[CRIS Project 5402-13660-006-00D]{.underline}

>  

**Title: [  ]{.underline}**Scaling and Modeling Space-Time Variability
of Landscape Processes to Enhance Management (Started 05/01/2002,
terminates 04/30/2007)

>  

**National Program Alignment:**

 

NP 201 (70%), NP 202 (30%)

**FY 2005 Funding for GPSR:**

 

> Net to Location                                               
> \$841,548
>
> Indirect Research costs                                 \$  83,136
>
> Available for travel                                           \$ 
> 18,000
>
> Publications                                                    \$   
> 1,380
>
> Equipment                                                       \$ 
> 10,000
>
> Supplies and Materials                                    \$  17,379
>
> Extramural                                                      \$   
> 2,300
>
>  

**Scientists (3.2 FTE)**

 

            T. Green, Co-Lead
Scientist                                       .80

            J. Ascough II, Co-Lead Scientist
                                 .70

            L. Ahuja, Co-Lead Scientist, Research Leader          .40

            G.
McMaster                                                               
.50

            G. Dunn
                                                                     
 .45

            Vacant -- plant stress
modeler                                    .35

           

**Support (5.85 FTE)**

>            
>
> L.
> Armand                                                                  
>   .45
>
> R.
> Erskine                                                                  
> 1.00
>
> J.
> Kozak                                                                     
>   .77
>
> M.
> Murphy                                                                   
> 1.00
>
> D.
> Palic                                                                      
>   .30
>
> I.
> Schneider                                                                
> 1.00
>
> L.
> Sherrod                                                                  
>   .50
>
> C.
> Haehn                                                                    
>   .18
>
> Vacant -
> hydrologist                                                      .35
>
> B.
> Vandenberg                                                           
>   .30
>
>  

**Current and Future Objectives:**

>  

Objective 1. Develop new statistical and physical scaling concepts and
methods to improve quantification of landscape processes causing
space-time variability in water, chemicals, soils and plants. Using data
from field experiments, relate spatial variability of soil and plant
attributes (e.g., soil hydraulic properties; plant biomass, maturity and
yield) to landscape topographic attributes, and develop these attributes
for spatial scaling.

 

Objective 2. Develop software tools for spatially distributed,
process-based simulation of agricultural systems. The simulation tools
will apply across multiple scales, and be capable of exploring
interactions between landscape variability and weather, responding to
effects of management and climate on production, and addressing both
on-site and off-site environmental effects due to spatially distributed
leaching, runoff of water, soil erosion, and sediment and chemical
transport.

 

Objective 3. Develop new methods and protocols for scale-appropriate
model parameterization and model evaluation.

 

**Guiding Hypotheses by Objectives:**

 

1.1.1        Soil-water processes and plant production may be scaled
using new physical/statistical methods incorporating spatial patterns of
measured landscape topography, soil properties and surface conditions.

1.1.2        Statistical measures (parametric and nonparametric) from
the above scaling investigations can used to identify sizes of
management units having specified internal variances.

1.1.3        Landscape topographic attributes can explain much of the
variability of soil and plant attributes observed within a field; thus,
land management units can be delineated based on topography and
associated soil and vegetation patterns.

2.1.1        A spatial model, containing scalable water and nutrient
simulation for C, N, and P management coupled with plant growth modules
providing site-specific simulation of yields, will provide a tool for
improved agricultural management prescriptions that will reduce adverse
impacts on water quality.

2.1.2        The model will predict causative factors affecting
landscape-related variability on farms, such that model testing and
application over a range of climatic and physiographic conditions
identify different dominant processes in space and time.

3.1              Model input parameters may be both scale and state
dependent, and are related to the sub-unit variability of properties
that can be measured or estimated using soil survey and topographic
information.

3.2              The distributed simulation model for management
prescription and space-time prediction of water quality impacts will be
scalable based on determination of appropriate input parameters.

