Snow and drought judgment method, device and equipment and storage medium

Abstract

The invention provides a snow and drought judgment method and device, equipment and a storage medium. Relates to the technical field of meteorological monitoring and agricultural disaster early warning. The method comprises the following steps: determining a target area, and constructing a data set containing grid scale snow water equivalent, air temperature and rainfall; arranging the data into a three-column format file of descending latitude, ascending longitude and corresponding data, and acquiring snow water equivalent, precipitation and daily average temperature in a historical cold season; calculating a cold season rainfall cumulative sum as an annual cold season rainfall amount value, a cumulative temperature sum higher than 0 DEG C as an ablation degree, and an annual cold season maximum single-day snow water equivalent as an annual snow water equivalent value, determining a judgment threshold value, and finally judging several snow drought types of dry snow drought, warm snow drought, warm dry snow drought and cold snow drought according to the judgment threshold value. According to the method, through ternary system partitioning and multi-index dynamic threshold calculation, the accuracy of snow and drought judgment is improved, cold snow and drought types are introduced, the blank of traditional classification is filled, and the influence of snow and drought on the ecological hydrological process is comprehensively reflected.

Description

Technical Field

[0001] This application relates to the field of meteorological monitoring and agricultural disaster early warning technology, and in particular to a method, device, equipment and storage medium for distinguishing between snow and drought. Background Technology

[0002] Existing zoning methods, such as "Temporal-spatial variations and influencing factors of vegetation cover in Xinjiang from 1982 to 2013 based on GIMMS-NDVI", are not applicable. 3g The paper proposes a vegetation cover-based zoning method, dividing the region into 11 sub-regions, including oases, mountains, and deserts. This method is primarily used for vegetation change research and has not been optimized for snow and drought monitoring.

[0003] Existing snow drought classification standards, such as "Snow drought risk and susceptibility in the Western United States and Southwestern Canada," have proposed classification standards for snow drought in western North America, covering three types: warm snow drought, dry snow drought, and warm-dry snow drought. These standards are based on SWE (snow water equivalent), precipitation, and temperature data, but do not address the differentiation of cold snow drought.

[0004] The partitioning method in Reference 1 includes three desert regions, which have little agricultural activity and lack value for snow and drought monitoring, increasing data redundancy and analysis difficulty.

[0005] The snow-dry classification criteria in Reference 2 do not cover cold snow-dry, which has a unique impact on spring snowmelt runoff and eco-hydrological processes. Its absence limits the applicability of the classification method.

[0006] In summary, existing methods are not optimized for specific geographical and climatic conditions, leading to discrepancies between classification results and actual snow and drought events. Summary of the Invention

[0007] This application provides a method, apparatus, equipment, and storage medium for snow and drought discrimination, and provides a snow and drought classification scheme for arid / semi-arid climate zones. This addresses the problems that existing methods have not been optimized for the special geographical and climatic conditions of arid and semi-arid regions, resulting in insufficient accuracy of snow and drought monitoring, and that existing classification methods lack cold snow and drought types, thus failing to fully reflect the impact of snow and drought on eco-hydrological processes.

[0008] Firstly, this application provides a method for distinguishing between snow and drought conditions, including: Define the target area; Construct a dataset for the target region; wherein the dataset includes raster-scale snow water equivalent, temperature and precipitation data; The dataset is organized into a three-column format file; wherein the three columns are latitude in descending order, longitude in ascending order, and snow water equivalent / temperature / precipitation data, respectively. The cold season was from November of the previous year to May of the current year. Historical data on snow water equivalent, precipitation, and average daily temperature for the cold season were obtained for each target region. The cumulative sum of daily precipitation during the annual cold season is calculated as the annual cold season precipitation value; The degree of ablation is calculated based on the daily average temperature during the cold season; wherein, the degree of ablation is the sum of the accumulated temperature above 0°C each day; The maximum daily snow water equivalent during the cold season is used as the annual snow water equivalent value to calculate the discrimination threshold. The snow drought type is determined based on the discrimination threshold; wherein, the snow drought type includes dry snow drought, warm snow drought, warm-dry snow drought and cold snow drought.

