New Soil Index Development Through Integration of Pedometrics and Econometrics

Research Team

Major Advisor:
Sabine Grunwald, Department of Soil and Water Sciences, University of Florida

Supervisory Committee Members (M.S. student committee):
Wendell P. Cropper, School of Forest Resources and Conservation, University of Florida
Wonsuk Lee, Agriculture and Biological Engineering Department, University of Florida
Gustavo G.M. Vasques, Embrapa Solos, Rio de Janeiro, Brazil

Supervisory Committee Members (Ph.D. student committee):
Allan Bacon, Department of Soil and Water Sciences, University of Florida
Wendell P. Cropper, School of Forest Resources and Conservation, University of Florida
Charles B. Moss, Food and Resource Economics, University of Florida

Collaborators:
Sanford V. Berg, Warrington College of Business, University of Florida
Michelle A. Phillips, Warrington College of Business, University of Florida

Graduate student:
Katsutoshi Mizuta, M.S. student, Department of Soil and Water Sciences, University of Florida (8/2014 to 8/2016)
Katsutoshi Mizuta, Ph.D. student, Department of Soil and Water Sciences, University of Florida (8/2016 to current)

Funding Source: Graduate student assistantship, Japan Student Services Organization (JASSO)

Research:

Katzutoshi Mizuta (2016). New soil index development and integration with econometric theory. M.S. thesis, University of Florida, Gainesville, FL.

Highlights:

(1) We  coined the term of the new research area, called Pedo-Econometrics (Soil Science + Economics + Mathematics/Statistics)

(2) Index studies for soil concepts with econometric production theory

  • Review of major soil concepts and identify shortcomings, progress and future needs of research.
  • Assess the pros and cons of indication system in soil and ecological studies.
  • Depict axiomatic features of indicators/indices (Ix/Ix) in soil and environmental sciences.
  • Describe the Data Envelopment Analysis (DEA) and Malmquist Index used in econometrics as an opportunity to develop soil In/Ix ideally suited to meet axiomatic index theory assumptions.

(3) Prototype application of a new indication method for soil science by integrating economic concepts using data envelopment analysis

  • Calculate soil carbon sequestration (SCseq) rates in the State of Florida
  • Implement the DEA to calculate the SCseq capability In/Ix (SCI) as a demonstration of a new indication assessment method
  • Predict SCI based on the SCseq estimated with spectroscopy data by multivariate calibration methods with various pre-processing methods.
  • Examine the relationships between soil orders, climatic factors and land use/ land cover types with SCI
  • Discuss improvements and potential applications of the new indication development method in soil science as well as emerging questions.

Summary: The quantitative assessment of soil quality, health, and security is critically important to sustain soil resources at regional and global scales. Numerous indicators (In) and indices (Ix) have been developed to quantify soil resources over the past decades. Our extensive literature review revealed that the traditional In/Ix metrics are still primitive and a new alternative approach is required. We identified a novel approach based on econometric theory using Data Envelopment Analysis (DEA) to overcome those limitations of conventional methods. The DEA has not been applied in soil science yet. Our first objective was to construct a prototype DEA to assess the Soil Carbon Sequestration (SCseq) Capability In/Ix (SCI) based on the soil carbon sequestration (SCseq) rate and climatic, biotic, and hydrologic factors in Florida. The SCseq was calculated based on lab-measured soil organic carbon (SOC) stocks in the top soil (~ 20cm). We also estimated the SCI based on spectral-predicted SOC stocks as our second research objective. The observed SCI scores were consistent with the literature reporting on the relationships between soil carbon and climatic, biological, and hydrological factors. Interestingly, the observed and estimated SCI showed a good fit with an R2 of 0.7 suggesting that spectroscopy data is well-suited to infer on SCI scores across Florida saving costs, time and labor. The successful DEA prototype application to derive SCI for a large multi-use, subtropical region demonstrated the extraordinary benefits which can quantitatively assess the soil concepts, such as quality, health and security.

Integral Soil Security

Research Team

Major Advisor:
Sabine Grunwald, Department of Soil and Water Sciences, University of Florida

Supervisory Committee Members:
Tracy A. Irani, Family Youth and Community Sciences, University of Florida
Monika Ardelt, Sociology, CLAS, University of Florida
Stefan Gerber, Department of Soil and Water Science, University of Florida

Graduate student:
Renita Kay Wilcox, Ph.D. student, Department of Soil and Water Sciences, University of Florida

Time: 8/2014 to current
Funding Source: Matching Assistantship UF and diverse other sources

Overview

Kay’s research interests include the human dimensions of natural resources, soil health and security, geographical information systems, and urban ecology.  The emphasis of the research she has undertaken for her PhD dissertation is the exploration of soil and human – soil interactions through the synthesis of soil quality/health data and individuals’ and community knowledge, understanding, experiences and values as related to soils.  The integral map of Integral Theory provides the framework for her research. The data collected in Kay’s research will support an assessment of soil security for South Florida.

Additionally, the data will be analyzed in order to understand differences in the soil data, knowledge, understanding, experiences and values as related to soils along the urban – rural gradient from the urban core area to the outlying rural area in the Greater Miami region.

Soil-Landscape Modeling for Agro-ecological Policy Development in the Developing Regions

Research Team

Major Advisor:
Sabine Grunwald, Department of Soil and Water Sciences, University of Florida

Supervisory Committee Members:
Denis Riveiro de Valle, School of Forest Resources and Conservation, University of Florida
Walter Bowen, Department of Soil and Water Science, University of Florida
Stefan Gerber, Department of Soil and Water Science, University of Florida

Graduate student:
Setyono Hari Adi, Ph.D. student, Department of Soil and Water Sciences, University of Florida

Time: 8/2015 to current
Funding Source: Graduate student assistantship, Government of Indonesia

Overview: 

Soils are underappreciated natural resources but its condition are inseparable from the major global issues including the food security and climate change. The status of soil properties, such as carbon and macronutrients, are important to be included in the land resources decision-making processes. However, due to the high cost of soil monitoring, such information is often neglected or at best assumed to be constant. Furthermore, the advances in soil spectroscopy have proven to become an effective solution to reduce the cost of traditional soil laboratory analysis, while maintaining the measurement accuracy. However, the remaining question is how to extrapolate point based soil spectroscopy measurement to the landscape scale that is applicable for the decision-making processes. This research, therefore, focuses on the study of the new pathway of soil prediction using multiple response modeling techniques to predict the soil Visible-Near Infrared (VNIR) spectrum. This soil spectrum is a set of the proportion of the reflected light, i.e. reflectance, that is measured at VNIR wavelength region at 350-2500 nm, which therefore represents multiple variables and contains information of multiple soil properties. The multi-response model is developed following the STEP-AWBH soil-factorial modeling framework using publicly-available global soil forming factor data from multiple online sources as the explanatory variables. Furthermore, my study focuses on two research locations: (1) The State of Florida, US, where the data is utilized for model development, and (2) Indonesia, where the prediction model is going to be applied.

Approach

Figure 1. Three different pathways of soil prediction modeling.

Figure 2. Pathway C of multivariate analysis for soil spectral spectrum prediction.

Research Highlights:

  • The model developed using the Redundancy Analysis technique yielded a reflectance prediction model with the correlation coefficient of the cross and test validation of 77 and 64%, respectively (RMSE 0.05 and 0.06).

Understanding Soil Carbon Dynamics in the Central Andes, Peru

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Research Team

Principal Investigator:
Sabine Grunwald, Department of Soil and Water Sciences, University of Florida

Co-Principal Investigators:
Roberto Quiroz, International Potato Center (CIP), Lima, Peru

Graduate students:
Carla Gavilan, Ph.D. student, Department of Soil and Water Sciences, University of Florida

Time: 2012 to current

Funding Source: International Potato Center (CIP), Consultative Group for International Agricultural Research (CGIAR)

Overview:

The Andes represent the largest and highest mountain range in the tropics. Even though the Andean Region is among the most threatened of ecosystems under current predicted global warming scenarios, there is a lack of knowledge on the interactive effects of land use, topography, and climate on soil organic carbon dynamics in this fragile ecosystem.

In this project we use proximal soil sensing (visible-near infrared and mid-infrared spectroscopy), geospatial and remote sensing technologies, integrative soil-landscape-climate simulation modeling (Roth-C) and agent-based models in order to bring new insights to the complex interrelationships between soils, topography, climate and humans in this remote and understudied region.

Development of a Geospatial Soil-Crop Inference Engine for Smallholder Farmers in India

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Research Team

Principal Investigator:
Sabine Grunwald, Department of Soil and Water Sciences, University of Florida

Co-Principal Investigators:
Scot E. Smith, Geomatics/School of Forest Resources and Conservation, University of Florida
Suhas Wani, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, India
K. Ramesh Reddy, Department of Soil and Water Science, University of Florida

Graduate students:
Christopher Clingensmith, Ph.D. student, Department of Soil and Water Science, University of Florida
Yiming Xu, Ph.D. student, dropped out of the project.

Collaborators:
Amr H. Abd-Elrahman, Geomatics/School of Forest Resources and Conservation, University of Florida
Yaduraju, International Crops Research Institute for the Semi-Arid Tropics, Patancheru, India
V. Balaji, Commonwealth of Learning, Vancouver, Canada
Walter Bowen, IFAS International Office, University of Florida

Time: 1/2012 to 12/2014
Funding Source: National Science Foundation (NSF) – EAGER

Overview:

In this project we develop an inference engine to transform smallholder farm production systems for resource optimization by utilizing spectral technology and geospatial modeling. We fuse innovative sensor technologies into a holistic engine aiming to enhance soil quality and optimize crop yield to support smallholder farmers. The overarching goal is to build a Geospatial Soil-Crop Inference Engine (GeoSCIE) that fuses ground-based and remotely-sensed spectral data with geo-referenced observations to derive critical metrics for crop and soil management. Several hypotheses are tested in two smallholder farm settings in Kothapally and Misuta, India.

The objectives are to:
(1) Develop and validate quantitative models that relate analytically-derived measures of soil indicators including soil texture, soil organic matter, macronutrients (nitrogen and phosphorus), micronutrients (sulfur, boron, and zinc) and soil spectral data derived from diffuse reflectance spectroscopy (DRS) (visible/near-infrared and mid-infrared spectral ranges);
(2) Design and implement a three-tiered multi-spectral system using ground-based spectral reflectance measurements, aerial sensors, and satellite multispectral scanners for a sequence of cropping seasons that aims to identify the optimal spatial resolution required for accurate assessment of crop-specific properties and stressors in smallholder farm settings;
(3a) Fuse soil DRS and remote sensing data into a GeoSCIE and calibrate and validate the engine by estimating a suite of critical soil and crop/vegetation-specific properties;
(3b) Apply a genetic algorithm to derive indices from fused spectral data to infer on soil quality, fertility, water deficiency, and crop stress; and
(4) Translate results from GeoSCIE into Reusable Learning Objects (RLOs) to provide training/learning material to smallholder farmers and streamline GeoSCIE-based  management recommendations into the agricultural-oriented social network application and information system AGROPEDIA used by smallholder farmers in India.

overview fig

Approaches

We used proximally collected hyperspectral data, specifically visible-near-infrared (VNIR) and mid-infrared (MIR) reflectance data, to predict measured soil properties using chemometric and data mining methods. This has been augmented by exploring how subsetting data for the creation of calibration and validation sets affects model prediction results. Additionally, new chemometric/genomic methods have been examined to observe their performance relative to more standard chemometric methods. In addition, the effects of transferring, spiking and scaling were investigated. These models are also being enhanced with the incorporation of spatial data or site characteristics when available with methods that can handle fixed effects with multivariable, multi-collinear random effects.

Pinemap: Integrating Research, Education and Extension for Enhancing Southern Pine Climate Change

Research Team

PI:
T.A. Martin, University of Florida

Cooperating Institutions:
University of Florida (lead)
Alcorn State University
Auburn University
Mississippi State University
North Carolina A&T University
North Carolina State University
Oklahoma State University
Texas A&M University
University of Georgia
USDA Forest Service
Virginia Polytechnic and State Institute
Virginia State University

among 50+ Co-PIs and Collaborators Dr. Sabine Grunwald is involved in the project as Co-PI to conduct multi-scale landscape modeling research. She has responsibility for the project data infrastructure (lab and field measurements).

Graduate (Ph.D.) student (supervised by Grunwald): C. Wade Ross

Database management / programmer: Brandon Hoover and Rosvel Bracho-Garillo

Time: 5/2011 to 5/2016
Funding Source: U.S. Department of Agriculture (USDA) – National Institute of Food and Agriculture (NIFA) / Agriculture and Food Research Initiative (AFRI) CAP Regional Project (funding amount for this project: $20 million)

Overview:

Project web site: http://www.pinemap.org

On Friday, Feb. 18, the U.S. Department of Agriculture’s National Institute of Food and Agriculture announced the award of a five-year, $20 million grant to a consortium led by the University of Florida, to fund research, outreach and education to develop and transfer better management methods for southern pine, notably loblolly pine.

From both an environmental and an economic standpoint, pine trees are one of the most important agricultural products in the southeastern U.S.

In an 11-state region reaching from Virginia to Texas, forests occupy about 60 percent of all land. That includes about 25 million acres of naturally occurring pine forest, and another 25 million acres of planted pine.

Among planted pine, the most important species is loblolly, which accounts for about 80 percent of planted forest in the Southeast. It’s widely used for lumber, pulp and paper production, and has great potential for biofuel production.

Southeastern pine forests produce about 16 percent of global industrial wood, more than any other country in the world.

The forest products industry is responsible for 5.5 percent of the jobs and 7.5 percent of the total industrial output of the region.

Two-thirds of all the drinking water in the U.S. comes from forested watersheds.

Forests in the SE U.S. store enough carbon each year to offset 13% of the regions greenhouse gas emissions.

Project goals are to create, synthesize, and disseminate the necessary knowledge to enable southern forest landowners to:

  • harness pine forest productivity to mitigate atmospheric carbon dioxide
  • more efficiently utilize nitrogen and other fertilizer inputs
  • adapt their forest management approaches to increase resilience in the face of changing climate.

The project is structured around six aims:

  • Aim 1 – Monitoring network establishment and measurement
  • Aim 2 – Multi-scale modeling
  • Aim 3 – Gene discovery and deployment guidelines
  • Aim 4 – Life cycle assessment; multi-scale policy and economic analysis; assessment of alternative management adoption
  • Aim 5 – Educational and training programs for stakeholders and students
  • Aim 6 – Extension program development and delivery

Disciplinary aims contribute to broader integrated project goals: Mitigation, adaptation, and education and extension.

Project summary: Over the last 50 years, cooperative research on planted southern pine management among SE U.S. universities, government agencies, and forest industry has developed and facilitated the widespread implementation of improved genetic and silvicultural technology. The impact of the regional research cooperatives is difficult to overstate, with current members managing 55% of the privately owned planted pine forestland, and producing 95% of the pine seedlings planted each year. Our team includes the eight major forestry cooperative research programs, scientists from nine land grant and three 1890s institutions, the US Forest Service, and climate modeling and adaptation specialists associated with the multi-state SE Climate Consortium and state climate offices. Our goal is to create and disseminate the knowledge that enables landowners to: harness planted pine forest productivity to mitigate atmospheric CO2; more efficiently use nitrogen and other fertilizer inputs; and adapt their forest management to increase resilience in the face of changing climate. We will integrate our team’s infrastructure and expertise to: 1) develop breeding, genetic deployment and innovative management systems to increase C sequestration and resilience to changing climate of planted southern pine forests ; 2) understand interactive effects of policy, biology, and climate change on sustainable management; 3) transfer new management and genetic technologies to private industrial and non-industrial landowners; and 4) educate a diverse cross-section of the public about the relevance of forests, forest management, and climate change. These efforts will enable our stakeholders to enhance the productivity of southern pine forests, while maintaining social, economic, and ecological sustainability.

Results – Modeling 

C.W. Ross (2017). A region-wide analysis of terrestrial carbon cycling across the “land of pines”. Ph.D. dissertation, University of Florida, Gainesville, FL.

Summary: Climate projections indicate that the Southern US will become warmer and potentially drier by the end of the 21st century. This is an important consideration for land managers as the region is home to some of the most productive and valuable timberlands in the world. Furthermore, these ecosystems mitigate a substantial fraction of anthropogenic emissions via carbon sequestration and contain a considerable amount of carbon in biomass and soil. Forest and soil carbon sequestration is predominantly a function of productivity and regional climate conditions; however, much of our knowledge regarding terrestrial carbon cycling in response to climate projections has been derived from global scale studies, which are too coarse to provide adequate guidance at local and regional scales. Thus, the overarching objectives of this dissertation were twofold: 1) quantify current terrestrial carbon stocks across the Southern US and 2) assess the response of forest productivity and carbon cycling to climate perturbations. We used a combination of mechanistic and machine learning methods to achieve these objectives. Stand-level biomass estimates and soil carbon measurements to 1m depth were collected from 326 research sites strategically positioned to capture the variation of climate and soils that characterize the region. Analysis of field data indicated that the largest fraction of terrestrial carbon was attributed to soil (53%), followed by stemwood (28%), coarse root (8%), branch (5%), stembark (4%), and foliage (2%). Terrestrial carbon stocks were modeled by applying data mining techniques to a large suite of spatially explicit environmental data (~ 7 TB) to identify important regional-scale predictors for Random Forest models. The best models achieved an adjusted R2 of 0.96 and 0.62 with model training and validation datasets, respectively. Forest productivity and carbon cycling in response to climate perturbations was assessed with a climate-driven experiment using the DayCent model. Regionally downscaled representative concentration pathways (RCP 4.5 and 8.5) were used to represent a range of potential climate scenarios. DayCent simulations covering the measurement period (2012 to 2014) were validated with field-based data and indicated robust agreement across the region, with mean absolute percentage error ranging from 6% for soil organic carbon to 51% for belowground net primary productivity. DayCent forecasts to the end of the 21st century demonstrate that forest productivity is clearly sensitive to climate perturbations. Our findings suggest that the terrestrial carbon sink capacity of pine forests will increase under a broad range of potential climate scenarios.

Soil Carbon Fractions

Figure 1. Distribution of soil samples and soil orders within the state of Florida. Soil orders were derived from the State Soil Geographic (STATSGO) database (Natural Resources Conservation Service, 2006). Abbreviation: SOC = soil organic carbon.

Research Team

Major Advisor / Supervisor:
Sabine Grunwald, Department of Soil and Water Sciences, University of Florida

Graduate student:
Gustavo G.M. Vasques, Ph.D. student, Department of Soil and Water Sciences, University of Florida (1/2006 to 8/2009)
Hamza Keskin, M.S. student, Department of Soil and Water Sciences, University of Florida (8/2013 to 12/2015)

Post-Doc:
Nicky Knox, Department of Soil and Water Sciences, University of Florida (11/2010 to 11/2011)
Deoyani V. Sarkhot Department of Soil and Water Sciences, University of Florida (9/2007 to 21/2009)

Research

Sarkhot D.V., S. Grunwald, Y. Ge and C.L.S. Morgan. 2011. Comparison and detection of soil carbon under Arundo Donax and coastal bermuda grass using visible/near infrared diffuse reflectance spectroscopy. Geoderma 164: 22-32. (pdf)

Knox N. M., S. Grunwald, M.L. McDowell, G.L.  Bruland, D.B. Myers, W.G. Harris. 2015. Modelling soil carbon fractions with VNIR and MIR spectroscopy. Geoderma 239-240: 229-239. (pdf)

Vasques G.M., S. Grunwald, N.B. Comerford and J.O. Sickman. 2010. Upscaling of dynamic soil organic carbon pools in a north-central Florida watershed. Soil Sci. Soc. Am. J. 74: 870-879. (pdf)

Keskin H. (2015). Digital mapping of soil carbon fractions. M.S. thesis, University of Florida, Gainesville, FL.

Summary: Our understanding of the spatial distribution of soil carbon (C) pools across diverse land uses, soils, and climatic gradients at regional scale is still limited. Research in digital soil mapping and modeling that investigates the interplay between (i) soil C pools and environmental factors (“deterministic trend model”) and (ii) stochastic, spatially dependent variations in soil C fractions (“stochastic model”) is just emerging. This evoked our motivation to investigate soil C pools in the State of Florida covering about 150,000 km2. Our specific objectives were to (i) compare different soil C pool models that quantify stochastic and/or deterministic components, (ii) assess the prediction performance of soil C models, and (iii) identify environmental factors that impart most control on labile and recalcitrant pools and total soil C (TC). We used soil data (0-20 cm) from a research project (USDA-CSREES-NRI grant award 2007-35107-18368) collected at 1,014 georeferenced sites including measured bulk density (BD), recalcitrant carbon (RC), labile (hot-water extractable) carbon (HC) and TC. A comprehensive set of 327 geospatial soil-environmental variables was acquired. The Boruta method was employed to identify “all-relevant” soil-environmental predictors. We employed eight methods – Classification and Regression Tree (CaRT), Bagged Regression Tree (BaRT), Boosted Regression Tree (BoRT), Random Forest (RF), Support Vector Machine (SVM), Partial Least Square Regression (PLSR), Regression Kriging (RK), and Ordinary Kriging (OK) – to predict soil C fractions and TC. Overall, 36, 20 and 25 predictors stood out as “all-relevant” to estimate TC, RC and HC, respectively. We predicted a mean of 5.39 ± 3.74 kg TC m-2 in the top 20 cm with the best model. The prediction performance assessed by the Ratio of Prediction Error to Inter-quartile Range for TC stocks was as follows: RF > SVM > BoRT > BaRT > PLSR > RK > CART > OK. The best models explained 71.6%, 71.7% and 30.5% of the total variation for TC, RC and HC, respectively. Biotic and hydro-pedological factors explained most of the variation in soil C pools and TC, lithologic and climatic factors showed some relationships to soil C pools and TC, whereas topographic factors faded from soil C models.

Global / Continental Digital Soil Mapping - Soil Carbon Modeling Along Ecological, Climatic, and Biotic Trajectories

Research Team

Major Advisor:
Sabine Grunwald, Department of Soil and Water Sciences, University of Florida

Graduate students:
Baijing Cao, School of Natural Resources and Environment, University of Florida
Xiong Xiong, Department of Soil and Water Sciences, University of Florida
C. Wade Ross, Department of Soil and Water Sciences, University of Florida

GIS Specialist:
Risa Patarasuk, Department of Soil and Water Sciences, University of Florida

Faculty advisors:
Ted Schuur, Department of Biology, University of Florida
Timothy Fik, Department of Geography, University of Florida
Stefan Gerber, Department of Soil and Water Science, University of Florida
Gustavo M. Vasques, Embrapa, Rio de Janeiro, Brazil

Collaborators:
National Soil Survey Center, Natural Resources Conservation Service (NRCS) – U.S. Department of Agriculture (USDA), Lincoln, NE, U.S.
Canadian Soil Information Service / Agriculture and Agri-Food Canada, Ottawa, ON, Canada
Embrapa Solos, Rio de Janeiro, Brazil

Time: 9/2010 to 12/2015
Funding Source: Ph.D. Chinese Scholarship Program and matching assistantship, University of Florida

Overview:

This research project is linked to activities of:

(1) GlobalSoilMap.net: http://www.globalsoilmap.net/

(2) Global Soil Partnership (GSP) for Food Security and Climate Change Mitigation and Adaptation (spearheaded by FAO; partners: European Commission, international agencies with global soil mandate, regional and national soil science associations and networks, and national soil science associations and universities)

Summary

Cao B. 2015. Soil carbon modeling along ecological, climatic and biotic trajectories at continental scale. Ph.D. dissertation, University of Florida, Gainesville, FL.

