Winter low-temperature drought monitoring and black disaster early warning technology

Abstract

The invention belongs to the technical field of meteorological disaster monitoring and early warning, and particularly relates to a winter low-temperature drought monitoring and black disaster early warning technology, which comprises a data acquisition module for fusing multi-source heterogeneous data, providing high-time-efficiency and high-spatial-resolution data support for low-temperature drought identification and black disaster early warning, establishing an exponential low-temperature risk function model, and establishing a low-temperature risk function model; continuous modeling of temperature intensity and duration is realized, traditional static threshold limitation is broken through, and a low-temperature stress response mechanism of an ecological system is more scientifically represented; a drought-low temperature joint index (LDCI) model is provided, remote sensing accumulated snow coverage and surface temperature data are fused, and the strength and distribution of the ecological system affected by dry-cold combination at the current stage are quantified; a black disaster risk index (BDRI) model is constructed, and quantitative evaluation and early warning grading of future black disaster occurrence possibilities are realized by combining current LLI and future LLI predicted values.

Description

Technical Field

[0001] The present invention belongs to the technical field of meteorological disaster monitoring and early warning, and in particular relates to a winter low temperature drought monitoring and black disaster early warning technology. Background Art

[0002] In arid and semi-arid regions of mid- and high-latitudes, a continuous lack of effective precipitation and abnormally low temperatures in winter often trigger a typical complex meteorological disaster: the black cloud. This disaster, resulting from a severe lack of snowfall in winter, leaves the ground bare, creating a stark visual contrast with the normal snow cover, hence the vivid name "black cloud." Black cloud not only reflects abnormal weather but also significantly impacts ecosystems.

[0003] Such disasters significantly weaken the soil's ability to retain heat and retain moisture, posing a complex threat to the living environment of plants and animals. On the one hand, plants are more susceptible to freezing damage in low temperatures. The expansion of water within cells during freezing damages cell membranes and walls, leading to structural damage and even death. On the other hand, animals face the dual pressures of water shortages and forage depletion, leading to health problems such as malnutrition, weakened immunity, and digestive and respiratory diseases, seriously threatening their survival and reproductive capacity.

[0004] Specifically:

[0005] 1) Impact on Plants: Prolonged winter drought significantly reduces soil moisture. Under extremely low temperature conditions, water within plant cells tends to freeze rapidly and form ice crystals. The expansion process causes irreversible physical damage to cell walls and membranes, destroying cell structure and even leading to tissue death. Under normal circumstances, moist soil has a high heat capacity, capable of storing and slowly releasing heat, providing a buffer against freezing for plants. However, under winter drought conditions, soil moisture is lost in large quantities, and its ability to retain heat is significantly reduced, making plants more susceptible to low temperature stress, especially during periods of sudden temperature drops, when plants are more susceptible to frost damage.

[0006] 2) Impacts on Animals (including Wildlife and Livestock): Prolonged winter droughts have a multifaceted impact on animal health, primarily manifesting as dehydration, electrolyte imbalances, nutritional deficiencies, decreased immunity, gastrointestinal diseases, respiratory infections, reproductive disorders, and parasitic infections. Wildlife, lacking water, suffers from dehydration, weakness, and abnormal temperature regulation, significantly increasing their risk of disease. Pasture degradation and insufficient forage supply further contribute to malnutrition and reduce disease resistance. Drought also increases stress levels in animals, impairing their physiological and psychological well-being and increasing the incidence of various diseases.

[0007] Currently, there is a lack of systematic, intelligent monitoring and early warning technologies for the black cloud risk caused by the combined effects of winter drought and extreme low temperatures. Traditional methods struggle to dynamically identify and assess the risk of coupled dry-cold processes, and they also struggle to effectively integrate remote sensing observations with future meteorological forecasts. This results in delayed black cloud early warning responses, unclear spatial representation, and inefficient interventions in ecological protection, grassland management, and animal husbandry disaster reduction practices. Therefore, we propose a winter low-temperature drought monitoring and black cloud early warning technology. Summary of the Invention

[0008] The purpose of the present invention is to provide a winter low temperature drought monitoring and black disaster early warning technology in response to the above-mentioned technical problems, so as to solve the problems raised in the above-mentioned background technology.