[0009] In one possible design, the annual precipitation value during the cool season is calculated using the following formula (1): ;

[0010] In the formula, This represents the annual precipitation value during the cold season. a Numbers indicating the number of days, Indicates the first a Daily rainfall, A This indicates the total number of days.

[0011] In one possible design, the degree of ablation is calculated based on the daily average temperature using the following formula: ;

[0012] In the formula, This indicates the degree of ablation, and max represents the maximum value function. a Numbers indicating the number of days, Indicates the first a Average daily temperature A This indicates the total number of days.

[0013] In one possible design, the discrimination threshold is calculated as follows: The empirical probability is calculated using the following formula: ; In the formula, Represents empirical probability. i This indicates the ranking of non-zero variables from their minimum to their maximum value. N Indicates the number of samples; 3-month points based on snow water equivalent To determine the ranking The calculation formula is: ; In the formula, , and They represent the first m -2, No. m -1 and the m The integral value of snow water equivalent per month; The standardized snow water equivalent index is calculated by converting empirical probabilities to a standard normal distribution. SWEI : ; In the formula, Indicates based on integral value The standardized snow water equivalent index was calculated. The cumulative distribution function represents the standard normal distribution. It is its inverse function, used to convert probability values ​​into quantiles of the standard normal distribution; The standardized snow water equivalent index was used as the discrimination threshold.

[0014] In one possible design, the discrimination threshold is calculated using the following formula (6) or formula (7): ; ; In the formula, Indicates the discrimination threshold. Indicates the first i The snowmelt equivalent, precipitation, or degree of melting during the cold season of a year, where n is the total number of years. This represents the 25-digit percentage of historical data for each grid point.

[0015] In one possible design, the discrimination threshold is calculated using the following formula (8). : ;

[0016] In the formula, Represents the j-th grid point. i Snow water equivalent, precipitation, or degree of melting during the cold season of the year. m It is the total number of grid points in the region. n This represents the total number of years.

[0017] In one possible design, the snow / drought type is determined based on the first and second thresholds using the following formulas (9) to (12): if{(SWE i <SWE μ)&(TP i <TP μ )&(TD i <TD μ )},D type =DRY(9) if{(SWE i <SWE μ )&(TP i >TP μ )&(TD i >TD μ )},D type =WARM(10) if{(SWE i <SWE μ )&(TP i <TP μ )&(TD i >TD μ )},D type =WARM&DRY(11) if{(SWE i <SWE μ )&(P i >TP μ )&(TD i <TD μ )},D type =COLD(12) In the formula, SWE i TP i and TD i SWE represents the snow water equivalent, cumulative precipitation, and ablation rate for each year's cold season. μ TP μ and TD μ D represents the discrimination thresholds for snow water equivalent, cumulative precipitation, and degree of ablation, respectively. type The terms indicate the type of snow drought: DRY, WARM, WARM&DRY, and COLD represent dry snow drought, warm snow drought, warm-dry snow drought, and cold snow drought, respectively.

[0018] Secondly, this application provides a snow-drought discrimination device, the device comprising: The region determination module is configured to determine the target region; The dataset construction module is configured to construct a dataset for the target region; wherein the dataset includes raster-scale snow water equivalent, temperature and precipitation data; The dataset organization module is configured to organize the dataset into a file with a three-column format; wherein the three columns are latitude in descending order, longitude in ascending order, and snow water equivalent / temperature / precipitation data, respectively. The cold season parameter acquisition module is configured to define the cold season as November of the previous year to May of the current year, and acquires the historical snow water equivalent, precipitation and daily average temperature of the cold season for each target area. The first calculation module is configured to calculate the cumulative sum of daily precipitation during the annual cold season as the annual cold season precipitation value. The second calculation module is configured to calculate the degree of ablation based on the daily average temperature during the cold season; wherein the degree of ablation is the sum of the accumulated temperature above 0°C each day; The third calculation module is configured to use the maximum daily snow water equivalent in the cold season as the annual snow water equivalent value to calculate the discrimination threshold. The type determination module is configured to determine the snow drought type based on the discrimination threshold; wherein the snow drought type includes dry snow drought, warm snow drought, warm-dry snow drought and cold snow drought.

