Antecedent lake conditions shape resistance and resilience of a shallow lake ecosystem following extreme wind storms

Extreme wind storms can strongly influence short-term variation in lake ecosystem functioning. Climate change is affecting storms by altering their frequency, duration, and intensity, which may have consequences for lake ecosystem resistance and resilience. However, catchment and lake processes are simultaneously affecting antecedent lake conditions which may shape the resistance and resilience landscape prior to storm exposure. To determine whether storm characteristics or antecedent lake conditions are more important for explaining variation in lake ecosystem resistance and resilience, we analyzed the effects of 25 extreme wind storms on various biological and physiochemical variables in a shallow lake. Using boosted regression trees to model observed variation in resistance and resilience, we found that antecedent lake conditions were more important (relative importance = 67%) than storm characteristics (relative importance = 33%) in explaining variation in lake ecosystem resistance and resilience. The most important antecedent lake conditions were turbidity, Schmidt stability, %O2 saturation, light conditions, and soluble reactive silica concentrations. We found that storm characteristics were all similar in their relative importance and results suggest that resistance and resilience decrease with increasing duration, mean precipitation, shear stress intensity, and time between storms. In addition, we found that antagonistic or opposing effects between the biological and physiochemical variables influence the overall resistance and resilience of the lake ecosystem under specific lake and storm conditions. The extent to which these results apply to the resistance and resilience of different lake ecosystems remains an important area for inquiry

Phytoplankton responses to repeated pulse perturbations imposed on a trend of increasing eutrophication

While eutrophication remains one of the main pressures acting on freshwater ecosystems, the prevalence of anthropogenic and nature-induced stochastic pulse perturbations is predicted to increase due to climate change. Despite all our knowledge on the effects of eutrophication and stochastic events operating in isolation, we know little about how eutrophication may affect the response and recovery of aquatic ecosystems to pulse perturbations. There are multiple ways in which eutrophication and pulse perturbations may interact to induce potentially synergic changes in the system, for instance, by increasing the amount of nutrients released after a pulse perturbation. Here, we performed a controlled press and pulse perturbation experiment using mesocosms filled with natural lake water to address how eutrophication modulates the phytoplankton response to sequential mortality pulse perturbations; and what is the combined effect of press and pulse perturbations on the resistance and resilience of the phytoplankton community. Our experiment showed that eutrophication increased the absolute scale of the chlorophyll-a response to pulse perturbations but did not change the proportion of the response relative to its pre-event condition (resistance). Moreover, the capacity of the community to recover from pulse perturbations was significantly affected by the cumulative effect of sequential pulse perturbations but not by eutrophication itself. By the end of the experiment, some mesocosms could not recover from pulse perturbations, irrespective of the trophic state induced by the press perturbation. While not resisting or recovering any less from pulse perturbations, phytoplankton communities from eutrophying systems showed chlorophyll-a levels much higher than non-eutrophying ones. This implies that the higher absolute response to stochastic pulse perturbations in a eutrophying system may increase the already significant risks for water quality (e.g., algal blooms in drinking water supplies), even if the relative scale of the response to pulse perturbations between eutrophying and non-eutrophying systems remains the same

Drivers of phytoplankton responses to summer wind events in a stratified lake: A modeling study

Extreme wind events affect lake phytoplankton by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases in phytoplankton concentration after strong wind events have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. We coupled a one-dimensional physical model to a biogeochemical model to investigate the factors regulating short-term phytoplankton responses to summer wind events, now and under expected warmer future conditions. We simulated physical, chemical, and biological dynamics in Lake Erken, Sweden, and found that strong wind could increase or decrease the phytoplankton concentration in the euphotic zone 1 week after the event, depending on antecedent lake physical and chemical conditions. Wind had little effect on phytoplankton concentration if the mixed layer was deep prior to wind exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted phytoplankton concentration, whereas higher surface water temperatures decreased concentrations after wind events. Medium-intensity wind events resulted in more phytoplankton than high-intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind events affect phytoplankton concentration. These findings help to better understand how wind impacts vary as a function of local environmental conditions and how climate warming and changing extreme weather dynamics will affect lake ecosystems

Drivers of phytoplankton responses to summer wind events in a stratified lake: A modeling study

Extreme wind events affect lake phytoplankton by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases in phytoplankton concentration after strong wind events have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. We coupled a one-dimensional physical model to a biogeochemical model to investigate the factors regulating short-term phytoplankton responses to summer wind events, now and under expected warmer future conditions. We simulated physical, chemical, and biological dynamics in Lake Erken, Sweden, and found that strong wind could increase or decrease the phytoplankton concentration in the euphotic zone 1 week after the event, depending on antecedent lake physical and chemical conditions. Wind had little effect on phytoplankton concentration if the mixed layer was deep prior to wind exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted phytoplankton concentration, whereas higher surface water temperatures decreased concentrations after wind events. Medium-intensity wind events resulted in more phytoplankton than high-intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind events affect phytoplankton concentration. These findings help to better understand how wind impacts vary as a function of local environmental conditions and how climate warming and changing extreme weather dynamics will affect lake ecosystems