[0009] In view of this, the present invention provides a winter low temperature drought monitoring and black disaster early warning technology, including: a data acquisition module, which integrates multi-source heterogeneous data to provide high-timeliness and high-spatial-resolution data support for low temperature drought identification and black disaster early warning;

[0010] The low-temperature drought identification and classification module (LDCI model) is used to identify the combined stress state of drought and low temperature in the current region, construct the drought-low temperature combined index (LDCI), and achieve spatial quantitative expression of ecological risks;

[0011] The Black Disaster Warning and Rating Module (BDRI Model) dynamically constructs a Black Disaster Risk Index (BDRI) based on the current LDCI value and integrates future weather forecast data to assess the development trend of black disasters in the future.

[0012] Output results and visualization module, the system ultimately outputs the following five types of spatial results, covering the three levels of current monitoring, future prediction and disaster warning:

[0013] LDCI raster map: represents the spatial distribution of the current low temperature and drought intensity;

[0014] BDRI grid map: expresses the probability of black disasters and risk areas in the future;

[0015] Low temperature and drought level map: risk level map based on the current LDCI classification;

[0016] Low temperature and drought prediction level map: future risk trend map calculated based on BDRI;

[0017] Black disaster warning map: A five-level warning level map based on BDRI values, used for pastoral area management and disaster response deployment.

[0018] In the above technical solution, further, the data acquisition module includes: remote sensing snow cover products (MOD10A1), with a spatial resolution of 500 meters, daily updates, used to extract current and historical snow cover status, identify snow-free days and reflect surface drought conditions, acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites. Including global daily, 8-day maximum and monthly snow cover data, with a spatial resolution of 500 meters and 0.5 kilometers; there are also daily cloud gap filling snow cover data, daily 500-meter resolution snow albedo data, etc. In addition, there are special Terra MODIS daily products for the Greenland Ice Sheet, including ice surface temperature, albedo and water vapor layers, with a spatial resolution of 0.78 kilometers;

[0019] The remote sensing land surface temperature product (MOD11A1), with a spatial resolution of 1 km, is used to identify surface temperature trends and assess the extent of low-temperature stress. MODIS is a remote sensing instrument jointly developed by NASA and the National Oceanic and Atmospheric Administration (NOAA), and is carried on the Terra and Aqua satellites. Daily data on land surface temperature (LST) and emissivity are obtained from MODIS data using a common split-window algorithm and a day-night algorithm. LST data are obtained at a 1-kilometer pixel level, and emissivity data are obtained at a 6-kilometer grid level.

[0020] Weather forecast data (NOAA / GFS0P25), with a spatial resolution of 0.25°, provides daily temperature and precipitation forecasts for up to 16 days ahead. This data is used for risk trend simulation and early warning deduction. NOAA / GFS0P25 weather forecast data is a meteorological data product generated by the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) under the National Oceanic and Atmospheric Administration (NOAA). Data features:

[0021] High resolution: A resolution of 0.25° can describe the distribution of meteorological elements on a global scale more finely, more accurately depict small and medium-scale meteorological systems and geographical features, and provide more detailed meteorological information for local areas.

[0022] Multi-factor coverage: Contains numerous meteorological elements, such as temperature, humidity, wind speed, wind direction, precipitation, atmospheric precipitable water, total cloud cover, downward shortwave radiation flux, etc., which can comprehensively reflect the state and changes of the atmosphere and provide rich data support for meteorological research and applications.

[0023] Regular updates: Global weather data is released four times a day, updated at 00:00, 06:00, 12:00 and 18:00 UTC. Each update provides a 384-hour forecast with a three-hour interval and a six-hour time resolution, reflecting the latest weather conditions and changing trends in a timely manner.

