Method, device and medium for flood regulation and breach risk assessment in arid regions

Abstract

This invention proposes a method, apparatus, and medium for flood control and breach risk assessment in arid areas. The method includes: dividing a target arid area, comprising multiple interception nodes, into multiple grids; multiplying the active interception efficiency calculated based on acquired arid area data for any one interception node by a baseline value of natural flood risk to obtain the active interception energy increment; calculating a dynamic defense threshold and a fully controlled risk value based on any grid, using arid area data and the active interception energy increment; when the fully controlled risk value is greater than the dynamic defense threshold, substituting the fully controlled risk value and the dynamic defense threshold into a nonlinear leakage function to obtain a nonlinear leakage coefficient and the fully controlled risk value to obtain a breach coupling risk value; and outputting a final report for the target arid area. According to the technical solution of this invention, the nonlinear leakage mechanism of the interception nodes is considered through a nonlinear leakage function, improving the accuracy of breach risk assessment for the interception nodes.

Description

Technical Field

[0001] This invention relates to the field of engineering flood control assessment technology, and in particular to a method, device and medium for flood control and breach risk assessment in arid areas. Background Technology

[0002] Currently, based on historical flood statistical sequences, design flood parameters for different return periods are determined through frequency analysis. This is then combined with reservoir flood regulation calculations and levee safety verification to assess the flood control standards of flood control projects. In existing technologies, flood inundation simulation is performed using a hydrodynamic model (Fast Analysis System for Flood, or FASFLOOD model). The hydrodynamic model solves shallow water equations numerically and outputs the flood inundation depth field, velocity field, and inundation range, thus describing "where the flood occurs and how deep the water is."

[0003] Current methods treat flood control engineering as a "linear risk reduction tool." However, in reality, while efficient control systems can suppress the load on dikes in the long term, keeping downstream flood peaks at a low level, this also means that the historical stress load on downstream dikes is consistently low. This leads to lower design and maintenance standards for downstream dikes. Once an extreme event exceeds the control capacity, the residual energy intensity faced by downstream dikes will far exceed that of a low-efficiency control system, resulting in extreme risks. This illustrates the nonlinear paradox that "the higher the control efficiency, the greater the risk of local failure." Therefore, existing technologies, by failing to consider the nonlinear leakage mechanisms in actual situations, cannot accurately assess the breach risk of interception nodes used for engineering control and the effectiveness of flood control. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method, apparatus, and medium for flood control and breach risk assessment in arid areas. Based on a nonlinear leakage function, it considers the nonlinear leakage mechanism of the interception node, thereby improving the accuracy of breach risk assessment at the interception node, and thus determining the flood control effect based on the breach risk assessment.

[0005] In a first aspect, embodiments of the present invention provide a method for flood control and breach risk assessment in arid areas, the method being applied to the SPRE-R energy transfer model, the method comprising: A target arid region is identified and divided into multiple grids, wherein the target arid region includes multiple interception nodes; Based on any one of the interception nodes, the active interception efficiency is calculated based on the acquired arid area data. The active interception efficiency is multiplied by the natural flood risk baseline value to obtain the active interception energy increment, wherein the active interception efficiency is used to characterize the flood energy interception efficiency. Based on any one of the grids, a dynamic defense threshold and a fully controllable risk value are calculated based on the arid zone data and the active interception energy increment. When the fully controllable risk value is greater than the dynamic defense threshold, the fully controllable risk value and the dynamic defense threshold are substituted into a nonlinear leakage function to obtain a nonlinear leakage coefficient. Based on the nonlinear leakage coefficient and the fully controllable risk value, a breach coupling risk value is obtained. The nonlinear leakage coefficient is used to characterize the energy release ratio when a breach occurs, and the breach coupling risk value is used to characterize the actual risk field. Based on all the breach coupling risk values, all the fully regulated risk values, the natural flood risk baseline value, and the whole basin interception efficiency obtained based on all the active interception efficiencies, a final output report for the target arid area is output, wherein the final output report includes the regulation mode of the target arid area and the basin breach risk assessment results.

[0006] According to some embodiments of the present invention, after dividing the target arid region into multiple grids, the method further includes: The water depth field data set is output based on the arid zone data using a pre-trained hydrodynamic model, wherein the water depth field data set includes the maximum inundation water depth field of all the grids. Based on any one of the grids, when the grid is a wide, shallow, low-slope floodplain, the maximum inundation depth field of the grid is converted into a reference energy field, wherein the expression for the reference energy field of the grid under the wide, shallow, low-slope floodplain condition is: , The reference energy field of the x-th grid. It is the acceleration due to gravity. Let x be the maximum flood depth field, where x is a positive integer; Alternatively, when the grid is located in a rapid canyon section, the maximum flood depth field is converted into the reference energy field, and a kinetic energy correction term is added based on the reference energy field. The expression for the reference energy field of the grid located in the rapid canyon section is: , For the kinetic energy correction term, Let x be the flow rate of the x-th grid.

[0007] According to some embodiments of the present invention, an active interception efficiency is calculated based on acquired arid zone data, and the active interception efficiency is multiplied by a baseline value of natural flood risk to obtain the active interception energy increment, including: The active interception efficiency was calculated based on the improved Bruns formula and the arid region data, wherein the expression of the improved Bruns formula is: , For the aforementioned active interception efficiency, To maximize interception efficiency, This is the efficiency growth coefficient. Let 'a' be the total reservoir capacity of the interception node, and 'a' be the reservoir capacity index. This is a correction item for the engineering grade; The product of the natural flood risk baseline value and the active interception efficiency equals the active interception energy increment, wherein the expression for the active interception energy increment is: , For the active interception of energy increment, This is the baseline value for the natural flood risk.

[0008] According to some embodiments of the present invention, a dynamic defense threshold and a fully controllable risk value are calculated based on the arid zone data and the active interception energy increment, including: Based on the arid region data, an effective attenuation coefficient and a dynamic defense threshold are calculated. A lower limit constraint is applied to the effective attenuation coefficient, wherein the formula for calculating the effective attenuation coefficient is: , Based on the attenuation coefficient, The soil erosion feedback coefficient, The soil erosion intensity of the grid is given. This is the slope or roughness feedback coefficient. Given the normalized riverbed slope or surface roughness of the grid, the formula for calculating the dynamic defense threshold is: , The dynamic defense threshold is used to characterize the actual defense threshold of the intercepting node. The maximum flood energy threshold, The effective attenuation coefficient is defined as the soil erosion reduction coefficient, and the lower limit constraint of the effective attenuation coefficient is: ; The fully modulated risk value is calculated based on the effective attenuation coefficient and the interception energy increment, wherein the calculation formula for the fully modulated risk value is: , The full control risk value for the x-th grid, Let x be the distance of the x-th grid cell along the river channel from the nearest upstream interception node. The reference distance for measuring the downstream distance is based on the interception node as the origin. The term is an exponential decay term, which characterizes the spatial dissipation of flood energy from the source node downstream.

[0009] According to some embodiments of the present invention, substituting the fully controllable risk value and the dynamic defense threshold into a nonlinear leakage function yields a nonlinear leakage coefficient. Based on the nonlinear leakage coefficient and the fully controllable risk value, a breach coupling risk value is obtained, including: Obtain the nonlinear leakage function, and substitute the fully controllable risk value and the dynamic defense threshold into the nonlinear leakage function to obtain the nonlinear leakage coefficient, wherein the expression of the nonlinear leakage function is: , The nonlinear leakage coefficient is... The maximum leakage ratio is used to characterize the upper limit of energy release when the dike completely fails. This represents the initial leakage rate. The logarithmic growth factor is used to control the rate at which the leakage ratio increases with the threshold amount. The nonlinear leakage coefficient is multiplied by the fully controllable risk value to obtain the nonlinear leakage increment of the grid. The fully controllable risk value is added to the nonlinear leakage increment to obtain the breach coupling risk value. The formula for calculating the nonlinear leakage increment is as follows: , The formula for calculating the breach coupling risk value, given the nonlinear leakage increment, is as follows: , The risk value for the breach coupling is given.

[0010] According to some embodiments of the present invention, before outputting the final output report of the target arid area based on all the breach coupling risk values, all the fully regulated risk values, the natural flood risk baseline value, and the basin-wide interception efficiency obtained based on all the active interception efficiencies, the method further includes: Multiple discrete candidate values ​​are generated for all pre-calibration parameters of the SPRE-R energy transfer model. Multiple candidate parameter combinations are formed based on all the discrete candidate values. Fine-structured grid search calibration is performed by traversing all the candidate parameter combinations through a full-parameter Cartesian product network. Based on any one of the candidate parameter combinations, the SPRE-R energy transfer model is configured based on the candidate parameter combinations, and the SPRE-R energy transfer model outputs the along-path distribution of all candidate breach coupling risk values ​​of the target arid area based on the arid area data; Obtain a dual objective function, calculate multiple objective function values ​​for all candidate parameter combinations based on the dual objective function, and determine the candidate parameter combination corresponding to the largest objective function value as the optimal parameter set, wherein the optimal parameter set includes the optimal calibration parameters corresponding to all pre-calibration parameters; The expression for the dual objective function is as follows: , The objective function value is the combination of the candidate parameters, and NSE is the Nash-Satcliffe efficiency coefficient. This is used to characterize the degree of agreement between the candidate breach coupling risk value and the reference energy field in terms of spatial morphology along the breach. The coupling risk value of the candidate breach. For spatial penalty terms, The first river channel location in the target arid zone when the fully regulated risk value is greater than the dynamic defense threshold.

[0011] According to some embodiments of the present invention, after determining the candidate parameter combination corresponding to the largest objective function value as the optimal parameter set, the method further includes: A plurality of perturbation parameters are obtained by applying uniformly distributed perturbations to all the optimal calibration parameters respectively. Multiple sets of perturbation parameters are generated based on all the perturbation parameters, wherein each set of perturbation parameters consists of multiple perturbation parameters, and all perturbation parameters in each set of perturbation parameters correspond to different optimal calibration parameters. For any set of disturbance parameters, the SPRE-R energy transfer model is configured based on the set of disturbance parameters, and the SPRE-R energy transfer model generates a watershed-scale decision index set based on the arid zone data; A preset confidence interval is constructed based on all the aforementioned watershed-scale decision indicators, and the confidence interval is written into the final output report. The watershed-scale decision indicators include regulation capacity, the proportion of breach risk sections, and the flood control effect-cost ratio. Multiple sensitivity parameter groups are generated based on all the optimal calibration parameters and all the disturbance parameters. The response change of each of the sensitivity parameter groups is calculated and determined. The measurement accuracy input of the optimal calibration parameter corresponding to the disturbance parameter corresponding to the response change is preferentially allocated based on the sorting order of all the response change from largest to smallest. The sensitivity parameter group includes multiple optimal calibration parameters and one disturbance parameter.

