氣候與土地利用變遷情境下的都市熱島效應—以桃園市區為例

Urban heat island under climate and land use changes: A case study of Taoyuan City

汪中安(Zhong-An Wang)

指導教授 ： 林裕彬

國立臺灣大學/生物資源暨農學院/生物環境系統工程學研究所/碩士(2022年)

https://doi.org/10.6342/NTU202200639

摘要

都市熱島效應描述因都市發展與擴張改變地表的生物物理特性，使得區域內溫度高於周圍的鄉村環境，此現象常見於全世界各氣候區的城市中。都市高溫會增加室內空調耗能，對於人員的勞動力、居民的環境舒適程度也有負面影響。在氣候變遷與都市持續擴張的情況下，都市熱環境將變得更加嚴峻，為訂定相關調適策略，必須了解未來情境下都市熱島效應的變化。過去都市熱島研究透過環境監測數據與不同的統計方法驗證，眾多結果顯示環境生物物理因子與空氣溫度之間的相關性，建立關係式並推估都市溫度分布。然而不同的研究受限於研究尺度、資料特性等因素，國內較少研究以都市尺度同時考量都市擴張與氣候變遷影響。因此本研究以台灣發展中城市—桃園市區為研究範例，討論夏季都市熱島於氣候與土地變遷情境下的變化與空間分布。
本研究以CA-Markov (Cellular automata-Markov)土地利用模型與隨機氣象產生器分別產生未來土地與氣候情境，並藉由InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) 的Urban Cooling模組中計算上述情境下都市熱島的空間分布。Urban Cooling模型依據土地利用之生物物理特性推估研究區空氣溫度分布，白天考量地表的蒸發散、反照率以及遮蔭程度，夜間則是考量建築密度的影響。其中，CA-Markov的土地情境設定，耦合馬爾克夫條件機率、二元羅吉斯迴歸以及隨機森林製作適宜性地圖。最終，以兩種2035土地利用情境結合3種代表濃度途徑情境(Representative Concentration Pathway)與7種大氣環流模式(General Circulation Model)，其中因ACCESS1-3與CMCC-CM模式沒有RCP 2.6之資料，故以38種組合模擬未來溫度空間分布。
CA-Markov土地利用模型2014年驗證結果顯示：模擬結果與真實情境相比，各土地分布較為聚集。三種適宜性地圖中，二元羅吉斯迴歸方法較不適用於桃園研究區及研究尺度，因其土地分配結果導致真實土地中分布隨機的建地消失，因此本研究後續以條件機率與隨機森林適宜性地圖模擬未來土地情境。2035年土地情境結果中，建地之需求量於將由2014年的27790公頃增加為33082公頃，並且以鄰近都市的區域向外擴張。本研究以2014年與2015年夏季平均溫度資料進行Urban Cooling的參數檢定驗證，比較都市空氣溫度分布模擬結果與真實測站資料，可知對於都市範圍高解析度的氣溫模擬具有足夠的可信度。未來氣候情境結果顯示無論晝夜的都市溫度、鄉村溫度與城鄉溫差皆有上升之情形，在都市區域增溫幅度較鄉村地區高。於2035之未來土地變遷與氣候情境下，研究區日夜溫度至少增加約1.2℃，土地利用將影響溫度空間分布。針對氣候變遷最極端之情境，RCP8.5的ACCESS1-3模擬結果午後兩點鄉村增溫2.56℃，而都市增溫幅度可能高達3.43℃；RCP4.5的CMCC-CM模擬顯示夜間十點鄉村與都市將分別增溫2.36℃與2.8℃。
本研究為國內首篇以Urban Cooling 模式討論都市熱島之研究，在結合土地利用、都市氣溫與氣候變遷模擬之架構下，Urban Cooling 模式能有效計算未來土地與氣候情境的都市溫度分布，且能作為有效比較不同情境差異的工具。本研究結果顯示未來情境對於都市熱島的影響中，氣候情境影響研究區整體的增溫幅度，而土地變遷情境則會改變都市溫度空間分布。後續研究能參考研究架構，作為都市能源計算與熱風險區域規劃所需的基本資訊。唯不同研究區需重新調整Urban Cooling模式參數方能應用，或是替換其他適合其研究區域與尺度的模擬工具。

