Floods are a major natural disaster aggravating poverty in the Indus and Ganges-Brahmaputra-Meghna (GBM) basins, which is home to over 600 million people and almost half of the world’s poor. The increasing frequency and intensity of transboundary flood events in the Hindu Kush Himalayan (HKH) region, which is likely to continue or worsen due to climate change, reinforces the importance of regional cooperation and capacity development in flood forecasting and early warning systems. The HKH HYCOS Initiative (2009-2014), implemented by ICIMOD jointly with the World Meteorological Organization (WMO) and partners from the regional member countries of Bangladesh, Bhutan, China, India, Nepal, and Pakistan, has focused on the establishment of a regional flood information system and methodologies to obtain real-time hydrological observations.

During the 2014 monsoon season, a pilot Regional Flood Outlook was set up for the Ganges and Brahmaputra basins. A hydrologic and hydrodynamic model using Mike 11 was developed, calibrated, and validated. The model produced three-day flood outlooks (i.e., 24 hour, 48 hour, and 72 hour), for 21 nodes in the Ganges and Brahmaputra basins. A flood situation was correctly estimated for the period 14–16 August 2014 in various river basins, including the Koshi at Chatara, the Karnali at Chisapani, and the Narayani at Devghat in Nepal when intense rainfall occurred across the Himalayas.

ICIMOD provided the flood outlook for various locations in Nepal to the Department of Hydrology and Meteorology (DHM). Based on the flood outlook, DHM prepared a flood bulletin and disseminated warnings to the Ministry of Home Affairs in Nepal and also issued a statement on their website. This allowed for the warnings to be widely disseminated for better flood preparedness when the water levels in the various rivers crossed the alert and danger levels and widespread flooding occurred. Based on the encouraging performance during the pilot phase, the regional flood outlook is being further improved to develop more reliable and accurate forecasts.