PERFORMANCE ASSESSMENT OF DIFFERENT BIAS CORRECTION METHODS IN STATISTICAL DOWNSCALING OF PRECIPITATION

Authors

  • Kamal Ahmed Department of Hydraulics and Hydrology, Faculty of Civil Engineering, University Technology Malaysia, 81310 Johor Bahru, Johor
  • Shamsuddin Shahid Department of Hydraulics and Hydrology, Faculty of Civil Engineering, University Technology Malaysia, 81310 Johor Bahru, Johor,
  • Sobri Harun Department of Hydraulics and Hydrology, Faculty of Civil Engineering, University Technology Malaysia, 81310 Johor Bahru, Johor
  • Nadeem Nawaz Department of Hydraulics and Hydrology, Faculty of Civil Engineering, University Technology Malaysia, 81310 Johor Bahru, Johor

DOI:

https://doi.org/10.11113/mjce.v27.15966

Keywords:

Downscaling, precipitation, arid, bias correction, multilayer perceptron.

Abstract

Global circulation models (GCMs) are widely used for the modeling and assessing the impacts of climate change. These models do not always accurately simulate climate variables due to the risk of considerable biases. Several bias correction methods have been proposed and applied so far. The selection and application of appropriate bias correction can improve accuracy and reduce uncertainty in downscaled precipitation in arid regions. In this study, initially multilayer perceptron (MLP) neural network was applied to downscale the mean monthly precipitation. The MLP model was calibrated by using National Center for environmental prediction (NCEP) reanalysis dataset and monthly precipitation observations located in selected hyper-arid, arid and semi-arid regions. Later, the performance of four bias correction methods namely, power transformation, simple multiplicative, variance inflation and quantile mapping were evaluated by comparing the mean and standard deviation of observed and downscaled precipitation. It has been found that the power transformation method is the most reliable and suitable method for downscaling precipitation in the arid region.

References

Acharya, N., Chattopadhyay, S., Mohanty, U. C., Dash, S. K. & Sahoo, L. N. (2013) On the bias

correction of general circulation model output for Indian summer monsoon. Meteorological

Applications 20(3):349-356.

Berg, P., Feldmann, H. & Panitz, H. J. (2012) Bias correction of high resolution regional climate

model data. Journal of Hydrology 448–449(0):80-92.

Cannon, A. J. (2008) Probabilistic Multisite Precipitation Downscaling by an Expanded

Bernoulli–Gamma Density Network. Journal of Hydrometeorology 9(6):1284-1300.

Chu, J. T., Xia, J., Xu, C. Y. & Singh, V. P. (2010) Statistical downscaling of daily mean

temperature, pan evaporation and precipitation for climate change scenarios in Haihe River,

China. Theoretical and Applied Climatology 99(1-2):149-161.

Elshamy, M. E., Seierstad, I. A. & Sorteberg, A. (2009) Impacts of climate change on Blue Nile

flows using bias-corrected GCM scenarios. Hydrol. Earth Syst. Sci. 13(5):551-565.

Gaitan, C., Hsieh, W., Cannon, A. & Gachon, P. (2013) Evaluation of Linear and Non-Linear

Downscaling Methods in Terms of Daily Variability and Climate Indices: Surface

Temperature in Southern Ontario and Quebec, Canada. Atmosphere-Ocean:1-11.

Gardner, M. W. & Dorling, S. R. (1998) Artificial neural networks (the multilayer perceptron)—

a review of applications in the atmospheric sciences. Atmospheric Environment 32(14–

:2627-2636.

Goyal, M., Burn, D. & Ojha, C. S. P. (2012) Evaluation of machine learning tools as a statistical

downscaling tool: temperatures projections for multi-stations for Thames River Basin,

Canada. Theoretical and Applied Climatology 108(3-4):519-534.

Gudmundsson, L., Bremnes, J. B., Haugen, J. E. & Engen Skaugen, T. (2012) Technical Note:

Downscaling RCM precipitation to the station scale using quantile mapping – a comparison

of methods. Hydrol. Earth Syst. Sci. Discuss. 9(5):6185-6201.

Hadipour, S., Harun, S. & Shahid, S. (2014) Genetic Programming for the Downscaling of

Extreme Rainfall Events on the East Coast of Peninsular Malaysia. Atmosphere 5(4):914-

Hannachi, A., Jolliffe, I. T. & Stephenson, D. B. (2007) Empirical orthogonal functions and

related techniques in atmospheric science: A review. International Journal of Climatology

(9):1119-1152.

