PERFORMANCE ASSESSMENT OF DIFFERENT BIAS CORRECTION METHODS IN STATISTICAL DOWNSCALING OF PRECIPITATION
DOI:
https://doi.org/10.11113/mjce.v27.15966Keywords:
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.