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.

Global environmental science challenges in the limnological research and applications communities can only be advanced when harnessing the collective expertise and capabilities of the satellite remote sensing community and well-established in situ communities such as the Global Lake Ecological Observatory Network (GLEON). At first glance, the groups seem wildly divergent: GLEON is a grass-roots effort which has been active since 2005 and connects researchers and practitioners from around the world to ask and answer questions about lake ecosystems. Earth observing missions can take a decade to plan, build, and launch. NASA and ESA have different missions as space agencies: one primarily focused on exploration and basic research with a year-to-year appropriations cycle, while the other presents a long-term commitment to address societal needs through the Copernicus program Sentinel satellite series. The Surface Biology and Geology (SBG) mission is a future NASA satellite that will launch toward the end of this decade as part of the Earth Systems Observatory. Working together to advance the science of lake ecosystem response to climate change, each group brings different complementary strengths and assets to this societal challenge. Increasing access through open science and cloud computing are creating opportunities for better collaboration. We describe our strategy for international engagement between these groups – cultural and methodological differences aside – to derive new information, learn new insights, and expand the body of knowledge around these unique natural resources.

Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water and air quality. Importantly, ecological forecasts can identify where uncertainty enters the forecasting system, which is necessary to refine and improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance (uncertainty) introduced by different sources, including specification of the model structure, errors in driver data, and estimation of initial state conditions. Uncertainty partitioning could be particularly useful in improving forecasts of high-density cyanobacterial events, which are difficult to predict and present a persistent challenge for lake managers. Cyanobacteria can produce toxic or unsightly surface scums and advance warning of these events could help managers mitigate water quality issues. Here, we calibrate fourteen Bayesian state-space models to evaluate different hypotheses about cyanobacterial growth using data from eight summers of weekly cyanobacteria density samples in an oligotrophic (low nutrient) lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We identify dominant sources of uncertainty for near-term (one-week to four-week) forecasts of G. echinulata densities over two years. Water temperature was an important predictor in calibration and at the four-week forecast horizon. However, no environmental covariates improved over a simple autoregressive (AR) model at the one-week horizon. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and often did not capture rare peak density occurrences, indicating that significant explanatory variables in calibration are not always effective for near-term forecasting of low-frequency events. Uncertainty partitioning revealed that model process specification and initial conditions uncertainty dominated forecasts at both time horizons. These findings suggest that observed densities result from both growth and movement of G. echinulata, and that imperfect observations as well as spatial misalignment of environmental data and cyanobacteria observations affect forecast skill. Future research efforts should prioritize long-term studies to refine process understanding and increased sampling frequency and replication to better define initial conditions. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

Assessing and understanding the extent and trajectory of change in inland waters is a great challenge, due in part to both differing methods — and cultures — of agencies that provide synoptic observations of Earth’s systems as well as the community of lake scientists whose research generates heterogeneous and distributed in situ data. Advancements require socio-technological initiatives that harness the resources of the highly diverse and distributed community of ecologists, as well as the products and expertise of the satellite remote sensing community. Here we describe a prototype for linking in situ and remotely sensed data for lakes through the collaborative efforts of the Global Lake Ecological Observatory Network (GLEON), the Environmental Data Initiative (EDI), and NASA. GLEON provides a community of lake scientists and data from lake observatories. EDI curates and publishes data and ensures conformity to rigorous FAIR principles. NASA provides the expertise and workflows to deliver remotely sensed data products on demand. The integration of the data and the communities provides a foundation for a new generation of lake science.