期刊
HYDROLOGICAL PROCESSES
卷 25, 期 4, 页码 650-660出版社
JOHN WILEY & SONS LTD
DOI: 10.1002/hyp.7857
关键词
hydrology; snow; remote sensing; arctic
资金
- NSF-Idaho EPSCoR
- National Science Foundation [EPS-0447689, EPS-0814387]
- Idaho Space Grant Consortium
- Directorate For Geosciences
- Division Of Earth Sciences [0930055] Funding Source: National Science Foundation
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [0854522] Funding Source: National Science Foundation
Describing the spatial variability of heterogeneous snowpacks at a watershed or mountain-front scale is important for improvements in large-scale snowmelt modelling. Snowmelt depletion curves, which relate fractional decreases in snow-covered area (SCA) against normalized decreases in snow water equivalent (SWE), are a common approach to scale-up snowmelt models. Unfortunately, the kinds of ground-based observations that are used to develop depletion curves are expensive to gather and for large areas. We describe an approach incorporating remotely sensed fractional SCA (FSCA) data with coinciding daily snowmelt SWE outputs during ablation to quantify the shape of a depletion curve. We joined melt estimates from the Utah Energy Balance Snow Accumulation and Melt Model (UEB) with FSCA data calculated from a normalized difference snow index snow algorithm using NASA's moderate resolution imaging spectroradiometer (MODIS) visible (0.545-0.565 mu m) and shortwave infrared (1.628-1.652 mu m) reflectance data. We tested the approach at three 500 m(2) study sites, one in central Idaho and the other two on the North Slope in the Alaskan arctic. The UEB-MODIS-derived depletion curves were evaluated against depletion curves derived from ground-based snow surveys. Comparisons showed strong agreement between the independent estimates. Copyright (C) 2010 John Wiley & Sons, Ltd.
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