Regional division method, device and equipment for reservoir over-standard flood inundation risk

Abstract

The embodiment of the invention discloses a reservoir over-standard flood inundation risk zoning method, device and equipment, and the method comprises the steps: determining a reservoir siltation influence probability factor and a reservoir dam break influence probability factor under a siltation condition; and on the basis of the reservoir siltation influence probability factor and the reservoir dam break influence probability factor, calibrating a reservoir inundation comprehensive risk degree of the siltation-dam break dual influence probability under the condition of the over-standard flood, and performing clustering analysis on the reservoir inundation comprehensive risk degree of each grid calculation unit by using a K-mean clustering algorithm to finally determine a flood risk level. The technical problem that in the prior art, the prediction zoning accuracy of the flood inundation risk of the reservoir area with the high sediment content and the weak desilting capacity is low is solved, and a scientific basis and a certain technical support are provided for reservoir flood inundation risk assessment and disaster prevention and reduction planning in the high sediment content basin area.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of information processing technology, and in particular to a method, device and equipment for zoning the risk of reservoir inundation by exceeding standard flooding. Background Art

[0002] When rivers with high sediment loads flow into reservoirs, if the reservoirs' desilting capacity is insufficient, the carried sediment will accumulate within the reservoir area, causing siltation. Reservoir siltation reduces the reservoir's effective storage capacity, reducing its ability to store water and prevent floods. This can lead to a rapid rise in reservoir water levels and increased downstream flow, especially during exceptional floods. This can increase the risk of flooding downstream.

[0003] At present, the methods for reservoir flood inundation risk zoning at home and abroad are relatively mature. However, for reservoir areas with high sediment content and weak silt removal capacity, few consider the impact of reservoir siltation and probabilistic dam failure on reservoir inundation risk. Summary of the Invention

[0004] The embodiments of the present invention provide a method, device and equipment for zoning the risk of excessive flooding in reservoirs, which solves the technical problem in the prior art of low accuracy in predicting and zoning the risk of flooding in reservoir areas with high sediment content and weak dredging capacity.

[0005] In a first aspect, an embodiment of the present invention provides a method for zoning reservoir inundation risk due to excessive flooding. The method divides the downstream area of ​​a target reservoir into multiple grid computing units. The method includes:

[0006] Determining the super-standard flood inundation risk of the corresponding grid computing unit based on the inundation risk basic elements of each grid computing unit, wherein the basic elements include at least the maximum inundation depth, the maximum flow velocity, and the maximum inundation duration of the corresponding grid computing unit;

[0007] Determining the reservoir siltation impact probability factor of the corresponding grid computing unit based on the relationship between the reservoir siltation degree of each grid computing unit and the super-standard flood inundation risk;

[0008] Determining the reservoir dam failure impact probability factor of the corresponding grid computing unit under siltation conditions based on the influence of the reservoir siltation degree and the frequency of super-standard floods on the dam failure probability of each grid computing unit, and the relationship between the above influence and the super-standard flood inundation risk;

[0009] Calibrate the comprehensive reservoir flooding risk of the probability of dual impact of siltation and dam break under super-standard flood conditions based on the reservoir siltation impact probability factor and the reservoir dam break 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 of the corresponding grid computing unit based on the inundation risk basic elements of each grid computing unit, the zoning method further includes:

[0012] Determining the relationship between the reservoir water level and the reservoir capacity based on the reservoir sedimentation, wherein the reservoir sedimentation is determined based on a historical measured reservoir capacity curve or predicted based on an auxiliary curve for determining sedimentation depth derived from a reservoir capacity and area calculation curve;

[0013] Calculating the super-standard flood inflow process based on the hydrological information of the upstream basin of the target reservoir;

[0014] Calculating the discharge flow process of the reservoir flood control operation based on the reservoir operation mode of the target reservoir and the storage capacity curve after sedimentation changes;

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

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

[0017] 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 super-standard flooding, α1 is the correction coefficient of the maximum flow velocity, α2 is the correction coefficient of the maximum flooding duration, and h is the maximum flooding depth.

[0018] Furthermore, the reservoir sedimentation impact probability factor is determined based on the relationship between the reservoir sedimentation degree and the above-mentioned super-standard flood inundation risk, including:

[0019] Determine the degree of reservoir siltation based on the amount of reservoir siltation at normal water level and the original storage capacity of the reservoir;

[0020] The reservoir siltation impact probability factor is determined based on the relationship between the reservoir siltation degree and the super-standard flood inundation risk.

[0021] Furthermore, based on the influence of the reservoir siltation degree and the frequency of super-standard floods on the probability of dam failure, and the relationship between the above influences and the inundation risk of super-standard floods, the reservoir dam failure impact probability factors under siltation conditions are determined to include:

[0022] Establishing a functional relationship between the reservoir siltation degree, the frequency of the super-standard flood, and the dam failure impact probability coefficient;

[0023] The established functional relationship is used to determine the probability factor of reservoir dam failure under siltation conditions.

[0024] Furthermore, the comprehensive reservoir flooding risk of the dual impact probability of sedimentation and dam break under super-standard flood conditions is calibrated based on the reservoir sedimentation impact probability factor and the reservoir dam break impact probability factor, including:

[0025] The comprehensive risk of reservoir inundation with the dual impact probability of siltation and dam break under super-standard flood conditions is calculated based on the formula R=a*b*H, where R is the comprehensive risk of reservoir inundation, a is the reservoir siltation impact probability factor, b is the reservoir dam break impact probability factor, and H is the equivalent water depth of the corresponding grid calculation unit.

[0026] In a second aspect, an embodiment of the present invention further provides a device for predicting the risk of reservoir inundation due to excessive flooding, wherein the downstream area of ​​a target reservoir is divided into a plurality of grid computing units, and the prediction device includes:

[0027] a basic risk determination unit, configured to determine the inundation risk of an excessive flood of a corresponding grid computing unit based on the inundation risk basic elements of each grid computing unit, wherein the basic elements include at least the maximum inundation depth, the maximum flow velocity, and the maximum inundation duration of the corresponding grid computing unit;

[0028] a sedimentation impact factor determination unit, configured to determine a reservoir sedimentation impact probability factor of a corresponding grid computing unit based on a relationship between the reservoir sedimentation degree of each grid computing unit and the risk of inundation by the super-standard flood;

[0029] a dam-break impact factor determination unit, configured to determine the reservoir dam-break impact probability factor of the corresponding grid computing unit under siltation conditions based on the influence of the reservoir siltation degree and the frequency of super-standard floods on the dam-break probability of each grid computing unit, and the relationship between the above influences and the inundation risk of super-standard floods;

[0030] A comprehensive risk determination unit, configured to calibrate a comprehensive reservoir flooding risk of the dual impact probability of siltation and dam break under super-standard flood conditions based on the reservoir siltation impact probability factor and the reservoir dam break impact probability factor;

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

[0032] In a third aspect, an embodiment of the present invention further provides an electronic device, including:

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

[0034] The memory stores computer-executable instructions;

[0035] The processor executes the computer-executable instructions stored in the memory to implement the method for zoning the reservoir's excessive flood inundation risk as described in the first aspect embodiment above.

