William Keenan Wayana Dolan Heather E. Golden Charles R. Lane Jay R. Christensen Kylen Solvik Adnan Rajib Melanie K. Vanderhoof 2026 <p><span id="_mce_caret" data-mce-bogus="1" data-mce-type="format-caret"><span>Globally, many wetlands and lakes are at risk for further loss, which can amplify downstream consequences of flood and drought events. We derived remotely sensed based time series of surface water storage (SW</span>_storage<span>) to determine when and where accounting for SW</span>_storage<span>&nbsp;dynamics improves predictions of river discharge. We trained four long short-term memory (LSTM) models, that differed in their inclusion of storage data and catchment characteristics, to simulate daily river discharge (2016–2023) for select watersheds across the conterminous United States. Adding SW</span>_storage<span>&nbsp;to a meteorology-only or meteorology-and-catchment characteristics model improved upon model Nash-Sutcliffe efficiency (NSE) in 80.6% of the watersheds. Residuals during low-flow (Q70) events decreased by 47.6% when adding storage to meteorological data. Improvements were most consistent in ecoregions with a greater abundance of non-floodplain lakes and wetlands. This effort represents the first exploration to train a multi-watershed LSTM on landscape-scale remotely sensed time series of SW</span>_storage<span>.</span></span></p> application/pdf 10.1080/02626667.2025.2593333 en Taylor & Francis Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models article