Contemporary community composition, spatial distribution patterns, and biodiversity characteristics of zooplankton in large alpine Lake Sevan, Armenia

We studied the quantitative composition, spatial distribution, and temporal dynamics of the zooplankton community of the alpine Lake Sevan, Armenia, the largest surface water in the Caucasus region. This article is providing a long-term information and fills the research gap of multiyear data on zooplankton, as the previous research on zooplankton provided only snapshots of the community, and a consistent assessment over multiple years was missing. However, an initial mini-review of historical studies indicated that zooplankton biomass and fish abundance were undergoing large fluctuations, indicating the importance of top-down control. We analysed 239 samples from the period 2016-2019 from 32 sampling sites in Lake Sevan and recorded 37 species of meso- and macrozooplankton (Rotifers, Copepods, Cladocera). Biomass fluctuations were high with peaking biomasses in 2016 and lowest biomasses in 2018, yearly averaged biomass varied about one order of magnitude. Variability over time was hence much higher than spatial variability. The pelagic habitat at the deepest part of the lake showed the highest diversity and biomasses but contrasts between sampling sites remained smaller than changes from year to year or seasonally. Many samples were dominated by a single species, and these key species explain observed biomass dynamics to a wide extent. We applied hierarchical clustering in order to identify phenological groups that appear to show similar patterns of occurrence. This clustering resulted in 6 groups where of 5 groups just consisting of one single species and these 5 key species were the Cladocerans Daphnia magna, Daphnia hyalina, Diaphanosoma sp. as well as the calanoids Arctodiaptomus bacillifer and Acanthodiaptomus denticornis. The most important species in Lake Sevan’s zooplankton during the observation period was D. magna, which reached high biomasses in 2016 and 2017 but then suddenly almost disappeared in 2018 and 2019. When there were more D. magna present, the water became clearer, which was measured using Secchi depth. This shows that these large water fleas effectively controlled the amount of phytoplankton in the water. Daphnia magna, in turn, managed to dominate zooplankton community only during times of extremely low fish biomass indicating strong top-down control of this large Cladoceran by fish. Both observations together imply a strong trophic linkage between fish, zooplankton, and phytoplankton and provide evidence for trophic cascades in Lake Sevan. Besides the novel insights into zooplankton community dynamics of this unique lake of high socio-economical, cultural, and ecological importance, our study also points to potential management opportunities for eutrophication control by biomanipulation, as well as our investigation allows us to conclude that probably biotic factors were more important than abiotic factors in explaining the observed changes and dynamics within the plankton community

Forecasting water temperature in lakes and reservoirs using seasonal climate prediction

ABSTRACT: Seasonal climate forecasts produce probabilistic predictions of meteorological variables for subsequent months. This provides a potential resource to predict the influence of seasonal climate anomalies on surface water balance in catchments and hydro-thermodynamics in related water bodies (e.g., lakes or reservoirs). Obtaining seasonal forecasts for impact variables (e.g., discharge and water temperature) requires a link between seasonal climate forecasts and impact models simulating hydrology and lake hydrodynamics and thermal regimes. However, this link remains challenging for stakeholders and the water scientific community, mainly due to the probabilistic nature of these predictions. In this paper, we introduce a feasible, robust, and open-source workflow integrating seasonal climate forecasts with hydrologic and lake models to generate seasonal forecasts of discharge and water temperature profiles. The workflow has been designed to be applicable to any catchment and associated lake or reservoir, and is optimized in this study for four catchment-lake systems to help in their proactive management. We assessed the performance of the resulting seasonal forecasts of discharge and water temperature by comparing them with hydrologic and lake (pseudo)observations (reanalysis). Precisely, we analysed the historical performance using a data sample of past forecasts and reanalysis to obtain information about the skill (performance or quality) of the seasonal forecast system to predict particular events. We used the current seasonal climate forecast system (SEAS5) and reanalysis (ERA5) of the European Centre for Medium Range Weather Forecasts (ECMWF). We found that due to the limited predictability at seasonal time-scales over the locations of the four case studies (Europe and South of Australia), seasonal forecasts exhibited none to low performance (skill) for the atmospheric variables considered. Nevertheless, seasonal forecasts for discharge present some skill in all but one case study. Moreover, seasonal forecasts for water temperature had higher performance in natural lakes than in reservoirs, which means human water control is a relevant factor affecting predictability, and the performance increases with water depth in all four case studies. Further investigation into the skillful water temperature predictions should aim to identify the extent to which performance is a consequence of thermal inertia (i.e., lead-in conditions).This is a contribution of the WATExR project (watexr.eu/), which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by MINECO-AEI (ES), FORMAS (SE), BMBF (DE), EPA (IE), RCN (NO), and IFD (DK), with co-funding by the European Union (Grant 690462 ). MINECO-AEI funded this research through projects PCIN- 2017-062 and PCIN-2017-092. We thank all water quality and quantity data providers: Ens d’Abastament d’Aigua Ter-Llobregat (ATL, https://www.atl.cat/es ), SA Water ( https://www.sawater.com. au/ ), Ruhrverband ( www.ruhrverband.de ), NIVA ( www.niva.no ) and NVE ( https://www.nve.no/english/ ). We acknowledge the contribution of the Copernicus Climate Change Service (C3S) in the production of SEAS5. C3S provided the computer time for the generation of the re-forecasts for SEAS5 and for the production of the ocean reanalysis (ORAS5), used as initial conditions for the SEAS5 re-forecasts

