Method for evaluating flood control influence of reservoir by coupling simulation of rain flood and grading of influence coefficient

Abstract

This application relates to a method for assessing the flood control impact of reservoirs using coupled rainfall-flood simulation and influence coefficient grading. The method includes: acquiring and preprocessing multi-source data from the study area to form a basic data layer; constructing a coupled rainfall-flood model that integrates distributed hydrology and a one-dimensional hydrodynamic model, and completing calibration and verification; simulating the discharge process and river cross-sectional flow under different flood-rainfall combination scenarios; calculating the river cross-sectional influence coefficient and dividing it into high, medium, and low influence intervals; analyzing the characteristics of the reservoir's impact on downstream rivers; using inverse distance weighted interpolation to extend the river's influence to urban space and generate an influence distribution map; and statistically analyzing the river length and spatial area proportion of each influence interval to quantitatively assess the upper limit of the reservoir's flood control protection capacity. This method achieves complete coupled simulation of the rainfall-flood process, pioneers a quantitative influence coefficient grading system, completes the spatial visualization of the reservoir's flood control impact from a "line" to a "surface," and accurately defines the upper limit of the reservoir's flood control protection capacity.

Description

Technical Field

[0001] This application relates to the fields of water conservancy engineering and urban flood control and disaster reduction technology, and in particular to a method for assessing the flood control impact of reservoirs using rainwater-flood coupling simulation and impact coefficient classification. Background Technology

[0002] Against the backdrop of intensifying global climate change and rapid urbanization, extreme rainfall events are becoming more frequent and intense. Downstream cities, due to hardened underlying surfaces and limited river network flood control and storage capacity, face severe flood risks from the combined effects of internal flooding and external flooding. Reservoirs, as core control projects in the basin's flood control system, directly determine the flood evolution of downstream rivers and urban flood safety through their discharge processes. Therefore, scientifically and accurately assessing the flood impact of reservoir discharges on downstream cities is a crucial prerequisite for optimizing reservoir scheduling decisions, formulating urban flood control strategies, and planning flood control projects.

[0003] Currently, the industry's methods for assessing the flood control impact of reservoirs have significant technical limitations, mainly in two aspects: First, insufficient model coupling leads to distorted characterization of impact boundaries. Traditional methods often employ single hydrological models or simplified hydrodynamic formulas, failing to achieve dynamic coupled simulation of the entire process of "rainfall-slope-convergence-river evolution-reservoir scheduling." This makes it difficult to accurately reflect the comprehensive response of complex urban underlying surfaces and river networks to flood events, resulting in insufficient prediction accuracy of key impact boundaries such as downstream river network water level changes and flood inundation ranges. Consequently, the assessment results deviate from the actual risk distribution. Second, the assessment indicators are qualitative, lacking a quantitative grading system. Existing technologies mostly qualitatively describe the "magnitude" or "regulatory role" of reservoirs, lacking universally applicable indicators to quantitatively characterize the intensity, scope, and upper limit of the reservoir's impact and protection capacity. This makes it impossible to scientifically classify the degree of impact, analyze the actual impact mechanism of reservoirs on different river sections and urban areas, and clarify the theoretical boundaries of their flood control protection capacity. Consequently, flood control decisions lack refined data support, easily leading to "one-size-fits-all" control measures, resulting in wasted flood control resources or insufficient local protection.

[0004] Therefore, existing technologies are no longer sufficient to meet the urgent need for refined, spatial, and quantitative assessment of the impact of reservoirs on flood control. Among related technologies, there is an urgent need for an innovative assessment method that can couple the complete rainwater and flood process, construct quantitative assessment indicators, and realize spatial classification of impacts, so as to provide scientific, accurate, and practical technical support for urban flood control safety. Summary of the Invention

[0005] Therefore, it is necessary to provide a reservoir flood control impact assessment method that can couple the complete rainwater and flood process, construct quantitative assessment indicators, and realize spatial classification of impacts through rainwater and flood coupling simulation and impact coefficient classification, in order to address the above-mentioned technical problems.

