A composite drought identification method and system based on coupling of SPEI and VPD threshold value

Abstract

The application discloses a composite drought identification method and system based on coupling of SPEI and VPD threshold values, and belongs to the field of meteorological disaster identification. The method comprises the following steps: obtaining SPEI data and VPD data of a research region at a target time scale, and performing spatial registration, time scale matching and resolution unification processing; then, drought states of supply sides and demand sides are identified based on the SPEI data and the VPD data respectively, and coupling determination is performed on the drought states at the same spatial position and the same time scale or within a set time window, so that composite drought pixels are identified. The application improves the accuracy and stability of composite drought identification by constructing a coupling determination mechanism of water supply and atmospheric demand, and is suitable for composite drought identification of regions with different land cover types.

Description

Technical Field

[0001] This invention relates to the field of meteorological disaster identification technology, and more specifically to a method and system for identifying compound drought based on the coupling of SPEI and VPD thresholds, which is applicable to the identification and spatial distribution extraction of regional-scale compound drought events. Background Technology

[0002] Drought is one of the most destructive meteorological disasters globally, with its frequency and intensity increasing significantly against the backdrop of global warming, severely threatening agricultural production, water resource security, and ecosystem stability. In recent years, under the combined effects of rising temperatures and changing precipitation patterns, complex drought events driven by multiple meteorological factors have become increasingly prominent. Complex droughts typically manifest as a superimposed effect of insufficient water supply and increased atmospheric evapotranspiration demand, with a wider impact range, longer duration, and profound influence on ecosystem structure and function, as well as carbon cycle processes. Therefore, accurately identifying complex drought events has become a crucial issue in climate change and ecological environment research.

[0003] Currently, existing drought identification methods mainly have the following shortcomings:

[0004] (1) A single indicator is insufficient to characterize the mechanisms of complex drought: Existing methods are mostly based on a single indicator for drought identification, which makes it difficult to simultaneously reflect the synergistic effect of insufficient water supply and increased atmospheric evapotranspiration demand, and easily underestimates the actual impact of complex drought events. (2) Lack of a unified multi-index coupling judgment method: Existing methods mostly employ empirical superposition or complex statistical models, resulting in complex implementation processes and a lack of a well-structured, clearly defined, and definite drought identification process, making it difficult to promote and apply them across different regions and scales. (3) Insufficient handling of spatiotemporal consistency: Existing methods lack a unified temporal scale and spatial resolution processing mechanism in the process of multi-source data fusion, which affects the stability and comparability of the results of compound drought identification.

[0005] Therefore, it is necessary to propose a complex drought identification method with a simple structure, clear judgment rules, and good versatility. Summary of the Invention

[0006] To address the limitations of existing drought identification methods, such as the reliance on single indicators and the lack of multi-factor coupling judgment mechanisms, this invention proposes a composite drought identification method and system based on the coupling of SPEI and VPD thresholds. By constructing a dual-constraint judgment system based on both water supply and atmospheric demand, the method achieves accurate identification of composite drought events.

[0007] To achieve the above objectives, the present invention adopts the following technical solution; In a first aspect, embodiments of the present invention provide a composite drought identification method based on the coupling of SPEI and VPD thresholds, comprising the following steps: S1. Acquire SPEI and VPD data of the study area at the target time scale, and perform spatial registration, time scale matching and resolution unification processing. S2. Determine the drought status on the water supply side based on the SPEI data. When the SPEI value of a pixel is less than a first preset threshold, it is marked as a water-deficient pixel. S3. Determine the atmospheric demand-side drought status based on the VPD data. When the VPD value of a pixel is greater than a preset quantile threshold, it is marked as an atmospheric drought pixel. S4. At the same spatial location and at the same time scale or within a set time window, the results of S2 and S3 are coupled and judged. When the same pixel is simultaneously marked as a water-deficient pixel and an atmospheric drought pixel, it is judged as a composite drought pixel. S5. Output the results of the complex drought identification, including the complex drought pixel distribution data or raster layer.

[0008] Preferably, in S1, the SPEI data is calculated based on the moisture balance of precipitation and potential evapotranspiration, and the VPD data is calculated based on air temperature and relative humidity.

[0009] Preferably, in S1, both the SPEI data and the VPD data are multi-timescale data, including 1-month, 3-month, 6-month, or 12-month scales. The corresponding scale is selected according to the scale of the study area and the target to be identified, and the spatial resolution is unified by a resampling method.

[0010] Preferably, in S2, the first preset threshold is an SPEI value less than -1.1, used to characterize a moderate or higher water deficit state.

