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题名: 中国城镇化的热环境效应研究
作者: 李元征1
学位类别: 博士
答辩日期: 2017-05
授予单位: 中国科学院大学
授予地点: 北京
导师: 胡聃
关键词: 气温变化、地表温度、地表城市热岛、影响因素、预测 ; Air tepmerature change, Land surface temperature, Surface urbanheat island, Influence factors, Prediction
其他题名: Study on Thermal Environment Effect of Urbanization in China
学位专业: 生态学
中文摘要: 全球正经历快速、高强度的城镇化,导致城市热岛加剧,并对城市、区域乃 至全球许多的生态环境要素直接或间接地产生多方面的影响,与人类福祉密切相 关。本文分析了中国 1449个城市 2010年的地表热岛强度(SUHII)的年内变化 时空规律、2003 - 2013年间这些城市及对应乡村的地表温度(LST)及这些城市 SUHII的年际变化规律;探索了影响 SUHII年内时空演变的相关决定要素并对其 进行预测;并从区域尺度上准确估算了 1977 - 2014年中国城镇化对区域气温变 化趋势的影响。主要的研究结果如下。 (1)中国白天较大的年均 SUHII出现在湿热的南方地区与湿冷的东北地区, 最小出现在干旱半干旱的西北部地区。最强的在长三角及台湾沿海的许多城市。 温带地区除西北部地区外,各季 SUHII的大小顺序为夏季 >春季 >秋季 >冬 季;热带地区雨季显著大于旱季,但雨旱两季内部的季节间并无显著差异。白天 全年最大的 SUHII在各环境区中均最多出现在夏季;最小则在冬季。夜间 SUHII 的时空变异性通常小于白天的,其季节变化规律复杂。北方地区的年均 SUHII 高于南方;最大和最小值分别出现在西北部地区及南方的亚热带地区。存有一定 的夜间冷岛现象。昼夜及全天的 SUHII的最大值与最小值在中国各环境区各时 间段均显著正相关,夜间关系最强,全天最弱。昼夜 SUHII幅度间并无显著或 仅有微弱的相关性。在 50%的情况下,昼夜平均 SUHII间并无显著相关性。 (2)2003 – 2013年间,中国绝大多数或大多数参考乡村的昼夜的 LST均未 显著变化。LST的显著变化具有明显的空间集聚和分异规律。夏季除在白天的新 疆的一些参考乡村的 LST显著降低外,其他的多为不显著或正向显著的变化, 其幅度多为 0.0 – 0.1 °C (yr-1),但仍有一些为 0.2 – 0.5 °C (yr-1)。与之鲜明对比 的是,冬季基本上或未显著变化,或为负向的显著变化,特别是东北和中北地区 的一些参考乡村,其幅度多为-0.4 – -0.1 °C (yr-1)。秋季昼夜显著变化的速率以负 值为主,其幅度多为-0.4 - -0.1 °C (yr-1 )。春季规律略复杂,其中白天东北地区中 南部的一些变化幅度可为-1.0 - -0.5 °C (yr-1)。 城区LST的变化规律与对应参考乡村的密切相关。白天,城乡间全年、冬季和秋季均值间的变化规律差异很小;夏季差异最大,显著正向变化的城市数量 和变化速率均明显加大;在春季,城区的变化速率有所加大,特别是在长江中下 游的一些城市。夜间,4季上城乡间均有一定的差异,城镇化可加速 LST增温。 中国绝大多数或大多数城市的 SUHII均未显著变化,但夜间的 SUHII显著 变化的比例明显高于城乡夜间 LST的。SUHII的变化具有明显的空间集聚和分 异性。全国昼夜的SUHII显著变化的速率的年均值分别为 0.03± 0.11和0.05± 0.03 °C (yr-1)。白天 SUHII的年际变化具有明显的季节规律,夏季变化速率明显 最大,范围也最大,春季次之,仍主要为正向速率,秋季再次之,冬季最弱,甚 至在中北地区的一些城市为负向速率。夜间 SUHII的变化的季节性波动不大, 变化速率基本不变,均多为 0.0 – 0.1 °C (yr-1 )。 (3)城乡夜间灯光强度和建筑密度差值与 SUHII的正向偏相关关系均在夜 间比白天存在的更广泛,后两者在夜间的关系也会更强;城乡增强型植被指数差 值普遍与昼夜 SUHII显著负向偏相关;城乡白色天空反照率差值与夜间 SUHII 普遍负向偏相关,但与白天的关系复杂;湿润指数或降水量分别与昼夜 SUHII 在一定情况下显著正向或负向偏相关;平均气温与夜间 SUHII在 40%的情况下 显著正向偏相关;城乡气溶胶的差值与昼夜 SUHII分别在 48.00%和 56.00%的情 况下显著正相关,相关系数在干旱半干旱的西北部地区明显要大,两者间显著的 偏相关关系很少存在且关系很弱;城区面积大小和总人口普遍与昼夜 SUHII显 著正相关,但很少显著偏相关;城乡人口密度差值与夜间 SUHII在 32%的情况 下显著正向偏相关,特别是在南方地区。在 72%的情况下,白天的解释率大于夜 间的。最大和最小的解释率分别出现在夏季白天的湿冷的东北地区及湿热的南方 地区,分别为 63.84%和 10.44%。 (4)基于遗传算法的支持向量机和与 SUHII显著偏相关的变量可较好预测 全国及各环境区内的全年及 4季的平均昼夜 SUHII。夜间的预测精度高于白天。 全国尺度上的 RMSE的范围为[0.49,1.54] °C,区域尺度上为[0.38,2.18] °C。 (5)均一化前后的平均最低气温(Tmin)、平均最高气温(Tmax)和平均 气温(Tm)的全年及 4季均值序列均差异显著。Tmin对观测系统的改变最为敏 感。大部分调整幅度在-1 – 1 °C,但一些调整的绝对值大于 1 °C,最大为 7.84 °C。 (6)1977 - 2014年间中国乡村背景场具有 6个不同气温变化趋势的分区:I: 东北和胶东半岛地区;II:北部及中部内蒙古地区;III:北疆外的西北地区;IV: 北疆地区;V:青藏滇高原地区;VI:长江-淮河流域及南部地区。中国乡村背景 场的年均 Tmin、Tmax和 Tm的变化速率分别为 0.424、0.339和 0.352 °C(10yr-1), Tmin、Tmax和 Tm的最大增速均出现在春季;Tmax和 Tm的最小增速出现在夏 季,Tmin的出现在冬季,但仅比夏季的低 0.011 °C(10yr-1)。近几十年来,Tmin、 Tmax和 Tm均普遍存在明显的跃变上升现象;近几年来,少数明显跃变下降。 (7)城镇化效应对气温变化速率的贡献量与 3种反映城镇化强度的指标间 显著的相关关系并不一直存在且正负均有。以不透水面为主的聚落生态系统面积 的增量是最好的相关指标。定标后的夜间灯光数据可克服未经定标数据的饱和问 题,更好地反映出城区内部的城镇化强度的变化。1977 - 2014年间中国城镇化效 应对区域尺度上年均 Tmin、Tmax和 Tm变化速率的贡献率分别为-0.08%、0.21% 和-0.09%。