[0012] According to some embodiments of the present invention, based on all the breach coupling risk values, all the fully regulated risk values, the natural flood risk baseline value, and the basin-wide interception efficiency obtained based on all the active interception efficiencies, a final output report for the target arid area is output, including: The risk status dashboard displays the basin-scale decision indicator group with the confidence interval, the fully regulated risk value, the natural flood risk baseline value, the breach coupling risk value, and the breach risk overflow value, wherein the breach risk overflow value is equal to the breach coupling risk value minus the nonlinear leakage increment. Based on all the fully regulated risk values, a residual energy intensity strip map along the river is generated, a comprehensive efficiency index of all the interception nodes is constructed, a bubble chart is generated based on all the comprehensive efficiency index, and the bubble chart is superimposed with the residual energy intensity strip map along the river to obtain a spatial matching degree map. The residual energy intensity strip map along the river is used to characterize the normalized intensity of all the fully regulated risk values ​​of the river channel in the target arid area. When the spatial matching degree map represents the residual energy concentration area in the residual energy intensity strip map along the river that highly overlaps with all the interception nodes, the watershed breach risk assessment result is a good match. Alternatively, if the residual energy concentration area is highly concentrated at the interception node located at the mid-stream altitude, and the active interception efficiency of the interception node located at the mid-stream altitude is the lowest, the basin breach risk assessment result is a matching deviation type; or, if the residual energy concentration area is evenly distributed throughout the entire river section of the target arid area, and there is no continuous section where the residual energy intensity of the residual energy concentration area exceeds the average value + 1 standard deviation of the entire river section, the basin breach risk assessment result is a fully vulnerable type. When the whole basin interception efficiency is greater than or equal to 50%, and the ratio of the breach coupling risk value of the first breach exceeding the threshold in the target arid area to the baseline value of the natural flood risk is greater than or equal to 50%, the regulation mode of the target arid area is to emphasize local high vulnerability. Alternatively, when the whole-basin interception efficiency is less than 40%, and the breach risk spillover value obtained after whole-basin equalization is within the minimum 20% range, the regulation mode of the target drought area is a weak regulation energy dissipation offset type; or, when the whole-basin interception efficiency is less than 35%, and the threshold locations of the target drought area are dispersed in the river channel of the target drought area, and there is no single dominant residual energy concentration area whose residual energy intensity exceeds the average value of the whole river section plus 1 standard deviation, the regulation mode of the target drought area is a dispersed, fragile, and non-concentrated hotspot type.

[0013] Secondly, embodiments of the present invention provide a device for flood control and breach risk assessment in arid areas, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which are executed by the at least one control processor to enable the at least one control processor to perform the flood control and breach risk assessment method in arid areas as described in the first aspect above.

[0014] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions for performing the flood control and breach risk assessment method for arid areas as described in the first aspect above.

[0015] According to an embodiment of the present invention, a method for flood control and breach risk assessment in arid areas, applied to the SPRE-R energy transfer model, has at least the following beneficial effects: A target arid area is identified, and the target arid area is divided into multiple grids, wherein the target arid area includes multiple interception nodes; based on any one of the interception nodes, an active interception efficiency is calculated based on acquired arid area data; the active interception efficiency is multiplied by a natural flood risk baseline value to obtain an active interception energy increment, wherein the active interception efficiency is used to characterize flood energy interception efficiency; based on any one of the grids, a dynamic defense threshold and a complete control risk value are calculated based on the arid area data and the active interception energy increment; when the complete control... If the risk value is greater than the dynamic defense threshold, the fully regulated risk value and the dynamic defense threshold are substituted into the nonlinear leakage function to obtain the nonlinear leakage coefficient. Based on the nonlinear leakage coefficient and the fully regulated risk value, the breach coupling risk value is obtained. The nonlinear leakage coefficient is used to characterize the energy release ratio when a breach occurs, and the breach coupling risk value is used to characterize the actual risk field. Based on all the breach coupling risk values, all the fully regulated risk values, the natural flood risk baseline value, and the whole basin interception efficiency obtained based on all the active interception efficiencies, the final output report of the target arid area is output. The final output report includes the regulation mode of the target arid area and the basin breach risk assessment results. According to the technical solution of this invention, by constructing a dynamic defense threshold and a nonlinear leakage function, the nonlinear physical process of progressive dam failure is transformed into an operable formula expression for the first time. The nonlinear paradox mechanism of actual floods is also transformed into a quantifiable formula system for the first time. This takes into account the nonlinear mechanism between the interception node and the flood, reducing the systematic deviation between the breach location and breach intensity, thus enabling a correct assessment of the breach risk of the interception node. Furthermore, it quantitatively reveals the formation mechanism of the "efficient regulation - local high vulnerability" paradox, and through a three-layer nested risk state—namely, the natural flood risk baseline value, the fully regulated risk value, and the breach coupling risk value—the nonlinear paradox is transformed into a quantifiable and comparable decision indicator, thereby correctly assessing the flood regulation effect of the interception node based on breach risk assessment. Attached Figure Description

[0016] Figure 1 This is a flowchart of a flood control and breach risk assessment method for arid areas provided in one embodiment of the present invention; Figure 2 This is a flowchart of a method for implementing flood control and breach risk assessment in arid areas using the SPRE-R energy transfer model, provided in another embodiment of the present invention. Figure 3This is a structural diagram of a flood control and breach risk assessment device for arid areas provided in another embodiment of the present invention. Detailed Implementation

[0017] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0018] In the description of this invention, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0019] In the description of this invention, "several" means one or more, "more than" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. The use of "first" and "second" in the description is merely for distinguishing technical features and should not be construed as indicating or implying relative importance, or implicitly indicating the number of indicated technical features, or implicitly indicating the order of the indicated technical features.

[0020] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0021] First, the terms used in this embodiment will be explained: SPRE-R energy transfer model: short for Source-Pathway-Receptor-Efficiency-Resilience framework, is a conceptual framework that models flood disaster processes as a "risk energy flow" propagating along river channels, and tracks its interception, attenuation and leakage process step by step; Source: This refers to the water source interception node, used to characterize the active interception process of inflow flood peak energy by reservoirs / dams within the watershed. The active interception efficiency of each interception node is calculated using a modified Brune-type efficiency formula. Its value is determined by the storage capacity. (10,000 m3), Engineering Grade Maximum interception rate and attenuation coefficient Exponential parameters Together, they determine that when multiple libraries are connected in series, a step-like energy drop occurs at each node; Pathway: A geomorphic modulation attenuation path, used to characterize the exponential attenuation process of flood energy as it propagates downstream along the river channel after being intercepted by the water source, modulated by both topographic roughness and soil erosion intensity. Effective attenuation coefficient. Based on the basic attenuation coefficient Feedback item on soil erosion intensity Slope or roughness item Joint decision; Receptor: Represents the receptor's defense threshold and leakage, used to characterize the passive defense process of levee or flood control projects against residual flood energy, and its dynamic defense threshold. Dynamically changing due to spatially heterogeneous soil erosion; when residual energy (i.e., the fully modulated risk value) exceeds the passive defense threshold, a nonlinear leakage increment described by a logarithmic function is generated. This leads to the risk value of breach coupling. A sudden increase; Efficiency: Refers to the efficiency of flood control, characterizing the rate of peak flow reduction achieved by a reservoir-levee engineering system under specified design flood conditions, in order to mitigate natural flood risks. To fully control the risk value The reduction magnitude is represented, with a 95% confidence interval; Resilience: Characterized by the ability of an engineering flood control system to withstand a sudden increase in the risk of breach under extreme flood conditions. It is measured by Observed Flood Risk (measured baseline flood risk value). Fully Regulated Risk (the theoretical lower limit of full regulation, i.e., the risk value of full regulation) Breach-Coupled Risk A comprehensive representation of three-layer nested risk states and their differences; Natural flood risk baseline value: via This indicates that the water depth field directly output by hydrodynamic models such as FASFLOOD is used to characterize the water depth field under current reservoir operating conditions. The derived baseline risk field, where, , serving as the starting benchmark for the SPRE-R energy transfer model; Fully controllable risk value: through This indicates that the theoretical risk lower limit field is used to characterize the situation where water source interception and landform modulation attenuation are fully utilized without any dike breach, reflecting the ideal protection effect of the engineering design.

[0022] Breach coupling risk value: through This indicates that it is used to characterize the nonlinear leakage increment caused by a levee breach. The actual engineering state risk field superimposed on fully controlled risks. It is used to characterize the true risk level under the combined effect of engineering defects and geological weak points; Nash-Sutcliffe Efficiency (NSE): Used to assess the breach coupling risk Rcp output by the SPRE-R model and the depth profile (reference energy field) along the hydrodynamic model. The degree of spatial morphological conformity between the two is used to assess internal consistency (i.e., non-predictive accuracy), requiring NSE > 0.67; Monte Carlo parameter uncertainty propagation: The nine parameters of the SPRE-R energy transfer model are randomly sampled n≥1000 times within ±20% of the optimal solution. The parameter uncertainty is propagated to watershed-scale decision indicators such as the estimated value of regulation capacity and the proportion of breach risk sections, and a 95% confidence interval is given. Improved Bruns formula: This is a Brune-type interception efficiency formula, the prototype of which was originally proposed by Brune (1953). This invention improves and extends the reservoir sediment interception efficiency formula by introducing an engineering grade correction term C and a two-parameter form ( and This makes it applicable to the description of flood energy interception efficiency in multi-level flood control projects; Two-dimensional shallow water equation hydrodynamic (Fast Analysis System for Flood, abbreviated as FASFLOOD): The conserved finite volume method and irregular triangular mesh are used for solution, providing the basic water depth field for this invention. enter.

[0023] This invention provides a method, apparatus, and medium for flood control and breach risk assessment in arid areas. The method is applied to the SPRE-R energy transfer model. The method includes: identifying a target arid area; dividing the target arid area into multiple grids, wherein the target arid area includes multiple interception nodes; calculating an active interception efficiency based on acquired arid area data using any one of the interception nodes; multiplying the active interception efficiency by a natural flood risk baseline value to obtain an active interception energy increment, wherein the active interception efficiency characterizes flood energy interception efficiency; and calculating a dynamic defense threshold and a complete control risk based on any one of the grids, using the arid area data and the active interception energy increment. When the fully regulated risk value is greater than the dynamic defense threshold, the fully regulated risk value and the dynamic defense threshold are substituted into the nonlinear leakage function to obtain the nonlinear leakage coefficient. Based on the nonlinear leakage coefficient and the fully regulated risk value, the breach coupling risk value is obtained. The nonlinear leakage coefficient is used to characterize the energy release ratio when a breach occurs, and the breach coupling risk value is used to characterize the actual risk field. Based on all the breach coupling risk values, all the fully regulated risk values, the natural flood risk baseline value, and the whole basin interception efficiency obtained based on all the active interception efficiencies, the final output report of the target arid area is output. The final output report includes the regulation mode of the target arid area and the basin breach risk assessment results. According to the technical solution of this invention, by constructing a dynamic defense threshold and a nonlinear leakage function, the nonlinear physical process of progressive dam failure is transformed into an operable formula expression for the first time. The nonlinear paradox mechanism of actual floods is also transformed into a quantifiable formula system for the first time. This takes into account the nonlinear mechanism between the interception node and the flood, reducing the systematic deviation between the breach location and breach intensity, thus enabling a correct assessment of the breach risk of the interception node. Furthermore, it quantitatively reveals the formation mechanism of the "efficient regulation - local high vulnerability" paradox, and through a three-layer nested risk state—namely, the natural flood risk baseline value, the fully regulated risk value, and the breach coupling risk value—the nonlinear paradox is transformed into a quantifiable and comparable decision indicator, thereby correctly assessing the flood regulation effect of the interception node based on breach risk assessment.

