Abstract. The century-long continuous daily observations from some stations are important for the study of long-term trends and extreme climate events in the past. In this paper, three daily data sources: (1) Department of Industry Agency of British Concession in Tianjin covering Sep 1 1890–Dec 31 1931 (2) Water Conservancy Commission of North China covering Jan 1 1932–Dec 31 1950 and (3) monthly journal sheets for Tianjin surface meteorological observation records covering Jan 1 1951–Dec 31 2019 have been collected from the Tianjin Meteorological Archive. The completed daily maximum and minimum temperature series for Tianjin from Jan 1 1887 (Sep 1 1890 for minimum) to Dec 31 2019 has been constructed and assessed for quality control and an early extension from 1890 to 1887. Several significant breakpoints are detected by the Penalized Maximal T-test (PMT) for the daily maximum and minimum time series using multiple reference series around Tianjin from monthly Berkeley Earth, CRUTS4.03 and GHCNV3 data. Using neighboring daily series the record has been homogenized with Quantile Matching (QM) adjustments. Based on the homogenized dataset, the warming trend in annual mean temperature in Tianjin averaged from the newly constructed daily maximum and minimum temperature is evaluated as 0.154 ± 0.013 °C decade-1 during the last 130 years. Trends of temperature extremes in Tianjin are all significant at the 5 % level, and have much more coincident change than those from the raw, with amplitudes of −1.454 d decade−1, 1.196 d decade−1, −0.140 d decade−1 and 0.975 d decade−1 for cold nights (TN10p), warm nights (TN90p), cold days (TX10p) and warm days (TX90p) at the annual scale. The adjusted daily maximum, minimum and mean surface air temperature dataset for Tianjin city presented here is publicly available at https://doi.pangaea.de/10.1594/PANGAEA.924561 (Si and Li, 2020).
摘要一些台站长达一个世纪的连续日观测对研究过去的长期趋势和极端气候事件具有重要意义。本文利用天津气象资料馆收集的三个日常资料来源:(1)天津英租界工署1890年9月1日—1931年12月31日(2)华北水利委员会1932年1月1日—1950年12月31日(3)1951年1月1日—2019年12月31日的天津地面气象观测月报。建立了天津1887年1月1日(最低为1890年9月1日)至2019年12月31日的日最高和最低气温序列，并对其进行了质量控制评估，并对1890年至1887年的日最高和最低气温序列进行了早期扩展。利用每月Berkeley Earth、CRUTS4.03和GHCNV3数据对天津周边多个参考序列进行了日最大和最小时间序列的惩罚极大t检验(PMT)，发现了几个显著的断点。利用相邻的日序列，用分位数匹配(QM)调整对记录进行均匀化。基于均质化数据，从新建日最高和最低气温计算得出近130 a来天津市年平均气温的增温趋势为0.154±0.013°C 10 -1。天津极端气温的变化趋势在5%水平上都很显著，且与原生地相比具有更强的一致性，年尺度上冷夜(TN10p)、暖夜(TN90p)、冷日(TX10p)和暖日(TX90p)的幅值分别为- 1.454 d 10年- 1、1.196 d 10年- 1、- 0.140 d 10年- 1和0.975 d 10年- 1。本文提供的天津市调整后的日最高、最低和平均地表气温数据集可在https://doi.pangaea.de/10.1594/PANGAEA.924561上公开获取(Si和Li, 2020)。
{"title":"Construction of homogenized daily surface air temperature for Tianjin city during 1887–2019","authors":"Peng Si, Qingxiang Li, P. Jones","doi":"10.5194/ESSD-2020-343","DOIUrl":"https://doi.org/10.5194/ESSD-2020-343","url":null,"abstract":"Abstract. The century-long continuous daily observations from some stations are important for the study of long-term trends and extreme climate events in the past. In this paper, three daily data sources: (1) Department of Industry Agency of British Concession in Tianjin covering Sep 1 1890–Dec 31 1931 (2) Water Conservancy Commission of North China covering Jan 1 1932–Dec 31 1950 and (3) monthly journal sheets for Tianjin surface meteorological observation records covering Jan 1 1951–Dec 31 2019 have been collected from the Tianjin Meteorological Archive. The completed daily maximum and minimum temperature series for Tianjin from Jan 1 1887 (Sep 1 1890 for minimum) to Dec 31 2019 has been constructed and assessed for quality control and an early extension from 1890 to 1887. Several significant breakpoints are detected by the Penalized Maximal T-test (PMT) for the daily maximum and minimum time series using multiple reference series around Tianjin from monthly Berkeley Earth, CRUTS4.03 and GHCNV3 data. Using neighboring daily series the record has been homogenized with Quantile Matching (QM) adjustments. Based on the homogenized dataset, the warming trend in annual mean temperature in Tianjin averaged from the newly constructed daily maximum and minimum temperature is evaluated as 0.154 ± 0.013 °C decade-1 during the last 130 years. Trends of temperature extremes in Tianjin are all significant at the 5 % level, and have much more coincident change than those from the raw, with amplitudes of −1.454 d decade−1, 1.196 d decade−1, −0.140 d decade−1 and 0.975 d decade−1 for cold nights (TN10p), warm nights (TN90p), cold days (TX10p) and warm days (TX90p) at the annual scale. The adjusted daily maximum, minimum and mean surface air temperature dataset for Tianjin city presented here is publicly available at https://doi.pangaea.de/10.1594/PANGAEA.924561 (Si and Li, 2020).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129062676","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. Quaternary deposits are complex and heterogeneous. They contain some of the most abundant and extensively used aquifers. In order to improve the knowledge of the spatial heterogeneity of such deposits, we acquired a large (more than 1400 hectares) and dense (20 m spacing) Time Domain ElectroMagnetic (TDEM) dataset in the upper Aare Valley, Switzerland. TDEM is a fast and reliable method to measure the magnetic field directly related to the resistivity of the underground. In this paper, we present the inverted resistivity models derived from this acquisition, and all the necessary data in order to perform different inversions on the processed data ( https://doi.org/10.5281/ZENODO.4269887 (Neven et al., 2020)). The depth of investigation ranges between 40 to 120 m depth, with an average data residual contained in the standard deviation of the data. These data can be used for many different purposes: from sedimentological interpretation of quaternary environments in alpine environments, geological and hydrogeological modeling, to benchmarking geophysical inversion techniques.