[0006] Firstly, this application provides a method for assessing the flood control impact of reservoirs using rainwater-flood coupling simulation and influence coefficient classification. The method includes: Hydrological and meteorological data, land use data, soil data, river network data, and digital elevation model data of the study area are acquired, and the data are preprocessed to form a basic data layer. A rain-flood coupled model is constructed based on the basic data layer, which couples a distributed hydrological model and a one-dimensional hydrodynamic model. The model is then calibrated and validated. The flood-rainfall coupling scenario of the reservoir under different design flood return periods and multiple rainstorm return periods was simulated using a calibration-verified rainstorm coupling model. The reservoir discharge process and the flow process lines of each downstream river section were obtained. For each simulation scenario, the influence coefficient of each downstream river section is calculated, and the degree of influence of the reservoir is divided into three levels based on the influence coefficient values. Based on the influence coefficients of different cross sections under different scenarios, the characteristics of the impact of reservoir discharge on the downstream river channel are analyzed. The spatial interpolation method is used to extend the river channel influence coefficient to each spatial unit within the study area, generating a spatial distribution map of the reservoir's influence. By statistically analyzing the proportion of river length and urban area corresponding to each affected area under different rainstorm and flood scenarios, the theoretical flood control protection capacity of the reservoir for different downstream areas is quantitatively assessed.

[0007] Optionally, in one embodiment of this application, the distributed hydrological model and the one-dimensional hydrodynamic model achieve bidirectional coupling and water exchange between the hydrological process and the hydrodynamic process through a dynamic link interface. The distributed hydrological model is a distributed hydrological model based on physical mechanisms, and the one-dimensional hydrodynamic model is a river hydrodynamic model based on the Saint-Venant equations.

[0008] Optionally, in one embodiment of this application, the rainwater coupling model is one of the following: the MIKE coupling system, the SWAT and HEC-RAS coupling system, and the VIC and TELEMAC-1D coupling system.

[0009] Optionally, in one embodiment of this application, the calibration and verification of the model includes: Historical rainfall and flood events in the study area were selected, and key parameters in the model were adjusted based on the measured cross-sectional flow process line of the river. The model was calibrated and validated using the coefficient of determination, Nash efficiency coefficient, and Kling-Gupta efficiency coefficient.

[0010] Optionally, in one embodiment of this application, the influence coefficient is the ratio of the peak value of the reservoir's designed discharge flow to the peak value of the natural flood flow at the cross-section without considering reservoir regulation.

[0011] Optionally, in one embodiment of this application, the impact of the reservoir is divided into high impact range, medium impact range, and low impact range based on the impact coefficient value. An impact coefficient greater than 0.5 is a high impact range, an impact coefficient between 0.2 and 0.5 is a medium impact range, and an impact coefficient less than 0.2 is a low impact range.

[0012] Optionally, in one embodiment of this application, the impact characteristics of the reservoir discharge on the downstream river channel include the spatial boundary of the impact range of the downstream river channel, the attenuation characteristics of the impact intensity along the course, and the distribution and transformation patterns of each impact interval under different return period scenarios.

[0013] Optionally, in one embodiment of this application, the spatial interpolation method is the inverse distance weighted interpolation method, which calculates the comprehensive influence value of each spatial unit in the study area and defines the spatial boundary of the reservoir's effect on the downstream urban built-up area.

[0014] Secondly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the steps of the methods described in the various embodiments above.

[0015] Thirdly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the methods described in the various embodiments above.

