RIPARIAN VEGETATION VARIABILITY ALONG PERENNIAL STREAMS IN CENTRAL ARIZONA

The spatial distribution of riparian vegetation is closely allied to abiotic processes along streams and rivers. There are dynamic relations between physical process, fluvial forms, and biotic structures. Explanation of these associations is critical to scientific understanding and practical management of riverine environments. Therefore, this study determines what geophysical parameters lead to the spatial patterns found in species of warm interior and cold montane riparian deciduous forests in central Arizona. Five riparian vegetation populations were examined along five perennial streams in the transition zone of central Arizona. The populations included Populus angustifolia (narrowleaf cottonwood), two commonly associated species of willow Salix lasiandra (western black willow) and Salix lasiolepis (arroyo willow), Alnus oblongifolia (Arizona alder), and Platanus wrightii (Arizona sycamore). Canonical correspondence analysis (CCA) with a forward selection was used to assess quantitatively the role of stream power in riparian vegetation patterns. Results indicated 40% of the spatial variability in the riparian populations was explained by channel morphology and several other variables related to changing channel geometry. Although floods are linked to the formation of geomorphic surfaces and the regeneration of riparian vegetation, changing fluvial landforms and channel patterns were closely related to the riparian species patterns in central Arizona. [Key words: Mogollon Rim, channel morphology, multivariate statistics.]     

A comparative analysis of DEM‐based models to estimate the solar radiation in mountainous terrain

Daily solar radiation estimates of four up‐to‐date solar radiation models (Solar Analyst, r.sun, SRAD and Solei‐32), based on a digital elevation model (DEM), have been evaluated and compared in a Mediterranean environment characterized by a complex topography. The models' estimates were evaluated against 40 days of radiometric data collected in 14 stations. Analyzed sky conditions ranged from completely overcast conditions to clear skies. Additionally, the role of the spatial resolution of the DEM has been evaluated through the use of two different resolutions: 20 and 100 m. Results showed that, under clear‐sky conditions, the daily solar radiation variability in the study area may be reasonably estimated with mean bias errors under 10% and root mean square error values of around 15%. On the other hand, results proved that the reliability of the estimates substantially decreases under overcast conditions for some of the solar radiation models. Regarding the role of the DEM spatial resolution, results suggested that the reliability of the estimates for complex topography areas under clear‐sky conditions improves using a higher spatial resolution.     

Constructing personalized transportation networks in multi-state supernetworks: a heuristic approach

An integrated view encompassing the networks for public and private transport modes as well as the activity programs of travelers is essential for accessibility analysis. In earlier research, the multi-state supernetwork has been put forward by the authors as a suitable technique to model the system in such an integrated fashion. An essential part of a supernetwork involving multi-modal and multi-activity is the personalized transportation network, which is an under-researched topic in the academic community. This article attempts to develop a heuristic approach to construct personalized transportation networks for an individual's activity program. In this approach, the personalized network consists of two types of network extractions from the original transportation system: public transport network and private vehicle network. Three examples are presented to illustrate that the public transport network and private vehicle network can represent an individual's attributes and be applied in large-scale applications for analyzing the synchronization of land-use and transportation systems.     

Statistics-based outlier detection for wireless sensor networks

Wireless sensor network (WSN) applications require efficient, accurate and timely data analysis in order to facilitate (near) real-time critical decision-making and situation awareness. Accurate analysis and decision-making relies on the quality of WSN data as well as on the additional information and context. Raw observations collected from sensor nodes, however, may have low data quality and reliability due to limited WSN resources and harsh deployment environments. This article addresses the quality of WSN data focusing on outlier detection. These are defined as observations that do not conform to the expected behaviour of the data. The developed methodology is based on time-series analysis and geostatistics. Experiments with a real data set from the Swiss Alps showed that the developed methodology accurately detected outliers in WSN data taking advantage of their spatial and temporal correlations. It is concluded that the incorporation of tools for outlier detection in WSNs can be based on current statistical methodology. This provides a usable and important tool in a novel scientific field.     

Stochastic model-based methods for handling uncertainty in areal interpolation

Handling of uncertainty in the estimation of values from source areas to target areas poses a challenge in areal interpolation research. Stochastic model-based methods offer a basis for incorporating such uncertainty, but to date they have not been widely adopted by the GIS community. In this article, we propose one use of such methods based in the problem of interpolating count data from a source set of zones (parishes) to a more widely used target zone geography (postcode sectors). The model developed also uses ancillary statistical count data for a third set of areas nested within both source and target zones. The interpolation procedure was implemented within a Bayesian statistical framework using Markov chain Monte Carlo methods, enabling us to take account of all sources of uncertainty included in the model. Distributions of estimated values at the target zone level are presented using both summary statistics and as individual realisations selected to illustrate the degree of uncertainty in the interpolation results. We aim to describe the use of such stochastic approaches in an accessible way and to highlight the need for quantifying estimation uncertainty arising in areal interpolation, especially given the implications arising when interpolated values are used in subsequent analyses of relationships.     