[0011] Preferably, in S3, the preset quantile threshold is the 90th quantile threshold obtained based on the statistical analysis of the historical VPD time series of each pixel.

[0012] Preferably, in S4, the determination function for coupling determination is:

[0013] In the formula, The t represents the spatial coordinates of a raster cell; t represents the time scale. This represents the SPEI value of the pixel at time t; This represents the VPD value of the pixel at time t; This represents the 90th percentile threshold of the current pixel in the historical time series summary; This indicates the result of the compound drought assessment for that pixel at time t; When =1, it indicates that the pixel is experiencing compound drought; When the value is 0, it indicates that the pixel has not experienced compound drought.

[0014] Preferably, in S4, the coupling determination is performed by superimposing the grid space and extracting the spatial intersection of the water-deficient pixels and the atmospheric drought pixels as a composite drought pixel.

[0015] Preferably, in S5, the results of the complex drought identification include spatial distribution visualization and statistical feature analysis of complex drought pixels, generating a spatial distribution map of complex drought and regional statistical information.

[0016] Secondly, embodiments of the present invention provide a composite drought identification system based on SPEI and VPD threshold coupling. This system is used to implement the composite drought identification method based on SPEI and VPD threshold coupling as described in any of the preceding claims, including: The data acquisition and processing module is used to acquire SPEI and VPD data of the study area at the target time scale, and perform spatial registration, time scale matching and resolution unification processing. The supply-side drought determination module is used to determine the drought status of the water supply side based on the SPEI data. When the SPEI value of a pixel is less than a first preset threshold, it is marked as a water-deficient pixel. The demand-side drought determination module is used to determine the atmospheric demand-side drought status based on the VPD data. When the VPD value of a pixel is greater than a preset quantile threshold, it is marked as an atmospheric drought pixel. The drought coupling determination module is used to couple and determine the results of S2 and S3 at the same spatial location and at the same time scale or within a set time window. When the same pixel is simultaneously marked as a water-deficient pixel and an atmospheric drought pixel, it is determined to be a composite drought pixel. The results output module is used to output the results of complex drought identification, including complex drought pixel distribution data or raster layers.

[0017] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the composite drought identification method based on the coupling of SPEI and VPD thresholds as described in any of the preceding claims.

[0018] This invention provides a method and system for identifying complex drought based on the coupling of SPEI and VPD thresholds. The aim is to construct a complex drought determination mechanism at a unified spatiotemporal scale by coupling water supply-side indicators and atmospheric demand-side indicators, thereby achieving effective identification of complex drought events.

[0019] Compared with existing technologies, the beneficial effects include at least the following: 1. Enhanced dual-constraint identification capability: Simultaneously introducing SPEI and VPD indicators, the assessment is conducted from two dimensions: water supply and atmospheric demand, which can more comprehensively reflect the drought occurrence mechanism. 2. Clear criteria for determining complex drought: A unified determination rule is established through threshold coupling to avoid misjudgment caused by single indicator identification; 3. Strong spatial recognition capability: Based on the grid pixel scale, it can obtain the spatial distribution characteristics of complex drought.

[0020] 4. Wide applicability: The method is not dependent on specific land types and can be applied to a variety of ecosystem regions.

[0021] 5. Simple and easy to implement: It can be implemented using conventional meteorological data and geographic information system tools, and has good application and promotion value. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the process of the composite drought identification method based on the coupling of SPEI and VPD thresholds provided in the embodiments of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] This invention discloses a method and system for identifying compound drought based on the coupling of SPEI and VPD thresholds. SPEI represents standardized precipitation evapotranspiration index data, which reflects changes in water supply and characterizes meteorological drought processes; VPD represents saturated vapor pressure difference data, which reflects the degree of atmospheric dryness and depicts atmospheric evapotranspiration demand and vegetation water stress. For ease of description, abbreviations will be used thereafter.

[0026] In this implementation, the identification method includes the following steps: S1. Acquire SPEI and VPD data of the study area at the target time scale, and perform spatial registration, time scale matching and resolution unification processing on the acquired data. S2. Determine the drought status on the water supply side based on the SPEI data. When the SPEI value of a pixel is less than a first preset threshold, it is marked as a water-deficient pixel. S3. Determine the atmospheric demand-side drought status based on the VPD data. When the VPD value of a pixel is greater than the preset quantile threshold of its corresponding historical time series, it is marked as an atmospheric drought pixel. S4. At the same spatial location and at the same time scale or within a set time window, the results of S2 and S3 are coupled and judged. When the same pixel is simultaneously marked as a water-deficient pixel and an atmospheric drought pixel, it is judged as a composite drought pixel. S5. Output the results of the complex drought identification, including the complex drought pixel distribution data or raster layer.

