A Hydroclimatological Analysis of Precipitation in the Ganges–Brahmaputra–Meghna River Basin

Abstract

Understanding seasonal precipitation input into river basins is important for linking large-scale climate drivers with societal water resources and the occurrence of hydrologic hazards such as floods and riverbank erosion. Using satellite data at 0.25-degree resolution, spatial patterns of monsoon (June-July-August-September) precipitation variability between 1983 and 2015 within the Ganges-Brahmaputra-Meghna (GBM) river basin are analyzed with Principal Component (PC) analysis and the first three modes (PC1, PC2 and PC3) are related to global atmospheric-oceanic fields. PC1 explains 88.7% of the variance in monsoonal precipitation and resembles climatology with the center of action over Bangladesh. The eigenvector coefficients show a downward trend consistent with studies reporting a recent decline in monsoon rainfall, but little interannual variability. PC2 explains 2.9% of the variance and shows rainfall maxima to the far western and eastern portions of the basin. PC2 has an apparent decadal cycle and surface and upper-air atmospheric height fields suggest the pattern could be forced by tropical South Atlantic heating and a Rossby wave train stemming from the North Atlantic, consistent with previous studies. Finally, PC3 explains 1.5% of the variance and has high spatial variability. The distribution of precipitation is somewhat zonal, with highest values at the southern border and at the Himalayan ridge. There is strong interannual variability associated with PC3, related to the El Nino/Southern Oscillation (ENSO). Next, we perform a hydroclimatological downscaling, as precipitation attributed to the three PCs was averaged over the Pfafstetter level-04 sub-basins obtained from the World Wildlife Fund (Gland, Switzerland). While PC1 was the principal contributor of rainfall for all sub-basins, PC2 contributed the most to rainfall in the western Ganges sub-basin (4524) and PC3 contributed the most to the rainfall in the northern Brahmaputra (4529). Monsoon rainfall within these two sub-basins were the only ones to show a significant relationship (negative) with ENSO, whereas four of the eight sub-basins had a significant relationship (positive) with sea surface temperature (SST) anomalies in the tropical South Atlantic. This work demonstrates a geographic dependence on climate teleconnections in the GBM that deserves further study.

Precipitation and Flow Variations in the Lancang–Mekong River Basin and the Implications of Monsoon Fluctuation and Regional Topography

“…Several large densely populated and flood-prone river basins in South and Southeast Asia, such as the Indus, Ganges, Yarlung Tsangpo-Brahmaputra, Irrawaddy and Lancang-Mekong River Basin, are characterized predominantly by the influences of strong monsoons and complex topography [35][36][37][38][39][40][41]. The comprehensive effects of monsoon fluctuation and regional topography determine the spatiotemporal distributions and variations of precipitation, and there are certain areas that make major contributions to both the amount and the variabilities of the river flow.…”

Precipitation Trends in the Ganges-Brahmaputra-Meghna River Basin, South Asia: Inconsistency in Satellite-Based Products

“…We analyze and compare the trend patterns of pre-monsoon and monsoon precipitation in the GBM river basin for the period of 1983-2019. Both products provide long-term precipitation data sets, and PERSIANN-CDR has been used frequently in the countries of the GBM river basin [36,38,[51][52][53][54].…”

“…Interestingly, the closest catchment to the GBM, located in southwestern China, showed the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) [35] as the superior product (see [33] Figure 3). PERSAINN-CDR has had a track record of success in estimating rainfall in South Asia [36][37][38][39][40].…”

Ottocodificação De Pequenas Bacias Hidrográficas Na Amazônia

“…Buscando definir um valor de área para classificar as pequenas bacias hidrográficas na Amazônia, o estudo de Santana e Blanco (2020) utilizou um Modelo Linear Simples (MLS) para observar a linearidade entre os processos chuva-vazão. Segundo Blanco et al (2013) (STEIN, 2018), sendo adotada em diversos estudos para delimitação e classificação de bacias hidrográficas (CURTIS et al, 2018;MULLER;ROLIM. ;MARCUZZO, 2018;TRINDADE-SANTOS et al, 2018;PACHRI et al, 2013).…”