Remediation of NAPL below the water table by steam-induced heat conduction

Previous experimental studies have shown that NAPL will be removed when it is contacted by steam. However, in full-scale operations, steam may not contact the NAPL directly and this is the situation addressed in this study. A two-dimensional intermediate scale sand box experiment was performed where an organic contaminant was emplaced below the water table at the interface between a coarse and a fine sand layer. Steam was injected above the water table and after an initial heating period the contaminant was recovered at the outlet. The experiment was successfully modeled using the numerical code T2VOC and the dominant removal mechanism was identified to be heat conduction induced boiling of the separate phase contaminant. Subsequent numerical modeling showed that this mechanism was insensitive to the porous medium properties and that it could be evaluated by considering only one-dimensional heat conduction.     

Modelling skewed data with many zeros: A simple approach combining ordinary and logistic regression

We discuss a method for analyzing data that are positively skewed and contain a substantial proportion of zeros. Such data commonly arise in ecological applications, when the focus is on the abundance of a species. The form of the distribution is then due to the patchy nature of the environment and/or the inherent heterogeneity of the species. The method can be used whenever we wish to model the data as a response variable in terms of one or more explanatory variables. The analysis consists of three stages. The first involves creating two sets of data from the original: one shows whether or not the species is present; the other indicates the logarithm of the abundance when it is present. These are referred to as the presence data and the log-abundance data, respectively. The second stage involves modelling the presence data using logistic regression, and separately modelling the log-abundance data using ordinary regression. Finally, the third stage involves combining the two models in order to estimate the expected abundance for a specific set of values of the explanatory variables. A common approach to analyzing this sort of data is to use a ln (y+c) transformation, where c is some constant (usually one). The method we use here avoids the need for an arbitrary choice of the value of c, and allows the modelling to be carried out in a natural and straightforward manner, using well-known regression techniques. The approach we put forward is not original, having been used in both conservation biology and fisheries. Our objectives in this paper are to (a) promote the application of this approach in a wide range of settings and (b) suggest that parametric bootstrapping be used to provide confidence limits for the estimate of expected abundance.     

Economics and Land-Use Change in Prioritizing Private Land Conservation

Abstract:  Incentive-based strategies such as conservation easements and short-term management agreements are popular tools for conserving biodiversity on private lands. Billions of dollars are spent by government and private conservation organizations to support land conservation. Although much of conservation biology focuses on reserve design, these methods are often ineffective at optimizing the protection of biological benefits for conservation programs. Our review of the recent literature on protected-area planning identifies some of the reasons why. We analyzed the site-selection process according to three important components: biological benefits, land costs, and likelihood of land-use change. We compared our benefit-loss-cost targeting approach with more conventional strategies that omit or inadequately address either land costs or likelihood of land-use change. Our proposed strategy aims to minimize the expected loss in biological benefit due to future land-use conversion while considering the full or partial costs of land acquisition. The implicit positive correlation between the likelihood of land-use conversion and cost of land protection means high-vulnerability sites with suitable land quality are typically more expensive than low-vulnerability sites with poor land quality. Therefore, land-use change and land costs need to be addressed jointly to improve spatial targeting strategies for land conservation. This approach can be extended effectively to land trusts and other institutions implementing conservation programs.     

Towards an ecological solution to the folivore paradox: patch depletion as an indicator of within-group scramble competition in red colobus monkeys (Piliocolobus tephrosceles)

A number of socioecological models assume that within-group food competition is either weak or absent among folivorous primates. This assumption is made because their food resources are presumed to be superabundant and evenly dispersed. However, recent evidence increasingly suggests that folivore group size is food-limited, that the primates prefer patchily distributed high-quality foods, and display some of the expected responses to within-group scramble competition. To investigate this apparent contradiction between theoretical models and recent empirical data, we examined the foraging behaviour of red colobus monkeys (Piliocolobus tephrosceles) in Kibale National Park, Uganda. We found that red colobus monkeys foraged in a manner that suggests they deplete patches of preferred foods: intake rate slowed significantly during patch occupancy while movement rate, an index of foraging effort, increased. Furthermore, patch occupancy was related to the size of the feeding group and the size of the patch. These results suggest that within-group scramble competition occurs, may limit folivore group size, and should be considered in models of folivore behavioural ecology.     

Exploring bicyclist injury severity in bicycle-vehicle crashes using latent class clustering analysis and partial proportional odds models

Introduction: Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019). Methods: This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns. Results: Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters. Conclusions: The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results.     

Modelling Replicated Weed Growth Data using Spatially-varying Growth Curves

Weed growth in agricultural fields constitutes a major deterrent to the growth of crops, often resulting in low productivity and huge losses for the farmers. Therefore, proper understanding of patterns in weed growth is vital to agricultural research. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data, which enable more sophisticated spatial analysis. Our current application concerns the development of statistical models for conducting spatial analysis of growth patterns in weeds. Our data comes from an experiment conducted in Waseca, Minnesota, that recorded growth of the weed Setariaspp. We capture the spatial variation in Setaria spp. growth using spatially-varying growth curves. An added challenge is that these designs are spatially replicated, with each plot being a lattice of sub-plots. Therefore, spatial variation may exist at different resolutions – a macro level variation between the plots and micro level variation between the sub-plots nested within each plot. We develop a Bayesian hierarchical framework for this setting. Flexible classes of models result which are fitted using simulation-based methods.     