[0036] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the method for zoning the risk of reservoir inundation caused by excessive flooding as described in the embodiment of the first aspect above.

[0037] In a fifth aspect, an embodiment of the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method for zoning the risk of reservoir inundation caused by excessive flooding as described in the embodiment of the first aspect above.

[0038] The embodiments of the present invention disclose a method, device, and equipment for zoning reservoir inundation risks due to super-standard floods. By determining the reservoir siltation impact probability factor and the reservoir dam breach impact probability factor under siltation conditions, the comprehensive reservoir inundation risk of the dual impact probability of siltation and dam breach under super-standard flood conditions is calibrated based on the reservoir siltation impact probability factor and the reservoir dam breach impact probability factor. The K-means clustering algorithm is then used to perform cluster analysis on the comprehensive reservoir inundation risk of each grid computing unit, ultimately determining the flood risk level. This method solves the technical problem of low accuracy in predicting and zoning flood inundation risks in reservoir areas with high sediment content and weak silt removal capacity in the prior art, providing a scientific basis and certain technical support for reservoir flood inundation risk assessment and disaster prevention and mitigation planning in high-sediment content basins. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of a method for zoning reservoir inundation risks of excessive flooding provided by an embodiment of the present invention;

[0040] Figure 2 This is a structural diagram of a device for predicting the risk of reservoir flooding exceeding the standard provided by an embodiment of the present invention;

[0041] Figure 3The original storage capacity curve of Reservoir A and the actual storage capacity curve of Reservoir A in 2017 after sedimentation changes;

[0042] Figure 4 This is a schematic diagram of the surface runoff flow process line of Reservoir A under the 100-year 24-hour rainfall condition;

[0043] Figure 5 This is a schematic diagram of the discharge flow and reservoir water level change process of Reservoir A under the 100-year flood disaster.

[0044] Figure 6 It is a structural schematic diagram of an electronic device for implementing the method for zoning the reservoir super-standard flood inundation risk according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0046] It should be noted that the terms "first," "second," and so on, in the specification, claims, and drawings of the present invention are used to distinguish different objects, and are not intended to limit a specific order. The following embodiments of the present invention can be implemented independently or in combination with each other, and the present invention does not impose specific limitations on this.

[0047] Figure 1 This is a flow chart of a method for zoning reservoir inundation risks of excessive flooding provided by an embodiment of the present invention.

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

[0049] S101, determining the super-standard flood inundation risk of the corresponding grid computing unit based on the basic elements of the inundation risk of each grid computing unit, wherein the basic elements at least include the maximum inundation depth, maximum flow velocity and maximum inundation duration of the corresponding grid computing unit.

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

[0051] S102: Determine the reservoir sedimentation impact probability factor of the corresponding grid calculation unit based on the relationship between the reservoir sedimentation degree of each grid calculation unit and the risk of excessive flooding.

[0052] Specifically, the reservoir sedimentation volume S t There is a certain positive correlation with the flood risk downstream of the reservoir. When the basin ecological environment and the operation and management of the reservoir do not change significantly, the reservoir sedimentation volume S t It will gradually increase with the operation time of the reservoir, but the change of reservoir sedimentation with the operation time of the reservoir has certain uncertainty, so when the reservoir sedimentation degree L is used to reflect the reservoir sedimentation amount S t As the reservoir operation time changes, the reservoir sedimentation volume S is considered. t To improve the accuracy and reliability of the reservoir sedimentation, the sedimentation impact probability coefficient a is defined as the reservoir sedimentation impact probability factor to comprehensively reflect the reservoir sedimentation amount S t Probabilistic impacts on flood inundation risk.

[0053] S103, determining the reservoir dam failure impact probability factor of the corresponding grid computing unit under siltation conditions based on the influence of the reservoir siltation degree and the frequency of super-standard floods on the dam failure probability of each grid computing unit, and the relationship between the above influence and the inundation risk of the super-standard flood.

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

[0055] S104, based on the reservoir sedimentation impact probability factor and the reservoir dam breach impact probability factor, calibrate the comprehensive reservoir inundation risk of the dual impact probability of sedimentation and dam breach under super-standard flood conditions.

[0056] Optionally, S104 specifically includes: calculating the comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam break under super-standard flood conditions based on the formula R=a*b*H, wherein R is the comprehensive risk of reservoir inundation, a is the reservoir siltation impact probability factor, b is the reservoir dam break impact probability factor, and H is the equivalent water depth of the corresponding grid calculation unit.

[0057] Specifically, the present invention takes into account the dual impact probability of siltation and dam break, and innovatively proposes a calculation formula for the comprehensive risk of reservoir inundation considering the dual impact probability of siltation and dam break under super-standard floods: R = a*b*H (Formula 7), where: H is the equivalent water depth H value corresponding to p of 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 the present invention; b is the dam break 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 the present invention; R is the comprehensive risk of flood inundation under a super-standard flood of a certain frequency after considering the dual impact probability of siltation and dam break.

[0058] The above method is applicable to the calculation of flood risk of a certain incident, and it provides a relatively intuitive and clear quantitative expression of the probabilistic influencing factors of reservoir siltation and dam failure in the comprehensive flood inundation risk. The coefficients a and b are exemplarily taken in tabular form in the embodiment of the present invention (a discrete expression of the functional relationship). When used in specific applications, they can be modified in combination with actual engineering conditions and expert experience, or the detailed expression of the functional relationship can be directly inferred.

[0059] S105 , using 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 R of the calculation unit through the above steps, the K-means clustering algorithm is used according to the R value of each calculation unit in the model to divide the comprehensive risk value into four levels, corresponding to low risk, medium risk, high risk and extremely high risk, which are used to characterize the flood risk level of each area (block) in the zoning analysis model and the flood risk zoning map. The threshold division of each risk level needs to be determined comprehensively based on the actual project situation, regional flood control requirements and the characteristics of the clustering results. Specifically:

[0061] (1) Construct a flood risk dataset S = {S1, S2, ..., S N}, initialize and define k cluster centers, each of which corresponds to a cluster, expressed as P = {P1, P2, ..., P k}, 1 <k≤N。

[0062] (2) Each data point in the data set is divided into the cluster with the closest Euclidean distance. After the data distribution is completed, the average value of the k cluster data is recalculated to obtain the corresponding new cluster center.

