

[0205] Table 11. Downstream inundation risk characteristics after reservoir discharge at different flood frequencies from Reservoir A

[0206] Flooding Indicators Original non-silted After siltation Maximum flood depth / m 2.895 2.992 <![CDATA[Total inundated area / km 2 > 11.372 11.674 <![CDATA[Area at submergence depth < 0.5m / km 2 > 5.609 5.532 <![CDATA[Area at 0.5m < submergence depth < 1.0m / km²]] 2 > 2.890 2.957 <![CDATA[Area at 1.0m < submergence depth < 1.5m / km²]]> 2 > 2.742 2.997 <![CDATA[Area at a submerged depth > 1.5 m / km 2 > 0.131 0.187

[0207] Table 11 shows the impact of siltation on downstream inundation risk under the same super-standard flood conditions: compared with the original non-silted-up condition, the maximum inundation depth and total inundation area both increase after siltation; furthermore, the proportion of the inundated area with deeper inundation depth also increases due to siltation. These results further illustrate the amplifying effect of siltation on downstream inundation risk.

[0208] According to the quantitative calculation method for the comprehensive risk of flood inundation in the embodiments of the present invention, considering siltation changes and dam failure probability, a quantitative calculation method for the comprehensive risk of reservoir operation inundation under the dual influence probability of siltation and dam failure is proposed, specifically including the following:

[0209] (1) Calculation of the risk of flooding exceeding the standard

[0210] The basic factors affecting the flood risk of the calculation unit are considered, including the maximum flood depth h, the maximum traveling velocity v, and the maximum flood duration t. Taking the maximum flood depth h as the main factor, and comprehensively considering the influence of the risk factors of the maximum traveling velocity v and the maximum flood duration t, the equivalent water depth H index is used to reflect the overall risk level of the calculation unit under a certain flood frequency, and is calculated according to Formula 3.

[0211] (2) Calculation of the probability factor of reservoir siltation impact

[0212] This method uses two operating conditions—the reservoir before siltation and the reservoir after siltation—to illustrate the method. The probability coefficient of siltation influence is determined according to Formula 4 and Table 6.

[0213] For the original non-silted working condition, the probability coefficient of siltation impact is a = 1;

[0214] For the post-siltation working condition, the degree of siltation L is calculated by formula 4: L = (944.77 - 644.58) / 944.77 = 31.8%. According to Table 6, the probability coefficient of siltation impact corresponding to this degree of siltation is a = 1.3.

[0215] (3) Calculation of the probability factor of reservoir dam failure under siltation conditions

[0216] Considering the indirect impact of reservoir siltation on dam failure under super-standard floods, and the direct impact of the recurrence period of super-standard floods on dam failure, a dam failure probability coefficient b is introduced to comprehensively reflect the probabilistic dam failure risk amplification effect on inundation under different super-standard floods and different siltation levels for calculating the comprehensive risk of the calculation unit.

[0217] For the original non-siltation working condition, according to Table 7, b = 1.0;

[0218] For the post-siltation condition, according to Table 7, considering the degree of siltation and that the highest water level (70.866m) during the reservoir discharge process did not exceed the reservoir check water level (75.170m), the probability coefficient of its dam failure impact is taken as b = 1.3.

[0219] The values ​​of the probability coefficients of siltation impact and dam failure impact are shown in Table 12 for the two working conditions: the original non-silted-up condition and the silted-up condition.

[0220] Table 12. Selection of Quantitative Coefficients for Comprehensive Risk Measurement of Reservoir Inundation under Different Siltation Conditions

[0221] Operating conditions degree of siltation siltation impact probability coefficient dam failure probability coefficient Original non-silted 0 1 1 After siltation 31％ 1.3 1.3

[0222] (4) Based on the formula 7 for calculating the comprehensive risk of reservoir flooding due to the dual impact of siltation and dam failure, the comprehensive risk of each small unit in the model is calculated. The results are sorted from largest to smallest. Some examples are shown in Table 13.

[0223] Table 13. Examples of partial results for calculating the comprehensive inundation risk of small units in the downstream model of the reservoir under different siltation conditions.

[0224]

[0225]

[0226] In summary, by analyzing and assigning values ​​to the two siltation scenarios, the comprehensive risk level of each computational grid cell downstream of the reservoir under the two siltation scenarios was obtained.

[0227] (4) After obtaining the comprehensive risk level of the calculation unit through the above steps, the comprehensive risk level is divided into four levels according to the R value of different units using the K-means clustering algorithm. The clustering results are assigned to each calculation unit to characterize its risk level in the flood risk zoning map. The risk levels are divided into four levels: low risk, medium risk, high risk, and extremely high risk. Finally, the risk zoning results of the reservoir exceeding the standard flood under the dual influence of siltation and dam failure probability are obtained, and the 100-year flood inundation risk map of Reservoir A after siltation and the 100-year flood inundation risk map of Reservoir A before siltation are generated. The two are compared to provide a scientific basis and technical support for the flood control risk management of Reservoir A.

[0228] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0229] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0230] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0231] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the zoning method for the risk of flooding exceeding the standard inundation of a reservoir.

[0232] In some embodiments, the method for zoning the risk of reservoir flooding exceeding standard levels can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for zoning the risk of reservoir flooding exceeding standard levels described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for zoning the risk of reservoir flooding exceeding standard levels by any other suitable means (e.g., by means of firmware).

[0233] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0234] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0235] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0236] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0237] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0238] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0239] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the zoning method for reservoir flooding risk exceeding standard levels as provided in any embodiment of this application.

