rapid prototyping capability project 2006

The University of Mississippi Geoinformatics Center NASA MRC RPC Review Meeting: 14-15 April, 2008

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

Rapid Prototyping Capability Project

2006

Lance Yarbrough (PI)Dath Mita (Co-PI)

The University of Mississippi

Ronald Bingner (Ron)USDA-ARS

National Sedimentation Lab

Robert RyanSSAI

John C. Stennis Space Center

Collaborators

Steve RunningThe Numerical Terradynamic Simulation Group

University of Montana

The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008

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


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


Study Area: Lower Mississippi,

The Yazoo River Basin, Yalobusha Watershed

0 25 5012.5 Miles 4

Yalobusha Watershed

Rationale:-long history of hydrologic work-extensive infrastructure-long history of hydrologic data-USDA-ARS NSL past & ongoing projects


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)


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


WindSolar Radiation

HumidityTemperature

PenmanEquation

ETpotential

WindSolar Radiation

HumidityTemperature

PenmanEquation

ETpotentialSoil

MoistureETactual

Project Overview Current AnnAGNPS ET input process involves:

1. The use of climate data and the PENMAN equation to estimate potential ET

2. Estimates of potential ET and soil moisture are used to generate actual ET


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


Project Outcomes

Enhancing AnnAGNPS as a Decision Support System by:

1. Providing a more efficient and effective ET input process a. Increased spatial coverageb. Reliable and consistent measurements

2. Performance evaluation of AnnAGNPS with satellite data from current (MODIS) and next generation (VIIRS) sensors


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.

The University of Mississippi Geoinformatics Center NASA MRC RPC Review Meeting: 14-15 April, 2008

Progress Report

April 15, 2008


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


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


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


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:

1. LAI (leaf area index) 2. FPAR (fraction of photosynthetically active radiation) 3. EVI (enhanced vegetation index) 4. ALBEDO

To generate ET using Virtual ET sensor algorithm and Revised RS-PM algorithm


Current Research Outputs


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


Gridded ET GIS layers (Virtual ET Stations)(2004, 8-day composites)

“Virtual” ET Stations


MODIS ET Surface Datasets (2004, 8-day)


Current AnnAGNPS ET estimate

variable inputs

MODISET data

AnnGNPS Climate Data Input Editor Modification


Modified AnnGNPS Climate Data Input Editor

MODIS/VIIRSET data

AnnAGNPS ET executable code -development -testing -implementation


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.


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


Watershed Drainage Area Subdivision by AnnAGNPS Cells


Assignment of MODIS-ET Grid to AnnAGNPS Watershed Cells


Watershed RUSLE LS-Factor(slope-length)

Subwta.shp

0.026 - 0.246

0.246 - 0.587

0.587 - 1.072

1.072 - 1.762

1.762 - 2.858


Watershed Soil Variability


MODIS/VIIRSData

Raw ET DataET Algorithm

GIS Protocol-scaling factors

-coefficients

Grid Potential ET Grid Actual ET

AnnAGNPS Climate Input Editor-gridded ET

-watershed cells

ET “Virtual” Sensor System


Remaining Major Tasks:

Continue VETS Model Testing, Evaluation, and Validation

Watershed simulation experiments:1. Penman ET based (2)2. MODIS ET (2)3. VIIRS simulated ET (2)

Analysis and evaluation of watershed simulation results

Preparation of final report and publication manuscripts


Project Schedule


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.


Greg Easson, Director UMGC

662 915 5995 [email protected]

Contact Information:

Dath Mita, Co-PI UMGC


Lance Yarbrough, PI UMGC


rapid prototyping capability project 2006

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