A space-time equivalent hydrological simulation method and device suitable for a data-deficient river basin

Abstract

This invention discloses a spatiotemporal equivalent hydrological simulation method and apparatus suitable for data-scarce watersheds, relating to the interdisciplinary fields of hydrology and ecohydrology. The method includes: constructing a digital elevation model and integrating vegetation, meteorological, and hydrological data to generate raw time-series numerical datasets; fitting meteorological and hydrological factors to obtain evapotranspiration, precipitation, and potential evapotranspiration datasets; calculating characteristic parameters such as evapotranspiration rate using water balance, runoff coefficient, and drought index models; extracting the underlying surface parameter set using a parameter optimizer with nonlinear fitting, and establishing a linear relationship model between the parameter set and environmental factors using the least squares method; finally, evaluating the spatiotemporal equivalence of the model based on the coefficient of determination and significance level. This invention can provide a simulation method for the hydrological processes of semi-arid ecosystems and a simulation technology approach with clear physical meaning and good generalization ability for hydrological prediction in data-scarce watersheds.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of hydrology and eco-hydrology, and in particular to a spatiotemporal equivalent hydrological simulation method and apparatus suitable for watersheds lacking data. Background Technology

[0002] Ecohydrological monitoring is a fundamental means of quantitatively understanding and dynamically assessing water resources, water environment, aquatic ecology, and land surface ecological processes, and is crucial data support for hydrological simulation and water resource management. However, in semi-arid regions, due to complex climate conditions, harsh natural environments, and economic and technological limitations, ecohydrological monitoring systems are generally weak, suffering from insufficient monitoring station density, lack of long-term observational data, and data discontinuity. This phenomenon is a typical manifestation of the Data-Deficient Watershed Prediction (PUB) problem proposed by the International Union of Hydrological Sciences (IUHMS), namely, how to achieve reliable hydrological process simulation and prediction under conditions of scarce or minimal observational data, which has become one of the frontiers and challenges in current hydrological research. Existing research on the PUB problem generally adopts the approach of "substituting time for space," that is, using spatial observational information from neighboring watersheds or regions to calibrate the model parameters of the target watershed, thereby achieving hydrological process simulation and prediction. However, the key premise for this method is that the watershed system should satisfy spatiotemporal symmetry, that is, the hydrological processes should have consistent patterns of change or "simultaneous effects" on both temporal and spatial scales. However, under the combined effects of climate change and human activities, the underlying surface conditions of watersheds (including vegetation cover, soil properties, and topography) continue to change, leading to significant differences in hydrological processes across time and space. This spatiotemporal heterogeneity makes it difficult to directly transfer parameters from traditional hydrological models across different scales, thus reducing model simulation accuracy and limiting their application in regional scales and data-deficient watersheds. Currently, extensive research has been conducted both domestically and internationally on the simulation of hydrological processes in semi-arid regions and the problem of data-deficient watersheds (PUBs), resulting in a technical system primarily based on empirical transfer, parameter regionalization, and water balance models. Among these, the Budyko model-based method, due to its simple structure and fewer parameters, is widely used in regional and global-scale hydrological simulations. The Budyko model describes the long-term water balance of a watershed through a functional relationship between the drought index and the evapotranspiration ratio. Its core parameters (such as the underlying surface parameter n) are used to characterize the water distribution characteristics under the combined effects of climate and underlying surface. Existing studies typically use multi-year average data to calibrate the parameters or establish connections between them and climate factors and soil properties through empirical relationships. However, traditional methods often treat the parameter n as a fixed value, making it difficult to reflect the dynamic evolution caused by vegetation change and climate fluctuations. Although some studies have attempted to correlate the parameter n with vegetation indices or land use types, these studies mostly remain at the level of static statistical relationships and fail to characterize the real-time impact of vegetation change on hydrological processes. Furthermore, the parameter relationships obtained by existing methods at different time and spatial scales are often inconsistent, indicating significant shortcomings in parameter representation and cross-scale applicability. Summary of the Invention

[0003] To address the technical problems of existing hydrological modeling techniques, such as the difficulty in unifying parameters across time and spatial scales, and shortcomings in dynamic parameter expression, cross-scale consistency, and characterization of eco-hydrological mechanisms, this invention provides a spatiotemporal equivalent hydrological simulation method and apparatus suitable for watersheds lacking data. The technical solution is as follows:

[0004] On the one hand, a spatiotemporal equivalent hydrological simulation method suitable for data-scarce watersheds is provided. This method is implemented by a spatiotemporal equivalent hydrological simulation device suitable for data-scarce watersheds, and includes: S1: Acquire digital elevation model data, collect vegetation data, meteorological data and hydrological data from multiple data sources, and perform preprocessing on the vegetation data, meteorological data and hydrological data to obtain raw data; S2: Generate an original time series numerical group based on the time sequence combination of each year in the original data, construct a set of year average numerical values ​​through moving average operation and integration, and update the set of year average numerical values ​​and the original data to obtain a time-series smoothed spatiotemporal sequence dataset. S3: Perform annual time-series trend correction on the time-series smoothed spatiotemporal sequence dataset to obtain the target spatiotemporal sequence dataset, and perform meteorological and hydrological factor fitting and hydrological feature calculation on the target spatiotemporal sequence dataset to obtain the hydro-meteorological dataset, which includes actual evapotranspiration data, precipitation data and potential evapotranspiration data; S4: Input the actual evapotranspiration data, precipitation data and potential evapotranspiration data contained in the hydro-meteorological dataset into the water balance calculation model, runoff coefficient calculation model and drought index calculation model respectively to obtain the first hydrological characteristic parameter, the second hydrological characteristic parameter and the third hydrological characteristic parameter. S5: Input the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter into the parameter optimizer to perform nonlinear fitting to obtain the underlying surface parameter set, and use the least squares method to perform linear regression analysis on the underlying surface parameter set, the third hydrological characteristic parameter, and the vegetation data in the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors; S6: Analyze the spatiotemporal equivalence between underlying surface parameters and environmental factors based on a linear relationship model, and evaluate the goodness of fit of the linear relationship model using the coefficient of determination and significance level values, so as to determine the spatiotemporal equivalence of the linear relationship model on a preset spatiotemporal scale.

