Water production prediction method and device and electronic equipment

Abstract

The application discloses a water yield prediction method and device and electronic equipment. The method comprises the following steps: acquiring grid-based basic data, precipitation observation data and to-be-corrected precipitation grid data corresponding to a target river basin; dividing the grid unit into a station-containing grid unit and a to-be-calculated grid unit according to the position information of the meteorological station and the grid unit; determining a station-level correction coefficient according to the measured precipitation of the meteorological station corresponding to the station-containing grid unit and the to-be-corrected precipitation grid data; determining a grid-level correction coefficient of the to-be-calculated grid unit in combination with the to-be-corrected precipitation grid data, temperature data, terrain data, spatial distance and environmental correlation information; determining a primary precipitation correction coefficient according to the station-level correction coefficient and the grid-level correction coefficient, and performing neighborhood stability adjustment to obtain an adjusted precipitation correction coefficient; correcting the to-be-corrected precipitation by using the adjusted precipitation correction coefficient to obtain corrected precipitation; and calculating a water yield prediction value according to the corrected precipitation, water yield calculation parameters and grid area. The application can reduce the water yield prediction deviation caused by the sparse distribution of meteorological stations, the mismatch between the distribution of stations and the distribution of precipitation and the poor simulation effect of precipitation grid data, and improve the water yield prediction accuracy in the complex terrain and the area with uneven distribution of precipitation.

Description

Technical Field

[0001] This application relates to the field of ecological prediction technology, and in particular to a method, apparatus and electronic equipment for predicting water production. Background Technology

[0002] Water yield assessment is a crucial technical component in watershed water resource evaluation, ecosystem service function assessment, water conservation capacity analysis, and regional water resource management. Current water yield assessments typically require spatial data such as precipitation, evapotranspiration, land cover, soil properties, and topographic conditions as input, and calculate water yield in different spatial units within the target watershed based on water balance relationships. With the development of remote sensing inversion, reanalysis data, and geographic information system (GIS) technologies, precipitation raster products or gridded precipitation data are widely used for regional-scale water yield calculations to compensate for insufficient spatial coverage of meteorological station observation data.

[0003] In practical applications, especially in areas with significant topographic relief, large spatial differences in precipitation, and uneven distribution of meteorological stations, the spatial representativeness of precipitation data directly affects the accuracy of water yield predictions. For example, in regions such as the southwestern mountainous areas, precipitation exhibits strong spatial heterogeneity due to topographic uplift, local circulation, and vertical climate differences. Meteorological stations are often concentrated in easily accessible or densely populated areas, and their spatial distribution does not perfectly correspond to the actual spatial distribution of precipitation. In such cases, relying solely on station observation data for spatial fitting can easily lead to overestimation or underestimation of precipitation in local areas.

[0004] On the other hand, while existing satellite-retrieved precipitation products, reanalysis precipitation products, meteorological interpolation precipitation products, and station-fused precipitation products can provide continuous spatial coverage, the simulation capabilities of precipitation grid data from different sources vary in complex terrain regions. Some precipitation products lack effective constraints in sparsely populated areas, while others may fail to accurately reflect local precipitation changes due to spatial resolution, inversion algorithm, or reanalysis model errors. Therefore, directly inputting uncorrected precipitation grid data into the water yield calculation process can easily lead to systematic biases in regional water yield prediction results.

[0005] In existing technologies, common precipitation correction methods often employ simple proportional correction, inverse distance weighted interpolation, or station residual interpolation to extend the observation errors at meteorological stations to surrounding areas. However, these methods typically focus primarily on spatial distance factors, making it difficult to fully incorporate environmental factors such as temperature and topography that influence the spatial differentiation of precipitation. Furthermore, they struggle to dynamically adjust correction weights based on the spatial distribution of precipitation across different observation periods. In addition, conventional interpolation correction processes often lack stability handling for spatial anomalies in correction coefficients; local anomalies at certain stations or in local anomalies may be directly propagated to adjacent grids, further impacting the accuracy of water yield calculations.

[0006] Therefore, in the face of situations such as sparse distribution of meteorological stations, mismatch between station distribution and precipitation distribution, and unstable simulation results of multi-source precipitation grid data, it is necessary to provide a water yield prediction method that can combine station observations, gridded precipitation data, topographic data, temperature data, and water yield calculation parameters. This would improve the spatial adaptability and stability of precipitation correction results in complex terrain and areas with non-uniform precipitation, and further enhance the reliability of water yield prediction results for the target watershed. Summary of the Invention

[0007] This application provides a method, apparatus, and electronic device for predicting water production, in order to solve the problem of low accuracy in water production prediction caused by the sparse distribution of meteorological stations and the mismatch between station distribution and precipitation distribution in the prior art.

[0008] This application provides a water yield prediction method, comprising: acquiring gridded baseline data, precipitation observation data, and precipitation grid data to be corrected corresponding to a target watershed; the gridded baseline data includes location information, topographic data, temperature data, grid area, and water yield calculation parameters of each grid unit within the target watershed; the precipitation observation data includes measured precipitation at each meteorological station within the target watershed for multiple observation periods; and the precipitation grid data to be corrected includes the precipitation to be corrected at each grid unit within the target watershed for multiple observation periods. Based on the location information of each meteorological station and each grid unit, the grid units within the target watershed are divided into station-containing grid units and grid units to be calculated. Based on the measured precipitation at the meteorological station corresponding to the station-containing grid unit and the precipitation grid data to be corrected, the station-level correction coefficients for each observation period of the station-containing grid unit are determined. Based on the precipitation grid data to be corrected, temperature data, topographic data, station-level correction coefficients, and the precipitation grid data of the station-containing grid unit and the precipitation grid data to be corrected, the method further refines the calculation. Based on the spatial distance and environmental association information between the grid cells to be calculated, the grid-level correction coefficients for each grid cell in each observation period are determined. Based on the station-level and grid-level correction coefficients, the original precipitation correction coefficients for each grid cell in each observation period are determined. Then, based on the neighborhood differences between the original precipitation correction coefficients of each grid cell, the stability of the original precipitation correction coefficients for each grid cell is adjusted to obtain the adjusted precipitation correction coefficients for each grid cell in each observation period. Based on the adjusted precipitation correction coefficients for each grid cell in each observation period, the precipitation to be corrected for the corresponding grid cell in the precipitation grid data is corrected for the corresponding observation period to obtain the corrected precipitation for each grid cell in each observation period. Based on the corrected precipitation, water yield calculation parameters, and grid area, the predicted water yield value for each grid cell is calculated, and the predicted water yield value for the target watershed is generated based on the predicted water yield value for each grid cell.

[0009] According to the embodiments of this application, the grid cell division process includes: determining grid cells containing meteorological stations as station-containing grid cells based on the location information of each meteorological station and the location information of each grid cell, and determining other grid cells in the target watershed besides station-containing grid cells as grid cells to be calculated; the station-level correction coefficient determination process includes: for each station-containing grid cell, determining the station-level correction coefficient corresponding to the station-containing grid cell in each observation period based on the measured precipitation corresponding to the meteorological station of the station-containing grid cell in each observation period, and the precipitation to be corrected corresponding to the station-containing grid cell in the precipitation grid data to be corrected; the grid-level correction coefficient determination process includes: based on precipitation spatial... The distribution characteristics, temperature factor weight, topographic factor weight, station representative parameters, coupled extrapolation weight, and station-level correction coefficients are used to obtain the grid-level correction coefficients for the grid cells to be extrapolated in each observation period. The stability adjustment process includes obtaining the adjusted precipitation correction coefficients based on the original precipitation correction coefficients, the set of neighboring grid cells, the coefficient difference value, the stable neighboring grid cells, the stable reference correction coefficients, and the stable adjustment coefficients. The water yield calculation process includes determining the corrected precipitation input and the actual evapotranspiration based on the corrected precipitation and water yield calculation parameters, determining the water yield depth per unit area based on the difference between the corrected precipitation input and the actual evapotranspiration, and calculating the predicted water yield value in combination with the grid area.

[0010] According to the embodiments of this application, the location information includes the coordinate data of each grid cell in a preset coordinate system; the terrain data includes one or more of the elevation data, slope data, aspect data and terrain relief data corresponding to each grid cell determined based on the digital elevation model; the temperature data includes the air temperature data corresponding to each grid cell in multiple observation periods; and the water production calculation parameters include one or more of the potential evapotranspiration parameters, soil property parameters, vegetation parameters, land cover parameters, root depth parameters and soil depth parameters.

[0011] According to the embodiments of this application, obtaining gridded baseline data, precipitation observation data, and precipitation grid data to be corrected for a target watershed includes: obtaining a multi-source candidate precipitation grid dataset for the target watershed, wherein the multi-source candidate precipitation grid dataset includes two or more candidate precipitation grid datasets obtained from different sources, and each candidate precipitation grid dataset includes candidate fitted precipitation amounts corresponding to each grid unit within the target watershed for multiple observation periods; for each candidate precipitation grid dataset, calculating the goodness-of-fit index corresponding to the candidate precipitation grid dataset based on the candidate fitted precipitation amounts in the grid units to which the candidate precipitation grid dataset belongs at each meteorological station, and the measured precipitation amounts at the corresponding meteorological stations in the precipitation observation data; and selecting candidate precipitation grid datasets as precipitation grid data to be corrected from the multi-source candidate precipitation grid dataset according to the preset selection rules corresponding to the goodness-of-fit index.

[0012] According to the embodiments of this application, the candidate precipitation grid data in the multi-source candidate precipitation grid dataset includes gridded precipitation data obtained from two or more sources, such as satellite inversion precipitation products, reanalysis precipitation products, meteorological interpolation precipitation products, and station fusion precipitation products.

[0013] According to the embodiments of this application, calculating the goodness-of-fit index corresponding to the candidate precipitation grid data includes: aggregating the candidate fitted precipitation amounts of the candidate precipitation grid data on the grid units to which each meteorological station belongs according to a preset fitting time scale to obtain candidate aggregated precipitation amounts; aggregating the measured precipitation amounts corresponding to each meteorological station according to a preset fitting time scale to obtain measured aggregated precipitation amounts; constructing station-level paired samples based on the candidate aggregated precipitation amounts and the measured aggregated precipitation amounts; calculating the coefficient of determination based on the station-level paired samples, and using the coefficient of determination as the goodness-of-fit index.

[0014] According to the embodiments of this application, multiple observation periods are monthly scale observation periods, and the preset fitting time scale includes an annual scale; according to the preset optimization rules corresponding to the goodness-of-fit index, candidate precipitation grid data are selected from the multi-source candidate precipitation grid dataset as precipitation grid data to be corrected, including: selecting the candidate precipitation grid data with the largest determination coefficient from the multi-source candidate precipitation grid dataset as precipitation grid data to be corrected.

[0015] According to the embodiments of this application, determining the station-level correction coefficients corresponding to a grid unit containing stations in each observation period includes: when the grid unit containing stations corresponds to one meteorological station, the measured precipitation of that meteorological station in the corresponding observation period is used as the grid representative measured precipitation of the grid unit containing stations in the corresponding observation period; when the grid unit containing stations corresponds to multiple meteorological stations, the measured precipitation of the multiple meteorological stations in the corresponding observation period is statistically processed to obtain the grid representative measured precipitation of the grid unit containing stations in the corresponding observation period; and the station-level correction coefficients corresponding to the grid unit containing stations in the observation period are determined based on the grid representative measured precipitation of the grid unit containing stations in the corresponding observation period and the precipitation to be corrected.

[0016] According to embodiments of this application, statistical processing includes at least one of averaging, distance-weighted averaging, median processing, outlier removal, and confidence-weighted processing.

[0017] According to an embodiment of this application, determining the station-level correction coefficient based on the grid-represented measured precipitation and the precipitation to be corrected includes: using the ratio between the grid-represented measured precipitation corresponding to the station-containing grid unit during the observation period and the precipitation to be corrected corresponding to the station-containing grid unit during the observation period as the station-level correction coefficient for the station-containing grid unit during the observation period; when the precipitation to be corrected corresponding to the station-containing grid unit during the observation period is less than a preset precipitation threshold, determining the station-level correction coefficient for the station-containing grid unit during the observation period based on the ratio between the grid-represented measured precipitation and the preset precipitation threshold.

[0018] According to an embodiment of this application, determining the weights of the temperature factor and the topographic factor includes: for the observation period, extracting the precipitation to be corrected corresponding to each grid unit in the target watershed from the precipitation grid data to obtain a precipitation distribution sample for the observation period; extracting the air temperature data corresponding to each grid unit in the target watershed during the observation period from the temperature data to obtain a temperature distribution sample for the observation period; extracting the topographic feature values ​​corresponding to each grid unit in the target watershed from the topographic data to obtain a topographic distribution sample; calculating the temperature correlation metric for the observation period based on the precipitation distribution sample and the temperature distribution sample; calculating the topographic correlation metric for the observation period based on the precipitation distribution sample and the topographic correlation metric; and determining the weights of the temperature factor and the topographic factor for the observation period based on the relative magnitudes between the temperature correlation metric and the topographic correlation metric.

[0019] According to embodiments of this application, the temperature correlation metric includes a coefficient of determination calculated based on precipitation distribution samples and temperature distribution samples; the topographic correlation metric includes a coefficient of determination calculated based on precipitation distribution samples and topographic distribution samples.

[0020] According to an embodiment of this application, the temperature factor weight and topographic factor weight corresponding to the observation period are determined based on the relative magnitude between the temperature correlation metric and the topographic correlation metric, including: using the ratio of the temperature correlation metric corresponding to the observation period to the sum of the temperature correlation metric and the topographic correlation metric as the temperature factor weight corresponding to the observation period; and using the ratio of the topographic correlation metric corresponding to the observation period to the sum of the temperature correlation metric and the topographic correlation metric as the topographic factor weight corresponding to the observation period.

