کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4978216 1452260 2017 21 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A remote sensing-based tool for assessing rainfall-driven hazards
ترجمه فارسی عنوان
یک ابزار مبتنی بر سنجش از راه دور برای ارزیابی خطرات ناشی از بارندگی
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزار
چکیده انگلیسی
RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, RainyDay can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, RainyDay can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. RainyDay can be useful for hazard modeling under nonstationary conditions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Modelling & Software - Volume 90, April 2017, Pages 34-54
نویسندگان
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