[0005] Preferably, step S1 involves acquiring digital elevation model data by collecting vegetation data, meteorological data, and hydrological data from multiple data sources, and performing preprocessing on the vegetation data, meteorological data, and hydrological data to obtain raw data, including: S11: Collect digital elevation data for the selected area; S12: Based on the digital elevation data, construct a digital elevation model to obtain digital elevation model data; S13: Collect vegetation data, meteorological data and hydrological data of a selected area in multiple years from multiple data sources, wherein the multiple data sources include: geographic information system data interface, remote sensing image analysis module, meteorological monitoring station network and hydrological monitoring station network, wherein the vegetation data includes vegetation cover and normalized vegetation cover. S14: Perform preprocessing on the vegetation data, meteorological data, and hydrological data to obtain raw data. The preprocessing includes: performing interpolation, discretization, spatiotemporal alignment, and format standardization to generate spatial data with uniform spatial granularity.

[0006] Preferably, step S2 generates an original time-series numerical set based on the time sequence combination of each year in the original data, constructs a set of yearly average values ​​through moving average calculation and integration, and updates the set of yearly average values ​​and the original data to obtain a time-series smoothed spatiotemporal sequence dataset, including: S21: Based on the time sequence of each year in the original data, combine the original values ​​corresponding to adjacent years to generate an original time sequence value group. S22: Perform a moving average operation on the original time series data set to generate a set of annual average values. The moving average operation includes performing a point-by-point moving average calculation on the original values ​​of adjacent years in the original time series data set, with the year as the moving dimension, to obtain a smoothed average value corresponding to each year. S23: Integrate the smoothed average values ​​corresponding to all years to construct the set of average values ​​for the years; S24: Update the set of average values ​​for each year and the original data to obtain a time-series smoothed spatiotemporal sequence dataset. The update process includes replacing the original values ​​of each year in the original data with the average values ​​of the corresponding years in the set of average values ​​for each year.

[0007] Preferably, step S3 performs annual time-series trend correction on the time-smoothed spatiotemporal sequence dataset to obtain a target spatiotemporal sequence dataset, and performs meteorological and hydrological factor fitting and hydrological feature calculation on the target spatiotemporal sequence dataset to obtain a hydrometeorological dataset. The hydrometeorological dataset includes actual evapotranspiration data, precipitation data, and potential evapotranspiration data, including: S31: Perform annual time-series trend correction on the time-smoothed spatiotemporal sequence dataset to obtain the target spatiotemporal sequence dataset; S32: Perform meteorological and hydrological factor fitting and hydrological feature calculation on the target spatiotemporal sequence dataset to obtain a hydro-meteorological dataset, which includes: actual evapotranspiration data, precipitation data and potential evapotranspiration data.

[0008] Preferably, in step S4, the actual evapotranspiration data, precipitation data, and potential evapotranspiration data contained in the hydro-meteorological dataset are input into the water balance calculation model, the runoff coefficient calculation model, and the drought index calculation model, respectively, to obtain the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter, including: S41: Input the actual evapotranspiration data, precipitation data and potential evapotranspiration data contained in the hydro-meteorological dataset into the water balance calculation model to obtain the first hydrological characteristic parameter, which is the evapotranspiration rate. The water balance calculation model includes the Budyko-Wang equation and the Budyko-Yang equation. S42: Input the precipitation data and actual evapotranspiration data contained in the hydro-meteorological dataset into the runoff coefficient calculation model to obtain the second hydrological characteristic parameter, which is the runoff coefficient, and the runoff coefficient is determined by the ratio of the actual evapotranspiration data to the precipitation data. S43: Input the potential evapotranspiration data and precipitation data contained in the hydro-meteorological dataset into the drought index calculation model to obtain the third hydrological characteristic parameter, which is the drought index. The drought index is determined by the ratio of the potential evapotranspiration data to the precipitation data.

[0009] Preferably, in step S5, the first, second, and third hydrological characteristic parameters are input into a parameter optimizer to perform nonlinear fitting to obtain a set of underlying surface parameters. Then, the least squares method is used to perform linear regression analysis on the underlying surface parameter set, the third hydrological characteristic parameter, and the vegetation data in the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors, including: S51: Input the first hydrological characteristic parameter, the second hydrological characteristic parameter and the third hydrological characteristic parameter into the parameter optimizer to perform nonlinear fitting and obtain the underlying surface parameter set; S52: The parameter optimizer is configured with a Budyko model function, which uses the drought index as the independent variable and the evapotranspiration rate or runoff coefficient as the dependent variable. S53: The underlying surface parameter set includes parameters n1 and n2 used to characterize the shape of the Budyko model function; S54: Using the least squares method, linear regression analysis is performed on the vegetation data in the underlying surface parameter set, the third hydrological characteristic parameters, and the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors. The environmental factor data includes vegetation cover, normalized vegetation cover, and drought index. The linear relationship model includes slope and intercept.

[0010] Preferably, step S6, which analyzes the spatiotemporal equivalence between underlying surface parameters and environmental factors based on a linear relationship model, and evaluates the goodness of fit of the linear relationship model using the coefficient of determination and significance level values, to determine the spatiotemporal equivalence of the linear relationship model at a preset spatiotemporal scale, includes: S61: Based on the linear relationship model, analyze the spatiotemporal equivalence between the underlying surface parameters and the environmental factors to obtain the spatiotemporal equivalence identification results; S62: Using the coefficient of determination and significance level as evaluation indicators, the accuracy and significance of the linear relationship model are evaluated to obtain the goodness-of-fit evaluation results; S63: Based on the goodness-of-fit evaluation results, determine whether the linear relationship model holds true on a preset spatiotemporal scale, so as to confirm its spatiotemporal equivalence.

