# Presentation: 665948

## Funded Applications Activities at Ames 

**YO Implementation Working Group**

**Ames Research Center**

**Funded Applications Activities at Ames **
**Steve Hipskind**
**Matt Fladeland**
**3 Februrary 2004**

## ESE Science questions from the Research PlanAreas of ARC Contribution

**Variability**

**Forcing**

**Response**

**Consequence**

**Prediction**

- Atmospheric Dynamics and Weather
- Global Carbon Cycle and Ecosystems
- Global Water and Energy Cycle
- Oceans, Ice and Climate
- Solid Earth

**Precipitation, evaporation & cycling of water changing? **

**Global ocean circulation ****varying?**

**Global ****ecosystems changing?****E I M A**

**Stratospheric ****ozone changing?**

**Ice cover mass changing?**

**Motions of Earth ****& interior processes?**

**Atmospheric constituents & solar radiation on climate?**** ****A M I P**

**Changes in ****land cover ****& land use?**

**Surface transformation?**

**Clouds & surface hydrological processes on climate?**

**Ecosystem res-ponses & affects on global carbon cycle? ****E I M A**** **

**Changes in ****global ocean circulation?**

**Stratospheric trace constituent responses?**

**Sea level ****affected by climate change?**

**Pollution effects?**

**Weather ****variation related to climate variation?**

**Consequences ****in land cover ****& land use?**

**Coastal region change?**

**Weather forecasting improvement?**

**Transient ****climate variations?**

**Trends in ****long-term climate?**

**Future atmospheric chemical impacts?**

**Future concen-trations of carbon dioxide and methane?**

**ESE Science**** ****questions**** ****from the Research Plan****Areas of ARC Contribution**

**V1**

**V2**

**V3**

**V5**

**V6**

**V4**

**F1**

**F2**

**F3**

**R1**

**R2**

**R3**

**R4**

**R5**

**R6**

**C1**

**C2**

**C3**

**P1**

**P2**

**P3**

**P4**

**P5**

- Atmospheric Composition

**E**  = Experimental
**L**  = Laboratory
**I**   = Instruments
**M** = Modeling
**A** = Analysis
**P** = Projects

**M A**

**A M I**

**E L I M A P**

**I A M P**

**E L**

**E L I M A P**

**Modeling**

**M**

**M P I A**

**M**

**A M**

**E I M A**

**P**

**A M**

**A M**

**AMP**

**M**

**F4**

## ARC’s Contributions to Earth Science Applications

- REAL and imagined

- Homeland Security

- Air Quality

- Energy Management

- Coastal Management

- Aviation

- Disaster Preparedness

- Water Management

- Carbon Management

- Public Health

- Ecological Forecasting

- Agricultural Efficiency

- Invasive Species

**r**
**p**
**i**
**e**

**PM****d**
**r**
**m**
**i**

**R****M**

**R****M****P****E****D****I**

**R****IMPED**

**R****MPID**

**R****IPD**

**R****M****E****P**

**R****P****M****E****D**

**R** = Remote Sensing, **M** = Models, **D** = Data, **P** = Partnerships, **PM** = Prog. Mgmt., **I** = Info. Technology, **E** = Education  training

**P**
**pm**
**i**

**YO Implementation Working Group**

**Ames Research Center**

-  **Ames Activities**
- 1. CASA / CQUEST - Potter
- 2. TOPS (REASoN) - Nemani / Golden
- 3. DEVELOP - Skiles / Schmidt
- 4. Wildfire Response - Ambrosia
- 5. Public Health + CHAART - Beck

**YO Implementation Working Group**

**Ames Research Center**

-  **Ames Activities (not currently funded, but areas of strength and potential future collaborations)**
- 1. High performance computing
- 2. Air Traffic Control - Decision Support Systems
- 3. INTEX Field Campaign - Project Scientist (formulation) & Project Management