[0240] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0241] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0242] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for zoning the risk of reservoir flooding exceeding standard levels, characterized in that, The downstream area of ​​the target reservoir is divided into multiple grid computing units, and the zoning method includes: The flood risk level of the corresponding grid computing unit is determined based on the basic elements of flood risk of each grid computing unit, wherein the basic elements include at least the maximum flood depth, maximum flow velocity and maximum flood duration of the corresponding grid computing unit. Based on the relationship between the degree of reservoir siltation in each of the grid computing units and the risk of flooding exceeding the standard, the probability factor of reservoir siltation impact for the corresponding grid computing unit is determined. Based on the impact of the reservoir siltation degree and the frequency of floods exceeding the standard on the probability of dam failure in each of the grid computing units, and the relationship between the above impact and the risk of flooding exceeding the standard, the probability factor of reservoir dam failure under siltation conditions for the corresponding grid computing unit is determined. The comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure is calibrated based on the reservoir siltation impact probability factor and the reservoir dam failure impact probability factor. The K-means clustering algorithm is used to perform cluster analysis on the comprehensive risk of reservoir inundation of each grid computing unit to determine the flood risk level.

2. The zoning method for the risk of reservoir flooding exceeding standard levels according to claim 1, characterized in that, Before determining the super-standard flood inundation risk level of the corresponding grid computing unit based on the basic elements of inundation risk of each grid computing unit, the zoning method further includes: The relationship between reservoir water level and reservoir capacity is determined based on reservoir siltation, wherein the reservoir siltation is determined based on historical measured reservoir capacity curves or predicted by an auxiliary curve derived from the reservoir capacity and area calculation curves to determine siltation depth. The inflow process of floods exceeding the standard is calculated based on the hydrological information of the upstream basin of the target reservoir; The reservoir flood control discharge process is calculated based on the reservoir scheduling and operation mode of the target reservoir and the reservoir capacity curve after siltation changes. Based on the calculation of the inflow process of the super-standard flood and the calculation of the outflow process of the reservoir flood control scheduling, a flood dynamic evolution model is constructed based on the multi-source topographic data of the target reservoir. The flood dynamic evolution model is used to simulate the dynamic change process of flood risk information of different grid computing units at different times.

3. The zoning method for the risk of reservoir flooding exceeding standard levels according to claim 1, characterized in that, The determination of the super-standard flood inundation risk level of each grid computing unit based on the basic elements of inundation risk includes: The equivalent water depth of each grid computing unit is determined based on the formula H = α1α2h, where H is the equivalent water depth of the grid computing unit, H is used to characterize the risk of flooding exceeding the standard, α1 is the correction coefficient for the maximum traveling velocity, α2 is the correction coefficient for the maximum flood duration, and h is the maximum flood depth.

4. The zoning method for the risk of reservoir flooding exceeding standard levels according to claim 1, characterized in that, The probability factors for determining the impact of reservoir siltation, based on the relationship between the degree of reservoir siltation and the risk of inundation by floods exceeding standard levels, include: The degree of reservoir siltation is determined based on the amount of siltation at the normal water level and the original reservoir capacity. The probability factor of reservoir siltation impact is determined based on the relationship between the degree of reservoir siltation and the risk of flooding exceeding the standard.

5. The zoning method for the risk of reservoir flooding exceeding standard levels according to claim 1, characterized in that, The probability factors for reservoir dam failure under siltation conditions are determined based on the impact of the reservoir siltation level and the frequency of floods exceeding standard levels on the probability of dam failure, and the relationship between the above impacts and the risk of flooding exceeding standard levels. Establish a functional relationship between the reservoir siltation degree, the frequency of floods exceeding standard levels, and the probability coefficient of dam failure impact; The probability factor of reservoir dam failure under siltation conditions is determined by using the established functional relationship.

6. The zoning method for the risk of reservoir flooding exceeding standard levels according to claim 1, characterized in that, The comprehensive risk level of reservoir inundation under the dual impact probability of sedimentation and dam failure, calibrated based on the reservoir sedimentation impact probability factor and the reservoir dam failure impact probability factor, includes: The comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure in the case of floods exceeding the standard is calculated based on the formula R = a * b * H, where R is the comprehensive risk of reservoir inundation, a is the probability factor of reservoir siltation, b is the probability factor of reservoir dam failure, and H is the equivalent water depth of the corresponding grid calculation unit.

7. A device for predicting the risk of reservoir flooding exceeding standard levels, characterized in that, The downstream area of ​​the target reservoir is divided into multiple grid computing units, and the prediction device includes: A basic risk determination unit is used to determine the flood risk level of the corresponding grid computing unit based on the basic flood risk elements of each grid computing unit, wherein the basic elements include at least the maximum flood depth, maximum flow velocity and maximum flood duration of the corresponding grid computing unit. The sedimentation impact factor determination unit is used to determine the reservoir sedimentation impact probability factor of the corresponding grid computing unit based on the relationship between the degree of reservoir sedimentation of each grid computing unit and the risk of flooding exceeding the standard. The dam failure impact factor determination unit is used to determine the dam failure impact probability factor of the corresponding grid computing unit under siltation conditions based on the impact of the reservoir siltation degree and the frequency of floods exceeding the standard on the dam failure probability of each grid computing unit, and the relationship between the above impact and the flood inundation risk of the flood exceeding the standard. The comprehensive risk determination unit is used to calibrate the comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure in the case of floods exceeding the standard, based on the reservoir siltation impact probability factor and the reservoir dam failure impact probability factor. The risk level determination unit is used to perform cluster analysis on the comprehensive reservoir inundation risk of each grid computing unit using the K-means clustering algorithm to determine the flood risk level.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the zoning method for reservoir flood inundation risk as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the zoning method for the risk of reservoir flooding exceeding the standard as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the zoning method for the risk of reservoir flooding exceeding the standard as described in any one of claims 1 to 6.

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