Stephen Wise

Increased Runoff from Melt from the Greenland Ice Sheet: A Response to Global Warming

The authors attribute significantly increased Greenland summer warmth and Greenland Ice Sheet melt and runoff since 1990 to global warming. Southern Greenland coastal and Northern Hemisphere summer temperatures were uncorrelated between the 1960s and early 1990s but were significantly positively correlated thereafter. This relationship appears to have been modulated by the North Atlantic Oscillation, whose summer index was significantly (negatively) correlated with southern Greenland summer temperatures until the early 1990s but not thereafter. Significant warming in southern Greenland since 1990, as also evidenced from Swiss Camp on the west flank of the ice sheet, therefore reflects general Northern Hemisphere and global warming. Summer 2003 was the warmest since at least 1958 in coastal southern Greenland. The second warmest coastal summer 2005 had the most extensive anomalously warm conditions over the ablation zone of the ice sheet, which caused a record melt extent. The year 2006 was the third warmest in coastal southern Greenland and had the third-highest modeled runoff in the last 49 yr from the ice sheet; five of the nine highest runoff years occurred since 2001 inclusive. Significantly rising runoff since 1958 was largely compensated by increased precipitation and snow accumulation. Also, as observed since 1987 in a single composite record at Summit, summer temperatures near the top of the ice sheet have declined slightly but not significantly, suggesting the overall ice sheet is experiencing a dichotomous response to the recent general warming: possible reasons include the ice sheet’s high thermal inertia, higher atmospheric cooling, or changes in regional wind, cloud, and/or radiation patterns.

Integrating GIS and spatial data analysis: problems and possibilities

Abstract This article is an agreed summary of a workshop held in Sheffield between 18-20 March 1991. The focus here is on three of the themes of the workshop: the mutual benefits of closer links between geographical information systems (GIS) and the methods of spatial data analysis (SDA); the specific areas of SDA that should be linked with GIS; how the linkage should be made in practice. Directions for future research are also reviewed. The emphasis throughout is on statistical SDA and principally from the perspective of human rather than physical geography.

Assessing the quality for hydrological applications of digital elevation models derived from contours

Digital elevation models (DEMs) are becoming increasingly important tools in hydrological research and in water resources management. The quality of DEMs, however, normally is reported simply as the root mean square error of elevation, a statistic that fails to capture the numerous sources of error in DEMs or to predict their effect on the result of using the DEM. This paper presents a review of other approaches to assessing DEM quality, and argues that a full assessment of DEM quality must focus on the accuracy and reliability of the final product of the DEM analysis. A number of DEMs for the Slapton Ley catchments in Devon derived from digitized contour data are compared in an initial assessment of their sustainability for use in hydrological work. Two are available for purchase from data suppliers, and five more were created using a variety of interpolation techniques in widely available geographical information system software. The different interpretation methods produce DEMs with different artefacts, although analyses of the distribution of elevation values, and visual techniques, suggested that none of these were of a particularly pronounced nature. The results of using the DEMs to derive drainage networks and catchment areas showed that at the broad scale there was a high level of agreement between the DEMs. There were, however, important differences of detail. For example, some DEMs predicted drainage lines that occasionally crossed the original contours. The results of calculating the TOPMODEL topographic index showed far more variation, because the index is calculated for each pixel in the area, rather than being an aggregate result derived from numerous pixels. The main conclusion was that care should always be taken to assess the quality of a DEM before attempting to use it, and that results should always be checked to ensure that they appear to be reasonable. Copyright

Outdoor Air Pollution and Stroke in Sheffield, United Kingdom: A Small-Area Level Geographical Study

Background and Purpose— Current evidence suggests that stroke mortality and hospital admissions should be higher in areas with elevated levels of outdoor air pollution because of the combined acute and chronic exposure effects of air pollution. We examined this hypothesis using a small-area level ecological correlation study. Methods— We used 1030 census enumeration districts as the unit of analysis and examined stroke deaths and hospital admissions from 1994 to 1998, with census denominator counts for people ≥45 years. Modeled air pollution data for particulate matter (PM10), nitrogen oxides (NOx), and carbon monoxide (CO) were interpolated to census enumeration districts. We adjusted for age, sex, socioeconomic deprivation, and smoking prevalence. Results— The analysis was based on 2979 deaths, 5122 admissions, and a population of 199 682. After adjustment for potential confounders, stroke mortality was 37% (95% CI, 19 to 57), 33% (95% CI, 14 to 56), and 26% (95% CI, 10 to 46) higher in the highest, relative to the lowest, NOx, PM10, and CO quintile categories, respectively. Corresponding increases in risk for admissions were 13% (95% CI, 1 to 27), 13% (95% CI, −1 to 29), and 11% (95% CI, −1 to 25). Conclusion— The results are consistent with an excess risk of stroke mortality and, to a lesser extent, hospital admissions in areas with high outdoor air pollution levels. If causality were assumed, 11% of stroke deaths would have been attributable to outdoor air pollution. Targeting policy interventions at high pollution areas may be a feasible option for stroke prevention.

