Spatio-temporal analysis

Understanding the spatial and temporal dynamics of the natural world is a fundamental prerequisite to making informed decisions about the sustainable management of interacting natural and human systems. Hence the spatio-temporal analysis group has underpinned studies by the Fenner School and research institutions around the world into biodiversity assessment, agricultural planning, integrated catchment modelling, water resources management, carbon accounting and assessment of the impacts of projected climate change.

These studies have direct requirements for spatially distributed climate and topographic data, at relatively fine spatial resolution. Translating, or downscaling, broad scale climate change scenarios to spatial scales sufficiently fine to be applicable to real management systems has been a particular contribution of these methods. A guiding principle of the group is to aim to develop and apply techniques that explicitly recognise fundamental driving processes at their appropriate temporal and spatial scales.

Publications

Foundation data for the Australian Hydrological Geofabric

  • ANU Fenner School of Environment and Society and Geoscience Australia, 2008. GEODATA 9 Second DEM and D8 Digital Elevation Model and Flow Direction Grid, User Guide. Geoscience Australia, 43 pp. http://www.ga.gov.au/image_cache/GA11644.pdf (PDF, 1 MB)
  • Hutchinson, M.F. 2008. Adding the Z-dimension. In: J.P. Wilson and A.S. Fotheringham (eds), Handbook of Geographic Information Science, Blackwell, pp 144-168.
  • Stein, J.L. and Hutchinson, M.F. 2008. A review of nested catchment reference systems for Version 1.0 of the National Catchments and Reporting Units, Report to the Bureau of Meteorology Australia, Fenner School of Environment and Society, Australian National University, Canberra.
  • Hutchinson, M.F., Stein, J.A., Stein, J.L. and Xu, T. 2009. Locally adaptive gridding of noisy high resolution topographic data. In Anderssen, R.S., R.D. Braddock and L.T.H. Newham (eds) 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, July 2009, pp. 2493-2499. ISBN: 978-0-9758400-7-8. http://www.mssanz.org.au/modsim09/F13/hutchinson.pdf (PDF, 824 KB)
  • Hutchinson, M.F., 2011. ANUDEM Version 5.3. Fenner School of Environment and Society, Australian National University. http://fennerschool.anu.edu.au/research/publications/software-datasets/anudem
  • Hutchinson, M.F., Xu, T. and Stein, J.A. 2011. Recent Progress in the ANUDEM Elevation Gridding Procedure. In: Geomorphometry 2011, edited by T. Hengel, I.S. Evans, J.P. Wilson and M. Gould, pp. 19-22. Redlands, California, USA. http://geomorphometry.org/HutchinsonXu2011
  • Gallant, J.C. and Hutchinson, M.F. 2011. A differential equation for specific catchment area. Water Resources Research 47, W05535, doi:10.1029/2009WR008540.
  • Pusey, B. J., Kennard, M. J., Stein, J. L., Olden, J. D., Mackay, S. J., Hutchinson, M. F. and Sheldon, F. (2008) (Eds.) Ecohydrological regionalisation of Australia: a tool for management and science. Innovations Project GRU36, Final Report to Land and Water Australia. http://lwa.gov.au/products/pn22591
  • Kennard, M.J., Pusey, B.J., Olden, J.D., Mackay, S.J., Stein, J.L. and Marsh, N. 2010. Classification of natural flow regimes in Australia to support environmental flow management. Freshwater Biology 55: 171-193.
 

