Modelling climate change effects on the spatial distribution of mountain permafrost at three sites in northwest Canada

Spatial models of present-day mountain permafrost probability were perturbed to examine potential climate change impacts. Mean annual air temperature (MAAT) changes were simulated by adjusting elevation in the models, and cloud cover changes were examined by altering the partitioning of direct beam and diffuse radiation within the calculation for potential incoming solar radiation (PISR). The effects of changes in MAAT on equilibrium permafrost distribution proved to be more important than those due to cloud cover. Under a ?2 K scenario (approximating Little Ice Age conditions), permafrost expanded into an additional 22?C43% of the study areas as zonal boundaries descended by 155?C290 m K^???1. Under warming scenarios, permafrost probabilities progressively declined and zonal boundaries rose in elevation. A MAAT change of +5 K, caused two of the areas to become essentially permafrost-free. The absolute values of these predictions were affected up to ±10% when lapse rates were altered by ±1.5 K km^???1 but patterns and trends were maintained. A higher proportion of diffuse radiation (greater cloud cover) produced increases in permafrost extent of only 2?C4% while decreases in the diffuse radiation fraction had an equal but opposite effect. Notwithstanding the small change in overall extent, permafrost probabilities on steep south-facing slopes were significantly impacted by the altered partitioning. Combined temperature and PISR partitioning scenarios produced essentially additive results, but the impact of changes in the latter declined as MAAT increased. The modelling illustrated that mountain permafrost in the discontinuous zone is sensitive spatially to long-term climate change and identified those areas where changes may already be underway following recent atmospheric warming.     

Mapping the vulnerability of crop production to drought in Ghana using rainfall, yield and socioeconomic data

This study evaluates new multi-scale, multi-indicator methods for assessing the vulnerability of crop production to drought at a national and regional scale. It does this by identifying differences across and within ten regions of Ghana, a country that faces many climate and crop production challenges typical of sub-Saharan Africa. In particular, we illustrate how a quantitative national and regional study is a critical first step in assessing differences in the drought sensitivity of food production systems and show how such an assessment enables the formulation of more targeted district and community level research that can explore the drivers of vulnerability and change on a local-scale. Finally, we propose methodological steps that can improve drought sensitivity and vulnerability assessments in dynamic dryland farming systems where there are multiple drivers of change and thresholds of risk that vary in both space and time. Results show that the vulnerability of crop production to drought in Ghana has discernible geographical and socioeconomic patterns, with the Northern, Upper West and Upper East regions being most vulnerable. Partly, this is because these regions have the lowest adaptive capacity due to low socioeconomic development and have economies based on rain-fed agriculture. Within these regions we find considerable differences between districts that can be explained only partly by socioeconomic variables with further community and household-scale research required to explain the causes of differences in vulnerability status. Our results highlight that national and regional scale multi-indicator vulnerability assessments are a vital (and often ignored) first step in assessing vulnerability across a large area. These inputs can guide both local-level research and also demonstrate the need for region-specific policies to reduce vulnerability and to enhance drought preparedness within dryland farming communities.     

Importance of inherited rift margin structures in the early North Alpine Foreland Basin, Switzerland

