A method, medium, device and product for determining large river-lake interzone confluence water quantity

Abstract

The present application provides a kind of large river lake interval confluence water quantity determination method, medium, equipment and product, it is related to data fusion and hydrological information technical field.The method obtains the remote sensing image of target area, terrain and hydrological observation data;Establish water balance equation, express interval confluence water quantity as the function of river lake storage water quantity change, inlet flow, outlet flow and main tributary (with hydrological station control) total flow;Space discretization is carried out to calculation unit and generates elevation grid and initial water level grid;Actual water area is extracted based on remote sensing image;By comparing the calculation water area with actual water area, iteratively adjust water level grid until the convergence condition is met, to obtain accurate storage water quantity;Finally, interval confluence water quantity is calculated by substituting water balance equation and analyzing its variation characteristics.The method fuses multi-source data and iterative optimization, improves the accuracy and reliability of water quantity calculation, and is suitable for water resources management and hydrological analysis of large rivers and lakes.

Description

Technical Field

[0001] This invention relates to the fields of data fusion and hydrological information technology, specifically to a method, medium, equipment, and product for determining the runoff volume of large river and lake sections. Background Technology

[0002] In large river and lake systems, there are usually a large number of small and medium-sized rivers flowing into them. Although the flow of each individual confluence is limited, due to their large number and dense water system, the cumulative water volume accounts for a considerable proportion of the total inflow, making them an indispensable part of the inflow structure of large rivers and lakes.

[0003] Taking large lakes as an example, to quantify the inter-regional runoff volume, existing studies generally introduce a correction coefficient to the flow of the main tributaries flowing into the lake to estimate the total inflow into the lake, and then inversely estimate the inter-regional runoff volume. The setting of this coefficient relies on theoretical assumptions such as the periodic balance of inflow and outflow or that the proportion of runoff-producing area is equivalent to the proportion of runoff volume. However, in reality, due to regional and climatic differences, the inter-regional runoff and the total inflow of the main tributaries do not exhibit a simple linear relationship throughout the year, resulting in limited accuracy and reliability of such estimation methods. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for determining the runoff volume of a large river-lake interval, comprising:

[0005] In a first aspect, embodiments of this application provide a method for determining the runoff volume of a large river-lake interval, the method comprising:

[0006] S1: Acquire remote sensing images and topographic data of the confluence area of ​​the target river and lake, as well as flow and water level data observed by hydrological or water level stations;

[0007] S2: Based on the data obtained from S1, determine the calculation unit for river and lake water balance, and establish the water balance equation of the calculation unit within a set time period;

[0008] S3: Express the interval runoff volume in the water balance equation as a functional relationship that includes the change in the stored water volume of the calculation unit, the known inlet flow, the outlet flow, and the total flow of the main tributaries;

[0009] S4: Spatial discretization of the computing unit is performed to generate a grid file, and elevation interpolation is performed on the grid nodes based on the terrain data to generate an elevation grid file that reflects the terrain undulation.

[0010] S5: The computational unit is partitioned based on water level data, and water level interpolation is performed on the grid nodes to generate an initial water level grid file that reflects the water surface morphology;

[0011] S6: Based on remote sensing images, extract the actual water area and spatial distribution of the computing unit at the corresponding time;

[0012] S7: Using the elevation grid file generated in S4 and the initial water level grid file generated in S5, calculate the initial water storage volume of the calculation unit and the corresponding calculated water area.

[0013] S8: Compare the calculated water area with the extracted actual water area, and iteratively adjust the water level values ​​in the initial water level grid file generated in S5 based on the comparison results. Repeat S7 until the difference between the calculated water area and the actual water area meets the preset convergence condition, thereby obtaining the accurate water storage volume of the calculation unit at the corresponding time.

[0014] S9: Substitute the known inlet flow rate, outlet flow rate, total flow rate of major tributaries, and precise stored water volume into the functional relationship described in S3 to calculate the interval runoff volume within the set time period, and analyze its variation characteristics based on the calculation results.

[0015] As a further technical solution, S1 further includes:

[0016] S11 specifies the remote sensing images, topographic data, and hydrological data to be collected; among them, the remote sensing images and topographic data should completely cover the entire target area, and the hydrological data should ensure: 1) measured flow data from upstream and downstream hydrological stations in the confluence area of ​​the river and lake area ( , ); 2) For large rivers, at least the measured water level data of the upstream and downstream water level stations of the main stream in the confluence section; 3) For large lakes, due to the large area and complex terrain of the lake area, at least the measured water level data of 3 water level stations in the confluence section, of which two water level stations need to be distributed at the outflow section of the lake area and the high point of the lake area terrain.