英文摘要: Rapid and high intensity urbanization is currently ccurring in the world, resulting in increasingly more serious urban heat island phenomenon. Urban heat islands have direct and indirect impacts on various ecoenvironment factors of cities, regions, and the world, which are closely related to the human well-being. This study analysed the inner-annual spatiotemporal variation laws of surface urban heat island intensity (SUHII) of 1449 cities in 2010 in China, and the interannual variability laws of land surface temperature (LST) of these cities and the corrosponding villiages, and SUHII of these cities from 2003 to 2013; explored the associated determinants of inner-annual spatiotemporal variation laws of SUHII, and did the prediction; and accurately estimated the effects of urbanization on variations in observed temperatures in region scale in China from 1977 to 2014. The main finding are as follows. (1) Larger annual mean daytime SUHII occurred in humid-hot southern China, or humid-cold northeastern China, while the smallest in the arid and semi-arid northeastern China. The strongest SUHII mainly occurred in most cities in the Yangtze River Delta and coastal areas of Taiwan. The season order for SUHII was summer > spring > autumn > winter in temperate regions except in northwestern China. While the SUHII was obviously larger in rainy season than dry season in tropical region. However, no significance difference existed between two seasons within the rainy or dry period. During daytime, the maximum SUHII mostly occurred in summer in each region, while the minimum in winter. The spatiotemporal variation degree was generally lesser for the SUHII during nighttime than during daytime. The seasonal variation laws were complex for nighttime SUHII. The annual SUHII was higher in northern China than southern. The maximum and minmum values occurred in the northwestern region and subtropic region in southern China, respectively. A certain cold island phenemon existed during nighttime. The maximum SUHII was almost significantly positive correlated with the minimum during daytime, nighttime and all-day in all environmental regions in the whole year and four seasons. This relationship was strongest and weakest during the nighttime and all-day, respectively. Moreover, the significant correlation did not exit or was weak for the daytime and nighttime range of SUHII. What’s more, the daytime SUHII was also insignificantly correlated with nightime SUHII in 50% cases. (2) The LST in overwhelming majority or most reference villiages did not changed significantly from 2003 to 2013 in China. The LST that have significantly changed showed obvious spatial agglomeration and spatial variation laws. In summer, except the LST in some reference villiages in Xinjiang significantly decreased during daytime, the others did not change significantly or was with significant positive change. The amplitudes were most 0.0 – 0.1 °C (yr-1 ). Some of them were 0.2 – 0.5 °C (yr-1 ). By contrast, the LST of reference villiages mainly did not change significantly or was with significant negative change, especially for some reference villiages in northeastern and northcentral regions, with the amlitudes of -0.4 – -0.1 °C (yr-1) in most cases. In autumn, the significant change rates were mainly negative, with the amlitudes of -0.4 - -0.1 °C (yr-1 ) in most cases. The laws were a little complicated. Note the variation rates were most -1.0 - -0.5 °C (yr-1 ) in some reference villiages in southcentral parts in northeastern region during daytime. The spatiotemporal variation laws of LST in cities were closely related with them in the corroponding reference villiages. During daytime, the differences were quite small between the spatiotemporal vartiations of LST in cities and their corresponding reference villiages. The largest differences occurred in summer. Not only the number of cities obviously increased whose mean LST significantly changed with positive rates, but also the variation rates obviously turned larger. In spring, the variation rates increased, especially for cities in the middle and lower reaches of Changjiang River. During nighttime, a certain differences exsited for the variation laws of LST between the cities and their corronponding reference villiages. The urbanization promoted the increase of LST. The overwhelming majority or most SUHII did not change significantly. But during the nighttime, the proportion with significant change of SUHII was obviously larger than of the LST of both the cities and reference villiages. The SUHII that have significantly changed showed obvious spatial agglomeration and spatial variation laws. The annual mean values of daytime and nighttime SUHII that significantly changed were 0.03± 0.11 and 0.05± 0.03 °C (yr-1 ), respectively. During daytime, the interannual variation of SUHII showed obviously seasonal laws. In summer, not only the variation rates but also the scope with significant varation were obviously the largest. Spring was the next and in which the variation rates were mainly positive. Then autumn. The varation was weakest in winter, when the varation rates in northcentral were even negative. During nighttime, the varation of SUHII was with less seasonal fluctuation. The varation rates almost unchanged, were most 0.0 – 0.1 °C (yr-1 ). (3) More significant partial relationships existed during nightime than during daytime between SUHII and both the difference between cities and villages for the night lights and difference between cities and villages for the built intensity (ΔBI). Moreover, the relationships between SUHII and ΔBI were stronger during nightime. The ΔEVI was commonly negatively partially correlated with daytime and nighttime SUHII. The ΔWSA was usually negatively partially correlated with night SUHII. During daytime, the relationship was complex. The IM or MAP was negative partially correlated with nighttime and positively with daytime SUHII in some cases, respectively. The MAT was positively partially correlated with nighttime SUHII in 40% cases. The ΔAOD was positively significantly related with nighttime and daytime SUHII in 56.00% and 48.00% cases. The correlation coefficients were obviously larger in arid and semi-arid northwestern China. Hoever, significant partial correlationships seldom existed, and with small partial coefficient. Although the UAS or TP was generally positively correlated with daytime and nighttime SUHII, significant partial correlation seldom existed. The