Lake water age estimation method, electronic equipment and storage medium

Abstract

The invention discloses a lake water age estimation method, electronic equipment and a storage medium. Comprising the following steps: acquiring multi-source remote sensing data, underwater topographic data, meteorological data and hydrological flow data of a target lake; performing inversion to obtain vegetation distribution data, and constructing a space-time dynamic resistance surface reflecting the motion resistance of water flow in the lake; at least one flow outlet of the lake is used as a source point, and the hydrological effective distance from each space unit in the lake to the source point is calculated based on the spatio-temporal dynamic resistance surface; estimating the reference two-dimensional water age of the lake; judging the vertical layering state of the lake, and calculating an aging coefficient representing the retention effect of a bottom water body; and correcting the reference two-dimensional water age by using the aging coefficient, and generating a lake three-dimensional water age field capable of reflecting plane and vertical differences. The problems that a traditional hydrological model is complex in calculation, seriously depends on dense field hydrological surveying and mapping and flow velocity observation, and is difficult to apply to large-range lake groups or areas lacking measured data are solved.

Description

Technical Field

[0001] This invention relates to the field of lake water age estimation. More particularly, it relates to a method, electronic device, and storage medium for lake water age estimation. Background Technology

[0002] In recent years, the water exchange capacity of lakes has mainly relied on the "water exchange cycle," an average indicator for the entire lake, which cannot quantify the water age of different areas within the lake (such as bays, shallows, and deep water areas). While traditional hydrological models can simulate hydrodynamic processes, their calculations are complex and rely on a large amount of measured hydrological data, making them difficult to apply over large areas and long time periods. Although remote sensing technology has been widely used for inverting water surface temperature and water quality parameters, it lacks a method to directly quantify the hydrodynamic processes and spatial differentiation of water age within lakes. Therefore, this invention proposes a method for estimating lake water age, an electronic device, and a storage medium to efficiently and dynamically characterize the spatial differentiation of water exchange within lakes. Summary of the Invention

[0003] This invention aims to at least partially solve one of the technical problems in related technologies. To this end, one objective of this invention is to propose a method for estimating lake water age. This invention couples hydrological resistance with vertical stratification effects, utilizes multi-source remote sensing data of lakes, underwater topography, and other data to construct a spatiotemporal dynamic resistance surface to calculate the effective hydrological distance, and incorporates a vertical aging coefficient for correction, ultimately generating a water age field that reflects the true spatiotemporal pattern of water exchange efficiency within the lake. To achieve the above objective, the following steps are included: Acquire multi-source remote sensing data, underwater topographic data, meteorological data, and hydrological flow data of the target lake; Based on the underwater topographic data, vegetation distribution data obtained from the multi-source remote sensing data, and wind field data extracted from the meteorological data, a spatiotemporal dynamic resistance surface reflecting the resistance of water flow within the lake is constructed. Using at least one outflow point of the lake as the source point, and based on the spatiotemporal dynamic resistance surface, calculate the effective hydrological distance from each spatial unit within the lake to the source point; Based on the effective hydrological distance and the overall water exchange cycle of the lake obtained from the hydrological flow data, the baseline two-dimensional water age of the lake is estimated. Based on the surface water temperature retrieved from the multi-source remote sensing data and the air temperature and wind speed provided by the meteorological data, the vertical stratification state of the lake is determined, and the aging coefficient characterizing the retention effect of the bottom water is calculated. The aging coefficient is used to correct the baseline two-dimensional water age, generating a three-dimensional water age field for the lake that reflects both planar and vertical differences.

[0004] In some implementations, constructing a spatiotemporal dynamic resistance surface that reflects the resistance to water flow within a lake includes: The topographic resistance factor is calculated based on water depth data, and the topographic resistance factor is negatively correlated with water depth. The vegetation resistance factor is calculated based on the vegetation index data, and the vegetation resistance factor is positively correlated with the vegetation index value. The wind field drag factor is calculated based on wind field data, and the wind field drag factor is negatively correlated with the effective wind zone length. After normalizing the terrain resistance factor, vegetation resistance factor, and wind field resistance factor, the spatiotemporal dynamic resistance surface is generated by weighted superposition based on the weighted linear superposition method.

