Long Short-Term Memory Model to Forecast River Ice Breakup Throughout Alaska USA

Long Short-Term Memory Model to Forecast River Ice Breakup Throughout Alaska USA | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 7 April 2025 V1 Latest version Share on Long Short-Term Memory Model to Forecast River Ice Breakup Throughout Alaska USA Authors : Russell Limber 0000-0002-0669-8206 [email protected] , Forrest M Hoffman 0000-0001-5802-4134 , Jon Schwenk 0000-0001-5803-9686 , and Jitendra Kumar 0000-0002-0159-0546 Authors Info & Affiliations https://doi.org/10.22541/au.174405211.12366502/v1 Published Water Resources Research Version of record Peer review timeline 364 views 173 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Predicting the timing of annual river ice breakup is crucial for residents to prepare for potential flooding and assess the safety of rivers for transportation. This analysis develops a deep learning approach using meteorological and geospatial data products to forecast river ice breakup. We selected 33 locations along eight major rivers across Alaska, USA, and Western Canada, leveraging annual breakup dates from the Alaska-Pacific River Forecast Center database. Daily meteorological data from Daymet, along with static watershed attributes from the pan-Arctic catchment database, were used to develop a Long Short-Term Memory model (LSTM) for predicting river ice breakup. Of the 33 locations, 23 were used for training the LSTM. The model demonstrated high efficacy, accurately predicting the annual breakup date with a mean absolute error (MAE) of 5.40 days, a standard deviation of 4.03 days and a mean absolute percentage error (MAPE) of 4.37%. The spatial generalizability of the LSTM was evaluated using the remaining 10 locations as holdouts, with eight of the 10 locations averaging a MAPE of less than 8% over the entire time series (1980 to 2023). Additionally, we retrieved 51 long-range seasonal forecast ensembles from the Copernicus Climate Data Store and applied trained LSTM to them to showcase the capability of the LSTM to predict future river ice breakup using operational weather forecasts. To analyze marginal contribution of LSTM inputs for predictions, Shapley values were calculated. A new temporal correction scheme was developed and applied to Shapley values to address unique features of the breakup data. Supplementary Material File (1029287_0_merged_1743114645.pdf) Download 31.52 MB File (supplementary_material.pdf) Download 7.20 MB Information & Authors Information Version history V1 Version 1 07 April 2025 Peer review timeline Published Water Resources Research Version of Record 20 Sep 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords alaska arctic climatology (global change) deep learning environmental sciences hydrology meteorology river ice breakup shapley values time series Authors Affiliations Russell Limber 0000-0002-0669-8206 [email protected] The University of Tennessee Knoxville View all articles by this author Forrest M Hoffman 0000-0001-5802-4134 Oak Ridge National Laboratory (DOE) View all articles by this author Jon Schwenk 0000-0001-5803-9686 Los Alamos National Laboratory View all articles by this author Jitendra Kumar 0000-0002-0159-0546 Oak Ridge National Laboratory (DOE) View all articles by this author Metrics & Citations Metrics Article Usage 364 views 173 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Russell Limber, Forrest M Hoffman, Jon Schwenk, et al. Long Short-Term Memory Model to Forecast River Ice Breakup Throughout Alaska USA. Authorea . 07 April 2025. DOI: https://doi.org/10.22541/au.174405211.12366502/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. 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