摘要第四纪沉积复杂而非均质。它们含有一些最丰富和最广泛使用的含水层。为了提高对此类矿床空间异质性的认识，我们在瑞士上Aare山谷获得了一个大型(超过1400公顷)和密集(20 m间距)的时域电磁(TDEM)数据集。TDEM是一种快速、可靠的测量与地下电阻率直接相关的磁场的方法。在本文中，我们介绍了从这次采集中获得的反演电阻率模型，以及所有必要的数据，以便对处理后的数据进行不同的反演(https://doi.org/10.5281/ZENODO.4269887 (Neven et al.， 2020))。调查深度在40 ~ 120m之间，数据标准差中包含平均数据残差。这些数据可以用于许多不同的目的:从第四纪环境的沉积学解释，地质和水文地质建模，到基准地球物理反演技术。
{"title":"tTEM20AAR: a benchmark geophysical dataset for unconsolidated\u0000fluvio-glacial sediments","authors":"A. Néven, P. Maurya, A. Christiansen, P. Renard","doi":"10.5194/ESSD-2020-390","DOIUrl":"https://doi.org/10.5194/ESSD-2020-390","url":null,"abstract":"Abstract. Quaternary deposits are complex and heterogeneous. They contain some of the most abundant and extensively used aquifers. In order to improve the knowledge of the spatial heterogeneity of such deposits, we acquired a large (more than 1400 hectares) and dense (20 m spacing) Time Domain ElectroMagnetic (TDEM) dataset in the upper Aare Valley, Switzerland. TDEM is a fast and reliable method to measure the magnetic field directly related to the resistivity of the underground. In this paper, we present the inverted resistivity models derived from this acquisition, and all the necessary data in order to perform different inversions on the processed data ( https://doi.org/10.5281/ZENODO.4269887 (Neven et al., 2020)). The depth of investigation ranges between 40 to 120 m depth, with an average data residual contained in the standard deviation of the data. These data can be used for many different purposes: from sedimentological interpretation of quaternary environments in alpine environments, geological and hydrogeological modeling, to benchmarking geophysical inversion techniques.","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132921052","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}
Joshua Lizundia-Loiola, M. Franquesa, M. Boettcher, G. Kirches, M. L. Pettinari, E. Chuvieco
Abstract. This paper presents a new global, operational burned area (BA) product at 300 m, called C3SBA10, generated from Sentinel-3 Ocean and Land Colour Instrument (OLCI) near-infrared (NIR) reflectance and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly data. This product was generated within the Copernicus Climate Change Service (C3S). Since C3S is a European service, it aims to use extensively the European Copernicus satellite missions, named Sentinels. Therefore, one of the components of the service is adapting previous developed algorithms to the Sentinel sensors. In the case of BA datasets, the precursor BA dataset (FireCCI51), which was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI), was based on the 250 m-resolution NIR band of the MODIS sensor, and the effort has been focused on adapting this BA algorithm to the characteristics of the Sentinel-3 OLCI sensor, which provides similar spatial and temporal resolution to MODIS. As the precursor BA algorithm, the OLCI's one combines thermal anomalies and spectral information in a two-phase approach, where first thermal anomalies with a high probability of being burned are selected, reducing commission errors, and then a contextual growing is applied to fully detect the BA patch, reducing omission errors. The new BA product includes the full time-series of S3 OLCI data (2017–present). Following the specifications of the FireCCI project, the final datasets are provided in two different formats: monthly full-resolution continental tiles, and monthly global files with aggregated data at 0.25-degree resolution. To facilitate the use by global vegetation dynamics and atmospheric emission models several auxiliary layers were included, such as land cover and cloud-free observations. The C3SBA10 product detected 3.77 Mkm2, 3.59 Mkm2, and 3.63 Mkm2 of annual BA from 2017 to 2019, respectively. The quality and consistency assessment of C3SBA10 and the precursor FireCCI51 was done for the common period (2017–2019). The global spatial validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 14–22 % and omission errors from 50 to 53 %, similar to those presented by the FireCCI51 product. The temporal reporting accuracy was also validated using 4.7 million active fires. 88 % of the detections were made within 10 days after the fire by both products. The spatial and temporal consistency assessment performed between C3SBA10 and FireCCI51 using four different grid sizes (0.05o, 0.10o, 0.25o, and 0.50o) showed global, annual correlations between 0.93 and 0.99. This high consistency between both products ensures a global BA data provision from 2001 to present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository (DOI: https://doi.org/10.24381/cds.f333cf85 , Lizundia-Loiola et al. (2020a)).
