Evaluation of Quantile Delta Mapping as a bias-correction method in maximum rainfall dataset from downscaled models in São Paulo state (Brazil)
(2022) In International Journal of Climatology 42(1). p.175-190- 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... (More)
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.
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- author
- Xavier, Ana Carolina Freitas ; Martins, Letícia Lopes ; Rudke, Anderson Paulo ; de Morais, Marcos Vinicius Bueno ; Martins, Jorge Alberto LU and Blain, Gabriel Constantino
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- block maxima, climate projections, data optimization, extreme values, NEX-GDDP, statistical techniques
- in
- International Journal of Climatology
- volume
- 42
- issue
- 1
- pages
- 175 - 190
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:85107879684
- ISSN
- 0899-8418
- DOI
- 10.1002/joc.7238
- language
- English
- LU publication?
- yes
- id
- 8e1cce31-cc05-44d5-b13f-7b817bf9733b
- date added to LUP
- 2021-07-15 14:26:03
- date last changed
- 2022-04-27 02:47:35
@article{8e1cce31-cc05-44d5-b13f-7b817bf9733b,
abstract = {{<p>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<sup>−2</sup>) 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 (d<sub>m</sub>), 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.</p>}},
author = {{Xavier, Ana Carolina Freitas and Martins, Letícia Lopes and Rudke, Anderson Paulo and de Morais, Marcos Vinicius Bueno and Martins, Jorge Alberto and Blain, Gabriel Constantino}},
issn = {{0899-8418}},
keywords = {{block maxima; climate projections; data optimization; extreme values; NEX-GDDP; statistical techniques}},
language = {{eng}},
number = {{1}},
pages = {{175--190}},
publisher = {{John Wiley & Sons Inc.}},
series = {{International Journal of Climatology}},
title = {{Evaluation of Quantile Delta Mapping as a bias-correction method in maximum rainfall dataset from downscaled models in São Paulo state (Brazil)}},
url = {{http://dx.doi.org/10.1002/joc.7238}},
doi = {{10.1002/joc.7238}},
volume = {{42}},
year = {{2022}},
}