A reservoir drought-flood rapid change event identification and evaluation method and system based on paired year analysis

Abstract

This invention discloses a method and system for identifying and assessing abrupt drought-flood transition events in reservoirs based on paired-year analysis. It solves the technical problem in existing methods where the independent impacts of these events are obscured by climate differences, leading to suboptimal assessment results. The method includes acquiring and preprocessing basic attribute data and meteorological / hydrological data for multiple target reservoirs within the study area, outputting target basic attribute data and target meteorological / hydrological data; then using gamma distribution based on daily precipitation sequence datasets to identify abrupt drought-flood transition events for multiple target time periods in each reservoir; selecting final paired years according to predefined meteorological similarity conditions and historical non-event years; combining the final paired years, event information, daily inflow / storage sequence datasets, and target basic attribute data to classify the impact degree of each reservoir's abrupt drought-flood transition event; conducting an impact assessment based on the classification results, and generating an integrated abrupt drought-flood transition event impact assessment report.

Description

Technical Field

[0001] This invention relates to the field of hydrology, water resources and reservoir operation management technology, and in particular to a method and system for identifying and assessing rapid drought-flood transition events in reservoirs based on paired-year analysis. Background Technology

[0002] Drought-Flood Abrupt Alternation (DFAA), a typical complex hydrological event, is characterized by the rapid alternation of drought and flood within a short period, disrupting the normal rhythm of natural hydrological processes. These events not only present the dual risks of water scarcity caused by drought and water surplus caused by flooding, but also, due to their rapid transition rate, wide impact range, and long disaster chain, cause a superimposed impact on regional water resource allocation, ecological environment stability, and socio-economic development. Reservoirs, as key infrastructure for regulating the spatial and temporal distribution of water resources and mitigating hydrological disasters, are directly related to core needs such as flood control safety, urban and rural water supply security, agricultural irrigation, and clean energy supply.

[0003] The dramatic fluctuations in the inflow process and sudden changes in water storage caused by DFAA events can easily exceed the conventional scheduling thresholds of reservoirs, triggering a chain of problems such as exceeding flood limits, water supply interruptions, and a sharp drop in power generation efficiency. In severe cases, it can even threaten the structural safety of reservoir projects. Therefore, the accurate identification and impact quantification of DFAA events have become an important issue for water resource security in the context of global climate change.

[0004] Existing methods for identifying and assessing rapid drought-flood transition events in reservoirs mostly focus on identifying and assessing single drought or flood events, or conduct statistical analysis at the watershed scale. They also generally compare the event year directly with the multi-year average, failing to fully consider the interference of interannual climate fluctuations. This causes the independent impact of rapid drought-flood transition events to be confused by climate differences, ultimately leading to poor assessment results. Summary of the Invention

[0005] This invention provides a method and system for identifying and assessing rapid drought-flood transition events in reservoirs based on paired-year analysis. It solves the technical problem in existing methods for identifying and assessing rapid drought-flood transition events in reservoirs where the independent impact of these events is confused by climate differences, ultimately leading to poor assessment results.

[0006] The first aspect of this invention provides a method for identifying and assessing rapid drought-flood transition events in reservoirs based on paired-year analysis, comprising:

[0007] Acquire basic attribute data and meteorological and hydrological data of multiple target reservoirs in the study area, preprocess the basic attribute data and meteorological and hydrological data of each target reservoir, and output the target basic attribute data and target meteorological and hydrological data of each target reservoir.

[0008] The gamma distribution is used to identify drought-flood transition events based on the daily precipitation sequence dataset in the target meteorological and hydrological data of each target reservoir, and outputs drought-flood transition events for multiple target time periods corresponding to each target reservoir.

[0009] Based on predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoirs, pairing years are selected according to the drought-flood transition events of multiple target time periods corresponding to each target reservoir and the daily precipitation sequence dataset, and the final paired years corresponding to each target reservoir are output.

[0010] Based on the final paired year corresponding to each target reservoir, the drought-flood transition events for multiple target time periods, the daily inflow sequence dataset and daily water storage sequence dataset in the target meteorological and hydrological data, and the target basic attribute data, the impact degree of the drought-flood transition events on each target reservoir is classified, and the classification results of the impact degree of the drought-flood transition events on each target reservoir are output.

[0011] Based on the classification results of the impact degree of the drought-flood transition events for each of the target reservoirs, an impact assessment of the drought-flood transition events is conducted, and an integrated impact assessment report of the drought-flood transition events is generated.

[0012] Optionally, the preprocessing of the basic attribute data and meteorological and hydrological data of each of the target reservoirs, and the output of the target basic attribute data and target meteorological and hydrological data of each of the target reservoirs, includes:

[0013] The basic attribute data and meteorological and hydrological data of each target reservoir are standardized in format, and the standardized basic attribute data and meteorological and hydrological data of each target reservoir are output.

[0014] Missing values ​​are filled in for the basic attribute data and meteorological and hydrological data of each target reservoir after the format is unified, and the missing value-filled basic attribute data and meteorological and hydrological data of each target reservoir are output.

[0015] Outlier removal is performed on the basic attribute data and meteorological and hydrological data of each target reservoir after missing value completion, and the target basic attribute data and target meteorological and hydrological data of each target reservoir are output.

[0016] Optionally, the step of using gamma distribution to identify drought-flood transition events based on the daily precipitation sequence dataset in the target meteorological and hydrological data of each target reservoir, and outputting drought-flood transition events for multiple target time periods corresponding to each target reservoir, includes:

[0017] The gamma distribution is used to analyze the daily precipitation sequence datasets in the target meteorological and hydrological data of each target reservoir to obtain the standardized precipitation index for multiple dates corresponding to each target reservoir.

[0018] Based on the standardized precipitation index for multiple dates corresponding to each of the target reservoirs, determine the drought and flood status labels and drought and flood start and end dates for multiple time periods corresponding to each of the target reservoirs;

[0019] Based on the drought and flood status labels and drought and flood start and end dates for multiple time periods corresponding to each target reservoir, the drought and flood abrupt change events for each time period corresponding to each target reservoir are identified, and the drought and flood abrupt change events for multiple target time periods corresponding to each target reservoir are output.

[0020] Optionally, based on predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoirs, the paired years are selected according to the drought-flood transition events of multiple target time periods corresponding to each target reservoir and the daily precipitation sequence dataset, and the final paired years corresponding to each target reservoir are output, including:

[0021] Based on the event years corresponding to the drought-flood transition events of multiple target time periods for each target reservoir, the precipitation data corresponding to the event years are extracted from the daily precipitation sequence dataset corresponding to each target reservoir, and the total annual precipitation and monthly precipitation sequence associated with each event year corresponding to each target reservoir are calculated.

[0022] Based on the predefined meteorological similarity conditions, multiple candidate pairing years are selected from multiple historical non-event years corresponding to the target reservoir;

[0023] Based on the total annual precipitation and monthly precipitation sequence associated with each event year corresponding to each of the target reservoirs, the final pairing year corresponding to each of the target reservoirs is selected from among the multiple candidate pairing years.

