Bias correction of modelled precipitation from CORDEX-CORE experiments over the Upper Teesta River Basin

For assessing the impact of climate change on socio-eco-hydrological systems at the catchment scale, reasonable and reliable meteorological input data are essential. The modelled, gridded meteorological datasets of several fields, e.g., precipitation, temperature, surface moisture, etc. from Regional Climate Models (RCMs) are widely used as inputs to numerically ecological or hydrological model and estimate the climate change impact on several natural systems inside a catchment. However, in the case of mountainous regions, like the Himalayas due to the scarcity of station observations or human inaccessibility, most of the RCMs contain inherent and systemic biases. Hence, before forcing these RCMs on ecological or hydrological models, the biases present in RCMs must be corrected. This study aims to provide a set of reasonable, bias-corrected precipitation field from 12 simulated CORDEX-CORE model experiments, to provide inputs for the estimation of hydrological changes over the mountainous Upper Teesta River Basin (UTRB) situated at the eastern Himalayas. The model precipitation field from 12 CORDEX-CORE model experiments and the corresponding observed precipitation field from CHELSA V2.1 climatic reanalysis were considered for the reference period of 1979–2005. Their performances were inter-compared and linear scaling, distribution mapping, and power transformation bias correction methods were applied on each grid of the precipitation fields from 12 CORDEX-CORE model experiments. After the application of bias correction methods, all the CORDEX-CORE model experiments show a reduction in bias. Among the bias correction methods, the distribution mapping method had altered the model precipitation fields while preserving the statistical characteristics of the observed data and was found to be more efficient than the other two, while the linear scaling method was found to be worst performing. Although the modified precipitation fields weren’t forced with a hydrological model in the present study, the evaluation of performances of bias-corrected model outputs shows that the precipitation field from the ERAINT-COSMO model experiment corrected with the distribution mapping method could be the best fit RCM for studying the hydrological impacts due to climatic changes over the data-scarce basins like the UTRB.