Precise estimation method and system for water surface evaporation of long and narrow reservoir and storage medium

Abstract

The application provides a long and narrow reservoir water surface evaporation accurate estimation method and system and a storage medium, wherein the long and narrow reservoir water surface evaporation accurate estimation method comprises collecting and preprocessing meteorological element data sets, hydrological element data sets and reservoir basic geographic data sets in the reservoir range, and establishing a reservoir gridded data set; based on the reservoir gridded data set, the daily average wind direction data of each pixel of a single reservoir is divided into 16 quantile wind directions; according to the wind direction frequency weight and the wind speed influence weight in each quantile wind direction, a dominant wind direction spatial weighting function is constructed, and the daily scale dominant wind direction of the reservoir is calculated; the application avoids the systematic error caused by the shape mismatch of the mean method in the long and narrow reservoir scene, and provides support for the accurate estimation of the long and narrow reservoir water surface evaporation.

Description

Technical Field

[0001] This invention relates to the field of hydrological technology, and in particular to a method, system, and storage medium for accurately estimating water surface evaporation in a long and narrow reservoir. Background Technology

[0002] Reservoirs, as artificial water storage projects, play a crucial role in flood control, water supply, power generation, and navigation, while also being of great significance to regional ecosystem services, socio-economic development, and climate adaptation. Reservoir evaporation is a vital component of the hydrological cycle, and its estimation accuracy directly impacts reservoir water balance calculations, water resource allocation, and management decisions. Therefore, accurate estimation of reservoir surface evaporation is of great importance for the efficient utilization of water resources in watersheds and for regional sustainable development.

[0003] Currently, methods for estimating reservoir evaporation mainly fall into two categories: one is the field observation method based on evaporation pans, which offers high data accuracy but is limited by the cost of site deployment and maintenance, making it unsuitable for large reservoir groups or areas without monitoring stations; the other is the model estimation method based on meteorological reanalysis data or remote sensing products, which is more prevalent in large-scale, multi-reservoir applications. However, existing model estimation methods generally use the average values ​​of meteorological elements within the reservoir area (average wind speed, average wind direction, average temperature, etc.) as input parameters, i.e., the "mean method." While this "mean method" is simple to calculate, it ignores the spatial non-uniformity and diurnal dynamic changes of meteorological elements within the reservoir area and fails to adapt to the morphological characteristics of long and narrow reservoirs, further amplifying the estimation error.

[0004] In actual hydrological conditions, reservoirs are characterized by complex shapes, large areas, and winding shorelines, with significant differences in meteorological conditions such as wind speed, wind direction, and temperature across different regions. Wind direction, in particular, plays a dominant role in controlling surface evaporation: it determines the spatial distribution of turbulent exchange and boundary layer characteristics, influencing momentum transfer and energy flux distribution, and thus altering local evaporation intensity. However, traditional mean-based methods cannot reflect these spatial differences, easily leading to systematic biases in surface evaporation estimations, especially for large reservoirs with vast areas, increasing the uncertainty in reservoir surface evaporation estimates.

[0005] Furthermore, wind direction affects the wind function by influencing the water intake length, which in turn affects the water surface evaporation estimation results (the wind function is an intermediate variable in the water surface evaporation estimation). For long and narrow reservoirs ( Figure 2 ), compared to non-narrow reservoirs ( Figure 3 The water intake length is more sensitive to the influence of wind direction. When the wind direction changes slightly, the change in the water intake length of a long and narrow reservoir will be very large, which will drastically increase the uncertainty of the water surface evaporation estimate.

[0006] To address the aforementioned issues, a method, system, and storage medium for accurately estimating water surface evaporation in long and narrow reservoirs are designed. Summary of the Invention

[0007] This application provides a method, system, and storage medium for accurately estimating the surface evaporation of a long and narrow reservoir. This addresses the problem that existing technologies, which use the mean method to estimate reservoir evaporation, fail to adapt to the spatial non-uniformity and diurnal dynamic changes of meteorological elements within the reservoir area, and thus cannot meet the high-precision evaporation estimation requirements of long and narrow reservoirs.

[0008] To achieve the above objectives, the technical solution adopted by this invention is: a method for accurately estimating water surface evaporation in a long and narrow reservoir, comprising the following steps: S1: Collect and preprocess meteorological element datasets, hydrological element datasets, and basic geographic datasets of the reservoir within its scope to establish a gridded dataset of the reservoir. S2: Based on the reservoir gridded dataset, the daily average wind direction data of each cell of a single reservoir is divided into 16 quantile wind directions; S3: Based on the wind direction frequency weight and wind speed influence weight of each quantile wind direction, construct the dominant wind direction spatial weighting function and calculate the daily dominant wind direction of the reservoir. S4: Calculate the weight of daily wind direction based on key driving factors of evapotranspiration, and generate the monthly dominant wind direction of the reservoir by combining the corresponding daily dominant wind direction; S5: Calculate the monthly water intake length of a long and narrow reservoir based on the prevailing monthly wind direction; S6: Calculate the monthly surface evaporation of a long and narrow reservoir based on the Penman equation.

[0009] Preferably, the meteorological element dataset in step S1 includes air temperature, wind speed, wind direction, and radiation, wherein the radiation is downward shortwave radiation from the ground surface; Hydrological data sets include water profile temperature; The basic geographic dataset for the reservoir includes reservoir vector boundary data and elevation data; The preprocessing includes spatial matching and spatiotemporal data completion of meteorological element datasets, hydrological element datasets, and reservoir basic geographic datasets. At the same time, wind speed and wind direction elements within the reservoir area are extracted, and the reservoir gridded dataset is established by combining the reservoir vector boundary data.

[0010] Preferably, in step S2, the daily average wind direction data of each pixel of a single reservoir is divided into 16 quantile wind directions, specifically 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, 157.5°, 180°, 202.5°, 225°, 247.5°, 270°, 292.5°, 315°, and 337.5°, where 0° and 360° wind directions coincide.