3.3              New statistical evaluation techniques can quantify
simulation model robustness and efficacy. Model evaluation criteria can
account for uncertainty, considering natural variability contained in
measured data.

 

**Expected Outcomes (Anticipated Products of Research)**

 

\(1\) New physical and statistical scaling methodology for improving
quantification of spatial landscape processes, including delineation of
agricultural land management units. (2) New methods for
scale-appropriate model parameterization and evaluation. (3)
GIS-integrated spatial analysis and simulation tools for predicting
plant growth and the movement of water and agricultural chemicals in
soils. These tools will be useful for evaluating site-specific soil,
water, crop, and grazing management strategies.

 

**Major Discoveries/Accomplishments over the Life of the Project: **

 

> A.      Based on our earlier research on a one-parameter model, we
> showed that soil water retention curves across soil types, from sands
> to clays, are related (can be scaled) through their log-log slope or
> the pore-size distribution index. We have also shown that all soil
> hydraulic parameters controlling infiltration, runoff, and soil water
> storage after rain are strongly related to the pore-size distribution
> index. Based on this new finding, we have developed relationships
> between pore size distributions of different soil textural classes and
> infiltration under different rain intensities, and between pore size
> distributions and soil water content with time after rainfall across
> the whole spectrum of soil classes (sands to clays). These
> relationships allow estimation of spatial variability of infiltration,
> runoff, and soil water content after a rainfall event across a
> landscape with known spatial distribution of soils. We have further
> shown that soil evaporation and transpiration are also related to
> their pore-size distribution across soil types. These discoveries can
> help guide site-specific management over space. We can build upon this
> fundamental finding to develop methods of scaling up field level
> effects of BMPs on water and water quality to farm and watershed
> scales. Theoretically sound upscaling is the most critical scientific
> barrier to realistic watershed modeling, and this new finding is a
> breakthrough that will help us achieve the goal (Williams & Ahuja,
> 2003; Kozak & Ahuja, 2005; Kozak et al., 2005; and several earlier
> papers).
>
>  
>
> B.     Simple fractal behavior is shown to be ubiquitous in terms of
> quantifying spatial patterns of crop yield, soil water and topographic
> attributes.  Crop yield on three fields and over two harvest years
> displayed fractal dimensions most similar to the specific catchment
> area or topographic wetness index. Surface soil water content
> displayed spatial structure despite very low correlations with
> topographic attributes.  A new measure of fractal anisotropy was
> introduced and estimated from field data (Green and Erskine, 2004).

 

> C.     Under a Specific Cooperative Agreement with Dr. Jose Salas,
> Colorado State University, we have investigated how space-time
> patterns in rainfall transfer to infiltration over a small watershed
> using actual topography, multifractal generated soil hydraulic
> conductivity, and a continuous cascade model for representing
> multifractal characteristics of rainfall data. A distributed
> rainfall-runoff model was developed, tested against runoff data, and
> used to simulate infiltration patterns. Specific relationships between
> input and output parameters have been identified to help explain
> infiltration and soil moisture variability. Fractal analyses of stream
> water quality with basin scale have shown that peak daily pesticide
> concentrations scale with simple fractal behavior (decreasing in a
> log-log linear fashion with watershed area) even though lower
> concentration quantiles do not scale in the same way. Furthermore, the
> scale-invariant dispersion approach was useful to scale PRZM model
> results from the edge-of-field to measured watershed scales (Meng et
> al., 2005; Gustafson et al., 2004).

 

> D.     Topographic attributes (e.g., elevation and slope) have been
> used to predict variability of soil, water, and plant processes, and
> can now be economically and accurately measured with GPS technology.
> We have evaluated spatial correlations with key topographic attributes
> for crop yield and near-surface (30 cm) soil moisture on the Lindstrom
> Farm, Sterling, CO (Green et al., 2001). A new Spatial Analysis Neural
> Network (SANN) method has been developed, tested and applied to the
> estimation of crop yield from topographic attributes, explaining up to
> 65% of the spatial variance (Green and Erskine, 2004). Landscape
> topographic attributes can explain much of the variability of soil and
> plant attributes observed within a field; thus, land management units
> can be delineated based on topography and associated soil and crop
> yield patterns.

