Evaluation of Quantile Delta Mapping as a bias‐correction method in maximum rainfall dataset from downscaled models in São Paulo state (Brazil)

Abstract

An essential step for improving climate change models' performance is to evaluate their ability to represent the current climate conditions, especially extreme events. On such background, this study aims at evaluating the performance of the Quantile Delta Mapping (QDM) as a bias correction method for annual maximum daily precipitation series (bmax) generated from downscaled climate change models under tropical–subtropical conditions of Brazil. We selected the QDM due to its ability to correct bias in extreme quantile of wet days. Climate projections obtained from 20 NASA Earth Exchange Daily Downscaled Projections models (NEX‐GDDP) from 1950 to 2005 were subjected to validation processes based on the QDM method. Two climate change scenarios (RCP 4.5 and RCP 8.5 W m−2) have also been considered. Several goodness‐of‐fit measures, such as root‐mean‐square‐error (RMSE), SD, percentual bias (pbias), mean absolute error (MAE), Pearson correlation test, modified Willmott test (dm), have been calculated from the outcomes of the models and their corresponding observed data (obtained from rain gauges). These goodness‐of‐fit measures were calculated before and after applying the QDM method. The QDM was able to correct virtually all biases. More specifically, the QDM successfully adjusted the empirical cumulative distribution of climate change projections, removing the systematic error of raw data. The QDM also presented a suitable performance when applied to future projections (2020–2095). This statement holds for all NEX‐GDDP models, except for the ACCESS1‐0 model in RCP 8.5. In such a scenario, this latter model presented unrealistic rainfall values. Finally, with the improvement resulting from applying the bias correction method QDM, there was an increase in the number of climate projections suitable for end‐users in the study region.

Future Projection of Extreme Precipitation Indices over the Qilian Mountains under Global Warming

“…The research results of correcting the earth system model of Beijing Normal University (BNU-ESM) by QDM and bias correction constructed analogs with quantile mapping reordering (BCCAQ) show that the QDM has a better performance compared to the BCCAQ method [ 35 ]. A study examined the performance of QDM in correcting the deviation of annual maximum daily precipitation, and the results showed that the QDM successfully adjusted the empirical cumulative distribution of climate change projections, removing the systematic error of raw data [ 36 ]. And the QDM also presented a suitable performance when applied to future projections.…”

Development of an ensemble Bayesian inference‐based copula approach for bivariate risk evaluation of extreme precipitation under climate change

“…For historical precipitation variable x , the projected can be written as (Eum et al ., 2020), where is relative ratio in a quantile between historical and future periods at time t , x m , f ( t ) and are raw uncorrected and bias‐corrected data from a climate model at time t within the projection period, F o , h is empirical cumulative density function (CDF) of observed values, F m , h and F m , f are the empirical CDFs of modelled data for historical and future periods, respectively. Brier score ( Bs ) is used as an indicator to reflect the effectiveness of QDM in precipitation projection (Miralha et al ., 2020; Xavier et al ., 2021), where M i and O i are the projected and observed values, respectively. T is the number o...…”

“…where 4 m t ð Þ is relative ratio in a quantile between historical and future periods at time t, x m,f (t) and b x m,f t ð Þ are raw uncorrected and bias-corrected data from a climate model at time t within the projection period, F o,h is empirical cumulative density function (CDF) of observed values, F m,h and F m,f are the empirical CDFs of modelled data for historical and future periods, respectively. Brier score (Bs) is used as an indicator to reflect the effectiveness of QDM in precipitation projection (Miralha et al, 2020;Xavier et al, 2021),…”

Evaluation of Bias Correction Methods for Regional Climate Models: Downscaled Rainfall Analysis Over Diverse Agroclimatic Zones of India

“…Thus, it becomes imperative to correct the RCM simulated meteorological variables before they could be used for impact studies (Mall et al., 2019; Seneviratne et al., 2012). Over the past decades, various bias correction methods are in use, these range from simple scaling approaches to sophisticated distribution mapping, quantile delta mapping and regional quantile delta mapping (RQDM) (Acharya et al., 2013; Chen et al., 2011, 2013; Fang et al., 2015; Gutjahr & Heinemann, 2013; Kim et al., 2021; Miralha et al., 2021; Piani et al., 2010; Sharma et al., 2007; A. Singh et al., 2017, 2021; Teutschbein & Seibert, 2012; Tiwari, Kar, Mohanty, Dey, Kumari, et al., 2016; Voropay et al., 2021; Xavier et al., 2021). Further, the bias in the daily rainfall values not only impact the monthly mean totals but also affect daily intensities, frequency, and other statistical properties at a different timescale that are used in agriculture as well as hydrological studies (Arnell et al., 2003; Choubin et al., 2019; Fowler et al., 2007).…”