Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin

Ansari, Rubina; Casanueva, Ana; Liaqat, Muhammad Usman; Grossi, Giovanna

doi:https://doi.org/10.5194/gmd-16-2055-2023

Articles | Volume 16, issue 7

https://doi.org/10.5194/gmd-16-2055-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-16-2055-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 7

Methods for assessment of models

|

17 Apr 2023

Methods for assessment of models |

| 17 Apr 2023

Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin

Rubina Ansari, Ana Casanueva, Muhammad Usman Liaqat, and Giovanna Grossi

Supplement

https://doi.org/10.5194/gmd-16-2055-2023-supplement

Data sets

WFDE5 over land merged with ERA5 over the ocean (W5E5). V. 1.0 Stefan Lange https://doi.org/10.5880/pik.2019.023

Model code and software

Climate change projections of wet and dry extreme events in the Upper Jhelum Basin using a multivariate drought index: Evaluation of bias correction Rubina Ansari, Ana Casanueva, Muhammad Usman Liaqat, and Giovanna Grossi https://doi.org/10.5281/zenodo.7296744

SantanderMetGroup/downscaleR: v3.3.3 (05 Jul 2021) (v3.3.3) Joaquin Bedia, Maialen Iturbide, Sixto Herrera García, szabotakacsb, Jose M. Gutierrez, Ana, Jorge Baño-Medina, Max Tuni, Matteo De Felice, and Jesus Fernandez https://doi.org/10.5281/zenodo.5070432

Package "SPEI". Calculation of the Standardised Precipitation-Evapotranspiration Index S. Beguería, S. M. Vicente-Serrano, and M. S. Beguería https://github.com/sbegueria/SPEI

Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? (https://rdocumentation.org/packages/MBC/versions/0.10-5) A. J. Cannon, S. R. Sobie, and T. Q. Murdock https://doi.org/10.1175/JCLI-D-14-00754.1

Short summary

Bias correction (BC) has become indispensable to climate model output as a post-processing step to render output more useful for impact assessment studies. The current work presents a comparison of different state-of-the-art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) for climate model simulations from three initiatives (CMIP6, CORDEX, and CORDEX-CORE) for a multivariate drought index (i.e., standardized precipitation evapotranspiration index).