語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Characterization of the comparative ...
~
Franz, Kristie Jean.
FindBook
Google Book
Amazon
博客來
Characterization of the comparative skill of conceptual and physically-based snow models for streamflow prediction.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Characterization of the comparative skill of conceptual and physically-based snow models for streamflow prediction./
作者:
Franz, Kristie Jean.
面頁冊數:
223 p.
附註:
Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 3662.
Contained By:
Dissertation Abstracts International67-07B.
標題:
Hydrology. -
電子資源:
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.
LDR
:02613nmm 2200277 4500
001
1829069
005
20071106080119.5
008
130610s2006 eng d
020
$a
9780542789717
035
$a
(UMI)AAI3225034
035
$a
AAI3225034
040
$a
UMI
$c
UMI
100
1
$a
Franz, Kristie Jean.
$3
1917939
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.
590
$a
School code: 0030.
650
4
$a
Hydrology.
$3
545716
650
4
$a
Engineering, Civil.
$3
783781
690
$a
0388
690
$a
0543
710
2 0
$a
University of California, Irvine.
$3
705821
773
0
$t
Dissertation Abstracts International
$g
67-07B.
790
1 0
$a
Sorooshian, Soroosh,
$e
advisor
790
$a
0030
791
$a
Ph.D.
792
$a
2006
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3225034
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9219932
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入