## CASA-HYDRA Ecosystem Forecasting

**Species Distributions**
**Ecosystem Fluxes**
**Ecosystem Productivity**
**Surface Water Flows**
**Land Cover Change**

-  NASA-CASA model for predicting ecosystem cover type, global  carbon pools and fluxes, and other GHG emissions
- HYDRA-CASA model for predicting land surface hydrologic flows

*Land cover*: MODIS, AVHRR, Landsat, ASTER
*Topography/Vegetation Structure*: SRTM, ASTER, IKONOS, LVIS, SLICER, Radars
*Primary Productivity/Phenology*: AVHRR, SeaWiFS, MODIS
*Atmosphere/Climate*: AIRS/AMSU/HSB, TRMM (PR, LIS, TMI), AVHRR, MODIS, MISR, CERES, QuikScat, SeaWinds
*Soils*: AMSR-E, AIRSAR

**Predict the impacts of changing climate on the ecosystem services that support all human enterprises **
**Develop ecological forecasts with reliable assessments of error**
**Agency Partners -- USDA, DOI/BLM, EPA**

**Land Cover/Land Use & Disturbances (e.g., fire)**
**Species Composition**
**Biomass/Productivity**
**Phenology**
**Vegetation Structure**
**Elevation**
**Surface Temperature**
**SST, SSH, Circulation, & Salinity**
**Atmospheric Temp.**
**Soil Moisture**
**Precipitation**
**Winds**

**Impact of ENSO & AO Events on Terrestrial Ecosystems and Stream Flow**

**Combine EOS satellite data and  ecosystem carbon  models to predict how climatological changes driven by ENSO & AO events will affect land cover composition, regional productivity, and biogenic gas emissions.**

**Combine EOS satellite data and  ecosystem hydrologic  models to predict how climatological changes driven by ENSO & AO events will affect stream flow at regional scales**
**ENSO = El Nino Southern Oscillation**
**AO = Arctic Oscillation**

**Integrated System Solutions**

## Carbon Sequestration Application:

**Evaluate major forest and agricultural sinks of atmospheric CO****2**** in the U. S. using EOS satellite data and ecosystem modeling.**
**Support the U. S. Government interagency program for registration of ****voluntary GHG emissions reductions under section 1605(b) of the 1992 Energy Policy Act.**
**Develop an internet-based Decision Support System (DSS) of carbon sequestration in U. S. ecosystems for users nationwide.**
**Investigators:  **
**	****Christopher Potter and Matthew Fladeland (NASA ARC)**
**	****Steven Klooster, Vanessa Genovese, and Marc Kramer (CSUMB)**

**Carbon Sequestration Application:**
** ****A Support System for the CASA Ecosystem Model**

**Supported by NASA Office of Earth Science Applications Division**

## Modeling the Terrestrial Carbon Cycle

## http://geo.arc.nasa.gov/sge/casa/cquest.html

*** ******CASA CQUEST  Viewer          ***

## Expected Users and Contributions

**The unique roles and contributions of this proposed DSS based on EOS remote sensing and the NASA-CASA carbon model, i.e., within the context of the overall USDA/NASA Global Change Program for CO****2**** sequestration are:**
** ****National C sequestration inventory toward "entity-wide" accounting.**
**  ****Historical reconstruction of carbon pool baselines for the past 15-20 years.**
**  ****Simulation of future scenarios, e.g.,  land use change "avoidance" projections.**
** ****Assessment of past and future climate variability and impacts of uncertainty.**
**"Low intensity" afforestation and recovery of croplands to natural forest cover.**
**  ****Soil carbon changes in C storage.**
**  ****Dead wood changes in C storage.**
**  ****Changes in N****2****O and CH****4**** soil emissions with changes in CO****2**** emissions and N inputs (fertilizer and deposition).**

***CASA CQUEST***

## Overview of voluntary GHG reductions 

**On February 14, 2002, the U.S. Government directed****:**
**The Secretary of Energy, In consultation with the Secretary of Commerce, the Secretary of Agriculture, and the Administrator of the Environmental Protection Agency, to propose improvements to the current voluntary emissions reductions registration program under section 1605(b) of the 1992 Energy Policy Act...;**
**The Secretary of Energy to recommend reforms to ensure that businesses and individuals that register reductions are not penalized under future climate policy and to give transferable credits to companies that can show real reductions;**
**The Secretary of Agriculture, in consultation with the Environmental Protection Agency and the Department of Energy, to ****develop accounting rules and guidelines for crediting sequestration projects, taking into account emerging domestic and international approaches.**