[0024] In the above technical solution, the low temperature drought identification and classification module (LDCI model) further includes: Drought factor calculation (D): Based on the MOD10A1 product, the number of consecutive days without snow in the time window is statistically analyzed as the drought intensity indicator:

[0025]

[0026] Where St is the snow cover status on day t (1 means snow, 0 means no snow).

[0027] In the above technical solution, the low temperature and drought identification and classification module (LDCI model) further includes: a low temperature stress score (CSS): based on MOD11A1 temperature data, an exponential continuous risk function is used to convert it into a daily risk weight, and the low temperature stress score is cumulatively calculated:

[0028]

[0029] Where Tt is the surface temperature on the tth day, Tref is the critical temperature (such as -10℃), and α and β are adjustment coefficients.

[0030] In the above technical solution, further, the low temperature drought identification and classification module (LDCI model) also includes: combined index calculation (LDCI): constructing a combined index by weighted combination of drought days and low temperature stress score:

[0031]

[0032] Where ω1, ω2 are weight coefficients, which can be set to 0.5 by default; LDCI∈[0,1].

[0033] In the above technical solution, the low temperature drought identification and classification module (LDCI model) further includes: risk level classification, which divides the area into five risk levels according to the LDCI value:

[0034] The BDRI range of 0–0.2 is risk-free;

[0035] The BDRI range of 0.2–0.4 indicates potential risk;

[0036] The BDRI range of 0.4–0.6 is moderate risk;

[0037] A BDRI range of 0.6–0.8 is high risk;

[0038] A BDRI range of 0.8–1.0 indicates very high risk.

[0039] In the above technical solution, further, the black disaster early warning and classification module (BDRI model) includes: index calculation structure:

[0040] BDRI=ω1·LDCI current +ω2·LDCI forecast

[0041] Among them, LDCIcurrent is the current index, LDCIforecast is the index obtained based on future weather forecast simulation, and ω1 and ω2 are weighting coefficients (such as 0.4 and 0.6).

[0042] In the above technical solution, further, the black disaster early warning and classification module (BDRI model) includes:

[0043] For drought prediction, we judge whether effective snow accumulation will form based on future snowfall forecasts, and count the number of consecutive days without effective snowfall;

[0044] For future low temperature simulation, the CSS is calculated by inserting the future temperature data into the exponential function to generate the LDCI value for the forecast period.

[0045] Risk level warning color classification, risk color classification according to the five risk levels:

[0046] The BDRI range of 0–0.2 is a no-warning color;

[0047] A BDRI range of 0.2–0.4 is a blue warning;

[0048] A BDRI range of 0.4–0.6 is a yellow warning;

[0049] The BDRI range of 0.6–0.8 is an orange alert;

[0050] A BDRI range of 0.8–1.0 indicates a red alert.

[0051] In the above technical solution, further, the LDCI raster map, BDRI raster map, low temperature drought level map, low temperature drought prediction level map, and black disaster warning map support dynamic updates, have standardized interface output capabilities, can be integrated into the WebGIS system, ecological protection platform or pastoral meteorological warning platform, support map service and JSON data interaction, and facilitate visual display and intelligent linkage applications.

[0052] The beneficial effects of the present invention are:

[0053] 1. This winter low-temperature drought monitoring and black disaster early warning technology establishes an exponential low-temperature risk function model to achieve continuous modeling of temperature intensity and duration, breaking through the traditional static threshold limitations and more scientifically characterizing the ecosystem's response mechanism to low-temperature stress; a drought-low temperature combined index (LDCI) model is proposed, integrating remote sensing snow cover and surface temperature data to quantify the intensity and distribution of the combined dry and cold impacts on the ecosystem at the current stage; a black disaster risk index (BDRI) model is constructed, which combines the current LDCI with future LDCI forecasts to achieve quantitative assessment and early warning grading of the possibility of future black disasters.

