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Title: Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate

Abstract

Here, recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2 = 0.60 ( RMSE = 18.72 W m-2) for all 27 sites and R2=0.62 ( RMSE 5 18.21 W m-2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of onlymore » 0.50 ( RMSE 5 20.74 W m-2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1];  [3]; ORCiD logo [4]
  1. Chinese Academy of Sciences (CAS), Beijing (China); Univ. of Chinese Academy of Sciences, Beijing (China)
  2. Chinese Academy of Sciences (CAS), Beijing (China)
  3. Univ. of Chinese Academy of Sciences, Beijing (China)
  4. Chinese Academy of Sciences (CAS), Beijing (China); Univ. of Chinese Academy of Sciences, Beijing (China); Univ. of Agriculture, Makurdi (Nigeria)
Publication Date:
Research Org.:
Oregon State Univ., Corvallis, OR (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1362031
Grant/Contract Number:  
FG02-04ER63917; FG02-04ER63911
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Advances in Modeling Earth Systems
Additional Journal Information:
Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Bai, Yun, Zhang, Jiahua, Zhang, Sha, Koju, Upama Ashish, Yao, Fengmei, and Igbawua, Tertsea. Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate. United States: N. p., 2017. Web. doi:10.1002/2016MS000702.
Bai, Yun, Zhang, Jiahua, Zhang, Sha, Koju, Upama Ashish, Yao, Fengmei, & Igbawua, Tertsea. Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate. United States. doi:10.1002/2016MS000702.
Bai, Yun, Zhang, Jiahua, Zhang, Sha, Koju, Upama Ashish, Yao, Fengmei, and Igbawua, Tertsea. Wed . "Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate". United States. doi:10.1002/2016MS000702. https://www.osti.gov/servlets/purl/1362031.
@article{osti_1362031,
title = {Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate},
author = {Bai, Yun and Zhang, Jiahua and Zhang, Sha and Koju, Upama Ashish and Yao, Fengmei and Igbawua, Tertsea},
abstractNote = {Here, recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2 = 0.60 (RMSE = 18.72 W m-2) for all 27 sites and R2=0.62 (RMSE 5 18.21 W m-2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 (RMSE 5 20.74 W m-2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.},
doi = {10.1002/2016MS000702},
journal = {Journal of Advances in Modeling Earth Systems},
number = 1,
volume = 9,
place = {United States},
year = {2017},
month = {1}
}

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