[0019] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to perform the snow and drought discrimination method as described in the first aspect and various possible designs of the first aspect.

[0020] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the snow and drought discrimination method described in the first aspect and various possible designs of the first aspect.

[0021] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the snow and drought discrimination method described in the first aspect and various possible designs of the first aspect.

[0022] The snow-dryness discrimination method, apparatus, equipment, and storage medium provided in this application have at least the following beneficial effects: This application improves the accuracy of snow-drought differentiation by using a ternary system partitioning and multi-index dynamic threshold calculation, and introduces a cold-snow-drought type, filling the gap in traditional classification and comprehensively reflecting the impact of snow-drought on eco-hydrological processes. Furthermore, the innovatively designed discrimination threshold application highlights spatiotemporal heterogeneity, providing a scientific basis for snow-drought monitoring in arid and semi-arid regions. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0024] Figure 1A flowchart illustrating a snow-dryness discrimination method provided in this application embodiment; Figure 2 This is a schematic diagram of snow and dry space partitioning provided in an embodiment of this application; Figure 3 This is a hotspot distribution map of four snow and drought types in a certain region, provided in an embodiment of this application. Figure 4 This is a structural diagram of the snow and drought discrimination device provided in the embodiments of this application.

[0025] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0026] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0027] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0028] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0029] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0030] To address the problem that existing classification methods lack cold-snow-drought types and cannot comprehensively reflect the impact of snow and drought on eco-hydrological processes, this application provides a snow-drought discrimination method. This method introduces a cold-snow-drought type, filling the gap in traditional snow-drought classification, comprehensively reflecting the impact of snow and drought on eco-hydrological processes, and employs a multi-index dynamic threshold calculation method (Method Four) to couple spatiotemporal heterogeneity, thereby improving the accuracy of snow-drought discrimination. Figure 1 The diagram shows a flowchart of a snow-drought discrimination method provided in an embodiment of this application. The snow-drought discrimination method includes the following steps S10-S80.

[0031] S10: Determine the target area.

[0032] In this embodiment, the method proposed in Reference 1 is used to delineate the snow and drought research area to determine the target area. An elevation boundary of 1300 m is set for some areas, and 2000 m for the remaining areas. Cultivated land and urban construction land are screened using land use maps. Oasis boundaries are determined through patch fusion and a 5 km buffer zone, constructing a ternary system of mountains, oases, and deserts. Three desert areas are excluded, retaining eight ecological zones as the target area. (See [reference]). Figure 2 .

[0033] S20: Construct a dataset for the target region; wherein the dataset includes raster-scale snow water equivalent, temperature and precipitation data.

[0034] In this embodiment, snow water equivalent, temperature, and precipitation data of the target area are collected and organized. Since this method uses raster data for calculation, the data collected in the target area are all raster-scale data, including the long-term snow depth dataset of China (1979-2024), the total precipitation and 2-meter temperature data of ERA5 reanalysis data from 1979 to 2021, and the precision of all datasets is 0.25°×0.25°.

[0035] S30: Organize the dataset into a three-column format file; wherein the three columns are latitude in descending order, longitude in ascending order, and snow water equivalent / temperature / precipitation data.

[0036] In this embodiment, all datasets can be processed using Python code and organized into a three-column txt file, with the columns being: latitude (arriving order), longitude (arriving order), and snow water equivalent / precipitation / temperature.

[0037] S40: The cold season is from November of the previous year to May of the current year. It obtains the snow water equivalent, precipitation and daily average temperature of the cold season for each target area in history.

