Human activities have significantly impacted the land surface temperature(LST),endangering human health;however,the relationship between these two factors has not been adequately quantified.This study comprehensively ...Human activities have significantly impacted the land surface temperature(LST),endangering human health;however,the relationship between these two factors has not been adequately quantified.This study comprehensively constructs a Human Activity Intensity(HAI)index and employs the Maximal Information Coefficient,four-quadrant model,and XGBoostSHAP model to investigate the spatiotemporal relationship and influencing factors of HAI-LST in the Yellow River Basin(YRB)from 2000 to 2020.The results indicated that from 2000 to 2020,as HAI and LST increased,the static HAI-LST relationship in the YRB showed a positive correlation that continued to strengthen.This dynamic relationship exhibited conflicting development,with the proportion of coordinated to conflicting regions shifting from 1:4 to 1:2,indicating a reduction in conflict intensity.Notably,only the degree of conflict in the source area decreased significantly,whereas it intensified in the upper and lower reaches.The key factors influencing the HAI-LST relationship include fractional vegetation cover,slope,precipitation,and evapotranspiration,along with region-specific factors such as PM_(2.5),biodiversity,and elevation.Based on these findings,region-specific ecological management strategies have been proposed to mitigate conflict-prone areas and alleviate thermal stress,thereby providing important guidance for promoting harmonious development between humans and nature.展开更多
Accurate land surface temperature(LST)assessment is crucial for comprehending and reducing the impacts of climate change and understanding land use evolution.This study presents an innovative method by utilizing ensem...Accurate land surface temperature(LST)assessment is crucial for comprehending and reducing the impacts of climate change and understanding land use evolution.This study presents an innovative method by utilizing ensemble models,advanced correlation analysis,and trend analysis to investigate its environmental influences.Google Earth Engine(GEE)was utilized to process the datasets from Landsat-7 and Landsat-8 for the five big cities of Punjab,Pakistan,from 2001 to 2023.Results from this study show significant urban warming trends,and a strong correlation between environmental variables and LST was identified.The ensemble-based three machine learning models,including XGBoost,AdaBoost,and random forest(RF),were adopted to improve the accuracy of LST evaluation.Although XGBoost and AdaBoost attained modest levels of accuracy,with R^(2) values of 0.767 and 0.706,respectively,the RF model outperformed them by achieving an exceptional R^(2) of 0.796 and RMSE of 0.476.Moreover,Pearson correlation analysis revealed a negative relationship between LST and normalized difference latent heat index(NDLI)with r=-0.67,normalized difference vegetation index(NDVI)with r=-0.6,and modified normalized difference water index(MNDWI)with the value of r as -0.57.In addition,wavelet analysis showed that vegetation and water offer long-term LST cooling,lasting up to 64 months,while built-up areas and bare soil contribute to short-term warming,lasting 4 to 8 months.Latent heat indicated variable cooling periods,surpassing 60 months in cities.These findings enhance the understanding of LST changes and the impact of climate change on the environment.展开更多