[0021] According to the embodiments of this application, determining the representative parameters of a station includes: for each station-containing grid cell, determining the local correction difference value corresponding to the station-containing grid cell based on the difference between the station-level correction coefficient corresponding to the station-containing grid cell in the observation period and the station-level correction coefficient corresponding to other station-containing grid cells within a preset range in the observation period; determining the representative parameters of the station-containing grid cell in the observation period based on the local correction difference value, wherein the larger the local correction difference value, the smaller the representative parameters of the station.

[0022] According to an embodiment of this application, determining the coupling estimation weight includes: for the grid cell to be estimated and each grid cell containing stations, determining a distance attenuation weight based on the spatial distance between the grid cell to be estimated and the grid cell containing stations; determining a temperature correlation weight based on the temperature difference between the grid cell to be estimated and the grid cell containing stations; determining a terrain correlation weight based on the terrain difference between the grid cell to be estimated and the grid cell containing stations; weighting the temperature correlation weight and the terrain correlation weight according to the temperature factor weight and the terrain factor weight corresponding to the observation period to obtain the environmental correlation weight of the grid cell to be estimated relative to the grid cell containing stations; determining the initial estimation weight of the grid cell to be estimated relative to the grid cell containing stations based on the distance attenuation weight, the environmental correlation weight, and the representative parameters of the stations corresponding to the grid cell containing stations; and normalizing the initial estimation weight of the grid cell to be estimated relative to each grid cell containing stations to obtain the coupling estimation weight of the grid cell to be estimated relative to each grid cell containing stations.

[0023] According to an embodiment of this application, determining the distance attenuation weight includes: determining the distance attenuation weight based on the negative power of the spatial distance between the grid cell to be calculated and the grid cell containing the station, wherein the distance attenuation index corresponding to the negative power is a preset value.

[0024] According to the embodiments of this application, determining the temperature association weight includes: normalizing the difference between the temperature data of the grid cell to be calculated and the grid cell containing the station during the corresponding observation period, and determining the temperature association weight based on the normalized temperature difference, so that the smaller the temperature difference, the greater the temperature association weight; determining the terrain association weight includes: normalizing the difference between the terrain feature values ​​of the grid cell to be calculated and the grid cell containing the station, and determining the terrain association weight based on the normalized terrain difference, so that the smaller the terrain difference, the greater the terrain association weight.

[0025] According to the embodiments of this application, determining the initial estimated weight includes: multiplying the distance attenuation weight, the environmental association weight, and the representative parameters of the station corresponding to the grid unit containing the station to obtain the initial estimated weight of the grid unit to be estimated relative to the grid unit containing the station.

[0026] According to an embodiment of this application, obtaining the grid-level correction coefficient includes: using the coupling calculation weight of the grid cell to be calculated relative to each grid cell containing stations as the weight, performing a weighted summation of the station-level correction coefficients corresponding to each grid cell containing stations during the observation period, and obtaining a weighted sum value; and using the ratio between the weighted sum value and the sum of each coupling calculation weight as the grid-level correction coefficient corresponding to the grid cell to be calculated during the observation period.

[0027] According to an embodiment of this application, determining the adjusted precipitation correction coefficient includes: calculating the absolute value of the difference between the original precipitation correction coefficient and the stable reference correction coefficient corresponding to the grid cell to obtain a stable deviation value; determining the stable adjustment coefficient corresponding to the grid cell in the observation period based on the ratio between the stable deviation value and a preset deviation threshold, wherein the larger the stable deviation value, the smaller the stable adjustment coefficient; weighting the original precipitation correction coefficient corresponding to the grid cell based on the stable adjustment coefficient, and weighting the stable reference correction coefficient based on the difference between the original and stable adjustment coefficients, and determining the sum of the weighted results as the adjusted precipitation correction coefficient corresponding to the grid cell in the observation period.

[0028] According to the embodiments of this application, before determining the grid-level correction coefficients corresponding to the grid cells to be calculated in each observation period, the method further includes: removing or limiting extreme values ​​in the station-level correction coefficients according to preset anomaly handling rules, and using the processed station-level correction coefficients to determine the grid-level correction coefficients.

[0029] According to an embodiment of this application, the method further includes: for a target grid cell to be calculated whose maximum value of the coupling calculation weight relative to all grid cells containing stations is lower than a preset lower limit, assigning a grid-level correction coefficient to the target grid cell to be calculated in the corresponding observation period according to a preset conservative strategy; the preset conservative strategy includes setting the grid-level correction coefficient to a neutral value that does not introduce correction, or setting the grid-level correction coefficient to the median of the station-level correction coefficients across the entire region.

[0030] According to an embodiment of this application, obtaining the corrected precipitation includes: for each grid cell and each observation period, multiplying the precipitation to be corrected corresponding to the grid cell in the observation period by the adjusted precipitation correction coefficient corresponding to the grid cell in the observation period to obtain the corrected precipitation corresponding to the grid cell in the observation period.

[0031] According to the embodiments of this application, determining the corrected precipitation input and the actual evapotranspiration includes: for each grid cell, summing the corrected precipitation for each observation period within the target calculation period to obtain the corrected precipitation input for that grid cell during the target calculation period; determining the potential evapotranspiration and evapotranspiration regulation coefficient for that grid cell during the target calculation period based on the water production calculation parameters corresponding to that grid cell; and determining the actual evapotranspiration for that grid cell during the target calculation period based on the corrected precipitation input, potential evapotranspiration, and evapotranspiration regulation coefficient.

[0032] According to the embodiments of this application, determining the actual evapotranspiration includes: determining the aridity index based on the ratio between the potential evapotranspiration and the corrected precipitation input; determining the actual evapotranspiration ratio based on the aridity index and the evapotranspiration adjustment coefficient; and determining the actual evapotranspiration of the grid cell in the target calculation period based on the actual evapotranspiration ratio and the corrected precipitation input.

[0033] According to the embodiments of this application, after generating the water production prediction result corresponding to the target watershed based on the water production prediction value corresponding to each grid unit, the method further includes: summarizing the water production prediction values ​​corresponding to each grid unit in the preset sub-watershed to obtain the sub-watershed water production prediction value corresponding to the preset sub-watershed; and calculating the water conservation capacity corresponding to the preset sub-watershed based on the sub-watershed water production prediction value and the measured runoff data corresponding to the preset sub-watershed.

[0034] According to an embodiment of this application, the method further includes: summarizing the predicted water yield values ​​corresponding to each grid unit within a preset sub-basin to obtain the predicted water yield value of the sub-basin corresponding to the preset sub-basin; performing feedback verification on at least one of the following: temperature factor weight, topographic factor weight, distance attenuation index of distance attenuation weight, and stability adjustment coefficient, based on the degree of deviation between the predicted water yield value of the sub-basin and the measured runoff corresponding to the preset sub-basin; iteratively adjusting the adjusted precipitation correction coefficient corresponding to each grid unit in each observation period based on the feedback verification results; and re-executing the correction of the precipitation to be corrected and the calculation of the predicted water yield value based on the iteratively adjusted adjusted precipitation correction coefficient until the degree of deviation corresponding to the iterative adjustment meets the preset convergence condition.

[0035] According to the embodiments of this application, before determining the station-level correction coefficients, data preprocessing is further performed on the gridded base data, precipitation observation data, and precipitation grid data to be corrected. The data preprocessing includes at least one of the following: unifying the gridded base data, precipitation observation data, and precipitation grid data to be corrected to the same coordinate system; resampling the gridded base data and precipitation grid data to be corrected at a uniform spatial resolution while maintaining grid alignment; organizing the precipitation observation data into vector point data according to the station number, longitude, latitude, and cumulative precipitation fields; removing outliers from the precipitation observation data; imputing missing values ​​in the precipitation observation data; and performing time aggregation processing on the precipitation observation data according to the time granularity corresponding to multiple observation periods.

[0036] According to the embodiments of this application, the method further includes: outputting one or more of the following: corrected precipitation data, original precipitation correction coefficient data, adjusted precipitation correction coefficient data, water yield prediction results, temperature factor weight time series data, topographic factor weight time series data, environmental correlation weight data, water yield time series data, sub-basin water yield statistics results, and water conservation data.

[0037] This application also provides a water yield prediction device, including a data acquisition module, a grid division module, a station correction module, a grid extrapolation module, a stabilization adjustment module, a precipitation correction module, and a water yield calculation module. The data acquisition module is used to acquire gridded baseline data, precipitation observation data, and precipitation grid data to be corrected corresponding to the target watershed. The grid division module is used to divide the grid units in the target watershed into station-containing grid units and grid units to be extrapolated. The station correction module is used to determine the station-level correction coefficients corresponding to the station-containing grid units in each observation period. The grid extrapolation module is used to determine the grid-level correction coefficients corresponding to the grid units to be extrapolated in each observation period. The stabilization adjustment module is used to perform stability adjustment on the original precipitation correction coefficients to obtain the adjusted precipitation correction coefficients. The precipitation correction module is used to obtain the corrected precipitation for each grid unit in each observation period. The water yield calculation module is used to calculate the predicted water yield value corresponding to each grid unit and generate the predicted water yield result for the target watershed.

[0038] This application also provides an electronic device, including a processor and a memory; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs any of the above-described water production prediction methods.

[0039] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-described water production prediction methods.

[0040] The water yield prediction method, apparatus, and electronic equipment provided in this application first distinguish between grid cells containing meteorological stations and grid cells to be predicted based on the location of the meteorological station and the location of the grid cells. Then, the difference between the measured precipitation at the grid cells containing the station and the precipitation to be corrected is used to determine the station-level correction coefficient. Furthermore, the correction information at the station is extrapolated to the grid cells to be predicted that lack station constraints by combining the spatial distribution characteristics of precipitation, temperature data, topographic data, spatial distance, and environmental correlation information reflected in the precipitation grid data to be corrected. At the same time, stability adjustment is performed based on the neighborhood differences between the original precipitation correction coefficients to obtain a more suitable grid-by-grid precipitation prediction method. The adjusted precipitation correction coefficient after water correction is used. Therefore, in situations where meteorological stations are sparsely distributed, the station distribution does not match the actual spatial distribution of precipitation, and the simulation effect of multi-source precipitation grid data is poor in complex terrain areas, it can reduce the regional precipitation bias caused by simply relying on station fitting or directly using uncorrected precipitation grid data. It also makes the corrected precipitation more closely resemble the spatial difference characteristics of precipitation in the target watershed. In turn, the predicted water yield is calculated by using the corrected precipitation, water yield calculation parameters, and grid area, thereby improving the accuracy and spatial reliability of water yield prediction in areas with uneven precipitation distribution, especially in areas with complex terrain and insufficient station representativeness.

[0041] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0042] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart of an embodiment of the water production prediction method provided in this application; Figure 2 A schematic diagram of the structure of an embodiment of the water production prediction device provided in this application; Figure 3 A schematic diagram of the structure of an embodiment of the electronic device provided in this application. Detailed Implementation

[0043] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0044] In the description of this application, it should be noted that the terms "first," "second," etc., are used only to distinguish descriptive purposes and should not be construed as indicating or implying relative importance. Unless otherwise expressly defined, the terms "connect," "acquire," "determine," "calculate," "generate," etc., should be interpreted broadly; for example, they may refer to direct processing or indirect processing through intermediate data. Those skilled in the art can understand the specific meaning of the above terms in this application according to specific application scenarios.

[0045] In the description of this application, it should also be noted that, unless otherwise expressly specified and limited, the terms "acquire," "computation," "determine," "generate," "fusion," and "diffusion," etc., should be interpreted broadly. For example, "acquire" can mean directly collecting data or reading data from a database, server, storage medium, or third-party platform; "computation" can be implemented by a processor executing a preset program or by a server, cloud platform, or edge computing device working together. Regarding the capabilities...

[0046] First Embodiment This application relates to technical fields such as watershed yield prediction, water resource assessment, precipitation grid data correction, and eco-hydrological spatial analysis. Yield prediction typically requires spatial data such as precipitation, evapotranspiration, land cover, soil properties, vegetation conditions, and topographic conditions as input, and calculates the yield of different grid units within the target watershed based on water balance relationships. With the development of remote sensing inversion, reanalysis data, and geographic information system (GIS) technologies, gridded precipitation data is widely used in regional-scale yield calculations to compensate for the insufficient spatial coverage of traditional meteorological station observation data.

[0047] However, in practical applications, target watersheds may exhibit challenges such as sparse distribution of meteorological stations, mismatch between station spatial distribution and actual precipitation spatial distribution, significant topographic relief, and strong spatial differences in precipitation. For example, in complex terrain regions such as the southwestern mountainous areas, precipitation is influenced by factors such as orographic uplift, slope aspect differences, local circulation, and vertical climate differentiation, resulting in a significantly non-uniform spatial distribution. Meteorological stations are typically located in areas with convenient transportation or concentrated population activity, making it difficult to comprehensively represent the actual precipitation conditions of special geomorphic units such as mountains, canyons, and high-altitude areas. Therefore, spatial fitting based solely on station precipitation data can easily lead to biases in local precipitation estimations.

[0048] Meanwhile, while existing satellite-retrieved precipitation products, reanalysis precipitation products, meteorological interpolation precipitation products, and site-fused precipitation products can provide continuous spatial coverage, the simulation results of precipitation grid data from different sources are not stable in complex terrain regions. Some precipitation products may introduce systematic errors due to insufficient spatial resolution, inversion models, reanalysis assimilation mechanisms, or insufficient site constraints. If uncorrected precipitation grid data is directly input into the water yield calculation process, the water yield prediction results will inherit the precipitation input errors, thereby reducing the accuracy of water yield calculations in the target watershed, especially in areas with uneven spatial distribution of precipitation.

