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Bayesian spatio-temporal models for stream networks

Author

Listed:
  • Santos-Fernandez, Edgar
  • Ver Hoef, Jay M.
  • Peterson, Erin E.
  • McGree, James
  • Isaak, Daniel J.
  • Mengersen, Kerrie

Abstract

Spatio-temporal models are widely used in many research areas including ecology. The recent proliferation of the use of in-situ sensors in streams and rivers supports space-time water quality modelling and monitoring in near real-time. A new family of spatio-temporal models is introduced. These models incorporate spatial dependence using stream distance while temporal autocorrelation is captured using vector autoregression approaches. Several variations of these novel models are proposed using a Bayesian framework. The results show that our proposed models perform well using spatio-temporal data collected from real stream networks, particularly in terms of out-of-sample RMSPE. This is illustrated considering a case study of water temperature data in the northwestern United States.

Suggested Citation

  • Santos-Fernandez, Edgar & Ver Hoef, Jay M. & Peterson, Erin E. & McGree, James & Isaak, Daniel J. & Mengersen, Kerrie, 2022. "Bayesian spatio-temporal models for stream networks," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:csdana:v:170:y:2022:i:c:s0167947322000263
    DOI: 10.1016/j.csda.2022.107446
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    References listed on IDEAS

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    1. David O'Donnell & Alastair Rushworth & Adrian W. Bowman & E. Marian Scott & Mark Hallard, 2014. "Flexible regression models over river networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 47-63, January.
    2. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    3. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    4. Guillaume Bal & Etienne Rivot & Jean-Luc Baglinière & Jonathan White & Etienne Prévost, 2014. "A Hierarchical Bayesian Model to Quantify Uncertainty of Stream Water Temperature Forecasts," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-24, December.
    5. Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
    6. Ver Hoef, Jay & Peterson, Erin & Clifford, David & Shah, Rohan, 2014. "SSN: An R Package for Spatial Statistical Modeling on Stream Networks," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i03).
    7. Felipe Tagle & Stefano Castruccio & Paola Crippa & Marc G. Genton, 2019. "A Non‐Gaussian Spatio‐Temporal Model for Daily Wind Speeds Based on a Multi‐Variate Skew‐t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(3), pages 312-326, May.
    8. Peterson, Erin & Ver Hoef, Jay, 2014. "STARS: An ArcGIS Toolset Used to Calculate the Spatial Information Needed to Fit Spatial Statistical Models to Stream Network Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i02).
    9. Michael Beenstock & Daniel Felsenstein, 2019. "The Econometric Analysis of Non-Stationary Spatial Panel Data," Advances in Spatial Science, Springer, number 978-3-030-03614-0.
    10. Jonathan R. Stroud & Peter Müller & Bruno Sansó, 2001. "Dynamic models for spatiotemporal data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 673-689.
    11. Posa, D., 1993. "A simple description of spatial-temporal processes," Computational Statistics & Data Analysis, Elsevier, vol. 15(4), pages 425-437, May.
    12. Michael Beenstock & Daniel Felsenstein, 2019. "Spatial Vector Autoregressions," Advances in Spatial Science, in: The Econometric Analysis of Non-Stationary Spatial Panel Data, chapter 0, pages 129-161, Springer.
    13. Ver Hoef, Jay M. & Peterson, Erin E., 2010. "A Moving Average Approach for Spatial Statistical Models of Stream Networks," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 6-18.
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