1. Enabling Ecological Forecasting by integrating surface, satellite, and climate data with ecosystem models Ramakrishna Nemani Petr Votava Andy Michaelis Forrest Melton Hirofumi Hashimoto Weile Wang Cristina Milesi Lee Johnson Lars Pierce Sam Hiatt Biospheric Sciences NASA Ames Research Center Terrestrial Observation and Prediction System

2. What is Ecological Forecasting? Ecological Forecasting (EF) predicts the effects of changes in the physical, chemical, and biological environments on ecosystem state and activity.

3. Short-term Monitoring and Forecasting Sacramento river flooding, California Irrigation requirements Based on weather forecasts, conditioned on historical ecosystem state Days

4. ENSO-Rainfall over U.S El Nino La Nina Based on ENSO forecasts Weeks to months Mid-term/Seasonal Forecasts of water resources, fire risk, phenology

5. Long-term Projected changes Based on GCM outputs Decades to centuries

6. Monitoring Modeling Forecasting Multiple scales Nemani et al., 2003, EOMWhite & Nemani, 2004, CJRS A common modeling framework Predictions are based on changes in biogeochemical cycles

7. Data – Model Integration in TOPS

8. TOPS-Gateway

9. Streamflow network Soil moisture network Fluxnet Weather network Access to a variety of observing networks

10. Access to a variety of remote sensing platforms Integration across Platforms, Sensors, Products, DAACs..Non-trivial

11. Ability to integrate a variety of models Biogeochemical Cycling Crop growth/yield Pest/Disease Global carbon cycle Prognostic/diagnostic models

12. Ability to work across different time and space scales Hours Days Weeks/Months Years/Decades Weather/Climate Forecasts at various lead times downscaling

13. Nemani et al., 2003, EOMWhite & Nemani, 2004, CJRS Research & Applications of TOPS Predictions are based on changes in biogeochemical cycles

14. Gridded Weather Surfaces for California using nearly 700 weather stations daily TMAX TMIN VPD SRAD PRECIP maps come with cross-validation statistics Weather networks often operated by different govt. agencies and/or private industry. Rarely integrated because they are intended for different audiences. We specialize in bringing them together to provide spatially continuous data.

15. Daily satellite mapping of CA landscapes SNOW COVER VEGETATION DENSITY VEGETATION PHENOLOGY FIRE

16. California : Ecological Daily Nowcast at 1km Biome-BGC Simulation models Outputs include plant growth, irrigation demand, streamflow Salt water incursion, water allocation, crop coefficients TP RAD Climate + Satellite Carbon and water cycles ET [Feb/01/2006] 0 2.5 5 GPP GPP (gC/m2/d) ET (mm/d)

17. Near realtime monitoring of global NPP anomalies Running et al., 2004, Bioscience, 54:547-560 Mapping changes in global net primary production near real-time depiction of the droughts in the Amazon and Horn of Africa, May 2005

18. 030 Forecast Irrigation (mm) Irrigation Forecast for week of July 19-26, 2005 Tokalon Vineyard, Oakville, CA CIMIS Measured Weather Data through July 18, 2005 NWS Forecast Weather Data July 19-26, 2005 01000meters N Irrigation Forecasts Fully automated web delivery to growersSeasonal

19. Understand the past Monitor/Manage the present Prepare for the future Adapting TOPS for NPS needs National Park Service

20. understand the past Ecosystem changes over continental scales

21. understand the past Interannual variability over Yosemite National Park Yosemite National Park

22. understand the past Watershed scale analysis of the anomalous 2004 using MODIS 250 data Yosemite National Park

23. monitor the present Snow monitoring using MODIS Yosemite National Park

24. monitor the present Monitoring stream flow Yosemite National Park

25. monitor the present Vegetation monitoring using MODIS FPAR Yosemite National Park

26. monitor the present Monitoring land surface temperature using MODIS Yosemite National Park

27. prepare for the future Impact of projected warming on Yosemite snow dynamics Yosemite National Park

28. prepare for the future Growing season dynamics under climate change Yosemite National Park

29. prepare for the future Projected trends in vegetation productivity Yosemite National Park

30. Potential exists for providing ecological forecasts of various lead times Characterizing and communicating uncertainty remains a key issue We need: Improved in-situ monitoring networks. Rapid access to satellite data. Better linkages among models. Comprehensive framework for data management Improved delivery systems to decision makers Summary