[0049] To address the aforementioned issues, this application provides a water yield prediction method. The method acquires gridded baseline data, precipitation observation data, and precipitation grid data to be corrected corresponding to the target watershed. Based on the positional relationship between meteorological stations and grid units, the grid units within the target watershed are divided into station-containing grid units and grid units to be calculated. Station-level correction coefficients are determined by utilizing the difference between the measured precipitation at the meteorological stations in the station-containing grid units and the precipitation grid data to be corrected. Then, combining the precipitation grid data to be corrected, temperature data, topographic data, spatial distance, and environmental correlation information, the station-level correction coefficients are extrapolated to the grid units to be calculated, resulting in grid-level correction coefficients. Subsequently, neighborhood stability adjustment is applied to the original precipitation correction coefficients corresponding to all grid units, and the adjusted precipitation correction coefficients are used to correct the precipitation to be corrected grid by grid, obtaining the corrected precipitation. Finally, the predicted water yield value is calculated based on the corrected precipitation, water yield calculation parameters, and grid area.

[0050] Through the above methods, this application combines station observation constraints, grid precipitation spatial distribution, temperature conditions, topographic conditions, and neighborhood stability processing. Therefore, in areas where meteorological stations are sparsely distributed and the precipitation data collected by the stations cannot fully reflect the true spatial distribution of precipitation, the impact of station fitting bias and precipitation grid product simulation errors on water yield calculation can be reduced. This improves the ability of the corrected precipitation data to express the spatial differences in true precipitation in the target watershed, and further enhances the spatial reliability and calculation accuracy of the water yield prediction results.

[0051] Second Embodiment Figure 1 This is a flowchart illustrating a method for predicting water production, provided in an embodiment of this application. This method can be executed by an electronic device, or by a server, terminal device, geographic information processing platform, hydrological simulation platform, or a combination thereof. This application does not specifically limit the executing entity. Figure 1 As shown, the method may include: S101. Obtain the gridded baseline data, precipitation observation data, and precipitation grid data to be corrected corresponding to the target watershed.

[0052] In step S101, the gridded baseline data, precipitation observation data, and precipitation grid data to be corrected corresponding to the target watershed can be obtained first. For example, in this embodiment of the application, the target watershed can be a watershed, sub-watershed, administrative region, ecological functional zone, or water conservation area that requires water yield prediction. The gridded baseline data can be obtained by spatially discretizing the target watershed according to a uniform spatial resolution. Each grid cell can correspond to a spatial location and a set of basic parameters required for water yield calculation.

[0053] The gridded baseline data can include location information, topographic data, temperature data, grid area, and yield calculation parameters for each grid cell within the target watershed. Location information can be used to determine the spatial correspondence between grid cells and meteorological stations; topographic data can be used to characterize factors such as elevation, slope, aspect, or topographic relief that affect the spatial differentiation of precipitation; temperature data can be used to characterize thermal conditions at different observation periods; grid area can be used to convert yield depth per unit area into grid yield; and yield calculation parameters can be used to calculate actual evapotranspiration, yield depth per unit area, or other water balance-related variables.

[0054] Precipitation observation data can include measured precipitation at various meteorological stations within the target watershed over multiple observation periods. These observation periods can be daily, decadal, monthly, seasonal, or annual scales, with the specific time scale determined based on the accuracy requirements and data availability of the water yield prediction task. Precipitation grid data to be corrected can include precipitation data to be corrected for each grid cell within the target watershed over multiple observation periods. This data can originate from satellite-retrieved precipitation products, reanalysis precipitation products, meteorological interpolation precipitation products, station-fused precipitation products, or other gridded precipitation data sources.

[0055] During data acquisition, the target watershed boundary can be used as the spatial clipping range. Data from different sources can be clipped, reprojected, resampled, and aligned to ensure consistency in spatial extent, coordinate system, and spatial resolution among the gridded baseline data, temperature data, topographic data, and precipitation grid data to be corrected. For precipitation observation data, it can be organized according to fields such as station number, latitude and longitude, observation time, and measured precipitation to facilitate subsequent matching of meteorological stations to corresponding grid cells.

[0056] Furthermore, in this embodiment, the location information may include coordinate data of each grid cell in a preset coordinate system; the terrain data may include one or more of the following: elevation data, slope data, aspect data, and terrain relief data corresponding to each grid cell determined based on the digital elevation model; the temperature data may include the air temperature data corresponding to each grid cell in multiple observation periods; and the water production calculation parameters may include one or more of the following: potential evapotranspiration parameters, soil property parameters, vegetation parameters, land cover parameters, root depth parameters, and soil depth parameters.

[0057] The coordinate data mentioned above can be the coordinates of the grid center point, grid boundary, or grid index. The digital elevation model (DEM) can be used to extract altitude and further calculate slope, aspect, and topographic relief. Temperature data can be consistent with precipitation observation periods to analyze the correlation between temperature conditions and the spatial distribution of precipitation at the same time scale. Water yield calculation parameters can be determined based on land use data, soil databases, vegetation cover data, ecological parameter tables, or remote sensing inversion results.

[0058] Furthermore, in this embodiment, the method further includes: acquiring a multi-source candidate precipitation grid dataset corresponding to the target watershed, wherein the multi-source candidate precipitation grid dataset includes two or more candidate precipitation grid data obtained from different sources, and each candidate precipitation grid data includes candidate fitted precipitation amounts corresponding to each grid unit within the target watershed at multiple observation periods; for each candidate precipitation grid data, calculating the goodness-of-fit index corresponding to the candidate precipitation grid data based on the candidate fitted precipitation amounts of the candidate precipitation grid data at each meteorological station's grid unit and the measured precipitation amounts at the corresponding meteorological stations in the precipitation observation data; and selecting candidate precipitation grid data from the multi-source candidate precipitation grid dataset as precipitation grid data to be corrected according to the preset selection rules corresponding to the goodness-of-fit index.

[0059] By introducing a multi-source candidate precipitation grid dataset, the basic fitting ability of different precipitation products can be screened before performing precipitation correction. Specifically, the candidate fitted precipitation amounts at the grid cells to which the meteorological stations belong can be paired with the station's measured precipitation amounts, and then the consistency index between the candidate precipitation grid data and the measured precipitation data can be calculated. The goodness-of-fit index can be the coefficient of determination, root mean square error, mean absolute error, correlation coefficient, or bias rate, etc. Preferably, the coefficient of determination can be used to characterize the explanatory power of the candidate precipitation grid data for changes in station-measured precipitation.

[0060] Furthermore, in this embodiment, the candidate precipitation grid data in the multi-source candidate precipitation grid dataset includes gridded precipitation data obtained from two or more sources, such as satellite inversion precipitation products, reanalysis precipitation products, meteorological interpolation precipitation products, and station fusion precipitation products.

[0061] The candidate precipitation grid data from the aforementioned different sources have different data generation mechanisms. Satellite inversion precipitation products can provide a large spatial coverage; reanalysis precipitation products can reflect atmospheric circulation and model assimilation information; meteorological interpolation precipitation products can better inherit station observation constraints; and station fusion precipitation products can integrate multiple observation and simulation information. By optimizing the multi-source candidate precipitation grid dataset, the initial error caused by the insufficient applicability of a single precipitation product in the target watershed can be reduced.

[0062] Furthermore, in this embodiment, the method may further include: aggregating the candidate fitted precipitation data of the candidate precipitation grid data on the grid units to which each meteorological station belongs according to a preset fitting time scale to obtain candidate aggregated precipitation; aggregating the measured precipitation corresponding to each meteorological station according to a preset fitting time scale to obtain measured aggregated precipitation; constructing station-level paired samples based on the candidate aggregated precipitation and the measured aggregated precipitation; calculating the coefficient of determination based on the station-level paired samples, and using the coefficient of determination as a goodness-of-fit index.

[0063] For example, when the original observation period is monthly, the monthly precipitation at each meteorological station can be summed to obtain the annual precipitation, and then the monthly candidate fitted precipitation at the corresponding grid cell of the candidate precipitation grid data can be summed to obtain the annual candidate aggregated precipitation. The station-level paired samples constructed in this way can reduce the impact of short-term random fluctuations and are more suitable for evaluating the overall applicability of candidate precipitation products in water yield prediction tasks.

[0064] Furthermore, in this embodiment, multiple observation periods are monthly-scale observation periods, and the preset fitting time scale includes an annual scale; according to the preset optimization rules corresponding to the goodness-of-fit index, candidate precipitation grid data are selected from the multi-source candidate precipitation grid dataset as precipitation grid data to be corrected, including: selecting the candidate precipitation grid data with the largest determination coefficient from the multi-source candidate precipitation grid dataset as precipitation grid data to be corrected.

[0065] In this implementation, monthly-scale observation periods can retain information on seasonal variations in precipitation, while annual-scale fitting evaluation can reflect the overall expressive ability of candidate precipitation products for annual water input. A higher coefficient of determination generally indicates a better fit between the candidate precipitation grid data and measured precipitation. Therefore, selecting the candidate precipitation grid data with the highest coefficient of determination as the precipitation grid data to be corrected can provide a better initial precipitation input for subsequent grid-by-grid correction and water yield calculation.

[0066] S102. Based on the location information of each meteorological station and the location information of each grid unit, the grid units within the target watershed are divided into grid units containing stations and grid units to be calculated.

[0067] In step S102, based on the location information of each meteorological station and each grid unit obtained in step S101, the grid units within the target watershed can be divided into grid units containing stations and grid units to be calculated. For example, in this embodiment, the latitude and longitude coordinates or projected coordinates of the meteorological stations can be spatially matched with the spatial range of the grid unit or the coordinates of the grid center point. If a meteorological station falls within the spatial range of a grid unit, that grid unit can be identified as a grid unit containing stations. Grid units within the target watershed that do not contain meteorological stations can be identified as grid units to be calculated.

[0068] In practical implementation, methods such as point-area overlay analysis, spatial index matching, nearest neighbor search, or coordinate range determination can be used to match meteorological stations with grid cells. For meteorological stations located near grid boundaries, the corresponding grid cells can be determined according to preset attribution rules, such as using inclusion relationships, nearest grid center point relationships, or administrative / basin boundary constraints for attribution determination.

[0069] Station-inclusive grid cells, as grid cells constrained by measured precipitation, can be used to calculate station-level correction coefficients. Grid cells to be extrapolated, lacking direct station observation constraints, require the inference of grid-level correction coefficients by combining correction information from station-inclusive grid cells with information such as spatial distance, temperature differences, and topographic differences. This partitioning method clarifies the source of correction coefficients for different grid cells and provides a foundation for subsequent spatial extrapolation.

[0070] In addition, the embodiments of this application may also include: determining the grid unit containing the meteorological station as the station-containing grid unit based on the location information of each meteorological station and the location information of each grid unit, and determining the other grid units in the target watershed other than the station-containing grid units as the grid units to be calculated.

[0071] The above method can be used to form a set of grid cells containing stations and a set of grid cells to be calculated. The set of grid cells containing stations is used to carry the error information between the measured precipitation at the stations and the precipitation to be corrected, while the set of grid cells to be calculated is used to receive the correction information calculated from the grid cells containing stations. This division result can also be used to subsequently determine the original precipitation correction coefficient for the entire region; that is, the grid cells containing stations use station-level correction coefficients, and the grid cells to be calculated use grid-level correction coefficients.

[0072] S103. Based on the measured precipitation and precipitation grid data to be corrected for the meteorological stations corresponding to the grid units containing stations, determine the station-level correction coefficients for each observation period of the grid units containing stations.

[0073] In step S103, the station-level correction coefficient for each observation period can be determined based on the measured precipitation and the precipitation grid data to be corrected for the meteorological station corresponding to the station-containing grid unit determined in step S102. For example, in this embodiment, the station-level correction coefficient is used to characterize the difference between the measured precipitation and the precipitation grid data to be corrected at the station-containing grid unit. For each station-containing grid unit, the measured precipitation for each observation period at the meteorological station corresponding to the station-containing grid unit can be extracted, and the precipitation to be corrected for the same observation period in the precipitation grid data to be corrected for the station-containing grid unit can be extracted. Then, the station-level correction coefficient is determined based on the proportional relationship or difference relationship between the two.

[0074] Station-level correction coefficients can be either proportional or bias-based. Proportional correction coefficients are obtained by the ratio of measured precipitation to the precipitation to be corrected, and are suitable for multiplicative correction of the precipitation grid data. Bias-based correction coefficients are obtained by the difference between measured precipitation and the precipitation to be corrected, and are suitable for additive correction. In a preferred implementation of this application, proportional correction coefficients are used to maintain the non-negative characteristic of precipitation and facilitate subsequent grid-by-grid multiplicative correction.

[0075] When determining station-level correction coefficients, they can be calculated separately for each observation period. For example, for a monthly observation period, the station-level correction coefficients for each month can be calculated separately for each grid cell containing stations. This reflects the dynamic deviation between the precipitation grid data to be corrected and the measured precipitation under different seasons or different precipitation events, avoiding the inability to fully correct seasonal errors due to the use of fixed correction coefficients.

[0076] Furthermore, the embodiments of this application may also: for each station-containing grid unit, determine the station-level correction coefficient corresponding to the station-containing grid unit in each observation period based on the measured precipitation corresponding to the meteorological station of the station-containing grid unit in each observation period, and the precipitation to be corrected corresponding to the station-containing grid unit in the precipitation grid data to be corrected.

