Flood storage and detention area water regimen element data assimilation method based on sky-ground integration

Abstract

The invention belongs to the technical field of flood storage and detention area data assimilation, and particularly relates to a flood storage and detention area water regimen element data assimilation method based on sky-ground integration, which comprises the following steps: S1, data acquisition, S2, data preprocessing, namely preprocessing acquired multi-source data, ensuring data quality and performing time-space alignment, the method comprises the steps of S1, constructing a hydrological model, S2, simulating water regimen changes by using the hydrological model to describe flowing, water storage and water drainage of flood in a flood storage and detention area and the influence of the flood on the downstream, S4, performing data assimilation, optimizing the prediction precision of the model by fusing observation data and a numerical simulation model result, S5, updating the model and outputting the result, and performing sky-ground data collection and combination to obtain a prediction result of the flood storage and detention area. According to the method, multi-source data support is provided, a plurality of links from data acquisition, preprocessing to assimilation, model updating, prediction application and the like are covered, the precision and timeliness of flood prediction can be remarkably improved through fusion and optimization of multi-source data, and powerful support is provided for effective management, scheduling and flood prevention and control of flood storage and detention areas.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of data assimilation of flood storage areas, in particular to a sky-ground integrated flood storage area water regime element data assimilation method. BACKGROUND

[0002] A flood storage area (also known as a flood regulation and storage area) is a region set up in a specific area to temporarily store floodwater in order to reduce flood disasters. These areas can regulate water levels, slow down the flow of floodwater, and reduce the pressure of floodwater downstream, thereby achieving the purpose of flood control and disaster reduction.

[0003] Flood storage areas usually control water flow through the regulation of reservoirs, sluices or other water conservancy facilities to ensure that floodwater does not flow too quickly downstream, thereby reducing the threat to people's lives and property.

[0004] Monitoring and sensing data is the premise and foundation for building a digital twin water conservancy system, promotes the scientific construction and high-frequency or online operation of a physical river basin monitoring system, enhances the intelligent monitoring and sensing capabilities of water conservancy objects in all aspects and throughout the management process, and provides strong calculation and guarantee support for the high-fidelity construction and operation of a digital twin water conservancy system. Therefore, there is an urgent need to provide a sky-ground integrated flood storage area water regime element data assimilation method. SUMMARY

[0005] This section aims to summarize some aspects of the embodiments of the application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, the abstract and the title, and such simplifications or omissions cannot be used to limit the scope of the application.

[0006] Therefore, the purpose of the present application is to provide a sky-ground integrated flood storage area water regime element data assimilation method, which provides multi-source data support through the combination of sky-ground data acquisition, covers multiple links from data acquisition, preprocessing to assimilation, model updating and prediction application, and significantly improves the accuracy and timeliness of flood prediction through the fusion and optimization of multi-source data, thereby providing strong support for the effective management, scheduling and flood control of flood storage areas.

[0007] To solve the above technical problems, according to one aspect of the present application, the present application provides the following technical scheme:

[0008] The sky-ground integrated flood storage area water regime element data assimilation method comprises the following operation steps:

[0009] S1, data acquisition - through the combination of sky-ground data acquisition, multi-source data support is provided:

[0010] S2, data preprocessing - preprocessing the collected multi-source data to ensure data quality and spatial-temporal alignment, in order to prepare for subsequent assimilation;

[0011] S3, building hydrological model - using hydrological model to simulate water regime changes to describe the flow, storage and discharge of flood in the detention basin and its impact on the downstream;

[0012] S4, data assimilation - by fusing observation data and numerical simulation model results, the prediction accuracy of the model is optimized;

[0013] S5, model updating and result output - after assimilation, the hydrological model state is updated and output, including the following steps:

[0014] State update: input the results of assimilation into the hydrological model to update the model state;

[0015] Short-term forecast: based on the prediction of the assimilated model, generate short-term, such as 1 hour, 6 hours, 24 hours of hydrological forecast results, including flood evolution, water level and flow of detention basin;

[0016] Warning and dispatching: output warning information, especially when the flood is about to affect the detention basin, to provide real-time data support for decision makers;

[0017] S6, result verification and evaluation - during the assimilation process, the prediction results of the model are verified to ensure the effectiveness and accuracy of the assimilation method;

[0018] S7, application decision and optimization - the results of assimilation provide decision support for flood dispatching, emergency response and ecological environment management of detention basin.

