کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6457740 1420854 2017 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Impact of canopy representations on regional modeling of evapotranspiration using the WRF-ACASA coupled model
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات علم هواشناسی
پیش نمایش صفحه اول مقاله
Impact of canopy representations on regional modeling of evapotranspiration using the WRF-ACASA coupled model
چکیده انگلیسی


- Impacts of canopy representation on reference and actual evapotranspirations are investigated.
- Two different complexity land surface models in WRF are compared.
- Model sensitivity to input data surface representation increases as model complexity increases.

In this study, we couple the Weather Research and Forecasting Model (WRF) with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model, to investigate the impact of canopy representation on regional evapotranspiration. The WRF-ACASA model uses a multilayer structure to represent the canopy, consequently allowing microenvironmental variables such as leaf area index (LAI), air and canopy temperature, wind speed and humidity to vary both horizontally and vertically. The improvement in canopy representation and canopy-atmosphere interaction allow for more realistic simulation of evapotranspiration on both regional and local scales. The coupled WRF-ACASA model is compared with the widely used intermediate complexity Noah land surface model in WRF (WRF-Noah) for both potential (ETo) and actual evapotranspiration (ETa). Two LAI datasets (USGS and MODIS) are used to study the model responses to surface conditions. Model evaluations over a diverse surface stations from the CIMIS and AmeriFlux networks show that an increase surface representations increase the model accuracy in ETa more so than ETo. Overall, while the high complexity of WRF-ACASA increases the realism of plant physiological processes, the model sensitivity to surface representation in input data such as LAI also increases.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Agricultural and Forest Meteorology - Volume 247, 15 December 2017, Pages 79-92
نویسندگان
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