Assessing climate change impacts on live fuel moisture and wildfire risk using a hydrodynamic vegetation model

Ma, Wu; Zhai, Lu; Pivovaroff, Alexandria; Shuman, Jacquelyn; Buotte, Polly; Ding, Junyan; Christoffersen, Bradley; Knox, Ryan; Moritz, Max; Fisher, Rosie A.; Koven, Charles D.; Kueppers, Lara; Xu, Chonggang

doi:https://doi.org/10.5194/bg-18-4005-2021

Articles | Volume 18, issue 13

https://doi.org/10.5194/bg-18-4005-2021

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

https://doi.org/10.5194/bg-18-4005-2021

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

Articles | Volume 18, issue 13

Research article

|

06 Jul 2021

Research article |

| 06 Jul 2021

Assessing climate change impacts on live fuel moisture and wildfire risk using a hydrodynamic vegetation model

Wu Ma, Lu Zhai, Alexandria Pivovaroff, Jacquelyn Shuman, Polly Buotte, Junyan Ding, Bradley Christoffersen, Ryan Knox, Max Moritz, Rosie A. Fisher, Charles D. Koven, Lara Kueppers, and Chonggang Xu

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Final revised paper (published on 06 Jul 2021)
Supplement to the final revised paper
Preprint (discussion started on 25 Nov 2020)
Supplement to the preprint

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Review', Anonymous Referee #1, 16 Dec 2020
- AC1: 'Reply on RC1', Wu Ma, 09 Mar 2021
RC2: 'Review of “Assessing Climate Change Impacts on Live Fuel Moisture and Wildfire Risk Using a Hydrodynamic Vegetation Model”', D. I. Kelley, 29 Jan 2021
- AC2: 'Reply on RC2', Wu Ma, 09 Mar 2021
RC3: 'Review', Anonymous Referee #3, 08 Feb 2021
- AC3: 'Reply on RC3', Wu Ma, 09 Mar 2021

Peer-review completion

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

ED: Reconsider after major revisions (15 Mar 2021) by Martin De Kauwe

AR by Wu Ma on behalf of the Authors (16 Mar 2021) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (16 Mar 2021) by Martin De Kauwe

RR by Anonymous Referee #1 (30 Mar 2021)

RR by Anonymous Referee #3 (06 Apr 2021)

Suggestions for revision or reasons for rejection

I thank the authors for their thoughtful responses to my suggestions and revisions. Their work has helped to improve the manuscript. However, there are a few key points that I made in my previous review that were not addressed that I think are important, so I will reiterate them here. Before I get to the paper, I would like to point out that the line numbers and pages are off in the R2R. This makes it a significant amount more work to review the revisions. I hope that the authors will check this more carefully next time.

One of my biggest concern that was not addressed is related to PFT diversity. I understand that the authors performed a clustering analysis on some key traits to identify unique PFTs and that this clustering analysis underlies their justification for 3 PFTs. However, implicitly underlying the clustering analysis is a hypothesis by the authors that the traits used in the clustering analysis will result in a functional difference in climate response/PFT performance. The authors did not test to see if there was a functional need to have 3 distinct PFTs through any simulations. I contend that PFT LA and PFT HA are functionally similar enough based on Figure 2 that there is not a need for all the PFTs used. This also ties into my criticism of H4 and its usefulness in this manuscript. These concerns were not addressed in the revision.

My other major concern is that the authors do not do any validation related to interannual variability, which is crucial for having confidence in model predictions on centennial timescales. The authors added an additional validation figure (S5) in response to my comment in the previous revision, but this is a poorly explained figure (I suggest the authors expand the figure legend if they keep this figure) and it is also only a single year. Further, it looks like the model-predicted variation in within-season LFMC (Fig. S5) substantially underestimates the observed LFMC peaks and troughs. Given that the model is not capturing the seasonal extremes, and there is little variation across climate forcings (Fig. S5), this makes me quite concerned about the model’s ability to capture interannual variability, especially since the model substantially overpredicts minimum soil moisture (Fig S3). If the authors want to make decade to centennial scale predictions, they need a longer time scale validation of some kind, despite the scarcity of data. This will help at the very least identify model biases that can be used to better interpret/caveat future projections

More minor comments:

Did the authors fit a linear model to the R and SR in Fig 4/5 of Saura-Mas and Lloret? That is what it appears to be from figure S4, but this should be spelled out in the methods

Re fig S3, it looks like the model substantially overestimates soil moisture relative to observations. How sensitive is the prognostic LFMC to soil moisture/ what implications does this have for future projections?

L 177. What do the authors mean by “recovered well”

The text is still too small in Fig 2

Figure S5 still shows a single seasonal cycle for LFMC and it seems to underestimate the within season variability of LFMC relative to the observations. This doesn’t get at my question about model validation of year to year variability. Additionally, it is difficult to tell what is being shown in Fig S5 without further explanation. I suggest the authors elaborate further if they would like to keep this in the SI

Why is there a No-RH included in Figure 5? This is not discussed in the text. I mentioned this previously and my comment was not addressed

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ED: Reconsider after major revisions (15 Apr 2021) by Martin De Kauwe

AR by Wu Ma on behalf of the Authors (20 May 2021) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (24 May 2021) by Martin De Kauwe

RR by Anonymous Referee #1 (31 May 2021)

ED: Publish as is (14 Jun 2021) by Martin De Kauwe

AR by Wu Ma on behalf of the Authors (15 Jun 2021) Manuscript

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Short summary

We use a hydrodynamic demographic vegetation model to estimate live fuel moisture dynamics of chaparral shrubs, a dominant vegetation type in fire-prone southern California. Our results suggest that multivariate climate change could cause a significant net reduction in live fuel moisture and thus exacerbate future wildfire danger in chaparral shrub systems.