[0016] Compared with the prior art, the present invention has the following significant advantages: First, it achieves complete coupling and high-precision simulation of the assessment process: This invention breaks through the limitations of traditional single hydrological models or simplified formulas. By dynamically coupling a distributed hydrological model and a one-dimensional hydrodynamic model, it fully depicts the entire chain response relationship of "rainfall - slope flow - river confluence - reservoir scheduling". This ensures that the simulation accuracy of downstream river network water level, flow and theoretical influence boundary is significantly improved under different complex scenarios, providing a reliable data foundation for assessment. Second, it pioneered a quantitative assessment index and grading system to make the degree of impact measurable and comparable: This invention defines the core quantitative index of "influence coefficient M" and sets 0.2 and 0.5 as thresholds to divide it into three influence ranges: high, medium and low. This completely changes the ambiguity of traditional qualitative descriptions, so that the intensity of the reservoir's impact on any downstream river section or urban area can be accurately measured and spatial differences can be clearly compared, providing a direct basis for the graded and classified formulation of flood control measures. Third, this invention enables visualization of the impact boundary and quantification of the upper limit of the capacity from the river channel to the urban space: By using inverse distance weighted spatial interpolation technology, the quantitative impact coefficient of the river channel section is extended to the entire urban space, generating an intuitive impact distribution map, realizing the spatialized and visualized expression of the reservoir's flood control impact from "line" to "area"; at the same time, by statistically analyzing the river length and spatial area ratio corresponding to each impact interval, the theoretical flood control protection capacity upper limit of the reservoir can be quantitatively defined, answering key questions such as "how much area can the reservoir protect" and "how does the protection capacity decrease with extreme levels", avoiding blindness and resource misallocation in flood control decision-making. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a reservoir flood control impact assessment method based on rainwater-flood coupling simulation and impact coefficient grading in one embodiment. Figure 2 The images show river channel M(a) under a 5-year flood with different return periods and river channel M(b) under a 200-year flood with different return periods in one embodiment of the present invention. Figure 3 This is an embodiment of the present invention showing the spatial M(a) of each county under a 5a flood and the spatial M(b) of each county under a 200a flood. Figure 4 This invention presents the percentage of the river channel length affected by rainfall with different return periods under a 5-year flood (a) and the theoretical cumulative regional impact of rainfall with different return periods under a 5-year flood (b) in one embodiment of the invention. Figure 5 This invention presents the percentage of the river channel length affected by rainfall with different return periods under a 200-year flood (a) and the theoretical cumulative regional impact of rainfall with different return periods under a 200-year flood (b) in one embodiment of the invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] In one embodiment, such as Figure 1 As shown, a method for assessing the flood control impact of reservoirs using coupled rainfall-flood simulation and influence coefficient classification is provided, including the following steps: S101: Acquire hydrological and meteorological data, land use data, soil data, river network data, and digital elevation model (DEM) data of the study area, and preprocess the data to form a basic data layer.

[0020] S102: Construct a rain-flood coupled model based on the basic data layer, which combines a distributed hydrological model and a one-dimensional hydrodynamic model, and calibrate and validate the model.

[0021] In one embodiment of this application, the distributed hydrological model and the one-dimensional hydrodynamic model achieve bidirectional coupling and water exchange between the hydrological and hydrodynamic processes through a dynamic link interface. The distributed hydrological model is a distributed hydrological model based on physical mechanisms, used to simulate the rainfall-runoff-confluence process of the watershed; the one-dimensional hydrodynamic model is a river hydrodynamic model based on the Saint-Venant equations, used to simulate the flood evolution process in the river channel.

[0022] In one embodiment of this application, the stormwater coupling model of the distributed hydrological model and the one-dimensional hydrodynamic model includes: (1) MIKE coupling system: It is constructed by bidirectional dynamic coupling of the three-dimensional distributed hydrological model MIKE SHE and the one-dimensional hydrodynamic model MIKE 11 through the MIKE SHE LINKS coupler. The input includes hydrological and meteorological data, geospatial data with a resolution of ≥30m, river network and hydraulic structure data and boundary conditions. MIKE SHE simulates the whole hydrological process through four modules with grid as the unit. MIKE 11 uses the implicit difference method to solve the hydrodynamics and outputs hydrological, hydrodynamic and coupling results. (2) SWAT+HEC-RAS coupling system: The distributed hydrological model SWAT and the one-dimensional hydrodynamic model HEC-RAS are coupled in one direction or two directions through the ArcSWAT plugin or HEC-DSS database. The input includes hydrological and meteorological data, geospatial data ≥30m, river channel and hydro-engineering structure and boundary data. SWAT simulates runoff generation and confluence in sub-basins / HRUs as units, and HEC-RAS solves based on the Saint-Venant equations or Manning equations, and outputs hydrological, hydrodynamic and comprehensive assessment results. (3) VIC+TELEMAC-1D Coupled System: The distributed land surface hydrological model VIC and the one-dimensional hydrodynamic model TELEMAC-1D are bidirectionally coupled through a custom script interface. The input includes hydrological and meteorological data, geospatial data of grids ≥50m, river network and hydraulic structure data. VIC simulates hydrological and land surface flux based on the "variable infiltration capacity" mechanism. TELEMAC-1D uses the finite volume method to solve the Saint-Venant equations and outputs hydrological and land surface processes, hydrodynamic processes and coupling results.

[0023] After the model is built, it needs to be calibrated and validated using historical hydrological observation data to ensure its simulation accuracy.

[0024] In one embodiment of this application, the calibration and verification of the model includes: Historical rainfall and flood events in the study area were selected, and key parameters in the model were adjusted based on the measured cross-sectional flow process line of the river. The model was calibrated and validated using the coefficient of determination, Nash efficiency coefficient, and Kling-Gupta efficiency coefficient.

