Spatiotemporal Variability of Current and Future Sub‐Daily Rainfall in Japan Using State‐Of‐The‐Art High‐Quality Data Sets

Abstract

Extreme rainfall intensities will likely increase in the future due to global warming-induced atmospheric moistening (Trenberth et al., 2003). Particularly, a rapid increase in rainfall intensities is projected for sub-daily scales (1-24 hr), primarily owing to more intense convective events (Zhao, Abhishek, Kinouchi, Ang, & Zhuang, 2022). These increases, combined with multiple anthropogenic factors, such as urbanization and land-use change, would likely lead to more frequent and intense rapidly developing (flash) flood events. This prospect of extreme events that occur with little warning can consequentially cause substantial damage to nature and people, including food reduction, infrastructure damage, economic decline, and more water-related diseases (

Estimation of Intensity‐Duration‐Area‐Frequency Relationships Based on the Full Range of Non‐Zero Precipitation From Radar‐Reanalysis Data

“…They provide the mathematical link between precipitation intensities (I), durations (D), areas (A), and frequency of occurrence (F). They are useful tools for engineers and hydrologists in hydrological design (see Bertini et al., 2020, for example), quantification of areal rainfall hazard (Mélèse et al., 2019; Overeem et al., 2010; Panthou et al., 2014; Zhao et al., 2023), storm characterization (Blanchet & Mélèse, 2020; Ceresetti et al., 2012; Ramos et al., 2005), and development of early warning systems (Panziera et al., 2016). IDAF models extend the well‐known Intensity Duration Frequency curves (IDF) by incorporating the spatial extent of precipitation (i.e., the area).…”

“…A rare application of GPD for threshold excesses is found in Zhao et al. (2023) for IDAF curve modeling. A major drawback of such approaches is the inefficient use of the data since only one value is retained in a block (usually the maximum in a year) or the excesses of a threshold (a tiny fraction of the data), and all the other data in the block is discarded.…”

“…This can result in significant uncertainty in estimation, especially in cases where the length of the data series is not sufficiently long. The problem of short record length is more apparent with radar and radar reanalysis products, which are usually used in IDAF curve modeling (Blanchet & Mélèse, 2020; Mélèse et al., 2019; Overeem et al., 2010; Zhao et al., 2023), due to the required spatial information they provide.…”

“…To elaborate more, all the authors used only extreme data in the form of block maxima and, as the distribution, the generalized extreme value (GEV) distribution (Ceresetti et al, 2012;Overeem et al, 2010;Panthou et al, 2014) or its special case, the Gumbel distribution (Bertini et al, 2020;Blanchet & Mélèse, 2020;Mélèse et al, 2019;Nhat et al, 2007), or log-normal distribution (De Michele et al, 2011). A rare application of GPD for threshold excesses is found in Zhao et al (2023) for IDAF curve modeling. A major drawback of such approaches is the inefficient use of the data since only one value is retained in a block (usually the maximum in a year) or the excesses of a threshold (a tiny fraction of the data), and all the other data in the block is discarded.…”

Modeling Areal Precipitation Hazard: A Data-driven Approach to Model Intensity-Duration-Area-Frequency Relationships using the Full Range of Non-Zero Precipitation in Switzerland

“…They provide the mathematical link between precipitation intensities (I), durations (D), areas (A), and frequency of occurrence (F). They are useful tools for engineers and hydrologists in hydrological design (see Bertini et al, 2020, for example), quantification of areal rainfall hazard (Overeem et al, 2010;Panthou et al, 2014;Mélèse et al, 2019;Zhao et al, 2023), storm characterization (Ramos et al, 2005;Ceresetti et al, 2012;Blanchet & Mélèse, 2020), and development of early warning systems (Panziera et al, 2016). IDAF models extend the well-known Intensity Duration Frequency curves (IDF) by incorporating the spatial extent of precipitation (i.e., the area).…”