Comparison of Three Spatial Sensitivity Analysis Techniques

International audienceThis paper compares the spatial Sobol' sensitivity approach to two other sensitivity analysis techniques on a model with spatially distributed inputs. The comparison is performed on AquiferSim, a model that simulates groundwater flow and nitrate transport from paddock to aquifer

How important is the description of soil unsaturated hydraulic conductivity values for simulating soil saturation level, drainage and pasture yield?

Accurate simulation of soil water dynamics is a key factor when using agricultural models for guiding management decisions. However, the determination of soil hydraulic properties, especially unsaturated hydraulic conductivity, is challenging and measured data are scarce. We investigated the use of APSIM (Agricultural Production Simulation Model) with SWIM3 as the water module, based on Richards equation and a bimodal pore system, to determine likely ranges of the hydraulic conductivity at field capacity (K-10; assumed at a matric potential of −10 kPa) for soils representing different drainage characteristics. Hydraulic conductivity measurements of soils with contrasting soil drainage characteristics and values for K-10 were extracted from New Zealand’s national soil database. The K-10 values were then varied in a sensitivity analysis from 0.02 to 5 mm d−1 for well-drained soils, from 0.02 to 1 mm d−1 for moderately well-drained soils, and from 0.008 to 0.25 mm d−1 for poorly drained soils. The value of K-10 had a large effect on the time it took for the soil to drain from saturation to field capacity. In contrast, the saturated hydraulic conductivity value had little effect. Simulations were then run over 20 years using two climatic conditions, either a general climate station for all seven different soils, or site-specific climate stations. Two values for K-10 were used, either the APSIM default value, or the soil-specific measured K-10. The monthly average soil saturation level simulated with the latter has a better correspondence with the morphology of the seven soils. Finally, the effect of K-10 on drainage and pasture yield was investigated. Total annual drainage was only slightly affected by the choice of K-10, but pasture yield varied substantially.Ministry of Business, Innovation and Employment’s Endeavour Fund, through the Manaaki Whenua-led ‘Next Generation S-map’ research programme, C09X161

Derivation of physically based soil hydraulic parameters in New Zealand by combining soil physics and hydropedology

Field-characterised soil morphological data (to 1 m depth) and modelled soil water release characteristics are recorded in the S-map database for soils cover- ing approximately 40% of New Zealand's soil area. This paper shows the devel- opment of the Smap-Hydro database that estimates hydraulic parameters by synergising soil morphologic data recorded in S-map and soil physics. The Smap-Hydro parameters were derived using the bi-modal Kosugi hydraulic function. The validity of the Smap-Hydro parameters was tested by applying them within an uncalibrated physically based hydrological model (HyPix) and comparing results with soil water content, θ, measured with Aquaflex soil moisture probes (0–40 cm deep) at 24 sites across New Zealand. The HyPix model provided an excellent fit with observed soil water content for 25% of the sites, a good fit for 33% of the sites and a poor fit for 42% of the sites. Applying the model to all soils in the S-map database required adjustments for the occurrence of rock fragments, hydraulic discontinuities caused by soil pans and required the addition of boundary conditions for water tables and the occurrence of impermeable rock. A discussion on how we can further syner- gise the development of pedotransfer functions with knowledge of soil physics is provided

