Drought and flood sudden turning event identification method and system based on time-space continuous monitoring and detection

Abstract

The invention discloses a drought and flood sudden change event identification method and system based on time-space continuous monitoring and detection, and relates to the technical field of monitoring and identification of extreme weather and climate events. The problems of single index, non-uniform time scale, lack of time-space continuity consideration and subjective definition of event intensity exist in identification of drought and flood sudden turning events in the prior art. According to the method, on the basis of site observation data, the sudden change event from drought to flood in a short time is judged by identifying time-space continuous meteorological drought and flood events, and the event intensity is defined by integrating the early-stage drought intensity, the later-stage flood intensity, the sudden change time and the influence range through an entropy weight method. According to the method, the starting time, the sudden turning time, the ending time and the influence range of the drought and flood sudden turning event can be accurately identified, the systematic understanding on the occurrence and development of the event is enhanced, and a basis is provided for evolution and forecasting of the drought and flood sudden turning event.

Description

Technical Field

[0001] This invention belongs to the field of monitoring and identifying extreme weather and climate events, and particularly relates to a method and system for identifying sudden shifts between drought and flood events based on continuous spatiotemporal monitoring and detection. Background Technology

[0002] Drought-flood transition events, as a special type of complex temperature-precipitation extreme event, involve both drought and flood processes and undergo phase transitions, thus being influenced by various factors. Drought processes have a longer temporal and spatial scale, while flood processes are closely related to heavy precipitation processes and have a relatively smaller temporal and spatial scale. This fundamental difference makes accurately identifying and treating drought-flood transition events a challenging problem. Currently, methods for identifying drought-flood transition events, both domestically and internationally, can be broadly categorized into two types: those based on precipitation indicators and those based on runoff or evapotranspiration indicators.

[0003] Methods based on precipitation indices assess changes in precipitation amount or precipitation anomaly percentage before and after a specific period to determine events, and have developed long- and short-period drought-flood abrupt change indices and standardized weighted average precipitation indices at seasonal, monthly, and daily scales. However, while these indices are progressively refined across time scales, they often fail to effectively reflect the "abrupt change" characteristic and may overlook events occurring at intramonthly scales. More importantly, they typically do not adequately consider the lagged effects of preceding precipitation on subsequent conditions, because whether a region experiences drought or flood events is influenced not only by the direct impact of precipitation but also by the combined effects of evapotranspiration, runoff, and the lagged responses of preceding precipitation on land surface hydrological processes.

[0004] Another type of approach is based on runoff or evapotranspiration data, such as using the standard runoff index or standardized precipitation evapotranspiration index. These methods provide an alternative when precipitation indicators are insufficient to accurately represent local drought and flood conditions, but they also have limitations. A summary of existing indicators reveals that their development has undergone refinement in terms of time scale and an evolution from single-factor to multi-factor integration. However, the time scales for identifying abrupt drought-flood transitions vary among indicators, and they do not adequately consider the impact of water changes in land surface hydrological processes. This results in an inability to accurately assess the multidimensional changes in the frequency, intensity, and duration of abrupt drought-flood transitions under the background of global climate change.

[0005] In practical applications, determining the occurrence of abrupt drought-flood transition events requires considering both drought and flood processes simultaneously. Identifying these processes relies not only on a single variable but is indicated by multiple indicators such as precipitation and evapotranspiration. Current technology lacks a comprehensive method that can comprehensively consider the influence of multiple factors, accurately identify the start, abrupt transition, and end times and impact range of events on a diurnal scale, and reasonably define intensity indicators. This technological deficiency limits the systematic understanding of the occurrence and development of abrupt drought-flood transition events and hinders their accurate forecasting and effective prevention. Therefore, there is an urgent need to develop a method and system for identifying abrupt drought-flood transition events that enables continuous spatiotemporal monitoring and detection. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes a method and system for identifying abrupt drought-flood transition events based on continuous spatiotemporal monitoring and detection. This method can accurately identify the start, transition, and end times and the scope of impact of abrupt drought-flood transition events, enhance the systematic understanding of the occurrence and development of these events, and provide a basis for the evolution and forecasting of such events.

