the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Mar 2026
Process-based evaluation of green roof models for assessment of heat mitigation efficacy in WRF (v4.3.1) and EnergyPlus (v8.6.0)
Abstract. Green roofs mitigate urban heat, but to fully assess their impact, green roof models must be integrated into urban climate models, where they provide critical surface boundary conditions. Ensuring their reliability requires evaluation, yet such efforts remain limited in the literature. This study addresses that gap by evaluating two configurations of EcoRoof, the green roof module from EnergyPlus (ERo for the original version of the model, and ERm for a modified version), and a multilayer green roof parametrization for WRF (WRF-MLGR) (Heusinger et al., 2018; Sailor, 2008; Zonato et al., 2021). These models were tested against field observations from a monitored green roof in London, Ontario, focusing on latent heat flux (Qe), surface temperature (Tsurf), storage heat flux (Qg) and soil water content (SWC). Model performance varied by variable. The two EcoRoof versions showed similar performance, with mean RMSE across study periods of approximately 56–60 W m-2 for Qe, 3–4 °C for Tsurf, 22 W m-2 for Qg, and 0.03 m3 m-3 for SWC. Performance for Qe was comparable across models; however, WRF-MLGR exhibited much larger errors for Qg (with mean RMSE exceeding 100 W m-2) and SWC (0.06 m3 m-3), along with higher Tsurf deviations (~5 °C). Overall, EcoRoof provided a more consistent representation of the daytime energy balance, whereas WRF-MLGR showed structural biases in surface heating. These findings highlight the importance of process-level evaluation for urban climate applications and underscore the need for continued model development to address the structural limitations in green roof parametrizations.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2026-981', Anonymous Referee #1, 21 Apr 2026
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AC1: 'Reply on RC1', Maria Martinez Mendoza, 12 Jun 2026
Egusphere-2026-981
Response to Reviewer 1’s comments.
General comments
This model evaluation paper compares two different green roof models for their ability to calculate the green roof surface energy balance in terms of the latent heat flux QE, the ground heat flux QG, the surface temperature (Tsurf), and substrate volumetric water content. The two models are the EnergyPLus EcoRoof formulation (ERo), a modified version of EcoRoof (ERm)and the green roof submodule of WRF (WRF-MLGR).
The analysis reveals that both models show certain biases for different reasons which are mainly related to differences in model structure and assumption. Generally, ER overestimates QE and Tsurf modestly, whereas WRF-MLGR more strongly underestimates QE but overestimates QG and Tsurf. The study clearly quantifies the deviations between model output and green roof observations from a test site in Ontario, Canada.
The paper is well written, well-structured and easy-to-follow. It presents and discusses all the mentioned terms of the surface energy balance in detail and quantifies deviations between measurements and model outputs. Hence, the paper is an important contribution to assess model performance for green roof studies in context of surface-atmosphere exchange studies, process analysis, climate change adaption studies, or urban climate research.
Response:
We thank the reviewer for their positive and constructive summary of the manuscript and for recognizing its relevance to the topic of green roof modelling and surface energy balance representation.
Specific comments
- Introduction, l.58: the state-of-the-art on earlier model evaluation is fairly short. You might briefly specify which general performance those evaluations found, e.g. whether energy fluxes were over- or underestimated and to what extent.
Response:
The state-of-the-art on earlier model evaluations has been revised (l. 58-81) to include the reported evaluation results from previous studies, focusing on typically assessed variables (e.g. soil temperature and atmospheric variables). This addition also helps clarify that most existing evaluations do not assess surface energy fluxes directly, which is consistent with the motivation of this study.
Note: All line numbers refer to the clean version of the manuscript.
- 73: I suppose QH was not evaluated in your study because it could not be measured directly, right?
Response:
Yes, Qh was not directly measured at the test site and is therefore not evaluated in this study, as it can only be estimated as a residual of the surface energy balance. This clarification has been added (l. 109).
- Methods, l.139. Due to the small dimensions of the green roof advective effects might play a role. As this was analysed in a Master Thesis (Kurukulaarachchi, 2017) which is not available to the public, could you briefly specify how advective effects were analysed in that study?
Response:
The paragraph (l. 158-163) was revised to describe in more detail the advection analysis used in Kurukulaarachchi (2017), which was based on diagnostic criteria. Moreover, the Master’s thesis is publicly accessible and a link has been added to the reference list (l. 760).
- Results, l.232: Here you state that 50% of the energy are partitioned into QH. However, the sensible heat flux was not directly measured – what is calculated as a residual of the surface energy balance?
Response:
Yes, for this statement Qh was derived as the residual of the surface energy balance. This has been clarified in the revised manuscript (l. 260-262).
- Discussion, l.437: I suggest to move the paragraph on the test of the elevated green roof module (and Fig. 12) to the results section.
Response:
The paragraph has been moved to the results section, under the subsection 3.2 Observed energy balance. (l. 272-282)
- Results, section 4.2.1.: I am wondering whether incorporation of specific CAM parameterisations/formulations in green roof models could (significantly) enhance model performance in term of QE and the surface energy balance representation? Could you briefly discuss that issue?
Response:
We add a paragraph to the discussion (l. 511-521) to address the potential role of CAM-specific parameterisations in green roof models and add a one-at-a-time sensitivity analysis in the supplement materials (S6). Although no studies were found explicitly applying CAM formulations in energy balance models, sensitivity analysis results from this study indicate strong model sensitivity to vegetation parameters in ER, supporting the need for improved physiological representation for Sedum vegetation.