關鍵字

都市生態系統服務 ； 都市熱島效應 ； InVEST模式 ； CA-Markov模式 ； 未來氣候情境

並列摘要

Urban heat island (UHI) is one of the common environmental problems in an urban area, which describes temperature in an urban area is higher than its surroundings. The warmer environment in a high-density city could raise the energy consumption of air conditioning, lower workers' efficiency and outdoor conform. Under the circumstance of climate change and urbanization, UHI may deteriorate in the near future. Studying how UHI would change with land use and climate change is essential for the mitigation plans. This study aimed to study future urban heat island in Taoyuan City, which is one of the developing cities in Taiwan, urder climate and land-use change scenarios.
In the study, Cellular automata-Markov (CA-Markov) land change model and stochastic weather generator were used to generate future urban land use and climate scenarios. Then, the diurnal spatial distribution of urban temperature would calculate by the Urban Cooling module of InVEST model, which estimates the land uses’ cooling capacity based on biophysical factors. For land-use modeling, CA-Markov model coupled with three suitability maps, made by Markov conditional probability, binary logistic regression and random forest. Future land use and climate scenarios, including two land-use maps, seven general circulation models and three representative concentration pathways, were considered to estimate future urban temperature.
Urban Cooling module and CA-Markov model were validated by data in 2014. The results of CA-Markov simulations showed that binary logistic regression is not suitable for suitability map for the research scale and Taoyuan area, due to its land-use allocation neglects the randomness of the built-up area near the farmland, which is a common rural landscape in Taiwan. Therefore, future land uses of 2035 are carried out with the method of conditional probability and random forest. A suitable combination of parameters for Urban Cooling was selected to calculate the urban temperatures, which its RMSE of urban summer day and night temperature were within 0.652℃ and 0.572℃, has proved that the model is reliable to estimate high resolution temperature for the cities scale.
Results of future climate scenarios have shown that urban temperatures, rural temperatures, and urban heat island intensities would all increase in 2035. The rise of temperature during the day is higher than at night, and the increase in an urban area is higher than it’s in rural. The simulations show that future temperatures in the Taoyuan area will increase at least 1.2℃. The result of RCP 8.5 and ACCESS1-3, which is the most severe condition of daytime, the increases in rural and urban areas may reach up to 2.56℃ and 3.43℃. In the nocturnal scenario of RCP4.5 and CMCC-CM, the temperature may increase by 2.36℃ in rural, and 2.8℃ in urban central.
The study had shown that, with the framework of land use, urban temperature and climate change simulation, the Urban Cooling model can not only provide the efficient and reliable calculation of temperature distribution under future scenarios, but also be a useful tool to analyze the difference between scenarios. The future scenarios of urban heat island reveal that climate change affects the temperature increase of the study area, and the change land-use would influence the spatial distribution of temperature. The framework can be the foundation of energy consumption projection or analysis of the high-risk area in the city for future study, any suitable stimulation module or scenario substitute to provide a more accurate result for the different study areas.

並列關鍵字

Urban Ecosystem Service ； Urban heat island ； InVEST model ； CA-Markov model ； Future climate scenario

參考文獻

Alexander, P., Prestele, R., Verburg, P. H., Arneth, A., Baranzelli, C., Batista e Silva, F., ... Rounsevell, M. D. (2017). Assessing uncertainties in land cover projections. Global change biology, 23(2), 767-781.

Arya, P. S. (2001). Introduction to micrometeorology. Elsevier.

Argüeso, D., Evans, J. P., Fita, L., Bormann, K. J. (2014). Temperature response to future urbanization and climate change. Climate dynamics, 42(7-8), 2183-2199.

Assessment, M. E. (2005). Ecosystems and human well-being (Vol. 5, p. 563). United States of America: Island press.

Aubrecht, C., Özceylan, D. (2013). Identification of heat risk patterns in the US National Capital Region by integrating heat stress and related vulnerability. Environment international, 56, 65-77.