The dynamic response of the two-electrode conductivity cell

The dynamic response of the two-electrode conductivity cell in the dual-needle configuration of M.C. Gregg et al. (1981) was investigated both theoretically and experimentally. A theory is presented which answers fundamental questions regarding the effective cell volume and the mathematical form of the transfer function. These characteristics are shown to be functionally dependent on the spacing between the wires. Experimental determinations of cell response were performed with a thermal plume as the stimulus rather than with the step-response-in-salinity method of Gregg et al. The data are shown to confirm the theoretical form of the transfer function.<>     

Detection of individual normal modes of oscillation of the Sun in the period range from 2 hr to 10 min in solar diameter studies

Solar Physics - New observations of solar oscillations are reported. Power density spectra derived from these observations reveal narrow-band oscillations that are spatially global, have spatial...     

A Model of Conductive Heat Flow in Forest Edges and Fragmented Landscapes

Although the creation of edges during forest fragmentation can have important abiotic and biotic impacts, especially under conditions of future climate change, mechanistic models of edge effects have not been forthcoming. A simple numerical model of two-dimensional heat flow is developed and applied to a vertical forest/clearcut edge profile and to simulated fragmented landscapes. Height-specific thermal diffusivity and conductivity in the forest were assumed to vary in proportion to foliage densities measured in the central Amazon. In the edge profile, the clearcut that abutted the edge served as a heat source and its temperature was maintained at a constant value higher than in the initially cooler forest. In the fragmented landscapes, simulated treefall gaps were heat sources whose temperature varied with sun movements during the day. Gap frequency was varied so as to approximate the gap coverage observed in selectively logged forests. In one set of simulations, temperature in the openings was systematically varied; in another, thermal diffusivity of the forest was varied. Along the edge profile, high temperatures in the clearcut were rapidly transmitted into the upper canopy due to additive edge effects. Temperatures in the forest understory were also very sensitive to clearcut temperatures due to relatively sparse understory foliage. An overall increase in forest diffusivity led to markedly higher temperatures close to the edge and a more even temperature distribution among height strata. In fragmented landscapes, total gap coverage and additivity from neighboring gaps strongly influenced forest temperatures. At low conductivities, heat flowed only into the forest close to the gaps and hence forest temperature increased almost linearly with gap area. However, at high conductivities, heat flowed far into the forest and forest temperature varied as a function of gap density in the surrounding neighborhood. Because of these additive effects, slight increases in total gap area led to disproportionate changes in the thermal profile of the landscape. These results have important implications for the conservation of forest ecosystems.     

Delineating groundwater potential zones in a hard-rock terrain using geospatial tool

Abstract

Geospatial techniques have become one of the leading tools in the field of natural sciences for assessment, monitoring and management of natural resources, particularly in groundwater research. The paper discusses the demarcation and assessment of groundwater potential zones using geospatial techniques in the Deccan Volcanic Province of Maharashtra, India, using multi-criteria analyses. The study incorporates integration of thematic information (geomorphology, lithology, drainage density, slope and lineaments) in a GIS environment in order to identify groundwater potential zones. The methodology adopted can be used as a rapid assessment tool in groundwater exploration and is helpful in predictive groundwater resource management. Multi-criteria evaluation techniques were used to integrate all the thematic layers. Individual themes and their corresponding categories were assigned a knowledge base ranking from 1 to 5, depending on their importance for groundwater potential. Using the Raster calculator tool in Arc GIS software, all thematic maps were integrated to produce a composite groundwater potential map of the study area. The identified groundwater potential zones were classified into four classes, from excellent to poor. The generated groundwater potential zones were validated with field checks and borewell/dugwell yield data, and showed consistency with the observations.

Citation Singh, P., Thakur, J. K., and Kumar, S. (2013) Delineating groundwater potential zones in a hard-rock terrain using geospatial tools. Hydrological Sciences Journal, 58 (1), 1–11.     

Control strategies for the Clam Wave Energy Device

A promising wave energy device being currently investigated is the ‘clam’. The clam extracts energy by pumping air through a specially designed (Wells) turbine. Although operation of the Wells turbine does not require a rectified air flow, some additional control will be necessary to optimize the phase of the clam motion for good efficiencies. An examination of the equation of motion in the time domain suggests the possibility of phase control by mechanical, power take-off, or pneumatic latching. Latching can be shown to increase the efficiency of the device in the longer wavelengths of the wave spectrum, i.e. those of high incident wave power. Equivalently latching could be used to keep the device efficiency high while reducing its size, possibly resulting in cheaper power extraction.     

Uncertainty and sensitivity in a bank stability model: implications for estimating phosphorus loading

Eutrophication of aquatic ecosystems is one of the most pressing water quality concerns in the United States and around the world. Bank erosion has been largely overlooked as a source of nutrient loading, despite field studies demonstrating that this source can account for the majority of the total phosphorus load in a watershed. Substantial effort has been made to develop mechanistic models to predict bank erosion and instability in stream systems; however, these models do not account for inherent natural variability in input values. To quantify the impacts of this omission, uncertainty and sensitivity analyses were performed on the Bank Stability and Toe Erosion Model (BSTEM), a mechanistic model developed by the US Department of Agriculture – Agricultural Research Service (USDA‐ARS) that simulates both mass wasting and fluvial erosion of streambanks. Generally, bank height, soil cohesion, and plant species were found to be most influential in determining stability of clay (cohesive) banks. In addition to these three inputs, groundwater elevation, stream stage, and bank angle were also identified as important in sand (non‐cohesive) banks. Slope and bank height are the dominant variables in fluvial erosion modeling, while erodibility and critical shear stress had low sensitivity indices; however, these indices do not reflect the importance of critical shear stress in determining the timing of erosion events. These results identify important variables that should be the focus of data collection efforts while also indicating which less influential variables may be set to assumed values. In addition, a probabilistic Monte‐Carlo modeling approach was applied to data from a watershed‐scale sediment and phosphorus loading study on the Missisquoi River, Vermont to quantify uncertainty associated with these published results. While our estimates aligned well with previous deterministic modeling results, the uncertainty associated with these predictions suggests that they should be considered order of magnitude estimates only. Copyright © 2016 John Wiley & Sons, Ltd.     

Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata

Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone.