Effects of different statistical distribution and threshold criteria in extreme precipitation modelling over global land areas

Abstract

In statistical modelling of extreme precipitation, most of previous studies were proposed for some certain regions, the effects of diverse extreme distribution and threshold remain unclear on a global scale. In this study, we evaluated the performance of different extreme distribution and threshold across the global land areas, and proposed the optimal criteria in distribution and threshold determination for monthly extreme precipitation modelling. The performance of the generalized extreme value (GEV) distribution and generalized Pareto distribution (GPD) models were compared and the applicability of GPD models with thresholds determined by different percentiles in each grid was analysed. The results show that the GPD model is more suitable than the GEV model for monthly extreme precipitation, and the threshold over global land areas is likely to be associated with the spatial distribution of annual precipitation magnitude. The 97–99th percentile is most applicable to arid regions (e.g., the northern edge of North America, central Asia, North Africa, southwest Asia, central South America). The 93–96th percentile is most applicable to semi‐arid and semi‐humid regions (e.g., eastern North America, a strip region in northern Eurasia, and inland semi‐arid areas in Asia). The 90–92 percentile is most applicable to humid regions (e.g., central and southern North America, most areas in South America, Eurasia [except central Eurasia], southern and central Africa).

Climate Projections for Precipitation and Temperature Indicators in the Douro Wine Region: The Importance of Bias Correction

“…For this purpose, several previous studies applied bias-correction methodologies [38][39][40][41][42][43]. These bias correction methods are of particular relevance when fixed thresholds of a given parameter or variable (e.g., temperature or precipitation) are important limiting factors for the targeted system [44][45][46], such as the number of days with maximum temperatures above 35 • C in viticulture. As such, several bias-correction methods and model output statistics (for example the delta change and quantile mapping) have been developed over the last decades to overcome this limitation [47].…”

A new bias-correction method for precipitation over complex terrain suitable for different climate states: a case study using WRF (version 3.8.1)

“…Not only are these biases produced by initial and boundary conditions provided by global climate models (GCMs), but they are also related to regions characterised by complex topography and to processes that correspond to a finer scale, such as cloud microphysical processes. These processes need to be parameterised as they cannot be explicitly resolved because of the RCM resolution used in CORDEX (Boer, 1993;Zhang and McFarlane, 1995;Fu, 1996;Haslinger et al, 2013;Yang et al, 2013;Warrach-Sagi et al, 2013;Maraun and Widmann, 2015;Hui et al, 2016). To overcome these shortcomings, RCMs need to be run at a resolution where they can explicitly resolve some of the relevant processes, such as convection (e.g.…”

“…for the forcing of impact models like glaciers or ice sheets (Jouvet et al, 2017;Jouvet and Huss, 2019), when temperature thresholds are important as limiting factor, e.g. for vegetation coverage, freezing of water, snowfall vs. rainfall, or when precipitation thresholds are essential (Liu et al, 2006;Zhao et al, 2017;Liu et al, 2018;Chen et al, 2019;Wang et al, 2020). So far, several correction methods have been suggested in the literature, e.g.…”

The potential habitat of desert locusts is contracting: predictions under climate change scenarios

“…Previous studies have suggested that during the recession period, desert locusts are usually confined to the semiarid and arid desert regions of Africa, the Middle East, and Southwest Asia, where the annual precipitation is usually less than 200 mm ( Uvarov, 1997 ; Cressman & Stefanski, 2016 ). However, in areas suitable for solitary desert locusts such as the Red Sea coast, and the India-Pakistan border, the annual precipitation reaches 200–500 mm ( Wang et al, 2020 ). Although these areas are relatively small, the MaxEnt model still recognized this detail.…”