[0054] 2. This winter low-temperature drought monitoring and black disaster early warning technology supports a refined assessment of ecological risks under persistent low-temperature conditions through the exponential function modeling method of the low-temperature stress index and its dynamic accumulation algorithm (CSS); the drought-low-temperature combined index (LDCI) construction method jointly assesses the degree of cold and drought stress and its risk level based on the number of snow-free days and an exponential low-temperature score; the black disaster risk index (BDRI) calculation method generates a black disaster probability risk value by integrating the current status with future weather forecasts, and performs standardized classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a module diagram of the present invention;

[0056] Figure 2 It is the risk level classification diagram of the present invention;

[0057] Figure 3 It is the early warning color diagram of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0059] In the description of this application, it should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to this application. For ease of description, the dimensions of the various parts shown in the drawings are not drawn according to the actual proportional relationship. Technologies, methods and equipment known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the technologies, methods and equipment should be considered as part of the authorization specification. In all examples shown and discussed here, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further discussed in subsequent figures.

[0060] Example 1:

[0061] See also Figure 1-Figure 3 As shown, this embodiment provides a winter low temperature drought monitoring and black disaster early warning technology.

[0062] It includes: data acquisition module, which integrates multi-source heterogeneous data to provide high-timeliness and high-spatial-resolution data support for low-temperature drought identification and black disaster warning;

[0063] The low-temperature drought identification and classification module (LDCI model) is used to identify the combined stress state of drought and low temperature in the current region, construct the drought-low temperature combined index (LDCI), and achieve spatial quantitative expression of ecological risks;

[0064] The Black Disaster Warning and Rating Module (BDRI Model) dynamically constructs a Black Disaster Risk Index (BDRI) based on the current LDCI value and integrates future weather forecast data to assess the development trend of black disasters in the future.

[0065] Output results and visualization module, the system ultimately outputs the following five types of spatial results, covering the three levels of current monitoring, future prediction and disaster warning:

[0066] LDCI raster map: represents the spatial distribution of the current low temperature and drought intensity;

[0067] BDRI grid map: expresses the probability of black disasters and risk areas in the future;

[0068] Low temperature and drought level map: risk level map based on the current LDCI classification;

[0069] Low temperature and drought prediction level map: future risk trend map calculated based on BDRI;

[0070] Black disaster warning map: A five-level warning level map based on BDRI values, used for pastoral area management and disaster response deployment.

[0071] Example 2:

[0072] This embodiment provides a winter low temperature drought monitoring and black disaster early warning technology, which, in addition to the technical solutions of the above embodiments, also has the following technical features.

[0073] In this embodiment, the data acquisition module includes: remote sensing snow cover products (MOD10A1), with a spatial resolution of 500 meters and daily updates, which are used to extract current and historical snow cover status, identify snow-free days and reflect surface drought conditions, and are acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites. It includes global daily, 8-day maximum and monthly snow cover data with spatial resolutions of 500 meters and 0.5 kilometers; there are also daily cloud gap filling snow cover data, daily 500-meter resolution snow albedo data, etc. In addition, there is a special Terra MODIS daily product for the Greenland Ice Sheet, which includes ice surface temperature, albedo and water vapor layers with a spatial resolution of 0.78 kilometers;

[0074] The remote sensing land surface temperature product (MOD11A1), with a spatial resolution of 1 km, is used to identify surface temperature trends and assess the extent of low-temperature stress. MODIS is a remote sensing instrument jointly developed by NASA and the National Oceanic and Atmospheric Administration (NOAA), and is carried on the Terra and Aqua satellites. Daily data on land surface temperature (LST) and emissivity are obtained from MODIS data using a common split-window algorithm and a day-night algorithm. LST data are obtained at a 1-kilometer pixel level, and emissivity data are obtained at a 6-kilometer grid level.

