the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: Sensitivity to vegetation phenology and maximum conductance
Yingqi Zheng
Minttu Havu
Huizhi Liu
Xueling Cheng
Yifan Wen
Hei Shing Lee
Joyson Ahongshangbam
Leena Järvi
Abstract. The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottomup approach to modelling carbon dioxide (CO2) emissions and sink in urban areas. In this study, SUEWS is evaluated against radiation flux observations and eddy covariance (EC) measured turbulent fluxes of sensible heat (QH), latent heat (QE), and CO2 (FC) at a densely built neighborhood in Beijing. The model sensitivity to maximum conductance (gmax) and leaf area index (LAI) is examined. Site-specific gmax is obtained from observations over local vegetation species, and LAI parameters by optimization with remotely sensed LAI obtained from a MODIS/Terra data product. For simulation of anthropogenic CO2 components, local traffic and population data are collected. In model evaluation, the mismatch between the measurement source area and simulation domain is also considered.
Using the optimized gmax and LAI, the modelling of heat fluxes is noticeably improved, showing higher correlation with observations, lower bias, and more realistic seasonal dynamics of QE and QH. In comparison to heat fluxes, the FC module shows lower sensitivity to the choice of gmax and LAI. This can be explained by the low relative contribution of vegetation to net FC in the modelled area. SUEWS successfully reproduces the average diurnal cycle of FC and annual cumulative sums. Depending on the size of the simulation domain, the modelled annual accumulated FC ranges from 7.2 to 8.5 kg C m−2 yr−1, when compared to 7.5 kg C m−2 yr−1 observed by EC. Traffic is the dominant CO2 source, contributing 63–73 % to the annual total CO2 emissions, followed by human metabolism (14–18 %), respiration released by vegetation and soil (6–11 %) and building heating (6–9 %). Vegetation photosynthesis offsets only 4–8 % of the total CO2 emissions. We highlight the importance of choosing optimal LAI parameters and gmax when SUEWS is used to model surface fluxes. The FC module of SUEWS is a promising tool in quantifying urban CO2 emissions at the local scale, and therefore assisting to mitigate urban CO2 emissions.
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Yingqi Zheng et al.
Status: open (until 01 May 2023)
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RC1: 'Comment on gmd-2022-305', Anonymous Referee #1, 15 Mar 2023
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Review of: Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: Sensitivity to vegetation phenology and maximum conductance
Author(s): Yingqi Zheng et al.
General Comments:
=================This manuscript evaluates the simulation results of the SUEWS on radiation flux, turbulent heat flux and CO2 flux at a densely built neighbourhood in Beijing. Using the site-specific gmax and optimized LAI parameters, the modelling of turbulent heat fluxes is improved.
The Fc module of SUEWS is applied in Beijing for the first time, and the simulation results of CO2 flux are satisfactory, which makes it possible to quantitatively evaluate the contribution of various CO2 sources and sinks and facilitate comparison with other observation sites.
However, there are some problems in the analysis and discussion results. For example, the radiation parameterization scheme NARP does not involve parameters gmax and LAI, so there is no way to say “gmax and LAI parameters has only a minor impact on the modelled radiation fluxes”.
Some of the analysis and discussion in the manuscript are relatively simple and one-sided. Please see the following specific comments.
Specific Comments
==================Line 167-176, Is the data during precipitation included in the deleted data?
In Table 2, is the TL set at -10 ℃ applicable to IAP, and is there any possibility that the air temperature in Beijing will be lower than -10 ℃?
Line 290-291, Generally, air temperature (including daily minimum air temperature, daily range of air temperature, accumulated temperature, etc.) is the main factor affecting urban vegetation phenology in Beijing. Vegetation in cities is irrigated frequently, so there are few cases where vegetation growth is limited by soil moisture, which is different from the non-urban areas Omidvar et al. (2022) studied. The optimization of the LAI model is of course necessary and recommended, but it is not reasonable to explain the factors affecting vegetation growth and senescence rate here.
Line 295-299, the radiation parameterization scheme (NARP) in the SUEWS does not involve gmax and LAI, so the simulation results of radiation flux among 4 cases should be identical, and the R2 is 1, but why are the RMSE and MBE are not equal to 0?
Line 307-308, In addition to the lower albedo of vegetation in summer, wet surface caused by frequent rainfall and radiation trapping caused by street canyons also lead to the decrease in surface albedo (Ao et al., 2016; Oke et al., 2017; Dou et al., 2019). Given the vegetated fraction is low, only adjusting the albedo for vegetation has a limited effect on improving the simulation results of radiation fluxes as stated by the author. Therefore, it is recommended to adjust albedos for all surface types in SUEWS, as Ward et al., (2016) did, in order to improve simulation results, especially Kup.
Line 313, the overestimated Lup might be induced by the lower emissivity of the building materials but does the Kdown of the reanalysis dataset WFDES also play a role? After all, it can be seen from Figure 5a-d that Kdown is obviously overestimated, especially in summer.
Line 349-351, the Parameterization scheme of Fc does not include the parameter gmax but is related to LAI. At IAP, compared with anthropogenic emissions, the amount of CO2 absorbed by plant photosynthesis is relatively small, so the Fc is not sensitive to the improvement of LAI model. However, this is different from the case where QE simulation results are highly dependent on gmax and LAI. It is inappropriate to simply say that Fc is less sensitive to the improvement of gmax and LAI than QE without further explanation.
Line 432, the NARP does not include the parameters gmax and LAI at all. They are not involved in the calculation of radiation fluxes, so it is not appropriate to say "hardly affected by gmax and LAI".
Line 433-435, For Beijing, plant phenology (leaf expansion time, leaf growth period, defoliation time, etc.) is generally more affected by temperature, not the transformation of dry-wet seasons.
Line 438, Case gmax_LAI improved the simulation effect of QE, but I am not sure whether R2 increased by 0.02 can be called remarkable better.
Technical corrections/suggestions/language edits (not exhaustive!)
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Line 90-91, “while outgoing longwave radiation (Lup) is estimated by a surface emissivity, α, Kdown, Lup and Tair”. The second Lup should be Ldown.
In Figure 5, the shaded area is recommended to be represented by the IQR rather than the standard deviation to display more data information. The same cases are in Figures 6, 8, and 9.
Line 309, that the average seasonal and diurnal cycles of Ldown are well captured by the model are shown in Fig.5 i-l rather than Fig.4 i-l.
Line 311, the full name of the NARP (Net All-wave Radiation Parameterization scheme) should be given when it is mentioned for the first time in the submitted manuscript.
Line 469, The cited reference is short of publication year.
In lines 33, Line 43, Line 360, Line 382-383, and Line 421, the arrangement of references is not consistent.
References:
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Dou, J. X., Grimmond, C. S. B., Cheng, Z. G., Miao, S. G., Feng, D. Y., and Liao, M. S.: Summertime surface energy balance fluxes at two Beijing sites, Int. J. Climatol., 39, 2793–2810, doi:10.1002/joc.5989, 2019.
Oke, T. R., Mills, G., Christen, A., Voogt, J. A. (2017) Urban Climates. Cambridge: Cambridge University Press. 134-137pp. https://doi.org/10.1017/9781139016476
Citation: https://doi.org/10.5194/gmd-2022-305-RC1
Yingqi Zheng et al.
Data sets
Datasets for simulating heat and CO2 fluxes in Beijing using SUEWS V2020b Yingqi Zheng, Huizhi Liu, and Xueling Cheng https://doi.org/10.5281/zenodo.7427361
Yingqi Zheng et al.
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