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Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake's watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near-stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near-stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake-specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality. ; U.S. National Science Foundation's Dynamics of Coupled Natural and Human Systems (CNH) Program [1517823]; U.S. National Science Foundation's Macrosystems Biology Program [EF-1638679, EF-1638554, EF-1638539, EF-1638550]; USDA National Institute of Food and Agriculture, Hatch project [1013544]; Hatch Appropriations [PEN04571, 1003346] ; This work was supported by the U.S. National Science Foundation's Dynamics of Coupled Natural and Human Systems (CNH) Program (award 1517823) and Macrosystems Biology Program (awards EF-1638679, EF-1638554, EF-1638539, and EF-1638550). PAS was also supported by the USDA National Institute of Food and Agriculture, Hatch project 1013544. ARK supported by Hatch Appropriations under Project #PEN04571 and Accession #1003346. We thank Sarah Collins for early input on the design and motivation for this study. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Author contributions: J. Stachelek conceived of the study, built models, analyzed data, and wrote the paper. C. C. Carey, A. R. Kemanian, and P. A. Soranno contributed to the conception of the manuscript and edited the paper. W. Weng, K. M. Cobourn, T. Wagner, K. C. Weathers, and P. A. Soranno provided interpretation of results and edited the paper. ; Public domain authored by a U.S. government employee

The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow-outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants. © 2019 Author(s). ; Acknowledgements. The primary code for GLM has been developed by Matthew R. Hipsey, Louise C. Bruce, Casper Boon, Brendan Busch, and David P. Hamilton at the University of Western Australia in collaboration with researchers participating in GLEON, with support provided by a National Science Foundation (NSF) (USA) Research Coordination Network Award. Whilst GLM is a new code, it is based on the large body of historical research and publications produced by the Centre for Water Research at the University of Western Australia, which we acknowledge for the inspiration, development, and testing of several of the model approaches that have been adopted. Funding for the initial development of the GLM code was from the U.S. NSF Cyber-enabled Discovery and Innovation grant awarded to Paul C. Hanson (lead investigator) and colleagues from 20092014 (NSF CDI-0941510); subsequent development was supported by the Australian Research Council projects awarded to Matthew R. Hipsey and colleagues (ARC projects LP0990428, LP130100756, and DP130104078). Funding for the optimization and improvement of the snow and ice model was provided by NSF MSB-1638704. Funding for the development of the GLM teaching module and GRAPLEr was provided by NSF ACI-1234983 and NSF EF-1702506 awarded to Cayelan C. Carey. Funding for glmtools was provided by the Department of the Interior Northeast Climate Science Center. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Provision of the environmental symbols used for the GLM scientific diagrams are courtesy of the Integration and Application Network, University of Maryland Center for Environmental Science. Joanne Moo and Aditya Singh also provided support in model set-up and testing. We gratefully acknowledge the anonymous reviewers whose contribution and editing have significantly improved the paper and model.

In many regions across the globe, extreme weather events such as storms have increased in frequency, intensity, and duration due to climate change. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. High winds and precipitation associated with storms can affect lakes via short-term runoff events from watersheds and physical mixing of the water column. In addition, lakes connected to rivers and streams will also experience flushing due to high flow rates. Although we have a well-developed understanding of how wind and precipitation events can alter lake physical processes and some aspects of biogeochemical cycling, our mechanistic understanding of the emergent responses of phytoplankton communities is poor. Here we provide a comprehensive synthesis that identifies how storms interact with lake and watershed attributes and their antecedent conditions to generate changes in lake physical and chemical environments. Such changes can restructure phytoplankton communities and their dynamics, as well as result in altered ecological function (e.g., carbon, nutrient and energy cycling) in the short- and long-term. We summarize the current understanding of storm-induced phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions across a gradient of lake types and environmental conditions. ; Main financial support for EMU: European Union's Horizon 2020 research and innovation programme Under the Marie Skłodowska-Curie Action, Innovative Training Networks, European Joint Doctorates. ; Project name, acronym and grant number: Management of climatic extreme events in lakes and reservoirs for the protection of ecosystem services, MANTEL, grant agreement No 722518. ; Publication date and, if applicable, length of embargo period: 22.04.2020, no embargo period. ; Main financial support for EMU: European Union's Horizon 2020 research and innovation programme Under the Marie Skłodowska-Curie Action, Innovative Training Networks, European Joint Doctorates

Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer 'growing seasons'. We executed the first global quantitative synthesis on under-ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter-summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake-specific, species-specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass. ; National Science Foundation (NSF DEB) [1431428, 1136637]; Washington State University; Russian Science Foundation [14-14-00400]; Ministry of education and science of Russia Gos-Zasanie project [1354-2014/51]; Natural Environment Research Council [NE/J00829X/1, 1230750, NE/G019622/1, NE/J010227/1] ; Funding was provided by the National Science Foundation (NSF DEB #1431428; NSF DEB #1136637) and Washington State University. M. Timofeyev and E. Silow were partially supported by Russian Science Foundation project No 14-14-00400 and Ministry of education and science of Russia Gos-Zasanie project No 1354-2014/51. We are grateful to Marianne Moore, Deniz Ozkundakci, Chris Polashenski and Paula Kankaala for discussions that greatly improved this work. We also gratefully acknowledge the following individuals for contributing to this project: John Anderson, Jill Baron, Rick Bourbonniere, Sandra Brovold, Lluis Camarero, Sudeep Chandra, Jim Cotner, Laura Forsstom, Guillaume Grosbois, Chris Harrod, Klaus D. Joehnk, T.Y. Kim, Daniel Langenhaun, Reet Laugaste, Suzanne McGowan, Virginia Panizzo, Giampaolo Rossetti, R.E.H. Smith, Sarah Spaulding, Helen Tammert, Steve Thackeray, Kyle Zimmer, Priit Zingel and two anonymous reviewers. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government.