[0005] In some implementations, calculating the effective wind zone length includes: traversing every effective cell on all spatial units; for any spatial unit on the lake surface, determining a ray originating from that unit along the prevailing wind direction; calculating the length of the line segment located inside the lake after the ray intersects with the lake water boundary; and using this length value as the effective zone length of that spatial unit.

[0006] In some implementations, the specific formula for estimating the baseline two-dimensional water age of a lake is as follows: ; Where Age_2D is the baseline two-dimensional water age, L_combined is the comprehensive hydrological effective distance, L_mean is the average hydrological effective distance of the entire lake, T is the overall water exchange cycle of the lake, and α and ү are empirical coefficients.

[0007] In some implementations, the rule for determining the vertical stratification state of a lake is as follows: if the average daily wind speed is consistently below a first threshold and the temperature difference between the surface water and the air temperature is consistently greater than a second threshold within a preset number of days, the lake is determined to be in a stable stratified state; otherwise, it is determined to be in a mixed state.

[0008] In some implementations, the specific formula for the aging coefficient, which characterizes the retention effect of bottom water, is as follows: When the water is determined to be in a stable stratified state, the age of the bottom water body increases, and the aging coefficient K is defined as follows: ; Where K is the aging coefficient, β is the calibration parameter, ΔT is the temperature difference between the surface water temperature and the air temperature, W is the wind speed, and D is the water depth; When the state is determined to be mixed, K=1.

[0009] In some implementations, the method for generating a three-dimensional water age field of a lake that reflects planar and vertical differences is as follows: When the condition is determined to be mixed, the aging coefficient of the entire water column is... ; When it is determined to be a stable stratification state , where K(z) is a function that varies with depth z; K(z) is defined as a piecewise function based on the water layer: surface layer ; Middle layer ; bottom layer ; Where d_s and d_b are the defined thicknesses of the surface and bottom layers, respectively, and H is the maximum depth of the lake; α (0≤α≤1) is the mid-layer attenuation coefficient, which characterizes the relative intensity of the mid-layer retention effect.

[0010] Some implementations also include: overlaying the three-dimensional water age field with the spatial distribution map of water quality parameters retrieved by synchronous remote sensing to identify water quality risk areas caused by excessively long water ages.

[0011] On the other hand, the present invention proposes an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor is used to implement a lake water age estimation method when executing the program stored in the memory.

[0012] On the other hand, the present invention proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a lake age estimation method.

[0013] The beneficial effects of this application are: This invention utilizes multi-source remote sensing, publicly available topographic data, and reanalysis meteorological data as the main inputs, minimizing reliance on traditional measured data and enabling large-scale, long-term dynamic monitoring. It effectively solves the problems of traditional hydrological models being computationally complex, heavily reliant on intensive on-site hydrological mapping and flow velocity observation, and difficult to apply in large-scale lake groups or areas lacking measured data. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0015] Figure 2 This is a flowchart for calculating the effective hydrological distance.

[0016] Figure 3 This is a flowchart for vertical stratification determination and aging coefficient correction. Detailed Implementation

[0017] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other. To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all of them. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0018] Reference Figures 1-3 As shown, this invention proposes a method for estimating lake water age. This invention calculates the effective hydrological distance by constructing a spatiotemporal dynamic resistance surface and corrects it using a vertical aging coefficient, ultimately generating a water age field distribution map that reflects the true spatiotemporal pattern of water exchange efficiency within the lake. The water age field is then spatially overlaid with synchronously sensed water quality parameters (such as chlorophyll a concentration) to identify water quality risk zones caused by excessively long water ages, providing precise decision support for ecological water replenishment and pollution control. Specifically, the method includes the following steps: S1. Acquire multi-source remote sensing data, underwater topographic data, meteorological data, and hydrological flow data of the target lake; All data obtained in this step was acquired from publicly available databases. Underwater topographic data: used to characterize the spatial morphology and basic hydrodynamic environment of lakes, specifically as follows: Lake vector boundary data: Defines the extent of the lake.

[0019] Lake bottom elevation (water depth) raster data: reflects the topographic relief of the lake bottom and the water depth at various points.

