A. Jolivot, V. Lebourgeois, M. Ameline, Valérie Andriamanga, Beatriz Bellón, Mathieu Castets, A. Crespin-Boucaud, P. Defourny, Santiana Diaz, M. Dièye, S. Dupuy, R. Ferraz, R. Gaetano, M. Gély, C. Jahel, Bertin Kabore, C. Lelong, G. le Maire, L. Leroux, D. Lo Seen, Mary Muthoni, B. Ndao, T. Newby, Cecília Lira Melo de Oliveira Santos, Eloise Rasoamalala, M. Simões, I. Thiaw, Alice Timmermans, A. Tran, A. Bégué
Abstract. The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural census are often poorly georeferenced, and crop types are difficult to photo-interpret directly from satellite imagery. In this paper, we present nine datasets collected in a standardized manner between 2013 and 2020 in seven tropical and subtropical countries within the framework of the international JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative. These quality-controlled datasets are distinguished by in situ data collected at field scale by local experts, with precise geographic coordinates, and following a common protocol. Altogether, the datasets completed 27 074 polygons (20 257 crop and 6 817 non-crop) documented by detailed keywords. These datasets can be used to produce and validate agricultural land use maps in the tropics, but also, to assess the performances and the robustness of classification methods of cropland and crop types/practices in a large range of tropical farming systems. The dataset is available at https://doi.org/10.18167/DVN1/P7OLAP .
{"title":"Harmonized in situ JECAM datasets for agricultural land use mapping and monitoring in tropical countries","authors":"A. Jolivot, V. Lebourgeois, M. Ameline, Valérie Andriamanga, Beatriz Bellón, Mathieu Castets, A. Crespin-Boucaud, P. Defourny, Santiana Diaz, M. Dièye, S. Dupuy, R. Ferraz, R. Gaetano, M. Gély, C. Jahel, Bertin Kabore, C. Lelong, G. le Maire, L. Leroux, D. Lo Seen, Mary Muthoni, B. Ndao, T. Newby, Cecília Lira Melo de Oliveira Santos, Eloise Rasoamalala, M. Simões, I. Thiaw, Alice Timmermans, A. Tran, A. Bégué","doi":"10.5194/ESSD-2021-125","DOIUrl":"https://doi.org/10.5194/ESSD-2021-125","url":null,"abstract":"Abstract. The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural census are often poorly georeferenced, and crop types are difficult to photo-interpret directly from satellite imagery. In this paper, we present nine datasets collected in a standardized manner between 2013 and 2020 in seven tropical and subtropical countries within the framework of the international JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative. These quality-controlled datasets are distinguished by in situ data collected at field scale by local experts, with precise geographic coordinates, and following a common protocol. Altogether, the datasets completed 27 074 polygons (20 257 crop and 6 817 non-crop) documented by detailed keywords. These datasets can be used to produce and validate agricultural land use maps in the tropics, but also, to assess the performances and the robustness of classification methods of cropland and crop types/practices in a large range of tropical farming systems. The dataset is available at https://doi.org/10.18167/DVN1/P7OLAP .","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133032185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. In recent decades the decline of the Arctic sea ice has modified vertical momentum fluxes from the atmosphere to the ice and the ocean, thereby affecting the surface circulation. In the past ten years satellite altimetry has contributed to understand these changes. However, data from ice-covered regions require dedicated processing, originating inconsistency between ice-covered and open ocean regions in terms of biases, corrections and data coverage. Thus, efforts to generate consistent Arctic-wide datasets are still required to enable the study of the Arctic Ocean surface circulation at basin-wide scales. Here we provide and assess a monthly gridded dataset of sea surface height anomaly and geostrophic velocity. This dataset is based on Cryosat-2 observations over ice-covered and open ocean areas of the Arctic up to 88° N for the period 2011 to 2018, interpolated using the Data-Interpolating Variational Analysis (DIVA) method. Geostrophic velocity was not available north of 82° N before this study. To examine the robustness of our results, we compare the generated fields to one independent altimetry dataset and independent data of ocean bottom pressure, steric height and near-surface ocean velocity from moorings. Results from the comparison to near-surface ocean velocity show that our geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km. We further discuss the seasonal cycle of sea surface height and geostrophic velocity in the context of previous literature. Large scale features emerge, i.e. Arctic-wide maximum sea surface height between October and January, with the highest amplitude over the shelves, and basin wide seasonal acceleration of Arctic slope currents in winter. We suggest that this dataset can be used to study not only the large scale sea surface height and circulation but also the regionally confined boundary currents. The dataset is available in netCDF format from PANGAEA at [data currently under review].