[0017] S12: Collect high-precision remote sensing images, topographic elevation scatter points or cross-sectional data, and daily average hydrological observation data of the confluence of the two regions; unify the collected data to the same geographic coordinate system and projection, and perform time consistency checks and data quality control; imput or remove missing data to form a data foundation that can be directly used in subsequent steps.

[0018] As a further technical solution, S2 further includes:

[0019] S21, combining the inflow intervals of the confluence of the intervals, as well as the distribution and frequency of hydrological stations, determines the large-scale river and lake calculation unit.

[0020] S22, Establish The water balance equation for a given period; where, for The initial water storage volume of the river and lake calculation unit at any given time. for time The water storage volume of the river and lake calculation unit. for The inflow rate at the inlet of a large-scale river computing unit at any given time. for Real-time outflow rate of a large river or lake computing unit. for The amount of water flowing into the calculation unit at any given time within the interval. The time step used for calculation can be 1 day, 1 month, 1 year, or many years, etc.

[0021] For a large river, its water balance equation is:

[0022] (1)

[0023] For a large lake, its water balance equation is:

[0024] (2)

[0025] As a further technical solution, S3 further includes:

[0026] S31, the calculation to be performed By rearranging the terms to one side of the equation, we can transform equations (1) and (2) into equations (3) and (4):

[0027] The water balance equation for large rivers is transformed into:

[0028] (3)

[0029] The water balance equation for a large lake is transformed into:

[0030] (4)

[0031] S32, based on the time period selected in S22, will The flow observation data of each major tributary within the time period are accumulated to obtain the cumulative value of the daily average flow of all major tributaries within the calculation unit. And input it into equation (3) or equation (4);

[0032] S33, based on the time period selected in S22, During the time period Cumulative value and Accumulate the value and input it into equation (3) or equation (4).

[0033] As a further technical solution, S4 further includes:

[0034] S41 discretizes the river and lake calculation unit, dividing it into a series of continuous, non-overlapping, and dense triangular units, ultimately generating a high-precision mesh file composed of nodes (vertices of triangles) and units (triangles themselves). This mesh file contains only geometric location information.

[0035] S42, combining the terrain elevation data collected in S12 and the high-precision grid file of the river and lake calculation unit obtained in S41, uses Kriging interpolation to assign terrain elevation values ​​to the grid nodes based on the planar position information of each node in the grid file. Each grid node is numbered k1, k2, k3, where k is the triangular grid number ( The final output is an elevation grid file that accurately reflects the topographical variations of rivers and lakes. .

[0036] As a further technical solution, S5 further includes:

[0037] S51, combining the water level data, hydrological station locations, and topographic relief features collected in S12, the calculation unit is partitioned into one or more regions; the partitioning principle is that there must be two water level stations upstream and downstream of the interval.

[0038] S52, within each divided region, based on the water level data collected in S12, linear interpolation is used to assign water level values ​​to the grid nodes (k1, k2, k3, where k is the triangular grid number), ultimately generating an initial water level grid file reflecting the morphology of the river and lake surface. .

[0039] As a further technical solution, S6 further includes:

[0040] S61, combined with the remote sensing image data collected in S12, performs targeted preprocessing on the original optical or radar images, such as radiometric calibration, geometric / topographic correction and quality control, to construct a standardized image dataset that can be used for subsequent analysis.

[0041] S62. Based on the remote sensing image dataset, water body discrimination features are constructed (water body index for optical images and backscattering / polarization features for SAR images) to initially distinguish water bodies from land areas and identify the water-land boundary. Then, pixel-level classification is achieved by threshold segmentation of the discrimination feature map. Finally, the extracted water body boundaries are finely adjusted to obtain a dataset of all or specified dates of river and lake water bodies within the target time period.

[0042] S63, based on the river and lake water body dataset obtained in S62, uses a geographic information system to calculate the water area and visualize the spatial distribution of the water body, generating a river and lake water area dataset for each time period within the target time period. Dataset, ) and spatial water distribution atlas.

[0043] As a further technical solution, S7 further includes:

[0044] S71, combining the elevation grid files obtained from S42 and S52 and initial water level grid file Based on equation (5), obtain in time( The water depth at each triangular grid node; in equation (5) For the k-th grid in Water depth (m) at the location of the triangular node at any given time. and Let represent the water level (m) and terrain elevation (m) at the three nodes of the k-th triangular grid, respectively; where the formula is:

[0045]

[0046] S72, combining the water depth calculation results from S71 and the mesh geometry data from S41, performs inundation determination. A water depth threshold δ is set to accurately define the wet-dry boundary; that is, if the water depth is greater than δ, the node is determined to be inundated. Based on this determination, in... time( The flooding status of each triangular mesh node is determined, and the distribution of its flooded area and its area calculation value are obtained. ( Based on equation (6), all grid-inundated areas are spatially aggregated to obtain the river and lake computing units. Calculated water area at time The data, including its spatial distribution, are used to generate a dataset of water area distribution maps and area calculations for each time period within the target time period.

[0047] (6)

[0048] S73, combining the water depth calculation results of S71 and the area calculation results of S72, obtains the river and lake calculation unit based on equation (7). Calculated value of water storage volume at any given time ; for the target time period …The above process is repeated at each moment to generate a dataset of calculated water storage volume values ​​for each moment;

[0049]

[0050] in, .

[0051] As a further technical solution, S8 further includes:

[0052] S81, the actual river and lake water area data at various times obtained in S63. Calculated values ​​obtained from S72 By comparison, the spatial difference area ratio between the calculated water area distribution and the actual water area distribution is obtained. ;

[0053] S82, perform spatial overlay analysis on the actual water distribution map obtained in S63 and the water distribution map calculated in S72 to identify the difference areas that do not overlap between the two;

[0054] S83, for false alarm areas, i.e., areas with water in the calculated graph but not in the actual graph, it is determined that the calculated water level at the corresponding location is too high, and the initial water level grid file is updated. The water level at the corresponding grid node is updated with a specified negative step size; for missed areas, i.e., areas with water in the actual graph but not in the calculated graph, it is determined that the calculated water level at the corresponding location is too low, and the initial water level grid file is updated accordingly. The water level at the corresponding grid node is updated with a specified positive step size;

[0055] In step S84, the updated water level grid file from S82 is used as the new initial condition, and the iteration calculation is restarted from S7. This process is repeated until the spatial difference area ratio is reached. If the preset convergence criterion is reached, such as less than 1%, the iteration stops; at this point, the result calculated by S73 is... The value is Precise volume values ​​of water storage in rivers and lakes at any given time; for the target time period …The above process is repeated at each moment, eventually generating a dataset of accurate water storage volume values ​​for each moment.

[0056] As a further technical solution, S9 further includes:

[0057] S91, the traffic data obtained in S32 and 33 ( , , ) and S84 calculation The precise value is used as an input and substituted into equation (3) or (4) to solve for the interval runoff volume of large rivers and lakes at each time point in the target period;

[0058] S92, plots the time intervals of the target time period with time as the horizontal axis. Distribution maps are used to analyze the annual, interannual, or specified time-period variation characteristics of the runoff volume in large river and lake areas.

[0059] Secondly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory is used to store computer programs;

[0060] The processor is used to load and execute computer programs to enable electronic devices to implement the above-described method for determining the runoff volume of large river and lake sections.

[0061] Thirdly, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a processor, implements the aforementioned method for determining the runoff volume of a large river and lake section.

[0062] Fourthly, embodiments of this application provide a computer program product, including a computer program, which, when executed, is used to perform the above-described method for determining the runoff volume of a large river-lake interval.

[0063] This invention provides a method, medium, equipment, and product for determining the runoff volume of large river and lake sections, which has the following beneficial effects:

[0064] 1. This invention solves the technical problems of large model simplification errors and weak theoretical foundation caused by relying on empirical assumptions such as the proportion of runoff generation area being equivalent to the proportion of runoff volume by constructing a water balance equation and combining it with the river and lake topographic grid modeling method. It improves the scientificity and physical consistency of the inter-regional runoff volume calculation model and can accurately characterize the spatiotemporal heterogeneity and instantaneous characteristics of inter-regional runoff volume.

[0065] 2. This invention uses actual water area data extracted from remote sensing images to iteratively calibrate and optimize the spatial consistency of model water level and inundation distribution. This solves the problem that the actual water surface morphology and water storage dynamics are difficult to accurately describe due to the long intervals between large river and lake hydrological (level) monitoring stations and the dispersion of observation points, and improves the accuracy of the inter-regional runoff calculation results.