Summary: Soil carbon (C) stored in the contiguous United States (U.S.) is critical to estimate the global soil C pool due to the large scale. To better understand the role this large SOC pool plays in the global carbon cycle, we need to know the SOC stock and its change at the continental scale. The incorporation of environmental variables into digital soil models has shown success to improve soil C predictions. The objective of this dissertation was to enhance our knowledge on the spatial and temporal variation of SOC in contiguous U.S. Firstly, a pilot test was conducted in Colorado and Florida, which are contrasting in ecological landscape. Results confirmed the Random Forest method is the best method to predict SOC and had decent predicting power in these two states. Secondly, it was explored to strategically select predictors from a comprehensive predictor pool of environmental variables to develop geospatial SOC prediction models. Results showed that the SOC stocks in the contiguous U.S. are controlled by a mix of soil, ecological, parent material, atmospheric and water environmental covariates and to lesser extent by biotic and topographic variables. Thirdly, SOC temporal change was analyzed in a long-term period from 1928 to 2011. Our results suggested that the trend in SOC stocks from 1928 to 2011 is non-monotonic but fluctuated with seven distinct stages; and in most ecoregions (97% area of the contiguous U.S.) it was driven by climate and land use type; also, socio-economic factors had a profound effect on SOC change. The study improved the knowledge of the spatial and temporal variation of SOC in the continental with implications for carbon cycling and sequestration, land resource management, and ecosystem service assessment.

U.S. Soil Carbon Assessment

RAPID

Research Team

PI:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida

Co-PIs:
Alex. McBratney, Pro-Dean and Professor, Faculty of Agriculture, Food and Natural Resources, University of Sydney
Budiman Minasny, Senior Research Fellow, Faculty of Agriculture, Food and Natural Resources, University of Sydney

Collaborators:
Larry T. West, National Leader – Soil Survey Research and Laboratory, USDA-NRCS, Lincoln, NE

Post-Doc:
Cleiton H. Sequeira, National Soil Survey Center (NCSS), USDA-NRCS, Lincoln, NE

Graduate Students:
Baijing Cao, School of Natural Resources and Environment, University of Florida
Xiong Xiong, Soil and Water Science Department, University of Florida

Time: 7/2011 to 7/2013
Funding Source: U.S. Department of Agriculture (USDA) – Natural Resources Conservation Service (NRCS)

Summary:

Project objectives:

  • Analysis of visible-near infrared (VNIR) spectra and other data collected as part of the Rapid Assessment of Soil Carbon in the U.S.
  • Evaluate soil property and land cover effects on soil carbon stocks
  • Develop a scientifically-based inventory of current soil carbon stocks for the U.S.

Methods: Soil data collected as part of NRCS’s “Rapid Assessment of U.S. Soil Carbon for Climate Change and Conservation Planning” project were utilized which cover the U.S. This dataset includes about 36,000 sample points representing 7,200 sites and about 144,000 horizons. VNIR spectra were fused with soil carbon analytical data to derive a national soil carbon spectral model that allowed estimating soil carbon at all sample locations. Current and historic soil carbon data were used to evaluate relationships among soil properties, land cover, and soil carbon stocks. The current and historic soil carbon data were integrated with available environmental co-variates to assess soil carbon across the U.S. using state-of-the-art digital soil mapping (DSM).

Rapid Assessment and Trajectory Modeling of Changes in Soil Carbon Across a Southeastern Landscape (Florida)

Core Project of the North American Carbon Program – Continental Carbon Budgets, Dynamics, Processes, and Management: http://www.nacarbon.org

Research Team

PI:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida

Co-PIs:
Nicholas B. Comerford, Soil and Water Sciences Department, University of Florida
Willie G. Harris, Soil and Water Sciences Department, University of Florida
Gregory L. Bruland, Department of Natural Resources & Environmental Management, University of Hawaii at Manoa

Post-Docs:
N.M. Knox, Soil and Water Sciences Department, University of Florida
D. Brenton Myers, Soil and Water Sciences Department, University of Florida
Deoyani V. Sarkhot, Soil and Water Sciences Department, University of Florida
Gustavo M. Vasques, Soil and Water Sciences Department, University of Florida

Graduate students:
Elena I. Azuaje (M.S.), Soil and Water Sciences Department, University of Florida
Xiong Xiong (Ph.D.), Soil and Water Sciences Department, University of Florida
Wade C. Ross (M.S.), Soil and Water Sciences Department, University of Florida

Technical / Lab Support:
Aja Stoppe, Soil and Water Science Department, University of Florida
Lisa Stanley, Soil and Water Science Department, University of Florida
Adriana Comerford, Soil and Water Science Department, University of Florida

Time: 09/2007 to 09/2011
Funding Source: U.S. Department of Agriculture (USDA) – National Research Initiative (NRI) – CSREES; now National Institute of Food and Agriculture (NIFA)

Summary:

The goal of this research is to assess the effects of land cover/land use (LC/LU) change on carbon stocks giving special attention to translating site-specific carbon pools (labile, recalcitrant and total carbon) to landscape scales. Our objectives are to: (i) Determine soil carbon pools in various ecosystem types across a large southeastern landscape (Florida) cutting across soil nutrient, LC/LU and climatic/hydrologic gradients; (ii) Investigate the strength and magnitude of relationships between environmental landscape properties and corresponding carbon pools within a GIS; (iii) Derive functional models relating measured soil carbon fractions to soil spectra in the visible/near-infrared (VNIR) range to develop rapid and cost-effective soil carbon prediction models; (iv) Model change trajectories, i.e. assess historic and actual soil carbon stocks and turnover rates in various ecotypes; and (v) Upscale site-specific VNIR-derived and laboratory-measured soil carbon pools to the landscape scale by modeling spatial autocorrelations and covariations with environmental landscape properties; and validate the derived geospatial soil carbon models using an independent dataset. Our methodology is based on comprehensive historic (~1,300 soil samples) and reconnaissance (~1,000) soil samples representing various ecotypes identified using a a stratified-random sampling design (strata: land use-suborder combinations). Various hypotheses are tested to investigate relationships between soil carbon pools and environmental landscape properties using analysis of variance, multivariate regression methods and Canonical Correlation Analysis. Chemometric modeling is used to relate spectra to analytical measures. Hybrid geospatial methods are used to develop soil carbon models for Florida. This proposal addresses USDA’s priority research areas including spatially-explicit soil carbon modeling.

Handout – project overview.

IFAS News (April 1, 2009).

field sampling
Photo: Field sampling to collect soil samples for carbon analysis.

Results

Peer-reviewed Publications:

Xiong, X., Grunwald, S., Corstanje, R., Yu, C., Bliznyuk, N., 2016. Scale-dependent variability of soil organic carbon coupled to land use and land cover. Soil Tillage Res. 160, 101–109. doi:10.1016/j.still.2016.03.001.

Knox N. M., S. Grunwald, M.L. McDowell, G.L.  Bruland, D.B. Myers, W.G. Harris. 2015. Modelling soil carbon fractions with VNIR and MIR spectroscopy. Geoderma 239-240: 229-239.

Xiong X., S. Grunwald, D. B. Myers, J. Kim, W. G. Harris, N. B. Comerford, and N. Bliznyuk. 2015. Assessing uncertainty in soil organic carbon modeling across a highly heterogeneous landscape. Geoderma 251-252: 105-116.

Xiong X., S. Grunwald, D.B. Myers, J. Kim, W.G. Harris and N.B. Comerford.. 2014. Holistic environmental soil-landscape modeling of soil organic carbon. Environmental Modeling and Software J. 57: 202-215.

Xiong X., S. Grunwald, D.B. Myers, C.W. Ross W.G. Harris and N.B. Comerford. 2014. Interaction effects of climate and land use/land cover change on soil organic carbon sequestration. Science of Total Environment J. 493: 974-982. http://dx.doi.org/10.1016/j.scitotenv.2014.06.088

Bliss C.M., N.B. Comerford, D.A. Graetz, S. Grunwald and A.M. Stoppe. 2014. Land use influence on carbon, nitrogen, and phosphorus in size fractions of sandy surface soils. Soil Sci. J. 178: 654-661

Ross C.W., S. Grunwald, D.B. Myers. 2013. Spatiotemporal modeling of soil organic carbon stocks across a subtropical region. Science of the Total Environment J. 461-462: 149-157.

Vasques G.M., S. Grunwald and D.B. Myers. 2012. Influence of the geographic extent and grain size on soil carbon models in Florida, USA. J. of Geophys. Research – Biogeosciences 117. G04004: 1-12. doi:10.1029/2012JG001982.

Vasques G.M., S. Grunwald and D.B. Myers. 2012. Associations between soil carbon and ecological landscape variables at escalating spatial scales in Florida, USA. Landscape Ecology J. 27: 355-367. doi:10.1007/s10980-011-9702-3.

Azuaje E.I., N.B. Comerford, W.G. Harris, J.B. Reeves III, and S. Grunwald. 2011. Loblolly and slash pine control aggregate soil carbon and soil carbon mineralization. Forest Ecology and Management J. 263: 1-8.

Vasques G.M., S. Grunwald, N.B. Comerford and J.O. Sickman. 2010. Regional modeling of soil carbon at multiple depths within a subtropical watershed. Geoderma 156: 326-336.

Vasques G.M., S. Grunwald and W.G. Harris. 2010. Building a spectral library to estimate soil organic carbon in Florida. J. Environ. Qual. 39: 923-934.

Vasques G.M., S. Grunwald, N.B. Comerford and J.O. Sickman. 2010. Upscaling of dynamic soil organic carbon pools in a north-central Florida watershed. Soil Sci. Soc. Am. J. 74: 870-879.

Vasques G.M., S. Grunwald and J.O. Sickman. 2009. Visible/near-infrared spectroscopy modeling of dynamic soil carbon fractions. Soil Sci. Soc. Am. J. 73: 176-184.

Vasques G.M., S. Grunwald and J.O. Sickman. 2008. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146: 14-25.

Theses and Dissertations:

Azuaje E. 2011. Loblolly and slash pine control aggregate soil carbon and soil carbon mineralization. M.S. thesis, Soil and Water Sciences Department, University of Florida.

Ross C.W. 2011. Spatial-temporal modelling of soil organic carbon across a subtropical region. M.S. thesis, Soil and Water Sciences Department, University of Florida.

Vasques G.M. 2009. Spatial and spectral models of soil carbon at multiple scales in Florida. Ph.D. dissertation, Soil and Water Sciences Department, University of Florida.

Assessment of Climate Regulation, Carbon Sequestration and Nutrient Cycling Ecosystem Services Impacted by Multiple Stressors

fractal

Research Team

Major Advisor:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida

Advisors:
Timothy A. Martin, School of Forestry and Conservation, University of Florida
Howard Beck, Agricultural and Biological Engineering, University of Florida
Alan W. Hodges, Food and Resource Economics, University of Florida
Amy Shober, Soil and Water Science Department, University of Florida

Graduate students:
Pasicha Chaikaew (Ph.D.), Soil and Water Science Department, University of Florida

Time: 09/2009 to 09/2013
Funding Source: Royal Thai Government – Ph.D. Fellowship

Overview

What are ecosystem services? The benefits people obtain from ecosystems (Millennium Ecosystem Assessment, 2005)

In this research, ecosystem service assessment is based on integrative analysis that aims to assess the benefits people obtain from ecosystems through (i) biophysical analysis of the ecosystem domain representing past and current conditions; (ii) linking ecosystem metrics derived from the ecosystem domain and valuation and perception of people to derive benefits, and (iii) investigating the change of those benefits due to select direct and indirect stressors (drivers) projected into the future.

The specific objectives were to:
(i) elucidate on the composition of terrestrial carbon (below and above ground carbon), carbon in surface waters, and green house gas (GHG) emissions in the Suwannee River Basin (FL) and to infer on relationships between carbon and environmental co-variates including nutrients.

(ii) analyze the ‘biophysical benefits of ecosystems’ to people (e.g. increased carbon sequestration, increased biomass production – bioenergy), or improved drinking water quality) which infer on ecosystem services from a biophysical perspective; and to assess ‘human perceived benefits to people’ through socio-economic valuation methods.

(iii) assess the effects of woody biomass for renewable energy production on climate, carbon, and nutrient regulation ecosystem services under different scenarios; and assess the effects of different climate change projections on carbon and nutrient regulation ecosystem services in Florida.

Results 

Pasicha Chaikaew (2014). Assessment of climate regulation, carbon sequestration, and nutrient cycling ecosystem services impacted by multiple stressors. Ph.D. dissertation, University of Florida, Gainesville, FL.

Summary: The importance of maintaining and enhancing ecosystem services is a vital basis for delivering benefits to human well-being. There are still several research gaps in linking biophysical and socio-economic characteristics, quantifying natural assets, and valuing services. The objective of this dissertation was to assess climate regulation, carbon sequestration, and nutrient cycling ecosystem services from the biophysical, ecological, and socio-economic perspectives. Findings suggest that top soils have acted as a carbon sink over the past decades in the Suwannee River Basin, Florida. The results coincided with an increase in total organic carbon (TOC) loads in surface waters of the Suwannee River, that were less pronounced than increases in total nitrogen (TN) and total phosphorus (TP) loads of which only a few of the drainage areas showed impairment by TN and TP. The net mineralization of TN and TP in soils and surface waters pinpointed to potential risks for nutrient enrichment in aquatic and terrestrial ecosystems. Biotic, soil, parent material, topographic, and water-related factors played crucial roles in predicting soil organic carbon (SOC) storage, whereas climatic factors were of much less importance. The model derived from simulated annealing and random forest estimating actual and attainable terrestrial carbon suggested the limitation of carbon enhancement in some areas (e.g., wetlands), while others showed potential to sequester more carbon (e.g., under row/field crops). The beliefs and perspectives of local residents identified nutrient cycling as the most important service, and climate regulation and carbon sequestration as the least important services, which somewhat contradicted the scientific-based knowledge from the empirical assessments. The willingness of the residents to pay for ecosystem services was extremely low (<$2/household/year). The socio-ecological outcomes from this study and secondary data from the literature and expert knowledge were then integrated in the Bayesian Belief Network (BBN) model under four distinct scenarios. Besides the natural processes and services, awareness, and adaptation through management were identified as key factors in manipulating these benefits. This dissertation took a big step forward in developing an ecosystem service concept from theory to a novel implementation that engaged pedogenic, hydrologic, biotic, atmospheric, and anthropogenic domains together.

Nanosatellites

Research Team

PI:
Jian Ge, Department of Astronomy, University of Florida

Co-PIs:
Alan George, Department of Electrical and Computer Engineering, University of Florida
Warren Dixon, Department of Mechanical and Aerospace Engineering, University of Florida
Sabine Grunwald, Soil and Water Sciences Department, University of Florida
Myoseon Jang, Department of Environmental Engineering Sciences, University of Florida

Time: 05/2011 to 04/2012
Funding Source: University of Florida Research Opportunity Fund

Overview

Our proposed design study provides a detailed remote sensing mission concept for deployment on one of the nanosatellite missions being planned jointly between our international partner, the Instituto Nacional de Tecnica Aeroespacial (INTA, Spain), and UF. We provide a complete nanosatellite mission concept including design reference science cases, technical requirements, science payload optical and mechanical design, interface and control design, data processing and transfer concept and science data operation and management concept. Our near term goal is to demonstrate remote sensing capability with this first nanosatellite mission.

Our long term goals are to use the nanosatellite and advanced miniaturized remote sensing spectrometer technologies for many remote sensing applications including both optical and near infrared to:

  • Detect biophysical properties in agricultural, natural, and human-impacted ecosystems in Florida
  • Evaluate the dispersion of air pollution plumes emitted from agricultural biomass burning over the Everglades National Park
  • monitor the air quality in large city area such as Miami, Tampa, and Orlando, and Jacksonville
  • Evaluate the impact of oil spills on the long term air, water quality, and coastal marshes
  • Monitor, detect, and alert outbreaks in diseases that may devastate crops across Florida and the globe
  • Detect fire and smoke in the earliest stages of forest fires
  • Apply it to future Mars missions to detect mineralogical information of astrobiological significance.

Multi-scaling Behavior of Carbon Across Various Spatial and Temporal Scales

fractal

Research Coordinator

Team Leader:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida

Collaborators:
D. Brenton Myers, Soil and Water Sciences Department, University of Florida
Gustavo M. Vasques, Soil and Water Sciences Department, University of Florida (current: Pedometria e Mapeamento Digital de Solos, Embrapa Solos, Rio de Janeiro, Brazil)
Jongsung Kim (Ph.D. student), Soil and Water Sciences Department, University of Florida
Xiong Xiong (Ph.D. student), Soil and Water Sciences Department, University of Florida

Time: 08/20089 to current
Funding Source: Various sources

Summary

Quantifying carbon (C) sources, sinks, and ecosystem processes that modulate the global C system is critical to identify imbalances and counteract global climate change. Soil organic C (SOC) patterns are dynamic in space and dependent on a multi-factorial and multi-scale system of environmental and anthropogenic drivers. Our overall objectives are to:

  1. Identify the critical (“tipping”) points at which SOC predictions shift from linear to non-linear (multi-fractal) process behavior
  2. Iidentify the underlying causative soil and environmental factors, their variability, and spatial distribution patterns that are causing such behavior to occur.

We improve techniques for accurate upscaling and downscaling of SOC by identifying factors that impart major control on SOC predictions. These factors may be environmental variables (such as land use or topography), soil properties, upscaling or downscaling methods (e.g. aggregation) or spatial dependence structures of SOC or environmental variables. To investigate which of these factors have the most impact on SOC scaling behavior we will rigorously test our hypotheses using an experimental landscape approach across three major nested scales: Field (< 5 km2), regional (~5-25,000 km2), and Florida State (> 150,000 km2) scale. We adopt a mixed deterministic/stochastic modeling approach using linear and non-linear parametric and non-parametric statistical and geostatistical methods to predict SOC and apply fractal methods to assess scaling behavior. This research has significance because it provides insight into complex scale-dependent soil processes in a mixed-use landscape consisting of agriculture, forest and rangeland.

concepts
Fig. 1. Concepts related to scaling of environmental properties and processes.

We are using different methods to test if soils and soil patterns show fractal (self-similar) or multi-fractal behavior:

  • Fractal dimension
  • Entropy and other landscape indices
  • Variography
  • Aggregation and disaggregation (grain analysis)
  • Up and down-scaling of properties and processes
  • Test transferabilty of soil prediction models
  • and more

Results

Vasques G.M., S. Grunwald and D.B. Myers. 2012. Influence of the geographic extent and grain size on soil carbon models in Florida, USA. J. of Geophys. Research – Biogeosciences 117. G04004: 1-12. doi:10.1029/2012JG001982. (pdf)

Vasques G.M., S. Grunwald and D.B. Myers. 2012. Associations between soil carbon and ecological landscape variables at escalating spatial scales in Florida, USA. Landscape Ecology J. 27: 355-367. doi:10.1007/s10980-011-9702-3. (pdf)

Soil Resource Data Bank (Soil DB)

SoilDB logo

Research Team

PI:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida

Co-PIs:
Faculty members in the Soil and Water Science Department: Mark Clark, George O’Connor, Samira Daroub, Carl Fitz, Willie Harris, Zhenli He, George Hochmuth, Jim Jawitz, Yungcong Li, Lena Ma, Cheryl Mackowiak, Kelly Morgan, Brent Myers, Peter Nkedi-Kizza, Andy Ogram, Arnold Schumann, Amy Shober, Maria Silveira, Craig Stanley, Max Teplitski, Gurpal Toor, Chris Wilson, and Alan Wright

Programmer:
Brandon Hoover

Time: 09/2009 to current
Funding Source: Seed funding provided by the Soil and Water Sciences Department, University of Florida

Summary

What is Soil DB? SoilDB allows online sharing of soil resource data collected across micro-, meso- and macro spatial scales and different time periods. The data bank includes physical, chemical, biological, and taxonomic soil data from historic and current projects generated by the Soil and Water Science Department, University of Florida, which will be complemented by other soil datasets in the next phase. The geographic focus is Florida, but includes other regions in and outside U.S. Soil resource data collected in different ecosystem types including agricultural, forest, rangeland, wetlands, urban, and natural conservation areas, are included. SoilDB will be linked to a Google Earth application allowing displaying and exploring soil datasets.

Aim: The aim of SoilDB is to facilitate synthesis of various soil properties to analyze spatial or temporal trends, compare site conditions, or conduct other types of meta-analysis by fusing soil data from the data bank. Soil data can be complemented by environmental datasets to perform more complex analysis and populate mechanistic, deterministic, or stochastic models. Synthesis of data will allow to gain new insights and enhance knowledge on various topical areas including soil and water contamination and public health, carbon management and ecosystem services, wetlands and aquatic ecosystems, landscape analysis and modeling, and nutrient, pesticide & water management.

Benefits: Soil data are costly to collect and require investment in labor and time and analytical expertise to derive biogeochemical properties. Since the data bank archives soil data it protects this investment. By reusing soil data for multiple projects value is added to ongoing and future research. Sharing of data facilitates to build on previous work and synthesize knowledge. Assembling soil data in a data bank allows generating knowledge by fusion of data into larger sets that encompass more observations, more biogeochemical properties, and/or extend the geographic region of an analysis. New insight can be gained by reanalyzing data in various combinations and fusing with ancillary environmental datasets. Sharing of data in form of a data bank stimulates collaboration among researchers, reduces investment to launch new projects, and stimulates new research ideas and projects through pooling of data.

architecture

Fig. 1. Architecture of Soil Resource Data Bank and geospatial outreach.

Results 

A prototype Soil Resource Data Bank was designed. Further development of the prototype lead to the design of the Terrestrial Carbon Information System (TerraC).

Terrestrial Carbon Information System (Terra C)

Logo

Research Team

PI:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida

Co-PIs:
Timothy A. Martin, School of Forest Resources and Conservation, University of Florida

Post-Doc:
Gustavo M. Vasques, Soil and Water Sciences Department, University of Florida

Programmer:
Brandon Hoover

Time: 09/2009 to 09/2013
Funding Source: Florida Energy Systems Consortium (FESC)

Motivation

Rising CO2 emissions in the atmosphere and effects on global climate change have been well documented, and future impacts are uncertain but potentially devastating. Florida’s natural and agro-forest ecosystems have much potential to sequester carbon in biomass and soils due to unique climatic and landscape conditions. However, research gaps exist to accurately assess carbon pools and fluxes at coarse scales, ranging from county to the region and larger. The overarching objective of this project is to address these obstacles by creating a database infrastructure for the carbon science community, focused on ecosystems in Florida and the southeastern United States.  The database is administered through the UF Carbon Resources Science Center, a multi-disciplinary Center dedicated to research in support of enhanced agricultural and natural resource carbon management. In addition to applied research, basic research remains necessary to quantify carbon pools and fluxes for many ecosystems, and to understand how perturbations such as land use change or climatic factors impact carbon balance.

UF Carbon Resources Science Center

Objectives

(1) Build the Terra C Information System: Develop a coherent, searchable, and expandable database that integrates terrestrial carbon and associated environmental datasets and provides information about carbon related to environmental stressors such as climate and land use change.

(2) Data synthesis: Conduct a synthesis of multiple large carbon datasets to gain insight into carbon cycling and dynamics across various spatial and temporal scales; upscaling of site‐specific carbon observations to landscape scales.

Results

The TerraC Information System has been utilized in the PINEMAP research project to support a shared database infrastructure.

