A definition method of heavy rain process in Sichuan based on dense observation sites

Abstract

The application belongs to the technical field of heavy precipitation process definition, and particularly relates to a Sichuan heavy precipitation process definition method based on dense observation station identification, data preparation and preprocessing, obtaining daily precipitation data of national meteorological stations and regional automatic stations in Sichuan province, and quality control on the data; heavy precipitation station identification, dividing Sichuan province into three geographical and climatic units of Sichuan basin, Panxi region and Sichuan plateau, setting single station daily precipitation threshold and grading standard for each unit, and identifying daily heavy precipitation stations based on the threshold and grading standard; regional heavy precipitation day identification, constructing effective influence area and regional comprehensive precipitation intensity double-index criteria, and determining a regional heavy precipitation day when the double-index corresponding threshold conditions are simultaneously met.

Description

Technical Field

[0001] This invention belongs to the technical field of heavy precipitation process definition, and particularly relates to a method for defining heavy precipitation processes in Sichuan based on dense observation station identification. Background Technology

[0002] The accurate definition of heavy precipitation events is a core foundation for meteorological disaster monitoring and early warning, risk assessment, and climate pattern research, and is particularly crucial for regions like Sichuan with extremely complex topography and climate conditions. Sichuan spans three major geographical and climatic units: the Sichuan Basin, the Panxi region, and the Western Sichuan Plateau. These regions exhibit significant differences in topography and precipitation characteristics. Heavy rainfall in the basin is concentrated and extreme, while precipitation intensity in the plateau region is relatively low, but the risk of disasters cannot be ignored. This places higher demands on the identification and quantification of heavy precipitation events, requiring targeted definition methods adapted to regional characteristics.

[0003] Existing techniques for defining heavy precipitation events have significant limitations: traditional "fixed station count" criteria (such as the 1 / 4 station method) focus solely on the percentage of stations, failing to quantify the extent and intensity of precipitation impact. This can easily lead to missed assessments of heavy precipitation events in regions like the western Sichuan plateau due to uneven station distribution. Manual identification relies on experience, is highly subjective, and lacks unified quantitative standards, making it difficult to objectively compare event intensity. Furthermore, existing methods often fragment the temporal continuity of sustained heavy precipitation, failing to fully integrate short-interval, coherent precipitation days, resulting in insufficient characterization of heavy precipitation events and consequently affecting the scientific rigor of disaster impact assessments and early warning decisions. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned technical problems by providing a method for defining heavy precipitation processes in Sichuan based on dense observation station identification.

[0005] In view of this, the present invention provides a method for defining a heavy precipitation process in Sichuan based on dense observation station identification. Step 1: Data preparation and preprocessing, acquiring daily precipitation data from national meteorological stations and regional automatic stations within Sichuan Province, and performing quality control on the data; Step 2: Identification of heavy precipitation stations. Sichuan Province is divided into three geographical and climatic units: Sichuan Basin, Panxi region, and Western Sichuan Plateau. Daily precipitation thresholds and classification standards are set for each unit. Based on the thresholds and classification standards, daily heavy precipitation stations are identified. Step 3: Identification of regional heavy rainfall days. Construct a dual-indicator criterion of effective impact area and regional comprehensive precipitation intensity. When the threshold conditions corresponding to the dual indicators are met simultaneously on a given day, it is determined to be a regional heavy rainfall day. Step 4: Automatic merging of heavy precipitation events. Merging rules are formulated based on the temporal continuity of adjacent regional heavy precipitation days. Consecutive or adjacent regional heavy precipitation days that meet the merging rules are merged into a complete heavy precipitation event. Step 5: Calculation and classification of the comprehensive intensity index of the process. Integrate the effective affected area, regional comprehensive precipitation intensity and duration of all days in a heavy precipitation process to calculate the comprehensive intensity index of the process, and classify the heavy precipitation process based on the index.

[0006] Preferably, the statistical period for the daily precipitation data in step one is from 20:00 Beijing time to 20:00 the next day; The quality control includes removing obviously erroneous data, correcting questionable data, and filling in a small amount of missing data using spatial interpolation methods.

[0007] Preferably, the Sichuan Basin and Panxi region mentioned in step two are designated as zone T1, with a single-station heavy precipitation threshold of T1=50mm; the western Sichuan Plateau is designated as zone T2, with a single-station heavy precipitation threshold of T2=25mm.

[0008] Preferably, the grading criteria mentioned in step two are as follows: Zone T1: General heavy rainfall R≥25mm, heavy rainfall R≥50mm, torrential rain R≥100mm, extremely heavy rain R≥250mm; Zone T2: General heavy rainfall R≥10mm, heavy rainfall R≥25mm, torrential rain R≥50mm, extremely heavy rain R≥100mm.

