Simulating heat and CO<sub>2</sub> fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance
<p>The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottom-up approach to modeling carbon dioxide (<span class="inline-formula">CO<sub>2</sub></span>) emissions and uptake in urban areas. In this study, SUEWS...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-08-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/16/4551/2023/gmd-16-4551-2023.pdf |
| _version_ | 1797745792971177984 |
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| author | Y. Zheng Y. Zheng Y. Zheng M. Havu H. Liu X. Cheng X. Cheng Y. Wen H. S. Lee H. S. Lee J. Ahongshangbam L. Järvi L. Järvi |
| author_facet | Y. Zheng Y. Zheng Y. Zheng M. Havu H. Liu X. Cheng X. Cheng Y. Wen H. S. Lee H. S. Lee J. Ahongshangbam L. Järvi L. Järvi |
| author_sort | Y. Zheng |
| collection | DOAJ |
| description | <p>The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottom-up approach to modeling carbon dioxide
(<span class="inline-formula">CO<sub>2</sub></span>) emissions and uptake in urban areas. In this study, SUEWS is evaluated against the measured eddy covariance (EC) turbulent fluxes of
sensible heat (<span class="inline-formula"><i>Q</i><sub>H</sub></span>), latent heat (<span class="inline-formula"><i>Q</i><sub>E</sub></span>), and <span class="inline-formula">CO<sub>2</sub></span> (<span class="inline-formula"><i>F</i><sub>C</sub></span>) in a densely built neighborhood in Beijing. The
model sensitivity to maximum conductance (<span class="inline-formula"><i>g</i><sub>max</sub></span>) and leaf area index (<span class="inline-formula">LAI</span>) is examined. Site-specific <span class="inline-formula"><i>g</i><sub>max</sub></span> is obtained from
observations over local vegetation species, and <span class="inline-formula">LAI</span> parameters are extracted by optimization with remotely sensed <span class="inline-formula">LAI</span> obtained from a Landsat 7 data product. For
the simulation of anthropogenic <span class="inline-formula">CO<sub>2</sub></span> components, local traffic and population data are collected. In the model evaluation, the mismatch
between the measurement source area and simulation domain is also considered.</p>
<p>Using the optimized <span class="inline-formula"><i>g</i><sub>max</sub></span> and <span class="inline-formula">LAI</span>, the modeling of heat fluxes is noticeably improved, showing higher correlation with observations, lower
bias, and more realistic seasonal dynamics of <span class="inline-formula"><i>Q</i><sub>E</sub></span> and <span class="inline-formula"><i>Q</i><sub>H</sub></span>. The effect of the <span class="inline-formula"><i>g</i><sub>max</sub></span> adjustment is more significant
than the <span class="inline-formula">LAI</span> adjustment. Compared to heat fluxes, the <span class="inline-formula"><i>F</i><sub>C</sub></span> module shows lower sensitivity to the choices of <span class="inline-formula"><i>g</i><sub>max</sub></span> and <span class="inline-formula">LAI</span>. This
can be explained by the low relative contribution of vegetation to the net <span class="inline-formula"><i>F</i><sub>C</sub></span> in the modeled area. SUEWS successfully reproduces the
average diurnal cycle of <span class="inline-formula"><i>F</i><sub>C</sub></span> and annual cumulative sums. Depending on the size of the simulation domain, the modeled annual accumulated
<span class="inline-formula"><i>F</i><sub>C</sub></span> ranges from 7.4 to 8.7 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M26" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">kg</mi><mspace linebreak="nobreak" width="0.125em"/><mi mathvariant="normal">C</mi><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">m</mi><mrow><mo>-</mo><mn mathvariant="normal">2</mn></mrow></msup><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">yr</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="007ce286dfe5231fe912e2d2b6c55f1c"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-16-4551-2023-ie00001.svg" width="64pt" height="15pt" src="gmd-16-4551-2023-ie00001.png"/></svg:svg></span></span>, compared to 7.5 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M27" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">kg</mi><mspace linebreak="nobreak" width="0.125em"/><mi mathvariant="normal">C</mi><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">m</mi><mrow><mo>-</mo><mn mathvariant="normal">2</mn></mrow></msup><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">yr</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="edd10d8104028cc3bc1eb02fb28fd8aa"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-16-4551-2023-ie00002.svg" width="64pt" height="15pt" src="gmd-16-4551-2023-ie00002.png"/></svg:svg></span></span> observed by EC. Traffic is
