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Ecosystems around the globe are experiencing increased variability due to land use and climate change. In response, ecologists are increasingly using near-term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near-term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost-prohibitive or impossible for forecasting ecological variables that lack high-frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used in-situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using FLARE (Forecasting Lake And Reservoir Ecosystems), an open-source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1 to 35-day-ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1-day-ahead forecast root mean square error (RMSE) of 0.94°C, mean 7-day RMSE of 1.33°C, and mean 35-day RMSE of 2.15°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1-7-day-ahead horizons, weekly data assimilation resulted in the most skillful forecasts at 8-35-day-ahead horizons. Within a year, daily to fortnightly data assimilation substantially outperformed monthly data assimilation in the stratified summer period, whereas all data assimilation frequencies resulted in skillful forecasts across depths in the mixed spring/autumn periods for shorter forecast horizons. Our results suggest that lower-frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high-frequency sensor data.

Ecological forecasting is an emerging approach to estimate the future state of an ecological system with uncertainty, allowing society to better manage ecosystem services. Ecological forecasting is a core mission of the U.S. National Ecological Observatory Network (NEON) and several federal agencies, yet, to date, forecasting training has focused on graduate students, representing a gap in undergraduate ecology curricula. In response, we developed a teaching module for the Macrosystems EDDIE (Environmental Data-Driven Inquiry and Exploration; MacrosystemsEDDIE.org) educational program to introduce ecological forecasting to undergraduate students through an interactive online tool built with R Shiny. To date, we have assessed this module, “Introduction to Ecological Forecasting,” at ten universities and two conference workshops with both undergraduate and graduate students (N = 136 total) and found that the module significantly increased undergraduate students’ ability to correctly define ecological forecasting terms and identify steps in the ecological forecasting cycle. Undergraduate and graduate students who completed the module showed increased familiarity with ecological forecasts and forecast uncertainty. These results suggest that integrating ecological forecasting into undergraduate ecology curricula will enhance students’ abilities to engage and understand complex ecological concepts. Published version

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. MINECO-AEI [PCIN-2017062, PCIN-2017-092]; FORMAS; BMBF; EPA; RCN [274208]; IFD; European Union [690462] Published version This work was carried out as part of the WATExR project, part of ERA4CS, an ERA-NET initiated by JPI Climate and funded by MINECO-AEI (ES; grant nos. PCIN-2017062 and PCIN-2017-092), FORMAS (SE), BMBF (DE), EPA (IE), RCN (NO; grant no. 274208), and IFD (DK), with co-funding by the European Union (grant no. 690462).

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., Stratification has a considerable influence on lake ecology, but there is little understanding of past or future changes in its seasonality. Here, the authors use modelling and empirical data to determine that between 1901–2099, climate change causes stratification to start earlier and end later.

Evaporation of surface water is critical to the basic functioning of lakes. It directly and, in some cases, substantially modifies the hydrologic, chemical, and energy budgets, making evaporation one of the most important physical controls on lake ecosystems. Predicting lake evaporation response to climate change is, therefore, of paramount importance. Most studies that simulate climate change impacts on lake evaporation have utilised only a single mechanistic model. Whilst such studies have merit, the advantage of applying multiple, independently developed models (i.e., an ensemble approach), is that some of the inherent uncertainties in the individual lake models due to, for example, different model structures, can be reduced thus enabling increased robustness of historic and future projections. In this study, we present results from the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP) Lake Sector, where lake evaporation responses to 20th and 21st century (1901-2099) climate change has been simulated with a suite of independently developed lake models under different climate change scenarios (Representative Concentration Pathways, RCP, 2.6, 6.0 and 8.5). Our study focuses on Lake Kinneret (Israel), a sub-tropical monomictic lake of socioeconomic importance. Our simulations are validated during the historic period with bulk evaporation estimates calculated from high frequency meteorological and in-lake observations. Our results demonstrate that the lake models provide an accurate representation of historical variability in lake evaporation, with promising comparisons of the magnitude, timing and seasonality of evaporative water loss. Future evaporation projections at Lake Kinneret show that evaporation anomalies will increase by the end of the century. We show that multi-model projections of lake evaporation can accurately represent the historic period and hence provide reliable future projections that will be vital for water management.

