PCLM-D-26-00041

Evaluating the potential for heat warning systems to account for intra-urban variability

PLOS Climate

Dear Dr. Ludwig,

Thank you for submitting your manuscript to PLOS Climate. After careful consideration, we feel that it has merit but does not fully meet PLOS Climate’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Kind regards,

Phil McManus

Academic Editor

PLOS Climate

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If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

This is a good piece of work that has been reviewed by two reviewers who have provided very helpful and detailed feedback based on a close reading of your work. I believe that once this feedback is considered and where appropriate incorporated into your origiinal submission then the revised version of your paper will be improved.

Reviewer 1 wrote that "overall, the manuscript presents an interesting and methodologically careful analysis, and the use of physiologically informed heat metrics such as UTCI and the Pedestrian Heat Stress Product is a clear strength. The spatial granularity of the measurements is also valuable and represents an important contribution to understanding local variation in heat stress."

Detailed feedback:

Reviewer 1:

Thank you for inviting me to review this important paper that interrogates the differences in heat risk at the neighborhood level, with particular focus given to differences in rural vs. urban areas.

1. Great, thorough introduction that highlights the underlying mechanisms, particularly for urban areas, from a range of perspectives.

2. The use of UTCI and the Pedestrian Heat Stress Product is appreciated, importantly how these relate to physiologically-based heat stress rather than a simple measure of ambient temperature/relative humidity.

3. The spatial granularity of the measurement is important and relatively novel.

4. The noted limitation of daytime vs. nighttime and the indoor environment is much appreciated. This is an important point.

Some points for consideration

1. It’s unclear, other than convenience, why the location of Freiburg is particularly important. Can you provide further justification either demographically, climate-wise or in terms of the human health impact of heat, as to why this location is of importance to study. If the advantage is the granularity of the data afforded please state that.

2. The methods section has plenty of technical nuance but lacks in operational detail. The calculation of Tmrt relies on globe temperature, but the manuscript does not provide sufficient information about the globe thermometer measurements. Please specify the instrument model, installation height, exposure conditions (e.g., direct solar exposure), and temporal resolution of globe temperature observations.

3. The results section would benefit from substantial tightening and clearer prioritization of the main findings. The current presentation is highly descriptive and metric-heavy, which makes the salient conclusions difficult to identify. Simply stating them upfront before going into more detail would be appreciated. Consider revising each subsection to begin with a concise statement of the key result, followed by only the most relevant supporting statistics. A clearer distinction between the two comparison exercises, PHSP vs observation-based warnings and PHSP vs official DWD warnings, would also help the reader to interpret the findings.

4. The discussion of transferability feels overstated in its current form. Although the methodological insights and approach may be portable, the study was conducted in Freiburg, a temperate and relatively affluent city with specific built-environment, infrastructural, and population characteristics. I would hesitate to say that these findings could be generalized in any way to more socially unequal urban settings, where exposure patterns, acclimatization, housing quality, air-conditioning access, and vulnerability profiles may alter both model performance and the practical relevance of heat warnings. I would revise to acknowledge the strengths of the unique methodological approach, while acknowledging that the results themselves may not translate so easily to cities with very different environmental and socioeconomic conditions.

Minor points

• Line 127, could use spelling out the source of the citation given the referencing style; as written “For instance, [31] developed a personalized”, does not read well as its currently stated. There are other instances of this style; i.e., on line 129, “Similarly, [21] highlight that incorporating urban-scale realism is a core requirement for…”

• 130 the next generation HHWSs

• Lines 127–128: Please clarify the meaning of “333 m-resolution hazard forecasts.” Do the authors mean 333-m spatial resolution of the forecasting grid? The wording could be clearer.

Reviewer 2:

This study “developed and evaluated neighborhood-scale Pedestrian Heat Stress Products (PHSP) using high-resolution Universal Thermal Climate Index (UTCI) predictions from a machine learning framework” for Freiburg, Germany, in the context of potential that the relevant “current county-level heat warning system might overlook urban–rural and intra-urban differences in heat exposure, limiting its usefulness for local action.”

In addressing this topic, the study tackles an important strategic problem for climate change and extreme weather-related heat-health early warning systems in urban areas, and variability of perceived heat across the cityscape: How best to alert residents to differential heat extremes across an urban heat island, and the recognized “need for neighborhood scale outdoor heat stress warnings.” The study employs machine learning techniques to identify fine-scale predictions of thermal comfort; such techniques are not yet routinely used in heat early warning systems, and show great potential for these systems as well as for those aimed at other extreme weather hazards.

