Search

Water temperature forecasting in lakes and reservoirs is a valuable tool to manage crucial freshwater resources in a changing and more variable climate, but previous efforts have yet to identify an optimal modeling approach. Here, we demonstrate the first multi‐model ensemble (MME) reservoir water temperature forecast, a forecasting method that combines individual model strengths in a single forecasting framework. We developed two MMEs: a three‐model process‐based MME and a five‐model MME that includes process‐based and empirical models to forecast water temperature profiles at a temperate drinking water reservoir. We found that the five‐model MME improved forecast performance by 8%–30% relative to individual models and the process‐based MME, as quantified using an aggregated probabilistic skill score. This increase in performance was due to large improvements in forecast bias in the five‐model MME, despite increases in forecast uncertainty. High correlation among the process‐based models resulted in little improvement in forecast performance in the process‐based MME relative to the individual process‐based models. The utility of MMEs is highlighted by two results: (a) no individual model performed best at every depth and horizon (days in the future), and (b) MMEs avoided poor performances by rarely producing the worst forecast for any single forecasted period (<6% of the worst ranked forecasts over time). This work presents an example of how existing models can be combined to improve water temperature forecasting in lakes and reservoirs and discusses the value of utilizing MMEs, rather than individual models, in operational forecasts. Aggregated lake temperature forecast skill was higher for multi‐model ensemble (MME) forecasts than individual model forecastsIncluding baseline empirical models (day‐of‐year, persistence) with process models improved MME forecast performanceMME forecasts improved forecast skill by “hedging,” as no individual model performed best at all horizons or depths Aggregated lake temperature forecast skill was higher for multi‐model ensemble (MME) forecasts than individual model forecasts Including baseline empirical models (day‐of‐year, persistence) with process models improved MME forecast performance MME forecasts improved forecast skill by “hedging,” as no individual model performed best at all horizons or depths

Water level drawdowns are increasingly common in lakes and reservoirs worldwide as a result of both climate change and water management. Drawdowns can have direct effects on physical properties of a waterbody (e.g., by altering stratification and light dynamics), which can interact to modify the waterbody's biology and chemistry. However, the ecosystem‐level effects of drawdown remain poorly characterized in small, thermally stratified reservoirs, which are common in many regions of the world. Here, we intensively monitored a small eutrophic reservoir for 2 years, including before, during, and after a month‐long drawdown that reduced total reservoir volume by 36%. During drawdown, stratification strength (maximum buoyancy frequency) and surface phosphate concentrations both increased, contributing to a substantial surface phytoplankton bloom. The peak in phytoplankton biomass was followed by cascading changes in surface water chemistry associated with bloom degradation, with sequential peaks in dissolved organic carbon, dissolved carbon dioxide, and ammonium concentrations that were up to an order of magnitude higher than the previous year. Dissolved oxygen concentrations substantially decreased in surface waters during drawdown (to 41% saturation), which was associated with increased total iron and manganese concentrations. Combined, our results illustrate how changes in water level can have cascading effects on coupled physical, chemical, and biological processes. As climate change and water management continue to increase the frequency of drawdowns in lakes worldwide, our results highlight the importance of characterizing how water level variability can alter complex in‐lake ecosystem processes, thereby affecting water quality. Changes in climate and water management are having substantial effects on the water level of lakes and reservoirs around the world. In particular, the frequency with which waterbodies experience water level drawdowns is increasing worldwide. However, the effects of drawdown on aquatic physics, chemistry, and biology remain poorly understood. Here, we intensively monitored before, during, and after a drawdown in Beaverdam Reservoir, VA, USA, during which water volume decreased by 36% over the course of 1 month. Physical changes to the waterbody during drawdown led to increased nutrient concentrations in surface water, which contributed to the formation of a substantial phytoplankton bloom. Degradation of the phytoplankton bloom then resulted in decreased dissolved oxygen and increased dissolved carbon dioxide in surface waters, among other chemical changes. Combined, our results illustrate how changes in water level can have cascading effects on coupled physical, chemical, and biological processes in lakes. As drawdowns become increasingly common, our results highlight the effects that drawdown can have on interconnected aquatic processes, with important implications for water quality worldwide. A month‐long drawdown (36% volume reduction) in a eutrophic reservoir substantially changed water quality relative to a reference yearCoincident with drawdown, both stratification strength at the thermocline and epilimnetic nutrient concentrations increasedA subsequent cyanobacterial bloom led to decreased surface dissolved oxygen and increases in solutes associated with bloom degradation A month‐long drawdown (36% volume reduction) in a eutrophic reservoir substantially changed water quality relative to a reference year Coincident with drawdown, both stratification strength at the thermocline and epilimnetic nutrient concentrations increased A subsequent cyanobacterial bloom led to decreased surface dissolved oxygen and increases in solutes associated with bloom degradation