{"title":"Sea surface height anomaly and geostrophic velocity from altimetry measurements over the Arctic Ocean (2011–2018)","authors":"Francesca Doglioni, R. Ricker, B. Rabe, T. Kanzow","doi":"10.5194/ESSD-2021-170","DOIUrl":"https://doi.org/10.5194/ESSD-2021-170","url":null,"abstract":"Abstract. In recent decades the decline of the Arctic sea ice has modified vertical momentum fluxes from the atmosphere to the ice and the ocean, thereby affecting the surface circulation. In the past ten years satellite altimetry has contributed to understand these changes. However, data from ice-covered regions require dedicated processing, originating inconsistency between ice-covered and open ocean regions in terms of biases, corrections and data coverage. Thus, efforts to generate consistent Arctic-wide datasets are still required to enable the study of the Arctic Ocean surface circulation at basin-wide scales. Here we provide and assess a monthly gridded dataset of sea surface height anomaly and geostrophic velocity. This dataset is based on Cryosat-2 observations over ice-covered and open ocean areas of the Arctic up to 88° N for the period 2011 to 2018, interpolated using the Data-Interpolating Variational Analysis (DIVA) method. Geostrophic velocity was not available north of 82° N before this study. To examine the robustness of our results, we compare the generated fields to one independent altimetry dataset and independent data of ocean bottom pressure, steric height and near-surface ocean velocity from moorings. Results from the comparison to near-surface ocean velocity show that our geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km. We further discuss the seasonal cycle of sea surface height and geostrophic velocity in the context of previous literature. Large scale features emerge, i.e. Arctic-wide maximum sea surface height between October and January, with the highest amplitude over the shelves, and basin wide seasonal acceleration of Arctic slope currents in winter. We suggest that this dataset can be used to study not only the large scale sea surface height and circulation but also the regionally confined boundary currents. The dataset is available in netCDF format from PANGAEA at [data currently under review].","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133392180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Duo Cui, Zhu Liu, Cuncun Duan, Z. Deng, Xiangzheng Deng, Xu Song, Xinyu Dou, Taochun Sun
Abstract. Tracking China's national and regional CO2 emission trends is becoming ever more crucial. The country recently pledged to achieve ambitious emissions reduction targets, however, high-resolution datasets for provincial level CO2 emissions in China are still lacking. This study provides daily CO2 emission datasets for China's 31 provinces, including for the first time, the province of Tibet. The inventory covers the emissions from three industrial sectors (power, industry and ground transport) during 2019 to 2020, with its temporal resolution at a daily level. In addition, the variations in CO2 emissions for seasonal, weekly and holiday periods have been uncovered at a provincial level for the first time. This new data was added to further analyze the impact that weekends and holidays have on China's CO2 emissions. Over weekend periods, carbon emissions are shown to reduce by around 3%. Spring Festival meanwhile, has the greatest impact on the reduction of China's CO2 emissions. This detailed and time-related inventory will facilitate a more local and adaptive management of China’s CO2 emissions during both the COVID-19 pandemic’s recovery and the ongoing energy transition. The data are archived at https://doi.org/10.5281/zenodo.4730175 (Cui et al., 2021).
摘要跟踪中国全国和地区的二氧化碳排放趋势变得越来越重要。中国最近承诺要实现雄心勃勃的减排目标，然而，中国省级二氧化碳排放的高分辨率数据集仍然缺乏。本研究提供了中国31个省的每日二氧化碳排放数据集，其中首次包括西藏省。该清单涵盖2019年至2020年三个工业部门(电力、工业和地面交通)的排放，其时间分辨率为每日水平。此外，首次在省级层面揭示了季节性、每周和节假日期间二氧化碳排放量的变化。加入这些新数据是为了进一步分析周末和节假日对中国二氧化碳排放的影响。在周末期间，碳排放量减少了约3%。同时，春节对中国二氧化碳排放量的减少影响最大。这份详细的、与时间相关的清单将有助于在2019冠状病毒病大流行的恢复和正在进行的能源转型期间对中国的二氧化碳排放进行更加本地化和适应性的管理。数据存档于https://doi.org/10.5281/zenodo.4730175 (Cui et al.， 2021)。
{"title":"Daily CO2 emission for China's provinces in 2019 and 2020","authors":"Duo Cui, Zhu Liu, Cuncun Duan, Z. Deng, Xiangzheng Deng, Xu Song, Xinyu Dou, Taochun Sun","doi":"10.5194/essd-2021-153","DOIUrl":"https://doi.org/10.5194/essd-2021-153","url":null,"abstract":"Abstract. Tracking China's national and regional CO2 emission trends is becoming ever more crucial. The country recently pledged to achieve ambitious emissions reduction targets, however, high-resolution datasets for provincial level CO2 emissions in China are still lacking. This study provides daily CO2 emission datasets for China's 31 provinces, including for the first time, the province of Tibet. The inventory covers the emissions from three industrial sectors (power, industry and ground transport) during 2019 to 2020, with its temporal resolution at a daily level. In addition, the variations in CO2 emissions for seasonal, weekly and holiday periods have been uncovered at a provincial level for the first time. This new data was added to further analyze the impact that weekends and holidays have on China's CO2 emissions. Over weekend periods, carbon emissions are shown to reduce by around 3%. Spring Festival meanwhile, has the greatest impact on the reduction of China's CO2 emissions. This detailed and time-related inventory will facilitate a more local and adaptive management of China’s CO2 emissions during both the COVID-19 pandemic’s recovery and the ongoing energy transition. The data are archived at https://doi.org/10.5281/zenodo.4730175 (Cui et al., 2021).