Development of open access tools in R for data-driven analysis and stochastic simulation of geophysical processes:
A simple, yet efficient, graphical method for synthesizing and comparing observed and modeled data across a range of spatiotemporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatiotemporal continuum. More information and an example of implementation can be found in Markonis et al. (2020).
Application of the Self-Organizing Maps technique for spatial classification of time series. The package uses spatial data, point or gridded, to create clusters with similar characteristics. The clusters can be further refined to a smaller number of regions by hierarchical clustering and their spatial dependencies can be presented as complex networks. Thus, meaningful maps can be created, representing the regional heterogeneity of a single variable. More information and an example of implementation can be found in Markonis and Strnad (2019).
A single framework, unifying, extending, and improving a general-purpose modelling strategy, based on the assumption that any process can emerge by transforming a specific ‘parent’ Gaussian process (Papalexiou, 2018).