[0007] To achieve the above objectives, this invention provides a method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection, including:

[0008] Based on station observation data, identify spatiotemporally continuous meteorological drought events;

[0009] Based on station observation data, identify spatiotemporally continuous meteorological and flood events;

[0010] For each identified meteorological flood event, it is determined whether a meteorological drought event existed within a preset time period before the occurrence of the meteorological flood event, and whether the proportion of the number of stations with spatial overlap between the meteorological flood event and the meteorological drought event exceeds a preset threshold. If the conditions are met, it is identified as a sudden drought-flood event.

[0011] Optionally, identifying spatiotemporally continuous meteorological drought events includes:

[0012] Based on station observation data, the meteorological drought index for each station is calculated;

[0013] For each station, it is determined whether an extreme drought event has occurred based on the meteorological drought index;

[0014] For each station, its drought anomaly rate is calculated, which is the proportion of stations within a preset radius around that station that experience extreme drought events.

[0015] Based on the drought anomaly rate, drought anomaly centers are identified, wherein the drought anomaly rate of the drought anomaly center exceeds a first preset threshold and the distance between the drought anomaly center and other drought anomaly centers exceeds a first preset distance;

[0016] Using each drought anomaly center as the center, sites within a preset radius that have experienced extreme drought events are grouped into the same drought anomaly zone;

[0017] Based on drought anomaly zones over multiple consecutive days, temporal continuity is determined. When the spatial overlap rate between the drought anomaly zone on a given day and the drought anomaly zone on the following day exceeds a second preset threshold, they are identified as the same continuous drought process.

[0018] Optionally, identifying spatiotemporally continuous meteorological flooding events includes:

[0019] Based on station observation data, the precipitation extreme value threshold for each station is calculated;

[0020] For each station, it is determined whether an extreme precipitation event has occurred based on whether its daily precipitation exceeds the precipitation extreme threshold.

[0021] For each station, calculate its flood anomaly rate, which is the proportion of stations within a preset radius around that station that experience extreme precipitation events.

[0022] Based on the flood anomaly rate, flood anomaly centers are identified, wherein the flood anomaly rate of the flood anomaly center exceeds a third preset threshold and the distance between it and other flood anomaly centers exceeds a second preset distance;

[0023] Using each flood anomaly center as the center, stations within a preset radius that experience extreme precipitation events are grouped into the same flood anomaly zone;

[0024] Based on the flood anomaly zones over multiple consecutive days, the temporal continuity is determined. When the spatial overlap rate between the flood anomaly zone on a given day and the flood anomaly zone on the following day exceeds a fourth preset threshold, they are identified as the same continuous flooding process.

[0025] Optionally, identifying drought-flood transition events also includes merging multiple drought-flood transition events; the process of merging multiple drought-flood transition events includes: when two drought-flood transition events are consecutive in time and have more than a preset number of overlapping stations in space, the two events are merged into one drought-flood transition event.

[0026] Optionally, the method further includes a process for determining the intensity of a sudden shift from drought to flood; the process for determining the intensity of the sudden shift from drought to flood includes:

[0027] To obtain multiple factors influencing the intensity of abrupt shifts between drought and flood, including the intensity of the preceding drought, the intensity of the subsequent flood, the timing of the shift, and the extent of the impact;

[0028] The weights of the multiple factors are determined using the entropy weight method;

[0029] Based on the weights and the multiple factors, the intensity index of the rapid shift from drought to flood is calculated.

[0030] Optionally, the meteorological drought index is a comprehensive meteorological drought index; the comprehensive meteorological drought index takes into account the impact of previous precipitation and evapotranspiration on the current drought.

[0031] Optionally, the precipitation extreme threshold is a preset quantile calculated based on a historical climate baseline period; the historical climate baseline period is daily precipitation data for thirty consecutive years.

[0032] Optionally, the affected area is the number of meteorological stations affected by the abrupt shift from drought to flood; the abrupt shift time is the time interval between the end of the drought event and the start of the flood event.