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.

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. Published version

As the global climate warms, the fate of lacustrine fish is of huge concern, especially given their sensitivity as ectotherms to changes in water temperature. The Arctic charr (Salvelinus alpinusL.) is a salmonid with a Holarctic distribution, with peripheral populations persisting at temperate latitudes, where it is found only in sufficiently cold, deep lakes. Thus, warmer temperatures in these habitats particularly during early life stages could have catastrophic consequences on population dynamics. Here, we combined lake temperature observations, a 1-D hydrodynamic model, and a multi-decadal climate reanalysis to show coherence in warming winter water temperatures in four European charr lakes near the southernmost limit of the species’ distribution. Current maximum and mean winter temperatures are on average ~ 1 °C warmer compared to early the 1980s, and temperatures of 8.5 °C, adverse for high charr egg survival, have frequently been exceeded in recent winters. Simulations of winter lake temperatures toward century-end showed that these warming trends will continue, with further increases of 3–4 °C projected. An additional 324 total accumulated degree-days during winter is projected on average across lakes, which could impair egg quality and viability. We suggest that the perpetuating winter warming trends shown here will imperil the future status of these lakes as charr refugia and generally do not augur well for the fate of coldwater-adapted lake fish in a warming climate.

Water temperature, ice cover, and lake stratification are important physical properties of lakes and reservoirs that control mixing as well as bio-geo-chemical processes and thus influence the water quality. We used an ensemble of vertical one-dimensional hydrodynamic lake models driven with regional climate projections to calculate water temperature, stratification, and ice cover under the A1B emission scenario for the German drinking water reservoir Lichtenberg. We used an analysis of variance method to estimate the contributions of the considered sources of uncertainty on the ensemble output. For all simulated variables, epistemic uncertainty, which is related to the model structure, is the dominant source throughout the simulation period. Nonetheless, the calculated trends are coherent among the five models and in line with historical observations. The ensemble predicts an increase in surface water temperature of 0.34 K per decade, a lengthening of the summer stratification of 3.2 days per decade, as well as decreased probabilities of the occurrence of ice cover and winter inverse stratification by 2100. These expected changes are likely to influence the water quality of the reservoir. Similar trends are to be expected in other reservoirs and lakes in comparable regions. Projekt DEAL; European Social Fund (ESF); Saxonian state parliament; National Science Foundation ABI development Grant [DBI 1759865]; European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant [722518]; WATExR project, ERA4CS, an ERA-NET by JPI Climate; MINECO (ES); FORMAS (SE); BMBF (DE); EPA (IE); RCN (NO); IFD (DK); European Union [690462]; NSF [DEB-1926050, DBI-1933016]; BMBF [FKZ 01LR 2005A] Published version Open Access funding enabled and organized by Projekt DEAL. Johannes Feldbauer is holder of a scholarship from the European Social Fund (ESF) and his work is co-financed by public tax funds as decided by the Saxonian state parliament. Robert Ladwig was funded through a National Science Foundation ABI development Grant (#DBI 1759865). Jorrit P. Mesman was funded by the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement No. 722518 (MANTEL ITN). Tadhg N. Moore was funded by: the WATExR project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by MINECO (ES), FORMAS (SE), BMBF (DE), EPA (IE), RCN (NO), and IFD (DK), with co-funding by the European Union (Grant Number: 690462) and also by NSF Grants DEB-1926050 and DBI-1933016. Thomas U Berendonk, Thomas Petzoldt, and Johannes Feldbauer also received funding from the BMBF project FKZ 01LR 2005A-Forderma ss nahme "Regionale Informationen zum Klimahandeln" (RegIKlim).

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.