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Spatiotemporal modeling of hydrological return levels: A quantile regression approach

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  • Maria Franco‐Villoria
  • Marian Scott
  • Trevor Hoey

Abstract

Extreme river flows can lead to inundation of floodplains, with consequent impacts for society, the environment, and the economy. Extreme flows are inherently difficult to model, being infrequent, irregularly spaced, and affected by nonstationary climatic controls. To identify patterns in extreme flows, a quantile regression approach can be used. This paper introduces a new framework for spatiotemporal quantile regression modeling, where the regression model is built as an additive model that includes smooth functions of time and space, as well as space–time interaction effects. The model exploits the flexibility that P‐splines offer and can be easily extended to incorporate potential covariates. We propose to estimate model parameters using a penalized least squares regression approach as an alternative to linear programming methods, classically used in quantile parameter estimation. The model is illustrated on a data set of flows in 98 rivers across Scotland.

Suggested Citation

  • Maria Franco‐Villoria & Marian Scott & Trevor Hoey, 2019. "Spatiotemporal modeling of hydrological return levels: A quantile regression approach," Environmetrics, John Wiley & Sons, Ltd., vol. 30(2), March.
    • Handle: RePEc:wly:envmet:v:30:y:2019:i:2:n:e2522
    DOI: 10.1002/env.2522
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    Cited by:

    1. Karen A. McKinnon & Andrew Poppick, 2020. "Estimating Changes in the Observed Relationship Between Humidity and Temperature Using Noncrossing Quantile Smoothing Splines," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 292-314, September.

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