[0038] In this embodiment, daily raster data of cold season SWE (snow water equivalent), precipitation (P), and daily average temperature (T) of each target area from 1980 to 2021 are collected as the data basis for subsequent processing steps.

[0039] S50: Calculate the cumulative sum of daily precipitation during the annual cold season as the annual precipitation value for the cold season.

[0040] In this embodiment, the annual precipitation value during the cold season is used to determine the snow-drought type, and its calculation formula is as follows: ;

[0041] In the formula, This represents the annual precipitation value during the cold season. a Numbers indicating the number of days, Indicates the first a Daily rainfall, A This indicates the total number of days.

[0042] S60: Calculate the degree of ablation based on the daily average temperature during the cold season; wherein the degree of ablation is the sum of the accumulated temperature above 0°C each day.

[0043] In this embodiment, the degree of ablation is calculated as the sum of the accumulated temperature above 0°C per day, used for snow and drought type identification. The calculation formula is as follows: ;

[0044] In the formula, This indicates the degree of ablation, and max represents the maximum value function. a Numbers indicating the number of days, Indicates the first a Average daily temperature A This indicates the total number of days.

[0045] S70: The maximum daily snow water equivalent during the cold season is used as the annual snow water equivalent value to calculate the discrimination threshold.

[0046] In this embodiment, four methods are used to calculate the raster data in order to determine the discrimination threshold.

[0047] Method 1: Calculate the Standardized Snow Water Equivalent Index (SWEI): ; in This is an empirical probability, where i is the rank of a non-zero variable from minimum to maximum, and N is the number of samples. It uses the 3-month SWE score. The ranking is determined by the following formula: ; In the formula, , and They represent the first m -2, No. m -1 and the m The integral value of snow water equivalent per month.

[0048] Finally, the standardized snow water equivalent index (SWEI) is calculated by converting the empirical probability p into a standard normal distribution: ; In the formula, Indicates based on integral value The standardized snow water equivalent index was calculated. The cumulative distribution function represents the standard normal distribution. It is its inverse function, used to convert probability values ​​into quantiles of the standard normal distribution; The standardized snow water equivalent index was used as the discrimination threshold.

[0049] Method 2: Calculate the historical average value of each grid point from 1980 to 2021 as the discrimination threshold µ, as shown in the following formula: ;

[0050] In the formula, Indicates the first i The annual snow water equivalent, precipitation value, or degree of melting during the cold season, where n is the total number of years (n=42 in this example).

[0051] Method 3: Use the 25th percentile of the historical data from 1980 to 2021 for each grid point as the threshold µ: ; In the formula, This represents the 25-digit percentage of historical data for each grid point.

[0052] Method 4: Calculate the mean value of all grid points within each region from 1980 to 2021 as the discrimination threshold µ. The calculation formula is as follows: ;

[0053] In the formula, Represents the j-th grid point. i Snow water equivalent, precipitation, or degree of melting during the cold season of the year. m It is the total number of grid points in the region. n This represents the total number of years.

[0054] S80: Determine the snow drought type based on the discrimination threshold; wherein, the snow drought type includes dry snow drought, warm snow drought, warm-dry snow drought and cold snow drought.

[0055] The snow-drought type is determined based on a discrimination threshold, and a new cold-snow-drought type is added to fill the gap in the traditional classification. The discrimination method is as follows: Method 1 directly uses the size of SWEI to determine whether snow drought has occurred. When SWEI < -0.5, it is judged as snow drought.