[0025] In one embodiment of this application, one or more typical and well-documented rainfall-flood events in the history of the study area are selected. Using the measured river cross-sectional flow process curve as a benchmark, key parameters in the model (such as soil hydraulic conductivity, Manning roughness coefficient, etc.) are adjusted and optimized to achieve the best fit between the simulated flow process curve and the measured process curve. The model performance is evaluated using quantitative indicators, including but not limited to the coefficient of determination (R²), Nash efficiency coefficient (NSE), and Kling-Gupta efficiency coefficient (KGE).

[0026]

[0027]

[0028]

[0029] in, It is the hourly measured flow rate. It is a time-based simulated flow; It is the measured average flow rate. It is the simulated average flow rate. It is the correlation coefficient. It is the standard deviation of the model's simulated values. It is the standard deviation of the observed values. It is the average value of the simulated values ​​from the model. It is the average of the observed values.

[0030] S103: Using a calibration-verified rain-flood coupling model, simulate the flood-rainfall coupling scenario of the reservoir under different design flood return periods and multiple rainstorm return period combinations, and obtain the reservoir discharge process and the flow process lines of each downstream river section.

[0031] In this embodiment, a storm-rainfall coupling model that has been verified by calibration is used to simulate the "flood-rainfall" coupling scenario of a reservoir under different combinations of design flood return periods (such as 5-year return period, 200-year return period) and multiple rainstorm return periods (such as 10- to 1000-year return periods), and to obtain the reservoir discharge process and the flow process lines of each downstream river section.

[0032] S104: Calculate the impact coefficient of each downstream river section for each simulation scenario, and classify the degree of reservoir impact into three levels based on the impact coefficient values.

[0033] In one embodiment of this application, the influence coefficient M is defined as the peak value of the reservoir design discharge flow ( The peak natural flood discharge at this cross-section, without considering reservoir regulation ( The ratio of ), i.e. This coefficient directly quantifies the relative proportion of reservoir discharge in the total river inflow. Based on the M value, the degree of influence is divided into three levels: M > 0.5 is the high-influence range, indicating that the reservoir's regulatory effect is significant; 0.2 ≤ M ≤ 0.5 corresponds to the medium-influence range, indicating that there is limited adjustment capacity; M < 0.2 is the low-influence range, indicating that the reservoir's influence is weak.

[0034] S105: Based on the influence coefficients of different scenarios at each cross section, analyze the characteristics of the impact of reservoir discharge on the downstream river channel.

[0035] In this embodiment of the application, based on the influence coefficient M value of each section under different scenarios, the spatial boundary of the influence range of the reservoir discharge on the downstream river channel, the attenuation characteristics of the influence intensity along the course, and the distribution and transformation law of high, medium and low influence intervals under different return period scenarios are analyzed.

[0036] S106: The spatial interpolation method is used to extend the river channel influence coefficient to each spatial unit within the study area, generating a spatial distribution map of the reservoir's influence.

[0037] In this embodiment of the application, based on the obtained influence coefficient M value distributed along the river channel, a spatial interpolation method (such as inverse distance weighted interpolation) is used to calculate the comprehensive influence degree value of each spatial unit (such as grid) in the study area, thereby extending the influence of the "linear" river channel to the "area" urban area, generating a spatial distribution map of the reservoir's influence, and clearly defining the spatial boundary of the reservoir's effect on the downstream urban built-up area.

[0038] S107: Statistically analyze the proportion of river length and urban space area corresponding to each affected area under different rainstorm and flood scenarios, and quantitatively assess the upper limit of the theoretical flood control protection capacity of the reservoir for different downstream areas.

[0039] In this embodiment, statistical analysis is performed on the proportion of river length and urban area within the high and medium impact ranges under different rainstorm and flood scenarios. By comparing and analyzing the changes in these proportions under different scenarios, the theoretical flood control protection capacity of the reservoir for different downstream areas (such as administrative districts and key protection areas) is quantitatively assessed, and its effective protection range under extreme conditions is clarified.

[0040] The following specific embodiment illustrates the detailed implementation steps of the reservoir flood control impact assessment method based on rainwater-flood coupling simulation and influence coefficient classification. Taking the Zipingpu Reservoir upstream of Chengdu City, Sichuan Province, and the main urban area of ​​Chengdu City and its surrounding areas under its influence as the study area, the method of this invention is applied to conduct a reservoir flood control impact assessment.

[0041] S1. Data Acquisition and Processing The data involved in this embodiment include hydrological and meteorological data, land use data, soil data, river network data, and DEM data.