[0024] The technical solutions of the embodiments of the present invention will be further illustrated in the following figures.

[0025] Reference Figure 1 , Figure 1 This is a flowchart illustrating a method for flood control and breach risk assessment in arid regions, provided by an embodiment of the present invention. This method is applied to the SPRE-R energy transfer model and includes, but is not limited to, the following steps: S10, determine the target arid area and divide the target arid area into multiple grids, where the target arid area includes multiple interception nodes.

[0026] It should be noted that the target arid area includes at least one arid watershed, which can be a river channel. To prevent flood discharge from the river channel, multiple interception nodes are set up along the river channel. The types of interception nodes include, but are not limited to, flood control engineering nodes such as reservoirs, dams, and dikes. The interception nodes are used to intercept floods. Along the direction of water flow in the river channel, the locations of the interception nodes include those located upstream, midstream, and downstream of the river channel.

[0027] It should be noted that the target arid area is divided into multiple grids, and the arid area watershed includes at least one grid, with a spatial resolution of no less than 30m.

[0028] S20, based on any interception node, the active interception efficiency is calculated based on the acquired arid area data. The active interception efficiency is multiplied by the natural flood risk baseline value to obtain the active interception energy increment. The active interception efficiency is used to characterize the flood energy interception efficiency. It should be noted that the SPRE-R energy transfer model includes a Source layer, a Pathway layer, and a Receptor layer. The Source layer is used to calculate the active interception efficiency of the interception nodes of reservoirs or dams. The active interception efficiency is used to characterize the flood energy interception efficiency of the interception nodes.

[0029] It should be noted that the baseline value of natural flood risk for each interception node is not necessarily the same; the baseline value of natural flood risk is used to characterize the maximum flood energy threat faced by the downstream disaster-bearing body under the current operating conditions of the reservoir (interception node) if the reservoir project fails completely (or there is no reservoir); it is the "starting point" of engineering regulation and also the benchmark for measuring the necessity of the project.

[0030] It should be noted that after obtaining the active interception efficiency and active interception energy increment of the interception nodes, multiple interception nodes in the arid watershed are assessed: when multiple upstream and downstream tandem reservoirs exist within the arid watershed, each interception node is processed sequentially from upstream to downstream. A stepped energy drop occurs at each interception node (typically 40%–50%), and after superposition, the pathway enters the exponential decay phase. In the multi-reservoir tandem operation mode, if the inflow to the downstream reservoir has already been regulated by the upstream reservoir, the active interception efficiency should be calculated based on the inflow process after upstream regulation, rather than the natural inflow process, to avoid redundant calculation of the regulation effect. For the joint optimization scheduling of the reservoir group, the output of the joint flood control calculation can directly replace the reservoir-by-reservoir calculation in the formula, and the joint flood control efficiency serves as the comprehensive interception efficiency input for the Source layer. In summary, after obtaining the active interception energy increment, and before calculating the effective decay coefficient, the superposition processing of multiple reservoirs in the arid watershed is completed.

[0031] S30, based on any grid, calculates the dynamic defense threshold and the fully controllable risk value based on arid zone data and active interception energy increment. When the fully controllable risk value is greater than the dynamic defense threshold, substitute the fully controllable risk value and the dynamic defense threshold into the nonlinear leakage function to obtain the nonlinear leakage coefficient. Based on the nonlinear leakage coefficient and the fully controllable risk value, the breach coupling risk value is obtained. The nonlinear leakage coefficient is used to characterize the energy release ratio when a breach occurs, and the breach coupling risk value is used to characterize the actual risk field. It should be noted that the SPRE-R energy transfer model also includes a Pathway layer and a Receptor layer. The Pathway layer is used to calculate the geomorphic modulation exponential decay, and the Receptor layer is used to calculate the dynamic defense threshold, the nonlinear leakage coefficient, and the nonlinear leakage increment.

[0032] It should be noted that the fully controlled risk value is used to characterize the theoretical minimum risk state when the engineering control of the interception nodes in the grid is fully utilized without breach. This invention introduces a spatially heterogeneous dynamic defense threshold, replacing the single fixed dike defense water level parameter in traditional methods. The dynamic defense threshold takes into account the effect of topographic conditions on flood energy attenuation, thus explaining why breach risk is highly concentrated in specific sections rather than evenly distributed across the entire river section in arid regions.

[0033] It should be noted that when the fully controllable risk value is less than or equal to the dynamic defense threshold, meaning the grid has not breached, the grid's breach coupling risk value is equal to the fully controllable risk value. When the fully controllable risk value is greater than the dynamic defense threshold, meaning the grid has breached, it is necessary to calculate the nonlinear leakage coefficient and nonlinear leakage increment, taking into account the energy release characteristics of the gradual breach of the dam and the nonlinear mechanism of the flood. The fully controllable risk value can characterize the theoretical minimum risk state when the reservoir interception and landform attenuation both achieve their design effects, and there are no breaches in the downstream levees, reflecting the protection level implied in the engineering design documents.

[0034] It should be noted that the logarithmic nonlinear leakage function truly reflects the physical mechanism of progressive dam failure, which is superior to the linear assumptions in existing technologies. Traditional linear excess flow models assume that the excess leakage is proportional to the excess magnitude, which is seriously inconsistent with the actual physical process of progressive dam failure, which exhibits a nonlinear characteristic of initial logarithmic growth followed by saturation. This invention, by introducing a logarithmic nonlinear leakage function, better matches the physical mechanism of progressive dam failure in mathematical form, while avoiding the numerical inconsistencies of unbounded linear functions.

[0035] It should be noted that after the SPRE-R energy transfer model outputs the nonlinear leakage increment, the three nested risk states of the core output of the SPRE-R energy transfer model have been established, including the natural flood risk baseline, the fully controllable risk value, and the breach coupling risk value. The breach coupling risk value can characterize the actual risk state after a breach occurs when residual energy exceeds the dynamic defense threshold under actual engineering conditions (considering the degradation of the levee threshold due to soil erosion); the breach coupling risk value is usually higher than the fully controllable risk value, revealing the gap between engineering design and actual conditions.

[0036] S40 outputs the final report for the target arid area based on all breach coupling risk values, all fully regulated risk values, natural flood risk baseline values, and the whole basin interception efficiency obtained based on all active interception efficiencies. The final output report includes the regulation mode of the target arid area and the basin breach risk assessment results.

[0037] It should be noted that, in this invention, by constructing a dynamic defense threshold and a nonlinear leakage function related to the degree of historical load suppression, this nonlinear paradox mechanism is transformed into a quantifiable formula system for the first time. Furthermore, a logarithmic nonlinear leakage coefficient function is introduced, transforming the nonlinear physical process of progressive dam failure into an operable formula expression for the first time. This takes into account the nonlinear mechanism between the interception node and the flood, reducing the systematic deviation between the breach location and breach intensity. Moreover, the formation mechanism of the "efficient regulation-local high vulnerability" paradox is quantitatively revealed, and through a three-layer nested risk state—the natural flood risk baseline value, the fully regulated risk value, and the breach coupling risk value—the nonlinear paradox is transformed into a quantifiable and comparable decision indicator.

[0038] Additionally, in one embodiment, in Figure 1 Following step S10, the following steps may also be included, but are not limited to: S101 outputs a water depth field data set based on arid zone data using a pre-trained hydrodynamic model. The water depth field data set includes the maximum inundation water depth field of all grids. S102, based on any grid, when the grid is a wide, shallow, low-slope floodplain, the maximum inundation depth field of the grid is converted into a reference energy field, where the expression for the reference energy field of the grid under the wide, shallow, low-slope floodplain condition is: , Let x be the reference energy field of the x-th grid. It is the acceleration due to gravity. The maximum flood depth field is represented by x, where x is a positive integer. Alternatively, when the grid is located in a rapid canyon section, the maximum flood depth field is converted into a reference energy field, and a kinetic energy correction term is added based on the reference energy field. The expression for the reference energy field of the grid located in the rapid canyon section is: , This is a kinetic energy correction term. Let x be the flow rate of the x-th grid cell.

[0039] It should be noted that this embodiment includes basic data acquisition and reference energy field construction. First, the hydrodynamic model is run to extract the maximum submerged depth field, and then the maximum submerged depth field is converted into a reference energy field, thereby completing the construction of the reference energy field.

[0040] It should be noted that by running a pre-trained FASFLOOD (or an equivalent-precision two-dimensional shallow water equation hydrodynamic model), a full-basin hydrodynamic simulation under the design flood condition of the target return period (usually 50 or 100 years) is completed. The following raster output is extracted, wherein the spatial resolution of the raster output is not less than 30m. The unit of the maximum inundation depth field is meters (m), which is used to characterize the maximum water depth that occurs in each raster during the simulation period, reflecting the maximum inundation intensity of the flood.

[0041] It should be noted that, in addition to the maximum inundation depth field, the hydrodynamic model also outputs the inundation duration field, flood arrival time field, depth-averaged velocity field, water depth profile curves of typical cross-sections along the flow path, and the quality control requirements for the hydrodynamic model. The inundation duration field is obtained through D... The time field, expressed in hours, represents the duration for which each grid cell remains submerged, reflecting the cumulative damage a flood can cause to assets. The parameters are: 1) In hours, representing the arrival time of the flood front at each grid, reflecting the available time window for downstream emergency response; 2) Depth-averaged velocity field, optional, used to correct the kinetic energy term, activated when there are localized jet stream sections within the target arid area; 3) Typical cross-sectional water depth profile curves along the route, used for parameter calibration. These curves are extracted from several representative longitudinal sections, serving as the reference energy profile for SPRE-R energy transfer model calibration; 4) Quality control requirements for the hydrodynamic model, including the difference between the calculated water depth and the design standard water depth. ≤0.1m, relative error of peak flow ≤10% (refer to IWHR national standard), and whole basin mass conservation error (volume closure error) ≤1.5%.