利用1979-2018年中国区域地面气象要素驱动数据集(0.1°×0.1°)作为大气强迫资料,驱动CLM5.0(Community Land Model version 5.0)模拟了青藏高原地区1979-2018年的土壤温湿度变化。将土壤冻融过程划分为冻结期和非冻结期,...利用1979-2018年中国区域地面气象要素驱动数据集(0.1°×0.1°)作为大气强迫资料,驱动CLM5.0(Community Land Model version 5.0)模拟了青藏高原地区1979-2018年的土壤温湿度变化。将土壤冻融过程划分为冻结期和非冻结期,通过两个阶段的CLM5.0模拟与站点观测资料、同化资料(GLDAS-Noah)、卫星遥感资料(MODIS土壤温度资料和ESA CCI-COMBINED土壤湿度资料)的对比验证,探讨CLM5.0模拟土壤温湿度在青藏高原的适用性。结果表明:(1)CLM5.0可较准确地描述站点土壤温湿度的动态变化,CLM5.0模拟的土壤温湿度与观测资料具有一致的变化特征且数值上较为接近。CLM5.0模拟的准确性高于GLDAS-Noah。CLM5.0对站点土壤温度的描述更为准确。(2)CLM5.0能够较准确地描述高原冻融过程中的土壤温湿度特征,CLM5.0模拟土壤温湿度与MODIS和ESA CCICOMBINED遥感资料在高原总体呈显著正相关,相关系数大多在0.9以上。CLM5.0对土壤温度的模拟能力相对较好,对非冻结期土壤湿度的模拟能力优于冻结期。CLM5.0整体高估了土壤温度,平均偏差大多在0~4℃之间。土壤湿度的平均偏差大多在-0.1~0.1 m^(3)·m^(-3)之间,非冻结期的平均偏差相对较小。(3)CLM5.0模拟、GLDAS-Noah、MODIS和ESA CCI-COMBINED遥感资料的土壤温湿度均具有相似的空间分布,其中土壤温度空间分布特征相似度更高。CLM5.0具有较高的空间分辨率和更为精细的土壤分层,对土壤温湿度细节的刻画更为完善。(4)CLM5.0模拟资料在高原整体呈增温变干趋势,MODIS和ESA CCI-COMBINED遥感资料整体呈增温增湿趋势。CLM5.0模拟的土壤温度变化趋势相对准确,土壤湿度的变化趋势则存在较大偏差。展开更多
气候变化正在对人类社会带来重大而深远的影响,气候对心理健康的影响及其机制,亟需深入探讨。基于2012—2020年中国家庭追踪调查(China Family Panel Studies,CFPS)成人库数据(N=58 256)和202个气象观测站气象数据,通过地级市中心经纬...气候变化正在对人类社会带来重大而深远的影响,气候对心理健康的影响及其机制,亟需深入探讨。基于2012—2020年中国家庭追踪调查(China Family Panel Studies,CFPS)成人库数据(N=58 256)和202个气象观测站气象数据,通过地级市中心经纬度坐标关联匹配,对季节气候条件如何影响人们的抑郁情绪展开研究,关键气候因子选取基本气象因素(气温、相对湿度)以及气候舒适度(温湿指数、寒冷指数和人体舒适度指数),得到以下结论。(1)春夏季节气象因素对抑郁情绪有显著影响,其中,气温和气候舒适度对抑郁情绪的影响程度较大。具体为适宜的气温、相对湿度、温湿指数和人体舒适度指标可以显著降低抑郁情绪,但是寒冷指数升高显著加重抑郁。人体舒适度指数每增加一个单位,抑郁情绪显著降低3%。(2)在春夏季节,当同时控制气温和相对湿度两个气象因素时,各自对抑郁情绪的影响比控制单个气象因素时更为明显。(3)春夏季节南北地区的气候因素对抑郁情绪存在明显差异。北方气候因素对抑郁情绪影响显著,而南方地区未发现显著影响。(4)在春夏季节,不同社会人口因素对抑郁情绪的影响也存在差异,比如未婚人口占比越大,公众抑郁情绪水平可能越高。本研究结果强调了非极端的气候条件对身心健康的潜在影响,以期推动我国对公众气候变化心理问题的关注,并为气候学、心理学相关学科和政府部门制定政策提供参考,助力构建全民参与的气候适应型社会。展开更多
基金Shanxi Province Graduate Research Practice Innovation Project,No.2023KY465Project on the Reform of Graduate Education and Teaching in Shanxi Province,No.2021YJJG146+1 种基金Research Project of Shanxi Provincial Cultural Relics Bureau,No.22-8-14-1400-119National Key R&D Program of China,No.2021YFB3901300。
文摘Human activities have significantly impacted the land surface temperature(LST),endangering human health;however,the relationship between these two factors has not been adequately quantified.This study comprehensively constructs a Human Activity Intensity(HAI)index and employs the Maximal Information Coefficient,four-quadrant model,and XGBoostSHAP model to investigate the spatiotemporal relationship and influencing factors of HAI-LST in the Yellow River Basin(YRB)from 2000 to 2020.The results indicated that from 2000 to 2020,as HAI and LST increased,the static HAI-LST relationship in the YRB showed a positive correlation that continued to strengthen.This dynamic relationship exhibited conflicting development,with the proportion of coordinated to conflicting regions shifting from 1:4 to 1:2,indicating a reduction in conflict intensity.Notably,only the degree of conflict in the source area decreased significantly,whereas it intensified in the upper and lower reaches.The key factors influencing the HAI-LST relationship include fractional vegetation cover,slope,precipitation,and evapotranspiration,along with region-specific factors such as PM_(2.5),biodiversity,and elevation.Based on these findings,region-specific ecological management strategies have been proposed to mitigate conflict-prone areas and alleviate thermal stress,thereby providing important guidance for promoting harmonious development between humans and nature.
基金supported by the National Natural Science Foundation of China(Grant Nos.52479045,52279042)the Key Research and Development Program in Guangxi(Grant No.AB23026021)the Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures,Guangxi Institute of Water Resources Research(Grant No.GXHRIWEMS-2022-07).