Sources of skill in lake temperature, discharge and ice-off seasonal forecasting tools

Despite high potential benefits, the development of seasonal forecasting tools in the water sector has been slower than in other sectors. Here we assess the skill of seasonal forecasting tools for lakes and reservoirs set up at four sites in Australia and Europe. These tools consist of coupled hydrological catchment and lake models forced with seasonal meteorological forecast ensembles to provide probabilistic predictions of seasonal anomalies in water discharge, temperature and ice-off. Successful implementation requires a rigorous assessment of the tools' predictive skill and an apportionment of the predictability between legacy effects and input forcing data. To this end, models were forced with two meteorological datasets from the European Centre for Medium-Range Weather Forecasts (ECMWF), the seasonal forecasting system, SEAS5, with 3-month lead times and the ERA5 reanalysis. Historical skill was assessed by comparing both model outputs, i.e. seasonal lake hindcasts (forced with SEAS5), and pseudo-observations (forced with ERA5). The skill of the seasonal lake hindcasts was generally low although higher than the reference hindcasts, i.e. pseudo-observations, at some sites for certain combinations of season and variable. The SEAS5 meteorological predictions showed less skill than the lake hindcasts. In fact, skilful lake hindcasts identified for selected seasons and variables were not always synchronous with skilful SEAS5 meteorological hindcasts, raising questions on the source of the predictability. A set of sensitivity analyses showed that most of the forecasting skill originates from legacy effects, although during winter and spring in Norway some skill was coming from SEAS5 over the 3-month target season. When SEAS5 hindcasts were skilful, additional predictive skill originates from the interaction between legacy and SEAS5 skill. We conclude that lake forecasts forced with an ensemble of boundary conditions resampled from historical meteorology are currently likely to yield higher-quality forecasts in most cases.publishedVersio

Lake Sevan modeling data 2008- 2017 including inflows, outflow, temperature and model setup

This dataset is regarding the modeling work of Lake Sevan, the largest freshwater lake in the Caucasus titled (Shikhani et al., Simulating thermal dynamics of the largest lake in the Caucasus region: The mountain Lake Sevan). This data was used to drive the General Lake Model, calibrate the model, and validate it. It contains the inflows, the outflow, the lake bathymetry, water level observations, and the meteorology (average of observed stations), as well as the temperature profiles in both of the sampling points. Moreover, it contains the nml file of the settings of the GLM used for this manuscript. Between 2008 and 2017

Forecasting water temperature in lakes and reservoirs using seasonal climate prediction

Seasonal climate forecasts produce probabilistic predictions of meteorological variables for subsequent months. This provides a potential resource to predict the influence of seasonal climate anomalies on surface water balance in catchments and hydro-thermodynamics in related water bodies (e.g., lakes or reservoirs). Obtaining seasonal forecasts for impact variables (e.g., discharge and water temperature) requires a link between seasonal climate forecasts and impact models simulating hydrology and lake hydrodynamics and thermal regimes. However, this link remains challenging for stakeholders and the water scientific community, mainly due to the probabilistic nature of these predictions. In this paper, we introduce a feasible, robust, and open-source workflow integrating seasonal climate forecasts with hydrologic and lake models to generate seasonal forecasts of discharge and water temperature profiles. The workflow has been designed to be applicable to any catchment and associated lake or reservoir, and is optimized in this study for four catchment-lake systems to help in their proactive management. We assessed the performance of the resulting seasonal forecasts of discharge and water temperature by comparing them with hydrologic and lake (pseudo)observations (reanalysis). Precisely, we analysed the historical performance using a data sample of past forecasts and reanalysis to obtain information about the skill (performance or quality) of the seasonal forecast system to predict particular events. We used the current seasonal climate forecast system (SEAS5) and reanalysis (ERA5) of the European Centre for Medium Range Weather Forecasts (ECMWF). We found that due to the limited predictability at seasonal time-scales over the locations of the four case studies (Europe and South of Australia), seasonal forecasts exhibited none to low performance (skill) for the atmospheric variables considered. Nevertheless, seasonal forecasts for discharge present some skill in all but one case study. Moreover, seasonal forecasts for water temperature had higher performance in natural lakes than in reservoirs, which means human water control is a relevant factor affecting predictability, and the performance increases with water depth in all four case studies. Further investigation into the skillful water temperature predictions should aim to identify the extent to which performance is a consequence of thermal inertia (i.e., lead-in conditions)