Spatial-temporal analysis of surface climate

  • Booth, T.H., Stein, J.A., Hutchinson, M.F. and Nix, H.A. 1990. Identifying areas within a country climatically suitable for particular tree species: an example using Zimbabwe. International Tree Crops Journal 6: 1-16.
  • Hijmans, R., S.E. Cameron, J. Parra, P. Jones, A. Jarvis, 2005. Very high resolution interpolated climate surface for global land areas. Int. J. Climatol., 25, 1965-1978.
  • Hutchinson, M.F. 1991. The application of thin plate smoothing splines to continent-wide data assimilation. In: Jasper, J.D. (ed), BMRC Research Report No.27, Data Assimilation Systems, Bureau of Meteorology, Melbourne, pp. 104-113.
  • Hutchinson, M.F. 1993. On thin plate splines and kriging. In: M.E.Tarter and M.D.Lock (eds), Computing Science and Statistics, Vol. 25. Interface Foundation of North America, University of California, Berkeley, pp. 55-62.
  • Hutchinson, M.F. 1995a. Interpolating mean rainfall using thin plate smoothing splines. International Journal of GIS9:385-403.
  • Hutchinson, M.F. 1995b. Stochastic space-time weather models from ground-based data. Agricultural and Forest Meteorology 73: 237-264.
  • Hutchinson, M.F. 1998a. Interpolation of rainfall data with thin plate smoothing splines: I. Two dimensional smoothing of data with short range correlation. Journal of Geographic Information and Decision Analysis 2(2):153-167.
  • Hutchinson, M.F. 1998b. Interpolation of rainfall data with thin plate smoothing splines: II. Analysis of topographic dependence. Journal of Geographic Information and Decision Analysis 2(2):168-185.
  • Hutchinson, M.F. 2008. Adding the Z-dimension. In: J.P. Wilson and A.S. Fotheringham (eds), Handbook of Geographic Information Science, Blackwell, pp 144-168.
  • Hutchinson, M.F. and Bischof, R.J. 1983. A new method for estimating the spatial distribution of mean seasonal and annual rainfall applied to the Hunter Valley, New South Wales. Australian Meteorological Magazine 31: 179-184.
  • Hutchinson, M.F., Booth, T.H., McMahon, J.P. and Nix, H.A. 1984. Estimating monthly mean values of daily total solar radiation for Australia. Solar Energy 32: 277-290.
  • Hutchinson, M.F. and Gessler, P.E. 1994. Splines - more than just a smooth interpolator. Geoderma 62: 45-67.
  • Hutchinson, M.F. and Kesteven, J.L. 1998. Monthly mean climate surfaces for Australia. Centre for Resource and Environmental Studies, Australian National University, Canberra. http://fennerschool.anu.edu.au/research/software-datasets/climate-surfaces
  • Hutchinson, M.F., McIntyre, S., Hobbs, R.J., Stein, J.L., Garnett, S. and Kinloch, J. 2005. Integrating a global agro-climatic classification with bioregional boundaries in Australia. Global Ecology and Biogeography 14: 197-211.
  • Jarvis, C.H., and N. Stuart, 2001: A comparison among stategies for interpolating maximum and minimum daily air temperature. Part II: The interaction between number of guiding variables and the type of interpolation method. Journal of Applied Meteorology 40: 1075-1084.
  • Kesteven, J.L. and Hutchinson, M.F. 1996. Spatial modelling of climatic variables on a continental scale. In: Proceedings of the Third International Conference/Workshop on Integrating GIS and Environmental Modeling, NCGIA, Santa Barbara, California.
  • Kesteven, J.L. and Landsberg, J. 2004. Developing a National Forest Productivity Model: National Carbon Accounting System, Technical Report No.23. Department of Climate Change, Australian Government, Canberra.
  • Lindenmayer, D.B., Nix, H.A., McMahon, J.P., Hutchinson, M.F. and Tanton, M.T. 1991. The conservation of Leadbeater's possum, Gymnobelideus leadbeateri: a case study of the use of bioclimatic modelling. Journal of Biogeography 18: 371-383.
  • McKenney, D.W., J.H. Pedlar, P. Papadopal, M.F. Hutchinson, 2006a: The development of 1901-2000 historical monthly climate models for Canada and the United States. Agricultural and Forest Meteorology 138: 69-81.
  • McKenney, D.W., Hutchinson, M.F., Papadopol, P., Lawrence, K., Pedlar, J., Campbell, K., Milewska, E., Hopkinson, R.F., Price, D. and Owen, T. 2011. Customized spatial climate models for North America. Bulletin of the American Meteorological Society December 2011, 1611-1622. doi:10.1175/BAMS-D-10-3132.1
  • McVicar, T.R., T.G. Van Niel, L.T. Li, M.F. Hutchinson, X.M. Mu, and Z.H. Liu, 2007: Spatially distributing monthly reference evapotranspiration and pan evaporation considering topographic influences. Journal of Hydrology338: 196-220.
  • New, M., D. Lister, M. Hulme, and I. Makin, 2002: A high-resolution data set of surface climate over global land areas. Climate Research 21: 1?5.
  • Price, D.T., McKenney, D.W., Nalder, I.A., Hutchinson, M.F. and Kesteven, J.L. (2000). A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agricultural and Forest Meteorology 101: 81-94.
  • Sharples, J.J., and M.F. Hutchinson,2004: Multivariate spatial smoothing using additive regression splines. ANZIAM Journal 45: C676-C692.
  • Sharples, J.J., M.F. Hutchinson, and D.R. Jellett, 2005: On the horizontal scale of elevation dependence of Australian monthly precipitation. Journal of Applied Meteorology 44: 1850-1865.
  • Smith, D.I., Hutchinson, M.F. and McArthur, R.J. 1992. Climatic and agricultural drought: payments and policy. Resource and Environmental Studies No. 7, Australian National University, 103 pp.  Download relevant chapter HERE
  • Wahba, G. abd Wendelberger, J. 1980. Some new marthematical metghiods for variational objective analysis using splines and cross-validation. Monthly Weather Review 108: 1122-1145.
  • Xu, T. and Hutchinson, M.F. 2012. ANUCLIM environmental modelling package ?new developments and applications. Submitted.

Updated:  13 December 2017/Responsible Officer:  Director Fenner School/Page Contact:  Webmaster Fenner School