[0063] (3) Repeat the iterative operation of step (2), redistribute the data, and continuously update the cluster center until the cluster center remains unchanged, thereby obtaining the optimal clustering result. i and the jth cluster center U j The Euclidean distance calculation formula is: d(S i , U j )=||S i -U j ||, where 1≤i≤N, 1≤j≤k.

[0064] From the above formula, we can see that for each cluster center, the smaller the sum of the Euclidean distances of all sample data in the cluster, the better the clustering effect is 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 cells are obtained, which can more accurately and reasonably simulate the short-, medium- and long-term inundation risk zoning of reservoirs at different scales under super-standard flood conditions, and provide a scientific basis and important technical support for flood control risk management of reservoirs in high-sediment content areas.

[0066] On the basis of the above technical solutions, before S101, the zoning method further includes:

[0067] S1. Determine the relationship between the reservoir water level and the reservoir capacity based on the reservoir siltation amount, wherein the reservoir siltation amount is determined based on the historical measured storage capacity curve or predicted based on the auxiliary curve derived from the storage capacity and area calculation curve for determining the siltation depth.

[0068] Specifically, it is necessary to first collect basic geographic data (administrative divisions, settlements, river systems, levee projects, digital elevation, remote sensing imagery, etc.) for the study area, reservoir design data, sedimentation change data, and flood disaster data, ensuring the authority, accuracy, and timeliness of this data, and then systematically compile and process it. Then, based on the reservoir's water level-capacity relationship (storage capacity curve) under sedimentation changes, and combining the data collected, calculate the reservoir's water level-capacity relationship under sedimentation changes.

[0069] Specifically, there are two main situations:

[0070] (1) The reservoir has a measured storage capacity curve in the recent period or in recent years. In this case, the reservoir sedimentation volume can be directly used as a known quantity in this method, taking into account the probabilistic changes in the reservoir sedimentation volume in the recent period or in recent years.

[0071] (2) If there is no recent measured reservoir storage data, the following method is used to predict the calculation (see the detailed explanation below), so as to derive the reservoir capacity curve under the change of siltation, which is suitable for the case that the siltation amount under the normal storage level of the reservoir, the original reservoir capacity curve, and the original area curve of the reservoir are known or predicted.

[0072] Among them, the original reservoir capacity and the original area curve are the basic data required for engineering design, and the siltation amount below the normal storage level of the reservoir is a known quantity, and the reservoir capacity curve under the change of siltation is derived. That is, the water level of the reservoir Z, the reservoir capacity V, and the siltation amount of the reservoir below the normal storage level S t There is a certain relationship among the three. Define Z = f (V, S t ) (formula 1) to express the functional relationship between the three under the above conditions V, wherein Z is the water level of the reservoir, the unit is m; V is the reservoir capacity, the unit is m 3 ; S t is the siltation amount of the reservoir, and is the siltation amount of the reservoir below the normal storage level, the unit is m 3 .

[0073] Specifically, the relationship between the volume and the water depth of a general reservoir can be represented by V = N * h m (formula 8), where the water depth h is obtained by h = Z i -Z0 (formula 9). In the formula, V represents the reservoir capacity, the unit is m 3 ; h represents the water depth, the unit is m; Z i represents a certain water level elevation, the unit is m; Z0 represents the reservoir capacity 0 point elevation, the unit is m; N is the coefficient; m is the index.

[0074] According to the index m of the power function of the reservoir depth-volume curve in formula 8, the reservoir is divided into four types, as shown in Table 1.

[0075] Table 1. Reservoir type table

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

[0077] The total siltation amount S t of the reservoir can be calculated by , in which S tis the total sedimentation of the reservoir; h0 represents the sedimentation depth in front of the dam at the starting point of sedimentation, that is, the original water depth in front of the dam at the starting point of sedimentation; A is the sedimentation area of ​​the reservoir at depth h, h <h0时,淤积面积即为原始水库面积相等;dh为深度增量;H0为正常蓄水位时的水库深度;Kf是h> When h0, the reservoir depth h corresponds to the reservoir sedimentation area, f is the relative sedimentation area, K is the proportional constant for converting the relative sedimentation area to the actual sedimentation area, for a known reservoir:

[0078] After integrating formula 10, we get Among them, V r V is the reservoir sedimentation volume below the sedimentation starting point; h is the sedimentation volume below the reservoir depth h; a0 is the relative sedimentation area corresponding to the sedimentation starting point when h=h0; A0 is the reservoir area corresponding to the sedimentation starting point when h=h0.

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

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

[0081] Reservoir type 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] The auxiliary curves for determining the sedimentation depth of four types of reservoirs are derived from the storage capacity and area calculation curves (the auxiliary curves have no mathematical expression. For ease of use, the relevant data of the curves are listed in Table 3 and can be used for interpolation calculations). Then, the total sedimentation volume and total water depth of the known reservoir are used to calculate the S of multiple different dam front sedimentation depths or relative depths P. t -V0 (the remaining sediment volume above the sedimentation surface in front of the dam, V0 is the sediment 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 value of the -V0) / H0A0 curve and the type curve determined by the index m value is the required relative depth P of siltation in front of the dam, from which the siltation distribution (storage capacity curve) of the reservoir can be determined.

[0084] Table 3. Auxiliary curves for determining the starting depth of sediment deposition

[0085]

[0086]

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

[0088] Specifically, the inflow flow process is calculated based on the hydrological information of the upstream basin of the reservoir according to the relevant model method to calculate the outflow flow process of the reservoir. It should be noted that the embodiment of the present invention preferably uses, but is not limited to, the SCS (Soil Conservation Service Model).

[0089] The SCS runoff model is used for hydrological forecasting in small watersheds. It calculates runoff depth (accumulated volume of rainwater) under given (extreme) rainfall conditions. It is a mathematical model for estimating surface runoff, characterized by its simple calculation process and few parameters. The SCS model is well suited for studying the impact of other factors on stormwater runoff and calculating surface runoff volumes within a catchment area.

[0090] The SCS model can derive the amount of surface runoff during rainfall through two basic assumptions: the water balance equation and the proportional equality assumption, and the initial loss (maximum potential retention) relationship assumption. The basic principle is: during rainfall, if the precipitation does not reach the initial absorption value of the soil, a , no runoff is generated; when the precipitation reaches I a After that, the 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 remainder after .