## Terrestrial Observations Predictions System

**Ecosystem fluxes**
**Forest Production**
**Water stress**
**Irrigation management**
**Crop/Range yields**
**Fire danger**
**Stream flow**
**Drought**
**Floods**

- Ecosystem (BIOME-BGC)
- Water Balance (VSIM, AGSIM)
- Microclimate (DAYMET, SOGS)
- Weather forecasts (RUC, ETA,MRF)
- Climate forecasts (IRI, SIO, NCEP)
- Climate Change/GCM (CCSM2, CHAMMP)
- Integrative (TOPS)

**U.N.E.P /FAO**

*Land cover, LAI, FPAR, ET, NPP, Snow, LST*: MODIS, ASTER, Landsat, IKONOS/QuickBird
*Phenology*: AVHRR, MODIS
*Topography/Vegetation Structure*: SRTM, Lidar/Radar
*Sub-surface*:Ground penetrating radar
*Atmosphere/Climate*: AIRS/AMSU/HSB, CIMIS ground network
- NWS weather network
- WMO global weather network
- FLUXNET

**Assessing the vulnerability of populations around the world to possible food shortages, floods and droughts.**

**Predict the impacts of changing land-use patterns, climate and atmospheric chemistry on the ecosystem services that support all human enterprises **
**Improved planning for effects of  short and long term changes in climatic conditions**
**Ability to assess the accuracy and uncertainty in ecological forecasts**

**Evapotranspiration**
**Biomass**
**Phenology**
**Vegetation structure**
**Air temperature**
**Precipitation**
**Solar radiation**
**Humidity**
**Soil moisture**
**Soil texture**
**CO2 exchange**

**USDA National Programs**

**Support USDA National Program efforts in Global Change and Water Quality/Management**

**•****Global Commodity Markets**

**Crop health, production potential**

**Integrated System Solutions**

## Terrestrial Observation and Prediction System (TOPS)

**Concept:**
**TOPS ****is a modeling software system that brings**
** ****together technologies in information technology, **
**weather/climate forecasting, ecosystem modeling, and satellite remote sensing to enhance management  decisions related to floods, droughts, forest fires, human health, and crop, range, and forest production.  **

**Terrestrial Observation and Prediction System (TOPS)**

**Partners:**
_**U.S. Forest Service**_**: mapping and forecasting wildland fire risk**
_**United Nations Environmental Program**_**: Assessment of trends and variability in net primary production**

**Resources**:
**Code YO : Carbon management (Ed Sheffner)**
**Code YS: EOS (Diane Wickland)**
**Code R: CICT/IDU (Joseph Coughlan)**

**ReASON Activity**
**Agent based support for ecological forecasting: Developing a natural language interface to TOPS**

**Deliverables**
**Nowcast and forecast maps of ecosystem conditions such as soil moisture, phenology, fire and flood risk and primary production at local to global scales**

**http://www.ntsg.umt.edu/tops**

## Trends in global NPP (1982-1999)

**Interannual variability and trends in global net primary production estimated using TOPS and historical AVHRR data from 1982 to 1999.**
**Published in Science, June 6****th****, 2003. Nemani et al., Science, **
**300, 1560-1563.**
**United Nations Environment Program showed keen interest in continuing this assessment as part of their annual global environmental outlook.**

**Nowcasts of global NPP are currently produced using TOPS and EOS MODIS data.**
**Current plans include operational ingestion of climate forecasts from  International Research Institute, Columbia and Scripps Institute of Oceanography/Experimental Climate Prediction Center for producing NPP forecasts.**
**Implementing TOPS at 250m resolution for the conterminous**
**United States to serve local users.**