[0077] In this implementation, the grid cell containing the station can serve as a spatial connection unit between the measured precipitation at the station and the grid precipitation. By comparing the measured precipitation and the precipitation to be corrected at the same grid location and during the same observation period, local correction information of the precipitation grid data to be corrected at that location can be obtained.

[0078] Furthermore, in this embodiment, the method may further include: when the grid unit containing the station corresponds to one meteorological station, the measured precipitation of that meteorological station during the corresponding observation period is used as the grid representative measured precipitation of the grid unit containing the station during the observation period; when the grid unit containing the station corresponds to multiple meteorological stations, the measured precipitation of the multiple meteorological stations during the corresponding observation period is statistically processed to obtain the grid representative measured precipitation of the grid unit containing the station during the observation period; and the station-level correction coefficient of the grid unit containing the station during the observation period is determined based on the grid representative measured precipitation of the grid unit containing the station during the observation period and the precipitation to be corrected.

[0079] In real-world data, a grid cell may contain one or more meteorological stations. When it contains only one meteorological station, the measured precipitation at that station can be directly used as the representative measured precipitation for the grid. When it contains multiple meteorological stations, the measured precipitation from multiple stations can be statistically fused to reduce the impact of outliers or local observation errors from a single station on the station-level correction coefficient.

[0080] Furthermore, in the embodiments of this application, the statistical processing includes at least one of the following: averaging, distance-weighted averaging, median processing, outlier removal, and confidence-weighted processing.

[0081] For example, the average of measured precipitation from multiple meteorological stations within the same grid cell can be taken, or a distance-weighted average can be calculated based on the distance between the station and the grid center. If there are obviously abnormal station observations, outlier removal can be performed first, followed by statistical processing. If different stations have different data quality levels or historical reliability, reliability-weighted processing can also be used to improve the reliability of the grid representing measured precipitation.

[0082] Furthermore, in this embodiment, the method may further include: using the ratio between the measured precipitation represented by the grid cell corresponding to the station-containing grid cell during the observation period and the precipitation to be corrected corresponding to the station-containing grid cell during the observation period as the station-level correction coefficient for the station-containing grid cell during the observation period; and determining the station-level correction coefficient for the station-containing grid cell during the observation period based on the ratio between the measured precipitation represented by the grid cell and the preset precipitation threshold when the precipitation to be corrected for the station-containing grid cell during the observation period is less than a preset precipitation threshold.

[0083] Setting a preset precipitation threshold can prevent the correction coefficient from becoming too large or impossible to calculate when the precipitation to be corrected is too small or zero. For example, when the precipitation to be corrected is close to zero while the measured precipitation is not zero, directly calculating the ratio may result in an extreme correction coefficient. By replacing the excessively small precipitation to be corrected with a preset precipitation threshold, the numerical stability of the station-level correction coefficient can be improved.

[0084] S104. Based on the precipitation grid data, temperature data, topographic data, station-level correction coefficients, and the spatial distance and environmental association information between the station-containing grid unit and the grid unit to be calculated, determine the grid-level correction coefficients corresponding to the grid unit to be calculated in each observation period.

[0085] In step S104, the grid-level correction coefficients for the grid units to be corrected in each observation period can be determined based on the precipitation grid data to be corrected obtained in step S103, the temperature data and topographic data obtained in step S101, the station-level correction coefficients determined in step S103, and the spatial distance and environmental association information between the grid units containing stations and the grid units to be calculated determined in step S102. For example, in this embodiment, the grid-level correction coefficients are used to characterize the degree of precipitation correction corresponding to the grid units to be calculated. Since the grid units to be calculated do not directly contain meteorological stations, the correction coefficients cannot be directly determined by the ratio between the measured precipitation and the precipitation to be corrected. Therefore, it is necessary to use the station-level correction coefficients of the grid units containing stations for spatial extrapolation.

[0086] Spatial extrapolation does not only consider distance but also takes into account the influence of temperature and topographic data on the spatial distribution of precipitation. Generally, the closer the distance between station-containing grid cells, the higher their reference value to the grid cell to be extrapolated; the more similar the temperature and topographic conditions of the station-containing grid cells, the more likely their station-level correction coefficients are to be applicable to the grid cell to be extrapolated. Therefore, the embodiments of this application comprehensively consider spatial distance, temperature correlation information, topographic correlation information, and station representative parameters to determine the coupling extrapolation weights.

[0087] For each observation period, the weights of the temperature factor and the topographic factor can be dynamically determined based on the precipitation distribution characteristics in the precipitation grid data to be corrected. For example, in a certain observation period, if the spatial distribution of precipitation is more correlated with topographic relief, the weight of the topographic factor can be larger; if the spatial distribution of precipitation is more correlated with temperature conditions, the weight of the temperature factor can be larger. This makes the calculation process of the correction coefficients for different observation periods time-varying and adaptable.

[0088] Furthermore, in this embodiment, it may also include: for each observation period, determining the spatial distribution characteristics of precipitation in the target watershed during the observation period based on the precipitation to be corrected for each grid unit in the precipitation grid data during that observation period, and determining the temperature factor weight and topographic factor weight for that observation period based on the correlation between the spatial distribution characteristics of precipitation and temperature data and topographic data respectively; for each observation period, determining the representative parameters of each station-containing grid unit during the observation period based on the spatial difference in the station-level correction coefficients corresponding to each station-containing grid unit during that observation period; for each grid unit to be calculated, under each observation period, based on the precipitation to be corrected for each station-containing grid unit, determining the representative parameters of each station-containing grid unit during the observation period based on the spatial difference in the station-level correction coefficients corresponding to each station-containing grid unit during that observation period; and for each grid unit to be calculated, under each observation period, determining the temperature factor weight and topographic factor weight for each station-containing grid unit to be calculated based on the temperature factor weight and topographic factor weight for each station-containing grid unit during that observation period. The spatial distance between the grid cell to be estimated and each grid cell containing stations, the weights of the temperature factor and the topographic factor corresponding to the observation period, the temperature correlation information and topographic correlation information between the grid cell to be estimated and each grid cell containing stations, and the representative parameters of the stations corresponding to each grid cell containing stations are used to determine the coupling estimation weight of the grid cell to be estimated relative to each grid cell containing stations. For each grid cell to be estimated, under each observation period, the station-level correction coefficients of each grid cell containing stations corresponding to the observation period are spatially weighted and estimated according to the coupling estimation weight of the grid cell to be estimated relative to each grid cell containing stations, so as to obtain the grid-level correction coefficients of the grid cell to be estimated corresponding to the observation period.

[0089] The representativeness parameter of a station can be used to reflect the reference capability of the station-level correction coefficient of a given grid cell to the surrounding area. When the station-level correction coefficient of a given grid cell differs significantly from that of surrounding grid cells, it may indicate the presence of local anomalies, observational anomalies, or local features unsuitable for extrapolation at that station. In such cases, the representativeness of that grid cell in spatial extrapolation can be reduced. Conversely, when the correction coefficient of a given grid cell is relatively consistent with that of surrounding grid cells, its representativeness parameter can be higher.

[0090] Furthermore, embodiments of this application may also include: for the observation period, extracting the precipitation to be corrected corresponding to each grid unit within the target watershed from the precipitation grid data to obtain a precipitation distribution sample for the observation period; extracting the air temperature data corresponding to each grid unit within the target watershed during the observation period from the temperature data to obtain a temperature distribution sample for the observation period; extracting the topographic feature values ​​corresponding to each grid unit within the target watershed from the topographic data to obtain a topographic distribution sample; calculating the temperature correlation metric for the observation period based on the precipitation distribution sample and the temperature distribution sample; calculating the topographic correlation metric for the observation period based on the precipitation distribution sample and the topographic correlation metric; and determining the temperature factor weight and topographic factor weight for the observation period based on the relative magnitude between the temperature correlation metric and the topographic correlation metric.

[0091] Temperature correlation measures can be used to assess the consistency or correlation between the spatial distribution of precipitation and the spatial distribution of temperature. Topographic correlation measures can be used to assess the consistency or correlation between the spatial distribution of precipitation and the spatial distribution of topography. Topographic features can be altitude, slope, aspect, topographic relief, or a combination of multiple topographic factors. Determining the weights of temperature and topographic factors based on their relative magnitudes allows the environmental correlation weights to better reflect the spatial genetic characteristics of precipitation during the current observation period.

[0092] Furthermore, in this embodiment, the temperature correlation metric includes the coefficient of determination calculated based on precipitation distribution samples and temperature distribution samples; the topographic correlation metric includes the coefficient of determination calculated based on precipitation distribution samples and topographic distribution samples.

[0093] The coefficient of determination can be used to characterize the extent to which one spatial variable explains the variation of another spatial variable. In this implementation, both temperature correlation and topographic correlation measures can be represented by the coefficient of determination, thus giving the weights of temperature factors and topographic factors a unified dimension and comparability.

[0094] Furthermore, in this embodiment of the application, the ratio of the temperature correlation metric corresponding to the observation period to the sum of the temperature correlation metric and the terrain correlation metric can be used as the temperature factor weight corresponding to the observation period; and the ratio of the terrain correlation metric corresponding to the observation period to the sum of the temperature correlation metric and the terrain correlation metric can be used as the terrain factor weight corresponding to the observation period.

[0095] This allows the sum of the temperature factor weight and the terrain factor weight to be 1, facilitating subsequent weighted combination of temperature and terrain correlation weights. When the temperature correlation metric is large, the temperature factor weight increases accordingly; when the terrain correlation metric is large, the terrain factor weight increases accordingly.

[0096] Furthermore, in this embodiment, the method may also include: for each grid cell containing a station, determining the local correction difference value corresponding to the grid cell containing a station based on the difference between the station-level correction coefficient corresponding to the grid cell containing a station during the observation period and the station-level correction coefficient corresponding to other grid cells containing stations within a preset range during the observation period; determining the station representative parameter corresponding to the grid cell containing a station during the observation period based on the local correction difference value, wherein the larger the local correction difference value, the smaller the station representative parameter.

[0097] The preset range can be a fixed distance range, a range of adjacent station numbers, or a watershed zoning range. Local correction difference values ​​can be calculated using methods such as mean absolute difference, root mean square error, median error, or standard deviation. When the local correction difference value is large, it indicates that the correction coefficient of the grid cell containing the station has a strong abrupt change relative to the surrounding stations. Reducing its station representativeness parameter can reduce the influence of abnormal local information on the extrapolation of the grid cell to be extrapolated.

[0098] Furthermore, in this embodiment, the method may further include: determining a distance attenuation weight based on the spatial distance between the grid unit to be estimated and each grid unit containing stations, for the grid unit to be estimated and each grid unit containing stations; determining a temperature correlation weight based on the temperature difference between the grid unit to be estimated and the grid unit containing stations; determining a terrain correlation weight based on the terrain difference between the grid unit to be estimated and the grid unit containing stations; weighting the temperature correlation weight and the terrain correlation weight according to the temperature factor weight and the terrain factor weight corresponding to the observation period to obtain the environmental correlation weight of the grid unit to be estimated relative to the grid unit containing stations; determining the initial estimation weight of the grid unit to be estimated relative to the grid unit containing stations based on the distance attenuation weight, the environmental correlation weight, and the representative parameters of the stations corresponding to the grid unit containing stations; and normalizing the initial estimation weight of the grid unit to be estimated relative to each grid unit containing stations to obtain the coupled estimation weight of the grid unit to be estimated relative to each grid unit containing stations.

[0099] Distance attenuation weights reflect spatial proximity, temperature correlation weights reflect similarity in thermal conditions, topographic correlation weights reflect similarity in topographic conditions, and station representativeness parameters reflect the extrapolability of station correction information. By coupling these factors, the problem of traditional inverse distance interpolation considering only spatial distance while ignoring environmental differences can be avoided, making it particularly suitable for mountainous areas, canyons, and regions with significant elevation changes.

[0100] In addition, this application embodiment may also include: determining the distance attenuation weight based on the negative power of the spatial distance between the grid cell to be calculated and the grid cell containing the station, wherein the distance attenuation index corresponding to the negative power is a preset value.

[0101] For example, the distance attenuation weight can decrease as spatial distance increases. The distance attenuation index can be preset based on the target watershed area, station density, terrain complexity, or historical verification results. The larger the distance attenuation index, the stronger the influence of nearby station-containing grid cells on the grid cells to be extrapolated.

[0102] Furthermore, in this embodiment, the method may also include: normalizing the difference between the temperature data of the grid cell to be calculated and the grid cell containing the station during the corresponding observation period, and determining the temperature association weight based on the normalized temperature difference, so that the smaller the temperature difference, the greater the temperature association weight; normalizing the difference between the terrain feature values ​​of the grid cell to be calculated and the grid cell containing the station, and determining the terrain association weight based on the normalized terrain difference, so that the smaller the terrain difference, the greater the terrain association weight.

[0103] Normalization eliminates the dimensional differences between temperature and topographic differences, allowing for a weighted combination of the two at a unified scale. For example, temperature and topographic differences can be mapped to values ​​between 0 and 1, and then the correlation weights can be calculated using an inverse relationship. The smaller the temperature or topographic difference, the more similar the environmental conditions are between the grid cell to be calculated and the grid cell containing the station, and the greater the corresponding correlation weight.

[0104] In addition, in this embodiment of the application, it may also include: multiplying the distance attenuation weight, the environmental association weight and the representative parameters of the station corresponding to the grid unit containing the station to obtain the initial estimated weight of the grid unit to be estimated relative to the grid unit containing the station.

[0105] By using a product approach, spatial distance, environmental similarity, and site representativeness can jointly constrain the inference weights. If a grid cell containing a station is far away, has significant environmental differences, or has low site representativeness, its initial inference weight will be reduced accordingly, thereby minimizing the impact of unsuitable reference information on the correction coefficients of the grid cells to be inferred.

