The information system of the French Peatland Observation Service: Service National d'Observation Tourbières – A valuable tool to assess the impact of global changes on the hydrology and biogeochemistry of temperate peatlands through long term monitoring

Mitigating and adapting to global changes requires a better understanding of the response of the Biosphere to these environmental variations. Human disturbances and their effects act in the long term (decades to centuries) and consequently, a similar time frame is needed to fully understand the hydrological and biogeochemical functioning of a natural system. To this end, the ‘Centre National de la Recherche Scientifique’ (CNRS) promotes and certifies long-term monitoring tools called national observation services or ‘Service National d'Observation’ (SNO) in a large range of hydrological and biogeochemical systems (e.g., cryosphere, catchments, aquifers). The SNO investigating peatlands, the SNO ‘Tourbières’, was certified in 2011 ( https://www.sno-tourbieres.cnrs.fr/ ). Peatlands are mostly found in the high latitudes of the northern hemisphere and French peatlands are located in the southern part of this area. Thus, they are located in environmental conditions that will occur in northern peatlands in coming decades or centuries and can be considered as sentinels. The SNO Tourbières is composed of four peatlands: La Guette (lowland central France), Landemarais (lowland oceanic western France), Frasne (upland continental eastern France) and Bernadouze (upland southern France). Thirty target variables are monitored to study the hydrological and biogeochemical functioning of the sites. They are grouped into four datasets: hydrology, fluvial export of organic matter, greenhouse gas fluxes and meteorology/soil physics. The data from all sites follow a common processing chain from the sensors to the public repository. The raw data are stored on an FTP server. After operator or automatic processing, data are stored in a database, from which a web application extracts the data to make them available ( https://data-snot.cnrs.fr/data-access/ ). Each year at least, an archive of each dataset is stored in Zenodo, with a digital object identifier (DOI) attribution ( https://zenodo.org/communities/sno_tourbieres_data/ ).     

Analysis of nonstationarity in low flow in the Loess Plateau of China

An approach for nonstationary low‐flow frequency analysis is developed and demonstrated on a dataset from the rivers on the Loess Plateau of China. Nonstationary low‐flow frequency analysis has drawn significant attention in recent years by establishing relationships between low‐flow series and explanatory variables series, but few studies have tested whether the time‐varying moments of low flow can be fully described by the time‐varying moments of the explanatory variables. In this research, the low‐flow distributions are analytically derived from the 2 basic explanatory variables—the recession duration and the recession coefficient—with the assumption that the recession duration and recession coefficient variables follow exponential and gamma distributions, respectively; the derived low‐flow distributions are applied to test whether the time‐varying moments of explanatory variables can explain the nonstationarities found in the low‐flow variable. The effects of ecosystem construction measures, that is, check dam, terrace, forest, and grassland, on the recession duration and recession coefficient are further discussed. Daily flow series from 11 hydrological stations from the Loess Plateau are used and processed with a moving average technique. Low‐flow data are extracted following the pit under threshold approach. Six of the 11 low‐flow series show significant nonstationarities at the 5% significance level, and the trend curves of the moments of low flow are in close agreement with the curves estimated from the derived distribution with time‐dependent moments of the recession duration and time‐constant moments of the recession coefficient. It is indicated that the nonstationarity in the low‐flow distribution results from the nonstationarity in the recession duration in all 6 cases, and the increase in the recession duration is resulted from large‐scale ecosystem constructions rather than climate change. The large‐scale ecosystem constructions are found to have more influence on the decrease in streamflow than on the increase in watershed storage, thus resulting in the reduction of low flow. A high return period for the initial fixed design value decreases dramatically with an increasing recession duration.     

