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Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France

Author

Listed:
  • Matthew Heiner

    (Brigham Young University)

  • Matthew J. Heaton

    (Brigham Young University)

  • Benjamin Abbott

    (Brigham Young University)

  • Philip White

    (Brigham Young University)

  • Camille Minaudo

    (Ecole Polytechnique Fédérale de Lausanne)

  • Rémi Dupas

    (INRAe, L’institut Agro)

Abstract

Elevated nitrate from human activity causes ecosystem and economic harm globally. The factors that control the spatiotemporal dynamics of riverine nitrate concentration remain difficult to describe and predict. We analyzed nitrate concentration from 4450 sites throughout France to group sites that exhibit similar trend and seasonal behaviors during 2010–2017 and relate these dynamics to catchment characteristics. We employed a latent-variable, Bayesian mixture of harmonic regressions model to infer site clustering based on multi-year trend and annual cycle amplitude and phase. We examined clustering patterns and relationships among nitrate level, trend, and seasonality parameters. Cluster membership probabilities were governed by continuous, latent variables that were informed with seven classes of covariates encompassing geology, hydrology, and land use. To relate interpretable parameters to the covariates, we modeled amplitude and phase separately in a novel framework employing a bivariate phase regression with the projected normal distribution. The analysis identified regional regimes of nitrate dynamics, including trend classifications. This approach can reveal general patterns that transcend small-scale heterogeneity, complementing site-level assessments to inform regional- to national-level progress in water quality. Supplementary materials accompanying this paper appear on-line.

Suggested Citation

  • Matthew Heiner & Matthew J. Heaton & Benjamin Abbott & Philip White & Camille Minaudo & Rémi Dupas, 2023. "Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 74-98, March.
    • Handle: RePEc:spr:jagbes:v:28:y:2023:i:1:d:10.1007_s13253-022-00513-2
    DOI: 10.1007/s13253-022-00513-2
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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. Alan R Pearse & James M McGree & Nicholas A Som & Catherine Leigh & Paul Maxwell & Jay M Ver Hoef & Erin E Peterson, 2020. "SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-17, September.
    3. Higgs, Megan Dailey & Hoeting, Jennifer A., 2010. "A clipped latent variable model for spatially correlated ordered categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1999-2011, August.
    4. 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.
    5. Berrett, Candace & Calder, Catherine A., 2012. "Data augmentation strategies for the Bayesian spatial probit regression model," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 478-490.
    6. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
    7. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    8. F. Y. Cheng & K. J. Van Meter & D. K. Byrnes & N. B. Basu, 2020. "Maximizing US nitrate removal through wetland protection and restoration," Nature, Nature, vol. 588(7839), pages 625-630, December.
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