气候变化领域集成服务门户(CCPortal): Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska

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DOI	10.5194/hess-23-2439-2019
	Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska
	Bennett K.E.; Cherry J.E.; Balk B.; Lindsey S.
发表日期	2019
ISSN	1027-5606
起始页码	2439
结束页码	2459
卷号	23 期号:5
英文摘要	Remotely sensed snow cover observations provide an opportunity to improve operational snowmelt and streamflow forecasting in remote regions. This is particularly true in Alaska, where remote basins and a spatially and temporally sparse gaging network plague efforts to understand and forecast the hydrology of subarctic boreal basins and where climate change is leading to rapid shifts in basin function. In this study, the operational framework employed by the United States (US) National Weather Service, including the Alaska Pacific River Forecast Center, is adapted to integrate Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed observations of fractional snow cover area (fSCA) to determine if these data improve streamflow forecasts in interior Alaska river basins. Two versions of MODIS fSCA are tested against a base case extent of snow cover derived by aerial depletion curves: the MODIS 10A1 (MOD10A1) and the MODIS Snow Cover Area and Grain size (MODSCAG) product over the period 2000-2010. Observed runoff is compared to simulated runoff to calibrate both iterations of the model. MODIS-forced simulations have improved snow depletion timing compared with snow telemetry sites in the basins, with discernable increases in skill for the streamflow simulations. The MODSCAG fSCA version provides moderate increases in skill but is similar to the MOD10A1 results. The basins with the largest improvement in streamflow simulations have the sparsest streamflow observations. Considering the numerous low-quality gages (discontinuous, short, or unreliable) and ungauged systems throughout the high-latitude regions of the globe, this result is valuable and indicates the utility of the MODIS fSCA data in these regions. Additionally, while improvements in predicted discharge values are subtle, the snow model better represents the physical conditions of the snowpack and therefore provides more robust simulations, which are consistent with the US National Weather Service's move toward a physically based National Water Model. Physically based models may also be more capable of adapting to changing climates than statistical models corrected to past regimes. This work provides direction for both the Alaska Pacific River Forecast Center and other forecast centers across the US to implement remote-sensing observations within their operational framework, to refine the representation of snow, and to improve streamflow forecasting skill in basins with few or poor-quality observations. © 2019 Author(s).
语种	英语
scopus关键词	Antennas; Climate change; Climate models; Forecasting; Radiometers; Remote sensing; Rivers; Runoff; Stream flow; Weather information services; Moderate resolution imaging spectroradiometer; National Weather Services; Physically based models; Remotely-sensed observations; River forecast centers; Streamflow forecasting; Streamflow simulations; US National Weather Service; Snow; climate change; estimation method; forecasting method; model validation; MODIS; remote sensing; river basin; satellite data; snow cover; snowpack; streamflow; subarctic region; telemetry; Alaska; United States; United States
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159676
作者单位	Bennett, K.E., International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AL 99775, United States, Water and Environmental Research Center, University of Alaska Fairbanks, Fairbanks, AL 99775, United States, Earth and Environmental Sciences, Los Alamos National Lab, Los Alamos, NM 87545, United States; Cherry, J.E., International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AL 99775, United States, Water and Environmental Research Center, University of Alaska Fairbanks, Fairbanks, AL 99775, United States, Alaska Pacific River Forecast Center, Anchorage, AL 99502, United States; Balk, B., Deltares USA, Silver Spring, MD 20910, United States; Lindsey, S., Alaska Pacific River Forecast Center, Anchorage, AL 99502, United States
推荐引用方式 GB/T 7714	Bennett K.E.,Cherry J.E.,Balk B.,et al. Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska[J],2019,23(5).
APA	Bennett K.E.,Cherry J.E.,Balk B.,&Lindsey S..(2019).Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska.Hydrology and Earth System Sciences,23(5).
MLA	Bennett K.E.,et al."Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in interior Alaska".Hydrology and Earth System Sciences 23.5(2019).