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"226 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132795617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Shrub biomass equations provide an accurate, efficient and convenient method in estimating biomass of shrubland ecosystems and biomass of the shrub layer in forest ecosystems at various spatial and temporal scales. In recent decades, many shrub biomass equations have been reported mainly in journals, books and postgraduate's dissertations. However, these biomass equations are applicable for limited shrub species with respect to a large number of shrub species widely distributed in China, which severely restricted the study of terrestrial ecosystem structure and function, such as biomass, production, and carbon budge. Therefore, we firstly carried out a critical review of published literature (from 1982 to 2019) on shrub biomass equations in China, and then developed biomass equations for the dominant shrub species using a unified method based on field measurements of 738 sites in shrubland ecosystems across China. Finally, we constructed the first comprehensive biomass equation dataset for China’s common shrub species. This dataset consists of 822 biomass equations specific to 167 shrub species and has significant representativeness to the geographical, climatic and shrubland vegetation features across China. The dataset is freely available at https://doi.org/10.11922/sciencedb.00641 for noncommercial scientific applications, and this dataset fills a significant gap in woody biomass equations and provides key parameters for biomass estimation in studies on terrestrial ecosystem structure and function.
{"title":"A biomass equation dataset for common shrub species in China","authors":"Yang Wang, Wenting Xu, Zhiyao Tang, Zongqiang Xie","doi":"10.5194/ESSD-2021-44","DOIUrl":"https://doi.org/10.5194/ESSD-2021-44","url":null,"abstract":"Abstract. Shrub biomass equations provide an accurate, efficient and convenient method in estimating biomass of shrubland ecosystems and biomass of the shrub layer in forest ecosystems at various spatial and temporal scales. In recent decades, many shrub biomass equations have been reported mainly in journals, books and postgraduate's dissertations. However, these biomass equations are applicable for limited shrub species with respect to a large number of shrub species widely distributed in China, which severely restricted the study of terrestrial ecosystem structure and function, such as biomass, production, and carbon budge. Therefore, we firstly carried out a critical review of published literature (from 1982 to 2019) on shrub biomass equations in China, and then developed biomass equations for the dominant shrub species using a unified method based on field measurements of 738 sites in shrubland ecosystems across China. Finally, we constructed the first comprehensive biomass equation dataset for China’s common shrub species. This dataset consists of 822 biomass equations specific to 167 shrub species and has significant representativeness to the geographical, climatic and shrubland vegetation features across China. The dataset is freely available at https://doi.org/10.11922/sciencedb.00641 for noncommercial scientific applications, and this dataset fills a significant gap in woody biomass equations and provides key parameters for biomass estimation in studies on terrestrial ecosystem structure and function.","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133863974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Long-term urban extent data are highly desirable for understanding urban land use patterns and achieving sustainable development goals. However, urban observation data based on remote sensing are typically confined to recent decades. In this study, we advance in this arena by reconstructing the urban extents for China that extend back from 15th century to 19th century based on multiple historical documents. Cities in late imperial China (the Ming and the Qing Dynasties, 1368–1911) generally had city walls, and these walls were usually built around the urban built-up area. By restoring the scope of the city walls, the urban extend in this period could be restored. Firstly, we collected the years of construction or reconstruction of city walls from the historical data. Specifically, the period in which the scope of the city wall keeps unchanged is recorded as a lifetime of it. Secondly, specialization of the scope of the city wall could be conducted based on the urban morphology method, and variety of documentation, including the historical literature materials, the military topographic maps of the first half of the 20th century, and the remote sensing images of the 1970s. Correlation and integration of the lifetime and the spatial data would produce China City Wall Areas Dataset (CCWAD) in late imperial. Based on the proximity to the time of most of the city walls, we generated China Urban Extent Dataset (CUED) in the 15th–19th centuries in six representative years (i.e., 1400, 1537, 1648, 1708, 1787, and 1866). These datasets are available at https://doi.org/10.6084/m9.figshare.14112968.v1
{"title":"An urban extent dataset in late imperial China in 15th–19th centuries","authors":"Qiaofeng Xue, Xiaobin Jin, Yinong Cheng, Xuhong Yang, Yinkang Zhou","doi":"10.5194/ESSD-2021-62","DOIUrl":"https://doi.org/10.5194/ESSD-2021-62","url":null,"abstract":"Abstract. Long-term urban extent data are highly desirable for understanding urban land use patterns and achieving sustainable development goals. However, urban observation data based on remote sensing are typically confined to recent decades. In this study, we advance in this arena by reconstructing the urban extents for China that extend back from 15th century to 19th century based on multiple historical documents. Cities in late imperial China (the Ming and the Qing Dynasties, 1368–1911) generally had city walls, and these walls were usually built around the urban built-up area. By restoring the scope of the city walls, the urban extend in this period could be restored. Firstly, we collected the years of construction or reconstruction of city walls from the historical data. Specifically, the period in which the scope of the city wall keeps unchanged is recorded as a lifetime of it. Secondly, specialization of the scope of the city wall could be conducted based on the urban morphology method, and variety of documentation, including the historical literature materials, the military topographic maps of the first half of the 20th century, and the remote sensing images of the 1970s. Correlation and integration of the lifetime and the spatial data would produce China City Wall Areas Dataset (CCWAD) in late imperial. Based on the proximity to the time of most of the city walls, we generated China Urban Extent Dataset (CUED) in the 15th–19th centuries in six representative years (i.e., 1400, 1537, 1648, 1708, 1787, and 1866). These datasets are available at https://doi.org/10.6084/m9.figshare.14112968.v1","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131454967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