[0024] Optionally, the step of classifying the impact degree of drought-flood transition events on each of the target reservoirs based on the final paired year corresponding to each target reservoir, the drought-flood transition events of multiple target time periods, the daily inflow sequence dataset and daily water storage sequence dataset in the target meteorological and hydrological data, and the target basic attribute data, and outputting the drought-flood transition event impact degree classification results for each of the target reservoirs, includes:

[0025] Based on the turning point of the drought-flood transition events for multiple target time periods corresponding to each target reservoir, a corresponding virtual turning point is set in the corresponding final paired year.

[0026] Starting from the virtual turning point corresponding to each of the target reservoirs and extending from there, a unified index calculation and observation window is obtained for each of the target reservoirs.

[0027] Based on the daily inflow sequence dataset and daily water storage sequence dataset corresponding to each of the target reservoirs, calculate the daily standardized anomaly sequence corresponding to each of the target reservoirs;

[0028] Based on the daily inflow sequence dataset and daily storage sequence dataset corresponding to each of the target reservoirs, the inflow quantile and storage quantile corresponding to each of the target reservoirs are statistically obtained.

[0029] Based on the daily standardized anomaly sequence, inflow quantile, storage quantile, and the defined unified index corresponding to each target reservoir, calculate the total number of days in the observation window, and calculate the average absolute standardized anomaly and the percentage of normal interval days corresponding to each target reservoir respectively.

[0030] Based on the average absolute standardized anomaly and the percentage of normal interval days corresponding to each of the target reservoirs, the difference index corresponding to each of the target reservoirs is calculated.

[0031] Based on the difference index corresponding to each target reservoir, the event type of the drought-flood transition event in each target time period, and the basic attribute data of the target, the impact degree of the drought-flood transition event on the corresponding target reservoir is classified, and the classification result of the impact degree of the drought-flood transition event on each target reservoir is output.

[0032] Optionally, the assessment of the impact of sudden drought-flood events based on the classification results of the impact degree of each target reservoir is performed to generate an integrated impact assessment report on sudden drought-flood events, including:

[0033] Based on the classification results of the impact degree of sudden drought-flood transition events of each of the target reservoirs, the distribution characteristics and risk patterns within the target area are statistically analyzed.

[0034] Based on the distribution characteristics and risk patterns data within the target area, a risk distribution map of sudden shifts between drought and flood events in the target area is constructed.

[0035] Based on the grading results of the impact degree of the sudden drought-flood transition events of each target reservoir, the distribution characteristics and risk pattern data of the target area, and the risk distribution map of the sudden drought-flood transition events, an integrated impact assessment report of the sudden drought-flood transition events is generated.

[0036] The second aspect of this invention provides a reservoir drought-flood transition event identification and assessment system based on paired-year analysis, comprising:

[0037] The acquisition module is used to acquire basic attribute data and meteorological and hydrological data of multiple target reservoirs in the study area, and to preprocess the basic attribute data and meteorological and hydrological data of each target reservoir, and output the target basic attribute data and target meteorological and hydrological data of each target reservoir.

[0038] The identification module is used to identify drought-flood transition events based on the daily precipitation sequence dataset in the target meteorological and hydrological data of each target reservoir using gamma distribution, and output drought-flood transition events for multiple target time periods corresponding to each target reservoir.

[0039] The selection module is used to select paired years based on predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoirs, according to the drought-flood transition events of multiple target time periods corresponding to each target reservoir and the daily precipitation sequence dataset, and output the final paired years corresponding to each target reservoir.

[0040] The grading module is used to grade the impact degree of drought-flood transition events on each of the target reservoirs based on the final paired year corresponding to each target reservoir, the drought-flood transition events of multiple target time periods, the daily inflow sequence dataset and daily water storage sequence dataset in the target meteorological and hydrological data, and the target basic attribute data, and output the grading results of the impact degree of drought-flood transition events on each of the target reservoirs.

[0041] The assessment module is used to assess the impact of sudden drought-flood events based on the classification results of the impact degree of sudden drought-flood events on each of the target reservoirs, and generate an integrated impact assessment report on sudden drought-flood events.

[0042] A third aspect of the present invention provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the reservoir drought-flood transition method based on paired-year analysis as described above.

[0043] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements the method for identifying and assessing reservoir drought-flood transition events based on paired-year analysis as described above.

[0044] The fifth aspect of the present invention provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein, when the program instructions are executed by a computer, the computer performs the steps of the method for identifying and assessing reservoir drought-flood transition events based on paired-year analysis as described above.

[0045] As can be seen from the above technical solutions, the present invention has the following advantages:

[0046] The above-mentioned technical solution of the present invention provides a method for identifying and evaluating reservoir drought-flood transition events based on paired-year analysis. It acquires basic attribute data and meteorological and hydrological data of multiple target reservoirs in the study area, preprocesses the basic attribute data and meteorological and hydrological data of each target reservoir, and outputs the target basic attribute data and target meteorological and hydrological data of each target reservoir. It uses gamma distribution to identify drought-flood transition events based on the daily precipitation sequence dataset in the target meteorological and hydrological data of each target reservoir, and outputs the drought-flood transition events for multiple target time periods corresponding to each target reservoir. Based on predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoirs, it selects paired years based on the drought-flood transition events for multiple target time periods corresponding to each target reservoir and the daily precipitation sequence dataset, and outputs the final paired year corresponding to each target reservoir. Finally, it uses the final paired year corresponding to each target reservoir, the drought-flood transition events for multiple target time periods, the daily inflow sequence dataset and the daily water storage sequence dataset in the target meteorological and hydrological data, and the target basic attributes... The data is used to classify the impact of abrupt drought-flood events on each target reservoir, and output the classification results for the impact of abrupt drought-flood events on each target reservoir. Based on the classification results for the impact of abrupt drought-flood events on each target reservoir, an impact assessment of the abrupt drought-flood events is conducted, generating an integrated impact assessment report for abrupt drought-flood events. Based on the above scheme, this invention uses a "paired year selection" step to select the final paired years based on the precipitation characteristics corresponding to the abrupt drought-flood events of each reservoir, ensuring that the paired years and the event years are consistent in terms of climate background. At the same time, in the process of impact classification, not only is the comparison data of the paired years combined, but also the daily inflow and storage sequences and basic attribute data of the target meteorological and hydrological data are incorporated, realizing a closed-loop data process from event identification to impact assessment, so that the impact classification results can accurately reflect the actual effect of abrupt drought-flood events on reservoir operation. Finally, by integrating the classification results of multiple reservoirs to generate an integrated assessment report, the accuracy of the assessment of individual reservoirs is ensured, and comprehensive judgment at the regional level is achieved, thereby improving the assessment effect. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A flowchart illustrating the steps of a method for identifying and assessing rapid drought-flood transition events in reservoirs based on paired-year analysis, provided in Embodiment 1 of the present invention.

[0049] Figure 2The three major reservoirs in the Dongjiang River Basin provided in Embodiment 1 of the present invention Result comparison chart;

[0050] Figure 3 This is a flowchart illustrating a method for identifying and assessing rapid drought-flood transition events in reservoirs based on paired-year analysis, provided in Embodiment 1 of the present invention.

