Representation of European hydroclimatic patterns with self-organizing maps

Abstract

Self-organizing maps provide a powerful, non-linear technique of dimensionality reduction that can be used to identify clusters with similar attributes. Here, they were constructed from a 1000-year-long gridded palaeoclimatic dataset, namely the Old World Drought Atlas, to detect regions of homogeneous hydroclimatic variability across the European continent. A classification scheme of 10 regions was found to describe most efficiently the spatial properties of Europe’s hydroclimate. These regions were mainly divided into a northern and a southern subset, linked together with a northwest-to-southeast orientation. Further analysis of the classification scheme with complex networks confirmed the divergence between the northern and southern components of European hydroclimate, also revealing that is not strongly correlated to the Iberian Peninsula. On the contrary, the region covering the British Isles, France and Germany appeared to be linked to both branches, implying links of hydroclimate with atmospheric/oceanic circulation.

The rise of compound warm-season droughts in Europe

“…The first layer consisted of a 5 × 5 = 25 nodes SOM. The dimensions of the SOM were determined by the variance minimization method ( 77 ). All events with duration of 3 months or more were classified over three regions (fig.…”

Revisiting glacier mass-balance sensitivity to surface air temperature using a data-driven regionalization

“…This technique has been previously tested in hydroclimatic research, suggesting that can be powerful for non-linear problems (e.g. Reusch and others, 2005; Markonis and Strnad, 2020). The use of PCA to provide a normalized set of variables for initialization of the SOM is a now common procedure in data analysis that tends to support reproducible results (Akinduko and others, 2016).…”

Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms

“…Machine and statistical learning algorithms (see, e.g., Alpaydin, 2014; Hastie et al., 2009; James et al., 2013; Witten et al., 2017) can be reliably automated and applied at scale (Papacharalampous et al., 2019). Therefore, they are befitting and increasingly adopted for solving urban water demand forecasting problems (see, e.g., Duerr et al., 2018; Herrera et al., 2010; Herrera et al., 2011; Lee & Derrible, 2020; Nunes Carvalho et al., 2021; Quilty & Adamowski, 2018; Quilty et al., 2016; Smolak et al., 2020; Xenochristou & Kapelan, 2020; Xenochristou et al., 2020; Xenochristou et al., 2021), and several other water informatics problems (see, e.g., Althoff, Dias, et al., 2020; Althoff, Filgueiras, & Rodrigues, 2020; Althoff, Bazame, & Garcia, 2021; Markonis & Strnad, 2020; Rahman, Hosono, Kisi, et al., 2020; Rahman, Hosono, Quilty, et al., 2020; Sahoo et al., 2019; Scheuer et al., 2021; Tyralis, Papacharalampous, & Langousis, 2021; Tyralis & Papacharalampous, 2017; Xu, Chen, Zhang, & Chen, 2020; Xu, Chen, Moradkhani, et al., 2020).…”

“…PAPACHARALAMPOUS AND LANGOUSIS 10.1029/2021WR030216 2 of 19 applied at scale (Papacharalampous et al, 2019). Therefore, they are befitting and increasingly adopted for solving urban water demand forecasting problems (see, e.g., Duerr et al, 2018;Herrera et al, 2010;Herrera et al, 2011;Lee & Derrible, 2020;Nunes Carvalho et al, 2021;Quilty & Adamowski, 2018;Quilty et al, 2016;Smolak et al, 2020;Xenochristou et al, 2021), and several other water informatics problems (see, e.g., Althoff, Dias, et al, 2020;Althoff, Bazame, & Garcia, 2021;Markonis & Strnad, 2020;Rahman, Hosono, Kisi, et al, 2020;Rahman, Hosono, Quilty, et al, 2020;Sahoo et al, 2019;Scheuer et al, 2021;Tyralis & Papacharalampous, 2017;Xu, Chen, Moradkhani, et al, 2020).…”