 

> E.     The AgSimGIS model is a prototype for integrating a complex,
> agricultural system water quality model (RZWQM) over multiple land
> units. It simulates: 1) hydrology and soil physics (i.e.,
> infiltration, soil water flow, kinematic wave overland flow, and soil
> evaporation); 2) chemical transport (i.e., nutrient cycling and
> leaching, pesticide transport and degradation); and 3) plant growth
> and development (i.e., basic plant phenology and development, and
> above- and below-ground biomass). Kinematic wave overland flow routing
> has been added to the previously developed version using a planar
> cascading approach. A stand-alone tool for visualization of spatial
> data has been completed and tested. The AgSimGIS framework provides a
> spatial foundation for coupling our future water quality modeling
> research with GIS. Spatially distributed process simulation with
> hydrologic and chemical interaction between land units will improve
> the assessment of complex interactions between landscape spatial
> variability and temporal variability of weather, leading to
> economically/environmentally sustainable management systems. (Ascough
> et al., 2004)
>
>  
>
> F.      The complete developmental sequences of the shoot apex of
> winter and spring wheat and barley, corn, and sorghum have been
> summarized and quantified for temperature responses and a prototype
> submodel released. In addition, the effects of water stress on wheat
> and barley phenology have been determined and incorporated into the
> prototype submodel. The prototype submodel uses a Java interface to
> access the underlying Fortran simulation program (McMaster and
> Wilhelm, 2003; McMaster, 2004).

 

> G.     Evaluated the temperature response functions/hypotheses
> commonly used in crop simulation models. As a result of this work, the
> current working paradigm on where to measure temperature was extended,
> the apparent linear response to temperature in the field that
> contradicts theory was explained, and improvements in quantifying leaf
> appearance rates and phenology completed. Also the SHOOTGRO cereal
> growth simulation model for water, climate, and management effects in
> the Czech Republic were evaluated, including a sensitivity analysis on
> important plant parameters. It was found that generic plant parameters
> provided with the model were sufficient to adequately simulate
> differing environmental conditions in the three crop production
> systems of the Czech Republic (McMaster et al., 2003; McMaster and
> Hunt, 2003; Hunt et al., 2003; Zalud et al., 2003).

 

> H.     A detailed data collection field experiment has been
> established at the Drake Farm, Ault, CO, a cooperator's farm. The
> Drake Farm experimental area (\~300 acres) is strip-cropped with
> winter wheat-fallow rotation under conventional tillage. Data
> collected so far include high-resolution (5m) elevation data; soil
> samples for bulk density, texture, and gravimetric water content, soil
> water content and temperature at various depths across strategic
> landscape positions; crop yield (from a combine yield monitor);
> surface water runoff; and plant measurements such as emergence, LAI,
> and phenology. On-farm field measurements of landscape variables and
> processes have been analyzed for their spatial and temporal behaviors,
> and methods for spatial analyses have been developed and tested to
> help classify land areas and guide spatially distributed sampling and
> simulation efforts. This research will enhance site-specific
> agriculture through identification of management scales based on
> variability in landscape and plant attributes and delineation of land
> management units based on topography and associated soil and
> vegetation patterns. (Green et al., 2003).
>
>  
>
> I.         We have developed new modules for interception of rain
> water by crop canopies and crop residues on the soil surface, and
> linked them to soil hydrology modules of RZWQM and future iFARM
> models. These modules are then used to evaluate the effects of
> interceptions on soil water balance and chemical movement for
> wheat-corn-fallow cropping system under no-tillage at Akron, CO. We
> also developed modules for soil surface depression storage of rainfall
> due to surface roughness, and for detention storage due to crop
> residues, both of which are important in increasing infiltration of
> water under conservation tillage systems (Kozak et al. 2005, J.
> Hydrology). We are currently testing and improving the effect of
> surface residues on water conservation and soil temperature, based on
> energy balance component of SHAW model. \[Common with iFARM Project\].