[0106] Furthermore, embodiments of this application may also: use the coupling calculation weight of the grid cell to be calculated relative to each grid cell containing stations as the weight, perform a weighted summation of the station-level correction coefficients corresponding to each grid cell containing stations during the observation period, and obtain a weighted sum value; use the ratio between the weighted sum value and the sum of each coupling calculation weight as the grid-level correction coefficient corresponding to the grid cell to be calculated during the observation period.

[0107] The above method can be used to obtain the weighted average correction coefficient of the grid cell to be calculated. If the coupled calculation weights have been normalized, the weighted sum can be directly used as the grid-level correction coefficient; if the coupled calculation weights have not been normalized, the grid-level correction coefficient can be obtained by the ratio between the weighted sum and the sum of the weights.

[0108] S105. Based on the station-level correction coefficient and the grid-level correction coefficient, determine the original precipitation correction coefficient corresponding to each grid unit in each observation period. Based on the neighborhood differences between the original precipitation correction coefficients corresponding to each grid unit, perform stability adjustment on the original precipitation correction coefficients corresponding to each grid unit to obtain the adjusted precipitation correction coefficients corresponding to each grid unit in each observation period.

[0109] In step S105, the original precipitation correction coefficients for each grid unit in each observation period can be determined based on the station-level correction coefficients determined in step S103 and the grid-level correction coefficients determined in step S104. Then, based on the neighborhood differences between the original precipitation correction coefficients of each grid unit, the stability of the original precipitation correction coefficients for each grid unit is adjusted to obtain the adjusted precipitation correction coefficients for each grid unit in each observation period. For example, in this embodiment, the station-level correction coefficients can be used as the original precipitation correction coefficients for grid units containing stations, and the grid-level correction coefficients can be used as the original precipitation correction coefficients for grid units to be calculated. Thus, the original precipitation correction coefficients covering all grid units of the target watershed can be obtained.

[0110] Due to factors such as anomalies in station observations, local errors in precipitation grid data, abrupt changes in terrain, or abnormal calculation weights, the original precipitation correction coefficients may exhibit unreasonable abrupt changes in local areas. Directly using these original precipitation correction coefficients for precipitation correction may result in locally abnormally high or low precipitation values ​​after correction. Therefore, this application embodiment further adjusts stability based on the differences in correction coefficients between neighboring grid cells.

[0111] Stability adjustment can be achieved by determining the set of neighboring grid cells for each grid cell and comparing the differences in the original precipitation correction coefficients between the central grid cell and its neighboring grid cells. When the correction coefficients of some neighboring grid cells differ little from those of the central grid cell, these neighboring grid cells can be considered to have good stability reference value; when the difference is too large, their weight in the stability reference can be reduced or they can be excluded.

[0112] Furthermore, in this embodiment, the method may further include: determining the original precipitation correction coefficients for each grid unit within the target watershed during each observation period based on the station-level correction coefficients corresponding to each grid unit containing stations and the grid-level correction coefficients corresponding to each grid unit to be calculated; for each grid unit and each observation period, determining the set of neighboring grid units corresponding to that grid unit within the target watershed, and calculating the coefficient difference between the original precipitation correction coefficients corresponding to that grid unit and the original precipitation correction coefficients corresponding to each neighboring grid unit in the set of neighboring grid units; selecting neighboring grid units from the set of neighboring grid units whose coefficient difference values ​​are less than or equal to a preset difference threshold as stable neighboring grid units, and determining the stable reference correction coefficients corresponding to that grid unit during the observation period based on the original precipitation correction coefficients corresponding to the stable neighboring grid units; determining the stable adjustment coefficients corresponding to that grid unit during the observation period based on the degree of deviation between the original precipitation correction coefficients corresponding to that grid unit and the stable reference correction coefficients; and performing a weighted calculation on the original precipitation correction coefficients and the stable reference correction coefficients corresponding to that grid unit based on the stable adjustment coefficients corresponding to that grid unit to obtain the adjusted precipitation correction coefficients corresponding to that grid unit during the observation period.

[0113] The neighborhood grid cell set can be an eight-neighborhood, a four-neighborhood, a fixed-radius neighborhood, or a neighborhood determined based on hydrological connectivity. The stability reference correction coefficient can be the average, median, distance-weighted average, or other statistical value of the original precipitation correction coefficients corresponding to the stable neighborhood grid cells. By introducing the stability reference correction coefficient, the original precipitation correction coefficients of local anomalies can be appropriately aligned with the neighborhood stability level.

[0114] Furthermore, in this embodiment, the method may further include: calculating the absolute value of the difference between the original precipitation correction coefficient and the stable reference correction coefficient corresponding to the grid cell to obtain a stable deviation value; determining the stable adjustment coefficient corresponding to the grid cell during the observation period based on the ratio between the stable deviation value and a preset deviation threshold, wherein the larger the stable deviation value, the smaller the stable adjustment coefficient; weighting the original precipitation correction coefficient corresponding to the grid cell based on the stable adjustment coefficient, and weighting the stable reference correction coefficient based on the difference between the original and stable adjustment coefficients, and determining the sum of the weighted results as the adjusted precipitation correction coefficient corresponding to the grid cell during the observation period.

[0115] When the deviation between the original precipitation correction coefficient and the stable reference correction coefficient is small, more information from the original precipitation correction coefficient can be retained; when the deviation is large, more information from the stable reference correction coefficient can be used. This can maintain local spatial differences while suppressing unreasonable local abrupt changes, thereby improving the spatial stability of the precipitation correction coefficient.

[0116] In addition, the embodiments of this application may also include: before determining the grid-level correction coefficients corresponding to the grid cells to be calculated in each observation period, removing or limiting extreme values ​​in the station-level correction coefficients according to preset anomaly handling rules, and using the processed station-level correction coefficients to determine the grid-level correction coefficients.

[0117] Pre-defined anomaly handling rules can include limiting amplitude based on upper and lower thresholds, truncating based on quantile ranges, identifying outliers based on mean and standard deviation, or removing outliers based on the historical observation quality of the station. By processing extreme values ​​in the station-level correction coefficients, the impact of abnormal stations or abnormal time periods on spatial extrapolation results can be reduced.

[0118] In addition, this application embodiment may also include: for a target grid cell to be calculated whose maximum value of the coupling calculation weight relative to all grid cells containing stations is lower than a preset lower limit, assigning a grid-level correction coefficient to the target grid cell to be calculated in the corresponding observation period according to a preset conservative strategy; the preset conservative strategy includes setting the grid-level correction coefficient to a neutral value that does not introduce correction, or setting the grid-level correction coefficient to the median of the station-level correction coefficients across the entire region.

[0119] When the spatial or environmental correlation between a given grid cell to be extrapolated and all grid cells containing stations is low, it indicates that the reliability of directly using the correction information from the grid cells containing stations is insufficient. Adopting a conservative strategy can avoid amplifying unreliable extrapolation correction errors. For example, for proportional correction coefficients, a neutral value of 1 indicates that the precipitation to be corrected is not adjusted.

[0120] S106. Based on the adjusted precipitation correction coefficients corresponding to each grid unit in each observation period, the precipitation amount to be corrected in the corresponding grid unit in the precipitation grid data to be corrected in the corresponding observation period is corrected to obtain the corrected precipitation amount corresponding to each grid unit in each observation period.

[0121] In step S106, the precipitation to be corrected in the corresponding grid cell of the precipitation grid data to be corrected during the corresponding observation period can be corrected according to the adjusted precipitation correction coefficient generated in step S105, thus obtaining the corrected precipitation for each grid cell during each observation period. For example, in this embodiment, the adjusted precipitation correction coefficient is used to correct the precipitation grid data to be corrected grid by grid and observation period by observation period. For any grid cell and any observation period, the precipitation to be corrected corresponding to that grid cell during that observation period can be extracted, and the corresponding adjusted precipitation correction coefficient can be extracted. Then, the corrected precipitation can be obtained based on both.

[0122] When using proportional correction coefficients, the corrected precipitation amount can be obtained by multiplying the precipitation to be corrected by the adjusted precipitation correction coefficient. This method is simple and stable, and allows station observation constraints and environmental extrapolation results to be directly applied to the original precipitation grid data. For additive correction coefficients, the corrected precipitation amount can also be obtained by adding the precipitation to be corrected to the correction deviation value.

[0123] The corrected precipitation data maintains the same spatial and temporal grid structure as the precipitation grid data to be corrected, meaning each grid cell corresponds to a corrected precipitation value for each observation period. Therefore, the corrected precipitation data can be directly used as the precipitation input data for subsequent water yield calculations.

[0124] In addition, the embodiments of this application may also include: correcting the precipitation amount to be corrected in the corresponding observation period of the corresponding grid unit in the precipitation grid data to be corrected according to the adjusted precipitation correction coefficient of each grid unit in each observation period, so as to obtain the corrected precipitation amount of each grid unit in each observation period.

[0125] This process can be performed grid-by-grid or in batches by layer matrix or array. When the number of grids in the target watershed is large, matrix operations, parallel computing, or block processing can be used to improve computational efficiency.

[0126] In addition, the embodiments of this application may also include: for each grid cell and each observation period, multiplying the precipitation to be corrected corresponding to the grid cell in the observation period by the adjusted precipitation correction coefficient corresponding to the grid cell in the observation period to obtain the corrected precipitation corresponding to the grid cell in the observation period.

[0127] Multiplicative correction can preserve the non-negative property of precipitation and effectively correct proportional biases in the original precipitation grid data. For example, when the original precipitation grid data for a certain area is generally low, the corresponding correction coefficient can be greater than 1; when the original precipitation grid data is generally high, the corresponding correction coefficient can be less than 1.

[0128] S107. Based on the corrected precipitation, water production calculation parameters, and grid area, calculate the predicted water production value for each grid cell, and generate the predicted water production result for the target watershed based on the predicted water production value for each grid cell.

[0129] In step S107, the predicted water yield for each grid cell can be calculated based on the corrected precipitation, water yield calculation parameters, and grid area determined in step S106. The predicted water yield for the target watershed is then generated based on these predicted values. For example, in this embodiment, the corrected precipitation for each grid cell within the target calculation period can be used as the precipitation input. The actual evapotranspiration is calculated in conjunction with the water yield calculation parameters, and the water yield depth per unit area is determined based on the water balance relationship. The target calculation period can be an annual scale, a seasonal scale, or any time period specified by the user.

[0130] The water yield depth per unit area can be determined based on the difference between the corrected precipitation input and the actual evapotranspiration. Subsequently, the water yield depth per unit area can be multiplied by the grid area to obtain the predicted water yield value for that grid cell. For the entire target watershed, the predicted water yield values ​​for all grid cells can be organized spatially to form the predicted water yield results, or the predicted water yield values ​​for each grid cell can be summarized to obtain the total predicted water yield value for the target watershed.

[0131] Water yield calculation parameters can include potential evapotranspiration, vegetation parameters, soil property parameters, land cover parameters, root depth parameters, and soil depth parameters. Different parameters can be used to constrain actual evapotranspiration and soil water storage capacity, thereby enabling water yield prediction results to reflect regional differences in underlying surface conditions.

[0132] In addition, the embodiments of this application may also include: determining the corrected precipitation input and actual evapotranspiration of each grid cell in the target calculation period based on the corrected precipitation and water production calculation parameters; determining the water production depth per unit area of ​​each grid cell based on the difference between the corrected precipitation input and the actual evapotranspiration of each grid cell; and calculating the predicted water production value of each grid cell based on the water production depth per unit area and the grid area of ​​each grid cell.

[0133] This calculation process converts the corrected precipitation input into water yield output. Since the corrected precipitation has been adjusted for factors such as station observations, spatial distance, temperature conditions, topographic conditions, and neighborhood stability, the water yield prediction based on the corrected precipitation can better reflect the spatial differences in the target watershed.

[0134] Furthermore, this embodiment may also include: for each grid cell, summing the corrected precipitation for each observation period within the target calculation period to obtain the corrected precipitation input for the grid cell during the target calculation period; determining the potential evapotranspiration and evapotranspiration regulation coefficient for the grid cell during the target calculation period based on the water production calculation parameters corresponding to the grid cell; and determining the actual evapotranspiration for the grid cell during the target calculation period based on the corrected precipitation input, potential evapotranspiration, and evapotranspiration regulation coefficient.

[0135] For example, when the observation period is on a monthly scale and the target calculation period is on an annual scale, the corrected precipitation for 12 months can be summed to obtain the annual corrected precipitation input. Potential evapotranspiration can be determined from meteorological data or existing evapotranspiration products, and the evapotranspiration regulation coefficient can be determined jointly by vegetation, soil, and land cover parameters.

[0136] Furthermore, the embodiments of this application may also include: determining the aridity index based on the ratio between potential evapotranspiration and corrected precipitation input; determining the actual evapotranspiration ratio based on the aridity index and the evapotranspiration adjustment coefficient; and determining the actual evapotranspiration of the grid cell in the target calculation period based on the actual evapotranspiration ratio and the corrected precipitation input.

[0137] The aridity index reflects the degree of matching between hydrothermal conditions. When the potential evapotranspiration is high relative to precipitation input, the region may be relatively arid, and the actual evapotranspiration ratio is limited by water availability. When precipitation input is sufficient, the actual evapotranspiration ratio is more influenced by energy conditions and underlying surface parameters. Determining the actual evapotranspiration using the aridity index and evapotranspiration regulation coefficient allows for water production calculations to better align with water balance mechanisms.

