@misc{MarceGeorgeBuscarinuetal.2016,
  author    = {Marce, Rafael and George, Glen and Buscarinu, Paola and Deidda, Melania and Dunalska, Julita and de Eyto, Elvira and Flaim, Giovanna and Grossart, Hans-Peter and Istvanovics, Vera and Lenhardt, Mirjana and Moreno-Ostos, Enrique and Obrador, Biel and Ostrovsky, Ilia and Pierson, Donald C. and Potuzak, Jan and Poikane, Sandra and Rinke, Karsten and Rodriguez-Mozaz, Sara and Staehr, Peter A. and Sumberova, Katerina and Waajen, Guido and Weyhenmeyer, Gesa A. and Weathers, Kathleen C. and Zion, Mark and Ibelings, Bas W. and Jennings, Eleanor},
  title     = {Automatic High Frequency Monitoring for Improved Lake and Reservoir Management},
  series = {Frontiers in plant science},
  volume    = {50},
  journal   = {Frontiers in plant science},
  publisher = {American Chemical Society},
  address   = {Washington},
  issn      = {0013-936X},
  doi       = {10.1021/acs.est.6b01604},
  pages     = {10780 -- 10794},
  year      = {2016},
  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.},
  language  = {en}
}
@article{BrentrupWilliamsonColomMonteroetal.2016,
  author    = {Brentrup, Jennifer A. and Williamson, Craig E. and Colom-Montero, William and Eckert, Werner and de Eyto, Elvira and Großart, Hans-Peter and Huot, Yannick and Isles, Peter D. F. and Knoll, Lesley B. and Leach, Taylor H. and McBride, Chris G. and Pierson, Don and Pomati, Francesco and Read, Jordan S. and Rose, Kevin C. and Samal, Nihar R. and Staehr, Peter A. and Winslow, Luke A.},
  title     = {The potential of high-frequency profiling to assess vertical and seasonal patterns of phytoplankton dynamics in lakes: an extension of the Plankton Ecology Group (PEG) model},
  series = {Inland waters : journal of the International Society of Limnology},
  volume    = {6},
  journal   = {Inland waters : journal of the International Society of Limnology},
  publisher = {Freshwater Biological Association},
  address   = {Ambleside},
  issn      = {2044-2041},
  doi       = {10.5268/IW-6.4.890},
  pages     = {565 -- 580},
  year      = {2016},
  abstract  = {The use of high-frequency sensors on profiling buoys to investigate physical, chemical, and biological processes in lakes is increasing rapidly. Profiling buoys with automated winches and sensors that collect high-frequency chlorophyll fluorescence (ChlF) profiles in 11 lakes in the Global Lake Ecological Observatory Network (GLEON) allowed the study of the vertical and temporal distribution of ChlF, including the formation of subsurface chlorophyll maxima (SSCM). The effectiveness of 3 methods for sampling phytoplankton distributions in lakes, including (1) manual profiles, (2) single-depth buoys, and (3) profiling buoys were assessed. High-frequency ChlF surface data and profiles were compared to predictions from the Plankton Ecology Group (PEG) model. The depth-integrated ChlF dynamics measured by the profiling buoy data revealed a greater complexity that neither conventional sampling nor the generalized PEG model captured. Conventional sampling techniques would have missed SSCM in 7 of 11 study lakes. Although surface-only ChlF data underestimated average water column ChlF, at times by nearly 2-fold in 4 of the lakes, overall there was a remarkable similarity between surface and mean water column data. Contrary to the PEG model's proposed negligible role for physical control of phytoplankton during the growing season, thermal structure and light availability were closely associated with ChlF seasonal depth distribution. Thus, an extension of the PEG model is proposed, with a new conceptual framework that explicitly includes physical metrics to better predict SSCM formation in lakes and highlight when profiling buoys are especially informative.},
  language  = {en}
}