Water resource monitoring method and device based on evapotranspiration, electronic equipment, medium and product

Abstract

The application discloses a water resource monitoring method and device based on evapotranspiration, electronic equipment, medium and product. The method comprises the following steps: obtaining water cycle reference data in a target area and surface water and heat multi-mode data comprising a target key parameter and a target evapotranspiration driving parameter; calculating an original water vapor pressure deficit, an air density and a temperature decrement rate constraint correction coefficient according to the target key parameter; correcting the original water vapor pressure deficit according to the temperature decrement rate constraint correction coefficient to obtain a temperature decrement rate constraint water vapor pressure deficit; taking the air density, the temperature decrement rate constraint water vapor pressure deficit and the target evapotranspiration driving parameter as inputs, calling a preset daily scale Penman-Monteith model to calculate a daily scale actual evapotranspiration of the target area; and generating a water resource monitoring result according to the water cycle reference data and the daily scale actual evapotranspiration. The method can effectively improve the inversion accuracy of the daily scale actual evapotranspiration.

Description

Technical Field

[0001] This application relates to the field of water resources monitoring technology, specifically to a water resources monitoring method, device, electronic equipment, readable storage medium, and computer program product based on evapotranspiration. Background Technology

[0002] Evapotranspiration is a crucial process in the terrestrial water cycle and surface energy balance. In water resource monitoring, evapotranspiration is of great significance for monitoring farmland water consumption, drought early warning, and regional water resource management. Existing water resource monitoring methods typically begin by acquiring satellite remote sensing imagery of the target area, then using a remote sensing evapotranspiration model to invert the regional evapotranspiration, and finally conducting water resource assessments and drought monitoring based on the calculated evapotranspiration. However, existing methods rely on polar-orbiting satellites to acquire thermal infrared remote sensing data. Effective observations for the same area are often only available every few days, and cloud cover and rain further extend the acquisition period, resulting in discontinuous evapotranspiration inversion results over time. This makes it difficult to meet the requirements for continuous daily monitoring, reducing the accuracy and reliability of water resource monitoring results. Summary of the Invention

[0003] In view of the above problems, this application provides a water resource monitoring method, device, electronic equipment, readable storage medium and computer program product based on evapotranspiration, which can solve the problem that the existing technology cannot accurately obtain the daily scale evapotranspiration, thus resulting in low accuracy and reliability of evapotranspiration monitoring results.

[0004] Firstly, this application provides a water resource monitoring method based on evapotranspiration, including: Acquire surface hydrothermal multi-model data and water cycle baseline data within the target area; wherein, the surface hydrothermal multi-model data includes target key parameters and target evapotranspiration driving parameters; the target key parameters include near-surface air temperature, near-surface air pressure, near-surface humidity data, lower-level air temperature, upper-level air temperature, lower-level height, and upper-level height; the target evapotranspiration driving parameters include daily net radiation, daily soil heat flux, aerodynamic drag, and surface drag; Calculate the original vapor pressure deficit, air density, and temperature lapse rate constraint correction coefficients based on the target key parameters. The original vapor pressure deficit is corrected according to the temperature lapse rate constraint correction coefficient to obtain the temperature lapse rate constrained vapor pressure deficit. Using the air density, temperature lapse rate constrained by water vapor pressure deficit, and the target evapotranspiration driving parameters as inputs, the preset diurnal-scale Penman-Monteith model is invoked to calculate the actual diurnal-scale evapotranspiration of the target region. Based on the water cycle baseline data and the actual daily evapotranspiration, water resource monitoring results are generated; wherein, the water resource monitoring results include water resource monitoring indicators and water resource management recommendations that match the water resource monitoring indicators.

[0005] In the above technical solution, this method can significantly improve the inversion accuracy and temporal continuity of daily-scale actual evapotranspiration by integrating multi-source surface water and heat multi-mode data with water cycle baseline data and introducing a temperature lapse rate constraint correction mechanism. This effectively overcomes the bottleneck of missing observations of polar-orbiting satellite thermal infrared data under cloud and rainy weather, thereby achieving daily dynamic monitoring of farmland water consumption and drought evolution. At the same time, this method can also combine water resource monitoring indicators and management suggestions generated from water cycle baseline data to provide more timely and reliable data support for regional water resource scheduling, drought early warning, and agricultural irrigation decisions, thereby greatly improving the practicality of water resource monitoring results in practical applications.

[0006] In some implementations, acquiring surface hydrothermal multi-model data and water cycle baseline data within the target area includes: Obtain the raw multimodal data within the target area; The original multimodal data is subjected to spatial scale matching processing based on a regular spatial grid to obtain first processed multimodal data; wherein, the first processed multimodal data is unified to the same regular spatial grid; The first processed multi-modal data is subjected to time-scale matching processing based on a daily scale to obtain the second processed multi-modal data; The second-processed multi-modal data is subjected to mathematical unit normalization to obtain the third-processed multi-modal data; The third-processed multi-modal data is subjected to missing test processing to obtain preprocessed multi-modal data; Surface water and heat multi-model data are extracted from the preprocessed multi-model data, and water cycle baseline data within the target area are obtained according to the preset water resource monitoring type.

[0007] In the above technical solution, the method can effectively eliminate the heterogeneity of multi-source data in terms of spatial resolution, temporal frequency, unit of measurement and data missingness, and ensure a high degree of consistency between surface water and heat multi-model data and model input requirements. This provides a standardized data foundation with controllable quality, spatiotemporal uniformity and physical dimension coordination for subsequent evapotranspiration inversion, thereby significantly improving the stability of model calculation and the reliability of inversion results.

[0008] In some implementations, the raw multi-model data includes near-surface meteorological variables, vertical air temperature profile variables, surface radiation flux variables, and underlying surface drag variables; The near-surface meteorological variables include near-surface air temperature, near-surface air pressure, and near-surface humidity data; The vertical profile variables of air temperature include lower-level air temperature, upper-level air temperature, lower-level altitude, and upper-level altitude; The surface radiation flux variables include daily net radiation and daily soil heat flux; The underlying surface drag variables include aerodynamic drag and surface drag; The water resources monitoring types include at least one of the following: farmland evapotranspiration monitoring, watershed hydrological simulation, ecological water consumption assessment, drought early warning, regional water resources management, and land-atmosphere interaction analysis. When the water resources monitoring type includes farmland evapotranspiration monitoring, the water cycle baseline data includes at least one of precipitation, irrigation amount and field holding capacity; When the water resources monitoring type includes watershed hydrological simulation, the water cycle baseline data includes at least one of watershed precipitation, watershed outlet runoff, and watershed water storage change. When the water resources monitoring type includes the ecological water consumption assessment type, the water cycle benchmark data includes at least one of ecological water demand quota and ecological available water limit; When the water resources monitoring type includes drought early warning type, the water cycle baseline data includes at least one of potential evapotranspiration, historical average evapotranspiration for the same period, and standard deviation of historical evapotranspiration for the same period; When the water resources monitoring type includes regional water resources management type, the water cycle benchmark data includes at least one of the total available water resources in the region and the water demand of other water users; When the water resource monitoring type includes land-atmosphere interaction analysis, the water cycle baseline data includes at least one of the following: sensible heat flux.

[0009] In the above technical solution, the method can match the corresponding water cycle benchmark data for different types of water resource monitoring (such as farmland evapotranspiration, watershed hydrological simulation, ecological water consumption assessment, drought early warning, regional water resource management and land-atmosphere interaction analysis), and combine the refined classification of multi-source raw multi-model data to achieve accurate correspondence between monitoring targets and data support. This allows for flexible adaptation to the differentiated monitoring needs under various application scenarios and significantly enhances the applicability and relevance of water resource monitoring methods in different business areas.

[0010] In some implementations, the step of calculating the original vapor pressure deficit, air density, and temperature lapse rate constraint correction coefficients based on the target key parameters includes: Calculate the saturated water vapor pressure based on the near-surface air temperature; The actual water vapor pressure is calculated based on the near-surface humidity data and the saturated water vapor pressure; wherein, the near-surface humidity data includes relative humidity; Calculate the original vapor pressure deficit based on the saturated vapor pressure and the actual vapor pressure. The air density is calculated based on the near-surface air temperature and the near-surface air pressure; wherein the air density is either the humid air density or the dry air density. Obtain the reference temperature lapse rate and the molar latent heat of vaporization of water vapor in the target region; Calculate the current temperature decay rate based on the lower-level air temperature, the upper-level air temperature, the lower-level height, and the upper-level height; Based on the current temperature lapse rate, the molar latent heat of vaporization of water vapor, and the near-surface air temperature, construct the current evaporation force conversion factor; A reference evaporation force conversion factor is constructed based on the reference temperature lapse rate, the latent heat of vaporization of water vapor, and the near-surface air temperature. Calculate the temperature lapse rate constraint correction factor based on the current evaporation force conversion factor and the reference evaporation force conversion factor.

[0011] In the above technical solution, the method can calculate saturated water vapor pressure, actual water vapor pressure and air density based on near-surface meteorological elements, and invert the current temperature lapse rate by combining vertical profile data. It further constructs two conversion coefficients of current and reference evaporation forces to obtain temperature lapse rate constraint correction coefficients, thereby realizing the quantitative characterization of the differences in atmospheric vertical thermal structure.

[0012] In some embodiments, calculating the air density based on the near-surface air temperature and the near-surface air pressure includes: When the air density is the humid air density, the air density is calculated based on the near-surface air temperature, the near-surface air pressure, the actual water vapor pressure, the preset dry air gas constant, and the preset water vapor gas constant. When the air density is the dry air density, the air density is calculated based on the near-ground air temperature, the near-ground air pressure, and the preset dry air gas constant.