[0027] It should be noted that the step numbers above are for descriptive convenience only and do not constitute a strict limitation on the order of step execution. In some implementations, some steps can be executed in parallel or their order can be adjusted in an equivalent manner.

[0028] In one alternative implementation, SPEI data is calculated based on the water balance of precipitation and potential evapotranspiration to characterize water supply-side constraints; VPD data is calculated based on air temperature and relative humidity to characterize atmospheric evapotranspiration demand intensity.

[0029] In one optional implementation, both the SPEI data and the VPD data are multi-timescale data, including 1-month, 3-month, 6-month, or 12-month scales. The corresponding scale is selected according to the scale of the study area and the identification target, and the data with different spatial resolutions are uniformly processed through a resampling method. The short-term timescale is used to identify short-term or sudden drought processes, while the long-term timescale is used to identify persistent or cumulative drought processes, thereby improving the applicability and accuracy of complex drought identification. The data can originate from various data sources such as surface meteorological observation data, reanalysis data, or remote sensing inversion data.

[0030] To ensure spatial consistency among different data sources, geographic information system tools are used for spatial registration, such as ArcGIS Pro or other software platforms with raster processing capabilities, to project data from different sources onto the same coordinate system (WGS1984). For data with inconsistent spatial resolutions, resampling methods are used for unified processing, including nearest neighbor or bilinear interpolation, to ensure that the data has consistent pixel size in space.

[0031] In one optional implementation, the first preset threshold is an SPEI value less than -1.1, used to characterize a moderate or higher water deficit state. In some implementations, pixels that meet the conditions are extracted by raster condition screening, and a water deficit pixel distribution layer is generated.

[0032] In one optional implementation, firstly, the VPD values ​​of each pixel are statistically analyzed based on historical time series to construct its distribution characteristics, and a threshold is determined accordingly. When the VPD value of a pixel is greater than the 90th percentile threshold of its historical time series, the pixel is determined to be in an atmospheric drought state. Simultaneously, pixels that meet the conditions are extracted through raster calculation, and an atmospheric drought pixel distribution layer is generated.

[0033] In one optional implementation, spatial overlay analysis is performed on water-deficient pixels and atmospheric drought pixels at the same spatial location and within the same time scale or a set time window, and their intersection is extracted; that is, the composite drought pixel is a pixel that simultaneously satisfies both water supply-side drought conditions and atmospheric demand-side drought conditions at the same time scale, and the determination function for coupling is:

[0034] In the formula, The t represents the spatial coordinates of a raster cell; t represents the time scale. This represents the SPEI value of the pixel at time t; This represents the VPD value of the pixel at time t; This represents the 90th percentile threshold of the current pixel in the historical time series summary; This indicates the result of the compound drought assessment for that pixel at time t; When =1, it indicates that the pixel is experiencing compound drought; When the value is 0, it indicates that the pixel has not experienced compound drought.

[0035] In this implementation scheme, the coupling determination is achieved by overlaying raster space and extracting the spatial intersection of water-deficient pixels and atmospheric drought pixels as composite drought pixels; at the same time, pixel scale identification is performed on the raster data based on geographic information system tools.

[0036] Furthermore, in an optional implementation, the output of the compound drought identification result includes: generating a raster data layer based on the identified compound drought pixels, and labeling the compound drought pixels. Optionally, the result is visualized using geographic information system tools to generate a spatial distribution map of compound drought. Simultaneously, statistical analysis can be performed on the compound drought pixels to obtain the spatial distribution characteristics of the compound drought-affected areas, including spatial distribution visualization and statistical feature analysis of the compound drought pixels, generating a spatial distribution map of compound drought and regional statistical information.

[0037] This invention is applicable to the identification of complex drought in regions with different land cover types, including grassland, farmland, forest and desert areas.

[0038] The following is an example of a drought event identification method applied globally. The process includes: 1. Data Acquisition The SPEI data is calculated based on precipitation and potential evapotranspiration, and can be derived from reanalysis data or publicly available drought data products, such as the SPEI dataset calculated based on ERA5 data. The VPD data is calculated based on air temperature and relative humidity, and can be derived from reanalysis data or climate data products, such as the VPD data provided by the TerraClimate dataset.