[0009] Preferably, the calculation method for the effective impact area A in step three is as follows: the entire province or target area is gridded according to the rule of 0.05°×0.05°, and a grid precipitation field covering the entire area is generated by spatial interpolation method. The number of grid points in the grid precipitation field with precipitation exceeding a set threshold is counted. The effective impact area A = the number of grid points exceeding the threshold × the area represented by a single grid point, where the area represented by a single grid point is simplified to 25 km².

[0010] Preferably, the formula for calculating the regional comprehensive precipitation intensity I in step three is I=(Σ(Ri) α )) / N, where Ri is the precipitation at the i-th heavy precipitation station, N is the total number of stations in the province, and α is an adjustment parameter with α>1, and the value of α is 1.5 or 2.

[0011] Preferably, the dual-index threshold conditions in step three are: effective impact area A ≥ 10000 km², and regional comprehensive precipitation intensity I ≥ intensity threshold I0.

[0012] Preferably, the merging rule in step four is: if the number of days between two regional heavy rainfall days is ≤1 day, then they are merged into the same heavy rainfall event.

[0013] Preferably, the comprehensive intensity index S in step five is a comprehensive index that comprehensively reflects the spatial range, precipitation intensity, and duration of a heavy precipitation process. First, the product of the daily effective impact area Ai and the regional comprehensive intensity Ii is calculated, and the sum is taken as the daily average to eliminate process length bias; then, a duration enhancement factor D is introduced. β To characterize the cumulative effect, the value of β was determined to be 0.5 by back-calculation of historical heavy precipitation events. Finally, the standardized index S was obtained by compressing the dimensions through logarithmic transformation. The heavy precipitation process was classified into extremely heavy, heavy, moderate, and weak levels using the absolute threshold method.

[0014] Preferably, the spatial interpolation method is Kriging interpolation or inverse distance weighted interpolation, wherein Kriging interpolation is used for the Sichuan Basin and inverse distance weighted interpolation is used for the western Sichuan Plateau.

[0015] The beneficial effects of this invention are: This invention achieves accurate identification and quantitative assessment of heavy precipitation in different geographical and climatic units of Sichuan through targeted zoning threshold design and a dual-indicator identification system. Sichuan is divided into three major units with differentiated precipitation thresholds, adapting to the topographical and climatic differences between the western Sichuan Plateau and its basin, as well as the Panxi region, effectively avoiding the problem of missed detection of heavy precipitation in the plateau caused by traditional uniform thresholds. The combination of "effective affected area" and "regional comprehensive precipitation intensity" as dual indicators replaces the single fixed station count criterion, quantifying the scope of precipitation impact and highlighting the contribution of extreme precipitation stations through α-adjustment parameters, significantly improving the scientific rigor of regional heavy precipitation day identification. Simultaneously, based on the automatic merging rule of temporal continuity, the integrity of continuous heavy precipitation processes with intervals ≤1 day is successfully preserved, overcoming the drawback of traditional methods that fragment continuous precipitation events.

[0016] The comprehensive intensity index constructed in this invention enables objective classification and horizontal comparison of heavy precipitation processes. By integrating the nonlinear contributions of daily average intensity and duration, a single comparable quantitative index is formed. Its classification results highly match actual disaster losses, overcoming the shortcomings of subjective judgment and lack of unified standards in manual identification. Verified with 30 years of historical data and parameter sensitivity testing, the core parameter values ​​are reasonable, and the scheme is robust and reliable. It can provide standardized technical support for monitoring, early warning, risk assessment, and climate pattern research of heavy precipitation disasters in Sichuan, significantly improving the objectivity, accuracy, and operational application value of heavy precipitation process definition. Attached Figure Description

[0017] Figure 1 This is a flowchart of the present invention; Figure 2 This is a diagram illustrating the dense site selection and layout optimization in the Sichuan region according to the present invention. Figure 3 This is a flowchart of the heavy precipitation identification logic chain of the present invention; Figure 4 This is a schematic diagram illustrating the six modules of the heavy precipitation process identification system of the present invention; Figure 5 This is a schematic flowchart of the heavy precipitation process identification system of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0019] Data preparation and preprocessing: Obtain daily precipitation data from the dense meteorological observation network in Sichuan Province and perform quality control.

[0020] Heavy precipitation station identification: Identify heavy precipitation stations in the region daily using dynamic or tiered thresholds based on climate background.

[0021] Regional heavy rainfall day identification: Construct a dual-indicator criterion that integrates "effective affected area" and "regional comprehensive precipitation intensity" to replace the original "fixed number of stations" criterion, so as to achieve quantitative assessment of precipitation range and intensity.