[0020] Multi-source remote sensing data: used to dynamically monitor the spatiotemporal changes in the Earth's surface and lake surface, specifically manifested as: Optical remote sensing imagery: Acquire Landsat satellite images during the growing season (such as summer), and use the near-infrared and red bands to calculate the normalized vegetation index, which is then used to invert the distribution of aquatic vegetation in lakes.

[0021] Thermal infrared remote sensing data: Acquire thermal infrared band data from sensors such as MODIS (MODIS Medium Resolution Imaging Spectroradiometer) to retrieve daily surface water temperature of lakes.

[0022] Meteorological data: used to drive the force field and determine the vertical stratification state, specifically manifested as follows: Wind field data: Time series data of wind speed (including east-west component u and north-south component v) at a height of 10 meters covering the target lake area were extracted from atmospheric reanalysis datasets such as ERA5. Based on this, the average wind field and prevailing wind direction were calculated for the study period (e.g., monthly or seasonal).

[0023] Hydrological flow data: used to quantify the input and output fluxes of lake water bodies, it is a key driving parameter for calculating water age timescales, specifically manifested as follows: Outflow data: Collect daily-scale flow observation or estimation data of the main outflows of lakes (such as dams and channels).

[0024] Furthermore, to ensure the reliability of the data in subsequent steps, the data is preprocessed: Optical remote sensing images (such as Landsat): First, radiometric calibration and atmospheric correction are performed (e.g., using the FLAASH algorithm). Then, cloud-covered pixels are removed by cloud masking (e.g., QA band filtering). Finally, NDVI (Normalized Difference Vegetation Index) is calculated, and an NDVI_water threshold (e.g., 0.1) is set to distinguish between water bodies and vegetation.

[0025] Thermal infrared data (such as MODIS): Perform geometric correction and reprojection (to a unified coordinate system, such as WGS84), and use spatiotemporal interpolation (such as Kriging) to fill in missing values ​​to generate a daily surface water temperature raster.

[0026] Meteorological and hydrological data: After extracting ERA5 wind field data, perform time averaging (e.g., calculate monthly average prevailing wind direction); flow data needs to be checked for completeness, and missing days are filled by interpolation from nearby stations or water balance method.

[0027] S2. Based on the underwater topographic data, vegetation distribution data retrieved from the multi-source remote sensing data, and wind field data extracted from the meteorological data, a spatiotemporal dynamic resistance surface reflecting the resistance to water flow within the lake is constructed, specifically as follows: The terrain resistance factor is calculated based on water depth data. This terrain resistance factor is negatively correlated with water depth. Specifically: based on lakebed elevation (water depth) data, assuming a water depth D, the terrain resistance value R_terrain can be calculated using the following function: ; Where a and b are empirical parameters.

[0028] The parameters a and b need to be determined based on historical hydrological data of the target lake or research findings on similar lakes. The specific calibration method is as follows: collect depth data (D) and measured flow resistance data (such as observed flow velocity) of the target lake or similar lakes, and use a nonlinear regression algorithm (such as least squares method) to fit the formula R_terrain = a * exp(-b * D) to obtain the optimal values ​​of a and b. If no measured data is available, the parameter ranges of typical lakes can be referenced (a is usually 0.1–10, b is 0.01–0.5 m⁻). 1 The intermediate values ​​are selected based on the lake's average water depth and bottom characteristics. For example, for shallow lakes (average depth <5m), a = 2.0 and b = 0.1 m⁻ 1 For deep lakes (average depth > 10 m), we can take a = 0.5 and b = 0.05 m⁻ 1 .

[0029] The vegetation resistance factor is calculated based on vegetation index data. This vegetation resistance factor is positively correlated with the vegetation index value. Specifically: Based on time-series NDVI (Normalized Difference Vegetation Index) data, a threshold was set to distinguish between water bodies and vegetation. For areas covered by aquatic vegetation, the vegetation resistance factor R_veg is positively correlated with the NDVI value. ; Where c is the weighting coefficient, and NDVI_water is the typical NDVI value for open water bodies (e.g., 0.1). Resistance in non-vegetated areas is set to 0.

[0030] The wind field drag factor is calculated based on wind field data. The wind field drag factor is negatively correlated with the effective wind zone length F. A longer wind zone means a more continuous wind driving force, which reduces water flow resistance.