[0051] Figure 4 This is a structural block diagram of a reservoir drought-flood transition event identification and assessment system based on paired-year analysis, provided in Embodiment 2 of the present invention. Detailed Implementation

[0052] This invention provides a method and system for identifying and assessing abrupt drought-flood transition events in reservoirs based on paired-year analysis. It solves the technical problem in existing methods where the independent impacts of these events are obscured by climate differences, leading to suboptimal assessment results. This invention is applicable to reservoir group operation assessment, risk warning, and auxiliary scheduling decisions.

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It should be noted that in the optional embodiments of the present invention, the object information and other related data involved require the permission or consent of the object when the embodiments of the present invention are applied to specific products or technologies, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. That is to say, if the embodiments of the present invention involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations, and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information requires the consent of the individual. If sensitive information is involved, the separate consent of the information subject is required, and the embodiments also need to be implemented with the authorization and consent of the object.

[0054] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of a method for identifying and assessing rapid drought-flood transition events in reservoirs based on paired-year analysis, as provided in Embodiment 1 of the present invention.

[0055] This invention provides a method for identifying and assessing rapid drought-flood transition events in reservoirs based on paired-year analysis, comprising:

[0056] Step 101: Obtain basic attribute data and meteorological and hydrological data of multiple target reservoirs in the study area, preprocess the basic attribute data and meteorological and hydrological data of each target reservoir, and output the target basic attribute data and target meteorological and hydrological data of each target reservoir.

[0057] The study area refers to a specific geographical area that includes multiple target reservoirs and their catchment areas, and is the spatial boundary for data collection, event identification, and impact assessment in this invention.

[0058] Multiple target reservoirs refer to several reservoirs located within the study area that require identification and impact assessment of sudden drought-flood transition events, and are the core research objects of this invention.

[0059] Basic attribute data refers to relevant data that characterizes the inherent engineering features of the target reservoir and serves as the foundational supporting data for conducting impact assessments of sudden shifts in reservoir drought and flood events.

[0060] Meteorological and hydrological data refers to data related to meteorological and hydrological processes in the area where the target reservoir is located, including daily precipitation series datasets, daily inflow series datasets, and daily water storage series datasets. These are core data for identifying sudden drought-flood transition events and assessing their impact.

[0061] It should be noted that the basic attribute data and meteorological and hydrological data of multiple target reservoirs in the study area were acquired, and the basic attribute data and meteorological and hydrological data of each target reservoir were preprocessed to output the target basic attribute data and target meteorological and hydrological data of each target reservoir. This preprocessing includes format standardization, missing value completion, and outlier removal, which provides standardized data support for subsequent identification of drought-flood transition events based on gamma distribution, selection of paired years, and classification of impact degree, ensuring the consistency and accuracy of the analysis in each stage.

[0062] Further, step 101 may include the following sub-steps:

[0063] S11. Standardize the format of the basic attribute data and meteorological and hydrological data of each target reservoir, and output the standardized basic attribute data and standardized meteorological and hydrological data of each target reservoir.

[0064] S12. Complete the missing values ​​of the basic attribute data and meteorological and hydrological data after the format is unified for each target reservoir, and output the basic attribute data and meteorological and hydrological data after missing value completion for each target reservoir.

[0065] S13. Perform outlier removal on the basic attribute data and meteorological and hydrological data of each target reservoir after missing value completion, and output the target basic attribute data and target meteorological and hydrological data of each target reservoir.

[0066] It should be noted that the selected study area and target reservoirs were used to collect basic attribute data and meteorological and hydrological data for each reservoir. During the data collection process, the following data were collected, including but not limited to: basic reservoir data (catchment boundary, total reservoir capacity, characteristic reservoir capacity), daily precipitation data, daily inflow data, and daily water storage data. Among them, the daily inflow or water storage data came from reservoir hydrological monitoring or simulation data.

[0067] Furthermore, the basic attribute data and meteorological and hydrological data of each target reservoir were standardized in format. Raw data from different sources and in different formats were converted into a unified structured format, outputting standardized basic attribute data and standardized meteorological and hydrological data for each target reservoir, providing a consistent analytical basis for subsequent data processing. Based on this, missing values ​​were filled in for the standardized basic attribute data and standardized meteorological and hydrological data of each target reservoir using common interpolation methods in the hydrological and meteorological fields, outputting completed basic attribute data and completed meteorological and hydrological data for each target reservoir, ensuring data continuity and integrity. Then, outlier removal was performed on the completed basic attribute data and completed meteorological and hydrological data of each target reservoir, identifying and removing extreme value interference using the 3σ principle, outputting the target basic attribute data and target meteorological and hydrological data for each target reservoir. This step, through a preprocessing workflow that unifies format, completes missing values, and removes outliers, effectively improves data quality and ensures that the output target basic attribute data and target meteorological and hydrological data are consistent, complete, and reliable. This provides accurate data support for subsequent identification of drought-flood transition events, selection of paired years, and classification of impact levels, avoiding analytical biases caused by chaotic, missing, or abnormal data formats.

[0068] Step 102: Using gamma distribution, identify drought-flood transition events based on the daily precipitation sequence dataset in the target meteorological and hydrological data of each target reservoir, and output the drought-flood transition events for multiple target time periods corresponding to each target reservoir.

[0069] It should be noted that, based on the target meteorological and hydrological data of each target reservoir output from the above steps, the daily precipitation sequence dataset is fitted with a gamma distribution to obtain a standardized precipitation index. Combined with the drought and flood period determination rules, the drought and flood abrupt transition events are identified, and the drought and flood abrupt transition events for multiple target periods corresponding to each target reservoir are output, providing accurate event references for subsequent pairing year selection.

[0070] Furthermore, step 102 may include the following sub-steps:

[0071] S21. Using the gamma distribution, the daily precipitation sequence datasets in the target meteorological and hydrological data of each target reservoir are used to obtain the standardized precipitation index for multiple dates corresponding to each target reservoir.

[0072] S22. Based on the standardized precipitation index corresponding to multiple dates for each target reservoir, determine the drought and flood status labels and drought and flood start and end dates for multiple time periods corresponding to each target reservoir;

[0073] S23. Based on the drought and flood status labels and drought and flood start and end dates for multiple time periods corresponding to each target reservoir, identify drought and flood abrupt transition events for each time period corresponding to each target reservoir, and output the drought and flood abrupt transition events for multiple target time periods corresponding to each target reservoir.

[0074] The gamma distribution refers to the probability distribution model used to fit daily precipitation sequence datasets and support the calculation of standardized precipitation index. It is a core computational tool for identifying sudden shifts between drought and flood events.

[0075] Standardized precipitation indexes for multiple dates refer to the standardized precipitation index sets for each target reservoir, covering multiple consecutive or discrete dates, providing continuous data support for subsequent division of drought and flood periods.

[0076] Drought and flood status labels are classification labels derived from standardized precipitation indices to identify whether a certain period is characterized by drought or flood, and serve as a direct basis for distinguishing between drought and flood periods.

[0077] The start and end dates of drought and flood periods refer to the start and end dates of each drought or flood period, determined based on the standardized precipitation index and duration rules, thus clarifying the time range of drought and flood periods.

[0078] Multiple target time periods refer to the set of drought-to-flood or flood-to-drought events that have been identified and confirmed for each target reservoir and occurred in different target time periods. This is the core basis for subsequent pairing year selection and impact degree classification.

