An Alternative to the Advection Dispersion Model for Interpreting Dye Tracing Studies in Fractured-Rock and Karst Aquifers

Due to the complexity of groundwater fl ow in fractured-rock and karst aquifers, solute transport models for these aquifers are typically stochastic models based on tracer transport studies. Water and tracers do not fl ow at one single advective velocity but experience a wide range of velocities, from rapid fl ow in conduits to near stagnant conditions in adjacent voids. This variance of velocities is referred to as dispersion and is traditionally described mathematically by the advection-dispersion equation (ADE). Analytical solutions to the ADE are available and are referred to as advection-dispersion models (ADM).The ADM is fitted to the tracer data by varying the parameters until a best-fit is achieved between the experimental residence time distribution (RTD) and the model RTD. The major shortcomings of this approach are due to the symmetry of the ADM and its associated prediction of finite concentrations at zero time and its inability to reflect the long upper tail typical in experimental RTD data. This paper presents an alternative conceptual approach to the ADM for modeling solute transport in fractured-rock and karst aquifers. In this approach the variance in fl ow velocities and fl ow path lengths are addressed directly by treating them as random, gamma distributed variables and deriving the RTD from a transformation of random variables based on the ratio of length to velocity and representing the RTD as a conditional probability distribution of time. The resulting four parameter (Gamma-RTD) model is relatively easily parameterized since the fl ow path length is tightly distributed about the known straight line distance between the injection point and the effluent. The model is demonstrated and contrasted to the ADM below by applying it to tracer data from a quantitative tracer study at Mammoth Cave National Park. The results indicate that the Gamma-RTD is superior to the ADM in modeling the shape as well as the area of the experimental RTD

ADOPT: a tool for predicting adoption of agricultural innovations

A wealth of evidence exists about the adoption of new practices and technologies in agriculture but there does not appear to have been any attempt to simplify this vast body of research knowledge into a model to make quantitative predictions across a broad range of contexts. This is despite increasing demand from research, development and extension agencies for estimates of likely extent of adoption and the likely timeframes for project impacts. This paper reports on the reasoning underpinning the development of ADOPT (Adoption and Diffusion Outcome Prediction Tool). The tool has been designed to: 1) predict an innovation‘s likely peak extent of adoption and likely time for reaching that peak; 2) encourage users to consider the influence of a structured set of factors affecting adoption; and 3) engage R, D & E managers and practitioners by making adoptability knowledge and considerations more transparent and understandable. The tool is structured around four aspects of adoption: 1) characteristics of the innovation, 2) characteristics of the population, 3) actual advantage of using the innovation, and 4) learning of the actual advantage of the innovation. The conceptual framework used for developing ADOPT is described.Adoption, Diffusion, Prediction, Research and Development/Tech Change/Emerging Technologies,

Conceptual Ecological Modelling of Shallow Sublittoral Mud Habitats to Inform Indicator Selection