D. Olefeldt, Mikael Hovemyr, McKenzie A. Kuhn, D. Bastviken, T. Bohn, J. Connolly, P. Crill, E. Euskirchen, S. Finkelstein, H. Genet, G. Grosse, L. Harris, L. Heffernan, M. Helbig, G. Hugelius, R. Hutchins, S. Juutinen, M. Lara, A. Malhotra, K. Manies, A. McGuire, S. Natali, J. O’Donnell, F. Parmentier, Aleksi Räsänen, C. Schädel, O. Sonnentag, M. Strack, S. Tank, C. Treat, R. Varner, T. Virtanen, Rebecca K. Warren, J. Watts
Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal-Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5° grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ~28 % each of the total wetland area, while the highest methane emitting marsh and tundra wetland classes occupied 5 and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low methane-emitting large lakes (> 10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 and 4 %, respectively. Small (< 0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area, but contributed disproportionally to the overall spatial uncertainty of lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain) of which 8 % was associated with high-methane emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that will have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake and river extents, and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern Boreal and Arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data is freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).
{"title":"The Boreal-Arctic Wetland and Lake Dataset (BAWLD)","authors":"D. Olefeldt, Mikael Hovemyr, McKenzie A. Kuhn, D. Bastviken, T. Bohn, J. Connolly, P. Crill, E. Euskirchen, S. Finkelstein, H. Genet, G. Grosse, L. Harris, L. Heffernan, M. Helbig, G. Hugelius, R. Hutchins, S. Juutinen, M. Lara, A. Malhotra, K. Manies, A. McGuire, S. Natali, J. O’Donnell, F. Parmentier, Aleksi Räsänen, C. Schädel, O. Sonnentag, M. Strack, S. Tank, C. Treat, R. Varner, T. Virtanen, Rebecca K. Warren, J. Watts","doi":"10.5194/ESSD-2021-140","DOIUrl":"https://doi.org/10.5194/ESSD-2021-140","url":null,"abstract":"Abstract. Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal-Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5° grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ~28 % each of the total wetland area, while the highest methane emitting marsh and tundra wetland classes occupied 5 and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low methane-emitting large lakes (> 10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 and 4 %, respectively. Small (< 0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area, but contributed disproportionally to the overall spatial uncertainty of lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain) of which 8 % was associated with high-methane emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that will have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake and river extents, and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern Boreal and Arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data is freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021). \u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115080401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qi Liu, M. Hernández‐Pajares, Heng Yang, E. Monte‐Moreno, D. Roma-Dollase, A. García‐Rigo, Zishen Li, Ningbo Wang, D. Laurichesse, A. Blot, Qile Zhao, Qiang Zhang, A. Hauschild, L. Agrotis, M. Schmitz, G. Wübbena, A. Stürze, A. Krankowski, S. Schaer, J. Feltens, A. Komjathy, R. Ghoddousi-Fard
Abstract. The Real-Time Working Group (RTWG) of the International GNSS Service (IGS) is dedicated to providing high-quality data, high-accuracy products for Global Navigation Satellite System (GNSS) positioning, navigation, timing, and Earth observations. As one part of real-time products, the IGS combined Real-Time Global Ionosphere Map (RT-GIM) has been generated by the real-time weighting of the RT-GIMs from IGS real-time ionosphere centers including the Chinese Academy of Sciences (CAS), Centre National d’Etudes Spatiales (CNES), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU). The performance of global Vertical Total Electron Content (VTEC) representation in all of the RT-GIMs has been assessed by VTEC from Jason3-altimeter during one month over oceans and dSTEC-GPS technique with 2-day observations over continental regions. According to the Jason3-VTEC and dSTEC-GPS assessment, the real-time weighting technique is sensitive to the accuracy of RT-GIMs. Compared with the performance of post-processed rapid Global Ionosphere Maps (GIMs) and IGS combined final GIM (igsg) during the testing period, the accuracy of UPC RT-GIM (after the transition of interpolation technique) and IGS combined RT-GIM (IRTG) is equivalent to the rapid GIMs and reaches around 2.7 and 3.0 TECU (TEC Unit, 1016 el/m2) over oceans and continental regions, respectively. The accuracy of CAS RT-GIM and CNES RT-GIM is slightly worse than the rapid GIMs, while WHU RT-GIM requires a further upgrade to obtain similar performance. In addition, the strong response to the recent geomagnetic storms has been found in the Global Electron Content (GEC) of IGS RT-GIMs (especially UPC RT-GIM and IGS combined RT-GIM). The IGS RT-GIMs turn out to be reliable sources of real-time global VTEC information and have great potential for real-time applications including range error correction for transionospheric radio signals (such as GNSS positioning, search and rescue, air traffic, radar altimetry, and radioastronomy), the monitoring of space weather (such as geomagnetic and ionospheric storms, ionospheric disturbance) and detection of natural hazards on a global scale (such as hurricanes/typhoons, ionospheric anomalies associated with earthquakes). All the IGS combined RT-GIMs generated and analyzed during the testing period are available at http://doi.org/10.5281/zenodo.4651445 (Liu et al., 2021b).