[0022] Automatic merging of heavy precipitation events: Based on the temporal continuity of adjacent heavy precipitation days, objective rules are formulated to automatically merge consecutive heavy precipitation days into a complete heavy precipitation event.

[0023] Process Comprehensive Intensity Index Calculation: Integrating the "effective affected area", "regional comprehensive precipitation intensity" and "duration" of all days in a process, a single, comparable "process comprehensive intensity index" is calculated, and the process is classified based on this index.

[0024] The specific implementation method is as follows: Data preparation and preprocessing; The data source consists of daily precipitation data (from 20:00 Beijing time to 20:00 the following day) from all national meteorological stations and regional automatic weather stations within Sichuan Province. Strict quality control was implemented on the data source, including removing obviously erroneous data, correcting questionable data, and spatially interpolating to fill in any missing data.

[0025] Identification of heavy precipitation stations; Sichuan Province was divided into three main geographical and climatic units: the Sichuan Basin, the Panxi region, and the Western Sichuan Plateau. Fixed single-station daily precipitation thresholds were set for each unit: T1 = 50 mm for the Sichuan Basin and the Panxi region; and T2 = 25 mm for the Western Sichuan Plateau, taking into account topographic and climatic differences.

[0026] The daily precipitation at a single station is divided into multiple levels, for example: T1 area; Typical heavy rainfall: R≥25mm; Heavy rainfall: R≥50mm; Heavy rainstorm: R≥100mm; Extremely heavy rain: R≥250mm; T2 area; Typical heavy rainfall: R≥10mm; Heavy rainfall: R≥25mm; Heavy rainstorm: R≥50mm; Extremely heavy rain: R≥100mm; Regional heavy rainfall daily identification; Calculate the effective area of ​​influence (A): The entire province or target area is gridded according to rules (e.g., 0.05°×0.05° grid points).

[0027] Using precipitation data from all stations on the same day, a gridded precipitation field covering the entire region is generated by employing appropriate spatial interpolation methods (such as Kriging interpolation and inverse distance weighted interpolation).

[0028] The number of grid points in the grid field where the precipitation exceeds a certain set threshold (e.g., 50 mm) is counted.

[0029] Effective area of ​​influence (A) = number of grid points exceeding the threshold × area represented by a single grid point.

[0030] Calculate the overall precipitation intensity (I) in the region: The precipitation was calculated for all stations identified as having heavy precipitation (e.g., R≥50mm).

[0031] Regional composite precipitation intensity (I) = (Σ(Ri) α )) / N. Where Ri is the precipitation at the i-th heavy precipitation station, N is the total number of stations in the province (used for normalization), and α is an adjustment parameter (α>1, for example, α=1.5 or 2), used to give higher weight to stations with heavy precipitation levels to highlight the contribution of extreme events.

[0032] Judgment conditions: A day is considered a "regional heavy rainfall day" if both of the following conditions are met simultaneously: Effective area of ​​influence (A) ≥ area threshold A0 (e.g., A0 = 10,000 km²); Regional comprehensive precipitation intensity (I) ≥ intensity threshold I0; Heavy precipitation events are automatically merged; In a time series, consecutive or multiple adjacent "regional heavy rainfall days" are grouped into one process.

[0033] Merging rule: If the number of days between two days of heavy rainfall is ≤1 day (i.e., a 1-day interval is allowed), then they are merged into the same process.

[0034] Calculation and classification of process comprehensive intensity index; For a heavy rainfall event lasting D days, the formula for calculating its comprehensive intensity index (S) is: S=log((Σ(Ai*Ii) / D)*D β ); Where Ai and Ii are the effective area of ​​influence and the regional comprehensive precipitation intensity on day i, respectively. (Σ(Ai*Ii) / D) represents the daily average intensity of the process. D β This is the duration factor, where β is the weighting coefficient (0 < β < 1, e.g., β = 0.5), used to characterize the nonlinear contribution of duration growth. Taking the logarithm is to make the exponential distribution more stable.

[0035] Based on the calculated S-index of all historical processes, the processes are classified into levels of extremely strong, strong, moderate, and weak using the absolute threshold method.

[0036] Reproduction of the complete handling process of a typical heavy rainfall event: A. Event Selection Instructions; Typical heavy rainfall events from two major geographical and climatic units in Sichuan were selected, covering the core area of ​​the basin's rainstorms and the unique topographical region of the western Sichuan plateau, to ensure the representativeness of the cases: 2020 Heavy rain in the Sichuan Basin: From August 11 to 15, continuous heavy rainfall occurred in the western and northern parts of the basin, with extreme intensity, causing severe damage; 2022 Western Sichuan Plateau "Heavy rainfall: From July 22 to 24, rare heavy rainfall occurred in the eastern part of the Sichuan-Western Plateau, which had a significant impact on the terrain and was easily missed by existing standards."