[0011] Preferably, in step S3, constructing a spatial weighted function for the dominant wind direction based on the wind direction frequency weight and wind speed influence weight for each quantile wind direction, and calculating the daily dominant wind direction of the reservoir includes the following steps: S31: Calculate the total frequency of each quantile wind direction appearing in all grid cells of the reservoir within the same time period. Normalize the frequency so that the sum of the weights of all quantile wind directions is 1, thus obtaining the wind direction frequency weight for each quantile wind direction. ; S32: Calculate the average of all wind speed data corresponding to each quantile wind direction within the same time period, and use it as the daily average wind speed for that quantile wind direction. The daily average wind speeds for all wind directions are normalized so that the sum of the weights is 1, thus obtaining the wind speed influence weight for each wind direction. ; S33: Normalize and average the wind direction frequency weight and wind speed influence weight of each quantile wind direction to obtain the comprehensive weight of that quantile wind direction. Combine the corresponding quantile wind direction angle to construct the dominant wind direction spatial weighting function. After weighted summation, the daily dominant wind direction of the reservoir is obtained.

[0012] Preferably, step S4, which calculates the weight of the daily wind direction based on the key driving factors of evapotranspiration and combines it with the corresponding daily dominant wind direction to generate the monthly dominant wind direction of the reservoir, includes the following steps: S41: Obtain the key drivers of evapotranspiration on a monthly scale, including the saturated vapor pressure differential (VPD) and the temperature TEM. S42: Normalize the daily VPD and TEM values ​​within the monthly scale, and then perform weighted summation to obtain the weight of the daily wind direction influencing factors within the monthly scale. S43: Weighting the impact of daily wind direction driving factors Multiply by the corresponding diurnal dominant wind direction The monthly dominant wind direction d is obtained by summing up all daily values ​​for the month.

[0013] Preferably, in step S42, calculating the weights of daily wind direction influencing factors on a monthly scale includes the following steps: For all days of the month, the reservoir Min-Max normalization calculations were performed to obtain the weight of the reservoir's daily VPD on wind direction. ; For all days of the month, the reservoir Min-Max normalization calculations were performed to obtain the weight of the daily TEM temperature of the reservoir on the influence of wind direction. ; Weighting the daily VPD of the reservoir on wind direction The weight of TEM temperature on wind direction The weights of the driving factors TEM and VPD on wind direction for the reservoir on a monthly scale are calculated by summing and dividing by the sum of the weights for the month. .

[0014] Preferably, the step S5, which calculates the monthly water intake length of the elongated reservoir based on the dominant monthly wind direction, is as follows: S51: Based on the direction of the dominant wind on the lunar scale To determine the prevailing path of wind blowing across the reservoir; S52: Based on the reservoir's geometry and area, calculate the farthest length parallel to the wind path along that direction, as the representative water intake length. .

[0015] Preferably, step S6, based on the Penman equation, calculates the monthly surface evaporation of a narrow reservoir, including the following steps: Based on the Penman model, combined with the principles of surface energy balance and aerodynamics, the water body heat storage variation term and the improved wind function are introduced to calculate the water surface evaporation rate of a long and narrow reservoir and obtain the monthly water surface evaporation. The calculated representative water intake length Substitute the following wind function Thus, an improved wind function is obtained; Considering the high specific heat capacity of reservoir water, an energy balance equation is introduced to extend the Penman model and calculate the equilibrium temperature of the water body, the temperature hysteresis effect, and the change in the water body's heat storage.

[0016] A precise estimation system for water surface evaporation in a long and narrow reservoir includes a memory and a processor. The memory stores a precise estimation program for water surface evaporation in a long and narrow reservoir, and the processor runs the precise estimation program for water surface evaporation in a long and narrow reservoir to execute a precise estimation method for water surface evaporation in a long and narrow reservoir.

[0017] A computer-readable storage medium storing a program for accurately estimating the evaporation of a narrow reservoir surface, wherein the program, when executed by a processor, implements a method for accurately estimating the evaporation of a narrow reservoir surface.

[0018] This invention provides a method, system, and storage medium for accurately estimating water surface evaporation in a long and narrow reservoir, with the following advantages: By incorporating gridded meteorological data and evapotranspiration driving factors from reservoirs, and introducing the spatiotemporal weighting factor of the prevailing wind direction and the furthest water intake length of the reservoir, the driving factors for evaporation on the surface of elongated reservoirs are refined. This reduces the uncertainty in estimating evaporation on the surface of elongated reservoirs and improves the accuracy of evaporation estimation. Addressing the inherent characteristics of elongated reservoirs, such as their winding shorelines, narrow shape, and drastic changes in water intake length with wind direction, the estimation model is adapted to the hydrological and meteorological characteristics of elongated reservoirs. This avoids the systematic errors caused by shape mismatch in the mean method for elongated reservoir scenarios, providing support for accurate estimation of evaporation on the surface of elongated reservoirs and facilitating the precise allocation of water use for irrigation, power generation, ecological purposes, and socio-economic development.

[0019] By dividing the wind direction in the reservoir area into 16 quantiles, calculating the frequency weight of wind direction and the influence weight of wind speed respectively, and then constructing the dominant wind direction on a daily scale through normalized averaging, the shortcomings of the traditional mean method in treating wind direction and wind speed in a singular way are avoided. This method considers both the probability of different wind directions and the actual contribution of wind speed intensity to evaporation, providing a more reasonable wind field input for evaporation estimation.

[0020] By using two key evapotranspiration drivers, temperature (TEM) and vapor pressure difference (VPD), and through normalization and weighted superposition, the daily wind direction influence weight is calculated, and then synthesized to obtain the monthly dominant wind direction. This combines the dynamic changes of meteorological elements with the dominant wind direction, avoiding the random fluctuations of single wind direction data, making the estimation results of the dominant wind direction more consistent with the actual monthly hydrological and meteorological patterns of the reservoir, and improving the stability of the estimation.