{"title":"Operational implementation of the burned area component of the\u0000Copernicus Climate Change Service: from MODIS 250 m to OLCI\u0000300 m data","authors":"Joshua Lizundia-Loiola, M. Franquesa, M. Boettcher, G. Kirches, M. L. Pettinari, E. Chuvieco","doi":"10.5194/ESSD-2020-399","DOIUrl":"https://doi.org/10.5194/ESSD-2020-399","url":null,"abstract":"Abstract. This paper presents a new global, operational burned area (BA) product at 300 m, called C3SBA10, generated from Sentinel-3 Ocean and Land Colour Instrument (OLCI) near-infrared (NIR) reflectance and Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly data. This product was generated within the Copernicus Climate Change Service (C3S). Since C3S is a European service, it aims to use extensively the European Copernicus satellite missions, named Sentinels. Therefore, one of the components of the service is adapting previous developed algorithms to the Sentinel sensors. In the case of BA datasets, the precursor BA dataset (FireCCI51), which was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI), was based on the 250 m-resolution NIR band of the MODIS sensor, and the effort has been focused on adapting this BA algorithm to the characteristics of the Sentinel-3 OLCI sensor, which provides similar spatial and temporal resolution to MODIS. As the precursor BA algorithm, the OLCI's one combines thermal anomalies and spectral information in a two-phase approach, where first thermal anomalies with a high probability of being burned are selected, reducing commission errors, and then a contextual growing is applied to fully detect the BA patch, reducing omission errors. The new BA product includes the full time-series of S3 OLCI data (2017–present). Following the specifications of the FireCCI project, the final datasets are provided in two different formats: monthly full-resolution continental tiles, and monthly global files with aggregated data at 0.25-degree resolution. To facilitate the use by global vegetation dynamics and atmospheric emission models several auxiliary layers were included, such as land cover and cloud-free observations. The C3SBA10 product detected 3.77 Mkm2, 3.59 Mkm2, and 3.63 Mkm2 of annual BA from 2017 to 2019, respectively. The quality and consistency assessment of C3SBA10 and the precursor FireCCI51 was done for the common period (2017–2019). The global spatial validation was performed using reference data derived from Landsat-8 images, following a stratified random sampling design. The C3SBA10 showed commission errors between 14–22 % and omission errors from 50 to 53 %, similar to those presented by the FireCCI51 product. The temporal reporting accuracy was also validated using 4.7 million active fires. 88 % of the detections were made within 10 days after the fire by both products. The spatial and temporal consistency assessment performed between C3SBA10 and FireCCI51 using four different grid sizes (0.05o, 0.10o, 0.25o, and 0.50o) showed global, annual correlations between 0.93 and 0.99. This high consistency between both products ensures a global BA data provision from 2001 to present. The datasets are freely available through the Copernicus Climate Data Store (CDS) repository (DOI: https://doi.org/10.24381/cds.f333cf85 , Lizundia-Loiola et al. (2020a)).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132017981","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. Seismologists and geoscientists in general often need earthquake catalogues for various types of research. This input usually contains basic earthquake parameters such as location (longitude, latitude, depth and origin time) as well as magnitude information. For the latter, the moment magnitude Mw has became the most sought after magnitude scale in the seismological community to characterize the size of an earthquake. In this contribution we provide an informative account of the Mw content for the newly rebuilt Bulletin of the International Seismological Centre (ISC, http://www.isc.ac.uk/), which is regarded as the most comprehensive record of the Earth's seismicity. From it, we extracted a list of hypocentres with Mw from a multitude of agencies reporting data to the ISC. We first summarize the main temporal-spatial features of the Mw provided by global agencies (i.e., providing results for moderate to great earthquakes worldwide) and regional ones (i.e., also providing results or small earthquakes in a specific area). Then we discuss their comparisons, not only by considering Mw but also the surface wave magnitude MS and short-period body wave magnitude mb. By using the Global Centroid Moment Tensor solutions as authoritative global agency, we identify regional agencies that best complement it and show examples of frequency-magnitude distributions in different areas obtained both from the Global Centroid Moment Tensor alone and complemented by Mw from regional agencies. The work done by the regional agencies in terms of Mw is fundamental to improve our understanding of the seismicity of an area and we call for the implementation of procedures to compute Mw in a systematic way in areas currently not well covered in this respect, such as vast parts of continental Asia and Africa. In addition, more studies are needed to clarify the causes of the apparent overestimation of global Mw estimations compared to regional Mw. Such difference is also observed in the comparisons of Mw with MS and mb. The results presented here are obtained from the dataset (Di Giacomo and Harris, 2020, https://doi.org/10.31905/J2W2M64S) stored at the ISC Dataset Repository (http://www.isc.ac.uk/dataset_repository/).