[0079] It should be noted that the SPI (Standardized Precipitation Index) is calculated based on gamma distribution fitting, standardizing the precipitation series to a standard normal distribution. Its calculation formula is as follows:

[0080] ;

[0081] in, =2.516, =0.803, =0.010, =1.433, =0.189, =0.001. It is the cumulative probability density of SPI. The conversion factor. When When ≤0.5, ;when When >0.5, .

[0082] Furthermore, when SPI > 0.5 and the duration is ≥ 10 days, it is determined to be a flood period; when SPI < -0.5 and the duration is ≥ 10 days, it is determined to be a drought period. If the interval between the two periods is ≤ 5 days, it is determined to be a drought-flood transition event. Among them, drought-flood transition events include drought-to-flood events and flood-to-drought events. When the time interval between any drought period and the subsequent adjacent flood period is not greater than a preset threshold D0, it is determined to be a drought-to-flood event; when the time interval between any flood period and the subsequent adjacent drought period is not greater than D0, it is determined to be a flood-to-drought event.

[0083] Specifically, the target meteorological and hydrological data of each target reservoir output from the above steps are fitted and calculated using a gamma distribution to obtain the standardized precipitation index for multiple dates corresponding to each target reservoir. Based on the standardized precipitation index for multiple dates corresponding to each target reservoir, the drought and flood status labels (drought or flood) and the start and end dates of drought and flood are determined for multiple time periods corresponding to each target reservoir according to the preset judgment rules (SPI≤-0.5 and duration≥10 days is drought state, SPI≥0.5 and duration≥10 days is flood state). Based on the drought and flood status labels and the start and end dates of drought and flood for multiple time periods corresponding to each target reservoir, the drought and flood abrupt transition events are identified for each time period corresponding to each target reservoir by judging whether the time interval between adjacent drought periods and flood periods meets the preset threshold, and the drought and flood abrupt transition events for multiple target time periods corresponding to each target reservoir are output. This invention identifies abrupt shifts between drought and flood events by fitting gamma distributions and using clear judgment rules. This ensures the standardization and accuracy of event identification, provides a clear and reliable event reference benchmark for subsequent year pairing, and avoids subsequent evaluation biases caused by fuzzy event identification.

[0084] Step 103: Based on predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoirs, select paired years according to the drought-flood transition events and daily precipitation sequence datasets for multiple target time periods corresponding to each target reservoir, and output the final paired years corresponding to each target reservoir.

[0085] It should be noted that, based on the drought-flood transition events for multiple target time periods corresponding to each target reservoir output by the above steps, daily precipitation sequence data for the corresponding event year are extracted. Combined with predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoir, candidate years matching the meteorological characteristics of the event year are selected from the historical non-event years. The pairing year selection for each target reservoir is completed, and the final pairing year is output, providing a consistent climatic background for subsequent assessment of the independent impact of drought-flood transition events.

[0086] Furthermore, step 103 may include the following sub-steps:

[0087] S31. Based on the event years corresponding to the drought-flood transition events of multiple target time periods corresponding to each target reservoir, extract the precipitation data of the corresponding event years from the daily precipitation sequence dataset corresponding to each target reservoir, and calculate the total annual precipitation and monthly precipitation sequence associated with each event year corresponding to each target reservoir.

[0088] S32. Based on predefined meteorological similarity conditions, select multiple candidate pairing years from multiple historical non-event years corresponding to the target reservoir;

[0089] S33. Based on the total annual precipitation and monthly precipitation sequence associated with each event year corresponding to each target reservoir, select the final pairing year corresponding to each target reservoir from multiple candidate pairing years.

[0090] Historical non-event years refer to the set of years corresponding to each target reservoir in the past when no sudden drought-flood transition events have occurred, which is the source of the selection range for candidate pairing years.

[0091] The event year refers to the specific year in which the drought-flood transition event occurred for each target reservoir during multiple target time periods. It serves as the reference year for selecting paired years and is also the time basis for extracting precipitation characteristic data.

[0092] It should be noted that, firstly, the total annual precipitation and monthly precipitation sequence of the event year are calculated; then, candidate paired years with an annual precipitation deviation of no more than 5% are selected from the precipitation records of multiple years; the correlation coefficient of the monthly precipitation sequence between the event year and the candidate years is calculated, and when the correlation coefficient is not less than 0.7, the year is determined as the paired year; when there are multiple candidate paired years, the year with the total annual precipitation closest to the event year is selected as the final paired year.

[0093] The predefined meteorological similarity conditions are as follows:

[0094] For the monthly precipitation sequence P of reservoir r in year y r,y and annual total precipitation Ap r,y For each identified DFAA event i (event year Ye) (i)), and selected from the reservoir's multi-year precipitation records the control year Yc in which no DFAA event occurred. (i) The following conditions must be met:

[0095] (1) The annual total precipitation deviation shall not exceed 5%:

[0096] ;

[0097] (2) The correlation coefficient of monthly precipitation is not less than 0.7:

[0098] ;

[0099] in, For reservoir r in the i-th DFAA event year Ye (i) The annual total precipitation is the reference value for calculating the deviation of the annual total precipitation; For the reservoir r in the year Ye (i) The correlation coefficient between the monthly precipitation series of y and the monthly precipitation series of candidate year y is used to measure the similarity of their monthly precipitation characteristics.

[0100] If multiple candidate years exist, the year with the closest annual precipitation to the event year is selected as the final paired year. A virtual turning point date is defined within the paired year. The observation window is set 60 days after the turning point of the event year, corresponding to the turning point of the event year.

[0101] Specifically, based on the abrupt shifts between drought and flood events corresponding to multiple target time periods for each target reservoir, the event year corresponding to each abrupt shift event is extracted. From the daily precipitation sequence dataset corresponding to each target reservoir, complete daily precipitation data for the corresponding event year is extracted. Through data aggregation, the total annual precipitation (cumulative daily precipitation throughout the year) and monthly precipitation sequences (a set of monthly precipitation data) associated with each event year for each target reservoir are calculated. Based on predefined meteorological similarity conditions (annual total precipitation deviation ≤ 5%, monthly precipitation sequence correlation coefficient)... (≥0.7) Among the multiple historical non-event years (past years without sudden drought-flood transition events) corresponding to each target reservoir, multiple candidate paired years that meet the meteorological similarity condition are compared and screened one by one. Based on the annual total precipitation and monthly precipitation sequence associated with each event year corresponding to each target reservoir as a reference benchmark, the annual total precipitation of each candidate paired year is further compared with that of the event year among the multiple selected candidate paired years. The year with the closest annual total precipitation to the corresponding event year is selected as the final paired year corresponding to each target reservoir. This invention, through the step-by-step screening logic of "extracting precipitation characteristics of event years - screening candidate years according to meteorological similarity conditions - selecting the closest candidate year", ensures that the final paired year has a consistent climate background with the event year, effectively eliminating the interference of interannual climate fluctuations, fundamentally solving the problem of the independent impact of sudden drought-flood transition events being confused by climate differences in existing methods, and significantly improving the accuracy and reliability of the assessment results.

[0102] Step 104: Based on the final paired year corresponding to each target reservoir, the drought-flood transition events for multiple target time periods, the daily inflow sequence dataset and daily water storage sequence dataset in the target meteorological and hydrological data, and the target basic attribute data, classify the impact degree of the drought-flood transition events on each target reservoir, and output the classification results of the impact degree of the drought-flood transition events on each target reservoir.