[0075] Weather forecast data (NOAA / GFS0P25), with a spatial resolution of 0.25°, provides daily temperature and precipitation forecasts for up to 16 days ahead. This data is used for risk trend simulation and early warning deduction. NOAA / GFS0P25 weather forecast data is a meteorological data product generated by the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) under the National Oceanic and Atmospheric Administration (NOAA). Data features:

[0076] High resolution: A resolution of 0.25° can describe the distribution of meteorological elements on a global scale more finely, more accurately depict small and medium-scale meteorological systems and geographical features, and provide more detailed meteorological information for local areas.

[0077] Multi-factor coverage: Contains numerous meteorological elements, such as temperature, humidity, wind speed, wind direction, precipitation, atmospheric precipitable water, total cloud cover, downward shortwave radiation flux, etc., which can comprehensively reflect the state and changes of the atmosphere and provide rich data support for meteorological research and applications.

[0078] Regular updates: Global weather data is released four times a day, updated at 00:00, 06:00, 12:00 and 18:00 UTC. Each update provides a 384-hour forecast with a three-hour interval and a six-hour time resolution, reflecting the latest weather conditions and changing trends in a timely manner.

[0079] Example 3:

[0080] This embodiment provides a winter low temperature drought monitoring and black disaster early warning technology, which, in addition to the technical solutions of the above embodiments, also has the following technical features.

[0081] In this embodiment, the low temperature drought identification and classification module (LDCI model) includes: Drought factor calculation (D): Based on the MOD10A1 product, the number of consecutive days without snow in the time window is statistically analyzed as the drought intensity indicator:

[0082]

[0083] Where St is the snow cover status on day t (1 means snow, 0 means no snow).

[0084] Example 4:

[0085] This embodiment provides a winter low temperature drought monitoring and black disaster early warning technology, which, in addition to the technical solutions of the above embodiments, also has the following technical features.

[0086] In this embodiment, the low temperature drought identification and classification module (LDCI model) also includes: a low temperature stress score (CSS): based on MOD11A1 temperature data, an exponential continuous risk function is used to convert it into a daily risk weight, and the low temperature stress score is cumulatively calculated:

[0087]

[0088] Where Tt is the surface temperature on the tth day, Tref is the critical temperature (such as -10℃), and α and β are adjustment coefficients.

[0089] Example 5:

[0090] This embodiment provides a winter low temperature drought monitoring and black disaster early warning technology, which, in addition to the technical solutions of the above embodiments, also has the following technical features.

[0091] In this embodiment, the low temperature drought identification and classification module (LDCI model) further includes: combined index calculation (LDCI): a combined index is constructed by weighted combination of drought days and low temperature stress score:

[0092]

[0093] Where ω1, ω2 are weight coefficients, which can be set to 0.5 by default; LDCI∈[0,1].

[0094] Example 6:

[0095] This embodiment provides a winter low temperature drought monitoring and black disaster early warning technology, which, in addition to the technical solutions of the above embodiments, also has the following technical features.

[0096] In this embodiment, the low temperature drought identification and classification module (LDCI model) also includes: risk level classification, which divides the area into five risk levels according to the LDCI value:

[0097] The BDRI range of 0–0.2 is risk-free;

[0098] The BDRI range of 0.2–0.4 indicates potential risk;

[0099] The BDRI range of 0.4–0.6 is moderate risk;

[0100] A BDRI range of 0.6–0.8 is high risk;

[0101] A BDRI range of 0.8–1.0 indicates very high risk.

[0102] Example 7:

[0103] This embodiment provides a winter low temperature drought monitoring and black disaster early warning technology, which, in addition to the technical solutions of the above embodiments, also has the following technical features.

[0104] In this embodiment, the black disaster early warning and classification module (BDRI model) includes: index calculation structure:

[0105] BDRI=ω1·LDCI current +ω2·LDCI forecast

[0106] Among them, LDCIcurrent is the current index, LDCIforecast is the index obtained based on future weather forecast simulation, and ω1 and ω2 are weighting coefficients (such as 0.4 and 0.6).