城乡过渡带空间演进与空间交互强度分析

采用NPP/VIIRS夜间灯光数据提取广州市城乡过渡带边界,利用基于sDNA模型修正的引力模型测算2012—2019年广州市城乡过渡带的空间交互强度,分析其空间演进特征与社会经济发展的关系。研究表明：1）2012—2019年广州市城乡过渡带总体呈扩大趋势,以北偏东方向发展为主,破碎区块逐渐演变为连续区域;2）广州市城乡过渡带之间的空间交互网络结构呈现“多方向化”趋势,从最初的西南侧单线网络结构逐步发育成多方向闭合网络联结结构,目前演变为南北两支、东西向的强空间交互态势,以白云区为代表的北部区域和以番禺区为代表的南部区域是广州市城乡过渡带的重要战略节点地域;3）广州市城乡过渡带社会经济优势度呈现出由内向外的3个空间层级结构,并由城市核心区辐射带动,向北、东北与东南3个方向推动发展为城乡过渡带;4）各城乡过渡带的发展速度不均衡现象仍较明显,广州市应结合本区域优势产业和自然资源,走联合与互补发展并存的道路,重视城乡过渡带区域发展引擎,增强其与周边地区联系的潜力。     

基于Item2Vec负采样优化的专题地图产品个性化推荐方法研究

建立适用于专题地图产品检索的用户偏好推荐模型是提高专题地图质量的有效方式之一,在专题地图产品推荐场景中,存在严重的内容冷启动和评论数据稀疏问题,现有的推荐算法无法为特定类用户推荐不同特征的专题地图产品,导致用户从专题地图中获取偏好信息受到限制。因此,本文构建基于负采样的连续词袋模型和基于Word2Vec的Item2Vec相结合的用户偏好推荐方法,用于专题地图产品推荐。① 计算用户行为日志文件中交互行为数据的隐性评分,以代替专题地图推荐信息流场景中稀疏的用户评论数据;② 基于负采样的连续词袋模型提取目标专题地图的前后地图序列感知特征信息,通过控制正负样本比例为1:2,提升目标专题地图潜在评分的预测精度;③ 通过Item2Vec将带有用户行为特征信息的专题地图映射到向量空间,计算用户对专题地图的相似度矩阵,根据用户偏好程度完成推荐。在构建的专题地图评分实验数据集Thematic CMaps和4个公开验证数据集MovieLens上的测试结果表明:与LFM、Personal Rank、Content Based和SVD 4种传统推荐算法相比,本文所提方法可有效提高潜在评分的预测精度,推荐性能最高达到27.85%;与以霍夫曼采样方式的Item2Vec基础方法和YouTubeNet 2种神经网络推荐算法相比,评分预测精度有一定提高,且推荐性能不断提升,最高达到2.97%和5.78%。以经典算法奇异值分解（SVD）为例,将MovieLens-20M数据集切分后,在数据量不断增大的数据子集中,本文所用方法的评分预测精度和性能均优于SVD方法。     

冷涡背景下山东省“5·17”极端强对流天气环境条件分析

2020年5月17日,山东省出现大范围强对流天气(简称“5·17”强对流),冰雹范围之广为近10年之首。对流风暴高度组织化,区域性的超级单体群以及一条长度超过500 km的强飑线造成此次极端强对流天气。利用ERA5再分析、加密自动气象观测站、多普勒天气雷达等资料,剖析了此次极端强对流天气的环境条件。结果表明:冷涡位于最有利于山东出现强对流的关键区,大尺度天气系统强迫强,对流层中层异常强的冷空气南下影响前期异常增暖的山东地区,造成“5·17”极端强对流。天气系统的异常程度更能代表动热力强迫的强度,异常程度达到2σ以上有可能造成极端强对流天气。当冷涡南下过程中强度减弱,但异常程度增加时,其东南象限仍能产生极端强对流天气。强的深层垂直风切变有利于对流风暴组织化发展,飑线的长轴走向与0～6 km垂直风切变矢量方向相同,新单体发生、发展、合并的区域位于风矢量差大值中心前沿。低层暖湿平流源源不断地向山东输送暖湿空气,是CAPE重建的机制,是超级单体群和长飑线得以长时间维持的主要能量来源。     

三维地图式可视化大数据

数据可视化技术利用计算机图形图像学以及数据挖掘技术,以交互方式将数据中的隐藏信息展示给用户,为用户决策提供参考。近年来,随着PC计算能力与互联网技术的快速发展,传统的数据可视化技术已无法满足人们日益增长的信息处理需求。目前,数据可视化在可视化过程中存在显示结果精确度低、表达方式单一、不能突出数字信息内含的规律性等问题。本文针对上述问题提出了基于三维地图的可视化大数据解决方案,该方案通过制作专题地图可视化数据,对数据的规律性进行了研究,从而方便用户理解数据的深层次信息,发现隐藏的特征、模式、趋势等信息。     