Harpham, C. & Dawson, C. W. (2006) The effect of different basis functions on a radial basis

function network for time series prediction: A comparative study. Neurocomputing 69(16–

:2161-2170.

Hessami, M., Gachon, P., Ouarda, T. B. M. J., Andr,& St-Hilaire (2008) Automated regressionbased

statistical downscaling tool. Environ. Model. Softw. 23(6):813-834.

Hsieh, W. W. (2009) Machine learning methods in the environmental sciences: Neural networks

and kernels. Cambridge university press.

Huth, R., Kliegrova, S. & Metelka, L. (2008) Non†linearity in statistical downscaling: does it

bring an improvement for daily temperature in Europe? International Journal of Climatology

(4):465-477.

Ines, A. V. & Hansen, J. W. (2006) Bias correction of daily GCM rainfall for crop simulation

studies. Agricultural and forest meteorology 138(1):44-53.

Lafon, T., Dadson, S., Buys, G. & Prudhomme, C. (2013) Bias correction of daily precipitation

simulated by a regional climate model: a comparison of methods. International Journal of

Climatology 33(6):1367-1381.

Leander, R., Buishand, T. A., Van Den Hurk, B. J. J. M. & De Wit, M. J. M. (2008) Estimated

changes in flood quantiles of the river Meuse from resampling of regional climate model

output. Journal of Hydrology 351(3–4):331-343.

Li, H., Sheffield, J. & Wood, E. F. (2010) Bias correction of monthly precipitation and

temperature fields from Intergovernmental Panel on Climate Change AR4 models using

equidistant quantile matching. Journal of Geophysical Research: Atmospheres

(D10):D10101.

Müller, M. F. & Thompson, S. E. (2013) Bias adjustment of satellite rainfall data through

stochastic modeling: Methods development and application to Nepal. Advances in Water

Resources 60:121-134.

Panofsky, H. A. & Brier, G. W. (1968) Some Applications of Statistics to Meteorology. Earth

and Mineral Sciences Continuing Education, College of Earth and Mineral Sciences.

Sachindra, D. A., Huang, F., Barton, A. & Perera, B. J. C. (2014) Statistical downscaling of

general circulation model outputs to precipitation—part 2: bias-correction and future

projections. International Journal of Climatology:

Salvi, K., Kannan, S. & Ghosh, S. (2011) Statistical Downscaling and Bias Correction for

Projections of Indian Rainfall and Temperature in Climate Change Studies. In Proceedings

of 2011 4th International Conference on Environmental and Computer Science (ICECS

.

Schoof, J. T. & Pryor, S. (2001) Downscaling temperature and precipitation: A comparison of

regression†based methods and artificial neural networks. International Journal of

Climatology 21(7):773-790.

Shabalova, M., Van Deursen, W. & Buishand, T. (2003) Assessing future discharge of the river

Rhine using regional climate model integrations and a hydrological model. Climate Research

(3):233-246.

Sharma, D., Das Gupta, A. & Babel, M. S. (2007) Spatial disaggregation of bias-corrected GCM

precipitation for improved hydrologic simulation: Ping River Basin, Thailand. Hydrol. Earth

Syst. Sci. 11(4):1373-1390.

Teutschbein, C. & Seibert, J. (2012a) Bias correction of regional climate model simulations for

hydrological climate-change impact studies: Review and evaluation of different methods.

Journal of Hydrology 456–457(0):12-29.

Teutschbein, C. & Seibert, J. (2012b) Is bias correction of Regional Climate Model (RCM)

simulations possible for non-stationary conditions? Hydrology and Earth System Sciences

Discussions 9(11):12765-12795.

Wang, L. & Chen, W. (2013) A CMIP5 multimodel projection of future temperature,

precipitation, and climatological drought in China. International Journal of Climatology.

Wilby, R. L. & Wigley, T. M. L. (1997) Downscaling general circulation model output: a review

of methods and limitations. Progress in Physical Geography 21(4):530-548.

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Published

2018-07-15

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How to Cite

PERFORMANCE ASSESSMENT OF DIFFERENT BIAS CORRECTION METHODS IN STATISTICAL DOWNSCALING OF PRECIPITATION. (2018). Malaysian Journal of Civil Engineering, 27. https://doi.org/10.11113/mjce.v27.15966