[0103] It should be noted that, based on the final paired year corresponding to each target reservoir, and combined with the turning point dates of drought-flood transition events for multiple target periods, a comparative observation window is set. The differences in the daily inflow sequence dataset and daily water storage sequence dataset in the target meteorological and hydrological data are compared and analyzed. The impact classification threshold is determined by incorporating the target basic attribute data. The impact degree of drought-flood transition events is classified for each target reservoir, and the impact degree classification results of drought-flood transition events for each target reservoir are output, providing core basis for individual reservoirs for subsequent regional overall impact assessment.

[0104] Furthermore, step 104 may include the following sub-steps:

[0105] S41. Based on the turning point of the drought-flood transition event for multiple target time periods corresponding to each target reservoir, set the corresponding virtual turning point in the corresponding final pairing year.

[0106] S42. Starting from the virtual turning point corresponding to each target reservoir and extending it, a unified index calculation and observation window corresponding to each target reservoir is obtained.

[0107] S43. Based on the daily inflow sequence dataset and daily water storage sequence dataset corresponding to each target reservoir, calculate the daily standardized anomaly sequence corresponding to each target reservoir.

[0108] S44. Based on the daily inflow sequence dataset and daily water storage sequence dataset corresponding to each target reservoir, the inflow quantile and water storage quantile corresponding to each target reservoir are statistically obtained.

[0109] S45. Calculate the total number of days in the observation window based on the daily standardized anomaly sequence, inflow quantile, storage quantile, and defined unified indicators for each target reservoir. Calculate the average absolute standardized anomaly and the percentage of normal interval days for each target reservoir.

[0110] S46. Based on the average absolute standardized anomaly and the percentage of normal interval days corresponding to each target reservoir, calculate the difference index corresponding to each target reservoir.

[0111] S47. Based on the difference index corresponding to each target reservoir, the event type of the drought-flood transition event in each target time period, and the basic attribute data of the target, classify the impact degree of the drought-flood transition event on the corresponding target reservoir, and output the classification result of the impact degree of the drought-flood transition event on each target reservoir.

[0112] The virtual turning point date refers to the date selected in the final paired year that coincides with the turning point date of the drought-flood transition event. It is the starting point for defining the observation window for the unified indicator calculation, and is used to ensure that the analysis time range of the event year and the paired year corresponds consistently.

[0113] Defining a unified indicator calculation observation window refers to a time interval extending backward from the virtual turning point date for a specific duration (such as 60 days). This serves as a unified time range for conducting comparative analysis of inflow and water storage data between the event year and the final paired year, ensuring consistency in the analysis dimensions.

[0114] Inflow quantiles refer to specific quantile values ​​(such as the 20th and 80th percentiles) obtained from a complete daily inflow sequence dataset. They serve as the benchmark for determining whether inflow is within the normal range.

[0115] Water storage quantiles refer to specific quantile values ​​(such as the 20th and 80th percentiles) obtained from a complete daily water storage sequence dataset. They serve as the benchmark for determining whether water storage is within the normal range.

[0116] The turning point of a drought-flood transition event refers to the critical date on which the drought state and the flood state transition between each other during the event. Specifically, it is the boundary date between the end date of the drought period and the start date of the flood period (or the boundary date between the end date of the flood period and the start date of the drought period) when the identification criterion of "the time interval between the drought period and the adjacent flood period / the time interval between the flood period and the adjacent drought period is ≤ 5 days" is met. This is the core time benchmark for setting virtual turning points and defining unified indicator calculation observation windows in the final paired year.

[0117] It should be noted that, based on the turning points of drought-flood transition events for multiple target time periods corresponding to each target reservoir, a date consistent with this turning point date is selected from the corresponding final paired year as the corresponding virtual turning point date. Extending 60 days forward from the virtual turning point date corresponding to each target reservoir, a unified indicator calculation observation window is obtained for each target reservoir, ensuring consistency in the analysis time range between the event year and the final paired year. Based on the unified indicator calculation observation window corresponding to each target reservoir, the daily inflow sequence dataset and daily storage sequence dataset within this window are extracted from the target meteorological and hydrological data. Combining the monthly multi-year mean and monthly standard deviation of inflow and storage, the daily standardized anomaly sequence between the event year and the final paired year for each target reservoir within this window is calculated. Based on the complete daily inflow sequence dataset and daily storage sequence dataset corresponding to each target reservoir, the 20th percentile and 80th percentile of inflow, as well as the 20th percentile of storage, are statistically obtained for each target reservoir. Based on the daily standardized anomaly series corresponding to each target reservoir, and combined with the inflow quantile and storage quantile, it is determined whether the data is within the normal range. Then, combined with the defined unified indicators, the total number of days in the observation window is calculated. The average absolute standardized anomaly (characterizing the degree of data fluctuation) and the percentage of days in the normal range (characterizing the degree of operational stability) of the event year and the final paired year corresponding to each target reservoir are calculated respectively. Based on the average absolute standardized anomaly and the percentage of days in the normal range of the event year and the final paired year corresponding to each target reservoir, the difference between the two is calculated to obtain the difference index corresponding to each target reservoir. Based on the difference index corresponding to each target reservoir, combined with the event type (drought to flood or flood to drought) of the drought-flood transition event in each target period, and referring to the total storage capacity and characteristic storage capacity in the target basic attribute data to match the classification threshold corresponding to the reservoir regulation capacity, the impact degree of the drought-flood transition event of the corresponding target reservoir is classified, and the classification result of the impact degree of the drought-flood transition event of each target reservoir is output. This invention ensures consistency in the analytical dimensions of the event year and the final paired year by using a "virtual turning point date + unified observation window". It combines daily standardized anomaly series to quantify data fluctuations and quantiles to define normal ranges. Furthermore, it uses difference indicators to remove the influence of basic fluctuations in the final paired year with consistent climate backgrounds. This avoids interference from climate differences throughout the entire process from data processing to indicator calculation. At the same time, it combines event type with target basic attribute data to match classification thresholds, ensuring that the classification of impact levels aligns with the actual regulation capacity of reservoirs. The final classification results accurately reflect the independent impact of abrupt drought-flood transition events on each reservoir.

[0118] To quantify the independent effects of DFAA events on reservoir inflow and storage processes, two basic indicators were established: Mean Absolute Standardized Anomaly (MAZA) and Percentage of Days in Normal Band (PDNB).

[0119] 1) Calculate the mean absolute standardized anomaly in the daily standardized anomaly series. Its formula is:

[0120] ;

[0121] in, This refers to daily inflow or water storage (i.e., daily inflow sequence dataset or daily water storage sequence dataset). and are the multi-year average and standard deviation of the month to which day t belongs, respectively, and represents the long-term average and standard deviation of the month to which day t belongs.

[0122] 2) The formulas for calculating MAZA and PDNB are as follows:

[0123] ;

[0124] ;

[0125] Where n is the number of days within the window; To define a unified index, the observation window must satisfy Q. 20 ≤ ≤Q 80 (or S) 20 ≤ ≤S 80 The number of days, N total The total number of days in the observation window is calculated to define a unified index. The smaller the PDNB value, the higher the proportion of the system operating under abnormal conditions, and the worse the operational stability. The larger the MAZA value, the more significant the deviation of the inflow or storage from the multi-year average during this period, reflecting a stronger hydrological anomaly.

