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Rapid Prototyping Capability Project 2006

Evaluation for the Integration of a Virtual Evapotranspiration Sensor Based on VIIRS and Passive Microwave Sensors into Annualized Agricultural Non-Point Source Pollution (AnnAGNPS) Model. Lance Yarbrough (PI) Dath Mita (Co-PI) The University of Mississippi.

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Rapid Prototyping Capability Project 2006

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  1. Evaluation for the Integration of a Virtual Evapotranspiration Sensor Based on VIIRS and Passive Microwave Sensors into Annualized Agricultural Non-Point Source Pollution (AnnAGNPS) Model Lance Yarbrough (PI) Dath Mita (Co-PI) The University of Mississippi Rapid Prototyping Capability Project 2006 Collaborators Robert Ryan SSAI John C. Stennis Space Center Ronald Bingner (Ron) USDA-ARS National Sedimentation Lab Steve Running The Numerical Terradynamic Simulation Group University of Montana

  2. Presentation Outline • Objectives • Background • Expected Impacts • Progress Report (April 2008) • Upcoming Major Tasks • Schedule • Future Science Questions

  3. Project Objectives • To develop and evaluate a “Virtual” Evapotranspiration (ET) Sensor (VETS) model for estimating ET using Moderate Resolution Imaging Spectroradiometer (MODIS) data • To evaluate the potential of applying VETS model with Annualized Agricultural Non-Point Source Pollution Model (AnnAGNPS) • To evaluate the use of VIIRS data (simulation) as a potential ET data source in place of MODIS 1

  4. Study Area:Lower Mississippi, The Yazoo River Basin, Yalobusha Watershed Rationale: -long history of hydrologic work -extensive infrastructure -long history of hydrologic data -USDA-ARS NSL past & ongoing projects

  5. Background: AnnAGNPS • AnnAGNPS is a watershed-scale simulation program (developed by USDA) • The model simulates point and non-point source quantities of: • surface water, • sediment, • nutrients, and • pesticides • The model output is expressed on an event basis for selected stream reaches and outlets • It is also used to evaluate Best Management Practices (BMPs)

  6. AnnAGNPS Input Parameters • Climate data • Hydrology – Daily soil moisture balance • Runoff – SCS curve number • Subsurface flow • Rill and sheet erosion – RUSLE • Sediment delivery – HUSLE • Gully Erosion • Channel Erosion • Chemical routing – Mass balance approach • Evapotranspiration (ET) – Penman equation 3

  7. Wind Solar Radiation Humidity Temperature Penman Equation ETpotential Wind Solar Radiation Humidity Temperature Penman Equation Soil Moisture ETpotential ETactual Project Overview • Current AnnAGNPS ET input process involves: • The use of climate data and the PENMANequation to estimate potential ET • Estimates of potential ETand soil moisture are used to generate actual ET

  8. Problem definition: • ET estimation: • Requires several climate data inputs (wind, temperature, solar radiation, humidity etc.) • Not all weather stations collect and record a complete set of the required climate parameters • Certain regions have limited ground weather stations and climate data • Can lead to limited and generalized watershed ET estimates

  9. Project Outcomes Enhancing AnnAGNPS as a Decision Support System by: • Providing a more efficient and effective ET input process • Increased spatial coverage • Reliable and consistent measurements 2. Performance evaluation of AnnAGNPS with satellite data from current (MODIS) and next generation (VIIRS) sensors

  10. Expected Impacts (Value and benefits to society) 1. Improvement in the accuracy of predicting non-point source pollution loadings within agricultural watersheds 2. Improvements in the design and implementation of watershed conservation programs. 3. Provision of short and long term improvements in water quality management and human health.

