DOI: 10.5194/hess-21-3557-2017
Scopus记录号: 2-s2.0-85024384813
论文题名: Incorporating remote sensing-based ET estimates into the Community Land Model version 4.5
作者: Wang D ; , Wang G ; , Parr D ; T ; , Liao W ; , Xia Y ; , Fu C
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期: 7 起始页码: 3557
结束页码: 3577
语种: 英语
Scopus关键词: Budget control
; Calibration
; Electromagnetic wave attenuation
; Evapotranspiration
; Soil moisture
; Surface measurement
; Bias-correction methods
; Calibration periods
; Community land models
; Continuous development
; Hydrological simulations
; Land surface models
; Physical parameterization
; Physically based modeling
; Aluminum alloys
; energy budget
; evapotranspiration
; land surface
; moisture content
; numerical model
; parameterization
; remote sensing
; runoff
; simulation
; soil moisture
; surface water
英文摘要: Land surface models bear substantial biases in simulating surface water and energy budgets despite the continuous development and improvement of model parameterizations. To reduce model biases, Parr et al. (2015) proposed a method incorporating satellite-based evapotranspiration (ET) products into land surface models. Here we apply this bias correction method to the Community Land Model version 4.5 (CLM4.5) and test its performance over the conterminous US (CONUS). We first calibrate a relationship between the observational ET from the Global Land Evaporation Amsterdam Model (GLEAM) product and the model ET from CLM4.5, and assume that this relationship holds beyond the calibration period. During the validation or application period, a simulation using the default CLM4.5 (CLM) is conducted first, and its output is combined with the calibrated observational-vs.-model ET relationship to derive a corrected ET; an experiment (CLMET) is then conducted in which the model-generated ET is overwritten with the corrected ET. Using the observations of ET, runoff, and soil moisture content as benchmarks, we demonstrate that CLMET greatly improves the hydrological simulations over most of the CONUS, and the improvement is stronger in the eastern CONUS than the western CONUS and is strongest over the Southeast CONUS. For any specific region, the degree of the improvement depends on whether the relationship between observational and model ET remains time-invariant (a fundamental hypothesis of the Parr et al. (2015) method) and whether water is the limiting factor in places where ET is underestimated. While the bias correction method improves hydrological estimates without improving the physical parameterization of land surface models, results from this study do provide guidance for physically based model development effort. © 2017 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79115
Appears in Collections: 气候变化事实与影响
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作者单位: School of Geography and Planning, Sun Yat-sen University, Guangzhou, China; Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, China; Key Laboratory of Water Cycle and Water Security in Southern China, Guangdong High Education Institute, Sun Yat-sen University, Guangzhou, China; Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, United States; National Centers for Environmental Prediction, Environmental Modeling Center, I. M. System Group, NCEP, EMC, College Park, MD, United States
Recommended Citation:
Wang D,, Wang G,, Parr D,et al. Incorporating remote sensing-based ET estimates into the Community Land Model version 4.5[J]. Hydrology and Earth System Sciences,2017-01-01,21(7)