[0021] Based on the dominant wind direction on a monthly scale, the representative water intake length of the reservoir is determined using geometric methods and incorporated into the wind function of the Penman model to form an improved wind function. This modification addresses the sensitivity of elongated reservoirs to wind direction changes, effectively reduces the drastic fluctuations in evaporation estimation results caused by small changes in wind direction, and significantly improves the anti-interference capability of evaporation estimation for elongated reservoirs.

[0022] By calculating the equilibrium temperature of the water body, the temperature lag effect, and the change in the water body's thermal storage, the Penman model is extended to reflect the impact of the reservoir's thermal storage effect on evaporation. This avoids the deficiency of ignoring the change in the water body's thermal storage, improves the physical basis of the model, and makes the evaporation estimation results closer to the actual hydrological process. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating the accurate estimation method for surface evaporation in a narrow reservoir with prevailing wind direction correction provided in this application embodiment; Figure 2 Schematic diagram a showing the impact of different wind direction calculation differences on the water intake length of long and narrow reservoirs and regular reservoirs, provided for embodiments of this application; Figure 3 Schematic diagram b showing the impact of different wind direction calculation differences on the water intake length of long and narrow reservoirs and regular reservoirs, provided for embodiments of this application; Figure 4 This is a schematic diagram of the water intake length provided in the embodiments of this application; Figure 5 This is a schematic diagram of the sixteenth percentile wind direction division. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] This application provides a method, system, and storage medium for accurately estimating the surface evaporation of a long and narrow reservoir. It can solve the problems in the prior art where the mean method is used to estimate the evaporation of the reservoir, the spatial non-uniformity of meteorological elements within the reservoir area and the dynamic changes at the diurnal scale are not adapted to the morphological characteristics of long and narrow reservoirs, and thus cannot meet the high-precision evaporation estimation requirements of long and narrow reservoirs.

[0027] like Figure 1-2 As shown, a method, system, and storage medium for accurately estimating water surface evaporation in a long and narrow reservoir include... S1: Collect and preprocess meteorological element datasets, hydrological element datasets, and basic geographic datasets of the reservoir within its scope to establish a gridded dataset of the reservoir. S2: Based on the reservoir gridded dataset, the daily average wind direction data of each cell of a single reservoir is divided into 16 quantile wind directions; S3: Based on the wind direction frequency weight and wind speed influence weight of each quantile wind direction, construct the dominant wind direction spatial weighting function and calculate the daily dominant wind direction of the reservoir. S4: Calculate the weight of daily wind direction based on key driving factors of evapotranspiration, and generate the monthly dominant wind direction of the reservoir by combining the corresponding daily dominant wind direction; S5: Calculate the monthly water intake length of a long and narrow reservoir based on the prevailing monthly wind direction; S6: Calculate the monthly surface evaporation of a long and narrow reservoir based on the Penman equation.

[0028] By incorporating gridded meteorological data and evapotranspiration driving factors from reservoirs, and introducing the spatiotemporal weighting factor of the prevailing wind direction and the furthest water intake length of the reservoir, the driving factors for evaporation on the surface of elongated reservoirs are refined. This reduces the uncertainty in estimating evaporation on the surface of elongated reservoirs and improves the accuracy of evaporation estimation. Addressing the inherent characteristics of elongated reservoirs, such as their winding shorelines, narrow shape, and drastic changes in water intake length with wind direction, the estimation model is adapted to the hydrological and meteorological characteristics of elongated reservoirs. This avoids the systematic errors caused by shape mismatch in the mean method for elongated reservoir scenarios, providing support for accurate estimation of evaporation on the surface of elongated reservoirs and facilitating the precise allocation of water use for irrigation, power generation, ecological purposes, and socio-economic development.

[0029] By dividing the wind direction in the reservoir area into 16 quantiles, calculating the frequency weight of wind direction and the influence weight of wind speed respectively, and then constructing the dominant wind direction on a daily scale through normalized averaging, the shortcomings of the traditional mean method in treating wind direction and wind speed in a singular way are avoided. This method considers both the probability of different wind directions and the actual contribution of wind speed intensity to evaporation, providing a more reasonable wind field input for evaporation estimation.

[0030] By using two key evapotranspiration drivers, temperature (TEM) and vapor pressure difference (VPD), and through normalization and weighted superposition, the daily wind direction influence weight is calculated, and then synthesized to obtain the monthly dominant wind direction. This combines the dynamic changes of meteorological elements with the dominant wind direction, avoiding the random fluctuations of single wind direction data, making the estimation results of the dominant wind direction more consistent with the actual monthly hydrological and meteorological patterns of the reservoir, and improving the stability of the estimation.

[0031] Based on the dominant wind direction on a monthly scale, the representative water intake length of the reservoir is determined using geometric methods and incorporated into the wind function of the Penman model to form an improved wind function. This modification addresses the sensitivity of elongated reservoirs to wind direction changes, effectively reduces the drastic fluctuations in evaporation estimation results caused by small changes in wind direction, and significantly improves the anti-interference capability of evaporation estimation for elongated reservoirs.

[0032] By calculating the equilibrium temperature of the water body, the temperature lag effect, and the change in the water body's thermal storage, the Penman model is extended to reflect the impact of the reservoir's thermal storage effect on evaporation. This avoids the deficiency of ignoring the change in the water body's thermal storage, improves the physical basis of the model, and makes the evaporation estimation results closer to the actual hydrological process.

[0033] In one embodiment, the meteorological element dataset in step S1 includes air temperature, wind speed, wind direction, and radiation, wherein the radiation is surface downwave shortwave radiation. Hydrological data sets include water profile temperature; The basic geographic dataset for the reservoir includes reservoir vector boundary data and elevation data; The preprocessing includes spatial matching and spatiotemporal data completion of meteorological element datasets, hydrological element datasets, and reservoir basic geographic datasets. At the same time, wind speed and wind direction elements within the reservoir area are extracted, and the reservoir gridded dataset is established by combining the reservoir vector boundary data.