[0031] The effective wind zone length refers to the unobstructed distance over which wind can continuously blow across an open water surface, thereby generating waves and currents. This length is a key parameter for calculating the wind field resistance factor. The longer the wind zone, the stronger the continuous effect of the wind on the water body, and the more conducive it is to water mixing and flow.

[0032] The calculation of the effective wind zone length is specifically as follows: traverse every effective cell across all spatial units. For any spatial unit on the lake surface, determine a ray emanating from that unit along the prevailing wind direction; calculate the length of the line segment within the lake after the ray intersects the lake's water boundary, and use this length as the effective zone length of that spatial unit. Specifically: The lake surface is divided into blocks, with the center point of each block representing the entire block, and each block is treated as a grid unit. First, the lake's vector boundary is converted into a raster format, generating a binary image where a pixel value of 1 represents water and 0 represents non-water surfaces (such as land). The lake surface is then divided into several regular blocks to improve computational efficiency, with each block represented by its center point. Based on this, given the prevailing wind direction (in degrees, measured clockwise from true north to indicate the wind's direction), for any point on the lake surface, a ray is drawn along this direction to the lake boundary. The straight-line distance between the starting point of this ray, which lies entirely within the water surface, and the first intersection point is the effective wind zone length at that point. To achieve efficient calculations for large-scale lakes, this method is based on block sampling, specifically: Step a: Read the lake surface raster image to obtain its size and spatial coordinate reference information. Simultaneously, read the lake's vector boundary data. Create a blank matrix of the exact same size as the raster image to store the final effective wind zone length calculation results for each pixel; initialize all values ​​to 0.

[0033] Step b: Divide the entire lake surface grid into multiple regular square blocks (e.g., each block consists of 10 rows x 10 columns of pixels). Each block is represented by its center point, which is used to calculate the effective wind zone length of the block. This length value is then assigned to all water pixels within the block.

[0034] Step c: For the center point of each block, perform the following operation: 1) Coordinate transformation: Convert the raster coordinates (rows, columns) of the point into real-world geographic coordinates (such as latitude and longitude).

[0035] 2) Construct wind direction ray: Based on the input prevailing wind direction angle, calculate the wind direction (wind direction angle ± 180°). Starting from the geographic coordinates of the point, construct a sufficiently long ray segment along the direction of the wind (the length must be long enough to cross the entire lake, for example, take the diameter of the lake's circumcircle).

[0036] 3) Geometric Intersection: Perform a spatial geometric intersection operation between the ray constructed in step b and the lake vector boundary polygon to identify the longest continuous line segment that is completely inside the lake's water area and starts from the starting point. The purpose of this operation is to find the longest continuous straight line segment that is completely inside the polygon and starts from the starting point.

[0037] 4) Extract distance: Extract the line segment from the starting point to the first downstream intersection point where the ray leaves the lake boundary from the intersection result, and calculate the actual geographical length of the line segment (considering the coordinate system, such as using the spherical distance formula). This length is the effective wind zone length of the center point.

[0038] Step d: Assign the effective wind zone length value of the center point of a single block obtained in step c to all water pixels within that block. After traversing all blocks, the initially created blank result matrix is ​​completely filled, generating a complete and spatially continuous effective wind zone length distribution map.

[0039] Step e: Save this resulting matrix as a standard GeoTIFF format raster file, which has the same spatial coordinates as the input data, facilitating visualization analysis and subsequent calculations in GIS software.

[0040] Through the above process, the generated effective wind zone length distribution map can clearly reveal the spatial differentiation of wind forces on the lake surface. For example, under the influence of southeasterly winds, the effective wind zone length is shorter along the western shore of Taihu Lake because it is close to the upwind shoreline; while in the central area of ​​the lake and along the eastern shore, the wind can penetrate far into the lake, resulting in a significantly longer effective wind zone length.

[0041] The specific manifestation of calculating the wind field drag factor R_wind is as follows: ; Where d is the weighting coefficient, and ε is a minimum value to prevent division by zero.

