SPATIAL DECISION SUPPORT FOR BIO-CONTROLHubert J. Montas, Prabhakar Reddy GVS, Adel Shirmohammadi and Ali Sadeghi

Model Analysis Laboratory, Biological Resources Engineering Department, University of Maryland at College Park, and

Hydrology Laboratory, USDA-ARS, BARC Center, Beltsville, MD.

Salmonella sp.

Escherichia coli

LITERATURE:Djojic, F., H.J. Montas, A. Shirmohammadi, L. Bergstrom and B. Ulen, 2002. Decision Support System for Phosphorus Management at Watershed Scale. Journal of Environmental Quality (in Press).

Sadeghi, A. and J. Arnold, 2002. A SWAT/Microbial Sub-Model for Predicting Pathogen Loadings in Surface and Groundwater at Watershed and Basin Scales. Proceedings of the March 11-13, 2002 TMDL Conference. Fort Worth Texas, ASAE Pub., ASAE, St.Joseph, MI.

Montas, H.J., P. Reddy GVS, T. Sohrabi, W. Devaney and A. Shirmohammadi, 2001. Wavelet-Stochastic Analysis of Two-Dimensional Biological Resources. ASAE Paper No. 01-3154 Presented at the 94th Annual International Meeting, July 29-August 1, 2001, Sacramento, CA, USA. ASAE, St-Joseph, MI.

Reddy, P. GVS, H.J. Montas, H. Samet and A. Shirmohammadi, 2001. Quadtree-Based Triangular Mesh Generation for Finite Element Analysis of Heterogeneous Spatial Data. ASAE Paper No. 01-3072 Presented at the 94th Annual International Meeting, July 29-August 1, 2001, Sacramento, CA, USA. ASAE, St-Joseph, MI.

Montas, H.J., L.E. Carr, T.H. Ifft and A. Shirmohammadi, 2000. Use of GIS to Estimate Waste Load Versus Available Land for Utilization. pp. 286-296 In: J.P Blake and P.H. Patterson (eds) Proceedings of the 2000 National Poultry Waste Management Symposium, October 16 to 18, 2000, Sheraton Fontainbleau, Ocean City, MD, National Poultry Waste Management Symposium Committee (NPWMSC) Pub., Auburn University Printing, Auburn University, Auburn, AL.

Montas, H.J., L. Moran, C. Peters, K. Shipman, T.H. Ifft, G.K. Felton and A. Shirmohammadi, 2000. Effectiveness of Riparian Buffers in Small Maryland Watersheds. ASAE Paper No. 00-2182, presented at the 2000 ASAE Annual International Meeting, Milwaukee, WI, July 2000. ASAE, St-Joseph, MI, 49085.

OBJECTIVE:The objective of this research is to develop computational tools that can aid in the development of appropriate strategies for controlling biological agents within extensive environments. Such control is complicated by at least three factors: 1) multiscale heterogeneity; 2) biodynamics, and; 3) the presence of uncontrollable driving forces (e.g. the weather).

This poster presents recent developments, by the investigators, of tools that can aid in identifying appropriate control strategies to prevent unintended propagation of bioagents and pathogens in watersheds. The focus is on plant nutrients and coliforms because of their well-known potential effects on downgradient biota and on drinking water supplies. Tools are being developed in three areas:

•Data pre-processing

•Bio-Transport Modeling

•Decision Support for Strategy Selection

Panchromatic Source DOQQ with Training Samples for Classification

Classification Results for the above Image using Intensity only

Classification Results for the Above Image using Stochastic Wavelets

Multi-Band DOQQ used to Delineate Land Cover Types

Land Cover Map Developed from DOQQs and Field Visits

Soils Map Developed from the USDA SSURGO Database

Elevation Map Developed from USGS 30m DEM

(1) ACQUISITION AND PRE-PROCESSING OF SPATIAL DATA

(2) TRANSPORT MODELING AND DETERMINATION OF HOT-SPOTS

(3) DECISION SUPPORT FOR SELECTING APPROPRIATE MANAGEMENT STRATEGIES

The spatial data needed to support the decision-making processes related to biological control typically come from a variety of sources including Digital Spatial Databases (SSURGO for soils and the USGS DEMs for topography). It is also frequently required to obtain detailed land cover data from field visits or by interpreting remote imagery.

To aid in the latter task, the investigators are developing new texture-based image segmentation procedures that use wavelets. Preliminary results (left column below) indicate that this new technique is significantly more accurate than classification based solely on pixel intensity. Research is underway to extend and validate this method for multi-band images.

Existing and newly developed transport models are used to identify Hot Spots of Bio-Export (bio-agents or pathogens) within the study watersheds.

Existing models such as HSPF, SWAT, AGNPS and ANSWERS are being investigated for their applicability in Maryland relative to their scale of representation, degree of process description, and accuracy.

The researchers are also developing new models that can be either embedded in GIS or linked to them.

An example of an embedded model is HydroSub which runs in ERDAS Inc. IMAGINE software and predicts the spatial distribution of Nitrate contribution to streams in a study watershed.

A pathogen transport model for field and watershed scales is also under development. The model is coupled with SWAT and its accuracy is being tested in Beltsville and Virginia.

The predictive accuracy of models often increases when they consider the heterogeneity that occurs at multiple scales within a bioenvironment. The investigators are pursuing several avenues for efficiently incorporating heterogeneity into models. Two such avenues are: 1) the development of heterogeneity-adapted meshing schemes for Finite-Element simulations of transport and, 2) the development of higher-order stochastic transport equations that predict both means and variances of transported bio-agents. The two techniques apply at different scales and are hence complementary.

Quadtree-based Heterogeneity Adapted Mesh for FEM Analysis of Bio-transport

Steady-State Water Table Elevations Calculated using the Heterogeneity-Adapted Mesh

E. coli - Corn

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Fecal coliforms - Corn

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Examples of Pathogens (left) for which the New Bio-Transport Model is being Developed (extending SWAT) along with Testing Facility (center) and Preliminary Results over Corn (right)

Third-Order Stochastic Predictions of Solute Transport in a Heterogeneous Soil. This New

Technique Predicts both Means and VariancesHydroSub Predictions of

Nitrate Delivery to Streams (red segments are hot spots)

Phosphorus Export Hot Spots (Red) from HSPF Predictions

(25 sub-watersheds)

Decision-Tree Implementation of an Expert System for Diagnosing the Most Probable Cause for Excessive P-Export in a Hot Spot

Schematic Representation of a Neural Network used to Suggest Appropriate Management Strategies for P-Control in Hot Spots

Map of Management Practices Suggested by an Expert System

for P-Control in a Swedish Watershed. The Effectiveness of

these Control Strategies was Verified with a Field-Based Model

(GLEAMS)

Once Hot Spots have been identified it becomes possible to determine appropriate management strategies that can minimize their negative effects on downgradient environments. The wide variety of conditions that may arise at hot spots may however render this determination quite time-consuming.

Artificial Intelligence tools are being developed to permit a more efficient and objective identification of effective management strategies for hot spots. Both neural networks (biomimetics) and expert systems (rooted in logic) are being developed and tested for their ability to reproduce the selections made by human experts when faced with selecting appropriate bio-control strategies for a given set of local conditions.