Cross-validation as a means of investigating DEM interpolation error

Studies of the detailed characteristics of DEM error have been hampered by the difficulty in obtaining a large sample of error values for a DEM. The approach proposed in this paper is to resample a DEM to a lower resolution and then reinterpolate back to the original resolution which produces a large sample of error values well distributed across the DEM. This method is applied to a sample area from Scotland, which contains a variety of terrain types. The results show that the standard measure of error, the root mean square error (RMSE) of elevation, shows only moderate correlation with a visual assessment of the quality of DEMs produced by a range of interpolation methods. The frequency distribution and strength of spatial autocorrelation are shown to vary with the initial data density and interpolation method. When the source data density is low, the error has strong spatial autocorrelation and a distribution that is close to being Gaussian. However, as the data density increases, levels of spatial autocorrelation drop and the distribution becomes leptokurtic with values very strongly clustered around zero. At the level of the individual DEM point, elevation error is shown to be a poor predictor of error in slope derivatives which depend on the spatial pattern of elevation errors around the point and are also sensitive to differences in terrain. At the level of a whole DEM, however, RMSE of elevation is a good predictor of RMSE in gradient and aspect but not of curvature.

Providing spatial statistical data analysis functionality for the GIS user: the SAGE project

Geographical Information Systems (GIS) are being used in a growing number of application areas. As a consequence there have been frequent calls to expand the range of spatial analysis tools available to users of GIS but a reluctance on the part of GIS software vendors to include such tools in standard software packages. An alternative approach is to link extra tools to GIS packages which raises a series of issues, such as, What sort of tools should be included? How should the linkage be done? To what extent can the functionality of the GIS be used? This paper draws on the results of a project in which software for statistical spatial data analysis (SSDA) was linked to ARC/INFO to produce a software system called SAGE. The statistical tools implemented included those which were felt to be useful to the general GIS user (as opposed to the specialist spatial statistician or econometrician), and they were linked to ARC/INFO using a client server architecture. The GIS was used within the context of SSDA for map drawing, spatial queries and operations on the topology of the spatial data, although it was found that the map drawing facilities of ARC/INFO were not well suited to the needs of this application. One of the conclusions of the project was that many of the techniques of exploratory spatial data analysis, such as providing graphical data summaries and linking these to cartographic views of the data could be easily integrated into existing GIS packages, providing a useful addition to their functionality for many GIS users. Many of the other SSDA facilities are probably still best provided in specialist software, but there is a need for a robust and standardised means for such software to extract information about the topology of spatial data from within GIS packages.

Designing and implementing software for spatial statistical analysis in a GIS environment

Abstract. This paper provides a description of SAGE, a software package linked to the Arc/Info GIS that can be used to undertake spatial statistical analysis of area based data. The paper is written from the perspective of the user who wishes to undertake exploratory and confirmatory spatial data analysis. The paper discusses design aspects of the package and also the statistical analysis philosophy underlying its contents. The paper describes the statistical analyses which SAGE can perform and details on how it performs them together with some illustrative examples. Detail on visualisation aspects of SAGE are discussed in a separate paper (Haining et al. 2000). The wider contribution of the paper is to build on earlier developments in this area and identify the needs of software packages if they are to enable users to implement effective spatial statistical analysis.

The effect of alternative representations of population location on the areal interpolation of air pollution exposure

Abstract Relatively new accurate commercial point databases incorporating population distribution are now available which could enhance areal interpolation estimates required for an underlying population. This paper explores the level to which results will be dependent on how well the underlying population is represented. Using a number of different levels of detail to represent the population distribution (postcode units, enumeration districts (ED) population centroids and no population information), this paper shows the differences that can occur in computing average ED dose levels due to the degree to which the population is represented. Although generally the differing methodologies gave similar overall patterns, there were substantial disparities between specific ED results obtained. As anticipated, the sensitivity of results were influenced by the degree to which the methods assumptions were adhered to. In the case of the ED population centroid approach, the extent to which the summarising points reflect the underlying spatial objects is critical to the estimated result. For areal weighting, the results were significantly influenced by the level of homogeneity within the variable of interest (population distribution). Generally, greater variation within the pollution surface exacerbates any breach in the methods underlying assumptions.

An integrated regionalization of earthquake, flood, and drought hazards in China

Earthquake, flood, and drought data from different sources are combined in a single data set using the same data structure, projection, and scale. The intensity and frequency of each hazard is classified into severe, heavy, modest, and light, producing a dassification with 64 combined states for the three kinds of hazard. These classes are then ranked according to severity. The three hazard coverages arc overlaid and the polygons that are produced are coded by the classification system. A map is produced that shows the distribution of these 64 classes in regions and their areas measured from the spatial topological data file in the GIs. Spatial analysis reveals the spatial association among the three hazards and between the three hazards and human factors. There is a brief discussion of the implications of the regionalized map for hazard monitoring.

Information entropy as a measure of DEM quality

The Shannon-Weaver Information statistic has been proposed as a useful measure of the quality of a Digital Elevation Model. However the statistic, usually referred to as entropy, is based purely on the range of values in a dataset and their relative proportion and is not directly related to the accuracy of those values or their spatial arrangement. These properties suggest that a better understanding is needed of how entropy behaves with respect to DEMs and that is what this paper seeks to provide. Previous work has suggested two uses for entropy: firstly as a measure of the loss of information caused by smoothing and aggregation of a DEM and secondly as a means of comparing DEMs and DEM products of varying quality. A series of theoretical and practical tests were used to test these ideas. It was found that entropy may well be useful as a measure of the information lost when a DEM is aggregated to a coarser scale but is not related to smoothing in any systematic way. There was no consistent relationship between entropy and the measures of DEM quality tested. Although it may have uses as a quality measure in some instances further work would be needed to establish its reliability.