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United States Department of Agriculture

Agricultural Research Service

Research Project: OBJECT MODELING AND SCALING OF LANDSCAPE PROCESSES AND CONSERVATION EFFECTS IN AGRICULTURAL SYSTEMS
2009 Annual Report


1a.Objectives (from AD-416)
Objective 1.Develop, implement, enhance, and maintain an object modeling system (OMS) and a library of modules for building agricultural system models at field to watershed scales for a variety of applications. [Contributes to Problem Area #1, Effectiveness of Conservation Practices, Product #5 of the new National Program (NP) 201 Action Plan (FY 2006 - 2010)] Objective 2.Develop, verify, and evaluate field to watershed modeling tools and techniques that quantify environmental outcomes of conservation practices in major agricultural regions, including modeling and decision aids for drainage water management systems. [Contributes to Problem Area #1, Effectiveness of Conservation Practices, Product #5 and Problem Area #3, Drainage Water Management Systems, Product #4 of the new NP 201 Action Plan (FY 2006 - 2010)] Objective 3.Develop improved space-time scaling and model parameterization approaches for landscape processes in new agricultural system models from field to watershed scales. [Contributes to Problem Area #1, Effectiveness of Conservation Practices, Product #5 of the new NP 201 Action Plan (FY 2006 - 2010), and to Goal 1.7.2 of NP 201 to develop methods to determine input model parameters, values, and state variables for multiple scales to account for the effect of management practices].


1b.Approach (from AD-416)
Objective 1. Hypothesis: The OMS framework can be used to develop customized, modular field to watershed ag system models with interchangeable components for assessing the effects of conservation practices. Experimental Design: OMS represents an ARS-led effort in partnership with the NRCS, USGS, and university collaborators (e.g. CO State University). Enhancing OMS functionality includes the development of improved capabilities for: .
1)model building .
2)code testing, data connectivity, and database integration; .
3)geospatial output visualization and model parallelization; and .
4)uncertainty, sensitivity analysis and parameter estimation. Objective 2 Hypothesis 2-1: A new prototype regionalized model can provide improved estimates of the effects of conservation practices on environmental responses at the field to watershed scales. Experimental Design: The overall goal is to develop an OMS-based modular simulation model with interchangeable components that can address regional soil and water conservation and water quality need from field to watershed scales. Specific task areas for Objective 2 are: .
1) Identify regions and define process modules for a selected regional area; .
2) Obtain needed scientific model components; .
3) Develop a prototype regionalized watershed model and perform a preliminary evaluation; .
4)Modify existing modules or identify and develop additional modules; .
5) Evaluate the prototype watershed model with various conservation practices; and .
6) Transfer the prototype model to NRCS. Hypothesis 2-2: An agricultural systems model, RZWQM2, can simulate and quantify the effects of BMPs under tile drainage for different Midwest climate and soil conditions. Experimental Design: In a collaborative research effort with the National Soil Tilth Laboratory (Ames, IA). Field experiments will be conducted in Iowa. RZWQM2 will be used to quantify controlled drainage and cover crop effects on drainage volumes, nitrate losses in drainage flow, and crop growth. Objective 3 Hypothesis: Soil, water and plant properties can be scaled over space and time to identify scale-appropriate behaviors and model parameters across agricultural landscapes. The resulting perameters can be used to improve the modeling of spatial interactions between land areas containing differential management and conservation practices. Experimental Design: The prototype regionalized watershed model will be used to assess the propogation of uncertainty in model structure, parameter values, and inputs to water quantity and quality effects up to watershed scales. Scale-dependence and uncertainty of model parameters will be evaluated as follows: .
1) Characterize the spatial and temporal variability of measured system variables in the prototype watershed model; .
2) Relate key model parameters to spatial surrogates; .
3) Generate high resolution inputs to detailed process modules and upscale the results; determine effective parameter values over the range of scales of interest; and.
4) Quantify parameter uncertainty and its impacts on model output uncertainty using a suite of object-based tools developed for parameter estimation.