[0034] Specifically, the preprocessing described in this implementation plan includes spatial matching and spatiotemporal data completion of meteorological element datasets, hydrological element datasets, and reservoir basic geographic datasets, comprising the following steps: The coordinate system transformation and spatial reference transformation includes: unifying the meteorological element dataset, hydrological element dataset, and reservoir basic geographic dataset to the same geographic coordinate system or projected coordinate system, preferably using the WGS84 geographic coordinate system; for processes involving area calculation, it can be further transformed into an equal-area projected coordinate system to ensure the consistency and accuracy of spatial analysis results.

[0035] Time scale unification and sequence reconstruction include: unifying meteorological element datasets and hydrological element datasets to a consistent time resolution (preferably daily or monthly scale); converting original hourly meteorological variables into target time scale data through averaging methods; and constructing continuous time series to ensure alignment of the time dimensions of each dataset.

[0036] The wind speed and direction data conversion process includes: converting the original wind field data (usually represented by u and v components) into wind speed and direction; and providing input parameters - wind speed and direction data - for subsequent water intake length calculation.

[0037] The saturation pressure difference is calculated based on the air temperature and dew point temperature data of ERA5.

[0038] The extraction and processing of wind speed and direction data within the reservoir area is based on the extraction of meteorological elements (including wind speed, air temperature and radiation, saturation pressure difference, etc.) and hydrological elements (including water profile temperature) from each grid within the reservoir area using the reservoir vector boundary data; thus obtaining the values ​​of meteorological and hydrological variables that characterize the overall features of the reservoir.

[0039] In one embodiment, a typical long and narrow river-type reservoir in southern my country is selected. This reservoir is distributed in a narrow strip shape with a winding shoreline, a length of approximately 60 km in the east-west direction, and a width of only 1-3 km in the north-south direction. It is extremely sensitive to changes in wind direction and is a typical long and narrow reservoir. For this long and narrow reservoir, ERA5 reanalysis meteorological data, measured hydrological data of the reservoir, and reservoir vector boundary data are used to collect and preprocess the meteorological element dataset, hydrological element dataset, and basic geographic dataset of the reservoir to establish a gridded dataset of the reservoir. The specific steps are as follows: Step 1: Coordinate System I and Spatial Datum Transformation (including...) The meteorological data set (ERA5 data), the hydrological data set (water profile temperature), and the reservoir basic geographic data set (reservoir vector boundary, DEM elevation data) will be uniformly converted to the same spatial reference datum. First, all data are converted to the WGS84 geographic coordinate system to ensure that the spatial positioning benchmark of each dataset is consistent and to eliminate spatial misalignment caused by coordinate system differences between different data sources. Secondly, for the subsequent calculation of reservoir water area, statistics of gridded cell area and calculation of evaporation, the dataset involved in area calculation will be further converted into the Lambert equal-area conic projection coordinate system to eliminate the area calculation error caused by projection distortion and ensure the consistency and accuracy of spatial analysis results in the area dimension.

[0040] Step Two: Time Scale Unification and Sequence Reconstruction (including...) The meteorological and hydrological data sets were unified to a daily time resolution, and continuous time series were constructed. For ERA5 meteorological variables (such as temperature, wind speed, and radiation) that are originally hourly, the arithmetic mean method is used to convert them into daily scale data. The average of the hourly wind speed data of a long and narrow reservoir from 0:00 to 23:00 on a certain day is calculated to obtain the daily average wind speed for that day. For missing instantaneous meteorological data, linear interpolation is used to fill in the gaps in time and space, and to construct a complete continuous time series. Ensure that all datasets are strictly aligned in the time dimension, so that meteorological and hydrological data of the same day correspond one-to-one on the time axis, providing a unified time framework for subsequent daily-scale dominant wind direction calculations.

[0041] Step 3: Wind speed and direction data conversion and processing The original wind field data of ERA5 (in... , (Stored in component form) is converted into wind speed and direction, which represent the physical meaning of water surface evaporation: Wind speed calculation: using vector composition formula Calculate the average daily wind speed for each grid cell; Wind direction calculation: using the quadrant determination formula And convert the angle to a standard orientation of 0°~360°; The wind speed and direction data are formatted to adapt to the input requirements of subsequent dominant wind direction statistical calculations and water intake length calculations.

[0042] Step 4: Calculate the saturation pressure difference Based on ERA5 air temperature and dew point temperature data, calculate the saturation pressure difference (VPD): First, calculate the saturated vapor pressure based on the air temperature. Calculate the actual water vapor pressure based on the dew point temperature. ;

[0043]

[0044] Secondly, calculate the saturated gas pressure difference. The VPD dataset, which characterizes the dynamic conditions of water evaporation, was obtained and serves as one of the key driving factors for subsequent monthly-scale calculations of the dominant wind direction.

[0045] (5) Extraction and processing of elements within the reservoir area Based on the reservoir vector boundary data, meteorological and hydrological elements of each grid within the reservoir area are extracted: Spatial masking was used to extract ERA5 gridded data using the reservoir vector boundary, retaining only image data within the reservoir water body outline and removing land surface clutter. The extracted meteorological elements include: daily average wind speed, daily average temperature, surface downwave radiation, and daily average saturation pressure difference (VPD). The extracted hydrological elements include: water profile temperature; The extracted grid data is statistically aggregated to calculate the average value of all pixels within the reservoir area, thereby obtaining the values ​​of meteorological and hydrological variables that characterize the overall features of the reservoir, and finally establishing a thematic dataset for the reservoir.

[0046] In one embodiment, in step S2, the daily average wind direction data of each pixel of a single reservoir is divided into 16 quantile wind directions, specifically 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, 157.5°, 180°, 202.5°, 225°, 247.5°, 270°, 292.5°, 315°, and 337.5°, where 0° and 360° wind directions coincide.