The earliest evolution of the North Alpine Foreland Basin in Switzerland was characterized by deposition in small, structurally partitioned sub-basins during the Late Cretaceous and Early Tertiary, rather than in a single, large foredeep. These sub-basins, which were probably located between old rift margin fault-blocks reactivated during Alpine compression, were incorporated into the thrust wedge during thin-skinned deformation. In eastern Switzerland, the most external sub-basins with respect to the orogenic wedge (North Helvetic Flysch and Blattengrat units) have at their base an unconformity attributed to flexural forebulge erosion. More internal sub-basins (Sardona and Prättigau units) contain a conformable succession from the underlying passive margin stage and are dominated by deep-water sedimentation. In western Switzerland, both external sub-basins, now found in the Helvetic Diablerets and Wildhorn nappes, and deep-water internal sub-basins (Höchst-Meilleret Flysch, Neisen Flysch, Tarentaise Flysch) preserve a well-developed basal unconformity. Comparison of the eastern and western Swiss transects shows important intrabasinal lateral variations to be present. The western Swiss area was a topographic high for much of the Late Cretaceous and Early Tertiary; this is demonstrated by the increased chronostratigraphic gap at the karstified basal unconformity surface in western Switzerland. The strata onlapping this unconformity young to the west, suggesting that drowning of the emergent area was delayed compared with the east. In addition, reactivation and uplift of the rift margin structures occurred earlier in western Switzerland compared with eastern Switzerland. There is therefore strong evidence for lateral topographic gradients in the early foreland basin caused by differential amounts of tectonic reactivation of rift margin structures. In the early foreland basin-fill, these lateral variations are as important in determining depositional patterns as strike-normal changes across the basin.     

Split-Match-Aggregate (SMA) algorithm: integrating sidewalk data with transportation network data in GIS

Sidewalk geodata are essential to understand walking behavior. However, such geodata are scarce, only available at the local jurisdiction and not at the regional level. If they exist, the data are stored in geometric representational formats without network characteristics such as sidewalk connectivity and completeness. This article presents the Split-Match-Aggregate (SMA) algorithm, which automatically conflates sidewalk information from secondary geometric sidewalk data to existing street network data. The algorithm uses three parameters to determine geometric relationships between sidewalk and street segments: the distance between streets and sidewalk segments; the angle between sidewalk and street segments; and the difference between the lengths of matched sidewalk and street segments. The SMA algorithm was applied in urban King County, WA, to 13 jurisdictions’ secondary sidewalk geodata. Parameter values were determined based on agreement rates between results obtained from 72 pre-specified parameter combinations and those of a trained geographic information systems (GIS) analyst using a randomly selected 5% of the 79,928 street segments as a parameter-development sample. The algorithm performed best when the distances between sidewalk and street segments were 12 m or less, their angles were 25° or less, and the tolerance was set to 18 m, showing an excellent agreement rate of 96.5%. The SMA algorithm was applied to classify sidewalks in the entire study area and it successfully updated sidewalk coverage information on the existing regional-level street network data. The algorithm can be applied for conflating attributes between associated, but geometrically misaligned line data sets in GIS.     

Application of a distributed blowing snow model to the Arctic

Transportation, sublimation and accumulation of snow dominate snow cover development in the Arctic and produce episodic high evaporative fluxes. Unfortunately, blowing snow processes are not presently incorporated in any hydrological or meteorological models. To demonstrate the application of simple algorithms that represent blowing snow processes, monthly snow accumulation, relocation and sublimation fluxes were calculated and applied in a spatially distributed manner to a 68-km² catchment in the low Arctic of north-western Canada. The model uses a Landsat-derived vegetation classification and a digital elevation model to segregate the basin into snow ‘sources’ and ‘sinks’. The model then relocates snow from sources to sinks and calculates in-transit sublimation loss. The resulting annual snow accumulation in specific landscape types was compared with the result of intensive surveys of snow depth and density. On an annual basis, 28% of annual snowfall sublimated from tundra surfaces whilst 18% was transported to sink areas. Annual blowing snow transport to sink areas amounted to an additional 16% of annual snowfall to shrub–tundra and an additional 182% to drifts. For the catchment, 19·5% of annual snowfall sublimated from blowing snow, 5·8% was transported into the catchment and 86·5% accumulated on the ground. The model overestimated snow accumulation in the catchment by 6%. The application demonstrates that winter precipitation alone is insufficient to calculate snow accumulation and that blowing snow processes and landscape patterns govern the spatial distribution and total accumulation of snow water equivalent over the winter. These processes can be modelled by relatively simple algorithms, and, when distributed by landscape type over the catchment, produce reasonable estimates of snow accumulation and loss in wind-swept regions. © 1997 John Wiley & Sons, Ltd.     