[0019] As a preferred scheme of the sky-ground integrated detention basin water regime element data assimilation method described in the application, in step S1, the sky-ground data collection includes the following:

[0020] S101, sky - using optical remote sensing and SAR remote sensing technology equipment:

[0021] Medium-high resolution data: realize large-scale global monitoring and perception of the underlying surface of the basin, river and lake water body, flood disaster and engineering appearance, provide wide range of water body, land use, precipitation, water depth information, help analyze the flood coverage and change trend of detention basin;

[0022] Observation parameters: precipitation, river flow, flood water depth, evaporation;

[0023] S102, air - using unmanned aerial vehicle, manned aircraft equipment, carrying laser radar monitoring instrument;

[0024] High-resolution data: high-precision monitoring of local areas, especially water level changes in flood inundated areas, flood storage capacity, and local precipitation, to achieve high-precision dynamic and timely monitoring of terrain and rainfall elements in key areas such as water area shorelines, flood storage areas, and mountain flood disaster-prone areas;

[0025] Fixed-point monitoring: real-time collection of water level, flow rate, and environmental change data for specific locations or important locations in a watershed;

[0026] S103, ground monitoring facilities such as high towers, unmanned ships, weather stations, and video monitoring stations;

[0027] Hydro-meteorological data: real-time precipitation, water level, flow rate, temperature, and humidity (air humidity) data obtained through ground monitoring stations to achieve accurate and real-time monitoring of rainfall and evaporation elements in key areas;

[0028] Watershed control point data: water level and flow rate key information in a specific watershed to help predict water level changes in the upstream and downstream.

[0029] As a preferred scheme of the sky-ground integrated water regime element data assimilation method for flood storage areas according to the present application, in step S2, data preprocessing includes the following operations:

[0030] S201, data cleaning: removing missing, incorrect, or abnormal values, processing noise, and ensuring data accuracy;

[0031] S202, time synchronization: different data sources may have inconsistent timestamps, and interpolation and synchronization in time are needed to assimilate at the same time;

[0032] S203, spatial matching: the resolution of satellite data may be low, and it needs to be converted to a spatial resolution that matches the ground monitoring data.

[0033] As a preferred scheme of the sky-ground integrated water regime element data assimilation method for flood storage areas according to the present application, in step S3, common hydrological models include:

[0034] 1) Watershed hydrological model: SCS-CN model, HEC-HMS model, and SWAT model;

[0035] 2) Hydrodynamic model: MIKE11 and Delft3D, used to simulate water flow and flood inundation process;

[0036] Hydrological forecasting model: predict future trends of precipitation and flow rate by establishing a hydro-meteorological forecasting system.

[0037] As a preferred scheme of the sky-ground integrated flood storage area water regime element data assimilation method according to the application, in step S4, the commonly used assimilation methods are:

[0038] 1) Kalman filter - processing linear dynamic system, gradually optimizing model prediction value by iteratively calculating the covariance matrix of prediction error, suitable for simple water flow dynamics and water level change prediction in the basin model;

[0039] 2) Extended Kalman filter - suitable for nonlinear systems, linearize the state space for assimilation, and when dealing with complex hydrological models such as nonlinear flow models, extended Kalman filter can better improve the assimilation accuracy;

[0040] 3) Particle filter - suitable for highly nonlinear and high-dimensional problems, processing model state update through random sampling and weighting, suitable for such dynamic and complex nonlinear systems as flood storage area, to effectively handle the nonlinear changes of water flow, especially in the case of heavy rainfall and sharp changes;

[0041] 4) Least squares assimilation - optimize model parameters and states by minimizing the sum of squares of errors between observed values and model predictions, used for water balance and flow prediction optimization of the basin.

[0042] As a preferred scheme of the sky-ground integrated flood storage area water regime element data assimilation method according to the application, in step S6:

[0043] Comparison with measured data: compare the assimilated results with the actual observation data, calculate the error and evaluate the assimilation effect;

[0044] Error analysis: analyze the influence of different observation data sources, assimilation methods and model parameters on the final results to determine the best data assimilation strategy;

[0045] Sensitivity analysis: study the sensitivity of hydrological model prediction to different input conditions such as precipitation and flow, to adjust model parameters and improve prediction accuracy.

