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Quantifying uncertainty in a remote sensing-based estimate of evapotranspiration over continental USA

Quantifying uncertainty in a remote sensing-based estimate of evapotranspiration over continental USA
Quantifying uncertainty in a remote sensing-based estimate of evapotranspiration over continental USA

We calculate evapotranspiration (E) from remote sensing (RS) data using the Penman-Monteith model over continental USA for four years (2003-2006) and explore, through an ensemble generation framework, the impact of input dataset (meteorological, radiation and vegetation) selection on performance (uncertainty) at the monthly time-scale. The impact of failed or missed RS retrievals and algorithmic assumptions are also quantified. To evaluate bias, we inter-compare RS-E with three independent sources of E: Variable Infiltration Capacity (VIC)model simulated, North American Regional Reanalysis (NARR) inferred, and Gravity Recovery and Climate Experiment (GRACE) inferred. Overall, we find that the choice of vegetation parameterization, followed by surface temperature, has the greatest impact on RS-E uncertainty. Additional uncertainty (4-18%) is linked to sources of net radiation-used to scale instantaneous RS-E under the assumption of constant daytime evaporative fraction-including the Surface Radiation Budget (SRB), International Satellite Cloud Climatology Project (ISCCP), and North American Land Data Assimilation System (NLDAS)-VIC. The ensemble median agrees to within 21% of VIC-modelled E, except for the Colorado and Great Basins for which the need for a soil moisture constraint on RS-E becomes evident.

0143-1161
3821-3865
Ferguson, Craig R.
b9d14f97-34c8-44c2-a0d7-f97147d0063c
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Wood, Eric F.
8352c1b4-4fd3-42fe-bd23-46619024f1cf
Gao, Huilin
0ab679b9-f396-48c1-b668-3f96e0f6a1c0
Ferguson, Craig R.
b9d14f97-34c8-44c2-a0d7-f97147d0063c
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Wood, Eric F.
8352c1b4-4fd3-42fe-bd23-46619024f1cf
Gao, Huilin
0ab679b9-f396-48c1-b668-3f96e0f6a1c0

Ferguson, Craig R., Sheffield, Justin, Wood, Eric F. and Gao, Huilin (2010) Quantifying uncertainty in a remote sensing-based estimate of evapotranspiration over continental USA. International Journal of Remote Sensing, 31 (14), 3821-3865. (doi:10.1080/01431161.2010.483490).

Record type: Article

Abstract

We calculate evapotranspiration (E) from remote sensing (RS) data using the Penman-Monteith model over continental USA for four years (2003-2006) and explore, through an ensemble generation framework, the impact of input dataset (meteorological, radiation and vegetation) selection on performance (uncertainty) at the monthly time-scale. The impact of failed or missed RS retrievals and algorithmic assumptions are also quantified. To evaluate bias, we inter-compare RS-E with three independent sources of E: Variable Infiltration Capacity (VIC)model simulated, North American Regional Reanalysis (NARR) inferred, and Gravity Recovery and Climate Experiment (GRACE) inferred. Overall, we find that the choice of vegetation parameterization, followed by surface temperature, has the greatest impact on RS-E uncertainty. Additional uncertainty (4-18%) is linked to sources of net radiation-used to scale instantaneous RS-E under the assumption of constant daytime evaporative fraction-including the Surface Radiation Budget (SRB), International Satellite Cloud Climatology Project (ISCCP), and North American Land Data Assimilation System (NLDAS)-VIC. The ensemble median agrees to within 21% of VIC-modelled E, except for the Colorado and Great Basins for which the need for a soil moisture constraint on RS-E becomes evident.

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e-pub ahead of print date: 4 August 2010

Identifiers

Local EPrints ID: 480884
URI: http://eprints.soton.ac.uk/id/eprint/480884
ISSN: 0143-1161
PURE UUID: 2490d2d3-f801-469a-be20-455ff8db7d72
ORCID for Justin Sheffield: ORCID iD orcid.org/0000-0003-2400-0630

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Date deposited: 10 Aug 2023 16:41
Last modified: 06 Jun 2024 01:54

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Contributors

Author: Craig R. Ferguson
Author: Eric F. Wood
Author: Huilin Gao

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