[0042] It should be noted that the maximum submerged water depth field output by the hydrodynamic model is... Transformed into a reference energy field As a calibration benchmark for the SPRE-R energy transfer model: ,in, For gravitational acceleration (9.81) ), This represents the maximum flood depth (m) output by the FASFLOOD model. The above approximation (using the hydrostatic potential energy term instead of the total specific energy) holds true under wide, shallow, low-slope floodplain conditions. The criterion is: depth-average flow velocity. When <1.5 m / s, the kinetic energy term ( The proportion of energy in the total energy is less than 8%, and the approximate accuracy is acceptable.

[0043] It should be noted that if there are rapids in the canyon section within the target arid area ( If the speed is greater than 1.5 m / s, then a kinetic energy correction term is added, and the complete specific energy is used: The physical significance of the reference energy field approximation for the supplementary kinetic energy correction term lies in the fact that the main mechanism by which floods threaten downstream dikes and disaster-bearing bodies is water level (pressure head) rather than flow velocity (kinetic energy). In the scenario of arid valley floods, flood energy is mainly stored and released in the form of potential energy, and the above approximation has sufficient physical rationality.

[0044] It should be noted that after converting the maximum inundation depth field into a reference energy field, an auxiliary data set is also required. This auxiliary data set includes raster soil erosion intensity, digital elevation model, reservoir technical parameters, dike technical parameters, and historical weak dike section survey records. Soil erosion intensity... The spatial resolution is no less than 100m, normalized to the [0,1] interval; the digital elevation model, or DEM, is used to extract the river slope. (If using the slope parameter), it can be NASADEMv1 (30m, filled with ICESat / GLAS voids) or a product of equivalent precision; Reservoir technical parameters include the total reservoir capacity S (10,000 m3), engineering grade C (determined according to flood control standards, such as C=1 for a 100-year flood, C=0.5 for a 50-year flood, and so on), reservoir capacity-water level relationship curve, discharge capacity curve, and design flood flood control calculation results (inflow / outflow process lines); Dike technical parameters include the design flood control level of each dike section (used to back-calculate the basic defense energy threshold). The data includes: levee material, completion date and most recent major repair date (used for prior estimation of α parameters); historical breach records (location, time, breach width, used for constructing spatial penalty terms during the calibration phase); and historical weak levee section investigation records derived from engineering inspection reports, hazard records, or potential risk sections marked in dam safety assessment reports after each flood. Reference for spatial penalty terms.

[0045] It should be noted that, in existing technologies, flood inundation simulation methods based on hydrodynamic models are currently the mainstream technology. Representative technologies in this field include HEC-RAS, MIKE FLOOD, LISFLOOD-FP, and FASFLOOD models. These methods numerically solve shallow water equations to output the flood inundation depth field, velocity field, and inundation extent, offering high accuracy in describing flood processes and making them the mainstream technology for flood risk mapping. However, the traditional hydrodynamic model framework was designed to describe the state of water movement, while the core requirement of flood risk management is to diagnose energy transfer mechanisms. Its core output is the "depth field," which precisely describes "where the flood occurs and how deep the water is," but it fundamentally fails to answer questions such as "where the energy driving the flood comes from, where it goes after passing through the engineering system, and why an efficient reservoir downstream experiences extreme local risks in a specific section of the dike." This fundamental deficiency stems from the essential mismatch between the original design intent of the traditional hydrodynamic model framework and the core requirements of flood risk management.

[0046] It should be noted that in this application, by converting the "water depth field" into an "energy field," the fundamental methodological gap between water depth field diagnosis and energy field mechanism is filled, achieving a paradigm shift from phenomenon description to mechanism quantification. Existing hydrodynamic models output water depth fields describing the location and intensity of floods, but cannot explain "why an efficient control system might generate extreme risks in a specific locality." This invention, by constructing a complete energy transport chain (Source→Pathway→Receptor), is the first to uniformly model the engineering response process of flood disasters as quantifiable energy flow, achieving an upgrade from "phenomenon description" to "mechanism diagnosis."

[0047] Additionally, in one embodiment, in Figure 1 In step S20, the active interception efficiency is calculated based on the acquired arid zone data. The active interception efficiency is then multiplied by the baseline value of natural flood risk to obtain the active interception energy increment, including but not limited to the following steps: S21, the active interception efficiency is calculated based on the improved Bruns formula and arid region data. The expression for the improved Bruns formula is: , To improve the efficiency of proactive interception, To maximize interception efficiency, This is the efficiency growth coefficient. Let 'a' be the total reservoir capacity of the intercepting node, and 'a' be the reservoir capacity index. This is a correction item for the engineering grade; S22, the product of the natural flood risk baseline value and the active interception efficiency equals the active interception energy increment, where the expression for the active interception energy increment is: , In order to actively intercept energy increments, This is the baseline value for natural flood risk.

[0048] It should be noted that the active interception efficiency of the reservoir is calculated at the Source layer of the SPRE-R energy transfer model; for each interception node (reservoir or dam) in the arid watershed, the active interception efficiency is calculated using a modified Brune-type formula. The expression of the modified Brune-type formula is as follows: , The maximum possible interception efficiency is defined as [0.6, 0.8], which is used to characterize the theoretical maximum flood interception capacity of the reservoir under design conditions. A higher value indicates that the reservoir has a large proportion of flood control capacity and strong discharge control capability. The efficiency growth factor, calibrated through grid search, is used to determine the saturation rate at which interception efficiency increases with storage capacity. Total reservoir capacity (10,000 sq m) The larger the total reservoir capacity, the higher the interception efficiency and the closer it is to the maximum possible interception efficiency. The storage capacity index, approximately equal to 6, is used to characterize the non-linear relationship between interception efficiency and storage capacity, causing the efficiency curve to grow slowly with small storage capacity and tend to saturate with large storage capacity. This is an engineering grade correction item, which is determined according to the reservoir flood control standard (100-year flood standard C=0, 50-year flood standard C=0.15, 20-year flood standard C=0.25, and so on), used to characterize the reduction of effective interception capacity due to flood control safety margin.

[0049] It should be noted that after obtaining the active interception efficiency of each interception node, the active interception energy increment at each interception node is calculated based on the active interception efficiency. The formula for calculating the active interception energy increment is as follows: ,in, Let be the baseline value of natural flood risk at the r-th interception node, where Depend on Approximate representation.

[0050] It should be noted that when the flood inflow far exceeds the reservoir's design discharge capacity, the active interception efficiency will experience a non-linear decline, and the calculation formula for the increase in active interception energy will be affected. The verification value under extreme inflow conditions (i.e., determined through flood control calculations) should be used, rather than the design value, to avoid systematically overestimating the control capacity.

[0051] Additionally, in one embodiment, in Figure 1In step S30, the dynamic defense threshold and the risk value for complete regulation are calculated based on arid zone data and the incremental energy of active interception, including but not limited to the following steps: S31, based on arid region data, calculates the effective attenuation coefficient and dynamic defense threshold, and applies a lower limit constraint to the effective attenuation coefficient. The formula for calculating the effective attenuation coefficient is as follows: , Based on the attenuation coefficient, The soil erosion feedback coefficient, The soil erosion intensity of the grid. This is the slope or roughness feedback coefficient. The formula for calculating the dynamic defense threshold, which is the normalized riverbed slope or surface roughness of the grid, is as follows: , The dynamic defense threshold is used to characterize the actual defense threshold of the intercepting node. The maximum flood energy threshold, Let be the soil erosion reduction coefficient, and the lower limit constraint for the effective attenuation coefficient is: ; S32, the fully modulated risk value is calculated based on the effective attenuation coefficient and the intercepted energy increment. The formula for calculating the fully modulated risk value is as follows: , The full control risk value for the x-th grid. Let x be the distance of the x-th grid cell along the river channel from the nearest upstream interception node. To measure the downstream reference distance with the interception node as the origin, The exponential decay term characterizes the spatial dissipation of flood energy from the source node downstream.

[0052] It should be noted that, as flood energy is intercepted at the water source node and propagates downstream along the river channel, it undergoes exponential attenuation due to the combined modulation of topographic roughness and soil erosion intensity. Therefore, the effective attenuation coefficient is calculated to characterize the exponential attenuation propagation of flood energy.

[0053] It should be noted that the calculation of geomorphic modulation exponential attenuation is completed at the Pathway layer of the SPRE-R energy transfer model. First, the effective attenuation coefficient and its physical mechanism are constructed. Then, based on the effective attenuation coefficient, the fully modulated risk field is calculated. Finally, the dynamic defense threshold, nonlinear leakage coefficient, and nonlinear leakage increment are calculated at the Receptor layer of the SPRE-R energy transfer model. This invention simultaneously models the three interacting physical processes of engineering interception, geomorphic attenuation, and levee threshold leakage within a unified parameter system, overcoming the systematic errors of isolated analysis.

[0054] It should be noted that after flood energy is intercepted at the water source, it undergoes exponential attenuation as it propagates downstream along the river channel and floodplain, modulated by both topographic roughness and soil erosion intensity. The formula for calculating the effective attenuation coefficient is: ,in: Basic attenuation coefficient (unit: ), used to characterize the basic energy loss rate under homogeneous floodplain conditions, with a value range of approximately 0.03 to 0.12 km⁻¹ (i.e., the typical range of a wide valley floodplain in arid regions). The soil erosion feedback coefficient is to be calibrated and is a positive value. It is used to characterize the enhancing effect of loose and porous soil on surface roughness in highly eroded areas. is the normalized soil erosion intensity at the x-th grid (its value ranges from 0 to 1); For slope or roughness, This is the slope / roughness feedback coefficient, to be calibrated. If high-precision slope data is unavailable, then... =0; Normalized riverbed slope or surface roughness is an optional parameter.

[0055] It should be noted that this invention imposes a strict lower limit constraint on the effective attenuation coefficient: The physical significance of the lower bound constraint is that, even on extremely flat and loose sandy floodplains, flood energy cannot propagate infinitely without any attenuation. This lower bound prevents numerical degradation while ensuring that the SPRE-R energy transfer model is physically reasonable for all geomorphic environments.

[0056] In existing technologies, soil erosion intensity In practical flood control systems, soil erosion intensity plays two opposing roles: along the propagation path, increased surface roughness in highly eroded areas accelerates the attenuation of flood energy along the path, making the soil erosion intensity parameter beneficial for flood control; however, at the receptor node, the foundation of dikes in highly eroded areas is weakened by erosion, lowering the effective defense threshold of the dikes and leading to a systematic overestimation of the flood control capacity of highly eroded sections, making the soil erosion intensity parameter detrimental to flood control. In existing technologies, these two effects are treated in isolation or completely ignored, resulting in a systematic overestimation of the overall flood control capacity of highly eroded sections and a severe underestimation of the risk of breach.

[0057] This invention considers the dual opposing effects of soil erosion, achieving for the first time a unified modeling of the dual opposing effects of soil erosion within a single SPRE-R energy transport model, thus eliminating systematic assessment errors. Soil erosion intensity This invention considers the opposing effects of soil erosion—accelerating decay at the path layer and weakening the threshold at the receptor layer—in the flood energy transport chain, and models these effects separately within a unified parameter system. Furthermore, this invention utilizes two independent parameters... and The joint calibration of the two factors, for the first time, quantitatively separates and balances the two opposing effects of soil erosion intensity, and significantly improves the accuracy of breach risk assessment in highly eroded sections by quantitatively separating their respective contributions through the calibration process.