[0013] In the above technical solution, the method can use moist air density (which is closer to the real physical process) when the data conditions are good, and dry air density (which lowers the data threshold) when the data conditions are limited, so as to ensure that the calculation can be completed under different meteorological observation levels.

[0014] In some implementations, constructing the current evaporation force conversion factor based on the current temperature lapse rate, the molar latent heat of vaporization of water vapor, and the near-surface air temperature includes: Calculate the vertical scale of water vapor phase change under the current temperature lapse rate, the molar latent heat of vaporization of water vapor, and the near-surface air temperature. The air gravity scale is calculated based on the preset air molar mass, the preset universal gas constant, the near-ground air temperature, and the preset gravitational acceleration. The water vapor gravity scale is calculated based on the preset water vapor molar mass, the universal gas constant, the near-surface air temperature, and the gravitational acceleration. Based on the vertical scale of the water vapor phase change and the gravity scale of the water vapor, calculate the relative attenuation intensity per unit height under water vapor phase change control and the relative attenuation intensity per unit height under water vapor gravity control. The difference between the relative attenuation intensity per unit height under the water vapor phase change control and the relative attenuation intensity per unit height under the water vapor gravity control is calculated to obtain the water vapor vertical attenuation difference term under the current temperature lapse rate state. The dimensionless processing of the vertical attenuation difference term of water vapor based on the air gravity scale yields the current evaporation force conversion coefficient.

[0015] In the above technical solution, the method can convert the current temperature lapse rate, which cannot be directly used in the calculation of water vapor pressure deficit, into the current evaporation force conversion factor, so that the current evaporation force conversion factor can be directly used in the correction calculation of water vapor pressure deficit.

[0016] Secondly, this application provides a water resource monitoring device based on evapotranspiration, comprising: The acquisition unit is used to acquire surface hydrothermal multi-model data and water cycle baseline data within the target area; wherein, the surface hydrothermal multi-model data includes target key parameters and target evapotranspiration driving parameters; the target key parameters include near-surface air temperature, near-surface air pressure, near-surface humidity data, lower-level air temperature, upper-level air temperature, lower-level height, and upper-level height; the target evapotranspiration driving parameters include daily net radiation, daily soil heat flux, aerodynamic drag, and surface drag; The first calculation unit is used to calculate the original water vapor pressure deficit, air density, and temperature lapse rate constraint correction coefficients based on the target key parameters. The correction unit is used to correct the original vapor pressure deficit according to the temperature lapse rate constraint correction coefficient to obtain the temperature lapse rate constraint vapor pressure deficit. The second calculation unit is used to take the air density, temperature lapse rate constrained water vapor pressure deficit and the target evapotranspiration driving parameters as inputs, and call the preset daily-scale Penman-Monteith model to calculate and obtain the daily-scale actual evapotranspiration of the target area. The generation unit is used to generate water resource monitoring results based on the water cycle baseline data and the actual daily evapotranspiration; wherein the water resource monitoring results include water resource monitoring indicators and water resource management recommendations that match the water resource monitoring indicators.

[0017] In the above technical solution, the device can significantly improve the inversion accuracy and temporal continuity of daily-scale actual evapotranspiration by integrating multi-source surface water and heat multi-mode data with water cycle baseline data and introducing a temperature lapse rate constraint correction mechanism. This effectively overcomes the bottleneck of missing observations of polar-orbiting satellite thermal infrared data under cloudy and rainy weather, thereby realizing daily dynamic monitoring of farmland water consumption and drought evolution. At the same time, the method can also combine water resource monitoring indicators and management suggestions generated from water cycle baseline data to provide more timely and reliable data support for regional water resource scheduling, drought early warning, and agricultural irrigation decisions, thereby greatly improving the practicality of water resource monitoring results in practical applications.

[0018] Thirdly, this application provides an electronic device including a memory and a processor, the memory storing a computer program, and the processor running the computer program to cause the electronic device to perform the water resource monitoring method based on evapotranspiration as described in any one of the first aspects.

[0019] Fourthly, this application provides a readable storage medium storing a computer program, which, when executed by a processor, performs the water resource monitoring method based on evapotranspiration as described in any one of the first aspects.

[0020] Fifthly, this application provides a computer program product comprising a computer program that, when executed by a processor, performs the water resource monitoring method based on evapotranspiration as described in the first aspect.

[0021] The beneficial effects of this application are as follows: it can introduce the constraint of the lower atmospheric vertical thermal structure on the basis of the traditional Penman-Monteith model, so that the water vapor pressure deficit can be adjusted with the temperature lapse rate, thereby solving the problem that traditional methods cannot distinguish the differences in evaporation demand under different vertical thermal structures; it can also provide differentiated treatment for the same near-surface water vapor pressure deficit but different temperature lapse rate conditions, and mark anomalous stratification under inversion or isothermal conditions to avoid mechanical application; it can also correct the aerodynamic terms without changing the radiation terms, drag terms and main framework of the model, maintaining compatibility with the existing calculation process; it can also simultaneously output multiple sets of variables such as the original water vapor pressure deficit, temperature lapse rate constrained water vapor pressure deficit, evaporation force conversion coefficient, correction coefficient and daily scale actual evapotranspiration, which can be used to distinguish the influence of near-surface dry and wet conditions and vertical thermal structure on evapotranspiration, facilitating diagnostic analysis. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a water resource monitoring method based on evapotranspiration in some embodiments of this application; Figure 2 This is a schematic diagram of the structure of a water resource monitoring device based on evapotranspiration in some embodiments of this application; Figure 3 This is a schematic diagram of the structure of an electronic device in some embodiments of this application. Detailed Implementation

[0024] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0026] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more (including two), similarly, "multiple sets" refers to two or more sets (including two sets), and "multiple pieces" refers to two or more pieces (including two pieces) unless otherwise explicitly defined.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0029] In existing methods for calculating evapotranspiration, the vapor pressure deficit is usually calculated based on near-surface temperature and humidity data, without taking into account the impact of the lower atmospheric temperature lapse rate on evaporation demand. This makes it difficult to distinguish the evapotranspiration calculation results under the same near-surface vapor pressure deficit conditions but with different vertical thermal structures.

[0030] To address the aforementioned technical problems, this application provides a water resource monitoring method based on evapotranspiration. This method aims to construct evaporation force conversion coefficients by using the current temperature lapse rate and the reference temperature lapse rate, and then take their ratio to constrain and correct the original vapor pressure deficit. The corrected vapor pressure deficit is then substituted into the Penman-Monteith model to calculate the actual daily evapotranspiration, so that the calculated evapotranspiration can change accordingly with the change of the lower atmospheric temperature lapse rate under the same near-surface conditions.

[0031] like Figure 1 As shown, some embodiments of this application provide a water resource monitoring method based on evapotranspiration, which includes: S100. Acquire surface hydrothermal multi-model data and water cycle baseline data within the target area; the surface hydrothermal multi-model data includes target key parameters and target evapotranspiration driving parameters.

[0032] In this embodiment, the key target parameters include near-surface air temperature, near-surface air pressure, near-surface humidity, lower-level air temperature, upper-level air temperature, lower-level altitude, and upper-level altitude.

[0033] In this embodiment, the target evapotranspiration driving parameters include daily net radiation, daily soil heat flux, aerodynamic drag, and surface drag.

[0034] S200, calculate the original vapor pressure deficit, air density, and temperature lapse rate constraint correction coefficients based on the target key parameters.

[0035] In this embodiment, the original water vapor pressure deficit refers to the difference between the near-surface air water vapor state and the saturated water vapor state on a diurnal scale, which is determined by the difference between the saturated water vapor pressure and the actual water vapor pressure.

[0036] In this embodiment, air density refers to the mass of air per unit volume, which is calculated based on near-surface air temperature, near-surface air pressure and actual water vapor pressure, and uses the density of moist air or the approximate density of dry air.

[0037] In this embodiment, the temperature lapse rate constraint correction coefficient refers to the enhancement or weakening effect of the current lower atmospheric vertical thermal structure on the original water vapor pressure deficit relative to the reference state, and is determined by the ratio of the current evaporation force conversion coefficient under the current temperature lapse rate state to the reference evaporation force conversion coefficient under the reference state.

[0038] S300. Correct the original vapor pressure deficit according to the temperature lapse rate constraint correction coefficient to obtain the temperature lapse rate constrained vapor pressure deficit.

[0039] In this embodiment, the method can utilize the temperature lapse rate constraint correction coefficient K. Γ The original vapor pressure deficit VPD0 is corrected to obtain the temperature lapse rate constrained vapor pressure deficit VPD. Γ The temperature lapse rate constraint on the vapor pressure deficit satisfies the following relationship: VPD Γ =K Γ VPD0; Among them, VPD Γ The vapor pressure deficit is constrained by the temperature lapse rate; VPD0 represents the original water vapor pressure deficit; K Γ This is the correction factor for the temperature lapse rate constraint.

[0040] In this embodiment, the temperature lapse rate constraint vapor pressure deficit is obtained by constraining and correcting the original vapor pressure deficit using the difference in evaporation force conversion coefficient between the current temperature lapse rate state and the reference state.

[0041] In this embodiment, when the temperature lapse rate constraint correction coefficient K Γ When K is less than the preset lower limit or greater than the preset upper limit, Γ Limit it to between the preset lower limit and the preset upper limit, or output the corresponding rule space grid or the corresponding date separately.