  11. Progress Report April 15, 2008

  12. Data Collection: Ground weather station data: Status: Complete Research Application: • To process ET data using the Penman equation • To run current AnnAGNPS watershed simulation process • To determine correlation and/or covariance with satellite-based ET

  13. Data Collection: MODIS (2004) Satellite data: Status: Complete • Acquired and pre-processed: • Surface Reflectance Daily L2G Global (MOD09GHK) • Surface Reflectance Quality L2G Global (MOD09GST) • Land Surface Temperature/Emissivity daily L3 (MOD11A1) • Landcover Types L3 Global (MOD12Q1) • Research Application: • To generate intermediate ET factors: • LAI (leaf area index) • FPAR (fraction of photosynthetically active radiation) • EVI (enhanced vegetation index) • ALBEDO • To generating ET using the Virtual ET sensor

  14. Data Collection: MODIS ET raw data (2004) Status: Complete • Acquired ET raw data (0.05 deg) processed using Revised RS-PM algorithm • In collaboration with The Numerical Terradynamic Simulation Group, University of Montana • Research Application: • To apply in the Virtual ET sensor system • Comparative analysis with Virtual sensor ET algorithm

  15. Data Collection: Simulated VIIRS data (ITD-SSAI Stennis) Status: In Progress • Surface-Reflectance band I1 (MOD1) and band I2 (MOD2) • Emissivity M15 (MOD31) • Emissivity M16 (MOD32) • Land surface temperature (LST) • Research Application: • To generate intermediate ET factors: • LAI (leaf area index) • FPAR (fraction of photosynthetically active radiation) • EVI (enhanced vegetation index) • ALBEDO • To generate ET using Virtual ET sensor algorithm and Revised RS-PM algorithm

  16. Current Research Outputs

  17. GIS layers of ground-based (2000-05) Climate Data: (monthly and daily precipitation and temperature)

  18. Gridded ET GIS layers (Virtual ET Stations) (2004, 8-day composites) “Virtual” ET Stations

  19. MODIS ET Surface Datasets (2004, 8-day)

  20. Current AnnAGNPS ET estimate variable inputs MODIS ET data AnnGNPS Climate Data Input Editor Modification

  21. MODIS/VIIRS ET data Modified AnnGNPS Climate Data Input Editor AnnAGNPS ET executable code -development -testing -implementation

  22. Challenge…… • Within AnnAGNPS, when the potential ET is combined with the soil moisture in the soil profile, the actual ET is determined. • When Actual ET is supplied by the user, the interaction with soil moisture still needs to be included in order to maintain the water balance in the system that effects surface and subsurface runoff as well as baseflow.

  23. Watershed Simulation • Collection and preprocessing of AnnAGNPS ancillary variables has been completed, • topographic, soils, landcover, land management datasets etc. • Approximately 95% of the pre-simulation preparations have been completed, including the incorporation of user defined actual ET effects on simulating soil moisture throughout the soil profile. • Major Simulation Parameters: Peak Runoff, Sediment Yield, and Nutrient Load

  24. Watershed Drainage Area Subdivision by AnnAGNPS Cells

  25. Assignment of MODIS-ET Grid to AnnAGNPS Watershed Cells

  26. Watershed RUSLE LS-Factor(slope-length)

  27. Watershed Soil Variability

  28. MODIS/VIIRS Data ET Algorithm Raw ET Data GIS Protocol -scaling factors -coefficients Grid Actual ET Grid Potential ET AnnAGNPS Climate Input Editor -gridded ET -watershed cells ET “Virtual” Sensor System

  29. Remaining Major Tasks: • Continue VETS Model Testing, Evaluation, and Validation • Watershed simulation experiments: • Penman ET based (2) • MODIS ET (2) • VIIRS simulated ET (2) • Analysis and evaluation of watershed simulation results • Preparation of final report and publication manuscripts

  30. Project Schedule

  31. Future Science Questions • How do watershed scale and the use of MODIS/VIIRS ET observations in hydrologic modeling affect the accuracy and precision of hydrologic assessments? • Compare in-situ and expected observations of watershed flow, sediment yield, and nutrient loading • How can we most effectively estimate the tolerances and optimization at local and regional scales? • How can we account for natural variability in ecological systems and allow for the most accurate and precise assessments possible.

  32. Contact Information: • Greg Easson, Director UMGC • 662 915 5995 • geasson@olemiss.edu • Lance Yarbrough, PI • UMGC • 662 915 6598 • ldyarbro@olemiss.edu • Dath Mita, Co-PI • UMGC • 662 915 5201 • mitadath@olemiss.edu

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