[0107] Example 8:

[0108] This embodiment provides a winter low temperature drought monitoring and black disaster early warning technology, which, in addition to the technical solutions of the above embodiments, also has the following technical features.

[0109] In this embodiment, the black disaster early warning and classification module (BDRI model) includes:

[0110] For drought prediction, we judge whether effective snow accumulation will form based on future snowfall forecasts, and count the number of consecutive days without effective snowfall;

[0111] For future low temperature simulation, the CSS is calculated by inserting the future temperature data into the exponential function to generate the LDCI value for the forecast period.

[0112] Risk level warning color classification, risk color classification according to the five risk levels:

[0113] The BDRI range of 0–0.2 is a no-warning color;

[0114] A BDRI range of 0.2–0.4 is a blue warning;

[0115] A BDRI range of 0.4–0.6 is a yellow warning;

[0116] The BDRI range of 0.6–0.8 is an orange alert;

[0117] A BDRI range of 0.8–1.0 indicates a red alert.

[0118] Example 9:

[0119] This embodiment provides a winter low temperature drought monitoring and black disaster early warning technology, which, in addition to the technical solutions of the above embodiments, also has the following technical features.

[0120] In this embodiment, the LDCI raster map, BDRI raster map, low temperature and drought level map, low temperature and drought prediction level map, and black disaster warning map support dynamic updates, have standardized interface output capabilities, can be integrated into the WebGIS system, ecological protection platform or pastoral meteorological warning platform, support map services and JSON data interaction, and facilitate visual display and intelligent linkage applications.

[0121] 3. This invention establishes an exponential low-temperature risk function model to achieve continuous modeling of temperature intensity and duration, breaking through the limitations of traditional static thresholds and more scientifically characterizing the ecosystem's response mechanism to low-temperature stress. It also proposes a combined drought-low temperature index (LDCI) model that integrates remotely sensed snow cover and surface temperature data to quantify the intensity and distribution of the combined effects of dryness and cold on the ecosystem at the current stage. It also constructs a black disaster risk index (BDRI) model that, by combining current LDCI with future LDCI forecasts, enables quantitative assessment and early warning grading of the likelihood of future black disasters.

[0122] The exponential function modeling method of the low temperature stress index and its dynamic accumulation algorithm (CSS) support the refined assessment of ecological risks under continuous low temperature conditions; the drought-low temperature combined index (LDCI) construction method jointly evaluates the degree of cold and drought stress and its risk level based on the number of snow-free days and the exponential low temperature score; the black disaster risk index (BDRI) calculation method generates the black disaster probability risk value by integrating the current status and future weather forecasts, and performs standardized classification.

[0123] The embodiments of the present application are described above in conjunction with the accompanying drawings. Unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A winter low temperature drought monitoring and black disaster early warning technology, characterized in that: include: The data acquisition module integrates multi-source heterogeneous data to provide high-timeliness and high-spatial-resolution data support for low-temperature drought identification and black disaster warning; The low-temperature drought identification and classification module (LDCI model) is used to identify the combined stress state of drought and low temperature in the current region, construct the drought-low temperature combined index (LDCI), and achieve spatial quantitative expression of ecological risks; The Black Disaster Warning and Rating Module (BDRI Model) dynamically constructs a Black Disaster Risk Index (BDRI) based on the current LDCI value and integrates future weather forecast data to assess the development trend of black disasters in the future. Output results and visualization module, the system ultimately outputs the following five types of spatial results, covering the three levels of current monitoring, future prediction and disaster warning: LDCI raster map: represents the spatial distribution of the current low temperature and drought intensity; BDRI grid map: expresses the probability of black disasters and risk areas in the future; Low temperature and drought level map: risk level map based on the current LDCI classification; Low temperature and drought prediction level map: future risk trend map calculated based on BDRI; Black disaster warning map: A five-level warning level map based on BDRI values, used for pastoral area management and disaster response deployment.