[0047] Specifically, in practical applications, assigning the daily average wind direction data of each pixel to the corresponding quantile wind direction includes: When a pixel's wind direction value is greater than or equal to 0° and less than or equal to 11.25°, the pixel's wind direction value is set to 0°; when the pixel's wind direction value is greater than 11.25° and less than or equal to 33.75°, the pixel's wind direction value is set to 22.5°; when the pixel's wind direction value is greater than 33.75° and less than or equal to 56.25°, the pixel's wind direction value is set to 45°; when the pixel's wind direction value is greater than 56.25° and less than or equal to 78.75°, the pixel's wind direction value is set to 67.5°; when the pixel's wind direction value is greater than 78.75° and less than or equal to 11.25°, the value is set to 0°; when the pixel's wind direction value is greater than 11.25° and less than or equal to 11.25°, the value is set to 22.5°; when the pixel's wind direction value is greater than 33.75° and less than or equal to 56.25°, the value is set to 45°; when the pixel's wind direction value is greater than 56.25° and less than or equal to 78.75°, the value is set to 67.5°; when the pixel's wind direction value is greater than 78.75° and less than or equal to 11.25°, the value is set to 0°; when the pixel's wind direction value is greater than 11.25° and less than or equal to 11.25°, the value is set to 11.25 ... When the wind direction value is 101.25°, the value is set to 90°; when the wind direction value is greater than 101.25° and less than or equal to 123.75°, the value is set to 112.5°; when the wind direction value is greater than 123.75° and less than or equal to 146.25°, the value is set to 135°; when the wind direction value is greater than 146.25° and less than or equal to 168.75°, the value is set to 157.5°; when the wind direction value is greater than 168.75° and less than or equal to 191.25°... When the pixel's wind direction value is greater than 191.25° and less than or equal to 213.75°, the value is set to 202.5°; when the value is greater than 213.75° and less than or equal to 236.25°, the value is set to 225°; when the value is greater than 236.25° and less than or equal to 258.75°, the value is set to 247.5°; when the value is greater than 258.75° and less than or equal to 281.25°, the value is set to 180°. The wind direction value is set to 270°; when the wind direction value of a pixel is greater than 281.25° and less than or equal to 303.75°, the wind direction value of that pixel is set to 292.5°; when the wind direction value of a pixel is greater than 303.75° and less than or equal to 326.25°, the wind direction value of that pixel is set to 315°; when the wind direction value of a pixel is greater than 326.25° and less than or equal to 348.75°, the wind direction value of that pixel is set to 337.5°; when the wind direction value of a pixel is greater than 348.75° and less than or equal to 360°, the wind direction value of that pixel is set to 360°.

[0048] In one embodiment, step S3, which involves constructing a spatial weighted function for the dominant wind direction based on the wind direction frequency weight and wind speed influence weight for each quantile wind direction, and calculating the daily dominant wind direction of the reservoir, includes the following steps: S31: Calculate the total frequency of each quantile wind direction appearing in all grid cells of the reservoir within the same time period. Normalize the frequency so that the sum of the weights of all quantile wind directions is 1, thus obtaining the wind direction frequency weight for each quantile wind direction. ; Specifically, the calculation method is as follows:

[0049] in, This represents the number of pixels in the reservoir corresponding to the i-th wind direction. It is the sum of the frequencies of the 16 wind directions (0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, 157.5°, 180°, 202.5°, 225°, 247.5°, 270°, 292.5°, 315°, 337.5° and 360°) that occur at the reservoir.

[0050] S32: Calculate the average of all wind speed data corresponding to each quantile wind direction within the same time period, and use it as the daily average wind speed for that quantile wind direction. The daily average wind speeds for all wind directions are normalized so that the sum of the weights is 1, thus obtaining the wind speed influence weight for each wind direction. ; Specifically, the calculation method is as follows:

[0051] in, This represents the average wind speed in the i-th wind direction within the reservoir. It is the sum of the average speeds of the 16 wind directions at the reservoir.

[0052] in, The weighted average velocity, calculated from the i-th wind direction in the reservoir, is obtained using the following formula:

[0053] in, It represents the sum of the daily average wind speeds of all pixels within this quantile wind direction, where n is the total number of pixels contained within this quantile wind direction.

[0054] S33: Normalize and average the wind direction frequency weight and wind speed influence weight of each quantile wind direction to obtain the comprehensive weight of that quantile wind direction. Combine the corresponding quantile wind direction angle to construct the dominant wind direction spatial weighting function. After weighted summation, the daily dominant wind direction of the reservoir is obtained.

[0055] The specifics are as follows: Weights of wind direction i at each quantile The weights are calculated by normalizing the average of wind direction frequency weights and wind speed influence weights, including the weights of each quantile wind direction i. Wind direction frequency weighting Weighting of wind speed influence The sum divided by all wind directions and The sum of these is expressed as the normalized sum, which is 2. The calculation formula is as follows: .

[0056] The dominant wind direction on a daily scale The calculation is achieved through weighted summation, and the formula is as follows:

[0057] in, It represents 16 wind direction units (including 0°, 22.5°, ..., 337.5°, 360°).

[0058] In one embodiment, step S4, which calculates the weight of daily wind direction based on key evapotranspiration drivers and combines it with the corresponding daily dominant wind direction to generate the monthly dominant wind direction of the reservoir, includes the following steps: S41: Obtain the key drivers of evapotranspiration on a monthly scale, including the saturated vapor pressure differential (VPD) and the temperature TEM. The key driving factors for daily evapotranspiration on a monthly scale include the following steps: based on the conclusions drawn from existing literature, correlation analysis is performed between indicators such as air temperature, surface radiation, saturated pressure difference, and wind speed and evapotranspiration, and those with high correlation coefficients are identified as key driving factors.