Opportunities for seasonal forecasting to support water management outside the tropics

Advance warning of seasonal conditions has the potential to assist water management in planning and risk mitigation, with large potential social, economic, and ecological benefits. In this study, we explore the value of seasonal forecasting for decision-making at five case study sites located in extratropical regions. The forecasting tools used integrate seasonal climate model forecasts with freshwater impact models of catchment hydrology, lake conditions (temperature, water level, chemistry, and ecology), and fish migration timing and were co-developed together with water managers. To explore the decision-making value of forecasts, we carried out a qualitative assessment of (1) how useful forecasts would have been for a problematic past season and (2) the relevance of any windows of opportunity (seasons and variables where forecasts are thought to perform well) for management. Overall, water managers were optimistic about the potential for improved decision-making and identified actions that could be taken based on forecasts. However, there was often a mismatch between those variables that could best be predicted and those which would be most useful for management. Reductions in forecast uncertainty and a need to develop practical, hands-on experience were identified as key requirements before forecasts would be used in operational decision-making. Seasonal climate forecasts provided little added value to freshwater forecasts in these extratropical study sites, and we discuss the conditions under which seasonal climate forecasts with only limited skill are most likely to be worth incorporating into freshwater forecasting workflows

Opportunities for seasonal forecasting to support water management outside the tropics

Advance warning of seasonal conditions has the potential to assist water management in planning and risk mitigation, with large potential social, economic, and ecological benefits. In this study, we explore the value of seasonal forecasting for decision-making at five case study sites located in extratropical regions. The forecasting tools used integrate seasonal climate model forecasts with freshwater impact models of catchment hydrology, lake conditions (temperature, water level, chemistry, and ecology), and fish migration timing and were co-developed together with water managers. To explore the decision-making value of forecasts, we carried out a qualitative assessment of (1) how useful forecasts would have been for a problematic past season and (2) the relevance of any windows of opportunity (seasons and variables where forecasts are thought to perform well) for management. Overall, water managers were optimistic about the potential for improved decision-making and identified actions that could be taken based on forecasts. However, there was often a mismatch between those variables that could best be predicted and those which would be most useful for management. Reductions in forecast uncertainty and a need to develop practical, hands-on experience were identified as key requirements before forecasts would be used in operational decision-making. Seasonal climate forecasts provided little added value to freshwater forecasts in these extratropical study sites, and we discuss the conditions under which seasonal climate forecasts with only limited skill are most likely to be worth incorporating into freshwater forecasting workflows.publishedVersio

Virtual Growing Pains: Initial Lessons Learned from Organizing Virtual Workshops, Summits, Conferences, and Networking Events during a Global Pandemic

For many, 2020 was a year of abrupt professional and personal change. For the aquatic sciences community, many were adapting to virtual formats for conducting and sharing science, while simultaneously learning to live in a socially distanced world. Understandably, the aquatic sciences community postponed or canceled most in-person scientific meetings. Still, many scientific communities either transitioned annual meetings to a virtual format or inaugurated new virtual meetings. Fortunately, increased use of video conferencing platforms, networking and communication applications, and a general comfort with conducting science virtually helped bring the in-person meeting experience to scientists worldwide. Yet, the transition to conducting science virtually revealed new barriers to participation whereas others were lowered. The combined lessons learned from organizing a meeting constitute a necessary knowledge base that will prove useful, as virtual conferences are likely to continue in some form. To concentrate and synthesize these experiences, we showcase how six scientific societies and communities planned, organized, and conducted virtual meetings in 2020. With this consolidated information in hand, we look forward to a future, where scientific meetings embrace a virtual component, so to as help make science more inclusive and global

AEMON-J/DSOS Archive: "Hacking Limnology" Workshop + Virtual Summit in Data Science & Open Science in Aquatic Research

This OSF project is meant to serve as a long-term storage repository for presentations and workshop materials for the Aquatic Ecosystem Modeling-Junior (AEMON-J) and Virtual Summit: Incorporating Data Science and Open Science (DSOS) communities. Contributors in this repository include past presenters and workshop organizers. Contributors are only responsible for those individual presentations that are labeled with their surname