Shrinks on Ice: a review of psychological research in Antarctica

The existence Of psychosocial research in Antarctica is little known but has improved understanding of the ways in which humans adapt and respond to the stresses of living in isolated and confined environments. A strong focus on determining who was most suited to Antarctic conditions resulted in a refinement of selection criteria and the incorporation of psychological testing by most national programmes. This literature review describes the stresses, psychological effects and coping stratagems used by staffin Antarctica. The evolution Of selection criteria from the days of heroic exploration to the current day is described. Some researchers have attempted to predict adaptive response from biographical data, personality traits, and stress level. The variety of results may be due in part to the microculture Of each group, therefore studies On the relevance Of leadership and group dynamics are reviewed. Finally, some suggestions for future directions in Antarctic psychosocial research are made. The review concludes by listing the key findings Of 'shrinks' working in Antarctica. The existence Of psychosocial research in Antarctica is little known but has improved understanding of the ways in which humans adapt and respond to the stresses of living in isolated and confined environments. A strong focus on determining who was most suited to Antarctic conditions resulted in a refinement of selection criteria and the incorporation of psychological testing by most national programmes. This literature review describes the stresses, psychological effects and coping stratagems used by staffin Antarctica. The evolution Of selection criteria from the days of heroic exploration to the current day is described. Some researchers have attempted to predict adaptive response from biographical data, personality traits, and stress level. The variety of results may be due in part to the microculture Of each group, therefore studies On the relevance Of leadership and group dynamics are reviewed. Finally, some suggestions for future directions in Antarctic psychosocial research are made. The review concludes by listing the key findings Of 'shrinks' working in Antarctica

The scale matcher: A framework for assessing scale compatibility of environmental data and models

Many organisations collect spatial data at a range of scales for a variety of purposes. Scientists apply new environmental knowledge either by improving existing simulation models or developing new ones. Technological advances, and the increasing availability of both models and the data required to run them, are potentially putting better information in the hands of environmental policy analysts, decision makers and land managers to solve environmental problems. A review of the literature, however, shows that each of this trio of Data–Model--Problem is scale dependent, and that this scale dependence can be very significant, potentially leading to the generation of information that is misleading or simply invalid. The research objective is to contribute to the area of spatial modelling by investigating how a best practice of applying environmental data and models, which considers the effects of scale, might be achieved. It is proposed that a systematic framework (dubbed the 'Scale Matcher') can be developed to identify and match the scale requirements of a problem with the scale limitations of spatial data and models. A bottom-up approach is taken by breaking scale into its constituent parts (extent, accuracy and precision), each of which is then analysed in some depth to understand the various ways in which scale can impact on data and simulation models. Each component of scale is quantified by identifying suitable metrics from the literature. Two new indices that take into account attribute imprecision are introduced. Spatial confusion measures the degree of overlap of the attribute values between mapunits. Colour purity measures the proportion of a map that is incorrectly shaded due to within-mapunit imprecision. Both measures can be calculated for continuous and categorical, raster and vector data, providing there is a parametric or empirical estimate of the distribution of sub-mapunit variability. The Scale Matcher comprises a set of 11 comparisons, derived from a systematic match between each combination of the scale components of data, model, and problem. Each match is named and described. In some cases the matches are simple comparisons of the relevant metrics. Others require the combination of data variability and model sensitivity to be investigated. By randomly simulating data and model imprecision and inaccuracy, sensitivity analysis can then be used to identify scales over which scale effects are minor or stable. The Scale Matcher procedure is specified in a flowchart format that leads the user through the relevant sequence of matches. Listing the results as a set of scale assumptions helps to draw attention to them making users more aware of the limitations of spatial modelling. The Scale Matcher is evaluated by assessing scale compatibility in a case study of nitrate leaching vulnerability. Some hypothetical examples are used to verify other routes through the procedure. The case study requires the development of some new representations of soil imprecision and inaccuracy, and loosely coupling a sensitivity analysis tool with a geographic information system. It was concluded that the scale-matching framework successfully broke down the scale issue into a series of comparisons, which, if performed should give the user more confidence in the scale validity of model output for a given problem.UnpublishedAalders, H. J. G. L. (1999). "The registration of quality in a GIS". In W. Shi, M. F. Goodchild & P. Fisher (eds), International Symposium on Spatial Data Quality. Hong Kong pp. 23-32. Addiscott, T. M. (1993). "Simulation modelling and soil behaviour". Geoderma. 60: 15-40. Addiscott, T. M. (1998). "Modelling concepts and their relation to the scale of the problem". Nutrient Cycling in Agroecosystems. 50: 239-245. Addiscott, T. M. & Wagenet, R. J. (1985). "Concepts of solute leaching in soils: a review of modelling approaches". Journal of Soil Science. 36: 411-424. AGI (1996). "GIS dictionary". Accessed: 15 June 2001. URL: http://www.geo.ed.ac.uk/agidict/welcome.html Alker, R. (1969). "A typology of ecological fallacies". In M. Dogan & M. Rokkan (eds), Quantitative Ecological Analysis in the Social Sciences. MIT Press. Cambridge, MA. Altman, D. G. & Bland, J. M. (1983). "Measurement in medicine: the analysis of method comparison studies". The Statistician. 32: 307-317. Amrhein, C. G. (1995). "Searching for the elusive aggregation effect: evidence from statistical simulations". Environment and Planning A. 27: 105-119. Amrhein, C. G. & Griffith, D. A. (1994). "Errors in spatial databases: a summary of results from several research projects". International Symposium on the Spatial Accuracy of Natural Resource Databases. ASPRS Williamsburg, Virginia pp. 214-226. Atkinson, P. M. (1997). "Scale and spatial dependence". In P. R. van Gardingen, G. M. Foody & P. J. Curran (eds), Scaling-up: from Cell to Landscape. Society for experimental biology seminar series Cambridge University Press. Cambridge pp. 35-60. Band, L. E. (1993). "Effect of land surface representation on forest, water and carbon budgets". Journal of Hydrology. 