[0056] Methods 2, 3, and 4 determine the presence and type of snow and drought based on dynamic thresholds. The determination formula is as follows: if{(SWE i <SWE μ )&(TP i <TP μ )&(TD i <TD μ )},D type =DRY(9) if{(SWE i <SWE μ )&(TP i >TP μ )&(TD i >TD μ )},D type =WARM(10) if{(SWE i <SWE μ )&(TP i <TP μ )&(TD i >TD μ )},D type =WARM&DRY(11) Building upon this foundation, the addition of a cold-snow-drought type classification fills a gap in the traditional three-category snow-drought zoning, indicating that in addition to temperature and precipitation, snow sublimation also has a significant impact on snow-drought formation. The formula for determining the cold-snow-drought type is as follows: if{(SWE i <SWE μ )&(P i >TP μ )&(TD i <TD μ )},D type =COLD(12) In equations (9) to (12), SWE i TP i and TD i SWE represents the snow water equivalent, cumulative precipitation, and ablation rate for each year's cold season. μ TP μ and TD μ D represents the discrimination thresholds for snow water equivalent, cumulative precipitation, and degree of ablation, respectively. typeThe terms indicate the type of snow drought: DRY, WARM, WARM&DRY, and COLD represent dry snow drought, warm snow drought, warm-dry snow drought, and cold snow drought, respectively.

[0057] By comparing the reasonableness and authenticity of the results from the four threshold calculation methods, as shown in Table 1 and... Figure 3 As shown, Method 1, which uses an integral algorithm, easily "smooths out" the effects of extreme values, and the method of calculating the standardized index by integration is not suitable for standard calculations of ablation degree and precipitation. Methods 2 and 3 can better reflect the concentrated effect of individual grid points over time, but lack cross-sectional comparisons of raster data within the same region. Method 4 can more comprehensively couple the spatiotemporal characteristics of grid point snow and drought, highlighting its spatiotemporal heterogeneity. After comprehensive consideration, Method 4 was selected as the most suitable method, and the calculated results are closest to the true values.

[0058] Table 1 Snow water equivalent, accumulated temperature, and cumulative precipitation thresholds for each target area.

[0059] This application also provides a snow and drought discrimination device, such as... Figure 4 As shown, the snow / drought discrimination device includes: The region determination module 401 is configured to determine the target region; Dataset construction module 402 is configured to construct a dataset for the target region; wherein the dataset includes raster-scale snow water equivalent, temperature and precipitation data; The dataset organization module 403 is configured to organize the dataset into a file with a three-column format; wherein the three columns are latitude in descending order, longitude in ascending order, and snow water equivalent / temperature / precipitation data, respectively. The cold season parameter acquisition module 404 is configured to take November of the previous year to May of the current year as the cold season, and acquires the snow water equivalent, precipitation and daily average temperature of the cold season for each target area in history. The first calculation module 405 is configured to calculate the cumulative sum of daily precipitation during the annual cold season as the annual cold season precipitation value. The second calculation module 406 is configured to calculate the degree of ablation based on the daily average temperature during the cold season; wherein the degree of ablation is the sum of the accumulated temperature above 0°C each day; The third calculation module 407 is configured to use the maximum daily snow water equivalent in the cold season as the annual snow water equivalent value to calculate the discrimination threshold. The type determination module 408 is configured to determine the snow drought type based on the discrimination threshold; wherein the snow drought type includes dry snow drought, warm snow drought, warm-dry snow drought and cold snow drought.

[0060] In some embodiments, the first calculation module is further configured to calculate the annual cold season precipitation value using the following formula (1): ;

[0061] In the formula, This represents the annual precipitation value during the cold season. a Numbers indicating the number of days, Indicates the first a Daily rainfall, A This indicates the total number of days.

[0062] In some embodiments, the second calculation module is further configured to calculate the degree of ablation based on the daily average temperature using the following formula: ;

[0063] In the formula, This indicates the degree of ablation, and max represents the maximum value function. a Numbers indicating the number of days, Indicates the first a Average daily temperature A This indicates the total number of days.

[0064] In some embodiments, the third calculation module is further configured to calculate the discrimination threshold in the following manner: The empirical probability is calculated using the following formula: ; In the formula, Represents empirical probability. i This indicates the ranking of non-zero variables from their minimum to their maximum value. N Indicates the number of samples; 3-month points based on snow water equivalent To determine the ranking The calculation formula is: ; In the formula, , and They represent the first m -2, No. m -1 and the m The integral value of snow water equivalent per month; The standardized snow water equivalent index is calculated by converting empirical probabilities to a standard normal distribution. SWEI : ; In the formula, Indicates based on integral value The standardized snow water equivalent index was calculated. The cumulative distribution function represents the standard normal distribution. It is its inverse function, used to convert probability values ​​into quantiles of the standard normal distribution; the standardized snow water equivalent index is used as the discrimination threshold.

