Zhen-Chun Hao, Jiaqi Jin, Runliang Xia, Shimin Tian, Wushuang Yang, Qixing Liu, Min Zhu, Tao Ma, Chen Jing
Abstract. We introduce the first large-scale catchment attributes and meteorological time series dataset of contiguous China. To develop the dataset, we compiled diverse data sources to generate basin-oriented features describing the characteristics of the catchment related to hydrological processes. The proposed dataset consists of catchment characteristics including soil, land cover, climate, topography, geology, and 29-year meteorological time series (from 1990 to 2018). The meteorological variables include precipitation, temperature, evapotranspiration, wind speed, ground surface temperature, pressure, humidity and sunshine duration. We also derived a daily potential evapotranspiration time series based on a modified Penman’s equation. The studied catchments are 4875 catchments within contiguous China derived from digital elevation models. The spatial variations of catchment characteristics are analysed and organized into a series of maps; the correlation analysis between attributes was conducted. Compared to the previously proposed datasets, we derived more catchment characteristics resulting in a total of 127 attributes, providing a complete description of the catchments. Besides, we propose Normal-Camels-YR, a hydrological dataset covering 102 basins of the Yellow River basin with normalized streamflow observations. The proposed dataset provides numerous opportunities for comparative hydrological research, such as examining the difference in hydrological behaviours across different catchments and building general rainfall-runoff modelling frameworks for many catchments instead of limited to a few. The dataset is freely available via http://doi.org/10.5281/zenodo.4704017 for community use. We will open-source the complement code for generating the dataset such that the user can generate meteorological series and catchment attributes for any watershedwithin contiguous China.
{"title":"Catchment attributes and meteorology for large sample study in contiguous China","authors":"Zhen-Chun Hao, Jiaqi Jin, Runliang Xia, Shimin Tian, Wushuang Yang, Qixing Liu, Min Zhu, Tao Ma, Chen Jing","doi":"10.5194/ESSD-2021-71","DOIUrl":"https://doi.org/10.5194/ESSD-2021-71","url":null,"abstract":"Abstract. We introduce the first large-scale catchment attributes and meteorological time series dataset of contiguous China. To develop the dataset, we compiled diverse data sources to generate basin-oriented features describing the characteristics of the catchment related to hydrological processes. The proposed dataset consists of catchment characteristics including soil, land cover, climate, topography, geology, and 29-year meteorological time series (from 1990 to 2018). The meteorological variables include precipitation, temperature, evapotranspiration, wind speed, ground surface temperature, pressure, humidity and sunshine duration. We also derived a daily potential evapotranspiration time series based on a modified Penman’s equation. The studied catchments are 4875 catchments within contiguous China derived from digital elevation models. The spatial variations of catchment characteristics are analysed and organized into a series of maps; the correlation analysis between attributes was conducted. Compared to the previously proposed datasets, we derived more catchment characteristics resulting in a total of 127 attributes, providing a complete description of the catchments. Besides, we propose Normal-Camels-YR, a hydrological dataset covering 102 basins of the Yellow River basin with normalized streamflow observations. The proposed dataset provides numerous opportunities for comparative hydrological research, such as examining the difference in hydrological behaviours across different catchments and building general rainfall-runoff modelling frameworks for many catchments instead of limited to a few. The dataset is freely available via http://doi.org/10.5281/zenodo.4704017 for community use. We will open-source the complement code for generating the dataset such that the user can generate meteorological series and catchment attributes for any watershedwithin contiguous China.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127397815","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}
Petra Zemunik, J. Šepić, Havu Pellikka, Leon Ćatipović, I. Vilibić
Abstract. Sea-level observations provide information on a variety of processes occurring over different temporal and spatial scales that may contribute to coastal flooding and hazards. However, global research of sea-level extremes is restricted to hourly datasets, which prevent quantification and analyses of processes occurring at timescales between a few minutes and a few hours. These shorter period processes, like seiches, meteotsunamis, infragravity and coastal waves, may even dominate in low-tidal basins. Therefore, a new global 1-minute sea-level dataset – MISELA (Minute Sea-Level Analysis) – has been developed, encompassing quality-checked records of nonseismic sea-level oscillations at tsunami timescales (T https://doi.org/10.14284/456 , Zemunik et al., 2021b). This paper describes data quality-control procedures applied to the MISELA dataset, world and regional coverage of tide-gauge sites and lengths of time-series. The dataset is appropriate for global, regional or local research of atmospherically-induced high-frequency sea-level oscillations, which should be included in the overall sea-level extremes assessments.