LakeEnsemblR: an R package that facilitates ensemble modelling of lakes

Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes

Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes

Small waterbodies have potentially high greenhouse gas emissions relative to their small footprint on the landscape, although there is high uncertainty in model estimates. Scaling their carbon dioxide (CO2) and methane (CH4) exchange with the atmosphere remains challenging due to an incomplete understanding and characterization of spatial and temporal variability in CO2 and CH4. Here, we measured partial pressures of CO2 (pCO2) and CH4 (pCH4) across 30 ponds and shallow lakes during summer in temperate regions of Europe and North America. We sampled each waterbody in three locations at three times during the growing season, and tested which physical, chemical, and biological characteristics related to the means and variability of pCO2 and pCH4 in space and time. Summer means of pCO2 and pCH4 were inversely related to waterbody size and positively related to floating vegetative cover; pCO2 was also positively related to dissolved phosphorus. Temporal variability in partial pressure in both gases weas greater than spatial variability. Although sampling on a single date was likely to misestimate mean seasonal pCO2 by up to 26%, mean seasonal pCH4 could be misestimated by up to 64.5%. Shallower systems displayed the most temporal variability in pCH4 and waterbodies with more vegetation cover had lower temporal variability. Inland waters remain one of the most uncertain components of the global carbon budget; understanding spatial and temporal variability will ultimately help us to constrain our estimates and inform research priorities

Phenological shifts in lake stratification under climate change

One of the most important physical characteristics driving lifecycle events in lakes is stratification. Already subtle variations in the timing of stratification onset and break-up (phenology) are known to have major ecological effects, mainly by determining the availability of light, nutrients, carbon and oxygen to organisms. Despite its ecological importance, historic and future global changes in stratification phenology are unknown. Here, we used a lake-climate model ensemble and long-term observational data, to investigate changes in lake stratification phenology across the Northern Hemisphere from 1901 to 2099. Under the high-greenhouse-gas-emission scenario, stratification will begin 22.0 ± 7.0 days earlier and end 11.3 ± 4.7 days later by the end of this century. It is very likely that this 33.3 ± 11.7 day prolongation in stratification will accelerate lake deoxygenation with subsequent effects on nutrient mineralization and phosphorus release from lake sediments. Further misalignment of lifecycle events, with possible irreversible changes for lake ecosystems, is also likely

Assessing future effects on lake ecosystem resilience using data analysis and dynamic modelling : Modelling the effects of extreme weather events and climate warming on lakes

Extreme weather events can have short-term and long-term effects on lake thermal structure, nutrient dynamics, and community composition. Moreover, changes in lake variables induced by global climate change may influence the response and recovery of lake ecosystems to extreme weather events. The linkage between extreme weather and lakes includes interactions between physics and biology, and long-term and short-term dynamics, which are not yet well understood. Process-based modelling is used in this thesis to further explore this topic, and to assess how lake responses to extreme weather events may change under the influence of climate warming. Lake-internal feedback mechanisms were shown to potentially cause sudden shifts in climate-induced transitions in lake mixing regimes, with a role for extreme weather events to induce such shifts. Additionally, one-dimensional physical lake models performed well in reproducing trends in lake variables during storms and heatwaves in a study covering multiple locations and models. However, extreme weather events still presented periods of increased model uncertainty, which should be taken into account. A software package was developed to promote the use of ensemble lake modelling, which is one way to include uncertainty in model forecasting efforts. This could be particularly helpful in periods of extreme weather. With tools and theory now in place, a coupled physical-biogeochemical model was then used to assess what are the most important drivers of how lake phytoplankton responds to storms, and how this response might change with climate warming. Storm intensity, thermal structure, nutrients, and light all affected the phytoplankton concentration after storms. Moderate wind speeds had increasing effects compared to high wind speeds, but a sufficiently deep mixed layer reduced the response to wind strongly. Higher nutrients and light promoted increasing effects of wind, and higher temperatures promoted decreasing effects. The response of phytoplankton to storms did not change markedly between present-day and future-climate scenarios. This thesis furthers our understanding of the processes involved in extreme events acting on lakes. A more complete understanding is necessary to develop more reliable models and anticipate future conditions. Furthermore, modelling was shown to be a viable approach to study these events and validation data and tools were provided to increase the reliability of this method. In these times of increasing environmental pressures and changing extreme weather patterns, more insight into future effects of extreme events is much needed.I do not yet have all the information about the defence; I do not know the exact room and address (but it will be in Geneva and livestreamed in Uppsala), nor do I have the Zoom link yet. I could not select a date without selecting a time; we do not have the exact time yet, but it will be in the 21st of October in the afternoon. 13:00 is a placeholder. I do not live in Sweden and will not be able to bring the copies to Carolina Redeviva in person. Another solution needs to be found, but perhaps my supervisor could do it? </p

Prediction of algal blooms via data-driven machine learning models : an evaluation using data from a well-monitored mesotrophic lake

With increasing lake monitoring data, data-drivenmachine learning (ML) models might be able to capture thecomplex algal bloom dynamics that cannot be completely described in process-based (PB) models. We applied two MLmodels, the gradient boost regressor (GBR) and long shortterm memory (LSTM) network, to predict algal blooms andseasonal changes in algal chlorophyll concentrations (Chl) ina mesotrophic lake. Three predictive workflows were tested,one based solely on available measurements and the othersapplying a two-step approach, first estimating lake nutrientsthat have limited observations and then predicting Chl usingobserved and pre-generated environmental factors. The thirdworkflow was developed using hydrodynamic data derivedfrom a PB model as additional training features in the twostep ML approach. The performance of the ML models wassuperior to a PB model in predicting nutrients and Chl. Thehybrid model further improved the prediction of the timingand magnitude of algal blooms. A data sparsity test based onshuffling the order of training and testing years showed theaccuracy of ML models decreased with increasing sampleinterval, and model performance varied with training–testingyear combinations