[0039] SPEI Data: A global diurnal SPEI-GD dataset developed based on precipitation data from the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset and potential evapotranspiration data from the Singer et al. dataset. This dataset has a spatiotemporal resolution of 0.25°, covers the period from 1982 to 2021, and provides five time scales (5, 30, 90, 180, and 360 days). VPD data: Global annual vapor pressure deficit (VPD) raster data from 1958 to 2024 at a resolution of 4.6 km (0.5°) were selected from the TerraClimate dataset.

[0040] 2. Data Preprocessing In ArcGIS Pro, the "Create NetCDF Raster Layer" tool was used to convert the nc-format SPEI data into raster data, with a unified WGS1984 projection. The VPD data, also in raster format, was directly imported into ArcGIS Pro. For ease of processing and recognition, annual SPEI-12 and yearly VPD data were selected, with a time scale of 1983-2021. The spatial resolution was unified to 0.5° (≈55km). During the spatial resolution unification process, for data with an original resolution of 0.25°, a resampling method from high resolution to low resolution (i.e., resampling from 0.25° to 0.5°) was adopted. Compared to resampling from low resolution to high resolution (e.g., from 0.5° to 0.25°), this processing method can effectively reduce interpolation errors and improve data consistency and result reliability.

[0041] 3. Determination of Complex Drought SPEI (Standardized Precipitation Evapotranspiration Index) follows an approximately standard normal distribution. According to drought severity classification standards, SPEI values ​​are generally between -3 and 3. To improve the rigor of drought identification and screen for persistent drought events, this embodiment selects SPEI-12 < -1.1 as the threshold. This threshold corresponds to approximately the 13th percentile in the standard normal distribution, effectively identifying areas with moderate to severe drought. Vapor Pressure Deficit (VPD) characterizes atmospheric dryness and atmospheric evaporation demand; a higher VPD value indicates drier air and stronger evapotranspiration demand. Since VPD is significantly influenced by regional climate background, with substantial differences in absolute values ​​across different regions, this embodiment uses a quantile method based on historical time series to determine the threshold. Specifically, based on the pixel-by-pixel VPD time series from 1983 to 2021, the 90th percentile value is calculated as the high-value threshold. When the VPD at a certain moment is greater than or equal to this threshold, it is determined to be an atmospheric drought state. The 90th percentile corresponds to the range where VPD is at its highest 10% under historical conditions, which can effectively identify abnormally high atmospheric dryness conditions.

[0042] As a preferred approach, the ArcGIS Pro tool "Raster Calculator" was used to extract pixels with SPEI-12 < -1.1 from 1983 to 2021. At the same time, pixels with VPD data greater than the 90th percentile in their historical time series from 1983 to 2021 were also extracted. The intersection of the two pixels was then searched, which were the pixels where the compound drought occurred.

[0043] 4. Result Generation The combined drought occurred over the largest area in 2020. Pixels showing combined drought that year were mainly distributed in East and South Asia, South America, and western North America, indicating a regionally concentrated distribution of combined drought. These results verify that the method of this invention can effectively identify the spatial distribution and variation characteristics of combined drought.

[0044] Based on the same inventive concept, embodiments of the present invention provide a composite drought identification system based on SPEI and VPD threshold coupling. This system is used to implement the composite drought identification method based on SPEI and VPD threshold coupling as described in any of the preceding claims, including: The data acquisition and processing module is used to acquire SPEI and VPD data of the study area at the target time scale, and perform spatial registration, time scale matching and resolution unification processing. The supply-side drought determination module is used to determine the drought status of the water supply side based on the SPEI data. When the SPEI value of a pixel is less than a first preset threshold, it is marked as a water-deficient pixel. The demand-side drought determination module is used to determine the atmospheric demand-side drought status based on the VPD data. When the VPD value of a pixel is greater than a preset quantile threshold, it is marked as an atmospheric drought pixel. The drought coupling determination module is used to couple and determine the results of S2 and S3 at the same spatial location and at the same time scale or within a set time window. When the same pixel is simultaneously marked as a water-deficient pixel and an atmospheric drought pixel, it is determined to be a composite drought pixel. The results output module is used to output the results of complex drought identification, including complex drought pixel distribution data or raster layers.

[0045] The composite drought identification system based on SPEI and VPD threshold coupling provided in this embodiment of the invention has the same principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the aforementioned method embodiment, and will not be repeated here.

[0046] Furthermore, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the composite drought identification method based on the coupling of SPEI and VPD thresholds as described in any of the preceding claims.

[0047] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, computer software program products, or electronic devices, etc. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0048] Any content or technical means not mentioned in the embodiments of this invention can be obtained by referring to the prior art. This disclosure does not limit the scope of the invention and therefore will not be elaborated further.

