Application of Statistical Bias correction method to the Yoshino River Basin

Abstract. 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 (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=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.

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-4-35-2007 ◽

2007 ◽

Vol 4 (1) ◽

pp. 35-74 ◽

Cited By ~ 14

Author(s):

D. Sharma ◽

A. Das Gupta ◽

M. S. Babel

Keyword(s):

River Basin ◽

Bias Correction ◽

Hydrological Modeling ◽

Correction Method ◽

Hydrologic Model ◽

Global Climate Models ◽

Model Parameters ◽

Bias Correction Method ◽

Spatial Disaggregation ◽

Grid Nodes

Abstract. 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 (β,σ2), based on multiplicative random cascade theory, are estimated using Mandelbrot-Kahane-Peyriere (MKP) function at q=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.

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Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand

Journal of Water and Climate Change ◽

10.2166/wcc.2020.021 ◽

2020 ◽

Author(s):

Srisunee Wuthiwongtyohtin

Keyword(s):

River Basin ◽

Bias Correction ◽

Climate Model ◽

Daily Rainfall ◽

Bias Error ◽

Statistical Bias ◽

Study Region ◽

Study Results ◽

Nonparametric Transformation ◽

Statistical Bias Correction

Abstract This study aims to investigate different statistical bias correction techniques to improve the output of a regional climate model (RCM) of daily rainfall for the upper Ping River Basin in Northern Thailand. Three subsamples are used for each bias correction method, which are (1) using full calibrated 30-year-period data, (2) seasonal subsampling, and (3) monthly subsampling. The bias correction techniques are classified into three groups, which are (1) distribution-derived transformation, (2) parametric transformation, and (3) nonparametric transformation. Eleven bias correction techniques with three different subsamples are used to derive transfer function parameters to adjust model bias error. Generally, appropriate bias correction methods with optimal subsampling are locally dependent and need to be defined specifically for a study area. The study results show that monthly subsampling would be well established by capturing the monthly mean variation after correcting the model's daily rainfall. The results also give the best-fitted parameter set of the different subsamples. However, applying the full calibrated data and the seasonal subsamples cannot substantially improve internal variability. Thus, the effect of internal climate variability of the study region is greater than the choice of bias correction methods. Of the bias correction approaches, nonparametric transformation performed best in correcting daily rainfall bias error in this study area as evaluated by statistics and frequency distributions. Therefore, using a combination of methods between the nonparametric transformation and monthly subsampling offered the best accuracy and robustness. However, the nonparametric transformation was quite sensitive to the calibration time period.

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Impact of statistical bias correction on the projected climate change signals of the regional climate model REMO over the Senegal River Basin

International Journal of Climatology ◽

10.1002/joc.4478 ◽

2015 ◽

Vol 36 (4) ◽

pp. 2035-2049 ◽

Cited By ~ 12

Author(s):

Mamadou L. Mbaye ◽

Andreas Haensler ◽

Stefan Hagemann ◽

Amadou T. Gaye ◽

Christopher Moseley ◽

...

Keyword(s):

Climate Change ◽

Regional Climate Model ◽

River Basin ◽

Bias Correction ◽

Climate Model ◽

Regional Climate ◽

Statistical Bias ◽

Senegal River ◽

Senegal River Basin ◽

Statistical Bias Correction

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Climate model bias correction and the role of timescales

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-7863-2010 ◽

2010 ◽

Vol 7 (5) ◽

pp. 7863-7898 ◽

Cited By ~ 6

Author(s):

J. O. Haerter ◽

S. Hagemann ◽

C. Moseley ◽

C. Piani

Keyword(s):

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Correction Method ◽

Statistical Properties ◽

Statistical Bias ◽

Negative Effects ◽

Climate Model Bias ◽

Corrected Data ◽

Statistical Bias Correction

Abstract. It is well known that output from climate models cannot be used to force hydrological simulations without some form of preprocessing to remove the existing biases. In principle, statistical bias correction methodologies act on model output so the statistical properties of the corrected data match those of the observations. However the improvements to the statistical properties of the data are limited to the specific time scale of the fluctuations that are considered. For example, a statistical bias correction methodology for mean daily values might be detrimental to monthly statistics. Also, in applying bias corrections derived from present day to scenario simulations, an assumption is made of persistence of the bias over the largest timescales. We examine the effects of mixing fluctuations on different time scales and suggest an improved statistical methodology, referred to here as a cascade bias correction method, that eliminates, or greatly reduces, the negative effects.

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Estimating monthly rainfall in rural river basins under climate change: an improved bias-correcting statistical downscaling approach

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-10-6847-2013 ◽

2013 ◽

Vol 10 (6) ◽

pp. 6847-6896

Author(s):

D. L. Jayasekera ◽

J. J. Kaluarachchi

Keyword(s):

Climate Change ◽

General Circulation Model ◽

River Basin ◽

Bias Correction ◽

General Circulation ◽

Circulation Model ◽

Correction Method ◽

Annual Rainfall ◽

Bias Correction Method ◽

Time Space

Abstract. This study extended the work of Kim et al. (2008) to generate future rainfall under climate change using a discrete-time/space Markov chain based on historical conditional probabilities. A bias-correction method is proposed by fitting suitable statistical distributions to transform rainfall from the general circulation model (GCM) scale to watershed scale. The demonstration example used the Nam Ngum River Basin (NNRB) in Laos which is a rural river basin with high potential for hydropower generation and significant rain-fed agriculture supporting rural livelihoods. This work generated weekly rainfall for a 100 yr period using historical rainfall data from 1961 to 2000 for ten selected weather stations. The bias-correction method showed the ability to reduce bias of the mean values of GCMs when compared to the observed mean amount at each station. The simulated rainfall series is perturbed using the delta change estimated at each station to project future rainfall for the Special Report on Emission Scenarios (SRES) A2. GCMs consisting of third generation coupled general circulation model (CGCM3.1 T63) and European center Hamburg model (ECHAM5) projected an increasing trend of mean annual rainfall in the NNRB. Seasonal rainfall percent changes showed an increase in the wet and dry seasons with the highest increase in the dry season mean rainfall of about 31% from 2051 to 2090. While the GCM projections showed good results with appropriate bias corrections, the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model significantly underestimated historical behavior and produced higher mean absolute errors compared to the corresponding GCM predictions.

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