Changes and variability of precipitation and temperature in the Ganges-Brahmaputra-Meghna River Basin based on global high-resolution reanalyses

Abstract

Previous studies suggest that climate change impacts significantly on the hydro-climatic processes within the Ganges–Brahmaputra–Meghna (GBM) River Basin (RB). This study examines the observed climate characteristics and potential strengths and limitations of three global high-resolution reanalyses and satellite remote-sensing products over the GBM RB for period 1980–2013 by (1) estimating trends and interannual variations of precipitation and temperature, and (2) isolating precipitation variations likely associated with El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). The surface temperature trends show widespread warming across the basin with a maximum increase of 0.6 °C decade−1 over western Nepal and southern Tibet from 1980 to 2013. Rainfall changes from 1980 to 2013 indicate pronounced decline over high rainfall regions of northeast India, Bhutan, Nepal, and Bangladesh, especially from 1998 to 2013. Basin-wide averaged trends show rainfall decline of up to 39 mm decade−1 in June–August in the Brahmaputra–Meghna RB from 1998 to 2013.Temperature variability based on Principal Component Analysis indicates that the first mode is associated with sea surface temperature (SST) warming in the Arabian Sea and the western tropical Pacific Ocean, while the second mode appears to be significantly correlated to SST anomalies in the western (eastern) tropical Indian (Pacific) Ocean. ENSO and IOD events are found to significantly influence rainfall variability contributing to about 10–20% (ENSO) and 8–10% (IOD) of the annual rainfall, mainly over Bhutan, Nepal, Bangladesh, and north-eastern India. Among the three reanalysis products: European Centre for Medium-Range Weather Forecasts (ECMWF) retrospective analysis (ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR), ERA-Interim (and MERRA) agrees well with the observed precipitation (temperature) data sets while, CFSR shows the least skills in representing the spatio-temporal variations of precipitation and temperature.