[0066] 3. This invention solves the problems of broken data links and calculation results that cannot support refined hydrological process simulation and control decisions by integrating multi-source data acquisition, gridded terrain processing, remote sensing image verification and water balance inversion into a full-process collaborative computing link. It improves the availability and decision support capabilities of inter-regional confluence data in hydrological forecasting, ecological scheduling and water resource management. Attached Figure Description

[0067] Figure 1 This is a schematic diagram of the process of the present invention;

[0068] Figure 2 A schematic diagram of a large river computing unit;

[0069] Figure 3A schematic diagram of a large lake computing unit;

[0070] Figure 4 This is a map showing the annual distribution of the monthly average water volume in the interval. Detailed Implementation

[0071] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] Example 1, as Figure 1-3 As shown, the remote sensing images, topographic data, and hydrological data to be collected are clearly defined; among them, the remote sensing images and topographic data should completely cover the entire target area, and the hydrological data should ensure: 1) the measured flow data of the upstream and downstream hydrological stations of the confluence area of ​​the river and lake ( , ); 2) For large rivers, at least the measured water level data of upstream and downstream water level stations of the main stream in the confluence section; 3) For large lakes, due to the large area and complex terrain of the lake area, at least the measured water level data of 3 water level stations in the confluence section, of which two water level stations need to be distributed at the outflow section and the high point of the lake terrain; collect high-precision remote sensing images, topographic elevation scatter points or cross-sectional data and daily average hydrological observation data of the confluence section; unify the collected data to the same geographic coordinate system and projection, and perform time consistency checks and data quality control, interpolate or remove missing data to form a data foundation that can be directly called upon in subsequent steps.

[0073] By combining the inflow intervals of the confluence of the river and the distribution of hydrological stations, large-scale river and lake calculation units are determined.

[0074] Establish The water balance equation for a given period; where, for The initial water storage volume of the river and lake calculation unit at any given time. for time The water storage volume of the river and lake calculation unit. for The inflow rate at the inlet of a large-scale river computing unit at any given time. for Real-time outflow rate of a large river or lake computing unit. for The amount of water flowing into the calculation unit at any given time within the interval. The time step used for calculation can be 1 day, 1 month, 1 year, or many years, etc.

[0075] For a large river, its water balance equation is:

[0076] (1)

[0077] For a large lake, its water balance equation is:

[0078] (2)

[0079] The calculation required By rearranging the terms to one side of the equation, we can transform equations (1) and (2) into equations (3) and (4):

[0080] The water balance equation for large rivers is transformed into:

[0081] (3)

[0082] The water balance equation for a large lake is transformed into:

[0083] (4)

[0084] Will The flow observation data of each major tributary within the time period are accumulated to obtain the cumulative value of the daily average flow of all major tributaries within the calculation unit. And input it into equation (3) or equation (4); During the time period Cumulative value and Accumulate the value and input it into equation (3) or equation (4).

[0085] The river and lake computational units are discretized into a series of continuous, non-overlapping, and dense triangular units, ultimately generating a high-precision grid file consisting of nodes (vertices of triangles) and units (triangles themselves). This grid file contains only geometric location information. Combining the collected terrain elevation data and the high-precision grid file of the river and lake computational units obtained by S41, Kriging interpolation is used to assign terrain elevation values ​​to the grid nodes based on the planar location information of each node in the grid file. Here, k1, k2, k3 are the triangular grid numbers within the grid nodes. The final result is an elevation grid file that accurately reflects the topographic undulations of the river and lake. .

[0086] Based on the collected water level data, hydrological station locations, and topographic relief characteristics, the computational unit is partitioned into one or more regions. The partitioning principle requires at least two water level stations upstream and downstream of each region. Within each partitioned region, linear interpolation is used to assign water level values ​​to the grid nodes (k1, k2, k3, where k is the triangular grid number) based on the collected water level data, ultimately generating an initial water level grid file reflecting the river and lake surface morphology. .