[0033] On the other hand, to achieve the above objectives, the present invention also provides a drought-flood transition event identification system based on spatiotemporal continuous monitoring and detection, comprising:

[0034] The drought event identification module is used to identify spatiotemporally continuous meteorological drought events based on station observation data;

[0035] The flood event identification module is used to identify spatiotemporally continuous meteorological flood events based on station observation data;

[0036] The abrupt change event judgment module is used to determine, for each meteorological flood event identified by the flood event identification module, whether there is a meteorological drought event identified by the drought event identification module within a preset time before its occurrence, and whether the proportion of the number of spatially overlapping stations of the meteorological flood event and the meteorological drought event exceeds a preset threshold, and to identify it as a drought-flood abrupt change event when the conditions are met.

[0037] Technical Effects of this Invention: This invention discloses a method and system for identifying abrupt drought-flood transition events based on continuous spatiotemporal monitoring and detection. It considers the lag effect of precipitation and the impact of evapotranspiration on drought events, and identifies abrupt drought-flood transition events from a diurnal scale perspective, improving the accuracy of research on these events. It can more effectively and rationally identify the rapid turning points contained within the events, thereby gaining a deeper understanding of the changes in abrupt drought-flood transition events. This invention employs a spatiotemporally continuous objective identification method for extreme events, which can better express the three-dimensional continuous changes of extreme events, accurately distinguish the beginning, development, and end stages, and provide the scope of influence at different stages. It reveals the evolutionary characteristics of extreme events under three-dimensional conditions, providing a basis for the evolution and forecasting of abrupt drought-flood transition events. Furthermore, this invention proposes a method for defining the intensity of abrupt drought-flood transition events based on the entropy weight method, which can unify the four indicators affecting the intensity of abrupt drought-flood transition events, providing an objective measurement of the intensity of abrupt drought-flood transition events. Attached Figure Description

[0038] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0039] Figure 1 This is a flowchart illustrating the method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection, according to an embodiment of the present invention. Detailed Implementation

[0040] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0041] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0042] like Figure 1 As shown, this embodiment provides a method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection, including:

[0043] Based on station observation data, identify spatiotemporally continuous meteorological drought events;

[0044] Based on station observation data, identify spatiotemporally continuous meteorological and flood events;

[0045] For each identified meteorological flood event, it is determined whether a meteorological drought event existed within a preset time period before the occurrence of the meteorological flood event, and whether the proportion of the number of stations with spatial overlap between the meteorological flood event and the meteorological drought event exceeds a preset threshold. If the conditions are met, it is identified as a sudden drought-flood event.

[0046] Furthermore, identifying spatiotemporally continuous meteorological drought events includes:

[0047] Based on station observation data, the meteorological drought index for each station is calculated;

[0048] For each station, it is determined whether an extreme drought event has occurred based on the meteorological drought index;

[0049] For each station, its drought anomaly rate is calculated, which is the proportion of stations within a preset radius around that station that experience extreme drought events.

[0050] Based on the drought anomaly rate, drought anomaly centers are identified, wherein the drought anomaly rate of the drought anomaly center exceeds a first preset threshold and the distance between the drought anomaly center and other drought anomaly centers exceeds a first preset distance;

[0051] Using each drought anomaly center as the center, sites within a preset radius that have experienced extreme drought events are grouped into the same drought anomaly zone;

[0052] Based on drought anomaly zones over multiple consecutive days, temporal continuity is determined. When the spatial overlap rate between the drought anomaly zone on a given day and the drought anomaly zone on the following day exceeds a second preset threshold, they are identified as the same continuous drought process.

[0053] Specifically, the implementation process of this embodiment includes:

[0054] Extreme threshold selection:

[0055] When defining the extremes of different variables, the threshold needs to be determined based on the research subjects;

[0056] For extreme meteorological flooding events, the relative threshold method is used to define precipitation exceeding the 95th percentile of its 30-year climate baseline period (1981-2010) as extreme precipitation.

