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.

An Iterative ICA-Based Reconstruction Method to Produce Consistent Time-Variable Total Water Storage Fields Using GRACE and Swarm Satellite Data

“…Largest decreasing trends are found in basins with large scale irrigation such as Ganges, Indus, Barhmaputra, and Yellow River Basins that are strongly affected by climate change. These results confirm the findings by [87,[105][106][107].…”

A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme

“…The average temperature of the country ranges from 17 • C to 20.6 • C during winter and 26.9 • C to 31.1 • C during summer (Rajib et al, 2011). Bangladesh is placed in the sub-tropical region with a humid, warm, and tropical climate, which is dominated by a subtropical monsoon originating over the Indian Ocean, which carry warm, moist, and unstable air (Ahmed, 2006;Khandu et al, 2017). An average drought frequency in the country is reported to be equivalent to 2.5 years (Adnan, 1993;Hossain, 1990) when rainfall, as the most important water supply, drops by almost 46% (Dey et al, 2011).…”

“…Incorporating more microwave sounding and imagery records as well as implementing better processing algorithms have caused a large improvement in this version of data (Huffman et al, 2012;Fleming and Awange, 2013). The data sets, validated by Khandu et al (2017) over the study region showed promising performance. The gridded (0.25 • ×0.25 • ) precipitation products (2003 to 2013) are converted to 1 • ×1 • and used to investigate their connection to water storage changes.…”

Bias correction of a gauge-based gridded product to improve extreme precipitation analysis in the Yarlung Tsangpo–Brahmaputra River basin

“…However, due to scarce spatial coverage of rainfall stations, short-length rainfall records, and high proportions of missing data, observations currently available in some remote basins are clearly inadequate to capture their precipitation characteristics. In addition, observed rainfall data are usually difficult to collect in international river basins because many countries may not share or freely distribute data (Lakshmi et al, 2018).…”