[0042] After normalizing the terrain resistance factor, vegetation resistance factor, and wind field resistance factor, the spatiotemporal dynamic resistance surface is generated by weighted superposition based on the weighted linear superposition method.

[0043] The three drag factor raster data are normalized to the same range (e.g., 0-1). A weighted linear superposition method is then used to generate the comprehensive spatiotemporal drag surface R_total. ; Here, w1, w2, and w3 are weights, and w1 + w2 + w3 = 1. The weights can be adjusted according to different seasons (e.g., the weight w2 for vegetation can be increased in summer).

[0044] S3. Taking at least one outflow point of the lake as the source point, and based on the spatiotemporal dynamic resistance surface, the specific manifestation of calculating the effective hydrological distance from each spatial unit within the lake to the source point is as follows: The complete process for calculating the effective hydrological distance in ArcGIS using the cost distance algorithm, with the main outflow point of the lake (such as a sluice gate) as the source point and based on the comprehensive spatiotemporal resistance surface R_total, is as follows: First, data preprocessing is performed to trim the resistance surface to the lake area to ensure spatial matching between the outflow outlet and the resistance surface. Then, the cost distance calculation is performed. With the outflow outlet as the source point and R_total as the cost grid, the algorithm will traverse each cell of the lake and calculate the minimum cumulative travel cost from the source point. This cumulative value is the hydrological effective distance, which quantifies the total resistance experienced by the water flowing from the cell to the outlet.

[0045] When a lake has multiple outflows, a separate hydrological effective distance raster map (Li) is calculated for each individual outflow. Discharge rate Q is used as the core weighting indicator. For each source (outflow), the weight Wout is... j This represents the proportion of the outflow from this outlet to the total outflow from the lake. The combined effective distance L_combined at any point within the lake can be expressed as a weighted average of the effective distances from all outlets: ; Among them, Lout j It is the effective distance from that point to the j-th outlet.

[0046] S4. Based on the effective hydrological distance and the overall water exchange cycle of the lake obtained from the hydrological flow data, the baseline two-dimensional water age of the lake is estimated. Specifically, a conversion relationship is established, assuming that the local water age at a certain point in the lake is directly proportional to the effective hydrological distance L at that point and inversely proportional to the average water exchange rate of the entire lake. The specific formula is as follows: ; Where Age_2D is the baseline two-dimensional water age, L_combined is the comprehensive hydrological effective distance, L_mean is the average hydrological effective distance of the entire lake, T is the overall water exchange cycle of the lake, and α and ү are empirical coefficients. Furthermore, T is calculated using the box model method. The empirical coefficients of α and ү can be obtained by fitting historical data or data from typical periods. S5. Based on the surface water temperature retrieved from the multi-source remote sensing data and the air temperature and wind speed provided by the meteorological data, determine the vertical stratification state of the lake (the surface and bottom layers in the vertical stratification state of the lake are functional layers based on the vertical profile of water temperature, and their boundaries are dynamically determined by the position of the thermocline), and calculate the aging coefficient characterizing the retention effect of the bottom water body. The rule for determining the vertical stratification state of a lake is as follows: if the average daily wind speed is consistently below the first threshold and the temperature difference between the surface water and the air temperature is consistently greater than the second threshold within a preset number of days, the lake is determined to be in a stable stratified state; otherwise, it is determined to be in a mixed state.

[0047] The specific formula for the aging coefficient, which characterizes the retention effect of bottom water, is as follows: When the stratification is determined to be stable, the age of the bottom water body increases, and an aging coefficient K is defined to quantify this effect: ; Where K is the aging coefficient, β is the calibration parameter, ΔT is the temperature difference between the surface water temperature and the air temperature, W is the wind speed, and D is the water depth; Based on the surface water temperature T_surface retrieved from multi-source remote sensing data and the near-surface air temperature T_air provided by ERA5, the temperature difference ΔT = T_surface - T_air is calculated. For example, if the daily average wind speed is below a threshold (e.g., 2 m / s) for N consecutive days (e.g., N=3), and ΔT is consistently above the threshold (e.g., 2°C), the lake is considered to be in a stable stratified state. Otherwise, it is considered to be in a mixed state.

[0048] When the state is determined to be mixed, K=1.

[0049] S6. The aging coefficient is used to correct the baseline two-dimensional water age to generate a three-dimensional water age field of the lake that reflects the differences in the plane and vertical direction.

[0050] The specific representation of generating a three-dimensional water age field for a lake that reflects both planar and vertical differences is as follows: For the bottom water body of a lake (which can be defined as a water layer of a certain thickness at the bottom, such as 1 meter above the lake bottom), its final water age Age_3D is: ; The aging coefficient is used to correct the baseline two-dimensional water age, generating a three-dimensional water age field: When the state is determined to be mixed, the aging coefficient of the entire water column K=1, Age_3D=Age_2D; When it is determined to be a stable stratification state , where K(z) is a function that varies with depth z.

[0051] K(z) is defined as a piecewise function based on the water layer: surface layer ; Middle layer ; bottom layer ; Where d_s and d_b are the defined thicknesses of the surface and bottom layers, respectively, H is the maximum depth of the lake, and α (0≤α≤1) is the mid-layer attenuation coefficient, characterizing the relative intensity of the mid-layer retention effect. Its value can be determined based on the morphology and hydrodynamic characteristics of the target lake: for lakes with sufficient vertical mixing and unstable stratification (e.g., lakes with shallow average depth and strong wind and wave action), the mid-layer water retention effect is not significant, and α can be taken as 0.3–0.5. For lakes or reservoirs that easily form stable thermoclines and have significant vertical stratification (e.g., water bodies with large average depth and strong topographical shielding), the mid-layer transition effect is obvious, and α can be taken as 0.6–0.8. In practical applications, the intermediate value α = 0.5 can be preferentially used for calculation.

[0052] This generates a three-dimensional water age field distribution map of the lake that reflects both planar and vertical differences.

[0053] Furthermore, the present invention also includes: overlaying and analyzing the three-dimensional water age field with the spatial distribution map of water quality parameters obtained by synchronous remote sensing inversion to identify water quality risk areas caused by excessively long water ages.

[0054] Based on the same technical concept, embodiments of the present invention also provide an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0055] Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the steps of the method of the present invention.

[0056] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0057] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0058] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0059] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0060] The computer program product of the method provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0061] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device, such as a personal computer, server, or network device, to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] Unless otherwise specified, all steps in this application may be performed sequentially or randomly, preferably sequentially. For example, the method includes steps (a) and (b), indicating that the method may include steps (a) and (b) performed sequentially, or it may include steps (b) and (a) performed sequentially. For example, the mention that the method may also include step (c) indicates that step (c) may be added to the method in any order. For example, the method may include steps (a), (b), and (c), or it may include steps (a), (c), and (b), or it may include steps (c), (a), and (b), etc.

[0063] The "range" disclosed in this application is defined by a lower limit and an upper limit. A given range is defined by selecting a lower limit and an upper limit, which define the boundaries of a particular range. Ranges defined in this way can include or exclude endpoints and can be arbitrarily combined; that is, any lower limit can be combined with any upper limit to form a range. For example, if ranges of 60-120 and 80-110 are listed for a specific parameter, it is expected that ranges of 60-110 and 80-120 are also included. Furthermore, if minimum range values ​​of 1 and 2 are listed, and if maximum range values ​​of 3, 4, and 5 are listed, then the following ranges are all expected: 1-3, 1-4, 1-5, 2-3, 2-4, and 2-5. In this application, unless otherwise stated, the numerical range "ab" represents a shortened representation of any combination of real numbers between a and b, where a and b are real numbers. For example, the numerical range "0-5" indicates that all real numbers between "0-5" have been listed in this article; "0-5" is simply a shortened representation of these numerical combinations. Furthermore, when a parameter is stated as an integer ≥2, it is equivalent to disclosing that the parameter is, for example, an integer such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, etc.

[0064] Unless otherwise specified, the terms "comprising" and "including" as used in this application are open-ended. For example, "comprising" and "including" may mean including or containing other components not listed.

[0065] Unless otherwise specified, all embodiments and optional embodiments of this application can be combined to form new technical solutions.

[0066] Unless otherwise specified, all technical features and optional technical features of this application may be combined to form new technical solutions.

