IDEAS home Printed from https://ideas.repec.org/a/spr/jstada/v7y2020i1d10.1186_s40488-020-00112-x.html
   My bibliography  Save this article

Spatio-temporal analysis of flood data from South Carolina

Author

Listed:
  • Haigang Liu

    (Department of Statistics, University of South Carolina)

  • David B. Hitchcock

    (Department of Statistics, University of South Carolina)

  • S. Zahra Samadi

    (Department of Civil and Environmental Engineering, University of South Carolina)

Abstract

To investigate the relationship between flood gage height and precipitation in South Carolina from 2012 to 2016, we built a conditional autoregressive (CAR) model using a Bayesian hierarchical framework. This approach allows the modelling of the main spatio-temporal properties of water height dynamics over multiple locations, accounting for the effect of river network, geomorphology, and forcing rainfall. In this respect, a proximity matrix based on watershed information was used to capture the spatial structure of gage height measurements in and around South Carolina. The temporal structure was handled by a first-order autoregressive term in the model. Several covariates, including the elevation of the sites and effects of seasonality, were examined, along with daily rainfall amount. A non-normal error structure was used to account for the heavy-tailed distribution of maximum gage heights. The proposed model captured some key features of the flood process such as seasonality and a stronger association between precipitation and flooding during summer season. The model is able to forecast short term flood gage height which is crucial for informed emergency decision. As a byproduct, we also developed a Python library to retrieve and handle environmental data provided by some main agencies in the United States. This library can be of general usefulness for studies requiring rainfall, flow, and geomorphological information over specific areas of the conterminous US.

Suggested Citation

  • Haigang Liu & David B. Hitchcock & S. Zahra Samadi, 2020. "Spatio-temporal analysis of flood data from South Carolina," Journal of Statistical Distributions and Applications, Springer, vol. 7(1), pages 1-19, December.
    • Handle: RePEc:spr:jstada:v:7:y:2020:i:1:d:10.1186_s40488-020-00112-x
    DOI: 10.1186/s40488-020-00112-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40488-020-00112-x
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40488-020-00112-x?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
    ---><---

    References listed on IDEAS

    as
    1. Victor Oliveira, 2012. "Bayesian analysis of conditional autoregressive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 107-133, February.
    2. Eric S Walsh & Betty J Kreakie & Mark G Cantwell & Diane Nacci, 2017. "A Random Forest approach to predict the spatial distribution of sediment pollution in an estuarine system," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-18, July.
    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. Ying C. MacNab, 2018. "Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 497-541, September.
    2. Ren, Cuirong & Sun, Dongchu, 2014. "Objective Bayesian analysis for autoregressive models with nugget effects," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 260-280.
    3. Cuirong Ren & Dongchu Sun, 2013. "Objective Bayesian analysis for CAR models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 457-472, June.
    4. Getayeneh Antehunegn Tesema & Zemenu Tadesse Tessema & Stephane Heritier & Rob G. Stirling & Arul Earnest, 2023. "A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research," IJERPH, MDPI, vol. 20(7), pages 1-24, March.
    5. Hong-Ding Yang & Yun-Huan Lee & Che-Yang Lin, 2023. "On Study of the Occurrence of Earth-Size Planets in Kepler Mission Using Spatial Poisson Model," Mathematics, MDPI, vol. 11(11), pages 1-14, May.
    6. Meyer, Hanna & Reudenbach, Christoph & Wöllauer, Stephan & Nauss, Thomas, 2019. "Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction," Ecological Modelling, Elsevier, vol. 411(C).
    7. KWON, Heeeun & HWANG, Beom Seuk, 2023. "Do Spatial Characteristics Affect Housing Prices in Korea? : Evidence from Bayesian Spatial Models," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 64(2), pages 109-124, December.
    8. Colette Mair & Sema Nickbakhsh & Richard Reeve & Jim McMenamin & Arlene Reynolds & Rory N Gunson & Pablo R Murcia & Louise Matthews, 2019. "Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-21, December.

    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:jstada:v:7:y:2020:i:1:d:10.1186_s40488-020-00112-x. 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.