Effect of Land-Use Change on the Urban Heat Island in the Fukuoka–Kitakyushu Metropolitan Area, Japan
<p>Geography of targeted area and “urban” land-use fraction in Fukuoka prefecture: (<b>a</b>) The geography of the Kyushu region. The locations of the Fukuoka district meteorological observatory, the Kumamoto local meteorological observatory, the Oita local meteorological observatory and the Nagasaki local meteorological observatory are also shown. (<b>b</b>) The “urban” land-use fraction in 1976. The degree of shading indicates the fraction of “urban” land-use within each grid cell. (<b>c</b>) Increase in the “urban” land-use fraction. The red indicates the degree of increase in the “urban” land-use fraction from 1976 to 2009, and the blue indicates the decrease. Numbers from 1 to 14 enclosed within circles indicate the observation sites of the Japan Meteorological Agency (Tokyo, Japan). Number 15 indicates the Doppler LiDAR observation site.</p> "> Figure 1 Cont.
<p>Geography of targeted area and “urban” land-use fraction in Fukuoka prefecture: (<b>a</b>) The geography of the Kyushu region. The locations of the Fukuoka district meteorological observatory, the Kumamoto local meteorological observatory, the Oita local meteorological observatory and the Nagasaki local meteorological observatory are also shown. (<b>b</b>) The “urban” land-use fraction in 1976. The degree of shading indicates the fraction of “urban” land-use within each grid cell. (<b>c</b>) Increase in the “urban” land-use fraction. The red indicates the degree of increase in the “urban” land-use fraction from 1976 to 2009, and the blue indicates the decrease. Numbers from 1 to 14 enclosed within circles indicate the observation sites of the Japan Meteorological Agency (Tokyo, Japan). Number 15 indicates the Doppler LiDAR observation site.</p> "> Figure 2
<p>Analysis domains for the Weather Research and Forecasting model (WRF) simulation. Domain 1 covers all of Japan, domain 2 covers the entire Kyushu region, and domain 3 covers the northern part of the Kyushu region, which was the main target area in this study.</p> "> Figure 3
<p>Differences in averaged air temperature between NLNI-76 and NLNI-09 over 54 days from 8 August to 30 September 2015. The blue tones show a temperature drop, and the red tones show a temperature rise from NLNI-76 to NLNI-09: (<b>a</b>) Difference in averaged air temperature for the whole period. The spatial average for this domain is +0.236 °C, with a minimum value of −0.171 °C and a maximum value of +2.049 °C. (<b>b</b>) Difference in averaged air temperature for the daytime period, from 09:00 to 18:00. The spatial average for this domain is +0.225 °C, with a minimum value of −0.144 °C and a maximum value of +1.317 °C. (<b>c</b>) Difference in averaged air temperature for the nighttime period, from 19:00 to 08:00 the next day. The spatial average for this domain is +0.245 °C, with a minimum value of −0.324 °C and a maximum value of +2.656 °C.</p> "> Figure 3 Cont.
<p>Differences in averaged air temperature between NLNI-76 and NLNI-09 over 54 days from 8 August to 30 September 2015. The blue tones show a temperature drop, and the red tones show a temperature rise from NLNI-76 to NLNI-09: (<b>a</b>) Difference in averaged air temperature for the whole period. The spatial average for this domain is +0.236 °C, with a minimum value of −0.171 °C and a maximum value of +2.049 °C. (<b>b</b>) Difference in averaged air temperature for the daytime period, from 09:00 to 18:00. The spatial average for this domain is +0.225 °C, with a minimum value of −0.144 °C and a maximum value of +1.317 °C. (<b>c</b>) Difference in averaged air temperature for the nighttime period, from 19:00 to 08:00 the next day. The spatial average for this domain is +0.245 °C, with a minimum value of −0.324 °C and a maximum value of +2.656 °C.</p> "> Figure 4
<p>Time–height cross sections of horizontal wind vectors at the observation site on 11 September 2015: (<b>a</b>) Doppler LiDAR observation, (<b>b</b>) Numerical simulation with NLNI-76, and (<b>c</b>) Numerical simulation with NLNI-09.</p> "> Figure 4 Cont.