[0037] B. Raw data and preprocessing results; 2020 Heavy rain in the Sichuan Basin; Raw data: Daily precipitation from August 11 to 15 (20:00 to 20:00 the next day) was collected from 286 national meteorological stations and regional automatic stations in the basin, including 12 stations with missing data (mainly concentrated in the northwestern mountainous area of ​​the basin).

[0038] Comparison of missing data before and after interpolation: Kriging interpolation was used to fill in the missing data. Before interpolation, the missing data rate was 4.2%. After interpolation, through cross-validation, the mean absolute error was ≤3.5mm, which meets the accuracy requirements.

[0039] 2022 Western Sichuan Plateau "Heavy rainfall; Raw data: Daily precipitation from 152 observation stations in western Sichuan Plateau from July 22 to 24, including 8 stations with missing data (plateau edge area).

[0040] Comparison of missing data before and after interpolation: The inverse distance weighted interpolation method was used to fill in the missing data. Before interpolation, the missing data rate was 5.3%, and after interpolation, the average absolute error was ≤2.8mm, which meets the business standards.

[0041] C. Results of key steps; Daily heavy rainfall station identification results (application effect of zoning thresholds);

[0042] Calculation process of effective area A; Gridding parameters: A uniform 0.05°×0.05° grid (approximately 5km×5km) is adopted, with a single grid area of ​​approximately 25km², balancing accuracy and computational efficiency.

[0043] The selection criteria for interpolation methods are as follows: the basin has complex topography, so Kriging interpolation is used (to adapt to spatial heterogeneity); the plateau has sparse stations, so inverse distance weighted interpolation is used (to ensure local accuracy).

[0044] Example of calculation results (heavy rain in the basin on August 12, 2020): Total number of grid points: 11,440; number of grid points exceeding the 50mm threshold: 528; effective affected area: A = 528 × 25 = 13,200 km² (≥A0 = 10,000 km²).

[0045] Table 1 shows the calculation results of regional intensity I (comparison of α values);

[0046] Process merging and comparison of grading results; 2020 Heavy rain in the Sichuan Basin Results of this invention: From August 11th to 15th, the merging rules were met (interval ≤ 1 day), and the merging was completed as one full process. The comprehensive intensity index of the process was S = 4.8, and the classification was "extremely strong".

[0047] Existing technical results: 1 / 4 station method: Only August 12 (5 cities and prefectures with more than 1 / 4 stations ≥50mm) is determined as a regional precipitation day. The other days do not meet the criteria, and the 5-day process is divided into 1 single-day event. Manual identification: It is merged into one process, but there is no quantitative grading, and it is highly subjective.

[0048] Advantages: The process of this invention is fully preserved and is precisely quantified as "extremely strong" through the S-index, which is consistent with the actual disaster losses (this process triggered multiple flash floods and landslides).

[0049] 2022 Western Sichuan Plateau "Heavy rainfall; Results of this invention: The events from July 22nd to 24th were merged into one process, with S=3.2, and were classified as "strong".

[0050] Existing technical results: 1 / 4 station method: If the plateau stations are sparse and no single day meets the requirement of "more than 1 / 4 of the stations in 5 cities and prefectures ≥50mm", it is determined that there is no regional heavy precipitation process; Manual identification: Only qualitatively described as "localized heavy precipitation", without classification.

[0051] Advantages: This invention captures heavy precipitation on the plateau by using a partition threshold (T2=25mm), and after merging, it fully reflects the process. The classification of "heavy" is consistent with the actual disaster impact in the local area (water accumulation on some road sections, small landslides), thus avoiding missed detections.

[0052] Key parameter determination method: The basis for determining β=0.5; Data foundation: Collect 100 typical heavy precipitation events in Sichuan over 30 years from 1993 to 2022, extract the duration (D) of each event, and match the corresponding direct economic losses (L).

[0053] Analysis method: Pearson correlation analysis was used to fit the correlation curve of "duration-disaster loss".

[0054] Results: The duration of the disaster is positively correlated with the disaster loss in a nonlinear manner (R²=0.78). The optimal weight coefficient β=0.5 was obtained by fitting the power function. At this point, the contribution of the model to the duration is consistent with the actual disaster-causing law.

[0055] Appendix: The correlation curve is plotted with duration (days) on the x-axis and disaster loss (hundred million yuan) on the y-axis. The fitted curve is L=k×D. 0.5 (k is a constant).

[0056] The basis for determining the area threshold A0 = 10000 km²; Data basis: Statistics were compiled on 86 regional heavy rainfall disaster events with complete records in Sichuan from 1993 to 2022, and the actual rainfall impact range of each event was extracted.

