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Transforming ecological sensor networks from data collectors to knowledge generators

Transforming ecological sensor networks from data collectors to knowledge generators. Paul Hanson, Fang-Pang Lin, Miron Livny, Chin Wu, Chris Solomon, Many colleagues of the GLEON. Questions.

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Transforming ecological sensor networks from data collectors to knowledge generators

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  1. Transforming ecological sensor networks from data collectors to knowledge generators Paul Hanson, Fang-Pang Lin, Miron Livny, Chin Wu, Chris Solomon,Many colleagues of the GLEON

  2. Questions • What are the patterns and surprises in sensor data, and what do they tell us about how external drivers influence lake physical, chemical, and biological processes? • How do large gradients in geology, hydrology, and climate influence lake responses to external drivers? • What are the essential emergent characteristics from lakes that allow us to generalize processes from a few, highly instrumented lakes to regional and global scales?

  3. 102 Level 3 model threshold (3-D models) Level 2 models 101 Level 2 model threshold (0,1-D models) Acceptable thresholds for different tasks. Time (minutes) Level 1 models 100 Level 1 model threshold (Transformations, simple QA/QC) Sandbox threshold (Select, visualize data) Query time with current system 10-1 107 108 109 Number of data 2008 2010 2012 2014 Year GLEON Projected Growth

  4. Web, e.g., dbBadger Mendota buoy LSPA dbBadger Software suite Stream data GLEON Observational Data Repositories Query and display observational data Existing New to this proposal 3D hydrodynamics wavelet Spectral anal. Multi-dimensional simulated data repository Surprise anal. Z Y X Condor

  5. Trout Bog Sparkling Lake Depth (m) Depth (m) Temperature (°C) Temperature (°C) A) TB B) SP PAR log10(µmol m-2 sec-1) PAR log10(µmol m-2 sec-1) Depth (m) Depth (m) C) TB D) SP Hanson, Hamilton, Stanley, Langman, Preston in prep. dcoc\simulations\CompareSimulationsMassesFluxes.m

  6. A) TB epi B) SP epi How does a large spring pulse of DOC affect other variables? DIC (mg L-1) C) TB hypo D) SP hypo F) SP epi E) TB epi Chl (µg L-1) G) TB hypo H) SP hypo Day of year Day of year I) TB epi J) SP epi T (°C) K) TB hypo L) SP hypo Detection band Pulse Day of year Day of year dcoc\simulations\PlotSeriesWithConfidence.m

  7. Ecosystem Higher trophic level processes biological Strong coupling Weak coupling Microbial processes Physical Physical, chemical processes chemical System level Hydrodynamics Hydrology, loading Morphometry Meteorology physical Landscape setting GLEON sites low high Predictive uncertainty

  8. Lake Mendota 2008 July thru Sept chlorophyll phycocyanin Power dissolved oxygen week day hour

  9. 10 min scale Phyco Chl DO 60 min scale

  10. Technology will… • access large repositories of data, and move data seamlessly through a web of models and repositories; • accomplish a complex series of tasks in dependable ways; • support the interconnection of models, some of which are extremely compute intensive, in flexible and fast ways; • provide on-demand access to GLEON scientists from around the world. This functionality extends existing GLEON technology by leveraging proven workflow and distributed technologies available through Condor and data access, visualization and transport technologies through NCHC.

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