[0046] As a preferred scheme of the sky-ground integrated flood storage area water regime element data assimilation method according to the application, in step S7:

[0047] Flood regulation: optimize the regulation strategy of the flood storage area, including water storage, water release and drainage operation, to ensure safe regulation of upstream flood and avoid disasters in the downstream;

[0048] Emergency response: according to the prediction results, make emergency plan in advance to guide personnel evacuation and disaster prevention;

[0049] Environmental monitoring: monitor the water quality change in the flood storage area, ecological environment influence, ensure ecological safety.

[0050] As a preferred embodiment of the sky-ground integrated flood storage area water regime element data assimilation method according to the application, wherein: the data assimilation method comprises the following steps:

[0051] M1, determining the water regime elements that need to be assimilated, precipitation, evapotranspiration, flow or water level;

[0052] M2, collecting and preprocessing observation data, collecting observation data related to water regime elements through sky-ground, such as sky-satellite data, ground-tower and unmanned ship monitoring data, and unmanned aerial vehicle collection data, and preprocessing observation data, including denoising, null value processing, time scale adjustment, to ensure data quality and consistency

[0053] M3, determining the assimilation target and variable, determining the water regime elements that need to be assimilated and their target variables, and selecting the most relevant variables for assimilation according to the specific application scenario;

[0054] M4, adopt two kinds of assimilation methods to contact and combine, establish fusion assimilation model, including the following:

[0055] Weighted average method: different weights are given to different data sources, and the observation values of multiple data sources are integrated to obtain the fusion result:

[0056]

[0057] Where D i is the i-th data source, w i is the corresponding weight, and the weight w i satisfies ∑w i =1;

[0058] Set Kalman wave: data fusion is carried out by using the formula, and the prior knowledge and observation data are updated to obtain the posterior distribution:

[0059] State equation: x k =Ax k-1 +Bu k +w k

[0060] Where: x k is the state at the current time, A is the state transition matrix, which describes how the state is transferred from the previous time to the current time, B is the control input matrix, which describes the influence of the control input on the state, u k is the control input, w k is the process noise, which is usually assumed to be zero-mean Gaussian noise, and the square value is Q;

[0061] Observation equation:

[0062] The assimilation framework is as follows: z k = Hx k + v k

[0063] Wherein: z k is the observation at the current time, H is the observation matrix, which describes how to obtain the observation from the state, v k is the observation noise, which is usually assumed to be zero-mean Gaussian noise with R as the square value;

[0064]

[0065] x k = Ax k-1 + Bu k + w k ;

[0066] z k = Hx k + v k ;

[0067] M5, performing data assimilation, using the selected assimilation method model, assimilating observation data and model prediction

[0068] Compared with the prior art, the present application has the beneficial effects that:

[0069] Through the combination of sky and ground data acquisition, multi-source data support is provided, covering multiple links from data acquisition, preprocessing to assimilation, model updating and prediction application, through the fusion and optimization of multi-source data, the accuracy and timeliness of flood prediction can be significantly improved, providing strong support for the effective management, scheduling and flood control of the storage and detention basin, and with the continuous updating of remote sensing, unmanned aerial vehicles and data assimilation technology, the water regime monitoring and management of the storage and detention basin will be more intelligent and refined, and the space, range, accuracy, frequency and other aspects are synergistically integrated, realizing intelligent monitoring and sensing of all elements of water conservancy objects and the whole process of management. BRIEF DESCRIPTION OF DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the present application will be described in detail below in combination with the drawings and detailed embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor. Among them:

[0071] Figure 1 is a schematic block diagram of the data assimilation method steps of the present application;

[0072] Figure 2 is an expanded schematic block diagram of step S2 of the data assimilation method of the present application;

[0073] Figure 3 The following is a schematic diagram of the assimilation model expansion of step M4 of the data assimilation method of the present application. DETAILED DESCRIPTION

[0074] In order to make the above objectives, features and advantages of the present application more apparent, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0075] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the concept of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0076] Secondly, the present application is described in detail in combination with the schematic diagram, and in the detailed description of the embodiments of the present application, the cross-sectional view of the device structure will be partially enlarged without the general proportion for the convenience of description, and the schematic diagram is only an example, which should not limit the scope of protection of the present application herein. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in actual manufacture.

