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United States Department of Agriculture

Agricultural Research Service

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: The inherent uncertainty of in-situ observations and its implications for modeling evapotranspiration

Author
item Alfieri, Joseph

Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: September 28, 2012
Publication Date: December 3, 2012
Citation: Alfieri, J.G. 2012. The inherent uncertainty of in-situ observations and its implications for modeling evapotranspiration [abstract]. American Geophysical Union. 2012 CDROM.

Technical Abstract: In-situ observations are essential to a broad range of applications including the development, calibration, and validation of both the numerical and remote sensing-based models. For example, observational data is requisite in order to evaluate the skill of these models both to represent the complex biogeophysical processes regulating evapotranspiration (ET) and to predict the magnitude of the moisture flux. As such, by propagating into these subsequent activities, any uncertainty or errors associated with the observational data have the potential to adversely impact the accuracy and utility of these models. It is, therefore, critical that the factors driving measurement uncertainty are fully understood so that the steps can be taken to account for its effects and mitigate its impact on subsequent analyses. Field measurements of ET can be collected using a variety of techniques including eddy covariance (EC), lysimetry (LY), and scintillometry (SC). Each of these methods is underpinned by a unique set of theoretical considerations and practical constraints; and, as a result, each method is susceptible to differing types of systematic and random error. Since the uncertainty associated with the field measurements is predicated on how well numerous factors – for example, environmental conditions – adhere to those prescribed by the underlying assumptions, the quality of in-situ observations collected via the differing methods can vary significantly both over time and from site-to-site. Using data from both site studies and large field campaigns, such as IHOP_2002 and BEAREX08, the sources of uncertainty in field observations will be discussed. The impact of measurement uncertainty on model validation will also be illustrated.

Last Modified: 8/27/2014
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