In vivo fluorometers use chlorophyll a fluorescence (F_chl) as a proxy to monitor phytoplankton biomass. However, the fluorescence yield of F_chl is affected by photoprotection processes triggered by increased irradiance (nonphotochemical quenching; NPQ), creating diurnal reductions in F_chl that may be mistaken for phytoplankton biomass reductions. Published correction methods are mostly designed for pelagic oceans and are ill suited for inland waters or for high‐frequency data collection. A machine learning‐based method was developed to correct vertical profiler data from an oligotrophic lake. NPQ was estimated as a percent reduction in F_chl by comparing daytime values to mean, unquenched values from the previous night. A random forest regression was trained on sensor data collected coincident with F_chl; including solar radiation, water temperature, depth, and dissolved oxygen saturation. The accuracy of the model was assessed using a grouped 10‐fold cross validation (mean absolute error [MAE]: 7.6%; root mean square error [RMSE]: 10.2%), which was then used to correct F_chl profiles. The model also predicted NPQ and corrected unseen F_chl profiles from a future period with excellent results (MAE: 9.0%; RMSE: 14.4%). F_chl profiles were then correlated to laboratory results, allowing corrected profiles to be compared directly to collected samples. The correction reduced error (RMSE) due to NPQ from 0.67 μg L⁻¹ to 0.33 μg L⁻¹ when compared to uncorrected F_chl data. These results suggest that the use of machine learning models may be an effective way to correct for NPQ and may have universal applicability.

A very detailed water budget analysis was conducted on Lake Trafford in South Florida. The inflow was dominated by surface water influx via five canals (61%), with groundwater influx constituting 12% and direct rainfall constituting 27%. Lake discharge was dominated by sheet flow (69%) and evapotranspiration (30.5%), with groundwater recharge of the hydraulically connected unconfined aquifer accounting for only 0.5%. The removal of 30 M tons (4.4 × 10⁶ m³) of organic sediment impacted the groundwater influx, causing enhanced groundwater flow into the deeper parts of the lake and mixed flow along the banks, creating a rather unusual pattern. The large number of groundwater seepage meters used during this investigation led to a very reliable set of measurements with occasional failure of only a few meters. A distinctive relationship was found between the wet-season lake stage, heavy rainfall events, and pulses of exiting sheet flow from the lake. Estimation of the evapotranspiration loss using data collected from a weather station on the lake allowed the use of three different models, which, when averaged, produced results comparable to Lake Okeechobee (South Florida). A limitation of this investigation was the inability to directly measure sheet-flow discharges, which had to be estimated as a residual within the calculated water budget.

Lake Trafford, a 600-ha subtropical lake in southwestern Florida, has suffered from over 50 years of cultural eutrophication, resulting in the invasion of Hydrilla verticillata and organic sediment accumulation due to herbicide treatments. This study aimed to assess the effects of dredging on nutrient dynamics. A pre-dredging nutrient budget, developed using land use models and climatic data, estimated nutrient loads of 190 kg d⁻¹ for total nitrogen (TN) and 18.6 kg d⁻¹ for total phosphorus (TP), with total maximum daily loads (TMDLs) of 70.4 kg d⁻¹ for TN and 4.15 kg d⁻¹ for TP. Post-dredging analysis, using detailed spatiotemporal data, showed higher nutrient loads of 274.3 kg d⁻¹ for TN and 24.2 kg d⁻¹ for TP. While dredging reduced legacy nutrient accumulation, it led to increased nutrient influx from groundwater, caused by the exposure of organic sediment, as evidenced by increased lake water electrical conductivity. These findings demonstrate the importance of conducting thorough pre-dredging assessments to mitigate unintended consequences, offering practical insights for managing nutrient loads and improving restoration strategies in eutrophic lakes.

A very detailed water budget was conducted on Lake Trafford in southern Florida. The inflow was dominated by surface-water influx via five canals (61%) with groundwater influx constituting 12% and direct rainfall 27%. Lake discharge was dominated by sheet-flow (69%) and evapotranspiration (30.5%) with groundwater recharge of the hydraulically connected unconfined aquifer accounting for only 0.5%. Removal of 30 M tons (4.4 x 106 m3) of organic sediment impacted groundwater influx, causing enhanced groundwater flows into the deeper parts of the lake and mixed flows along the banks creating a rather unusual pattern. The large number of groundwater seepage meters used during the investigation led to a very reliable set of measurements with occasional failure of only a few meters. A distinctive relationship was found between the wet season lake stage, heavy rainfall events and pulses of exiting sheet-flow from the lake. Estimation of the evapotranspiration loss using data collected from a weather station on the lake allowed the use of three different models which, when averaged, produced results comparable to Lake Okeechobee (southern Florida). A limitation of the investigation was the inability to directly measure sheet-flow discharges, which had to be estimated as a residual within the calculated water budget.

To measure chlorophylla(Chla) fluorescence (F_chl), fluorometers use an excitation wavelength that is within the visible spectrum of most zooplankton, and as a result has the potential to cause a phototactic response in zooplankton. The transparent bodies of herbivorous zooplankton may allow viable chlorophyllawithin an individual's digestive tract to fluoresce in response to sensor excitation light, resulting in measurement bias. To test for this bias, a fully factorial (± zooplankton and ± light) experiment was conducted in an oligotrophic lake. Excitation light from fluorometers triggered a positive phototactic response during nighttime hours, resulting in swarms of zooplankton congregating beneath the sensor. The maximum hourly meanF_chlfrom nighttime/open treatments was higher and more variable than nighttime/zooplankton exclusion treatments, with the greatest single hour difference of 7.34 relative fluorescence units (RFU) vs. 0.26 RFU. In open treatments, sustained periods ofF_chlexceeded 31x the values of exclusion treatments. A second series of experiments pulsed excitation lights in alternating periods in order to characterize zooplankton response times. Sensor bias was detected in as little as 20 s after initial illumination. Collectively, these results suggest that swarms of phototactic zooplankton can cause substantial bias inF_chlmeasurements at night. To correct for this bias, post‐processing methods using time series decomposition were demonstrated to remove the majority ofF_chlbias.

Cyanobacterial harmful algal blooms (cyanoHABs) are often associated with warm water temperatures and low wind speeds, but quantifying thresholds in meteorological conditions is challenging. Using 4208 cyanoHAB reports from 405 lakes in New York State (USA), we calculated anomalies in daily wind speed and air temperature for each report and the 5 d preceding it. On the day of a cyanoHAB, lakes had lower than average wind speeds and higher than average air temperatures. Notably, the magnitude of these anomalies was influenced by the trophic state of each lake, with nutrient‐limited lakes having larger anomalies on the day of a cyanoHAB. Additionally, the absence of either anomaly type reduced the likelihood of a cyanoHAB, particularly in nutrient‐poor lakes. These results enhance our understanding of the proximate drivers and thresholds regulating cyanoHABs across varying trophic states, suggesting that large‐scale changes in climate may substantially impact future cyanoHAB development.