[0057] The meteorological drought is identified using the Meteorological Drought Composite Index (MCI) developed by the National Climate Center. This index comprehensively considers the impacts of precipitation and evapotranspiration from different periods in the preceding timeframe on the current drought, and it has been applied to the drought monitoring operations of the National Climate Center of the China Meteorological Administration. The criteria for defining extreme drought events are as follows: This represents moderate to more severe drought events (i.e., dry soil, insufficient soil moisture, wilting of crop leaves; water shortage, impacting production and daily life).

[0058] Calculate the anomaly rate:

[0059] Calculate the daily anomalous rate for each station, including the anomalous rates for floods and droughts. The flood anomalous rate for a given station is the proportion of extreme precipitation events occurring among all stations within a 250km radius, and the drought anomalous rate is the proportion of drought events occurring among all stations within a 250km radius.

[0060] Identify the anomaly center:

[0061] We selected drought and flood centers, defining an anomaly rate standard of 0.4 to ensure the identification of large-scale drought events; for flood events, we defined an anomaly rate standard of 0.25 to ensure the identification of small-scale flood events.

[0062] Identify abnormal bands:

[0063] Sites experiencing flooding or drought are assigned to the aforementioned anomalous zones to obtain daily drought and flood zones.

[0064] Selection of time continuity:

[0065] The overlap rate between the daily anomaly band and the next day's anomaly band exceeded 20%, identifying all consecutive drought and flood events. Since drought events are slowly changing processes, while flood events are rapidly changing processes, the minimum duration of a drought event is defined as exceeding 15 days to account for the impact of sudden droughts; the minimum duration of a flood event is defined as exceeding 1 day.

[0066] Furthermore, identifying spatiotemporally continuous meteorological flooding events includes:

[0067] Based on station observation data, the precipitation extreme value threshold for each station is calculated;

[0068] For each station, it is determined whether an extreme precipitation event has occurred based on whether its daily precipitation exceeds the precipitation extreme threshold.

[0069] For each station, calculate its flood anomaly rate, which is the proportion of stations within a preset radius around that station that experience extreme precipitation events.

[0070] Based on the flood anomaly rate, flood anomaly centers are identified, wherein the flood anomaly rate of the flood anomaly center exceeds a third preset threshold and the distance between it and other flood anomaly centers exceeds a second preset distance;

[0071] Using each flood anomaly center as the center, stations within a preset radius that experience extreme precipitation events are grouped into the same flood anomaly zone;

[0072] Based on the flood anomaly zones over multiple consecutive days, the temporal continuity is determined. When the spatial overlap rate between the flood anomaly zone on a given day and the flood anomaly zone on the following day exceeds a fourth preset threshold, they are identified as the same continuous flooding process.

[0073] Specifically, the implementation process of this embodiment includes:

[0074] Calculate the daily extreme (outlier) values ​​for each site:

[0075] For any given meteorological station, based on all historical meteorological data, its historical extreme records are calculated. Taking extreme precipitation as an example, although there is a unified standard definition of heavy rain (daily rainfall exceeding 50 mm) for both the south and north, the north and south have different tolerances to extreme precipitation. The same level of heavy rain will have different impacts on the north and south. Therefore, it is necessary to use relative thresholds to define extreme precipitation in the north and south. The specific calculation details are as follows:

[0076] For meteorological station n, assume it has j daily precipitation meteorological data records from 1961 to 2024:

[0077] ;

[0078] Extract k daily records with values ​​greater than 0 from the 30-year climate baseline period of 1981-2010:

[0079] ;

[0080] Arrange these k precipitation records in ascending order, and take the 95th percentile value as the extreme precipitation threshold. Precipitation exceeding this threshold is defined as extreme precipitation.

[0081] After calculating the threshold values ​​for all m meteorological stations, the daily extreme precipitation for each station is calculated, and finally the daily extreme precipitation for all stations is obtained.

