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Hydrology Research Special Issue on

Advanced Geostatistics for Hydrological Applications




Geostatistical methods are commonly applied in the Water, Earth and Environmental sciences to quantify spatial variation, produce interpolated maps with quantified uncertainty and optimize spatial sampling designs. Space-time geostatistics explores the dynamic aspects of environmental processes and characterise the dynamic variation in terms of correlations. Geostatistics can also be combined with machine learning and mechanistic models to improve the modelling of real-world processes and patterns. Such methods are used to model soil properties, produce climate model outputs, simulate hydrological processes, and to better understand and predict uncertainties overall. Big data analysis and data fusion have become major topics of research due to technological advances and the abundance of new data sources from remote and proximal sensing as well as a multitude of environmental sensor networks. Methodological advances, such as hierarchical Bayesian modeling, machine learning, sparse Gaussian processes, local interaction models, as well as advances in geostatistical software modules in R and Python have enhanced the geostatistical toolbox.


Potential areas of interest include but are not limited to the following:

  • Space-time geostatistics for hydrology, soil, climate system observations and modelling
  • Hybrid methods: Integration of geostatistics with optimization and machine learning approaches
  • Advanced parametric and non-parametric spatial estimation and prediction techniques
  • Big spatial data: analysis and visualization
  • Optimisation of spatial sampling frameworks and space-time monitoring designs
  • Algorithms and applications on Earth Observation Systems
  • Data Fusion, mining and information analysis
  • Geostatistical characterization of uncertainties and error propagation
  • Bayesian geostatistical analysis and hierarchical modelling
  • Functional data analysis approaches to geostatistics
  • Multiple point geostatistics


Guest Editors

Emmanouil Varouchakis, Associate Editor Hydrology Research journal, Technical University of Crete, Greece

Gerald Corzo Perez, IHE-Delft, Netherlands


Key dates:

  • Deadline for manuscript submission: 30 November 2021

Note: accepted manuscripts will be published online rapidly following acceptance.


How to submit:

Please make sure that your paper follows the Instructions to Authors, before submitting your paper directly to Hydrology Research’s peer review system.  You will need to select the article type, 'Special Issue Paper OA', and the submission category: 'Special Issue: Advanced Geostatistics for Hydrological Applications'.

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