Ramakrishna Nemani Petr Votava Andy Michaelis Forrest Melton Hirofumi Hashimoto Weile Wang
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Transcript of Ramakrishna Nemani Petr Votava Andy Michaelis Forrest Melton Hirofumi Hashimoto Weile Wang
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Enabling Ecological Forecastingby integrating surface, satellite, and climate data with ecosystem models
Ramakrishna NemaniPetr VotavaAndy MichaelisForrest MeltonHirofumi HashimotoWeile WangCristina MilesiLee JohnsonLars PierceSam Hiatt
Biospheric SciencesNASA Ames Research Center
Terrestrial Observation and Prediction System
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What is Ecological Forecasting?
• Ecological Forecasting (EF) predicts the effects of changes in the physical, chemical, and biological environments on ecosystem state and activity.
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Short-term Monitoring and Forecasting
Sacramento river flooding, California Irrigation requirements
Based on weather forecasts, conditioned on historical ecosystem stateDays
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ENSO-Rainfall over U.S
El Nino
La Nina
Based on ENSO forecastsWeeks to months
Mid-term/Seasonal Forecasts of water resources, fire risk, phenology
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Long-term Projected changes
Based on GCM outputsDecades to centuries
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Monitoring
Modeling
Forecasting
Multiple scales
Nemani et al., 2003, EOM White & Nemani, 2004, CJRS
A common modeling framework
Predictions are based onchanges in biogeochemicalcycles
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Data – Model Integration in TOPS
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TOPS-Gateway
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Streamflow network Soil moisture network
FluxnetWeather network
Access to a variety of observing networks
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Access to a variety of remote sensing platforms
Integration across Platforms, Sensors, Products, DAACs ..Non-trivial
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Ability to integrate a variety of models
Biogeochemical CyclingCrop growth/yieldPest/Disease Global carbon cycle
Prognostic/diagnostic models
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Ability to work across different time and space scales
Hours
Days
Weeks/Months
Years/Decades
Weather/Climate Forecasts at various lead timesdownscaling
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Nemani et al., 2003, EOM White & Nemani, 2004, CJRS
Research & Applications of TOPS
Predictions are based onchanges in biogeochemicalcycles
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Gridded Weather Surfaces for Californiausing nearly 700 weather stations daily
TMAXTMIN
VPD
SRADPRECIP
maps come with cross-validation statistics
Weather networks often operatedby different govt. agencies and/or private industry. Rarely integratedbecause they are intended fordifferent audiences. We specializein bringing them together to providespatially continuous data.
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Daily satellite mapping of CA landscapes
SNOW COVER VEGETATION DENSITY
VEGETATION PHENOLOGY FIRE
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California : Ecological Daily Nowcast at 1km
Biome-BGCSimulation models
Outputs include plant growth, irrigation demand, streamflowSalt water incursion, water allocation, crop coefficients
T P
RAD
Climate + Satellite Carbon and water cycles
ET
[Feb/01/2006]
0 2.5 5
GPP
GPP (gC/m2/d) ET (mm/d)
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Near realtime monitoring of global NPP anomalies
Running et al., 2004, Bioscience, 54:547-560
Mapping changes in global net primary productionnear real-time depiction of the droughts in the Amazon and Horn of Africa, May 2005
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0 30
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
0 1000meters N
Irrigation Forecasts
Fully automated web delivery to growersSeasonal
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•Understand the past
•Monitor/Manage the present
•Prepare for the future
Adapting TOPS for NPS needs
National Park Service
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understand the past
Ecosystem changes over continental scales
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understand the past
Interannual variability over Yosemite National Park
Yosemite National Park
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understand the past
Watershed scale analysis of the anomalous 2004 using MODIS 250 data
Yosemite National Park
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monitor the present
Snow monitoring using MODIS
Yosemite National Park
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monitor the present
Monitoring stream flow
Yosemite National Park
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monitor the present
Vegetation monitoring using MODIS FPAR
Yosemite National Park
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monitor the present
Monitoring land surface temperature using MODIS
Yosemite National Park
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prepare for the future
Impact of projected warming on Yosemite snow dynamics
Yosemite National Park
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prepare for the future
Growing season dynamics under climate change
Yosemite National Park
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prepare for the future
Projected trends in vegetation productivity
Yosemite National Park
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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