Statistical Approaches Versus Weather Generator to Downscale RCM Outputs to Point Scale: A Comparison of Performances

Abstract

Abstract:To properly evaluate weather variables regulating the occurrence of geo-hydrological hazards, the current constraints of climate models imply the need of adopting statistical approaches in cascade to GCM/RCM for the assessment of the potential variations associated to climate changes. Since, in the last years, several approaches, often freely available, have been proposed and applied to investigate various hazards in different geographical areas and geomorphological contexts, a deeper understanding about their performances and constraints is crucial; in the work, it is carried out focusing the attention on two kind of approaches widely adopted in impact studies: bias correction methods (in particular, quantile mapping tools) and weather generators. Both methodology have been applied to outputs of an high resolution RCM simulation carried out on Italian territory for analyzing two very localized (and then challenging) landslide case studies. Beyond an assessment about relative performances in reproducing weather variables on the areas, the goal concerns an increasing awareness about how these approaches could affect the climate signal, physically detected by RCM, not only in outputs weather variables but also in derived components of soil surface budgets strictly governing the occurrence of landslide phenomena.

Evaluation of Variations in Frequency of Landslide Events Affecting Pyroclastic Covers in Campania Region under the Effect of Climate Changes

“…Even if this refinement makes it possible to accurately evaluate a remarkable fraction of weather patterns, RCD may misrepresent orography, land surface feedbacks and sub-grid processes, thus inducing biases preventing their use for impact analysis ( [33][34][35][36]). To overcome this issue, different approaches, known as Bias Correction (BC) methods, have been proposed in recent years ( [10,37,38]). They can be defined as statistical regression models calibrated, for current periods in order to detect and correct biases, assumed to systematically affect the climate simulations.…”

“…Lastly, model outputs are bias-corrected (BC) using an empirical quantile-mapping approach that makes use of a quantile-based transformation of distributions where "a quantile of the present day simulated distribution is replaced by the same quantile of the present-day observed distribution" [36]. This approach has proven outperforming the others for different weather variables or geographical areas ( [10,37,43]).…”

“…Similarly, over recent years, the increase in papers accounting for the effect of climate change on landslide activities is nevertheless less than that of papers concerning landslide issues in general ( [4,5]). On the whole, they tend to carry out analysis on the local scale (slope or basins), for which they adopt two main approaches: (i) assessment of past/ongoing variations in landslide occurrence based on long datasets of weather forcing and induced landslide events (e.g., [6,7]); or (ii) evaluations of future trends in landslide activities coupling climate and slope stability models (e.g., [8][9][10][11][12]).…”

Temporal evolution of landslide hazard for a road infrastructure in the Municipality of Nocera Inferiore, Italy, under the effect of climate change

“…For instance, to characterize uncertainty associated to future projections, climate multi-models ensemble can be utilized where different combinations of GCM and 25 RCM run on fixed grid and domain. Furthermore, statistical approaches (e.g., Maraun 2013;Villani et al 2015;Lafon et al 2013) can be pursued to reduce biases assumed as systematic in simulations. More specifically, quantile mapping approaches have been applied with satisfactory results in recent years for different impact studies.…”

Changes in future rainfall extremes over Northeast Bangladesh: A Bayesian model averaging approach

“…Among the various methods, distribution mapping-based methods are getting more popular and have been applied to the downscale and correct temperature and precipitation data from RCMs in hydrological studies (Ashfaq et al, 2010;Piani et al, 2010;Dosio and Paruolo, 2011;Themeßl et al, 2012). Therefore, in this study, we used the quantile mapping bias correction method because it has been successfully and widely applied in climate change studies Seibert, 2010, 2012;Räisänen and Räty, 2013;Villani et al, 2015). In this method, cumulative distribution functions (CDFs) were first generated for both the observed and RCM-simulated rainfall.…”