Regional selenium cycling in an irrigated agricultural groundwater system: conceptualization, modeling, and mitigation

2012 Summer.Includes bibliographical references.Selenium (Se) is an element that occurs naturally as a trace constituent in geologic formations and associated soils and, although an essential nutrient for animals and humans, can prove detrimental to health at high concentrations. Over the previous decades, the presence of either deficient or elevated concentrations of Se in groundwater, surface water, and associated plants and cultivated crops has emerged as a serious issue in many regions of the world, including the United States, northern and western Europe, the Middle East, and East Asia. Regardless of the nature of concern regarding Se, whether concentrations are deficient or elevated in water supplies and cultivated crops, there is a basic need for a thorough description of the movement and chemical processes of Se within a dynamic soil-aquifer system influenced by agricultural practices, and for the development of numerical simulation tools that allow these processes to be simulated in assessing baseline conditions and exploring remediation best-management practices (BMPs). While the individual processes controlling Se speciation, transformation, and movement within soil systems have been well documented, their synthesis into a comprehensive numerical model of Se fate and transport within an alluvial aquifer system influenced by agricultural practices has not yet been realized. This dissertation presents the development of a numerical model that can simulate the fate and transport of Se species in irrigation-influenced agricultural soil and groundwater systems at a regional scale. The model was developed by first, linking RT3D, modified to handle multi-species reactive transport in variably-saturated porous media, to MODFLOW, which uses the UZF1 (Unsaturated Zone Flow) package to simulate groundwater flow in the unsaturated zone; and second, developing an Se reaction module for RT3D that accounts for the cycling, chemical activity, and transport of Se species in regional-scale agricultural soil and groundwater systems. The module also accounts for the influence of other chemical species such as dissolved oxygen and nitrate (NO3). The resulting model, referred to as UZF-RT3DAG, is applied to a 50,600 ha regional site in the Lower Arkansas River Valley (LARV) in southeastern Colorado for the years 2006 through 2009. Using the calibrated model, multiple BMPs for remediation of Se contamination in the LARV are investigated. These strategies include decreasing annual loading of nitrogen fertilizer, decreasing species concentration in canal water, decreasing applied volume of irrigation water, and increasing chemical activity within riparian areas. Research results are presented through a series of published and submitted articles and modeling results that outline the progression of model development and model application. Results of the BMP scenario testing indicate that alternative land-management practices can have a significant impact in decreasing the concentration of dissolved Se in groundwater by up to 5-8% as well as mass loadings of Se to the Arkansas River by as much as 20-30%. Practices also have a significant impact on decreasing NO3 concentrations and loadings by up to 50% and 45%, respectively. As the alluvial aquifer in the LARV is similar to other Se-contaminated aquifer systems, the results of this research are pertinent to the assessment and remediation of Se contamination world-wide

2019 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

Schedule and abstract book for the Eleventh Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: November 16-17, 2019Location: UT Conference Center, KnoxvilleKeynote Speaker: Sadie Ryan, Medical Geography, Univ. of Florida; Director, Quantitative Disease Ecology & Conservation Lab (QDEC Lab)Featured Speaker: Christopher Strickland, Mathematics, Univ. of Tennessee, Knoxvill

Ecological Determinants of Highly Pathogenic Avian Influenza (H5N1) Outbreaks in Bangladesh