[0011] On the other hand, a spatiotemporal equivalent hydrological simulation device suitable for data-scarce watersheds is provided. This device is applied to a spatiotemporal equivalent hydrological simulation method suitable for data-scarce watersheds. The device includes: Raw data module: Used to acquire digital elevation model data, collect vegetation data, meteorological data and hydrological data from multiple data sources, and perform preprocessing on the vegetation data, meteorological data and hydrological data to obtain raw data; The sequence dataset module is used to generate an original time series numerical group based on the time sequence combination of each year in the original data, construct a set of year average values ​​through moving average calculation and integration, and update the set of year average values ​​and the original data to obtain a time-series smoothed spatiotemporal sequence dataset. Hydrological and meteorological data module: used to perform annual time series trend correction on the time-series smoothed spatiotemporal sequence dataset to obtain the target spatiotemporal sequence dataset, and to perform meteorological and hydrological factor fitting and hydrological feature calculation on the target spatiotemporal sequence dataset to obtain the hydrological and meteorological dataset, which includes actual evapotranspiration data, precipitation data and potential evapotranspiration data; The calculation model module is used to input the actual evapotranspiration data, precipitation data and potential evapotranspiration data contained in the hydro-meteorological dataset into the water balance calculation model, runoff coefficient calculation model and drought index calculation model respectively, to obtain the first hydrological characteristic parameter, the second hydrological characteristic parameter and the third hydrological characteristic parameter; Linear Relationship Module: This module is used to input the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter into the parameter optimizer to perform nonlinear fitting to obtain the underlying surface parameter set. It then uses the least squares method to perform linear regression analysis on the underlying surface parameter set, the third hydrological characteristic parameter, and the vegetation data in the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors. Spatiotemporal equivalence module: used to analyze the spatiotemporal equivalence between underlying surface parameters and environmental factors based on a linear relationship model, and to evaluate the goodness of fit of the linear relationship model using the coefficient of determination and significance level values, so as to determine the spatiotemporal equivalence of the linear relationship model on a preset spatiotemporal scale.

[0012] On the other hand, a spatiotemporal equivalent hydrological simulation device suitable for watersheds lacking data is provided. The spatiotemporal equivalent hydrological simulation device for watersheds lacking data includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method described in any one of the above-described spatiotemporal equivalent hydrological simulation methods for watersheds lacking data is implemented.

[0013] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: The system identifies the spatiotemporal equivalence relationships between water balance elements and their core parameters and vegetation cover. It introduces a vegetation dynamics mechanism to enhance the model's response to climate change and vegetation change, improves the physical rationality of the Budyko model, and achieves cross-scale equivalent expression of model parameters, providing a more accurate eco-hydrological simulation method for data-scarce watersheds. Based on the spatiotemporal equivalent expression method of Budyko model parameters based on the synergistic effect of climate and vegetation, it achieves a unified description of model parameters on both temporal and spatial scales, thereby improving the cross-scale applicability of hydrological simulation and the predictive ability of data-scarce watersheds. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of a spatiotemporal equivalent hydrological simulation method for watersheds lacking data, provided by an embodiment of the present invention. Figure 2 This invention provides a relationship diagram between underlying surface parameter n1 and vegetation cover M, and a relationship diagram between underlying surface parameter n2 and vegetation cover M. Figure 3This is a block diagram of a spatiotemporal equivalent hydrological simulation device suitable for watersheds lacking data, provided by an embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of a spatiotemporal equivalent hydrological simulation device suitable for watersheds lacking data, provided by an embodiment of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0017] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0018] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0019] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0020] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0021] This invention provides a spatiotemporal equivalent hydrological simulation method suitable for watersheds lacking data. This method can be implemented using a spatiotemporal equivalent hydrological simulation device suitable for watersheds lacking data, which can be a terminal or a server. Figure 1 The flowchart shown is for a spatiotemporal equivalent hydrological simulation method applicable to watersheds with insufficient data. The processing flow of this method may include the following steps:

[0022] Digital elevation model data is acquired by collecting vegetation data, meteorological data, and hydrological data from multiple data sources, and preprocessing the vegetation data, meteorological data, and hydrological data to obtain raw data. Preferably, digital elevation model data is acquired by collecting vegetation data, meteorological data, and hydrological data from multiple data sources, and preprocessing the vegetation data, meteorological data, and hydrological data to obtain raw data, including: Collect digital elevation data for the selected area; Based on the digital elevation data, a digital elevation model is constructed to obtain digital elevation model data; Vegetation data, meteorological data, and hydrological data of a selected area over multiple years are collected from multiple data sources, including: a geographic information system data interface, a remote sensing image analysis module, a meteorological monitoring station network, and a hydrological monitoring station network. The vegetation data includes vegetation cover and normalized difference vegetation cover. The vegetation data, meteorological data, and hydrological data are preprocessed to obtain raw data. The preprocessing includes interpolation, discretization, spatiotemporal alignment, and format standardization to generate spatial data with uniform spatial granularity.

[0023] In some embodiments, digital elevation model (DEM) data of the Hailar River Basin, as well as vegetation, meteorological, and hydrological data from 1982 to 2015, are collected.

[0024] Table 1. Meteorological, hydrological, and vegetation-related data for the study period.