[0059] Existing literature includes: Impact of climate change on “evaporation paradox” in province ofJiangsu in southeastern China; Spatiotemporal variation of potential evapotranspiration and its dominant factors during 1970 2020 across the Sichuan-Chongqing region, China; Vegetation and Evapotranspiration Responses to Increased AtmosphericVapor Pressure Deficit across the Global Forest.

[0060] Meanwhile, ArcGIS software was used to conduct correlation analysis between indicators such as air temperature, surface radiation, saturation pressure difference, and wind speed and evapotranspiration.

[0061] In one embodiment, taking the monthly-scale water surface evaporation estimation of a long and narrow reservoir as an example, the key driving factors of daily evapotranspiration (saturated vapor pressure difference VPD and air temperature TEM) within the monthly scale are obtained through the following steps: According to existing research findings in the field of hydrometeorology, air temperature (TEM) and saturated vapor pressure difference (VPD) are the core driving factors controlling the evapotranspiration process of water surface. Air temperature directly affects the thermodynamic conditions of water evaporation, while VPD reflects the air's capacity to hold water vapor. Both have a significant positive correlation with the water surface evaporation rate.

[0062] To further validate and screen key factors, Pearson correlation analysis was conducted on meteorological indicators such as air temperature, surface radiation, saturation pressure difference (VPD), and wind speed with actual evapotranspiration data from the reservoir. The results showed that: The correlation coefficient between temperature (TEM) and evapotranspiration was approximately 0.78 (p<0.01), indicating a strong positive correlation. The correlation coefficient between saturated vapor pressure difference (VPD) and evapotranspiration is approximately 0.82 (p<0.01), indicating a very strong positive correlation. The correlation coefficients of other indicators (such as surface radiation and wind speed) were all below 0.6, indicating weak significance.

[0063] Therefore, air temperature (TEM) and saturated vapor pressure difference (VPD) were ultimately identified as the key driving factors for this monthly-scale dominant wind direction calculation.

[0064] Taking the month in which the reservoir is located (30 days in total) as an example, obtain the daily temperature TEM and saturated vapor pressure difference VPD data: The daily average temperature data within the reservoir area is extracted from the ERA5 reanalysis data, i.e., the average temperature of all grids in the reservoir is calculated to obtain the daily TEM1, TEM2, ..., TEM values ​​for the current month. 30 ; Based on the air temperature and dew point temperature data of ERA5, the daily average VPD within the reservoir area is calculated, that is, the average VPD of all grids in the reservoir is calculated to obtain the daily VPD1, VPD2, ..., VPD for the current month. 30 ; S42: Normalize the daily VPD and TEM values ​​within the monthly scale, and then perform weighted summation to obtain the weight of the daily wind direction influencing factors within the monthly scale. The weighting of daily wind direction is calculated as follows: For all days of the month, the reservoir Min-Max normalization calculations were performed to obtain the weight of the reservoir's daily VPD on wind direction. ; The calculation process is as follows:

[0065] This refers to the VPD value of the reservoir on that day. This refers to the minimum daily VPD value of the reservoir in that month. This refers to the maximum daily VPD value of the reservoir in that month.

[0066] Similarly, for all days of that month at that reservoir... Min-Max normalization calculations were performed to obtain the weight of the daily TEM temperature of the reservoir on the influence of wind direction. ; The calculation process is as follows:

[0067] in, This refers to the average daily temperature value of the reservoir on that day. This refers to the minimum daily average temperature of the reservoir in that month. This refers to the highest daily average temperature of the reservoir in that month.

[0068] By weighting the daily VPD of the reservoir on the impact of wind direction The weight of TEM temperature on wind direction The weights of the driving factors TEM and VPD on wind direction for the reservoir on a monthly scale are calculated by summing and dividing by the sum of the weights for the month. : The calculation process is as follows:

[0069] in, The denominator is the sum of the TEM and VPD weights for all days in the month, ensuring the weights of all driving factors for the month are calculated. The sum is 1.

[0070] S43: Weighting the impact of daily wind direction driving factors Multiply by the corresponding diurnal dominant wind direction The monthly dominant wind direction d is obtained by summing up all daily values ​​for the month.

[0071] The calculation formula is as follows:

[0072] In the formula, n is the number of days in a month.

[0073] like Figure 3 As shown, in one embodiment, the step S5 of calculating the monthly water intake length of the elongated reservoir based on the monthly prevailing wind direction is as follows: S51: Based on the direction of the dominant wind on the lunar scale To determine the prevailing path of wind blowing across the reservoir; S52: Based on the reservoir's geometry and area, calculate the farthest length parallel to the wind path along that direction, as the representative water intake length. .

[0074] Based on the reservoir's geometry and area, the farthest length parallel to the wind path is calculated and used as the representative water intake length. ; In one embodiment, a typical long and narrow river reservoir in southern my country is selected. This reservoir is distributed in a long and narrow strip with a winding shoreline. It is about 60 km long in the east-west direction and only 1-3 km wide in the north-south direction. It is extremely sensitive to changes in wind direction and is a typical long and narrow reservoir.

[0075] The number of pixels for each wind direction at each quantile is counted, and the frequency weights are obtained by normalization. For example, the 45° quantile on a given day has 150 pixels, after normalization...

[0076] Calculate the daily average wind speed for each quantile wind direction. Wind speed weights are obtained by normalization using 45° quantiles. The prevailing wind direction on the daily scale is calculated to obtain That is, the prevailing wind direction on that day was northeast-east. Through literature and correlation analysis, temperature (TEM) and volume temperature deviation (VPD) were identified as key driving factors. Daily average TEM and VPD data for the 30 days of the month were obtained, and TEM and VPD were normalized using the Min-Max method. The daily driving factor weights were then obtained by summing the data. .

[0077] Dominant wind direction on a lunar scale The calculation is as follows: The calculation for that month =82.5 That is, the dominant wind direction on a monthly scale is northeast-east.