{"title":"The cooperative IGS RT-GIMs: a global and accurate estimation of the ionospheric electron content distribution in real-time","authors":"Qi Liu, M. Hernández‐Pajares, Heng Yang, E. Monte‐Moreno, D. Roma-Dollase, A. García‐Rigo, Zishen Li, Ningbo Wang, D. Laurichesse, A. Blot, Qile Zhao, Qiang Zhang, A. Hauschild, L. Agrotis, M. Schmitz, G. Wübbena, A. Stürze, A. Krankowski, S. Schaer, J. Feltens, A. Komjathy, R. Ghoddousi-Fard","doi":"10.5194/ESSD-2021-136","DOIUrl":"https://doi.org/10.5194/ESSD-2021-136","url":null,"abstract":"Abstract. The Real-Time Working Group (RTWG) of the International GNSS Service (IGS) is dedicated to providing high-quality data, high-accuracy products for Global Navigation Satellite System (GNSS) positioning, navigation, timing, and Earth observations. As one part of real-time products, the IGS combined Real-Time Global Ionosphere Map (RT-GIM) has been generated by the real-time weighting of the RT-GIMs from IGS real-time ionosphere centers including the Chinese Academy of Sciences (CAS), Centre National d’Etudes Spatiales (CNES), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU). The performance of global Vertical Total Electron Content (VTEC) representation in all of the RT-GIMs has been assessed by VTEC from Jason3-altimeter during one month over oceans and dSTEC-GPS technique with 2-day observations over continental regions. According to the Jason3-VTEC and dSTEC-GPS assessment, the real-time weighting technique is sensitive to the accuracy of RT-GIMs. Compared with the performance of post-processed rapid Global Ionosphere Maps (GIMs) and IGS combined final GIM (igsg) during the testing period, the accuracy of UPC RT-GIM (after the transition of interpolation technique) and IGS combined RT-GIM (IRTG) is equivalent to the rapid GIMs and reaches around 2.7 and 3.0 TECU (TEC Unit, 1016 el/m2) over oceans and continental regions, respectively. The accuracy of CAS RT-GIM and CNES RT-GIM is slightly worse than the rapid GIMs, while WHU RT-GIM requires a further upgrade to obtain similar performance. In addition, the strong response to the recent geomagnetic storms has been found in the Global Electron Content (GEC) of IGS RT-GIMs (especially UPC RT-GIM and IGS combined RT-GIM). The IGS RT-GIMs turn out to be reliable sources of real-time global VTEC information and have great potential for real-time applications including range error correction for transionospheric radio signals (such as GNSS positioning, search and rescue, air traffic, radar altimetry, and radioastronomy), the monitoring of space weather (such as geomagnetic and ionospheric storms, ionospheric disturbance) and detection of natural hazards on a global scale (such as hurricanes/typhoons, ionospheric anomalies associated with earthquakes). All the IGS combined RT-GIMs generated and analyzed during the testing period are available at http://doi.org/10.5281/zenodo.4651445 (Liu et al., 2021b).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114734315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cui Duo, Liu Zhu, cuncun Duan, D. Zhu, Xiangzheng Deng, Xuanren Song, Dou Xinyu, Taocun Sun
Abstract. Tracking China's national and regional CO2 emission trends is becoming ever more crucial. The country recently pledged to achieve ambitious emissions reduction targets, however, high-resolution datasets for provincial level CO2 emissions in China are still lacking. This study provides daily CO2 emission datasets for China's 31 provinces, including for the first time, the province of Tibet. The inventory covers the emissions from three industrial sectors (power, industry and ground transport) during 2019 to 2020, with its temporal resolution at a daily level. In addition, the variations in CO2 emissions for seasonal, weekly and holiday periods have been uncovered at a provincial level for the first time. This new data was added to further analyze the impact that weekends and holidays have on China's CO2 emissions. Over weekend periods, carbon emissions are shown to reduce by around 3%. Spring Festival meanwhile, has the greatest impact on the reduction of China's CO2 emissions. This detailed and time-related inventory will facilitate a more local and adaptive management of China’s CO2 emissions during both the COVID-19 pandemic’s recovery and the ongoing energy transition. The data are archived at https://doi.org/10.5281/zenodo.4730175 (Cui et al., 2021).
