Transforming Atmospheric and Remotely Sensed Information to Hydrologic Predictability in South Asia

South Asia is a flashpoint for natural disasters with profound societal impacts for the region and globally. Although close to 40% of the world’s population depends on the Greater Himalaya’s great rivers, $20 Billion of GDP is affected by river floods each year. The frequent occurrence of floods, combined with large and rapidly growing populations with high levels of poverty, make South Asia highly susceptible to humanitarian disasters. The challenges of mitigating such devastating disasters are exacerbated by the limited availability of real-time rain and stream gauge measuring stations, lack of transboundary data sharing, the impacts of climate change, and by constrained institutional commitments to overcome these challenges. To overcome some of these limitations, India and the World Bank have committed resources to the National Hydrology Project III, with the development objective to improve the extent, quality, and accessibility of water resources information and to strengthen the capacity of targeted water resources management institutions in India.

The availability and application of remote sensing products and weather forecasts from ensemble prediction systems (EPS) have transformed river forecasting capability over the last decade, and is of interest to India. In this talk, we review the potential predictability of river flow contributed by remotely-sensed and weather forecasting products within the framework of the physics of water migration through a watershed in the context of the Ganges, Brahmaputra, and Meghna river basins. We focus on satellite rainfall estimation, river height and width estimation, and EPS weather forecasts. We discuss how atmospheric predictability, as measured by an EPS, is transformed into hydrometeorological predictability, providing in some cases skillful daily flow forecasts out to weeks in advance. We provide an overview of the strengths and weaknesses of each of these data sets to the river flow prediction problem, generalizing their utility across spatial- and temporal-scales, and highlight the benefits of joint utilization and multi-modeling to minimize uncertainty and enhance operational robustness. Finally, we compare improved forecasts utilizing information from these data sets with our past efforts in the region.