Soil Iodine Determination in Deccan Syneclise,India: Implications for Near Surface Geochemical Hydrocarbon Prospecting

The association of iodine with organic matter in sedimentary basins is well documented. High iodine concentration in soils overlying oil and gas fields and areas with hydrocarbon microseepage has been observed and used as a geochemical exploratory tool for hydrocarbons in a few studies. In this study, we measure iodine concentration in soil samples collected from parts of Deccan Syneclise in the west central India to investigate its potential application as a geochemical indicator for hydrocarbons. The Deccan Syneclise consists of rifted depositional sites with Gondwana–Mesozoic sediments up to 3.5 km concealed under the Deccan Traps and is considered prospective for hydrocarbons. The concentration of iodine in soil samples is determined using ICP-MS and the values range between 1.1 and 19.3 ppm. High iodine values are characteristic of the northern part of the sampled region. The total organic carbon (TOC) content of the soil samples range between 0.1 and 1.3%. The TOC correlates poorly with the soil iodine (r ² < 1), indicating a lack of association of iodine with the surficial organic matter and the possibility of interaction between the seeping hydrocarbons and soil iodine. Further, the distribution pattern of iodine compares well with two surface geochemical indicators: the adsorbed light gaseous hydrocarbons (methane through butane) and the propane-oxidizing bacterial populations in the soil. The integration of geochemical observations show the occurrence of elevated values in the northern part of the study area, which is also coincident with the presence of exposed dyke swarms that probably serve as conduits for hydrocarbon microseepage. The corroboration of iodine with existing geological, geophysical, and geochemical data suggests its efficacy as one of the potential tool in surface geochemical exploration of hydrocarbons. Our study supports Deccan Syneclise to be promising in terms of its hydrocarbon prospects.     

Surface energy and water vapor fluxes observed in a desert plantation in central Iran

Summertime observations of surface radiation budget, energy balance and atmospheric surface layer meteorology were made on an arid valley floor planted with Haloxylon aphyllum to combat desertification in central Iran. The surface microclimate is characterized and compared with other arid regions and the role of ‘desert greening’ on surface fluxes is considered. A high surface albedo (0.265) and large longwave radiation loss produced relatively low net radiation. Energy partitioning was dominated by sensible and ground heat fluxes with opposing diurnal asymmetry governed by strong diurnal variability in eddy diffusivity. The Bowen ratio was 2.53, which fell inside the range of other vegetated arid surfaces. Surface temperature gradients were strong both in the atmospheric surface layer and in the substrate, with consistent lapse conditions by day and inversions at night. The wind regime included a moderate daytime regional wind which displayed Coriolis turning and weaker nocturnal slope flows. Actual evapotranspiration (1 mm dy⁻¹) was only a small fraction of potential evapotranspiration. The diurnal pattern of AET indicates strong stomatal control. The desert greening effect of Haloxylon plantations provided atmospheric water and reduced sensible heat flux by up to 40%.     

A caveat regarding diatom-inferred nitrogen concentrations in oligotrophic lakes

Atmospheric deposition of reactive nitrogen (Nr) has enriched oligotrophic lakes with nitrogen (N) in many regions of the world and elicited dramatic changes in diatom community structure. The lakewater concentrations of nitrate that cause these community changes remain unclear, raising interest in the development of diatom-based transfer functions to infer nitrate. We developed a diatom calibration set using surface sediment samples from 46 high-elevation lakes across the Rocky Mountains of the western US, a region spanning an N deposition gradient from very low to moderate levels (<1 to 3.2 kg Nr ha⁻¹ year⁻¹ in wet deposition). Out of the fourteen measured environmental variables for these 46 lakes, ordination analysis identified that nitrate, specific conductance, total phosphorus, and hypolimnetic water temperature were related to diatom distributions. A transfer function was developed for nitrate and applied to a sedimentary diatom profile from Heart Lake in the central Rockies. The model coefficient of determination (bootstrapping validation) of 0.61 suggested potential for diatom-inferred reconstructions of lakewater nitrate concentrations over time, but a comparison of observed versus diatom-inferred nitrate values revealed the poor performance of this model at low nitrate concentrations. Resource physiology experiments revealed that nitrogen requirements of two key taxa were opposite to nitrate optima defined in the transfer function. Our data set reveals two underlying ecological constraints that impede the development of nitrate transfer functions in oligotrophic lakes: (1) even in lakes with nitrate concentrations below quantification (<1 μg L⁻¹), diatom assemblages were already dominated by species indicative of moderate N enrichment; (2) N-limited oligotrophic lakes switch to P limitation after receiving only modest inputs of reactive N, shifting the controls on diatom species changes along the length of the nitrate gradient. These constraints suggest that quantitative inferences of nitrate from diatom assemblages will likely require experimental approaches.     

Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression

This work has evaluated the probability of earthquake-triggered landslide occurrence in the whole of El Salvador, with a Geographic Information System (GIS) and a logistic regression model. Slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness are the predictor variables used to determine the dependent variable of occurrence or non-occurrence of landslides within an individual grid cell. The results illustrate the importance of terrain roughness and soil type as key factors within the model — using only these two variables the analysis returned a significance level of 89.4%. The results obtained from the model within the GIS were then used to produce a map of relative landslide susceptibility.