دانلود مقاله ISI انگلیسی شماره 95963
ترجمه فارسی عنوان مقاله

یک چارچوب برای بهبود شبیه سازی هیدرولوژیکی بیش از حد قطع در مناطق برف تحت تاثیر قرار

عنوان انگلیسی
A framework to improve hyper-resolution hydrological simulation in snow-affected regions
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
95963 2017 12 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Hydrology, Volume 552, September 2017, Pages 1-12

ترجمه کلمات کلیدی
تاج، مدل هیدرولوژیکی توزیع، فرآیند برف، وضوح بالا، سیل
کلمات کلیدی انگلیسی
CREST; Distributed hydrological model; Snow process; Hyper-resolution; Flood;
پیش نمایش مقاله
پیش نمایش مقاله  یک چارچوب برای بهبود شبیه سازی هیدرولوژیکی بیش از حد قطع در مناطق برف تحت تاثیر قرار

چکیده انگلیسی

Snow processes in mid- and north-latitude basins and their interaction with runoff generation at hyperresolution (<1 km and <hourly) pose challenges in current state-of-the-art distributed hydrological models. These models run typically at macro to moderate scales (>5 km), representing land surface processes based on simplified couplings of snow thermal physics and the water cycle in the soil-vegetation-atmosphere (SVA) layers. This paper evaluates a new hydrological model capable of simulating river flows for a range of basin scales (100 km2 to >10,000 km2), and a particular focus on mid- and north-latitude regions. The new model combines the runoff generation and fully distributed routing framework of the Coupled Routing and Excess STorage (CREST) model with a new land surface process model that strictly couples water and energy balances at the SVA layer, imposing closed energy balance solutions. The model is vectorized and parallelized to achieve long-term (>30 years) high-resolution (30 m to 500 m and subhourly) simulations of large river basins utilizing high-performance computing. The model is tested in the Connecticut River basin (20,000 km2), where flooding is frequently associated with interactions of snowmelt triggered by rainfall events. Model simulations of distributed evapotranspiration (ET) and snow water equivalence (SWE) at daily time step are shown to match accurately ET estimates from MODIS (average NSCE and bias are 0.77 and 6.79%) and SWE estimates from SNODAS (average correlation and normalized root mean square error are 0.94 and of 19%); the modeled daily river flow simulations exhibit an NSCE of 0.58 against USGS streamflow observations.