Spatiotemporal variability of event‐based rainstorm: The perspective of rainfall pattern and concentration

He, Shixu; Wang, Zhaoli; Wang, Dagang; Liao, Weilin; Wu, Xushu; Lai, Chengguang

doi:10.1002/joc.7588

Cited by 19 publications

(6 citation statements)

References 94 publications

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“…To extract the most significant extreme precipitation events in the study area over the last 50 years, the Chinese extreme precipitation algorithm was employed, building upon existing research [45,46]. Five criteria were then re-evaluated and established to select typical extreme precipitation events: (1) The precipitation duration is greater than or equal to 24 h. (2) The total precipitation amount of each precipitation event is greater than or equal to 200 mm.…”

Section: Extraction Of Extreme Precipitation Eventsmentioning

confidence: 99%

Spatiotemporal Variation and Causes of Typical Extreme Precipitation Events in Shandong Province over the Last 50 Years

Liu,

Li,

2024

Remote Sensing

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In this study, based on hourly ERA5 reanalysis data from July to September, from 1971 to 2020, for Shandong Province, we used mathematical statistical analysis, the Mann–Kendall nonparametric statistical test, cluster analysis, and other methods to extract and analyze the spatiotemporal variation characteristics and causes of typical extreme precipitation events. The results indicated the following: (1) The total number and duration of precipitation events show a nonsignificant upward trend, while the average and extreme rainfall intensities show a nonsignificant downward trend. (2) Extreme precipitation events are primarily concentrated in Qingdao, Jinan, Heze, and Binzhou, with fewer events occurring in central Shandong Province. (3) Extreme precipitation events are classified into four types (namely, patterns I, II, III, and IV). Pattern I exhibits two rain peaks, with the primary rain peak occurring after the secondary rain peak. Similarly, pattern II also displays two rain peaks, with equivalent rainfall amounts for both peaks. In contrast, pattern III has multiple, evenly distributed rain peaks. Finally, pattern IV shows a rain peak during the first half of the precipitation event. Pattern I has the highest occurrence probability (46%), while pattern IV has the lowest (7%). (4) The spatial distributions of the different rain patterns are similar, with most being found in the eastern coastal and western regions. (5) Extreme precipitation events result from interactions between large-scale circulation configurations and mesoscale convective systems. The strong blocking situation and significant circulation transport at middle and low latitudes in East Asia, along with strong convergent uplift, abnormally high specific humidity, and high-water-vapor convergence centers, play crucial roles in supporting large-scale circulation systems and triggering mesoscale convective systems.

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Section: Extraction Of Extreme Precipitation Eventsmentioning

confidence: 99%

Spatiotemporal Variation and Causes of Typical Extreme Precipitation Events in Shandong Province over the Last 50 Years

Liu,

Li,

2024

Remote Sensing

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“…The characteristics of rainfall include return periods and rainfall patterns. The return period is related to the rainfall amount, while rainfall pattern refers to the temporal distribution of rainfall intensity during a rainfall event (He et al 2022).…”

Section: Rainfall Event Designmentioning

confidence: 99%

A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model

Liao

Wang

Lai

et al. 2023

Int J Disaster Risk Sci

Self Cite

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Fast and accurate prediction of urban flood is of considerable practical importance to mitigate the effects of frequent flood disasters in advance. To improve urban flood prediction efficiency and accuracy, we proposed a framework for fast mapping of urban flood: a coupled model based on physical mechanisms was first constructed, a rainfall-inundation database was generated, and a hybrid flood mapping model was finally proposed using the multi-objective random forest (MORF) method. The results show that the coupled model had good reliability in modelling urban flood, and 48 rainfall-inundation scenarios were then specified. The proposed hybrid MORF model in the framework also demonstrated good performance in predicting inundated depth under the observed and scenario rainfall events. The spatial inundated depths predicted by the MORF model were close to those of the coupled model, with differences typically less than 0.1 m and an average correlation coefficient reaching 0.951. The MORF model, however, achieved a computational speed of 200 times faster than the coupled model. The overall prediction performance of the MORF model was also better than that of the k-nearest neighbor model. Our research provides a novel approach to rapid urban flood mapping and flood early warning.

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“…To explore the sensitive factors of waterlogging under various rainfall conditions, this study utilized the Chicago hyetograph method to provide rainfall input data. He, Wang [30] pointed out that over 70% of the rainfall in Shenzhen follows a single-peak pattern. Consequently, this study focused solely on designing single-peak rainfall patterns.…”

Section: Rainfall Datamentioning

confidence: 99%

Exploring the Sensitivity Range of Underlying Surface Factors for Waterlogging Control

Liu,

Qi,

Wei

et al. 2023

Water

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To mitigate the incidence of waterlogging to livelihoods and property security, a combination of management measures has been necessary to achieve optimal benefits, reducing the risk caused by waterlogging to the development of the urban ecology. Thus, this study aims to analyze the sensitivity and sensitivity range of management measures under different rainfall conditions, focusing on establishing a foundation for their combined implementation. Based on different rainfall scenarios, the modified Morris method is employed to assess the sensitivity of key factors and subsequently determine their respective sensitivity ranges. The findings reveal that the sensitivity rankings for total overflow volume and maximum pipe flow are as follows: pipe volume per hectare (PV-H), proportion of impervious area (P-Imperv), and slope. Additionally, analyzing the variation pattern of sensitivity with factors highlight the high sensitivity ranges. As for total overflow volume, a very high sensitivity is observed when the P-Imperv ranges from 36.8% to 82.7% (Niujiaolong community) and from 82.7% to 94.5% (Zhuyuan community). Similarly, when PV-H is less than 148 (Niujiaolong community) and 89.6 (Zhuyuan community), the sensitivity of PV-H to total overflow volume is very high. Nevertheless, the slope had a lower influence on the sensitivity in the study areas. These findings provide a complete analysis of the management measures sensitivity, which can be valuable for creating optimal urban waterlogging management systems.

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Spatiotemporal variability of event‐based rainstorm: The perspective of rainfall pattern and concentration

Cited by 19 publications

References 94 publications

Spatiotemporal Variation and Causes of Typical Extreme Precipitation Events in Shandong Province over the Last 50 Years

Spatiotemporal Variation and Causes of Typical Extreme Precipitation Events in Shandong Province over the Last 50 Years

A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model

Exploring the Sensitivity Range of Underlying Surface Factors for Waterlogging Control

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