Using Diffuse Reflectance Spectroscopy to Quantify and Predict Soil Carbon Content in Agricultural Soils in Hawai’i

Hawaii

Research Team

PI:
Gregory L. Bruland, University of Hawai’i Manoa

Co-PIs:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida
Jonathan Deenik, University of Hawai’i Manoa
Ray Uchida, University of Hawai’i Manoa

Post-Doc:
Meryl L. McDowell, University of Hawai’i Manoa

Time: 09/2009 to 09/2011
Funding Source: UU TSTAR-Pacific

Summary

In Hawai‘i there is an increasing demand for techniques that can quantify soil carbon (C) over space and time to assess changes in fertility and C sequestration with changes in management.  However, traditional analysis required for accurate assessment of soil C is so time-consuming and expensive that it limits the degree to which this variability can be characterized.  Thus, there is a need for new techniques to measure C.  Visible/near-infrared (VNIR)- and mid-infrared (MIR)-diffuse reflectance spectroscopy (DRS) has the potential to revolutionize soil monitoring by allowing for samples to be scanned rapidly, inexpensively, and non-destructively.  The spectra can be related to laboratory measurements of C with chemometrics.  This allows for better characterization of soil C across Hawai‘i’s agroecosystems.  Thus, our project objectives are to:

  1. Employ VNIR/MIR-DRS to scan samples from the NRCS National Soils Database for which laboratory measurements of soil C are available
  2. Model relationships between spectra and laboratory-measured C
  3. Use chemometric models to predict C in freshly-collected samples from agricultural fields
  4. Validate predictive models for use by UH Mānoa Agricultural Diagnostic Service Center (ADSC).

The project tests two hypotheses:

  1. Fundamental wavelength regions in the MIR range will produce better predictions of C than combination or overtone regions in the VNIR range
  2. Subsetting the data (i.e. by texture, taxonomy) will result in better predictive models of C than will global datasets.

Results

McDowell M.D., G.L. Bruland, J.L. Deenik and S. Grunwald. 2012. Effects of subsetting by carbon content, soil order, and spectral classification on prediction of soil total carbon with diffuse reflectance spectroscopy. Applied and Environmental Soil Science J. Vol. 2012, Article ID 294121, 1-14. doi:10.1155/2012/294121. (pdf)

McDowell M.L., G.L. Bruland, J.L. Deenik, S. Grunwald and M.M Knox. 2012. Soil total carbon analysis in Hawaiian soils with visible, near-infrared and mid-infrared diffuse reflectance spectroscopy. Geoderma 189-190: 312-320. doi:10.1016/j.geoderma.2012.06.009. (pdf)

Assessment of Soil Carbon Storage Using Analytical and Spectral Methods (bioenergy crop - TX)

Photos: Study sites with E-grass.

Research Team

PI:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida

Co-PI:
Cristine M. Morgan, Soil and Crop Sciences Department, Texas A&M University

Post-Docs:
Deoyani Sarkhot, Soil and Water Science Department, University of Florida
Yufeng Ge, Soil and Crop Sciences Department, Texas A&M University

Time: 03/2008 to 07/2009

Funding Source: Biomass Investment Group Inc.

Objectives
(1) Compare the baseline soil carbon (C) storage of an energy crop (E-Grass) to a control site located in the Quemado Valley region of northwestern Maverick County, TX.
(2) Assess the spatial distribution and variability of soil C accumulated under E-Grass.
(3) Develop soil C prediction models based on visible/near-infrared spectroscopy for the study region.
(4) Monitor the rate of soil and biomass C build-up over time on field plots under E-Grass.

Results

Sarkhot D.V., S. Grunwald, Y. Ge and C.L.S. Morgan. 2012. Soil carbon storage under the perennial bioenergy crop Arundo Donax L. J. Biomass and Bioenergy 41: 122-130. doi:10.1016/j.biombioe.2012.02.015. (pdf)

Ge Y., C.L.S. Morgan, S. Grunwald, D.J. Brown and D.V. Sarkhot. 2011. Comparison of soil reflectance spectra and calibration models obtained using multiple spectrometers. Geoderma 161(3-4): 202-211. (pdf)

Sarkhot D.V., S. Grunwald, Y. Ge and C.L.S. Morgan. 2011. Comparison and detection of soil carbon under Arundo Donax and coastal bermuda grass using visible/near infrared diffuse reflectance spectroscopy. Geoderma 164: 22-32. (pdf)

Opportunities for Greenhouse Gas Reduction by Forestry and Agriculture in Florida

Photos: Soil profiles of Spodosols. These soils stores substantial amounts of soil carbon, specifically in the spodic horizon which is carbon-rich.

Research Team

PI:
Stephen Mulkey, School of Natural Resources and Environment, Department of Botany, University of Florida

Co-PIs:
Janaki Alavalapati, School of Forest Resources and Conservation, University of Florida

Alan Hodges, Food and Resource Economics Department, University of Florida

Ann C. Wilkie, Soil and Water Science Department, University of Florida

Sabine Grunwald, Soil and Water Sciences Department, University of Florida

Time: 01/2007 to 10/2007

Funding Source: Environmental Defense, Washington D.C.

Publications

Mulkey S., J. Alavalapati, A. Hodges, A.C. Wilkie and S. Grunwald. 2008. Opportunities for greenhouse gas reduction by agriculture and forestry in Florida. University of Florida, School of Natural Resources and Environment. Project sponsored by Environmental Defense, Washington D.C. (pdf)

Introduction and Overview
Stephen Mulkey
School of Natural Resources and Environment and Department of Botany

Role of Florida forests in reducing greenhouse gases
Janaki Alavalpati
School of Forest Resources and Conservation

Opportunities for greenhouse gas reduction through biofuels in Florida
Alan Hodges
Food and Resource Economics Department

Opportunities for greenhouse gas reduction through livestock waste management
Ann C. Wilkie
Soil and Water Science Department

Role of Florida soils in carbon sequestration
Sabine Grunwald
Soil and Water Science Department

Project Workshop, Gainesville, FL, July 25, 2007

Talk on soil carbon (S. Grunwald) [pdf]

Linking Terrestrial Nutrients to Red Tide Incidences in the Tampa Bay

Research Team 

PI:
Gurpal Toor, Soil and Water Sciences Department, University of Florida, Gulf Coast REC, Balm, FL

Co-PI:
Amy Shober, Soil and Water Sciences Department, University of Florida, Gulf Coast REC, Balm, FL
Sabine Grunwald, Soil and Water Sciences Department, University of Florida, Gainesville, FL
Geoff Denny, Environmental Horticulture, Gulf Coast REC, Balm, FL
Chris Martinez, Agricultural and Biological Engineering, University of Florida, Gainesville, FL

Graduate Students:
Lori Clark, Soil and Water Science Department, University of Florida

Time: 09/2007 to 06/2008

Funding Source: Research Innovation Fund, IFAS, University of Florida

Overview

Florida’s water resources are being threatened by rapid population growth and land use change. We use remote sensing and GIS tools to develop a framework to conduct soil and water sampling in the Alafia river of Tampa Bay Watershed. This information is used to develop relationship between the spatial distribution of nutrients in the various land-use categories and water quality parameters. The increased understanding of land-water linkage facilitates to identify solutions to mitigate impacts of urbanization on water quality, specifically on red tide outbreaks.

Rationale:
Harmful algal blooms have long plagued the west coast of Florida. Karenia bevis is a photosynthetic dinoflagellate known to cause red tides. K. bevis produces brevetoxins, which binds with sodium channels and affect nervous system response resulting in killing of fish and other marine life. The natural concentrations of K. bevis in the Gulf of Mexico are ~1,000 cells per liter of water (Tester & Steidinger, 1997). When concentrations of K. bevis approach 100,000 cells per liter, it result in large dense blooms and fish kills. Shellfish can also concentrate brevetoxins produced by K. bevis and may harm human health. Brevetoxins can also cause respiratory problems when they become aerosolized. To control incidences of red tide blooms, a comprehensive evaluation of its origin, physiology, and persistence is needed. Ii is known that red tide blooms are initiated offshore and then move inshore with wind or water currents. Whether anthropogenic pollution prolongs the duration of red tide is a topic of controversy among scientists but most agree that blooms are increasing in magnitude and are more abundant close to the shore. Figure 1 shows that the concentrations of K. bevis were approximately 20-fold greater within 5-km of shoreline (often approaching >1,000,000 cells per liter in Tampa bay) than 20-30 km offshore. This points out to possible contribution of terrestrial nutrients that increased concentrations of K. bevis close to the shore.

Results

Clark L. (2008). Observing changes in water quality parameters and land use affects in the Alafia River Watershed. M.S. non-thesis major paper.

Remote Sensing Supported Digital Soil Mapping in South Florida (Water Conservation Areas, Everglades)

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Research Team

PI:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida

Co-PI:
Todd Z. Osborne, Soil and Water Sciences Department, University of Florida

Collaborators:
Rick Robins, Natural Resources Conservation Service (NRCS)
Tom Weber, Natural Resources Conservation Service (NRCS)

Graduate student:
Jongsung Kim, Soil and Water Sciences Department, University of Florida

Time: 09/2008 to 09/2010

Funding Source:
Cooperative Ecosystem Studies Unit (CESU) – Natural Resources Conservation Service (NRCS)- NRCS, Gainesville, FL- National Geospatial Development Center, Morgantown, WV and Planet Action.

Summary

Remote sensing supported digital soil mapping improves soil surveying and mapping of the spatial variability of various soil properties over large regions. In this project we used satellite imagery, GIS and site-specific soil data to develop predictive models of soil biogeochemical properties in Water Conservation Area 2A, Greater Everglades, Florida, USA. Digital soil modeling is used to produce soil maps of various taxonomic, morphological, and physico-chemical properties. The digital soil models were tested in an adjacent wetland (Water Conservation Area 3 North).

DSM methods
Fig 1. Overview of digital soil modeling approach.

Objectives

(1) Develop models to predict various types of soil properties: (i) soil taxonomic classes; (ii) soil morphological properties; and (iv) physico-chemical soil properties.

(2) Assess the usefulness to incorporate remote sensing imagery into soil prediction models.

(3) Develop a tutorial for remote-sensing supported digital soil mapping that enables transfer of the methods to other soil survey regions.

Results

Tutorial for Remote-sensing Supported Digital Soil Mapping (pdf)

Kim J. and S. Grunwald. 2016. Assessment of carbon stocks in the topsoil using Random Forest and remote sensing images. J. of Env. Qual. 45, 1910-1918. doi:10.2134/jeq2016.03.0076. (pdf)

Kim J., S. Grunwald and R.G. Rivero. 2014.  Soil phosphorus and nitrogen predictions across spatial escalating scales in an aquatic ecosystem using remote sensing images. IEEE Trans. on Geoscience and Remote Sensing J.: 52(10): 6724-6737. (pdf)

Kim J., S. Grunwald, R.G. Rivero and R. Robbins. 2012. Multi-scale modeling of soil series using remote sensing in a wetland ecosystem. Soil Sci. Soc. Am. J. 76: 2327-2341. doi:10.2136/sssaj2012.0043. (pdf)

Kim J. (2012). Upscaling of soil properties across landscapes of south Florida. Ph.D. Dissertation, University of Florida, Gainesville, FL.

Summary: Soil nutrients stored in wetland soils are critical to assess the effectiveness of restoration efforts, yet it is challenging to accurately derive soil heterogeneity. The incorporation of remote sensing (RS) data into digital soil models has shown success to improve soil predictions. However, the effects of multi-resolution imagery on modeling of soil properties and classes in aquatic ecosystems are still poorly understood. The objectives of this study were to develop prediction models for soil properties including total phosphorus (TP), total nitrogen (TN), total carbon (TC), and soil series utilizing RS images and environmental ancillary data and elucidate the effect of different spatial resolutions of RS images on inferential modeling of those soil properties. The study was conducted in subtropical wetlands: Water Conservation Area-2A (480 km2) and 3A North (722 km2), the Florida Everglades, U.S. The spectral data and derived indices from remote sensing images, which have different spatial resolutions, included: Moderate Resolution Imaging Spectroradiometer (MODIS, 250 m), Landsat Enhanced Thermatic Mapper Plus (ETM+, 30 m), and Satellite Pour l’Observation de la Terre (SPOT, 10 m). Classification Tree, Block kriging, and Random Forest were employed to predict soil series and biogeochemical properties, respectively, using RS image derived spectral input variables, environmental ancillary data, and soil observations. Most of the models could explain > 60% of the spatial variability of the soil properties. This study provided ample evidence that RS-informed prediction models can successfully infer on multiple biophysical properties in soils and soil classes in aquatic ecosystems. The spatial distributions of soil properties, major controlling environmental factors for each of the soil properties, and absolute storage amounts for soil TP, TN, and TC were assessed. Interestingly, there was no noticeable distinction among different spatial resolutions of RS images to develop prediction models for soil properties. Results provided a better understanding of how fine and coarse grain resolutions of RS images impact soil modeling, model transferability, and scaling. Also this study showed the potential use of visible-near infrared spectroscopy for wetland soil property estimations.

Implementation and Verification for BMPs for Reducing Phosphorus Loading in the Everglades Agricultural Area

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Research Team

PI:
Samira Daroub, Soil and Water Sciences Department, University of Florida, Belle Glade

Team:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida, Gainesville
Timothy A. Lang , Soil and Water Sciences Department, University of Florida, Belle Glade
Orlando A. Diaz, Soil and Water Sciences Department, University of Florida, Belle Glade

Statistical Support:
Meghan Brennan, Statistics Department, University of Florida, Gainesville

Time: 09/2006 to 09/2007

Funding Source: Everglades Environmental Protection District

Objectives

The main goal of this research project is to enhance best management practices (BMP) performance on Everglades Agricultural Area (EAA) farm basins through improved selection and implementation of current BMPs by the EAA grower community. This is achieved by incorporating the results and recommendations from this study into BMP implementation training modules, BMP implementation extension documents, and the BMP consultation program.

Specific Objectives:

(1) Conduct a detailed statistical analysis to identify factors affecting phosphorus loading from EAA farms.

(2) Identify BMP implementation research gaps and recommend small scale field investigations to address those gaps.

Results

Daroub S.H., T.A. Lang, O.A Diaz and S. Grunwald. 2009. Long-term water quality trends after implementing best management practices in south Florida. J. Environ. Qual. 38(4): 1683-1693. (pdf)

Grunwald S., S.H. Daroub, T.A. Lang and O.A. Diaz. 2009. Tree-based modeling of complex interactions of phosphorus loadings and environmental factors. Sci. of the Total Environ. 407: 3772-3783. (pdf)

U.S. India Agricultural Knowledge Initiative: E-Learning and Capacity Building (Water Management)

Team

PI:
K. Ramesh Reddy, Soil and Water Sciences Department, University of Florida

Co-PIs:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida
Lisette Staal, International Programs IFAS. University of Florida

Collaborators:
Venkataraman Balaji, Head of Knowledge Management and Sharing, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Andhra Pradesh, Hyderabad, India.

B. Bhaskar Reddy, Director International Programs, Administrative office, Acharya N. G. Ranga Agricultural University (ANGRAU), Rajendranagar, Hyderabad, Andhra Pradesh, India.

B.S. Dhillon, Director of Research, Punjab Agricultural University, Ludhiana, India.

Rao Mylavarapu, Soil and Water Science Department, University of Florida

Craig Stanley, Soil and Water Science Department, University of Florida

Samira Daroub, Soil and Water Science Department, University of Florida

Dorota Haman, Agricultural and Bioengineering Department, University of Florida

Jasmeet Judge, Agricultural and Bioengineering Department, University of Florida

Time: 9/2006 – 9/2009

Funding Source: National Association of State Universities and Land-Grant Colleges (NASULGC) – U.S. Department of Agriculture (USDA) – Indian Council of Agricultural Research (ICAR). U.S. India Agricultural Knowledge Initiative

Summary

New technologies, both in the technical and educational arenas, can contribute significantly to building long-term capacity helping to solve problems of increasing population, limited water resources, land and water degradation and food security in India.  Modern information technologies in water management, including geographical information systems (GIS), remote sensing, and internet-based education tools, provide new efficient and cost-effective approaches for the assessment of water resources and quality.  The proposed project addresses two key components of the US- India Agricultural Knowledge Initiative (AKI), Capacity Building and Water Management.  The project partners developed skills and collaborative digital learning resources to strengthen education and technical training for extension and outreach to maximize the use of innovative tools focused on sustainable management of water resources.

The project between the University of Florida (UF), Gainesville, Florida, International Crops Research Institute for the Semiarid Tropics (ICRISAT), Acharya N. G. Ranga Agricultural University (ANGRAU), Punjab Agricultural University (PAU), and Tamil Nadu Agricultural University (TNAU) built on the established consortium among Indian agricultural universities and partners with its mission to form a long-term sustainable digital educational learning grid.  This project allowed creation of critical human and material resources needed for the application of new methods and tools to address pressing issues in water management in India.  The individual and institutional capacity building was fostered at US and Indian academic institutions providing a platform for sustainable education, extension and outreach that will foster globalization, knowledge sharing and awareness building.  We achieved the following outcomes:  (1) an Indian-US grid of agricultural and environmental educators contributing to and using interdisciplinary learning objects that increases accessible educational resources, (2) the RLOs will enhance student’s scientific training and stimulate critical thinking skills to address complex problems at the agricultural-environmental interface, (3)  a partnership of academic and research institutions that is linked by shared online instructional resources will provide the seed to grow into a global network that fosters agricultural and environmental education, and (4) the experiences shared in this project will leverage future large-scale joint grant activity.

This project utilized the EcoLearnIT Reusable Learning Object System: http://ecolearnit.ifas.ufl.edu  designed by Sabine Grunwald and web programmer Brandon Hoover.

RLO example
Fig. 1. Snapshot of an Reusable Learning Object implemented within EcoLearnIT.

Objectives

The specific objectives were to: (1) identify needs in the use of GIS and remote sensing tools in water management, explore best practices in the use of innovative e-technologies to support extension, outreach, certificate and in-service training programs, (2) build individual and institutional capacity through faculty/scientists/extension specialists exchange and training programs among partner institutions and create development teams based on specific needs, and (3) provide local/regional relevant interdisciplinary reusable learning objects (RLOs) streamlined into a shared online library that is sustainable beyond the time-frame of this project to establish a long-term partnership.

These objectives were accomplished through specific tasks: (1) a workshop on innovative e-technologies and water management to be held in India, (2) dynamic team building, collaborative planning and communication, (3) joint reciprocal training in RLO development, and (4) integration of RLOs into various modes of learning activities in the US and India.

Project Documentation

Grunwald S., K.R. Reddy, and V. Balaji. 2007. Reusable Learning Materials for Water Management: Indo-U.S. Partnership under the Agricultural Knowledge Initiative. Sloan-C International Meeting on Online Learning, Orlando, FL, Nov. 8-9, 2007. (pdf)

Report IFAS/UF – India Collaborative Activities, trip to Hyderabad, India (2006) (pdf)

Grunwald S., K.R. Reddy, and V. Balaji. 2007. Reusable Learning Materials for Water Management: Indo-U.S. Partnership under the Agricultural Knowledge Initiative. Sloan-C International Meeting on Online Learning, Orlando, FL, Nov. 8-9, 2007. (pdf – poster).

Grunwald S. and B. Hoover. 2006. A digital repository of Reusable Learning Objects – EcoLearnIT. Soil and Water Science Research Forum, Gainesville, FL Sept. 15, 2006. (pdf – poster).

Grunwald S. 2007. Reusable Learning Objects. Indo-U.S. Workshop on Innovative E-technologies for Distance Education and Extension/Outreach for Efficient Water Management. March 5-9, 2007, ICRISAT Patancheru/Hyderabad, India (pdf)

Grunwald S. 2007. E-delivery methods of learning materials. Indo-U.S. Workshop on Innovative E-technologies for Distance Education and Extension/Outreach for Efficient Water Mangagement. March 5-9, 2007, ICRISAT Patancheru/Hyderabad, India (pdf)

Grunwald S. and S. Daroub. 2007. E-delivery tools and distance education. Indo-U.S. Workshop on Innovative E-technologies for Distance Education and Extension/Outreach for Efficient Water Mangagement. March 5-9, 2007, ICRISAT Patancheru/Hyderabad, India (pdf)

Grunwald S. 2007. Geographic Information Systems (GIS). Indo-U.S. Workshop on Innovative E-technologies for Distance Education and Extension/Outreach for Efficient Water Mangagement. March 5-9, 2007, ICRISAT Patancheru/Hyderabad, India (pdf)

Access the digital repository of RLOs – EcoLearnIT: http://ecolearnit.ifas.ufl.edu

Reusable Learning Objects (RLOs) - EcoLearnIT

EcoLearnIT Reusable Learning Object System: http://ecolearnit.ifas.ufl.edu

EcoLearnIT logo

Team

PI:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida, University of Florida

Programmer / Web-Design:
Brandon Hoover, Soil and Water Sciences Department, University of Florida, University of Florida

Time: Spring 2006 – 2010

Funding Source: AKI U.S.-Indo cooperative and Soil and Water Science Department, University of Florida

What are RLOs?

Digital libraries of Reusable Learning Objects (RLO) offer new avenues to support education efforts that benefit multiple instructors, courses and graduate programs. Because RLOs are shared educational resources they are cost-effective and can be used to support distance education and on-campus instruction. Reusable Learning Objects are a new type of online instruction that provide a digital educational resource that can be reused, scaled and shared from a central online repository in the support of instruction and learning. Each RLO supports a single learning objective, which are streamlined into a Digital Library of RLOs. These include, but are not limited to text entries, web sites, bibliographies, charts, figures, maps, models, photographs, illustrations, mini-case studies, assessments, tutorials, simulations, animations, audio and video clips, movies, and interactive tools. They vary in size, scope and level of granularity ranging from small chunks of instruction to a series of combined resources to provide a more complex learning experience.

RLO snapshot
Fig. 1. Snapshot of an Reusable Learning Object implemented within EcoLearnIT.

Objectives

(1) Develop a RLO repository for soil, water and environmental sciences
(2) Develop a suite of prototype RLOs

Results

Presentations:

Grunwald S., B. Hoover and K.R. Reddy. 2009. Reusable learning objects to support soil, water and environmental education. ASA-CSSA-SSSA Meeting in Pittsburgh, PA, Nov. 1-5, 2009.

Hoover B., S. Grunwald and K.R. Reddy. 2009. eExtension and eLearning in soil and water sciences. Soil and Water Science Research Forum, Gainesville, FL, Sept. 11, 2009.

Grunwald S. and B. Hoover. 2009. Workshop session “Empowered learning and instruction through shared learning resources”. College of Agriculture and Life Sciences Teaching Enhancement Symposium, Gainesville, FL, Aug. 11, 2009.

Grunwald S. 2009. Reusable learning objects and eLearning. Tamil Nadu Agricultural University, Coimbatore, India, July 21, 2009.

Grunwald S. 2009.  Shared experiences with distance learning and instruction.  Joint annual meeting of the Florida State Horticultural Society & Soil and Crop Science Society of Florida, Jacksonville, FL, June 7-9, 2009.

Grunwald S. and K.R. Reddy. 2009. Information and communication technologies for capacity building in water management: U.S. India collaborative extension/outreach and distance education. Colloquium UF AKI Projects – U.S. India Agricultural Knowledge Initiative. Gainesville, FL, Jan 26, 2009.

Hoover B. and S. Grunwald. 2008. Reusable Learning Objects. Soil and Water Science Research Forum, Gainesville, FL, Sept. 12, 2008.

Beck H.W., S. Grunwald and B. Hoover. 2008. Session on “The Object is to Learn – Learning Objects” offered at the CALS Teaching Enhancement Symposium, Gainesville, FL, Aug. 11, 2008.

Grunwald S. 2008. EcoLearnIT – A collection of soil and water science e-learning material based on the concept of Reusable Learning Objects. Soil and Water Science seminar, Gainesville, FL, April 11, 2008.

Grunwald S. and B. Hoover. 2007. Assessment of barriers and usage of computer-mediated communication and learning tools in a Distance Education M.S. Program in Environmental Sciences. Sloan-C Conference on Online Learning, Orlando, FL, Nov. 7-9, 2007.

Grunwald S., K.R. Reddy and V. Balaji. 2007. Reusable e-Learning materials for water management: Indo-US partnership under the Agricultural Knowledge Initiative. Sloan-C Conference on Online Learning, Orlando, FL, Nov. 7-9, 2007. (pdf)

Grunwald S. and B. Hoover. 2007. EcoLearnIT – A digital library of Reusable Learning Objects (RLOs). UF-CALS Teaching Enhancement Symposium 2007, Gainesville, FL, Aug. 13, 2007.

