Abstract
Abstract. A novel approach to stochastic rainfall generation that can reproduce various statistical characteristics of observed rainfall at hourly to yearly timescales is presented. The model uses a seasonal autoregressive integrated moving average (SARIMA) model to generate monthly rainfall. Then, it downscales the generated monthly rainfall to the hourly aggregation level using the Modified Bartlett–Lewis Rectangular Pulse (MBLRP) model, a type of Poisson cluster rainfall model. Here, the MBLRP model is carefully calibrated such that it can reproduce the sub-daily statistical properties of observed rainfall. This was achieved by first generating a set of fine-scale rainfall statistics reflecting the complex correlation structure between rainfall mean, variance, auto-covariance, and proportion of dry periods, and then coupling it to the generated monthly rainfall, which were used as the basis of the MBLRP parameterization. The approach was tested on 34 gauges located in the Midwest to the east coast of the continental United States with a variety of rainfall characteristics. The results of the test suggest that our hybrid model accurately reproduces the first- to the third-order statistics as well as the intermittency properties from the hourly to the annual timescales, and the statistical behaviour of monthly maxima and extreme values of the observed rainfall were reproduced well.
Highlights
IntroductionIntroduction and backgroundMost human and natural systems affected by rainfall react sensitively to temporal variability of rainfall across small (e.g. quarter-hourly) to large (e.g. monthly, yearly) timescales
Introduction and backgroundMost human and natural systems affected by rainfall react sensitively to temporal variability of rainfall across small to large timescales
This study proposes a composite rainfall generation model that can reproduce various statistical properties of observed rainfall at timescales ranging between 1 h and 1 year
Summary
Introduction and backgroundMost human and natural systems affected by rainfall react sensitively to temporal variability of rainfall across small (e.g. quarter-hourly) to large (e.g. monthly, yearly) timescales. Small-scale rainfall temporal variability influences short-term watershed responses such as flash floods (Reed et al, 2007) and subsequent transport of sediments (Ogston et al, 2000) and contaminants (Zonta et al, 2005). Large-scale rainfall temporal variability (Iliopoulou et al, 2016; Tyralis et al, 2018) influences long-term resilience of human–flood systems (Yu et al, 2017), human health (Patz et al, 2005), food production (Shisanya et al, 2011), and the evolution of human society (Warner and Afifi, 2014) and ecosystems (Borgogno et al, 2007; Fernandez-Illescas and Rodriguez-Iturbe, 2004). The rainfall records do not exist when the risks need to be assessed for the future. Stochastic rainfall generators, which can create synthetic rainfall records with infinite length, have been frequently used to provide rainfall input data to the modelling studies for risk assessment
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