Relationships between Genetic Diversity of Vascular Plant Species and Climate Factors

Genetic diversity is crucial for plants to respond to global climate change, and exploring relationships between genetic diversity and climatic factors may help predict how global climate change will shape the genetic diversity of plants in the future. So far, however, the extent and magnitude of the impact of climatic factors on the genetic diversity of plants has not been clarified. We collected data from 68 published papers on two widely used measures of genetic diversity of populations (average expected heterozygosity (He) and average observed heterozygosity (Ho)) and on localities of populations of 79 vascular plants, and extracted data on 19 climatic factors from WorldClim. We then explored the relationships between measures of genetic diversity and climatic factors using linear regressions. He of plant populations was significantly correlated with climatic factors in 58.7% (44) of the 75 species that used He as a measure of genetic diversity, and Ho was correlated with climatic factors in 65.1% (41) of the 63 species that used this genetic diversity measure. In general, Mean Temperature of Wettest Quarter, Precipitation Seasonality, Precipitation of Driest Quarter and Temperature Seasonality played a vital role in shaping He, and Ho was mostly correlated with Precipitation of Warmest Quarter, Mean Temperature of Wettest Quarter, Precipitation of Driest Quarter and Precipitation of Driest Month. Also, the proportion of the significant correlations between genetic diversity of populations and climatic factors was higher for woody than for herbaceous species, and different climatic factors played different roles in shaping genetic diversity of these two growth forms. Our results suggest that climate may play an important role in shaping genetic diversity of plant populations, that climatic change in the future may alter genetic diversity of plants, and that genetic diversity of different plant forms may respond to climatic change differently.     

基于组网观测的那曲土壤湿度不同时间尺度的变化特征

利用第三次青藏高原大气科学试验的土壤湿度观测数据,分析了那曲多空间尺度组网观测的28个站2、5、10、20和30 cm 5个不同深度土壤湿度的季节变化和日变化特征,并对比讨论了土壤湿度站点间的差异。分析表明,各层土壤湿度均存在显著的季节变化。冬春季节,20 cm以上土壤湿度随深度变浅而减小。夏秋季节土壤湿度随深度增加而减小,并分别在7月上、中旬和9月出现两个峰值。10月以后进入土壤湿度衰减期。土壤温度和土壤湿度存在协同变化关系。在一定的温度范围内,土壤发生冻结-融化过程,引起土壤湿度变化。在太阳辐射加热下,土壤表层水分蒸发,进而影响土壤温度。不同观测站间土壤湿度差异较大,夏秋季离散性大于冬春季。不同季节土壤湿度的日变化存在差异。春季10 cm以上土壤湿度日变化明显,08-10时（北京时）达到最低,19-20时达到最高。夏季土壤湿度日变化较为平缓。秋季2 cm深度土壤湿度日变化明显。线性拟合结果表明,1、4、10月土壤湿度和土壤温度为正相关关系。但是在夏季,土壤湿度与土壤温度为负相关。站点间土壤湿度变化的离散性表明,多测站才能全面体现青藏高原某区域的陆面状态。文中结果为青藏高原地区土壤湿度卫星参数验证和数值模式参数化提供了多角度的观测依据。      

甘肃省气候舒适度时空分异特征研究

利用甘肃省37个气象台站的气候资料,采用温湿指数、风寒指数、着衣指数及综合舒适度指数等气候舒适度指标和反距离权重空间插值分析方法,对甘肃省气候舒适度的时空分异特征进行了详细分析,研究表明,4—10月气候舒适度都比较优越,6—8月气候舒适度最佳,1月、12月气候舒适度较差。气候舒适度整体呈纬度地带性变化,由东南向西北递减,同时垂直地带性影响显著,导致海拔较高的南部和中部地区气候舒适度较差,其中陇南市、天水市和白银市气候舒适度最好,其次为平凉市、庆阳市等地区,比较差的有甘南藏族自治州、张掖市和金昌市等地区。研究结果全面反映了甘肃省气候舒适度的时空分异规律,对于研究区旅游规划、人口分布研究等有一定参考价值。     