Grunwald S. 2007. Reusable Learning Objects. Indo-U.S. Workshop on Innovative E-technologies for Distance Education and Extension/Outreach for Efficient Water Mangagement. March 5-9, 2007, ICRISAT Patancheru/Hyderabad, India. (pdf)

Grunwald S. and B. Hoover. 2006. A digital repository of Reusable Learning Objects – EcoLearnIT. Soil and Water Science Research Forum, Gainesville, FL Sept. 15, 2006. (pdf – poster).

Textbook Chapter:
Grunwald S., B. Hoover and G.L. Bruland. 2009. An eLearning portal to teach geographic information sciences. pp. 234-245. In Syed M. (ed.) Methods and Applications for Advancing Distance Education Technologies: “International Issues and Solutions” included in the Advances in Distance Education Technologies Book Series (Vol. 3), IGI Global Publ., London, UK.

Concept Papers and Guides:
Grunwald S., K.R. Reddy and B. Hoover. 2007. Concept guide on Reusable Learning Objects with application to soil, water and environmental sciences. Agricultural Knowledge Initiative Capacity Building and Water Management Available at: http://ecolearnit.ifas.ufl.edu/documentation.asp. Grunwald S. and B. Hoover. 2007. EcoLearnIT© Manual (Version 1.0) – digital repository of Reusable Learning Objects.

Object-Oriented Modeling System (Modeling of Phosphorus Transport - Sugarcane)

Research Team

PI:
Howard W. Beck, Agricultural and Biological Engineering, University of Florida

Co-PIs:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida
Kelly Morgan, Soil and Water Sciences Department, University of Florida

Post-Doc:
Ho-Young Kwon, Soil and Water Science Department, University of Florida

Graduate student:
Yunchul Jung, Agricultural and Biological Engineering, University of Florida

Time: 8/2006 to 8/2009

Funding Source: Florida Department of Environmental Protection

Summary

An expert system that assists growers in selection and application of best management practices (BMPs) related to water and nutrition in ridge citrus (NUMAPS) has been expanded to flatwood citrus and sugarcane. The objectives are to model (calibrate and validate) water flux and phosphorus transport in sugarcane in low-relief basins. We adopted an ontology-based modeling design to provide flexibility to simulate processes in small basins. Our web-based software environment incorporates easily into day-to-day production operation management. BMPs in the form of rules are compiled into a database and displayed in the software as appropriate for on-site decision making. Computer simulations of the plant, soil moisture, and nutrient uptake enhance rule-based BMPs by providing more accurate assessments of water and nutrient utilization and runoff. The system is designed to optimize yields in an economically effective manner while greatly reducing the environmental impact of excess fertilizer and water applications.

Results

Peer-reviewed Publications:

Beck H.W., K.T. Morgan, Y. Jung, S. Grunwald, H.-Y. Kwon and J. Wu. 2010. Ontology-based simulation in agricultural systems modeling. Agricultural Systems J. 103: 463-477. (pdf)

Kwon H.-Y., S. Grunwald, H.W. Beck, Y. Jung, S.H. Daroub, T.A. Lang and K.T. Morgan. 2010. Ontology-based simulation of water flow in organic soils applied to Florida’s sugarcane. Agric. Wat. Manag. J. 97: 112-122. (pdf)

Kwon H.-Y., S. Grunwald, H.W. Beck, Y. Jung, S.H. Daroub, T.A. Lang and K.T. Morgan. 2010. Modeling of phosphorus loads in sugarcane in a low-relief landscape using ontology-based simulation. J. Environ. Qual. 39: 1-11. (pdf)

Beck. H., K. Morgan, Y. Jung, S. Grunwald, H.-Y. Kwon. 2008. Ontology-based simulation applied to soil, water and nutrient management. pp. 209-243. In P. Papajorgji (ed.) Advances in Modeling Agricultural Systems, Springer, Berlin.

Presentations:

Kwon H.Y., S. Grunwald, H.W. Beck, Y. Jung, S.H. Daroub, T.A. Lang and K.T. Morgan. 2009. Modeling of phosphorus loads from Florida sugarcane farms using ontology-based simulation. ASA-CSSA-SSSA Meeting in Pittsburgh, PA, Nov. 1-5, 2009. (poster – pdf)

Kwon H.Y., S. Grunwald, H.W. Beck, Y. Jung, S.H. Daroub and T.A. Lang. 2009. Automatic calibration of ontology-based model for simulating water table fluctuations on farms in the Everglades Agricultural Area. Soil and Water Science Research Forum, Gainesville, FL, Sept. 11, 2009.

Kwon H.-Y., S. Grunwald, H.W. Beck, Y.C. Jung, S. Daroub, T.A. Lang and K.T. Morgan. 2008. Ontology-based simulation of water flow and phosphorus in organic soils used for sugarcane. Joint Meeting ASA-CSSA-SSSA and Geological Society of America in Houston, TX, Oct. 5-9, 2008.

Kwon H.-Y., S. Grunwald, H.W. Beck. Y. Jung, S. Daroub, T.A. Lang and K.T. Morgan. 2008. Ontology-based simulation of daily water table fluctuations on Histosols in the Everglades Agricultural Area. Soil and Water Science Research Forum, Gainesville, FL, Sept., 2008. (poster – pdf)

Kwon H.-Y., S. Grunwald, H. W. Beck and K. T. Morgan. 2007. Ontology-based simulations for minimizing environmental impacts of Florida sugarcane production. ASA-CSSA-SSSA Meeting in New Orleans, LA, Nov. 4-8, 2007.

Kwon H.-Y., S. Grunwald, H. W. Beck and K. T. Morgan. 2007. A nutrient management plan support system for assessing water and nutrient utilization in Florida sugarcane production. Soil and Water Science Research Forum, Gainesville, FL, Sept. 14, 2007. (poster – pdf)

Beck H., K. Morgan, J. Scholberg, and S. Grunwald. 2006. Implementation of in-season irrigation and nutrient yools for minimizing environmental impacts of citrus and sugarcane production. World Congress on Computers in Agriculture, Orlando, FL, July 24-26, 2006. (pdf)

Morgan K., H. Beck, J. Scholberg, and S. Grunwald. 2006. In-season irrigation and nutrient decision suport system for citrus production. World Congress on Computers in Agriculture, Orlando, FL, July 24-26, 2006. (pdf)

Linking Experimental and Soil Spectral Sensing for Prediction of Soil Carbon Pools and Carbon Sequestration at the Landscape Scale (Santa Fe River Watershed)

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Research Team

PI: Sabine Grunwald, Soil and Water Sciences Department, University of Florida, University of Florida

Co-PIs:
Jim O. Sickman, Soil and Water Sciences Department, University of Florida (UC-Riverside)
Nick B. Comerford, Soil and Water Sciences Department, University of Florida

Post-Doc: Mi-Youn Ahn and Christine Bliss

Graduate students: Gustavo M. Vasques, Sanjay Lamsal and Jinseok Hong

Undergraduate student: Nicholas DiGruttolo

Lab analysis: Chunhao Xu

Time: 09/2005 to 05/2008

Funding Source:
Natural Resources Conservation Service (NRCS)- Cooperative Ecosystem Studies Unit (CESU)

Phase 1: Linking Experimental and Soil Spectral Sensing for Prediction of Soil Carbon Pools and Carbon Sequestration at the Landscape Scale.

Phase 2: Linking Experimental and Soil Spectral Sensing for Prediction of Microbial Bioavailability of Organic C, N and P in Soils at the Landscape Scale.

Study area: Santa Fe River Watershed, Florida.

Rationale

Globally, more than four times as much carbon (C) is held in soils as in vegetation and changes in soil C stocks are expected to have large effects on the global carbon budget. Man induced land use/land cover (LU/LC) change, global climate warming, rising levels of CO2 in the atmosphere, and shifts in local temperature/hydrologic conditions are expected to significantly impact soil C pools (labile, recalcitrant, and total soil C). But understanding the response of local and global C cycling and pools due to multi-factorial forces across larger landscapes is limited. Carbon pools are indicators for locally operating processes such as organic matter decomposition, mineralization, and immobilization that are variable in space and time. A transfer of local labile and recalcitrant soil imprints into larger landscapes would elucidate mechanisms that induce soil C change. Specifically in the southeastern U.S., the distribution of prominent deep, recalcitrant soil C stocks (spodic horizon C) is poorly understood.

TC SFRW
Fig. 1. Total carbon stocks (0- 100 cm) assessed using Ordinary Kriging (OK) in the Santa Fe River Watershed, Florida. Additional maps show soil drainage and soil order distributions (Soil Data Mart, NRCS) and land use (Florida Fish and Wildlife Conservation Commission, 2003).

Summary

Phase 1: The project describes a novel quantitative framework to assess soil C pools and sequestration at the landscape scale that addresses the spatial variability of soil properties and processes and how they relate to coevolving landscape factors such as land use, geology and topography. We link conceptual, experimental and geospatial methods to assess total, stable and labile C pools in a large, mixed-use watershed in north-central Florida. Site-specific process-based observations are used to characterize different C pools (total, recalcitrant, hydrolysable and hot water extractable) that are linked via chemometric modeling to soil spectral characteristics derived using visible, near-infrared diffuse reflectance spectroscopy. Soil spectroscopy is a rapid and cost-effective method that provides inferences on multiple soil properties. Site-specific observations are upscaled to the landscape scale (1000-5000 km2) using exhaustive, spatial environmental datasets stored in a geographic information system. Environmental correlation and hybrid/geostatistical modeling are used to produce multiple regional, continuous soil C maps.

Phase 2: Bioavailability of soil organic matter (SOM) to heterotrophic microbial populations largely controls the turnover time of SOM and is an important factor in determining the long-term carbon sequestration potential of soils. Both nitrogen (N) and phosphorus (P) are primarily supplied/immobilized by the process of microbial mineralization because of the low ion exchange capacity and low P sorption capacity of soils in the southeastern U.S. Since the C-cycle is closely linked to the cycling of N and P we propose a holistic geospatial modeling approach to assess C, N and P mineralizable pools across the Santa Fe River Watershed with diverse land use, soils, and terrain patterns. We propose an integrative approach combining analytical, spectral soil measurements and geospatial modeling that will provide rapid inferences on multiple soil properties related to C, N and P mineralization and hence the biogeochemistry of these macronutrients. Phase 2 will continue phase 1 to identify those environmental factors that control carbon pools across a landscape. Our carbon predictive models will be validated using an independent dataset from a landscape unit (650 ha) nested within the Santa Fe River Watershed (3,500 km2). To synergize results we will streamline all soil maps (N, P and C) into a grid-based Geospatial Soil Knowledge Management System, which may serve as template for other similar landscape scale studies.

Objectives

(1) To determine soil carbon pools and carbon sequestration (actual and potential) across a mixed-use watershed (Santa Fe River Watershed) in north-central Florida.

(2) To develop a rapid assay of soil carbon pools that will result in cost-effective upscaling of site-specific observations to landscape scale (1000-5000 km2).

(3) To understand the relationships between predominant ecosystem characteristics and their corresponding soil carbon pools and soil carbon sequestration capacity.

(4) To develop a conceptual framework to derive rapid and cost-effective soil carbon spectral signatures that can be used by researchers, students, extension agents, farmers and ranchers to optimize land resource management.

(5) To assess the spatial distribution of soil phosphorus and covariation to environmental landscape properties.

Results

Publications:

Bliss C.M., N.B. Comerford, D.A. Graetz, S. Grunwald and A.M. Stoppe. 2014. Land use influence on carbon, nitrogen, and phosphorus in size fractions of sandy surface soils. Soil Sci. J. 178: 654-661.

Vasques G.M., S. Grunwald, N.B. Comerford and J.O. Sickman. 2010. Regional modeling of soil carbon at multiple depths within a subtropical watershed. Geoderma 156: 326-336. (pdf)

Vasques G.M., S. Grunwald, N.B. Comerford and J.O. Sickman. 2010. Upscaling of dynamic soil organic carbon pools in a north-central Florida watershed. Soil Sci. Soc. Am. J. 74: 870-879. (pdf)

Vasques G.M., S. Grunwald and W.G. Harris. 2010. Spectroscopic models of soil organic carbon in Florida, USA. J. Environ. Qual. 39: 923-934. (pdf)

Vasques G.M., S. Grunwald and J.O. Sickman. 2009. Visible/near-infrared spectroscopy modeling of dynamic soil carbon fractions. Soil Sci. Soc. Am. J. 73: 176-184. (pdf)

Bruland G.L., C.M. Bliss, S. Grunwald, N.B. Comerford and D.A. Graetz. 2008. Soil nitrate-nitrogen in forested versus non-forested ecosystems in a mixed-use watershed. Geoderma 148: 220-231. (pdf)

Vasques G.M., S. Grunwald and J.O. Sickman. 2008. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146: 14-25. (pdf)

Lamsal S., S. Grunwald, G.L. Bruland, C.M. Bliss and N.B. Comerford. 2006. Regional hybrid geospatial modeling of soil nitrate-nitrogen in the Santa Fe River Watershed. Geoderma 135: 233-247. (pdf)

Oral and poster presentations:
Vasques, G.M., Grunwald, S., Myers, D.B. 2010. Search for a multiscale soil organic carbon spatial model in Florida. Part I: influence of extent, resolution, and geographic region. Global Workshop on Digital Soil Mapping, Rome, Italy, May 24-26, 2010.

Vasques, G.M., Grunwald, S., Myers, D.B., 2009. Analysis of spatial scaling behavior of soil organic carbon in Florida. ASA-CSSA-SSSA Annual Meeting, Pittsburgh, PA, November 1-5, 2009. Abstract No. 332-2.

Hong J., S. Grunwald and N.B. Comerford. 2009. Soil phosphorus modeling using ancillary spatial environmental datasets in the Santa Fe River Watershed. ASA-CSSA-SSSA Meeting in Pittsburgh, PA, Nov. 1-5, 2009.

Vasques, G.M., Grunwald, S., 2009. Soil carbon in Florida: estimates derived from legacy data. Soil and Water Science Research Forum, Gainesville, FL, September 11, 2009.

Vasques, G.M., Grunwald, S., Hong, J., Myers, D.B., 2009. Multi-scale behavior of soil organic carbon at nested locations in Florida. Association of American Geographers Annual Meeting, Las Vegas, NV, March 22-27, 2009.

Hong J.-S., S. Grunwald, N.B. Comerford and S.E. Smith. 2008. Modeling of soil phosphorus using satellite imagery and ancillary spatial environmental datasets. Joint Meeting ASA-CSSA-SSSA and Geological Society of America in Houston, TX, Oct. 5-9, 2008.

Vasques, G.M., Grunwald, S., 2008. Soil organic carbon estimation from lab-based spectroscopy in the State of Florida. Global Workshop on Digital Soil Mapping, Logan, UH, September 30-October 3, 2008.

Vasques, G.M., Grunwald, S., Harris, W.G., 2008. Estimation of soil organic carbon in the State of Florida using visible/near-infrared spectroscopy. Soil and Water Science Research Forum, Gainesville, FL, September 12, 2008.

Vasques G.M., N. DiGruttolo and S. Grunwald. 2008. Comparison of soil information system and field data to measure soil organic carbon. Southern Regional Cooperative Soil Science Conference, Gainesville, FL, July 14-17, 2008. (pdf)

Ahn M.-Y., N.B. Comerford, A.R Zimmerman, S. Grunwald and J. O. Sickman. The rate of soil organic carbon mineralization in 140 soils of a North Florida watershed. ASA-CSSA-SSSA Meeting, New Orleans, Nov. 4-8, 2007.

Stoppe A. M., N.B. Comerford, S. Grunwald and J. Sickman. Nitrogen and phosphorus mineralization as influenced by land use and soil characteristics in a north Florida watershed. ASA-CSSA-SSSA Meeting, New Orleans, Nov. 4-8, 2007.

Vasques G.M., S. Grunwald and J.O. Sickman. 2007. Assessment of dynamic soil carbon pools using visible/near-infrared diffuse reflectance spectroscopy (VNIRS) and various multivariate methods. ASA-CSSA-SSSA Meeting, New Orleans, Nov. 4-8, 2007.

Vasques G.M., S. Grunwald, J.O. Sickman and N.B. Comerford. Assessment of dynamic soil carbon pools at the watershed scale using regression kriging. ASA-CSSA-SSSA Meeting, New Orleans, Nov. 4-8, 2007.

Grunwald S., G.M. Vasques, N.B. Comerford, G.L. Bruland, C.M. Bliss, D.A. Graetz, and J.O. Sickman. 2007. Integration of carbon, nitrogen and phosphorus into a spatially-explicit soil-landscape model using geostatistical methods. ASA-CSSA-SSSA Meeting, New Orleans, Nov. 4-8, 2007.

Vasques G.M., S. Grunwald, J.O. Sickman and N.B. Comerford. 2007. Geospatial modeling of dynamic soil carbon pools at the watershed scale. Soil and Water Science Research Forum, Gainesville, FL Sept. 14, 2007.

DiGruttolo N., G.M. Vasques, S. Grunwald and J.O. Sickman. 2007. GIS-based assessment of soil carbon storage along soil-land use trajectories. Soil and Water Science Research Forum, Gainesville, FL Sept. 14, 2007 (poster – pdf)

Vasques G.M., S. Grunwald and J.O. Sickman. 2007. Assessment of total soil carbon and carbon fractions at the watershed scale using geospatial upscaling techniques. International Pedometrics 2007 meeting, Tuebingen, Germany, Aug. 27-31, 2007.

Grunwald S., G.M. Vasques and J.O. Sickman. 2007. Spectral signatures for soil carbon: Assessment of total, recalcitrant, hydrolysable and dissolved organic fractions. International Pedometrics 2007 meeting, Tuebingen, Germany, Aug. 27-31, 2007. (poster – pdf)

Vasques G.M., S. Grunwald, and J.O. Sickman. 2006. Assessment of total, stable and labile carbon using visible, near-infrared diffuse reflectance spectroscopy. Soil and Water Science Research Forum, Gainesville, FL Sept. 15, 2006. (poster – pdf)

Vasques G.M., S. Grunwald, and J.O. Sickman. 2006. Assessment of total, stable and labile carbon using visible, near-infrared diffuse reflectance spectroscopy. World Congress of Soil Science – Frontiers of Soil Science, Philadelphia, Pennsylvania, July 9-15, 2006. (poster – pdf)

Baseline Characterization of Pilot Stormwater Treatment Areas (STAs) in the Lake Okeechobee Watershed (Taylor Creek STA)

Research Team

PI:
K. Ramesh Reddy, Soil and Water Sciences Department, University of Florida

Co-PIs:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida
Tom DeBusk, DB Environmental Inc.
Vimala Nair, Soil and Water Sciences Department, University of Florida

GIS analysis:
Andre Moura, Soil and Water Sciences Department, University of Florida

Analytical Analysis:
Yu Wang, Soil and Water Sciences Department, University of Florida

Time: 2005 to 2006
Funding Source: South Florida Water Management District

Objectives

The objective of this project is to characterize the environmental base conditions of Taylor Creek Stormwater Treatment Area (STA) before going into operation. Multiple soil and vegetation properties were mapped including bulk density, pH, loss on ignition, total nitrogen, total phosphorus, total carbon, and several phosphorus variants.

Results

Results of this project: Final project report and Power Point presentation.


Fig. 2. Total phosphorus distribution in 0-10 cm and 10-30 cm soils across Taylor Creek Storm Water Treatment Area.

Rapid Assessment of Restoration Performance Measures at Multiple Scales in the Greater Everglades Using Near Infrared Reflectance Spectroscopy

VNIR

Research Team

PI:
Matt Cohen, Soil and Water Sciences Department, University of Florida

Co-PIs:
K. Ramesh Reddy, Soil and Water Sciences Department, University of Florida
Sabine Grunwald, Soil and Water Sciencse Department, University of Florida
Mark W. Clark, Soil and Water Sciences Department, University of Florida

Time: 2005 to 2006
Funding Source: Critical Ecosystem Studies Initiative (CESI)

Summary

Large-scale restoration requires ecosystem performance measures that can function as rapid quantitative benchmarks of recovery or degradation over time. Soil provides a useful indicator of ecosystem condition because it is sensitive to change, ubiquitous and responds in relatively well-understood ways to anthropogenic impairment gradients. This project presents a method for rapid assessment of a wide array of soil performance measures based on Visible Near-Infrared Reflectance Spectroscopy (VNIRS), a technique that relates high resolution light reflectance characteristics to physical, chemical and biological attributes of soil and plant sample attributes. Here we explore applications at three nested spatial scales of analysis. First, we use a soil archive that covers the entire Greater Everglades region to develop chemometric models that relate spectra and numerous ecosystem performance measures. Second, we explore specific nutrient enrichment gradients in the northern Everglades to determine if spectral reflectance can provide low cost indication and early detection of enrichment processes. Finally, we link spectral reflectance models to a variety of process and state indicators of landscape dynamics in the ridge-slough mosaic, including mineralization rates, carbon quality and marl/peat development. At each scale, we provide chemometric models that have multiple advantages:

  1. rapid to implement
  2. low cost (sample pre-processing is minimal and the instrument and operating costs are relatively inexpensive) and
  3. they offer a unique view of the soil ecosystem that integrates across biota, mineralogy and exogenous forcing functions.While we link the results to water quality and quantity parameters at multiple scales, we foresee this tool providing assessment and monitoring support across a much wider range of restoration applications.

Objectives

(1) Develop calibrations between routine soil performance measures and spectral reflectance signatures using a spatially comprehensive soil archive (n ~ 5000) collected throughout the Everglades by the Wetland Biogeochemistry Laboratory (WBL) at the Univ. of Florida (UF) during 2003.

(2) Explore spectroscopic methods (using calibration and calibration-free methods) to predict integrated soil performance measures (nutrient enrichment, changes in carbon quality, changes in microbial indicators, carbon and nitrogen mineralization rates) along known environmental and anthropogenic disturbance gradients.

(3) Develop and disseminate a suite of analytical tools that apply to performance assessment across scales in support of several Comprehensive Everglades Restoration Plan (CERP) objectives (e.g. fine scale spatial analysis for hydrologic, contaminant and ecological model boundary conditions; baseline condition development for simulation model support; changes in landscape patterns – e.g. ridge and slough soil accretion models; detailed studies of biogeochemical kinetics) for decision support and improved system assessment.

GIS-Based Spatial Analysis of Movement of Silverleaf Whitefly and Begomovirus

Research Team

PI:
David J. Schuster, Entomology Department, University of Florida

Co-PIs:
Craig D. Stanley, Soil and Water Sciences Department, University of Florida
Sabine Grunwald, Soil and Water Sciences Department, University of Florida
J.E. Polston, Entomology Department, University of Florida
A.L. Gonzalez
L.I. Rivera Vargas

Time: 09/2005 to present
Funding Source: USDA T-STAR Tropical/Subtropical Agricultural Research

Objectives

The overall objective of this study is to utilize silverleaf whitefly population data and tomato yellow leaf curl (TYLCV) incidence and intensity information in GIS-based analyses to determine environmental and cultural factors that affect distribution and spread of both pests. The information is used to prevent or minimize the impacts of these pests by understanding the dynamics involved with their movement. The specific objectives are to:

(1) Use commercial scouting data in Florida to initiate the evaluation of various GIS spatial analysis techniques to interpret TYLCV and silverleaf whitefly movement trends in time.

(2) Collect additional TYLCV incidence and severity data and silverleaf whitefly population data in selected geographical areas of Puerto Rico and Florida for GIS evaluation to determine additional data needs for improving trend analyses (i.e. more detailed information about factors which my influence pest movement and intensity such as neighboring fields and alternative plant hosts cultural practices, temperature, wind direction and speed, etc.)

Soil Mapping and Geospatial Analysis in the Greater Everglades


Photo: Greater Everglades.