[0138] In addition, this application embodiment may also include: after generating the water production prediction result corresponding to the target watershed based on the water production prediction value corresponding to each grid unit, summarizing the water production prediction values ​​corresponding to each grid unit in the preset sub-watershed to obtain the sub-watershed water production prediction value corresponding to the preset sub-watershed; and calculating the water conservation capacity corresponding to the preset sub-watershed based on the sub-watershed water production prediction value and the measured runoff data corresponding to the preset sub-watershed.

[0139] In this implementation, grid cells can be partitioned and aggregated according to sub-basin boundaries to obtain predicted water yield values ​​for each sub-basin. Measured runoff data can be obtained from hydrological station monitoring data. By comparing the predicted water yield values ​​with the measured runoff data, the water conservation capacity of the sub-basin can be further calculated or assessed, providing a data foundation for ecosystem service assessment.

[0140] Furthermore, the embodiments of this application may also: summarize the predicted water yield values ​​corresponding to each grid unit within the preset sub-basin to obtain the predicted water yield value of the sub-basin corresponding to the preset sub-basin; based on the degree of deviation between the predicted water yield value of the sub-basin and the measured runoff corresponding to the preset sub-basin, perform feedback verification on at least one of the following: temperature factor weight, topographic factor weight, distance attenuation index of distance attenuation weight, and stability adjustment coefficient; iteratively adjust the adjusted precipitation correction coefficient corresponding to each grid unit in each observation period based on the feedback verification results; and re-execute the correction of the precipitation to be corrected and the calculation of the predicted water yield value based on the iteratively adjusted adjusted precipitation correction coefficient until the degree of deviation corresponding to the iterative adjustment meets the preset convergence condition.

[0141] Feedback verification can be used to check the reasonableness of precipitation correction and yield calculation results at the sub-basin scale. When the predicted yield of a sub-basin deviates significantly from the measured runoff, the weights of temperature factors, topographic factors, distance decay exponent, or stability adjustment coefficient can be adjusted to gradually bring the corrected precipitation and yield predictions closer to an acceptable range. Preset convergence conditions can include a deviation less than a threshold, reaching the upper limit of the number of iterations, or a change in the results between two adjacent iterations less than a threshold.

[0142] Furthermore, this embodiment may also include: performing data preprocessing on the gridded base data, precipitation observation data, and precipitation grid data to be corrected before determining the station-level correction coefficients; the data preprocessing includes at least one of the following: unifying the gridded base data, precipitation observation data, and precipitation grid data to be corrected to the same coordinate system; resampling the gridded base data and precipitation grid data to be corrected at a uniform spatial resolution while maintaining grid alignment; organizing the precipitation observation data into vector point data according to the station number, longitude, latitude, and cumulative precipitation fields; removing outliers from the precipitation observation data; imputing missing values ​​in the precipitation observation data; and performing time aggregation processing on the precipitation observation data according to the time granularity corresponding to multiple observation periods.

[0143] Data preprocessing can improve spatiotemporal consistency between different data sources and reduce subsequent errors caused by inconsistencies in coordinates, resolution, time scales, or outlier observations. By performing preprocessing before calculating station-level correction coefficients, the data quality of the entire water yield prediction process can be improved.

[0144] In addition, the embodiments of this application may also include one or more of the following: output corrected precipitation data, original precipitation correction coefficient data, adjusted precipitation correction coefficient data, water yield prediction results, temperature factor weight time series data, topographic factor weight time series data, environmental correlation weight data, water yield time series data, sub-basin water yield statistics results, and water conservation data.

[0145] The output data can be used for result visualization, error analysis, subsequent hydrological simulation, ecosystem service assessment, or water resource management decision-making. For example, time series data with weighted temperature factors and topographic factors can be used to analyze the degree of influence of environmental factors on precipitation correction under different observation periods; adjusted precipitation correction coefficient data can be used to analyze the spatial distribution characteristics of the correction process; and water yield prediction results can be used for watershed water resource assessment.

[0146] Third Embodiment Figure 2 This is a structural block diagram of a water production prediction device provided in an embodiment of this application. Figure 2 As shown, the water production prediction device may include: a data acquisition module 21, a grid division module 22, a site correction module 23, a grid calculation module 24, a stability adjustment module 25, a precipitation correction module 26, and a water production calculation module 27.

[0147] The data acquisition module 21 can be used to acquire gridded baseline data, precipitation observation data and precipitation grid data to be corrected corresponding to the target watershed.

[0148] In this embodiment, the data acquisition module 21 serves as the data entry point for the device, and can acquire data corresponding to the target watershed from databases, remote sensing product servers, meteorological station data platforms, geographic information system files, local storage media, or user input interfaces. The gridded base data may include the location information, topographic data, temperature data, grid area, and water yield calculation parameters of each grid cell. Precipitation observation data may include the locations of meteorological stations and the measured precipitation at those stations. The precipitation grid data to be corrected may include the precipitation to be corrected corresponding to each grid cell in multiple observation periods.

[0149] The data acquisition module 21 can provide the location information of meteorological stations and grid units to the grid division module 22; provide precipitation observation data and precipitation grid data to be corrected to the station correction module 23; provide precipitation grid data to be corrected, temperature data and topographic data to the grid extrapolation module 24; provide precipitation grid data to be corrected to the precipitation correction module 26; and provide grid area and water production calculation parameters to the water production calculation module 27.

[0150] In addition, in this embodiment of the application, the data acquisition module 21 can also acquire a multi-source candidate precipitation grid dataset, which includes two or more candidate precipitation grid data obtained from different sources; for each candidate precipitation grid data, the goodness-of-fit index corresponding to the candidate precipitation grid data is calculated; and according to the preset optimization rules corresponding to the goodness-of-fit index, candidate precipitation grid data is selected from the multi-source candidate precipitation grid dataset as precipitation grid data to be corrected.

[0151] The data acquisition module 21 can read and process at least two of the following: satellite-retrieved precipitation products, reanalysis precipitation products, meteorological interpolated precipitation products, and station-fused precipitation products. It then filters the precipitation grid data to be corrected based on the goodness of fit between candidate fitted precipitation amounts and measured precipitation amounts. Thus, the device can obtain a more suitable initial precipitation input for the target watershed before subsequent correction.

[0152] In addition, in this embodiment of the application, the data acquisition module 21 can also perform data preprocessing on the gridded base data, precipitation observation data and precipitation grid data to be corrected; the data preprocessing includes at least one of coordinate system one, spatial resolution resampling, grid alignment, precipitation observation data vectorization, outlier removal, missing value imputation and time aggregation processing.

[0153] By performing preprocessing through the data acquisition module 21, it can be ensured that the data processed by subsequent modules have a unified spatial and temporal reference, thereby reducing calculation errors caused by inconsistencies in data format, spatial range, or time scale.

[0154] The grid division module 22 can be used to divide the grid units in the target watershed into grid units containing stations and grid units to be calculated, based on the location information of each meteorological station and the location information of each grid unit.

[0155] In this embodiment, the grid division module 22 can receive the meteorological station locations and grid cell locations from the data acquisition module 21. The grid division module 22 can determine the grid cell to which the meteorological station belongs through methods such as spatial overlay, coordinate matching, point-to-surface inclusion judgment, or nearest neighbor matching. Grid cells containing meteorological stations can be divided into station-containing grid cells, and other grid cells can be divided into grid cells to be calculated.

[0156] The grid generation module 22 can output grid cells containing stations to the station correction module 23, enabling the station correction module 23 to calculate station-level correction coefficients for the grid cells containing stations. The grid generation module 22 can also output grid cells containing stations and grid cells to be calculated to the grid extrapolation module 24, enabling the grid extrapolation module 24 to extrapolate the grid cells to be calculated based on the correction information of the grid cells containing stations.

[0157] In addition, in this embodiment of the application, the grid division module 22 can also determine the grid unit containing the meteorological station as the station-containing grid unit, and determine the other grid units in the target watershed other than the station-containing grid units as the grid units to be calculated.

[0158] The module's partitioning results clarify the method for determining the correction coefficients for each grid unit. Grid units containing stations directly calculate their correction coefficients based on measured precipitation at the stations, while grid units to be extrapolated rely on spatial distance and environmental correlation information to extrapolate their correction coefficients.

[0159] The station correction module 23 can be used to determine the station-level correction coefficients for each observation period based on the measured precipitation of the meteorological station corresponding to the station-containing grid unit and the precipitation grid data to be corrected.

[0160] In this embodiment, the station correction module 23 can receive precipitation observation data and precipitation grid data to be corrected from the data acquisition module 21, and receive grid cells containing stations from the grid division module 22. For each grid cell containing stations, the station correction module 23 can extract the measured precipitation of the corresponding meteorological station in each observation period, and extract the precipitation to be corrected in the precipitation grid data containing stations for that grid cell, thereby determining the station-level correction coefficient.

[0161] The site correction module 23 can output the site-level correction coefficients to the grid calculation module 24 and the stability adjustment module 25. The grid calculation module 24 can use the site-level correction coefficients to calculate the grid-level correction coefficients corresponding to the grid cells to be calculated; the stability adjustment module 25 can use the site-level correction coefficients and the grid-level correction coefficients to construct the global original precipitation correction coefficients.

[0162] In addition, in this embodiment of the application, the station correction module 23 can also use the measured precipitation of a meteorological station during the corresponding observation period as the grid representative measured precipitation when the grid unit corresponds to a meteorological station; when the grid unit corresponds to multiple meteorological stations, it can perform statistical processing on the measured precipitation of multiple meteorological stations during the corresponding observation period to obtain the grid representative measured precipitation; and determine the station-level correction coefficient based on the grid representative measured precipitation and the precipitation to be corrected.

[0163] Statistical processing can include at least one of the following: averaging, distance-weighted averaging, median processing, outlier removal, and confidence-weighted processing. Statistical processing can improve the representativeness of observational data from multiple meteorological stations within a grid cell and reduce the impact of anomalies from individual stations on the correction coefficient.

[0164] In addition, in this embodiment of the application, the station correction module 23 can use the ratio between the measured precipitation represented by the grid and the precipitation to be corrected as the station-level correction coefficient; when the precipitation to be corrected is less than the preset precipitation threshold, the station-level correction coefficient is determined according to the ratio between the measured precipitation represented by the grid and the preset precipitation threshold.

[0165] By setting a preset precipitation threshold, the problem of abnormally amplified station-level correction coefficients due to insufficient precipitation to be corrected can be avoided, thereby improving the stability of the subsequent spatial extrapolation process.

[0166] The grid extrapolation module 24 can be used to determine the grid-level correction coefficients of the grid cells to be extrapolated for each observation period based on the precipitation grid data, temperature data, topographic data, station-level correction coefficients, and the spatial distance and environmental association information between the station-containing grid cells and the grid cells to be extrapolated.

[0167] In this embodiment, the grid extrapolation module 24 can receive precipitation grid data, temperature data, and topographic data to be corrected from the data acquisition module 21, receive grid cells containing stations and grid cells to be extrapolated from the grid division module 22, and receive station-level correction coefficients from the station correction module 23. The grid extrapolation module 24 can determine the coupling extrapolation weight of the grid cells to be extrapolated relative to the grid cells containing stations based on these input data, and perform spatial weighted extrapolation of the station-level correction coefficients based on the coupling extrapolation weight.

[0168] The grid extrapolation module 24 can output the grid-level correction coefficients corresponding to the grid cells to be extrapolated to the stability adjustment module 25. The grid extrapolation module 24 can also output intermediate data such as temperature factor weights, terrain factor weights, environmental correlation weights, site representative parameters, or coupled extrapolation weights for subsequent result analysis or feedback verification.

[0169] In addition, in this embodiment of the application, the grid calculation module 24 can determine the spatial distribution characteristics of precipitation in the target watershed for each observation period based on the precipitation amount to be corrected for each grid unit in the precipitation grid data to be corrected during that observation period, and determine the weight of temperature factor and the weight of topography factor based on the correlation between the spatial distribution characteristics of precipitation and temperature data and topographic data respectively.

[0170] The grid extrapolation module 24 can calculate correlation metrics using precipitation distribution samples, temperature distribution samples, and topographic distribution samples, and determine the weights of the temperature factor and the topographic factor based on the correlation metrics. This allows the weights to be dynamically adaptive across different observation periods.

[0171] In addition, in this embodiment, the grid calculation module 24 can determine the representative parameters of each station-containing grid unit based on the degree of spatial difference in the station-level correction coefficients of each station-containing grid unit during the corresponding observation period.

[0172] When the station-level correction coefficient of a certain station-containing grid cell differs significantly from that of surrounding station-containing grid cells, the grid estimation module 24 can reduce the station representative parameters of that station-containing grid cell, thereby reducing its influence in the estimation of grid-level correction coefficients.

[0173] In addition, in this embodiment, the grid estimation module 24 can determine the distance attenuation weight based on the spatial distance, the temperature correlation weight based on the temperature difference, and the terrain correlation weight based on the terrain difference. The temperature correlation weight and the terrain correlation weight are weighted and combined according to the temperature factor weight and the terrain factor weight to obtain the environmental correlation weight. The initial estimation weight is determined according to the distance attenuation weight, the environmental correlation weight and the representative parameters of the site. The initial estimation weight is then normalized to obtain the coupled estimation weight.

[0174] The above processing enables the grid estimation module 24 to consider not only spatial distance, but also temperature similarity, terrain similarity and site representativeness, which helps to improve the rationality of the correction coefficient of the grid unit to be estimated in complex terrain areas.