[0042] The specific methods for obtaining the data for each of the above elements include: (1) Hydrological data are obtained from the measured hourly flow data of four hydrological stations (Dujiangyan, Shidiyan, Huanglongxi and Pengshan) in Chengdu from 2006 to 2023, and are obtained from the flood control data of Chengdu. Meteorological data are obtained from the "Daily Value Dataset of China Surface Climate Data (V3.0)" published by China Meteorological Data Network.

[0043] (2) The land use data comes from the 2020 GlobeLand 30 dataset released by the China Resources and Environment Science Data Center (RESDC), with a spatial resolution of 30m×30m.

[0044] (3) The soil dataset is from the China Soil Database (v1.1) released by the National Center for Glacier, Permafrost and Desert Science. This dataset is based on the World Soil Database (HWSD) and adopts the FAO-90 international standard classification system. The soil resolution is 1km×1km.

[0045] (4) The river network data comes from the OpenStreetMap (OSM) dataset, which can be downloaded from https: / / www.openstreetmap.org. The river distribution data of the study area is obtained by cropping the vector data of rivers from level I to level V across the country.

[0046] (5) The DEM data comes from the global electronic terrain data released by NASA in 2020, with a horizontal resolution of 30m×30m.

[0047] All spatial data undergo preprocessing including coordinate system 1, cropping, and format conversion.

[0048] S2. Construction and Calibration of Rainfall-Flood Coupling Model A rainwater-flood coupling model was built based on the MIKE model system platform.

[0049] First, the study area boundary, river system, DEM, soil type, land use, and meteorological data were imported into MIKE SHE, and computational grids and hydrophysical process modules (slope flow, unsaturated zone, and saturated zone) were set up. Second, a one-dimensional hydrodynamic model was constructed in MIKE 11 based on the generalized river network (retaining the main channel and merging minor tributaries), and the channel cross-section and boundary conditions were defined. Finally, MIKE SHE and MIKE 11 were dynamically coupled using the MIKE SHE LINKS coupler: the grid runoff calculated by MIKE SHE was injected laterally into the MIKE 11 channel, while the channel water level calculated by MIKE 11 was fed back to MIKE SHE to drive groundwater exchange. Two typical rainstorm-flood events in 2018 and 2020 were selected to calibrate and validate the coupled model. Based on the measured flow rate at the Dujiangyan hydrological station, key hydrological and hydrodynamic parameters (such as Manning roughness and soil hydraulic conductivity) were adjusted, and the performance was evaluated using the coefficient of determination (R²), Nash efficiency coefficient (NSE), and Kling-Gupta efficiency coefficient (KGE). During both calibration and validation periods, the NSE was greater than 0.75, and the R² was greater than 0.85, indicating that the established model has reliable simulation accuracy and can be used for scenario simulation.

[0050] S3, Multi-scenario flood process simulation To analyze the combined impact of upstream floods and local torrential rains, multiple "flood-rainfall" coupled scenarios were designed. Two design discharge scenarios for the Zipingpu Reservoir were set: a 5-year flood discharge (representing a common flood) and a 200-year flood discharge (representing an exceptionally large flood). For each flood scenario, seven design torrential rain scenarios with 10, 20, 50, 100, 200, 500, and 1000-year return periods were superimposed (using the Chengdu torrential rain intensity formula and the Chicago rainfall pattern to generate the design rainfall pattern). Using the calibrated coupled model, the reservoir discharge process and the flood evolution process at various cross-sections of the downstream Chengdu Plain river network were simulated under these 14 combined scenarios, and the flow process curves of each cross-section were output.

[0051] S4. Calculation and Classification of Influence Coefficient For each simulation scenario and each river cross-section, two key peak flows are extracted: ① the peak design discharge flow of the reservoir under this scenario. ② In this scenario, the peak natural flood flow at this cross-section is not considered when the Zipingpu Reservoir regulates the water flow. (This can be simulated by setting the reservoir module in the model to "unregulated" state).

[0052] According to the formula Calculate the influence coefficient M for each cross-section. For example, at a certain cross-section, if... It is 1500 m³ / s. If it is 3000 m³ / s, then Based on the grading criteria shown in Table 1, the M values ​​of all cross sections are divided.

[0053] Table 1. Influence Coefficient Classification Standard

[0054] S5. River Channel Impact Analysis To quantify the proportion of reservoir flow in different downstream river channels and the attenuation characteristics of each river channel's influence coefficient along its course, and to clarify the degree of reservoir influence on the urban river network and its spatial boundaries, a rain-flood coupling model was used. Based on this model, the evolution of river floods under historical rainstorm floods and rainstorm floods with different return periods in the study area was simulated, and the theoretical influence intervals were calculated.