[0042] In this embodiment, the vapor pressure deficit constrained by the temperature lapse rate is not derived by reverse calculation of the evaporation force deficit using the same conversion factor. Instead, it is obtained by constraining and correcting the original vapor pressure deficit using the difference in the evaporation force conversion factor between the current temperature lapse rate state and the reference state. Therefore, implementing this method avoids the problem of cyclic calculations.

[0043] S400 uses air density, temperature lapse rate constraints, vapor pressure deficit, and target evapotranspiration driving parameters as inputs, and calls the preset diurnal-scale Penman-Monteith model to calculate the actual diurnal-scale evapotranspiration of the target area.

[0044] In this embodiment, the daily-scale Penman-Monteith model refers to a physical model that calculates the actual evapotranspiration of the Earth's surface based on energy balance and aerodynamic theory with a daily time step. The model is driven by daily-scale net radiation, daily-scale soil heat flux, air density, air specific heat at constant pressure, water vapor pressure deficit, aerodynamic drag, and surface drag. It outputs the actual daily-scale evapotranspiration through physical parameters such as the slope of the saturated water vapor pressure curve, the wet and dry surface constants, and the latent heat of water vaporization.

[0045] In this embodiment, the temperature lapse rate constrains the vapor pressure deficit (VPD). Γ As input variables characterizing atmospheric evaporation demand, they are substituted into the diurnal Penman-Monteith model. The diurnal Penman-Monteith model outputs the actual diurnal evapotranspiration of the target region. This actual diurnal evapotranspiration is determined by diurnal net radiation, diurnal soil heat flux, air density, air specific heat at isobaric pressure, temperature lapse rate constrained vapor pressure deficit, aerodynamic drag, and surface drag. The actual diurnal evapotranspiration satisfies the following relationship: ; Among them, ET a This represents the actual evapotranspiration on a daily scale. s is the slope of the saturated water vapor pressure curve; R n Net radiation on a daily scale; G represents the daily soil heat flux; ρ air density; c p The specific heat of air at constant pressure; VPD Γ The vapor pressure deficit is constrained by the temperature lapse rate; r a For aerodynamic drag; r s For surface resistance; λ is the latent heat of water vaporization; γ p This is the constant of the wet / dry meter.

[0046] In this embodiment, the method can output the original vapor pressure deficit, temperature lapse rate constrained vapor pressure deficit, and daily-scale actual evapotranspiration of the target area so that the user can know them.

[0047] In this embodiment, the method can output the original vapor pressure deficit VPD0 and the temperature lapse rate constrained vapor pressure deficit VPD according to the regular spatial grid, watershed, administrative region, or target study area. Γ Daily actual evapotranspiration ET a Synchronously output the corresponding rule space grid, date, current temperature lapse rate Γ, reference temperature lapse rate Γ0, and current evaporation force conversion factor C under the current temperature lapse rate state. Γ Reference evaporation force conversion factor C0 under reference conditions, and temperature lapse rate constraint correction factor K. Γ , saturated water vapor pressure e s Actual water vapor pressure e a air density ρ And missing test processing mark.

[0048] In this embodiment, the method can constrain the original vapor pressure deficit VPD0 and the temperature lapse rate to the vapor pressure deficit VPD. Γ Daily actual evapotranspiration ET a It can be output to inform users. It can also be used for monitoring farmland evapotranspiration, watershed hydrological simulation, ecological water consumption assessment, drought early warning, regional water resource management, or land-atmosphere interaction analysis.

[0049] S500. Based on water cycle baseline data and daily actual evapotranspiration, generate water resource monitoring results; the water resource monitoring results include water resource monitoring indicators and water resource management recommendations that match the water resource monitoring indicators.

[0050] In this embodiment, the method is applicable to technical fields such as eco-hydrological assessment, agricultural meteorological support, and land-atmosphere interaction monitoring. Specifically, step S500 can comprehensively determine the water cycle baseline data, such as calculated daily actual evapotranspiration, precipitation, runoff, and historical average evapotranspiration, based on calculated daily actual evapotranspiration. This generates monitoring indicators reflecting the regional water surplus / deficit status and automatically matches differentiated management recommendations, thereby providing direct data decision-making basis for practical operations such as farmland irrigation scheduling, watershed hydrological regulation, and drought early warning response.

[0051] In the above embodiments, this method can significantly improve the inversion accuracy and temporal continuity of daily-scale actual evapotranspiration by fusing multi-source surface water and heat multi-mode data with water cycle baseline data and introducing a temperature lapse rate constraint correction mechanism. This effectively overcomes the bottleneck of missing observations of polar-orbiting satellite thermal infrared data under cloud and rainy weather, thereby enabling daily dynamic monitoring of farmland water consumption and drought evolution. At the same time, this method can also combine water resource monitoring indicators and management suggestions generated from water cycle baseline data to provide more timely and reliable data support for regional water resource scheduling, drought early warning, and agricultural irrigation decisions, thereby greatly improving the practicality of water resource monitoring results in real-world applications.

[0052] In some embodiments, step S100 may include: S110. Obtain the original multi-modal data within the target area.

[0053] In some embodiments, the raw multi-model data includes near-surface meteorological variables, vertical air temperature profile variables, surface radiation flux variables, and underlying surface drag variables; Near-surface meteorological variables include near-surface air temperature, near-surface air pressure, and near-surface humidity data; The vertical profile variables for air temperature include lower-level air temperature, upper-level air temperature, lower-level altitude, and upper-level altitude; Surface radiation flux variables include daily net radiation and daily soil heat flux; The variables of underlying surface drag include aerodynamic drag and surface drag; Water resource monitoring types include at least one of the following: farmland evapotranspiration monitoring, watershed hydrological simulation, ecological water consumption assessment, drought early warning, regional water resource management, and land-atmosphere interaction analysis. When water resource monitoring types include farmland evapotranspiration monitoring, the water cycle baseline data includes at least one of precipitation, irrigation amount, and field holding capacity. When the water resources monitoring type includes watershed hydrological simulation, the water cycle baseline data includes at least one of watershed precipitation, watershed outlet runoff and watershed storage change. When the water resources monitoring type includes the ecological water consumption assessment type, the water cycle baseline data includes at least one of the ecological water demand quota and the ecological available water limit; When the water resources monitoring type includes drought early warning, the water cycle baseline data includes at least one of potential evapotranspiration, historical average evapotranspiration for the same period, and standard deviation of historical evapotranspiration for the same period. When the water resources monitoring type includes the regional water resources management type, the water cycle baseline data includes at least one of the total available water resources in the region and the water demand of other water users. When the water resources monitoring type includes land-atmosphere interaction analysis, the water cycle baseline data includes at least one of the sensible heat fluxes.

[0054] S120. Perform spatial scale matching processing based on regular spatial grid on the original multi-modal data to obtain the first processed multi-modal data; wherein, the first processed multi-modal data is unified into the same regular spatial grid.

[0055] In this embodiment, the method can perform a spatial consistency check before executing step S120. If the check fails, step S120 is executed. If the check passes, the original multi-modal data can be directly used as the first processed multi-modal data.

[0056] In this embodiment, the method can resample, interpolate, or aggregate near-surface meteorological variables, vertical air temperature profile variables, surface radiation flux variables, and underlying surface resistance variables to unify them into the same regular spatial grid.

[0057] In this embodiment, when unifying the original multi-modal data to the same regular spatial grid, if the spatial resolution of the input data is higher than that of the regular spatial grid, the data falling within the same regular spatial grid are subjected to spatial averaging or area-weighted averaging; if the spatial resolution of the input data is lower than that of the regular spatial grid, the nearest neighbor interpolation, bilinear interpolation, or conservative resampling method is used to match it to the regular spatial grid.

[0058] S130. Perform time-scale matching processing based on the daily scale on the first-processed multi-mode data to obtain the second-processed multi-mode data.

[0059] In this embodiment, the method can perform a time consistency check before executing step S130. If the check fails, step S130 is executed. If the check passes, the first processed multi-mode data can be directly used as the second processed multi-mode data.

[0060] In this embodiment, the method can match near-surface air temperature, near-surface air pressure, near-surface humidity data, lower-level air temperature, upper-level air temperature, daily net radiation, daily soil heat flux, aerodynamic drag, and surface drag to the same daily scale. Specifically, near-surface air temperature, lower-level air temperature, and upper-level air temperature are taken at the same time or within the same daily average window, and lower-level and upper-level altitudes use the same altitude reference.

[0061] In this embodiment, when the time resolution of different input data is inconsistent, the high time resolution data is averaged or accumulated to the daily scale, and the low time resolution data is interpolated or maintained to the daily scale.

[0062] S140. Perform mathematical unit normalization on the second-processed multi-modal data to obtain the third-processed multi-modal data.

[0063] In this embodiment, the method can standardize the units of the input data. Specifically, the units for near-surface air temperature, lower-level air temperature, and upper-level air temperature are standardized to K; the units for lower-level and upper-level altitudes are standardized to m; the units for near-surface air pressure are standardized to Pa; the units for saturated vapor pressure and actual vapor pressure are standardized to Pa; and the units for daily net radiation and daily soil heat flux are standardized to W·m³. -2 The unified unit for aerodynamic drag and surface drag is s·m. -1 .

[0064] S150. Perform missing test processing on the third-processed multi-mode data to obtain preprocessed multi-mode data.

[0065] In this embodiment, the method can perform missing test processing on the third-processed multi-modal data (i.e., input data).

