Statistical modeling of rates and trends in Holocene relative sea level
Characterizing the spatio-temporal variability of relative sea level (RSL) and estimating local, regional, and global RSL trends requires statistical analysis of RSL data. Formal statistical treatments, needed to account for the spatially and temporally sparse distribution of data and for geochronol...
| Main Authors: | , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/143148 |
| _version_ | 1826127719517126656 |
|---|---|
| author | Ashe, Erica L. Cahill, Niamh Hay, Carling Khan, Nicole S. Kemp, Andrew Engelhart, Simon E. Horton, Benjamin Peter Parnell, Andrew C. Kopp, Robert E. |
| author2 | Asian School of the Environment |
| author_facet | Asian School of the Environment Ashe, Erica L. Cahill, Niamh Hay, Carling Khan, Nicole S. Kemp, Andrew Engelhart, Simon E. Horton, Benjamin Peter Parnell, Andrew C. Kopp, Robert E. |
| author_sort | Ashe, Erica L. |
| collection | NTU |
| description | Characterizing the spatio-temporal variability of relative sea level (RSL) and estimating local, regional, and global RSL trends requires statistical analysis of RSL data. Formal statistical treatments, needed to account for the spatially and temporally sparse distribution of data and for geochronological and elevational uncertainties, have advanced considerably over the last decade. Time-series models have adopted more flexible and physically-informed specifications with more rigorous quantification of uncertainties. Spatio-temporal models have evolved from simple regional averaging to frameworks that more richly represent the correlation structure of RSL across space and time. More complex statistical approaches enable rigorous quantification of spatial and temporal variability, the combination of geographically disparate data, and the separation of the RSL field into various components associated with different driving processes. We review the range of statistical modeling and analysis choices used in the literature, reformulating them for ease of comparison in a common hierarchical statistical framework. The hierarchical framework separates each model into different levels, clearly partitioning measurement and inferential uncertainty from process variability. Placing models in a hierarchical framework enables us to highlight both the similarities and differences among modeling and analysis choices. We illustrate the implications of some modeling and analysis choices currently used in the literature by comparing the results of their application to common datasets within a hierarchical framework. In light of the complex patterns of spatial and temporal variability exhibited by RSL, we recommend non-parametric approaches for modeling temporal and spatio-temporal RSL. |
| first_indexed | 2024-10-01T07:13:28Z |
| format | Journal Article |
| id | ntu-10356/143148 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:13:28Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1431482022-05-14T20:11:12Z Statistical modeling of rates and trends in Holocene relative sea level Ashe, Erica L. Cahill, Niamh Hay, Carling Khan, Nicole S. Kemp, Andrew Engelhart, Simon E. Horton, Benjamin Peter Parnell, Andrew C. Kopp, Robert E. Asian School of the Environment Earth Observatory of Singapore Science::Geology Hierarchical Statistical Modeling Sea Level Characterizing the spatio-temporal variability of relative sea level (RSL) and estimating local, regional, and global RSL trends requires statistical analysis of RSL data. Formal statistical treatments, needed to account for the spatially and temporally sparse distribution of data and for geochronological and elevational uncertainties, have advanced considerably over the last decade. Time-series models have adopted more flexible and physically-informed specifications with more rigorous quantification of uncertainties. Spatio-temporal models have evolved from simple regional averaging to frameworks that more richly represent the correlation structure of RSL across space and time. More complex statistical approaches enable rigorous quantification of spatial and temporal variability, the combination of geographically disparate data, and the separation of the RSL field into various components associated with different driving processes. We review the range of statistical modeling and analysis choices used in the literature, reformulating them for ease of comparison in a common hierarchical statistical framework. The hierarchical framework separates each model into different levels, clearly partitioning measurement and inferential uncertainty from process variability. Placing models in a hierarchical framework enables us to highlight both the similarities and differences among modeling and analysis choices. We illustrate the implications of some modeling and analysis choices currently used in the literature by comparing the results of their application to common datasets within a hierarchical framework. In light of the complex patterns of spatial and temporal variability exhibited by RSL, we recommend non-parametric approaches for modeling temporal and spatio-temporal RSL. Ministry of Education (MOE) National Research Foundation (NRF) Submitted/Accepted version This work was supported by National Science Foundation grants OCE-1458904 (ELA, REK, and BPH), OCE-1458903 (SEE), OCE1458921 (ACK), OCE-1702587 (ELA and REK), Singapore Ministry of Education Academic Research Fund Tier 2 MOE218-T2-1-030, the National Research Foundation of Singapore, and the Singapore Ministry of Education under the Research Centres of Excellence initiative (NSK and BPH), Science Foundation Ireland Career Development Award grant number 17/CDA/4695 (ACP), and is a contribution to IGCP Project 639, INQUA Project CMP1601P “HOLSEA” and PALSEA3. 2020-08-05T07:49:59Z 2020-08-05T07:49:59Z 2018 Journal Article Ashe, E. L., Cahill, N., Hay, C., Khan, N. S., Kemp, A., Engelhart, S. E., Horton, B. P., Parnell, A. C. & Kopp, R. E. (2018). Statistical modeling of rates and trends in Holocene relative sea level. Quaternary Science Reviews, 204, 58-77. https://dx.doi.org/10.1016/j.quascirev.2018.10.032 0277-3791 https://hdl.handle.net/10356/143148 10.1016/j.quascirev.2018.10.032 2-s2.0-85057841108 204 58 77 en MOE218-T2-1-030 Quaternary Science Reviews © 2018 Elsevier Ltd. All rights reserved. This paper was published in Quaternary Science Reviews and is made available with permission of Elsevier Ltd. application/pdf |
| spellingShingle | Science::Geology Hierarchical Statistical Modeling Sea Level Ashe, Erica L. Cahill, Niamh Hay, Carling Khan, Nicole S. Kemp, Andrew Engelhart, Simon E. Horton, Benjamin Peter Parnell, Andrew C. Kopp, Robert E. Statistical modeling of rates and trends in Holocene relative sea level |
| title | Statistical modeling of rates and trends in Holocene relative sea level |
| title_full | Statistical modeling of rates and trends in Holocene relative sea level |
| title_fullStr | Statistical modeling of rates and trends in Holocene relative sea level |
| title_full_unstemmed | Statistical modeling of rates and trends in Holocene relative sea level |
| title_short | Statistical modeling of rates and trends in Holocene relative sea level |
| title_sort | statistical modeling of rates and trends in holocene relative sea level |
| topic | Science::Geology Hierarchical Statistical Modeling Sea Level |
| url | https://hdl.handle.net/10356/143148 |
| work_keys_str_mv | AT asheerical statisticalmodelingofratesandtrendsinholocenerelativesealevel AT cahillniamh statisticalmodelingofratesandtrendsinholocenerelativesealevel AT haycarling statisticalmodelingofratesandtrendsinholocenerelativesealevel AT khannicoles statisticalmodelingofratesandtrendsinholocenerelativesealevel AT kempandrew statisticalmodelingofratesandtrendsinholocenerelativesealevel AT engelhartsimone statisticalmodelingofratesandtrendsinholocenerelativesealevel AT hortonbenjaminpeter statisticalmodelingofratesandtrendsinholocenerelativesealevel AT parnellandrewc statisticalmodelingofratesandtrendsinholocenerelativesealevel AT kopproberte statisticalmodelingofratesandtrendsinholocenerelativesealevel |