the dominant <span class="inline-formula">CO<sub>2</sub></span> source, contributing 59 %–70 % to the annual total <span class="inline-formula">CO<sub>2</sub></span> emissions, followed by human metabolism
(14 %–18 %), buildings (11 %–14 %), and <span class="inline-formula">CO<sub>2</sub></span> release by vegetation and soil respiration (6 %–10 %). Vegetation
photosynthesis offsets only 5 %–10 % of the total <span class="inline-formula">CO<sub>2</sub></span> emissions. We highlight the importance of choosing the optimal <span class="inline-formula">LAI</span>
parameters and <span class="inline-formula"><i>g</i><sub>max</sub></span> when SUEWS is used to model surface fluxes. The <span class="inline-formula"><i>F</i><sub>C</sub></span> module of SUEWS is a promising tool in quantifying
urban <span class="inline-formula">CO<sub>2</sub></span> emissions at the local scale and therefore assisting in mitigating urban <span class="inline-formula">CO<sub>2</sub></span> emissions.</p> |
| first_indexed | 2024-03-12T15:28:05Z |
| format | Article |
| id | doaj.art-eb3d1252ff2b47e9835010deec02851a |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-03-12T15:28:05Z |
| publishDate | 2023-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-eb3d1252ff2b47e9835010deec02851a2023-08-10T11:05:12ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032023-08-01164551457910.5194/gmd-16-4551-2023Simulating heat and CO<sub>2</sub> fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductanceY. Zheng0Y. Zheng1Y. Zheng2M. Havu3H. Liu4X. Cheng5X. Cheng6Y. Wen7H. S. Lee8H. S. Lee9J. Ahongshangbam10L. Järvi11L. Järvi12State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00560, FinlandCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100029, ChinaInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00560, FinlandDepartment of Atmospheric Sciences, Yunnan University, Kunming, 650091, ChinaState Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100029, ChinaSchool of Environment, State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing, 100084, ChinaInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00560, FinlandHelsinki Institute of Sustainability Science, University of Helsinki, Helsinki, 00560, FinlandInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00560, FinlandInstitute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, 00560, FinlandHelsinki Institute of Sustainability Science, University of Helsinki, Helsinki, 00560, Finland<p>The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottom-up approach to modeling carbon dioxide (<span class="inline-formula">CO<sub>2</sub></span>) emissions and uptake in urban areas. In this study, SUEWS is evaluated against the measured eddy covariance (EC) turbulent fluxes of sensible heat (<span class="inline-formula"><i>Q</i><sub>H</sub></span>), latent heat (<span class="inline-formula"><i>Q</i><sub>E</sub></span>), and <span class="inline-formula">CO<sub>2</sub></span> (<span class="inline-formula"><i>F</i><sub>C</sub></span>) in a densely built neighborhood in Beijing. The model sensitivity to maximum conductance (<span class="inline-formula"><i>g</i><sub>max</sub></span>) and leaf area index (<span class="inline-formula">LAI</span>) is examined. Site-specific <span class="inline-formula"><i>g</i><sub>max</sub></span> is obtained from observations over local vegetation species, and <span class="inline-formula">LAI</span> parameters are extracted by optimization with remotely sensed <span class="inline-formula">LAI</span> obtained from a Landsat 7 data product. For the simulation of anthropogenic <span class="inline-formula">CO<sub>2</sub></span> components, local traffic and population data are collected. In the model evaluation, the mismatch between the measurement source area and simulation domain is also considered.