Articles | Volume 18, issue 5
Hydrol. Earth Syst. Sci., 18, 1679–1693, 2014
https://doi.org/10.5194/hess-18-1679-2014
Hydrol. Earth Syst. Sci., 18, 1679–1693, 2014
https://doi.org/10.5194/hess-18-1679-2014

Research article 09 May 2014

Research article | 09 May 2014

Predicting streamflows in snowmelt-driven watersheds using the flow duration curve method

D. Kim and J. Kaluarachchi D. Kim and J. Kaluarachchi
  • College of Engineering, Utah State University, Logan, UT 84322-4100, USA

Abstract. Predicting streamflows in snow-fed watersheds in the Western United States is important for water allocation. Since many of these watersheds are heavily regulated through canal networks and reservoirs, predicting expected natural flows and therefore water availability under limited data is always a challenge. This study investigates the applicability of the flow duration curve (FDC) method for predicting natural flows in gauged and regulated snow-fed watersheds. Point snow observations, air temperature, precipitation, and snow water equivalent were used to simulate the snowmelt process with the SNOW-17 model, and extended to streamflow simulation using the FDC method with a modified current precipitation index. For regulated watersheds, a parametric regional FDC method was applied to reconstruct natural flow. For comparison, a simplified tank model was used considering both lumped and semi-distributed approaches. The proximity regionalization method was used to simulate streamflows in the regulated watersheds with the tank model. The results showed that the FDC method is capable of producing satisfactory natural flow estimates in gauged watersheds when high correlation exists between current precipitation index and streamflow. For regulated watersheds, the regional FDC method produced acceptable river diversion estimates, but it seemed to have more uncertainty due to less robustness of the FDC method. In spite of its simplicity, the FDC method is a practical approach with less computational burden for studies with minimal data availability.

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