Modelling tide–surge interaction effects using finite volume and finite element models of the Irish Sea

An unstructured mesh model of the west coast of Britain, covering the same domain and using topography and open boundary forcing that are identical to a previous validated uniform grid finite difference model of the region, is used to compare the performance of a finite volume (FV) and a finite element (FE) model of the area in determining tide–surge interaction in the region. Initial calculations show that although qualitatively both models give comparable tidal solutions in the region, comparison with observations shows that the FV model tends to under-estimate tidal amplitudes and hence background tidal friction in the eastern Irish Sea. Storm surge elevations in the eastern Irish Sea due to westerly, northerly and southerly uniform wind stresses computed with the FV model tend to be slightly higher than those computed with the FE model, due to differences in background tidal friction. However, both models showed comparable non-linear tide–surge interaction effects for all wind directions, suggesting that they can reproduce the extensive tide–surge interaction processes that occur in the eastern Irish Sea. Following on from this model comparison study, the physical processes contributing to surge generation and tide–surge interaction in the region are examined. Calculations are performed with uniform wind stresses from a range of directions, and the balance of various terms in the hydrodynamic equations is examined. A detailed comparison of the spatial variability of time series of non-linear bottom friction and non-linear momentum advection terms at six adjacent nodes at two locations in water depths of 20 and 6 m showed some spatial variability from one node to another. This suggests that even in the near coastal region, where water depths are of the order of 6 m and the mesh is fine (of order 0.5 km), there is significant spatial variability in the non-linear terms. In addition, distributions of maximum bed stress due to tides and wind forcing in nearshore regions show appreciable spatial variability. This suggests that intensive measurement campaigns and very high-resolution mesh models are required to validate and reproduce the non-linear processes that occur in these regions and to predict extreme bed stresses that can give rise to sediment movement. High-resolution meshes will also be required in pollution transport problems.     

Land cover mapping at Alkali Flat and Lake Lucero,White Sands,New Mexico,USA using multi-temporal and multi-spectral remote sensing data

The goal of this research is to map land cover patterns and to detect changes that occurred at Alkali Flat and Lake Lucero, White Sands using multispectral Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and hyperspectral Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data. The other objectives of this study were: (1) to evaluate the information dimensionality limits of Landsat 7 ETM+, ASTER, ALI, Hyperion, and AVIRIS data with respect to signal-to-noise and spectral resolution, (2) to determine the spatial distribution and fractional abundances of land cover endmembers, and (3) to check ground correspondence with satellite data. A better understanding of the spatial and spectral resolution of these sensors, optimum spectral bands and their information contents, appropriate image processing methods, spectral signatures of land cover classes, and atmospheric effects are needed to our ability to detect and map minerals from space. Image spectra were validated using samples collected from various localities across Alkali Flat and Lake Lucero. These samples were measured in the laboratory using VNIR–SWIR (0.4–2.5 μm) spectra and X-ray Diffraction (XRD) method. Dry gypsum deposits, wet gypsum deposits, standing water, green vegetation, and clastic alluvial sediments dominated by mixtures of ferric iron (ferricrete) and calcite were identified in the study area using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-D Visualization. The results of MNF confirm that AVIRIS and Hyperion data have higher information dimensionality thresholds exceeding the number of available bands of Landsat 7 ETM+, ASTER, and ALI data. ASTER and ALI data can be a reasonable alternative to AVIRIS and Hyperion data for the purpose of monitoring land cover, hydrology and sedimentation in the basin. The spectral unmixing analysis and dimensionality eigen analysis between the various datasets helped to uncover the most optimum spatial–spectral–temporal and radiometric-resolution sensor characteristics for remote sensing based on monitoring of seasonal land cover, surface water, groundwater, and alluvial sediment input changes within the basin. The results demonstrated good agreement between ground truth data and XRD analysis of samples, and the results of Matched Filtering (MF) mapping method.