A modified DRASTIC model for Siting Confined Animal Feeding Operations in Williams County,Ohio, USA

Three of DRASTIC’s parameters (Depth to Water, Soil Media, and Topography) were modified and another parameter was added (land use/land cover) to the model to determine the potential impact on groundwater from Confined Animal Feeding Operations (CAFO) manure lagoon settings and manure application as fertilizer. Williams County is a mostly agricultural county located in northwest Ohio, USA. It currently has three CAFOs, all dairy, with the possibility of the construction of a multi-million chicken egg CAFO in the near future. A Geographic Information System (GIS) was utilized to modify the Ohio Department of Natural Resources (ODNR) DRASTIC map for the county to fully assess the county-wide pollution potential of CAFOs. The CAFO DRASTIC map indicates that almost half of Williams County has elevated groundwater pollution potential. The rest of the county, primarily the southeast corner, has lower CAFO groundwater pollution potential. Future CAFO development within the county should focus on the southeastern portion of the county where the groundwater table is deeper, and the aquifer is composed of shale substrate with low hydraulic conductivity. The CAFO DRASTIC results are intended to be used as a screening tool and are not to replace site-specific hydrogeologic investigations.     

Deciphering potential groundwater zone in hard rock through the application of GIS

Remote Sensing and Geographic Information System has become one of the leading tools in the field of hydrogeological science, which helps in assessing, monitoring and conserving groundwater resources. It allows manipulation and analysis of individual layer of spatial data. It is used for analysing and modelling the interrelationship between the layers. This paper mainly deals with the integrated approach of Remote Sensing and geographical information system (GIS) to delineate groundwater potential zones in hard rock terrain. The remotely sensed data at the scale of 1:50,000 and topographical information from available maps, have been used for the preparation of ground water prospective map by integrating geology, geomorphology, slope, drainage-density and lineaments map of the study area. Further, the data on yield of aquifer, as observed from existing bore wells in the area, has been used to validate the groundwater potential map. The final result depicts the favourable prospective zones in the study area and can be helpful in better planning and management of groundwater resources especially in hard rock terrains.     

Assessing soil erosion in Mediterranean karst landscapes of Lebanon using remote sensing and GIS

Maps showing the potential for soil erosion at 1:100,000 scale are produced in a study area within Lebanon that can be used for evaluating erosion of Mediterranean karstic terrain with two different sets of impact factors built into an erosion model. The first set of factors is: soil erodibility, morphology, land cover/use and rainfall erosivity. The second is obtained by the first adding a fifth factor, rock infiltration. High infiltration can reflect high recharge, therefore decreasing the potential of surface runoff and hence the quantity of transported materials. Infiltration is derived as a function of lithology, lineament density, karstification and drainage density, all of which can be easily extracted from satellite imagery. The influence of these factors is assessed by a weight/rate approach sharing similarities between quantitative and qualitative methods and depending on pair-wise comparison matrix.The main outcome was the production of factorial maps and erosion susceptibility maps (scale 1:100,000). Spatial and attribute comparison of erosion maps indicates that the model that includes a measure of rock infiltration better represents erosion potential. Field investigation of rills and gullies shows 87.5% precision of the model with rock infiltration. This is 17.5% greater than the precision of the model without rock infiltration. These results indicate the necessity and importance of integrating information on infiltration of rock outcrops to assess soil erosion in Mediterranean karst landscapes.     

Greening as strategic development in industrial change - Why companies participate in eco-networks

Networking between companies and other regional key actors has grown into a widespread instrument for economic development since the 1990s. Participatory networks have formed the concept for many activities of Local Agenda 21. Simultaneously, economic geography has focused on network theories to explain innovation in and the economic success of regions.This article focuses on the reasons why individual actors participate in environmentally oriented information networks. The questions approached are: Does the concept of learning within networks influence a firm’s development? How do the participants interact with each other? What effects do the networks have on their environment (arenas)?The changing arenas of a firm are analysed with regard to environmental concerns. These changes need to be anticipated for reliable strategies. To gain information and knowledge about current behaviour and activities, the functions of networks as learning platforms are discussed. Drawing upon arguments from different network theories, the motivations of participating in a network are threefold: actor and firm oriented, network internal (inter-firm), and network external. From these, a generic target cube of network motivations and actions is set up.The concept is reviewed by examining 12 ‘regional environmental information-oriented corporation networks’ (RUN) in the Greater Munich Area. The empirical material reveals that this type of network does not provide a guarantee for significant improvements in a firm’s environmental behaviour. However, participants learn about best practices while pursuing explicit or implicit aims as to influence their arenas.     

