Bias correction capabilities of quantile mapping methods for rainfall and temperature variables

Abstract This study aims to conduct a thorough investigation to compare the abilities of QM techniques as a bias correction method for the raw outputs from GCM/RCM combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the CORDEX dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.

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Multilevel Drought Hazard Assessment under Climate Change Scenarios in Semi-Arid Regions—A Case Study of the Karkheh River Basin in Iran

Water ◽

10.3390/w9040241 ◽

2017 ◽

Vol 9 (4) ◽

pp. 241 ◽

Cited By ~ 17

Author(s):

Bahareh Kamali ◽

Delaram Houshmand Kouchi ◽

Hong Yang ◽

Karim Abbaspour

Keyword(s):

Climate Change ◽

River Basin ◽

Hazard Assessment ◽

Arid Regions ◽

Climate Change Scenarios ◽

Drought Hazard ◽

Semi Arid ◽

Karkheh River Basin ◽

The Karkheh River

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

Hydrology and Earth System Sciences ◽

10.5194/hess-11-1373-2007 ◽

2007 ◽

Vol 11 (4) ◽

pp. 1373-1390 ◽

Cited By ~ 131

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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Analysis and prediction of meteorological drought using SPI index and ARIMA model in the Karkheh River Basin, Iran

Extreme Hydrology and Climate Variability ◽

10.1016/b978-0-12-815998-9.00026-9 ◽

2019 ◽

pp. 343-353 ◽

Cited By ~ 1

Author(s):

Mahshid Karimi ◽

Assefa M. Melesse ◽

Khabat Khosravi ◽

Melkamu Mamuye ◽

Jiahua Zhang

Keyword(s):

River Basin ◽

Arima Model ◽

Meteorological Drought ◽

Spi Index ◽

Karkheh River Basin ◽

The Karkheh River

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Streamflow regionalization using a similarity approach in ungauged basins: Application of the geo-environmental signatures in the Karkheh River Basin, Iran

CATENA ◽

10.1016/j.catena.2019.104128 ◽

2019 ◽

Vol 182 ◽

pp. 104128 ◽

Cited By ~ 13

Author(s):

Bahram Choubin ◽

Karim Solaimani ◽

Fereidoun Rezanezhad ◽

Mahmoud Habibnejad Roshan ◽

Arash Malekian ◽

...

Keyword(s):

River Basin ◽

Ungauged Basins ◽

Karkheh River Basin ◽

The Karkheh River

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Probability Distributions for a Quantile Mapping Technique for a Bias Correction of Precipitation Data: A Case Study to Precipitation Data Under Climate Change

Water ◽

10.3390/w11071475 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1475 ◽

Cited By ~ 7

Author(s):

Jun-Haeng Heo ◽

Hyunjun Ahn ◽

Ju-Young Shin ◽

Thomas Rodding Kjeldsen ◽

Changsam Jeong

Keyword(s):

Climate Change ◽

Bias Correction ◽

Shape Parameter ◽

Mapping Method ◽

Distribution Model ◽

Parameter Distribution ◽

Precipitation Data ◽

Distribution Models ◽

Quantile Mapping

The quantile mapping method is a bias correction method that leads to a good performance in terms of precipitation. Selecting an appropriate probability distribution model is essential for the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping method in the bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping, for a bias correction of simulated precipitation data from a climate model under a climate change scenario. Additionally, the impacts of distribution selection on the frequency analysis of future extreme precipitation from climate are investigated. Generalized Lindley, Burr XII, and Kappa distributions are used, and their fits and appropriateness are compared to those of conventional distributions in a case study. Applications of two-shape parameter distributions do lead to better performances in reproducing the statistical characteristics of observed precipitation, compared to those of conventional distributions. The Kappa distribution is considered the best distribution model, as it can reproduce reliable spatial dependences of the quantile corresponding to a 100-year return period, unlike the gamma distribution.

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Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes

Hydrology and Earth System Sciences ◽

10.5194/hess-21-2649-2017 ◽

2017 ◽

Vol 21 (6) ◽

pp. 2649-2666 ◽

Cited By ~ 34

Author(s):

Matthew B. Switanek ◽

Peter A. Troch ◽

Christopher L. Castro ◽

Armin Leuprecht ◽

Hsin-I Chang ◽

...

Keyword(s):

Error Correction ◽

Bias Correction ◽

Climate Model ◽

Correction Method ◽

Climate Change Signal ◽

Quantile Mapping ◽

Temperature And Precipitation ◽

Distribution Mapping ◽

Projected Changes ◽

Time Invariant

Abstract. Commonly used bias correction methods such as quantile mapping (QM) assume the function of error correction values between modeled and observed distributions are stationary or time invariant. This article finds that this function of the error correction values cannot be assumed to be stationary. As a result, QM lacks justification to inflate/deflate various moments of the climate change signal. Previous adaptations of QM, most notably quantile delta mapping (QDM), have been developed that do not rely on this assumption of stationarity. Here, we outline a methodology called scaled distribution mapping (SDM), which is conceptually similar to QDM, but more explicitly accounts for the frequency of rain days and the likelihood of individual events. The SDM method is found to outperform QM, QDM, and detrended QM in its ability to better preserve raw climate model projected changes to meteorological variables such as temperature and precipitation.

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