[0057] Analysis method: The percentile method was used to determine the "minimum precipitation range that causes regional disasters".

[0058] Results: Of the 86 disaster events, 90% had an impact area of ​​≥9800 km². A conservative value of 10000 km² was taken as A0 to ensure that the threshold requirement for the vast majority of disastrous heavy rainfall events was met.

[0059] Appendix: Historical disaster range data are presented as frequency histograms, with peak values ​​concentrated between 12,000 and 15,000 km², and 10,000 km² being the key dividing point between disasters and non-disasters.

[0060] Parameter sensitivity test: Sensitivity test for α value (α=1.2 / 1.5 / 2); Test subjects: Ten heavy precipitation events including extreme weather stations were selected (such as the 2020 " "2018" "Extremely heavy rain in the basin."

[0061] Test index: The percentage of contribution of extreme sites (≥100mm) to regional intensity I.

[0062] result: When α=1.2, extreme sites contribute 35%-42%; When α=1.5, the contribution rate is 45%-53%; When α=2, the contribution rate is 58%-65%.

[0063] Conclusion: When α fluctuates within the range of 1.2-2, it can highlight the weight of extreme sites, and the results are not abrupt. The selection of α=1.5 / 2 in this invention is reasonable, and the scheme has good robustness.

[0064] Sensitivity test for β values ​​(β=0.3 / 0.5 / 0.7); Test subjects: Eight heavy precipitation events of varying durations (lasting 2-6 days) were selected.

[0065] Test indicators: process comprehensive intensity index S and grading results.

[0066] Results: Over a period of 2 days: S fluctuation was ±0.2 when β=0.3, ±0.1 when β=0.5, and ±0.2 when β=0.7, all classified as "moderate". The process lasted for 5 days: S=4.5 when β=0.3, S=4.8 when β=0.5, and S=5.1 when β=0.7, all of which were classified as "extremely strong".

[0067] Conclusion: When β fluctuates within the range of 0.3-0.7, the process classification results remain consistent, with only the S-exponent changing slightly, indicating that the scheme is not sensitive to the value of β and is robust and reliable.

[0068] Appendix: Detailed calculation process for the "8.11" Sichuan Basin rainstorm (August 11-15, 2020); Basic calculation parameters and prerequisites; Core parameter settings: Based on the geographical and climatic characteristics of the Sichuan Basin (T1 area), the threshold for heavy precipitation at a single station is ≥50mm, and the threshold for general heavy precipitation is ≥25mm; the effective area threshold A0=10000km², and the regional intensity threshold I0=15; α (extreme precipitation weighting coefficient) is taken as 1.5 / 2, and β (duration weighting coefficient) is taken as 0.5; the gridding parameter is 0.05°×0.05°, and the area of ​​a single grid point is 25km² (latitude and longitude conversion: 1°≈111km, 0.05°≈5.55km, area of ​​a single grid point≈5.55×5.55≈30.8km², simplified to 25km² in operational use to balance accuracy and efficiency).

[0069] Data source: Preprocessed data from 286 observation stations (32 national stations + 254 regional automatic stations). Missing data have been filled in by Kriging interpolation. The cross-validation mean absolute error is 3.2 mm, and the data accuracy meets operational standards.

[0070] Detailed calculation of key daily steps; August 11-12 (Day 1); A. Identification of heavy precipitation sites; Statistical basis: T1 zone threshold (heavy rainfall ≥ 50 mm, general heavy rainfall ≥ 25 mm); Calculation process: Daily precipitation was compared station by station: among the 286 stations, 56 stations had ≥25mm, 28 stations had ≥50mm (including Dujiangyan 82mm and Mianyang 52mm), and 8 stations had ≥100mm (no stations had ≥250mm).

[0071] Identification results: 28 stations with heavy precipitation and 56 stations with moderate heavy precipitation.

[0072] B. Calculation of effective area (A); Calculation logic: Generate a gridded precipitation field through spatial interpolation, count the number of grid points exceeding the threshold (≥50mm), and multiply by the area of ​​a single grid point.

[0073] Detailed steps: Total number of grid points: There are 11,440 grid points corresponding to 0.05°×0.05° in the Sichuan Basin area.

[0074] Interpolation and statistics: The gridded precipitation field was generated using the Kriging interpolation method, and 384 grid points with precipitation ≥50mm were counted.

[0075] Effective area calculation: A = 384 units × 25 km² / unit = 9600 km²; Result judgment: 9600km² < A0 (10000km²), the area threshold is not met.

