Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

Abstract

For effective management practices and decision-making, the uncertainty associated with Regional Climate Models (RCMs) and their scenarios need to be assessed in the context of climate change. The present study analyzes the various uncertainties in the precipitation and temperature datasets of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under Representative Concentrative Pathways (RCPs) 4.5 and 8.5 over the Munneru river basin, in India, using the Reliable Ensemble Averaging (REA) method. From the available 21 RCMs, the top five ranked are ensembled and bias-corrected at each grid using the non-parametric quantile mapping method for the precipitation and temperature datasets. The spatio-temporal variations in precipitation and temperature data for the future periods, i.e., 2021–2039 (near future), 2040–2069 (mid future) and 2070–2099 (far future) are analyzed. For the period 2021–2099, annual average precipitation increases by 233 mm and 287 mm, respectively, the in RCP 4.5 and RCP 8.5 scenarios when compared to the observed period (1951–2005). In both the RCP 4.5 and RCP 8.5 scenarios, the annual average maximum temperature rises by 1.8 °C and 1.9 °C, respectively. Similarly, the annual average minimum temperature rises by 1.8 °C and 2.5 °C for the RCP 4.5 and RCP 8.5 scenarios, respectively. The spatio-temporal climatic variations for future periods obtained from high-resolution climate model data aid in the preparation of water resource planning and management options in the study basin under the changing climate. The methodology developed in this study can be applied to any other basin to analyze the climatic variables suitable for climate change impact studies that require a finer scale, but the biases present in the historical simulations can be attributed to uncertainties in the estimation of climatic variable projections. The findings of the study indicate that NEX-GDDP datasets are in good agreement with IMD datasets on monthly scales but not on daily scales over the observed period, implying that these data should be scrutinized more closely on daily scales, especially when utilized in impact studies.