[0082] Calculate the daily anomaly rate for each site:

[0083] For any weather station n, assuming a certain day t, the total number of stations within a 250km radius of station n is M, and among them, m stations have precipitation exceeding the extreme precipitation threshold. Then the anomaly rate of site n on this day is defined. for:

[0084] ;

[0085] Define the anomaly center:

[0086] For a given day t, based on the calculated anomaly rates of all meteorological stations, select the anomaly rate r. n Sites with an anomaly rate >0.25 are selected, and all sites are sorted in descending order of their anomaly rates. Assuming there are N sites that meet the criteria on a given day, these are potential anomaly centers. The site with the highest anomaly rate r is selected as the first anomaly center. For the remaining N-1 potential anomaly centers, the following two conditions must be met to be defined as a new anomaly center:

[0087] ;

[0088] r n >0.25;

[0089] Where d represents the distance between any potential anomaly center and other already identified anomaly centers. This represents the anomalous rate of the potential anomalous center; all potential anomalous centers are judged in descending order of anomalous rate, assuming that K anomalous centers are finally obtained.

[0090] Define exception bands:

[0091] According to the definition of the anomaly center on day t, for the first anomaly center, all adjacent stations within a 250km radius that exceed the extreme threshold can be defined as belonging to the anomaly zone. Within, in the abnormal zone Within, if there is a site r n If the value is greater than 0.25 and does not belong to any other abnormal zone, then station n and its neighboring stations (stations exceeding the extreme threshold) also belong to the abnormal zone. Based on this, the main features of the L anomaly bands can be selected.

[0092] For any one of the anomalous bands Each of them belongs to an abnormal zone All abnormal sites within a 250km radius of an abnormal site are defined as belonging to the abnormal zone. By performing this operation on all L abnormal bands, an abnormal band can be defined.

[0093] Define the temporal continuity of the exception band:

[0094] Based on the defined daily anomaly bands, for a certain anomaly band on day t... Identify and check if there are one or more other abnormal bands in day t+1. able to If the locations of the stations overlap, and the number of overlapping stations exceeds 20%, then an anomaly zone is defined for day t. and one or more abnormal bands on day t+1 This refers to the continuity of the same event across different time periods. Based on the method described above, the temporal continuity of an anomaly band can be defined.

[0095] Furthermore, identifying drought-flood transition events also includes merging multiple drought-flood transition events; the process of merging multiple drought-flood transition events includes: when two drought-flood transition events are consecutive in time and have more than a preset number of overlapping stations in space, the two events are merged into one drought-flood transition event.

[0096] Specifically, the implementation process of this embodiment includes:

[0097] Merging of Drought-Flood Transition Events: In the process of identifying drought-flood transition events based on flood events, the flood events themselves are often relatively scattered and short in duration, resulting in multiple different flood events occurring after the same drought event. When the identified different drought-flood transition events occur within one day before or after each other, and the locations of the events overlap at more than 3 stations, the several events are merged into one drought-flood transition event.

[0098] Furthermore, the method also includes a process for determining the intensity of a sudden shift from drought to flood; the process for determining the intensity of a sudden shift from drought to flood includes:

[0099] To obtain multiple factors influencing the intensity of abrupt shifts between drought and flood, including the intensity of the preceding drought, the intensity of the subsequent flood, the timing of the shift, and the extent of the impact;

[0100] The weights of the multiple factors are determined using the entropy weight method;

[0101] Based on the weights and the multiple factors, the intensity index of the rapid shift from drought to flood is calculated.

[0102] Specifically, the implementation process of this embodiment includes:

[0103] There are four direct factors influencing the intensity of abrupt shifts between drought and flood: the intensity of the preceding drought, the intensity of the subsequent flood, the timing of the shift, and the extent of impact. The stronger the drought / flood intensity, the shorter the shift time, and the larger the impact area, the stronger the abrupt shift event. To comprehensively consider these four factors, a unified intensity index needs to be determined. In this embodiment, the entropy weight method is used to determine the weights of the four factors to further specify the intensity of the abrupt shift event. The entropy weight method is an objective evaluation method because it depends only on the dispersion of the indicators, a point that has been widely used. The entropy weight method can be used to determine the weights of the indicators affecting abrupt shift events between drought and flood.