<p>Time–height cross sections of horizontal wind vectors at the observation site on 11 September 2015: (<b>a</b>) Doppler LiDAR observation, (<b>b</b>) Numerical simulation with NLNI-76, and (<b>c</b>) Numerical simulation with NLNI-09.</p> "> Figure 5
<p>Vertical wind profiles from Doppler LiDAR observations and from simulations with NLNI-76 and NLNI-09, and potential temperature profiles from simulations with NLNI-76 and NLNI-09, on 11 September 2015: (<b>a</b>) At 08:00, before the sea breeze blows into the Ohashi Doppler LiDAR observation site; (<b>b</b>) At 09:00, the simulation with NLNI-76 captured the sea breeze, while the Doppler LiDAR did not; (<b>c</b>) At 10:00, the Doppler LiDAR captured the sea breeze; (<b>d</b>) At 14:00, the simulation with NLNI-76 showed a potential temperature drop below 50 m above ground level (AGL); (<b>e</b>) At 15:00, the observed sea breeze became the strongest on that day; and (<b>f</b>) At 18:00, the potential temperature profile difference showed a large difference between NLNI-76 and NLNI-09 below 100 m AGL.</p> "> Figure 5 Cont.
<p>Vertical wind profiles from Doppler LiDAR observations and from simulations with NLNI-76 and NLNI-09, and potential temperature profiles from simulations with NLNI-76 and NLNI-09, on 11 September 2015: (<b>a</b>) At 08:00, before the sea breeze blows into the Ohashi Doppler LiDAR observation site; (<b>b</b>) At 09:00, the simulation with NLNI-76 captured the sea breeze, while the Doppler LiDAR did not; (<b>c</b>) At 10:00, the Doppler LiDAR captured the sea breeze; (<b>d</b>) At 14:00, the simulation with NLNI-76 showed a potential temperature drop below 50 m above ground level (AGL); (<b>e</b>) At 15:00, the observed sea breeze became the strongest on that day; and (<b>f</b>) At 18:00, the potential temperature profile difference showed a large difference between NLNI-76 and NLNI-09 below 100 m AGL.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Outline of the Meso-Scale Meteorological Model
2.2. Outline of the National Land Numerical Information Land-Use Datasets
2.3. Analysis Conditions
2.4. Outline of the Doppler LiDAR System
3. Results and Discussion
3.1. Comparing Observation and Simulation Results of the Surface Air Temperature
3.2. Surface Air Temperature Change Caused by Land-Use Change
3.3. Transition of the Sea Breeze over the Urban Area
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Land-Use Categories in WRF (Provided by the U.S. Geological Survey) | Land-Use Categories in NLNI in 1976 | Land-Use Categories in NLNI in 2009 |
---|---|---|
Irrigated Cropland and Pasture | Paddy field | Paddy field |
Mixed Dryland/Irrigated Cropland and Pasture | Field | Other agricultural land |
Orchard | ||
Other tree plantation | ||
Mixed Forest | Forest | Forest |
Barren or Sparsely Vegetated | Wasteland | Wasteland |
Other land | Other land | |
Golf course | ||
Urban and Built-Up Land | Land for building | Land for building |
Trunk transportation land | Trunk transportation land (Road) | |
