Stochastic Environmental Research and Risk Assessment (SERRA) publishes research papers, reviews and technical notes on stochastic (i.e., probabilistic and statistical) approaches to environmental sciences and engineering, including the description, modelling and prediction of the spatiotemporal evolution of natural and engineered systems under conditions of uncertainty, risk assessment, interactions of terrestrial and atmospheric environments with people and the ecosystem, and environmental health. Its core aim is to bring together research in various fields of environmental, planetary and health sciences and engineering, providing an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of novel stochastic techniques used in various fields. Contributions may cover measurement, instrumentation and probabilistic / statistical modelling approaches in various fields of science and engineering, including (but not limited to):

  • Surface and subsurface hydrology, including stochastic hydrology and hydraulics, scale invariant phenomena, fractals and multifractals.
  • Climate science, meteorology and hydrometeorology, including hydrologic/hydroclimatic variability, hydrologic scaling, and climate change impact assessment.
  • Natural hazards, environmental risk modelling and assessment, including statistical estimation and modelling of hydrologic and hydroclimatic extremes (i.e., precipitation, droughts and floods), spatiotemporal modelling of the availability of surface-water and groundwater resources, hydroclimatic and environmental risk quantification, catastrophe risk and management of insurance and re-insurance.
  • Public health and environmental epidemiology, including statistical epidemiology, spatiotemporal spread of infectious diseases, as well as human exposure assessment.
  • Stochastic approaches (probabilistic and statistical) to modelling the Water, Food, Energy and Health nexus, including socio-economic concepts, complex inter-linkages and compound risk resulting from hydroclimatic conditions, water quality, food security and energy production, as well as population health.
  • Stochastic approaches to modelling and assessment of the efficiency and sustainability of green infrastructure concepts, including smart solutions and modelling approaches for water usage, reduction of water losses, agricultural irrigation, environmental and ecological health monitoring, and associated modelling approaches.
  • Compound risk modelling and assessment for the design of critical infrastructure under uncertainty (e.g., observation networks, water supply and sewerage works, flood retention structures, etc.), including modelling tools for organizing integrated solutions for water supply, precision agriculture, ecosystem health monitoring, and characterization of environmental conditions.
  • Probabilistic assessment of the sustainability of the natural environment, including soil contamination and remediation, air pollution monitoring and control, as well as environmental health effects.
  • Enviroinformatics and hydroinformatics, including application of data-driven approaches as well as machine and deep learning techniques for estimation, prediction and control of natural and engineered systems.
  • Geostatistics, spatial and spatiotemporal statistics and analyses of environmental (geophysical and biophysical) processes, including cross-scale integration of Earth system spatiotemporal data (ground level and airborne (e.g. UAV) observations, remote sensing products, model simulation outputs, etc.), emerging patterns and scalable properties of environmental processes, as well as statistical downscaling, interpolation and forecasting techniques for environmental variables.

 

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Editor-in-Chief
  • Andreas Langousis
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Hybrid (Transformative Journal). How to publish with us, including Open Access

Journal metrics

3.821 (2021)
Impact factor
3.355 (2021)
Five year impact factor
15 days
Submission to first decision (Median)
249,387 (2021)
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  • 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.
     

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1436-3259
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1436-3240
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