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Characterization of the comparative ...

Franz, Kristie Jean.

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Characterization of the comparative skill of conceptual and physically-based snow models for streamflow prediction.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Characterization of the comparative skill of conceptual and physically-based snow models for streamflow prediction./
Author:	Franz, Kristie Jean.
Description:	223 p.
Notes:	Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3662.
Contained By:	Dissertation Abstracts International67-07B.
Subject:	Hydrology. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3225034
ISBN:	9780542789717

Characterization of the comparative skill of conceptual and physically-based snow models for streamflow prediction.
Franz, Kristie Jean.

Characterization of the comparative skill of conceptual and physically-based snow models for streamflow prediction. - 223 p.

Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3662.

Thesis (Ph.D.)--University of California, Irvine, 2006.

The National Weather Service (NWS) SNOW17 is a conceptual, temperature-index model used by the NWS to aid in the generation of short- to long-term streamflow predictions for river basins across the U.S. Numerous snow models explicitly represent physical snow processes; however, the inability to meet extensive data requirements is stated as the primary reason for keeping more advanced models out of operations. With the continual advances in data acquisition techniques the lack of data may soon no longer be a limitation for operational forecasting. This leads to the question of whether the increased skill, if any, of streamflow predictions is worth the efforts of implementing a more complex model. This study compares the physically-based, energy balance snow model component of the Snow-Atmosphere-Soil Transfer model (SAST) to the SNOW17 for simulating snow processes at the Mammoth Mountain snow study site, California and an at the Reynolds Creek Experimental Watershed, Idaho. A systematic framework to evaluate hydrologic models for streamflow prediction is presented. Significant improvements in modeling snow accumulation and melt using the SAST (over the SNOW-17) were not observed for the locations studied. The SAST and SNOW17 performed equally well for the simulation of SWE and discharge for most years. Hindcasts reveal that the SAST does have skill for predicting snow water equivalent and the subsequent streamflow, but overall is not better than the SNOW17. The implication of this finding is that advanced snow models will have no consequences for improving streamflow prediction until data collection and modeling technologies advance further.

ISBN: 9780542789717Subjects--Topical Terms:

545716
Hydrology.

Characterization of the comparative skill of conceptual and physically-based snow models for streamflow prediction.
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245 1 0 $a Characterization of the comparative skill of conceptual and physically-based snow models for streamflow prediction.
300 $a 223 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3662.
500 $a Adviser: Soroosh Sorooshian.
502 $a Thesis (Ph.D.)--University of California, Irvine, 2006.
520 $a The National Weather Service (NWS) SNOW17 is a conceptual, temperature-index model used by the NWS to aid in the generation of short- to long-term streamflow predictions for river basins across the U.S. Numerous snow models explicitly represent physical snow processes; however, the inability to meet extensive data requirements is stated as the primary reason for keeping more advanced models out of operations. With the continual advances in data acquisition techniques the lack of data may soon no longer be a limitation for operational forecasting. This leads to the question of whether the increased skill, if any, of streamflow predictions is worth the efforts of implementing a more complex model. This study compares the physically-based, energy balance snow model component of the Snow-Atmosphere-Soil Transfer model (SAST) to the SNOW17 for simulating snow processes at the Mammoth Mountain snow study site, California and an at the Reynolds Creek Experimental Watershed, Idaho. A systematic framework to evaluate hydrologic models for streamflow prediction is presented. Significant improvements in modeling snow accumulation and melt using the SAST (over the SNOW-17) were not observed for the locations studied. The SAST and SNOW17 performed equally well for the simulation of SWE and discharge for most years. Hindcasts reveal that the SAST does have skill for predicting snow water equivalent and the subsequent streamflow, but overall is not better than the SNOW17. The implication of this finding is that advanced snow models will have no consequences for improving streamflow prediction until data collection and modeling technologies advance further.
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