[0058] It should be noted that the dual effects of soil erosion mentioned above have never been uniformly modeled in existing technologies, leading to a systematic overestimation of the comprehensive flood control capacity of highly eroded sections. In the SPRE-R energy transport model, the pathway layer... Parameters and Receptor layer The parameters control the intensity in these two directions respectively, and the two automatically reach an optimal balance through the NSE objective function during the calibration process.

[0059] It should be noted that in the Pathway layer (i.e., the favorable direction) of the SPRE-R energy transport model, the surface soil in highly eroded areas is loose, the surface roughness is increased, and the energy loss due to friction between floodwater and the ground is increased, thus reducing the effective attenuation coefficient. The rise in elevation accelerates the attenuation of energy along the course of the flood, which has a positive effect on reducing the intensity of downstream floods.

[0060] It should be noted that in the SPRE-R energy transfer model, within the Receptor layer (i.e., the unfavorable direction), the toe of the embankment and the foundation of the embankment in highly eroded areas are weakened by long-term erosion, thus reducing the effective dynamic defense threshold. Lowering the load makes the dikes more prone to seepage and breaches under the same external load, which has a negative impact on flood control safety.

[0061] It should be noted that the fully controllable risk value is calculated based on the effective attenuation coefficient. The fully controllable risk value represents the theoretical lowest risk state when the engineering control is fully utilized without breach. The formula for calculating the fully controllable risk value is as follows: ,in, Let x be the full control risk value of the x-th grid. Let x be the baseline value for natural flood risk in the x-th grid. This represents the incremental interception energy at the nearest upstream interception node. The distance (in km) of the x-th grid from the nearest upstream interception node along the river channel. The reference distance for the interception node location (usually set to 0, meaning the downstream distance is measured with the interception node as the origin); exponential decay term. It is used to characterize the spatial dissipation process of flood energy from the water source node to the downstream.

[0062] It should be noted that the physical image representation of the calculation formula for fully controlled risk value is that after the flood energy experiences a step-like drop at each reservoir, it continues to decay at an exponential rate in the downstream floodplain. The decay rate is modulated in real time by the local soil erosion conditions and the spatial variation of topographic roughness. The decay is faster in high erosion areas and slower in low erosion areas such as hard clay layers.

[0063] It should be noted that the dynamic defense threshold, nonlinear leakage coefficient, and nonlinear leakage increment are calculated in the Receptor layer of the SPRE-R energy transfer model. This invention introduces a spatially heterogeneous dynamic defense threshold, replacing the single, fixed dike defense water level parameter in traditional methods. The formula for calculating the dynamic defense threshold is as follows: ,in, The maximum flood energy threshold that a dike can withstand under ideal maintenance conditions in a target arid region is calculated from the design flood control level and the corresponding water depth (dimensions: ...). (Consistent); α is the soil erosion reduction factor, which needs to be calibrated. Its value range is [0.2, 0.6], which is used to characterize the weakening strength of soil erosion on the dam threshold. The larger the α, the more serious the damage to the integrity of the dam by erosion. The normalized soil erosion intensity (ranging from 0 to 1). The physical meaning of the formula for calculating the dynamic defense threshold is that in areas with severe soil erosion ( →1), the actual defense threshold of the dike Lowest price This refers to 40% to 80% of the nominal defense capability. This spatially varying defense threshold is one of the core mechanisms that causes the risk of breaching to be highly concentrated in specific sections (rather than being evenly distributed throughout the entire river).

[0064] Additionally, in one embodiment, in Figure 1 In step S30, the fully controllable risk value and the dynamic defense threshold are substituted into the nonlinear leakage function to obtain the nonlinear leakage coefficient. Based on the nonlinear leakage coefficient and the fully controllable risk value, the breach coupling risk value is obtained, including but not limited to the following steps: S33, Obtain the nonlinear leakage function. Substitute the fully controllable risk value and the dynamic defense threshold into the nonlinear leakage function to obtain the nonlinear leakage coefficient. The expression for the nonlinear leakage function is: , The nonlinear leakage coefficient is... The maximum leakage ratio is used to characterize the upper limit of energy release when the dike completely fails. This represents the initial leakage rate. The logarithmic growth factor is used to control the rate at which the leakage ratio increases with the threshold excess. S34, multiplying the nonlinear leakage coefficient by the fully controllable risk value yields the nonlinear leakage increment of the grid. Adding the fully controllable risk value to the nonlinear leakage increment equals the breach coupling risk value. The formula for calculating the nonlinear leakage increment is: , The formula for calculating the breach coupling risk value, which represents the nonlinear leakage increment, is as follows: , The risk value for the breach coupling is given.

[0065] It should be noted that this embodiment is used to complete the threshold judgment and nonlinear leakage increment calculation. For each grid in the arid watershed, a judgment is made based on the fully controllable risk value and the dynamic defense value. If the grid has not experienced a breach, then the nonlinear leakage increment is... The risk of breach coupling at this location is equal to the risk of complete control, that is... .

[0066] It should be noted that when If a breach occurs in the grid, the leakage increment is calculated using the following nonlinear formula (logarithmic leakage coefficient function): The expression for the nonlinear leakage coefficient formula is: The expression for the nonlinear leakage increment is: The formula for calculating the risk value of the breach coupling is: ; The maximum leakage rate, ∈[0.7, 0.9], used as the upper limit of energy release when the dike completely fails; ≈0.5 is the initial leakage ratio (i.e., the initial value when the threshold is about to be exceeded). ∈[0.1, 0.2] is the logarithmic growth coefficient, which controls the rate at which the leakage ratio increases with the threshold. The core advantage of the logarithmic form is that it is effective when the threshold is small (close to the threshold). The growth rate is relatively fast, thus capturing the danger of initial leakage; as the amount exceeding the threshold continues to increase, Approaching This reflects that there is an upper limit to the physical leakage ratio, avoiding the physical irrationality of "infinite increase" caused by the linear assumption, and more realistically simulating the energy release characteristics during the gradual collapse of a dam.

[0067] Additionally, in one embodiment, in Figure 1 Before step S40 shown, the following steps may also be included, but are not limited to: S51 generates multiple discrete candidate values ​​for all pre-calibrated parameters of the SPRE-R energy transfer model, forms multiple candidate parameter combinations based on all discrete candidate values, and performs fine-structured grid search calibration by traversing all candidate parameter combinations through a full-parameter Cartesian product network. S52, based on any candidate parameter combination, configure the SPRE-R energy transfer model based on the candidate parameter combination, and output the along-path distribution of all candidate breach coupling risk values ​​of the target arid area based on arid area data; S53, obtain the dual objective function, calculate multiple objective function values ​​for all candidate parameter combinations based on the dual objective function, and determine the candidate parameter combination corresponding to the largest objective function value as the optimal parameter set, wherein the optimal parameter set includes the optimal calibration parameters corresponding to all pre-calibration parameters; The expression for the biobjective function is: , Here, NSE represents the objective function value for the candidate parameter combination, and NSE is the Nash-Satcliffe efficiency coefficient. Used to characterize the degree of agreement between candidate breach coupling risk values ​​and reference energy fields in the spatial morphology along the breach. For spatial penalty terms, The location of the first river channel in the target arid zone when the risk value of complete regulation exceeds the dynamic defense threshold.

[0068] It should be noted that before outputting the final output report, the SPRE-R energy transfer model is calibrated using a structured grid search. First, the parameter space and calibration objective are determined; then, the structured grid search scheme is determined, and the design of the dual objective function is completed.

[0069] It should be noted that the SPRE-R energy transfer model includes nine parameters that require calibration, i.e., pre-calibration parameters. All pre-calibration parameters constitute the following parameter space: The pre-calibration parameters together determine the spatial distribution of the breach coupling risk value output by the SPRE-R energy transfer model. It is necessary to compare the calibration with the reference energy field output by the hydrodynamic model to find the candidate parameter combination that makes the two have the highest spatial fit, that is, to determine the optimal parameter set.

[0070] It should be noted that the objective calibration design using a dual objective function (NSE + spatial penalty term) ensures spatial positioning accuracy. Traditional single-objective calibration using only the NSE objective function may improve the index value by adjusting the global energy amplitude rather than the correct spatial distribution, leading to a systematic shift in the predicted breach location. This invention introduces a spatial penalty term for the breach location. This method mandates that the model possess physical rationality in the spatial positioning dimension, marking the first time in its class that "breach location prediction accuracy" has been explicitly incorporated into the technical design of the calibration objective function.

[0071] It should be noted that, in response to the specific requirements of the SPRE-R energy transfer model, this invention designs a bi-objective function with two components. The expression of the bi-objective function is as follows: The first component of the dual objective function is the Nash-Satcliffe efficiency coefficient (NSE), which quantifies the breach coupling risk of the SPRE-R energy transport model output. With reference energy field The degree of fit in terms of spatial morphology along the route. NSE=1 indicates perfect fit, and NSE>0 indicates the model is better than the mean prediction. The calibration target is NSE>0.67 (referring to the "good" standard in the field of hydrological models). The second component of the biobjective function is... , The distance between the simulated first threshold location (i.e., the earliest river channel location where the fully controllable risk value exceeds the dynamic defense threshold) and the nearest location in the historically weak levee section or the high-risk breach corridor marked by the engineering design (the unit of river distance is km). This represents the spatial penalty weight (ranging from 0.1 to 0.3). The purpose of introducing the penalty term is to prevent the SPRE-R energy transfer model from relying solely on adjusting the global energy amplitude to improve NSE, thus failing to accurately describe the spatial distribution and ensuring that the SPRE-R energy transfer model has spatial positioning accuracy in predicting the core question of "where breaches are most likely to occur." After calibration, >0.67; Simulated distance between the first threshold-exceeding location and the historically weak levee section <5km, meaning within 5% to 10% of the total river length.

[0072] It should be noted that this invention employs a structured grid search method for parameter calibration: for each pre-calibration parameter, 3 to 5 discrete candidate values ​​are determined based on the physical prior range, for example: Choose {0.60, 0.65, 0.70, 0.75, 0.80}. Choose {0.2, 0.3, 0.4, 0.5, 0.6}. We select parameters such as {0.70, 0.75, 0.80, 0.85, 0.90}; construct a full-parameter Cartesian product grid, and traverse all candidate parameter combinations; within an acceptable computation time, there are approximately 4^9 = 262,144 groups of 9 parameters with 4 levels each. In practice, this can be reduced to about 1 / 10 of the scale through physical constraints before performing a fine-grained grid search; for each candidate parameter combination, we run the SPRE-R energy transfer model in its entirety to calculate the along-path distribution of the breach coupling risk value; calculate the objective function value, and retain the parameter combination with the largest objective function value as the optimal calibration result.