[0087] Preprocessing and water body calculation are performed on collected optical or radar remote sensing image data. Taking radar imagery as an example, the original radar imagery is first radiometrically calibrated, speckle noise filtered, and topographically corrected to generate a standardized image dataset for subsequent analysis. Then, a water body discrimination index is constructed based on dual-polarization backscattering features, and threshold segmentation is used to achieve pixel-level water / non-water body classification. Post-processing, including morphological operations and connected component constraints, refines the water body boundaries to obtain river and lake water body datasets for each time period within the target time frame. Based on the obtained river and lake water body datasets, a geographic information system is used to calculate the water area and visualize the spatial distribution of the water bodies, generating a river and lake water area dataset. Dataset, ) and spatial distribution atlas.

[0088] Combined with the obtained elevation grid file and initial water level grid file Based on equation (5), obtain in time( The water depth at each triangular grid node; in equation (5) For the k-th grid in Water depth (m) at the location of the triangular node at any given time. and Let represent the water level (m) and terrain elevation (m) at the three nodes of the k-th triangular grid, respectively; where the formula is:

[0089]

[0090] By combining water depth calculation results and grid geometry data, inundation judgment is performed, and a water depth threshold is set. The water depth threshold is greater than 0 to accurately define the wet-dry boundary; based on this, it is determined that... time( The flooding status of each triangular grid is determined, and the distribution of its flooded area and its area calculation value are obtained. ( Based on equation (6), all grid-inundated areas are spatially aggregated to obtain the river and lake computing units. Calculated water area at time Its spatial distribution; within the target time period …The above process is repeated at each moment, eventually generating a dataset of water area distribution maps and their area calculation values ​​for each moment; (6)

[0091] Combining the water depth calculation results and area calculation results, the river and lake calculation unit is obtained based on equation (7). Calculated value of water storage volume at any given time ; for the target time period …The above process is repeated at each moment to generate a dataset of calculated water storage volume values ​​for each moment;

[0092]

[0093] in, .

[0094] Data on the actual river and lake water area at various times will be obtained. With the obtained calculated value By comparison, the spatial difference area ratio between the calculated water area distribution and the actual water area distribution is obtained. Spatial overlay analysis is performed between the acquired actual water distribution map and the calculated water distribution map to identify discrepancies where they do not overlap. For false alarm areas—areas where water is present in the calculated map but not in the actual map—the calculated water level at the corresponding location is determined to be too high, and the initial water level grid file is updated accordingly. The water level at the corresponding grid node is updated with a specified negative step size; for missed areas, i.e., areas with water in the actual graph but not in the calculated graph, it is determined that the calculated water level at the corresponding location is too low, and the initial water level grid file is updated accordingly. The water level at the corresponding grid node is updated with a specified positive step size; the updated water level grid file is used as the new initial condition, and the iterative calculation is restarted; this process is repeated until the spatial difference area ratio is reached. The iteration stops when the preset convergence criterion is met, such as when the convergence rate is less than 1%. At this point, the calculated convergence rate is... The value is Precise volume values ​​of water storage in rivers and lakes at any given time; for the target time period …The above process is repeated at each moment, eventually generating a dataset of accurate water storage volume values ​​for each moment.

[0095] The acquired traffic data ( , , ) and calculation The precise value is used as input and substituted into equation (3) or (4) to solve for the interval runoff volume of large rivers and lakes at each time point in the target period; plot the time points with time as the horizontal axis. Distribution maps are used to analyze the annual, interannual, or specified time-period variation characteristics of the runoff volume in large river and lake areas.

[0096] Example 2: The implementation of the method of the present invention begins with the collection and preprocessing of multi-source data, and then establishes a mathematical model based on the principle of water balance. The storage volume is calculated by constructing a refined numerical grid model of the computing unit, and the actual water area monitored by remote sensing is introduced to iteratively calibrate the volume calculation result. Finally, the target interval runoff volume is inverted using the calibrated accurate storage volume data.

[0097] To implement this method, the target large river and lake and its specific inflow and outflow intervals must first be identified. Then, three types of core data for the region are collected: 1) topographic data, which reflects the morphology of the riverbed or lake bottom; 2) hydrological observation data, including flow and water level, which are used to drive water balance calculations and water surface modeling; and 3) remote sensing image data, which are used to obtain real water distribution information. After collection, all data must undergo temporal consistency checks and spatial registration to be unified to the same geographic coordinate system and projection system, forming a standardized dataset with a standardized format.

[0098] like Figure 2 As shown, based on the collected hydrological station (flow station, water level station) distribution information, the spatial scope of water balance calculation, i.e. the calculation unit, is defined. For large rivers, the calculation unit is the main stream section between the upstream and downstream hydrological control sections. For large lakes, it is the entire lake area between the inflow sections of all major tributaries and the outflow section of the lake. The establishment of the calculation unit clarifies the spatial boundary of water balance analysis.