[0126] To eliminate the influence of climate differences, a difference index between the event year and the paired year is defined:

[0127] ;

[0128] In this context, the superscripts (e) and (c) represent the event year and the control year (the final paired year), respectively. When A value greater than 0 indicates that the DFAA event amplified the abnormal deviation; when A value less than 0 indicates that the DFAA event has led to a decrease in the stability of reservoir operation.

[0129] Step 105: Based on the classification results of the impact degree of the drought-flood transition event for each target reservoir, conduct an impact assessment of the drought-flood transition event and generate an integrated impact assessment report of the drought-flood transition event.

[0130] It should be noted that, based on the classification results of the impact degree of the drought-flood transition events of each target reservoir, the distribution characteristics and risk patterns of the drought-flood transition events in the region are summarized and analyzed. The assessment conclusions of individual reservoirs are integrated with the comprehensive regional assessment to carry out the impact assessment of the drought-flood transition events, and an integrated impact assessment report of the drought-flood transition events is generated, which includes event impact analysis, risk conclusions and scheduling decision recommendations.

[0131] Furthermore, step 105 may include the following sub-steps:

[0132] S51. Based on the classification results of the impact degree of sudden drought-flood events on each target reservoir, statistically analyze the distribution characteristics and risk patterns within the target area.

[0133] S52. Based on the distribution characteristics and risk patterns data within the target area, construct a risk distribution map of sudden shifts between drought and flood events in the target area;

[0134] S53. Based on the grading results of the impact degree of the sudden drought-flood transition events of each target reservoir, the distribution characteristics and risk pattern data of the target area, and the risk distribution map of the sudden drought-flood transition events, generate an integrated impact assessment report of the sudden drought-flood transition events.

[0135] The distribution characteristics and risk patterns data within the target area refer to the data set that characterizes the risk-related features of drought and flood transition events within the target area, obtained from the statistical results of the classification of the impact degree of drought and flood transition events of each target reservoir. This includes the distribution of reservoirs with different impact levels, the proportion of event types, and the clustering characteristics of high-risk areas. It is the core data support for constructing risk distribution maps and generating integrated reports.

[0136] The risk distribution map of sudden drought-flood transition events refers to a map constructed using spatial visualization based on the distribution characteristics and risk patterns data of a target area. It is used to intuitively present the high and low risk zones of sudden drought-flood transition events in the area, the location of high-risk areas, and the risk clustering situation. It is a visualization tool for regional risk assessment.

[0137] An integrated impact assessment report on sudden shifts between drought and flood events refers to a comprehensive report that integrates the conclusions on the impact level classification of each target reservoir, the analysis of regional distribution characteristics and risk patterns, the interpretation of risk distribution maps, and targeted prevention and control recommendations. It covers assessment content at both the individual reservoir and regional levels and is the final output of the technical solution.

[0138] It should be noted that, based on the classification results of the impact degree of abrupt drought-flood transition events for each target reservoir, the number proportion, spatial distribution pattern, distribution of abrupt drought-flood transition event types (drought to flood / flood to drought), and clustering characteristics of high-risk reservoirs within the target area are statistically analyzed to form distribution characteristics and risk pattern data within the target area. Based on this data, spatial visualization is used to visually present the high and low risk zones, high-risk area distribution, and spatial clustering of different risk levels of abrupt drought-flood transition events in the region in the form of a map, constructing a risk distribution map of abrupt drought-flood transition events for the target area. Based on the classification results of the impact degree of abrupt drought-flood transition events for each target reservoir, the distribution characteristics and risk pattern data within the target area, and the risk distribution map, the system integrates the single reservoir impact assessment conclusions, regional risk pattern analysis, and map visualization interpretation, incorporating targeted reservoir scheduling optimization suggestions and regional risk prevention and control measures to generate an integrated impact assessment report for abrupt drought-flood transition events. This invention summarizes and categorizes results to form regional distribution characteristics and risk patterns data, constructs a visualized risk distribution map, and finally generates an integrated report. This upgrades the approach from precise assessment of a single reservoir to comprehensive regional analysis. It not only retains the advantages of separating climate differences and accurately quantifying the independent impact of events, but also clearly presents the spatial pattern and clustering pattern of the risk of rapid shifts between drought and flood in the region. It completely solves the problems of existing methods that can only perform single assessments, cannot achieve regional integrated analysis, and have poor assessment results. This provides comprehensive and accurate decision support for the coordinated scheduling of reservoir groups and precise risk prevention and control at the regional level.

[0139] This includes calculating the response levels at the inflow and storage ends of each reservoir and outputting the reservoir-level DFAA impact classification results. The output includes:

[0140] 1. The number and type of DFAA events for each reservoir;

[0141] 2. Abnormal indicators of inflow and water storage (MAZA, PDNB and their differences);

[0142] 3. Risk distribution and statistical results at the regional scale.

[0143] For example, this invention collects daily hydrological and meteorological data from three large reservoirs (Xinfengjiang, Fengshuba, and Baipenzhu) in the Dongjiang River basin. The daily inflow and storage data used in this embodiment are derived from measured data from the reservoirs, and the precipitation data are derived from measured meteorological data from meteorological stations. Based on the daily precipitation data, the Standardized Precipitation Index (SPI) is calculated to identify drought and flood periods in the reservoir catchment areas. In this embodiment, an SPI < -0.5 is used to define a drought period, and an SPI > 0.5 is used to define a flood period. The abrupt transition events between drought and flood periods are extracted using runs theory. The event type (drought to flood or flood to drought), start and end dates, and transition date are thus determined.

[0144] Next, based on the monthly precipitation sequence corresponding to the event year, control years without abrupt shifts between drought and flood events were selected from multi-year precipitation records. In this embodiment, the selection criteria were an annual total precipitation deviation of no more than 5% and a correlation coefficient of monthly precipitation sequences of no less than 0.7. Finally, a pairing year was determined for Xinfengjiang, Fengshuba, and Baipenzhu Reservoir. Using the event turning point date as a reference, a 60-day comparison window was extracted between the event year and the pairing year. The standardized anomaly sequence was calculated, and the mean absolute anomaly (MAZA) and the percentage of normal interval days (PDNB) were obtained. Further calculations were performed to determine the difference index between the event year and the pairing year. and This is used to quantify the independent impact of sudden shifts between drought and flood events.

[0145] like Figure 2 As shown, calculations using this invention indicate that the water storage indicators of the three reservoirs generally exhibited the following characteristics during the identified drought-to-flood and flood-to-drought events: >0、 <0. The above results indicate that the reservoir's water storage status deviated significantly from historical climatological conditions during the event year, with a decrease in the proportion of the normal operating range. Additionally, the inflow-side indicators ( , The different response amplitudes observed in different reservoirs and different types of events indicate that reservoirs have varying sensitivities to sudden shifts between meteorological drought and flood events.

[0146] In summary, the method of this invention can output and quantify the operational deviation characteristics and stability changes of different reservoirs during the event period. The results are repeatable and operable, and can be used for reservoir operation impact analysis and management auxiliary decision-making.