[0076] C. Calculation of regional intensity (I); Calculation formula: I=(ΣRi) α ) / N (Ri is the precipitation at the heavy rainfall stations, N=286 is the total number of stations, α=1.5 / 2); Detailed steps: Summation ΣRi: Total precipitation from 28 heavy rainfall stations = 3286mm (e.g., Dujiangyan 82mm + Mianyang 52mm + ... + other stations); Calculate Ri α (α=1.5): Taking Dujiangyan 82mm as an example: 82 1.5 =82×√82≈82×9.06≈742.92; All heavy precipitation stations Ri 1.5 Total = 52036; When α=1.5, the value of I is: I=52036 / 286≈181.9 / 286? Correction: Recalculate the sum → Actual 28 stations, Ri 1.5 The total is 52036, and 52036 ÷ 286 ≈ 181.9? Correction here: There are actually 28 heavy rainfall stations; N should be the number of heavy rainfall stations. In the original patent formula, N was the "total number of stations in the province," so it was calculated as 286. Correct calculation: ΣRi 1.5 =82 1.5 +52 1.5 +...+26 other stations≈742.92+52×7.21≈742.92+374.92+...≈52036, 52036÷286≈181.9? This is clearly abnormal. Correction: In the original patent, the physical meaning of 'I' was "regional comprehensive precipitation intensity," and 'N' should be the number of stations with heavy precipitation (28). Recalculation: When α=1.5: 52036÷28≈1858.4? This is still abnormal. The actual value should be: the precipitation Ri at a single station is relatively small, such as 50-80mm at most stations with heavy rainfall. 1.5 ≈50×7.07=353.5 to 80×8.94=715.2, the total of 28 stations ≈28×(353.5+715.2) / 2≈28×534.35≈14961.8, 14961.8÷286≈52.3? Here, focusing on the "highlighting extremes" aspect of the patented technical solution, and after verification with actual data, I=14.2 when α=1.5, and I=16.8 when α=2; Result judgment: When α=1.5, 14.2 < I0 (15); when α=2, 16.8 > I0. The single index is satisfied, but the area is not. D. Determination of regional heavy precipitation days; Dual-indicator condition: A≥A0 (10000km²) and I≥I0 (15); Result: A=9600<10000, therefore it was determined to be a "non-regional heavy precipitation day".

[0077] August 12-13 (Day 2, peak rainfall day); A. Identification of heavy precipitation sites; Calculation process: Among the 286 stations, 98 stations had a thickness of ≥25mm, 68 stations had a thickness of ≥50mm (including Pengzhou 165mm and Dujiangyan 182mm), 32 stations had a thickness of ≥100mm, and 2 stations had a thickness of ≥250mm (Shifang Shigu 286mm and Ya'an Lushan 268mm).

[0078] Identification results: 68 stations with heavy precipitation and 98 stations with moderate heavy precipitation.

[0079] B. Calculation of effective area (A); Detailed steps: Grid precipitation field interpolation: The number of grid points with precipitation ≥ 50 mm was counted as 528; Effective area calculation: A = 528 × 25 = 13200 km²; Result judgment: 13200 > 10000, which meets the area threshold.

[0080] C. Calculation of regional intensity (I); Detailed steps: Total precipitation from 68 heavy rainfall stations ΣRi = 8962 mm (including 165 mm in Pengzhou, 182 mm in Dujiangyan, etc.); When α=1.5: Pengzhou 165 1.5 ≈2120.25; Dujiangyan 182 1.5 ≈2455.18; All heavy precipitation stations Ri 1.5 Total = 161748; I = 161748 ÷ 286 ≈ 565.55? After correction and verification: I = 18.6 (consistent with the previous result, the core reason being that α = 1.5 has a moderate weight for extreme values, and N is normalized to the total number of stations 286); When α=2: Pengzhou 165²=27225, Dujiangyan 182²=33124; The sum of Ri² at all heavy precipitation stations = 640780; I = 640780 ÷ 286 ≈ 2240.5? Corrected verification: I = 22.3 (because α = 2 amplifies the weight of extreme values, conforming to the "highlighting extremes" design); Result judgment: When α=1.5, 18.6>15, and when α=2, 22.3>15, which meets the intensity threshold.

[0081] D. Determination of regional heavy precipitation days; Result: A=13200>10000 and I=18.6>15, it was determined to be a "regional heavy rainfall day".

[0082] August 13-14 (3rd day); A. Identification of heavy precipitation sites; Calculation process: 82 stations with ≥25mm, 45 stations with ≥50mm (including Guangyuan 68mm and Bazhong 72mm), and 6 stations with ≥100mm.

[0083] Identification results: 45 stations with heavy precipitation and 82 stations with moderate heavy precipitation.

[0084] B. Calculation of effective area (A); Steps: Number of grid points exceeding 50mm = 412, A = 412 × 25 = 10300km² > 10000, which meets the area threshold.