[0099] For a given computational unit, establish a discrete time step. The water balance equation within a lake; taking a large lake as an example, its basic equation is:

[0100] ;in, for The initial water storage volume of the river and lake calculation unit at any given time. for time( + The water storage volume of the river and lake calculation unit. for Real-time outflow rate of a large river or lake computing unit. for The amount of water flowing into the calculation unit at any given time within the interval. The time step used for calculation can be 1 day, 1 month, 1 year, or many years; by rearranging terms, the equation is transformed into a form that directly solves for the inter-regional runoff volume: Therefore, the solution is... The key lies in accurately obtaining the computing unit's... and The amount of water stored at any given time.

[0101] To accurately calculate the water storage volume, a three-dimensional digital model of the calculation unit is required. First, using finite element mesh generation technology, the calculation unit region is discretized into an unstructured mesh composed of a large number of triangular elements. The mesh resolution is set according to the research accuracy requirements and terrain complexity; for example, a mesh system containing hundreds of thousands of triangular elements can be generated.

[0102] Then, assign values ​​to the mesh properties:

[0103] 1. Based on the collected discrete terrain point data, the Kriging space interpolation method is used to calculate and assign terrain elevation values ​​to each grid node. This generates a digital elevation model that accurately reflects the underwater topographic relief.

[0104] 2. Since the surface of natural water bodies is not perfectly flat, a spatially distributed water level field needs to be constructed based on measured data from multiple water level stations. According to the location of the water level stations, the computational unit is divided into several sub-regions. Within each sub-region, assuming a linear change in water level, initial water level elevation values ​​are assigned to all grid nodes using the measured water level values ​​from the stations within the region via linear interpolation. This forms the initial water level grid.

[0105] To calibrate the water storage volume calculated based on the initial water level, it is necessary to extract real water area information from remote sensing imagery. Taking Sentinel-1 SAR data as an example, the imagery is first preprocessed with radiometric calibration, speckle noise filtering, and geometric fine correction. Then, a multi-level water body extraction algorithm is employed: first, a water body discrimination index is constructed based on dual-polarization scattering features to achieve coarse water body classification; then, the Canny operator is used for edge detection to delineate the water body contour; next, single-band threshold segmentation is used to achieve pixel-level classification; finally, an active contour model is used to refine the extracted boundaries, obtaining high-precision water body boundary vector data. The actual water area can then be calculated using this data on a GIS platform. Its spatial distribution map serves as a true reference for subsequent calibration.

[0106] Through iterative optimization, the water state calculated by this method is made to be consistent with the actual state observed by remote sensing, thereby obtaining an accurate value of the water storage volume; the specific process is as follows:

[0107] 1. Regarding date and time , initial water level grid With terrain grid Subtracting the values ​​at corresponding nodes yields the water depth at each node. Set a water depth threshold, such as δ=0.01m, to determine whether a grid is submerged. For example, if the water depth at a node is greater than 0.01m, then that location is considered submerged. Accumulate the volume and area of ​​all submerged grids according to the formula...

[0108] ,

[0109]

[0110] Calculate the initial water volume and corresponding water area for that day. ;

[0111] 2. Calculate the water area The actual water area as measured by remote sensing on the same day Compare and calculate the relative error. Simultaneously, the calculated inundation range and the actual water body range are superimposed spatially to identify false alarm areas and missed alarm areas;

[0112] 3. Based on the spatial difference analysis results, the water level grid is corrected: for grid nodes in the false alarm area, the water level value is adjusted down by one step, such as... =-0.05m; For nodes within the missed reporting area, their water level values ​​are adjusted upwards by one step, such as =+0.05m; After correction, use the new water level grid to repeat step 1 for water depth, flooding determination, and volume calculation, to obtain a new round of... and And calculate the error again;

[0113] 4. Repeat the iterative cycle of steps 1-3 above until the relative area error is less than the preset convergence criterion; at this point, the calculated water surface morphology is in full agreement with the actual situation, and the final calculated water storage volume is the accurate volume value for that date.

[0114] Obtain accurate time-series data on water storage. Then, by combining the synchronously collected measured flow data with the derived water balance equation, the flow volume of the interval corresponding to the target time period can be calculated, forming a complete interval flow time series.

