Journal updates

  • Call for Papers: Spatiotemporal Data Science: Theoretical Advances and Applications

    This special issue aims at exploring the new challenges and opportunities opened by the spread of data-driven statistical learning approaches in Earth and Soil Sciences. We invite cutting-edge contributions related to methods of spatio-temporal statistics and data mining on topics including, but not limited to advances  in  spatio-temporal  modeling  using  geostatistics  and  machine learning; uncertainty quantification and representation; innovative  techniques of  knowledge  extraction  based  on  clustering,  pattern recognition and, more generally, data mining.

  • Call for Papers: Advances in Uncertainty Quantification for Water Resources Applications

    Quantification and characterization of uncertainty are two key features of modern science-based predictions. When applied to water resources, these tasks must be able to handle many degrees of freedom, complexity of physical and (bio)chemical processes, sparsity and/or poor quality of data, and medium- and long-term variability of natural/anthropic stresses imposed on a system.