1.青藏高原1公里分辨率多年冻土概率图(2019)

    基于最新发布的青藏高原多年冻土存在性证据数据集,利用统计模型计算得到了1公里分辨率青藏高原多年冻土概率分布图。该图考虑了气温、积雪和植被这三个多年冻土分布控制性因素,因此能够准确地反应青藏高原冻土的空间异质性。根据1000多个实测资料验证和与已有多年冻土图的对比结果显示,该图的整体分布精度为82.5%,卡帕系数可达到0.62,在多年冻土下界表现出了更好的分类效果。结果显示,青藏高原多年冻土区面积约为1.54 (1.35–1.66) 百万平方公里, 约占陆地面积的 60.7 (54.5– 65.2)% 。多年冻土面积 约为 1.17 (0.95–1.35)百万平方公里, 约占46 (37.3–53.0)%.

    doi:10.11888/Geocry.tpdc.270215 在线下载 2019-09-13

    2.青藏高原土地利用数据(1992、2005和2015)(V1.0)

    基于2015年欧空局ESA GlobCover全球陆地覆盖数据,结合中科院地理资源所土地利用数据NLCD-China、清华大学全球土地覆被FROM-GLC数据、美国NASA的MODIS全球土地覆被MCD12Q1数据、马里兰大学全球土地覆被UMD、美国USGS土地覆被数据IGBP DISCover,构建了青藏高原LUC分类系统以及其余数据分类系统的转换规则,构建土地覆被分类置信度函数和地类融合规则,进行土地覆被产品融合与修正,完成了青藏高原土地利用数据V1.0(1992,2005,2015,,300m×300m栅格,一级分类)

    doi:10.11888/Geogra.tpdc.270198 在线下载 2019-09-12

    3.一带一路核心国家2015年土地利用数据(V1.0)

    依据多土地覆被类型数据包括有欧空局全球陆地覆盖数据(ESA CCI-LC,300m栅格)、清华大学全球土地覆被数据(FROM-GLC,30m栅格)和美国NASA的LP DAAC 中心的MODIS全球土地覆被数据(MCD12Q1,300m栅格)等3个土地覆被产品数据。个别类别土地覆被数据包括有美国地质调查局USGS的全球耕地数据(GFSAD30,30m)、日本宇宙航空研究开发机构JAXA的全球林地数据(PALSAR/PALSAR-2,25m)、欧盟联合研究中心(JRC, EC)的全球水体数据(GSWD,Global Surface Water Data)和中山大学基于Google earth engine提取的全球城市用地数据(GULM,Global Urban Land Map)。构建了一带一路区域LUC分类系统以及其余数据分类系统的转换规则,构建土地覆被分类置信度函数和地类融合规则,进行土地覆被产品融合与修正,完成了一带一路核心国家2015年土地利用数据(V1.0)。

    doi:10.11888/Geogra.tpdc.270197 在线下载 2019-09-09

    4.青藏高原多种分辨率月温度递减率网格数据集

    数据内容:本数据集包含3种分辨率(0.25度、0.75度和2度)青藏高原多年平均月温度递减率(单位:℃/m)网格数据 数据来源及加工方法:基于高程标准差和相关性阈值动态检测不同分辨率网格内MODIS地温-海拔样本的有效性来获得局部可靠的温度递减率 数据质量描述:基于青藏高原113个站点的1980-2014年间日平均气温观测,对ERA-Interim气温数据应用0.75度气温递减率产品进行日平均气温的空间降尺度,使其验证误差(均方根误差)由~4℃降低到~2℃。 数据应用成果及前景:该数据集可应用于多种再分析资料的气温降尺度。

    doi:10.11888/Meteoro.tpdc.270211 在线下载 2019-09-08

    5.全球重大林火灾害数据集 (2018.01-2019.06)