[0055] Based on the local rainfall intensity calculation formula and design rainfall pattern in the study area, this implementation case designed seven rainfall scenarios with different frequencies: no rainfall, 10-year return period, 20-year return period, 50-year return period, 100-year return period, 200-year return period, 500-year return period, and 1000-year return period. Through systematic analysis of the actual inflow into the Neijiang River from the design flood discharge of the Zipingpu Reservoir, the design frequencies of 5-year and 200-year return periods were selected as the flood scenario settings.

[0056] The evolution of river levels and flow in Chengdu is mainly caused by upstream inflows and rainfall. To quantitatively explain the impact of upstream floods and rainstorms on river flood processes, this embodiment combines model simulation with scenario setting. By setting different return periods for floods and rainfall, the return period of one factor is fixed first, and then the return period of another factor is gradually changed. The flood evolution process of Chengdu's rivers under each scenario is compared and analyzed. Based on the above analysis, this embodiment designs two flood scenarios with different return periods (5-year and 200-year) and seven rainstorm scenarios with different return periods (10-year, 20-year, 50-year, 100-year, 200-year, 500-year, and 1000-year). After pairwise coupling, a total of 14 scenario designs are obtained, as shown in Table 2.

[0057] Table 2. Scenario Design of Rainstorms and Floods with Different Return Periods

[0058] The evolution of river floods in the study area under 5-year and 10-year design floods and different return periods (10-1000 years) was simulated and analyzed. The evolution process of the river network and the results of the influence boundary are shown in [the table below]. Figure 2See Tables 3 and 4. Under the 5-year design flood scenario, as the rainfall return period increases, the impact of Zipingpu Reservoir on the rivers of Chengdu City decreases significantly, mainly in the Neijiang area. Moreover, this impact is significantly negatively correlated with the rainfall return period. Most of the original high-impact river sections have transformed into low-impact river sections. Specifically, as the rainfall return period increases from 10 years to 1000 years, the boundary of the impact of Zipingpu Reservoir on the downstream river channels shows a clear upstream characteristic, and most of the red-orange high-impact river sections have turned into blue low-impact river sections. Under the 200-year design flood scenario, the impact zones of different rainfall return periods show significant variation patterns. Under this scenario, the proportion of rivers in the high-impact zone remains the lowest among all impact zones, all below 5%; the proportion of rivers in the medium-impact zone decreases from 32.90% to 14.28% for the 1000-year return period, a decrease of approximately 59.60%; the proportion of rivers in the low-impact zone remains the highest, increasing from 63.69% for the 10-year return period to 84.15% for the 1000-year return period, an increase of 20.46%.

[0059] Overall, under the design flood scenarios of 5-year and 200-year return periods, the proportion of river channels corresponding to the high and medium impact zones decreased by 57.97% and 55.26% respectively from the 10-year return period to the 1000-year return period, while the proportion decreased by 56.60%. The relatively small difference in these proportions indicates that the influence of the Zipingpu Reservoir on downstream river channels is significantly reduced by the increased return period of heavy rainfall. Specifically, as the return period of rainfall increases, the proportion of reservoir discharge in the total river flow gradually decreases, leading to a shrinking spatial range of the high and medium impact zones. The fundamental reason for this is the significantly enhanced regional runoff intensity under extreme rainfall conditions, which dilutes the relative weight of the reservoir's influence.

[0060] Table 3. Influence range of river channels with different return periods under a 5-year flood.

[0061] Table 4. River channel impact ranges under different rainfall return periods during a 200-year flood.

[0062] S6, Urban Spatial Impact Interpolation By combining the river influence coefficient and the degree of influence of the surrounding rivers of each unit grid, spatial interpolation is performed on the degree of influence of each unit grid in space to analyze the spatial boundary of the reservoir's impact on the city. The specific operation is as follows: Based on the simulation results of the river stormwater model, GIS spatial analysis technology is used in combination with the inverse distance weight (IDW) interpolation method. Based on the degree of influence of the surrounding rivers of each unit grid, spatial interpolation is performed on the degree of influence of each unit grid in space. The degree of influence of each interpolation point is determined by the combined influence of multiple nearby rivers. This is used to analyze the spatial boundary of the reservoir's impact on the downstream city.

