ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Study on differences of sliding and fixed-hour heavy precipitation during the flood season in Shandong based on minute-scale precipitation data
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Abstract:
Fixed-hourly precipitation data may underestimate the characteristics of heavy rainfall at the hourly scale. To gain a deeper understanding of the variation patterns of hourly heavy rainfall, this study utilizes minute-level precipitation observations from 123 national meteorological stations in Shandong Province from June to September during the flood seasons of 2004–2024. It compares the differences between the 60-minute sliding window and fixed-hourly statistical methods in identifying heavy rainfall events, aiming to quantitatively assess the impact of different methods on the frequency, intensity, and spatio-temporal distribution of heavy rainfall. The results indicate that the 60-minute sliding window method captures heavy rainfall events more comprehensively than the fixed-hourly method. The average frequency of heavy rainfall across the province was 124.3 times using the sliding method, which is 1.48 times that of the fixed-hourly method (84.3 times), with regional ratios ranging between 1.31 and 1.81. The maximum precipitation captured by the sliding method was on average 10.0 mm higher, exceeding 30.0 mm at some stations. While the spatial distributions of the two methods are generally consistent, the sliding method exhibits greater spatial variability and extremity. It also more clearly reveals increasing trends in both the frequency and intensity of heavy rainfall, with climatic tendency rates of 1.31 times·(10a)?1 and 4.514 mm·(10a)?1, respectively. Moreover, monthly and diurnal variation characteristics are more distinct with the sliding method, particularly during the high-frequency periods from July to August and from afternoon to early morning. The traditional fixed-hourly statistical system underestimates the frequency and extreme intensity of short-duration heavy rainfall. The 60-minute sliding window method provides a more accurate representation of heavy rainfall characteristics, offering important applications for improving the accuracy of storm monitoring and enhancing early warning capabilities for disaster risk.
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