[0077] In order to make the objectives, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail below in combination with the accompanying drawings.

[0078] The present application provides a data assimilation method for water regime elements of a flood storage and detention area based on the integration of sky and ground, which provides multi-source data support through the combination of sky and ground data collection, covers multiple links from data collection, preprocessing to assimilation, model updating and prediction application, and can significantly improve the accuracy and timeliness of flood prediction through the fusion and optimization of multi-source data, thereby providing strong support for the effective management, scheduling and flood control of the flood storage and detention area. Please refer to Figures 1-2 , which includes the following operation steps:

[0079] S1, data collection - through the combination of sky and ground data collection, multi-source data support is provided:

[0080] S2, data preprocessing - the collected multi-source data is preprocessed to ensure data quality and perform spatio-temporal alignment, so as to prepare for subsequent assimilation;

[0081] S3, constructing a hydrological model - a hydrological model is used to simulate water regime changes to describe the flow, storage and discharge of flood in the flood storage and detention area and its influence on the downstream;

[0082] S4, data assimilation - through the fusion of observation data and numerical simulation model results, the prediction accuracy of the model is optimized;

[0083] S5, model updating and result output - after assimilation, the hydrological model state is updated and output, including the following steps:

[0084] State update: input the assimilated results into the hydrological model to update the model state;

[0085] Short-term forecast: based on the assimilated model prediction, generate short-term hydrological forecast results such as 1 hour, 6 hours, 24 hours, including flood evolution, water level of detention basin, flow;

[0086] Warning and dispatching: output warning information, especially when the flood is about to affect the detention basin, provide real-time data support for decision-makers;

[0087] S6, result verification and evaluation - during the assimilation process, the prediction results of the model are verified to ensure the effectiveness and accuracy of the assimilation method;

[0088] S7, application decision and optimization - the results after assimilation provide decision support for flood regulation, emergency response and ecological environment management of detention basin.

[0089] In step S1, the sky-ground data collection includes the following:

[0090] S101, Sky - use optical remote sensing, SAR remote sensing technology equipment:

[0091] Medium-high resolution data: realize large-scale global monitoring and perception of the underlying surface of the basin, river and lake water body, flood disaster, engineering appearance, provide wide range of water body, land use, precipitation, water depth information, help analyze the flood coverage and change trend of detention basin;

[0092] Observation parameters: precipitation, river flow, flood water depth, evaporation;

[0093] S102, Air - use unmanned aerial vehicle, manned aircraft equipment, carry laser radar monitoring instrument;

[0094] High-resolution data: high-precision monitoring of local area, especially water level change of flood submerged area, detention water volume, local precipitation, realize high-precision dynamic and timely monitoring and perception of topography, rainfall elements of water area coastline, detention basin, mountain flood disaster prone area key area;

[0095] Fixed-point monitoring: real-time collection of water level, flow, environmental change data for specific locations or important positions of the basin;

[0096] S103, Ground - use high tower, unmanned ship, weather station, video monitoring station ground monitoring facilities;

[0097] Hydrometeorological data: Real-time precipitation, water level, flow, temperature, humidity (air humidity) data are obtained through ground monitoring stations to realize accurate real-time monitoring and sensing of rainfall and evaporation elements in key regional points;

[0098] Basin control point data: Water level and flow rate key information in a specific basin to help predict water level changes upstream and downstream.

[0099] In step S2, data preprocessing includes the following operations:

[0100] S201, Data cleaning: Remove missing, incorrect or abnormal values, handle noise and ensure data accuracy;

[0101] S202, Time synchronization: Different data sources may have inconsistent timestamps, so interpolation and synchronization in time are needed to assimilate at the same time;

[0102] S203, Spatial matching: The resolution of satellite data may be low, which needs to be converted to a spatial resolution matching the ground monitoring data.