ΔPD was positively partially correlated with nighttime SUHII in 32% cases, especially in southern China. In total, the explanation rates during daytime were larger than during nighttime in 72% cases. The largest and least was in summer day in humid cold northeaster China, and in southern China with the proportion of 63.84% and 10.44%, respectively. (4) Both the daytime and nighttime SUHII can be well predicted in the whole country and each environmental region in the whole year and four seasons in China, by adoping the Support Vector Machine based on Genetic Algorithm and using the driving indices that was significantly partially correlated with the SUHII. The prediction accuracy was always higher during nighttime than daytime. The RMSE ranged from 0.49 to 1.54 °C in national scale, while from 0.38 to 2.18 °C in regional scale. (5) Significant differences existed between the series of annual and four seasonal of mean minimum air tempeature (Tmin), mean maximum air tempeature (Tmax), and mean air tempeature (Tm) before and after homogenization. The Tmin was the most sensitive index to the change of observation system. The most adjusted amlitudes ranged from -1 to 1 °C, several absolute values of adjustments were larger than 1 °C, and the maximum adjusted value reached 7.84 °C. (6) China was divided to six regions with different rural temperature change trends from 1977 to 2014: I: Northeastern China and the Jiaodong Peninsula; II: Northern China and Middle Inner Mongolia; III: Northnorthwestern China, except for Northern Xinjiang; IV: Northern Xinjiang; V: the Qinghai - Tibet - Yunan Plateau and the Yangtze-Huaihe River Basin; and VI: Southern China. The rural temperature significantly increased in China, and the rates of annual Tmin, Tmax and Tm were 0.424, 0.339, and 0.352 °C (10 * yr-1 ), respectively. All maximum rates occurred in spring for Tmin, Tmax and Tm. The minimum rates occurred in summer for Tmax and Tm, as well as in winter for Tmin, which was 0.011 °C (10 * yr-1 ) less than in summer. Abrupt increases in Tmin, Tmax and Tm commonly occurred in China in recent decades, while a few abrupt decreases in recent years. (7) Significant correlations did not always exist between the temperature variation rates contributed by urbanization and three indices of urbanization intensity, and the correlation coefficients can be both positive or negative. The change in the area of the settelement ecosytem that was mainly compositional by impervious surfaces was the best related factor. Adopting the nighttime light data after calibration can overcome the saturation issue exsiting in the raw data, thus indicate the varation of urbanization intensity within cities. The percentage contributions of urbanization to annual Tmin, Tmax, and Tm were -0.08%, 0.21% and -0.09%, respectively in region scale in China from 1977 to 2014.
内容类型: 学位论文
URI标识: http://ir.rcees.ac.cn/handle/311016/38653
Appears in Collections:城市与区域生态国家重点实验室_学位论文

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作者单位: 1.中国科学院生态环境研究中心

Recommended Citation:
李元征. 中国城镇化的热环境效应研究[D]. 北京. 中国科学院大学. 2017.
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