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1. Uncertainty in projections of future lake thermal dynamics is differentially driven by lake and global climate models

   https://doi.org/10.7717/peerj.15445  

   Wynne, Jacob H.; Woelmer, Whitney; Moore, Tadhg N.; Thomas, R. Quinn; Weathers, Kathleen C.; Carey, Cayelan C. (January 2023, PeerJ)

   Freshwater ecosystems provide vital services, yet are facing increasing risks from global change. In particular, lake thermal dynamics have been altered around the world as a result of climate change, necessitating a predictive understanding of how climate will continue to alter lakes in the future as well as the associated uncertainty in these predictions. Numerous sources of uncertainty affect projections of future lake conditions but few are quantified, limiting the use of lake modeling projections as management tools. To quantify and evaluate the effects of two potentially important sources of uncertainty, lake model selection uncertainty and climate model selection uncertainty, we developed ensemble projections of lake thermal dynamics for a dimictic lake in New Hampshire, USA (Lake Sunapee). Our ensemble projections used four different climate models as inputs to five vertical one-dimensional (1-D) hydrodynamic lake models under three different climate change scenarios to simulate thermal metrics from 2006 to 2099. We found that almost all the lake thermal metrics modeled (surface water temperature, bottom water temperature, Schmidt stability, stratification duration, and ice cover, but not thermocline depth) are projected to change over the next century. Importantly, we found that the dominant source of uncertainty varied among the thermal metrics, as thermal metrics associated with the surface waters (surface water temperature, total ice duration) were driven primarily by climate model selection uncertainty, while metrics associated with deeper depths (bottom water temperature, stratification duration) were dominated by lake model selection uncertainty. Consequently, our results indicate that researchers generating projections of lake bottom water metrics should prioritize including multiple lake models for best capturing projection uncertainty, while those focusing on lake surface metrics should prioritize including multiple climate models. Overall, our ensemble modeling study reveals important information on how climate change will affect lake thermal properties, and also provides some of the first analyses on how climate model selection uncertainty and lake model selection uncertainty interact to affect projections of future lake dynamics. 

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2. A General Lake Model (GLM 2.4) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)

   https://doi.org/0.5194/gmd-2017-257  

   Matthew R Hipsey, Louise C (November 2017, Geoscientific model development)

   The General Lake Model (GLM) is a one-dimensional open-source model code designed to simulate the hydrodynamics of lakes, reservoirs and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of lake sensors and researchers attempting to understand lake functioning and address questions about how lakes around the world vary in response to climate and land-use change. The scale and diversity of lake types, locations and sizes, as well as the observational data within GLEON, created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management needs of the GLEON community. This paper summarises the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice-cover dynamics, vertical mixing and inflow/outflow dynamics. A summary of typical parameter values for lakes and reservoirs collated from a range of sources is included. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health. An overview of approaches for integration with other models, and utilities for the analysis of model outputs and for undertaking sensitivity and uncertainty assessments is also provided. Finally, we discuss application of the model within a distributed cloud-computing environment, and as a tool to support learning of network participants. 

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3. Remote Sensing of Water Quantity and Quality in Geospatial Education: Lake Sidney Lanier, Georgia, USA

   Brookins-Jackson, C.D.; Boyd, Z.L.; Freeland, A.C.; Mann, B.L.; Perry, M.K.; Ignatius, A.R. (January 2022, Apalachicola-Chattahoochee-Flint Waters Conference: Shared Resources in Changing Times. Albany, GA)

   To increase geospatial awareness about local water resources, our team developed learning resources for the 150 km² Lake Sidney Lanier reservoir located in North Georgia, USA. The reservoir is vital for hydroelectric power generation, recreation, tourism, and consumptive uses. Using geospatial analysis in Google Earth Engine (GEE), we analyzed precipitation trends in the watershed using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. We also quantified expansion and contraction of reservoir surface area using Landsat-derived Global Surface Water data. As Lake Sidney Lanier is a managed reservoir, surface water extent fluctuations are related to climatic variables, consumptive use, and hydropower generation. Water temperature varies based on seasonality, water depth, water clarity, and lake stratification. Changing temperature dynamics affect ecosystem health and determine other important water quality parameters such as dissolved oxygen concentrations. Landsat 8 Thermal Infrared Sensor (TIRS) data were used to examine temperature trends over multiple years and investigate the timing of lake stratification and mixing. Highly turbid waters are associated with pollutants and lower water quality and can affect ecosystem productivity by minimizing sunlight penetration into the water column. Sentinel 2 MSI data were processed using a turbidity algorithm to analyze temporal trends and spatial correlations with reservoir inflows. Finally, high concentrations of chlorophyll a were used as a proxy to identify harmful algal blooms. The spatial differences in headwaters and near-dam locations were examined and near real-time satellite data were explored for potential development of early-warning systems to protect ecosystem and human health. 