[0115] Ultimately, in-depth analysis is conducted based on this sequence, such as plotting the annual process line of the inter-regional runoff, analyzing its seasonal variation characteristics, the time of extreme value occurrence, and the phase relationship with the inflow of major tributaries, thereby providing a key data foundation for water resource assessment, flood control scheduling, and ecological research.

[0116] Example 3: A certain lake has five main tributaries flowing into it, each with a hydrological observation station. The outflow section is controlled by a specific hydrological station. Based on the collected data on the locations and long-term hydrological observation sequences of the main tributary hydrological stations, the outflow section hydrological station, and six water level stations in the lake area, the calculation unit for the lake's water balance is first determined and established. Water balance equation; in the equation and The average daily flow rate of the aforementioned hydrological stations was accumulated and calculated. Subsequently, the calculation unit was divided into 717,158 triangular grid units (366,912 nodes). Elevation grid files were generated by interpolating the grid nodes using scattered lake topographic data and an interpolation method. Then, based on the locations and observation data of six hydrological (level) stations in the lake area, the lake area was divided into zones, and linear water level interpolation was performed to form an initial water level grid file. Next, based on 12 periods from January to December of a certain year... High-resolution radar remote sensing images (one issue at the end of each month) were used to preprocess and extract water information from the monthly images, resulting in the monthly distribution map and area values ​​of the actual water bodies. Then, a water depth threshold of 0.01m was set, and the monthly cumulative change value of the water storage volume of the calculation unit and the initial calculated value of the water area at the end of the adjacent months were obtained by combining equations (5) and (7). The calculated water area distribution and the actual water area distribution were spatially overlaid and analyzed. The convergence standard of the spatial difference area ratio was set to 10%. The water level of the grid nodes corresponding to the non-overlapping areas was adjusted positively or negatively according to the specific situation with a step size of 0.05m and recalculated until the spatial difference area ratio of all dates met the requirement of less than 10%. Finally, the accurate value of the monthly cumulative water storage volume output in equation (5) was substituted into the water balance equation to solve the monthly cumulative interval runoff volume value of a certain year and calculate the average. The annual distribution of the monthly average value is shown in the figure. Figure 4 As shown; by Figure 4 It is evident that the inflow volume between the two lake sections is well correlated with the inflow volume of the main tributaries, both exhibiting a distribution pattern of smaller inflows during the dry season and larger inflows during the flood season. In a certain year, the total inflow volume between the two lake sections accounted for approximately 18% of the total inflow volume into the lake. However, the proportion of the inflow volume between the two sections to the total inflow volume in each month varied significantly, fluctuating between 12% and 33%.

[0117] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for determining the runoff volume of a large river-lake interval, characterized in that, Includes the following steps: S1: Acquire remote sensing images and topographic data of the confluence area of ​​the target river and lake, as well as flow and water level data observed by hydrological or water level stations; S2: Based on the data obtained from S1, determine the calculation unit for river and lake water balance, and establish the water balance equation of the calculation unit within a set time period; S3: Express the interval runoff volume in the water balance equation as a functional relationship that includes the change in the stored water volume of the calculation unit, the known inlet flow, the outlet flow, and the total flow of the main tributaries; S4: Spatial discretization of the computational units is performed to generate a grid file, and elevation interpolation is performed on the grid nodes based on terrain data to generate an elevation grid file reflecting the terrain undulations. ; S5: Based on water level data, the computational cells are partitioned, and water level interpolation is performed on the grid nodes to generate an initial water level grid file reflecting the water surface morphology. ; S6: Based on remote sensing images, extract the actual water area and spatial distribution of the computing unit at the corresponding time; S7: Based on elevation grid file The topographic elevation and initial water level of each grid node in the grid file. The water surface elevation of the corresponding node is obtained through Calculate the water depth at each of the three vertices of each triangular mesh at the corresponding time. Then, using a water depth greater than zero as the flooding criterion, determine the flooding state of each mesh and calculate the area contribution of each flooded triangular mesh. Next, the area contribution of all submerged grids is spatially aggregated, based on... The total area of ​​the calculated water body at the corresponding time is obtained by the calculation unit. Finally, according to the formula By combining the area of ​​each submerged grid and the water depth at its three vertices, the initial water storage volume of the computing unit at the corresponding time is calculated. This enables the collaborative calculation of water volume and area based on gridded terrain and water level data, where N is the total number of submerged grids. S8: Compare the calculated water area with the extracted actual water area, and iteratively adjust the water level values ​​in the initial water level grid file generated in S5 based on the comparison results. Repeat S7 until the difference between the calculated water area and the actual water area meets the preset convergence condition, thereby obtaining the accurate water storage volume of the calculation unit at the corresponding time. S9: Substitute the known inlet flow, outlet flow, total flow of major tributaries, and precise river and lake storage into the functional relationship described in S3 to calculate the interval runoff volume within the set time period, and analyze its variation characteristics based on the calculation results.