BACKGROUND: The agro-ecology and poultry husbandry of the south Asian and south-east Asian countries share common features, however, with noticeable differences. Hence, the ecological determinants associated with risk of highly pathogenic avian influenza (HPAI-H5N1) outbreaks are expected to differ between Bangladesh and e.g., Thailand and Vietnam. The primary aim of the current study was to establish ecological determinants associated with the risk of HPAI-H5N1 outbreaks at subdistrict level in Bangladesh. The secondary aim was to explore the performance of two different statistical modeling approaches for unmeasured spatially correlated variation. METHODOLOGY/PRINCIPAL FINDINGS: An ecological study at subdistrict level in Bangladesh was performed with 138 subdistricts with HPAI-H5N1 outbreaks during 2007-2008, and 326 subdistricts with no outbreaks. The association between ecological determinants and HPAI-H5N1 outbreaks was examined using a generalized linear mixed model. Spatial clustering of the ecological data was modeled using 1) an intrinsic conditional autoregressive (ICAR) model at subdistrict level considering their first order neighbors, and 2) a multilevel (ML) model with subdistricts nested within districts. Ecological determinants significantly associated with risk of HPAI-H5N1 outbreaks at subdistrict level were migratory birds' staging areas, river network, household density, literacy rate, poultry density, live bird markets, and highway network. Predictive risk maps were derived based on the resulting models. The resulting models indicate that the ML model absorbed some of the covariate effect of the ICAR model because of the neighbor structure implied in the two different models. CONCLUSIONS/SIGNIFICANCE: The study identified a new set of ecological determinants related to river networks, migratory birds' staging areas and literacy rate in addition to already known risk factors, and clarified that the generalized concept of free grazing duck and duck-rice cultivation interacted ecology are not significant determinants for Bangladesh. These findings will refine current understanding of the HPAI-H5N1 epidemiology in Bangladesh

Earth Observations and Integrative Models in Support of Food and Water Security

Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries

How to adequately represent biological processes in modeling multifunctionality of arable soils

Essential soil functions such as plant productivity, C storage, nutrient cycling and the storage and purification of water all depend on soil biological processes. Given this insight, it is remarkable that in modeling of these soil functions, the various biological actors usually do not play an explicit role. In this review and perspective paper we analyze the state of the art in modeling these soil functions and how biological processes could more adequately be accounted for. We do this for six different biologically driven processes clusters that are key for understanding soil functions, namely i) turnover of soil organic matter, ii) N cycling, iii) P dynamics, iv) biodegradation of contaminants v) plant disease control and vi) soil structure formation. A major conclusion is that the development of models to predict changes in soil functions at the scale of soil profiles (i.e. pedons) should be better rooted in the underlying biological processes that are known to a large extent. This is prerequisite to arrive at the predictive models that we urgently need under current conditions of Global Change

Master of Science

thesisTurbulent dispersion is one of the most important transport mechanisms in the life cycle of many fungal plant pathogens. Without turbulent dispersion, inoculum spread would be confined to adjacent leaves, limiting the severity of epidemics. Thus, understanding the mechanisms that influence and control dispersion from disease foci are of primary importance towards improving our ability to prevent and respond to disease outbreaks. In sparse canopy environments, the influence of canopy geometry (row spacing, canopy height, and plant density) on turbulent fluxes can be problematic for traditional dispersion modeling techniques that rely on assumptions of steady or horizontally homogeneous velocity fields. Here, the link between canopy geometry, turbulent fluxes and particle dispersion gradients in sparse agricultural canopies was explored using a Lagrangian particle dispersion model linked to velocity fields from large-eddy simulations. In particular, particle dispersion from line sources in plant canopies with geometry characteristic of grape vineyards were examined. Simulations were performed with varying row spacing and plant density to characterize particle dispersion within the canopy over a large range of length scales. It was of primary importance to examine how changing plant geometry could limit the spread of pathogens over large length scales, thus limiting the speed at which epidemics spread. Unresolved particle motion was modeled by solving a form of the Langevin equation and particle deposition onto vegetation is modeled using a stochastic technique. Results show that as overall canopy density decreases, bulk velocity in the canopy increases exponentially. This has a substantial impact on particle concentrations downstream of the source, as mean particle velocity influences concentrations. Furthermore, as canopy density decreases, particles tend to travel further before being deposited. However, as canopy density decreases, fewer particles tend to escape the canopy, which corresponds to a lower probability of long-distance transport. Thus, in less dense geometries, particles tend to spread further in near-source areas inside the canopy, but transport is more likely to be confined to smaller length scales. More dense canopies tend to limit transport near the source due to increased drag and deposition, but increased canopy escape increases the probability of transport over large length scales

Climatological, Hydrological, and Economic Analysis of Agriculture in Montana and the Western U.S.A.