[0065] In some embodiments, the third calculation module is further configured to calculate the discrimination threshold using the following formula (6) or formula (7): ; ; In the formula, Indicates the discrimination threshold. Indicates the first i The snowmelt equivalent, precipitation, or degree of melting during the cold season of a year, where n is the total number of years. This represents the 25-digit percentage of historical data for each grid point.

[0066] In some embodiments, the third calculation module is further configured to calculate the discrimination threshold using the following formula (8). : ;

[0067] In the formula, Represents the j-th grid point. i Snow water equivalent, precipitation, or degree of melting during the cold season of the year. m It is the total number of grid points in the region. n This represents the total number of years.

[0068] In some embodiments, the type determination module is further configured to determine the snow / drought type based on the first threshold and the second threshold using the following formulas (9) to (12): if{(SWE i <SWE μ )&(TP i <TP μ )&(TD i <TD μ )},D type =DRY(9) if{(SWE i <SWE μ )&(TP i >TP μ )&(TD i >TD μ )},D type =WARM(10) if{(SWE i <SWE μ )&(TP i <TPμ )&(TD i >TD μ )},D type =WARM&DRY(11) if{(SWE i <SWE μ )&(P i >TP μ )&(TD i <TD μ )},D type =COLD(12) In the formula, SWE i TP i and TD i SWE represents the snow water equivalent, cumulative precipitation, and ablation rate for each year's cold season. μ TP μ and TD μ D represents the discrimination thresholds for snow water equivalent, cumulative precipitation, and degree of ablation, respectively. type The terms indicate the type of snow drought: DRY, WARM, WARM&DRY, and COLD represent dry snow drought, warm snow drought, warm-dry snow drought, and cold snow drought, respectively.

[0069] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0070] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0071] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0072] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0073] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the snow and drought discrimination method described above.

[0074] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the snow and drought discrimination method in the above embodiments.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0076] The modules described as separate components may or may not be physically separate. The components shown as modules 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 modules can be selected to implement the solution of this embodiment according to actual needs.

[0077] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0078] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0079] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0080] The memory may include high-speed RAM, and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0081] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0082] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0083] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0084] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A snow drought discrimination method characterized by comprising: The method comprises: determining a target region; constructing a data set of the target region; wherein the data set comprises snow water equivalent, air temperature and precipitation data at a grid scale; organizing the data set into a three-column format file; wherein the three-column format is in turn latitude in descending order, longitude in ascending order, and snow water equivalent / air temperature / precipitation data; taking November of the previous year to May of the current year as the cold season, obtaining the snow water equivalent, precipitation and daily average temperature of the cold season of each target region in history; calculating the cumulative sum of daily precipitation in the cold season as the precipitation value of the cold season; calculating the degree of ablation according to the daily average temperature in the cold season; wherein the degree of ablation is the sum of daily accumulated temperature above 0℃; taking the maximum single-day snow water equivalent in the cold season as the annual snow water equivalent value, and calculating the discrimination threshold; judging the snow drought type based on the discrimination threshold; wherein the snow drought type includes dry snow drought, warm snow drought, warm dry snow drought and cold snow drought.

2. The snow drought discrimination method according to claim 1, characterized by, The precipitation value of the cold season is calculated by the following formula (1): ； wherein represents the precipitation value in the cold season of the year, a represents the number of days, represents the precipitation of the a day, A represents the total number of days.

3. The snow drought discrimination method according to claim 1, characterized by, The degree of ablation is calculated according to the daily average temperature by the following formula: ； In the formula, denotes the degree of ablation, max denotes the maximum function, a denotes the number of days, denotes the average temperature on the a day, A denotes the total number of days.