[0091] First: (1) According to the assumption of equal proportions, the ratio of the actual infiltration volume F to the maximum possible retention volume S at that time is equal to the actual surface direct runoff volume Q and the maximum possible runoff volume PI a The ratio of is expressed as: Where P is the total rainfall, in mm; Q is the surface runoff, in mm; I a is the initial loss (initial absorption value of soil), in mm, including interception, surface water storage, etc.; F is excluding I a The cumulative infiltration volume (actual infiltration volume) is in mm; S is the maximum possible retention volume (maximum potential retention volume) at that time, in mm.

[0092] (2) According to the water balance equation of the basin, the total rainfall P is equal to the initial loss of the catchment area in the basin (the initial absorption value of the soil) I a , the sum of the actual infiltration volume F and the surface direct runoff volume Q, the expression of which is 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 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, and the empirical value λ is usually taken as 0.2.

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

[0095] (5) To find the maximum possible retention capacity S of the basin at that time, the model introduces the parameter CN. As can be seen from Formula 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 in the basin. S varies in different basins and it is difficult to determine its value. Therefore, the comprehensive parameter CN that reflects the basin characteristics is introduced into the SCS model to obtain the value of S. The relationship is:

[0096] Where: CN is a parameter that describes the land's ability to retain rainwater and reflects the runoff capacity of a regional underlying surface unit. It is influenced by underlying surface factors such as previous soil moisture, soil type, land use type, slope, and vegetation. The CN value is determined based on information on these influencing factors using the CN value lookup table provided by the U.S. Soil and Water Conservation Service. The CN value ranges from 0 to 100, with smaller CN values ​​indicating greater infiltration.

[0097] Second: The SCS model considers the impact of previous rainfall on runoff and introduces the previous rainfall index AMC, which is calculated as follows: Where: P i It is the daily rainfall in the last five days, in mm.

[0098] (1) According to the AMC index, the effect of previous rainfall on soil moisture is divided into three types: I (dry), II (medium), and III (wet), as shown in Table 4.

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

[0100]

[0101] Among them, the CN values ​​under conditions I and III are calculated according to the following formulas:

[0102]

[0103] (2) The calculation formula for the previous impact rainfall is:

[0104]

[0105] Where: P a,t represents the previous rainfall on day t, in mm; P t represents the rainfall on day t, in mm; parameter K is the daily decay coefficient of soil moisture; E m represents the daily evapotranspiration capacity of the basin, in mm; W m is the tension water storage capacity, in mm.

[0106] S3, calculating the discharge flow process of the reservoir flood control operation based on the reservoir operation mode of the target reservoir and the storage capacity curve after sedimentation changes;

[0107] Specifically, according to the reservoir scheduling operation mode, combined with the storage capacity curve after considering the sedimentation change, the reservoir flood control scheduling discharge flow process is calculated. The embodiment of the present invention is explained by taking the general practical weir flow with gates as an example.

[0108] The basic principle of reservoir flood regulation discharge calculation 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 It is the inflow flow at the beginning and end of the time period, in m 3 / s;q t ,q t+1 It is the outbound flow at the beginning and end of the period, in m 3 / s;V t 、V t+1 is the reservoir water storage at the beginning and end of the period, in m 3 By combining the reservoir inflow process, the storage capacity curve under siltation changes, and the reservoir scheduling method, the reservoir flood control scheduling discharge process can be iteratively calculated. In the scheduling calculation process, it is necessary to consider whether the reservoir will burst, which is mainly divided into the following two situations:

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

[0112] (2) During the flood control calculation process, if the maximum water level of the reservoir reaches or exceeds the reservoir verification water level, the risk and probability of reservoir dam failure are considered. When considering the probability of reservoir dam failure, an appropriate breach theory is selected to calculate the dam failure flow process based on factors such as the specific reservoir type, engineering structure, and hydrogeology.

[0113] According to 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 dam instantaneous collapse model is introduced in detail below.

[0115] A major form of dam failure, instantaneous total collapse is the simplest and most direct form of dam failure. It forms an important foundation for studying gradual or partial dam failure. Some small and medium-sized dams often fail, scour, and collapse within minutes or even less, sending a torrent of water cascading down. Therefore, instantaneous total collapse is highly representative. Calculating the breach flood is crucial for dam failures. Once the maximum discharge at the breach is determined, the breach flood hydrograph can be derived.

[0116] The maximum flow rate of instantaneous dam break can be calculated according to the following empirical formula:

[0117]

[0118] In formula 22: Q M is the maximum flow rate of the breach, in m 3 / s; B is the dam length, unit is m 3 / s; b is the average width of the breach, in m; H s is the upstream water depth before the dam breaks, in meters; h is the effective water depth, in meters. According to experience, the size of the b value of the average width of the breach directly affects the size of the breach flow, and is equal to the dam length when the breach is complete. When the reservoir capacity of the breached dam V ≥ 1 million m 3 When Estimation (K1 is called the material coefficient of the dam body, for clay, clay core wall or inclined wall, as well as soil, stone, concrete, etc. K1 = 1.19, for homogeneous loam K1 = 1.98), when V < 1 million m 3 When pressing b=K2(VH0) 1 / 4 Estimate (K2 is 6.6 for dam construction and management with good quality, and 9.1 for poor quality).

[0119] The instantaneous dam-break flow process line is derived by using the generalized typical flow process line method. The instantaneous dam-break flow process line and the maximum flow Q M , the discharge flow before dam break Q0 and the reservoir capacity W that can be discharged after dam break (which can be determined by the final residual height of the dam break and the reservoir capacity curve). Its linear shape can be generalized as a 4th order parabola or a 2.5th order parabola, that is, at the moment of dam break, the discharge increases sharply to Q M , followed by a rapid decline in flow, forming a concave curve, ultimately approaching the original discharge flow, Q0. The quadratic and 2.5-order parabolas that generalize typical flow hydrographs are shown in Table 5 below, where T is the time it takes for the dam to empty its reservoir capacity, and t is an arbitrary moment. Currently, the quadratic parabola is often used to generalize flow hydrographs.

[0120] Table 5. Generalized typical flow hydrograph table

[0121]

[0122] When Q M , Q0 and W are known, the flow hydrograph can be determined by trial and error, the steps of which are as follows:

[0123] 1) Preliminarily determine the emptying time T according to Q M and W. T is calculated by T = K (W / Q M ) (formula 23); in formula 23, K is a coefficient, for a fourth-order parabola, K is generally 4-5, and for a 2.5-order parabola, K = 3.5.

