Ecological Forecasting by integrating surface, satellite, and climate data with ecosystem models

1
Ecological Forecastingby integrating surface,
satellite, and climate data with ecosystem models
Ramakrishna Nemani Ames Research Center Moffett
Field, CA With contributions from Kazuhito
Ichii Petr Votava Andy Michaelis Michael
White Hirofumi Hashimoto Ranga Myneni Forrest
Melton Cristina Milesi Steve Running
Global Vegetation Workshop, Missoula, MT, August
8-10, 2006
2
Outline
- Ecological forecasting
- Why we need ecological forecasts?
- Types of ecological forecasts
- modeling framework for producing EF
- the Terrestrial Observation and Prediction
System
- Ecological nowcasts and forecasts
- Global net primary production monitoring
- historical analysis of global NPP and ENSO
- realtime mapping of NPP anomalies
- California carbon and water budgets
- Ecological forecasts for premium wine industry
Summary
3
What is Ecological Forecasting?

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

4
Changing surface temperatures
Why we need ecological forecasting?
5
Changing Sea Levels
40
20
Sea Level Anomaly (mm)
0
-20
Why we need ecological forecasting?
Cabanes, C. et. al.,, Science, 294, pp. 840-842,
2001)
6
Night lights 2000
Growing global population and urbanization
Night lights 2070
Source Nakicenovic et al., 2000
7
Why are Ecological Forecasts important?

• Ecological forecasts offer decision makers
  estimates of ecological vulnerabilities and
  potential outcomes given specific natural events,
  and/or management or policy options.
• Ecological forecasting is critical in
  understanding potential changes in ecological
  services, before they happen (early warning), and
  are critical in developing strategies to off-set
  or avoid catastrophic losses of services
• Goal is to develop management strategies and
  options to prevent or reverse declining trends,
  reduce risks, and to protect important ecological
  resources and associated processes

Foster interdisciplinary activity
Bruce Jones, NCSE, Forecasting Environmental
Changes, 2005
8
Short-term Monitoring and Forecasting
Irrigation requirements
Sacramento river flooding, California
Based on weather forecasts, conditioned on
historical ecosystem state Days to a week
9
Mid-term/Seasonal Forecasts of water resources,
fire risk, phenology
ENSO-Rainfall over U.S
El Nino
La Nina
Based on ENSO forecasts Weeks to months
10
Long-term Projected changes
Based on GCM outputs Decades to centuries
11
From data to knowledge/decisions
12
A common modeling framework
Monitoring Modeling Forecasting Multiple scales
Predictions are based on changes in
biogeochemical cycles
Nemani et al., 2003, EOM
White Nemani, 2004, CJRS
13
System Architecture
14
Access to a variety of observing networks
Soil moisture network
Streamflow network
15
Access to a variety of remote sensing platforms
Integration across Platforms, Sensors, Products,
DAACs ..Non-trivial
16
Ability to integrate a variety of models
Biogeochemical Cycling Crop growth/yield Pest/Dise
ase Global carbon cycle
Prognostic/diagnostic models
17
Ability to work across different time and space
scales
Hours
Years/Decades
Days
Weeks/Months
Weather/Climate Forecasts at various lead
times downscaling
18
Prognostic/Diagnostic Analysis
19
Rapid Prototyping of TOPS products for NACP
20
Validation of model results
21
Improvement of Snow Model
Empirical Model
Process-based Model
Model SnowSeasonLength (days)
Ground Obs. (SNOTEL) SnowSeasonLength (days)
22
TOPS can serve many of these applications
23
ENSO impacts on the biosphere
Nemani et al., Science, 2003
24
Significant correlations between MEI and NPP were
found over 63 of the vegetated surface,
inhabited by 3.3 billion people
Hirofumi et al., JGR-atm, december, 2004 Milesi
et al., Glob. Pl. Change, 2005
25
Near realtime monitoring of global NPP anomalies
Running et al., 2004, Bioscience, 54547-560
26
Forecasting the onset of growing season
Based on White and Nemani, RSE, 2006 GS/Health
27
Gridded Weather Surfaces for California using
nearly 700 weather stations daily
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.
maps come with cross-validation statistics
28
California Ecological Daily Nowcast at 1km
Feb/01/2006
Climate Satellite
Carbon and water cycles
T
P
ET
GPP
0
2.5
5
GPP (gC/m2/d) ET (mm/d)
Biome-BGC Simulation models
RAD
Outputs include plant growth, irrigation demand,
streamflow Salt water incursion, water
allocation, crop coefficients
29
Ecological Forecasting with economic implications
Annually worth 30 Billion Intense capital
investment 50-70K per acre to acquire
land Produces nearly 80 of U.S premium
wine Highly sensitive to weather events
In collaboration with Mondavi Wineries
30
Extreme temperatures under global warming causes
near disappearance of premium wine producing
areas in the U.S
Decadal/Century
White, M. A. et al. (2006) Proc. Natl. Acad. Sci.
USA 103, 11217-11222
31
Strong maritime influence creates ideal wine
producing climate
interannual
Nemani et al., 2001 Climate Research, 19 25-34
32
Monthly SST-Tair Correlation
Warmer than normal SST Better than average
vintages
33
Interannual changes in SST from Satellites
1997
1998
1999
El Nino
La Nina
interannual
34
Modeled water stress as a predictor of
vintage 1997 moderate water stress, best vintage
1998 is warm and wet, 1999 cool, dry
interannual
35
TOPS Irrigation Scheduling
LAI from NDVI Imagery
Limited Farm-scale Soils Data
Crop Params from Variety
Met Data from CIMIS
Irrigation Forecasts Crop Monitoring
Forecast from NWS
Inputs
Modeling
Outputs
36
Irrigation Forecasts
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
30
Forecast Irrigation (mm)
0
1000
meters
N
Fully automated web delivery to growers
Seasonal
37
Delivering information to users
Solar power Crop growth modeling
38
Mapping Weather Observations, April, 4, 2006
39
Soil Moisture Status over A.P, May 2006
40
Vegetation Health status over A.P, May 2006
41
Summary

• Potential exists for mimicking the
  weather/climate services with ecological
  forecasts of various lead times.
• Characterizing and communicating uncertainty
  remains a key issue.
• Further progress depends on
• 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