Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia
Journal Article
·
· Frontiers in Earth Science
- Science Applications International Corporation, McLean, VA (United States); NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
- NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
- University of Maryland, College Park, MD (United States)
- Johns Hopkins University, Baltimore, MD (United States)
- NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); Univ. of Maryland, College Park, MD (United States)
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- University of Utah, Salt Lake City, UT (United States)
- George Mason University, Fairfax, VA (United States)
- Washington State University, Pullman, WA (United States)
- University of Washington, Seattle, WA (United States)
- NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); Science Systems and Applications, Inc., Lanham, MD (United States)
- Athabasca University, Edmonton, AB (Canada); Indian Inst. of Technology, Kharagpur (India)
- Indian Institute of Technology, Kharagpur (India)
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.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1530880
- Alternate ID(s):
- OSTI ID: 2349310
OSTI ID: 1631708
- Report Number(s):
- PNNL-SA--148780
- Journal Information:
- Frontiers in Earth Science, Journal Name: Frontiers in Earth Science Vol. 7; ISSN 2296-6463
- Publisher:
- Frontiers Research FoundationCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia
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journal | September 2019 |
Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
|
journal | October 2019 |
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