2. The method for determining the runoff volume of a large river-lake interval according to claim 1, characterized in that: In step S2, the physical boundaries of the inflow and outflow intervals between rivers and lakes are spatially defined based on the hydrological or water level stations corresponding to the inflow and outflow rates, thereby determining the calculation units. Subsequently, based on the target river / lake type, water balance equations are constructed. For large rivers, the equation is as follows: ,in, for The initial water storage volume of the river and lake calculation unit at any given time, where , for The water storage volume of the river and lake calculation unit at any given time, of which + , for The inflow rate at the inlet of a large-scale river computing unit at any given time. for Real-time outflow rate of a large river or lake computing unit. for The amount of water flowing into the calculation unit at any given time within the interval. This represents the total discharge of each major tributary; for large lakes, the equation does not include independent... Item, constructed as .

3. The method for determining the runoff volume of a large river-lake interval according to claim 2, characterized in that: Based on the water balance equation established in S2, determine the known quantities in the equation. , , and Values ​​and unknowns Move the unknowns in the equation to one side of the equation; where It is obtained by accumulating flow observation data from each major tributary; and The values ​​are all directly controlled by the hydrological stations. The value is obtained through S7 and S8; where, The water balance equation for large rivers is transformed into: The water balance equation for a large lake is transformed into: 。 4. The method for determining the runoff volume of a large river-lake interval according to claim 1, characterized in that: In step S4, the elevation grid file is generated as follows: First, the computational units determined in S2 are geometrically discretized into several triangular grid units. Then, using topographic data of rivers or lakes, elevation interpolation is performed on the grid nodes based on the Kriging interpolation method to generate an elevation grid file reflecting the topographic undulations of the river or lake. Where k is the triangular mesh number, and k1, k2, and k3 correspond to the three nodes of the triangular mesh, respectively. , , These represent the terrain elevation values ​​calculated using the Kriging interpolation method at nodes k1, k2, and k3 of the kth triangular mesh.

5. The method for determining the runoff volume of a large river-lake interval according to claim 1, characterized in that: Step S5 generates the initial water level grid file. The process involves: assigning water levels to different regions of the computational unit based on the location of the hydrological station and topographic relief characteristics; using linear interpolation to interpolate the water level elevation within each region and combining the observed water levels from the hydrological station with the grid nodes, thus generating an initial water level grid file reflecting the morphology of the river and lake surface. , , , These represent the water level elevation values ​​calculated based on linear interpolation at nodes k1, k2, and k3 of the kth triangular mesh.

6. The method for determining the runoff volume of a large river-lake interval according to claim 1, characterized in that: In S8, the water area is calculated. Compared with the actual water area Spatial difference area ratio The spatial distribution maps of the two datasets were overlaid and analyzed to identify non-overlapping areas of difference. Subsequently, the causes of these differences were determined and water levels were adjusted accordingly. False alarm areas were then identified. There is water in it For areas without water, determine the calculated water level of the corresponding grid nodes. If the water level is too high, reduce it by a specified negative step size; for areas that are missed in reporting, i.e. There is water in it For areas without water, determine the calculated water level of the corresponding grid nodes. If the water level is too low, increase it by a specified positive step size; update the adjusted water level value to the initial water level grid file, and use the updated file as the new input condition, then return to execute S7 to recalculate the stored water volume and calculate the water area. This process is repeated until the spatial difference area ratio reaches the preset convergence standard. At this time, the water storage volume finally calculated by S7 is the accurate water storage volume of the calculation unit at the corresponding time.

7. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory is used to store computer programs; The processor is used to load and execute a computer program to enable the electronic device to implement the method for determining the runoff volume of a large river and lake area as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a method for determining the runoff volume of a large river-lake interval as described in any one of claims 1-6.

9. A computer program product, characterized in that, It includes a computer program, which, when executed, performs a method for determining the runoff volume of a large river-lake interval as described in any one of claims 1-6.

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