{"title":"Complementing regional moment magnitudes to GCMT: a\u0000perspective from the rebuilt ISC Bulletin","authors":"D. Di Giacomo, James Harris, D. Storchak","doi":"10.5194/ESSD-2020-371","DOIUrl":"https://doi.org/10.5194/ESSD-2020-371","url":null,"abstract":"Abstract. Seismologists and geoscientists in general often need earthquake catalogues for various types of research. This input usually contains basic earthquake parameters such as location (longitude, latitude, depth and origin time) as well as magnitude information. For the latter, the moment magnitude Mw has became the most sought after magnitude scale in the seismological community to characterize the size of an earthquake. In this contribution we provide an informative account of the Mw content for the newly rebuilt Bulletin of the International Seismological Centre (ISC, http://www.isc.ac.uk/), which is regarded as the most comprehensive record of the Earth's seismicity. From it, we extracted a list of hypocentres with Mw from a multitude of agencies reporting data to the ISC. We first summarize the main temporal-spatial features of the Mw provided by global agencies (i.e., providing results for moderate to great earthquakes worldwide) and regional ones (i.e., also providing results or small earthquakes in a specific area). Then we discuss their comparisons, not only by considering Mw but also the surface wave magnitude MS and short-period body wave magnitude mb. By using the Global Centroid Moment Tensor solutions as authoritative global agency, we identify regional agencies that best complement it and show examples of frequency-magnitude distributions in different areas obtained both from the Global Centroid Moment Tensor alone and complemented by Mw from regional agencies. The work done by the regional agencies in terms of Mw is fundamental to improve our understanding of the seismicity of an area and we call for the implementation of procedures to compute Mw in a systematic way in areas currently not well covered in this respect, such as vast parts of continental Asia and Africa. In addition, more studies are needed to clarify the causes of the apparent overestimation of global Mw estimations compared to regional Mw. Such difference is also observed in the comparisons of Mw with MS and mb. The results presented here are obtained from the dataset (Di Giacomo and Harris, 2020, https://doi.org/10.31905/J2W2M64S) stored at the ISC Dataset Repository (http://www.isc.ac.uk/dataset_repository/).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134258609","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}
F. Dottori, L. Alfieri, A. Bianchi, J. Skøien, P. Salamon
Abstract. Continental scale hazard maps for riverine floods have grown in importance in the last years. Nowadays, they are used for a variety of research and commercial activities, such as evaluating present and future risk scenarios and adaptation strategies, as well as a support of national and local flood risk management plans. Here, we present a new set of hazard maps for river flooding that covers most of the geographical Europe and all the river basins entering the Mediterranean and Black Seas in the Caucasus, Middle East and Northern Africa countries. Maps represent inundation along 329’000 km of river network at 100 m resolution, for six different flood return periods. The input river flow data is produced by the hydrological model LISFLOOD, while inundation simulations are performed with the 2D hydrodynamic modelling LISFLOOD-FP. To provide an overview of the skill of the new maps, we undertake a detailed validation exercise of the new maps using official hazard maps for Hungary, Italy, Norway, Spain and the United Kingdom. We find that modelled maps can identify on average two-thirds of reference flood extent, however they also overestimate flood-prone areas for flood probabilities below 1-in-100-year, while for return periods equal or above 500 years the maps can correctly identify more than half of flooded areas. We attribute the observed skill to a number of shortcomings of the modelling framework, such as the absence of flood protections and rivers with upstream area below 500 km2, and the limitations in representing river channels and topography of low land areas. In addition, the large variability of reference maps affects the correct identification of the areas for the validation, thus penalizing scores. However, modelled maps achieve comparable results to existing large-scale flood models when using similar parameters for the validation. We conclude that recently released high-resolution elevation datasets combined with reliable data of river channel geometry may greatly contribute to improve future versions of continental-scale flood hazard maps. The database is available for download at https://data.jrc.ec.europa.eu/dataset/1d128b6c-a4ee-4858-9e34-6210707f3c81 (Dottori et al., 2020a).
摘要在过去的几年里，大陆尺度的河流洪水危险地图变得越来越重要。如今，它们被用于各种研究和商业活动，例如评估当前和未来的风险情景和适应战略，以及支持国家和地方洪水风险管理计划。在这里，我们提出了一套新的河流洪水危害图，涵盖了欧洲大部分地区以及高加索、中东和北非国家进入地中海和黑海的所有河流流域。地图以100米的分辨率显示了329,000公里河网的六个不同的洪水回潮期的淹没情况。输入的河流流量数据由水文模型LISFLOOD生成，而洪水模拟使用二维水动力模型LISFLOOD- fp进行。为了对新地图的技能进行概述，我们使用匈牙利、意大利、挪威、西班牙和英国的官方危险地图对新地图进行了详细的验证。我们发现，模拟地图平均可以识别参考洪水范围的三分之二，然而，对于洪水概率低于100年1次的地区，它们也高估了洪水易发地区，而对于回复期等于或高于500年的地区，地图可以正确识别超过一半的洪水地区。我们将观察到的技能归因于建模框架的一些缺点，例如缺乏防洪设施和上游面积低于500平方公里的河流，以及在表示河道和低地地区地形方面的局限性。此外，参考地图的巨大可变性影响了验证区域的正确识别，从而降低了分数。然而，当使用相似的参数进行验证时，模拟地图的结果与现有的大尺度洪水模型相当。我们的结论是，最近发布的高分辨率高程数据集与可靠的河道几何数据相结合，可能极大地有助于改进未来大陆尺度洪水灾害图的版本。该数据库可从https://data.jrc.ec.europa.eu/dataset/1d128b6c-a4ee-4858-9e34-6210707f3c81下载(Dottori et al.， 2020a)。