[0073] In another embodiment, after determining the candidate parameter combination corresponding to the largest objective function value as the optimal parameter set in step S53, the following steps are included, but are not limited to: S531, apply uniformly distributed perturbations to all optimal calibration parameters to obtain multiple perturbation parameters, and generate multiple sets of perturbation parameter sets based on all perturbation parameters. Each perturbation parameter set consists of multiple perturbation parameters, and all perturbation parameters in the perturbation parameter set correspond to different optimal calibration parameters. S532, for any set of perturbation parameters, configure the SPRE-R energy transfer model based on the set of perturbation parameters, and the SPRE-R energy transfer model generates a watershed-scale decision index set based on arid zone data; S533: Based on the decision-making indicator group at the whole watershed scale, a preset confidence interval is constructed and the confidence interval is written into the final output report. The watershed scale decision-making indicator group includes regulation capacity, the proportion of breach risk sections, and the flood control effect cost ratio. S534 generates multiple sensitivity parameter groups based on all optimal calibration parameters and all disturbance parameters, calculates and determines the response change of each sensitivity parameter group, and prioritizes the allocation of measurement accuracy input of the optimal calibration parameter corresponding to the disturbance parameter corresponding to the response change based on the sorting order of all response changes from largest to smallest. The sensitivity parameter group includes multiple optimal calibration parameters and one disturbance parameter.

[0074] It should be noted that, due to the inherent uncertainty of parameter calibration results—that is, different parameter combinations may produce similar objective function values—and because the nine parameters of the SPRE-R energy transfer model all originate from physical quantities with certain measurement errors and spatial variability (such as the accuracy of soil erosion intensity grids and reservoir capacity measurement errors), this invention employs the Monte Carlo method for uncertainty propagation to quantify the impact of parameter uncertainty in the SPRE-R energy transfer model on the final decision indicators. After determining the uncertainty propagation scheme, uncertainty analysis and sensitivity identification are performed.

[0075] It should be noted that Monte Carlo uncertainty quantification provides complete information on decision reliability. Existing engineering assessment methods generally provide point estimates (single control capacity values), lacking a systematic assessment of the uncertainty of the estimates. This invention, through Monte Carlo parameter perturbation propagation, provides 95% confidence intervals for all key decision indicators, enabling decision-makers to clearly understand the reliability of their conclusions and providing sufficient uncertainty information for high-risk flood control decisions. This embodiment, through structured grid search and Monte Carlo propagation, provides objective and reproducible parameter calibration results and a complete uncertainty quantification report.

[0076] For example, in the vicinity of the optimal parameter set, a uniformly distributed perturbation of ±20% is independently applied to all nine optimal calibration parameters in the optimal parameter set, generating n≥1000 sets of perturbation parameter sets; for each set of perturbation parameter sets, the SPRE-R energy transfer model is fully run to calculate watershed-scale decision indicators, including regulation capacity (%), the proportion of breach risk sections (%), and the flood control benefit-cost ratio (BCR); the 2.5 percentile and 97.5 percentile are extracted from the 1000 calculation results to construct a 95% confidence interval, which is then included in the final output report.

[0077] It should be noted that, based on Monte Carlo propagation, a one-time parameter sensitivity analysis (One-At-a-Time, OAT) is added; each parameter is perturbed by ±20% while other parameters remain at their optimal values, the response changes of each decision index are recorded, the parameters with the greatest impact on the output are identified, and the measurement accuracy of the parameters with the greatest impact on the output is prioritized.

[0078] It should be noted that, in typical arid inland river application scenarios, the maximum possible interception efficiency... (Upper limit of active interception efficiency) and maximum leakage ratio (i.e., the upper limit of the nonlinear leakage increment) affects the accuracy of breach location prediction. The impact of the position shift is the greatest (200~300m / 20% parameter change), while its contribution to the watershed-scale regulation capacity is the second greatest (approximately ±4~6 percentage points / 20% parameter change).

[0079] Additionally, in one embodiment, in Figure 1 Step S40 shown includes, but is not limited to, the following steps: S41, the risk status dashboard displays the basin-scale decision indicator group with confidence intervals, the fully regulated risk value, the natural flood risk baseline value, the breach coupling risk value, and the breach risk spillover value, wherein the breach risk spillover value is equal to the breach coupling risk value minus the nonlinear leakage increment. S42, Generate a residual energy intensity strip map along the river based on all fully regulated risk values, construct a comprehensive efficiency index for all interception nodes, generate a bubble chart based on all comprehensive efficiency indexes, and overlay the bubble chart with the residual energy intensity strip map along the river to obtain a spatial matching degree map. The residual energy intensity strip map along the river is used to characterize the normalized intensity of all fully regulated risk values ​​of the river channel in the target arid area. S43, when the residual energy concentration area in the spatial matching degree map representing the residual energy intensity strip map along the river highly overlaps with all interception nodes, the basin breach risk assessment result is a good matching type. S44, or, when the residual energy concentration area is highly concentrated at the interception node located at the mid-stream height, and the active interception efficiency of the interception node located at the mid-stream height is the lowest, the basin breach risk assessment result is a matching deviation type; or, when the residual energy concentration area is evenly distributed throughout the entire river section of the target arid area, and there is no continuous section where the residual energy intensity of the residual energy concentration area exceeds the average value of the entire river section + 1 standard deviation, the basin breach risk assessment result is a full-line vulnerable type; S45. When the interception efficiency of the entire basin is greater than or equal to 50%, and the ratio of the breach coupling risk value of the first breach exceeding the threshold in the target arid area to the baseline value of natural flood risk is greater than or equal to 50%, the regulation mode of the target arid area is to emphasize local high vulnerability. S46, or, when the interception efficiency of the entire basin is less than 40%, and the breach risk spillover value obtained after basin-wide equalization is within the minimum 20% range, the regulation mode of the target arid area is a weak regulation energy dissipation offset type; or, when the interception efficiency of the entire basin is less than 35%, and the threshold locations of the target arid area are dispersed in the river channels of the target arid area, and there is no single dominant residual energy concentration area with residual energy intensity exceeding the average value of the entire river section + 1 standard deviation mileage, the regulation mode of the target arid area is a dispersed, fragile, and non-concentrated hotspot type.

[0080] It should be noted that the breach risk spillover value is equal to the breach coupling risk value minus the fully controlled risk value. The breach risk spillover value quantifies the "additional risk" that exceeds the design expectations due to the degradation (erosion) of the dike threshold and nonlinear leakage mechanism, and is a core diagnostic indicator for identifying weak links in the project.

[0081] It should be noted that, based on the calculation results of the aforementioned steps, a three-layer nested risk status dashboard is constructed. This three-layer risk status dashboard is used to display the following core information: Average value and spatial distribution (measured flood risk baseline); Mean value and spatial distribution (lower limit of the theory of complete control), and with The difference (the reduction amount under basin-wide regulation); Average value and spatial distribution (breach coupling risk value), and relatively The baseline ratio (residual risk ratio); The spatial distribution of the difference (breach risk spillover value) identifies high spillover hotspots. All the above indicators are accompanied by confidence intervals from Monte Carlo propagation results; in this embodiment, these are 95% confidence intervals.

[0082] It should be noted that the horizontal axis is based on the river mileage (%, normalized relative to the total river length), and the vertical axis is based on the river channel mileage (%, normalized relative to the total river length). Using the normalized intensity along the river channel as the vertical axis, a residual energy intensity strip map is plotted along the river's course to identify "hotspot mileage sections" where energy concentration is concentrated. These "hotspot mileage sections" are continuous segments where the residual energy intensity exceeds the river-wide average plus one standard deviation, i.e., high-risk corridors. The residual energy intensity strip map along the river's course serves as a crucial bridge connecting the physical diagnosis of the SPRE-R energy transport model with engineering investment decisions. It directly reveals "where flood energy is most abundant and where engineering interception is most needed," information completely invisible in traditional water depth field analysis.

[0083] It should be noted that a comprehensive efficiency index for each interception node is constructed and visualized in the form of a bubble chart (the bubble position corresponds to the river mileage, the bubble color ranges from blue (inefficient) to dark red (efficient) to indicate high efficiency, and the bubble area represents the investment scale). By overlaying the bubble chart with the residual energy intensity strip map along the river, the spatial matching degree of "engineering investment - breach risk" is quantitatively assessed: A good match (e.g., the Hotan River model): the residual energy concentration area highly overlaps with the efficient protection node, indicating that the engineering investment has accurately targeted the highest risk section; a mismatch (e.g., the Tizinafu River model): residual energy is highly concentrated in the middle reaches, but the protection node in this section has the lowest efficiency, indicating a significant spatial mismatch between engineering investment and risk distribution, requiring priority adjustment; a vulnerable situation along the entire river (e.g., the Qiemo River model): residual energy is evenly distributed throughout the river section, with no obvious hotspots, few engineering nodes, high systemic vulnerability, and extremely low marginal benefits of single-point investment, requiring overall planning. This invention directly outputs a spatial ranking of "which locations' flood control investments can most effectively reduce the risk of breaches" by superimposing a normalized residual energy intensity strip map along the river channel with a bubble chart representing the comprehensive efficiency index of each interception node. This provides quantitative decision support for the precise allocation of limited flood control funds.

[0084] It should be noted that this invention, by overlaying residual energy strip diagrams with engineering node efficiency bubble diagrams, directly outputs a spatial ranking of the "most worthwhile flood control locations for investment," providing water conservancy authorities with limited funds with a precise tool for prioritizing investment and avoiding a homogenized investment model. Through investment-risk spatial matching analysis, it directly serves precise flood control investment decisions.

[0085] It should be noted that this invention includes the following three criteria for determining the control mode: High-Regulation Paradox Mode: The determination condition is the average value across the entire watershed. ≥50%, and the first breach exceeding the threshold ≥50%; This signifies that the reservoir's efficient interception has suppressed most of the risks, but the risk of extreme local breaches is concentrated at the first discharge point; the management recommendation is to strengthen the dike reinforcement along the first high-risk breach corridor, focusing on improving... And establish flood diversion plans for floods exceeding the standard; Low-Regulation Cancellation Mode: the discrimination condition is the average of the entire basin. <40%, and The difference is less than 20% after equalization across the entire basin; this indicates weak reservoir interception effect, with the energy reduction from the reservoir almost entirely offset by leakage due to the dense breaches in the middle reaches; management recommendations are to prioritize improving the flood control capacity of reservoirs or constructing new flood diversion areas in the middle reaches, while systematically repairing the dikes along the densely breached corridors in the middle reaches; Distributed Fragility Mode: the criterion is the entire basin. The percentage of rivers exceeding the threshold is less than 35%, and these locations are widely dispersed across the upper, middle, and lower reaches, with no single dominant hotspot. This indicates that the entire river section is in a state of low regulation and low defense, making it impossible to address system vulnerability through single-point reinforcement. Management recommendations include a comprehensive planning approach for constructing new reservoir groups or a cascade reservoir system, combined with ecological revetment to enhance natural attenuation capacity. This invention provides a theoretical basis for empirical transfer to rivers in different types of arid regions through the three-category regulation mode discrimination criteria output by the SPRE-R energy transfer model.