In my view, this study/paper is a welcome example of using machine learning to move toward more effective extreme weather early warning systems. Within an overall positive review, my main substantive comment is that the draft misses an opportunity to address actual health impacts of heat (in the EU, Germany and Freiburg). The paper aims to be health-focused and yet health is referenced rather than directly discussed. Particularly in light of growing interest in developing “impact-based forecasts” for heat and other climate-related extreme weather hazards that link hazards directly to specific negative effects on population wellbeing (and the potential for machine learning tools and modeling to support this), discussing actual population health outcomes in the Introduction, Methods and in particular in the Discussion sections would seem important.

More minor comments follow, hoping these will be useful for the research team:

Introduction

Lines 49-50: A minor point, it seems “C” for Celsius is intended rather than “K” for Kelvin…

Line 74: The model is calibrated to an “active average person;” how to adjust for the more vulnerable individuals (see comment in Discussion on vulnerability mapping)

Lines 86-87: The background well identifies that varying scales and factors are used in heat health warnings across locations, e.g., warnings at city level such as Italy potentially better able to capture variability than those at county level such as Germany. This is important context that is often overlooked.

Research Hypothesis

Lines 139-144: This section appropriately describes the three-part research hypothesis. I would have liked to see this structure reflected more systematically in organization of the Results and Discussion sections. A minor point, logically it would seem that the second part of hypothesis 1 “remaining undetected by county-level warning systems” may be covered within hypothesis 3.

Materials and Methods

Lines 170-172: Has the HTC-NN of Briegel et al. been used elsewhere for this or similar studies? If so, these studies should be mentioned at least in the Discussion section. If not, this should also be noted.

Lines 170-188: There are very many abbreviations used in this paper, in particular in this section. I would recommend ensuring all abbreviations are clearly and carefully defined in the text, and that the written explanation (particularly of the modeling methods) be as clear as possible.

Lines 198-199: When documenting physiological heat stress measurements, it would be useful to “translate” the skin and rectal temperatures to health outcomes. See my main comment above, and general comment in Discussion section about enhancing the linkage with actual heat-related population health outcomes.

Line 227: An additional reason [“for choosing” is missing?] Freiburg…

Results

Results are clearly reported. Consideration could be given to framing the results more directly/explicitly in terms of the three hypotheses.

Discussion

This section identifies a number of study limitations (measurement stations, UTCI threshold chosen, etc.), and discusses the findings in the context of the three hypotheses, though it could help if the section headers referred to the three hypotheses more clearly.

Lines 548-562: This section emphasizes the relevance for the physiologically-based PHSP approach from a health perspective. However, the study says nothing about actual heat-related population health impacts (heat stress, heat stroke, CVD risks, CKD risks, etc.), beyond implying them via targeting hospitals and “public health interventions” broadly. As noted in the main comment above, this seems an important gap and missed opportunity. My suggestion would be to make heat-health outcomes (and health burden of heat locally) feature somewhat more prominently throughout, including in the Introduction, the Methods and in the Discussion. In addition, vulnerability of different populations should be at least mentioned, even if the goal of this study did not include addressing this critical aspect of heat health early warning systems.

Lines 563-573: In this vein, I would also suggest the team consider discussing (possibly in this section) the potential for this modeling effort to help develop impact-based forecasts (i.e., associating heat hazard with specific health impacts in the context of warnings), an increasingly important aspect of extreme weather early warning systems. As above, currently “health” in the sense of heat health impacts on populations is not discussed and there is clearly an opportunity to enhance the impact of the research by doing so.

Lines 603-610: The applicability of this model to other cities is well noted.

Lines 613-616: As mentioned above, population vulnerability mapping and implications for modeling and early warning should be more fully discussed. At the very least, this important topic requires its own section in Discussion with somewhat more comprehensive treatment of potential next steps.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Climate’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Climate does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for the opportunity to review this manuscript examining spatial variability in heat exposure and risk across urban and rural environments. Overall, the manuscript presents an interesting and methodologically careful analysis, and the use of physiologically informed heat metrics such as UTCI and the Pedestrian Heat Stress Product is a clear strength. The spatial granularity of the measurements is also valuable and represents an important contribution to understanding local variation in heat stress. I've included more thoughts for revisions in my review.

Reviewer #2: This study “developed and evaluated neighborhood-scale Pedestrian Heat Stress Products (PHSP) using high-resolution Universal Thermal Climate Index (UTCI) predictions from a machine learning framework” for Freiburg, Germany, in the context of potential that the relevant “current county-level heat warning system might overlook urban–rural and intra-urban differences in heat exposure, limiting its usefulness for local action.”

In addressing this topic, the study tackles an important strategic problem for climate change and extreme weather-related heat-health early warning systems in urban areas, and variability of perceived heat across the cityscape: How best to alert residents to differential heat extremes across an urban heat island, and the recognized “need for neighborhood scale outdoor heat stress warnings.” The study employs machine learning techniques to identify fine-scale predictions of thermal comfort; such techniques are not yet routinely used in heat early warning systems, and show great potential for these systems as well as for those aimed at other extreme weather hazards.