3.Progress Report
The Object Modeling System (OMS) was enhanced to better support parameter estimation through integration of a Java component for multiobjective parameter estimation. Other system improvements included development of spatial database components for OMS data provisioning with NRCS data warehouses and the addition of SQL support for the OMS data API. Java standalone software tools were developed for:.
1)spatial “4D” data visualization across model output response variables, time, space, and scenarios; and.
2)development of a client-server tool for web-based watershed delineation including boundaries, hydrologic response units, and flow routing networks. Work continued on the development of a standalone Java interface for the OMS-based prototype watershed model using NASA World Wind geospatial technology with emphasis on creating relational inputs (i.e., defining linkages between watershed elements and model input files) and manipulation of GIS input data layers.

RZWQM2 parameter estimation work was conducted that focused on soil parameters and two estimation approaches:.
1)Global search analysis and local optimization methods were explored to calibrate soil hydraulic parameters in RZWQM2. Six methods of estimating soil parameters of the soil water retention curve and saturated hydraulic conductivity were evaluated to simulate soil water dynamics. Hierarchical estimation methods in RZWQM2 that limit the number of free parameters generally fit observed soil water contents as well or better than full parameter sets over all depths. .
2)Surface infiltration of rainfall and vertical soil water redistribution in different hypothetical soil types were computed using RZWQM2. We investigated whether an effective field saturated hydraulic conductivity and correlated hydraulic parameters obtained for instant ponding high rainfalls could give reasonable results for infiltration of lower rainfall rates. Out of three different effective parameter sets developed, one set was identified that gave reasonable predictions for both infiltration and subsequent soil water contents for several different rainfall intensities.

The Unified Plant Growth Model (UPGM) was modified to improve crop growth simulation responses to varying water deficits. Major enhancements were adding modules for seedling emergence, crop phenology, and canopy height that responded better to varying levels of soil water. Work on restructuring the code for inclusion of these components and/or UPGM into the OMS-based prototype watershed model are underway.

Autoregressive techniques, quality assured weather data, and the automatic parameter calibration program PEST were used to show that RZWQM2 simulates significantly lower nitrate concentration in discharge from late spring nitrate test (LSNT) treatments compared to fall N fertilizer applications within the tile drained Walnut Creek Iowa watershed (> 5 mg N/L difference for the third year of the treatment, 1999). The model results are similar to field measured data from a paired watershed design and suggest that RZWQM2 is a promising tool to accurately estimate the water quality effects of LSNT.


4.Accomplishments
1. OMS-Based Prototype Watershed Model, Version 0.10. New watershed models are needed that can assess the outcome of implementing conservation practices at multiple spatial scales and also be customized to regional processes and concerns. An initial version of a Java-based prototype watershed model (containing verified modular components for water balance, infiltration, groundwater recharge, runoff and stream flow dynamics, erosion, nutrient cycling, and plant growth) was developed and applied for simulation of runoff and stream flow between land units and stream reaches. The model is now ready for evaluation of nitrogen and sediment transport, and has the potential (due to fully-distributed simulation capability) to better quantify conservation impacts on water quality at field to watershed scales.

2. Improved Modeling Through Estimating Effective Soil Properties of Heterogeneous Areas. Field scale water infiltration and soil-water models require spatially-averaged “effective” soil hydraulic parameters whose values may vary for different conditions, processes, and component soils in a field. Out of three different effective parameter sets developed, we identified one set that gave reasonable predictions for both infiltration for several different rainfall intensities and the subsequent soil water contents. The results are an important step toward devising a scheme for scaling-up or estimating effective properties and results of hydrologic units in watershed modeling.