Mapping ambivalence: Exploring the geographies of community change and rails-to-trails development using photo-based Q method and PPGIS

As the literature on trail development suggests, recreational trail projects can generate conflicts and controversies, particularly when built on abandoned rail corridors through developed areas. These conflicts are often understood as “not in my back yard” (NIMBY) reactions, suggesting a spatial proximity to conflict which increases as one draws closer to the proposed trail. This research seeks to understand local residents’ perceptions and reactions to recreational trail development in the City of Delaware (Ohio, USA). It addresses two spatially infused questions: Does the potential for conflict related to trail development increase as people live closer to a potential trail (the NIMBY factor)? Can important qualitative factors about favorable and unfavorable land uses including potential recreational trail sites be defined using a participatory methodology and then represented in GIS? The study used a mixed-method approach to collect and analyze qualitative data from a group of local residents. Each participant was interviewed and asked to sort 19 pictures related to trail development. After each of the sorts, participants were asked to explain why they ranked the pictures the way they did. Results of the picture sorts were then analyzed using Q method and mapped with GIS. The results show that spatial proximity matters in the context of trail development and potential NIMBY reactions to trails. Significant differences were found in the picture sorts that reveal the importance of proximity and location, although in a manner contrary to the assumptions in the writings on rails-to-trails. Through combining qualitative methods, Q analysis and PPGIS analysis, the research shows that qualitative place-based studies are capable of generating insights about the complexities of situated geographic change such as recreational trail development.     

Contribution of the SISCam Web-based GIS to the seismotectonic study of Campania (Southern Apennines): an example of application to the Sannio-area

In this article the implementation and potential of the Seismotectonic Information System of the Campania Region (SISCam) are described, in particular an application of this Web-based GIS system to the seismotectonic analysis of the Sannio area (Southern Apennines) is performed. WEB-GIS technologies greatly contribute to both the environmental monitoring and the disaster management of areas affected by high natural risks. Specifically the SISCam system has been developed with the aim of providing easy access and fast diffusion, through Internet technology, of the most significant geological, geophysical, and territorial data relative to the Campania Region. The Sannio area has been selected as our application example because it is among the most active seismic regions in Italy. This portion of the Southern Apennines which was hit by the June 5, 1688 strong earthquake (M _W = 6.7, CPTI 1999) and by some low- and moderate-energy seismic sequences (1990–1992, 1997), is characterized by a complex inherited tectonic setting and low-tectonic deformation rates that hide the seismogenic sources position. Since this case study turned out to be complicated, the use of the SIScam WEB-GIS has become indispensable because it allowed us to visualize, integrate and analyze all the data available, in order to obtain an accurate and direct picture of the seismotectonic setting of the area. Moreover, a different approach of data analysis was necessary, due to the lack of up-to-date neotectonic and structural data; therefore, the operation of this GIS system enabled us to process and generate some original informative layers, through image analysis, such as new structural lineaments represented on a map of the potential active faults of the area, which has been the final result of our application, as a contribution to new knowledge about the local seismic risk parameters.     

Numerical estimation of REV and permeability tensor for fractured rock masses by composite element method

The Monte Carlo method is used to generate parent stochastic discrete fracture network, from which a series of fractured rock samples of different sizes and orientations are extracted. The fracture network combined with a regular grid forms composite element mesh of the fractured rock sample, in which each composite element is composed of sub‐elements incised by fracture segments. The composite element method (CEM) for the seepage is implemented to obtain the nodal hydraulic potential as well as the seepage flow rates through the fractured rock samples. The application of CEM enables a large quantity of stochastic tests for the fractured rock samples because the pre‐process is facilitated greatly. By changing the sizes and orientations of the samples, the analysis of the seepage characteristics is realized to evaluate the variation of the permeability components, the existence of the permeability tensor and the representative element volume. The feasibility and effectiveness are illustrated in a numerical example. Copyright © 2008 John Wiley & Sons, Ltd.     

Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey)

In the international literature, although considerable amount of publications on the landslide susceptibility mapping exist, geomorphology as a conditioning factor is still used in limited number of studies. Considering this factor, the purpose of this article paper is to implement the geomorphologic parameters derived by reconstructed topography in landslide susceptibility mapping. According to the method employed in this study, terrain is generalized by the contours passed through the convex slopes of the valleys that were formed by fluvial erosion. Therefore, slope conditions before landsliding can be obtained. The reconstructed morphometric and geomorphologic units are taken into account as a conditioning parameter when assessing landslide susceptibility. Two different data, one of which is obtained from the reconstructed DEM, have been employed to produce two landslide susceptibility maps. The binary logistic regression is used to develop landslide susceptibility maps for the Melen Gorge in the Northwestern part of Turkey. Due to the high correct classification percentages and spatial effectiveness of the maps, the landslide susceptibility map comprised the reconstructed morphometric parameters exhibits a better performance than the other. Five different datasets are selected randomly to apply proper sampling strategy for training. As a consequence of the analyses, the most proper outcomes are obtained from the dataset of the reconstructed topographical parameters and geomorphologic units, and lithological variables that are implemented together. Correct classification percentage and root mean square error (RMSE) values of the validation dataset are calculated as 86.28% and 0.35, respectively. Prediction capacity of the different datasets reveal that the landslide susceptibility map obtained from the reconstructed parameters has a higher prediction capacity than the other. Moreover, the landslide susceptibility map obtained from the reconstructed parameters produces logical results.