[0063] In real-world scenarios, the low-impact zone is less affected and difficult to regulate in a timely manner during heavy rain. Therefore, the high- and medium-impact zones are considered as the zones where upstream reservoirs can effectively influence downstream cities.

[0064] In this embodiment, the spatial variation of the impact under the 10-year and 1000-year return periods of rainfall is taken as an example. Based on the simulation results of the river stormwater model, GIS spatial analysis technology combined with the inverse distance weighted (IDW) interpolation method is used. According to the degree of influence of the river around each unit grid, the degree of influence of each unit grid is spatially interpolated. By analyzing the changes in the proportion of the impact area of ​​each district and county in Chengdu under different flood and rainfall return periods, the spatial boundary of the impact of Zipingpu Reservoir on the downstream area is identified. Figure 3 The impact of the Zipingpu Reservoir on downstream areas weakens with increasing rainfall recurrence intervals. Under a 10-year return period rainfall pattern, the project has some impact on the upper and middle reaches of the river system, but the overall impact coefficient for Xindu District, Chenghua District, Longquanyi District, Qingbaijiang District, and Jintang County is already in the low-impact range, with minimal impact from the project. Under a 1000-year return period rainfall pattern, the project's impact area shrinks further, only having a certain degree of effective impact on Dujiangyan City, Wenjiang District, Xinjin County, and Shuangliu District near the Jinma River, as well as Pidu District located upstream of the river system.

[0065] Under a 200-year flood scenario, the impact proportions of each region are generally similar to those under a 5-year flood scenario. Under a 10-year rainfall scenario, the Zipingpu Reservoir continues to have an effective impact on the nine regions already affected under the 5-year scenario, and also adds an impact on Xindu District. However, the impact coefficients of Chenghua District, Longquanyi District, Qingbaijiang District, and Jintang County remain in the low-impact range, indicating that their impact from the Zipingpu Reservoir is still relatively limited under all conditions. Under a 1000-year rainfall scenario, the impact on downstream areas is basically the same as under a 5-year flood scenario, and the scope of the project's impact remains unchanged.

[0066] S7. Quantitative Assessment of Flood Control Capacity To quantitatively assess the upper limit of the reservoir's theoretical flood control protection capacity for different downstream areas, the flood control capacity under different scenarios was quantitatively statistically analyzed based on spatial interpolation results. Specifically, the proportion of river length and urban area within the high and medium impact zones under different rainstorm and flood scenarios was statistically analyzed to clarify its effective protection range under extreme conditions.

[0067] River protection capacity: This involves statistically analyzing the proportion of total river length in the high- and medium-impact zones under different scenarios, relative to the total river length of the study area. For example... Figure 4 (a) and Figure 5 As shown in the cumulative curve of (a), under a 5-year flood and a 10-year rainfall event, the Zipingpu Reservoir can effectively influence more than 60% of the river channel (M>0.2); while under a 1000-year rainfall event, this proportion drops to about 5%; under a 200-year flood and a 10-year rainfall event, the Zipingpu Reservoir can effectively influence more than 60% of the river channel (M>0.2); while under a 1000-year rainfall event, this proportion drops to about 29%.

[0068] Regional protection capacity: Under different scenarios, the proportion of raster areas with influence values ​​higher than the medium influence interval threshold (i.e., M>0.2) in the raster map was statistically analyzed to quantify the urban area that the reservoir can effectively protect. Results are as follows: Figure 4 (b) and Figure 5 As shown in (b). For example, under a 5-year flood and a 10-year rainfall scenario, the Zipingpu Reservoir provides effective protection for approximately 33.9% of the Chengdu area; however, when the rainfall reaches a 1000-year return period, this effective protection range drops sharply to 13.8%. Under a 200-year flood and a 10-year rainfall scenario, the Zipingpu Reservoir provides effective protection for approximately 30.9% of the Chengdu area; however, when the rainfall reaches a 1000-year return period, this effective protection range drops sharply to 0.29%.

[0069] In general, compared with the 5-year flood condition, the reservoir's overall impact capacity is lower under the 200-year flood condition, the annual average decrease rate in each threshold range is smaller, and the regulation effect of the Zipingpu Water Conservancy Project on the Chengdu regional river channel shows a significant nonlinear attenuation relationship as the rainfall recurrence period increases.

