Analysis of Mapping Techniques for Mountain Precipitation: A Case Study of Alpine Region, Austria

  • A. N. Laghari Department of Energy and Environment Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah, Pakistan
  • G. D. Walasai Department of Mechanical Engineering, Quaid -e- Awam University of Engineering, Science and Technology, Nawabshah, Pakistan
  • D. K. Bangwar Civil Engineering Department, Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah, Pakistan
  • A. H. Memon Department of Civil Engineering, Quaid-e-Awam University of Engineering Science and Technology, Nawabshah, Pakistan
  • A. H. Shaikh Department of Civil Engineering, Quaid -e- Awam University of Engineering, Science and Technology, Nawabshah, Pakistan
Volume: 8 | Issue: 4 | Pages: 3213-3217 | August 2018 |


Truly representative precipitation map generation of mountain regions is a difficult task. Due to poor gauge representativity, complex topography and uneven density factors make the generation of representative precipitation maps a very difficult task. To generate representative precipitation maps, this study focused on analyzing four different mapping techniques: ordinary kriging, spline technique (SP), inverse distance weighting (IDW) and regression kriging (RK). The generated maps are assessed through cross-validation statistics, spatial cross-consistency test and by water balance approach. The largest prediction error is produced by techniques missing information on co-variables. The ME and RMSE values show that IDW and SP are the most biased techniques. The RK technique produced the best model results with 1.38mm and 72.36mm ME and RMSE values respectively. The comparative analysis proves that RK model can produce reasonably accurate values at poorly gauged areas, where geographical information compensated the poor availability of local data.

Keywords: mountain regions, poor gauge representativity, spatial interpolation techniques


Download data is not yet available.


B. Klove, P. Ala-Aho, G. Bertrand, J. J. Gurdak, H. Kupfersberger, J. Kværner, T. Muotka, H. Mykra, E. Preda, P. Rossi, C. B. Uvo, E. Velasco, M. Pulido-Velazquez, “Climate change impacts on groundwater and dependent ecosystems”, Journal of Hydrology, Vol. 518B, pp. 250-266, 2014 DOI:

H.-M. Fussel, A. Jol, A. Marx, M. Hilden, A. Aparicio, A. Bastrup-Birk, A. Bigano, S. Castellari, M. Erhard, B. Georgi, Climate change, impacts and vulnerability in Europe 2016-An indicator-based report, EU Publications, 2017

M. Burke, J. Dykema, D. B. Lobell, E. Miguel, S. Satyanath, “Incorporating climate uncertainty into estimates of climate change impacts”, Review of Economics and Statistics, Vol. 97, No. 2, pp. 461-471, 2015 DOI:

C. B. Field, V. R. Barros, K. Mach, M. Mastrandrea, Climate change 2014: impacts, adaptation, and vulnerability. Vol. 1, Cambridge University Press, 2014

A. Laghari, D. Vanham, W. Rauch, “To what extent does climate change result in a shift in Alpine hydrology? A case study in the Austrian Alps”, Hydrological Sciences Journal, Vol. 57, No. 1, pp. 103-117, 2012 DOI:

D. Viviroli, M. Zappa, J. Gurtz, R. Weingartner, “An introduction to the hydrological modelling system PREVAH and its pre-and post-processing-tools”, Environmental Modelling & Software, Vol. 24, No. 10, pp. 1209-1222, 2009 DOI:

R. Cibin, P. Athira, K. Sudheer, I. Chaubey, “Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty”, Hydrological Processes, Vol. 28, No. 4, pp. 2033-2045, 2014 DOI:

Y. Chen, J. Li, H. Xu, “Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization”, Hydrology and Earth System Sciences, Vol. 20, No. 1, pp. 375-392, 2016 DOI:

K. X. Yu, L. Gottschalk, L. Xiong, Z. Li, Li, “Estimation of the annual runoff distribution from moments of climatic variables”, Journal of Hydrology, Vol. 531, pp. 1081-1094, 2015 DOI:

Z. K. Tesemma, Y. Wei, M. C. Peel, A. W. Western, “Including the dynamic relationship between climatic variables and leaf area index in a hydrological model to improve streamflow prediction under a changing climate”, Hydrology and Earth System Sciences, Vol. 19, No. 6, pp. 2821-2836, 2015 DOI:

K. Beven, “How far can we go in distributed hydrological modelling?”, Hydrology and Earth System Sciences, Vol. 5, No. 1, pp. 1-12, 2001 DOI:

E. Lepuschitz, “Geographic information systems in mountain risk and disaster management”, Applied Geography, Vol. 63, pp. 212-219, 2015 DOI:

S. Takaoka, “Origin and geographical characteristics of ponds in a high mountain region of central Japan”, Limnology, Vol. 16, No. 2, pp. 103-112, 2015 DOI:

N. Boers, B. Bookhagen, N. Marwan, J. Kurths, “Spatiotemporal characteristics and synchronization of extreme rainfall in South America with focus on the Andes Mountain range”, Climate Dynamics, Vol. 46, No. 1-2, pp. 601-617, 2016 DOI:

J. Creutin, C. Obled, “Objective analyses and mapping techniques for rainfall fields: an objective comparison”, Water Resources Research, Vol. 18, No. 2, pp. 413-431, 1982 DOI:

D. D. Weber, E. J. Englund, “Evaluation and comparison of spatial interpolators II”, Mathematical Geology, Vol. 26, No. 5, pp. 589-603, 1994 DOI:

C. Prudhomme, D. W. Reed, “Mapping extreme rainfall in a mountainous region using geostatistical techniques: a case study in Scotland”, International Journal of Climatology, Vol. 19, No. 12, pp. 1337-1356, 1999 DOI:<1337::AID-JOC421>3.0.CO;2-G

D. Vanham, E. Fleischhacker, W. Rauch, “Technical Note: Seasonality in alpine water resources management? a regional assessment”, Hydrology and Earth System Sciences, Vol. 12, No. 1, pp. 91-100, 2008 DOI:

M. Knotters, D. Brus, J. O. Voshaar, “A comparison of kriging, co-kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations”, Geoderma, Vol. 67, No. 3-4, pp. 227-246, 1995 DOI:

I. O. A. Odeha, A. B. McBratney, D. Chittleborough, “Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging”, Geoderma, Vol. 67, No. 3-4, pp. 215-226, 1995 DOI:

I. O. A. Odeha, A. B. McBratney, D. J. Chittleborough, “Spatial prediction of soil properties from landform attributes derived from a digital elevation model”, Geoderma, Vol. 63, No. 3-4, pp. 197-214, 1994 DOI:

V. Chaplot, C. Walter, P. Curmi, “Improving soil hydromorphy prediction according to DEM resolution and available pedological data”, Geoderma, Vol. 97, No. 3-4, pp. 405-422, 2000 DOI:

I. D. Moore, P. E. Gessler, G. A. E. Nielsen, G. A. Peterson, “Soil attribute prediction using terrain analysis”, Soil Science Society of America Journal, Vol. 57, No. 2, pp. 443-452, 1993 DOI:

J. L. Richardson, W. J. Edmonds, “Linear regression estimations of Jenny's relative effectiveness of state factors equation”, Soil Science, Vol. 144, No. 3, pp. 203-208, 1987 DOI:

J. A. Thompson, J. C. Bell, C. A. Butler, “Digital elevation model resolution: effects on terrain attribute calculation and quantitative soil-landscape modeling”, Geoderma, Vol. 100, No. 1-2, pp. 67-89, 2001 DOI:

M. R. Holdaway, “Spatial modeling and interpolation of monthly temperature using kriging”, Climate Research, Vol. 6, pp. 215-225, 1996 DOI:

A. Martinez-Cob, “Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain”, Journal of Hydrology, Vol. 174, No. 1-2, pp. 19-35, 1996 DOI:

D. L. Phillips, J. Dolph, D. Marks, “A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain”, Agricultural and forest meteorology, Vol. 58, No. 1-2, pp. 119-141, 1992 DOI:

P. Goovaerts, Geostatistics for natural resources evaluation. Oxford University Press, 1997

T. Hengl, G. B. Heuvelink, A. Stein, Comparison of kriging with external drift and regression kriging, ITC, 2003

P. A. Burrough, R. A. McDonnell, C. D. Lloyd, Principles of geographical information systems, Oxford University Press, 2015

D. R. Legates, C. J. Willmott, “Mean seasonal and spatial variability in global surface air temperature”, Theoretical and Applied Climatology, Vol. 41, No. 1-2, pp. 11-21, 1990 DOI:

C. Stallings, R. Huffman, S. Khorram, Z. Guo, Linking gleams and GIS, Paper-American Society of Agricultural Engineers (USA), 1992

M. Hutchinson, P. Gessler, “Splines—more than just a smooth interpolator”, Geoderma, Vol. 62, No. 1-3, pp. 45-67, 1994 DOI:

G. Matheron, Le krigeage universel: cahiers du Centre de Morphologie Mathematique, École nationale supérieure des mines de Paris, 1969 (in French)

R. Webster, M. A. Oliver, Geostatistics for environmental scientists, John Wiley & Sons, 2007 DOI:

J. C. Davis, Statistics and Data Analysis in Geology, John Wiley & Sons, 1986

C. V. Deutsch, A. G. Journel, GSLIB Geostatistical Software Library and User’s Guide, Oxford University Press, 1992

G. W. Heine, “A controlled study of some two-dimensional interpolation methods”, COGS Computer Contributions, Vol. 3, No. 2, pp. 60-72, 1986

N. S. N. Lam, “Spatial interpolation methods: a review”, The American Cartographer, Vol. 10, No. 2, pp. 129-150, 1983 DOI:

A. G. Royle, F. L. Clausen, P. Frederiksen, “Practical universal kriging and automatic contouring”, Geoprocessing, Vol. 1, pp. 377-394, 1981

S. Ahmed, G. De Marsily, “Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity”, Water Resources Research, Vol. 23, No. 9, pp. 1717-1737, 1987 DOI:

J. Delhomme, “Kriging in the hydrosciences”, Advances in Water Resources, Vol. 1, pp. 251-266, 1978 DOI:

J. Hofierka, J. Parajka, H. Mitasova, L. Mitas, “Multivariate interpolation of precipitation using regularized spline with tension”, Transactions in GIS, Vol. 6, No. 2, pp. 135-150, 2002 DOI:

M. Kuhn, H. Escher-Vetter, “Die Reaktion der österreichischen Gletscher und ihres Abflusses auf Änderungen von Temperatur und Niederschlag”, Osterreichische Wasser-und Abfallwirtschaft, Vol. 56, No. 1-2, pp. 10-16, 2004

S. G. Custer, P. Fames, J. P. Wilson, R. D. Snyder, “A Comparison of Hand and Spline-Drawn Precipitation Maps for mantainous Montana”, Journal of the American Water Resources Association, Vol. 32, No. 2, pp. 393-405, 1996 DOI:


Abstract Views: 351
PDF Downloads: 133

Metrics Information
Bookmark and Share

Most read articles by the same author(s)

> >>