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4. Deeper waters are changing less consistently than surface waters in a global analysis of 102 lakes

   https://doi.org/10.1038/s41598-020-76873-x  

   Pilla, Rachel M.; Williamson, Craig E.; Adamovich, Boris V.; Adrian, Rita; Anneville, Orlane; Chandra, Sudeep; Colom-Montero, William; Devlin, Shawn P.; Dix, Margaret A.; Dokulil, Martin T.; et al (December 2020, Scientific Reports)

   null (Ed.)

   Abstract Globally, lake surface water temperatures have warmed rapidly relative to air temperatures, but changes in deepwater temperatures and vertical thermal structure are still largely unknown. We have compiled the most comprehensive data set to date of long-term (1970–2009) summertime vertical temperature profiles in lakes across the world to examine trends and drivers of whole-lake vertical thermal structure. We found significant increases in surface water temperatures across lakes at an average rate of + 0.37 °C decade −1 , comparable to changes reported previously for other lakes, and similarly consistent trends of increasing water column stability (+ 0.08 kg m −3 decade −1 ). In contrast, however, deepwater temperature trends showed little change on average (+ 0.06 °C decade −1 ), but had high variability across lakes, with trends in individual lakes ranging from − 0.68 °C decade −1 to + 0.65 °C decade −1 . The variability in deepwater temperature trends was not explained by trends in either surface water temperatures or thermal stability within lakes, and only 8.4% was explained by lake thermal region or local lake characteristics in a random forest analysis. These findings suggest that external drivers beyond our tested lake characteristics are important in explaining long-term trends in thermal structure, such as local to regional climate patterns or additional external anthropogenic influences. 

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5. Diagnosing Discrepancies between Observations and Models of Surface Energy Fluxes in a Midlatitude Lake

   https://doi.org/10.1175/JHM-D-21-0141.1  

   Taebel, Zachary W.; Reed, David E.; Desai, Ankur R. (June 2022, Journal of Hydrometeorology)

   Abstract The physical processes of heat exchange between lakes and the surrounding atmosphere are important in simulating and predicting terrestrial surface energy balance. Latent and sensible heat fluxes are the dominant physical process controlling ice growth and decay on the lake surface, as well as having influence on regional climate. While one-dimensional lake models have been used in simulating environmental changes in ice dynamics and water temperature, understanding the seasonal to daily cycles of lake surface energy balance and its relationship to lake thermal properties, atmospheric conditions, and how those are represented in models is still an open area of research. We evaluated a pair of one-dimensional lake models, Freshwater Lake (FLake) and the General Lake Model (GLM), to compare modeled latent and sensible heat fluxes against observed data collected by an eddy covariance tower during a 1-yr period in 2017, using Lake Mendota in Madison, Wisconsin, as our study site. We hypothesized transitional periods of ice cover as a leading source of model uncertainty, and we instead found that the models failed to simulate accurate values for large positive heat fluxes that occurred from late August into late December. Our results ultimately showed that one-dimensional models are effective in simulating sensible heat fluxes but are considerably less sensitive to latent heat fluxes than the observed relationships of latent heat flux to environmental drivers. These results can be used to focus future improvement of these lake models especially if they are to be used for surface boundary conditions in regional numerical weather models. Significance Statement While lakes consist of a small amount of Earth’s surface, they have a large impact on local climate and weather. A large amount of energy is stored in lakes during the spring and summer, and then removed from lakes before winter. The effect is particularly noticeable in high latitudes, when the seasonal temperature difference is larger. Modeling this lake energy exchange is important for weather models and measuring this energy exchange is challenging. Here we compare modeled and observed energy exchange, and we show there are large amounts of energy exchange happening in the fall, which models struggle to capture well. During periods of partial ice coverage in early winter, lake behavior can change rapidly. 

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