[0049] The embodiments of the present invention have been described in detail above, and the principles and implementation methods of the present invention have been explained. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of the present invention. It should be noted that the word "comprising" does not exclude the presence of components or steps not listed in the claims. The words "a" or "an" preceding a component do not exclude the presence of a plurality of such components. The present invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer.

[0050] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0051] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A composite drought identification method based on the coupling of SPEI and VPD thresholds, characterized in that, Includes the following steps: S1. Acquire SPEI and VPD data of the study area at the target time scale, and perform spatial registration, time scale matching and resolution unification processing. S2. Determine the drought status on the water supply side based on the SPEI data. When the SPEI value of a pixel is less than a first preset threshold, it is marked as a water-deficient pixel. S3. Determine the atmospheric demand-side drought status based on the VPD data. When the VPD value of a pixel is greater than a preset quantile threshold, it is marked as an atmospheric drought pixel. S4. At the same spatial location and at the same time scale or within a set time window, the results of S2 and S3 are coupled and judged. When the same pixel is simultaneously marked as a water-deficient pixel and an atmospheric drought pixel, it is judged as a composite drought pixel. S5. Output the results of the complex drought identification, including the complex drought pixel distribution data or raster layer.

2. The composite drought identification method based on SPEI and VPD threshold coupling as described in claim 1, characterized in that, In S1, the SPEI data is calculated based on the moisture balance of precipitation and potential evapotranspiration, and the VPD data is calculated based on air temperature and relative humidity.

3. The composite drought identification method based on SPEI and VPD threshold coupling as described in claim 1, characterized in that, In S1, both the SPEI data and the VPD data are multi-timescale data, including 1-month, 3-month, 6-month, or 12-month scales. The corresponding scale is selected according to the scale of the study area and the target to be identified, and the spatial resolution is unified by resampling.

4. The composite drought identification method based on SPEI and VPD threshold coupling as described in claim 1, characterized in that, In S2, the first preset threshold is an SPEI value less than -1.1, which is used to characterize a moderate or higher water deficit state.

5. The composite drought identification method based on SPEI and VPD threshold coupling as described in claim 1, characterized in that, In S3, the preset quantile threshold is the 90th percentile threshold obtained based on the statistical analysis of the historical VPD time series of each pixel.

6. The composite drought identification method based on SPEI and VPD threshold coupling as described in claim 1, characterized in that, In S4, the determination function for coupling determination is: In the formula, The t represents the spatial coordinates of a raster cell; t represents the time scale. Represents a pixel The SPEI value at time t; Represents a pixel The VPD value at time t; Represents a pixel The 90th percentile threshold for historical time series aggregation; Represents a pixel The result of the combined drought assessment at time t; =1 indicates a pixel Complex drought occurred; =0 indicates a pixel No compound drought occurred.

7. The composite drought identification method based on SPEI and VPD threshold coupling as described in claim 1, characterized in that, In S4, the coupling determination is achieved by superimposing the grid space and extracting the spatial intersection of the water-deficient pixels and the atmospheric drought pixels as a composite drought pixel.

8. The composite drought identification method based on SPEI and VPD threshold coupling as described in claim 1, characterized in that, In S5, the results of the complex drought identification include spatial distribution visualization and statistical feature analysis of complex drought pixels, generating a spatial distribution map of complex drought and regional statistical information.

9. A composite drought identification system based on SPEI and VPD threshold coupling, characterized in that, The method for implementing the composite drought identification method based on SPEI and VPD threshold coupling as described in any one of claims 1 to 8 includes: The data acquisition and processing module is used to acquire SPEI and VPD data of the study area at the target time scale, and perform spatial registration, time scale matching and resolution unification processing. The supply-side drought determination module is used to determine the drought status of the water supply side based on the SPEI data. When the SPEI value of a pixel is less than a first preset threshold, it is marked as a water-deficient pixel. The demand-side drought determination module is used to determine the atmospheric demand-side drought status based on the VPD data. When the VPD value of a pixel is greater than a preset quantile threshold, it is marked as an atmospheric drought pixel. The drought coupling determination module is used to couple and determine the results of S2 and S3 at the same spatial location and at the same time scale or within a set time window. When the same pixel is simultaneously marked as a water-deficient pixel and an atmospheric drought pixel, it is determined to be a composite drought pixel. The results output module is used to output the results of complex drought identification, including complex drought pixel distribution data or raster layers.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the composite drought identification method based on the coupling of SPEI and VPD thresholds as described in any one of claims 1 to 8.

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