{"title":"A new dataset of river flood hazard maps for Europe and \u0000the Mediterranean Basin region","authors":"F. Dottori, L. Alfieri, A. Bianchi, J. Skøien, P. Salamon","doi":"10.5194/ESSD-2020-313","DOIUrl":"https://doi.org/10.5194/ESSD-2020-313","url":null,"abstract":"Abstract. Continental scale hazard maps for riverine floods have grown in importance in the last years. Nowadays, they are used for a variety of research and commercial activities, such as evaluating present and future risk scenarios and adaptation strategies, as well as a support of national and local flood risk management plans. Here, we present a new set of hazard maps for river flooding that covers most of the geographical Europe and all the river basins entering the Mediterranean and Black Seas in the Caucasus, Middle East and Northern Africa countries. Maps represent inundation along 329’000 km of river network at 100 m resolution, for six different flood return periods. The input river flow data is produced by the hydrological model LISFLOOD, while inundation simulations are performed with the 2D hydrodynamic modelling LISFLOOD-FP. To provide an overview of the skill of the new maps, we undertake a detailed validation exercise of the new maps using official hazard maps for Hungary, Italy, Norway, Spain and the United Kingdom. We find that modelled maps can identify on average two-thirds of reference flood extent, however they also overestimate flood-prone areas for flood probabilities below 1-in-100-year, while for return periods equal or above 500 years the maps can correctly identify more than half of flooded areas. We attribute the observed skill to a number of shortcomings of the modelling framework, such as the absence of flood protections and rivers with upstream area below 500 km2, and the limitations in representing river channels and topography of low land areas. In addition, the large variability of reference maps affects the correct identification of the areas for the validation, thus penalizing scores. However, modelled maps achieve comparable results to existing large-scale flood models when using similar parameters for the validation. We conclude that recently released high-resolution elevation datasets combined with reliable data of river channel geometry may greatly contribute to improve future versions of continental-scale flood hazard maps. The database is available for download at https://data.jrc.ec.europa.eu/dataset/1d128b6c-a4ee-4858-9e34-6210707f3c81 (Dottori et al., 2020a).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116874086","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}
T. Doumbia, C. Granier, N. Elguindi, I. Bouarar, S. Darras, G. Brasseur, B. Gaubert, Yiming Liu, Xiaoqing Shi, T. Stavrakou, S. Tilmes, F. Lacey, A. Deroubaix, Tao Wang
Abstract. In order to fight the spread of the global COVID-19 pandemic, most of the world countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to global and regional emission inventories has been developed. This dataset provides, for the January–August 2020 period, gridded AFs at a 0.1 × 0.1 latitude/longitude degree resolution, on a daily or monthly basis for the transportation (road, air and ship traffic), power generation, industry and residential sectors. The quantification of AFs is based on activity data collected from different databases and previously published studies. A range of AFs is provided at each grid point for model sensitivity studies. The emission AFs developed in this study are applied to the CAMS global inventory (CAMS-GLOB-ANT_v4.2_R1.1), and the changes in emissions of the main pollutants are discussed for different regions of the world and the first six months of 2020. Maximum decreases in the emissions are found in February in Eastern China, with an average reduction of 20–30 % in NOx, NMVOCs and SO2 relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20–30 % for NOx, NMVOCs and CO in Europe and North America and larger decreases (30–50 %) in South America. In India and African regions, NOx and NMVOCs emissions are reduced by 15–30 %. For the others species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC are estimated. As discussed in the paper, reductions vary highly across regions and sectors, due to the differences in the duration of the lockdowns before partial or complete recovery. The dataset providing a range of AFs (average and average ± standard deviation) is called CONFORM (COvid adjustmeNt Factor fOR eMissions) (https://doi.org/10.25326/88). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/).
{"title":"Changes in global air pollutant emissions during the COVID-19\u0000pandemic: a dataset for atmospheric chemistry modeling","authors":"T. Doumbia, C. Granier, N. Elguindi, I. Bouarar, S. Darras, G. Brasseur, B. Gaubert, Yiming Liu, Xiaoqing Shi, T. Stavrakou, S. Tilmes, F. Lacey, A. Deroubaix, Tao Wang","doi":"10.5194/ESSD-2020-348","DOIUrl":"https://doi.org/10.5194/ESSD-2020-348","url":null,"abstract":"Abstract. In order to fight the spread of the global COVID-19 pandemic, most of the world countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to global and regional emission inventories has been developed. This dataset provides, for the January–August 2020 period, gridded AFs at a 0.1 × 0.1 latitude/longitude degree resolution, on a daily or monthly basis for the transportation (road, air and ship traffic), power generation, industry and residential sectors. The quantification of AFs is based on activity data collected from different databases and previously published studies. A range of AFs is provided at each grid point for model sensitivity studies. The emission AFs developed in this study are applied to the CAMS global inventory (CAMS-GLOB-ANT_v4.2_R1.1), and the changes in emissions of the main pollutants are discussed for different regions of the world and the first six months of 2020. Maximum decreases in the emissions are found in February in Eastern China, with an average reduction of 20–30 % in NOx, NMVOCs and SO2 relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20–30 % for NOx, NMVOCs and CO in Europe and North America and larger decreases (30–50 %) in South America. In India and African regions, NOx and NMVOCs emissions are reduced by 15–30 %. For the others species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC are estimated. As discussed in the paper, reductions vary highly across regions and sectors, due to the differences in the duration of the lockdowns before partial or complete recovery. The dataset providing a range of AFs (average and average ± standard deviation) is called CONFORM (COvid adjustmeNt Factor fOR eMissions) (https://doi.org/10.25326/88). