Hydrologic Regionalization under Data Scarcity: Implications for Streamflow Prediction
Date of Original Version
Continuous streamflow prediction is crucial in many applications of water resources planning and management. However, streamflow prediction is challenging, particularly in data-scarce regions. This paper demonstrates an approach to regionalize the flow duration curve for predicting daily streamflow in the data-scare region of the central Himalayas. We developed a regression-based model to estimate streamflow at various segments of a flow duration curve by incorporating basin characteristics and climate variables. This study analyzes the sensitivities of proximity and characteristics between the donor (gauged) and receptor (ungauged) basins for time-series streamflow prediction. Our results show that regionalization techniques perform better in low to medium flows over high flows. Our findings are significant in the central Himalayan regional context to inform operational and management decisions in water sector projects like hydropower plants, which generally rely on low-to-medium streamflow information. Although the quantitative results are region-specific, the approach and insights are generalizable to the Himalayan region.
Publication Title, e.g., Journal
Journal of Hydrologic Engineering
Panthi, Jeeban, Rocky Talchabhadel, Ganesh R. Ghimire, Sanjib Sharma, Piyush Dahal, Rupesh Baniya, Thomas Boving, Soni M. Pradhanang, and Binod Parajuli. "Hydrologic Regionalization under Data Scarcity: Implications for Streamflow Prediction." Journal of Hydrologic Engineering 26, 9 (2021). doi: 10.1061/(ASCE)HE.1943-5584.0002121.