4.7 Article

Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models

Journal

ADVANCES IN WATER RESOURCES
Volume 56, Issue -, Pages 77-89

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.advwatres.2013.03.006

Keywords

Snow; Energy-balance; Temperature-index; Hydrologic model

Funding

  1. Duke University
  2. NASA [NNX11AK35A]
  3. NSF [CBET-0838607]
  4. NSF-CBET [0854553]
  5. USDA-ARS CRIS Snow and Hydrologic Processes in the Intermountain West [5362-13610-008-00D]
  6. USDA-NRCS Water and Climate Center-Portland, Oregon [5362-13610-008-03R]
  7. USDA-ARS Headquarters Postdoctoral Research Associate Program [0101-88888-016-00D]
  8. USDA-NRCS Conservation Effects Assessment Project [5352-13610-009-14R]
  9. USDA-ARS CRIS Preserving water quality and availability for agriculture in the Lower Mississippi River Basin [7408-13000-024-00D]
  10. Div Of Chem, Bioeng, Env, & Transp Sys
  11. Directorate For Engineering [0854553] Funding Source: National Science Foundation
  12. NASA [143049, NNX11AK35A] Funding Source: Federal RePORTER

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Two commonly used strategies in modeling snowmelt are the energy balance and temperature-index methods. Here we evaluate the distributed hydrologic impacts of these two different snowmelt modeling strategies, each in conjunction with a physics-based hydrologic model (PIHM). Results illustrate that both the Isnobal energy-balance and calibrated temperature-index methods adequately reproduce snow depletion at the observation site. However, the models exhibit marked differences in the distribution of snowmelt. When combined with PIHM, both models capture streamflow reasonably during calibration year (WY06), but Isnobal model gives better streamflow results in the validation year (WY07). The uncalibrated temperature-index model predicts streamflow poorly in both years. Differences between distributed snowmelt, as predicted by Isnobal and calibrated temperature-index method, and its consequent effect on predicted hydrologic states suggest the need to carefully calibrate temperature-index models in both time and space. Combined physics-based snow and hydrologic models provide the best accuracy, while a temperature-index model using coefficients from the literature the poorest. (c) 2013 Elsevier Ltd. All rights reserved.

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