Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces

Wang, Ren; Gentine, Pierre; Yin, Jiabo; Chen, Lijuan; Chen, Jianyao; Li, Longhui

doi:https://doi.org/10.5194/hess-25-3805-2021

Articles | Volume 25, issue 7

https://doi.org/10.5194/hess-25-3805-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-3805-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 7

Research article

|

02 Jul 2021

Research article |

| 02 Jul 2021

Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces

Ren Wang, Pierre Gentine, Jiabo Yin, Lijuan Chen, Jianyao Chen, and Longhui Li

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Final revised paper (published on 02 Jul 2021)
Supplement to the final revised paper
Preprint (discussion started on 21 Nov 2020)
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Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review of Wang et al. "Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces"', Rene Orth, 16 Dec 2020
- RC2: 'Comments', Anonymous Referee #2, 03 Jan 2021
  - AC1: 'Reply on RC2', Ren Wang, 27 Jan 2021
- AC2: 'Reply on RC1', Ren Wang, 27 Jan 2021
- AC3: 'Additional Reply on RC1', Ren Wang, 29 Jan 2021

Peer-review completion

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

ED: Reconsider after major revisions (further review by editor and referees) (01 Feb 2021) by Adriaan J. (Ryan) Teuling

AR by Ren Wang on behalf of the Authors (15 Mar 2021) Author's response Manuscript

ED: Referee Nomination & Report Request started (23 Mar 2021) by Adriaan J. (Ryan) Teuling

RR by Anonymous Referee #2 (25 Mar 2021)

RR by Rene Orth (15 Apr 2021)

Suggestions for revision or reasons for rejection

Second review of Wang et al.
"Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces"

The paper has overall improved as the authors have addressed some of
the concerns raised by me and the other reviewer.
However, at the same time important issues remain unresolved:

(1)
As pointed out in main comment (1) of my previous review,
the validation of the inferred time series against independent data such as the Fluxnet-MTE product,
and observed streamflow is not convincing.
The differences are not satisfactorily explained by the authors in their rebuttal.
Figure S11 clearly shows diverging trends in the tropics, and the author's explanation about
"different models, forcing data and time scales" does not provide evidence why their product
(and trends therein) is more reliable than the Fluxnet-MTE reference product.
Moreover, the different runoff trends between Figures 4c and S14 cannot be entirely explained with
human influence on the observed streamflow, I think, as even the large-scale patterns
differ clearly, for example in Europe and Australia.
Given that the differences between the products here are partly so remarkable I feel that
they need to be understood better in order to illustrate the reliability of the newly derived dataset.

(2)
As pointed out in main comment (2) of my previous review,
different time periods are considered during which trends from the data derived in this study are determined versus the trends from the CMIP5 data. The authors acknowledge that matching time periods would be "better, but difficult to achieve", and state that this might not be so important as the evaluation
focuses on directions of change rather than the actual magnitude. I do not agree with this.
Even the sign of change may be different in different time periods, as for example introduced
by decreasing and later increasing radiation within the global dimming and brightening periods.
Further, decadal climate variability can introduce varying trends over time.
Therefore, I think that with using different time periods, the comparison of trends between the
data derived in this study and the CMPI5 data is not meaningful.

(3)
Even though I requested this in main comment (4) of my previous review,
the authors did not explain why they chose to employ artificial neural networks
over alternative machine learning approaches.

(4)
Even though I mentioned this in main comment (5) of my previous review,
there are still many small language errors throughout the manuscript,
for example in lines 71, 77, 131, 155, 159, 196/197, 205, 224, and 249.

(5)
One additional issue caught my attention this time, sorry for realizing this only now.
The ANN model is trained with target data from flux tower sites which typically cover 5-15 years.
I doubt that CO2 in such short time periods would change sufficiently to lead to an emerging
influence of CO2 fertilization on ET or EF.
Therefore I think the reasoning of the CO2 fertilization effect on stomatal conductance
potentially explaining differences in the partitioning between ET and runoff over time,
which can be found throughout the manuscript, is not valid.

I do not wish to remain anonymous - René Orth.

------------------

Specific comments:

lines 66-67: not clear what is meant with "boundary layer budget" and "non-linearity"

lines 70-74: The authors state that existing datasets are somewhat biased towards the (flux) data availability
which is largely concentrated in the northern hemisphere. This is true, but it is also true
for the data derived in this study even if the weather stations are more distributed,
because the model training is confined to the flux tower sites, as for the existing products.

lines 156-157: Why was exactly this combination of input variables chosen while other combinations
showed even better performance in Table S1, for example after including precipitation?

lines 165-166: What do you mean with initial tests and how did that influence the final decision?
How was this formula applied to guide these decisions?

lines 204-213: Why are there different p-value thresholds used here?

line 220: Why >12000?

lines 220-225: How are these trends computed? As linear trends? Over which time period?

line 241: Why are you using the 11-year old Fluxnet-MTE product from Jung et al. 2010
rather than the more recent Fluxcom product from Jung et al. 2019?

line 298: Which CMPI5 models are used in the considered ensemble, and how were their simulations aggregated?

Figures 3-7: How did you determine the area of the Sahara?

References:
Jung, M., et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes, Scientific Data 6, 74 (2019).
Jung, M., et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951–954 (2010).

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ED: Publish subject to revisions (further review by editor and referees) (15 Apr 2021) by Adriaan J. (Ryan) Teuling

AR by Ren Wang on behalf of the Authors (07 May 2021)

ED: Referee Nomination & Report Request started (13 May 2021) by Adriaan J. (Ryan) Teuling

RR by Rene Orth (27 May 2021)

ED: Publish subject to minor revisions (review by editor) (27 May 2021) by Adriaan J. (Ryan) Teuling

AR by Ren Wang on behalf of the Authors (31 May 2021) Author's response Manuscript

ED: Publish as is (31 May 2021) by Adriaan J. (Ryan) Teuling

AR by Ren Wang on behalf of the Authors (02 Jun 2021)

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Short summary

Assessment of changes in the global water cycle has been a challenge. This study estimated long-term global latent heat and sensible heat fluxes for recent decades using machine learning and ground observations. The results found that the decline in evaporative fraction was typically accompanied by an increase in long-term runoff in over 27.06 % of the global land areas. The observation-driven findings emphasized that surface vegetation has great impacts in regulating water and energy cycles.