</p> <p>Using the optimized <span class="inline-formula"><i>g</i><sub>max</sub></span> and <span class="inline-formula">LAI</span>, the modeling of heat fluxes is noticeably improved, showing higher correlation with observations, lower bias, and more realistic seasonal dynamics of <span class="inline-formula"><i>Q</i><sub>E</sub></span> and <span class="inline-formula"><i>Q</i><sub>H</sub></span>. The effect of the <span class="inline-formula"><i>g</i><sub>max</sub></span> adjustment is more significant than the <span class="inline-formula">LAI</span> adjustment. Compared to heat fluxes, the <span class="inline-formula"><i>F</i><sub>C</sub></span> module shows lower sensitivity to the choices of <span class="inline-formula"><i>g</i><sub>max</sub></span> and <span class="inline-formula">LAI</span>. This can be explained by the low relative contribution of vegetation to the net <span class="inline-formula"><i>F</i><sub>C</sub></span> in the modeled area. SUEWS successfully reproduces the average diurnal cycle of <span class="inline-formula"><i>F</i><sub>C</sub></span> and annual cumulative sums. Depending on the size of the simulation domain, the modeled annual accumulated <span class="inline-formula"><i>F</i><sub>C</sub></span> ranges from 7.4 to 8.7 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M26" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">kg</mi><mspace linebreak="nobreak" width="0.125em"/><mi mathvariant="normal">C</mi><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">m</mi><mrow><mo>-</mo><mn mathvariant="normal">2</mn></mrow></msup><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">yr</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="007ce286dfe5231fe912e2d2b6c55f1c"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-16-4551-2023-ie00001.svg" width="64pt" height="15pt" src="gmd-16-4551-2023-ie00001.png"/></svg:svg></span></span>, compared to 7.5 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M27" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">kg</mi><mspace linebreak="nobreak" width="0.125em"/><mi mathvariant="normal">C</mi><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">m</mi><mrow><mo>-</mo><mn mathvariant="normal">2</mn></mrow></msup><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">yr</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="edd10d8104028cc3bc1eb02fb28fd8aa"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="gmd-16-4551-2023-ie00002.svg" width="64pt" height="15pt" src="gmd-16-4551-2023-ie00002.png"/></svg:svg></span></span> observed by EC. Traffic is the dominant <span class="inline-formula">CO<sub>2</sub></span> source, contributing 59 %–70 % to the annual total <span class="inline-formula">CO<sub>2</sub></span> emissions, followed by human metabolism (14 %–18 %), buildings (11 %–14 %), and <span class="inline-formula">CO<sub>2</sub></span> release by vegetation and soil respiration (6 %–10 %). Vegetation photosynthesis offsets only 5 %–10 % of the total <span class="inline-formula">CO<sub>2</sub></span> emissions. We highlight the importance of choosing the optimal <span class="inline-formula">LAI</span> parameters and <span class="inline-formula"><i>g</i><sub>max</sub></span> when SUEWS is used to model surface fluxes. The <span class="inline-formula"><i>F</i><sub>C</sub></span> module of SUEWS is a promising tool in quantifying urban <span class="inline-formula">CO<sub>2</sub></span> emissions at the local scale and therefore assisting in mitigating urban <span class="inline-formula">CO<sub>2</sub></span> emissions.</p>https://gmd.copernicus.org/articles/16/4551/2023/gmd-16-4551-2023.pdf |
| spellingShingle | Y. Zheng Y. Zheng Y. Zheng M. Havu H. Liu X. Cheng X. Cheng Y. Wen H. S. Lee H. S. Lee J. Ahongshangbam L. Järvi L. Järvi Simulating heat and CO<sub>2</sub> fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance Geoscientific Model Development |
| title | Simulating heat and CO<sub>2</sub> fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance |
| title_full | Simulating heat and CO<sub>2</sub> fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance |
| title_fullStr | Simulating heat and CO<sub>2</sub> fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance |
| title_full_unstemmed | Simulating heat and CO<sub>2</sub> fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance |
| title_short | Simulating heat and CO<sub>2</sub> fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance |
| title_sort | simulating heat and co sub 2 sub fluxes in beijing using suews v2020b sensitivity to vegetation phenology and maximum conductance |
| url | https://gmd.copernicus.org/articles/16/4551/2023/gmd-16-4551-2023.pdf |
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