[0086] This invention directly outputs the spatial priority ranking of flood control investment through the superposition analysis of residual energy spatial distribution and engineering node efficiency; furthermore, it establishes a classification framework for control modes that can be transferred across watersheds, enhancing the universality of the method. The three types of control modes proposed in this invention (emphasis on control - locally highly vulnerable type, weak control - energy dissipation offset type, and dispersed vulnerability - no concentrated hotspot type) are not only applicable to the three studied rivers in southern Xinjiang Uygur Autonomous Region, but their classification criteria and mechanism explanations also have physical universality and cross-watershed transferability. They can be directly transferred to the assessment of reservoir-levee joint flood control systems in other arid rivers worldwide and even in warm and humid regions, significantly enhancing the promotional value of this invention's method.

[0087] To better understand the flood control and breach risk assessment methods for arid areas presented in this application, refer to... Figure 2 , Figure 2 This is a flowchart illustrating the SPRE-R energy transfer model's method for flood control and breach risk assessment in arid regions, provided in another embodiment of the present invention. The technical solution of the present invention consists of seven interconnected steps, forming a complete technical chain: "data input → reference energy field → source interception → pathway attenuation → receptor leakage → parameter calibration → uncertainty propagation → risk diagnosis output". The overall process is as follows: Step 1 (Basic Data Acquisition and Reference Energy Field Construction): This includes hydrodynamic model operation and water depth field extraction, construction of the reference energy field Eref(x), and auxiliary data acquisition; Hydrodynamic Model Operation and Water Depth Field Extraction: Run the hydrodynamic model to perform full-basin hydrodynamic simulation under design flood conditions, and extract the following raster outputs: maximum inundation depth field, inundation duration field, flood arrival time field, depth-average velocity field, water depth profile curves of typical cross sections along the pipeline, and quality control requirements for the hydrodynamic model; Construction of the reference energy field Eref(x): The water depth field output by the hydrodynamic model is converted into the reference energy field; Auxiliary data collection: collecting soil erosion intensity, digital elevation model, reservoir technical parameters, dike technical parameters, and historical weak dike section survey records.

[0088] Step 2 (Source Layer - Reservoir Active Interception Efficiency Calculation): This includes basic interception efficiency calculation, interception energy increment calculation, and multi-reservoir series superposition processing; Basic interception efficiency calculation: Active interception efficiency is calculated using the improved Brune formula. Improved Brune formula: ; Interception energy increment calculation: Calculate the active interception energy increment at each interception node. The expression for the active interception energy increment is as follows: ; Overlay processing of multiple reservoirs connected in series: When there are multiple upstream and downstream connected reservoirs in the basin, the interception nodes are overlaid sequentially from upstream to downstream.

[0089] Step 3 (Pathway Layer - Geomorphic Modulation Index Attenuation): This includes the construction of the effective attenuation coefficient and the physical mechanism, as well as the calculation of the fully modulated risk field; Construction and physical mechanism of effective attenuation coefficient: The formula for calculating the effective attenuation coefficient is as follows: A lower limit constraint is imposed on the effective attenuation coefficient: ; Calculation of a fully controlled risk field: Formula for calculating the fully controlled risk value: .

[0090] Step 4 (Receptor Layer - Dynamic Defense Threshold and Non-linear Leakage): Includes dynamic defense threshold. The construction, threshold judgment and nonlinear leakage increment calculation, and physical interpretation of the three-layer risk state; Dynamic defense threshold Construction: The formula for calculating the dynamic defense threshold is: ; Threshold detection and nonlinear leakage increment calculation: For each grid cell within the watershed, if If a breach occurs at that location, calculate the nonlinear leakage coefficient, the nonlinear leakage increment, and the breach coupling risk value. The expression for the nonlinear leakage formula is: The expression for the nonlinear leakage increment is: The expression for the risk value of the breach coupling is: ; Physical Interpretation of the Three-Layer Risk State: The core output of the SPRE-R energy transfer model – a three-layer nested risk state, including: (Natural flood risk baseline) (Completely controllable risk value) (Breach coupling risk value) and breach risk spillover value.

[0091] Step 5 (Structured Grid Search Parameter Calibration): This includes the design of the parameter space and calibration objective, the structured grid search scheme, and the bi-objective function; Parameter Space and Calibration Objectives: The expression for the parameter space of the SPRE-R energy transfer model is as follows: By calibrating and comparing with the reference energy field Eref based on the hydrodynamic model, the parameter combination that achieves the highest spatial fit between the two is found. Structured grid search scheme: Multiple discrete candidate values ​​are set for each parameter (determined based on physical prior range), a fully parametric Cartesian product grid is constructed, all candidate parameter combinations are traversed, and for each parameter combination, the SPRE-R transport chain (steps two to four) is fully run to calculate... The distribution along the process is used to calculate the objective function value, and the parameter combination with the largest objective function value is retained as the optimal calibration result. Design of the dual objective function: The expression for the dual objective function is: After calibration: >0.67; Simulated distance between the first threshold-exceeding location and the historically weak levee section <5 km.

[0092] Step Six (Monte Carlo Parameter Uncertainty Propagation): This includes uncertainty propagation schemes and uncertainty analysis and sensitivity identification; Uncertainty propagation scheme: In the vicinity of the optimal parameter set, apply uniformly distributed perturbations of ±20% independently to all 9 parameters to generate n≥1000 sets of perturbation parameter sets; for each set of perturbation parameter sets, run the SPRE-R transmission chain in its entirety to calculate the following watershed-scale decision indicators: regulation capacity (%), proportion of breach risk sections (%), and flood control benefit-cost ratio (BCR). Extract two percentiles from the 1000 calculation results to construct confidence intervals; Uncertainty analysis and sensitivity identification: Based on Monte Carlo propagation, one-at-a-time (OAT) parameter sensitivity analysis is added: each parameter is subjected to a ±20% perturbation (other parameters are kept at their optimal values), the response changes of each decision index are recorded, and the parameters with the greatest impact on the output are identified (prioritizing the measurement accuracy of these parameters).

[0093] Step 7 (Three-layer risk diagnosis, energy distribution map and investment efficiency assessment): This includes dashboard output of three-layer risk status, normalized residual energy intensity strip map along the river channel, bubble chart of investment efficiency of flood control project nodes and spatial matching analysis, and discrimination and diagnostic criteria for three types of control modes. Three-tiered risk status dashboard output: A three-tiered nested risk status dashboard displays... Average value and spatial distribution (measured flood risk baseline) Average value and spatial distribution (lower limit of the theory of complete regulation) Average and The difference (the reduction amount due to basin-wide regulation) Average value and spatial distribution (breach coupling risk) relatively The baseline ratio (residual hazard ratio) and Spatial distribution of the difference (risk spillover from breach); all the above indicators are given 95% confidence intervals (from Monte Carlo propagation results); Normalized residual energy intensity strip along the river channel: with river channel mileage (%, normalized relative to total river length) as the horizontal axis, and... Using the normalized intensity along the river channel as the vertical axis, plot the residual energy intensity strips along the river. Bubble chart and spatial matching analysis of investment efficiency at flood control project nodes: The comprehensive efficiency index of each interception node is visualized in the form of a bubble chart. The bubble chart is overlaid with the residual energy intensity strip chart to quantitatively assess the spatial matching degree of "project investment - breach risk" and determine the well-matched, poorly matched, and vulnerable types. The criteria for identifying and diagnosing the three types of regulatory modes are: strong regulation - local high vulnerability, weak regulation - energy dissipation and offsetting, and dispersed vulnerability - no concentrated hotspots.

[0094] In summary, the key point of this invention lies in proposing a flood control and breach risk assessment method for arid regions based on the SPRE-R energy transfer model. Compared with traditional technologies, this invention, through the chain-like energy transfer diagnostic framework of the SPRE-R energy transfer model, uniformly models the flood disaster process as a "risk energy flow" propagating along the river channel, constructing a three-stage cascade diagnostic chain of Source (water interception) → Pathway (geomorphic modulation attenuation) → Receptor (receptor threshold leakage), thus solving the fundamental deficiency of traditional hydrodynamic models that can only output water depth fields and cannot diagnose energy transfer mechanisms. Furthermore, it achieves unified modeling of the bidirectional opposing effects of soil erosion, simultaneously modeling soil erosion intensity within the same parameter system. Positive attenuation enhancement effect on the Pathway layer (through) (parameters) and the negative weakening effect on the defense threshold of the Receptor layer (through) This invention addresses the problem of existing technologies treating flood control in highly eroded areas in isolation, leading to a systematic overestimation of flood control capacity. It introduces a dynamic defense threshold modulated by soil erosion by constructing a dynamic defense threshold and a nonlinear leakage mechanism. Logarithmic nonlinear leakage coefficient This invention establishes a nonlinear breach coupling risk calculation method based on the assumption that "exceeding the threshold indicates an event, and the greater the exceedance of the threshold, the higher the leakage rate," thus solving the problem that traditional linear exceedance assumptions cannot reflect the physical mechanism of progressive dam failure. The invention also includes a structured grid search calibration with a spatial penalty term, proposing a method that uses the NSE (Non-Self-Surveillance) and the spatial penalty term of the breach location. The constructed dual objective function performs structured grid search calibration on the 9-parameter space, propagates parameter uncertainties to watershed-scale decision indicators via Monte Carlo, and provides a complete 95% confidence interval in this invention.

[0095] like Figure 3 As shown, Figure 3 This is a structural diagram of a flood control and breach risk assessment device for arid areas provided in one embodiment of the present invention. The present invention also provides a flood control and breach risk assessment device for arid areas, comprising: The processor 601 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 602 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 602 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called and executed by the processor 601 to execute the drought-area flood control and breach risk assessment method of the embodiments of this application. The input / output interface 603 is used to implement information input and output; The communication interface 604 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 605 transmits information between various components of the device (e.g., processor 601, memory 602, input / output interface 603, and communication interface 604); The processor 601, memory 602, input / output interface 603, and communication interface 604 are connected to each other within the device via bus 605.

[0096] This application also provides an electronic device, including the flood control and breach risk assessment device for arid areas as described above.

[0097] This application embodiment also provides a storage medium, which is a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for flood control and breach risk assessment in arid areas.