[0147] For comparison of technical effects, existing technologies can be used as a reference. Drought-Flood Abrupt Alternation (DFAA) refers to a complex extreme event in which drought and flood alternate rapidly, often manifesting as "drought turning into flood" or "flood turning into drought." These events change rapidly, significantly disturbing the inflow process and water storage regulation of reservoirs, increasing the risks to flood control, water supply, and power generation operations.

[0148] Existing research largely focuses on the identification and assessment of single drought or flood events, or on conducting statistical analyses at the watershed scale. However, it lacks a systematic approach for reservoir-scale analysis to quantitatively identify DFAA events and their independent impacts on reservoir operation. Furthermore, traditional methods often directly compare the event year with multi-year averages, which is easily affected by interannual climate fluctuations, leading to unstable results and poor comparability.

[0149] To solve the above problems, it is necessary to establish a system that can:

[0150] (1) Automatically identify DFAA events within the reservoir's catchment area;

[0151] (2) Select a control year without events under similar climate background conditions;

[0152] (3) Use multidimensional indicators to characterize the reservoir response features and quantify the rapid change effect;

[0153] (4) An evaluation system that enables batch and automated processing.

[0154] This invention proposes a method for identifying and assessing rapid drought-flood transition events in reservoirs based on paired-year analysis. By organically combining event identification, paired comparison, and response index calculation, it can accurately assess the operational anomalies and stability changes of reservoirs during rapid drought-flood transitions while eliminating interannual climate differences.

[0155] Specifically, such as Figure 3 As shown, using daily / monthly hydrological and meteorological data from the reservoir's catchment area as input, this invention first automatically identifies drought-to-flood and flood-to-drought events based on the Standardized Precipitation Index (SPI) and runs theory. Secondly, it constructs a dual-constraint paired-year screening mechanism (annual total precipitation deviation ≤5%, monthly precipitation sequence correlation coefficient ≥0.7) to select event-free control years under similar climatic backgrounds. Then, it calculates response indicators such as the Mean Absolute Standardized Anomaly (MAZA) and the Percentage of Normal Days (PDNB), and uses the difference between the event year and the paired year to characterize the independent effect of abrupt transition events. Finally, it outputs reservoir-level and regional-level impact classifications and reports. This invention, through an integrated process of "event identification—paired comparison—response quantification—risk output," effectively eliminates interannual climate fluctuations, accurately characterizes the deviation magnitude, stability, and recovery capacity of reservoirs during DFAA processes, and has advantages such as high automation, batch calculation capability, and strong engineering applicability.

[0156] In this embodiment of the invention, a method for identifying and evaluating rapid drought-flood transition events in reservoirs based on paired-year analysis is provided. The method acquires basic attribute data and meteorological and hydrological data of multiple target reservoirs in the study area, preprocesses the basic attribute data and meteorological and hydrological data of each target reservoir, and outputs target basic attribute data and target meteorological and hydrological data for each target reservoir. Using gamma distribution, rapid drought-flood transition events are identified based on the daily precipitation sequence dataset in the target meteorological and hydrological data of each target reservoir, outputting rapid drought-flood transition events for multiple target time periods corresponding to each target reservoir. Based on predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoirs, paired years are selected based on the rapid drought-flood transition events for multiple target time periods corresponding to each target reservoir and the daily precipitation sequence dataset, outputting the final paired year corresponding to each target reservoir. Finally, based on the final paired year corresponding to each target reservoir, the rapid drought-flood transition events for multiple target time periods, the daily inflow sequence dataset and daily water storage sequence dataset in the target meteorological and hydrological data, and the target basic attribute data... This invention uses data to classify the impact of sudden drought-flood events on each target reservoir, outputting the classification results. Based on these classifications, an integrated impact assessment report is generated. The invention employs a "paired year selection" step, selecting the final paired years based on the precipitation characteristics corresponding to each reservoir's sudden drought-flood event, ensuring consistency between the paired years and the event year in terms of climate background. Furthermore, the impact classification process incorporates not only the comparison data of the paired years but also daily inflow and storage sequences from the target meteorological and hydrological data, as well as the target's basic attribute data. This achieves a closed-loop data process from event identification to impact assessment, ensuring the classification results accurately reflect the actual impact of sudden drought-flood events on reservoir operation. Finally, by integrating the classification results of multiple reservoirs to generate an integrated assessment report, the accuracy of individual reservoir assessments is guaranteed, while comprehensive regional analysis is achieved, thereby improving the overall assessment effect.

[0157] Please see Figure 4 , Figure 4 This is a structural block diagram of a reservoir drought-flood transition event identification and assessment system based on paired-year analysis, provided in Embodiment 2 of the present invention.

[0158] This invention provides a reservoir drought-flood transition event identification and assessment system based on paired-year analysis, comprising:

[0159] The acquisition module 401 is used to acquire basic attribute data and meteorological and hydrological data of multiple target reservoirs in the study area, and to preprocess the basic attribute data and meteorological and hydrological data of each target reservoir, and output the target basic attribute data and target meteorological and hydrological data of each target reservoir.

[0160] The identification module 402 is used to identify drought-flood transition events based on the daily precipitation sequence dataset in the target meteorological and hydrological data of each target reservoir using gamma distribution, and output drought-flood transition events for multiple target time periods corresponding to each target reservoir.

[0161] The selection module 403 is used to select paired years based on predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoir, according to the drought-flood transition events and daily precipitation sequence datasets for multiple target time periods corresponding to each target reservoir, and output the final paired years corresponding to each target reservoir.

[0162] The grading module 404 is used to grade the impact degree of drought and flood events on each target reservoir based on the final paired year corresponding to each target reservoir, the drought and flood abrupt transition events of multiple target time periods, the daily inflow sequence dataset and daily water storage sequence dataset in the target meteorological and hydrological data, and the target basic attribute data, and output the grading results of the impact degree of drought and flood abrupt transition events on each target reservoir.

[0163] The assessment module 405 is used to assess the impact of drought and flood transition events based on the classification results of the impact degree of each target reservoir, and generate an integrated drought and flood transition event impact assessment report.

[0164] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0165] This invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program; when the computer program is executed by the processor, the processor performs the steps of the reservoir drought-flood transition method based on paired-year analysis as described in the above embodiments.

[0166] This invention also provides a computer-readable storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements the steps of the reservoir drought-flood transition method based on paired-year analysis as described in the above embodiments.

[0167] This invention also provides a computer program product, including a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the reservoir drought-flood transition method based on paired-year analysis as described in the above embodiments.

