IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v28y2023i1d10.1007_s13253-022-00513-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-022-00513-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-022-00513-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Seoncheol Park & Hee‐Seok Oh, 2022. "Lifting scheme for streamflow data in river networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(2), pages 467-490, March.
    2. Lynsie R. Warr & Matthew J. Heaton & William F. Christensen & Philip A. White & Summer B. Rupper, 2023. "Distributional Validation of Precipitation Data Products with Spatially Varying Mixture Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(1), pages 99-116, March.
    3. 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).
    4. Brian Gray & Vyacheslav Lyubchich & Yulia Gel & James Rogala & Dale Robertson & Xiaoqiao Wei, 2016. "Estimation of river and stream temperature trends under haphazard sampling," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 89-105, March.
    5. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
    6. Jeong Eun Lee & Christian Robert, 2013. "Imortance Sampling Schemes for Evidence Approximation in Mixture Models," Working Papers 2013-42, Center for Research in Economics and Statistics.
    7. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2012. "The directional identification problem in Bayesian factor analysis: An ex-post approach," Kiel Working Papers 1799, Kiel Institute for the World Economy (IfW Kiel).
    8. Sun-Joo Cho & Allan S. Cohen, 2010. "A Multilevel Mixture IRT Model With an Application to DIF," Journal of Educational and Behavioral Statistics, , vol. 35(3), pages 336-370, June.
    9. Brian Hartley, 2020. "Corridor stability of the Kaleckian growth model: a Markov-switching approach," Working Papers 2013, New School for Social Research, Department of Economics, revised Nov 2020.
    10. Papastamoulis, Panagiotis, 2018. "Overfitting Bayesian mixtures of factor analyzers with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 220-234.
    11. Simen Alexander Linge Johnsen & Jörg Bollmann, 2020. "Coccolith mass and morphology of different Emiliania huxleyi morphotypes: A critical examination using Canary Islands material," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-29, March.
    12. Nichole E. Carlson & Timothy D. Johnson & Morton B. Brown, 2009. "A Bayesian Approach to Modeling Associations Between Pulsatile Hormones," Biometrics, The International Biometric Society, vol. 65(2), pages 650-659, June.
    13. Montanari, Angela & Viroli, Cinzia, 2011. "Maximum likelihood estimation of mixtures of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2712-2723, September.
    14. Daniel W Kennedy & Jessica Cameron & Paul P -Y Wu & Kerrie Mengersen, 2021. "Peer groups for organisational learning: Clustering with practical constraints," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    15. Stéphane Bonhomme & Koen Jochmans & Jean-Marc Robin, 2016. "Non-parametric estimation of finite mixtures from repeated measurements," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 211-229, January.
    16. Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
    17. Xue, Jiacheng & Yao, Weixin, 2022. "Machine Learning Embedded Semiparametric Mixtures of Regressions with Covariate-Varying Mixing Proportions," Econometrics and Statistics, Elsevier, vol. 22(C), pages 159-171.
    18. Liqun Wang & James Fu, 2007. "A practical sampling approach for a Bayesian mixture model with unknown number of components," Statistical Papers, Springer, vol. 48(4), pages 631-653, October.
    19. Royce Anders & William Batchelder, 2015. "Cultural Consensus Theory for the Ordinal Data Case," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 151-181, March.
    20. AWLP Thilan & P Menéndez & JM McGree, 2023. "Assessing the ability of adaptive designs to capture trends in hard coral cover," Environmetrics, John Wiley & Sons, Ltd., vol. 34(6), September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jagbes:v:28:y:2023:i:1:d:10.1007_s13253-022-00513-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.