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121582877","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 climate data and high-quality baseline climatology surface with high resolution are highly essential to multiple fields in climatological, ecological, hydrological, and environmental sciences. Here, we created a brand-new baseline climatology surface (ChinaClim_baseline) and developed a 1 km monthly precipitation and temperatures dataset in China during 1952–2019 (ChinaClim_timeseries). Thin plate spline (TPS) algorithm in each month with different model formulations by accounting for satellite-driven products, was used to generate ChinaClim_baseline and monthly climate anomaly surface. Meanwhile, climatologically aided interpolation (CAI) was used to superimpose monthly anomaly surface with ChinaClim_baseline to generate ChinaClim_timeseries. Our results showed that ChinaClim_baseline exhibited very high performance. For precipitation estimation, the value of all R2 was over 0.860, and the values of RMSEs and MAEs were 8.149 mm~21.959 mm and 2.787~14.125 mm, respectively. Temperature elements had an average R2 of 0.967~0.992, an average MAEs of 0.321~0.785 °C, and an average RMSEs between 0.485 and 1.233 °C for all months. ChinaClim_baseline performed much better than WorldClim2 and CHELSA and there were many spatial discrepancies captured among those surfaces, especially in summer months and the regions with low-density weather stations in temperate continental and high cold Tibetan Plateau. For ChinaClim_timeseries, precipitation had an average R2 of 0.699~0.923, an average RMSE between 7.449 mm and 56.756 mm, and an average of MAE of 4.263~40.271 mm for all months. Temperature elements had an average R2 of 0.936~0.985, an average RMSE between 0.807 °C and 1.766 °C, and an average MAE of 0.548~1.236 °C for all months. Compared with Peng's climate surface and CHELSAcruts, R2 increased by approximately 6 %, RMSE and MAE decreased by approximately 15 % for precipitation; R2 of temperatures had no obviously changes, but RMSE and MAE decreased by 8.37~34.02 %. The results showed that the interannual variations of ChinaClim_timeseries performed much better than other datasets, thanks to the help of ChinaClim_baseline and satellite-driven products. However, ChinaClim_baseline did not significantly improve the accuracy of precipitation estimation, but it greatly improved the accuracy of temperature estimation; the satellite-driven TRMM3B43 anomaly greatly improve the accuracy of precipitation estimation after 1998, while the LST anomaly did not effectively improve the accuracy of temperature estimation. ChinaClim_baseline can be used as an excellent baseline climatology surface for obtaining high-quality and long-term climate datasets from past to future. In the meantime, ChinaClim_timeseries of 1 km spatial resolution based on ChinaClim_baseline, is very suitable for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China. Here, ChinaClim_baseline is available at https://doi.org/1
{"title":"1 km Monthly Precipitation and Temperatures Dataset for China from 1952 to 2019 based on a Brand-New and High-Quality Baseline Climatology Surface","authors":"Haibo Gong, Xueqiao Xiang, Huiyu Liu, Xiaojuan Xu, Fusheng Jiao, Zhen-shan Lin","doi":"10.5194/ESSD-2020-361","DOIUrl":"https://doi.org/10.5194/ESSD-2020-361","url":null,"abstract":"Abstract. Long-term climate data and high-quality baseline climatology surface with high resolution are highly essential to multiple fields in climatological, ecological, hydrological, and environmental sciences. Here, we created a brand-new baseline climatology surface (ChinaClim_baseline) and developed a 1 km monthly precipitation and temperatures dataset in China during 1952–2019 (ChinaClim_timeseries). Thin plate spline (TPS) algorithm in each month with different model formulations by accounting for satellite-driven products, was used to generate ChinaClim_baseline and monthly climate anomaly surface. Meanwhile, climatologically aided interpolation (CAI) was used to superimpose monthly anomaly surface with ChinaClim_baseline to generate ChinaClim_timeseries. Our results showed that ChinaClim_baseline exhibited very high performance. For precipitation estimation, the value of all R2 was over 0.860, and the values of RMSEs and MAEs were 8.149 mm~21.959 mm and 2.787~14.125 mm, respectively. Temperature elements had an average R2 of 0.967~0.992, an average MAEs of 0.321~0.785 °C, and an average RMSEs between 0.485 and 1.233 °C for all months. ChinaClim_baseline performed much better than WorldClim2 and CHELSA and there were many spatial discrepancies captured among those surfaces, especially in summer months and the regions with low-density weather stations in temperate continental and high cold Tibetan Plateau. For ChinaClim_timeseries, precipitation had an average R2 of 0.699~0.923, an average RMSE between 7.449 mm and 56.756 mm, and an average of MAE of 4.263~40.271 mm for all months. Temperature elements had an average R2 of 0.936~0.985, an average RMSE between 0.807 °C and 1.766 °C, and an average MAE of 0.548~1.236 °C for all months. Compared with Peng's climate surface and CHELSAcruts, R2 increased by approximately 6 %, RMSE and MAE decreased by approximately 15 % for precipitation; R2 of temperatures had no obviously changes, but RMSE and MAE decreased by 8.37~34.02 %. The results showed that the interannual variations of ChinaClim_timeseries performed much better than other datasets, thanks to the help of ChinaClim_baseline and satellite-driven products. However, ChinaClim_baseline did not significantly improve the accuracy of precipitation estimation, but it greatly improved the accuracy of temperature estimation; the satellite-driven TRMM3B43 anomaly greatly improve the accuracy of precipitation estimation after 1998, while the LST anomaly did not effectively improve the accuracy of temperature estimation. ChinaClim_baseline can be used as an excellent baseline climatology surface for obtaining high-quality and long-term climate datasets from past to future. In the meantime, ChinaClim_timeseries of 1 km spatial resolution based on ChinaClim_baseline, is very suitable for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China. Here, ChinaClim_baseline is available at https://doi.org/1","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133397767","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}