[0025] The original time series numerical data is generated by combining the time sequence of each year in the original data. The average value set of the years is constructed by moving average operation and integration. The average value set of the years and the original data are then updated to obtain a time-series smoothed spatiotemporal sequence dataset. Preferably, an original time-series numerical set is generated based on the chronological combination of each year in the original data. This set is then constructed by performing a moving average operation and integrating the data. The set of year averages and the original data are then updated to obtain a time-smoothed spatiotemporal sequence dataset, including: Based on the time sequence of each year in the original data, the original values ​​corresponding to adjacent years are combined to generate an original time series value group. For the original time series data set, a moving average operation is performed to generate a set of annual average values. The moving average operation includes performing a point-by-point moving average calculation on the original values ​​of adjacent years in the original time series data set, with the year as the moving dimension, to obtain a smoothed average value corresponding to each year. The smoothed average values ​​corresponding to all years are integrated to construct the set of average values ​​for the years; The set of average values ​​for each year and the original data are updated to obtain a spatiotemporal sequence dataset that has undergone time-series smoothing. The update process includes replacing the original values ​​of each year in the original data with the average values ​​of the corresponding years in the set of average values ​​for each year.

[0026] In some embodiments, an 11-year moving average is used, where the average of each adjacent 11-year period is taken and used as the value for the corresponding year. Therefore, the original 34 data points from 1982 to 2015 are transformed into 24 data points from 1987 to 2010.

[0027] Annual time-series trend correction is performed on the time-series smoothed spatiotemporal sequence dataset to obtain the target spatiotemporal sequence dataset. Meteorological and hydrological factor fitting and hydrological feature calculation are then performed on the target spatiotemporal sequence dataset to obtain the hydrometeorological dataset, which includes actual evapotranspiration data, precipitation data, and potential evapotranspiration data. Preferably, annual time-series trend correction is performed on the time-smoothed spatiotemporal sequence dataset to obtain a target spatiotemporal sequence dataset. Meteorological and hydrological factor fitting and hydrological characteristic calculation are then performed on the target spatiotemporal sequence dataset to obtain a hydrometeorological dataset. The hydrometeorological dataset includes actual evapotranspiration data, precipitation data, and potential evapotranspiration data, including: The time-smoothed spatiotemporal sequence dataset is subjected to annual time-series trend correction to obtain the target spatiotemporal sequence dataset; For the target spatiotemporal sequence dataset, meteorological and hydrological factor fitting and hydrological feature calculation are performed to obtain a hydro-meteorological dataset, which includes: actual evapotranspiration data, precipitation data and potential evapotranspiration data.

[0028] It should be further explained that, considering the influence of the soil water storage variable ∆S, in order to obtain the time-varying Budyko shape parameter and eliminate the influence of the connection between water storage years on the Budyko water balance model, an 11-year moving average was used (using an 11-year moving window to analyze time series data in the 11-year overlapping interval) to obtain a stable water balance index. After the 11-year moving average, the change in the soil water storage variable ∆S can be regarded as approaching 0 and ignored.

[0029] The actual evapotranspiration data, precipitation data and potential evapotranspiration data contained in the hydro-meteorological dataset are respectively input into the water balance calculation model, runoff coefficient calculation model and drought index calculation model to obtain the first hydrological characteristic parameter, the second hydrological characteristic parameter and the third hydrological characteristic parameter. Preferably, the actual evapotranspiration data, precipitation data, and potential evapotranspiration data contained in the hydro-meteorological dataset are input into the water balance calculation model, the runoff coefficient calculation model, and the drought index calculation model, respectively, to obtain the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter, including: The actual evapotranspiration data, precipitation data and potential evapotranspiration data contained in the hydro-meteorological dataset are input into the water balance calculation model to obtain the first hydrological characteristic parameter, which is the evapotranspiration rate. The water balance calculation model includes the Budyko-Wang equation and the Budyko-Yang equation. The precipitation data and actual evapotranspiration data contained in the hydro-meteorological dataset are input into the runoff coefficient calculation model to obtain the second hydrological characteristic parameter, which is the runoff coefficient, and the runoff coefficient is determined by the ratio of the actual evapotranspiration data to the precipitation data. The potential evapotranspiration data and precipitation data contained in the hydro-meteorological dataset are input into the drought index calculation model to obtain the third hydrological characteristic parameter, which is the drought index. The drought index is determined by the ratio of the potential evapotranspiration data to the precipitation data.

[0030] In some embodiments, typical water balance calculation models include the Budyko–Wang equation and the Budyko–Yang equation. The Budyko–Wang equation is a classical analytical formula for hydrothermal coupling balance, which can accurately characterize the quantitative relationship between actual evapotranspiration and precipitation and potential evapotranspiration under different underlying surface conditions, and is suitable for quantitative extrapolation of watershed hydrological situation. The Budyko–Yang equation is a general Budyko framework calculation formula that can efficiently complete watershed evapotranspiration accounting in multiple climate zones, and is often used in the analysis of hydrological element changes and water resource situation.

[0031] In some embodiments, two alternative functional forms of the Budyko model are considered, each involving different Budyko shape parameters. Specifically, a new Budyko parameter n1 and the widely used parameter n2 are considered. The Budyko–Wang water balance equation can be expressed as follows:

[0032]

[0033] The Budyko–Yang water balance equation can be expressed as follows:

[0034] Where n1 and n2 are shape parameters used to test whether spatiotemporal symmetry exists within the watershed in relation to the annual water balance index. E represents actual evapotranspiration; P represents precipitation; E p For potential evaporation.