150: 749-772. Barringer, J. & Lilburne, L. (2000). "Developing fundamental data layers to support environmental modelling in New Zealand: progress and problems". 4th International Conference on Integrating GIS and Environmental Modeling (GIS/EM4). Banff, Canada. Barringer, J. R. F. & Lilburne, L. R. (1997). "An evaluation of digital elevation models for upgrading New Zealand land resource inventory slope data". In R. T. Pascoe (ed.), GeoCornputation 97 (incl. SIRC 97). Spatial Information Research Centre, University of Otago Dunedin, NZ pp. 109--116. Barry, R. G. (1992). Mountain weather and climate. 2nd edn. Routledge. London. Batty, M. (1974). "Spatial entropy". Geographical Analysis. 6: 1-31. Becker, A. & Braun, P. (1999). "Disaggregation, aggregation and spatial scaling in hydrological modelling". Journal of Hydrology. 217: 239-252. Bedward, M., Keith, D. A. & Pressey, R. L. (1992). "Homogeneity analysis: assessing the utility of classifications and maps of natural resources". Australian Journal of Ecology. 17: 133-139. Bekesi, G. & McConchie, J. (1999). "Groundwater recharge modelling using the Monte Carlo technique, Manawatu region, New Zealand". Journal of Hydrology. 224: 137-148. Bergstrom, S. & Graham, L. P. (1998). "On the scale problem in hydrological modelling". Journal of Hydrology. 211: 253-265. Bian, L. (1997). "Multiscale nature of spatial data in scaling up environmental models". In D. A. Quattrochi & M. F. Goodchild (eds), Scale in Remote Sensing and GIS. Lewis Publishers. Boca Raton pp. 13-26. Bian, L. & Walsh, S. J. (1993). "Scale dependencies of vegetation and topography in a mountainous environment of Montana". Professional Geographer. 45(1): 1-11. Bloschl, G. (1999). "Scaling issues in snow hydrology". Hydrological Processes. 13: 2149-2175. Bloschl, G. & Sivapalan, M. (1995). "Scale issues in hydrological modelling: a review". Hydrological Processes. 9(3/4): 9-48. Boldocchi, D. D. & Meyers, T. P. (1998). "On using eco-physiological, micrometeorological and biochemical theory to evaluate carbon dioxide, water vapor and gaseous deposition fluxes over vegetation". Agricultural and Forest Meteorology. 90: 1-26. Bouma, J., Booltink, H. W. G. &. Finke, P. A. (1996). "Use of soil survey data for modeling solute transport in the vadose zone". Journal of Environmental Quality. 25: 519-526. Brasington, J. & Richards, K. (1998), "Interactions between model predictions, parameters and DTM scales for topmodel". Computers and Geosciences. 24(4): 299-314. Brassel, K., Bucher, F., Stephan, E.-M. & Vckovski, A. (1995). "Completeness". In S. C. Guptill & J. L. Morrison (eds), Elements of Spatial Data Quality. Elsevier. Oxford pp. 81-107. Bresnahan, P. A. & Miller, D. R. (1997). "Choice of data scale: predicting resolution error in a regional evapotranspiration model". Agricultural and Forest Meteorology. 84: 97-113. Brooks, R. J. & Tobias, A. M. (1996). "Choosing the best model: level of detail, complexity, and model performance". Mathematical and Computing Modelling. 24(4): 1-14. Brown, D. G., Bian, L. & Walsh, S. J. (1993). "Response of a distributed watershed erosion model to variations in input data aggregation levels". Computers and Geosciences. 19(4): 499-509. Burden, R. J. (1982). "Nitrogen contamination of New Zealand aquifers: a review". New Zealand Journal of Science. 25: 205-220. Burrough, P. A. (1989). "Modelling land qualities in space and time: the role of geographical information systems". In J. Bouma & A. Bregt (eds), Land Qualities in Space and Time. Pudoc. Wageningen, the Netherlands pp. 45-59. Burrough, P. A. (1996). "Opportunities and limitations of GIS-based modeling of solute transport at the regional scale". Application of GIS to the Modeling of Non-point Source Pollutants in the Vadose Zone. SSSA Special Publication 48 Soil Science Society of America. Madison, Wisconsin pp. 19-38. Burrough, P. A. & McDonnell, R. A. (1998). Principles of geographic information systems. Spatial Information Systems 2nd edn. Oxford University Press. Oxford. Caldwell, M. M., Matson, P. A., Wessman, C. & Gamon, J. (1993). "Prospects for scaling". In J. R. Ehleringer & C. B. Field (eds), Scaling Physiological Processes: Leaf to Globe. Academic Press. San Diego pp. 223-230. CAMASE (1996). "The CAMASE register of agro-ecosystems models". Accessed: Nov. 2001. URL: http://www.agralin.nl/camase/ Canters, F., Eerens, H. & Veroustraete, F. (1999). "Estimation of land-cover proportions from aggregated medium-resolution satellite data". In K. Lowell & A. Jaton (eds), Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources. Ann Arbor Press. Michigan pp. 263-270. Cao, C. & Lam, N. S.-N. (1997). "Understanding the scale and resolution effects in remote sensing and GIS". In D. A. Quattrochi & M. F. Goodchild (eds), Scale in Remote Sensing and GIS. Lewis Publishers. Boca Raton pp. 57-72. CartoCorner (n.d.). "Glossary of cartographic terms". Accessed: 15 June 2001. URL: http://atlas.gc.ca/english/carto/cartglos.htrnl Carver, S. J. & Brunsdon, C. F. (1994). "Vector to raster conversion error and feature complexity: an empirical study using simulated data". International Journal of Geographical Information Systems. 8(3): 261-270. Century (2000). "Century model". Accessed: 21 Aug. 2001. 