Many studies have addressed the impact of climate on agriculture; however, fewer studies addressed how farmers adapt to climate change, to what extent implementation of adaptation strategies mitigates economic losses or alters the hydrologic system. Analyses of how historical climate affected not only farmer decision making, but also the economic and hydrological consequences of farmers’ adaptations to climate variations, and projections of the spatiotemporal climatic regimes at finer regional scales are critical for aiding in actionable climate change adaptations. This dissertation helps fill knowledge gaps on the impacts of climate change in rural regions of the agricultural western U.S.A. and provides a baseline to understand what crops farmers in the region will prioritize under future climates, and what will be the economic and hydrologic costs of adaptation. The first project modeled producer behavior under end-of-century climate projections. We applied a stochastic, integrated hydro-economic model that simulates land and water allocations to analyze Montana farmer adaptations to a range of projected climate conditions and the response of the hydrologic system to those adaptations. Results show a state-wide increase in agricultural water use leading to decreased summer streamflows. Land use for irrigated crops increased while rainfed crops decreased, implying state-level decrease in planted area. Both irrigated and rainfed crop production and farmer revenue decreased. The second project used historical data to quantify the climate water deficit (CWD) threshold where farmers’ perception swings towards repurposing crops instead of harvesting for grain. We analyzed the relationship between crop repurposing (the ratio of acres harvested for grain to the total planted acres) to seasonal CWD, and to isolate the climate signal from economic factors, our analysis accounted for the influence of crop prices on grain harvest. Results indicate that farmers are less likely to harvest barley and spring wheat for grain when the spring CWD is above average. For the majority of major crop growing regions, grain prices increased with lower levels of grain harvest. The third project used the most current climate change forecasts to predict future climate regimes of important rainfed winter wheat growing regions and compare current yields of climate analog regions. Using a suite of climate models, we evaluate which model(s) best simulated seasonal historical distributions of five climatic variables using the energy distance statistical metric, then use the best performing models to predict and map mid-century climate analog locations across the western U.S.A. Results show significant western and/or southern shifts in analog locations, regardless of season. These shifts to warmer, dryer regions do not conclusively imply decreased yields, however land use devoted to rainfed winter wheat in analog regions was dramatically lower

Investigating Daily Summertime Circulation and Precipitation Over West Africa with the WRF Model: A Regional Climate Model Adaptation Study

This dissertation a) evaluates the performance of the NCAR Weather and Research Forecasting (WRF) model as a West African Sahel regional-atmospheric model and b) investigates the utility of regional modeling to meeting user-needs. This work represents the beginning of an effort to adapt the model as a regional climate model (RCM) for the Sahel. Two independent studies test WRF sensitivity to 64 configurations of alternative parameterizations in a series of September simulations. In all, 104 12-day simulations during 11 consecutive years are examined. Simulated daily and mean circulation results are validated against NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) and NCEP Reanalysis-2. Modeled daily and total precipitation results are validated against NASA's Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) data. Particular attention is given to westward-propagating precipitation maxima associated with transient African Easterly Waves (AEWs). A wide range of 700-hPa vorticity and daily precipitation validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve circulation correlations against reanalysis of 0.40-0.60 and realistic amplitudes of spatiotemporal variability for the 2006 focus year, but they get time-longitude precipitation correlations (against GPCP) of between 0.35-0.42. A parallel-benchmark-simulation by the NASA Regional Model-3 (RM3) achieves higher correlations but less realistic spatiotemporal variability. The largest favorable impact on WRF vorticity and precipitation validation is achieved by selecting the Grell-Devenyi cumulus convection scheme, resulting in higher correlations against reanalysis and GPCP than simulations using the Kain-Fritch convection. Other parameterizations have less-obvious impact. A comparison of reanalysis circulation against two NASA-radiosonde stations confirms that both reanalyses represent observations well enough to validate WRF results. A rain-gauge comparison does the same for GPCP and TRMM