[0035] The first, second, and third hydrological characteristic parameters are input into a parameter optimizer to perform nonlinear fitting to obtain a set of underlying surface parameters. The least squares method is then used to perform linear regression analysis on the underlying surface parameter set, the third hydrological characteristic parameter, and the vegetation data in the target spatiotemporal sequence dataset to obtain a linear relationship model between underlying surface parameters and environmental factors. Preferably, the first, second, and third hydrological characteristic parameters are input into a parameter optimizer to perform nonlinear fitting to obtain a set of underlying surface parameters. Then, the least squares method is used to perform linear regression analysis on the underlying surface parameter set, the third hydrological characteristic parameter, and vegetation data in the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors, including: The first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter are input into the parameter optimizer to perform nonlinear fitting and obtain the underlying surface parameter set. The parameter optimizer is configured with a Budyko model function, which uses the drought index as the independent variable and the evapotranspiration rate or runoff coefficient as the dependent variable. The underlying surface parameter set includes parameters n1 and n2 used to characterize the shape of the Budyko model function; The least squares method is used to perform linear regression analysis on the vegetation data in the underlying surface parameter set, the third hydrological characteristic parameter, and the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors. The environmental factor data includes vegetation cover, normalized difference vegetation cover, and drought index. The linear relationship model includes slope and intercept.

[0036] It should be noted that, based on 24 data points, the evapotranspiration rate E / P, runoff coefficient Q / P, drought index Φ (Ep / P), underlying surface parameter n1, and underlying surface parameter n2 were calculated. The drought index Φ (Ep / P) was used as the independent variable (horizontal axis), and the evapotranspiration rate E / P and runoff coefficient Q / P were used as the dependent variables (vertical axis). The results were then nonlinearly fitted using the Budyko model function curve.

[0037] In some embodiments, the specific value of the shape parameter n is analyzed by nonlinear fitting, and the best fitting curve is determined by the least squares method. The best function fit for the data is found by minimizing the sum of squares of the optimal errors. Let the parameter er be the sum of squares of the residuals. The general form of the least squares optimization problem is:

[0038]

[0039] Among them, y i The parameter n1 or n2 varies with time; x i M represents vegetation cover; j represents the number of data points. The results for k and b are analyzed by calculating the partial derivatives of er with respect to k and b when they are zero (when er reaches its minimum). Furthermore, R is used... 2 The p-value is used to assess the accuracy and significance of the model.

[0040] The spatiotemporal equivalence between underlying surface parameters and environmental factors is analyzed based on a linear relationship model. The goodness of fit of the linear relationship model is evaluated using the coefficient of determination and significance level values ​​to determine the spatiotemporal equivalence of the linear relationship model on a preset spatiotemporal scale.

[0041] Preferably, the spatiotemporal equivalence between underlying surface parameters and environmental factors is analyzed based on a linear relationship model, and the goodness of fit of the linear relationship model is evaluated using the coefficient of determination and significance level values ​​to determine the spatiotemporal equivalence of the linear relationship model at a preset spatiotemporal scale, including: Based on the linear relationship model, the spatiotemporal equivalence between the underlying surface parameters and the environmental factors is analyzed to obtain the spatiotemporal equivalence identification results. The coefficient of determination and significance level were used as evaluation indicators to evaluate the accuracy and significance of the linear relationship model, and the goodness-of-fit evaluation results were obtained. Based on the goodness-of-fit evaluation results, it is determined whether the linear relationship model holds true on a preset spatiotemporal scale, so as to confirm its spatiotemporal equivalence.

[0042] In some embodiments, the spatiotemporal equivalence identification of water balance elements is based on watershed E, E p After calculating the static shape parameters n1 and n2 from the P data, the drought index Φ (E) is then calculated. p Using E / P as the independent variable (horizontal axis) and Evapotranspiration rate E / P and runoff coefficient Q / P as the dependent variables (vertical axis), nonlinear fitting was performed using the Budyko model function curve to determine the spatiotemporal equivalence of static Budyko water balance elements.

[0043] It should be noted that, for the identification of the spatiotemporal equivalence between underlying surface parameters and vegetation cover and drought index, since the obtained shape parameters are insufficient to capture the temporal dynamics of watershed attributes and their relationship with temporal changes in vegetation cover, the drought index Φ(E) is also used. p / P) Correlates time-varying Budyko shape parameters with vegetation cover to determine the spatiotemporal equivalence of the relationship between underlying surface parameters and vegetation cover and drought index.

[0044] It needs to be further explained that, respectively,M and M / Φ Using the x-axis as the horizontal axis and the Budyko model parameters n1 and n2 as the y-axis, the data points are fitted to determine the spatiotemporal equivalence of the relationship between underlying surface parameters, vegetation cover, and drought index.

[0045] Preferably, spatiotemporal symmetry refers to the phenomenon that the fitting results of the model exhibit the same parameters and effects across both time and spatial scales. The time scale refers to the interannual variation scale of hydrological elements, while the spatial scale refers to the multi-year average scale of hydrological elements across watersheds. Specific characteristics of spatiotemporal symmetry include... Figure 2 As shown: It should be noted that, Figure 2 (1a) and Figure 2 (1c) shows the fit of the underlying surface parameters n1 and n2 of the Budyko model between the sub-basins from 1987 to 2010 after an 11-year moving average. The fitting results are as follows: R = 1.67. 2 When R reaches 0.52 and the p-value of the p-test is less than 0.01, R0.52 is achieved when n2 = 1.35. 2 The p-value reached 0.54 and the p-value of the p-test was less than 0.01. And... Figure 2 (1b) and Figure 2 (1d) shows the interannual fit of the underlying surface parameters n1 and n2 of the Budyko model from 1987 to 2010 for each sub-basin after an 11-year moving average. Each circle corresponds to the water balance element of the corresponding year for the sub-basin. The fitting results are as follows: R = 1.66. 2 When R reaches 0.51 and the p-value of the p-test is also less than 0.01, R0.01 is achieved when n2 = 1.36. 2 The p-value reached 0.53, and the p-value of the p-test was also less than 0.01. Clearly, the water balance elements in the Budyko model exhibit spatiotemporal equivalence.