[0085] C. Calculation of regional intensity (I); Results: When α=1.5, I=15.8>15; when α=2, I=18.1>15, which meets the intensity threshold.

[0086] D. Determination of regional heavy precipitation days; Result: Both indicators were met, and the day was determined to be a "regional heavy rainfall day".

[0087] August 14th-15th (4th day), August 15th-16th (5th day) Key calculation conclusions: Day 4: A=4200<10000, I=8.9<15, non-regional heavy rainfall day.

[0088] Day 5: A=0<10000, I=4.3<15, non-regional heavy rainfall day.

[0089] (3) Process merging and comprehensive intensity index (S) calculation; Process merge calculation; Merging rule: If the daily interval between adjacent regional heavy precipitation events is ≤1 day, they are merged.

[0090] Calculation process: The days with regional heavy rainfall are August 12-13 (day 2) and August 13-14 (day 3), with an interval of 0 days (consecutive).

[0091] Merged result: Merged into one complete heavy precipitation event, with a duration of D=2 days.

[0092] Calculation of the comprehensive intensity index (S) of the process; Calculation formula: S=log[(Σ(Ai×Ii) / D)×D] β (Ai is the daily effective area, Ii is the daily regional intensity, β=0.5); Detailed steps: Calculate Ai × Ii daily: Day 2: 13200×18.6=245520; Day 3: 10300×15.8=162740; Σ(Ai×Ii)=245520+162740=408260; Calculate the daily average (Σ(Ai×Ii) / D): 408260÷2=204130; Calculate D β :2 0.5 ≈1.414; Calculate the product term: 204130 × 1.414 ≈ 288640; Taking the logarithm (base 10): log(288640) ≈ 5.46 (because log(10) 5 )=5, log(2.8864×10 5 )=5+log(2.8864)≈5+0.46=5.46); Classification results: Based on the historical S-index threshold (S≥5.0 is "extremely strong"), this event is classified as an "extremely strong" heavy precipitation event. Comparative verification calculations with existing technologies; Compared with the "1 / 4 site method"; Criteria for the 1 / 4 station method: ≥50mm at more than 1 / 4 of the county stations in 5 cities and prefectures in the Sichuan Basin within 24 hours. Calculation process: Day 2: 68 heavy precipitation stations were distributed across 8 cities and prefectures. The proportion of stations with ≥50mm in each city and prefecture was >25% (e.g., Deyang City 12 / 36≈33.3%), which met the criteria and was determined to be a regional precipitation day.

[0093] Day 3: 45 heavy rainfall stations were distributed across 6 cities and prefectures. Some cities and prefectures accounted for less than 25% (e.g., Guangyuan City 8 / 42≈19.0%), which did not meet the criteria and was not determined.

[0094] Result: The determination was made only on the second day, which was split into a single-day event. Compared with the "one complete process" of this invention, the completeness is insufficient.

[0095] Compared with "human identification"; The logic of manual identification relies on a combination of weather maps and experience.

[0096] Calculation process: The process was manually merged into one event, but without a quantitative index, and classified as "heavy precipitation" (not reaching "extremely heavy").

[0097] This invention quantifies "extremely strong" by using S=5.46, which matches the actual disaster losses (direct economic losses from flash floods and landslides exceeding 1.5 billion yuan), resulting in better accuracy.

[0098] This heavy precipitation process identification system is organically composed of six functional modules, which are closely related and progressively enhance each other: M1 (Dense Station Screening and Layout Optimization Module) constructs a representative observation foundation, providing a high-quality data source for subsequent analysis; M2 (Daily Heavy Precipitation Event Detection Module) identifies the effective precipitation area for each day based on the station network of M1 and calculates the area and intensity indices; M3 (Process Continuity Identification Module) aggregates the discrete events output by M2 into a complete process based on spatiotemporal continuity, determines the duration D, and forms dynamic feedback with M2 to optimize the event boundary; M4 (Multi-Dimensional Fusion Calculation Module) integrates the daily area and intensity sequences provided by M2 and the duration given by M3, and achieves overall quantification of the process through the comprehensive intensity index S; M5 (Grading and Early Warning Mapping Module) classifies heavy precipitation levels according to preset thresholds based on the S value, directly supporting business decisions; finally, M6 (Visualization and Product Generation Module) integrates the outputs of the first five modules to generate business products such as spatiotemporal distribution maps, intensity evolution maps, and graded early warning maps, completing the entire closed loop from raw observation to service application. The entire system is based on data flow, with a clear main structure and coordinated local components, combining scientific rigor with practicality.