Trunk transportation land (Rail) | ||
Water Bodies | Lake | River basin, lake, and marsh |
River | ||
Beach | Beach | |
Seawater body | Seawater body |
Site | NLNI-09 | USGS | ||
---|---|---|---|---|
ME (°C) | RMSE (°C) | ME (°C) | RMSE (°C) | |
1. Munakata | −0.741 | 2.553 | −1.722 | 3.170 |
2. Yahata | 0.633 | 3.150 | −1.038 | 3.324 |
3. Kuko-kitamachi | 0.093 | 3.036 | −0.044 | 2.979 |
4. Yukuhashi | −0.492 | 3.160 | −1.608 | 3.590 |
5. Iizuka | −0.172 | 3.164 | −1.628 | 3.748 |
6. Maebaru | 0.052 | 3.191 | −0.998 | 3.296 |
7. Fukuoka | 1.430 | 3.397 | 0.674 | 3.081 |
8. Hakata | 1.032 | 3.317 | −1.066 | 3.652 |
9. Dazaifu | 0.290 | 3.184 | −0.996 | 3.377 |
10. Soeda | −1.238 | 3.435 | −0.153 | 3.006 |
11. Asakura | −0.208 | 3.473 | −1.193 | 3.708 |
12. Kurume | 0.845 | 3.427 | −0.626 | 3.548 |
13. Kurogi | −0.665 | 3.435 | −0.904 | 3.466 |
14. Omuta | 0.924 | 3.407 | 1.060 | 3.408 |
Total | 0.128 | 3.247 | −0.731 | 3.391 |
Land-Use Fraction in NLNI-09 | Dominant Land-Use Category in USGS | ||||||
---|---|---|---|---|---|---|---|
Urban and Built-Up Land | Irrigated Cropland and Pasture | Mixed Dryland/Irrigated Cropland and Pasture | Mixed Forest | Barren or Sparsely Vegetated | Water Bodies | ||
1. Munakata | 26.7% | 41.9% | 4.2% | 18.8% | 1.7% | 6.7% | Shrubland |
2. Yahata | 67.2% | 0.0% | 0.0% | 12.4% | 20.4% | 0.0% | Cropland/Grassland Mosaic |
3. Kuko-kitamachi | 0.1% | 0.0% | 0.0% | 0.0% | 21.3% | 78.5% | Water Bodies |
4. Yukuhashi | 50.7% | 39.8% | 1.0% | 0.3% | 3.3% | 4.9% | Cropland/Grassland Mosaic |
5. Iizuka | 53.7% | 7.0% | 0.0% | 17.1% | 8.8% | 13.5% | Shrubland |
6. Maebaru | 45.0% | 44.5% | 2.0% | 0.0% | 2.5% | 6.0% | Dryland Cropland and Pasture |
7. Fukuoka | 74.6% | 0.0% | 0.0% | 8.7% | 8.3% | 8.4% | Urban and Built-up Land |
8. Hakata | 46.2% | 0.0% | 0.0% | 6.8% | 45.9% | 1.1% | Shrubland |
9. Dazaifu | 57.1% | 5.4% | 9.4% | 18.7% | 8.1% | 1.3% | Shrubland |
10. Soeda | 10.4% | 13.8% | 5.2% | 62.3% | 4.8% | 3.5% | Urban and Built-up Land |
11. Asakura | 28.6% | 62.1% | 7.2% | 1.7% | 0.4% | 0.0% | Irrigated Cropland and Pasture |
12. Kurume | 44.5% | 44.0% | 0.9% | 0.0% | 7.4% | 3.2% | Irrigated Cropland and Pasture |
13. Kurogi | 3.3% | 34.2% | 48.0% | 13.0% | 0.0% | 1.5% | Cropland/Woodland Mosaic |
14. Omuta | 60.3% | 11.2% | 8.2% | 16.3% | 3.5% | 0.5% | Urban and Built-up Land |
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Kawamoto, Y. Effect of Land-Use Change on the Urban Heat Island in the Fukuoka–Kitakyushu Metropolitan Area, Japan. Sustainability 2017, 9, 1521. https://doi.org/10.3390/su9091521
Kawamoto Y. Effect of Land-Use Change on the Urban Heat Island in the Fukuoka–Kitakyushu Metropolitan Area, Japan. Sustainability. 2017; 9(9):1521. https://doi.org/10.3390/su9091521
Chicago/Turabian StyleKawamoto, Yoichi. 2017. "Effect of Land-Use Change on the Urban Heat Island in the Fukuoka–Kitakyushu Metropolitan Area, Japan" Sustainability 9, no. 9: 1521. https://doi.org/10.3390/su9091521