[0066] In this embodiment, the method can use near-surface air temperature, near-surface air pressure, near-surface humidity data, lower-level air temperature, upper-level air temperature, lower-level height, and upper-level height as key input parameters, and use daily net radiation, daily soil heat flux, aerodynamic drag, and surface drag as evapotranspiration driving input parameters. For key input parameters whose missing time is less than or equal to the preset continuous missing threshold, linear interpolation of neighboring days within the same rule space grid is used to complete the missing data. For key input parameters that are missing in local spatial measurements at the same diurnal scale, interpolation using nearby regular spatial grids or spatial averages under the same climatic background are used to complete the measurements. For key input parameters whose missing time exceeds the preset continuous missing threshold and cannot be filled, the corresponding rule space grid or the corresponding date is removed. For missing values ​​of evapotranspiration-driven input parameters, the missing values ​​are filled by the average value of neighboring regular spatial grids within the same daily scale, the average value of neighboring daily grids within the same regular spatial scale, or preset model parameters. After the missing data processing is completed, a missing data processing identifier is generated. The missing data processing identifier is used to record the original valid data, temporal interpolation data, spatial interpolation data, model completion data, or data that has been removed. When the vertical profile variable of air temperature does not have a clear height coordinate, or when the height of the lower and upper floors cannot be unified to the same height reference, the corresponding rule space grid or the corresponding date is removed.

[0067] S160. Extract surface water and heat multi-model data from the preprocessed multi-model data, and obtain water cycle baseline data within the target area according to the preset water resource monitoring type.

[0068] In this embodiment, the method can, after the above-mentioned spatial scale matching, temporal scale matching, unit unification and missing measurement processing, extract surface water and heat multi-model data from preprocessed multi-model data, and together with water cycle reference data, form a meteorological-thermal-evapotranspiration multi-model dataset with regular spatial grids as the calculation units.

[0069] In the above embodiments, the method can effectively eliminate the heterogeneity of multi-source data in terms of spatial resolution, temporal frequency, unit of measurement, and data missingness, and ensure a high degree of consistency between surface water and heat multi-model data and model input requirements. This provides a standardized data foundation with controllable quality, spatiotemporal uniformity, and physical dimension coordination for subsequent evapotranspiration inversion, thereby significantly improving the stability of model calculation and the reliability of inversion results.

[0070] In some embodiments, step S200 may include: S210. Calculate the saturated vapor pressure based on the near-surface air temperature.

[0071] In this embodiment, the saturated water vapor pressure satisfies the following relationship: e s =0.6108 exp(17.27 T c / (T c +237.3)); Among them, e s For saturated water vapor pressure, T c The value represents the near-surface air temperature in Celsius. When the saturated vapor pressure obtained from the above formula is in kPa, it should be converted to Pa before being used in subsequent calculations.

[0072] S220. Calculate the actual water vapor pressure based on near-surface humidity data and saturated water vapor pressure; whereby near-surface humidity data includes relative humidity.

[0073] In this embodiment, the actual water vapor pressure satisfies the following relationship: e a =e s RH / 100; Among them, e a e represents the actual water vapor pressure. s RH represents saturated water vapor pressure and relative humidity.

[0074] S230. Calculate the original vapor pressure deficit based on the saturated vapor pressure and the actual vapor pressure.

[0075] In this embodiment, the method calculates the difference between the saturated vapor pressure and the actual vapor pressure to obtain the original vapor pressure deficit. The original vapor pressure deficit satisfies the following relationship: VPD0=es -e a ; Where VPD0 is the original water vapor pressure deficit, e s e is the saturated vapor pressure. a This is the actual water vapor pressure.

[0076] In this embodiment, the original vapor pressure deficit is used to characterize the difference between the near-surface air vapor state and the saturated vapor state on a diurnal scale.

[0077] S240. Calculate the air density based on the near-surface air temperature and near-surface air pressure; where the air density is either the wet air density or the dry air density.

[0078] In this embodiment, before calculating the water vapor pressure deficit, the saturated water vapor pressure, actual water vapor pressure, and near-surface air pressure are all calculated using Pa as the unit of measurement.

[0079] To ensure that calculations can be completed under different meteorological observation levels, step S240 may include: S241. When the air density is the same as that of moist air, calculate the air density based on the near-surface air temperature, near-surface air pressure, actual water vapor pressure, preset dry air gas constant, and preset water vapor gas constant.

[0080] In this embodiment, the density of moist air satisfies the following relationship: ρ =((pe a ) / R d T)+(e a / R v T) in, ρ where p is the air density and e is the near-surface air pressure. a R is the actual water vapor pressure. d R is the gas constant for dry air. v Let T be the water vapor gas constant, and T be the Kelvin temperature value of the near-surface air temperature.

[0081] S242. When the air density is the dry air density, calculate the air density based on the near-surface air temperature, near-surface air pressure, and the preset dry air gas constant.

[0082] In this embodiment, the approximate density of dry air satisfies the following relationship: ρ =p / (R d T); in, ρ R is the air density, p is the near-surface air pressure, and R is the air density. d Let T be the gas constant for dry air, and T be the Kelvin temperature value of the near-surface air temperature.

[0083] In this embodiment, when the actual water vapor pressure or the parameters required for calculating the humid air density are missing, the dry air density is used as an approximate density for calculation; otherwise, the humid air density is used.

[0084] S250, obtain the reference temperature lapse rate and the molar latent heat of vaporization of water vapor in the target area.

[0085] In this embodiment, the reference temperature lapse rate is denoted as Γ0, which refers to the temperature lapse rate corresponding to the vertical thermal state of the atmosphere under the reference state. Γ0 is determined based on the historical temperature vertical structure, climatological temperature vertical structure, or preset standard temperature lapse rate of the target area. Specifically, it can be any one of the following: the multi-year average temperature lapse rate of the target area, the climatological temperature lapse rate of the same month, the historical average temperature lapse rate of the target area, or the preset standard temperature lapse rate.

[0086] In this embodiment, the reference temperature decay rate is preferably the multi-year average temperature decay rate of the same regular spatial grid and the same month; when the multi-year average temperature decay rate is not available, the historical average temperature decay rate of the target area or the preset standard temperature decay rate can be used.

[0087] S260. Calculate the current temperature decay rate based on the lower-level air temperature, upper-level air temperature, lower-level height, and upper-level height.

[0088] In this embodiment, the low-level air temperature and the high-level air temperature are respectively taken from two preset height layers within the same regular spatial grid, wherein the low-level height and the high-level height are the height coordinates of the corresponding preset height layers.

[0089] In this embodiment, the lower floor height and the upper floor height use the same height reference, and the lower floor height is lower than the upper floor height.

[0090] In this embodiment, the current temperature decay rate satisfies the following relationship: Γ = -(T2 - T1) / (z2 - z1); Where Γ is the current temperature decay rate, T1 is the lower-level air temperature, T2 is the upper-level air temperature, z1 is the lower-level altitude, and z2 is the upper-level altitude.

[0091] In this embodiment, the current temperature lapse rate is uniformly calculated using K / m. When the input temperature lapse rate is in K / km, it is converted to K / m before being used in subsequent calculations. When z2-z1 is less than or equal to a preset height difference threshold, the corresponding regular spatial grid or the corresponding date is removed. When Γ is less than or equal to 0, the corresponding regular spatial grid or the corresponding date is marked as an inversion or isothermal condition and removed or output separately according to preset quality control rules.

[0092] S270. Based on the current temperature lapse rate, the molar latent heat of vaporization of water vapor, and the near-surface air temperature, construct the current evaporation force conversion coefficient.

[0093] In this embodiment, the current evaporation force conversion factor is used to characterize the effective difference between the vertical decay intensity of water vapor phase change and the vertical decay intensity of water vapor gravity under the current vertical thermal structure of the lower atmosphere.

[0094] In this embodiment, the current evaporation force conversion coefficient is not an empirical parameter, but is constructed based on the vertical decay scale under water vapor phase change control, the vertical decay scale under water vapor gravity control, and the air gravity scale.

[0095] In order to allow the current temperature lapse rate to be directly used in the correction calculation for the vapor pressure deficit, step S270 may further include: S271. Based on the current temperature lapse rate, the molar latent heat of vaporization of water vapor, and the near-surface air temperature, calculate the vertical scale of water vapor phase change under the current temperature lapse rate condition.

[0096] In this embodiment, the vertical scale of the water vapor phase change satisfies the following relationship: ; Among them, h c The vertical scale of water vapor phase change under the current temperature lapse rate is expressed in meters. R is the universal gas constant, with units of J·mol⁻¹. -1 ·K -1 The value is 8.314; T is the Kelvin temperature value of the near-surface air temperature, expressed in absolute temperature, with the unit being K. Γ represents the current temperature lapse rate, in K·m. -1 ; J·mol represents the molar latent heat of vaporization of water vapor, which is the latent heat of vaporization required for one mole of water to change from a liquid state to water vapor. -1 Its value is determined based on the near-surface air temperature on a daily scale, or by using a preset constant value, with a range of 40.0. 10 3 Up to 45.1 10 3 .

[0097] S272. Calculate the air gravity scale based on the preset air molar mass, preset universal gas constant, near-ground air temperature, and preset gravitational acceleration.

[0098] In this embodiment, the air gravity scale satisfies the following relationship: h a =(R T) / (Ma g); Among them, h a This is a scale for air gravity, measured in meters (m). R is the universal gas constant, with units of J·mol⁻¹. -1 ·K -1 The value is 8.314; T is the Kelvin temperature value of the near-surface air temperature, expressed in absolute temperature, with the unit being K. M a This refers to the molar mass of air, expressed in kg·mol⁻¹. -1 The value is 28.97. 10 -3 ; g is the acceleration due to gravity, and its unit is m·s². -2 The value is 9.80665.