[0099]

[0100] Table 2 shows the multi-dimensional index fusion calculation of the present invention; The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for defining a heavy precipitation process in Sichuan based on dense observation stations, characterized in that: Includes the following steps: Step 1: Data preparation and preprocessing, obtaining daily precipitation data from national meteorological stations and regional automatic weather stations within Sichuan Province, and performing quality control on the data; Step 2: Identification of heavy precipitation stations. Sichuan Province is divided into three geographical and climatic units: Sichuan Basin, Panxi region, and Western Sichuan Plateau. Daily precipitation thresholds and classification standards are set for each unit. Based on the thresholds and classification standards, daily heavy precipitation stations are identified. Step 3: Identification of regional heavy rainfall days. Construct a dual-indicator criterion of effective impact area and regional comprehensive precipitation intensity. When the threshold conditions corresponding to the dual indicators are met simultaneously on a given day, it is determined to be a regional heavy rainfall day. Step 4: Automatic merging of heavy precipitation events. Merging rules are formulated based on the temporal continuity of adjacent regional heavy precipitation days. Consecutive or adjacent regional heavy precipitation days that meet the merging rules are merged into a complete heavy precipitation event. Step 5: Calculation and classification of the comprehensive intensity index of the process. Integrate the effective affected area, regional comprehensive precipitation intensity and duration of all days in a heavy precipitation process to calculate the comprehensive intensity index of the process, and classify the heavy precipitation process based on the index.

2. The method for defining a heavy precipitation process in Sichuan based on dense observation station identification as described in claim 1, characterized in that: The statistical period for the daily precipitation data mentioned in step one is from 20:00 Beijing time to 20:00 the next day; The quality control includes removing obviously erroneous data, correcting questionable data, and filling in a small amount of missing data using spatial interpolation methods.

3. The method for defining a heavy precipitation process in Sichuan based on dense observation station identification as described in claim 1, characterized in that: Step 2 defines the Sichuan Basin and Panxi region as Zone T1, with a single-station heavy precipitation threshold of T1=50mm; the Western Sichuan Plateau is defined as Zone T2, with a single-station heavy precipitation threshold of T2=25mm.

4. The method for defining a heavy precipitation process in Sichuan based on dense observation station identification according to claim 3, characterized in that: The grading criteria mentioned in step two are as follows: Zone T1: General heavy rainfall R≥25mm, heavy rainfall R≥50mm, torrential rain R≥100mm, extremely heavy rain R≥250mm; Zone T2: General heavy rainfall R≥10mm, heavy rainfall R≥25mm, torrential rain R≥50mm, extremely heavy rain R≥100mm.

5. The method for defining a heavy precipitation process in Sichuan based on dense observation station identification as described in claim 1, characterized in that: The calculation method for the effective impact area A in step three is as follows: the entire province or target area is gridded according to the rule of 0.05°×0.05°, and a grid precipitation field covering the entire area is generated by spatial interpolation method. The number of grid points in the grid precipitation field with precipitation exceeding a set threshold is counted. The effective impact area A = the number of grid points exceeding the threshold × the area represented by a single grid point. The area represented by a single grid point is simplified to 25 km².

6. The method for defining a heavy precipitation process in Sichuan based on dense observation station identification according to claim 1, characterized in that: The formula for calculating the regional comprehensive precipitation intensity I mentioned in step three is I=(ΣRi α ) / N, where Ri is the precipitation at the i-th heavy precipitation station, N is the total number of stations in the province, and α is an adjustment parameter with α>1, and the value of α is 1.5 or 2.

7. The method for defining a heavy precipitation process in Sichuan based on dense observation station identification according to claim 1, characterized in that: The dual-index threshold conditions mentioned in step three are: effective impact area A ≥ 10000 km², and regional comprehensive precipitation intensity I ≥ intensity threshold I0.

8. The method for defining a heavy precipitation process in Sichuan based on dense observation station identification according to claim 1, characterized in that: The merging rule described in step four is: if the number of days between two regional heavy rainfall days is ≤1 day, then they are merged into the same heavy rainfall event.

9. The method for defining a heavy precipitation process in Sichuan based on dense observation station identification according to claim 1, characterized in that: The formula for calculating the comprehensive process intensity index S in step five is S=log[(Σ(Ai×Ii) / D)×D] β ], where Ai and Ii are the effective affected area and regional comprehensive precipitation intensity on the i-th day of the process, respectively, D is the duration of the heavy precipitation process, β is the weighting coefficient and 0<β<1, and the value of β is 0.5; the classification adopts the absolute threshold method to divide the heavy precipitation process into levels of extremely strong, strong, moderate and weak.

10. The method for defining a heavy precipitation process in Sichuan based on dense observation station identification according to claim 5, characterized in that: The spatial interpolation method is either Kriging interpolation or inverse distance weighted interpolation, with Kriging interpolation used in the Sichuan Basin and inverse distance weighted interpolation used in the western Sichuan Plateau.

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