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We monitored water quality in Beaverdam Reservoir (Vinton, Virginia, USA, 37.31288, -79.8159) with high-frequency (10-minute and 15-minute) sensors in 2016-2022. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of 2 data sets: BVR_sensor_string_2016_2020.csv and BVR_platform_data_2020_2022.csv. BVR_sensor_string_2016_2022.csv consists of a temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by a dissolved oxygen logger at 5 m or 10 m depending on the time of year. A sonde measuring temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was deployed at ~1.5 m depth. This initial data set spans 2016 to 2020, with no additional data collection beyond the last observation. The second data set is BVR_platform_data_2020_2022.csv, with data collection still ongoing. This data set contains 1) a temperature string with 13 temperature sensors ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; 2) two oxygen sensors, one in the middle of the string and one sensor above the sediments; and 3) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 data set is also included in this 2020-2022 data set, deployed at 1.5 m below the surface. The temperature string with the thermistors, dissolved oxygen sensor, and pressure sensor are permanently fixed to the platform and water level changes around them. In the methods we describe how to add a depth measurement to each observation. 

Sediment traps were deployed to assess the mass and composition (iron, manganese, total organic carbon, and total nitrogen) of settling particulates in the water column of two drinking water reservoirs—Beaverdam Reservoir and Falling Creek Reservoir, both located in Vinton, Virginia, USA. Sediment traps were deployed at two depths in each reservoir to capture both epilimnetic and hypolimnetic (total) sediment flux. The particulates were collected from the traps approximately fortnightly from April to December from 2018 to 2022, then filtered, dried, and analyzed for either iron and manganese or total organic carbon and total nitrogen. Beaverdam and Falling Creek are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia. The sediment trap dataset consists of logs detailing the sample filtering process, the mass of dried particulates from each filter, and the raw concentration data for iron (Fe) and manganese (Mn), total organic carbon (TOC) and total nitrogen (TN). The final products are the calculated downward fluxes of solid Fe, Mn, TOC and TN during the deployment periods. 

Near-term, ecological forecasting with iterative model refitting and uncertainty partitioning has great promise for improving our understanding of ecological processes and the predictive skill of ecological models, but to date has been infrequently applied to predict biogeochemical fluxes. Bubble fluxes of methane (CH 4 ) from aquatic sediments to the atmosphere (ebullition) dominate freshwater greenhouse gas emissions, but it remains unknown how best to make robust near-term CH 4 ebullition predictions using models. Near-term forecasting workflows have the potential to address several current challenges in predicting CH 4 ebullition rates, including: development of models that can be applied across time horizons and ecosystems, identification of the timescales for which predictions can provide useful information, and quantification of uncertainty in predictions. To assess the capacity of near-term, iterative forecasting workflows to improve ebullition rate predictions, we developed and tested a near-term, iterative forecasting workflow of CH 4 ebullition rates in a small eutrophic reservoir throughout one open-water period. The workflow included the repeated updating of a CH 4 ebullition forecast model over time with newly-collected data via iterative model refitting. We compared the CH 4 forecasts from our workflow to both alternative forecasts generated without iterative model refitting and a persistence null model. Our forecasts with iterative model refitting estimated CH 4 ebullition rates up to 2 weeks into the future [RMSE at 1-week ahead = 0.53 and 0.48 log e (mg CH 4 m −2 d −1 ) at 2-week ahead horizons]. Forecasts with iterative model refitting outperformed forecasts without refitting and the persistence null model at both 1- and 2-week forecast horizons. Driver uncertainty and model process uncertainty contributed the most to total forecast uncertainty, suggesting that future workflow improvements should focus on improved mechanistic understanding of CH 4 models and drivers. Altogether, our study suggests that iterative forecasting improves week-to-week CH 4 ebullition predictions, provides insight into predictability of ebullition rates into the future, and identifies which sources of uncertainty are the most important contributors to the total uncertainty in CH 4 ebullition predictions.