- Results, l. 507. I would suggest to, in the context of green roofs, we referred to “substrates” rather than “soil,” since no actual soil is used on green roofs (they typically consist of small rock fragments, at least in most European green roofs)
Response:
The correction has been made replacing the word ‘soil’ with ‘substrate’ where applicable.
Technical corrections
Fig 3: labels on x and y-axis are quite small
Fig. 4: The figure legend is hardly to read
Response:
We thank the reviewer for highlighting these issues. Both figures have been revised to improve readability. Axis label font sizes have been increased, and the legends have been repositioned outside the plot area to avoid overlap and enhance clarity.
We thank the reviewer for these comments and hope the revisions are satisfactory.
Sincerely,
Maria Martinez Mendoza
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AC1: 'Reply on RC1', Maria Martinez Mendoza, 12 Jun 2026
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RC2: 'Comment on egusphere-2026-981', Anonymous Referee #2, 13 May 2026
This study provides a high-quality assessment of existing green roof models, addressing a gap in the literature. Overall, I find the evaluation scientifically sound and the discussion insightful. I have two minor suggestions:
- Move the green roof variable setup from the supplement into the main manuscript (currently Table S3).
- Present time-series plot or scatter plots in addition to time-averaged results to show deviations during dry and rainy periods for different fluxes.
Citation: https://doi.org/10.5194/egusphere-2026-981-RC2 -
AC2: 'Reply on RC2', Maria Martinez Mendoza, 12 Jun 2026
Egusphere-2026-981
Response to Reviewer 2’s comments.
General comments
This study provides a high-quality assessment of existing green roof models, addressing a gap in the literature. Overall, I find the evaluation scientifically sound and the discussion insightful.
Response:
We sincerely thank the reviewer for this encouraging comment and for recognizing the value of our work in addressing this gap in the literature.
Specific comments
I have two minor suggestions:
- Move the green roof variable setup from the supplement into the main manuscript (currently Table S3).
Response:
The green roof variable setup previously presented in Table S3 has been moved to the main manuscript and is now included as Table 2 in the “Model setup” subsection (l. 199).
Note: All line numbers refer to the clean version of the manuscript.
- Present time-series plot or scatter plots in addition to time-averaged results to show deviations during dry and rainy periods for different fluxes.
Response:
Figure S6 has been added to the supplementary materials to illustrate model behavior at the hourly scale under dry and rainy conditions, complementing the time-averaged results in the main text. The scatter plots provide the full distribution of hourly model–observation pairs and show the same dry-versus-rainy contrasts in bias and spread observed in the time-averaged results.
We thank the reviewer for these suggestions and hope the revisions are satisfactory.
Sincerely,
Maria Martinez Mendoza
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Maria Martinez Mendoza et al.: Process-based evaluation of green roof models for assessment of heat mitigation efficacy in WRF (v4.3.1) and EnergyPlus (v8.6.0), egusphere-2026-981
General comments
This model evaluation paper compares two different green roof models for their ability to calculate the green roof surface energy balance in terms of the latent heat flux QE, the ground heat flux QG, the surface temperature (Tsurf), and substrate volumetric water content. The two models are the EnergyPLus EcoRoof formulation (ER0), a modified version of EcoRoof (ERm)and the green roof submodule of WRF (WRF-MLGR).
The analysis reveals that both models show certain biases for different reasons which are mainly related to differences in model structure and assumption. Generally, ER overestimates QE and Tsurf modestly, whereas WRF-MLGR more strongly underestimates QE but overestimates QG and Tsurf. The study clearly quantifies the deviations between model output and green roof observations from a test site in Ontario, Canada.
The paper is well written, well-structured and easy-to-follow. It presents and discusses all the mentioned terms of the surface energy balance in detail and quantifies deviations between measurements and model outputs. Hence, the paper is an important contribution to asses model performance for green roof studies in context of surface-atmosphere exchange studies, process analysis, climate change adaption studies, or urban climate research.
Specific comments
Introduction, l.58: the state-of-the-art on earlier model evaluation is fairly short. You might briefly specify which general performance those evaluations found, e.g. whether energy fluxes were over- or underestimated and to what extent.
l. 73: I suppose QH was not evaluated in your study because it could not be measured directly, right?
Methods, l.139. Due to the small dimensions of the green roof advective effects might play a role. As this was analysed in a Master Thesis (Kurukulaarachchi, 2017) which is not available to the public, could you briefly specify how advective effects were analysed in that study?
Results, l.232: Here you state that 50% of the energy are partitioned into QH. However, the sensible heat flux was not directly measured – what is calculated as a residual of the surface energy balance?
Discussion, l.437: I suggest to move the paragraph on the test of the elevated green roof module (and Fig. 12) to the results section.
Results, section 4.2.1.: I am wondering whether incorporation of specific CAM parameterisations/formulations in green roof models could (significantly) enhance model performance in term of QE and the surface energy balance representation? Could you briefly discuss that issue?
Results, l. 507. I would suggest to, in the context of green roofs, we referred to “substrates” rather than “soil,” since no actual soil is used on green roofs (they typically consist of small rock fragments, at least in most European green roofs)
Technical corrections
Fig 3: labels on x and y-axis are quite small
Fig. 4: The figure legend is hardly to read