E. Martini, Simon Kögler, M. Kreck, K. Roth, U. Werban, U. Wollschläger, S. Zacharias
Abstract. The Schafertal hillslope site is part of the TERENO Harz/Central German Lowland Observatory and its soil water dynamics is being monitored intensively as part of an integrated, long-term, multi-scale and multi-temporal research framework linking hydrological, pedological, atmospheric and biodiversity-related research to investigate the influences of climate and land use change on the terrestrial system. Here, a new soil monitoring network, indicated as STH-net, has been recently implemented to provide high-resolution data about the most relevant hydrological variables and local soil properties. The monitoring network is spatially optimized, based on previous knowledge from soil mapping and soil moisture monitoring, in order to capture the spatial variability of soil properties and soil water dynamics along a catena across the site as well as in depth. The STH-net comprises eight stations instrumented with time-domain reflectometry (TDR) probes, soil temperature probes and piezometers. Furthermore, a weather station provides data about the meteorological variables. A detailed soil characterization exists for locations where the TDR probes are installed. All data are measured at a 10-minutes interval since January 1st, 2019. The STH-net is intended to provide scientists with high-quality data needed for developing and testing modelling approaches in the context of vadose-zone hydrology at spatial scales ranging from the pedon to the hillslope. The data are available from the EUDAT portal ( https://b2share.eudat.eu/records/e2a2135bb1634a97abcedf8a461c0909 ) (Martini et al., 2020).
摘要Schafertal山坡场址是TERENO Harz/德国中部低地观测站的一部分，其土壤水动态正在作为一个综合、长期、多尺度和多时间研究框架的一部分进行密切监测，该框架将水文、土壤学、大气和与生物多样性有关的研究联系起来，以调查气候和土地利用变化对陆地系统的影响。在这里，最近实施了一个新的土壤监测网络，称为STH-net，以提供有关最相关的水文变量和当地土壤性质的高分辨率数据。监测网络在空间上进行了优化，基于以前的土壤制图和土壤湿度监测知识，以便捕捉整个场地以及深度上的土壤性质和土壤水动力学的空间变异性。地网包括8个台站，配备时域反射探针、土壤温度探针和压力计。此外，气象站提供有关气象变量的数据。对于安装TDR探针的地点，存在详细的土壤特征。所有数据自2019年1月1日起每隔10分钟测量一次。STH-net旨在为科学家提供所需的高质量数据，以便在从土壤到山坡的空间尺度上开发和测试水汽带水文学背景下的建模方法。数据可从EUDAT门户网站(https://b2share.eudat.eu/records/e2a2135bb1634a97abcedf8a461c0909)获得(Martini et al.， 2020)。
{"title":"STH-net: a model-driven soil monitoring network for process-based \u0000hydrological modelling from the pedon to the hillslope scale","authors":"E. Martini, Simon Kögler, M. Kreck, K. Roth, U. Werban, U. Wollschläger, S. Zacharias","doi":"10.5194/essd-2020-363","DOIUrl":"https://doi.org/10.5194/essd-2020-363","url":null,"abstract":"Abstract. The Schafertal hillslope site is part of the TERENO Harz/Central German Lowland Observatory and its soil water dynamics is being monitored intensively as part of an integrated, long-term, multi-scale and multi-temporal research framework linking hydrological, pedological, atmospheric and biodiversity-related research to investigate the influences of climate and land use change on the terrestrial system. Here, a new soil monitoring network, indicated as STH-net, has been recently implemented to provide high-resolution data about the most relevant hydrological variables and local soil properties. The monitoring network is spatially optimized, based on previous knowledge from soil mapping and soil moisture monitoring, in order to capture the spatial variability of soil properties and soil water dynamics along a catena across the site as well as in depth. The STH-net comprises eight stations instrumented with time-domain reflectometry (TDR) probes, soil temperature probes and piezometers. Furthermore, a weather station provides data about the meteorological variables. A detailed soil characterization exists for locations where the TDR probes are installed. All data are measured at a 10-minutes interval since January 1st, 2019. The STH-net is intended to provide scientists with high-quality data needed for developing and testing modelling approaches in the context of vadose-zone hydrology at spatial scales ranging from the pedon to the hillslope. The data are available from the EUDAT portal ( https://b2share.eudat.eu/records/e2a2135bb1634a97abcedf8a461c0909 ) (Martini et al., 2020).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"141 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134063721","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. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China Land Cover Dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics of China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China’s Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Using 335,709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial-temporal filtering and logical reasoning to further improve the spatial-temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5,463 visually-interpreted samples. A further assessment based on 5,131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC, and GlobaLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China’s LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease of cropland (−4.85 %) and grassland (−3.29 %), increase of forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g., Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at http://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).