[0175] In addition, in this embodiment of the application, the grid estimation module 24 can use the coupling estimation weight of the grid cell to be estimated relative to each grid cell containing stations as the weight, and perform a weighted summation of the station-level correction coefficients corresponding to each grid cell containing stations during the observation period to obtain a weighted sum value; the ratio between the weighted sum value and the sum of each coupling estimation weight is used as the grid-level correction coefficient of the grid cell to be estimated during the observation period.

[0176] Through this weighted calculation method, the grid calculation module 24 can extend the measured correction information of the grid unit containing the station to the area lacking meteorological stations, so that the grid unit to be calculated can also obtain a precipitation correction coefficient that is compatible with the spatial environment conditions.

[0177] The stabilization adjustment module 25 can be used to determine the original precipitation correction coefficient of each grid unit in each observation period based on the station-level correction coefficient and the grid-level correction coefficient, and to perform stability adjustment on the original precipitation correction coefficient of each grid unit based on the neighborhood difference between the original precipitation correction coefficients of each grid unit, so as to obtain the adjusted precipitation correction coefficient of each grid unit in each observation period.

[0178] In this embodiment, the stabilization adjustment module 25 can receive station-level correction coefficients from the station correction module 23, grid-level correction coefficients from the grid calculation module 24, and station-containing grid cells and grid cells to be calculated from the grid division module 22. The stabilization adjustment module 25 can merge the station-level correction coefficients corresponding to the station-containing grid cells and the grid-level correction coefficients corresponding to the grid cells to be calculated into a global original precipitation correction coefficient.

[0179] The stabilization adjustment module 25 can determine a set of neighboring grid cells for each grid cell based on the adjacency relationship of the grid space, and select stable neighboring grid cells based on the difference in the original precipitation correction coefficient between the central grid cell and the neighboring grid cells. Subsequently, the stabilization adjustment module 25 can determine the stabilization reference correction coefficient based on the original precipitation correction coefficient of the stable neighboring grid cells, and further determine the stabilization adjustment coefficient to obtain the adjusted precipitation correction coefficient.

[0180] In addition, in this embodiment of the application, the stabilization adjustment module 25 can calculate the absolute value of the difference between the original precipitation correction coefficient and the stable reference correction coefficient to obtain the stable deviation value; determine the stabilization adjustment coefficient based on the ratio between the stable deviation value and the preset deviation threshold; weight the original precipitation correction coefficient based on the stabilization adjustment coefficient, and weight the stable reference correction coefficient based on the difference between the original and the stabilization adjustment coefficient, and determine the sum of the weighted results as the adjusted precipitation correction coefficient.

[0181] In this way, the stabilization module 25 can suppress local anomaly correction coefficients, making the precipitation correction coefficients more spatially smooth and stable, while still retaining reasonable local differences.

[0182] In addition, in this embodiment, the stabilization adjustment module 25 or the site correction module 23 can also remove or limit extreme values ​​in the site-level correction coefficients according to preset anomaly handling rules, and use the processed site-level correction coefficients to determine the grid-level correction coefficients.

[0183] By processing extreme values, the transmission impact of abnormal stations, abnormal observation periods, or abnormal raw precipitation data on the global correction coefficient can be reduced.

[0184] In addition, in this embodiment, the stabilization adjustment module 25 or the grid calculation module 24 may assign a grid-level correction coefficient to the target grid cell to be calculated in the corresponding observation period according to a preset conservative strategy, for the target grid cell to be calculated whose maximum value of the coupling calculation weight relative to all grid cells containing stations is lower than the preset association lower limit.

[0185] When the correlation between the grid cell to be extrapolated and all grid cells containing stations is weak, a conservative strategy can be adopted to avoid the expansion of correction error caused by unreliable extrapolation.

[0186] The precipitation correction module 26 can be used to correct the precipitation amount to be corrected in the corresponding grid unit in the precipitation grid data to be corrected in the corresponding observation period according to the adjusted precipitation correction coefficient of each grid unit in each observation period, so as to obtain the corrected precipitation amount of each grid unit in each observation period.

[0187] In this embodiment, the precipitation correction module 26 can receive precipitation grid data to be corrected from the data acquisition module 21, receive adjusted precipitation correction coefficients from the stabilization adjustment module 25, and perform precipitation correction processing according to the correspondence between grid units and observation periods.

[0188] The precipitation correction module 26 can extract the precipitation to be corrected and the adjusted precipitation correction coefficient on a grid-by-grid and observation-period basis, and calculate the corrected precipitation. The precipitation correction module 26 can output the corrected precipitation to the water production calculation module 27 as the precipitation input for water production prediction.

[0189] In addition, in this embodiment of the application, the precipitation correction module 26 can multiply the precipitation to be corrected corresponding to the grid unit in the observation period by the adjusted precipitation correction coefficient corresponding to the grid unit in the observation period for each grid unit and each observation period to obtain the corrected precipitation corresponding to the grid unit in the observation period.

[0190] When using multiplicative correction, an adjusted precipitation correction factor greater than 1 indicates that the original precipitation to be corrected is too low and needs to be adjusted upward; an adjusted precipitation correction factor less than 1 indicates that the original precipitation to be corrected is too high and needs to be adjusted downward; an adjusted precipitation correction factor equal to 1 indicates that no correction is introduced.

[0191] The water production calculation module 27 can be used to calculate the predicted water production value corresponding to each grid cell based on the corrected precipitation, water production calculation parameters and grid area, and generate the predicted water production result corresponding to the target watershed based on the predicted water production value corresponding to each grid cell.

[0192] In this embodiment, the water production calculation module 27 can receive corrected precipitation from the precipitation correction module 26 and water production calculation parameters and grid area from the data acquisition module 21. The water production calculation module 27 can accumulate the corrected precipitation according to the target calculation period to obtain the corrected precipitation input, and determine the actual evapotranspiration based on the water production calculation parameters.

[0193] The water production calculation module 27 can determine the water production depth per unit area based on the difference between the corrected precipitation input and the actual evapotranspiration, and calculate the predicted water production value corresponding to the grid cell based on the grid area. The predicted water production values ​​corresponding to multiple grid cells can be organized according to spatial location to form the predicted water production results for the target watershed.

[0194] In addition, in this embodiment of the application, the water production calculation module 27 can accumulate the corrected precipitation for each grid cell in each observation period within the target calculation period to obtain the corrected precipitation input; determine the potential evapotranspiration and evapotranspiration adjustment coefficient according to the water production calculation parameters; and determine the actual evapotranspiration according to the corrected precipitation input, potential evapotranspiration and evapotranspiration adjustment coefficient.

[0195] The water production calculation module 27 can further determine the aridity index based on the ratio between potential evapotranspiration and corrected precipitation input, determine the actual evapotranspiration ratio based on the aridity index and evapotranspiration adjustment coefficient, and determine the actual evapotranspiration based on the actual evapotranspiration ratio and corrected precipitation input.

[0196] In addition, in this embodiment, the water production calculation module 27 can also summarize the predicted water production values ​​of each grid unit in the preset sub-basin to obtain the predicted water production value of the sub-basin; and calculate the water conservation capacity of the preset sub-basin based on the predicted water production value of the sub-basin and the measured runoff data corresponding to the preset sub-basin.

[0197] By aggregating at the sub-basin scale, grid-scale water yield prediction results can be transformed into statistical scales commonly used in water resource management or ecosystem service assessment.

[0198] In addition, in this embodiment, the water production calculation module 27 can perform feedback verification on at least one of the temperature factor weight, topographic factor weight, distance attenuation index of distance attenuation weight, and stability adjustment coefficient based on the degree of deviation between the predicted water production value and the measured runoff of the sub-basin, and iteratively adjust the adjusted precipitation correction coefficient based on the feedback verification results.

[0199] Through feedback verification and iterative adjustments, the consistency between the predicted water yield of the target watershed and the measured hydrological response can be further improved.

[0200] In addition, the embodiments of this application may also output one or more of the following from the water production calculation module 27: corrected precipitation data, original precipitation correction coefficient data, adjusted precipitation correction coefficient data, water production prediction results, temperature factor weight time series data, topographic factor weight time series data, environmental correlation weight data, water production time series data, sub-basin water production statistics results, and water conservation data.

[0201] Fourth embodiment The above describes the internal functions and structure of the water production prediction device, which can be implemented as an electronic device. Figure 3 A schematic diagram illustrating the structure of an embodiment of the electronic device provided in this application. (See attached diagram.) Figure 3 As shown, the electronic device includes a memory 31 and a processor 32.

[0202] Memory 31 is used to store programs. In addition to the programs described above, memory 31 can also be configured to store various other data to support operation on the electronic device. Examples of this data include instructions for any application or method used to operate on the electronic device, contact data, phonebook data, messages, pictures, videos, etc.

[0203] The memory 31 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.

[0204] Processor 32 is not limited to a central processing unit (CPU), but may also be a graphics processing unit (GPU), a field-programmable gate array (FPGA), an embedded neural network processor (NPU), or an artificial intelligence (AI) chip. Processor 32 is coupled to memory 31 and executes the program stored in memory 31. When the program runs, it executes the water production prediction method of Embodiment 2 described above.

[0205] Furthermore, such as Figure 3 As shown, the electronic device may also include other components such as a communication component 33, a power supply component 34, an audio component 35, and a display 36. Figure 3 The diagram only shows some components and does not mean that the electronic device includes only these components. Figure 3 The components shown.

[0206] Communication component 33 is configured to facilitate wired or wireless communication between electronic devices and other devices. The electronic devices can access wireless networks based on communication standards, such as WiFi, 3G, 4G, or 5G, or combinations thereof. In one exemplary embodiment, communication component 33 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 33 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0207] Power supply component 34 provides power to various components of the electronic device. Power supply component 34 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device.

[0208] Audio component 35 is configured to output and / or input audio signals. For example, audio component 35 includes a microphone (MIC) configured to receive external audio signals when the electronic device is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 31 or transmitted via communication component 33. In some embodiments, audio component 35 also includes a speaker for outputting audio signals.

[0209] Display 36 includes a screen, which may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touchscreen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can detect not only the boundaries of touch or swipe actions, but also the duration and pressure associated with the touch or swipe operation.

[0210] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0211] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended 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. Such 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.

Claims

1. A method for predicting water production, characterized in that, include: The process involves acquiring gridded baseline data, precipitation observation data, and precipitation grid data to be corrected for the target watershed. The gridded baseline data includes the location information, topographic data, temperature data, grid area, and water yield calculation parameters of each grid unit within the target watershed. The precipitation observation data includes the measured precipitation at each meteorological station within the target watershed during multiple observation periods. The precipitation grid data to be corrected includes the precipitation to be corrected for each grid unit within the target watershed during the multiple observation periods. Based on the location information of each meteorological station and the location information of each grid unit, the grid units within the target watershed are divided into grid units containing stations and grid units to be calculated. Based on the measured precipitation of the meteorological station corresponding to the grid unit containing the station and the precipitation grid data to be corrected, the station-level correction coefficients of the grid unit containing the station are determined for each observation period. Based on the precipitation grid data to be corrected, the temperature data, the topographic data, the station-level correction coefficients, and the spatial distance and environmental association information between the station-containing grid unit and the grid unit to be calculated, the grid-level correction coefficients corresponding to the grid unit to be calculated in each observation period are determined. Based on the station-level correction coefficient and the grid-level correction coefficient, the original precipitation correction coefficient corresponding to each grid unit in each observation period is determined, and the original precipitation correction coefficient corresponding to each grid unit is adjusted for stability based on the neighborhood difference between the original precipitation correction coefficients corresponding to each grid unit, so as to obtain the adjusted precipitation correction coefficient corresponding to each grid unit in each observation period. Based on the adjusted precipitation correction coefficients corresponding to each grid cell in each observation period, the precipitation amount to be corrected in the corresponding grid cell in the precipitation grid data to be corrected in the corresponding observation period is corrected to obtain the corrected precipitation amount corresponding to each grid cell in each observation period. Based on the corrected precipitation, the water production calculation parameters, and the grid area, the predicted water production value corresponding to each grid cell is calculated, and the predicted water production value corresponding to the target watershed is generated based on the predicted water production value corresponding to each grid cell.