[0046] By analyzing the water balance elements and underlying surface parameters of the Budyko, n 1 and n 2. With vegetation cover M The identification of spatiotemporal symmetry between them, through the spatiotemporal equivalence of the hydrological model, allows the use of time instead of space. Parameters are calibrated based on spatial observations around the data-deficient watershed area, and hydrological runoff simulation (Q= P - E) is achieved through meteorological data in the data-deficient watershed.

[0047] Using interannual time-varying underlying surface parameters n 1 and n 2. Replace the fitted macroscopic static underlying surface parameters n By introducing a drought index Φ( E p / P ), to obtain Figure 2 (2) and Figure 2 (3) shows the fitting result. Figure 2 (2a) and Figure 2 As shown in (2b), different watersheds n 1 / Φ and M / Φ The fitting parameters maintain a consistent linear trend across watersheds and between years. k Similarly significantly similar (between watersheds) k =1.94, interannual k =2.25), and simultaneously determine the coefficient. R 2 The ratio is relatively high, especially between river basins. R 2 It reached 0.95 and p <0.01, interannual R 2 It also reached 0.83 and p The value is <0.01, therefore it is considered that the fitting curves of its inter-basin and inter-annual relationship models exhibit significant spatiotemporal symmetry. Meanwhile, Figure 2 As shown in (3a) and 2(3b), the underlying surface parameters n 2. A more complex exponential form, namely exp(- n 2) with M The existence of inter-relationships and n 1. Similar spatiotemporal symmetry, where watersheds... R 2 It reached 0.83 and p <0.01, interannual R 2 It also reached 0.70 and p <0.01, fitting parameters between watersheds and between years k The values ​​are 0.42 and 0.50, respectively.

[0048] The above is an introduction to the method embodiments. The following describes the solution described in this application through device embodiments.

[0049] Figure 3 This is a block diagram illustrating a spatiotemporal equivalent hydrological simulation apparatus suitable for data-scarce watersheds, according to an exemplary embodiment. The apparatus is used in a spatiotemporal equivalent hydrological simulation method suitable for data-scarce watersheds. (Refer to...) Figure 3 The device includes a raw data module 310, a sequence dataset module 320, a hydrological and meteorological data module 330, a computational model module 340, a linear relationship module 350, and a spatiotemporal equivalence module 360.

[0050] Raw data module 310: used to acquire digital elevation model data, collect vegetation data, meteorological data and hydrological data from multiple data sources, and perform preprocessing on the vegetation data, meteorological data and hydrological data to obtain raw data; Sequence Data Set Module 320: Used to generate an original time series numerical set based on the time sequence combination of each year in the original data, construct a set of year average values ​​through moving average calculation and integration, and update the set of year average values ​​and the original data to obtain a time-series smoothed spatiotemporal sequence dataset; Hydrometeorological data module 330: used to perform annual time series trend correction on the time-series smoothed spatiotemporal sequence dataset to obtain a target spatiotemporal sequence dataset, and to perform meteorological and hydrological factor fitting and hydrological feature calculation on the target spatiotemporal sequence dataset to obtain a hydrometeorological dataset, wherein the hydrometeorological dataset includes actual evapotranspiration data, precipitation data and potential evapotranspiration data; Calculation model module 340: is used to input the actual evapotranspiration data, precipitation data and potential evapotranspiration data contained in the hydro-meteorological dataset into the water balance calculation model, runoff coefficient calculation model and drought index calculation model respectively, to obtain the first hydrological characteristic parameter, the second hydrological characteristic parameter and the third hydrological characteristic parameter; Linear Relationship Module 350: This module is used to input the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter into the parameter optimizer to perform nonlinear fitting to obtain the underlying surface parameter set, and to use the least squares method to perform linear regression analysis on the vegetation data in the underlying surface parameter set, the third hydrological characteristic parameter, and the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors. Spatiotemporal equivalence module 360: used to analyze the spatiotemporal equivalence between underlying surface parameters and environmental factors based on a linear relationship model, and to evaluate the goodness of fit of the linear relationship model using the coefficient of determination and significance level values, so as to determine the spatiotemporal equivalence of the linear relationship model on a preset spatiotemporal scale.

[0051] A spatiotemporal equivalent hydrological simulation device suitable for watersheds lacking data, the device comprising: a processor; and a memory storing computer-readable instructions, wherein when executed by the processor, the computer-readable instructions implement any of the above-described spatiotemporal equivalent hydrological simulation methods for watersheds lacking data.

[0052] Figure 4 This is a schematic diagram of the structure of a spatiotemporal equivalent hydrological simulation device suitable for watersheds lacking data, provided by an embodiment of the present invention. Figure 4As shown, spatiotemporal equivalent hydrological simulation equipment suitable for data-scarce watersheds may include the above-mentioned... Figure 3 The illustrated spatiotemporal equivalent hydrological simulation device is suitable for watersheds lacking data. Optionally, the spatiotemporal equivalent hydrological simulation device 410 suitable for watersheds lacking data may include a first processor 2001.

[0053] Optionally, the spatiotemporal equivalent hydrological simulation device 410, suitable for watersheds lacking data, may also include a memory 2002 and a transceiver 2003.

[0054] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0055] The following is combined Figure 4 A detailed introduction to each component of the spatiotemporal equivalent hydrological simulation device 410, applicable to watersheds with scarce data: The first processor 2001 is the control center of the spatiotemporal equivalent hydrological simulation device 410 suitable for watersheds lacking data. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0056] Optionally, the first processor 2001 can perform various functions of the spatiotemporal equivalent hydrological simulation device 410 suitable for data-deficient watersheds by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0057] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 4 CPU0 and CPU1 are shown in the diagram.

