Toward a Global Lake Ecological Observatory Network

1. Toward a Global Lake Ecological Observatory Network Tim Kratz Center for Limnology University of Wisconsin-Madison USA http://gleon.org Yuan Yang Lake, Taiwan ; photo by Matt Van de Bogert

2. The Global Lake Ecological Observatory Network (GLEON) A grassroots network of lake scientists, engineers, information technology experts institutions and programs instruments data Linked by a common cyberinfrastructure With a goal of understanding lake dynamicsat local, regional, continental, and global scales gleon.org Yuan Yang Lake, Taiwan ; photo by Matt Van de Bogert

3. Science depends on inextricable link among questions, models, and observations Observations Questions sensor networks Models Observations Existing Sensor Networks 100 km 10 km Spatial extent 1 km 100 m 10 m random selection from Ecology 2003 1 m 10 cm Annual Monthly Weekly Daily Hourly Min. Sec. Frequency of measurement Slide courtesy of Paul Hanson Source: Porter, Arzberger,Hanson, Lin, Kratz, et al. Bioscience (2005)

4. GLEON’s Mission Facilitate interaction and build collaborations among an international, multidisciplinary community of researchers focused on understanding, predicting, and communicating the impact of natural and anthropogenic influences on lake ecosystems by developing, deploying, and using networks of emerging observational system technologies and associated cyberinfrastructure. http://gleon.org

5. Some current issues in limnology amenable to “observatory” approach • Source, movement, and fate of carbon in lakes and watersheds • Pelagic/littoral coupling • Role of episodic events, thresholds, and non-linear dynamics • Coupling of physical and biological processes

6. GLEON Activities • Share experience, expertise, and data • Catalyze joint projects • Develop tools • Conduct multi-site training • Create opportunities for students • Meet and communicate regularly

7. Why Grassroots? • Members decide the science agenda • Members initiate activities • Leads to innovative science • Enhances collaborative science • Shortens lag time between ideas and action • Members share expertise and experience • Open to all who share GLEON vision • Allows flexibility

8. GLEON: Shared Vision* • Participation: contribute to GLEON mission • Openness: share experience and expertise • Data: share data as openly as possible • Informal: “flat” organization – “grassroots” • Transparent: open decision-making • Training: integration of students • Diversity: gender, geography, discipline *Abstracted from: GLEON Operating Principles and Procedures, Aug 2007

9. GLEON Steering Committee Tim Kratz Univ. Wisconsin USA Lauri Arvola Univ. Helsinki Finland Kathie Weathers Institute for Ecosystem Studies USA Peter Arzberger Univ. California-San Diego USA Justin Brooks Univ Adelaide Australia David Hamilton Univ. Waikato New Zealand Fang-Pang Lin National Center for High Performance Computing Taiwan Thorsten Blenckner Uppsala Univ. Sweden Boqiang Qin Nanjing Inst. Hydrology and Limnology China Paul Hanson Univ. Wisconsin USA Ami Nishri Limnology and Oceanographic Research Center Israel

10. How do I become a member of GLEON? • Agree to shared vision • Be nominated by two existing members • Fill out membership form on “gleon.org” website Please join us!!!

11. GLEON Working Groups • Lake Metabolism – Diel O2 dynamics • Microbial • DOC • Cyberinfrastructure • ???

12. GLEON Sites Lake site Cyber-support site

13. GLEON 1 San Diego US March 2005 GLEON 3 Hsinchu TW October 2006 GLEON 2 Townsville AU March 2006 GLEON 4 Lammi FI March 2007 GLEON 5 Montreal CA August 2007 GLEON 6 Archbold Field Station Florida, US February 2008

14. GLEON and Students

15. The Old Model Manual recording at weather station D2 on Niwot Ridge, Colorado, USA Photo circa 1953, courtesy of Niwot Ridge LTER web site

16. The Current Model Portable Lake Metabolism Buoy North Temperate Lakes LTER Wisconsin • Instrumented Platforms • make high frequency observations of key variables • send data to web-accessible database in near real time

17. Base Station/Internet Lake Buoy USGS Gauging Station Relay Tower ~5 km Northern Highland Lake District, Wisconsin Wireless Sensor Network – 900-915 MHz Ethernet

18. Yuan-Yang Lake ( 湖）Ecosite Source Fang-Pang Lin

19. Data available online in near real time Lake Rotorua, NZ

20. Near Real Time Data from Wisconsin GLEON Lakes

21. Scalable instrumentation and cyberfrastructure is critical We can do this scale now http://lakemetabolism.org

22. Problematic, but possible with today’s cyberinfrastructure

23. Not currently possible Scale needed to answer regional/continental questions

24. A global network???

25. Yuan-Yang Lake, Taiwan The Future Model Data Repository NCHC: Taiwan • Web Services • metabolism models • intelligent agents • data retrieval Application Client Data Repository Wisconsin • Requires significant partnerships among • lake scientists • information managers • middleware developers http://gleon.org Trout Bog Lake, Wisconsin

26. Planning the cyberinfrastructure Tony Fountain Sameer Tilak Ken Chiu Barbara Benson Paul Hanson Luke Winslow et al.

27. GLEON Lake Metadata Website Dave Balsiger Barbara Benson John Byrne

28. 2005 Typhoon Season An example of episodic events and threshold dynamics Yuan Yang Lake, Taiwan Japanese National Institute of Informatics

29. Access can be difficult during the most interesting times Photo by Peter Arzberger, October 2004 Image from NOAA web site

30. Yuan Yang Lake, 2005 Slide courtesy of Stuart Jones

31. Epilimnion Hypolimnion Time Since Mixing 2004 and 2005 Bacterial Community Composition Axis 1 Axis 2 Slide courtesy of Stuart Jones

32. Slide courtesy of Stuart Jones

33. Diel O2 Dynamics GLEON promotes discovery science

34. Also seen in Australia and China, etc.

35. Detection Preprocessing Feature Extraction Classification Verification Analysis The Algorithm (Overview) • Use engineering techniques from signal processing to create the algorithm. • The feature extraction is similar to the technique used to find peaks in gas chromatography. Slide courtesy of Laurence Choi

36. Feature Extraction Preprocessing Feature Extraction Classification • We use a sunrise/sunset calculator to define night-time. • From these data windows, we search for the surge and extract two features: volume and height. Verification Example Feature Extraction Dissolved Oxygen (mg/L) Height Volume Slide courtesy of Laurence Choi

37. Algorithm Results • False alarm rate of ~1%. • Data sets which did not exhibit periodic cycles were excluded from these results. Slide courtesy of Laurence Choi

38. What causes these “bumps?” Possible mechanisms Enhanced atmospheric flux due to cooling Vertical mixing due to cooling Horizontal mixing Unknown biological process Challenge: 1 free drink for plausible mechanism 2 free drinks if the mechanism turns out to be correct!

39. Next Steps Grow network Persistent Scalable Functional Advance understanding of water resources Locally and at larger scales Enhance student opportunities

40. Near Real Time Data from the Taipei Harbor Photo by Matt Van de Bogert

41. Acknowledgements Peter Arzberger David Hamilton Fang-Pang Lin GLEON members US National Science Foundation Gordan and Betty Moore Foundation Multiple domestic funding sources of members