Submitted as: methods for assessment of models
05 Dec 2022
Submitted as: methods for assessment of models |  | 05 Dec 2022
Status: a revised version of this preprint was accepted for the journal GMD.

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

Abstract. Bias correction (BC) is often a necessity to improve the applicability of global and regional climate model (GCM and RCM, respectively) outputs to impact assessment studies, which usually depend on multiple potentially dependent variables. To date, various BC methods have been developed which adjust climate variables separately (univariate BC) or jointly (multivariate BC) prior to their application in impact studies (i.e., the component-wise approach). Another possible approach is to first calculate the multivariate hazard index from the original, biased simulations, and bias-correct the impact model output or index itself using univariate methods (direct approach). This has the advantage of circumventing the difficulties associated with correcting the inter-variable dependence of climate variables which is not considered by univariate BC methods.

Using a multivariate drought index (i.e., SPEI) as an example, the present study compares different state-ofthe- art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) applied to climate model simulations stemming from different experiments at different spatial resolutions (namely CORDEX, CORDEX-CORE and CMIP6). The BC methods are calibrated and evaluated over the same historical period (1986–2005). The proposed framework is demonstrated as a case study over a transboundary watershed, i.e. the Upper Jhelum Basin (UJB) in the Western Himalaya.

Results show that (1) there is some added value of multivariate BC methods over the univariate methods in adjusting the inter-variable relationship, however, comparable performance is found for SPEI indices. (2) The best performing BC methods exhibits a comparable performance under both approaches with a slightly better performance for the direct approach. (3) The added value of the high-resolution experiments (CORDEX-CORE) compared to their coarser resolution counterparts (CORDEX) are not apparent in this study.

Rubina Ansari et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2022-237: reference', Jorn Van de Velde, 20 Dec 2022
    • AC1: 'Reply on CC1', Rubina Ansari, 24 Dec 2022
  • RC1: 'Comment on gmd-2022-237', Anonymous Referee #1, 18 Jan 2023
  • RC2: 'Comment on gmd-2022-237', Anonymous Referee #2, 24 Jan 2023

Rubina Ansari et al.

Rubina Ansari et al.


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Short summary
Bias correction has become indispensable to climate model output as a post-processing step to render climate model 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).