    此数据集包含2018全年及2019上半年全球重大林火案例数据,包括2018年11月美国加州林火、2018年7月希腊阿提卡区林火及2019年3月中国山西省林火3个案例数据。具体数据包括:监测范围的火烧强度数据及灾前灾后植被指数变化数据。该数据集主要用于描述2018-2019上半年全球重大林火事件的发生、发展、影响及恢复,数据主要来源于NASA官网和EM-DAT数据库,在EXCEL和ArcGIS中运用统计与空间分析方法,对数据进行处理。数据来源可靠,处理方法科学严谨,可有效运用于全球(林火)灾害案例分析研究。

    doi:10.11888/Disas.tpdc.270203 在线下载 2019-09-02

    6.南部非洲热带气旋“伊代”及继发洪灾数据集(2019年3月)

    1)数据包含了2019年3月发生于南半球的热带气旋“伊代”的路径数据,以及由其引发的南部非洲洪灾受淹范围的数据,是2019年全球重大热带气旋灾害的重要数据源补充。 2)热带气旋路径数据整理自国家卫星气象中心的监测数据,使用ArcGIS软件读取经纬度坐标获得;南部非洲洪灾的受淹范围数据是中科院遥感所基于高分三号卫星影像提取的。 3)数据质量还可以。 4)可用于热带气旋“伊代”的路径分析、受影响情况分析和灾损评估等。

    doi:10.11888/Disas.tpdc.270207 在线下载 2019-09-01

    7.全球干旱强度及主要气象因子距平数据集(2018)

    本数据集主要包含2018年12个月全球SPEI空间分布、2018年全球干旱强度,以及降水、陆表温度、0-10cm土壤湿度与过去10年(2009-2018)的距平;数据使用了距平指数法,最大值合成法以及趋势分析法计算得到了2018年全球干旱强度以及主要气象因子距平数据,数据时间尺度为2018-01-01到2018-12-31,空间分辨率为0.5度,数据可为分析2018年全球干旱分布、干旱评价提供科学参考。

    doi:10.11888/Meteoro.tpdc.270208 在线下载 2019-09-01

    8.全球强震时空分布数据集(1989-2018)

    此数据集是对近30年全球的强震(Mw≥5)活动性水平进行分析,并将其进行空间化呈现,可以得到近年来全球强震频发、活动性水平高的分布区域,将2018年强震分布与其进行对比,得到2018年全球强震活动的分布特征。强震原始数据来自USGS,对其进行局部密度计算作为频度信息,对所有震例的震级进行全球范围的插值,之后对频度和震级进行相乘作为其强震活动性水平。该数据集为TIff格式,空间分辨率约为80km。该数据集可以在全球尺度上对强震的活动水平强弱分析提供参考,对全球地震危险性分析、防震减灾体系的构建具有一定辅助意义。

    doi:10.11888/Disas.tpdc.270206 在线下载 2019-09-01

    9.全球台风路径数据集(2018)

    1)数据包含了2018年29个发生在西北太平洋的台风路径点的数据,数据包含时间、经纬度、中心气压、风速风力、未来移向、未来移速、风力等级; 2)数据来源于中央气象台台风网(http://typhoon.nmc.cn/web.html),使用python抓取了网页发布的台风路径数据,并将抓取的excel数据表整理导成shapefile形式,按照台风风力等级划分标准赋予每一个路径点风力等级; 3)数据质量较好; 4)数据可以应用于基于台风路径点的移动情况、风速风力等的特征、影响分析。

    doi:10.11888/Disas.tpdc.270205 在线下载 2019-08-31

    10.全球重大洪水灾害典型案例数据集(2018.01-2018.12)