2. The winter low temperature drought monitoring and black disaster early warning technology according to claim 1 is characterized in that: The data acquisition module includes: remote sensing snow cover products (MOD10A1), with a spatial resolution of 500 meters and daily updates, which are used to extract current and historical snow cover status, identify snow-free days and reflect surface drought conditions; Remote sensing surface temperature product (MOD11A1), with a spatial resolution of 1 km, is used to obtain surface temperature trends and assess the degree of low temperature stress; Meteorological forecast data (NOAA / GFS0P25), with a spatial resolution of 0.25°, provides daily temperature and precipitation forecasts for up to 16 days in the future, which are used for risk trend simulation and early warning deduction.

3. The winter low temperature drought monitoring and black disaster early warning technology according to claim 1 is characterized in that: The low temperature drought identification and classification module (LDCI model) includes: Drought factor calculation (D): Based on the MOD10A1 product, the number of consecutive days without snow in the time window is statistically analyzed as the drought intensity indicator: Where St is the snow cover status on day t (1 means snow, 0 means no snow).

4. The winter low temperature drought monitoring and black disaster early warning technology according to claim 1 is characterized in that: The low temperature drought identification and classification module (LDCI model) also includes: low temperature stress score (CSS): based on MOD11A1 temperature data, an exponential continuous risk function is used to convert it into a daily risk weight, and the low temperature stress score is cumulatively calculated: Where Tt is the surface temperature on the tth day, Tref is the critical temperature, and α and β are adjustment coefficients.

5. The winter low temperature drought monitoring and black disaster early warning technology according to claim 1 is characterized in that: The low temperature drought identification and classification module (LDCI model) also includes: combined index calculation (LDCI): constructing a combined index by weighted combination of drought days and low temperature stress score: Where ω1, ω2 are weight coefficients, which can be set to 0.5 by default; LDCI∈[0,1].

6. The winter low temperature drought monitoring and black disaster early warning technology according to claim 1 is characterized in that: The low temperature drought identification and classification module (LDCI model) also includes: risk level classification, which divides the area into five risk levels according to the LDCI value: The BDRI range of 0–0.2 is risk-free; The BDRI range of 0.2–0.4 indicates potential risk; The BDRI range of 0.4–0.6 is moderate risk; A BDRI range of 0.6–0.8 is high risk; A BDRI range of 0.8–1.0 indicates very high risk.

7. The winter low temperature drought monitoring and black disaster early warning technology according to claim 1 is characterized in that: The black disaster early warning and classification module (BDRI model) includes: index calculation structure: BDRI=ω1·LDCI current +ω2·LDCI forecast Among them, LDCIcurrent is the current index, LDCIforecast is the index obtained based on future weather forecast simulation, and ω1 and ω2 are weighting coefficients (such as 0.4 and 0.6).

8. The winter low temperature drought monitoring and black disaster early warning technology according to claim 1 is characterized in that: The black disaster early warning and classification module (BDRI model) includes: For drought prediction, we judge whether effective snow accumulation will form based on future snowfall forecasts, and count the number of consecutive days without effective snowfall; For future low temperature simulation, the CSS is calculated by inserting the future temperature data into the exponential function to generate the LDCI value for the forecast period. Risk level warning color classification, risk color classification according to the five risk levels: The BDRI range of 0–0.2 is a no-warning color; A BDRI range of 0.2–0.4 is a blue warning; A BDRI range of 0.4–0.6 is a yellow warning; The BDRI range of 0.6–0.8 is an orange alert; A BDRI range of 0.8–1.0 indicates a red alert.

9. The winter low temperature drought monitoring and black disaster early warning technology according to claim 1 is characterized in that: The LDCI raster map, BDRI raster map, low temperature and drought level map, low temperature and drought prediction level map, and black disaster warning map support dynamic updates, have standardized interface output capabilities, can be integrated into WebGIS systems, ecological protection platforms, or pastoral meteorological warning platforms, support map services and JSON data interaction, and facilitate visual display and intelligent linkage applications.

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