Stochastic Environmental Research and Risk Assessment publishes research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. Its core aim is to bring together stochastic modeling research papers in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Coverage includes modeling, measurements and instrumentation in the areas of Spatiotemporal analysis and mapping of natural processes; Enviroinformatics; Environmental risk assessment, reliability analysis and decision making; Surface and subsurface hydrology and hydraulics; Multiphase porous media domains and contaminant transport modeling; and more. See full Aims and Scope for more details.

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All articles accepted after February 1, 2014 will have free color online and in print.

  • Pursues stochastic and probabilistic approaches to environmental sciences and engineering
  • Studies interactions of earth and atmospheric environments with people and ecosystems
  • Brings together stochastic modeling research papers in various fields of environmental sciences
  • 97% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again

Journal information

  • George Christakos
Publishing model
Hybrid. Open Access options available

Journal metrics

2.351 (2019)
Impact factor
2.721 (2019)
Five year impact factor
55 days
Submission to first decision
231 days
Submission to acceptance
163,734 (2019)

Latest articles

  1. Authors

    • Bellie Sivakumar
    • Content type: Editorial
This journal has 85 open access articles

Journal updates

  • 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. 

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