title: Hydro meteorological drivers and extended range flood forecasting for the Brahmaputra river basin in Bangladesh
Creators: name: Hossain, Md Sazzad
Publisher: name: University of Reading
Year: 2023

description: While flooding is an annual occurrence in the Brahmaputra basin during the South Asian summer 
monsoon, there is large variability in the flood characteristics that drive risk: flood duration, rate of water 
level rise and peak water level. Here, three distinct objectives are adopted; first, to assess
hydroclimatological characteristics of floods with respect to the key hydrometeorological drivers. 
Secondly, to understand flood early warning from the users’ perspectives. Thirdly, to understand global 
model skill to simulate flood behaviour and assessment of forecast skill for different flood preparedness 
decisions for early action.
Historical flood records have been analysed to understand flood dynamics focusing on three 
extraordinary floods in 1998 (long duration), 2017 (rapid rise) and 2019 (high water level). The long 
duration floods in the basin have been driven by basin-wide seasonal rainfall extremes associated with 
the development phase of strong La Niña events, whereas floods with a rapid rate of rise have been 
driven by more localised rainfall falling in a hydrological ‘sweet spot’ that leads to a concurrent 
contribution from the tributaries into the main stem of the river. The recent record high water levels are 
not coincident with extreme river flows, hinting that other drivers such as sedimentation and 
morphological changes are also important drivers of flood risk that should be further investigated. 
Communities are aware of the flood season as it is an annual phenomenon in the basin, but they can 
only anticipate floods events 2 to 3 days beforehand based on the available early warnings and their 
risk knowledge. This study finds that a lead time of 10 to 20 days allows better flood preparedness 
decisions to be taken for agricultural planning. Stakeholders specified that they would need a forecast 
probability of 50% and above to activate preparedness action. Capacity development of the local 
community is necessary to improve understanding of the probabilistic forecast and overcome 
communication challenges.
The Global Flood Awareness System (GloFAS) flood forecasting model has been upgraded from 
version 2.1 to 3.1 with a significant change to its hydrological model structure. Skillful global models 
can be used for anticipatory action by humanitarian agencies and also support capacity development
in national hydrometeorological services to provide improved early warning. Reforecasts of two GloFAS 
model versions (version 2.1 and 3.1) have been assessed for the period 1999–2018. GloFAS models 
are skillful in simulating the hydrological behaviour of the Brahmaputra River in Bangladesh. The 
forecast skill shows that GloFAS 2.1 performs better than GloFAS 3.1 in predicting floods. Both versions 
have acceptable skill and also provide acceptable FAR and POD for decision makers in predicting low 
(90th percentile) and medium (95th percentile) threshold floods, with only limited skill for extreme floods 
(99th percentile). 
The thesis provides important information on hydrometeorological drivers related to flood 
characteristics, users’ perspectives on flood preparedness decisions and global flood forecasting model 
skill for the Brahmaputra basin in Bangladesh. The study results will be applied to improve existing flood 
early warning in Bangladesh.