全球气温–相对湿度–人口驱动型制冷度日数时空演变、影响因素及模拟  

Spatio-temporal variations,associated determinants and simulation of global cooling degree days driven by temperature,relative humidity and population

作　　者：李元征 王怡君 赵国松 贺添[4] 王昉琳 孙永胜[5] Li Yuanzheng;Wang Yijun;Zhao Guosong;He Tian;Wang Fanglin;Sun Yongsheng(School of Resources and Environment,Henan University of Economics and Law,Zhengzhou 450046,Henan,China;Academician Laboratory for Urban and Rural Spatial Data Mining of Henan Province,Zhengzhou 450046,Henan,China;Hubei Key Laboratory of Regional Ecology and Environmental Change,China University of Geosciences,Wuhan 430074,Hubei,China;School of Geoscience and Technology,Zhengzhou University,Zhengzhou 450051,Henan,China;Key Research Institute of Yellow River Civilization and Sustainable Development&Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education,Henan University,Kaifeng 475001,Henan,China)

机构地区：[1]河南财经政法大学资源与环境学院,河南郑州450046 [2]河南省城乡空间数据挖掘院士工作站,河南郑州450046 [3]中国地质大学(武汉)区域生态过程与环境演变湖北省重点实验室,湖北武汉430074 [4]郑州大学地球科学与技术学院,河南郑州450051 [5]河南大学黄河文明与可持续发展研究中心暨黄河文明省部共建协同创新中心,河南开封475001

出　　处：《地理科学》2024年第8期1406-1416,共11页Scientia Geographica Sinica

基　　金：国家自然科学基金项目(31600375,41901238);河南省高等学校重点科研项目(24A170005)资助。

摘　　要：制冷度日数(Cooling degree days,CDDs)可指示空间制冷能耗与室外热环境,但在全球栅格尺度上同时考虑气温、相对湿度与人口的CDDs分析鲜见报道。据此,本文利用气象、人口、遥感等数据,曼−肯德尔法、相对重要性分析、机器学习等方法在全球0.25°栅格尺度上开展气温−相对湿度−人口驱动型CDDs时空变化、影响因素与模拟研究。结果表明,①全球基于湿球温度计算的CDDs(CDDs_(wb),CDDs based on wet bulb temperature)在30°N~30°S间除北非与西亚外的不少地区均高于567(℃·d),极高值[1469~2677(℃·d)]主要分布在亚马孙平原、东南亚中南半岛南侧及其以南地区。基于湿球温度与人口计算的CDDs(CDDs based on wet bulb temperature and population,CDDs_(wb_pop))大多低于17×10^(6)(℃·d·人),高值[277×10^(6)~2144×10^(6)(℃·d·人)]主要在恒河平原与印度南端、尼日利亚沿海、越南南北平原与爪哇岛。②1970—2018年CDDs_(wb)与2000—2018年CDDs_(wb_pop)在中高纬度呈现极高年际间变异,全球未来变化趋势多与过去保持强一致性。CDDs_(wb)显著增加(P<0.05)地区主要分布在北非与西亚、澳大利亚、里海东部、印尼西部的一些地区,显著降低区域主要分布在拉美、撒哈拉以南非洲、中国胡焕庸线以南及中南半岛的一些地区。CDDs_(wb_pop)在一些地区显著增加,速率基本小于8×10^(6)(℃·d·人)/a,集中发布在北非、西亚与里海东部的一些地区。③纬度与高程均分别与CDDs_(wb)及其变异系数呈现显著负向与正向偏相关关系(P<0.05);在不同大洲内,年降水量、夏季反照率、增强型植被指数与PM_(2.5)对CDDs_(wb)影响不同,夜间灯光影响不大。CDDs_(wb)实际值与模拟值间R2大多高于0.935,平均绝对误差百分比多小于6.77%,均方根误差在15.63~184.51(℃·d)。It is rarely reported that the global cooling degree days(CDDs)analysis simultaneously considers the air temperature,relative humidity and population at the grid scale.Thus,the paper used multi-source data(e.g.meteorological,population,remote sensing data,etc.)and several methods(e.g.Mann-Kendall test,relative importance analysis,Generalized Regression Neural Networks,etc.)to study the spatio-temporal variations,influencing factors and simulation of global CDDs driven by air temperature,relative humidity and population on the 0.25°×0.25°grid scale.The main findings were as follows.1)Global CDDs_(wb)exceeded 567(℃·d)in most regions between 30°N and 30°S except North Africa and West Asia.The extremely high values[1469~2677(℃·d)]were in the Amazon Plain,South of Indochina Peninsula and its southern regions belonging to Southeast Asia,etc.The CDDs driven by air temperature,relative humidity and population(CDDs_(wb_pop))were mostly less than 17×10^(6)(℃·d·person).High values[277×10^(6)~2144×10^(6)(℃·d·person)]were mainly in the Ganges Plain and the southern part of India,coastal plain of Nigeria,southern plain of Vietnam and island of Java,etc.2)Most CDDs_(wb)from 1970 to 2018 and CDDs_(wb_pop)from 2000 to 2018 showed extremely high variability in the middle and high latitudes,and most of the change trend types in the future were strongly persistent.The significant positive changes(P<0.05)of CDDs_(wb)mainly occurred in some regions of North Africa,Western Asia,Australia,the eastern Caspian Sea and western Indonesia,while the negative changes were mainly in some areas of Latin America,sub-Saharan Africa,south of China’s Hu Huanyong Line and Indochina Peninsula.CDDs_(wb_pop)increased significantly in some areas,basically with the rate less than 8×10^(6)(℃·d·person)/yr,concentrated in some regions of North Africa,Western Asia and eastern Caspian Sea regions.3)Both latitude and elevation showed significant negative and positive partial correlation with CDDs_(wb)and its coefficient of variation

关 键 词：制冷度日数 相对湿度 人口加权 PM_(2.5) 广义回归神经网络 

分 类 号：P461[天文地球—大气科学及气象学]