[0067] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. All should be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for estimating the water age of a lake, characterized in that, Includes the following steps: Acquire multi-source remote sensing data, underwater topographic data, meteorological data, and hydrological flow data of the target lake; Based on the underwater topographic data, vegetation distribution data is obtained by inversion from the multi-source remote sensing data, and wind field data is extracted from the meteorological data, a spatiotemporal dynamic resistance surface reflecting the resistance of water flow within the lake is constructed. Using at least one outflow point of the lake as the source point, and based on the spatiotemporal dynamic resistance surface, calculate the effective hydrological distance from each spatial unit within the lake to the source point; Based on the effective hydrological distance and the overall water exchange cycle of the lake obtained from the hydrological flow data, the baseline two-dimensional water age of the lake is estimated. Based on the surface water temperature retrieved from the multi-source remote sensing data and the air temperature and wind speed provided by the meteorological data, the vertical stratification state of the lake is determined, and the aging coefficient characterizing the retention effect of the bottom water is calculated. The aging coefficient is used to correct the baseline two-dimensional water age, generating a three-dimensional water age field for the lake that reflects both planar and vertical differences.

2. The method for estimating lake water age according to claim 1, characterized in that, Constructing a spatiotemporal dynamic resistance surface that reflects the resistance to water flow within a lake includes: The topographic resistance factor is calculated based on water depth data, and the topographic resistance factor is negatively correlated with water depth. The vegetation resistance factor is calculated based on the vegetation index data, and the vegetation resistance factor is positively correlated with the vegetation index value. The wind field drag factor is calculated based on wind field data, and the wind field drag factor is negatively correlated with the effective wind zone length. After normalizing the terrain resistance factor, vegetation resistance factor, and wind field resistance factor, the spatiotemporal dynamic resistance surface is generated by weighted superposition based on the weighted linear superposition method.

3. The method for estimating lake water age according to claim 2, characterized in that, Calculating the effective wind zone length involves: traversing every effective cell across all spatial units; for any spatial unit on the lake surface, determining a ray originating from that unit along the prevailing wind direction; and calculating the length of the line segment within the lake after the ray intersects the lake's water boundary. This length is the effective wind zone length of the spatial unit.

4. The method for estimating lake water age according to claim 1, characterized in that, The specific formula for estimating the baseline two-dimensional water age of a lake is as follows: ； in, As a baseline two-dimensional water age, To determine the effective distance based on comprehensive hydrological data, The average effective hydrological distance for the entire lake. For the overall water exchange cycle of the lake, and This is an empirical coefficient.

5. The method for estimating lake water age according to claim 1, characterized in that, The rule for determining the vertical stratification of a lake is as follows: when the average daily wind speed is consistently below the first threshold and the temperature difference between the surface water and the air temperature is consistently greater than the second threshold within a preset number of days, the lake is determined to be in a stable stratification state. Otherwise, it is determined to be a mixed state.

6. The method for estimating lake water age according to claim 1, characterized in that, The specific formula for the aging coefficient, which characterizes the retention effect of bottom water, is as follows: When a stable stratification state is determined, the age of the bottom water body increases, and an aging coefficient is defined. : ； in, The aging factor is... For calibration parameters, The temperature difference between the surface water and the air. For wind speed, For water depth; When determined to be in a mixed state .

7. The method for estimating lake water age according to claim 1, characterized in that, The method for generating a three-dimensional water age field for lakes that reflects planar and vertical differences is as follows: When the condition is determined to be mixed, the aging coefficient of the entire water column is... ; When it is determined to be a stable stratification state , where K(z) is a function that varies with depth z; Defined as a piecewise function based on the water layer: surface layer ; Middle layer ; bottom layer ; in, and The thicknesses of the surface and bottom layers are defined, respectively, and H is the maximum depth of the lake. is the mid-layer attenuation coefficient, which characterizes the relative intensity of the mid-layer retention effect.

8. The method for estimating lake water age according to claim 1, characterized in that, Also includes: By overlaying the three-dimensional water age field with the spatial distribution map of water quality parameters retrieved by synchronous remote sensing, water quality risk areas caused by excessively long water ages can be identified.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor, when executing the program stored in the memory, implements the lake water age estimation method according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the lake age estimation method according to any one of claims 1-8.

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