[0103] In step S3, common hydrological models include:

[0104] 3) Watershed hydrological model: SCS-CN model, HEC-HMS model, SWAT model;

[0105] 4) Hydrodynamic model: MIKE11, Delft3D, used to simulate water flow and flood inundation process;

[0106] Hydrological forecasting model: Predict future trends of precipitation and flow changes by establishing a hydro-meteorological forecasting system.

[0107] In step S4, common assimilation methods:

[0108] 1) Kalman filter - processing linear dynamic systems, gradually optimizing model predictions by iteratively calculating the covariance matrix of prediction errors, suitable for relatively simple water flow dynamics and water level change prediction in basin models;

[0109] 2) Extended Kalman filter - suitable for nonlinear systems, linearizes the state space for assimilation, and can better improve assimilation accuracy when dealing with complex hydrological models such as nonlinear flow models;

[0110] 3) Particle filter - suitable for highly nonlinear and high-dimensional problems, handles model state updates through random sampling and weighting, suitable for dynamic complex and nonlinear systems such as flood detention areas to effectively handle nonlinear changes in water flow, especially in the case of heavy rainfall and rapid changes;

[0111] 4) Least squares assimilation - optimize model parameters and states by minimizing the sum of squared errors between observed and model predicted values, for water balance and flow prediction optimization of the basin.

[0112] In step S6:

[0113] Comparison with actual data: compare the assimilated results with actual observation data, calculate the error and evaluate the assimilation effect;

[0114] Error analysis: analyze the influence of different observation data sources, assimilation methods and model parameters on the final results, and determine the best data assimilation strategy;

[0115] Sensitivity analysis: study the sensitivity of hydrological model prediction to different input conditions such as precipitation and flow, to adjust model parameters and improve prediction accuracy.

[0116] As a preferred scheme of the sky-ground integrated flood storage area water regime element data assimilation method described in the present application, in step S7:

[0117] Flood regulation: optimize the regulation strategy of the flood storage area, including water storage, water release and drainage operation, to ensure safe regulation of upstream flood and avoid disaster in downstream;

[0118] Emergency response: according to the prediction results, make flood emergency plan in advance to guide personnel evacuation and disaster prevention;

[0119] Environmental monitoring: monitor the water quality change and ecological environment influence in the flood storage area to ensure ecological safety.

[0120] The data assimilation method includes the following steps:

[0121] M1, determine the water regime elements that need to be assimilated, such as precipitation, evapotranspiration, flow or water level;

[0122] M2, collect and preprocess observation data, collect observation data related to water regime elements through sky-ground, such as sky-satellite data, ground-tower and unmanned ship monitoring data, and air-unmanned aerial vehicle collection data, and preprocess the observation data, including denoising, null value processing and time scale adjustment, to ensure data quality and consistency

[0123] M3, determine the assimilation target and variable, determine the water regime elements and target variables that need to be assimilated, and select the most relevant variables for assimilation according to the specific application scenario;

[0124] M4, adopt two assimilation methods to contact and combine, establish a fusion assimilation model, including the following:

[0125] Weighted average method: different data sources are given different weights, and the observation values of multiple data sources are integrated to obtain the fusion result:

[0126]

[0127] where D i is the i-th data source, w i is the corresponding weight, and the weight w i satisfies ∑w i =1;

[0128] Set Kalman wave: data fusion is carried out by using the formula, and the prior knowledge and observation data are combined to update the posterior distribution:

[0129] State equation: x k =Ax k-1 +Bu k +w k

[0130] where: x k is the state at the current time, A is the state transition matrix, which describes how the state is transferred from the previous time to the current time, B is the control input matrix, which describes the influence of the control input on the state, u k is the control input, w k is the process noise, which is usually assumed to be zero-mean Gaussian noise, and the square value is Q;

[0131] Observation equation:

[0132] The assimilation framework is as follows: z k =Hx k +v k

[0133] where: z k is the observation at the current time, H is the observation matrix, which describes how to obtain the observation from the state, v k is the observation noise, which is usually assumed to be zero-mean Gaussian noise, and the square value is R;

[0134]

[0135] x k =Ax k-1 +Bu k +w k ;

[0136] z k =Hx k +v k ;

[0137] M5, perform data assimilation, and assimilate the observation data and model prediction by using the selected assimilation method model.