In my view, this study/paper is a welcome example of using machine learning to move toward more effective extreme weather early warning systems. Within an overall positive review, my main substantive comment is that the draft misses an opportunity to address actual health impacts of heat (in the EU, Germany and Freiburg). The paper aims to be health-focused and yet health is referenced rather than directly discussed. Particularly in light of growing interest in developing “impact-based forecasts” for heat and other climate-related extreme weather hazards that link hazards directly to specific negative effects on population wellbeing (and the potential for machine learning tools and modeling to support this), discussing actual population health outcomes in the Introduction, Methods and in particular in the Discussion sections would seem important.

More minor comments follow, hoping these will be useful for the research team:

Introduction

Lines 49-50: A minor point, it seems “C” for Celsius is intended rather than “K” for Kelvin…

Line 74: The model is calibrated to an “active average person;” how to adjust for the more vulnerable individuals (see comment in Discussion on vulnerability mapping)

Lines 86-87: The background well identifies that varying scales and factors are used in heat health warnings across locations, e.g., warnings at city level such as Italy potentially better able to capture variability than those at county level such as Germany. This is important context that is often overlooked.

Research Hypothesis

Lines 139-144: This section appropriately describes the three-part research hypothesis. I would have liked to see this structure reflected more systematically in organization of the Results and Discussion sections. A minor point, logically it would seem that the second part of hypothesis 1 “remaining undetected by county-level warning systems” may be covered within hypothesis 3.

Materials and Methods

Lines 170-172: Has the HTC-NN of Briegel et al. been used elsewhere for this or similar studies? If so, these studies should be mentioned at least in the Discussion section. If not, this should also be noted.

Lines 170-188: There are very many abbreviations used in this paper, in particular in this section. I would recommend ensuring all abbreviations are clearly and carefully defined in the text, and that the written explanation (particularly of the modeling methods) be as clear as possible.

Lines 198-199: When documenting physiological heat stress measurements, it would be useful to “translate” the skin and rectal temperatures to health outcomes. See my main comment above, and general comment in Discussion section about enhancing the linkage with actual heat-related population health outcomes.

Line 227: An additional reason [“for choosing” is missing?] Freiburg…

Results

Results are clearly reported. Consideration could be given to framing the results more directly/explicitly in terms of the three hypotheses.

Discussion

This section identifies a number of study limitations (measurement stations, UTCI threshold chosen, etc.), and discusses the findings in the context of the three hypotheses, though it could help if the section headers referred to the three hypotheses more clearly.

Lines 548-562: This section emphasizes the relevance for the physiologically-based PHSP approach from a health perspective. However, the study says nothing about actual heat-related population health impacts (heat stress, heat stroke, CVD risks, CKD risks, etc.), beyond implying them via targeting hospitals and “public health interventions” broadly. As noted in the main comment above, this seems an important gap and missed opportunity. My suggestion would be to make heat-health outcomes (and health burden of heat locally) feature somewhat more prominently throughout, including in the Introduction, the Methods and in the Discussion. In addition, vulnerability of different populations should be at least mentioned, even if the goal of this study did not include addressing this critical aspect of heat health early warning systems.

Lines 563-573: In this vein, I would also suggest the team consider discussing (possibly in this section) the potential for this modeling effort to help develop impact-based forecasts (i.e., associating heat hazard with specific health impacts in the context of warnings), an increasingly important aspect of extreme weather early warning systems. As above, currently “health” in the sense of heat health impacts on populations is not discussed and there is clearly an opportunity to enhance the impact of the research by doing so.

Lines 603-610: The applicability of this model to other cities is well noted.

Lines 613-616: As mentioned above, population vulnerability mapping and implications for modeling and early warning should be more fully discussed. At the very least, this important topic requires its own section in Discussion with somewhat more comprehensive treatment of potential next steps.

**********

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Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: PLOS Climate Review_March 20.docx

Evaluating the potential for heat warning systems to account for intra-urban variability

PCLM-D-26-00041R1

Dear Ms Ludwig,

Thank you for your positive response to the thoughtful feedback provided by reviewers on the original submission.  We are pleased to inform you that your revised manuscript 'Evaluating the potential for heat warning systems to account for intra-urban variability' has been provisionally accepted for publication in PLOS Climate.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Climate.

Best regards,

Phil McManus

Academic Editor

PLOS Climate

***********************************************************

Additional Editor Comments (if provided):

Thank you for responding positively to the very helpful comments of the two reviewers. The paper was already very good but has been improved through this process. The clarifications made in the revised version of the paper are very good and the inclusion of health data to highlight the dangers of heatwaves definitely improves the overall paper without changing your initial conclusions. Well done.

Reviewer Comments (if any, and for reference):