Small freshwater reservoirs are ubiquitous and likely play an important role in global greenhouse gas (GHG) budgets relative to their limited water surface area. However, constraining annual GHG fluxes in small freshwater reservoirs is challenging given their footprint area and spatially and temporally variable emissions. To quantify the GHG budget of a small (0.1 km2) reservoir, we deployed an Eddy covariance (EC) system in a small reservoir located in southwestern Virginia, USA over 2 years to measure carbon dioxide (CO2) and methane (CH4) fluxes near‐continuously. Fluxes were coupled with in situ sensors measuring multiple environmental parameters. Over both years, we found the reservoir to be a large source of CO2(633–731 g CO2‐C m−2yr−1) and CH4(1.02–1.29 g CH4‐C m−2yr−1) to the atmosphere, with substantial sub‐daily, daily, weekly, and seasonal timescales of variability. For example, fluxes were substantially greater during the summer thermally stratified season as compared to the winter. In addition, we observed significantly greater GHG fluxes during winter intermittent ice‐on conditions as compared to continuous ice‐on conditions, suggesting GHG emissions from lakes and reservoirs may increase with predicted decreases in winter ice‐cover. Finally, we identified several key environmental variables that may be driving reservoir GHG fluxes at multiple timescales, including, surface water temperature and thermocline depth followed by fluorescent dissolved organic matter. Overall, our novel year‐round EC data from a small reservoir indicate that these freshwater ecosystems likely contribute a substantial amount of CO2and CH4to global GHG budgets, relative to their surface area. Freshwater ecosystems release substantial amounts of greenhouse gases, especially carbon dioxide (CO2) and methane, to the atmosphere. Small waterbodies, such as lakes and reservoirs, are common in the landscape and may release particularly high levels of greenhouse gases, though their overall contribution remains unknown. The most common methods to date for estimating greenhouse gas emissions from freshwaters typically involve only measuring concentrations during the daytime on a handful of days throughout the year. Thus, there is a clear need for near‐continuous measurements of CO2and methane from small waterbodies throughout the year on multiple timescales (hours to years). To do this, we measured fluxes of CO2and methane from a small reservoir using eddy covariance over 2 years. We found this small reservoir to be a large source of both CO2and methane to the atmosphere over 2 years and found high variability in fluxes measured at short (sub‐daily) to long (seasonal) timescales. Overall, this study demonstrates the importance of small reservoirs as greenhouse gas sources to the atmosphere and emphasizes the need for additional measurements to estimate their contribution to global greenhouse gas budgets. We measured high annual CO2and CH4fluxes over 2 years from a small reservoirFluxes were higher in the summer than winter, with statistically higher fluxes during intermittent ice‐on as compared to continuous ice‐onSurface water temperature, thermocline depth, and dissolved organic matter concentrations were correlated with reservoir fluxes We measured high annual CO2and CH4fluxes over 2 years from a small reservoir Fluxes were higher in the summer than winter, with statistically higher fluxes during intermittent ice‐on as compared to continuous ice‐on Surface water temperature, thermocline depth, and dissolved organic matter concentrations were correlated with reservoir fluxes