[0124] 2) Preliminarily determine the flow hydrograph according to T, Q M , Q0 and Table 5.

[0125] 3) Verify whether the water volume between the flow hydrograph and the Q = Q0 straight line is equal to the dam-break reservoir capacity (the reservoir capacity above the dam-break bottom), if not, the preliminarily determined T value needs to be adjusted until the two are equal.

[0126] It should be noted that there are many related calculation formulas for dam-break situations, and the appropriate calculation formula should be determined according to the corresponding conditions.

[0127] S4, based on the multi-source terrain data of the target reservoir, a flood dynamic evolution model is constructed according to the calculation of the super-standard flood inflow process and the calculation of the reservoir flood control discharge process, wherein the flood dynamic evolution model is used to simulate the dynamic change process of the flood risk information of different grid calculation units at different times.

[0128] Specifically, based on multi-source data such as reservoir downstream terrain elevation, river system, dike engineering, remote sensing image, etc., a two-dimensional hydrodynamic model of the whole river channel-flood area downstream of the reservoir based on hydrodynamic theory is constructed, and the reservoir flood control discharge process is taken as the flow limit condition of the inflow boundary, the whole process of flood dynamic evolution and inundation downstream of the reservoir is simulated, and the dynamic change process of the flood risk information of different grid units at different times is calculated to obtain data such as flood inundation water depth, flood flow velocity, and inundation duration.

[0129] Through the above principles and steps, combined with the foregoing related materials and data, the dynamic evolution process of the reservoir downstream discharge flood under different return periods is simulated and calculated to obtain the downstream inundation results of the flood under different return periods.

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

[0131] 1) Model principle: The flood routing and flood risk simulation model of the reservoir downstream river channel and the floodplain on both banks adopts a two-dimensional water dynamic model. The basic equations for two-dimensional unsteady flow calculation include continuity equation and momentum equation as follows:

[0132] ① Continuity equation:

[0133]

[0134] ② Momentum equation:

[0135]

[0136] In the formula: is the average flow rate based on water depth, with the unit of m / s; t is time, with the unit of s; x, y and z are Cartesian coordinates; η is the river-floodplain surface elevation, with the unit of m; d is the static water depth, with the unit of m; h is the total water head, with the unit of m; S is the point source flow size, with the unit of m / s; u, v are velocity components in x and y directions, with the unit of m / s; g is the gravity acceleration, with the unit of m / s2; ρ is the water density, with the unit of kg / m3; sxx, sxy, syx and syy are components of radiation stress, with the unit of N; pa is the atmospheric pressure, with the unit of kPa; ρ0 is the relative density of water; us and vs are flow rates of source and sink terms, with the unit of m / s. 3 3 3

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

[0138]

[0139] 2) Model construction:

[0140] ① Determination of modeling range: according to the CAD elevation points of the reservoir downstream river channel and the area outside the river channel, along the reservoir downstream river channel, considering the maximum floodplain inundation range of river channel flood overflow, a “river-floodplain closed boundary” is established.

[0141] ② Grid subdivision: extract the model calculation boundary line and CAD elevation point data, perform unstructured triangular grid subdivision on the model calculation area, and assign the grid cell terrain elevation attribute, and process the maximum area and minimum angle of the calculation cell grid in the important range.

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

[0143] ④ Parameter setting: According to the actual situation of the underlying surface in the study area and combined with the empirical coefficient setting value, set the data information such as eddy viscosity coefficient, roughness, dry and wet 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 super-standard flooding, α1 is the correction coefficient for the maximum flow velocity, α2 is the correction coefficient for the maximum flooding duration, and h is the maximum flooding depth.

[0146] Illustratively, 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 reservoir siltation at normal water level and the original storage capacity of the reservoir; the probability factor of reservoir siltation impact is determined based on the relationship between the degree of reservoir siltation and the risk of super-standard flood inundation.

[0149] Specifically, first, the formula The reservoir siltation level L is calculated, and then the innovative formula a = f(L) (Formula 5) is established to represent the functional relationship between the siltation impact probability coefficient a and the reservoir siltation level L. Table 6 exemplifies this functional relationship, but the corresponding relationship between the reservoir siltation level L and the siltation impact probability coefficient a in Table 6 is not limited to this. For specific reservoirs, the corresponding relationship in Table 6 can be adjusted based on actual project conditions and expert experience, or the detailed expression of Formula 5 can be directly derived.

[0150] Table 6. Relationship between sedimentation impact probability coefficient and reservoir sedimentation degree

[0151] Reservoir siltation degree L Sedimentation impact probability coefficient 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] Among them, L represents the degree of reservoir siltation, which is related to the reservoir operation time and generally increases with the increase of reservoir operation time; S t is the amount of siltation under the normal water level of the reservoir, V0 is the original storage capacity of the reservoir, that is, the storage capacity below the normal water level before siltation occurs. When the reservoir siltation degree L is 0-10%, the siltation impact probability coefficient a=1.0, which means the reservoir siltation amount S t There is no effect on the aforementioned equivalent water depth H; the probability coefficient of siltation influence a gradually increases with the increase of reservoir siltation degree L, indicating that the reservoir siltation amount S t Amplify the risk of flooding.

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

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

[0155] Specifically, the innovative formula b = g(L, p) (Formula 6) is established to represent the functional relationship between the dam failure probability coefficient b, the reservoir siltation level L, and the frequency of excessive floods p. The dam failure probability coefficient b and the reservoir siltation level L are conditional probabilistic relationships.

[0156] In the embodiment of the present 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 when the standard flood frequency exceeds the standard flood frequency in Table 7. It should be noted that the data in Table 7 will vary slightly in specific reservoirs and can be adjusted based on actual project conditions and expert experience, rather than being limited to the data set in the table or directly deducing the detailed expression of Formula 6.

[0157] Table 7. Relationship between dam failure probability coefficient, reservoir sedimentation degree, and super-standard flood

[0158]

[0159] Among them, b is the dam break probability coefficient, and b starts from 1, which means that the dam break probability has no effect on the equivalent water depth H. b increases with the increase of the reservoir siltation level L, which means that the reservoir siltation amount S t At the same time, under super-standard floods with a frequency of p, the value of b also considers whether the reservoir water level exceeds the check level during the reservoir discharge process to reflect the impact of p on b.

