Inland Waters, Vol 5, No 1 (2015)

A Global Lake Ecological Observatory Network (GLEON) for synthesising high–frequency sensor data for validation of deterministic ecological models

David P Hamilton, Cayelan C Carey, Lauri Arvola, Peter Arzberger, Carol Brewer, Jon J Cole, Evelyn Gaiser, Paul C Hanson, Bas W Ibelings, Eleanor Jennings, Tim K Kratz, Fang-Pang Lin, Chris G McBride, David de Motta Marques, Kohji Muraoka, Ami Nishri, Boqiang Qin, Jordan S Read, Kevin C Rose, Elizabeth Ryder, Kathleen Weathers, Guangwei Zhu, Dennis Trolle, Justin D Brookes
Pages: 49-56


A Global Lake Ecological Observatory Network (GLEON; has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate process-based ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest.