Numerical Study on the Influence of Rivers on the Urban Microclimate: A Case Study in Chengdu, China
Abstract
:1. Introduction
- This study mainly focuses on the influence of inland rivers on the microclimate of Chengdu.
- The influence mechanism of rivers, trees, and roads on the urban riverfront space of an inland city is analyzed.
- Outdoor predicted mean vote (PMV) is used to evaluate the influence of rivers, trees, and green plants on the outdoor thermal comfort of urban riverfront spaces.
2. Data and Methodology
2.1. Study Area
2.2. Meteorological Data Acquisition
2.3. ENVI-Met Modeling and Parameter Settings
2.4. Validation of ENVI-Met Simulation Results
3. Results and Discussions
3.1. The Simulation Results of Air Temperature
3.2. The Simulation Results of Relative Air Humidity
3.3. The Simulation Results of Thermal Comfort in the Study Area
4. Conclusions
- (1)
- The river had a significant cooling effect on air temperature in the riverfront spaces. The temperature in the downwind region was lower than that of the upwind region. Conversely, the road had a warming effect on the air temperature. The order of influence of different underlying surfaces on air temperature was determined as: rivers > roads > trees.
- (2)
- The river had an obvious humidifying effect on the relative air humidity, while the road had little effect. The order of influence of different underlying surfaces on the relative air humidity was determined as: rivers > trees > roads.
- (3)
- The PMV results showed that the thermal comfort on the left bank was better than that on the right bank. Rivers, trees, and green plants play a crucial role in alleviating the urban heat islands and regulating the local microclimate.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Time | Solar Radiation Intensity (W/m2) | Air Temperature (°C) | Relative Air Humidity (%) |
---|---|---|---|
7:00 | 75.82 | 27.28 | 90.60 |
8:00 | 130.19 | 28.69 | 86.67 |
9:00 | 74.92 | 29.89 | 79.85 |
10:00 | 49.80 | 30.22 | 78.57 |
11:00 | 90.94 | 29.94 | 79.08 |
12:00 | 285.99 | 30.19 | 76.95 |
13:00 | 646.91 | 30.75 | 73.27 |
14:00 | 471.55 | 31.82 | 70.51 |
15:00 | 800.08 | 33.16 | 60.08 |
16:00 | 191.87 | 34.76 | 61.30 |
17:00 | 247.59 | 33.78 | 65.47 |
18:00 | 173.75 | 33.03 | 67.29 |
19:00 | 76.48 | 32.25 | 70.99 |
Length (m) | 600 | Width (m) | 300 |
Grid size (m) | 2 | Expansion coefficient | 20% |
The lowest height of the building (m) | 140 |
Simulation Duration (h) | 24 |
Air temperature (°C) | Min: 27.28 Max: 34.76 |
Relative air humidity (%) | Min: 60.08 Max: 90.60 |
Solar radiation intensity (W/m2) | Max: 800.08 |
Wind speed (m/s) | 1.21 |
Soil temperature (°C) | 19.85 |
Soil moisture (%) | 70.00 |
Surface roughness | 0.02 |
9:00 | 12:00 | 16:00 | |||||||
---|---|---|---|---|---|---|---|---|---|
Measuring Point | Measured Value (°C) | Simulation Value (°C) | RE (%) | Measured Value (°C) | Simulation Value (°C) | RE (%) | Measured Value (°C) | Simulation Value (°C) | RE (%) |
1 | 25.3 | 26.1 | 3.16 | 27.5 | 26.5 | 3.64 | 30.9 | 30.5 | 1.29 |
2 | 25.6 | 25.7 | 0.39 | 27.4 | 26.7 | 2.55 | 30.6 | 30.9 | 0.98 |
3 | 25.7 | 25.2 | 1.95 | 28.3 | 27.1 | 4.24 | 31.0 | 31.5 | 1.61 |
4 | 26.4 | 25.9 | 1.89 | 27.3 | 26.4 | 3.30 | 30.2 | 31.1 | 2.98 |
5 | 25.8 | 25.6 | 0.78 | 27.1 | 26.9 | 0.74 | 29.7 | 29.4 | 1.01 |
6 | 25.2 | 24.6 | 2.38 | 27.3 | 26.4 | 3.30 | 29.4 | 29.2 | 0.68 |
9:00 | 12:00 | 16:00 | |||||||
---|---|---|---|---|---|---|---|---|---|
Measured Value (%) | Simulation Value (%) | RE(%) | Measured Value (%) | Simulation Value (%) | RE (%) | Measured Value (%) | Simulation Value (%) | RE (%) | |
1 | 75.9 | 73.4 | 3.29 | 67.6 | 69.2 | 2.37 | 60.8 | 60.4 | 0.66 |
2 | 70.4 | 71.5 | 1.56 | 69.2 | 68.8 | 0.58 | 62.5 | 64.9 | 3.84 |
3 | 71.5 | 71.9 | 0.56 | 68.9 | 69.3 | 0.58 | 57.1 | 59.1 | 3.50 |
4 | 72.7 | 71.5 | 1.65 | 70.1 | 69.7 | 0.57 | 61.8 | 61.3 | 0.81 |
5 | 71.6 | 73.4 | 2.51 | 68.8 | 70.6 | 2.62 | 60.2 | 62.6 | 3.99 |
6 | 76.7 | 73.9 | 3.65 | 72.8 | 69.6 | 4.40 | 59.8 | 59.5 | 0.50 |
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Qi, X.; Zhao, X.; Fu, B.; Xu, L.; Yu, H.; Tao, S. Numerical Study on the Influence of Rivers on the Urban Microclimate: A Case Study in Chengdu, China. Water 2023, 15, 1408. https://doi.org/10.3390/w15071408
Qi X, Zhao X, Fu B, Xu L, Yu H, Tao S. Numerical Study on the Influence of Rivers on the Urban Microclimate: A Case Study in Chengdu, China. Water. 2023; 15(7):1408. https://doi.org/10.3390/w15071408
Chicago/Turabian StyleQi, Xuejun, Xing Zhao, Bin Fu, Lanjing Xu, Haibin Yu, and Shuyan Tao. 2023. "Numerical Study on the Influence of Rivers on the Urban Microclimate: A Case Study in Chengdu, China" Water 15, no. 7: 1408. https://doi.org/10.3390/w15071408