reslr: An R Package for Relative Sea Level Modelling

We present reslr, an R package to perform Bayesian modelling of relative sea level data. We include a variety of different statistical models previously proposed in the literature, with a unifying framework for loading data, fitting models, and summarising the results. Relative sea-level data often contain measurement error in multiple dimensions, and so our package allows for these to be included in the statistical models. When plotting the output sea level curves, the focus is often on comparing rates of change, and so our package allows for computation of the derivatives of sea level curves with appropriate consideration of the uncertainty. We provide a large example dataset from the Atlantic coast of North America and show some of the results that might be obtained from our package.

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Publication Details

Journal
The R Journal
Published
2025-03-09
DOI
https://doi.org/10.32614/rj-2024-018
Primary Topic
Hydrological Forecasting Using AI
Type
article
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article

reslr: An R Package for Relative Sea Level Modelling

Andrew Parnell, Maeve Upton, Niamh Cahill
The R Journal
Hydrological Forecasting Using AI
article

reslr: An R Package for Relative Sea Level Modelling

Andrew Parnell, Maeve Upton, Niamh Cahill
article en

Abstract

We present reslr, an R package to perform Bayesian modelling of relative sea level data. We include a variety of different statistical models previously proposed in the literature, with a unifying framework for loading data, fitting models, and summarising the results. Relative sea-level data often contain measurement error in multiple dimensions, and so our package allows for these to be included in the statistical models. When plotting the output sea level curves, the focus is often on comparing rates of change, and so our package allows for computation of the derivatives of sea level curves with appropriate consideration of the uncertainty. We provide a large example dataset from the Atlantic coast of North America and show some of the results that might be obtained from our package.

The R JournalVol. 16(2)
National University of Ireland, Maynooth (IE)
Life below water
Openalex Percentile: Top 89%
Hydrological Forecasting Using AI
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reslr: An R Package for Relative Sea Level Modelling — Andrew Parnell, Maeve Upton, et al. · The R Journal (2025) | TGRS Research Map | TGRS