[0138] Although the present application has been described with reference to the embodiments above, various changes and modifications can be suggested to one skilled in the art, and it is intended that the present application encompass such changes and modifications as fall within the scope of the appended claims. Particularly, each feature disclosed in the description and / or the claims can be used in the combination with each of the features disclosed in the description and / or the claims, unless specifically stated otherwise. Therefore, the present application is not intended to be limited to the particular embodiments disclosed in the description and / or the claims.

Claims

1. A method for assimilating water regime data of flood storage and detention areas based on sky-ground integration is characterized by: The steps are as follows: S1. Data Collection - Provide multi-source data support through the combination of air and ground data collection: S2. Data preprocessing: preprocess the collected multi-source data to ensure data quality and perform spatiotemporal alignment to prepare for subsequent assimilation; S3. Build a hydrological model - Use a hydrological model to simulate water regime changes to describe flood flow, water storage, and water release within the flood storage area and its impact on downstream areas; S4, Data Assimilation - Optimizing the model's prediction accuracy by fusing observational data with numerical simulation model results; S5. Model update and result output - After assimilation, the hydrological model status is updated and output, including the following steps: Status update: input the assimilated results into the hydrological model and update the model status; Short-term forecast: Based on the assimilated model predictions, short-term hydrological forecast results such as 1 hour, 6 hours, and 24 hours are generated, including flood evolution, water level and flow in the detention area; Early warning and dispatch: Output early warning information, especially when floods are about to affect flood storage areas, to provide real-time data support to decision makers; S6. Result Verification and Evaluation - During the assimilation process, the model prediction results are verified to ensure the effectiveness and accuracy of the assimilation method; S7. Application of decision-making and optimization: The assimilated results provide decision support for flood control, emergency response and ecological environment management in flood storage and detention areas.

2. The method for assimilating water regime element data of flood storage and detention areas based on sky-ground integration according to claim 1 is characterized in that: In step S1, the sky-ground data collection includes the following: S101, Sky - Using optical remote sensing and SAR remote sensing technology equipment: Medium- and high-resolution data: This enables large-scale, global monitoring and perception of the basin's underlying surface, river and lake water bodies, flood disasters, and engineering projects. It provides information on a wide range of water bodies, land use, precipitation, and water depth, helping to analyze flood coverage and changing trends in flood storage and detention areas. Observation parameters: precipitation, river flow, flood depth, evaporation; S102, Air - Use UAVs and manned equipment equipped with laser radar monitoring equipment; High-resolution data: Provides high-precision monitoring of local areas, especially water level changes, flood storage volume, and local precipitation in flood-inundated areas. This enables timely and dynamic monitoring of small- and medium-scale topography and rainfall elements in key areas such as waterways, flood storage areas, and flash flood-prone areas. Fixed-point monitoring: Targeting specific locations or important locations in a river basin, collecting real-time data on water level, flow, and environmental changes; S103, Ground - Utilize ground monitoring facilities such as towers, unmanned vessels, weather stations, and video monitoring stations; Hydrometeorological data: Real-time precipitation, water level, flow, temperature, and humidity (air humidity) data are obtained through ground monitoring stations to achieve accurate real-time monitoring and perception of rainfall and evaporation factors at key regional locations; Watershed control point data: Key information on water levels and flow rates in a specific watershed, helping to predict changes in water conditions upstream and downstream.

3. The method for assimilating water regime element data of flood storage and detention areas based on sky-ground integration according to claim 2 is characterized in that: In step S2, data preprocessing includes the following operations: S201. Data cleaning: remove missing, erroneous or outlier values, process noise, and ensure data accuracy; S202, time synchronization: The timestamps of different data sources may be inconsistent, and time interpolation and synchronization are required so that assimilation can be performed at the same time; S203, spatial matching: The resolution of satellite data may be low and needs to be converted to a spatial resolution that matches the ground monitoring data.

4. The method for assimilating water regime element data of flood storage and detention areas based on sky-ground integration according to claim 3 is characterized in that: In step S3, common hydrological models include: 1) Basin hydrological models: SCS-CN model, HEC-HMS model, SWAT model; 2) Hydrodynamic models: MIKE11 and Delft3D, used to simulate water flow and flooding processes; 3) Hydrological forecast model: Predict future trends in precipitation and flow by establishing a hydro-meteorological forecast system.