4. The snow drought discrimination method according to claim 1, characterized by, The discrimination threshold is calculated by the following method: The empirical probability is calculated by the following formula: ； wherein denotes the empirical probability, i denotes the rank of a non-zero variable from the minimum to the maximum, N denotes the number of samples; 3-month points based on snow water equivalent To determine the ranking The calculation formula is: ； wherein, , and represent the snow water equivalent integrated values of the first m -2, the first m -1 and the first m months, respectively. Standardized snow water equivalent index calculated by converting empirical probability to standard normal distribution SWEI : ； wherein denotes the standardized snow water equivalent index calculated based on the integral value denotes the cumulative distribution function of the standard normal distribution, is the inverse function thereof for converting a probability value into a quantile of the standard normal distribution;​ The standardized snow water equivalent index is taken as the discrimination threshold.

5. The snow drought discrimination method according to claim 1, characterized by, The discrimination threshold is calculated by the following formula (6) or formula (7): ； ； wherein denotes a discrimination threshold, denotes the i snow water equivalent, precipitation value or degree of thawing of the cold season of the year n, n is the total number of years, denotes the 25th percentile of the historical data of each grid point.

6. The snow drought discrimination method according to claim 1, characterized by, The discrimination threshold is calculated by the following equation (8) : ； In the formula, represents the jth grid point in the i th year i snow water equivalent, precipitation value or thawing degree in the cold season of the year, m is the total number of grids in the region, n is the total number of years.

7. The snow drought discrimination method according to any one of claims 1 to 6, characterized by, The snow drought type is judged based on the first threshold and the second threshold by the following formula (9) to (12): if {(SWE i < SWE μ ) & (TP i < TP μ ) & (TD i < TD μ )}, D type = DRY(9) if {(SWE i < SWE μ ) & (TP i > TP μ ) & (TD i > TD μ )}, D type = WARM (10) if {(SWE i < SWE μ ) & (TP i < TP μ ) & (TD i > TD μ )}, D type = WARM & DRY (11) if {(SWE i < SWE μ ) & (P i > TP μ ) & (TD i < TD μ )}, D type = COLD (12) where SWE i , TP i , and TD i represent the snow water equivalent, cumulative precipitation, and degree of thawing of each year's cold season, respectively, SWE μ , TP μ , and TD μ represent the discriminant thresholds of snow water equivalent, cumulative precipitation, and degree of thawing, respectively, D type represents the snow drought type, and DRY, WARM, WARM&DRY, and COLD represent dry snow drought, warm snow drought, warm dry snow drought, and cold snow drought, respectively.

8. A snow drought discrimination device characterized by comprising: The device comprises: a region determination module configured to determine a target region; a data set construction module configured to construct a data set of the target region; wherein the data set comprises snow water equivalent, air temperature and precipitation data at a grid scale; a data set organization module configured to organize the data set into a three-column format file; wherein the three-column format is in turn latitude in descending order, longitude in ascending order, and snow water equivalent / air temperature / precipitation data; a cold season parameter acquisition module configured to take November of the previous year to May of the current year as the cold season, and obtain the snow water equivalent, precipitation and daily average temperature of the cold season of each target region in history; a first calculation module configured to calculate the cumulative sum of daily precipitation in the cold season as the precipitation value of the cold season; a second calculation module configured to calculate the degree of ablation according to the daily average temperature in the cold season; wherein the degree of ablation is the sum of daily accumulated temperature above 0℃; a third calculation module configured to take the maximum single-day snow water equivalent in the cold season as the annual snow water equivalent value, and calculate the discrimination threshold; a type judgment module configured to judge the snow drought type based on the discrimination threshold; wherein the snow drought type includes dry snow drought, warm snow drought, warm dry snow drought and cold snow drought.

9. An electronic device, comprising: It comprises: a processor, and a memory connected in communication with the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to realize the snow drought discrimination method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to realize the snow drought discrimination method according to any one of claims 1-7.

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