摘要跟踪中国全国和地区的二氧化碳排放趋势变得越来越重要。中国最近承诺要实现雄心勃勃的减排目标，然而，中国省级二氧化碳排放的高分辨率数据集仍然缺乏。本研究提供了中国31个省的每日二氧化碳排放数据集，其中首次包括西藏省。该清单涵盖2019年至2020年三个工业部门(电力、工业和地面交通)的排放，其时间分辨率为每日水平。此外，首次在省级层面揭示了季节性、每周和节假日期间二氧化碳排放量的变化。加入这些新数据是为了进一步分析周末和节假日对中国二氧化碳排放的影响。在周末期间，碳排放量减少了约3%。同时，春节对中国二氧化碳排放量的减少影响最大。这份详细的、与时间相关的清单将有助于在2019冠状病毒病大流行的恢复和正在进行的能源转型期间对中国的二氧化碳排放进行更加本地化和适应性的管理。数据存档于https://doi.org/10.5281/zenodo.4730175 (Cui et al.， 2021)。
{"title":"Daily CO 2 emission for China's provinces in 2019 and 2020","authors":"Cui Duo, Liu Zhu, cuncun Duan, D. Zhu, Xiangzheng Deng, Xuanren Song, Dou Xinyu, Taocun Sun","doi":"10.5281/ZENODO.4730175","DOIUrl":"https://doi.org/10.5281/ZENODO.4730175","url":null,"abstract":"Abstract. Tracking China's national and regional CO2 emission trends is becoming ever more crucial. The country recently pledged to achieve ambitious emissions reduction targets, however, high-resolution datasets for provincial level CO2 emissions in China are still lacking. This study provides daily CO2 emission datasets for China's 31 provinces, including for the first time, the province of Tibet. The inventory covers the emissions from three industrial sectors (power, industry and ground transport) during 2019 to 2020, with its temporal resolution at a daily level. In addition, the variations in CO2 emissions for seasonal, weekly and holiday periods have been uncovered at a provincial level for the first time. This new data was added to further analyze the impact that weekends and holidays have on China's CO2 emissions. Over weekend periods, carbon emissions are shown to reduce by around 3%. Spring Festival meanwhile, has the greatest impact on the reduction of China's CO2 emissions. This detailed and time-related inventory will facilitate a more local and adaptive management of China’s CO2 emissions during both the COVID-19 pandemic’s recovery and the ongoing energy transition. The data are archived at https://doi.org/10.5281/zenodo.4730175 (Cui et al., 2021).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125122203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Horwath, B. D. Gutknecht, A. Cazenave, H. Palanisamy, F. Marti, B. Marzeion, F. Paul, R. Le Bris, A. Hogg, Inès N. Otosaka, A. Shepherd, P. Döll, Denise Cáceres, Hannes Müller Schmied, J. Johannessen, J. Nilsen, R. Raj, R. Forsberg, L. Sandberg Sørensen, V. Barletta, S. Simonsen, P. Knudsen, O. Andersen, Heidi Randall, S. Rose, C. Merchant, C. Macintosh, K. von Schuckmann, K. Novotny, A. Groh, M. Restano, J. Benveniste
Abstract. Studies of the global sea-level budget (SLB) and the global ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributions. Here we present datasets for times series of the SLB and OMB elements developed in the framework of ESA's Climate Change Initiative. We use these datasets to assess the SLB and the OMB simultaneously, utilising a consistent framework of uncertainty characterisation. The time series, given at monthly sampling, include global mean sea-level (GMSL) anomalies from satellite altimetry; the global mean steric component from Argo drifter data with incorporation of sea surface temperature data; the ocean mass component from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry; the contribution from global glacier mass changes assessed by a global glacier model; the contribution from Greenland Ice Sheet and Antarctic Ice Sheet mass changes, assessed from satellite radar altimetry and from GRACE; and the contribution from land water storage anomalies assessed by the WaterGAP global hydrological model. Over the period Jan 1993–Dec 2016 (P1, covered by the satellite altimetry records), the mean rate (linear trend) of GMSL is 3.05 ± 0.24 mm yr−1. The steric component is 1.15 ± 0.12 mm yr−1 (38 % of the GMSL trend) and the mass component is 1.75 ± 0.12 mm yr−1 (57 %). The mass component includes 0.64 ± 0.03 mm yr−1 (21 % of the GMSL trend) from glaciers outside Greenland and Antarctica, 0.60 ± 0.04 mm yr−1 (20 %) from Greenland, 0.19 ± 0.04 mm yr−1 (6 %) from Antarctica, and 0.32 ± 0.10 mm yr−1 (10 %) from changes of land water storage. In the period Jan 2003–Aug 2016 (P2, covered by GRACE and the Argo drifter system), GMSL rise is higher than in P1 at 3.64 ± 0.26 mm yr−1. This is due to an increase of the mass contributions (now about 2.22 ± 0.15 mm yr−1, 61 % of the GMSL trend), with the largest increase contributed from Greenland. The SLB of linear trends is closed for P1 and P2, that is, the GMSL trend agrees with the sum of the steric and mass components within their combined uncertainties. The OMB budget, which can be evaluated only for P2, is also closed, that is, the GRACE-based ocean-mass trend agrees with the sum of assessed mass contributions within uncertainties. Combined uncertainties (1-sigma) of the elements involved in the budgets are between 0.26 and 0.40 mm yr−1, about 10 % of GMSL rise. Interannual variations that overlie the long-term trends are coherently represented by the elements of the SLB and the OMB. Even at the level of monthly anomalies the budgets are closed within uncertainties, while also indicating possible origins of remaining misclosures.