[0058] In a specific implementation, as one example, the spatiotemporal equivalent hydrological simulation device 410 suitable for data-scarce watersheds may also include multiple processors, for example... Figure 4The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0059] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0060] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via an interface circuit of the spatiotemporal equivalent hydrological simulation device 410 suitable for data-deficient watersheds. Figure 4 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0061] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0062] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 4 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0063] Optionally, the transceiver 2003 can be integrated with the first processor 2001 or exist independently, and can be connected via the interface circuit of the spatiotemporal equivalent hydrological simulation device 410 suitable for data-scarce watersheds. Figure 4 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0064] It should be noted that, Figure 4 The structure of the spatiotemporal equivalent hydrological simulation device 410 shown in the figure for data-deficient watersheds does not constitute a limitation on the router. Actual knowledge structure identification devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0065] Furthermore, the technical effects of the spatiotemporal equivalent hydrological simulation device 410 applicable to watersheds lacking data can be referred to the technical effects of the spatiotemporal equivalent hydrological simulation method applicable to watersheds lacking data described in the above method embodiments, and will not be repeated here.

[0066] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0067] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0068] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0069] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0070] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0071] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0072] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0073] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0074] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0077] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A spatiotemporal equivalent hydrological simulation method suitable for watersheds lacking data, characterized in that, The method includes: S1: Acquire digital elevation model data, collect vegetation data, meteorological data and hydrological data from multiple data sources, and perform preprocessing on the vegetation data, meteorological data and hydrological data to obtain raw data; S2: Generate an original time series numerical group based on the time sequence combination of each year in the original data, construct a set of year average numerical values ​​through moving average operation and integration, and update the set of year average numerical values ​​and the original data to obtain a time-series smoothed spatiotemporal sequence dataset. S3: Perform annual time-series trend correction on the time-series smoothed spatiotemporal sequence dataset to obtain the target spatiotemporal sequence dataset, and perform meteorological and hydrological factor fitting and hydrological feature calculation on the target spatiotemporal sequence dataset to obtain the hydro-meteorological dataset, which includes actual evapotranspiration data, precipitation data and potential evapotranspiration data; S4: Input the actual evapotranspiration data, precipitation data and potential evapotranspiration data contained in the hydro-meteorological dataset into the water balance calculation model, runoff coefficient calculation model and drought index calculation model respectively to obtain the first hydrological characteristic parameter, the second hydrological characteristic parameter and the third hydrological characteristic parameter. S5: Input the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter into the parameter optimizer to perform nonlinear fitting to obtain the underlying surface parameter set, and use the least squares method to perform linear regression analysis on the underlying surface parameter set, the third hydrological characteristic parameter, and the vegetation data in the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors; S6: Analyze the spatiotemporal equivalence between underlying surface parameters and environmental factors based on a linear relationship model, and evaluate the goodness of fit of the linear relationship model using the coefficient of determination and significance level values, so as to determine the spatiotemporal equivalence of the linear relationship model on a preset spatiotemporal scale.

2. The spatiotemporal equivalent hydrological simulation method for watersheds lacking data, as described in claim 1, is characterized in that... The step S1 involves acquiring digital elevation model data by collecting vegetation data, meteorological data, and hydrological data from multiple data sources, and performing preprocessing on the vegetation data, meteorological data, and hydrological data to obtain raw data, including: S11: Collect digital elevation data for the selected area; S12: Based on the digital elevation data, construct a digital elevation model to obtain digital elevation model data; S13: Collect vegetation data, meteorological data and hydrological data of a selected area in multiple years from multiple data sources, wherein the multiple data sources include: geographic information system data interface, remote sensing image analysis module, meteorological monitoring station network and hydrological monitoring station network, wherein the vegetation data includes vegetation cover and normalized vegetation cover. S14: Perform preprocessing on the vegetation data, meteorological data, and hydrological data to obtain raw data. The preprocessing includes: performing interpolation, discretization, spatiotemporal alignment, and format standardization to generate spatial data with uniform spatial granularity.

3. The spatiotemporal equivalent hydrological simulation method for watersheds lacking data, as described in claim 1, is characterized in that... S2 generates an original time-series numerical set based on the time sequence combination of each year in the original data. This set is then constructed by moving average calculation and integration to create a set of yearly average values. Finally, the set of yearly average values ​​and the original data are updated to obtain a time-smoothed spatiotemporal sequence dataset, including: S21: Based on the time sequence of each year in the original data, combine the original values ​​corresponding to adjacent years to generate an original time sequence value group. S22: Perform a moving average operation on the original time series data set to generate a set of annual average values. The moving average operation includes performing a point-by-point moving average calculation on the original values ​​of adjacent years in the original time series data set, with the year as the moving dimension, to obtain a smoothed average value corresponding to each year. S23: Integrate the smoothed average values ​​corresponding to all years to construct the set of average values ​​for the years; S24: Update the set of average values ​​for each year and the original data to obtain a time-series smoothed spatiotemporal sequence dataset. The update process includes replacing the original values ​​of each year in the original data with the average values ​​of the corresponding years in the set of average values ​​for each year.

4. The spatiotemporal equivalent hydrological simulation method for watersheds lacking data, as described in claim 1, is characterized in that... S3 performs annual time-series trend correction on the time-smoothed spatiotemporal sequence dataset to obtain a target spatiotemporal sequence dataset, and performs meteorological and hydrological factor fitting and hydrological feature calculation on the target spatiotemporal sequence dataset to obtain a hydrometeorological dataset. The hydrometeorological dataset includes actual evapotranspiration data, precipitation data, and potential evapotranspiration data, including: S31: Perform annual time-series trend correction on the time-smoothed spatiotemporal sequence dataset to obtain the target spatiotemporal sequence dataset; S32: Perform meteorological and hydrological factor fitting and hydrological feature calculation on the target spatiotemporal sequence dataset to obtain a hydro-meteorological dataset, which includes: actual evapotranspiration data, precipitation data and potential evapotranspiration data.

