The leading hydrologists around the world have been working hard to develop some kind of preventive measures to reduce the disastrous consequences of a flash flood in advance. For this purpose, a flash flood early-warning and forecasting system that can accurately and timely forecast an coming flash flood has being the research focus in this field, despite its difficulties and complexities. An ideal to specify those areas that are subject at high risk to flash flood in terms of precipitation intensity in a relatively large region is proposed in this paper. It is accomplished through the design of the High Risk Flash Flood Rainstorm Area (HRFFRA) with a certain return period for a given duration based on the application of the end-to-end Regional L-moments Approach to precipitation frequency analysis. A HRFFRA is defined as the area potentially under hitting by higher intense-precipitation for a given duration with certain return period that may cause a flash flood disaster in the area. An example to develop the HRFFRA has been demonstrated in detail in this paper through the application of the Regional L-Moments Approach to precipitation frequency analysis in Jiangxi Province, South China Mainland. The high risk areas that will be hit by an forthcoming flash flood can be visually showed by the HRFFRA, with its help, hydrologists and governments can substantially reduce the disastrous outcome of a flash flood beforehand.
There are little researchers that combined satellite-based products and L-moments methods in frequency analysis. The objective of this study was to explore and evaluate the satellite precipitation data applied in univariate L-moments method. The Shaoguan City of South China was selected as the study area. Annual maximum precipitation (AMP) respectively from observational rainfall and Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) gridded rainfall was used for the calculation of estimates using regional L-moments analysis (RLMA) method. Then, the quantiles at grids were corrected by mean bias correction (MBC) method with the estimates at sites. The results showed that the number of homogeneous regions as well as the spatial distribution of parameters ($${C}_{v}$$ C v , $${C}_{s}$$ C s , and $${C}_{k}$$ C k ) for two data sets are different. When the return periods are larger than 40 years, the estimates at sites are also larger than those at grids and the difference will increase as return period increases. After being modified, the quantiles from IMERG are more reasonable and combine the spatial distribution characteristic of estimates from two cases, which indicates that the estimation calculated by satellite products with L-moments approach is applicable and more reasonable. This study can also provide reference for solving the issues regarding the density and coverage of rain gauges that affect the accuracy of frequency analysis, especially in remote areas of China or most parts of the word.
Storm separation is a key step when carrying out storm transposition analysis for Probable Maximum Precipitation (PMP) estimation in mountainous areas. The World Meteorological Organization (WMO) has recommended the step-duration-orographic-intensification-factor (SDOIF) method since 2009 as an effective storm separation technique to identify the amounts of precipitation caused by topography from those caused by atmospheric dynamics. The orographic intensification factors (OIFs) are usually developed based on annual maximum rainfall series under such assumption that the mechanism of annual maximum rainfalls is close to that of the PMP-level rainfall. In this paper, an alternative storm separation technique using rainfall quantiles, instead of annual maximum rainfalls, with rare return periods estimated via Regional L-moments Analysis (RLMA) to calculate the OIFs is proposed. Based on Taiwan’s historical 4- and 24-h precipitation data, comparisons of the OIFs obtained from annual maximum rainfalls with that from extreme rainfall quantiles at different return periods, as well as the PMP estimates of Hong Kong from transposing the different corresponding separated nonorographic rainfalls, were conducted. The results show that the OIFs obtained from rainfall quantiles with certain rare probabilities are more stable and reasonable in terms of stability and spatial distribution pattern.
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