[0099] S273. Calculate the water vapor gravity scale based on the preset water vapor molar mass, universal gas constant, near-surface air temperature, and gravitational acceleration.

[0100] In this embodiment, the water vapor gravity scale satisfies the following relationship: h v =(R T) / (M v g); Among them, h v This is a water vapor gravity scale, with units in meters (m). R is the universal gas constant, with units of J·mol⁻¹. -1 ·K -1 The value is 8.314; T is the Kelvin temperature value of the near-surface air temperature, expressed in absolute temperature, with the unit being K. M v The molar mass of water vapor is expressed in kg·mol⁻¹. -1 The value is 18.015. 10 -3 ; g is the acceleration due to gravity, and its unit is m·s². -2 The value is 9.80665.

[0101] S274. Based on the vertical scale of water vapor phase change and the gravity scale of water vapor, calculate the relative attenuation intensity per unit height under the control of water vapor phase change and the relative attenuation intensity per unit height under the control of water vapor gravity.

[0102] In this embodiment, the relative attenuation intensity per unit height under water vapor phase change control satisfies the following relationship: S c =1 / hc ; Among them, S c The relative attenuation intensity per unit height under water vapor phase change control; h c The vertical scale of water vapor phase change under the current temperature lapse rate condition.

[0103] In this embodiment, the relative attenuation intensity per unit height under water vapor gravity control satisfies the following relationship: S v =1 / h v ; Among them, S v The relative attenuation intensity per unit height under the control of water vapor gravity; h v It is a water vapor gravity scale.

[0104] S275. Calculate the difference between the relative attenuation intensity per unit height under water vapor phase change control and the relative attenuation intensity per unit height under water vapor gravity control to obtain the water vapor vertical attenuation difference term under the current temperature lapse rate.

[0105] In this embodiment, the difference in vertical water vapor attenuation satisfies the following relationship: S Γ =S c -S v =1 / h c -1 / h v ; S Γ This is the difference in vertical water vapor decay under the current temperature lapse rate, used to characterize the difference between water vapor phase change decay controlled by the temperature lapse rate and water vapor decay due to gravity. S c The relative attenuation intensity per unit height under water vapor phase change control; h c The vertical scale of water vapor phase change under the current temperature lapse rate condition; S v The relative attenuation intensity per unit height under the control of water vapor gravity; h v It is a water vapor gravity scale.

[0106] S276. The vertical attenuation difference term of water vapor is dimensionlessly processed according to the air gravity scale to obtain the current evaporation force conversion coefficient.

[0107] In this embodiment, the water vapor molar mass ratio parameter satisfies the following relationship: β v =M v / M a ; Where, β vM is the water vapor molar mass ratio parameter. v M is the molar mass of water vapor. a This is the molar mass of air.

[0108] In this embodiment, the current evaporation force conversion factor satisfies the following relationship: ; Among them, C Γ This is the conversion factor of the current evaporation force under the current temperature lapse rate, used to characterize the effective difference between the vertical decay intensity of water vapor phase change and the vertical decay intensity of water vapor gravity under the current vertical thermal structure of the lower atmosphere. β v This is the water vapor molar mass ratio parameter; h a Measured by atmospheric gravity; S Γ This represents the difference in vertical water vapor decay under the current temperature lapse rate. S c The relative attenuation intensity per unit height under water vapor phase change control; S v The relative attenuation intensity per unit height under the control of water vapor gravity; h c The vertical scale of water vapor phase change under the current temperature lapse rate condition; h v Water vapor gravity scale M a The molar mass of air; g is the acceleration due to gravity; T is the Kelvin temperature value of the near-surface air temperature; Γ represents the current temperature lapse rate; It is the latent heat of vaporization of water vapor.

[0109] S280. Based on the reference temperature lapse rate, the molar latent heat of vaporization of water vapor, and the near-surface air temperature, a reference evaporation force conversion factor is constructed.

[0110] In this embodiment, the reference evaporation force conversion factor satisfies the following relationship: ; C0 is the reference evaporation force conversion factor under reference conditions; Γ0 represents the reference temperature lapse rate; The latent heat of vaporization of water vapor; M a The molar mass of air; g is the acceleration due to gravity; T is the Kelvin temperature value of the near-surface air temperature; β v This is the water vapor molar mass ratio parameter.

[0111] S290. Calculate the temperature lapse rate constraint correction factor based on the current evaporation force conversion factor and the reference evaporation force conversion factor.

[0112] In this embodiment, the method can be based on the current evaporation force conversion factor C under the current temperature lapse rate state. Γ The temperature lapse rate constraint correction factor K is determined by using the reference evaporation force conversion factor C0 under the reference condition. Γ The formula is determined as follows: K Γ =C Γ / C0; Among them, K Γ This is a temperature lapse rate constraint correction coefficient, used to characterize the enhancement or weakening effect of the current lower atmospheric vertical thermal structure on the original water vapor pressure deficit relative to the reference state. C0 is the reference evaporation force conversion factor under reference conditions; C Γ This is the conversion factor for the current evaporation force under the current temperature lapse rate.

[0113] In this embodiment, when C Γ If the absolute value of C0 is less than or equal to a preset threshold, the corresponding rule space grid or the corresponding date will be removed, or the corresponding rule space grid or the corresponding date will be output separately.

[0114] In the above embodiments, the method can calculate saturated water vapor pressure, actual water vapor pressure and air density based on near-surface meteorological elements, and invert the current temperature lapse rate by combining vertical profile data. It further constructs two conversion coefficients of current and reference evaporation forces to obtain temperature lapse rate constraint correction coefficients, thereby realizing a quantitative characterization of the differences in atmospheric vertical thermal structure.

[0115] For example, in order to illustrate the differentiated response capability of this application compared with the traditional day-scale Penman-Monteith method, the following reproducible comparative example is set up.

[0116] Assume that the near-surface air temperature, humidity, air pressure, daily net radiation, daily soil heat flux, aerodynamic drag, and surface drag remain constant for a certain regular spatial grid on a certain date.

[0117] The calculation parameters are set as follows: The near-surface air temperature is T = 298.15 K; The initial vapor pressure deficit VPD0 = 2000 Pa; Daily effective energy term = Daily net radiation - Daily soil heat flux; Daily net radiation - Daily soil heat flux = R n -G=120W·m -2 ; air density ρ =1.18kg·m -3 ; specific heat of air at constant pressure c p =1004 J·kg -1 ·K -1 ; Aerodynamic drag r a =100s·m -1 ; Surface resistance r s =70s·m -1 ; The slope of the saturated water vapor pressure curve is s = 188.7 Pa·K. -1 ; Wet and dry surface constant γ p =66Pa·K -1 ; The latent heat of water vaporization λ = 2.45 10 6 J·kg -1 ; Molar latent heat of vaporization of water vapor =44.0 10 3 J·mol -1 ; air molar mass M a =28.97 10 -3 kg·mol -1 ; Molar mass of water vapor M v =18.015 10 -3 kg·mol -1 ; The acceleration due to gravity is g = 9.80665 m / s². -2 ; Reference temperature lapse rate Γ0 = 6.5 K·km -1 .

[0118] In the traditional diurnal Penman-Monteith method, the original vapor pressure deficit (VPD0) is directly used as the input for the aerodynamic term. Under the above conditions, the actual diurnal evapotranspiration calculated by the traditional method is: ET PM =5.43mm·d -1 .

[0119] In this method, if the current temperature lapse rate is Γ = 8.0 K·km -1 Then, based on the ratio of the current evaporation force conversion factor under the current temperature lapse rate state to the reference evaporation force conversion factor under the reference state, we obtain: K Γ =C Γ / C0=1.283; Therefore, the temperature lapse rate constrained vapor pressure deficit is: VPD Γ =1.283 VPD0 = 2565.7 Pa; VPD Γ Substituting into the day-scale Penman-Monteith model, we obtain: ET a =6.22mm·d -1 .

[0120] In this method, if the current temperature lapse rate is Γ = 4.0 K·km -1 Then we get: K Γ =C Γ / C0=0.529; Therefore, the temperature lapse rate constrained vapor pressure deficit is: VPD Γ =0.529 VPD0 = 1057.1 Pa; VPD Γ Substituting into the day-scale Penman-Monteith model, we obtain: ET a =4.12mm·d -1 .

[0121] The comparative examples above show that, under the condition that the near-surface air temperature, humidity, air pressure, daily net radiation, daily soil heat flux, aerodynamic drag, and surface drag remain consistent, the traditional daily-scale Penman-Monteith method, due to its direct use of the original vapor pressure deficit, cannot respond to changes in the current temperature lapse rate. In contrast, this method can constrain and correct the vapor pressure deficit entering the aerodynamic term based on the difference between the current temperature lapse rate and the reference temperature lapse rate, thereby enabling the daily-scale actual evapotranspiration calculation results to respond to changes in the vertical thermal structure of the lower atmosphere.

[0122] When the current temperature lapse rate is less than or equal to 0, corresponding to inversion or isothermal conditions, this method does not directly include these conditions as normal stratification states in the actual diurnal evapotranspiration calculation. Instead, it marks the corresponding regular spatial grid or the corresponding date as an anomalous stratification condition and discards or outputs them separately according to preset quality control rules. Based on this, this processing can avoid mechanically applying the traditional original vapor pressure deficit for evapotranspiration calculation under inversion or isothermal conditions.

