Evaluation and selection of CORDEX-SA datasets and bias correction methods for a hydrological impact study in a humid tropical river basin, Kerala

Abstract

It is well recognised that the performance of climate model simulations and bias correction methods is region specific, and, therefore, careful validation should always be performed. This study evaluates the performance of five general circulation model–regional climate model (GCM–RCM) combinations selected from CORDEX–SA datasets over a humid tropical river basin in Kerala, India, for climate variables such as precipitation, maximum and minimum temperatures. This involves ranking of the selected climate models based on an EDAS (Evaluation Based on Distance from Average Solution) method and the selection of an appropriate bias correction method for the selected three climate variables. A range of indices are used to evaluate the performance of the bias-corrected climate models to simulate observed climate data. Finally, the hydrological impact of the bias-corrected ranked models is assessed by simulating streamflow over the river basin using individual models and different combinations of models based on rank. According to the findings, hydrological simulation using an average of all GCM–RCM pairs provides the best model output in simulating streamflow, with an NSE value of 0.72. The results confirm the importance of a multimodel ensemble for improving the reliability and minimising the uncertainty of climate predictions for impact studies.

Bias correction of CMIP6 simulations of precipitation over Indian monsoon core region using deep learning algorithms

“…From the analysis, we can see that the LSTM‐ED algorithm has a closer range of values with respect to the observed data than the result obtained with the CNN1D algorithm. We have also measured the 90th and 95th percentiles to check the skill of bias correction algorithms for extreme precipitation events by calculating the difference (Saranya & Nair Vinish, 2021) between IMD and bias‐corrected data. A minimum difference was found in the case of the LSTM‐ED technique than in the CNN1D.…”

Investigating the Impact of Multiple Factors on CO2 Emissions: Insights from Quantile Analysis

“…Over the last six decades, substantial economic advancement and a significant increase in the world's population have been accompanied by negative impacts on the environment [1]. The Fifth Assessment Report [2] provides an important framework for understanding the impacts of climate change on natural and human systems across the globe [3,4]. This urgent call for action stems from the growing scientific evidence that shows the detrimental effects of global warming and the escalating environmental damage caused by carbon dioxide (CO 2 ) emissions [5].…”