[0098] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate, and may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0099] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically include computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0100] The above provides a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for flood control and breach risk assessment in arid areas, characterized in that, The method is applied to the SPRE-R energy transfer model, and the method includes: A target arid region is identified and divided into multiple grids, wherein the target arid region includes multiple interception nodes; Based on any one of the interception nodes, the active interception efficiency is calculated based on the acquired arid area data. The active interception efficiency is multiplied by the natural flood risk baseline value to obtain the active interception energy increment, wherein the active interception efficiency is used to characterize the flood energy interception efficiency. Based on any one of the grids, a dynamic defense threshold and a fully controllable risk value are calculated based on the arid zone data and the active interception energy increment. When the fully controllable risk value is greater than the dynamic defense threshold, the fully controllable risk value and the dynamic defense threshold are substituted into a nonlinear leakage function to obtain a nonlinear leakage coefficient. Based on the nonlinear leakage coefficient and the fully controllable risk value, a breach coupling risk value is obtained. The nonlinear leakage coefficient is used to characterize the energy release ratio when a breach occurs, and the breach coupling risk value is used to characterize the actual risk field. Based on all the breach coupling risk values, all the fully regulated risk values, the natural flood risk baseline value, and the whole basin interception efficiency obtained based on all the active interception efficiencies, a final output report for the target arid area is output, wherein the final output report includes the regulation mode of the target arid area and the basin breach risk assessment results.

2. The method for flood control and breach risk assessment in arid areas according to claim 1, characterized in that, After dividing the target arid region into multiple grids, the method further includes: The water depth field data set is output based on the arid zone data using a pre-trained hydrodynamic model, wherein the water depth field data set includes the maximum inundation water depth field of all the grids. Based on any one of the grids, when the grid is a wide, shallow, low-slope floodplain, the maximum inundation depth field of the grid is converted into a reference energy field, wherein the expression for the reference energy field of the grid under the wide, shallow, low-slope floodplain condition is: , The reference energy field of the x-th grid. It is the acceleration due to gravity. Let x be the maximum flood depth field, where x is a positive integer; Alternatively, when the grid is located in a rapid canyon section, the maximum flood depth field is converted into the reference energy field, and a kinetic energy correction term is added based on the reference energy field. The expression for the reference energy field of the grid located in the rapid canyon section is: , For the kinetic energy correction term, Let x be the flow rate of the x-th grid.

3. The method for flood control and breach risk assessment in arid areas according to claim 1, characterized in that, The active interception efficiency is calculated based on the acquired arid zone data. Multiplying this active interception efficiency by the baseline value of natural flood risk yields the active interception energy increment, including: The active interception efficiency was calculated based on the improved Bruns formula and the arid region data, wherein the expression of the improved Bruns formula is: , For the aforementioned active interception efficiency, To maximize interception efficiency, This is the efficiency growth coefficient. Let 'a' be the total reservoir capacity of the interception node, and 'a' be the reservoir capacity index. This is a correction item for the engineering grade; The product of the natural flood risk baseline value and the active interception efficiency equals the active interception energy increment, wherein the expression for the active interception energy increment is: , For the active interception of energy increment, This is the baseline value for the natural flood risk.

4. The method for flood control and breach risk assessment in arid areas according to claim 3, characterized in that, Based on the arid region data and the active interception energy increment, a dynamic defense threshold and a fully controllable risk value are calculated, including: Based on the arid region data, an effective attenuation coefficient and a dynamic defense threshold are calculated. A lower limit constraint is applied to the effective attenuation coefficient, wherein the formula for calculating the effective attenuation coefficient is: , Based on the attenuation coefficient, The soil erosion feedback coefficient, The soil erosion intensity of the grid is given. This is the slope or roughness feedback coefficient. Given the normalized riverbed slope or surface roughness of the grid, the formula for calculating the dynamic defense threshold is: , The dynamic defense threshold is used to characterize the actual defense threshold of the intercepting node. The maximum flood energy threshold, The effective attenuation coefficient is defined as the soil erosion reduction coefficient, and the lower limit constraint of the effective attenuation coefficient is: ; The fully modulated risk value is calculated based on the effective attenuation coefficient and the interception energy increment, wherein the calculation formula for the fully modulated risk value is: , The full control risk value for the x-th grid, Let x be the distance of the x-th grid along the river channel from the nearest upstream interception node. The reference distance for measuring the downstream distance is based on the interception node as the origin. The term is an exponential decay term, which characterizes the spatial dissipation of flood energy from the source node downstream.

5. The method for flood control and breach risk assessment in arid areas according to claim 4, characterized in that, Substituting the fully controllable risk value and the dynamic defense threshold into the nonlinear leakage function yields the nonlinear leakage coefficient. Based on the nonlinear leakage coefficient and the fully controllable risk value, the breach coupling risk value is obtained, including: Obtain the nonlinear leakage function, and substitute the fully controllable risk value and the dynamic defense threshold into the nonlinear leakage function to obtain the nonlinear leakage coefficient, wherein the expression of the nonlinear leakage function is: , The nonlinear leakage coefficient is... The maximum leakage ratio is used to characterize the upper limit of energy release when the dike completely fails. This represents the initial leakage rate. The logarithmic growth factor is used to control the rate at which the leakage ratio increases with the threshold amount. The nonlinear leakage coefficient is multiplied by the fully controllable risk value to obtain the nonlinear leakage increment of the grid. The fully controllable risk value is added to the nonlinear leakage increment to obtain the breach coupling risk value. The formula for calculating the nonlinear leakage increment is as follows: , The formula for calculating the breach coupling risk value, given the nonlinear leakage increment, is as follows: , The risk value for the breach coupling is given.

6. The method for flood control and breach risk assessment in arid areas according to claim 5, characterized in that, Before outputting the final output report for the target arid region based on all the aforementioned breach coupling risk values, all the aforementioned fully regulated risk values, the aforementioned natural flood risk baseline values, and the basin-wide interception efficiency obtained based on all the aforementioned active interception efficiencies, the report further includes: Multiple discrete candidate values ​​are generated for all pre-calibration parameters of the SPRE-R energy transfer model. Multiple candidate parameter combinations are formed based on all the discrete candidate values. Fine-structured grid search calibration is performed by traversing all the candidate parameter combinations through a full-parameter Cartesian product network. Based on any one of the candidate parameter combinations, the SPRE-R energy transfer model is configured based on the candidate parameter combinations, and the SPRE-R energy transfer model outputs the along-path distribution of all candidate breach coupling risk values ​​of the target arid area based on the arid area data; Obtain a dual objective function, calculate multiple objective function values ​​for all candidate parameter combinations based on the dual objective function, and determine the candidate parameter combination corresponding to the largest objective function value as the optimal parameter set, wherein the optimal parameter set includes the optimal calibration parameters corresponding to all pre-calibration parameters; The expression for the dual objective function is as follows: , The objective function value is the combination of the candidate parameters, and NSE is the Nash-Satcliffe efficiency coefficient. This is used to characterize the degree of agreement between the candidate breach coupling risk value and the reference energy field in terms of spatial morphology along the breach. The coupling risk value of the candidate breach. For spatial penalty terms, The first river channel location in the target arid zone when the fully regulated risk value is greater than the dynamic defense threshold.

7. The method for flood control and breach risk assessment in arid areas according to claim 6, characterized in that, After determining the candidate parameter set corresponding to the largest objective function value as the optimal parameter set, the method further includes: A plurality of perturbation parameters are obtained by applying uniformly distributed perturbations to all the optimal calibration parameters respectively. Multiple sets of perturbation parameters are generated based on all the perturbation parameters, wherein each set of perturbation parameters consists of multiple perturbation parameters, and all perturbation parameters in each set of perturbation parameters correspond to different optimal calibration parameters. For any set of disturbance parameters, the SPRE-R energy transfer model is configured based on the set of disturbance parameters, and the SPRE-R energy transfer model generates a watershed-scale decision index set based on the arid zone data; A preset confidence interval is constructed based on all the aforementioned watershed-scale decision indicators, and the confidence interval is written into the final output report. The watershed-scale decision indicators include regulation capacity, the proportion of breach risk sections, and the flood control effect-cost ratio. Multiple sensitivity parameter groups are generated based on all the optimal calibration parameters and all the disturbance parameters. The response change of each of the sensitivity parameter groups is calculated and determined. The measurement accuracy input of the optimal calibration parameter corresponding to the disturbance parameter corresponding to the response change is preferentially allocated based on the sorting order of all the response change from largest to smallest. The sensitivity parameter group includes multiple optimal calibration parameters and one disturbance parameter.

8. The method for flood control and breach risk assessment in arid areas according to claim 7, characterized in that, Based on all the aforementioned breach coupling risk values, all the aforementioned fully regulated risk values, the aforementioned natural flood risk baseline values, and the basin-wide interception efficiency obtained based on all the aforementioned active interception efficiencies, the final output report for the target arid area is output, including: The risk status dashboard displays the basin-scale decision indicator group with the confidence interval, the fully regulated risk value, the natural flood risk baseline value, the breach coupling risk value, and the breach risk overflow value, wherein the breach risk overflow value is equal to the breach coupling risk value minus the nonlinear leakage increment. Based on all the fully regulated risk values, a residual energy intensity strip map along the river is generated, a comprehensive efficiency index of all the interception nodes is constructed, a bubble chart is generated based on all the comprehensive efficiency index, and the bubble chart is superimposed with the residual energy intensity strip map along the river to obtain a spatial matching degree map. The residual energy intensity strip map along the river is used to characterize the normalized intensity of all the fully regulated risk values ​​of the river channel in the target arid area. When the spatial matching degree map represents the residual energy concentration area in the residual energy intensity strip map along the river that highly overlaps with all the interception nodes, the watershed breach risk assessment result is a good match. Alternatively, if the residual energy concentration area is highly concentrated at the interception node located at the mid-stream altitude, and the active interception efficiency of the interception node located at the mid-stream altitude is the lowest, the basin breach risk assessment result is a matching deviation type; or, if the residual energy concentration area is evenly distributed throughout the entire river section of the target arid area, and there is no continuous section where the residual energy intensity of the residual energy concentration area exceeds the average value + 1 standard deviation of the entire river section, the basin breach risk assessment result is a fully vulnerable type. When the whole basin interception efficiency is greater than or equal to 50%, and the ratio of the breach coupling risk value of the first breach exceeding the threshold in the target arid area to the baseline value of the natural flood risk is greater than or equal to 50%, the regulation mode of the target arid area is to emphasize local high vulnerability. Alternatively, when the whole-basin interception efficiency is less than 40%, and the breach risk spillover value obtained after whole-basin equalization is within the minimum 20% range, the regulation mode of the target drought area is a weak regulation energy dissipation offset type; or, when the whole-basin interception efficiency is less than 35%, and the threshold locations of the target drought area are dispersed in the river channel of the target drought area, and there is no single dominant residual energy concentration area whose residual energy intensity exceeds the average value of the whole river section plus 1 standard deviation, the regulation mode of the target drought area is a dispersed, fragile, and non-concentrated hotspot type.

9. A device for flood control and breach risk assessment in arid areas, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which, when executed by the at least one control processor, enable the at least one control processor to perform the flood control and breach risk assessment method for arid areas as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to perform the flood control and breach risk assessment method for arid areas as described in any one of claims 1 to 8.

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