[0070] Conclusion: The method of this invention not only clearly visualizes the spatial differentiation pattern of the impact of Zipingpu Reservoir on flood control in Chengdu, but also... Figure 2 , Figure 3 More importantly, it is the first time that the theoretical upper limit of the reservoir's protection capacity for river channels and urban areas under different extreme scenarios has been quantitatively given. Figure 4 , Figure 5This quantitative assessment directly indicates that the regulatory role of reservoirs is significantly diluted during extreme local rainfall events, requiring downstream cities to rely more heavily on local drainage systems and regional flood control projects. This provides crucial and precise decision-making support for Chengdu to develop tiered and zoned flood control plans and optimize the layout of flood control projects.

[0071] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0072] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0073] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0074] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0075] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0076] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0077] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0078] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for assessing the flood control impact of reservoirs using coupled rainfall-flood simulation and influence coefficient classification, characterized in that, The method includes: Hydrological and meteorological data, land use data, soil data, river network data, and digital elevation model data of the study area are acquired, and the data are preprocessed to form a basic data layer. A rain-flood coupled model is constructed based on the basic data layer, which couples a distributed hydrological model and a one-dimensional hydrodynamic model. The model is then calibrated and validated. The flood-rainfall coupling scenario of the reservoir under different design flood return periods and multiple rainstorm return periods was simulated using a calibration-verified rainstorm coupling model. The reservoir discharge process and the flow process lines of each downstream river section were obtained. For each simulation scenario, the influence coefficient of each downstream river section is calculated, and the degree of influence of the reservoir is divided into three levels based on the influence coefficient values. Based on the influence coefficients of different cross sections under different scenarios, the characteristics of the impact of reservoir discharge on the downstream river channel are analyzed. The spatial interpolation method is used to extend the river channel influence coefficient to each spatial unit within the study area, generating a spatial distribution map of the reservoir's influence. By statistically analyzing the proportion of river length and urban area corresponding to each affected area under different rainstorm and flood scenarios, the theoretical flood control protection capacity of the reservoir for different downstream areas is quantitatively assessed.

2. The reservoir flood control impact assessment method based on rainwater-flood coupling simulation and influence coefficient classification according to claim 1, characterized in that, The distributed hydrological model and the one-dimensional hydrodynamic model achieve bidirectional coupling and water exchange between the hydrological and hydrodynamic processes through a dynamic link interface. The distributed hydrological model is a distributed hydrological model based on physical mechanisms, and the one-dimensional hydrodynamic model is a river hydrodynamic model based on the Saint-Venant equations.

3. The reservoir flood control impact assessment method based on rainwater-flood coupling simulation and influence coefficient classification according to claim 2, characterized in that, The rainwater coupling model is one of the following: the MIKE coupling system, the SWAT and HEC-RAS coupling system, or the VIC and TELEMAC-1D coupling system.

4. The reservoir flood control impact assessment method based on rainwater-flood coupling simulation and influence coefficient classification according to claim 1, characterized in that, The calibration and validation of the model includes: Historical rainfall and flood events in the study area were selected, and key parameters in the model were adjusted based on the measured cross-sectional flow process line of the river. The model was calibrated and validated using the coefficient of determination, Nash efficiency coefficient, and Kling-Gupta efficiency coefficient.

5. The reservoir flood control impact assessment method based on rainwater-flood coupling simulation and influence coefficient classification according to claim 1, characterized in that, The influence coefficient is the ratio of the peak value of the reservoir's designed discharge flow to the peak value of the natural flood flow at that cross-section without considering reservoir regulation.

6. The reservoir flood control impact assessment method based on rainwater-flood coupling simulation and influence coefficient classification according to claim 4, characterized in that, Based on the impact coefficient value, the impact of the reservoir is divided into high impact range, medium impact range, and low impact range. An impact coefficient greater than 0.5 is a high impact range, an impact coefficient between 0.2 and 0.5 is a medium impact range, and an impact coefficient less than 0.2 is a low impact range.

7. The reservoir flood control impact assessment method based on rainwater-flood coupling simulation and influence coefficient classification according to claim 1, characterized in that, The characteristics of the impact of reservoir discharge on downstream river channels include the spatial boundary of the impact range of the downstream river channels, the attenuation characteristics of the impact intensity along the course, and the distribution and transformation patterns of each impact interval under different return period scenarios.

8. The reservoir flood control impact assessment method based on rainwater-flood coupling simulation and influence coefficient classification according to claim 1, characterized in that, The spatial interpolation method is the inverse distance weighted interpolation method. This method calculates the comprehensive influence of each spatial unit within the study area and defines the spatial boundary of the reservoir's effect on the downstream urban built-up area.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images