3. Fractal Geometry Helps Explain Complex Patterns of Soil-Water Infiltration. Spatial patterns of soil hydraulic properties that control water infiltration must be characterized and quantified to predict rainfall infiltration, surface runoff, and soil-water redistribution in farm fields. Analyses of 150 clustered steady infiltration measurements across different landscape positions and denser (5 m) terrain attributes revealed fractal patterns. However, the fractal exponent changed with the area size, meaning that a “universal fractal” could not describe the variability at all scales, and patterns were most organized (least random) at hillslope scales (about 200 m). These results contribute to basic scientific understanding, challenging some previous assumptions, and provide information for precision agriculture - particularly in the semi-arid west where 200 m is a practical scale for variable rate prescriptions and management.

4. Spatially Variable Plant Development Complicates Precision Farming Prescriptions and Development of Digital Information Technologies. Characterizing and quantifying plant phenology across complex terrains comprising large fields is required to create detailed precision management plans. A six-year study revealed that winter wheat reached each developmental stage at different times within the field, yet consistent relationships did not occur between the timing of the developmental stage and either temperature accumulation at the field location or terrain attributes (as expected). This finding may limit our decision support tools in aiding in the writing of prescriptions, thus reducing the effectiveness of many management practices.

5. Using Object-Oriented (O-O) Computer Programming to Advance Crop Growth Modeling. Current crop simulation models differ greatly in the scale of resolution ranging from the dynamic addition and subtraction of phytomer units on each shoot up to the whole plant and knowledge representation of these processes. A prototype crop simulation model, named CANON, was developed to demonstrate how using object-oriented (O-O) design and languages enables:.
1)incorporation of code for different levels of scale from the individual phytomer unit on a shoot up to a whole plant; and.
2)using a mixture of existing and new code. The CANON prototype helps improve our crop simulation models by taking advantage of advances in O-O design and programming languages for more efficient and robust simulation model development, including easier incorporation of different scales of resolution and alternative representations of system processes.


6.Technology Transfer

Number of Web Sites Managed1

Review Publications
Zhu, Y., Li, W., Ye, H., Mcmaster, G.S., Cao, W. 2008. Modeling Grain Protein Formation in Relation to N Uptake and Remobilzation in Rice. In: Ma, L., Ahuja, L.R., Bruulsema, T.W., editors. Quantifying and Understanding Plant Nitrogen Uptake for Systems Modeling. Boca Raton, FL: CRC Press. p. 147-167.

Ma, L., Malone, R.W., Jaynes, D.B., Thorp, K.R., Ahuja, L.R. 2008. Simulated Effects of Nitrogen Management and Soil Microbes on Soil Nitrogen Balance and Crop Production. Soil Science Society of America Journal. 72:1594-1603.

Sophocleous, M., Townsend, M.A., Vocasek, F., Ma, L., Ashok, K.C. 2009. Soil Nitrogen Balance Under Wastewater Management: Field Measurements and Simulation Results. Journal of Environmental Quality. 38:1286-1301.

Green, T.R., Dunn, G.H., Erskine, R.H., Salas, J.D., Ahuja, L.R. 2009. Fractal Analyses of Steady Infiltration and Terrain on an Undulating Agricultural Field. Vadose Zone Journal. 8(2)310-320.

Mcmaster, G.S. 2009. Development of the Wheat Plant. In: Carver, B.F., editor. Wheat:: Science & Trade. Blackwell Publishing. p. 31-55.

Larocque, G.R., Bhatti, J.S., Liu, J., Ascough II, J.C., Luckai, N., Gordon, A.M. 2008. The Importance of Uncertainty and Sensitivity Analyses in Process-Based Models of Carbon and Nitrogen Cycling in Terrestrial Ecosystems with Particular Emphasis on Forest Ecosystems. Ecological Modeling. 219(3-4):261-263.

Ascough II, J.C., Maier, H.R., Ravalco, J.K., Strudley, M.W. 2008. Future Research Challenges for Incorporating Uncertainty in Environmental and Ecological Decision-Making. Ecological Modeling. 219(3-4):383-399.

Last Modified: 10/23/2014
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