Photo: Greater Everglades National Park


Photo: Helicopter-based sampling in the Greater Everglades (courtesy of Todd Osborne and David Genchi)


Photo: Helicopter-based sampling in the Greater Everglades (courtesy of Todd Osborne and David Genchi)


Photo: Big Cypress National Park.

Research Team

PI:
K.Ramesh Reddy, Soil and Water Sciences Department, University of Florida

Co-PI:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida

Field data collection:
Todd Z. Osborne, Soil and Water Sciences Department, University of Florida

Laboratory analysis:
Yu Wang, Soil and Water Sciences Department, University of Florida

Geospatial modeling:
Sabine Grunwald, Soil and Water Sciences Department, University of Florida
Greg L. Bruland, Soil and Water Sciences Department, University of Florida
Rosanna G. Rivero, Soil and Water Sciences Department, University of Florida
Ron Corstanje, Soil and Water Sciences Department, University of Florida
T. Kapner, Soil and Water Sciences Department, University of Florida

Time: 05/2003 to 05/2005
Funding Source: Data collection and analytical analysis: South Florida Water Management District (Program Manager: Sue Newman). The geospatial analysis and data integration were funded by various misc. funding sources from Dr. Grunwald’s Core laboratory.

Objectives

(1) Characterize multiple key biogeochemical soil properties throughout the Greater Everglades. (2) Quantify spatial relationships between soil and vegetative properties mapped by remote sensing.


Photo: Soil sampling at 1,341 sites was conducted in 3 depth increments: floc/detritus, 0-10 and 10-20 cm soil.


The map shows the hydrologic units and soil sampling locations – Greater Everglades ecosystem. The following properties were analyzed in floc, 0-10 and 10-20 cm soil layers: Bulk density, total phosphorus, inorganic phosphorus, total carbon, total nitrogen, total calcium, total magnesium, total iron, total aluminum, and loss on ignition.

Results

Peer-refereed publications:

Rivero R.G., S. Grunwald, M.W. Binford and T.Z. Osborne. 2009. Integrating spectral indices into prediction models of soil phosphorus in a subtropical wetland. Remote Sensing of Environment J. 113: 2389-2403. (pdf)

Reddy K.R. and R.D. DeLaune. 2008. Biogeochemistry of wetlands – Science and applications. CRC Press, Baton Rouge FL.

See chapter 17 and page 636 of this book to view the total phosphorus (TP) map of the Greater Everglades Ecosystem. Unfortunately the map in the book is printed in black/white colors and does not allow to discern high and low TP values. Contact Grunwald to receive the colored map and a detailed description of geospatial methods and validation results which she was not given permission by the project Principal Investigator to publish.

Corstanje R., S. Grunwald, and R.M. Lark. 2008. Inferences from fluctuations in the local variogram about the assumption of stationarity in the variance. Geoderma, 143: 123-132. (pdf).

Rivero R.G., S. Grunwald and G.L. Bruland. 2007. Incorporation of spectral data into multivariate geostatistical models to map soil phosphorus variability in a Florida wetland. Geoderma 140: 428-443. (pdf)

Rivero R.G., S. Grunwald, T.Z. Osborne, K.R. Reddy and S. Newman. 2007. Characterization of the spatial distribution of soil properties in Water Conservation Area -2A, Everglades, Florida. Soil Science 172(2): 149-166 (reprints available upon request).

Bruland G.L., T.Z. Osborne, K.R. Reddy, S. Grunwald, S. Newman and W.F. DeBusk. 2007. Recent changes in soil total phosphorus in the Everglades: Water Conservation Area 3. Environ. Monit. Assess 129: 379-395. (pdf)

Corstanje R., S. Grunwald, K.R. Reddy, T.Z. Osborne and S. Newman. 2006. Assessment of the Spatial Distribution of Soil Properties in a Northern Everglades. J. Environ Qual. 35: 938-949. (pdf)

Bruland G.L., S. Grunwald, T.Z. Osborne, K.R. Reddy and S. Newman. 2006. Spatial distribution of soil properties in Water Conservation Area 3 of the Everglades. SSSA J., 70: 1662-1676. (pdf)

Oral and poster presentations:

Grunwald S., G.L. Bruland, R. Corstanje, R.G. Rivero, P. Goovaerts and K.R. Reddy. 2008. Landscape models for spatial upscaling of biogeochemical parameters. Symposium on Biogeochemistry and Water Quality of the Greater Everglades: Fate and Transport of Nutrients and Other Contaminants – Greater Everglades Ecosystem Restoration Conference, Naples, FL, July 28-Aug. 1, 2008 (invited talk).
Rivero R.G., S. Grunwald, M.W. Binford, T.Z. Osborne, and K.R. Reddy. 2008. Applications of remote sensing and multivariate geostatistics in order to improve spatial modeling of soil phosphorus predictions in wetland areas. Study case: WCA-2A, Everglades. Symposium on Biogeochemistry and Water Quality of the Greater Everglades: Fate and Transport of Nutrients and Other Contaminants – Greater Everglades Ecosystem Restoration Conference, Naples, FL, July 28-Aug. 1, 2008.
Kapner T., S. Grunwald, K.R. Reddy, and T.Z. Osborne. 2008. Remote-sensing supported analysis of soil properties in Water Conservation Area 1, Everglades. Southern Regional Cooperative Soil Science Conference, Gainesville, FL, July 14-17. 2008.(poster)
Rivero R. G., S. Grunwald, G.L. Bruland, M.W. Binford, K.R. Reddy, T.Z. Osborne and S. Newman. 2007. Spectral inferential modeling of soil phosphorus using hybrid geostatistical methods. ASA-CSSA-SSSA Meeting in New Orleans, LA, Nov. 4-8, 2007.
Kapner T., S. Grunwald, K.R. Reddy, T.Z. Osborne and S. Newman. GIS-based analysis of physico-chemical soil properties, nutrient influx points and vegetative patterns characterized by field and remote sensing data. ASA-CSSA-SSSA Meeting in New Orleans, LA, Nov. 4-8, 2007.
Grunwald S., K.R. Reddy, and T.Z. Osborne. 2007. How to translate biogeochemical models into spatially-explicit context – a case study from the Greater Everglades. 10th International Symposium on Wetland Biogeochemistry – Frontiers in Biogeochemistry. Annapolis, Maryland, April 1-4, 2007. (talk)
Rivero R.G., S. Grunwald, and K.R. Ramesh. 2007. Development of predictive models of soil phosphorus in Water Conservation Area-2A (Everglades, Florida, USA) integrating GIS, remote sensing and geostatistics. 10th International Symposium on Wetland Biogeochemistry – Frontiers in Biogeochemistry. Annapolis, Maryland, April 1-4, 2007.
Grunwald S., G.L. Bruland, T.Z. Osborne, S. Newman and K.R. Reddy. 2005. Spatial principal component mapping of physico-chemical soil properties in the Greater Everglades. ASA-CSSA-SSSA Meeting, Salt Lake City, UT, Nov. 7-10, 2005. (poster)
Rivero R.G., S. Grunwald, T.Z. Osborne, S. Newman, K.R. Reddy. 2005. Comparative analysis of hybrid geostatistical methods for advanced soil mapping in WCA-2A, Everglades, Florida. ASA-CSSA-SSSA Meeting, Salt Lake City, UT, Nov. 7-10, 2005. (poster)
Bruland G.L., S. Grunwald, T.Z. Osborne, K.R. Reddy and S. Newman. 2005. Spatial distribution of soil properties and soil physical-chemical diversity in the Greater Everglades Ecosystem. ASA-CSSA-SSSA Meeting, Salt Lake City, UT, Nov. 7-10, 2005. (poster)
Osborne T.Z., G.L. Bruland, K.R. Reddy, S. Newman and S. Grunwald. 2005. Spatial distribution and linkages of soil biogeochemical properties in the Everglades National Park. ASA-CSSA-SSSA Meeting, Salt Lake City, UT, Nov. 7-10, 2005.
Grunwald S., G.L. Bruland and P. Goovaerts. 2005. Independent validation of soil predictions – the act of testing the truth? Pedometrics 2005 – International Meeting of Commission 1.5 of the Int. Union of Soil Sciences. Naples, Florida, Sept. 12-14, 2005.
Bruland G.L., S. Grunwald, K.R. Reddy, T.Z. Osborne and S. Newman. 2005. A spatially-explicit Mantel test framework to investigate relationships among soil, water, landscape and vegetative properties. Pedometrics 2005 – International Meeting of Commission 1.5 of the Int. Union of Soil Sciences. Naples, Florida, Sept. 12-14, 2005.
Rivero R.G., S. Grunwald, S. Newman, T.Z. Osborne and K.R. Reddy. 2005. Incorporation of ASTER satellite imagery into multi-variate geostatistical models to predict soil phosphorus. Pedometrics 2005 – International Meeting of Commission 1.5 of the Int. Union of Soil Sciences. Naples, Florida, Sept. 12-14, 2005. (Graduate student Rosanna Rivero ranked 3nd in the student award competition)
Corstanje R., R.M. Lark and S. Grunwald. 2005. To detect the breakdown of assumptions of statistical stationarity in the soil variation of a complex landscape. Pedometrics 2005 – International Meeting of Commission 1.5 of the Int. Union of Soil Sciences. Naples, Florida, Sept. 12-14, 2005.
Rivero R.G., S. Grunwald, T.Z. Osborne, K.R. Reddy, and S. Newman. 2005. Incorporation of Aster satellite imagery into geospatial modeling of soil total phosphorus in Water Conservation Area 2A. SWS Department. Research Forum, Gainesville, FL, Sept. 2, 2005. (Graduate student Rosanna Rivero won poster award)
Grunwald S., R. Corstanje, G.L. Bruland, T.Z. Osborne, R.G. Rivero, S. Newman and K.R. Reddy. 2005. Geospatial mapping of soil total phosphorus in the Greater Everglades ecosystem. 9th Int. Symposium on Biogeochemistry of Wetlands. Baton Rouge, LA, March 20-23, 2005.(poster)
Bruland G.L., S. Grunwald, T.Z. Osborne*, K.R. Reddy and S. Newman. 2005. Geostatistical analyses of soils data from Water Conservation Area 3, South Florida. 9th Int. Symposium on Biogeochemistry of Wetlands. Baton Rouge, LA, March 20-23, 2005.
Grunwald S., G.L. Bruland, R. Corstanje, R.G. Rivero, K.R. Reddy, T.Z. Osborne and S. Newman. 2005. Geostatistical modeling of soil property variability within the Greater Everglades ecosystem. 20th Annual Symposium of the U.S. Regional Chapter of the Int. Association for Landscape Ecology, Syracuse, NY, March 12-16, 2005. (poster)
Rivero R.G., S. Grunwald, T.Z. Osborne, S. Newman and K.R. Reddy. 2004. Application of soil mapping and modeling efforts in WCA2 integrating GIS, geostatistics and remote sensing techniques. National Conference on Ecosystem Restoration, Lake Buena Vista, FL, Dec. 6-10, 2004.
Bruland G.L., S. Grunwald, T.Z. Osborne, K.R. Reddy and S. Newman. 2004. Statistical and geostatistical analyses of soils data from Water Conservation Area 3, South Florida. National Conference on Ecosystem Restoration, Lake Buena Vista, FL, Dec. 6-10, 2004.
Grunwald S., K.R. Reddy, T.Z. Osborne, R. Corstanje, M.W. Clark and S. Newman. 2004. Spatially-explicit modeling of soil phosphorus across the Greater Everglades. National Conference on Ecosystem Restoration, Lake Buena Vista, FL, Dec. 6-10, 2004.
Grunwald S., K.R. Reddy, T.Z. Osborne and S. Newman. 2004. Geostatistical modeling of soil phosphorus in the Greater Everglades Ecosystem. ASA-CSSA-SSSA Meeting, Seattle, WA, Oct. 31 – Nov. 4, 2004. Abstract No.: 5009. (talk)
Corstanje R., S. Grunwald, K.R. Reddy and T.Z. Osborne. 2004. Modeling of the spatial distribution of selected soil nutrients in Loxahatchee National Park. ASA-CSSA-SSSA Meeting, Seattle, WA, Oct. 31 – Nov. 4, 2004. Abstract No.: 5824.
Bruland G.L., R.G. Rivero, R. Corstanje, S. Grunwald, T.Z. Osborne**, K.R. Reddy and S. Newman. 2004. Spatial distribution of total soil phosphorus in the Greater Everglades ecosystem. SWS Department Research Forum, Gainesville, FL, Sept. 2, 2004. Thesis projects:
Tiffany Kapner. 2007. Spatial relationships between physico-chemical soil properties andvegetative patterns in Everglades Water Conservation Area 1.
Rosanna G. Rivero. 2006. Development of predictive models of soil phosphorus in Water Conservation Area -2A (Everglades), integrating remote sensing, GIS and geostatistics.

Florida’s Wetlands WebGIS

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Florida’s Wetlands WebGIS

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Florida’s Wetlands WebGIS

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Florida’s Wetlands WebGIS

Team

PI:
S. Grunwald, Soil and Water Science Department, University of Florida
K.R. Reddy, Soil and Water Science Department, University of Florida
V. Mathiyalagan (implementation of the interactive geo-database)
S. Bloom (ArcIMS implementation)

Dataset

Thousands of data records of physical, chemical and biological geo-referenced measurements collected in Florida wetlands from 1987 to ~2000 (courtesy of K.R. Reddy, Wetland Biogeochemistry Laboratory). Data mining from various wetland studies was conducted (2001-2002) and a harmonized database build.

Wetland GIS

Methodology

We standardized the measurements, developed a metadata set, and used ArcIMS to create a webGIS for global data sharing. Users can query the geo-database and download data to their local machines.

Objectives

To develop an interactive web-based tool to integrate and visualize geospatial data and information for Florida’s wetlands providing map and data services to users.

Results

Unfortunately, the project web site had to be disconnected because Dr. K.R. Reddy reconsidered data sharing of historic wetland data. The project web site was disconnected February 22, 2011.

Publication:

Mathiyalagan V., S. Grunwald, K.R. Reddy and S.A. Bloom. 2005. A WebGIS and geodatabase for Florida’s wetlands. Computers & Electronics in Agriculture, 47: 69-75.

Space-Time Modeling of Water Table Dynamics in a Flatwood Landscape in Florida

Team

PI:
S. Grunwald, Soil and Water Science Department, University of Florida

Collaborators:
N. B. Comerford, Soil and Water Science Department, University of Florida
V. Ramasundaram, Computer Science and Engineering, University of Florida
A. Mangeot, Soil and Water Science Department, University of Florida
C.M. Bliss, Soil and Water Science Department, University of Florida

Dataset

Soil properties/horizon, topographic data and water table measurements collected at 123 wells, over a 5-year period (1992 – 1998), on a 42-ha flatwood landscape 33 km northeast of Gainesville

Methodology

Employ 2D and 3D ordinary kriging to reconstruct 3D soil-landscape models and to utilize the Virtual Reality Modeling Language (VRML), External Authoring Interfaces (EAI), eXtensible 3D (X3D), and Java for space-time modeling and visualization of dynamic properties (e.g. water table dynamics)

Objectives

Space-time modeling and visualization of water table dynamics in a flatwood landscape in Florida

Results


Fig. 1. 3D soil horizon model.

Fig. 2. Space-time models of inundation.

Publications:
Grunwald S., V. Ramasundaram, N.B. Comerford and C.M. Bliss. 2006. Are current scientific visualization and virtual reality techniques capable to represent real soil-landscapes? pp. 571-580 (chapter 42). In Lagacherie P., A.B. McBratney and M. Voltz (eds.),Digital Soil Mapping – An Introductory Perspective. Developments in Soil Science Vol. 31, Elsevier, Berlin.

Ramasundaram V., S. Grunwald, A. Mangeot, N.B. Comerford and C.M. Bliss. 2005. Development  of an environmental virtual field laboratory. J. Computers & Education, 45: 21-34.

Grunwald S. 2005. Reconstruction and three-dimensional scientific visualization of soil-landscapes, pp. 373-392. In Grunwald S. (ed.), Environmental Soil-Landscape Modeling – Geographic Information Technologies and Pedometrics. CRC Press, New York.

Geo-Temporal Estimation and Visualization of Nitrogen and Other Soil Properties in a Mixed-Use Watershed (Santa Fe River Watershed, Florida)

Ownership/use of data from Santa Fe project – Request permission

Research Team

PI:
S. Grunwald, Soil and Water Science Department, University of Florida

Co-PIs:
N.B. Comerford, Soil and Water Science Department, University of Florida
M.W. Clark, Soil and Water Science Department, University of Florida
D.A. Graetz, Soil and Water Science Department, University of Florida

Post-Docs:
C.M. Bliss, Soil and Water Science Department, University of Florida
G.L. Bruland, Soil and Water Science Department, University of Florida
Collaborator:
R. Srinivasan, Director of the Spatial Science Lab, Associate Professor, Texas A & M University

Graduate Students:
Aarthy Sabesan (M.S.)
Sanjay Lamsal (Ph.D.)

Time: 09/2002 to 09/2006
Funding Source: United States Department of Agriculture (USDA) – Nutrient Science for Improved Watershed Management Program.

Study Area

The Santa Fe River Watershed, a tributary of the Suwannee Basin that drains into the Gulf of Mexico, spans over an area of approximately 3,585 sq. km across eight counties in north-east Florida. The watershed covers about 13.8 % of the Suwannee Basin but contributed 20% of the total NO3-N loads that drained from the Suwannee Basin into the Gulf of Mexico accounting for about 2,900 tons NO3-N (Suwannee River Water Management District, 2003). Water quality monitoring data indicated an increasing trend in nitrate-nitrogen concentrations in ground, surface and spring waters, and drinking water wells. Ground water from the Floridan aquifer is the source of the drinking water in the Santa Fe River Watershed. The long-term goal is to maintain and improve surface and ground water quality in the Suwannee Basin.

Fig. 1. Map shows the coastline of Florida and the boundaries of the Santa Fe River Watershed (data source: Suwannee River Water Management District).


Fig. 2. County and watershed boundaries (data sources: U.S. Census Bureau and Suwannee River Water Management District).

The soils of the Santa Fe River Watershed are predominantly sandy in texture with loamy to clayey deposits, organics and sites with sand hill karst terrain with many solution basins. According to the Soil Survey Geographic Database (SSURGO) Ultisols cover about 36.7%, Spodosols (25.8%), and Entisols (14.7%) representing the dominant soil orders in the watershed. Less prominent are Histosols (2.0%), Inceptisols (1.1%) and Alfisols (1.0%).


Fig. 3. Santa Fe River, Florida.

Fig. 4. Santa Fe River, Florida.

Land use (1995) consisted of pine plantation (32.2%), wetlands (16.2%), upland forest (14.7%), improved pasture (14.0%), urban (8.8%), forest regeneration (6.0%), crops (5.0%), rangeland (3.7%) and a variety of high intensity land uses such as tree groves, dairies, and feeding operations.

Specialty land uses in the watershed are diverse, ranging from corn, peanuts, tobacco, vegetables, watermelons, strawberries, blueberries, and pecans. Wetlands and a few lakes are distributed widely in the watershed while urban areas are sparsely distributed.


Fig. 5. Major land cover (1995) and soil types within the Santa Fe River Watershed.

The elevation ranges from around 3 m to over 90 m above mean sea level. Generally, the land is level (0-2 % slopes) to gently sloping and undulating (0-5% slopes), with the major exception to this pattern being the moderately and strongly sloping land (5-12% slopes) along the Cody Scarp.


Fig. 6. Digital elevation model (top) and slope (bottom) – Santa Fe River Watershed (data source: National Elevation Dataset, USGS).

Two main physiographic regions in the watershed are the Gulf Coastal Lowlands and the Northern Highlands, which are separated from one another by the Cody Scarp.

Underlying geologic units include Eocene limestone (which occurs near the ground surface in the high-recharge, strongly karst-influenced Gulf Coastal Lowlands), capped by Miocene sediments which tend to be rather clayey and phosphatic (occurring at or near the surface along the Cody Scarp), in turn capped by Pliocence and Pleistocene-Holocene sediments which tend to be sandy at the surface but having loamy subsoils or substrate at varying depths.

Objectives

Goals:

Gain better understanding how soil properties are linked to ecosystem processes and environmental landscape properties across multiple spatial scales.

Objectives:

(1) Assess space-time variation of soil nitrate-nitrogen across the Santa Fe River Watershed

(2) Develop predictive geospatial models for various soil properties (C, N, P and texture) that incorporate spatial autocorrelation of soil properties and covariation with environmental properties.

(3) Evaluate the effects of environmental ancillary datasets to improve predictions of soil properties across multiple spatial scales.

Data and Methods:

(1) Characterize environmental landscape properties: A comprehensive set of spatial environmental data were assembled using the ArcGIS Geographic Information System including: geology, soils, topography (Digital Elevation Model – DEM), land use / land cover, climate, stream network, major roads, and more.

(2) Document land use/land cover shifts in the watershed: Remote sensing imagery (Landsat TM and ETM+) were used to characterize land cover shifts from 1990 to 2003. A complimentary project that investigates relationships between biophysical landscape properties (IKONOS, ASTER, Landsat, and MODIS) and soil properties at multiple scales (ongoing).

(3) Geospatial and temporal mapping of soil nitrate-nitrogen: Soil samples were collected during Sept. 2003, January 2004, May 2004, January 2005, May 2005 and September 2005 from four depth increments (0-30, 30-60, 60-120 and 120-180 cm) in composites proportional to the depth of sampling at ~140 observation sites (random-stratified sampling design). Multiple hybrid/geostatistical methods (e.g. Regression Kriging, Generalized Linear Models, Classification and Regression Trees, spatial stochastic simulations) were used to upscale site-specific observations with the support of ancillary environmental data (e.g. remote sensing data derived landcover, DEM) to the watershed scale.

(4) Geospatial mapping of other soil properties (phosphorus, soil texture, and soil organic carbon): One time soil sampling; 4 depths (0-30, 30-60, 60-120 and 120-180 cm); 143 observation sites: nitrate-N, total N, Mehlich 1 extractable P, and soil texture. One time soil sampling; 0-10 cm depth; 143 observation sites: soil organic carbon and N of 4 particle size classes (2000µ-250µ, 250µ-150µ, 150µ-45µ and <45µ) and P mineralization by particle size class. Complimentary research projects:

(1) Linking experimental and soil spectral sensing for prediction of soil carbon pools (total, labile and recalcitrant carbon); assessment of carbon sequestration potential at the landscape scale (Santa Fe River Watershed): project link

(2) High-intensity study site – Santa Fe Beef Cattle Research Station (650 ha) nested within the Santa Fe River Watershed: ~150 observation sites sampled at 4 different depths (0-30; 30-60; 60-120; 120-180 cm); soil properties: nitrate-N, soil organic carbon; visible/near-infrared spectral data; topographic data derived from LIDAR.

Results

Publications:

Grunwald S., G.M. Vasques, N.B. Comerford, G.L. Bruland and C.M. Bliss. 2010. Regional modeling of carbon, nitrogen and phosphorus geospatial patterns. In Hanrahan G. (ed.) Modeling of Pollutants in Complex Environmental Systems Vol. II. ILM Publ., Hertfordshire, UK.

Bruland G.L., C.M. Bliss, S. Grunwald, N.B. Comerford, and D.A. Graetz. 2008. Soil nitrate-nitrogen in forested versus non-forested ecosystems in a mixed-use watershed. Geoderma 148: 220-231. (pdf)

Vasques G.M., S. Grunwald, and J.O. Sickman. 2008. Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra. Geoderma 146: 14-25. (pdf)

Vasques G.M., S. Grunwald and J.O. Sickman. 2008. Visible/near-infrared spectroscopy modeling of dynamic soil carbon fractions. Soil Sci. Soc. Am. J. (in press).

