the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Mar 2026
Process-based upgrades to the WRF multi-layer green-roof scheme (WRF-MLGR v2.0) and evaluation against field observations
Abstract. Green roofs can moderate urban heat by increasing latent heat flux and reducing sensible heat flux. However, capturing these effects in models depends on accurate representation of key green-roof processes, including substrate heat and moisture transport, soil-vegetation-atmosphere energy and moisture exchanges, interactions with the underlying roof, and drainage. Here we introduce targeted, process-based updates to the green-roof scheme (hereafter, MLGR) within the multi-layer urban canopy model (BEP-BEM) in the WRF mesoscale model to address key limitations in the original formulation. The updates include a non-linear dependence of soil thermal conductivity on moisture, vegetation-modulated surface thermal conductivity, explicit soil-surface evaporation, multi-layer root water uptake for transpiration, and canopy interception with evaporation and dew formation. We evaluate the original and modified MLGR schemes using hourly observations from an extensive sedum roof in London, Canada, for ‘summer’ (1 July–31 August 2014) and ‘fall’ (1 September–31 October 2014) periods. We also analyze 11–18 October 2025, when green roof modules were placed directly on the roof deck – which corresponds to the model’s lower boundary assumption. Following implementation of the process-based improvements, model–measurement agreement for the conductive heat flux is markedly improved: RMSE is reduced from 105.9 to 24.0 W m⁻² in summer and from 94.2 to 24.0 W m⁻² in fall and the model produces more realistic overall green roof energy partitioning. The modified model better captures post-rain increases in latent heat flux (QE) and improves the timing and magnitude of daytime turbulent latent and sensible heat flux peaks (QE and QH). Drainage is reduced relative to the original scheme; however, it remains slightly underestimated during summer and slightly overestimated during fall, and biases persist in surface temperature (warm during the day and cool at night) and in the magnitude and variability of QE. Overall, the revised MLGR physics improves surface-flux realism, and future development should focus on developing a more realistic vegetation canopy submodule.
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Status: open (until 29 Aug 2026)
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RC1: 'Comment on egusphere-2026-984', Anonymous Referee #1, 11 May 2026
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AC1: 'Reply on RC1', Alireza Saeedi, 29 Jun 2026
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We sincerely thank Anonymous Referee #1 for the constructive and helpful comments. We are currently preparing a detailed point-by-point response and corresponding manuscript revisions. A complete response addressing all referee comments will be provided with the revised manuscript.
Citation: https://doi.org/10.5194/egusphere-2026-984-AC1
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AC1: 'Reply on RC1', Alireza Saeedi, 29 Jun 2026
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RC2: 'Comment on egusphere-2026-984', Anonymous Referee #2, 10 Aug 2026
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The study improves the previous green roof parameterization scheme in the WRF-BEP+BEM module. The topic is worth investigating. There are some concerns regarding the results presented in the current manuscript:
- As mentioned by the authors, there are five parts being improved/revised. However, based on the current methodology, it is not clear how these five parts affect the physical processes included in the module. In addition to a clearer description, a schematic figure showing the structure and improved processes could help. It would also be better to show a comparison of the module structures between the old and modified versions.
- Some technical details regarding the module improvement: 1) How many layers of soil are below the green vegetation for the roof? 2) Is it possible to show the soil layer temperature and moisture? 3) Does the indoor temperature influence the simulation, especially the storage heat? How does the current model deal with this?
- A clarification is needed for the reason for the included validation variables. Why are the sensible heat flux and near-surface 2-m air temperature not included? Is it because of the limits of the field measurement dataset? Since the field measurement description is very limited in the current manuscript, I suggest adding a bit more detail, along with the previous reference; this would be more rigorous and comprehensive for the current focus.
- As mentioned by the authors, the green-roof scheme in the single-layer urban canopy model, I would like to ask how its performance compares to the previous scheme and the current modified scheme. Because the green-roof scheme itself has been quite intensively studied, it is essential to show why and how the current improvements are significant.
- The figures shown in the current manuscript are quite unclear, which may be due to the resolution of the figures. In addition, in both Figures 2 and 3b, there are grey lines underlying the figures; what do those lines represent?
- For the results, I have one major question. As shown by the current results, the storage heat is largely improved; the error improved from around 100W/m2 to around 20 W/m2, but the surface temperature results are comparable. This means the energy partitioning in the previous and current/modified modules is quite different. This part of the results needs further and deeper explanation and discussion. I suggest a comprehensive analysis of each energy partitioning and an explanation of the reasons/possible reasons behind it. Figure. 6 has addressed this partially, but it is still a bit superficial and only describes the differences.
Citation: https://doi.org/10.5194/egusphere-2026-984-RC2 -
RC3: 'Comment on egusphere-2026-984', Anonymous Referee #3, 10 Aug 2026
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In this manuscript, the authors implement process-based upgrades to the existing multi-layer green-roof scheme in the WRF BEP+BEM urban canopy model, with the aim of improving simulations of surface energy and water exchanges. The authors have tried to perform validation study against observations with interesting green roof setup. The manuscript makes an important contribution to the WRF model development and shows interesting improvements to the energy balance with the improved model. While the manuscript fits within the scope of GMD and of interest to the community, the presentation of the results and statistical analysis does not seem to be rigorous. There are glaring issues in the presentation of statistical values and in general presentation of the results. Furthermore there are some inconsistencies in the model setup, general lack of information about the WRF version they used in the study. Manuscript needs major revision before it can be considered for publication.