[0104] This embodiment also provides a drought-flood transition event identification system based on spatiotemporal continuous monitoring and detection, including: a drought event identification module, used to identify spatiotemporally continuous meteorological drought events based on station observation data;

[0105] The flood event identification module is used to identify spatiotemporally continuous meteorological flood events based on station observation data;

[0106] The abrupt change event judgment module is used to determine, for each meteorological flood event identified by the flood event identification module, whether there is a meteorological drought event identified by the drought event identification module within a preset time before its occurrence, and whether the proportion of the number of spatially overlapping stations of the meteorological flood event and the meteorological drought event exceeds a preset threshold, and to identify it as a drought-flood abrupt change event when the conditions are met.

[0107] An application example of this invention:

[0108] This invention is applied to the identification of abrupt drought-flood transition events in China. A specific example from a 2024 drought-flood transition event will be used for detailed explanation. The goal is to objectively identify and assess the intensity of such events using the MCI index and precipitation data from observation stations. For this particular event, the period from June 25th to July 2nd, 2024 (the initial drought period), July 3rd (the turning point), and July 4th to July 11th (the subsequent rainfall and flooding process) will be used as an example for demonstration. Figure 1 The specific steps are as follows:

[0109] S1: Identify spatiotemporally continuous meteorological drought events based on station observation data;

[0110] S1.1: Based on the precipitation data observed at the stations, calculate the MCI index of each station daily in 2024. Points with an MCI less than -1 are selected as drought stations.

[0111] S1.2: In order to identify specific drought anomaly centers, the drought anomaly rate was calculated for each drought station on a daily basis in 2024, and stations with a drought anomaly rate exceeding 0.4 were selected as potential drought anomaly centers. It was calculated that there were eight potential drought anomaly centers on June 25.

[0112] S1.3: Identifying the Drought Anomaly Center. Based on the potential drought anomaly centers defined in S1.2, they are sorted in descending order of their drought anomaly rate. The station with the highest drought anomaly rate is selected as the first drought anomaly center. The remaining potential drought anomaly centers are then evaluated one by one. Only if the distance between the station and a confirmed drought anomaly center is greater than 250 km is it identified as a drought anomaly center. Through these steps, only one drought anomaly center was ultimately identified for June 25th.

[0113] S1.4: Define the drought anomaly zone. Based on the drought anomaly center defined in S1.3, the range of stations experiencing drought within 250 km of the drought anomaly center is defined as the scope of the drought anomaly zone. Therefore, the drought anomaly zone on June 25 is finally identified.

[0114] S1.5: Temporal Continuity of Drought Anomaly Zones. To reflect the temporal continuity of the drought process, based on the content of S1.1-S1.4, the drought anomaly zones for each day from June 25 to July 2, 2024 are calculated. If the spatial overlap rate between the drought anomaly zone of a day and the drought anomaly zone of the next day exceeds 20%, it is judged to be a continuous drought process.

[0115] S2: Identifying spatiotemporally continuous meteorological and flood events based on station observation data;

[0116] S2.1: Based on the precipitation data observed at the stations, a 95% relative threshold is selected as the extreme precipitation threshold. Points with daily precipitation exceeding the extreme precipitation threshold are selected as extreme precipitation stations.

[0117] S2.2: In order to identify specific flood anomaly centers, the flood anomaly rate was calculated for each extreme precipitation station for each day in 2024, and five stations with a flood anomaly rate exceeding 0.25 were selected as potential flood anomaly centers.

[0118] S2.3: Identify Flood Anomaly Centers. Based on the potential flood anomaly centers defined in S2.2, they are sorted in descending order of their flood anomaly rate. The station with the highest flood anomaly rate is selected as the first flood anomaly center. The remaining potential flood anomaly centers are then evaluated one by one. Only stations with a distance greater than 250 km from the identified flood anomaly centers are considered flood anomaly centers. Through these steps, two flood anomaly centers were finally identified for July 4th.

[0119] S2.4: Define flood anomaly zones. Based on the flood anomaly center defined in S2.3, the range of stations experiencing extreme precipitation within a 250km radius around it is defined as the scope of the flood anomaly zone. Therefore, two flood anomaly zones were finally identified on July 4.