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"The use of upscaling procedures in the application of soil acidification models at different scales". Nutrient Cycling in Agroecosystems. 50: 223-236. Deutsch, C. V. & Journel, A. G. (1992). GSLIB: Geostatistical software library and user's guide. Oxford University Press. New York. Di, H. J. & Aylmore, L. A. G. (1997). "Modeling the probabilities of groundwater contamination by pesticides". Soil Science Society of America Journal. 61: 17-23. Drummond, J. (1995). "Positional accuracy". In S. Guptill & J. Morrison (eds), Elements of Spatial Data Quality. Elsevier. Oxford pp. 31--58. Duckham, M. (1999). "Implementing an object-oriented error-sensitive GIS". In K. Lowell & A. Jaton (eds), Spatial Accuracy Assessment: Land Information Uncertainty in Natural Resources. Ann Arbor Press. Michigan pp. 209-215. Ehlschlaeger, C. R. & Goodchild, M. F. (1994). "Dealing with uncertainty in categorical coverage maps: defining, visualising, and managing errors". In N. R. Adam, B. K. Bhargava & Y. Yesha (eds), Third International Conference on Information and Knowledge Management (CIKM94). ACM Press Gaithersburg, MD pp. 86-91. Ehlschlaeger, C. R., Shortridge, A. M. & Goodchild, M. F. (1997). "Visualizing spatial data uncertainty using animation". Computers and Geosciences. 23(4): 387-395. ERDAS (1997). "ERDAS field guide". ERDAS. Georgia, USA. ESRI (1991). "ARC/INFO". ESRI. Redlands, CA. Everett, J. & Simonett, D. S. (1976). "Principles, concepts and philosophical problems in remote sensing". In J. Lintz & D. S. Simonett (eds), Remote Sensing of the Environment. Addison-Wesley. Reading, MA pp. 85-127. Finke, P. A., Wladis, D., Kros, J., Pebesma, E. J. & Reinds, G. J. (1999). "Quantification and simulation of errors in categorical data for uncertainty analysis of soil acidification modelling". Geoderma. 93(3-4): 177-194. Finn, J. T. (1993). "Use of the average mutual information index in evaluating classification error and consistency". 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Unwin (eds), Visualization in Geographical Information Systems. John Wiley & Sons. London pp. 181— 185. Fisher, P. F., Abrahart, R. J. & Herbinger, W. (1997). "The sensitivity of two distributed non-point source pollution models to the spatial arrangement of the landscape". Hydrological Processes. 11: 241-252. Floate, M. & Cossens, G. (1992). "Land resources". In M. Floate (ed.), Guide to Tussock Grassland Farming. AgResearch. Invermay pp. 23-37. Florinsky, I. V. (1998). "Accuracy of local topographic variables derived from digital elevation models". International Journal of Geographical Information Systems. 12(1): 47-61. Florinsky, I. V. & Kuryakova, G. A. (2000). "Determination of grid size for digital terrain modelling in landscape investigations - exemplified by soil moisture distribution at a micro-scale". International Journal of Geographical Information Systems. 14(8): 815-832. Foody, G. M. (1992). 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"Spatial scale influence on longterm temporal patterns of a semi-arid grassland". Landscape Ecology. 11(2): 107- 113. Gallant, J. C., Hutchinson, M. F. & Wilson, J. P. (2000). "Future directions for terrain analysis". In J. P. Wilson & J. C. Gallant (eds), Terrain Analysis: Principles and Applications. John Wiley & Sons. New York pp. 423-427. GAO (1979). "Guidelines for model evaluation". Technical Report PAD-79-17. US General Accounting Office. Gao, J. (1997). "Resolution and accuracy of terrain representation by grid DEMs at a micro-scale". International Journal of Geographical Information Science. 11(2): 199-212. Gardner, R. H. & Turner, M. G. (1990). "Future directions in quantitative landscape ecology". In M. G. Turner & R. H. Gardner (eds), Quantitative Methods in Landscape Ecology: the Analysis and Interpretation of Landscape Heterogeneity. Springer-Verlag. New York pp. 519-525. Goodchild, M. F. (1993). "Data models and data quality: problems and prospects". In M. Goodchild, B. 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(1997). "Scale in a digital geographic world". Geographical and Environmental Modelling. 1(1): 5-23. Goodchild, M. F. & Quattrochi, D. A. (1997). "Introduction: scale, multiscaling, remote sensing and GIS". In D. A. Quattrochi & M. F. Goodchild (eds), Scale in Remote Sensing and GIS. Lewis Publishers. Boca Raton pp. 1-11. Grayson, R. B., Bloschl, G., Barling, R. D. & Moore, I. D. (1993). "Process, scale and constraints to hydrological modelling in GIS". HydroGIS 93: Application of Geographical Information Systems in Hydrology and Water Resources. IAHS Publication 211 Vienna. Griffin, C. B. (1995). "Data quality issues affecting GIS use for environmental problem-solving". Environmental Problem Solving with Geographic Information Systems. Cincinnati, Ohio. Guptill, S. C. & Morrison, J. L. (eds) (1995). Elements of spatial data quality. Elsevier. Oxford. Hamby, D. M. (1994). "A review of techniques for parameter sensitivity analysis of environmental models". 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The scale matcher: A framework for assessing scale compatibility of environmental data and models