Integrated Use of GLEAMS and GIS to Prevent Groundwater Pollution Caused by Agricultural Disposal of Animal Waste

/ In modern intensive animal farming the disposal of a large amount of waste is of great concern, as, if not properly performed, it can cause the pollution of water, mainly because of the high content of nitrate and phosphate. This paper presents the results of a study intended to assess the environmental sustainability of animal waste disposal on agricultural soils in the alluvial plain of the River Chiana (Tuscany, Italy), a particularly sensitive area because of the high vulnerability of the shallow aquifer and of the intensive agricultural and breeding activities. With this aim, a strategy has been employed, that consists of the integrated use of a management model and GISs. The consequences on groundwater of applying animal waste to different kind of soils and crop arrangements have been simulated by means of the management model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems, ver 2.01). As the huge amount of data required by such a sophisticated model does not allow applications at a scale larger than the field size, IDRISI and GRASS GIS packages have been used to divide the study area into land units, with homogeneous environmental characteristics, and then to generalize on these units the outputs of the model. The main conclusions can be synthesized as follows: The amount of animal waste produced in some of the investigated areas (i.e., municipal territory) is greater than that disposable on their own agricultural soil with no risks to the groundwater; consequently a cooperative approach among municipalities is necessary in order to plan waste disposal in a comprehensive and centralized way.KEY WORDS: Land use; Animal waste disposal; Groundwater protection; GIS, Management models     

SELECTED CHALLENGES IN RUNOFF GENERATION RESEARCH IN FORESTS FROM THE HILLSLOPE TO HEADWATER DRAINAGE BASIN SCALE1

ABSTRACT: When faced with practical forest land management issues such as the impacts of logging or forest conversion to other land uses, planners ideally require a comprehensive understanding of within drainage basin hydrological processes to determine the most vulnerable areas to increased storm runoff and erosion. Land managers in particular need to know the source areas and magnitude of inputs to the storm hydrograph, in terms of water quantity, sediment and solute transport; and the routing of such hydrographs from headwater to larger drainage basins. The latter includes an overall assessment at various scales of the impacts of forest disturbance and conversion on the water balance. This paper will focus on runoff generation in terms of identifying the various pathways and source areas. Such aspects will be linked with the need for a more comprehensive effort towards the field testing of so-called ‘physically based’ models of runoff generation. Some of the controversial issues arising from the difficulties in reconciling results from hydrochemical investigations with complementary hydrometric studies will be highlighted. Subsequently, attention will be given to topographic-wetness models, which have promising applications in forestland management. In addition, alternative simple models for application at the catchment scale will be assessed. The latter is in recognition that at smaller scales, heterogeneity both in time and space of soil hydraulic properties demand a greater number of parameters in modelling. Such considerations can even prove an obstacle in terms of the confident application of ‘physically based’ models.     

A SIMULATION OF RIVER-BASIN RESPONSE TO MESOSCALE METEOROLOGICAL FORCING: THE SUSQUEHANNA RIVER BASIN EXPERIMENT (SRBEX)1

ABSTRACT: A mesoscale meteorological model, a surface hydrology model, and a ground-water hydrology model are linked to simulate the hydrographic response of a large river basin to a single storm. Synoptic climatology is employed to choose a representative hydro-climatic event. The mesoscale meteorological model uses three nested domains to simulate relatively high-resolution precipitation over a sub-basin of the Susquehanna River Basin. The hydrology models simulate surface runoff and ground-water baseflow using both analyzed and simulated precipitation. The hydrologic abstractions are handled using both Curve Number and Green-Ampt routines. To support the linkage of the numerical models, special attention is given to data resampling and reprojection. The mesoscale meteorological model simulation captures the spatial and temporal structure of the storm event, while the hydrology models represent the timing of the event well. The Curve Number method generates a realistic hydrograph with both analyzed and simulated precipitation. In contrast, the hydrographic response generated by the Green-Ampt routine is inferior. Several interrelated factors contribute to these results, including: the nature of the precipitation event chosen for the experiment; the tendency of the mesoscale meteorological model to underpredict low intensity, widespread precipitation in this case; and the influence of the surface soil-texture characteristics on infiltration rates.     

CRITICAL DATA REQUIREMENTS FOR PREDICTION OF EROSION AND SEDIMENTATION IN MOUNTAIN DRAINAGE BASINS1

ABSTRACT: The potential for understanding and, where necessary, managing sedimentation in humid mountain drainage basins increases with awareness of the conditions that lead to shallow landsliding, debris flows, and catastrophic sedimentation in stream channels. Progress in understanding has involved: improved recognition of source areas and the potential for downstream effects of slope failure; improved understanding of hydrological conditions required for failure; and a general theory of slope stability in shallow colluvium, including the role of plants, fires, timber harvest, and other disturbances. The theory acknowledges spatial variability in topographic and geotechnical terrain characteristics, the stochastic nature of climatic triggering events such as forest fires and rainstorms, and the integrating nature of channel networks in modulating the cumulative effects of transient processes within a basin. Anthropogenic fire regimes, road effects, and timber harvest can readily be included. Continued application and modification of the theory over an expanded geographical range require improvements in field data and their systematic storage in spatial databases. Improvements in digital topographic data for mountain basins, systematic network-wide surveys of channel conditions, and new technology for rapid documentation of soil depths in landslide source areas would enhance the prediction of mass failure, its consequences for channel habitat, and the basin-wide or regional distribution of hillslope and channel conditions. Computations of the probabilities of transient effects throughout basins could then form the basis of ecological risk analyses. Large-scale spatial data sets of a few critical variables are required before this next level of understanding can be developed and applied to sedimentation impacts on ecosystems and other resources.