2. The water production prediction method according to claim 1, characterized in that, The step of dividing the grid units within the target watershed into station-containing grid units and grid units to be calculated, based on the location information of each meteorological station and the location information of each grid unit, includes: Based on the location information of each meteorological station and the location information of each grid unit, the grid unit containing the meteorological station is determined as the station-containing grid unit, and the other grid units in the target watershed other than the station-containing grid units are determined as the grid units to be calculated. The step of determining the station-level correction coefficients for each observation period based on the measured precipitation at the meteorological station corresponding to the station-containing grid unit and the precipitation grid data to be corrected includes: For each of the aforementioned station-containing grid units, the station-level correction coefficients for each observation period are determined based on the measured precipitation at the meteorological station corresponding to the station-containing grid unit in each observation period and the precipitation to be corrected corresponding to the station-containing grid unit in the precipitation grid data to be corrected. The step of determining the grid-level correction coefficient for each observation period of the grid unit to be corrected, based on the precipitation grid data to be corrected, the temperature data, the topographic data, the station-level correction coefficient, and the spatial distance and environmental association information between the station-containing grid unit and the grid unit to be calculated, includes: For each observation period, based on the precipitation amount to be corrected for each grid cell in the precipitation grid data to be corrected during that observation period, the spatial distribution characteristics of precipitation in the target watershed during that observation period are determined. Based on the correlation between the spatial distribution characteristics of precipitation and the temperature data and the topographic data, the weights of the temperature factor and the topographic factor for that observation period are determined. For each observation period, based on the spatial difference of the site-level correction coefficients of each of the station-containing grid units in that observation period, the site representative parameters of each of the station-containing grid units in that observation period are determined. The site representative parameters are used to characterize the correction reference capability of the site-level correction coefficients of the corresponding station-containing grid unit for the surrounding grid units. For each of the grid cells to be extrapolated, in each observation period, the coupling extrapolation weight of the grid cell to be extrapolated relative to each of the grid cells containing stations is determined based on the spatial distance between the grid cell to be extrapolated and each of the grid cells containing stations, the temperature factor weight and the terrain factor weight corresponding to the observation period, the temperature association information and terrain association information between the grid cell to be extrapolated and each of the grid cells containing stations, and the representative parameters of the stations corresponding to each of the grid cells containing stations. For each of the grid cells to be calculated, in each observation period, based on the coupling calculation weight of the grid cell to be calculated relative to each of the grid cells containing stations, the station-level correction coefficients of each of the grid cells containing stations in that observation period are spatially weighted and calculated to obtain the grid-level correction coefficients of the grid cell to be calculated in that observation period. The process involves determining the original precipitation correction coefficient for each grid cell in each observation period based on the station-level correction coefficient and the grid-level correction coefficient, and then adjusting the stability of the original precipitation correction coefficient for each grid cell based on the neighborhood differences between the original precipitation correction coefficients for each grid cell, to obtain the adjusted precipitation correction coefficient for each grid cell in each observation period. This includes: Based on the station-level correction coefficients corresponding to each of the station-containing grid units and the grid-level correction coefficients corresponding to each of the grid units to be calculated, the original precipitation correction coefficients for each grid unit in the target watershed during each observation period are determined. For each grid cell and each observation period, the set of neighboring grid cells corresponding to the grid cell is determined from the target watershed, and the coefficient difference between the original precipitation correction coefficient corresponding to the grid cell and the original precipitation correction coefficients corresponding to each neighboring grid cell in the set of neighboring grid cells is calculated. Neighboring grid cells whose coefficient difference value is less than or equal to a preset difference threshold are selected from the set of neighboring grid cells as stable neighboring grid cells, and the stable reference correction coefficient corresponding to the grid cell in the observation period is determined according to the original precipitation correction coefficient corresponding to the stable neighboring grid cell. Based on the degree of deviation between the original precipitation correction coefficient and the stable reference correction coefficient corresponding to the grid cell, the stable adjustment coefficient corresponding to the grid cell in the observation period is determined. Based on the stable adjustment coefficient corresponding to the grid cell, the original precipitation correction coefficient and the stable reference correction coefficient corresponding to the grid cell are weighted and calculated to obtain the adjusted precipitation correction coefficient corresponding to the grid cell in the observation period. The step of correcting the precipitation amount of the corresponding grid cell in the precipitation grid data to be corrected during the corresponding observation period according to the adjusted precipitation correction coefficient of each grid cell in each observation period, to obtain the corrected precipitation amount of each grid cell in each observation period, includes: Based on the adjusted precipitation correction coefficients corresponding to each grid cell in each observation period, the precipitation amount to be corrected in the corresponding grid cell in the precipitation grid data to be corrected in the corresponding observation period is corrected to obtain the corrected precipitation amount corresponding to each grid cell in each observation period. The step of calculating the predicted water yield for each grid cell based on the corrected precipitation, the water yield calculation parameters, and the grid area includes: Based on the corrected precipitation and the water production calculation parameters, the corrected precipitation input and actual evapotranspiration of each grid cell in the target calculation period are determined. Based on the difference between the corrected precipitation input and the actual evapotranspiration of each grid cell, the water production depth per unit area of ​​each grid cell is determined. Based on the water production depth per unit area of ​​each grid cell and the grid area, the predicted water production value of each grid cell is calculated.

3. The water production prediction method according to claim 1, characterized in that, The acquisition of the gridded baseline data, precipitation observation data, and precipitation grid data to be corrected corresponding to the target watershed includes: Obtain a multi-source candidate precipitation grid dataset corresponding to the target watershed, wherein the multi-source candidate precipitation grid dataset includes two or more candidate precipitation grid data obtained from different sources, and each candidate precipitation grid data includes the candidate fitted precipitation amount corresponding to each grid unit in the target watershed during the multiple observation periods; For each candidate precipitation grid data, the goodness-of-fit index corresponding to the candidate precipitation grid data is calculated based on the candidate fitted precipitation amount in the grid unit to which each meteorological station belongs, and the measured precipitation amount of the corresponding meteorological station in the precipitation observation data. According to the preset optimization rules corresponding to the goodness-of-fit index, candidate precipitation grid data are selected from the multi-source candidate precipitation grid dataset as the precipitation grid data to be corrected.

4. The water production prediction method according to claim 2, characterized in that, For each observation period, based on the precipitation amount to be corrected for each grid cell in the precipitation grid data to be corrected during that observation period, the spatial distribution characteristics of precipitation in the target watershed during that observation period are determined. Then, based on the correlation between the spatial distribution characteristics of precipitation and the temperature data and the topographic data, the temperature factor weights and topographic factor weights corresponding to that observation period are determined, including: For the observation period, the precipitation to be corrected corresponding to each grid unit in the target watershed is extracted from the precipitation grid data to be corrected, and the precipitation distribution sample corresponding to the observation period is obtained. Extract the air temperature data of each grid unit in the target watershed during the observation period from the temperature data to obtain the temperature distribution sample for the observation period. The terrain feature values ​​corresponding to each grid cell within the target watershed are extracted from the terrain data to obtain a terrain distribution sample. Based on the precipitation distribution sample and the temperature distribution sample, calculate the temperature correlation metric corresponding to the observation period; Based on the precipitation distribution sample and the topography distribution sample, calculate the topography correlation metric corresponding to the observation period; The weights of the temperature factor and the topography factor are determined based on the relative magnitudes between the temperature correlation metric and the topography correlation metric for the observation period.

5. The water production prediction method according to claim 2, characterized in that, For each of the grid cells to be extrapolated, in each observation period, the coupling extrapolation weight of the grid cell to be extrapolated relative to each of the grid cells containing stations is determined based on the spatial distance between the grid cell to be extrapolated and each of the grid cells containing stations, the temperature factor weight and the terrain factor weight corresponding to the observation period, the temperature association information and terrain association information between the grid cell to be extrapolated and each of the grid cells containing stations, and the representative parameters of the stations corresponding to each of the grid cells containing stations. This includes: For the grid cell to be calculated and each grid cell containing a station, the distance attenuation weight is determined based on the spatial distance between the grid cell to be calculated and the grid cell containing the station. The temperature correlation weight is determined based on the temperature difference between the grid cell to be calculated and the grid cell containing the station. The terrain association weight is determined based on the terrain differences between the grid cell to be calculated and the grid cell containing the station. Based on the temperature factor weight and terrain factor weight corresponding to the observation period, the temperature correlation weight and the terrain correlation weight are weighted and combined to obtain the environmental correlation weight of the grid cell to be calculated relative to the grid cell containing the station. Based on the distance attenuation weight, the environmental association weight, and the representative parameters of the stations corresponding to the grid unit containing the station, the initial estimation weight of the grid unit to be estimated relative to the grid unit containing the station is determined. The initial estimation weights of the grid cell to be estimated relative to each of the grid cells containing stations are normalized to obtain the coupled estimation weights of the grid cell to be estimated relative to each of the grid cells containing stations. The step of determining the distance attenuation weight based on the spatial distance between the grid cell to be calculated and the grid cell containing the station includes: The distance attenuation weight is determined by a negative power of the spatial distance between the grid cell to be calculated and the grid cell containing the station, wherein the distance attenuation index corresponding to the negative power is a preset value. The step of determining the temperature correlation weight based on the temperature difference between the grid cell to be calculated and the grid cell containing the station includes: The temperature difference between the grid cell to be calculated and the grid cell containing the station during the corresponding observation period is normalized, and the temperature correlation weight is determined based on the normalized temperature difference, so that the smaller the temperature difference, the larger the temperature correlation weight. The step of determining the terrain association weight based on the terrain difference between the grid cell to be calculated and the grid cell containing the station includes: The differences between the terrain feature values ​​of the grid cell to be calculated and the grid cell containing the station are normalized, and the terrain association weight is determined based on the normalized terrain differences, such that the smaller the terrain differences, the larger the terrain association weight. The step of determining the initial estimation weight of the grid cell to be estimated relative to the grid cell containing the station, based on the distance attenuation weight, the environmental association weight, and the representative parameters of the stations corresponding to the grid cell containing the station, includes: Multiply the distance attenuation weight, the environmental association weight, and the representative parameters of the station corresponding to the grid cell containing the station to obtain the initial estimated weight of the grid cell to be estimated relative to the grid cell containing the station.

6. The water production prediction method according to claim 2, characterized in that, For each of the grid cells to be calculated, in each observation period, based on the coupling calculation weight of the grid cell to be calculated relative to each of the grid cells containing stations, the station-level correction coefficients of each of the grid cells containing stations are spatially weighted and calculated in that observation period to obtain the grid-level correction coefficients of the grid cell to be calculated in that observation period, including: The coupling calculation weight of the grid cell to be calculated relative to each grid cell containing stations is used as the weight. The station-level correction coefficients of each grid cell containing stations during the observation period are summed in a weighted manner to obtain the weighted sum value. The ratio between the weighted sum and the sum of each of the coupled calculation weights is used as the grid-level correction coefficient for the grid cell to be calculated during the observation period.

7. The water production prediction method according to claim 2, characterized in that, The process involves determining the stable adjustment coefficient for a grid cell during the observation period based on the deviation between the original precipitation correction coefficient and the stable reference correction coefficient. Then, based on the stable adjustment coefficient, a weighted calculation is performed on the original precipitation correction coefficient and the stable reference correction coefficient for that grid cell to obtain the adjusted precipitation correction coefficient for that grid cell during the observation period. This includes: Calculate the absolute value of the difference between the original precipitation correction coefficient corresponding to the grid cell and the stable reference correction coefficient to obtain the stable deviation value; The stability adjustment coefficient of the grid cell during the observation period is determined based on the ratio between the stable deviation value and the preset deviation threshold, wherein the larger the stable deviation value, the smaller the stability adjustment coefficient. The original precipitation correction coefficient corresponding to the grid cell is weighted according to the stability adjustment coefficient, and the stability reference correction coefficient is weighted according to the difference between the original and the stability adjustment coefficient. The sum of the weighted results is determined as the adjusted precipitation correction coefficient corresponding to the grid cell in the observation period.

8. The water production prediction method according to claim 2, characterized in that, The step of determining the corrected precipitation input and actual evapotranspiration of each grid cell in the target calculation period based on the corrected precipitation and the water production calculation parameters includes: For each grid cell, the corrected precipitation for each observation period within the target calculation period is accumulated to obtain the corrected precipitation input for that grid cell during the target calculation period. Based on the water production calculation parameters corresponding to the grid cell, determine the potential evapotranspiration and evapotranspiration regulation coefficient of the grid cell in the target calculation period; Based on the corrected precipitation input, the potential evapotranspiration, and the evapotranspiration adjustment coefficient, the actual evapotranspiration of the grid cell corresponding to the target calculation period is determined, and The step of determining the actual evapotranspiration of the grid cell corresponding to the target calculation period based on the corrected precipitation input, the potential evapotranspiration, and the evapotranspiration adjustment coefficient includes: The aridity index is determined based on the ratio between the potential evapotranspiration and the corrected precipitation input. The actual evaporation ratio is determined based on the dryness index and the evaporation adjustment coefficient. Based on the actual evapotranspiration ratio and the corrected precipitation input, the actual evapotranspiration of the grid cell corresponding to the target calculation period is determined.

9. A water production prediction device, characterized in that, include: The data acquisition module is used to acquire gridded baseline data, precipitation observation data, and precipitation grid data to be corrected corresponding to the target watershed. The gridded baseline data includes the location information, topographic data, temperature data, grid area, and water yield calculation parameters of each grid unit in the target watershed. The precipitation observation data includes the measured precipitation at each meteorological station in the target watershed during multiple observation periods. The precipitation grid data to be corrected includes the precipitation to be corrected for each grid unit in the target watershed during the multiple observation periods. The grid division module is used to divide the grid units within the target watershed into grid units containing stations and grid units to be calculated, based on the location information of each meteorological station and the location information of each grid unit. The station correction module is used to determine the station-level correction coefficients of the station-containing grid unit in each observation period based on the measured precipitation of the meteorological station corresponding to the station-containing grid unit and the precipitation grid data to be corrected. The grid extrapolation module is used to determine the grid-level correction coefficient of the grid unit to be extrapolated in each observation period based on the precipitation grid data to be corrected, the temperature data, the topographic data, the station-level correction coefficient, and the spatial distance and environmental association information between the station-containing grid unit and the grid unit to be extrapolated. The stabilization adjustment module is used to determine the original precipitation correction coefficient corresponding to each grid unit in each observation period based on the station-level correction coefficient and the grid-level correction coefficient, and to perform stability adjustment on the original precipitation correction coefficient corresponding to each grid unit based on the neighborhood difference between the original precipitation correction coefficients corresponding to each grid unit, so as to obtain the adjusted precipitation correction coefficient corresponding to each grid unit in each observation period. The precipitation correction module is used to correct the precipitation amount to be corrected in the corresponding grid unit in the precipitation grid data to be corrected in the corresponding observation period according to the adjusted precipitation correction coefficient of each grid unit in each observation period, so as to obtain the corrected precipitation amount of each grid unit in each observation period. The water production calculation module is used to calculate the predicted water production value corresponding to each grid cell based on the corrected precipitation, the water production calculation parameters, and the grid area, and to generate the predicted water production result corresponding to the target watershed based on the predicted water production value corresponding to each grid cell.

10. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the water production prediction method according to any one of claims 1 to 8.

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