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Title: Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia

Abstract

This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff (R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11 ± 0.45, 1.26 ± 0.11, and 0.85 ±more » 0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [8];  [2];  [9];  [10];  [1];  [11];  [12];  [13]
  1. Science Applications International Corporation, McLean, VA (United States); NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
  2. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
  3. Univ. of Maryland, College Park, MD (United States)
  4. Johns Hopkins Univ., Baltimore, MD (United States)
  5. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); Univ. of Maryland, College Park, MD (United States)
  6. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  7. Univ. of Utah, Salt Lake City, UT (United States)
  8. George Mason Univ., Fairfax, VA (United States)
  9. Washington State Univ., Pullman, WA (United States)
  10. Univ. of Washington, Seattle, WA (United States)
  11. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); Science Systems and Applications, Inc., Lanham, MD (United States)
  12. Athabasca Univ., Edmonton, AB (Canada); Indian Inst. of Technology, Kharagpur (India)
  13. Indian Inst. of Technology, Kharagpur (India)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1530880
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Frontiers in Earth Science
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2296-6463
Publisher:
Frontiers Research Foundation
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; High Mountain Asia; precipitation; terrestrial water budget; uncertainty; land surface modeling; triple collocation

Citation Formats

Yoon, Yeosang, Kumar, Sujay V., Forman, Barton A., Zaitchik, Benjamin F., Kwon, Yonghwan, Qian, Yun, Rupper, Summer, Maggioni, Viviana, Houser, Paul, Kirschbaum, Dalia, Richey, Alexandra, Arendt, Anthony, Mocko, David, Jacob, Jossy, Bhanja, Soumendra, and Mukherjee, Abhijit. Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia. United States: N. p., 2019. Web. doi:10.3389/feart.2019.00120.
Yoon, Yeosang, Kumar, Sujay V., Forman, Barton A., Zaitchik, Benjamin F., Kwon, Yonghwan, Qian, Yun, Rupper, Summer, Maggioni, Viviana, Houser, Paul, Kirschbaum, Dalia, Richey, Alexandra, Arendt, Anthony, Mocko, David, Jacob, Jossy, Bhanja, Soumendra, & Mukherjee, Abhijit. Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia. United States. doi:10.3389/feart.2019.00120.
Yoon, Yeosang, Kumar, Sujay V., Forman, Barton A., Zaitchik, Benjamin F., Kwon, Yonghwan, Qian, Yun, Rupper, Summer, Maggioni, Viviana, Houser, Paul, Kirschbaum, Dalia, Richey, Alexandra, Arendt, Anthony, Mocko, David, Jacob, Jossy, Bhanja, Soumendra, and Mukherjee, Abhijit. Fri . "Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia". United States. doi:10.3389/feart.2019.00120. https://www.osti.gov/servlets/purl/1530880.
@article{osti_1530880,
title = {Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia},
author = {Yoon, Yeosang and Kumar, Sujay V. and Forman, Barton A. and Zaitchik, Benjamin F. and Kwon, Yonghwan and Qian, Yun and Rupper, Summer and Maggioni, Viviana and Houser, Paul and Kirschbaum, Dalia and Richey, Alexandra and Arendt, Anthony and Mocko, David and Jacob, Jossy and Bhanja, Soumendra and Mukherjee, Abhijit},
abstractNote = {This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff (R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11 ± 0.45, 1.26 ± 0.11, and 0.85 ± 0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.},
doi = {10.3389/feart.2019.00120},
journal = {Frontiers in Earth Science},
number = ,
volume = 7,
place = {United States},
year = {2019},
month = {5}
}

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