Franco-Villoria, M., Scott, M. and Hoey, T. (2019) Spatiotemporal modeling of hydrological return levels: A quantile regression approach. Environmetrics, 30(2), e2522. (doi: 10.1002/env.2522)
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164925.pdf - Accepted Version 4MB |
Abstract
Extreme river flows can lead to inundation of floodplains, with consequent impacts for society, the environment and the economy. Extreme flows are inherently diffcult to model being infrequent, irregularly spaced and affected by non-stationary climatic controls. To identify patterns in extreme flows a quantile regression approach can be used. This paper introduces a new framework for spatio-temporal quantile regression modelling, 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 exibility 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 rivers across Scotland.
| Item Type: | Articles |
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| Additional Information: | The authors would like to thank the Kelvin-Smith postgraduate studentship scheme from the University of Glasgow for partially funding this research. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Hoey, Professor Trevor and Scott, Professor Marian |
| Authors: | Franco-Villoria, M., Scott, M., and Hoey, T. |
| College/School: | College of Science and Engineering > School of Geographical and Earth Sciences College of Science and Engineering > School of Mathematics and Statistics > Statistics |
| Journal Name: | Environmetrics |
| Publisher: | Wiley |
| ISSN: | 1180-4009 |
| ISSN (Online): | 1099-095X |
| Published Online: | 21 August 2018 |
| Copyright Holders: | Copyright © 2018 John Wiley and Sons Ltd |
| First Published: | First published in Environmetrics 30:e2522 |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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