| 21. |
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| 22. |
- Jiménez-Navarro, Inmaculada C., et al.
(författare)
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Application of an integrated catchment-lake model approach for simulating effects of climate change on lake inputs and biogeochemistry
- 2023
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Ingår i: Science of the Total Environment. - : Elsevier. - 0048-9697 .- 1879-1026. ; 885
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Tidskriftsartikel (refereegranskat)abstract
- Climate change is simultaneously affecting lakes and their catchments, resulting in altered runoff patterns in the catchment and modified mixing and biogeochemical dynamics in lakes. The effects of climate change in a catchment will eventually have an impact on the dynamics of a downstream water body as well. An integrated model would allow considering how changes in the watershed affect the lake, but coupled modelling studies are rare. In this study we integrate a catchment model (SWAT+) and a lake model (GOTM-WET) to obtain holistic predictions for Lake Erken, Sweden. Using five different global climate models, projections of climate, catchment loads and lake water quality for the mid and end of the 21st century have been obtained under two future scenarios (SSP 2-45 and SSP 5-85). Temperature, precipitation and evapotranspiration will increase in the future, overall resulting in an increase in water inflow to the lake. An increasing importance of surface runoff will also have consequences on the catchment soil, hydrologic flow paths, and the input of nutrients to the lake. In the lake, water temperatures will rise, leading to increased stratification and a drop in oxygen levels. Nitrate levels are predicted to remain unchanged, while phosphate and ammonium levels increase. A coupled catchment-lake configuration such as that illustrated here allows prediction of future biogeochemical conditions of a lake, including linking land use changes to changing lake conditions, as well as eutrophication and browning studies. Since climate affects both the lake and the catchment, simulations of climate change should ideally take into account both systems.
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| 23. |
- Lin, Shuqi, et al.
(författare)
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Multi-Model Machine Learning Approach Accurately Predicts Lake Dissolved Oxygen With Multiple Environmental Inputs
- 2024
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Ingår i: Earth and Space Science. - : American Geophysical Union (AGU). - 2333-5084. ; 11:7
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Tidskriftsartikel (refereegranskat)abstract
- As a key water quality parameter, dissolved oxygen (DO) concentration, and particularly changes in bottom water DO is fundamental for understanding the biogeochemical processes in lake ecosystems. Based on two machine learning (ML) models, Gradient Boost Regressor (GBR) and long-short-term-memory (LSTM) network, this study developed three ML model approaches: direct GBR; direct LSTM; and a 2-step mixed ML model workflow combining both GBR and LSTM. They were used to simulate multi-year surface and bottom DO concentrations in five lakes. All approaches were trained with readily available environmental data as predictors. Indices of lake thermal structure and mixing provided by a one-dimensional (1-D) hydrodynamic model were also included as predictors in the ML models. The advantages of each ML approach were not consistent for all the tested lakes, but the best one of them was defined that can estimate DO concentration with coefficient of determination (R2) up to 0.6-0.7 in each lake. All three approaches have normalized mean absolute error (NMAE) under 0.15. In a polymictic lake, the 2-step mixed model workflow showed better representation of bottom DO concentrations, with a highest true positive rate (TPR) of hypolimnetic hypoxia detection of over 90%, while the other workflows resulted in, TPRs are around 50%. In most of the tested lakes, the predicted surface DO concentrations and variables indicating stratified conditions (i.e., Wedderburn number and the temperature difference between surface and bottom water) are essential for simulating bottom DO. The ML approaches showed promising results and could be used to support short- and long-term water management plans.
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| 24. |
- Lin, Shuqi, et al.
(författare)
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Prediction of algal blooms via data-driven machine learning models : an evaluation using data from a well-monitored mesotrophic lake
- 2023
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Ingår i: Geoscientific Model Development. - : Copernicus Publications. - 1991-959X .- 1991-9603. ; 16:1, s. 35-46
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 25. |
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| 26. |
- Mantzouki, Evanthia, et al.
(författare)
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A European Multi Lake Survey dataset of environmental variables , phytoplankton pigments and cyanotoxins
- 2018
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 5:October, s. 1-13
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Tidskriftsartikel (refereegranskat)abstract
- Under ongoing climate change and increasing anthropogenic activity, which continuously challenge ecosystem resilience, an in-depth understanding of ecological processes is urgently needed. Lakes, as providers of numerous ecosystem services, face multiple stressors that threaten their functioning. Harmful cyanobacterial blooms are a persistent problem resulting from nutrient pollution and climate-change induced stressors, like poor transparency, increased water temperature and enhanced stratification. Consistency in data collection and analysis methods is necessary to achieve fully comparable datasets and for statistical validity, avoiding issues linked to disparate data sources. The European Multi Lake Survey (EMLS) in summer 2015 was an initiative among scientists from 27 countries to collect and analyse lake physical, chemical and biological variables in a fully standardized manner. This database includes in-situ lake variables along with nutrient, pigment and cyanotoxin data of 369 lakes in Europe, which were centrally analysed in dedicated laboratories. Publishing the EMLS methods and dataset might inspire similar initiatives to study across large geographic areas that will contribute to better understanding lake responses in a changing environment.