 

> J.       We have also greatly improved quantification and modeling of
> management effects on soil properties and processes, with subsequent
> effects on water quantity and quality, crop yield, and soil quality
> (soil carbon). These improvements provide a more reliable simulation
> of BMP effects on production and the environment. (See numerous
> publications by Ahuja et al., Green et al., Ma et al., and coauthored
> papers with our numerous collaborators in the publication list.)
> \[Common with iFARM Project\].

 

**Anticipated Information Technologies for Customers:**

 

Our research products will initially be distributed to other scientists
performing precision agriculture and environmental research for water
quality protection and enhancing production all over the U.S.  With
additional training, professionals in federal action agencies and state
extension agencies, private consultants and information-based industries
will also be able to use these products for recommending spatially
variable management scenarios under various landscape and climate
conditions at field and farm scales.  The following federal agencies
will also benefit from this research:  BLM, EPA, FS, NPS, NRCS and USGS.

 

**Milestones for the Next Three Years:**

 

[FY2005:]{.underline}

>          Complete Spatial Artificial Neural Network analyses relating
> crop yield to topographic attributes.
>
>          Complete surface water routing algorithms for the spatial
> model.  These are now implemented in the prototype.
>
>          Initiate lateral subsurface flow modeling in FY2005.
>
>          Begin testing of tillage effects and variable-rate management
> scenarios within AgSimGIS (prototype).
>
>          Update the literature review and assessment of stochastic
> theory and new methods for deriving scale-dependent functions for
> model parameters..
>
>          Predict soil parameters from available data using similar
> media concepts.
>
>          Explore potential CRADA partners, giving opportunity for
> early feedback.

 

[FY2006:]{.underline}

>          Complete relationships between fractal dimensions and
> potential management scales.
>
>          Complete land unit delineations for model applications; use
> Drake Farm data to date.
>
>          Complete plant developmental sequences for phonological
> responses to environmental stress.
>
>          Complete the management effects submodel.
>
>          Complete initial nutrient data collection, but this activity
> will likely continue into the next 5 years.

 

[FY2007:]{.underline}

>          Archive full database for use in future model testing.
>
>          Complete final summer of field data collection in this CRIS
> cycle.
>
>          Complete soil erosion/deposition module.
>
>          Complete investigations of three-dimensionality.
>
>          Compare management scenarios for off-site effects on runoff,
> sediment and nutrient loads.
>
>          Compare existing plant models with new methods using
> plot-scale stress responses.
>
>          Complete plant model testing at land unit scales.
>
>          Implement scale-dependent model parameters.
>
>          Complete whole-system model testing, and apply completed
> model to cooperator sites.
>
>          Test model scalability, and apply model evaluation methods.
>
>          Deliver tools developed and tested for space-time modeling
> and scaling.

 

**Relevance to ARS National Programs:**

 

There are broad interests and stated needs beyond ARS for improved
methods of predicting and scaling spatial variability in agricultural
landscapes for various management issues. The proposed research
addresses the following specific goals of ARS National Program 201 --
Water Quality and Management: 1) Predict impacts of changes in land use
and management on watershed response based on hydrologic processes,
watershed variability, and watershed characteristics (Goal 1.2.1); 2)
Develop methods to determine input model parameters, values, and state
variables for multiple scales to account for the effect of management
practices (Goal 1.7.2); 3) Test and improve models by comparing results
against field databases for different conditions at the field, farm, and
watershed scale (Goal 3.6.2);and 4) Determine the impact of management
practices developed at the field and farm scales on water quality within
watersheds (Goal 3.7.3). This research also addresses the following
goals of the ARS National Program 202 -- Soil Resource Management: 1)
Develop quantitative approaches for evaluating the environmental and
economic benefits of site-specific soil management strategies; and 2)
Develop science-based tools and decision aids that enable land managers
to understand the effects of various combinations of management
practices. This research also will serve a fundamental role in
addressing components of the new ARS NP 201 Water Quality Management
initiatives on: Water Supply and Draught (A1), Total Maximum Daily Loads
(A5), Remote Sensing Applications (A6), and Water Resource Models (A8).

