Climate change projections of wet and dry extreme events in the Upper Jhelum Basin using a multivariate drought index: Evaluation of bias correction

Abstract

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.

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

“…The potential evapotranspiration (PET) and standardized precipitation evapotranspiration index (SPEI) were calculated with the R package "SPEI" (version 1.7) available from a GitHub repository (https://github.com/sbegueria/SPEI, last access: August 2022, Beguería et al, 2017). All the code to perform the derived analyses, calculations, and plots is also based on R scripts and ArcMap, which are available at https://doi.org/10.5281/zenodo.7296744 (Ansari et al, 2022a).…”

Projected changes in daily precipitation, temperature and wet‐bulb temperature across Arizona using statistically downscaled CMIP6 climate models

“…For example, the eqm is a non‐parametric method using empirical CDFs, whereas the gpqm and the pqm are the parametric methods employing probability distributions (e.g., generalized Pareto, gamma, and Gaussian) for the CDFs. The dqm initially removes a long‐term linear trend before applying standard quantile mapping to detrended data (Cannon et al, 2015); the qdm is similar to the dqm but it preserves the model‐projected relative changes and apply quantile mapping to correct the remaining biases (Ansari et al, 2022). Other methods (i.e., loci, mva, ptr, scaling, and variance) employ linear transformation or scaling factors to match the statistical properties (i.e., mean, variance or standard deviation) for bias correction (Cannon et al, 2015; Fang et al, 2015; Teutschbein & Seibert, 2012).…”