Wildfires are becoming larger and more frequent across much of the US due to a combination of climate change and land use activities. Increasing wildfires have begun to raise concerns about effects on fresh waters, including water quality and other ecosystem services. Despite this, previous research mostly consists of short-term case studies and focuses on streams and rivers rather than lakes and reservoirs (hereafter, lakes). Using the Monitoring Trends in Burn Severity (MTBS) database, we show that 4.5% of lakes ≥ 1 ha in the continental US experienced at least one watershed wildfire from 1984-2016. Interestingly, lake watershed fires are not restricted to the western US. Of all the lower 48 states, Florida, Texas and Kansas were the top 3 states with the most lakes experiencing wildfire, whereas Idaho, Arizona and Nevada were the top 3 states by percentage of lakes in respective states experiencing wildfire. Using the LAGOS-US database, we present new regional-scale findings demonstrating effects of large wildfires on lake water quality. For example, we found a negative correlation between post-fire lake water clarity and the proportion of a lake’s watershed burned in 11 Minnesota and Wisconsin lakes (r = -0.61). We highlight the urgent need for more broad-scale studies that encompass an ecologically diverse set of waterbodies, landscapes and fire regimes, particularly in landscapes in which humans depend on lakes for fresh water. Finally, we emphasize that growing data sources such as MTBS and continental-scale water quality databases (e.g., LAGOS-US) offer prime opportunities for research advances that can help scale up findings from local case studies.

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.