夏季黄河流域降水气候特征及其与大气环流的关系

本文基于1958—2015年夏季黄河流域55个观测站降水量和NCEP/NCAR再分析1高度场等资料,使用MannKendall突变检验、合成分析和Monte Carlo检验等气候统计方法,分析了黄河流域58年夏季降水量的气候变化特征,以及导致其变化的大气环流成因。58年期间,黄河流域夏季降水量总体呈减少趋势,尤其在河套北部有显著性减少趋势,其主要原因是欧亚中高纬度等压面升高、西风带减弱所致;1975年和1996年是黄河流域夏季降水的两个明显年代际气候变化转折点,在1958—1975年期间,黄河流域夏季降水量年际变化大,异常偏多和偏少年出现频次较高,期间欧亚中高纬度及其以南包括黄河流域地区高度场偏低,主要受高空低压系统和较强冷空气影响;在1976—1995年期间,黄河流域大部降水偏多,其主要环流成因为乌拉尔山阻塞高压发展、贝加尔湖到东北亚一带受负高度距平控制高空槽加深,同时,来自南方的暖湿气流输送增强;到1996—2015年最近20年间,乌拉尔山北部环流高度场偏低、里海至贝加尔湖再到东北亚一带高度场一致偏高,黄河流域一带西风带强度和冷空气势力均较弱,流域受高压影响导致大部区域降水偏少。不同时期黄河各流域段降水量与中高纬度阻塞高压以及与西北太平洋副热带高压的相关关系分析进一步说明了上述结论。     

冬季北大西洋涛动与中国北方极端低温相关性的年代际变化

本文基于中国地面气温日值网格数据集（V2.0）,采用滑动相关和相关分析等方法,揭示了冬季北大西洋涛动（NAO）对中国北方极端低温影响的事实,进一步证实了东北后冬（1、2月）冷日（夜）与同期NAO相关性的年代际变化。研究发现:在20世纪80年代中期前,东北后冬冷日（夜）频发,与NAO的相关性较好,而在80年代中期后东北后冬冷日（夜）少发,与NAO的相关性减弱。其中,1月在1969~1988阶段,东北冷日（夜）与NAO的相关性最好,相关区域显著,相关系数可达-0.68（-0.66）,而在1989~2009阶段二者相关性最弱,相关区域不显著。进一步分析发现,在不同年代际背景下,NAO引起的大气环流异常是导致东北1月冷日（夜）与1月NAO相关性年代际变化的重要原因。相关性较好的年代,NAO引起的环流异常有利于冷涡等天气系统维持在贝加尔湖到东北一带,使东北地区气温偏低,冷日（夜）频发;相关性较弱的年代,不利于冷空气南下,使东北地区气温偏高,冷日（夜）少发。     

空间功能视角下的公共服务对房价的影响——以成都市为例

房价的快速上涨和城市内部房价的巨大差异引起社会广泛关注,调控房价,防止局部区域房价过热势在必行。本文从城市空间功能的视角出发,以成都市2016年房价为例,基于地理探测器分析公共服务对房价的影响。结果表明：成都平均房价为8480元/m²,并从市中心沿交通环线和放射状干线同时向郊区递减,形成圈层加放射格局,总体上呈现西高东低、南高北低的特点。公共服务(主要包括医疗、金融和教育服务)对房价的影响以第三圈层和西南方最为显著,且高于城市层面上整体的影响。公共服务对房价的影响显著受不同区域的空间功能差异的影响,公共服务和空间功能差异会加剧房价的分异格局,并推动局部房价过热。因此,显著地受到在房价调控中,不仅要有传统的金融、经济政策,还要注重空间功能和公共服务的优化。     