Lamsal S., S. Grunwald, G.L. Bruland, C.M. Bliss and N.B. Comerford. 2006. Regional hybrid geospatial modeling of soil nitrate-nitrogen in the Santa Fe River Watershed. Geoderma, 135: 233-247.(pdf)

Grunwald S., P. Goovaerts, C.M. Bliss, N.B. Comerford, and S. Lamsal. 2006. Incorporation of auxiliary information in the geostatistical simulation of soil nitrate-nitrogen. Vadose Zone J. 5: 391-404 (invited). (pdf)

Oral and poster presentations:

Vasques G.M., S. Grunwald, and J.O. Sickman. 2006. Assessment of total, stable and labile carbon using visible, near-infrared diffuse reflectance spectroscopy. Soil and Water Science Research Forum, Gainesville, FL Sept. 15, 2006.

Grunwald S., G.W. Hurt, G.L. Bruland, and N.B. Comerford. 2006. SCORPAN-based soil-landscape modeling in north-east Florida. World Congress of Soil Science – Frontiers of Soil Science, Philadelphia, Pennsylvania, July 9-15, 2006.

Vasques G.M., S. Grunwald, and J.O. Sickman. 2006. Assessment of total, stable and labile carbon using visible, near-infrared diffuse reflectance spectroscopy. World Congress of Soil Science – Frontiers of Soil Science, Philadelphia, Pennsylvania, July 9-15, 2006.

Grunwald S., N.B. Comerford, D.A. Graetz, M.W. Clark, C.M. Bliss and G.L. Bruland. 2006. Digital soil mapping in the Santa Fe River Watershed. Suwannee River Partnership Steering Committee meeting, Live Oak, FL, June 29, 2006.

Lamsal S., S. Grunwald, G.L. Bruland, C.M. Bliss, and N.B. Comerford. 2006.
Geospatial Footprints of Nitrogen Mapped across the Santa Fe River Watershed in Florida. IFAS Research Forum, University of Florida, Gainesville, FL, March 24, 2006.

Bliss C.M., G.L. Bruland, I. Lopez-Zamora, N.B. Comerford, D.A. Graetz and S. Grunwald. 2005. Carbon, nitrogen, and phosphorus in soil size fractions: influence of land use and soil type in a north Florida watershed. ASA-CSSA-SSSA Meeting, Salt Lake City, UT, Nov. 7-10, 2005.[poster]

Grunwald S., C.M. Bliss, G.L. Bruland, I. Lopez-Zamora, D.A. Graetz and N.B. Comerford. 2005. Geospatial modeling of phosphorus within a multi-functional and multi-use watershed. ASA-CSSA-SSSA Meeting, Salt Lake City, UT, Nov. 7-10, 2005.

Bruland G.L., C.M. Bliss, S. Grunwald, N.B. Comerford and D.A. Graetz. 2005. Soil nitrate in forested versus non-forested land-uses in the Santa Fe Watershed, Florida. ASA-CSSA-SSSA Meeting, Salt Lake City, UT, Nov. 7-10, 2005.

Stoppe A., C.M. Bliss, N.B. Comerford, D.A. Graetz and S. Grunwald. 2005. Phosphorus and nitrogen in soil size fractions: mineralizability of each fraction as affected by land use and soil type. ASA-CSSA-SSSA Meeting, Salt Lake City, UT, Nov. 7-10, 2005.

Lamsal S., S. Grunwald, G.L. Bruland, C.M. Bliss, and N.B. Comerford. 2005. Modeling of regional soil nitrate-nitrogen patterns using a mixed geospatial modeling approach. Pedometrics 2005 – International Meeting of Commission 1.5 of the Int. Union of Soil Sciences. Naples, Florida, Sept. 12-14, 2005 (Graduate student Sanjay Lamsal ranked 2nd in the student award competition). [poster]

Bruland G.L., C.M. Bliss, S. Grunwald, N.B. Comerford, and D.A. Graetz. 2005. Contrasting soil nitrate-nitrogen across land-uses and soil orders in a north-central Florida watershed. SWS Department. Research Forum, Gainesville, FL, Sept. 2, 2005. [poster]

Lamsal S., S. Grunwald, C.M. Bliss, G.L. Bruland, N.B. Comerford. 2005. Regional modeling of soil nitrate-nitrogen with auxiliary environmental datasets in the Santa Fe River Watershed. SWS Department. Research Forum, Gainesville, FL, Sept. 2, 2005 (Graduate student Sanjay Lamsal won poster award).

Grunwald S., G.L. Bruland, S. Lamsal, C.M. Bliss, N.B. Comerford and M.W. Clark. 2005. Development of predictive geospatial soil nitrate-nitrogen models in the Santa Fe River Watershed. CSREES National Water Quality Conference, San Diego, CA, Febr. 7-9, 2005.

Sabesan A., S. Grunwald, M.W. Binford and M.W. Clark. 2004. Backcasting of land use pattern using remote sensing to support soil-landscape modeling. ASA-CSSA-SSSA Meeting, Seattle, WA, Oct. 31 – Nov. 4, 2004. Abstract No.: 5000.

Lamsal S., S. Grunwald, C.M. Bliss, I. Lopez-Zamora, M.W. Clark and N.B. Comerford. 2004. Comparison of multivariate non-parametric methods for predictive modeling of soil properties in a mixed-use watershed in Florida. ASA-CSSA-SSSA Meeting, Seattle, WA, Oct. 31 – Nov. 4, 2004. Abstract No.: 4997.

Lamsal S., S. Grunwald, C. M. Bliss, I. Lopez-Zamora, N. B. Comerford, M.W. Clark, G. W. Hurt. 2004. Spatial upscaling of site-specific nitrate-nitrogen measurements to the watershed scale in the Santa Fe River Watershed. SWS Department Research Forum, Gainesville, FL, Sept. 2, 2004.

Lamsal S., S. Grunwald, C.M. Bliss, I. Lopez-Zamora and N.B. Comerford. 2004. Upscaling site-specific nitrate-nitrogen measurements to watershed scale in the Santa Fe River Watershed. Soil and Crop Science Society of Florida, Tallahassee, FL, May 20-21, 2004 (Graduate student was awarded price in student competition).

Sabesan A., S. Grunwald, M.W. Clark, N.B. Comerford, D.A. Graetz and R.B. Brown. 2003. Targeting sampling locations using GIS and remote sensing datasets. ASA-CSSA-SSSA Annual Meeting, Denver, CO, Nov. 2-6, 2003, S05-sabesan265102-poster.

Sabesan A., S. Grunwald, M.W. Binford and M.W. Clark. 2003. Linking land use dynamics to soil and water quality using geographic techniques. Soil and Water Science Departmental Research Forum, Gainesville, FL, Sept. 4, 2003.

Lamsal S., S. Grunwald, N.B. Comerford, D.A. Graetz, K.M. Portier, W.G. Harris and G.W. Hurt, G. 2003. Development of holistic, quantitative soil-landscape models in north-eastern Florida. Soil and Water Science Departmental Research Forum, Gainesville, FL, Sept. 4, 2003.

Florida Soil Characterization Database

Research Team

PI:
S. Grunwald, Soil and Water Sciences Department, University of Florida

Co-PIs:
W.G. Harris, Soil and Water Sciences Department, University of Florida
G.W. Hurt, Soil and Water Sciences Department, University of Florida

Technical support:
S.A. Bloom, Soil and Water Sciences Department, University of Florida

R.G. Rivero, Soil and Water Sciences Department, University of Florida

V. Ramasundaram, Computer Science and Engineering Department, University of Florida

Time: 05/2003 to 05/2004
Funding Source: Florida Department of Transportation

Summary

From the early 1970s until 1996, the Accelerated Soil Survey Program provided funding for extensive soil mapping in Florida. Thousands of pedons were sampled and analyzed at the Environmental Pedology Laboratory (University of Florida, Soil and Water Sciences Department). These data represent the most comprehensive body of knowledge ever collected for Florida soils. In this project, we standardized, integrated and geo-referenced (latitude/longitude) the Florida soil data comprising about 1,300 soil profiles and 8,325 horizons distributed in 58 of 67 counties. The indexed data were ported to a web-based SQL database. The dataset includes soil profile descriptions (morphological data), soil taxonomic information, latitude and longitude, and 144 different physical and chemical soil properties. The interactive web interface enables users to query the database by soil characteristics and geographic attributes.

Results

The Florida Soil Characterization Database provides a rich resource of historical soil conditions in the State of Florida, USA. A documentation of the methods used to digitize the data can be found in the Final project report.

Access the historical soil data (pedon data): http://flsoils.ifas.ufl.edu. 

Phosphorus Retention and Storage by Isolated and Constructed Wetlands in the Lake Okeechobee Basin

Research Team

PIs:
K. Ramesh Reddy, Soil and Water Science Department, University of Florida
W. Graham, Agricultural and Biological Engineering, University of Florida

Co-PI: M.W. Clark, Soil and Water Science Department, University of Florida
S. Grunwald, Soil and Water Science Department, University of Florida
J. Jawitz, Soil and Water Science Department, University of Florida
M. Annable, Environmental Engineering Sciences, University of Florida
W. Wise, Environmental Engineering Sciences, University of Florida
T.A. DeBusk, DB Environmental Labs

Graduate student (sub-project):
Kathleen McKee (Major advisor: S.Grunwald)

Time: 08/2002 to 08/2007
Funding Source: South Water Management District and Florida DEP

Objectives (Sub-project)

(1) Evaluation of isolated wetlands for phosphorus retention
(2) Synoptic survey of ambient phosphorus storage in isolated wetlands in the Okeechobee Basin

Spatial Modeling of Nitrogen Emissions from Poultry Operations and their Influence on Pitch Canker in Pinus Elliottii

Collaborators

PI:
E. Jokela, School of Forest Resources and Conservation, University of Florida

Co-PIs:
N. B. Comerford, Soil and Water Science Department, University of Florida
S. Grunwald, Soil and Water Science Department, University of Florida

Post-Docs:
C.M. Bliss I. Lopez Undergraduate student: G. Vasques

Time: 1/2003 to 1/2004
Funding Source: Department of Agriculture and Consumer Services (DACS), Division of Forestry

Objectives

  1. Quantify the periodic N emission from a poultry house
  2. Quantify the local spatial variability of accumulated N in the soils and foliage of a developing slash pine plantation in proximity to poultry houses, with particular attention to the distance of influence that extends from the poultry house
  3. Quantify the temporal and spatial impact of the forest stand on filter N emissions through the judicious use of throughfall and minor use of atmospheric collectors
  4. Investigate the relationship between the spatial distributions of accumulated N and pitch canker in adjacent pine stands

Total N
Fig. 1. Spatial distribution of forest floor nitrogen concentrations (%) in a slash pine stand planted adjacent to a poultry operation (black rectangles). Note: The poultry houses are air ventilated externally by fans on the east (south) side.

Results

Publications:

Lopez-Zamora I., C.M. Bliss, E.J. Jokela, N.B. Comerford, S. Grunwald, E. Barnard and G.M. Vasquez. 2007. Spatial relationships between nitrogen status and pitch canker disease in slash pine planted adjacent to a poultry operation. Environmental Pollution J. 147: 101-111 (pdf).

Bliss C.M., I. Lopez-Zamora, N.B. Comerford, S. Grunwald, E.J. Jokela and E. Barnard. 2005. Spatial distribution of nitrogen and carbon in soil size fractions in pine plantations affected by poultry emissions. ASA-CSSA-SSSA Meeting, Salt Lake City, UT, Nov. 7-10, 2005.

Jokela E.J., C.M. Bliss, I. Lopez-Zamora, N.B. Comerford, S. Grunwald and E. Barnard. 2005. Spatial distribution of phosphorus and cations in slash pine foliage and forest floor: effects of emissions from poultry operations in north Florida. ASA-CSSA-SSSA Meeting, Salt Lake City, UT, Nov. 7-10, 2005.

Bliss C.M., I. Lopez-Zamora, N.B. Comerford, S. Grunwald, E.J. Jokela, E. Barnard, G.M. Vasques. 2004. Relationship between nitrogen loading and pitch canker disease in slash pine stands adjacent to poultry operations. ASA-CSSA-SSSA Meeting, Seattle, WA, Oct. 31 – Nov. 4, 2004. Abstract No.: 5643 (best paper award). (poster)

Lopez-Zamora I., C.M. Bliss, N.B. Comerford, E.J. Jokela, S. Grunwald, E. Barnard, G.M. Vasques. 2004. Effect of nitrogen emissions from poultry operations on nitrogen deposition and pitch canker disease in slash pine stands. ASA-CSSA-SSSA Meeting, Seattle, WA, Oct. 31 – Nov. 4, 2004. Abstract No.: 5616 (best paper award).

Implementation and Growers Evaluation of a Web-Based Nutrient Management Plan Support (NUMAPS) System for Florida Crops

Research Team

Principal Investigators:
J.M. Scholberg, Agronomy Department, University of Florida (phase 1)
H.W. Beck, Information Technology, Agricultural and Biological Engineering, University of Florida (phase 2)

Co-Principal Investigators:
K. Morgan, Soil and Water Science Department, University of Florida
S. Grunwald, Soil and Water Science Department, University of Florida
T. Obreza, Soil and Water Science Department, University of Florida

Time: 10/2002 to 10/2006 (phase 1); 10/2006 to 8/2010 (phase 2)

Funding Source: Florida Department of Environmental Protection

Objectives

Phase 1: The objective of this project is to implement a web-based decision support (expert) system that will combine environmentally sound and economically feasible crop production practices with production system-specific information for the design and implementation of Nutrient Management Plans (NMPs) and Best Management Practices (BMPs). We aim to take full advantage of current advances in computer technology to link scientific and site-specific information via user-friendly web-based computer systems that provides optimal nutrient, irrigation and/or other crop management recommendations. The implementation of this project is based on object-oriented modeling design. The specific objectives of this project are to use the modeling tool to simulate nitrogen cycling in Florida citrus.

Phase 2: Ontology-based simulation modeling of phosphorus transport in sugarcane in south Florida (OntoSim-Sugarcane model).

Results

Publications:

Kwon H.-Y., S. Grunwald, H.W. Beck, Y. Jung, S.H. Daroub, T.A. Lang, and K.T. Morgan. 2009. Ontology-based simulation of water flow in organic soils applied to Florida’s sugarcane. Agric. Wat. Manag. J. (in press).

Beck. H., K. Morgan, Y. Jung, S. Grunwald, H.-Y. Kwon. 2008. Ontology-based simulation applied to soil, water and nutrient management. In P. Papajorgji (ed.) Advances in Modeling Agricultural Systems, Springer, Berlin.

Select poster presentations:

Kwon H.-Y., S. Grunwald, H. Beck, Y. Jung, S. Daroub and T.A. Lang. 2009. Automatic calibration of ontology-based model for simulating water table fluctuations on farms in the Everglades Agricultural Area of South Florida. Research Forum Soil and Water Science Department, University of Florida, Gainesville, FL, Sept. 2009.

Kwon H.-Y., S. Grunwald, H. Beck, Y. Jung, S. Daroub, T.A. Lang, and K. Morgan. 2008. Ontology-based simulation of water flow in organic soils used for sugarcane. ASA-CSSA-SSSA Meeting, Houston, TX, Oct. 5-9, 2008.

Kwon H.-Y., S. Grunwald, H.W. Beck and K.T. Morgan. 2007. Ontology-based simulations for minimizing environmental impacts of Florida sugarcane production. ASA-CSSA-SSSA Meeting in New Orleans, LA, Nov. 4-8, 2007.

Inventory of Physico-Chemical Soil Properties and Spatial Pattern Analyses in the Everglades Ecosystem

Collaborators

PI:
S. Grunwald, Soil and Water Science Department, UH

Collaborator:
W.F. DeBusk, Soil and Water Science Department, UF

Acknowledgement:
Data were made available for this project by the Wetland Biogeochemistry Laboratory, University of Florida http://wetlands.ifas.ufl.edu/ (courtesy of K.R. Reddy)

Time: 10/1/2001 to 7/1/2002

Funding Source: School of Natural Resources, University of Florida.

Summary

This project contributes to the restoration efforts outlined in the Comprehensive Everglades Restoration Plan (CERP) by revisiting physical and chemical soil properties measured within a time frame of more than a decade by the scientist of the Soil and Water Science Department, IFAS, University of Florida, in the Everglades Ecosystem. These geo-referenced point measurements of dissolved reactive phosphorus (P), HCL extractable P, total P, pH, bulk density, dissolved O2, and others are a valuable resource to describe spatial patterns of physico-chemical soil properties in the Everglades ecosystem. Recently, novel geostatistical methods emerged, which were not available at the time the data were collected. Quantifying these spatial patterns of soil properties enable us to look from a different perspective at historic conditions of the Everglades ecosystem. Given such a valuable spatial dataset, we can reconstruct the history of degradation of the Everglades ecosystem putting us in a position to better understand the ongoing dynamics, assess the potential for its reversal, and evaluate resilience of such a complex and fragile ecosystem. Preserving such a comprehensive dataset of geo-data in a readily available state-of-the-art GIS format is beneficial to the restoration effort in south Florida.

Objectives

  1. Restore and catalogue systematically physico-chemical soil properties measured in the wetlands biogeochemistry laboratory, Soil and Water Science Department, from 1987 to present in a GIS database.
  2. Integration of other readily available natural resource GIS data layers to assist in interpretation of spatial patterns of physico-chemical soil properties (e.g. hydrologic patterns, vegetation patterns).
  3. Explicit spatial modeling utilizing different types of geo-analytical methods:
    • Global estimation methods
    • Local estimation methods
    • Interpolation using geostatistics (kriging) – these methods attempt to optimize interpolation by dividing spatial variation into 3 components: (i) deterministic variation, (ii) spatially autocorrelated but physically difficult to explain variations, and (iii) uncorrelated noise.
    • Neighborhood analysis (technique: convolution). Common measures for diversity are ‘entropy’ or ‘lacunarity’, which measure the heterogeneity of specific property values across a study area.

Everglades Ecosystems

Many of the problems with declining ecosystem health in southern Florida revolve around four interrelated factors: (1) Water quantity (2) Water quality (3) Timing (4) Distribution. The major goal of restoration is to deliver the right amount of water that is clean enough to the right places and at the right time.

EAA: Everglades Agricultural Area LNWR: Loxahatchee National Wildlife Refugee WCA: Water Conservation Area ENP: Everglades National Park

Major stresses imposed on the Everglades ecosystem:

  • Water management (water quantity)
  • Urban development
  • Agriculture (input of phosphorus)
  • Recreation
  • Exotic species
STATSGO soil map, Florida

Related links:
South Florida Information Access (SOFIA): http://sofia.usgs.gov/
South Florida Water Management District: http://www.sfwmd.gov/
Everglades Restoration Plan: http://www.evergladesplan.org/

Geo-Database

Archive of chemical and physical soil properties (current status: 1987 – 1995). Currently, there are 1905 data records stored in the geo-database. Metadata

Variable Description Units
SOILS_ID Index
DATE Date of collection of sample
PROJECT_ID Project identifier
LAT Latitude degrees
LONG Longitude degress
BEGIN Beginning depth of sample cm
END Ending depth of sample cm
Soil physical parameters
pH pH of wet-sample
ASH Ash content of dried sub-sample %
WATER Water content of fresh sub-sample %
TC Total carbon content * mg kg-1
Parameters related to phosphorus
CA 1 M hydrochloric acid extractable calcium content * mg kg-1
MG 1 M hydrochloric acid extractable magnesium content * mg kg-1
FE 1 M hydrochloric acid extractable iron content * mg kg-1
AL 1 M hydrochloric acid extractable aluminum content * mg kg-1
Phosphorus (P)
TP Total phosphorus content * mg kg-1
TPI Summation of all inorganic phosphorus forms mg kg-1
TPO Summation of all organic phosphorus forms mg kg-1
NAHCO3 Sodium bicarbonate extractable phosphorus (represents the amount of bioavailable P) ** mg kg-1
KCLP1M Potassium chloride extractable phosphorus content (P that can be easily desorbed from soil and sediment, and thus is also considered bioavailable) ** mg kg-1
NAOHPI Sodium hydroxide extractable inorganic phosphorus content (the sodium hydroxide extraction is thought to liberate P associated with iron and aluminum compounds) ** mg kg-1
NAOHPO Sodium hydroxide extractable organic phosphorus content (This form of P is considered moderately labile and its mobility is related to the iron and aluminum content of the soil and redox conditions) ** mg kg-1
HCLPI 1 N hydrochloric acid extractable inorganic phosphorus content ** mg kg-1
HCLP1M 1 M hydrochloric acid extractable phosphorus * mg kg-1
SOL_P Porewater dissolved reactive phosphorus concentration mg L-1
FLUX_P Field-measured flux of phosphorus mg m-2 day-1
Nitrogen (N)
TN Total nitrogen content * mg kg-1
KCLN2M 2M potassium chloride extractable ammonium-N * mg kg-1
SOL_N Porewater ammonium-N concentration mg L-1
FLUX_N Field-measured flux of N mg m-2 day-1

* of dried sample ** of wet sub-sample

Interactive Analysis of Geo-Data

You can access, analyze, select, and filter all soil physical and chemical data collected in Water Conservation Area 2 interactively utilizing the Treemap tool. Treemaps are a space-filling visualization for hierarchical structures that are extremely effective in showing attributes by size and color coding. Treemaps enable users to compare sizes of nodes and of sub-trees, and are especially strong in spotting unusual patterns. They were developed by Ben Schneiderman at the Human-Computer Interaction Laboratory (HCIL) of the University of Maryland. Treemaps functions:

Access raw data
Select variables and display them by size and color. Identify pattern in datasets. For example, show differences in total phosphorus between different soil depths and sampling locations.
Use the filter routine to highlight values of an attribute exceeding a user-defined threshold value. For example, highlight all values of total phosphorus exceeding 500 mg/kg.
Steps to use treemap: (1) Download the Treemap tool (2) Unzip the file treemap.zip (3) If you have not installed a Java-plug on your machine you need to download a java-plugin to run treemap (current version 1.4 or higher). Available at: http://java.sun.com/products/plugin/index.html (4) Start treemap either using treemap-3.2.bat or treemap-3.2.jar (depending on settings on your local machine). (5) Use FILE / OPEN and select file wca-2-layer-loc.tm3 Example snapshot: total phosphorus TP in WCA2Documentation Treemap

Phosphorus Mechanisms

Phosphorus (P) pools and mechanisms

Orthophosphate (H2PO4 and HPO42-) that is released by mineralization is rapidly adsorbed by the soil particles where its availability steadily declines with time (phosphate fixation). Soil pH, clay, sesquioxide content, and redox potential influence the phosphorus availability. Phosphorus pools range from labile phosphorus that can be readily desorbed plus phosphorus in solution, to inorganic phosphorus associated with calcium, magnesium, iron, and aluminum, to non-labile (stable) phosphorus held in insoluble metallo-organic complexes. The reduction of redox potential following flooding can cause transformations of crystalline aluminum and iron minerals to the amorphous forms. Amorphous aluminum and iron hydrous oxides have higher phosphorus sorption capacity than crystalline oxides due to their larger number of singly coordinated surface hydroxyl ions. Water Conservation Areas The level of impairment in phosphorus differed in Water Conservation Areas 1, 2, and 3. Typically phosphorus content decreased with depth.

Total phosphorus (TP) Porewater dissolved reactive phosphorus concentration (Sol_P) 1 M hydrochloric acid extractable phosphorus (HCLP1M)

Water Conservation Area 2

Generally, correlations between total phosphorus (TP) and other physical and chemical properties were low for most projects in the geo-database. Example: Significant correlations for Water Conservation Area 2 are listed in the table below. Correlations

TP
TN -0.363**
TC -0.229*
SOL_P 0.663**
SOL_N 0.396**
NAHCO3 0.589**
KCLN2M -0.343**
HCLP1M 0.855**
CA 0.375**
Mg 0.461**

** correlation is significant at the 0.01level (2-tailed) * correlation is significant at the 0.05 level (2-tailed) It is difficult to develop general empirical prediction models for phosphorus due to many interrelating factors and processes. To gain an understanding of underlying processes and factors across an area of interest it is necessary to analyze the spatial variability.

