Abstract/Summary

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.