Microwave implementation of two-source energy balance  approach for estimating evapotranspiration

Holmes, Thomas R. H.; Hain, Christopher R.; Crow, Wade T.; Anderson, Martha C.; Kustas, William P.

doi:https://doi.org/10.5194/hess-22-1351-2018

Articles | Volume 22, issue 2

https://doi.org/10.5194/hess-22-1351-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-22-1351-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 22, issue 2

Research article

|

23 Feb 2018

Research article |

| 23 Feb 2018

Microwave implementation of two-source energy balance approach for estimating evapotranspiration

Thomas R. H. Holmes, Christopher R. Hain, Wade T. Crow, Martha C. Anderson, and William P. Kustas

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Final revised paper (published on 23 Feb 2018)
Preprint (discussion started on 06 Jun 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review', Carlos Jimenez, 11 Jul 2017
- AC1: 'Reply to the comments of C. Jimenez', Thomas Holmes, 15 Sep 2017
RC2: 'Comments to 'Microwave implementation of the two-source energy balance approach for estimating evapotranspiration'', Anonymous Referee #2, 11 Aug 2017
- AC2: 'Reply to comments of Anonymous Referee #2', Thomas Holmes, 15 Sep 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to revisions (further review by Editor and Referees) (01 Oct 2017) by Bob Su

AR by Thomas Holmes on behalf of the Authors (10 Nov 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (03 Dec 2017) by Bob Su

RR by Anonymous Referee #2 (04 Dec 2017)

RR by Carlos Jimenez (14 Dec 2017)

Suggestions for revision or reasons for rejection

The paper has been improved and the authors have addressed most of my concerns by better explaining in the new manuscript the conflicting or unclear statements. The more comprehensive roadmap at the end also signals more clearly the limitations of the present exercise, and remove some of my concerns. In my opinion, the paper is ready for publication.

A couple of very minor things, that could even be addressed in the editing phase, no need for a new revision:

P4. L6. I think this is not clear: ” In practice this means taking an average of all needed inputs on the “clear-sky” days in the 7-day period and running ALEXI. Based on the resulting “clear sky” ET, we calculate a ratio of instantaneous latent heat flux to incoming solar radiation (fSUN). This fSUN is held constant over the 7-day period and used to calculate daily ET on each day, which is then summed to a 7-day total ET”. I think you have not stated the “instantaneous” calculation of ALEXIS at noon time before. Much better this from one one of your answers to the reviewers: “The ALEXI model computes the energy balance at two instantaneous points during the morning hours (post-dawn and pre-noon) using LST data available at those times. The latent heat estimate at the second time is then upscaled to a daily flux, conserving a flux ratio metric.” Please, rephrase to make it clear to non-ALEXIS experts would be nice.

Reply to original P3.L11 comment. You say: “However, we prefer that ALEXI remains a purely diagnostic model to maintain its role as independent estimate of ET, not requiring precipitation data as input”. I wonder what makes you claim that not using precipitation makes your model more “diagnostic” and “independent” than a model that uses precipitation (e.g., GLEAM). Satellite-based precipitation products exist, so ingesting that should not make the model less “diagnostic”. Also, in principle, there is nothing making them more “dependent” of whatever you may have in mind, compared with other products such as radiation. For instance, radiation at the surface typically needs an atmospheric model and atmospheric inputs to remove the atmospheric component, where the inputs are typically outsourced from atmospheric reanalysis. So their “independence” may also be questioned. Perhaps, you refer to evaluate ALEXIS ET later on with correlated hydrological datasets such as precipitation, or soil moisture.

P6.L4. “This overlap in coverage” may be unclear. There is also overlap between MSG and GCOM-W, but the key is that it is not global. Better to say again that the calibration with MODIS is preferred as the present calibration with MSG is not global.

Reply to original P11. L15 comment. This does not look like a serious answer. I think you should explain why we should be surprised or not, and it seems that you were surprised. Perhaps ALEXIS is very sensitive to small change in LST, so you were expecting a worst agreement between ALEXIS MW and IR given the differences between both LTS, even if the LST differences were small. Or perhaps the MW and IR LST estimates were closer than you expected, so even if ALEXIs is not very robust to LST uncertainties, the ALEXIS MW and IR ET are closer than expected.

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ED: Publish subject to minor revisions (review by editor) (21 Dec 2017) by Bob Su

AR by Thomas Holmes on behalf of the Authors (22 Dec 2017) Author's response Manuscript

ED: Publish as is (08 Jan 2018) by Bob Su

Short summary

In an effort to apply cloud-tolerant microwave data to satellite-based monitoring of evapotranspiration (ET), this study reports on an experiment where microwave-based land surface temperature is used as the key diagnostic input to a two-source energy balance method for the estimation of ET. Comparisons of this microwave ET with the conventional thermal infrared estimates show widespread agreement in spatial and temporal patterns from seasonal to inter-annual timescales over Africa and Europe.