Geostatistics

Variography: The process of estimating the theoretical semivariogram.Steps: (1) exploratory data analysis, (2) check for global trend, (3) computation of the empirical semivariogram, (4) binning and fitting a semivariogram model, (5) computation of directional variograms to identify anisotropy.

Geostatistics is based on the assumption that observations close to each other are more likely to be similar than observations at a larger distance from each other. The spatial correlation between observations is described by the semivariance, which is half the average squared difference between paired data values. The fitted semivariogram model is defined by nugget, sill, and range. The nugget represents the measurement error and fine-scale variability (variation at spatial scales too fine to detect). The distance at which the sill is reached is called the range. Beyond the range the observations are not spatially correlated. The identified spatial structure in the variogram is used to estimate values at previously unsampled locations to create a continuous map (kriging). Kriging, which is a weighted interpolation method, is similar to inverse distance in that it weights the surrounding measured values to derive a prediction for each location. However, the weights are based no only on the distance between the measured points and the prediction location but also on the overall spatial arrangement among the measured points. To use the spatial arrangement in the weights the spatial autocorrelation must be quantified.

Results

Spatial pattern analyses can reveal relationships between variables at geographic locations. Three-dimensional (3D) spatial models were created identifying cross-relationships between soil chemical and physical variables. Total phosphorus (TP) was represented as height and physical and chemical properties by color (e.g. bulk density, magnesium content). For example we found that bulk density was large in the south-west of the study area while total phosphorus was large. In contrast, small bulk densities were predicted in the north-east of the study area associated with large total phosphorus. Likewise other spatial cross-relationships between physical and chemical variables were identified in the models below.

Multi-dimensional ordinary kriging was used to create a 3D model showing the spatial distribution of total phosphorus in 3D geographic space.

Select publications:

Mathiyalagan V., S. Grunwald, K.R. Reddy and S.A. Bloom. 2005. A WebGIS and geodatabase for Florida’s wetlands. Computers & Electronics in Agriculture, 47: 69-75.

Grunwald S., K.R. Reddy, S. Newman and W.B. DeBusk. 2004. Spatial variability, distribution, and uncertainty assessment of soil phosphorus in a south Florida wetland. J. of Environmetrics, 15: 811-825.

Development of 3D Soil-Landscape Models in Florida

Objective

Reconstruct and visualize a soil-landscape in Alachua county, Florida using readily available soil and topographic data. Project conducted by: S. Grunwald.

Data

Readily available soil data from the SSURGO database were used to create a soil-landscape model for a small site in northern Florida. The following soil series were found:

  • Lockloosa (loamy, siliceous, semiactive, hyperthermic Aquic Arenic Paleudults)
  • Pomona (sandy, siliceous, hyperthermic Ultic Alaquods)
  • Moneocha (sandy, siliceous, hyperthermic Ultic Alaquods)
  • Plummer (loamy, siliceous, subactive, thermic Grossarenic Paleaquults)
  • Millhopper (loamy, siliceous, semiactive, hyperthermic Grossarenic Paleudults)
  • Tavares (hyperthermic, uncoated Typic Quartzipsamments)

Soil profile information for soils were downloaded from the Official Soil Series Description Database (NRCS) and Topgraphic data, 5-foot contour lines (data source: USGS, 1997) were downloaded from theFlorida Geographic Data Library (FGDL).

Methods

The SSURGO data layer was imported into ArcView GIS 3.2. Attribute tables were matched to polygons of the GIS layer via the common variable Muid. A point data layer was created in shapefile format. The geo-referenced points were exported and an ASCII file prepared containing soil profile information for each point location. This file was imported into EVS-PRO software (CTech Development Corporation, Huntington Beach, CA) and 2D ordinary kriging in the horizontal plane and linear interpolation in the vertical plane was used to create face geometry of soil layers.


SSURGO soil data, study site in Alachua County, Florida

Soil profiles

Results

The stratigraphic soil-landscape model visualized the spatial distribution of soil horizons continuously in 3D space. Such a model is helpful to get a better understanding of soils and how they relate to topography. Reconstruction of soil-landscapes is a prerequisite for simulation of transport and transformation processes (e.g. lateral flow, nitrate leaching, nitrification). Higher resolution soil data are desirable to improve 3D reconstruction of soil-landscapes.


3D stratigraphic soil-landscape model

Publications:

Grunwald S. and P. Barak. 2003. 3D Geographic reconstruction and visualization techniques applied to land resource management. Transactions in GIS 7(2): 231-241.

Grunwald S. and P. Barak. 2001. The use of VRML for virtual soil landscape modeling. Systems Analysis Modelling Simulation 41: 755-776.

3D Reconstruction and Scientific Visualization of Soil-Landscapes (WI and OH)

Collaborators

S. Grunwald, P. Barak, K. McSweeney, and B. Lowery Department of Soil Science, University of Wisconsin-Madison.

Data collection: P.J. Fagan, G. Hart, and A.I. Malik

Time: 01/1999 to 03/2000

Funding Source: Department of Soil Science, University of Wisconsin-Madison

Objective

The objective was to investigate the use of Virtual Reality Modeling Language (VRML) to create 3-D soil landscape models at different scales ranging from pedon (1 m2), catena (2.73 ha), catchment (23.7 ha) to soil region scale (>100.0 ha). These soil-landscape models describe and visualize the three-dimensional distribution of soil and landscape attributes.

Scientific Visualization / Virtual Reality / VRML

Scientific visualization (SciVis) transforms numerical symbolic data into geometric computer generated images. According to Barraclough and Guymer (1998) it is one of the most powerful communicators of spatial information. Advanced visualization techniques better communicate spatial information between people of different backgrounds such as scientists, administrators, educators and the public. Just as maps can visually enhance the spatial and temporal understanding of phenomena, 3D representations can enhance our understanding of soil patterns. Interactivity enhances the perception and interpretation of soil-landscapes. According to Stibbard (1997) information is absorbed best when using more than one human sense:

  • 10 % taken in by reading
  • 30 % by reading and visual
  • 50 % by reading, visuals and sound
  • 80 % by reading, visuals, sound and interaction

Virtual reality (VR) has different meanings. Full or immersive virtual reality requires the participant to be subject to stimuli affecting many senses, including vision, hearing, balance and touch. Such systems require head-mounted displays, audio speakers, moving platforms, and tactile gloves. Immersive VR systems are expensive and access is limited. Problems associated with immersive VR encompass cybersickness (problems with orientation and motion sickness symptoms) and psychological disturbance when the user returns from the synthetic world to the real world. Desktop virtual reality is the most commonly used form of VR systems, due to the fact that it can be presented on standard computer monitors. Here, conventional PC software is used to create and view artificial worlds in the office and over the internet. The World Wide Web (www) provides a desktop-based virtual environment (VE) where users can interactively navigate though VEs, they can interact in real time with objects, and have feelings of presence. Desktop VR is useful for representations of environmental systems, because it provides 3D capabilities, interactivity, and assists making extremely complex system transparent and supporting scientific interpretation and analysis of the natural environments. >

Virtual Reality Modeling Language (VRML) is a 3D object-oriented graphics language, suitable for stand-alone or browser-based interactive viewing. It is portable across platforms >and has an open standard (current ISO standard ‘VRML97’ accepted by International Standards Organization’s (ISO) JTC1/SC24 committee). VRML worlds are accessible via the www. In VRML, 3-D objects are models extending in three dimensions. A VRML object has a form or geometry that defines its 3-D structure and it has an appearance based on the material and color from which it is made and its surface texture, like wood or brick. Coordinates of objects are defined using a 3-D coordinate system with x, y, z axis. The origin is defined by the triplet 0 0 0 representing x, y, and z-axis. The shape of every object has to be defined using triplets in relation to the origin.

References:
Barraclough A. and I. Guymer. 1998. Virtual reality – a role in environmental engineering education? Water Sci. Tech., 38(11): 303-310.

Stibbard A., 1997, Warwick University Forum, No. 6.

Soil-Landscape Representations

Commonly, 2D maps are used to visualize the spatial distribution of soil and landscape patterns. 2D digital elevation model (DEM) and soils map (SSURGO data, NRCS) for a site in southern Wisconsin Geographic information systems (GIS) are still the most common tools to store, analyze, and visualize digital soil and landscape data. The most widely used digital soil data in the U.S. provided by the Natural Resources Conservation Service (NRCS) in the National Soil Geographic Database (NATSGO), State Soil Geographic Database (STATSGO), and Soil Survey Geographic Database (SSURGO) are attribute tables and 2D ArcView GIS shape- files. Other soil-landscape representations use a 2½D design, where soil or land use data are draped over a digital elevation model (DEM) to produce a 3D view. Since this technique describes patterns on 2D landscape surfaces rather than the spatial distribution of subsurface attributes (e.g., soil texture, soil horizons) it fails to address three-dimensional soil-landscape reality. Numerous 3D sketches of soil-landscapes can be found in Soil Survey Manuals. However, these mental models do not utilize field data nor do they utilize a geostatistical method. The relatively few 3D representations of soils at landscape-scale currently available are striking. For example, the Cooperative Research Center for Landscape Evolution and Mineral Exploration constructed a 3-D regolith model of the Temora study area in Central New South Wales, Australia, and a 3D soil horizon model in a Swiss floodplain was created by Mendonça Santos et al. (2000) using a quadratic finite-element method. Sirakow and Muge (2001) developed a 3D Subsurface Objects Reconstruction and Visualization System (3D SORS) in which 2D planes are used to assemble 3D subsurface objects.

References:

Mendonça Santos M.L., C. Guenat, M. Bouzelboudjen, and F. Golay. 2000. Three-dimensional GIS cartography applied to the study of the spatial variation of soil horizons in a Swiss floodplain. Geoderma 97: 351-366.

Sirakov N.M. and F.H. Muge. 2001. A system for reconstructing and visualizing three-dimensional objects. Computers & Geosciences 27: 59-69.

Methods

(1) Reconstruction and scientific visualization of soil-landscapes (geo-data modeling): The simplest geographic data model of reality is a basic spatial entity, which is further specified by attributes and geographical location (spatial coordinates or geometry) and relationships (topology). This can be further subdivided according to one of the three basic geographical data primitives, namely a ‘point’, a ‘line’, or an ‘area’. We used the geographic entities ‘pixels’ and ‘voxels’ (volume cells) to model real soil-landscapes. The input datasets to create soil-landscape models comprised x, y, and z (depth) coordinates, elevations, and subsurface attribute values (e.g. texture, bulk density, soil water content, etc.). Geostatistical methods were used to create continuous soil-landscape representations. We distinguished three different model types: (a) Models representing subsurface attributes as points: These are the simplest type of virtual soil-landscape models. The PointSet VRML class (node) was used to create point geometry. (b) Models representing subsurface attributes as polyhedrons or “volume objects” (stratigraphic models). Surfaces of models were created using 2D ordinary kriging and volumes (layers) were created with linear interpolation in the vertical direction between these surfaces. The IndexedFaceSet VRML class was employed to render polyhedrons (e.g., representing soil horizons) Steps (pdf file) (c) Models representing subsurface attributes as voxels (block models). Soil attributes were interpolated based on the spatial structure identified in 3D variograms, which plot semivariance on the z-axis, distance (h) between data pairs in the x-y plane (horizontal) on the x-axis, and distance (h) between data pairs in the z-plane (vertical) on the y-axis. Variograms are displayed in three-space as a surface. The observed values were interpolated horizontally and vertically using 3D ordinary kriging. The IndexedFaceSet VRML class was employed to render voxels. Steps (pdf file) We used Environmental Visualization System (EVS) (EVS-PRO; CTech Development Corporation, Huntington Beach, CA) to create soil-landscape models in VRML format. Our models visualize the 3D spatial distribution of subsurface and topographic attributes. Colors and surface textures were used to specify the appearance of objects. Subsurface attributes were portrayed using the red-green-blue (RGB) color specification system and topographic attributes were portrayed on the z-axis. The VRML capable browser automatically computes shading to give objects a 3D appearance.

Results

3D point model showing the spatial distribution of soil cores which were analyzed for bulk density in 5-cm depth increments (site location: southern Wisconsin)


3D Soil-layer model (stratigraphic model) showing the spatial distribution of soil layers across a 2.73-ha site in southern Wisconsin

3D block model showing the spatial distribution of bulk density across a 2.73-ha site in southern Wisconsin

Access VRML 3D models (You need a VRML plug-in in your web browser)

4D simulation:

Quick time movie showing changes in soil water content over time simulated on a topo-sequence in northwestern Ohio utilizing the Soil and Water Assessment Tool (SWAT).

Publications:

Grunwald S., V. Ramasundaram, N.B. Comerford and C.M. Bliss. 2006. Are current scientific visualization and virtual reality techniques capable to represent real soil-landscapes? pp. 571-580 (chapter 42). InLagacherie P., A.B. McBratney and M. Voltz (eds.), Digital Soil Mapping – An Introductory Perspective. Developments in Soil Science Vol. 31, Elsevier, Berlin.

Grunwald S. 2006. Reconstruction and three-dimensional scientific visualization of soil-landscapes, pp. 373-392. In Grunwald S. (ed.), Environmental Soil-Landscape Modeling – Geographic Information Technologies and Pedometrics. CRC Press, New York.

Chen S.-S. and S. Grunwald. 2005. The spatial/temporal indexing and information visualization genre for environmental digital libraries. J. of Zhejiang University Science (JZUS) 6A(11): 1235-1248.

Grunwald S. and P. Barak. 2003. 3D Geographic reconstruction and visualization techniques applied to land resource management. Transactions in GIS 7(2): 231-241.

Grunwald S. and P. Barak. 2001. The use of VRML for virtual soil landscape modeling. Systems Analysis Modelling Simulation 41: 755-776.

Grunwald S., K. McSweeney, D.J. Rooney, and B. Lowery. 2001. Soil layer models created with profile cone penetrometer data. Geoderma 103: 181-201.

Grunwald S., P. Barak, K. McSweeney, and B. Lowery. 2000. Soil landscape models at different scales portrayed in Virtual Reality Modeling Language (VRML). Soil Sci. 165(8): 598-615.

Profile Cone Penetrometer (PCP) Applications

Collaborators

S. Grunwald, K. McSweeney, B. Lowery (Department of Soil Science, University of Wisconsin-Madison)

Data collection: D.J. Rooney, G. Hart, and A. I. Malik

Time: 08/1997 – 03/2000

Funding Source: Department of Soil Science, University of Wisconsin-Madison

Study Area

A 2.73-ha site located on the Agricultural Research Station West Madison, University of Wisconsin-Madison, in southern Wisconsin. Soils were formed in reworked loess overlying glacial till. The site was mapped as fine-loamy, mixed, mesic Typic Argiudolls. Land use was alfalfa (Medicago sativa) for the past three years. Mean annual precipitation is 720-mm. The soil moisture regime is udic.

PCP Specifications

Profile cone penetrometers measure penetration resistance expressed as cone index (kPa). The PCP system components included a PCP probe, a hydraulic truck- mounted push system (Gidding #9HD; Fort Collins, CO)1, a load cell (1360-kg capacity; Omegadyne LC 101; Sunbury, OH), a depth transducer (Unimeasure HX-EP; Corvallis, OR) and data aquisition system.

PCP 1: 60 degree cone angle, 2-cm diameter surface area, no sleeve friction measurement, and datalogger (Campbell Scientific 21X; Logan, UT).

PCP 2: 30 degree cone angle, tip and sleeve friction measurements and real-time data aquisistion and analysis system (Applied Research Associates, Inc., South Royalton, Vermont).


PCP 1


PCP 2


Real-time data aquisition system and GPS


Continuous cone index curve

Data / Sampling Design

Data set 1: grid sampling (10-m spacing); 273 penetrations; 21 soil core (4.3-cm diameter) samples randomly taken at grid locations; soil cores were analyzed by horizons for texture, bulk density, and water content. Soil texture was analyzed by the UW Soil and Plant Analysis Laboratory (Madison, WI) based on the hydrometer method. Soil water content was derived by collecting known volumes of samples and oven drying them to obtain volumetric values. Bulk density was determined by oven drying a known volume of soil and presented on dry weight basis.

Data set 2: targeted sampling design based on an entropy analysis of topographic attributes; 77 penetrations conducted with PCP probe 1 and 2; 77 soil cores analyzed in 5-cm depth increments for bulk density and water content. A Trimble 4600 LS differential global positioning system (GPS) (Trimble, 1996; Sunnyvale, CA) was used to georeference sampling locations and to develop a digital elevation model (DEM) for the study site.

Objectives A

To use PCP and soil property data to distinguish among soil materials (reworked loess and glacial till)

Analyses A

Steps (data set 1):
(1) We grouped cone index profiles (dense data set) using a hierarchical cluster analysis
(2) We used landform element classes as supplementary information to define groups
(3) Experts defined characteristics of soil materials
(4) We calculated mean and standard deviation for soil attributes (sparse data set) and identified soil materials
(5) We combined cone index and soil property data – transfer of soil material information from the sparse soil property data set to the dense cone index data set
(6) We created a 3D soil layer model showing the spatial distribution of soil materials. 2D ordinary kriging was used to generate surfaces of layers and volumes of layers were created with linear interpolation in the vertical direction between the surfaces (EVS-PRO software)

Results A

Grunwald S., B. Lowery, D.J. Rooney, and K. McSweeney. 2001. Profile cone penetrometer data used to distinguish between soil materials. Soil & Tillage Research 62: 27-40.

Grunwald S., B. Lowery, M.K. Clayton, K. McSweeney, G.L. Hart, and D.J. Rooney. 1999. Using a penetrometer to produce a 3-D Model of soil patterns. ASA-CSSA-SSSA Annual Meeting, Salt Lake City, Oct. 31- Nov. 4, 1999, Div. S-5, pp. 271.

Rooney D.J., J.M. Norman, S. L. Lieberman, and S. Grunwald. 1999. Characterizing soil properties in 3-D using multiple sensor penetrometer technology. ASA-CSSA-SSSA Annual Meeting, Salt Lake City, Oct. 31 – Nov. 4, 1999, Div. S-1, pp. 185.

Grunwald S., K. McSweeney, B. Lowery, and D.J. Rooney. 1998. Continuous description of soil attributes on a landscape in southern Wisconsin. ASA-CSSA-SSSA Annual Meeting, Baltimore, Oct. 18-22, Div. S-5, pp. 253.

Mean and standard deviation for clay, silt and sand content, bulk density (rb) and soil volumetric water content (q) for layers identified by the hierarchical cluster analysis. Cluster B was further classified using landform element information into cluster B1 and B2. Table 1. Mean and standard deviation of soil attributes.

Attribute

Cluster A

Cluster B

Cluster B1

Cluster B2

Cluster C

Cluster D

1 Clay (%)

13.5†; 0.9‡

21.7; 1.4

20.5; 1.2

11.8; 0.6

21.5; 1.4

22.5; 0.6

Silt (%)

63.0; 1.9

59.5; 2.0

58.6; 1.4

60.3; 1.7

58.3; 1.4

65.5; 1.5

Sand (%)

22.1; 2.0

21.7; 1.9

20.6; 1.4

22.7; 1.8

21.2; 2.4

10.1; 1.0

rb (Mg m-3)

1.30; 0.12

1.39; 0.07

1.45; 0.05

1.32; 0.11

1.60; 0.10

1.71; 0.10

q (m3 m-3)

0.19; 0.012

0.188;0.006

0.184; 0.006

0.192; 0.006

0.188; 0.007

0.194; 0.005

2 Clay (%)

13.3; 1.0

21.6; 1.9

20.7; 1.1

22.5; 0.7

26.0; 0.9

24.3; 0.7

Silt (%)

62.1; 1.8

62.5; 2.0

61.9; 1.1

63.1; 0.9

67.2; 1.1

62.2; 1.5

Sand (%)

36.3; 7.9

15.7; 6.1

20.3; 1.5

11.1; 1.2

12.1; 1.6

11.2; 1.7

rb (Mg m-3)

1.38; 0.02

1.43; 0.09

1.50; 0.07

1.36; 0.05

1.38; 0.13

1.37; 0.05

q (m3 m-3)

0.198; 0.003

0.197;0.006

0.200; 0.011

0.194; 0.012

0.197; 0.005

0.199; 0.004

3 Clay (%)

9.1; 1.2

20.0; 15.1

10.3; 0.7

30.0; 0.5

Silt (%)

9.1; 1.0

36.3; 28.5

10.4; 1.2

62.1; 0.7

Sand (%)

80.5; 9.1

43.9; 35.0

79.0; 1.3

8.8; 0.9

rb (Mg m-3)

1.71; 0.05

1.58; 0.11

1.65; 0.07

1.51; 0.10

q (m3 m-3)

0.127; 0.006

0.169;0.049

0.121; 0.012

0.217; 0.004

† Mean, ‡ SD

Reworked loess
Glacial till


3D soil-layer model showing the spatial distribution of soil materials (top layer, reworked loess, and glacial till).

Conclusion / Summary A

In this study, we used a heuristic approach using a working hypothesis about the spatial distribution of soil materials. The objective o this study was to distinguish among soil materials found in a glaciated landscape in southern Wisconsin. We used a dense data set of cone penetrations, a sparse set of laboratory measured soil properties, and landform element classes to accomplish our goal. Thus, we combined rapid sampling techniques with cost-intensive and labor intensive procedures. This method yielded good results mapping contrasting soil materials such as reworked loess and glacial till. Results indicated that combined information consisting of cone index, soil properties, and geographic location improved soil material mapping.

Objectives B (a)

To describe continuously the spatial distribution of soil materials and layers in three space dimensions. (b) To evaluate two different approaches to create 3D soil layer models utilizing cone index profiles and soil property data

Analyses B

Steps:
(1) Crisp hierarchical clustering / vertical point inflection method (data set 1)
(2) Fuzzy k-mean classification / defuzzification (data set 1)
(3) Validation procedure (data set 2): the root mean square error (RMSE) was used to evaluate the accuracy of predictions and the coefficient of efficiency (E) was used to evaluate the fit of measured vs. predicted values
(4) We created 3D soil layer models showing the spatial distribution of soil materials. 2D ordinary kriging was used to generate surfaces of layers and volumes of layers were created with linear interpolation in the vertical direction between the surfaces (EVS-PRO software)

Results B

Grunwald S., K. McSweeney, D.J. Rooney, and B. Lowery. 2001. Soil layer models created with profile cone penetrometer data. Geoderma 103: 181-201.

Grunwald S., D.J. Rooney, K. McSweeney, and B. Lowery. 1999. Application of a profile cone penetrometer to distinguish between soil materials. Proceedings 3rd Conference of the Working Group on Pedometrics of the International Union of Soil Science (IUSS), Sept. 27-29, 1999, Sydney, Australia.

The RMSE and E coefficient for different layers and methods:

Layers Crisp method Fuzzy method
RMSE E RMSE E
1 3.81 0.96 3.67 0.97
2 4.18 0.90 15.69 0.51
3 4.98 0.86 17.33 0.40


3D soil layer models created with two different methods

Conclusions / Summary B

Results indicate that the crisp method overall produced better predictions than the fuzzy method. The crisp method yielded excellent results predicting the spatial variability of changes in layer depth and / or soil material across the 2.73-ha site in southern Wisconsin. An advantage of using cone index data is rapid and non-invasive data collection. These data, combined with limited soil coring are ideal to create 3D soil layer models.

Objectives C

(a) To develop pedotransfer functions, which describe the relationship between cone index and

  • soil texture,
  • bulk density, and
  • water content

(b) To evaluate the sensitivity of parameters used in these functions

Analyses C

Steps (data set 1):
(1) We derived cone index layer profiles (CILP) using horizontal hierarchical clustering and a vertical point inflection method
(2) Within each CILP group, cone index was regressed with soil physical properties to develop pedotransfer functions
(3) Pedotransfer functions were evaluated using the coefficient of determination (R2)
(4) We conducted a sensitivity analysis to evaluate the relative importance of different parameters in the regression models

Results C

Grunwald S., D.J. Rooney, K. McSweeney, and B. Lowery. 2001. Development of pedotransfer functions for a profile cone penetrometer. Geoderma, 100: 25-47.

