Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand

Other literature type, Article English OPEN
Sharma , D. ; Das Gupta , A. ; Babel , M. S. (2007)
  • Publisher: European Geosciences Union
  • Journal: (issn: 1607-7938, eissn: 1607-7938)
  • Related identifiers: doi: 10.5194/hess-11-1373-2007
  • Subject: [ SDU.STU ] Sciences of the Universe [physics]/Earth Sciences | [ SDU.ENVI ] Sciences of the Universe [physics]/Continental interfaces, environment | [ SDU.OCEAN ] Sciences of the Universe [physics]/Ocean, Atmosphere

International audience; Global Climate Models (GCMs) precipitation scenarios are often characterized by biases and coarse resolution that limit their direct application for basin level hydrological modeling. Bias-correction and spatial disaggregation methods are employed to improve the quality of ECHAM4/OPYC SRES A2 and B2 precipitation for the Ping River Basin in Thailand. Bias-correction method, based on gamma-gamma transformation, is applied to improve the frequency and amount of raw GCM precipitation at the grid nodes. Spatial disaggregation model parameters (?,?<sup>2</sup>), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at <i>q</i>=1 for each month. Bias-correction method exhibits ability of reducing biases from the frequency and amount when compared with the computed frequency and amount at grid nodes based on spatially interpolated observed rainfall data. Spatial disaggregation model satisfactorily reproduces the observed trend and variation of average rainfall amount except during heavy rainfall events with certain degree of spatial and temporal variations. Finally, the hydrologic model, HEC-HMS, is applied to simulate the observed runoff for upper Ping River Basin based on the modified GCM precipitation scenarios and the raw GCM precipitation. Precipitation scenario developed with bias-correction and disaggregation provides an improved reproduction of basin level runoff observations.
  • References (26)
    26 references, page 1 of 3

    G u¨ntner, A., Olsson, J., Calver, A., and Gannon, B.: Cascade-based disaggregation of continuous rainfall time series: the influence of climate, Hydrol. Earth Syst. Sci., 5, 145-164, 2001,

    Gupta, V. K. and Waymire, E. C.: A statistical analysis of mesoscale rainfall as a random cascade, J. Appl. Meteorol., 32, 251-267, 1993.

    Gupta, V. K. and Waymire, E. C.: Multiscaling properties of spatial rainfall and river flow distributions, J. Geophys. Res., 95, 1999-2009, 1990.

    Hamlet, A. F., Snover, A., and Lettenmaier, D. P.: Climate change scenarios for Pacific Northwest water planning studies: motivation, methodologies, and a user's guide to applications, Technical Document, 1-8, archive/hamleaf/bams paper/technical documentation.pdf, 2003.

    Hydrologic Engineering Center (HEC): Geospatial hydrologic modeling extension HECGeoHMS: user's manual (version 1.1), U.S. Army Corps of Engineers, Davis, California, 2003.

    Hydrologic Engineering Center (HEC): Hydrologic modeling system HEC-HMS: user's manual (version 3.0.0), U.S. Army Corps of Engineers, Davis, California, 2005.

    Ines, A. V. M. and Hansen, J. W.: Bias correction of daily GCM rainfall for crop simulation studies, Agric. Forest Meteorol., 138, 44-53, 2006.

    Ines, A. V. M.: GCM bias correction tool (version 0.3a), IRI-Columbia university, New York, USA, 2004.

    IPCC (Inter Governmental Panel on Climate Change): Climate Change 2001: Impacts, Adaptation and Vulnerability, Cambridge University Press, tar/wg2, 2001.

    Jothityangkoon, C., Sivapalan, M., and Viney, N. R.: Tests of a space-time model of daily rainfall in southwestern Australia based on non-homogenous random cascades, Water Resour. Res., 36, 267-284, 2000.

  • Metrics
    No metrics available
Share - Bookmark