 

This project will make a major contribution to the ARS National
Initiative on "Water Resources Models, Decision Support Tools, and
Information Databases." It will address the following two proposed
deliverables of the Initiative: 1) Tested and improved models and
decision support tools for managing water resources and agriculture; and
2) An integrated framework to guide selection and application of models
for management. The project will provide improved models of
spatially-variable processes for guiding site-specific management of
agricultural landscapes to protect water quality and optimize
production, through new scaling and parameterization methods. The new
scaling methods will allow the transfer of measured and modeled results
(e.g., pollutants) from a land management unit level to field, farm, and
larger scales, and vice versa. Thus, this project will also contribute
to the National Initiative on "Total Maximum Daily Load (TMDL)
Monitoring and Research" by addressing two of its proposed
deliverables: 1) Determine the sources and processes of TMDL pollutants
and their fate from source areas to water bodies; and 2) Develop and
evaluate regionally cost-effective BMP's for farmers and producers to
reduce TMDL pollutants to natural or acceptable levels using
conservation, management, and stabilization practices, methods, and
techniques

 

**Research Quality, Capacity, and Unit's Leadership:**

 

The review of this project by the Office of Science Quality review
(OSQR) panel gave it an "Excellent" rating, requiring no revisions. This
project is at the frontiers of science, as the scaling up of measured or
simulating results from plots to fields to farm and watersheds is a
major science challenge at this time. Also on the forefronts of science
are the development of a spatial model to help evaluate and guide
site-specific management that is a foremost need for moving precision
agriculture forward, and new methods to parameterize heterogeneity
within land units and management practices. Our Unit team is emminently
qualified to meet these enormous challenges. However, we realize that
these objectives are ambitious, and we do not have all the data and
expertise. Thus we have to collaborate closely with some key scientists
and teams. Locally, we are working on farmer fields, with Colorado State
University (Drs. Peterson, Hansen, Salas) and ARS Range Resouces and
Water Management Research Units. Our national/international
collaborations are given below. We also realize that these objectives
are long-term and will continue beyond the current Project Plan. A
priority for this CRIS, as for the IFARM CRIS, is to fill our plant
water stress modeling position with a bright scientist.

 

**National/International Collaborations**

 

We have started to collaborate with the following five ARS Laboratories
across the country: Hydrology Lab., Beltsville, MD; Grazingland Research
Lab., El Reno, OK; Southwest Watershed Research Center, Tucson, AZ;
Southeast Watershed Research Lab., Tifton, GA; and the National Soil
Tilth Lab., Ames, IA. We are part of the CSREES Multi-State Project
W1188, dealing with scaling up and modeling of soil water and chemical
transport. We have also established international collaborations with
the Swiss Federal Institute of Technology (ETH) for this research.
Specific collaborators include: Gary Heathman (ARS, IN), John Sadler
(ARS, MO)  Pat Starks (ARS, OK), Mark Tomer (ARS, IO), Gordon Starr
(ARS, ME), Jorge Ramirez, Jose Salas, Gary Peterson, Neal Hanson, and
Luis Garcia (Colorado State U.), Rainer Schulin and and Hannes Fluehler
(ETH).

 

**Status of the Technology Transfer Efforts:**

 

ARS scientists at El Reno, OK and Columbia, MO will begin using our
spatial model prototype AgSimGIS for their spatial precision agriculture
data in Spring 2005.

 

ARS scientists at West Lafayette, IN are applying RZWQM to their
watershed study to simulate pesticide transport.