[0078] Based on the dominant wind direction on a monthly scale =82.5 Determine the prevailing wind path (approximately east-west across the reservoir).

[0079] Based on the geometry of the reservoir's vector boundary, the following calculations are implemented using code: Generate a scan line parallel to the wind path and traverse the reservoir boundary; Calculate the vertical distance from the reservoir boundary point to the wind direction scan line, find the two farthest points to calculate the water intake width, and use the formula: reservoir area / water intake width = water intake length. Obtain representative water sampling length =52.3km, used for subsequent Penman equation wind function calculations.

[0080] Finally, based on the Penman equation, substituting... Calculate the wind function Then calculate the evaporation rate. With monthly evaporation This yielded a precise estimate of the reservoir's monthly evaporation.

[0081] In one embodiment, step S6, based on the Penman equation, calculates the monthly surface evaporation of a long and narrow reservoir, including the following steps: Based on the Penman model, this study combines surface energy balance and aerodynamic principles, while also incorporating a water body thermal storage variation term. and improved wind function The evaporation rate of the water surface in a long and narrow reservoir was calculated to obtain the monthly water surface evaporation. The calculation is as follows:

[0082]

[0083] In the formula, Evaporation rate of water surface; Net radiation (MJ / (m²·d)); The change in the thermal storage of a single reservoir (MJ / (m²·d)); and These are the air temperatures The saturated vapor pressure and actual vapor pressure (kPa) at the specified values. Latent heat of vaporization (MJ / kg); The slope of the saturated vapor pressure curve; These are the wet-bulb and dry-bulb constants; E is the evaporation rate of a single reservoir, AREA is the surface area of ​​the water body of each reservoir in the current month, E is the evaporation rate, and D is the number of days in the current month. This is a wind function used to reduce errors introduced when using terrestrial meteorological data to calculate water surface evapotranspiration.

[0084] The wind function is related to the water intake length in the formula. The calculation of water surface evaporation involves variables related to surface energy radiation and aerodynamics, and takes into account the influence of water body heat storage on the evaporation rate of open water. The calculated representative water intake length Substitute the following wind function This allows us to obtain an improved wind function; The calculation formula is as follows:

[0085] Considering the high specific heat capacity of reservoir water, an energy balance equation is introduced to extend the Penman model and calculate the equilibrium temperature of the water body, the temperature hysteresis effect, and the change in the water body's heat storage. It should be noted that water bodies (such as reservoirs / lakes) have a high specific heat capacity. In spring and summer / during the day, water bodies absorb solar radiation and store a large amount of heat; in autumn and winter or at night, reservoirs release the previously stored heat, according to the energy balance equation. The equilibrium temperature is calculated in the Penman model as follows:

[0086] in the formula It is the albedo of the water surface. , These are constant parameters, with values ​​of 0.46 MJ / (m 2 ·d·℃) and 23.38MJ / (m 2 ·d).

[0087]

[0088]

[0089]

[0090] In the formula, It represents the downward shortwave radiation at the Earth's surface (MJ / (m²·d)). It is the emissivity of water. It is the emissivity of the air; It is the lag time (d); It is the depth of the reservoir (m); It is the density of the water. It is the specific heat capacity of water (MJ / (kg·℃)); and These are the wet-bulb temperature and the equilibrium temperature of the water body (°C), respectively. and These are the current and previous water profile temperatures, respectively. and It is a constant.

[0091] A precise estimation system for water surface evaporation in a long and narrow reservoir includes a memory and a processor. The memory stores a precise estimation program for water surface evaporation in a long and narrow reservoir, and the processor runs the precise estimation program for water surface evaporation in a long and narrow reservoir to execute a precise estimation method for water surface evaporation in a long and narrow reservoir.

[0092] In one embodiment, the processor includes a data preprocessing module, a wind direction quantization processing module, a daily dominant wind direction calculation module, a monthly dominant wind direction calculation module, a water intake length calculation module, and an evaporation calculation module. The data preprocessing module is used to collect and preprocess meteorological element datasets, hydrological element datasets, and reservoir basic geographic datasets to establish a reservoir gridded dataset. The wind direction quantile processing module is used to divide the daily average wind direction data of each cell of a single reservoir into 16 quantile wind directions based on the reservoir gridded dataset. The daily dominant wind direction calculation module is used to construct a spatial weighting function for the dominant wind direction based on the wind direction frequency weight and wind speed influence weight of each quantile wind direction, and to calculate the daily dominant wind direction of the reservoir. The monthly dominant wind direction calculation module is used to calculate the weight of the daily wind direction based on the key driving factors of evapotranspiration, and generate the monthly dominant wind direction of the reservoir by combining the corresponding daily dominant wind direction. The water intake length calculation module is used to calculate the monthly water intake length of a long and narrow reservoir based on the prevailing monthly wind direction. The evaporation calculation module is used to calculate the monthly water surface evaporation of a long and narrow reservoir based on the PENMAN equation and the monthly water intake length. The memory is used to store the datasets, intermediate calculation results, and program instructions required for the operation of the above modules.

[0093] A computer-readable storage medium storing a program for accurately estimating the evaporation of a narrow reservoir surface, wherein the program, when executed by a processor, implements a method for accurately estimating the evaporation of a narrow reservoir surface.

[0094] If the aforementioned integrated modules are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0095] The technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0096] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0097] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0098] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for accurately estimating water surface evaporation in a long and narrow reservoir, characterized in that, Includes the following steps: S1: Collect and preprocess meteorological element datasets, hydrological element datasets, and basic geographic datasets of the reservoir within its scope to establish a gridded dataset of the reservoir. S2: Based on the reservoir gridded dataset, the daily average wind direction data of each cell of a single reservoir is divided into 16 quantile wind directions; S3: Based on the wind direction frequency weight and wind speed influence weight of each quantile wind direction, construct the dominant wind direction spatial weighting function and calculate the daily dominant wind direction of the reservoir. S4: Calculate the weight of daily wind direction based on key driving factors of evapotranspiration, and generate the monthly dominant wind direction of the reservoir by combining the corresponding daily dominant wind direction; S5: Calculate the monthly water intake length of a long and narrow reservoir based on the prevailing monthly wind direction; S6: Calculate the monthly surface evaporation of a long and narrow reservoir based on the Penman equation.