5. The method for assimilating water regime elements in flood storage and detention areas based on sky-ground integration according to claim 4 is characterized in that: In step S4, the commonly used assimilation method is: 1) Kalman filter - processes linear dynamic systems and gradually optimizes the model prediction value by iteratively calculating the covariance matrix of the prediction error. It is suitable for relatively simple water flow dynamics and water level change forecasts in watershed models; 2) Extended Kalman Filter - Applicable to nonlinear systems, it performs assimilation by linearizing the state space. When dealing with complex hydrological models, such as nonlinear water flow models, the extended Kalman filter can better improve the assimilation accuracy; 3) Particle filtering - Applicable to highly nonlinear and high-dimensional problems, it handles model state updates through random sampling and weighting. It is suitable for dynamic, complex, nonlinear systems such as flood storage and detention areas to effectively handle nonlinear changes in water flow, especially in cases of heavy rainfall and rapid changes; 4) Least Squares Assimilation - Optimize model parameters and states by minimizing the sum of squared errors between observations and model predictions, which is used for water balance and flow forecast optimization in the basin.

6. The method for assimilating water regime element data of flood storage and detention areas based on sky-ground integration according to claim 5 is characterized in that: In the step S6: Comparison with measured data: Compare the assimilated results with the actual observed data, calculate the error and evaluate the assimilation effect; Error analysis: Analyze the impact of different observation data sources, assimilation methods, and model parameters on the final results to determine the optimal data assimilation strategy; Sensitivity analysis: Study the sensitivity of different input conditions such as precipitation and flow to hydrological model predictions in order to adjust model parameters and improve prediction accuracy.

7. The method for assimilating water regime element data of flood storage and detention areas based on sky-ground integration according to claim 6 is characterized in that: In step S7: Flood control: Optimize flood storage and detention area control strategies, including water storage, release, and drainage operations, to ensure safe upstream flood control and avoid disasters downstream; Emergency response: Prepare flood emergency plans in advance based on forecast results, and provide guidance on evacuation and disaster prevention; Environmental monitoring: Monitor water quality changes and ecological environmental impacts in flood storage areas to ensure ecological safety.

8. The method for assimilating water regime element data of flood storage and detention areas based on sky-ground integration according to claim 7 is characterized in that: The data assimilation method comprises the following steps: M1. Determine the water regime elements that need to be assimilated, such as precipitation, evapotranspiration, flow or water level; M2. Collect and pre-process observation data. Collect observation data related to water conditions from the sky and ground, such as satellite data, ground-based tower and unmanned vessel monitoring data, and air-based drone data. Pre-process the observation data, including denoising, null value processing, and time scale adjustment, to ensure data quality and consistency. M3. Determine assimilation targets and variables. Identify the water regime elements and target variables that need to be assimilated. Select the most relevant variables for assimilation based on the specific application scenario. M4. Use the two assimilation methods to connect and combine and establish a fusion assimilation model, including the following: Weighted average method: assign different weights to different data sources, combine the observation values ​​of multiple data sources, and obtain the fusion result: Among them, D i is the i-th data source, w i is the corresponding weight, and the weight w i Satisfy ∑w i =1; Ensemble Kalman Wave: Use the formula to fuse data and update the posterior distribution by combining prior knowledge and observed data: Equation of state: x k =Ax k-1 +Bu k +w k Where: x k is the state at the current moment, A is the state transfer matrix, which describes how the state is transferred from the previous moment to the current moment, B is the control input matrix, which describes the influence of the control input on the state, u k is the control input, w k is the process noise, usually assumed to be zero-mean Gaussian noise, with a square value of Q; Observation equation: The assimilation framework is as follows: k =Hx k +v k Where: z k is the observation quantity at the current moment, H is the observation matrix, which describes how to obtain the observation from the state, v k is the observation noise, which is usually assumed to be zero-mean Gaussian noise with a square value of R; x k =Ax k-1 +Bu k +w k ; z k =Hx k +v k ; M5. Perform data assimilation, using the selected assimilation method model to assimilate the observed data and model forecasts.

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