کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8894642 1629892 2018 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Influence of rainfall data scarcity on non-point source pollution prediction: Implications for physically based models
ترجمه فارسی عنوان
تأثیر کمبود داده های بارش در پیش بینی آلودگی منابع غیر نقطه ای: پیامدهای مدل های مبتنی بر
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
چکیده انگلیسی
Hydrological and non-point source pollution (H/NPS) predictions in ungagged basins have become the key problem for watershed studies, especially for those large-scale catchments. However, few studies have explored the comprehensive impacts of rainfall data scarcity on H/NPS predictions. This study focused on: 1) the effects of rainfall spatial scarcity (by removing 11%-67% of stations based on their locations) on the H/NPS results; and 2) the impacts of rainfall temporal scarcity (10%-60% data scarcity in time series); and 3) the development of a new evaluation method that incorporates information entropy. A case study was undertaken using the Soil and Water Assessment Tool (SWAT) in a typical watershed in China. The results of this study highlighted the importance of critical-site rainfall stations that often showed greater influences and cross-tributary impacts on the H/NPS simulations. Higher missing rates above a certain threshold as well as missing locations during the wet periods resulted in poorer simulation results. Compared to traditional indicators, information entropy could serve as a good substitute because it reflects the distribution of spatial variability and the development of temporal heterogeneity. This paper reports important implications for the application of Distributed Hydrological Models and Semi-distributed Hydrological Models, as well as for the optimal design of rainfall gauges among large basins.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 562, July 2018, Pages 1-16
نویسندگان
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