**Trends in global NPP (1982-1999)**

**Near Realtime Monitoring of global NPP**

## WNV Study, California

**DEVELOP TEAM, SUMMER 2003**

**Presentation to the Monterey County**
**Board of Supervisors, September '03**

**Agricultural**
**Competitiveness**

**Invasive Species**

**Water**
**Management**

**Discussions with Pyramid Lake Paiute Tribe,**
**June '03**

**WNV Study, California**

**Invasive Species Study, Nevada**

## Wildfire Response Team Partnered With U.S. Forest Service

- 5 year REASoN Project under way at NASA-AMES (2003-2007)
- PI is Vince Ambrosia
- Objective: NASA and Cal State University Monterey Bay personnel to lead and direct R&D and technology development in fire imaging to assist the US Forest Service in improving information gathering, reporting and managing of wildfire events in the United States.
- NASA focus: Development of imaging technologies, test and evaluation of data telemetry technologies, and improved data and information handling for decision support systems.
- Interface with Tactical Fire Remote Sensing Steering Committee to set priorities and effect tech transfer.

## Accomplishments in 2003

- Upgraded Airborne Infrared Disaster Assessment System (AIRDAS) instrument for long endurance fire monitoring missions
- Flew Montana Fires (August 2003) to evaluate fire detection algorithm modifications to AIRDAS product
- Flew Southern California fires in November to provide time-critical images to U.S. Forest Service
- Established partnership and Steering Committee with Remote Sensing Applications Center and National Interagency Fire Center
- Organized special sessions for 2004 Remote Sensing Conference

**AIRDAS data collected over the Otay Fire complex on 5 November 2003.**

## YO Implementation Working Group

**A major goal of the Ames program is to build relationships that expand the use of NASA technologies in the human health community:**** **

**YO Implementation Working Group**

**Ames Research Center**

**Public Health**

-  NIH, through training and participation in NIH-funded research
-  CDC, through transfer of proven technologies and science
-  State and local governments, through application projects
-  Other national and international institutions, through training and peer-reviewed research

## London School of Hygiene & Trop. Medicine:  Chagas’ Disease in Venezuela and Colombia; funded by the Wellcome Trust
U. Illinois Urbana-Champaign (UIUC):  Schistosomiasis in Kenya; funded by NIH’s Emerging Infectious Disease Program
State U. New York (SUNY) Buffalo:  Population Biology of African Malaria Vectors; funded by NIH’s Training Program
SUNY Buffalo:  Highland Malaria in Kenya; funded by NIH’s Emerging Infectious Disease Program
UIUC:  Chagas’ Disease in NW Argentina; funded by NIH’s Emerging Infectious Disease Program
CDC Environmental Public Health Tracking Network:  Training in the use of NASA science and technologies for health tracking; funded by Code YO Health Applications Program

**YO Implementation Working Group**

**Ames Research Center**

- London School of Hygiene & Trop. Medicine:  Chagas’ Disease in Venezuela and Colombia; funded by the Wellcome Trust
- U. Illinois Urbana-Champaign (UIUC):  Schistosomiasis in Kenya; funded by NIH’s Emerging Infectious Disease Program
- State U. New York (SUNY) Buffalo:  Population Biology of African Malaria Vectors; funded by NIH’s Training Program
- SUNY Buffalo:  Highland Malaria in Kenya; funded by NIH’s Emerging Infectious Disease Program
- UIUC:  Chagas’ Disease in NW Argentina; funded by NIH’s Emerging Infectious Disease Program
- CDC Environmental Public Health Tracking Network:  Training in the use of NASA science and technologies for health tracking; funded by Code YO Health Applications Program

**Current CHAART Collaborations**

## Projects primarily through competitive solicitation and selection
Build on strengths in science and technology (YS funded)
Partner with other enterprises at Ames, and with other centers (collaborating with IT; forging ties with ATC DSS’s)
Pioneer use of NASA science and technology into new areas, such as the leading work in applying NASA remote sensing capabilities to public health
Support federal partners and their DSS’s (Forest Service, USDA, NOAA, EPA, FAA)
Use Roadmaps to guide focus areas (need direction for investments of time and resources)
Support 9 of the 12 National Applications - often in a cross-cutting approach

