Temporal disaggregation of hourly precipitation under changing climate over the Southeast United States

Abstract

Climate change impacts on precipitation characteristics will alter the hydrologic characteristics, such as peak flows, time to peak, and erosion potential of watersheds. However, many of the currently available climate change datasets are provided at temporal and spatial resolutions that are inadequate to quantify projected changes in hydrologic characteristics of a watershed. Therefore, it is critical to temporally disaggregate coarse-resolution precipitation data to finer resolutions for studies sensitive to precipitation characteristics. In this study, we generated novel 15-minute precipitation datasets from hourly precipitation datasets obtained from five NA-CORDEX downscaled climate models under RCP 8.5 scenario for the historical (1970–1999) and projected (2030–2059) years over the Southeast United States using a modified version of the stochastic method. The results showed conservation of mass of the precipitation inputs. Furthermore, the probability of zero precipitation, variance of precipitation, and maximum precipitation in the disaggregated data matched well with the observed precipitation characteristics. The generated 15-minute precipitation data can be used in all scientific studies that require precipitation data at that resolution.

Sub‐daily rainfall intermittency: Is it really stochastic? A test at two Australian locations

“…A more complete understanding of sub‐daily intermittency would also contribute usefully to the development and evaluation of numerical methods for simulating rainfall arrival at high temporal resolution, using, for instance, statistical downscaling methods such as random cascade models (Onof et al, 2005; Molnar & Burlando, 2005; Connolly et al, 1998; Koutsoyiannis et al, 2003; Frost et al, 2004; Pui et al, 2012; McIntyre et al, 2016; Lombardo et al, 2017; Kossieris et al, 2018; Bohn et al, 2019; Brigandì & Aronica, 2019; Müller‐Thomy, 2020; Park et al, 2021; Manikanta et al, 2023). These procedures are also being applied to the generation of scenario data for future decades under climate change (Takhellambam et al, 2022). However, given that many of these approaches subdivide the day into rain and rainless periods in an entirely stochastic fashion (Brigandì & Aronica, 2019), including the number and duration of dry intervals (e.g., see figures 10 and 11 of Lombardo et al, 2017) it is pertinent to enquire whether the distribution of such intervals is indeed essentially random, or whether it might have metric properties linked to the diurnal cycle.…”

Temporal Scaling Characteristics of Sub‐Daily Precipitation in Qinghai‐Tibet Plateau

“…The quality of any empirical temporal downscaling strongly depends on the number of field observations and the statistical methods used. Compared to the complex and indescribable relationship between daily and sub‐daily precipitation generated by machine learning and deep learning methods in previous studies (Müller & Haberlandt, 2018; Socolofsky et al., 2001; Takhellambam et al., 2022), it could be a more effective and feasible strategy to establish a describable equation of the relationship of precipitation between daily and sub‐daily scales, to quantify the temporal scaling characteristics of sub‐daily precipitation. The motivation for this strategy is the observation and expectation that the temporal scaling characteristics of sub‐daily precipitation has simple and smooth shapes with regional similarities (Parding et al., 2023).…”

“…In the framework of statistical downscaling, many machine learning and deep learning methods were used to explore the connections between low‐ and high‐time resolution scales, such as the artificial neural network method (Vu et al., 2015), fractal‐multifractal method (Maskey et al., 2019), multiplicative cascade method (Müller & Haberlandt, 2018), extreme learning machine method (MoradiKhaneghahi et al., 2019), and stochastic disaggregation method (Socolofsky et al., 2001; Takhellambam et al., 2022). The above temporal downscaling methods usually pursue a statistically perfect fit; however, their effectiveness depends not only on the data availability but also on physical mechanisms of precipitation.…”

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

“…Regarding the observed sub‐daily rainfall data sets, a handful of studies have used the observations provided by the rain gauges, satellites, and radars (Hosseinzadehtalaei et al., 2020, 2021; Marra & Morin, 2015; Minh et al., 2022; Takhellambam et al., 2022b; Zhao, Abhishek, & Kinouchi, 2022), and the reanalysis data sets (e.g., ERA5) (e.g., Hosseinzadehtalaei et al., 2020; Zhao, Abhishek, Kinouchi, Ang, & Zhuang, 2022). However, these data sets have inherent limitations, such as limited spatial representativeness of rain‐gauge data sets, poor representation of convective process (e.g., reanalysis data sets), and quantitative precipitation estimation (QPE) uncertainty in radars.…”