摘要对全球海平面预算(SLB)和全球海洋质量预算(OMB)的研究对于评估我们关于海平面变化及其贡献的知识的可靠性至关重要。在这里，我们展示了在欧空局气候变化倡议框架下开发的SLB和OMB元素的时间序列数据集。我们使用这些数据集同时评估SLB和OMB，利用一致的不确定性表征框架。每月采样的时间序列包括来自卫星测高的全球平均海平面(GMSL)异常;Argo漂船数据的全球平均空间分量与海面温度数据的结合;重力恢复和气候实验(GRACE)卫星重力测量的海洋质量分量;用全球冰川模式评估全球冰川质量变化的贡献由卫星雷达测高和GRACE评估的格陵兰冰盖和南极冰盖质量变化的贡献;以及由WaterGAP全球水文模型评估的陆地蓄水异常的贡献。在1993年1月至2016年12月(P1，卫星测高记录覆盖)期间，GMSL的平均速率(线性趋势)为3.05±0.24 mm yr - 1。空间分量为1.15±0.12 mm yr−1(占GMSL趋势的38%)，质量分量为1.75±0.12 mm yr−1(占57%)。质量分量包括来自格陵兰和南极洲以外冰川的0.64±0.03 mm yr - 1(占GMSL趋势的21%)、格陵兰的0.60±0.04 mm yr - 1(占20%)、南极洲的0.19±0.04 mm yr - 1(占6%)和陆地蓄水变化的0.32±0.10 mm yr - 1(占10%)。2003年1月至2016年8月(P2, GRACE和Argo漂移系统覆盖)，GMSL上升幅度高于P1，为3.64±0.26 mm yr - 1。这是由于质量贡献的增加(现在约为2.22±0.15 mm /年，占GMSL趋势的61%)，其中最大的贡献来自格陵兰岛。P1和P2的线性趋势的SLB是闭合的，即GMSL趋势与空间分量和质量分量在其组合不确定性范围内的总和一致。只能对P2进行评估的OMB预算也是封闭的，即基于grace的海洋质量趋势与不确定范围内评估质量贡献的总和一致。预算中涉及的要素的综合不确定性(1-sigma)在0.26至0.40 mm /年- 1之间，约占GMSL上升的10%。覆盖长期趋势的年际变化由SLB和OMB的要素一致地表示。即使在每月异常的水平上，预算也在不确定的范围内关闭，同时也表明剩余的错误关闭的可能根源。
{"title":"Global sea-level budget and ocean-mass budget, with focus on advanced data products and uncertainty characterisation","authors":"M. Horwath, B. D. Gutknecht, A. Cazenave, H. Palanisamy, F. Marti, B. Marzeion, F. Paul, R. Le Bris, A. Hogg, Inès N. Otosaka, A. Shepherd, P. Döll, Denise Cáceres, Hannes Müller Schmied, J. Johannessen, J. Nilsen, R. Raj, R. Forsberg, L. Sandberg Sørensen, V. Barletta, S. Simonsen, P. Knudsen, O. Andersen, Heidi Randall, S. Rose, C. Merchant, C. Macintosh, K. von Schuckmann, K. Novotny, A. Groh, M. Restano, J. Benveniste","doi":"10.5194/ESSD-2021-137","DOIUrl":"https://doi.org/10.5194/ESSD-2021-137","url":null,"abstract":"Abstract. Studies of the global sea-level budget (SLB) and the global ocean-mass budget (OMB) are essential to assess the reliability of our knowledge of sea-level change and its contributions. Here we present datasets for times series of the SLB and OMB elements developed in the framework of ESA's Climate Change Initiative. We use these datasets to assess the SLB and the OMB simultaneously, utilising a consistent framework of uncertainty characterisation. The time series, given at monthly sampling, include global mean sea-level (GMSL) anomalies from satellite altimetry; the global mean steric component from Argo drifter data with incorporation of sea surface temperature data; the ocean mass component from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry; the contribution from global glacier mass changes assessed by a global glacier model; the contribution from Greenland Ice Sheet and Antarctic Ice Sheet mass changes, assessed from satellite radar altimetry and from GRACE; and the contribution from land water storage anomalies assessed by the WaterGAP global hydrological model. Over the period Jan 1993–Dec 2016 (P1, covered by the satellite altimetry records), the mean rate (linear trend) of GMSL is 3.05 ± 0.24 mm yr−1. The steric component is 1.15 ± 0.12 mm yr−1 (38 % of the GMSL trend) and the mass component is 1.75 ± 0.12 mm yr−1 (57 %). The mass component includes 0.64 ± 0.03 mm yr−1 (21 % of the GMSL trend) from glaciers outside Greenland and Antarctica, 0.60 ± 0.04 mm yr−1 (20 %) from Greenland, 0.19 ± 0.04 mm yr−1 (6 %) from Antarctica, and 0.32 ± 0.10 mm yr−1 (10 %) from changes of land water storage. In the period Jan 2003–Aug 2016 (P2, covered by GRACE and the Argo drifter system), GMSL rise is higher than in P1 at 3.64 ± 0.26 mm yr−1. This is due to an increase of the mass contributions (now about 2.22 ± 0.15 mm yr−1, 61 % of the GMSL trend), with the largest increase contributed from Greenland. The SLB of linear trends is closed for P1 and P2, that is, the GMSL trend agrees with the sum of the steric and mass components within their combined uncertainties. The OMB budget, which can be evaluated only for P2, is also closed, that is, the GRACE-based ocean-mass trend agrees with the sum of assessed mass contributions within uncertainties. Combined uncertainties (1-sigma) of the elements involved in the budgets are between 0.26 and 0.40 mm yr−1, about 10 % of GMSL rise. Interannual variations that overlie the long-term trends are coherently represented by the elements of the SLB and the OMB. Even at the level of monthly anomalies the budgets are closed within uncertainties, while also indicating possible origins of remaining misclosures.