[0123] It should be noted that the numerical examples described above are used to illustrate the differentiated response capability of this application to the vertical thermal structure of the lower atmosphere compared to traditional methods, and are not directly equivalent to the conclusion of improved accuracy. When observational or verification data such as flux towers, lysimeters, or regional evapotranspiration inversion products are available, indicators such as Bias, MAE, and RMSE can be further used to evaluate the errors in the calculation results of the traditional diurnal Penman-Monteith method and this method.

[0124] The error evaluation index can take the following form: ; ; ; Among them, ET cal,i To calculate the actual daily evapotranspiration, ET obs,i n represents the daily evapotranspiration corresponding to the observed or verified data, where n is the sample size. Bias measures the direction and degree of systematic deviation of the calculated result from the true value. A positive bias indicates that the calculated result is systematically too large, and a negative bias indicates that the calculated result is systematically too small. The closer the absolute value of the bias is to zero, the smaller the systematic error of the calculated result. MAE (Mean Absolute Error) measures the average absolute deviation of a calculated result from the true value; the closer the MAE is to zero, the higher the overall accuracy of the calculated result. RMSE (Root Mean Square Error) measures the overall dispersion of the calculated result relative to the true value. It is more sensitive to large errors than the mean absolute error. The closer the RMSE is to zero, the smaller the overall error of the calculated result.

[0125] Based on this, since the error evaluation indicators Bias, MAE, and RMSE are standard formulas, no further explanation or description will be provided here.

[0126] Therefore, by calculating the Bias, MAE, and RMSE corresponding to the traditional diurnal Penman-Monteith method and this method respectively, the impact of the temperature lapse rate constraint correction on the evapotranspiration calculation results can be further evaluated.

[0127] In some embodiments, the method generates water resource monitoring results corresponding to each type by combining daily-scale actual evapotranspiration with corresponding water cycle baseline data, based on different water resource monitoring types.

[0128] For example, when the water resource monitoring type is farmland evapotranspiration monitoring, the water cycle baseline data includes precipitation, irrigation amount, soil moisture content, and field capacity. Based on this, the method may include: The difference between the sum of precipitation and irrigation and the actual evapotranspiration is the daily water surplus / deficit of farmland. Among them, the water resources monitoring indicators under the water resources monitoring type include daily water surplus / deficit of farmland, soil moisture content and field capacity. When the daily water deficit in farmland is greater than zero and the soil moisture content is lower than the field capacity, it indicates that the farmland's water income exceeds its expenditure, and the soil moisture is replenished, generating information on improved soil moisture. When the daily water deficit in farmland is less than zero and the soil moisture content is lower than the field capacity, it indicates that the farmland's water expenditure exceeds its income, and the soil moisture is continuously consumed, generating information on the cumulative risk of drought. Based on information on soil moisture improvement and water resource monitoring indicators under different water resource monitoring types, recommendations for raw water resource management are provided.

[0129] Among them, the water resource management recommendations include: when the daily water deficit of farmland is continuously negative and the cumulative deficit exceeds the preset deficit threshold, irrigation recommendations are generated; when the daily water deficit of farmland is continuously positive and the soil moisture content exceeds the field capacity, drainage recommendations or waterlogging warning information are generated.

[0130] For example, when the water resources monitoring type is watershed hydrological simulation, the water cycle baseline data includes watershed precipitation, watershed outlet runoff, and changes in watershed storage. Based on this, the method may include: Subtracting the basin outlet runoff from the basin precipitation and then subtracting the change in basin water storage yields the basin evapotranspiration calculated based on water balance. The difference between the actual daily evapotranspiration and the basin evapotranspiration calculated based on water balance is the basin evapotranspiration simulation deviation. Among them, the water resources monitoring indicators under the basin hydrological simulation type include the basin evapotranspiration simulation deviation and the change in basin water storage. When the absolute value of the deviation in the basin evapotranspiration simulation is less than or equal to the preset deviation threshold, it is determined that the actual daily evapotranspiration is consistent with the closure result of the basin water balance, and the reliability confirmation information of the basin hydrological simulation is generated. When the absolute value of the deviation in the watershed evapotranspiration simulation is greater than the preset deviation threshold, it is determined that the watershed water balance is not closed or there is a deviation in the evapotranspiration simulation, and a model parameter calibration prompt message is generated. Meanwhile, based on the simulation deviation of watershed evapotranspiration and the change in watershed water storage, watershed water resource management recommendations are generated.

[0131] Among them, the water resources management recommendations include: when the deviation of the basin evapotranspiration simulation is consistently large and the change in basin water storage is negative, a risk warning of basin runoff attenuation will be generated; when the deviation of the basin evapotranspiration simulation is consistently small, a warning of underestimation of basin evapotranspiration or abnormal baseflow recharge will be generated.

[0132] For example, when the water resource monitoring type is ecological water consumption assessment, the water cycle baseline data includes ecological water demand quotas and ecological available water limits. Based on this, the method may include: The ratio obtained by dividing the actual daily evapotranspiration by the ecological water demand quota is the ecological water consumption satisfaction rate. The ecological water consumption pressure index is obtained by dividing the actual daily evapotranspiration by the ecological available water quota and converting it into water depth equivalent. Among them, the water resource monitoring indicators under the ecological water consumption assessment type include the ecological water consumption pressure index and the ecological water consumption satisfaction. Ecological water demand information is generated based on the ecological water consumption satisfaction level; wherein, the ecological water demand information is either information on the suitability of ecosystem water conditions or information on ecological water deficit, specifically: When the ecological water consumption satisfaction level is greater than or equal to the preset satisfaction level threshold, it indicates that the actual evapotranspiration meets the ecological water demand requirements, and information on suitable ecosystem water conditions is generated; when the ecological water consumption satisfaction level is less than the preset satisfaction level threshold, it indicates that the actual evapotranspiration is lower than the ecological water demand quota, and information on ecological water deficit is generated. When the ecological water consumption pressure index is greater than the preset pressure index threshold, it indicates that the actual evapotranspiration exceeds the ecological available water limit, and an ecological water use over-limit warning is generated; where the preset satisfaction threshold can be 1 and the preset pressure index threshold can be 1. Based on the ecological water consumption pressure index, ecological water consumption satisfaction, and ecological water demand information, water resource management recommendations are generated.

[0133] Among them, water resource management recommendations include: when there is an ecological water deficit and the ecological water consumption pressure index exceeds the preset pressure index threshold, generating ecological water replenishment recommendations or ecological water allocation recommendations; when an ecological water use exceeding the limit warning is triggered, generating recommendations to restrict ecological water use or adjust the ecological water demand quota.

[0134] For example, when the water resource monitoring type is drought early warning, the water cycle baseline data includes potential evapotranspiration, historical average evapotranspiration for the same period, and the standard deviation of historical evapotranspiration for the same period. Based on this, the method may include: The evapotranspiration drought index is obtained by subtracting the potential evapotranspiration from the actual daily evapotranspiration and dividing the difference by the standard deviation of evapotranspiration. The standard deviation of evapotranspiration is the standard deviation of daily evapotranspiration for multiple years in the same historical period. The difference between the actual daily evapotranspiration and the historical average evapotranspiration is divided by the historical average evapotranspiration and then multiplied by 100%. The resulting percentage is the evapotranspiration anomaly percentage. Among them, the water resource monitoring indicators under the ecological water consumption assessment type include the evapotranspiration drought index and the evapotranspiration anomaly percentage. Drought status is determined based on the evapotranspiration drought index and evapotranspiration anomaly percentage; among which, drought status types include drought-free status, mild drought status, moderate drought status, severe drought status, or extreme drought status, specifically: When the evapotranspiration drought index is greater than the first drought index threshold and the evapotranspiration anomaly percentage is greater than the first anomaly percentage threshold, it is determined to be a drought-free state. When the evapotranspiration drought index is greater than the second drought index threshold and less than or equal to the first drought index threshold, and the evapotranspiration anomaly percentage is greater than the second anomaly percentage threshold and less than or equal to the first anomaly percentage threshold, it is judged as a mild drought state. When the evapotranspiration drought index is greater than the third drought index threshold and less than or equal to the second drought index threshold, and the evapotranspiration anomaly percentage is greater than the third anomaly percentage threshold and less than or equal to the second anomaly percentage threshold, it is judged as a moderate drought state. When the evapotranspiration drought index is greater than the fourth drought index threshold and less than or equal to the third drought index threshold, and the evapotranspiration anomaly percentage is greater than the fourth anomaly percentage threshold and less than or equal to the third anomaly percentage threshold, it is judged as a severe drought state. When the evapotranspiration drought index is less than or equal to the fourth drought index threshold, and the evapotranspiration anomaly percentage is less than or equal to the fourth anomaly percentage threshold, it is determined to be a state of extreme drought.

[0135] Based on the water resource monitoring indicators and drought status type under the ecological water consumption assessment type, water resource management recommendations are generated.

[0136] The water resource management recommendations include: generating drought alerts when the drought is classified as mild; generating drought warnings and recommending the implementation of water-saving measures when the drought is classified as moderate; and generating drought emergency response recommendations when the drought is classified as severe or extreme, including at least one of activating backup water sources, adjusting planting structures, and implementing water restriction measures.

