TY  - JOUR
A1  - Marce, Rafael
A1  - George, Glen
A1  - Buscarinu, Paola
A1  - Deidda, Melania
A1  - Dunalska, Julita
A1  - de Eyto, Elvira
A1  - Flaim, Giovanna
A1  - Grossart, Hans-Peter
A1  - Istvanovics, Vera
A1  - Lenhardt, Mirjana
A1  - Moreno-Ostos, Enrique
A1  - Obrador, Biel
A1  - Ostrovsky, Ilia
A1  - Pierson, Donald C.
A1  - Potuzak, Jan
A1  - Poikane, Sandra
A1  - Rinke, Karsten
A1  - Rodriguez-Mozaz, Sara
A1  - Staehr, Peter A.
A1  - Sumberova, Katerina
A1  - Waajen, Guido
A1  - Weyhenmeyer, Gesa A.
A1  - Weathers, Kathleen C.
A1  - Zion, Mark
A1  - Ibelings, Bas W.
A1  - Jennings, Eleanor
T1  - Automatic High Frequency Monitoring for Improved Lake and Reservoir Management
JF  - Frontiers in plant science
N2  - 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.
Y1  - 2016
U6  - https://doi.org/10.1021/acs.est.6b01604
SN  - 0013-936X
SN  - 1520-5851
VL  - 50
SP  - 10780
EP  - 10794
PB  - American Chemical Society
CY  - Washington
ER  - 
TY  - JOUR
A1  - Brentrup, Jennifer A.
A1  - Williamson, Craig E.
A1  - Colom-Montero, William
A1  - Eckert, Werner
A1  - de Eyto, Elvira
A1  - Großart, Hans-Peter
A1  - Huot, Yannick
A1  - Isles, Peter D. F.
A1  - Knoll, Lesley B.
A1  - Leach, Taylor H.
A1  - McBride, Chris G.
A1  - Pierson, Don
A1  - Pomati, Francesco
A1  - Read, Jordan S.
A1  - Rose, Kevin C.
A1  - Samal, Nihar R.
A1  - Staehr, Peter A.
A1  - Winslow, Luke A.
T1  - 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
JF  - Inland waters : journal of the International Society of Limnology
N2  - 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.
KW  - chlorophyll fluorescence
KW  - Global Lake Ecological Observatory Network (GLEON)
KW  - high-frequency sensors
KW  - PEG model
KW  - phytoplankton
KW  - profiling buoys
KW  - subsurface chlorophyll maximum
Y1  - 2016
U6  - https://doi.org/10.5268/IW-6.4.890
SN  - 2044-2041
SN  - 2044-205X
VL  - 6
SP  - 565
EP  - 580
PB  - Freshwater Biological Association
CY  - Ambleside
ER  -