中国流动人口的城市宜居性感知及其对定居意愿的影响

流动人口是中国城镇化进程的重要参与者,加强其定居意愿特征分析对制定中国新型城镇化发展政策具有重要参考价值。基于中国40个主要城市的宜居城市问卷调查数据,运用描述统计和地理探测器方法,揭示中国流动人口的城市宜居性感知特征及其对定居意愿的影响。研究结果表明：①中国流动人口的城市宜居性感知评价分异明显,呈现出“公共服务设施便利性>自然环境舒适性>社会人文环境舒适性>环境健康性>交通便捷性>城市安全性”的递减特征;②中国流动人口的公共服务设施便利性、社会人文环境舒适性、环境健康性和城市安全性等4个维度城市宜居性感知对其定居意愿具有显著影响,且影响强度依次递减,但二者关系并不完全呈简单的线性特征。③相对他人收入满意度、生活满意度、住房价格感知和地理区位等参照变量,以及年龄、性别和职业等个体与家庭属性也是影响中国流动人口定居意愿的重要因素。     

Classifying natural-language spatial relation terms with random forest algorithm

The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.     

Characterization of Black Carbon in the Ambient Air of Agra,India:Seasonal Variation and Meteorological Influence

This study characterizes the black carbon in Agra, India home to the Taj Mahal—and situated in the Indo-Gangetic basin.The mean black carbon concentration is 9.5 μg m~(-3)and, owing to excessive biomass/fossil fuel combustion and automobile emissions, the concentration varies considerably. Seasonally, the black carbon mass concentration is highest in winter, probably due to the increased fossil fuel consumption for heating and cooking, apart from a low boundary layer. The nocturnal peak rises prominently in winter, when the use of domestic heating is excessive. Meanwhile, the concentration is lowest during the monsoon season because of the turbulent atmospheric conditions and the process of washout by precipitation. The ratio of black carbon to brown carbon is less than unity during the entire study period, except in winter(December). This may be because that biomass combustion and diesel exhaust are major black carbon contributors in this region, while a higher ratio in winter may be due to the increased consumption of fossil fuel and wood for heating purposes. ANOVA reveals significant monthly variation in the concentration of black carbon; plus, it is negatively correlated with wind speed and temperature. A high black carbon mass concentration is observed at moderate(1–2 m s~(-1)) wind speed, as compared to calm or turbulent atmospheric conditions.     

Why Was the Strengthening of Rainfall in Summer over the Yangtze River Valley in 2016 Less Pronounced than that in 1998 under Similar Preceding El Niño Events?——Role of Midlatitude Circulation in August

It is widely recognized that rainfall over the Yangtze River valley (YRV) strengthens considerably during the decaying summer of El Niño, as demonstrated by the catastrophic flooding suffered in the summer of 1998. Nevertheless, the rainfall over the YRV in the summer of 2016 was much weaker than that in 1998, despite the intensity of the 2016 El Niño having been as strong as that in 1998. A thorough comparison of the YRV summer rainfall anomaly between 2016 and 1998 suggests that the difference was caused by the sub-seasonal variation in the YRV rainfall anomaly between these two years, principally in August. The precipitation anomaly was negative in August 2016——different to the positive anomaly of 1998. Further analysis suggests that the weaker YRV rainfall in August 2016 could be attributable to the distinct circulation anomalies over the midlatitudes. The intensified "Silk Road Pattern" and upper-tropospheric geopotential height over the Urals region, both at their strongest since 1980, resulted in an anticyclonic circulation anomaly over midlatitude East Asia with anomalous easterly flow over the middle-to-lower reaches of the YRV in the lower troposphere. This easterly flow reduced the climatological wind, weakened the water vapor transport, and induced the weaker YRV rainfall in August 2016, as compared to that in 1998. Given the unique sub-seasonal variation of the YRV rainfall in summer 2016, more attention should be paid to midlatitude circulation——besides the signal in the tropics——to further our understanding of the predictability and variation of YRV summer rainfall.