[0137] The drought index thresholds are as follows: the first drought index threshold is greater than the second drought index threshold, the second drought index threshold is greater than the third drought index threshold, and the third drought index threshold is greater than the fourth drought index threshold. The anomaly percentage thresholds decrease sequentially from first to fourth, meaning the first anomaly percentage threshold is greater than the second, the second is greater than the third, and the third is greater than the fourth. All thresholds are negative; the smaller the value, the more severe the drought.

[0138] Specifically, the first drought index threshold can be -0.5; the second drought index threshold can be -1.0; the third drought index threshold can be -1.5; and the fourth drought index threshold can be -2.0. The first anomaly percentage threshold can be -10%; the second anomaly percentage threshold can be -20%; the third anomaly percentage threshold can be -30%; and the fourth anomaly percentage threshold can be -40%. The above specific values ​​are only a preferred embodiment of this example. In practical applications, the above thresholds can be adaptively adjusted according to the climate characteristics of the target area, historical drought frequency, and monitoring sensitivity requirements.

[0139] For example, in humid regions, the threshold values ​​for each drought index can be appropriately lowered to reduce false alarms, while in arid regions, the threshold values ​​for each drought index can be appropriately raised to improve the sensitivity of early warnings.

[0140] For example, when the water resource monitoring type is regional water resource management, the water cycle baseline data includes the total available water resources in the region and the water demand of other water users. Based on this, the method may include: Multiply the actual daily evapotranspiration by the area of ​​the region and then by one-thousandth to get the total daily ecological water consumption of the region. The ratio obtained by dividing the total available water resources in the region by the sum of the total daily ecological water consumption in the region and the water demand of other water users is the regional water supply and demand balance index; among them, the water resource monitoring indicators under the regional water resource management type include the regional water supply and demand balance index. The water supply and demand status is determined based on the regional water supply and demand balance index; among which, the water supply and demand status includes a state of abundant water supply, a state of balanced water supply and demand, a state of tight water supply, or a state of severe water shortage, specifically: When the regional water supply and demand balance index is greater than or equal to the first supply and demand balance index threshold, it is determined to be a state of abundant water supply. When the regional water resources supply and demand balance index is greater than or equal to the second supply and demand balance index threshold and less than the first supply and demand balance index threshold, it is determined to be a water resources supply and demand balance state. When the regional water supply and demand balance index is greater than or equal to the third supply and demand balance index threshold and less than the second supply and demand balance index threshold, it is determined to be a state of water supply shortage. When the regional water supply and demand balance index is less than the third supply and demand balance index threshold, it is judged to be a state of severe water supply shortage. The first supply-demand balance index threshold is higher than the second supply-demand balance index threshold, and the second supply-demand balance index threshold is higher than the third supply-demand balance index threshold. All thresholds are positive numbers; the larger the value, the more abundant the regional water resource supply, and the smaller the value, the tighter the water resource supply. Specifically, the first supply-demand balance index threshold can be 1.2; the second supply-demand balance index threshold can be 0.9; and the third supply-demand balance index threshold can be 0.7. These specific values ​​are merely a preferred embodiment of this practice. In practical applications, the thresholds can be adaptively adjusted according to the water resource endowment, water supply guarantee rate, and monitoring sensitivity requirements of the target area. For example, in water-rich areas, the thresholds for each supply-demand balance index can be appropriately lowered to reduce false alarms, while in water-scarce areas, the thresholds can be appropriately raised to improve early warning sensitivity. Based on the regional water supply and demand balance index and the water supply and demand status, water resource management recommendations are generated.

[0141] The water resource management recommendations include: when the water supply is determined to be tight, generating recommendations for optimizing water use structure and water-saving measures; when the water supply is determined to be severely insufficient, generating at least one of the following: recommendations for water resource allocation, recommendations for restricting water use in high water-consuming industries, and recommendations for activating emergency water supply plans.

[0142] For example, when the water resource monitoring type is land-atmosphere interaction analysis, the water cycle baseline data includes sensible heat flux. Based on this, the method may include: Multiply the actual daily evapotranspiration by the latent heat of water vaporization, and the product is the daily latent heat flux. Dividing the sensible heat flux by the latent heat flux yields the diurnal Bovn ratio; among them, the water resource monitoring index under the land-atmosphere interaction analysis type includes the diurnal Bovn ratio. The current moisture level is determined based on the diurnal scale Bowen ratio; wherein, the current moisture level includes a wet state, a moderately wet state, a moderately dry state, or a dry state, specifically: When the daily scale Bovn ratio is less than the first Bovn ratio threshold, it is determined to be a wet state, and the surface energy is mainly exchanged by latent heat. When the daily Bovn ratio is greater than or equal to the first Bovn ratio threshold and less than the second Bovn ratio threshold, it is judged as a moderately humid state, with both latent heat and sensible heat exchange being relatively active. When the daily scale Bowen ratio is greater than or equal to the second Bowen ratio threshold and less than the third Bowen ratio threshold, it is judged as a moderate drought state, and the surface energy is mainly sensible heat exchange. When the daily scale Bowen ratio is greater than or equal to the third Bowen ratio threshold, it is determined to be a drought state, in which sensible heat exchange is absolutely dominant. Among them, the first wave Von der ... Specifically, the first Bowen ratio threshold can be 0.5; the second Bowen ratio threshold can be 1.0; and the third Bowen ratio threshold can be 2.0. These specific values ​​are merely a preferred embodiment of this practice. In practical applications, the thresholds can be adaptively adjusted according to the underlying surface type, climate background, and monitoring sensitivity requirements of the target area. For example, in areas with high forest coverage and high evaporation, the Bowen ratio thresholds can be appropriately increased, while in desert or semi-desert areas, they can be appropriately decreased. Based on the diurnal wave ratio and current moisture level, recommendations for land-atmosphere interaction analysis are generated.

[0143] Among them, the land-atmosphere interaction analysis recommendations include: when the state is determined to be humid or moderately humid, the analysis conclusion is that the land-atmosphere water and heat exchange is active and the evaporative cooling effect is significant; when the state is determined to be moderately arid or arid, the analysis conclusion is that the land-atmosphere water and heat exchange is limited and the surface heating effect is significant.

[0144] like Figure 2 As shown, some embodiments of this application provide a structural schematic diagram of a water resource monitoring device based on evapotranspiration. It should be understood that this device is related to... Figure 1 The method executed in the middle corresponds to the steps involved in the aforementioned method. The specific functions and effects of the device can be found in the description above. To avoid repetition, detailed descriptions are omitted here.

[0145] The water resource monitoring device based on evapotranspiration includes: The acquisition unit 610 is used to acquire surface hydrothermal multi-model data and water cycle baseline data within the target area. The surface hydrothermal multi-model data includes target key parameters and target evapotranspiration driving parameters. The target key parameters include near-surface air temperature, near-surface air pressure, near-surface humidity, lower-level air temperature, upper-level air temperature, lower-level height, and upper-level height. The target evapotranspiration driving parameters include daily net radiation, daily soil heat flux, aerodynamic drag, and surface drag. The first calculation unit 620 is used to calculate the original vapor pressure deficit, air density, and temperature lapse rate constraint correction coefficients based on the target key parameters. The correction unit 630 is used to correct the original vapor pressure deficit according to the temperature lapse rate constraint correction coefficient to obtain the temperature lapse rate constrained vapor pressure deficit. The second calculation unit 640 is used to calculate the actual daily evapotranspiration of the target area by calling the preset daily-scale Penman-Monteith model, with air density, temperature lapse rate constrained water vapor pressure deficit and target evapotranspiration driving parameters as inputs. The generation unit 650 is used to generate water resource monitoring results based on water cycle baseline data and daily-scale actual evapotranspiration; wherein, the water resource monitoring results include water resource monitoring indicators and water resource management recommendations that match the water resource monitoring indicators.

[0146] In this embodiment, the specific execution logic of each unit corresponds one-to-one with steps S100 to S500 in the aforementioned method embodiment. The sub-unit division and functional implementation of each unit have been described in detail in the aforementioned method embodiment, so they will not be repeated here.

[0147] like Figure 3 As shown, this application provides an electronic device 700, which includes a processor 701 and a memory 702. The processor 701 and the memory 702 are interconnected and communicate with each other through a communication bus 703 and / or other forms of connection mechanism (not shown). The memory 702 stores a computer program that can be executed by the processor 701. When the computing device is running, the processor 701 executes the computer program to perform the method in any of the aforementioned optional implementations.

[0148] This application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the method in any of the aforementioned optional implementations.