Table 2. Coefficients for multiple linear regression equations for prediction of cone index (CI) in kPa.

Data sets M Intercept Depth (cm) Sand (%) Silt (%) Clay (%) rb (Mg m-3) q (m3 m-3) R2 Eq.

G

S

818.45

13.15

0.345

1.1

S

-2,318.77

10.39

2,348.36

0.409

1.2

S

-1,434.77

12.88

-25.61

2,031.77

0.446

1.3

S

-3,615.65

13.52

-32.74

2,704.26

69.61

0.468

1.4

E

-3,371.53

12.52

-12.54

-27.48

2,614.97

101.29

0.480

1.5

CILP1

S

1,029.54

14.75

0.445

1.6

E

4,992.29

3.75

1.16

-39.80

-1,203.12

-80.87

0.621

1.7

CILP2

S

1,820.82

-30.57

0.331

1.8

S

1,082.59

37.81

-46.14

0.422

1.9

E

5,853.12

48.16

86.76

15.04

-1,483.21

-288.05

0.760

2.0

CILP3

S

852.58

8.53

0.510

2.1

S

-820.41

6.88

1,284.51

0.655

2.2

E

-3,209.55

6.49

15.82

-1.87

2,410.55

-4.78

0.700

2.3

CILP4

S

-18,182.50

735.50

0.455

2.4

S

-23,509.00

574.02

482.03

0.601

2.5

S

-19,755.80

606.14

-2,107.70

411.32

0.622

2.6

E

23,395.32

56.29

527.97

-12,414.9

-1,110.04

0.630

2.7

CILP5

S

532.47

33.21

0.851

2.8

S

-4679.62

30.32

2,883.32

69.20

0.900

2.9

E -5,777.01 27.54 29.03 -90.66 3,775.17 117.09 0.980 3.0

G: global data set
CLIP1 to CILP5: cone index layer profiles
M: Methods, S: stepwise; E: enter

Example Eq. 1.2: CI = -2,318.77 + 10.39 * depth + 2,348.36 * rb

Spatial distribution of CILP in 3D geographic space

Pedotransfer functions using the global data set showed large sensitivities for bulk density and soil water content. Similar results were calculated for all other CILPs, except CILP4 where clay content showed large sensitivity.

Conclusions / Summary C

The R2 for pedotransfer funcitons ranged from 0.345 up to 0.98. Depth, bulk density, clay content and water content showed largest predictability for cone index using the global data set. Textural variables had strong predictive power in the top layers, CILP1 and CILP2. In CILP4, clay content along with bulk density and soil water content were variables with large predictive power. In contrast, the predictive power of bulk density and soil water content was strong in layers CILP3 and CILP5, whereas soil textural characteristics showed low predictability of cone index.

The sensitivity analysis calculated the relative importance of variables using a specific pedotransfer function to predict cone index. Sensitivity values have merit for any user who might apply one of the pedotransfer functions. Parameters with largest sensitivity have the greatest impact on model output and therefore should be collected/measured carefully.

Penetrometer measurements are useful to describe gradual and continuous variation of cone index and associated soil properties within fields and within soil map units. Besides detailed measurements of soil physical and hydraulic properties, the collection of cone index facilitates to improve our knowledge of the within-field spatial variability of soil properties. This is the basis for effective site-specific farming which is environ- mentally sound and economically. The costs for measurements of texture, bulk density and water content with high precision would be higher when compared with the approach presented in this paper. A drawback of using pedotransfer functions is the uncertainty associated with estimates.

We believe that pedotransfer functions for cone penetrometers would make these devices widely applicable. Measurements of cone index are rapid and the costs are modest when compared to traditional soil sampling of soil physical properties. We conclude that this technique shows promise to improve fine-scale sampling of soil physical properties. Considering the limited nature of this study, these results strongly indicate the feasibility of this approach.

GIS-Based Water Quality Modeling with SWAT

Collaborators

PI:
Sabine Grunwald, Soil and Water Science Department, University of Florida

Co-PI:
Kevin Czaijkowski, Department of Geography and Planning, University of Toledo, OH

Collaborators:
Jeff Arnold, United States Department of Agriculture (USDA) – Agricultural Research Service (ARS), Temple, TX and A. van Griensven

Graduate Student:
Kevin Alicia, Department of Geography and Planning, University of Toledo, OH
Chen Qi, Civil Engineering, University of Florida

Time: 09/2000 to 06/2003
Funding Source: Ohio Lake Erie Commission

Soil and Water Assessment Tool (SWAT)

SWAT is a quasi physically-based water quality simulation model that operates on a daily time step. It is a basin-scale model developed by Dr. Jeff Arnold, USDA-ARS, in Temple, TX. SWAT was developed to predict the impact of land management practices on water, sediment and agricultural chemical yields in complex watersheds with varying soils, land use and management conditions over long periods of time. The SWAT model components include:
(i) hydrology,
(ii) weather,
(iii) sedimentation,
(iv) soil temperature,
(v) crop growth,
(vi) nutrients,
(vii) pesticides, and
(viii) agricultural management (Neitsch et al., 1999).

To accurately predict movement of sediment, nutrients and pesticides, the hydrologic cycle as simulated by the model must conform to what is happening in the watershed. For simulations the watershed of interest is subdivided into simulation elements, either grid cells, virtual basins or subbasins. For each simulation element water flux and transport of sediment and agrichemicals are simulated and then routed through a watershed, i.e., water and chemicals are transported from one simulation element to the next depending on flow characteristics. An ArcView based interface is available to input GIS into SWAT (DiLuzio et al., 1997).

More information about SWAT is available at: http://www.brc.tamus.edu/swat/

References:
DiLuzio, M., R., Srinivasan and J.G., Arnold. 1997. An integrated user interface for SWAT using ArcView and Avenue. ASAE Meeting, Minneapolis, MN Aug. 10-14, 1997; Paper No. 972235.

Neitsch, S.L., J.G., Arnold and J.R., Williams. 1999. SWAT – Soil and Water Assessment Tool – user’s manual Version 99.2. Grassland, Soil and Water Research Laboratory & Blackland Research Center, USDA-ARS, Temple, TX.

Objectives

  1. Assemble a high-quality Geographic Information System (GIS) dataset for the Sandusky Watershed using readily available data, which includes soils, land use, management, geology, topography, and climate. Our goal is to develop a new GIS land use layer utilizing Landsat TM images.
  2. Simulate transport processes such as infiltration, sediment, nutrient (N, P) and pesticide transport to assess point and non-point source pollution in the Sandusky Watershed utilizing SWAT.
  3. Utilize existing detailed loading data to test model predictions.
  4. Identify areas that show large loads of sediments and agrichemicals.
  5. Describe and visualize the spatial distribution of water quality indicators over time in the Sandusky Watershed.

Study Area: Sandusky Watershed, Ohio

The Sandusky Watershed, with a drainage area at Fremont of 3,240 km2, is located within the Lake Erie Watershed and Great Lakes basin. The Sandusky River is the second largest of the Ohio rivers draining into Lake Erie. Analysis of 1994 LANDSAT data indicates that 84% of the land is used for agriculture, 12.6% is wooded, 1.2% is urban and 1.1% is non-forested wetlands. Major crops based on county-level estimates in 1985 were corn (Zea mays L.) with 35.6% of cropland acreage, soybeans (Glycine max L.) with 44.9% and wheat (Triticum aestivum L.) with 19.5%. Crop production was similar in 1995 with 32.1% in corn, 49.1% in soybeans and 18.7% in wheat. Tillage practices shifted from 86% conventional management in 1985 to 50.5% in 1995, as farmers replaced conventional with conservation tillage practices. Tile drainage is used extensively throughout the watershed. Urban areas within the Sandusky Watershed are Bucyrus, Fremont, Tiffin, and Upper Sandusky, and numerous smaller communities. The river and its major tributaries support important recreational uses for watershed residents. Bedrock underlying the watershed is primarily Silurian dolostone and Devonian limestone. In the eastern portion of the watershed, Devonian shale and Mississippian sandstone are present. Surface features of the Sandusky Watershed reflect the effects of the Wisconsinian glaciers, which retreated approximately 13,000 years ago. This resulted in two physiographic regions in the watershed, the Lake Plains in the northern portion and the Till Plains in the central and southern portions. The landscape of the Lake Plains is an extremely flat plain of fine clay sediments, formed by wave action of glacial meltwater lakes that preceded Lake Erie. Sandier soils are present in the remnants of occasional beach ridges from these lakes. The Till Plains consists of flat to gently rolling plains with heavy till soils. Most of the relief within the Till Plains is located in three end moraines that lie in an east-west orientation. The majority of the Till Plaines consists of flatter, ground moraines which lie between the end moraines. Besides glacial till, lacustrine sediments and alluvial deposits along the drainage system of the Sandusky River are found. Dominant soils are Hapludalfs, Ochraqualfs, Fragiaqualfs, Medisaprists, Fluvaquents, and Argiaquolls. Textures are mainly silt loam and silty clay loam. Average annual precipitation ranges from 881 mm at Fremont to 964 mm at Bucyrus. Historic precipitation data for the watershed show highest amount for July (99 mm) and smallest for February (48 mm). Annual mean discharge for the Sandusky Watershed at Fremont is 29.1 m3s-1, Honey Creek at Melmore is 3.8 m3s-1, and Rock Creek at Tiffin is 0.88 m3s-1.



Hydrologic units and monitoring stations in the Sandusky Watershed.

Impairment of the Sandusky Watershed

Ohio EPA’s 1998 field survey of stream segments of the Sandusky Watershed indicated the following causes for impairment:
(1) Lower Sandusky Watershed (incl. Wolf, Green, Indian, and Muddy creeks) 84.4 stream miles assessed from total of 279.3 stream miles
(2) Middle Sandusky Watershed (incl. Sugar, Willow, Rock, Honey, and Broken Knife creeks) 118.6 miles assessed from total of 256.0 stream miles
(3) Tymochtee Creek 21.4 miles assessed from total of 187.8 stream miles
(4) Upper Sandusky Watershed (incl. Sandusky River, Negro Run, Rock Run, Broken Sword Creek, Paramour creeks, Crestline Tributary) 99.4 miles assessed from a total of 222.7 miles

Impairments Miles impaired
Lower Sandusky Watershed Habitat alterations 22.0
Flow alterations 21.0
Nutrient enrichment 17.0
Siltation 18.0
Ammonia 17.0
Middle Sandusky Watershed Habitat alterations 6.5
Nutrient enrichment 2.5
Siltation 13.0
Total organics 2.5
Cause unknown 18.0
Tymochtee Creek Habitat alterations 21.4
Siltation 21.4
Upper Sandusky Watershed Habitat alterations 18.0
Nutrient enrichment 41.0
Siltation 75.0
Ammonia 29.0
Metals 21.0
Oil and greese 5.0

Water Quality Monitoring

Monitoring results of the Water Quality Laboratory (WQL) indicate that unit area loads in the Sandusky Watershed are highest for total phosphorus, suspended sediments, and nitrates out of seven major watersheds in Ohio and higher than most other locations. In the Sandusky Watershed point sources of total phosphorus averaged 5.5% and do not constitute more than 15% of the annual loads in any year of the period 1975-1995.

Results

Publications:

van Griensven, A, T. Meixner, S. Grunwald, and R. Srinivasan. 2008. Fit-for-purpose uncertainty versus calibration uncertainty in model-based decision making, Hydrological Sciences Journal.

Grunwald S. and C. Qi. 2006. GIS-based water quality modeling in the Sandusky Watershed. J. of the American Water Resources Association, 42(4): 957-973.

van Griensven A., T. Meixner, S. Grunwald, A. Di Luzio and R. Srinivasan. 2006. A global sensitivity analysis tool for the parameters of multi-variable watershed models. J. of Hydrology, 324: 10-23.

Qi C. and S. Grunwald. 2005. GIS-based hydrologic modeling in the Sandusky Watershed. Transactions of the ASAE 48(1): 169-180.

van Griensven A., T. Meixner, S. Grunwald and R. Srinivasan. 2005. Evaluation methods for SWAT models. SWAT 3rd Int. Conference, Zuerich, Switzerland, July 11-15, 2005.

Qi C. and S. Grunwald. 2004. GIS-based spatially-distributed water quality modeling in the Sandusky Watershed. ASA-CSSA-SSSA Meeting, Seattle, WA, Oct. 31 – Nov. 4, 2004. (poster)

GIS-Based Simulations of Non-Point Source Pollution Using AGNPSm Model

Collaborators

Ph.D. project – Sabine Grunwald, Department of Natural Resources, University of Giessen, Germany

Major advisor:
Professor H.G. Frede, Department of Natural Resources, University of Giessen, Germany

Collaborators:
D.L. Norton; National Soil Erosion Research Laboratory, United States Department of Agriculture (USDA) Agricultural Research Service (ARS), West Lafayette, IN, USA.
S. Haverkamp, M. Rode, and F. Lotz; Department of Natural Resources, University of Giessen, Germany.
Chaubey I. and C.T. Haan; Department of Biosystems and Agricultural Engineering, Oklahoma State University.

Time: 01/1993 to 12/1996
Funding Source: Honors fellowship State Hessen, Germany

AGNPS

Agricultural Non-Point Source Pollution Model
AGNPS is an event-based, deterministic-analytical water quality model (Young et al, 1987; 1994).

Algorithms References
Surface runoff – Curve Number (CN) method (USDA-SCS, 1972)
Peak flow Smith et al. (1980)
Runoff velocity Manning’s equation
Soil loss – Modified USLE (Universal Soil Loss Equation) (Wischmeier et al., 1978)
Sediment transport capacity – modified stream power equation Bagnold (1966)
Sediment transport – stationary continuity equation Foster et al. (1981) and Lane (1982)
Nutrient transport (N, P) Frere et al. (1980)

References
Bagnold R.A., 1966. An approach to the sediment transport problem from general physics. U.S. Geological Survey Professional Paper 422-I, Washington.

Foster G.R., L.J. Lane, J.D. Nowlin, L.M. Laflen, and R.A. Young. 1981. Estimating erosion and sediment yield on field-sized areas. Trans. of the ASAE, 24 (5): 1253-1262.

Frere M.H., J.D. Ross, and L.J. Lane. 1980. The nutrient submodel. In: Knisel W.G. (ed.).1980. CREAMS: A field-scale model for chemicals, runoff, and erosion from agricultural management systems. U.S. Department of Agriculture, Conservation Research Report, No. 26.

Lane L.J., 1982. Development of a procedure to estimate runoff and sediment transport in ephemeral streams. In: Recent developments in the explanation and prediction of erosion and sediment yield. Publ. No. 137, Int. Assoc. Hydrological Science, Wallingford, England: 275-282.

Smith R.E., and J.R. Williams. 1980. Simuation of the surface water hydrology. In: Knisel W.G. (ed.): CREAMS: A field-scale model for chemicals, runoff, and erosion from agricultural management systems. USDA, Conservation Research Report, 26: 13-35.

USDA-SCS. 1972. United States Department of Agriculture – Soil Conservation Service. National Engineering Handbook, Sec. 4. Hydrology.

Wischmeier W., and D.D. Smith. 1978. Predicting rainfall erosion losses – A guide to conservation planning. USDA, Handbook No.537.

Young R.A., C.A. Onstad, D.D. Bosch, and W.P. Anderson. 1987. AGNPS, Agricultural Non-Point Source Pollution Model – A watershed analysis tool. United States Department of Agriculture, Conservation Research Report 35.

Young R.A., C.A. Onstad, D.D. Bosch, W.P. Anderson. 1994. Agricultural Non-Point Source Pollution Model, Version 4.03 – AGNPS User’s Guide.

AGNPSm – Modified AGNPS

Modifications were integrated into the source code of AGNPS in order to adjust to Western European climate and land use conditions (model transfer), as well as to improve simplified AGNPS model routines. Changes made to the source code of AGNPS included the following:

(1) Replacement of the SCS Curve Number method by the Lutz method (Lutz, 1984) for calculation of surface runoff
(2) Replacement of the LS factor algorithm of Wischmeier & Smith by the algorithm of Moore et al. (1986) based on stream power theory
(3) Linkage of channel erosion by individual categories of particle size to runoff velocity
(4) Replacement of uniform precipitation input by grid-based precipitation input.

References
Lutz W., 1984. Berechnung von Hochwasserabfluessen unter Anwendung von Gebiets-kenngroessen. Ph.D. Thesis, Karlsruhe University, Germany.

Moore I.D., and G.J. Burch. 1986: Physical basis of the Length-Slope Factor in the Universal Soil Loss Equation. Soil Sci. Soc. Am. J., 50: 1294-1298.

Objectives

To simulate surface runoff, sediment and nutrient (N, P) yield using the event-based modified AGNPS model (modified Agricultural Non-Point Source Pollution Model) and validate model simulations in four watersheds showing contrasting landscape characteristics.

Study Areas

Glonn watersheds (G1 and G2) (drainage area: 1.2 and 1.6 km2, respectively)
Weiherbach Watershed (drainage area: 3.5 km2)
Salzboede Watershed (size: 81.7 km2)

Location and size of study areas Watershed characteristics

Linkage Between GIS input data and AGNPSm

Spatial data were stored and manipulated using SPANS geographic information system (GIS). An interface was coded in C++ to link spatial input data (land use, soils, topography, climate) to the AGNPSm.


Modeling framework

Entropy / Sensitivity Analysis

An entropy analysis was conducted in order to evaluate the degree of heterogeneity/homogeneity of spatial natural resources. A moving window technique was used to calculate entropy for soils, land use, elevation, and slope utilizing different grid sizes. We evaluated the loss of information associated with data aggregation. Topographic data showed the largest entropy and therefore the largest heterogeneity. Topographic data were most sensitive to information loss when aggregated.

A spatial sensitivity analysis was conducted to assess sensitivity of different grid sizes to simulation output. Sediment yield showed large, peak flow moderate and surface runoff small sensitivity to grid size variation.

AGNPSm Simulation Results

Simuation output was compared to measured data and evaluated using the Coefficient of Efficiency (E) by Nash and Sutcliffe (1970). Simulation of the hydrological routines in watershed G1 (Glonn) provided highly satisfactory results. Runoff volume showed an E of 0.96 for 19 flood events (validation). Peak flow yielded an E of 0.84. Modifications to the sediment routine allowed the E of 0.26 (AGNPS) to be raised to 0.90 (AGNPSm). Nutrients attached to sediment was satisfactorily calculated with an E of 0.71 (P in sediment) and 0.79 (N in sediment), respectively. Simulations for dissolved nutrient transport (an E of 0.40 for P and an E of 0.60 for N) were poor.

The results calculated for watershed G2 (Glonn) were similar to those obtained for watershed G1 (Glonn). Runoff volume was simulated with an E of 0.83, peak flow rate with an E of 0.82. Sediment transport had an E of 0.72, and nutrients attached to sediment had E’s of 0.64 (P) and 0.40 (N). Simulation of dissolved nutrient transport was unsatisfactory, with Es of -1.92 (P) and 0.13 (N).

In Weiherbach watershed E for runoff volume was 0.88 and 0.36 for peak flow. In Salzboede watershed, grid-based precipitation input (rN) was tested via sensitivity analysis with varying synthetic precipitation fields. A comparison was also made between uniform and grid-based precipitation inputs to the outputs surface runoff, peak flow, and sediment yield. Runoff volume yielded an E of 0.87 (rN) and an E for peak flow of 0.57 (rN). The E for sediment delivery was 0.49.

References
Nash J.E., and J.V. Sutcliffe. 1970: River flow forecasting through conceptual models – Part I: A discussion of principles. J. of Hydrology, 10: 282-290.

Validations results:


surface runoff & peak flow rate

sediment delivery

phosphorus and nitrogen

Simulation results for the rainfall-runoff event on Sept. 29, 1981 in Glonn G1 Watershed:

Simulation results for the rainfall-runoff event on Nov. 22, 1984 in Salzboede Watershed:


precipitation

Predicted surface runoff using uniform precipitation input

Predicted surface runoff using grid-based precipitation input

Predicted sediment delivery using grid-based precipitation input

Deviations in sediment delivery between grid-based and uniform precipitation input

Predicted particulate phosphorus using grid-based precipitation input

Deviations in particulate phosphorus between grid-based and uniform precipitation input

Conclusions

AGNPSm predictions for runoff volume, peak flow, and sediment yield were robust in four different watersheds with contrasting landscape characteristics and size. Simulation of dissolved nutrient transport was unsatisfactory.

Publications

Grunwald S. and H.-G. Frede. 2000. Application of modified AGNPS in German watersheds (book chapter pp. 43-58). In: Schmidt J.‚ Application of Physically-Based Soil Erosion Models, Springer, Berlin, New York.

Grunwald S. and L.D. Norton. 2000. Calibration and validation of a non-point source pollution model. Agricultural Water Management, 45: 17-39.

Grunwald S. and L.D. Norton. 1999. An AGNPS-based runoff and sediment yield model for two small watersheds in Germany. Transactions of the ASAE, 42(6):1723-1731.

Grunwald S., and H.-G. Frede. 1999. Using AGNPS in German watersheds. Catena, 37(3-4): 319-328.

Chaubey I., C.T. Haan, J.M. Salisbury, and S. Grunwald. 1999. Quantifying model output uncertainty due to spatial variability of rainfall. J. of American Water Resources Association, 35(5):1113-1123.

Chaubey I., C.T. Haan, S. Grunwald, and J. M. Salisbury. 1999. Uncertainty in the model parameters due to spatial variability of rainfall. J. of Hydrology, 220: 48-61.

Grunwald S. 1998. AGNPS (Agricultural Non-Point Source Pollution Model). Wiener Mitteilg. Wasser – Abwasser – Gewaesser – Experiences with Soil Erosion Models, 151: 77-88.

Grunwald S. and H.-G. Frede. 1998. Application of AGNPSm in German Watersheds. Wiener Mitteilg. Wasser – Abwasser – Gewaesser – Experiences with Soil Erosion Models, 151: 183-189.

Frede H.-G., S. Haverkamp, S. Grunwald, and N. Fohrer. 1998. Assessment of MEKA subsidies for soil and water protection by AGNPS model. World Congress of Soil Science, Montpellier, France, Aug. 20 – 26., Symposium 31, No. 2044.

Grunwald S. 1997. GIS based simulations of non-point source pollution using AGNPSm model. Ph.D. thesis, Department of Natural Resources Management, University of Giessen, Germany.

Haverkamp S., S. Grunwald, and H.-G. Frede. 1997. Erhoehter Erosionsschutz und verminderter Naehrstoffaustrag durch das MEKA-Foerderprogramm. Mitteilg. der Deutschen Bodenkundlichen Gesellschaft (German Soil Science Society), 85, III: 1447-1452.

Grunwald S., S. Haverkamp, M. Bach, and H.-G. Frede. 1997. Assessment of MEKA subsidies for soil and water protection by AGNPSm model J. of Rural Engineering and Development, 38(6): 260-265.

Chaubey I., C.T. Haan, J.M. Salisbury, and S. Grunwald. 1997. Effect of spatial variability of rainfall on modeling hydrologic/water quality processes. ASAE Annual Int. Meeting, Minneapolis, MN, Aug. 10-14, 1997. Paper No. 972099.

Grunwald S., and H.-G. Frede. 1996: GIS-based modeling of water quality using AGNPS. In: Dollinger F., Strobl J. (ed.): Salzburger Geographic Materials – Applied GIS VIII, AGIT Symposium, 3.-5. Juli 1996, 24: 231-236.

Rode M., S. Grunwald, and H.-G. Frede. 1995: Modeling of water quality using AGNPS and GIS. J. of Rural Engineering and Development, 36(2): 63-68.