[0120] S2.5: Temporal Continuity of Flood Anomaly Zones. To reflect the temporal continuity of flood events, based on the content of S2.1-S2.4, the flood anomaly zones for each day from July 4 to July 11, 2024, are calculated. If the spatial overlap rate between the flood anomaly zone of a given day and the flood anomaly zone of the following day exceeds 20%, it is determined to be a continuous flood process, and multiple flood events are ultimately identified.

[0121] S3: Identify spatiotemporally continuous rapid drought-flood transition events based on station observation data;

[0122] S3.1: Based on the spatiotemporally continuous drought and flood events identified in S1 and S2, it can be found that the drought events identified in S1 have a wide impact range and a long duration, while the flood events identified in S2 have a smaller impact range, which is basically within the scope of the drought events. Therefore, according to the discrimination requirements, the multiple flood events identified in S2 can be merged into a single drought-flood transition event.

[0123] S3.2: Based on the drought-flood transition events identified in S3.1, it was found that several events occurred sequentially and had spatial overlap. Therefore, by merging them, they were identified as a single drought-flood transition event, namely, the drought period from June 25 to July 2, 2024, the transition period on July 3, and the flood period from July 4 to July 11.

[0124] S4: Defines the intensity of the rapid shift from drought to flood;

[0125] S4.1: Based on the drought-flood abrupt transition events identified in S1, S2, and S3, obtain multiple factors influencing the intensity of these events, including the intensity of the preceding drought, the intensity of the subsequent flood, the timing of the transition, and the extent of impact. Drought intensity can be the average MCI index of all drought-affected stations; flood intensity can be the average cumulative precipitation of all extreme precipitation stations; the timing of the transition can be the number of days between the end of the drought and the beginning of the flood; and the extent of impact can be the total number of stations affected by the drought-flood abrupt transition event.

[0126] S4.2: Using all samples of abrupt shifts between drought and flood, the weight coefficients of the above four factors are determined by the entropy weight method, and the comprehensive intensity of the abrupt shifts between drought and flood from June 25 to July 11, 2024 is obtained by weighted summation.

[0127] This invention considers the lag effect of precipitation and the impact of evapotranspiration on drought events, and identifies abrupt drought-flood transition events from a diurnal scale perspective. This innovative perspective not only introduces novel research methods to the field of abrupt drought-flood transition event research but also improves the accuracy of such research. Based on this, it more effectively and rationally identifies the rapid turning points contained in abrupt drought-flood transition events, allowing for a deeper understanding of the changes in these events. This invention employs a spatiotemporally continuous objective identification method for extreme events. Compared to traditional research methods, this method can better express the three-dimensional continuous changes of extreme events, accurately distinguishing the beginning, development, and end stages, and providing the scope of influence at different stages. Based on this, it reveals the evolutionary characteristics of extreme events under three-dimensional conditions, thus providing a basis for the evolution and forecasting of abrupt drought-flood transition events. This invention proposes a method for defining the intensity of abrupt drought-flood transition events based on the entropy weight method. This method unifies the four indicators affecting the intensity of abrupt drought-flood transition events, eliminating the need to consider differences between different variables and providing an objective measurement of the intensity of abrupt drought-flood transition events.

[0128] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection, characterized in that, include: Based on station observation data, identify spatiotemporally continuous meteorological drought events; Based on station observation data, identify spatiotemporally continuous meteorological and flood events; For each identified meteorological flood event, it is determined whether a meteorological drought event exists within a preset time period before the occurrence of the meteorological flood event, and the proportion of the number of stations with spatial overlap between the meteorological flood event and the meteorological drought event to the number of stations with the meteorological flood event exceeds a preset threshold. If the conditions are met, it is identified as a sudden shift from drought to flood.