环境约束下中国工业化与城镇化的关系演变——效率分析视角

将工业化视为投入,城镇化视为产出,基于中国各省份2001~2012年的工业化、城镇化和污染物排放数据,测算了传统效率值、环境效率值及效率指数,据此分析各省份工业化与城镇化的关系及其演变。研究发现：全国总体层面的城镇化传统效率值和环境效率值均在下降。分地区来看,东部地区通过工业化来推进城镇化的实施空间有限,中部地区需要调整发展方式,西部地区尚有一定余地。在城镇化过程中,工业化存在前期“负向规律机制”和后期“正向反馈机制”两种作用。为实现城镇化低碳发展,需要转变工业和经济发展方式,改善对外贸易结构,同时提高环境污染治理投资效果。     

THE UNDERSTANDING OF ENSO CYCLE MECHANISM AND ENSO POTENTIAL PREDICTION ABILITY*

In recent years,the dynamic coupled models of ocean-atmosphere and statistical models have been used in routine operation for issuing long-lead forecasts.The dynamic coupled models consist of models with varying degrees of complexity,ranging from simplified coupled models of the shallow water to coupled general circulation models.During the period of 1980-1992,some models performed considerably better than the persistence forecast on predicting typical indices of ENSO for lead time of 6 to 12 months.It seems that ENSO is predictable at least one year in advance.However.nearly all the models have lost their skill of forecasting sea surface temperature(SST)changes in the eastern equatorial Pacific since 1992.It is a challenge not only to the dynamic models but also to the understanding of the ENSO cycle mechanism.This paper examines multiple time-space scales of the ocean-atmosphere interactions and potential prediction ability of ENSO event by using data analysis and model study.     

基于期刊论文的中国地级以上城市知识专业化研究

将287个地级以上城市（不包括港澳台地区）作为研究对象,以中国知网发布的168个学科领域期刊论文数量为数据来源,对中国地级以上城市知识专业化水平、影响因素及其与经济发展的关系进行了研究。研究发现：① 中国地级以上城市的专业化水平总体偏低,多样化水平较为明显;知识专业化水平西部地区>中部地区>东部地区;地区相对专业化指数随城市行政等级依次升高,即直辖市<副省级城市<省会城市<普通地级市。具有资源优势的城市,其知识专业化领域高度集中在以资源为基础的领域;中小城市的知识专业化领域相对较少,专业化程度高;大中城市知识专业化领域多,多样化趋势明显,但各领域的知识专业化程度相对较低。从城市等级上来看,知识专业化程度表现为五线城市>四线城市>三线城市>二线城市>一线城市。② 知识专业化水平主要受科教经济因素、人才潜力和服务业因素、工业基础因素、人口规模因素四大因素影响。③ 知识专业化与经济发展关系密切,整体呈现倒U形关系;但各区域随发展阶段不同,表现出不同的形式可以分为U形、倒U形、M形和W形4种大类型。     

1975-2015年鄂尔多斯市沙漠化的时空演变过程

鄂尔多斯市地处温带草原向荒漠草原过渡的半干旱区,是中国沙漠化问题较为严重的区域之一,其土地沙漠化发展和逆转过程及影响因素在北方农牧交错带具有代表性。以7期Landsat卫星遥感数据为信息源提取沙漠化土地类型及程度的时空格局信息,分析了该地区自1975年以来的土地沙漠化过程,并结合气象及社会经济数据采用主成分分析法对驱动因子进行定量分析。结果表明：在脆弱生态环境背景下,鄂尔多斯市土地沙漠化受人口数量增加、过垦及过牧等人为因素的严重影响。不同时段土地沙漠化发展的方向与驱动力都有所不同,1975-2000年是土地沙漠化的急剧发展期,驱动力以人为因素为主导,自然因素为基础;2000-2015年是土地沙漠化的逆转期,驱动力以自然因素为主,人为因素为辅。