文摘Accurate land surface temperature(LST)assessment is crucial for comprehending and reducing the impacts of climate change and understanding land use evolution.This study presents an innovative method by utilizing ensemble models,advanced correlation analysis,and trend analysis to investigate its environmental influences.Google Earth Engine(GEE)was utilized to process the datasets from Landsat-7 and Landsat-8 for the five big cities of Punjab,Pakistan,from 2001 to 2023.Results from this study show significant urban warming trends,and a strong correlation between environmental variables and LST was identified.The ensemble-based three machine learning models,including XGBoost,AdaBoost,and random forest(RF),were adopted to improve the accuracy of LST evaluation.Although XGBoost and AdaBoost attained modest levels of accuracy,with R^(2) values of 0.767 and 0.706,respectively,the RF model outperformed them by achieving an exceptional R^(2) of 0.796 and RMSE of 0.476.Moreover,Pearson correlation analysis revealed a negative relationship between LST and normalized difference latent heat index(NDLI)with r=-0.67,normalized difference vegetation index(NDVI)with r=-0.6,and modified normalized difference water index(MNDWI)with the value of r as -0.57.In addition,wavelet analysis showed that vegetation and water offer long-term LST cooling,lasting up to 64 months,while built-up areas and bare soil contribute to short-term warming,lasting 4 to 8 months.Latent heat indicated variable cooling periods,surpassing 60 months in cities.These findings enhance the understanding of LST changes and the impact of climate change on the environment.
文摘利用1979-2018年中国区域地面气象要素驱动数据集(0.1°×0.1°)作为大气强迫资料,驱动CLM5.0(Community Land Model version 5.0)模拟了青藏高原地区1979-2018年的土壤温湿度变化。将土壤冻融过程划分为冻结期和非冻结期,通过两个阶段的CLM5.0模拟与站点观测资料、同化资料(GLDAS-Noah)、卫星遥感资料(MODIS土壤温度资料和ESA CCI-COMBINED土壤湿度资料)的对比验证,探讨CLM5.0模拟土壤温湿度在青藏高原的适用性。结果表明:(1)CLM5.0可较准确地描述站点土壤温湿度的动态变化,CLM5.0模拟的土壤温湿度与观测资料具有一致的变化特征且数值上较为接近。CLM5.0模拟的准确性高于GLDAS-Noah。CLM5.0对站点土壤温度的描述更为准确。(2)CLM5.0能够较准确地描述高原冻融过程中的土壤温湿度特征,CLM5.0模拟土壤温湿度与MODIS和ESA CCICOMBINED遥感资料在高原总体呈显著正相关,相关系数大多在0.9以上。CLM5.0对土壤温度的模拟能力相对较好,对非冻结期土壤湿度的模拟能力优于冻结期。CLM5.0整体高估了土壤温度,平均偏差大多在0~4℃之间。土壤湿度的平均偏差大多在-0.1~0.1 m^(3)·m^(-3)之间,非冻结期的平均偏差相对较小。(3)CLM5.0模拟、GLDAS-Noah、MODIS和ESA CCI-COMBINED遥感资料的土壤温湿度均具有相似的空间分布,其中土壤温度空间分布特征相似度更高。CLM5.0具有较高的空间分辨率和更为精细的土壤分层,对土壤温湿度细节的刻画更为完善。(4)CLM5.0模拟资料在高原整体呈增温变干趋势,MODIS和ESA CCI-COMBINED遥感资料整体呈增温增湿趋势。CLM5.0模拟的土壤温度变化趋势相对准确,土壤湿度的变化趋势则存在较大偏差。
文摘气候变化正在对人类社会带来重大而深远的影响,气候对心理健康的影响及其机制,亟需深入探讨。基于2012—2020年中国家庭追踪调查(China Family Panel Studies,CFPS)成人库数据(N=58 256)和202个气象观测站气象数据,通过地级市中心经纬度坐标关联匹配,对季节气候条件如何影响人们的抑郁情绪展开研究,关键气候因子选取基本气象因素(气温、相对湿度)以及气候舒适度(温湿指数、寒冷指数和人体舒适度指数),得到以下结论。(1)春夏季节气象因素对抑郁情绪有显著影响,其中,气温和气候舒适度对抑郁情绪的影响程度较大。具体为适宜的气温、相对湿度、温湿指数和人体舒适度指标可以显著降低抑郁情绪,但是寒冷指数升高显著加重抑郁。人体舒适度指数每增加一个单位,抑郁情绪显著降低3%。(2)在春夏季节,当同时控制气温和相对湿度两个气象因素时,各自对抑郁情绪的影响比控制单个气象因素时更为明显。(3)春夏季节南北地区的气候因素对抑郁情绪存在明显差异。北方气候因素对抑郁情绪影响显著,而南方地区未发现显著影响。(4)在春夏季节,不同社会人口因素对抑郁情绪的影响也存在差异,比如未婚人口占比越大,公众抑郁情绪水平可能越高。本研究结果强调了非极端的气候条件对身心健康的潜在影响,以期推动我国对公众气候变化心理问题的关注,并为气候学、心理学相关学科和政府部门制定政策提供参考,助力构建全民参与的气候适应型社会。