Major comments
- Please verify the statistical calculations in Table 1. Modified and Original RMSE for QE for the Fall is showing 316.2 and 369.7 corresponding to the MAE values. It is anomalously high in multiple places, while the scatter plots show something else. Please check this.
- How can R2 be negative in Figure 1 (c,d)? Please define R2, if it is the squared of the correlation coefficient it cannot be negative.
- The experimental modules have a total substrate depth of 0.15 m (lines 81–84), whereas Section 2.2 describes ten model layers spanning approximately 0.30 m. Section 2.1.2 also states that layer depths were prescribed from site measurements. Please explain the discrepancy in this data, this will have effect on all the results.
- Line 70 there is a mention of energy and mass conservation, however I do not see any diagnostics of mass conservation, perhaps that needs to be included to support the results. The claim of mass conservation needs to be quantified.
- Eqn (6) explain the importance of \theta_{cap} and its implications. Because as \theta_1 = \theta_{cap} the value suddenly/abruptly jumps to 1. This introduces an abrupt discontinuity. Is this intentional, and could it affect numerical behavior or simulated evaporation?
- Figure 4, why is the observed QE not added in this figure? Please add that and discuss the implications.
- Simulation details are missing especially WRF model version and details, whether it is fully online atmosphere coupled simulation or offline simulation.
- Line 245: Discussion about amplitude and timing and peak would require referring to actual time series of the figures rather than the scatter plots. I suggest presenting time series first and then present the scatter plot i.e. make Figure 2 as Figure 1. Also Figure 2 and Figure 3 could simply be merged by using different line styles. 3 lines in a single plot is not really a lot. This would make it easier for the reader to easily compare the old and the new method than scrolling down to see if there is a mismatch in the timing of the amplitude etc.
- Figure 1: Scatter plot instead of plotting summer and fall images together. Considering plotting original and modified methods for summer in the left column and fall in the right column.
- Line 265 -- 270 : So does that mean the original model was doing a good job in terms of soil water content even with simplifications? Discuss the reasons for this.
- Line 270--275: Comparing the timing and peaks of different models would require that they be plotted in the same figure.
- Line 260--265: I agree that QG is substantially improved, but the modified model still produces pronounced negative nighttime peaks (Fig. 2c), while the observations remain close to zero. Moreover, the air gap and exposed lateral boundaries could influence measurements during both daytime and nighttime. Please discuss their implications across the complete diurnal cycle.
- Readability of results section can be further improved by dividing it into sub sections based on energy or mass balance analysis.
Minor comments
- In general image quality is quite low. Please use vector image formats or high DPI images.
- In some figures (Fig 2 for example), the tick sizes of the figure are small, thumb rule should be that the font sizes in the figures would be approximately same as the text size in the manuscript.
- Perhaps include the statistical metrics in Figure 1, which would make it easier for the reader to compare the values directly. Mention these metrics under R2.
- Instead of just colours, please use different markers (circles, squares or open circles) to denote summer and fall. Considering the low quality images it is really difficult to differentiate between different points.
- In general please do a thorough revision w.r.t readability of the figures and presentation of the sections in general.
Citation: https://doi.org/10.5194/egusphere-2026-984-RC3
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This study improves a multi-layer green roof scheme coupled to the BEM+BEP urban canopy model within the WRF framework. The model development is clearly described, the validation is robust, and the authors clearly identify the contributions of individual modifications. The work would make a valuable contribution to the urban-climate and building-energy modeling community. I have several comments and suggestions below.
1. The green‑roof soil depth is only 0.03 m per layer. Please discuss potential numerical stability issues under extreme conditions (e.g., prolonged heat waves) when soil moisture may dry out and temperatures may rise rapidly. Have you tested model behavior under such extremes, and are any timestep or scheme adjustments needed?
2. Section 2.2 describes the original model in text. Consider adding a schematic or flow diagram that contrasts the original scheme and the modified scheme to make the differences clearer (e.g., layer structure, heat flux pathways, and where parameter changes are applied).
3. In Sections 2.3.1 and 2.3.2, please quantify how much the soil and vegetation thermal conductivities were changed relative to the original model. Are the modified values still within physically reasonable/observed ranges? If possible, include a table or figure showing the original vs. modified conductivity values and their sources or justification.
4. Table 1 and Fig. 1 show degraded performance in Ts prediction across all cases. Please discuss possible causes. Clarify which surface temperature is reported (soil surface, vegetation canopy surface, or some aggregated surface skin temperature). You mention two thermal conductivity calculations—how do those relate to the reported Ts, and might they explain the degradation? Consider adding separate diagnostics for soil-surface and vegetation-surface temperatures if available.
5. The resolution of all figures is quite low. Please replace figures with higher-resolution versions and ensure axes, legends, and labels are clearly legible.