[0160] In this embodiment of the present invention, a method for zoning reservoir flood risk exceeding the designated flood standard, considering the dual impact probabilities of both siltation and dam failure, is proposed to address the practical engineering challenges of reservoir sedimentation changes and dam failure probabilities in high-sediment-content areas. First, the method considers the probabilistic impact of reservoir sedimentation changes over reservoir operation time, innovatively proposing a sedimentation impact probability coefficient. Second, the method considers the risk amplification effect of sedimentation changes on dam failure under exceptional flood conditions, innovatively proposing a dam failure impact probability coefficient. Finally, an innovative method for calculating the comprehensive risk of reservoir inundation under exceptional flood conditions, considering the dual impact probabilities of both sedimentation and dam failure, is proposed.

[0161] On the one hand, the embodiment of the present invention proposes a new perspective on the flood inundation risk zoning method in reservoir flood control and management, effectively improving the ability to deal with diversified and complex problems in river basin flood control, and providing new ideas for reservoir flood control and management; on the other hand, using this method for flood risk zoning can help provide guidance for river basin flood control and disaster reduction from the perspective of siltation and dam breach, or further assist existing flood control risk zoning schemes from a new perspective, making reservoir flood control schemes more multidimensional and scientific, and providing a scientific basis and certain technical support for reservoir flood control risk management in high-sand content areas.

[0162] Figure 2 This is a structural diagram of a device for predicting the risk of reservoir inundation exceeding the standard flood level provided by an embodiment of the present invention.

[0163] The embodiment of the present invention also provides a device for predicting the risk of reservoir flooding exceeding the standard. Figure 2 As shown, the prediction device specifically includes:

[0164] A basic risk determination unit 21 is configured to determine the inundation risk of an excessive flood of a corresponding grid computing unit based on the inundation risk basic elements of each grid computing unit, wherein the basic elements include at least the maximum inundation depth, the maximum flow velocity, and the maximum inundation duration of the corresponding grid computing unit;

[0165] A sedimentation impact factor determination unit 22 is configured to determine a reservoir sedimentation impact probability factor of a corresponding grid calculation unit based on a relationship between the reservoir sedimentation degree of each grid calculation unit and the risk of excessive flooding;

[0166] a dam-break impact factor determination unit 23 for determining a reservoir dam-break impact probability factor of a corresponding grid computing unit under siltation conditions based on the influence of the reservoir siltation degree and the frequency of super-standard floods on the dam-break probability of each grid computing unit, and the relationship between the above influences and the inundation risk of the super-standard floods;

[0167] A comprehensive risk determination unit 24 is configured to calibrate a comprehensive reservoir flooding risk of the dual impact probability of sedimentation and dam breach under super-standard flood conditions based on a reservoir sedimentation impact probability factor and a reservoir dam breach impact probability factor;

[0168] The 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 excessive flood inundation risk of the corresponding grid computing unit based on the basic inundation risk elements of each grid computing unit, the prediction device further includes:

[0170] A first calculation unit is configured to determine a relationship between a reservoir water level and a reservoir capacity based on a reservoir sedimentation amount, wherein the reservoir sedimentation amount is determined based on a historical measured reservoir capacity curve or is predicted based on an auxiliary curve for determining sedimentation depth derived from a reservoir capacity and area calculation curve;

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

[0172] The third calculation unit is used to calculate the discharge flow process of the reservoir flood control scheduling based on the reservoir scheduling operation mode of the target reservoir and the storage capacity curve after the sedimentation change;

[0173] The model building unit is used to construct a flood dynamic evolution model based on the multi-source terrain data of the target reservoir according to the calculation of the inflow process of super-standard floods and the discharge process of reservoir flood control scheduling. The flood dynamic evolution model is used to simulate the dynamic change process of obtaining flood risk information of different grid calculation units at different times.

[0174] Optionally, the basic risk determination unit 21 is specifically configured to:

[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 super-standard flooding, α1 is the correction coefficient for the maximum flow velocity, α2 is the correction coefficient for the maximum flooding duration, and h is the maximum flooding depth.

[0176] Optionally, the sedimentation impact factor determination unit 22 is specifically configured to:

[0177] Determine the degree of reservoir siltation based on the amount of reservoir siltation at normal water level and the original storage capacity of the reservoir;

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

[0179] Optionally, the dam-break impact factor determination unit 23 is specifically configured to:

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

[0181] The established functional relationship is used to determine the probability factor of reservoir dam failure under siltation conditions.

[0182] Optionally, the comprehensive risk determination unit 24 is specifically configured to:

[0183] The comprehensive risk of reservoir inundation under the dual impact probability of siltation and dam failure under super-standard flood conditions 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 impact, b is the probability factor of reservoir dam failure impact, and H is the equivalent water depth of the corresponding grid calculation unit.

[0184] The device for predicting the risk of reservoir flooding exceeding the standard provided by the embodiment of the present invention can execute the method for zoning the risk of reservoir flooding exceeding the standard provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0185] The following is an introduction to the method for zoning the reservoir's inundation risk of exceeding standard flooding provided by the above embodiment of the present invention using a specific example.

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

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

[0188] Table 8. Comparative analysis of the two measured storage capacity curves of Diaoyutai Reservoir

[0189]

[0190] Considering that the amount of reservoir sedimentation usually increases parabolically over time (i.e., the sedimentation rate gradually decreases over time), and the normal and reasonable operation of the reservoir in recent years, the sedimentation status in 2017 is selected as a case for illustration in the embodiment of the present invention, and subsequent calculations are carried out based on the measured data in 2017, which is reasonable and has reference value.

[0191] In addition, in order to compare and illustrate the effect and rationality of the present invention, the original storage capacity curve of the reservoir without siltation is used for parallel calculation and analysis. The original storage capacity curve of the reservoir and the storage capacity curve of the reservoir in 2017 after siltation changes are obtained from the data. Figure 3 shown.

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

[0193] Due to the lack of measured runoff and rainfall data for Reservoir A, a related hydrological model is used to calculate the upstream flood process. Based on the 100-year 24-hour design surface rainfall in the A reservoir area, the hourly rainfall process of the design rainstorm is obtained according to the rainstorm schedule. Combined with the SCS model and the instantaneous unit line model, the surface runoff flow process line when p = 1% is derived, as shown in the following figure: Figure 4 As shown in the figure, during the design flood, surface runoff is the primary inflow due to its rapid flow, large volume, and direct impact on the reservoir. However, subsurface runoff is a slow and complex process, with a relatively weak short-term impact. Therefore, it is reasonable to consider only surface runoff here. Therefore, the calculated surface runoff flow process is used as the super-standard flood process for Reservoir A.

[0194] Table 9. A Reservoir Design Area Rainfall Results

[0195]

[0196] (2) Discharge process of Reservoir A.