The purpose of this study is to produce a series of Conceptual Ecological Models (CEMs) that represent the shallow sublittoral mud habitat in the UK. CEMs are diagrammatic representations of the influences and processes that occur within an ecosystem. The models can be used to identify critical aspects of an ecosystem that may be developed for further study, or serve as the basis for the selection of indicators for environmental monitoring purposes. The models produced by this project are ‘control diagrams’, representing the unimpacted state of the environment, free from anthropogenic pressures. It is intended that the models produced by this project will be used to guide indicator selection for the monitoring of this habitat in UK waters. CEMs will eventually be produced for a range of habitat types defined under the UK Marine Biodiversity Monitoring R&D Programme (UKMBMP), which, along with stressor models designed to show the interactions within impacted habitats, would form the basis of a robust method for indicator selection. This project builds on the work to develop CEMs for shallow sublittoral coarse sediment habitats (Alexander. 2014). The project scope included the Marine Strategy Framework Directive (MSFD) predominant habitat type ‘shallow sublittoral mud’. This definition includes those habitats that fall into the EUNIS Level 4 classifications A5.33 Infralittoral Sandy Mud, A5.34 Infralittoral Fine Mud, A5.35 Circalittoral Sandy Mud and A5.36 Circalittoral Fine Mud, along with their constituent Level 5 biotopes which are relevant to UK waters. A species list of characterising fauna to be included within the scope of the models was identified using an iterative process to refine the full list of species found within the relevant Level 5 biotopes. A literature review was conducted using a pragmatic and iterative approach to gather evidence regarding species traits and information that would be used to inform the models and the interactions that occur within the shallow sublittoral mud habitat. All information gathered during the literature review was entered into a data logging pro forma spreadsheet which accompanies this report. Wherever possible, attempts were made to collect information from UK-specific peer-reviewed studies, although other sources were used where necessary. All data gathered was subject to a detailed confidence assessment. Expert judgement by the project team was utilised to provide information for aspects of the models for which references could not be sourced within the project timeframe. A model hierarchy was developed based on groups of fauna with similar species traits which aligned with previous sensitivity studies of ecological groups. One general control model was produced that indicated the high level drivers, inputs, biological assemblages, ecosystem processes and outputs that occur in shallow sublittoral mud habitats. In addition to this, five detailed sub-models were produced, which each focussed on a particular functional group of fauna within the habitat: tube building fauna, burrowing fauna, suspension and deposit feeding infauna, mobile epifauna, scavengers and predators, and echinoderms and sessile epifauna. Each sub-model is accompanied by an associated confidence model that presents confidence in the links between each model component. The models are split into seven levels and take spatial and temporal scale into account through their design, as well as magnitude and direction of influence. The seven levels include regional to global drivers, water column processes, local inputs/processes at the seabed, habitat and biological assemblage, output processes, local ecosystem functions, and regional to global ecosystem functions. The models indicate that whereas the high level drivers which affect each functional group are largely similar, the output processes performed by the biota and the resulting ecosystem functions vary both in number and importance between groups. Confidence within the Conceptual Ecological Modelling of Shallow Sublittoral Mud Habitats models as a whole is generally high, reflecting the level of information gathered during the literature review. Important drivers that influence the ecosystem include factors such as wave exposure, depth, water currents, climate and propagule supply. These factors, in combination with seabed and water column processes, such as primary production, suspended sediments, water chemistry, temperature and recruitment define and influence the food sources consumed by the biological assemblages of the habitat, and the biological assemblages themselves. In addition, the habitat sediment type plays an important factor in shaping the biology of the habitat. Output processes performed by the biological assemblage are variable between functional faunal groups depending on the specific fauna present and the role they perform within the ecosystem. Important processes include secondary production, biodeposition, bioturbation, bioengineering and the supply of propagules; these in turn influence ecosystem functions at the local scale such as nutrient and biogeochemical cycling, supply of food resources, sediment stability, habitat provision and in some cases microbial activity. The export of biodiversity and organic matter, biodiversity enhancement and biotope stability are the resulting ecosystem functions that occur at the regional to global scale. Features within the models that are most useful for monitoring habitat status and change due to natural variation have been identified; as have those which may be useful for monitoring to identify anthropogenic causes of change within the ecosystem. Physical and chemical features of the ecosystem have mostly been identified as potential indicators to monitor natural variation, whilst biological factors have predominantly been identified as most likely to indicate change due to anthropogenic pressures

Identifying Opportunities for Improved Adoption of New Grazing Innovations

Those aiming for high levels of adoption of grazing-related innovation are often frustrated at low and slow uptake by farmers. This paper describes a new tool, ADOPT (Adoption and Diffusion Outcome Prediction Tool), that can be used to evaluate the potential adoptability of grazing innovations (Kuehne et al. 2012). ADOPT aims to: (1) predict an innovation’s likely peak level of adoption and likely time for reaching that peak; (2) encourage users to consider factors affecting adoption during project design; and (3) engage R, D & E managers and practitioners by making adoptability knowledge and considerations more transparent and understandable

Partners in Water Quality Monitoring at Mammoth Cave National Park, Kentucky

Water resources are essential to landscape development and maintenance of the extraordinary ecosystem at Mammoth Cave National Park, Kentucky. The National Park Service has implemented many policies and management practices in an effort to maintain and improve the water quality in the park. As part of their resources management, the Park evaluates current hydrologic conditions, as well as, anticipates and responds to emerging issues. With regards to that goal, Mammoth Cave National Park Service partnered with Tennessee State University, the Mammoth Cave International Center for Science and Learning, and the U.S. Geological Survey on a series of water-related projects from 2007-2013. The objective of this paper is to highlight some of the findings and lessons learned from the past 6 years. Many of the results presented in this paper have been presented at other conferences or published in other reports. Collaborative projects included storm-water runoff from parking lots and roads, evaluating storm-water filters, and transport of chemicals in the caves. These projects purposefully engaged students to provide professional experience and educational outreach opportunities. Over 50 student presentations related to these monitoring activities have been made at regional and national conferences in the past 6 years, resulting in numerous awards and publications. Major funding or in-kind services were provided by the partnering agencies and institutions. Additional funding for supplies and student support was provided by the National Science Foundation (Opportunity for Enhancing Diversity in Geoscience, 2007-8; Undergraduate Research and Mentoring, 2009-13), and, the Department of Energy (Massey Chair – NNSA, 2007-13). The following summaries are excerpts from previously published student papers (West et al., 2010; Diehl et al., 2012, Embry, et al., 2012, West et al., 2012)