2. The method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection as described in claim 1, characterized in that, Identifying spatiotemporally continuous meteorological drought events includes: Based on station observation data, the meteorological drought index for each station is calculated; For each station, it is determined whether an extreme drought event has occurred based on the meteorological drought index; For each station, its drought anomaly rate is calculated, which is the proportion of stations within a preset radius around that station that experience extreme drought events. Based on the drought anomaly rate, drought anomaly centers are identified, wherein the drought anomaly rate of the drought anomaly center exceeds a first preset threshold and the distance between the drought anomaly center and other drought anomaly centers exceeds a first preset distance; Using each drought anomaly center as the center, sites within a preset radius that have experienced extreme drought events are grouped into the same drought anomaly zone; Based on drought anomaly zones over multiple consecutive days, temporal continuity is determined. When the spatial overlap rate between the drought anomaly zone on a given day and the drought anomaly zone on the following day exceeds a second preset threshold, they are identified as the same continuous drought process.

3. The method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection as described in claim 1, characterized in that, Identifying spatiotemporally continuous meteorological flooding events includes: Based on station observation data, the precipitation extreme value threshold for each station is calculated; For each station, it is determined whether an extreme precipitation event has occurred based on whether its daily precipitation exceeds the precipitation extreme threshold. For each station, calculate its flood anomaly rate, which is the proportion of stations within a preset radius around that station that experience extreme precipitation events. Based on the flood anomaly rate, flood anomaly centers are identified, wherein the flood anomaly rate of the flood anomaly center exceeds a third preset threshold and the distance between it and other flood anomaly centers exceeds a second preset distance; Using each flood anomaly center as the center, stations within a preset radius that experience extreme precipitation events are grouped into the same flood anomaly zone; Based on the flood anomaly zones over multiple consecutive days, the temporal continuity is determined. When the spatial overlap rate between the flood anomaly zone on a given day and the flood anomaly zone on the following day exceeds a fourth preset threshold, they are identified as the same continuous flooding process.

4. The method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection as described in claim 1, characterized in that, Identifying sudden drought-to-flood transition events also includes merging multiple sudden drought-to-flood transition events; The process of merging multiple drought-flood transition events includes: when two drought-flood transition events are consecutive in time and have more than a preset number of overlapping stations in space, the two events are merged into one drought-flood transition event.

5. The method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection as described in claim 1, characterized in that, The method also includes a process for determining the intensity of a sudden shift from drought to flood; The process of determining the intensity of a sudden shift from drought to flood includes: To obtain multiple factors influencing the intensity of abrupt shifts between drought and flood, including the intensity of the preceding drought, the intensity of the subsequent flood, the timing of the shift, and the extent of the impact; The weights of the multiple factors are determined using the entropy weight method; Based on the weights and the multiple factors, the intensity index of the rapid shift from drought to flood is calculated.

6. The method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection as described in claim 2, characterized in that, The meteorological drought index is a comprehensive meteorological drought index; the comprehensive meteorological drought index takes into account the impact of previous precipitation and evapotranspiration on the current drought.

7. The method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection as described in claim 3, characterized in that, The precipitation extreme threshold is a preset quantile calculated based on a historical climate baseline period; the historical climate baseline period is daily precipitation data for thirty consecutive years.

8. The method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection as described in claim 5, characterized in that, The affected area refers to the number of meteorological stations affected by the abrupt shift from drought to flood; the abrupt shift time is the time interval between the end of the drought event and the start of the flood event.

9. A drought-flood transition event identification system based on spatiotemporal continuous monitoring and detection, characterized in that, A method for identifying sudden drought-flood transition events based on spatiotemporal continuous monitoring and detection as described in any one of claims 1-8, comprising: The drought event identification module is used to identify spatiotemporally continuous meteorological drought events based on station observation data; The flood event identification module is used to identify spatiotemporally continuous meteorological flood events based on station observation data; The abrupt change event judgment module is used to determine, for each meteorological flood event identified by the flood event identification module, whether there is a meteorological drought event identified by the drought event identification module within a preset time before its occurrence, and whether the proportion of the number of spatially overlapping stations of the meteorological flood event and the meteorological drought event exceeds a preset threshold, and to identify it as a drought-flood abrupt change event when the conditions are met.

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