Many organisations collect spatial data at a range of scales for a variety of purposes. Scientists apply new environmental knowledge either by improving existing simulation models or developing new ones. Technological advances, and the increasing availability of both models and the data required to run them, are potentially putting better information in the hands of environmental policy analysts, decision makers and land managers to solve environmental problems. A review of the literature, however, shows that each of this trio of Data–Model--Problem is scale dependent, and that this scale dependence can be very significant, potentially leading to the generation of information that is misleading or simply invalid. The research objective is to contribute to the area of spatial modelling by investigating how a best practice of applying environmental data and models, which considers the effects of scale, might be achieved. It is proposed that a systematic framework (dubbed the 'Scale Matcher') can be developed to identify and match the scale requirements of a problem with the scale limitations of spatial data and models. A bottom-up approach is taken by breaking scale into its constituent parts (extent, accuracy and precision), each of which is then analysed in some depth to understand the various ways in which scale can impact on data and simulation models. Each component of scale is quantified by identifying suitable metrics from the literature. Two new indices that take into account attribute imprecision are introduced. Spatial confusion measures the degree of overlap of the attribute values between mapunits. Colour purity measures the proportion of a map that is incorrectly shaded due to within-mapunit imprecision. Both measures can be calculated for continuous and categorical, raster and vector data, providing there is a parametric or empirical estimate of the distribution of sub-mapunit variability. The Scale Matcher comprises a set of 11 comparisons, derived from a systematic match between each combination of the scale components of data, model, and problem. Each match is named and described. In some cases the matches are simple comparisons of the relevant metrics. Others require the combination of data variability and model sensitivity to be investigated. By randomly simulating data and model imprecision and inaccuracy, sensitivity analysis can then be used to identify scales over which scale effects are minor or stable. The Scale Matcher procedure is specified in a flowchart format that leads the user through the relevant sequence of matches. Listing the results as a set of scale assumptions helps to draw attention to them making users more aware of the limitations of spatial modelling. The Scale Matcher is evaluated by assessing scale compatibility in a case study of nitrate leaching vulnerability. Some hypothetical examples are used to verify other routes through the procedure. The case study requires the development of some new representations of soil imprecision and inaccuracy, and loosely coupling a sensitivity analysis tool with a geographic information system. It was concluded that the scale-matching framework successfully broke down the scale issue into a series of comparisons, which, if performed should give the user more confidence in the scale validity of model output for a given problem.UnpublishedAalders, H. J. G. L. (1999). "The registration of quality in a GIS". In W. Shi, M. F. Goodchild & P. Fisher (eds), International Symposium on Spatial Data Quality. Hong Kong pp. 23-32. Addiscott, T. M. (1993). "Simulation modelling and soil behaviour". Geoderma. 60: 15-40. Addiscott, T. M. (1998). "Modelling concepts and their relation to the scale of the problem". Nutrient Cycling in Agroecosystems. 50: 239-245. Addiscott, T. M. & Wagenet, R. J. (1985). "Concepts of solute leaching in soils: a review of modelling approaches". Journal of Soil Science. 36: 411-424. AGI (1996). "GIS dictionary". Accessed: 15 June 2001. URL: http://www.geo.ed.ac.uk/agidict/welcome.html Alker, R. (1969). "A typology of ecological fallacies". In M. Dogan & M. Rokkan (eds), Quantitative Ecological Analysis in the Social Sciences. MIT Press. Cambridge, MA. Altman, D. G. & Bland, J. M. (1983). "Measurement in medicine: the analysis of method comparison studies". The Statistician. 32: 307-317. Amrhein, C. G. (1995). "Searching for the elusive aggregation effect: evidence from statistical simulations". Environment and Planning A. 27: 105-119. Amrhein, C. G. & Griffith, D. A. (1994). "Errors in spatial databases: a summary of results from several research projects". 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Statistical analysis of soil data from the McMurdo Dry Valleys, Antarctica