[0149] The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0150] This application provides a computer program product, which includes a computer program that, when run by a processor, executes the method in any of the aforementioned optional implementations.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application. In particular, as long as there is no conflict, the various technical features mentioned in the embodiments can be combined in any way. This application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A water resource monitoring method based on evapotranspiration, characterized by, include: Acquire surface hydrothermal multi-model data and water cycle baseline data within the target area; wherein, the surface hydrothermal multi-model data includes target key parameters and target evapotranspiration driving parameters; the target key parameters include near-surface air temperature, near-surface air pressure, near-surface humidity data, lower-level air temperature, upper-level air temperature, lower-level height, and upper-level height; the target evapotranspiration driving parameters include daily net radiation, daily soil heat flux, aerodynamic drag, and surface drag; Calculate the original vapor pressure deficit, air density, and temperature lapse rate constraint correction coefficients based on the target key parameters. The original vapor pressure deficit is corrected according to the temperature lapse rate constraint correction coefficient to obtain the temperature lapse rate constrained vapor pressure deficit. Using the air density, temperature lapse rate constrained by water vapor pressure deficit, and the target evapotranspiration driving parameters as inputs, the preset diurnal-scale Penman-Monteith model is invoked to calculate the actual diurnal-scale evapotranspiration of the target region. Based on the water cycle baseline data and the actual daily evapotranspiration, water resource monitoring results are generated; wherein, the water resource monitoring results include water resource monitoring indicators and water resource management recommendations that match the water resource monitoring indicators; The step of calculating the original vapor pressure deficit, air density, and temperature lapse rate constraint correction coefficients based on the target key parameters includes: Calculate the saturated water vapor pressure based on the near-surface air temperature; The actual water vapor pressure is calculated based on the near-surface humidity data and the saturated water vapor pressure; wherein, the near-surface humidity data includes relative humidity; Calculate the original vapor pressure deficit based on the saturated vapor pressure and the actual vapor pressure. The air density is calculated based on the near-surface air temperature and the near-surface air pressure; wherein the air density is either the humid air density or the dry air density. Obtain the reference temperature lapse rate and the molar latent heat of vaporization of water vapor in the target region; Calculate the current temperature decay rate based on the lower-level air temperature, the upper-level air temperature, the lower-level height, and the upper-level height; Based on the current temperature lapse rate, the molar latent heat of vaporization of water vapor, and the near-surface air temperature, construct the current evaporation force conversion factor; A reference evaporation force conversion factor is constructed based on the reference temperature lapse rate, the latent heat of vaporization of water vapor, and the near-surface air temperature. Calculate the temperature lapse rate constraint correction factor based on the current evaporation force conversion factor and the reference evaporation force conversion factor; The step of constructing the current evaporation force conversion factor based on the current temperature lapse rate, the molar latent heat of vaporization of water vapor, and the near-surface air temperature includes: Calculate the vertical scale of water vapor phase change under the current temperature lapse rate, the molar latent heat of vaporization of water vapor, and the near-surface air temperature. The air gravity scale is calculated based on the preset air molar mass, the preset universal gas constant, the near-ground air temperature, and the preset gravitational acceleration. The water vapor gravity scale is calculated based on the preset water vapor molar mass, the universal gas constant, the near-surface air temperature, and the gravitational acceleration. Based on the vertical scale of the water vapor phase change and the gravity scale of the water vapor, calculate the relative attenuation intensity per unit height under water vapor phase change control and the relative attenuation intensity per unit height under water vapor gravity control. The difference between the relative attenuation intensity per unit height under the water vapor phase change control and the relative attenuation intensity per unit height under the water vapor gravity control is calculated to obtain the water vapor vertical attenuation difference term under the current temperature lapse rate state. The dimensionless processing of the vertical attenuation difference term of water vapor based on the air gravity scale yields the current evaporation force conversion coefficient.

2. The evapotranspiration-based water resource monitoring method of claim 1, wherein, The acquisition of surface water and heat multi-model data and water cycle baseline data within the target area includes: Obtain the raw multimodal data within the target area; The original multimodal data is subjected to spatial scale matching processing based on a regular spatial grid to obtain first processed multimodal data; wherein, the first processed multimodal data is unified to the same regular spatial grid; The first processed multi-modal data is subjected to time-scale matching processing based on a daily scale to obtain the second processed multi-modal data; The second-processed multi-modal data is subjected to mathematical unit normalization to obtain the third-processed multi-modal data; The third-processed multi-modal data is subjected to missing test processing to obtain preprocessed multi-modal data; Surface water and heat multi-model data are extracted from the preprocessed multi-model data, and water cycle baseline data within the target area are obtained according to the preset water resource monitoring type.

3. The evapotranspiration-based water resource monitoring method of claim 2, wherein, The original multi-model data includes near-surface meteorological variables, vertical air temperature profile variables, surface radiation flux variables, and underlying surface drag variables; The near-surface meteorological variables include near-surface air temperature, near-surface air pressure, and near-surface humidity data; The vertical profile variables of air temperature include lower-level air temperature, upper-level air temperature, lower-level altitude, and upper-level altitude; The surface radiation flux variables include daily net radiation and daily soil heat flux; The underlying surface drag variables include aerodynamic drag and surface drag; The water resources monitoring types include at least one of the following: farmland evapotranspiration monitoring, watershed hydrological simulation, ecological water consumption assessment, drought early warning, regional water resources management, and land-atmosphere interaction analysis. When the water resources monitoring type includes farmland evapotranspiration monitoring, the water cycle baseline data includes at least one of precipitation, irrigation amount and field holding capacity; When the water resources monitoring type includes watershed hydrological simulation, the water cycle baseline data includes at least one of watershed precipitation, watershed outlet runoff, and watershed water storage change. When the water resources monitoring type includes the ecological water consumption assessment type, the water cycle benchmark data includes at least one of ecological water demand quota and ecological available water limit; When the water resources monitoring type includes drought early warning type, the water cycle baseline data includes at least one of potential evapotranspiration, historical average evapotranspiration for the same period, and standard deviation of historical evapotranspiration for the same period; When the water resources monitoring type includes regional water resources management type, the water cycle benchmark data includes at least one of the total available water resources in the region and the water demand of other water users; When the water resource monitoring type includes land-atmosphere interaction analysis, the water cycle baseline data includes at least one of the following: sensible heat flux.

4. The evapotranspiration-based water resource monitoring method of claim 1, wherein, The calculation of air density based on the near-surface air temperature and the near-surface air pressure includes: When the air density is the humid air density, the air density is calculated based on the near-surface air temperature, the near-surface air pressure, the actual water vapor pressure, the preset dry air gas constant, and the preset water vapor gas constant. When the air density is the dry air density, the air density is calculated based on the near-ground air temperature, the near-ground air pressure, and the preset dry air gas constant.

5. A water resource monitoring device based on evapotranspiration, characterized in that, The water resource monitoring device based on evapotranspiration includes: The acquisition unit is used to acquire surface hydrothermal multi-model data and water cycle baseline data within the target area; wherein, the surface hydrothermal multi-model data includes target key parameters and target evapotranspiration driving parameters; the target key parameters include near-surface air temperature, near-surface air pressure, near-surface humidity data, lower-level air temperature, upper-level air temperature, lower-level height, and upper-level height; the target evapotranspiration driving parameters include daily net radiation, daily soil heat flux, aerodynamic drag, and surface drag; The first calculation unit is used to calculate the original water vapor pressure deficit, air density, and temperature lapse rate constraint correction coefficients based on the target key parameters. The correction unit is used to correct the original vapor pressure deficit according to the temperature lapse rate constraint correction coefficient to obtain the temperature lapse rate constraint vapor pressure deficit. The second calculation unit is used to take the air density, temperature lapse rate constrained water vapor pressure deficit and the target evapotranspiration driving parameters as inputs, and call the preset daily-scale Penman-Monteith model to calculate and obtain the daily-scale actual evapotranspiration of the target area. The generation unit is used to generate water resource monitoring results based on the water cycle baseline data and the actual daily evapotranspiration; wherein the water resource monitoring results include water resource monitoring indicators and water resource management recommendations that match the water resource monitoring indicators. Specifically, the first calculation unit is used to: calculate saturated vapor pressure based on the near-surface air temperature; calculate actual vapor pressure based on the near-surface humidity data and the saturated vapor pressure; wherein the near-surface humidity data includes relative humidity; calculate the original vapor pressure deficit based on the saturated vapor pressure and the actual vapor pressure; calculate air density based on the near-surface air temperature and the near-surface air pressure; wherein the air density is either moist air density or dry air density; obtain the reference temperature lapse rate and water vapor molar latent heat of vaporization for the target area; calculate the current temperature lapse rate based on the lower-level air temperature, the upper-level air temperature, the lower-level altitude, and the upper-level altitude; calculate the vertical scale of water vapor phase change under the current temperature lapse rate state based on the current temperature lapse rate, the water vapor molar latent heat of vaporization, and the near-surface air temperature; and calculate the water vapor phase change vertical scale based on a preset air molar mass, a preset universal gas constant, the near-surface air temperature, and a preset gravitational acceleration. The following steps are taken:

1. Calculate the air gravity scale; 2. Calculate the water vapor gravity scale based on the preset water vapor molar mass, the universal gas constant, the near-surface air temperature, and the gravitational acceleration; 3. Calculate the relative attenuation intensity per unit height under water vapor phase change control and the relative attenuation intensity per unit height under water vapor gravity control based on the water vapor phase change vertical scale and the water vapor gravity scale; 4. Calculate the difference between the relative attenuation intensity per unit height under water vapor phase change control and the relative attenuation intensity per unit height under water vapor gravity control to obtain the water vapor vertical attenuation difference term under the current temperature lapse rate; 5. Perform dimensionless processing on the water vapor vertical attenuation difference term based on the air gravity scale to obtain the current evaporation force conversion coefficient; 6. Construct a reference evaporation force conversion coefficient based on the reference temperature lapse rate, the water vapor molar latent heat of vaporization, and the near-surface air temperature; 7. Calculate the temperature lapse rate constraint correction coefficient based on the current evaporation force conversion coefficient and the reference evaporation force conversion coefficient.

6. An electronic device, comprising: The electronic device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the water resource monitoring method based on evapotranspiration as described in any one of claims 1 to 4.

7. A readable storage medium, characterized by, The readable storage medium stores a computer program, which, when executed by a processor, performs the water resource monitoring method based on evapotranspiration as described in any one of claims 1 to 4.

8. A computer program product, characterised in that, The computer program product includes a computer program that, when executed by a processor, performs the water resource monitoring method based on evapotranspiration as described in any one of claims 1 to 4.

Images