摘要土地覆盖(LC)决定了地球各圈之间的能量交换、水和碳循环。准确的LC信息是环境和气候研究的基本参数。由于近几十年来中国的土地利用状况随着经济的发展发生了巨大的变化，因此迫切需要对土地利用状况进行连续的、精细的监测。然而，由于缺乏足够的训练样本和计算能力，目前中国通常无法获得由观测图像生成的高分辨率年度LC数据集。为了解决这一问题，我们在谷歌地球引擎(GEE)平台上制作了第一个基于landsat的年度中国土地覆盖数据集(CLCD)，该数据集包含了1990 - 2019年中国的30 m年度土地覆盖及其动态。首先，我们将中国土地利用/覆盖数据集(CLUD)提取的稳定样本与卫星时间序列数据、谷歌地球和谷歌地图的视觉解释样本相结合，收集训练样本。利用335709张陆地卫星遥感影像，构建了多个时间尺度，并将其输入随机森林分类器，得到分类结果。在此基础上，提出了一种结合时空滤波和逻辑推理的后处理方法，进一步提高了CLCD的时空一致性。最后，基于5463个目视解译样本，CLCD的总体准确率达到79.31%。基于5131个第三方测试样本的进一步评估表明，CLCD的整体准确性优于MCD12Q1、ESACCI_LC、FROM_GLC和GlobaLand30。此外，我们还将CLCD与多个landsat衍生专题产品进行了对比，结果表明CLCD与全球森林变化、全球地表水和三个不透水表面产品具有良好的一致性。基于CLCD，揭示了1985 - 2019年中国LC的变化趋势和格局，主要表现为不透水面(+ 148.71%)和水(+ 18.39%)的扩大，耕地(- 4.85%)和草地(- 3.29%)的减少，森林(+ 4.34%)的增加。总体而言，CLCD反映了中国快速城市化和一系列生态工程(如Gain for Green)，揭示了气候变化条件下LC的人为影响，表明其在全球变化研究中的潜在应用价值。本文介绍的CLCD数据集可在http://doi.org/10.5281/zenodo.4417810免费获得(Yang and Huang, 2021)。
{"title":"30 m annual land cover and its dynamics in China from 1990 to 2019","authors":"Jie Yang, Xin Huang","doi":"10.5194/ESSD-2021-7","DOIUrl":"https://doi.org/10.5194/ESSD-2021-7","url":null,"abstract":"Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China Land Cover Dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics of China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China’s Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Using 335,709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial-temporal filtering and logical reasoning to further improve the spatial-temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5,463 visually-interpreted samples. A further assessment based on 5,131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC, and GlobaLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China’s LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease of cropland (−4.85 %) and grassland (−3.29 %), increase of forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g., Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at http://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129708471","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. High quality and long-term soil moisture productions are significant for hydrologic monitoring and agricultural management. However, the acquired daily soil moisture productions are incomplete in global land (just about 30 %∼80 % coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieving algorithms. To solve this inevitable problem, we develop a novel 3D spatio-temporal partial convolutional neural network (CNN) for Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture productions gap-filling. Through the proposed framework, we generate the seamless global daily (SGD) AMSR2 soil moisture long-term productions from 2013 to 2019. To further validate the effectiveness of these productions, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. Results show that the seamless global daily soil moisture productions have reliable cooperativity with the selected in-situ values. The evaluation indexes of the reconstructed (original) dataset are R: 0.683 (0.687), RMSE: 0.099 m3/m3 (0.095 m3/m3), and MAE: 0.081 m3/m3 (0.078 m3/m3), respectively. Temporal consistency of the reconstructed daily soil moisture productions is ensured with the original time-series distribution of valid values. Besides, the spatial continuity of the reconstructed regions is also accorded with the context information (R: 0.963∼0.974, RMSE: 0.065∼0.073 m3/m3, and MAE: 0.044∼0.052 m3/m3). More details of this work are released at https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2/ . This dataset can be downloaded at https://zenodo.org/record/3960425 (Zhang et al., 2020. DOI: https://doi.org/10.5281/zenodo.3960425 ).
{"title":"SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Products (2013-2019)","authors":"Qiang Zhang, Q. Yuan, Jie Li, Yuanhong Wang, Fujun Sun, Liangpei Zhang","doi":"10.5281/ZENODO.3960425","DOIUrl":"https://doi.org/10.5281/ZENODO.3960425","url":null,"abstract":"Abstract. High quality and long-term soil moisture productions are significant for hydrologic monitoring and agricultural management. However, the acquired daily soil moisture productions are incomplete in global land (just about 30 %∼80 % coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieving algorithms. To solve this inevitable problem, we develop a novel 3D spatio-temporal partial convolutional neural network (CNN) for Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture productions gap-filling. Through the proposed framework, we generate the seamless global daily (SGD) AMSR2 soil moisture long-term productions from 2013 to 2019. To further validate the effectiveness of these productions, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. Results show that the seamless global daily soil moisture productions have reliable cooperativity with the selected in-situ values. The evaluation indexes of the reconstructed (original) dataset are R: 0.683 (0.687), RMSE: 0.099 m3/m3 (0.095 m3/m3), and MAE: 0.081 m3/m3 (0.078 m3/m3), respectively. Temporal consistency of the reconstructed daily soil moisture productions is ensured with the original time-series distribution of valid values. Besides, the spatial continuity of the reconstructed regions is also accorded with the context information (R: 0.963∼0.974, RMSE: 0.065∼0.073 m3/m3, and MAE: 0.044∼0.052 m3/m3). More details of this work are released at https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2/ . This dataset can be downloaded at https://zenodo.org/record/3960425 (Zhang et al., 2020. DOI: https://doi.org/10.5281/zenodo.3960425 ).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126686669","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：