Interannual Sea Level Variability Amplifies Coastal Wetland Loss

Interannual Sea Level Variability Amplifies Coastal Wetland Loss | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 February 2026 V1 Latest version Share on Interannual Sea Level Variability Amplifies Coastal Wetland Loss Authors : Mead A. Allison 0000-0001-9090-1811 [email protected] , Ahmed M. Khalifa 0000-0002-0502-8980 , Sönke Dangendorf 0000-0002-3679-5234 , Ehab Meselhe 0000-0002-5832-8864 , and Kelin Hu 0000-0003-4120-8222 Authors Info & Affiliations https://doi.org/10.22541/au.177063499.99667650/v1 134 views 89 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Accelerating rates of global sea level rise (SLR) are a primary land loss driver today for coastal wetlands. Predictive models to anticipate rates and locations of land loss typically incorporate smoothed SLR projections that lack interannual variability. Here we utilize a biophysical numerical model to examine the impact of randomly generated sea-level variability derived from tide gauge observations on projections of wetland loss timing and magnitude to 2100 for a deltaic subbasin (Barataria Basin) of the Mississippi River Delta in Louisiana, USA. We demonstrate that periods of enhanced SLR rate that have been observed as a SLR hotspot on the US Atlantic and Gulf Coasts in recent decades can irreversibly accelerate land loss by several decades relative to state-of-the-art smooth SLR projections. These trends are even more negative when marsh edge erosion induced by storms is included in model projections. Our projections also demonstrate a limited or no ability of marsh ecotypes to backstep to accommodate these regional interannual accelerations. Our results emphasize the importance of (1) identifying other oceanographically linked regional hotspots in SLR, and (2) modifying SLR projections to accommodate this variability in coastal protection and restoration planning. Supplementary Material File (hydrologicalprocesseswetlandpaper_submittaldraft.docx) Download 2.98 MB Information & Authors Information Version history V1 Version 1 09 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords barataria basin mississippi delta numerical modeling sea level projections sea level rise wetland loss Authors Affiliations Mead A. Allison 0000-0001-9090-1811 [email protected] Tulane University View all articles by this author Ahmed M. Khalifa 0000-0002-0502-8980 Tulane University View all articles by this author Sönke Dangendorf 0000-0002-3679-5234 Tulane University View all articles by this author Ehab Meselhe 0000-0002-5832-8864 Tulane University View all articles by this author Kelin Hu 0000-0003-4120-8222 Tulane University View all articles by this author Metrics & Citations Metrics Article Usage 134 views 89 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mead A. Allison, Ahmed M. Khalifa, Sönke Dangendorf, et al. Interannual Sea Level Variability Amplifies Coastal Wetland Loss. Authorea . 09 February 2026. DOI: https://doi.org/10.22541/au.177063499.99667650/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.177063499.99667650/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe4192ded8209d6',t:'MTc3OTIwNDE1OQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();