**YO Implementation Working Group**

**Ames Research Center**

- Projects primarily through competitive solicitation and selection
- Build on strengths in science and technology (YS funded)
- Partner with other enterprises at Ames, and with other centers (collaborating with IT; forging ties with ATC DSS’s)
- Pioneer use of NASA science and technology into new areas, such as the leading work in applying NASA remote sensing capabilities to public health
- Support federal partners and their DSS’s (Forest Service, USDA, NOAA, EPA, FAA)
- Use Roadmaps to guide focus areas (need direction for investments of time and resources)
- Support 9 of the 12 National Applications - often in a cross-cutting approach

**Approach**

## Carbon Management
Ecological Forecasting
Air Quality
Disaster Management
Homeland Security
Agricultural Efficiency
Invasive Species
Public Health
Coastal Management

**YO Implementation Working Group**

**Ames Research Center**

- Carbon Management
- Ecological Forecasting
- Air Quality
- Disaster Management
- Homeland Security
- Agricultural Efficiency
- Invasive Species
- Public Health
- Coastal Management

**Applications Supported**

## Wildfire Response (Ambrosia)
TOPS - Natural language interface (Nemani, Golden)

**YO Implementation Working Group**

**Ames Research Center**

- Wildfire Response (Ambrosia)
- TOPS - Natural language interface (Nemani, Golden)

**REASON Activities**

## Web based tools (CASA-CQUEST)/1605b compliance aids
Natural language interface to TOPS forecast tools
Reports, presentations, peer-reviewed articles
Nemani - Science article on greening of planet
UN Report and presentation (on above work)
Wildfire response
Systems design
Data delivery
Tech insertion planning and training
Benchmark improvements to fire response
CHAART
Education and training

**YO Implementation Working Group**

**Ames Research Center**

- Web based tools (CASA-CQUEST)/1605b compliance aids
- Natural language interface to TOPS forecast tools
- Reports, presentations, peer-reviewed articles
- Nemani - Science article on greening of planet
- UN Report and presentation (on above work)
- Wildfire response
- Systems design
- Data delivery
- Tech insertion planning and training
- Benchmark improvements to fire response
- CHAART
- Education and training

**Deliverables**

## Education of PI’s, management, partners in YO approach (as Mike Luther mentioned last week, capitalizing on NASA capabilities requires patience).
Educating partners on ESE capabilities - importance of cross-fertilization of program plans
Final step of getting from research to operations - partners’ identification of resources to take on operational responsibility
Specific inter-agency issues (such as access to high-res USDA inventory datasets)
Some application areas (such as public health) are largely implemented by state and local agencies - how do we work with / impact them.

**YO Implementation Working Group**

**Ames Research Center**

- Education of PI’s, management, partners in YO approach (as Mike Luther mentioned last week, capitalizing on NASA capabilities requires patience).
- Educating partners on ESE capabilities - importance of cross-fertilization of program plans
- Final step of getting from research to operations - partners’ identification of resources to take on operational responsibility
- Specific inter-agency issues (such as access to high-res USDA inventory datasets)
- Some application areas (such as public health) are largely implemented by state and local agencies - how do we work with / impact them.

**Difficulties, Challenges, Issues**

## Similar to Issues

**YO Implementation Working Group**

**Ames Research Center**

**Communication Problems**

## Meetings/workshops like this one.
Getting Washingtonians out to the West Coast (winter weather much better for meetings).
Better identification and documentation of center capabilities and capacities
Encourage inter-center collaborations in solicitations

**YO Implementation Working Group**

**Ames Research Center**

- Meetings/workshops like this one.
- Getting Washingtonians out to the West Coast (winter weather much better for meetings).
- Better identification and documentation of center capabilities and capacities
- Encourage inter-center collaborations in solicitations

**Building Well Balanced Team**