2. The method for accurately estimating water surface evaporation in a long and narrow reservoir according to claim 1, characterized in that: The meteorological element dataset in step S1 includes air temperature, wind speed, wind direction, and radiation, wherein the radiation is surface downwave shortwave radiation. Hydrological data sets include water profile temperature; The basic geographic dataset for the reservoir includes reservoir vector boundary data and elevation data; The preprocessing includes spatial matching and spatiotemporal data completion of meteorological element datasets, hydrological element datasets, and reservoir basic geographic datasets. At the same time, wind speed and wind direction elements within the reservoir area are extracted, and the reservoir gridded dataset is established by combining the reservoir vector boundary data.

3. The method for accurately estimating water surface evaporation in a long and narrow reservoir according to claim 1, characterized in that: In step S2, the daily average wind direction data of each pixel of a single reservoir is divided into 16 quantile wind directions, specifically 0°, 22.5°, 45°, 67.5°, 90°, 112.5°, 135°, 157.5°, 180°, 202.5°, 225°, 247.5°, 270°, 292.5°, 315°, and 337.5°, where 0° and 360° wind directions coincide.

4. The method for accurately estimating water surface evaporation in a long and narrow reservoir according to claim 1, characterized in that: In step S3, the dominant wind direction spatial weighting function is constructed based on the wind direction frequency weight and wind speed influence weight for each quantile wind direction. The calculation of the daily dominant wind direction of the reservoir includes the following steps: S31: Calculate the total frequency of each quantile wind direction appearing in all grid cells of the reservoir within the same time period. Normalize the frequency so that the sum of the weights of all quantile wind directions is 1, thus obtaining the wind direction frequency weight for each quantile wind direction. ; S32: Calculate the average of all wind speed data corresponding to each quantile wind direction within the same time period, and use it as the daily average wind speed for that quantile wind direction. The daily average wind speeds for all wind directions are normalized so that the sum of the weights is 1, thus obtaining the wind speed influence weight for each wind direction. ; S33: Normalize and average the wind direction frequency weight and wind speed influence weight of each quantile wind direction to obtain the comprehensive weight of that quantile wind direction. Combine the corresponding quantile wind direction angle to construct the dominant wind direction spatial weighting function. After weighted summation, the daily dominant wind direction of the reservoir is obtained.

5. The method for accurately estimating water surface evaporation in a long and narrow reservoir according to claim 1, characterized in that: Step S4, which calculates the weight of daily wind direction based on key evapotranspiration drivers and combines it with the corresponding daily dominant wind direction to generate the monthly dominant wind direction of the reservoir, includes the following steps: S41: Obtain the key drivers of evapotranspiration on a monthly scale, including the saturated vapor pressure differential (VPD) and the temperature TEM. S42: Normalize the daily VPD and TEM values ​​within the monthly scale, and then perform weighted summation to obtain the weight of the daily wind direction influencing factors within the monthly scale. S43: Weighting the impact of daily wind direction driving factors Multiply by the corresponding diurnal dominant wind direction The monthly dominant wind direction d is obtained by summing up all daily values ​​for the month.

6. The method for accurately estimating water surface evaporation in a long and narrow reservoir according to claim 5, characterized in that: In step S42, calculating the weights of daily wind direction influencing factors on a monthly scale includes the following steps: For all days of the month, the reservoir Min-Max normalization calculations were performed to obtain the weight of the reservoir's daily VPD on wind direction. ; For all days of the month, the reservoir Min-Max normalization calculations were performed to obtain the weight of the daily TEM temperature of the reservoir on the influence of wind direction. ; Weighting the daily VPD of the reservoir on wind direction The weight of TEM temperature on wind direction The weights of the driving factors TEM and VPD on wind direction for the reservoir on a monthly scale are calculated by summing and dividing by the sum of the weights for the month. .

7. The method for accurately estimating water surface evaporation in a long and narrow reservoir according to claim 1, characterized in that: In step S5, the calculation of the monthly water intake length of the elongated reservoir based on the dominant monthly wind direction is as follows: S51: Based on the direction of the dominant wind on the lunar scale To determine the prevailing path of wind blowing across the reservoir; S52: Based on the reservoir's geometry and area, calculate the furthest length parallel to the wind path along that direction, as the representative water intake length. .

8. The method for accurately estimating water surface evaporation in a long and narrow reservoir according to claim 1, characterized in that: Step S6, based on the Penman equation, calculates the monthly surface evaporation of a long and narrow reservoir, including the following steps: Based on the Penman model, combined with the principles of surface energy balance and aerodynamics, the water body heat storage variation term and the improved wind function are introduced to calculate the water surface evaporation rate of a long and narrow reservoir and obtain the monthly water surface evaporation. The calculated representative water intake length Substitute the following wind function Thus, an improved wind function is obtained; Considering the high specific heat capacity of reservoir water, an energy balance equation is introduced to extend the Penman model and calculate the equilibrium temperature of the water body, the temperature hysteresis effect, and the change in the water body's heat storage.

9. A precise estimation system for water surface evaporation in a long and narrow reservoir, characterized in that, The device includes a memory and a processor. The memory stores a program for accurately estimating the evaporation of the surface of a long and narrow reservoir. The processor runs the program to perform the method for accurately estimating the evaporation of the surface of a long and narrow reservoir as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for accurately estimating the evaporation of the surface of a narrow reservoir. When the program is executed by a processor, it implements the method for accurately estimating the evaporation of the surface of a narrow reservoir as described in any one of claims 1-8.

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