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| 27. |
- Mantzouki, Evanthia, et al.
(författare)
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Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins
- 2018
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Ingår i: Toxins. - : MDPI. - 2072-6651. ; 10:4
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Tidskriftsartikel (refereegranskat)abstract
- Insight into how environmental change determines the production and distribution of cyanobacterial toxins is necessary for risk assessment. Management guidelines currently focus on hepatotoxins (microcystins). Increasing attention is given to other classes, such as neurotoxins (e.g., anatoxin-a) and cytotoxins (e.g., cylindrospermopsin) due to their potency. Most studies examine the relationship between individual toxin variants and environmental factors, such as nutrients, temperature and light. In summer 2015, we collected samples across Europe to investigate the effect of nutrient and temperature gradients on the variability of toxin production at a continental scale. Direct and indirect effects of temperature were the main drivers of the spatial distribution in the toxins produced by the cyanobacterial community, the toxin concentrations and toxin quota. Generalized linear models showed that a Toxin Diversity Index (TDI) increased with latitude, while it decreased with water stability. Increases in TDI were explained through a significant increase in toxin variants such as MC-YR, anatoxin and cylindrospermopsin, accompanied by a decreasing presence of MC-LR. While global warming continues, the direct and indirect effects of increased lake temperatures will drive changes in the distribution of cyanobacterial toxins in Europe, potentially promoting selection of a few highly toxic species or strains.
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| 28. |
- Marcé, Rafael, et al.
(författare)
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Automatic High Frequency Monitoring for Improved Lake and Reservoir Management
- 2016
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Ingår i: Environmental Science and Technology. - : American Chemical Society (ACS). - 0013-936X .- 1520-5851. ; 50:20, s. 10780-10794
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Forskningsöversikt (refereegranskat)abstract
- Recent technological developments have increased the number of variables being monitored in lakes and reservoirs using automatic high frequency monitoring (AHFM). However, design of AHFM systems and posterior data handling and interpretation are currently being developed on a site-by-site and issue-by-issue basis with minimal standardization of protocols or knowledge sharing. As a result, many deployments become short-lived or underutilized, and many new scientific developments that are potentially useful for water management and environmental legislation remain underexplored. This Critical Review bridges scientific uses of AHFM with their applications by providing an overview of the current AHFM capabilities, together with examples of successful applications. We review the use of AHFM for maximizing the provision of ecosystem services supplied by lakes and reservoirs (consumptive and non consumptive uses, food production, and recreation), and for reporting lake status in the EU Water Framework Directive. We also highlight critical issues to enhance the application of AHFM, and suggest the establishment of appropriate networks to facilitate knowledge sharing and technological transfer between potential users. Finally, we give advice on how modern sensor technology can successfully be applied on a larger scale to the management of lakes and reservoirs and maximize the ecosystem services they provide.
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| 29. |
- Mesman, Jorrit P., 1993-
(författare)
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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
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 30. |
- Mesman, Jorrit P., 1993-, et al.
(författare)
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Drivers of phytoplankton responses to summer wind events in a stratified lake : a modelling study
- 2022
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Ingår i: Limnology and Oceanography. - : John Wiley & Sons. - 0024-3590 .- 1939-5590. ; 67:4, s. 856-873
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Tidskriftsartikel (refereegranskat)abstract
- Extreme wind events affect lake phytoplankton amongst others by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases of phytoplankton biomass after storms have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. In this study, we coupled a one-dimensional physical model to a biogeochemical model to investigate the factors regulating short-term phytoplankton responses to summer storms, now and under expected warmer future conditions. We simulated physical, chemical and biological dynamics in Lake Erken, Sweden, and found that wind storms could increase or decrease the phytoplankton concentration one week after the storm, depending on antecedent lake physical and chemical conditions. Storms had little effect on phytoplankton biomass if the mixed layer was deep prior to storm exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted growth, whereas higher surface water temperatures decreased phytoplankton concentration after storms. Medium-intensity wind speeds resulted in more phytoplankton biomass after storms than high-intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind affects phytoplankton growth following storms. Our study shows that storm impacts on lake phytoplankton are complex and likely to vary as a function of local environmental conditions.
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