[0168] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0169] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0170] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0171] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying and assessing rapid drought-flood transition events in reservoirs based on paired-year analysis, characterized in that, include: Acquire basic attribute data and meteorological and hydrological data of multiple target reservoirs in the study area, preprocess the basic attribute data and meteorological and hydrological data of each target reservoir, and output the target basic attribute data and target meteorological and hydrological data of each target reservoir. The gamma distribution is used to identify abrupt drought-flood transition events based on the daily precipitation sequence dataset in the target meteorological and hydrological data of each target reservoir. The results output abrupt drought-flood transition events for multiple target time periods corresponding to each target reservoir, including: The gamma distribution is used to analyze the daily precipitation sequence datasets in the target meteorological and hydrological data of each target reservoir to obtain the standardized precipitation index for multiple dates corresponding to each target reservoir. Based on the standardized precipitation index for multiple dates corresponding to each of the target reservoirs, determine the drought and flood status labels and drought and flood start and end dates for multiple time periods corresponding to each of the target reservoirs; Based on the drought and flood status labels and drought and flood start and end dates of multiple time periods corresponding to each of the target reservoirs, the drought and flood abrupt change events are identified for each time period corresponding to each of the target reservoirs, and the drought and flood abrupt change events for multiple target time periods corresponding to each of the target reservoirs are output. Based on predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoirs, pairing years are selected according to the drought-flood transition events of multiple target time periods corresponding to each target reservoir and the daily precipitation sequence dataset, and the final paired years corresponding to each target reservoir are output. Based on the final paired year corresponding to each target reservoir, abrupt drought-flood transition events for multiple target time periods, daily inflow sequence datasets and daily water storage sequence datasets from the target meteorological and hydrological data, and target basic attribute data, the impact degree of abrupt drought-flood transition events on each target reservoir is classified, and the classification results of the impact degree of abrupt drought-flood transition events on each target reservoir are output, including: Based on the turning point of the drought-flood transition events for multiple target time periods corresponding to each target reservoir, a corresponding virtual turning point is set in the corresponding final paired year. Starting from the virtual turning point corresponding to each of the target reservoirs and extending from there, a unified index calculation and observation window is obtained for each of the target reservoirs. Based on the daily inflow sequence dataset and daily water storage sequence dataset corresponding to each of the target reservoirs, calculate the daily standardized anomaly sequence corresponding to each of the target reservoirs; Based on the daily inflow sequence dataset and daily storage sequence dataset corresponding to each of the target reservoirs, the inflow quantile and storage quantile corresponding to each of the target reservoirs are statistically obtained. Based on the daily standardized anomaly sequence, inflow quantile, storage quantile, and the defined unified index corresponding to each target reservoir, calculate the total number of days in the observation window, and calculate the average absolute standardized anomaly and the percentage of normal interval days corresponding to each target reservoir respectively. Based on the average absolute standardized anomaly and the percentage of normal interval days corresponding to each of the target reservoirs, the difference index corresponding to each of the target reservoirs is calculated. Based on the difference index corresponding to each target reservoir, the event type of the drought-flood transition event in each target time period, and the basic attribute data of the target, the impact degree of the drought-flood transition event of the corresponding target reservoir is classified, and the classification result of the impact degree of the drought-flood transition event of each target reservoir is output. Based on the classification results of the impact degree of the drought-flood transition events for each of the target reservoirs, an impact assessment of the drought-flood transition events is conducted, and an integrated impact assessment report of the drought-flood transition events is generated.

2. The method for identifying and assessing reservoir drought-flood transition events based on paired-year analysis according to claim 1, characterized in that, The preprocessing of the basic attribute data and meteorological and hydrological data of each of the target reservoirs, and the output of the target basic attribute data and target meteorological and hydrological data of each of the target reservoirs, includes: The basic attribute data and meteorological and hydrological data of each target reservoir are standardized in format, and the standardized basic attribute data and meteorological and hydrological data of each target reservoir are output. Missing values ​​are filled in for the basic attribute data and meteorological and hydrological data of each target reservoir after the format is unified, and the missing value-filled basic attribute data and meteorological and hydrological data of each target reservoir are output. Outlier removal is performed on the basic attribute data and meteorological and hydrological data of each target reservoir after missing value completion, and the target basic attribute data and target meteorological and hydrological data of each target reservoir are output.

3. The method for identifying and assessing reservoir drought-flood transition events based on paired-year analysis according to claim 1, characterized in that, The process involves selecting paired years based on predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoirs, according to the drought-flood transition events of multiple target time periods corresponding to each target reservoir and the daily precipitation sequence dataset. The final paired years corresponding to each target reservoir are then output, including: Based on the event years corresponding to the drought-flood transition events of multiple target time periods for each target reservoir, the precipitation data corresponding to the event years are extracted from the daily precipitation sequence dataset corresponding to each target reservoir, and the total annual precipitation and monthly precipitation sequence associated with each event year corresponding to each target reservoir are calculated. Based on the predefined meteorological similarity conditions, multiple candidate pairing years are selected from multiple historical non-event years corresponding to the target reservoir; Based on the total annual precipitation and monthly precipitation sequence associated with each event year corresponding to each of the target reservoirs, the final pairing year corresponding to each of the target reservoirs is selected from among the multiple candidate pairing years.

4. The method for identifying and assessing reservoir drought-flood transition events based on paired-year analysis according to claim 1, characterized in that, The impact assessment of the sudden drought-flood transition event is conducted based on the classification results of the impact degree of each target reservoir, and an integrated impact assessment report of the sudden drought-flood transition event is generated, including: Based on the classification results of the impact degree of sudden drought-flood transition events of each target reservoir, the distribution characteristics and risk patterns within the target area are statistically analyzed. Based on the distribution characteristics and risk patterns data within the target area, a risk distribution map of sudden shifts between drought and flood events in the target area is constructed. Based on the grading results of the impact degree of the sudden drought-flood transition events of each target reservoir, the distribution characteristics and risk pattern data of the target area, and the risk distribution map of the sudden drought-flood transition events, an integrated impact assessment report of the sudden drought-flood transition events is generated.

5. A reservoir drought-flood transition event identification and assessment system based on paired-year analysis, applied to the reservoir drought-flood transition event identification and assessment method based on paired-year analysis as described in claim 1, characterized in that, include: The acquisition module is used to acquire basic attribute data and meteorological and hydrological data of multiple target reservoirs in the study area, and to preprocess the basic attribute data and meteorological and hydrological data of each target reservoir, and output the target basic attribute data and target meteorological and hydrological data of each target reservoir. The identification module is used to identify drought-flood transition events based on the daily precipitation sequence dataset in the target meteorological and hydrological data of each target reservoir using gamma distribution, and output drought-flood transition events for multiple target time periods corresponding to each target reservoir. The selection module is used to select paired years based on predefined meteorological similarity conditions and multiple historical non-event years corresponding to the target reservoirs, according to the drought-flood transition events of multiple target time periods corresponding to each target reservoir and the daily precipitation sequence dataset, and output the final paired years corresponding to each target reservoir. The grading module is used to grade the impact degree of drought-flood transition events on each of the target reservoirs based on the final paired year corresponding to each target reservoir, the drought-flood transition events of multiple target time periods, the daily inflow sequence dataset and daily water storage sequence dataset in the target meteorological and hydrological data, and the target basic attribute data, and output the grading results of the impact degree of drought-flood transition events on each of the target reservoirs. The assessment module is used to assess the impact of sudden drought-flood events based on the classification results of the impact degree of sudden drought-flood events on each of the target reservoirs, and generate an integrated impact assessment report on sudden drought-flood events.

6. An electronic device, characterized in that, The system includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method for identifying and assessing reservoir drought-flood transition events based on paired-year analysis as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the method for identifying and assessing reservoir drought-flood transition events based on paired-year analysis as described in any one of claims 1-4.

8. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the steps of the method for identifying and assessing reservoir drought-flood transition events based on paired-year analysis as described in any one of claims 1-4.

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