Pedological data from over a decade of field trips to the McMurdo Dry valleys has been collated into a dataset. This data includes site observations (location, topographical position, estimated glacial history and soil age), morphological observations (full pit profile description), soil taxonomic classification, surface observations (weathering characteristics of boulders), chemical measurements of the major anions (Cl, S04, N04) and cations (Ma, Mg, Ca, K), electrical conductivity and pH. An exploratory statistical analysis was performed on this dataset to determine which analyses were appropriate given the format of the dataset, and its quality and quantity. Box plots were used to study the variability of variables according to different groupings. Multivariate analyses including a factor analysis, discriminant analysis, cluster analysis and machine learning algorithms were all applied. Geostatistical analyses investigated the spatial dependence of some of the observations. Most of the variability analyses indicated little differences in the ranges of soil properties between groups (weather stage, eco-climatic zone, taxonomic class, geological age). Where there were differences some trends were obvious and others were unexpected. The multivariate analyses did separate the pits and observations into groups that seem reasonably sensible. Little spatial dependence was found. It is concluded that the Bockheim dataset is sufficiently comprehensive for statistical analyses. The next stage in this work requires pedological input to refine those analyses that either have results of interest or have the potential to provide information of interest

Sensitivity Analysis of Spatial Models

Sensitivity analysis involves determining the contribution of individual input factors to uncertainty in model predictions. A number of techniques exist to carry out sensitivity analysis from a set of Monte Carlo simulations, some more efficient than others, depending on the approach used to sample the space of the uncertainties and on calculation methods. The most common approaches are summarised in this paper. In particular, the limitations of each in the context of a sensitivity analysis of a spatial model are critically examined. A novel approach for undertaking a spatial sensitivity analysis (based on the Sobol' method) is proposed and tested. This method is global, variance-based, and model-free, and enables the analysis of space-dependent uncertain inputs. The proposed approach is illustrated with a simple test model and a groundwater contaminantJRC.G.9-Econometrics and applied statistic