5. The spatiotemporal equivalent hydrological simulation method for watersheds lacking data, as described in claim 1, is characterized in that... S4 inputs the actual evapotranspiration data, precipitation data, and potential evapotranspiration data contained in the hydrological and meteorological dataset into the water balance calculation model, runoff coefficient calculation model, and drought index calculation model, respectively, to obtain the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter, including: S41: Input the actual evapotranspiration data, precipitation data and potential evapotranspiration data contained in the hydro-meteorological dataset into the water balance calculation model to obtain the first hydrological characteristic parameter, which is the evapotranspiration rate. The water balance calculation model includes the Budyko-Wang equation and the Budyko-Yang equation. S42: Input the precipitation data and actual evapotranspiration data contained in the hydro-meteorological dataset into the runoff coefficient calculation model to obtain the second hydrological characteristic parameter, which is the runoff coefficient, and the runoff coefficient is determined by the ratio of the actual evapotranspiration data to the precipitation data. S43: Input the potential evapotranspiration data and precipitation data contained in the hydro-meteorological dataset into the drought index calculation model to obtain the third hydrological characteristic parameter, which is the drought index. The drought index is determined by the ratio of the potential evapotranspiration data to the precipitation data.

6. The spatiotemporal equivalent hydrological simulation method for watersheds lacking data, as described in claim 1, is characterized in that... S5 inputs the first, second, and third hydrological characteristic parameters into a parameter optimizer to perform nonlinear fitting to obtain a set of underlying surface parameters. Then, it uses the least squares method to perform linear regression analysis on the underlying surface parameter set, the third hydrological characteristic parameter, and vegetation data in the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors, including: S51: Input the first hydrological characteristic parameter, the second hydrological characteristic parameter and the third hydrological characteristic parameter into the parameter optimizer to perform nonlinear fitting and obtain the underlying surface parameter set; S52: The parameter optimizer is configured with a Budyko model function, which uses the drought index as the independent variable and the evapotranspiration rate or runoff coefficient as the dependent variable. S53: The underlying surface parameter set includes parameters n1 and n2 used to characterize the shape of the Budyko model function; S54: Using the least squares method, linear regression analysis is performed on the vegetation data in the underlying surface parameter set, the third hydrological characteristic parameters, and the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors. The environmental factor data includes vegetation cover, normalized vegetation cover, and drought index. The linear relationship model includes slope and intercept.

7. The spatiotemporal equivalent hydrological simulation method for watersheds lacking data, as described in claim 1, is characterized in that... The S6 method analyzes the spatiotemporal equivalence between underlying surface parameters and environmental factors based on a linear relationship model, and evaluates the goodness of fit of the linear relationship model using the coefficient of determination and significance level values ​​to determine the spatiotemporal equivalence of the linear relationship model at a preset spatiotemporal scale, including: S61: Based on the linear relationship model, analyze the spatiotemporal equivalence between the underlying surface parameters and the environmental factors to obtain the spatiotemporal equivalence identification results; S62: Using the coefficient of determination and significance level as evaluation indicators, the accuracy and significance of the linear relationship model are evaluated to obtain the goodness-of-fit evaluation results; S63: Based on the goodness-of-fit evaluation results, determine whether the linear relationship model holds true on a preset spatiotemporal scale, so as to confirm its spatiotemporal equivalence.

8. A spatiotemporal equivalent hydrological simulation device suitable for watersheds lacking data, wherein the spatiotemporal equivalent hydrological simulation device is used to implement the spatiotemporal equivalent hydrological simulation method for watersheds lacking data as described in any one of claims 1-7, characterized in that, The device includes: Raw data module: Used to acquire digital elevation model data, collect vegetation data, meteorological data and hydrological data from multiple data sources, and perform preprocessing on the vegetation data, meteorological data and hydrological data to obtain raw data; The sequence dataset module is used to generate an original time series numerical group based on the time sequence combination of each year in the original data, construct a set of year average values ​​through moving average calculation and integration, and update the set of year average values ​​and the original data to obtain a time-series smoothed spatiotemporal sequence dataset. Hydrological and meteorological data module: used to perform annual time series trend correction on the time-series smoothed spatiotemporal sequence dataset to obtain the target spatiotemporal sequence dataset, and to perform meteorological and hydrological factor fitting and hydrological feature calculation on the target spatiotemporal sequence dataset to obtain the hydrological and meteorological dataset, which includes actual evapotranspiration data, precipitation data and potential evapotranspiration data; The calculation model module is used to input the actual evapotranspiration data, precipitation data and potential evapotranspiration data contained in the hydro-meteorological dataset into the water balance calculation model, runoff coefficient calculation model and drought index calculation model respectively, to obtain the first hydrological characteristic parameter, the second hydrological characteristic parameter and the third hydrological characteristic parameter; Linear Relationship Module: This module is used to input the first hydrological characteristic parameter, the second hydrological characteristic parameter, and the third hydrological characteristic parameter into the parameter optimizer to perform nonlinear fitting to obtain the underlying surface parameter set. It then uses the least squares method to perform linear regression analysis on the underlying surface parameter set, the third hydrological characteristic parameter, and the vegetation data in the target spatiotemporal sequence dataset to obtain a linear relationship model between the underlying surface parameters and environmental factors. Spatiotemporal equivalence module: used to analyze the spatiotemporal equivalence between underlying surface parameters and environmental factors based on a linear relationship model, and to evaluate the goodness of fit of the linear relationship model using the coefficient of determination and significance level values, so as to determine the spatiotemporal equivalence of the linear relationship model on a preset spatiotemporal scale.

9. A spatiotemporal equivalent hydrological simulation device suitable for watersheds lacking data, characterized in that, The spatiotemporal equivalent hydrological simulation device applicable to watersheds lacking data includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 7.

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