    该数据集分析了2018-2019年全球典型洪水灾害事件的时空分布规律、影响及损失情况。2018年,全球洪水灾害发生次数共109起,死亡人口1995人,受灾人口总数达1262万人次,直接经济损失约为45亿美元,在全球近30年中处于较低水平。2018年全球洪灾事件发生次数上半年较下半年多,5月至7月发生频次较高。因此,以2018年美国弗罗伦斯飓风洪水、2018年尼日利亚尼日尔河洪水及2018年中国山东寿光洪水等三个典型灾害事件为案例,从灾害背景、致灾因子、受灾情况等方面进行了分析。

    doi:10.11888/Disas.tpdc.270209 在线下载 2019-08-30

The 1-km Permafrost Zonation Index Map over the Tibetan Plateau

Based on a recently developed inventory of permafrost presence or absence from 1475 in situ observations, we developed and trained a statistical model and used it to compile a high‐resolution (30 arc‐ seconds) permafrost zonation index (PZI) map. The PZI model captures the high spatial variability of permafrost distribution over the QTP because it considers multi- ple controlling variables, including near‐surface air temperature downscaled from re‐ analysis, snow cover days and vegetation cover derived from remote sensing. Our results showed the new PZI map achieved the best performance compared to avail- able existing PZI and traditional categorical maps. Based on more than 1000 in situ measurements, the Cohen's kappa coefficient and overall classification accuracy were 0.62 and 82.5%, respectively. Excluding glaciers and lakes, the area of permafrost regions over the QTP is approximately 1.54 (1.35–1.66) ×106 km2, or 60.7 (54.5– 65.2)% of the exposed land, while area underlain by permafrost is about 1.17 (0.95–1.35) ×106 km2, or 46 (37.3–53.0)%.

2019-10-02 131 8 online More

Land use of the Tibet Plateau in 2015 (Version 1.0)

Based on 2015 ESA global land cover data (ESA GlobCover), combined with the Tsinghua university global land cover data (FROM GLC)、NASA MODIS global land cover data (MCD12Q1)、University of Maryland global land data (UMD)、USGS global land data (IGBP DISCover),we build the LUC classification system in the Tibet Plateau and the rest of the data transformation rules of the classification system. We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Tibet Plateau V1.0.

2019-09-12 363 43 online More

Land cover of core countries of the Belt and Road in 2015 (Version 1.0)

Based on 2015 ESA global land cover data (ESA GlobCover, 300 m grid), combined with the tsinghua university global land cover data (FROM GLC, 30 m grid)、NASA MODIS global land cover data (MCD12Q1, 300 m grid)、the United States Geological Survey (USGS global land data (GFSAD30, 30 m)、Japanese global forest data (PALSAR/PALSAR - 2, 25 m),we build the LUC classification system in the Belt and Road’s region and the rest of the data transformation rules of the classification system.We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Belt and Road’s region V1.0(64 + 1 countries, 2015, 1 km x 1 km grid, the first level classification).

2019-09-16 183 16 online More

Gridded Monthly Temperature Lapse Rates of the Tibetan Plateau

1) Data content (including elements and meanings): Gridded multiyear-average monthly air temperature lapse rate data over the Tibetan Plateau at three kinds of resolutions (i.e. 0.25°, 0.75° and 2°) 2) Data source and processing method: Locally reliable temperature lapse rates are created from filtered MODIS LST-elevation samples by using the thresholds of standard error of elevation and correlation coefficient 3) Data quality description: For ERA-Interim, the validation accuracy (based on 1980-2014 daily mean aire temperature records from 113 stations across the Tibetan Plateau) decreases from ~4℃ to ~2℃ after using the 0.75° temperaturel lapse rate. 4) Data application results and prospects: This dataset can be used for downscaling air temperature from multiple reanalysis datasets.

2019-09-24 70 3 online More

Dataset of major global forest fire disasters (2018.01-2019.06)

This data set contains 2018 global forest fire case data for the whole year and 2019, including the forest fire in California in November 2018, the forest fire in Attica, Greece in July 2018, and the forest fire in Shanxi Province in March 2019. Case data. Specific data include: fire intensity data of the monitoring range and data of vegetation index changes before and after the disaster. The data set is mainly used to describe the occurrence, development, impact and recovery of major global forest fire events in the first half of 2018-2019. The data mainly comes from NASA official website and EM-DAT database, it was processed by statistical and spatial analysis methods using EXCEL and ArcGIS tools. The data source is reliable, the processing method is scientific and rigorous, and it can be effectively applied to global (forest fire) disaster case analysis research.