GIS, expert systems and interoperability

Part of the GeoComputation '96 Special Issue 96/25; follow the "related link" to download the entire collection as a single document.How should geographic information systems be developed? There is a strong demand from users for enhanced functionality and power. Vendors can and do respond to these demands. But where will this lead? Will the result be one all-embracing and all-conquering program or geographic information system (GIS)? A GIS could grow to incorporate all statistical functions, all visualisation techniques, all data management functions etc. It is possible to perceive a scenario in which GIS is developed to ‘bloatware’ proportions. An alternative scenario is one in which a GIS is interfaced with other software systems. Embedding database bridges and other product-specific links, providing data import and export routines, and system calls are all ways of interfacing GIS with other systems. GIS vendors could opt to produce a ‘linkware’ GIS, interfaced to as many third party systems as possible. Given these two alternatives to GIS development, an interesting set of questions arises. How far do vendors go with enhancing their systems compared with interfacing with third party systems? Is there a balance? Or do GIS users just keep calling for ‘more’, regardless of the solution set? There is a balance. GIS is likely to be developed by being enhanced AND by being interfaced with third party software. In a way, this is a third developmental track leading to an increasingly functional GIS whose ability to interact with other systems is greatly improved. This interoperable GIS allows flexible combinations of systems components while still providing a comprehensive range of spatial operations and analytical functions. Of these three developmental tracks, this paper presents an example of what can be achieved with the interoperable GIS. Expert systems are introduced along with the client/server and object-oriented paradigms. By using these paradigms, a generic, spatial, rule-based toolbox called SES (spatial expert shell) has been created. SES is described using examples and contrasted with other documented expert system-GIS linkages. But first integration is modelled in three dimensions to highlight the need for improvements in how GISs can interact with other systems.UnpublishedOpen GIS Consortium, Inc. 1996. Open GIS. WWW ref: http://www.opengis.org. Bleecker, M., Hutson, J. L., and Waltman, S. W. 1990. Mapping groundwater contamination potential using integrated simulation modeling and GIS. Proceedings of Application of geographic information systems, simulation models, and knowledge-based systems for landuse management, Blacksburg, VA. Booch, G. 1994. Object-oriented analysis and design, The Benjamin/Cummings Publishing Company Inc., Redwood City, California. Burrough, P. A. 1986. Principles of geographical information systems for land resources assessment, Oxford University Press. Burrough, P. A. 1992. Development of intelligent geographical information system. International journal of geographical information systems, 6(1). Crossland, M. D. 1990. HyrdoLOGIC - a prototype GIS expert system for examining an AI application in a GIS environment. Proceedings of GIS/LIS '90. Davis, J. R., Cuddy, S. M., Laut, P., Goodspeed, M. J., and Whigham, P. A. 1991. Testing of soil moisture prediction model for army land managers. Journal of irrigation and drainage engineering, 117(4), p. 476-489. Durkin, J. 1994. Expert systems: design and development, Macmillan Publishing. Fedra, D. K. 1993. GIS and environmental modeling. In Environmental modeling with GIS, M. Goodchild, B. Parks, and L. Steyaert, eds., Oxford University Press. Fedra, K. 1995. Decision support for natural resources management: models, GIS and expert systems. AI Applications, 9(3), p. 3-19. Fischer, M. M. 1994. From conventional to knowledge-based geographic information systems. Computers, environment and urban systems, 18(4), p. 233-42. Fisher, P. F., Mackaness, W. A., Peacegood, G., and Wilkinson, G. G. 1988. Artificial intelligence and expert systems in geodata processing. Progress in physical geography, 12, p. 371-388. Goodchild, M., Haining, R., and Wise, S. 1992. Integrating GIS and spatial data analysis: problems and possibilities. International journal of geographical information systems, 6(5), p. 407-423. Jackson, P. 1990. Introduction to expert systems, Addison-Wesley Publishing Co. Lam, D. C. L., and Swayne, D. A. 1993. An expert system approach of integrating hydrological database, models and GIS: application of the RAISON System. Proceedings of HydroGIS 93: Application of geographical information systems in hydrology and water resources, Vienna, IAHS Publication 211, p. 23. Lein, J. K. 1992. Modeling environmental impact using an expert-geographic information system. Proceedings of GIS/LIS '92, San Jose, CA, USA, p. 436-44. Leung, Y. 1993. Towards the development of an intelligent spatial decision support system. In Geographic information systems, spatial modelling and policy evaluation, M. Fischer and P. Nijkamp, eds., Springer-Verlag. Lilburne, L. 1996. The integration challenge. Proceedings of SIRC '96, Dunedin. Loh, D. K., and Rykiel, E. J. 1992. Integrated resource management systems: coupling expert systems with database management and GIS. Environmental management, 16(2), p. 167-177. Nyerges, T. L. 1992. Coupling GIS and spatial analytical models. Proceedings of 5th international symposium on spatial data handling, Charleston, South Carolina, p. 534-543. Robinson, V. B., Frank, A. U., and Karimi, H. A. 1987. Expert systems for GIS in resource management. AI Applications, 1(1). Sharma, J., Flewelling, D. M., and Egenhofer, M. J. 1994. A qualitative spatial reasoner. 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