[0197] The dispatching rules for Reservoir A are: "Before entering the main flood season, the reservoir water level will be lowered to 69.0m through beneficial dispatching measures; when the main flood season arrives and the reservoir water level exceeds the flood limit of 70.0m, all spillway gates will be opened to release water, and the reservoir water level will be strictly controlled below the flood limit of 70.0m. The reservoir discharge flow does not need to consider the flood-carrying capacity of the main river downstream of the reservoir. At the same time, the hydropower station generates electricity at full load, and the discharge flow from the power generation tunnel is kept at the maximum."

[0198] In the embodiment of the present invention, a 100-year super-standard flood is taken as an example. Considering the flood risk prevention and control needs downstream of Reservoir A, when a 100-year super-standard flood occurs, the reservoir water level is lowered to the dead water level (66m). After the reservoir stores water and regulates floods, it starts to discharge when the water level exceeds the flood limit water level (70.0m). At the same time, all the spillway gates are kept open, and the discharge flow of the power generation tunnel of the hydropower station is kept at full load to maintain the maximum.

[0199] Therefore, combined with the different storage capacity-water level relationships before and after siltation, and the combined discharge mode of the two discharge modes of A Reservoir's spillway open discharge and power generation irrigation tunnel discharge (where the relevant discharge function and coefficient are obtained based on the corresponding data and specifications), the hourly discharge flow and reservoir water level change process of the reservoir under different siltation states are obtained, as shown below: Figure 5 As shown in Table 10, the characteristic parameters of the reservoir discharge flow and reservoir water level change process under siltation changes are shown in Table 10.

[0200] Table 10. Characteristic parameters of reservoir discharge and reservoir water level changes under different sedimentation conditions

[0201]

[0202] In the present embodiment, the highest water level never exceeded the check water level, so the discharge flow process was calculated according to the normal scheduling process. Table 10 also shows the impact of siltation on the reservoir discharge process under the same flood standard: as the degree of siltation increases, the peak discharge flow and peak discharge water level both increase, and the discharge start and peak discharge times are both advanced, indicating that siltation can weaken the reservoir's ability to store water and regulate floods.

[0203] (3) Dynamic evolution of floods and submergence risk simulation downstream of the reservoir.

[0204] Based on multiple data sources, including terrain elevation, river systems, embankment projects, and remote sensing imagery downstream of the reservoir, a two-dimensional free-surface flow model based on hydrodynamic theory was constructed to analyze the flood risk of the river channel and floodplain downstream of the reservoir. The unstructured grid finite volume method was used to solve the two-dimensional shallow water equations. Considering the relatively flat inundation area and neglecting vertical flow acceleration, the vertically averaged flow factor was used as the research object to simulate the dynamic evolution of floods and water levels in the river channel and floodplain. Using the reservoir's flood control operation calculation results as the flow restriction condition at the inflow boundary, the dynamic evolution of floods and the inundation risk distribution characteristics of the river channel and floodplain downstream of the reservoir were simulated under the discharge operation of a 100-year super-standard flood. Downstream inundation results for the 100-year super-standard flood under different sedimentation conditions were obtained, and the inundation risk characteristics are shown in Table 11.

[0205] Table 11. Downstream flooding risk characteristics of Reservoir A after reservoir operation with different flood frequencies

[0206] Submergence Indicator Original unsilted After siltation Maximum submerged depth / m 2.895 2.992 <![CDATA[淹没总面积 / km 2 ]]> 11.372 11.674 <![CDATA[淹没水深<0.5m处面积 / km 2 ]]> 5.609 5.532 <![CDATA[0.5m<淹没水深<1.0m处面积 / km 2 ]]> 2.890 2.957 1.0 < Submerged water depth < 1.5 m Area / km 2 ]]> 2.742 2.997 Area in km at submergence depth > 1.5 m 2 ]]> 0.131 0.187

[0207] Table 11 shows the impact of siltation on the flooding risk downstream of the reservoir under the same super-standard flood: compared with the original unsilted condition, the maximum flooding depth and total flooded area increased after siltation. Furthermore, the proportion of areas with deeper flooding depths also increased due to siltation. These results further illustrate the amplifying effect of siltation on the flooding risk downstream of the reservoir.

[0208] According to the quantitative calculation method of the comprehensive flood inundation risk in the embodiment of the present invention, a quantitative calculation method of the comprehensive flood inundation risk of reservoir operation under the dual impact probability of siltation and dam break is proposed, which specifically includes the following contents:

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

[0210] Consider the basic factors that influence the flooding risk of a calculation unit, including the maximum flooding depth h, the maximum flow velocity v, and the maximum flooding duration t. Taking the maximum flooding depth h as the primary factor and comprehensively considering the impact of the maximum flow velocity v and the maximum flooding duration t, the equivalent water depth H indicator is used to comprehensively reflect the risk level of the calculation unit under a certain level of flood frequency. This is calculated according to Formula 3.

[0211] (2) Calculation of reservoir sedimentation impact probability factors

[0212] This method uses two working conditions: the original unsilted reservoir and the reservoir after siltation to illustrate the method. The siltation impact probability coefficient is determined according to Formula 4 and Table 6.

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

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

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

[0216] Taking into account the indirect impact of the degree of reservoir siltation on the reservoir dam break under super-standard floods, and the direct impact of the recurrence period of super-standard floods on the reservoir dam break, the dam break impact probability coefficient b is introduced into the calculation of the comprehensive risk of the calculation unit to comprehensively reflect the risk expansion effect of probabilistic dam break on flooding under different super-standard floods and different degrees of siltation.

[0217] For the original unsilted condition, the values ​​are taken according to Table 7, b = 1.0;

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

[0219] The values ​​of the probability coefficient of siltation impact and the probability coefficient of dam break impact under the two working conditions of original unsilted and after siltation are shown in Table 12.

[0220] Table 12. Selection of quantification coefficients for comprehensive risk of reservoir inundation under different sedimentation conditions

[0221] Working conditions Degree of siltation Sedimentation impact probability coefficient Dam break impact probability coefficient Original unsilted 0 1 1 After siltation 31％ 1.3 1.3

[0222] (4) Based on the calculation formula 7 for the comprehensive risk of reservoir inundation caused by super-standard flooding with the dual impact probability of siltation and dam failure, the comprehensive risk is calculated for each small unit divided in the model. The calculation results are sorted from large to small. Some example results are shown in Table 13.

[0223] Table 13. Examples of some results of calculation of comprehensive flooding risk of small units in the reservoir downstream model under different sedimentation conditions

[0224]

[0225]

[0226] In summary, by analyzing and assigning values ​​to the two siltation situations, the comprehensive risk of each calculation grid unit downstream of the reservoir under the two siltation situations was obtained.