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131396412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qianfeng Wang, Rongrong Zhang, Yanping Qu, J. Zeng, Xiaoping Wu, Xiao-feng Zhou, Binyu Ren, Xiaohang Li, D. Zhou
Abstract. With the increasing shortage of water resources, drought has become one of the hot issues in the world. The standardized precipitation index (SPI) is one of the widely used drought assessment indicators because of its simple and effective calculation method, but it can only assess drought events more than one month. We developed a new multi-scale daily SPI dataset to make up for the shortcomings of the commonly used SPI and meet the needs of drought types at different time scales. Taking three typical stations in Henan, Yunnan and Fujian Province as examples, the drought events identified by SPI with different scales were consistent with the historical drought events recorded. Meanwhile, we took the 3-month scale SPI of soil and agricultural drought as an example, and analyzed the characteristics of drought events in 484 stations in Chinese mainland. The results showed that most of the drought events the mainland China did not increase significantly, and some parts of the northwestern Xinjiang and Northeast China showed signs of gradual relief. In short, our daily SPI data set is freely available to the public on the website https://doi.org/10.6084/m9.figshare.14135144 , and can effectively capture drought events of different scales. It can also meet the needs of drought research in different fields such as meteorology, hydrology, agriculture, social economy, etc.
{"title":"Daily standardized precipitation index with multiple time scale for monitoring water deficit across the mainland China from 1961 to 2018","authors":"Qianfeng Wang, Rongrong Zhang, Yanping Qu, J. Zeng, Xiaoping Wu, Xiao-feng Zhou, Binyu Ren, Xiaohang Li, D. Zhou","doi":"10.5194/ESSD-2021-105","DOIUrl":"https://doi.org/10.5194/ESSD-2021-105","url":null,"abstract":"Abstract. With the increasing shortage of water resources, drought has become one of the hot issues in the world. The standardized precipitation index (SPI) is one of the widely used drought assessment indicators because of its simple and effective calculation method, but it can only assess drought events more than one month. We developed a new multi-scale daily SPI dataset to make up for the shortcomings of the commonly used SPI and meet the needs of drought types at different time scales. Taking three typical stations in Henan, Yunnan and Fujian Province as examples, the drought events identified by SPI with different scales were consistent with the historical drought events recorded. Meanwhile, we took the 3-month scale SPI of soil and agricultural drought as an example, and analyzed the characteristics of drought events in 484 stations in Chinese mainland. The results showed that most of the drought events the mainland China did not increase significantly, and some parts of the northwestern Xinjiang and Northeast China showed signs of gradual relief. In short, our daily SPI data set is freely available to the public on the website https://doi.org/10.6084/m9.figshare.14135144 , and can effectively capture drought events of different scales. It can also meet the needs of drought research in different fields such as meteorology, hydrology, agriculture, social economy, etc.","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127291388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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