2019-09-29 96 4 online More

Dataset of tropical cyclone Idai and subsequent flood disaster in Southern Africa (March 2019)

1) The data includes the path data of the tropical cyclone “Idai” that occurred in the southern hemisphere in March 2019, and the data on the flooding in southern Africa caused by it. It is an important data source supplement to the major tropical cyclone disasters in 2019. 2) The tropical cyclone path data is compiled from the monitoring data of the National Satellite Meteorological Center, and the ArcGIS software is used to read the latitude and longitude coordinates. The flooded area data of the floods in southern Africa is extracted by the high-resolution satellite imagery of the Chinese Academy of Sciences Remote Sensing. 3) Data quality is OK. 4) It can be used for path analysis, affected situation analysis and damage assessment of tropical cyclone “Idai”.

2019-09-30 69 1 online More

Global drought intensity and major meteorological factors anomaly dataset (2018)

This dataset mainly includes the spatial distribution of global SPEI in 1218 in 2018, the global drought intensity in 2018, and the anomalies of precipitation, land surface temperature, 0-10 cm soil moisture and the past 10 years (2009-2018); The flat index method, the maximum value synthesis method and the trend analysis method calculate the global drought intensity and the main meteorological factor anomaly data for 2018. The data time scale is 2018-01-01 to 2018-12-31, and the spatial resolution is 0.5 degree. The data can provide a scientific reference for the analysis of global drought distribution and drought assessment in 2018.

2019-09-30 166 10 online More

Dataset of spatial and temporal distribution of global strong earthquakes (1989-2018)

This data set is used to analyze the global activity level of strong earthquakes (Mw 5) in the past 30 years, and to present it spatially. It can be used to obtain the distribution areas of strong earthquakes with high frequency and activity level in recent years. By comparing the distribution of strong earthquakes in 2018 with that in 1989-2018, the distribution characteristics of global strong earthquakes in 2018 are obtained. The original data of strong earthquakes are from USGS, and the local density is calculated as frequency information. The magnitudes of all earthquake cases are interpolated globally, and then the frequency and magnitude are multiplied as the activity level of strong earthquakes. The data set is in TIff format with a spatial resolution of about 80 km. The data set can provide a reference for the analysis of strong earthquake activity level on the global scale, and is helpful for the analysis of global earthquake risk and the construction of earthquake prevention and disaster reduction system.

2019-09-29 64 5 online More

Global Typhoon path dataset (2018)

1) The data includes data on 29 typhoon path points occurring in the Pacific Northwest in 2018. The data includes time, latitude and longitude, central air pressure, wind speed wind power, future direction, future speed, and wind level; 2) The data comes from the Central Meteorological Observatory Typhoon Network (http://typhoon.nmc.cn/web.html), using python to capture the typhoon path data published by the webpage, and sorting the extracted excel data table into shapefile form. According to the typhoon wind level classification standard, the wind level is given to each path point; 3) The data quality is good; 4) The data can be applied to typhoon characteristics and impact analysis based on typhoon path point movement, wind speed, wind power, and the like.

2019-09-29 105 8 online More

Typical case dataset of major global flood disasters (2018.01-2018.12)

The data set analyzes the spatial and temporal distribution, impact and loss of typical global flood disasters from 2018 to 2019. In 2018, there were 109 flood disasters in the world, with a death toll of 1995. The total number of people affected was 12.62 million. The direct economic loss was about 4.5 billion US dollars, which was at a low level in the past 30 years. The number of global flood incidents in 2018 was higher in the first half of the year than in the second half of the year, and the frequency of occurrence was higher from May to July. Therefore, based on three typical disaster events such as the hurricane flood in Florence in the United States in 2018, the flooding of the Niger River in Nigeria in 2018, and the Shouguang flood in Shandong Province in 2018, the disaster background, hazard factors, and disaster situation were analyzed. .

2019-09-30 98 4 online More