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

[0228] Figure 6A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

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

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

[0231] Processor 11 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processors, controllers, microcontrollers, etc. Processor 11 executes the various methods and processes described above, such as the method for zoning reservoir inundation risk due to excessive flooding.

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

[0233] Various embodiments of the systems and techniques described 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), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0234] Computer programs for implementing 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 the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

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

[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 can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0237] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0238] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within a cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0239] An embodiment of the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the method for zoning the risk of reservoir inundation caused by excessive flooding as provided in any embodiment of the present application.

[0240] The computer program product may be implemented by writing computer program code for performing the operations of the present invention in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone 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 a remote computer, the remote computer may 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 may 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 the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0242] Note that the above are only preferred embodiments 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 that various obvious changes, readjustments, and substitutions can be made by those skilled in the art 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 the present invention is determined by the scope of the appended claims.

Claims

1. A method for zoning reservoir inundation risk due to excessive flooding, characterized in that: The downstream area of ​​the target reservoir is divided into multiple grid computing units. The zoning method includes: Determining the super-standard flood inundation risk of the corresponding grid computing unit based on the inundation risk basic elements of each grid computing unit, wherein the basic elements include at least the maximum inundation depth, the maximum flow velocity, and the maximum inundation duration of the corresponding grid computing unit; Determining the reservoir siltation impact probability factor of the corresponding grid computing unit based on the relationship between the reservoir siltation degree of each grid computing unit and the super-standard flood inundation risk; Determining the reservoir dam failure impact probability factor of the corresponding grid computing unit under siltation conditions based on the influence of the reservoir siltation degree and the frequency of super-standard floods on the dam failure probability of each grid computing unit, and the relationship between the above influences and the inundation risk of super-standard floods; Calibrate the comprehensive reservoir flooding risk of the probability of dual impact of siltation and dam break under super-standard flood conditions based on the reservoir siltation impact probability factor and the reservoir dam break 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 reservoir submergence risk zoning method according to claim 1 is characterized in that: Before determining the inundation risk degree of the grid computing unit exceeding the standard flood based on the inundation risk basic elements of each grid computing unit, the zoning method further includes: Determining the relationship between the reservoir water level and the reservoir capacity based on the reservoir sedimentation, wherein the reservoir sedimentation is determined based on a historical measured reservoir capacity curve or predicted based on an auxiliary curve for determining sedimentation depth derived from a reservoir capacity and area calculation curve; Calculating the super-standard flood inflow process based on the hydrological information of the upstream basin of the target reservoir; Calculating the discharge flow process of the reservoir flood control operation based on the reservoir operation mode of the target reservoir and the storage capacity curve after sedimentation changes; According to the calculation of the inflow flow process of the super-standard flood and the calculation of the outflow flow process of the reservoir flood control scheduling, a flood dynamic evolution model is constructed based on the multi-source terrain data of the target reservoir, wherein the flood dynamic evolution model is used to simulate the dynamic change process of obtaining flood risk information of different grid computing units at different times.

3. The reservoir submergence risk zoning method according to claim 1 is characterized in that: Determining the inundation risk degree of the grid computing unit exceeding the standard flood based on the inundation risk basic elements of each grid computing unit includes: 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 super-standard flooding, α1 is the correction coefficient of the maximum flow velocity, α2 is the correction coefficient of the maximum flooding duration, and h is the maximum flooding depth.

4. The reservoir submergence risk zoning method according to claim 1 is characterized in that: The reservoir sedimentation impact probability factors determined based on the relationship between the reservoir sedimentation degree and the above-mentioned super-standard flood inundation risk include: Determine the degree of reservoir siltation based on the amount of reservoir siltation at normal water level and the original storage capacity of the reservoir; The reservoir siltation impact probability factor is determined based on the relationship between the reservoir siltation degree and the super-standard flood inundation risk.

5. The method for zoning reservoir submergence risk due to excessive flooding according to claim 1, characterized in that: Based on the influence of the reservoir siltation degree and the frequency of super-standard floods on the probability of dam failure, and the relationship between the above influences and the inundation risk of super-standard floods, the reservoir dam failure impact probability factors under siltation conditions are determined to include: Establishing a functional relationship between the reservoir siltation degree, the frequency of the super-standard flood, and the dam failure impact probability coefficient; The established functional relationship is used to determine the probability factor of reservoir dam failure under siltation conditions.

6. The reservoir submergence risk zoning method according to claim 1 is characterized in that: The comprehensive reservoir flooding risk degree based on the reservoir sedimentation impact probability factor and the reservoir dam breach impact probability factor calibrates the probability of sedimentation-dam breach dual impact under super-standard flood conditions, including: The comprehensive risk of reservoir inundation with the dual impact probability of siltation and dam break under super-standard flood conditions is calculated based on the formula R=a*b*H, where R is the comprehensive risk of reservoir inundation, a is the reservoir siltation impact probability factor, b is the reservoir dam break impact probability factor, and H is the equivalent water depth of the corresponding grid calculation unit.

7. A device for predicting the risk of reservoir flooding exceeding the standard, characterized in that: The downstream area of ​​the target reservoir is divided into a plurality of grid computing units. The prediction device includes: a basic risk determination unit, configured to determine the inundation risk of an excessive flood of a corresponding grid computing unit based on the inundation risk basic elements of each grid computing unit, wherein the basic elements include at least the maximum inundation depth, the maximum flow velocity, and the maximum inundation duration of the corresponding grid computing unit; a sedimentation impact factor determination unit, configured to determine a reservoir sedimentation impact probability factor of a corresponding grid computing unit based on a relationship between the reservoir sedimentation degree of each grid computing unit and the risk of inundation by the super-standard flood; a dam-break impact factor determination unit, configured to determine the reservoir dam-break impact probability factor of the corresponding grid computing unit under siltation conditions based on the influence of the reservoir siltation degree and the frequency of super-standard floods on the dam-break probability of each grid computing unit, and the relationship between the above influences and the inundation risk of super-standard floods; A comprehensive risk determination unit, configured to calibrate a comprehensive reservoir flooding risk of the dual impact probability of siltation and dam break under super-standard flood conditions based on the reservoir siltation impact probability factor and the reservoir dam break impact probability factor; The risk level determination unit is used to perform cluster analysis on the comprehensive risk of reservoir inundation of each grid computing unit using a 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-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method for zoning the reservoir's inundation risk of exceeding standard flooding according to 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 method for zoning the reservoir's inundation risk of excessive flooding according to any one of claims 1 to 6.

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

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