摘要海平面观测提供了在不同时间和空间尺度上发生的各种过程的信息，这些过程可能导致沿海洪水和灾害。然而，全球对海平面极端事件的研究仅限于每小时的数据集，这妨碍了对在几分钟到几小时的时间尺度上发生的过程进行量化和分析。这些较短周期的过程，如海啸、气象海啸、重力不足和海岸波，甚至可能在低潮盆地占主导地位。因此，我们开发了一个新的全球1分钟海平面数据集——MISELA (Minute sea-level Analysis，分钟海平面分析)，其中包含海啸时间尺度下非地震海平面振荡的质量检查记录(T https://doi.org/10.14284/456, Zemunik et al.， 2021b)。本文介绍了应用于MISELA数据集的数据质量控制程序，潮汐测量站点的世界和区域覆盖以及时间序列的长度。该数据集适用于全球、区域或地方的大气引起的高频海平面振荡研究，这应包括在总体海平面极端事件评估中。
{"title":"MISELA: 1-minute sea-level analysis global dataset","authors":"Petra Zemunik, J. Šepić, Havu Pellikka, Leon Ćatipović, I. Vilibić","doi":"10.5194/ESSD-2021-134","DOIUrl":"https://doi.org/10.5194/ESSD-2021-134","url":null,"abstract":"Abstract. Sea-level observations provide information on a variety of processes occurring over different temporal and spatial scales that may contribute to coastal flooding and hazards. However, global research of sea-level extremes is restricted to hourly datasets, which prevent quantification and analyses of processes occurring at timescales between a few minutes and a few hours. These shorter period processes, like seiches, meteotsunamis, infragravity and coastal waves, may even dominate in low-tidal basins. Therefore, a new global 1-minute sea-level dataset – MISELA (Minute Sea-Level Analysis) – has been developed, encompassing quality-checked records of nonseismic sea-level oscillations at tsunami timescales (T https://doi.org/10.14284/456 , Zemunik et al., 2021b). This paper describes data quality-control procedures applied to the MISELA dataset, world and regional coverage of tide-gauge sites and lengths of time-series. The dataset is appropriate for global, regional or local research of atmospherically-induced high-frequency sea-level oscillations, which should be included in the overall sea-level extremes assessments.","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130493652","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. According to the living data process in ESSD, this publication presents extensions of a comprehensive hydrometeorological and glaciological data set for several research sites in the Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria). Whereas the original dataset has been published in a first original version in 2018 (https://doi.org/10.5194/essd-10-151-2018), the new time series presented here originate from meteorological and snow-hydrological recordings that have been collected from 2017 to 2020. Some data sets represent continuations of time series at existing locations, others come from new installations complementing the scientific monitoring infrastructure in the research catchment. Main extensions are a fully equipped automatic weather and snow monitoring station, as well as extensive additional installations to enable continuous observation of snow cover properties. Installed at three high Alpine locations in the catchment, these include automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water content, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow redistribution. They are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. The data sets represent a unique time series of high-altitude mountain snow and meteorology observations. We present three years of data for temperature, precipitation, humidity, wind speed, and radiation fluxes from three meteorological stations. The continuous snow measurements are explored by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations are demonstrated with examples of measured wind speeds, snow drift rates and redistributed snow amounts in December 2019 when a tragic avalanche accident occurred in the vicinity of the station. All new data sets are provided to the scientific community according to the Creative Commons Attribution License by means of the PANGAEA repository (https://www.pangaea.de/?q=%40ref104365).
摘要根据ESSD的实时数据过程，本出版物介绍了Rofental几个研究站点(1891-3772 m a.s.l)的综合水文气象和冰川学数据集的扩展。(Ötztal阿尔卑斯山，奥地利)。虽然原始数据集已于2018年以第一个原始版本发布(https://doi.org/10.5194/essd-10-151-2018)，但这里展示的新时间序列来自2017年至2020年收集的气象和雪水记录。一些数据集代表现有地点时间序列的延续，其他数据集来自补充研究集水区科学监测基础设施的新装置。主要的扩建部分是一个设备齐全的自动天气和积雪监测站，以及大量的额外设施，以便持续观测积雪特性。安装在三个高山地区的集水区，这些包括自动测量雪深，雪水当量，体积固体和液态水含量，雪密度，分层雪温度剖面和雪表面温度。一个观测站通过特别安排两个雪深和水当量记录装置来扩展，以观察和量化风驱动的雪再分布。它们被安装在附近迎风和避风的地方，并辅以一个基于声学的雪漂传感器。这些数据集代表了一个独特的高海拔山区积雪和气象观测的时间序列。本文介绍了三年来三个气象站的温度、降水、湿度、风速和辐射通量数据。通过对气象和积雪资料的综合分析，探讨了连续积雪测量方法，以显示典型的季节性积雪特征。通过2019年12月在该站附近发生悲惨雪崩事故时测量的风速、雪漂率和重新分配的雪量的例子，证明了雪漂观测的潜力。所有新的数据集都根据知识共享署名许可协议，通过PANGAEA知识库(https://www.pangaea.de/?q=%40ref104365)提供给科学界。
{"title":"Operational and experimental snow observation systems in the upper Rofental: data from 2017–2020","authors":"M. Warscher, T. Marke, U. Strasser","doi":"10.5194/ESSD-2021-68","DOIUrl":"https://doi.org/10.5194/ESSD-2021-68","url":null,"abstract":"Abstract. According to the living data process in ESSD, this publication presents extensions of a comprehensive hydrometeorological and glaciological data set for several research sites in the Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria). Whereas the original dataset has been published in a first original version in 2018 (https://doi.org/10.5194/essd-10-151-2018), the new time series presented here originate from meteorological and snow-hydrological recordings that have been collected from 2017 to 2020. Some data sets represent continuations of time series at existing locations, others come from new installations complementing the scientific monitoring infrastructure in the research catchment. Main extensions are a fully equipped automatic weather and snow monitoring station, as well as extensive additional installations to enable continuous observation of snow cover properties. Installed at three high Alpine locations in the catchment, these include automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water content, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow redistribution. They are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. The data sets represent a unique time series of high-altitude mountain snow and meteorology observations. We present three years of data for temperature, precipitation, humidity, wind speed, and radiation fluxes from three meteorological stations. The continuous snow measurements are explored by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations are demonstrated with examples of measured wind speeds, snow drift rates and redistributed snow amounts in December 2019 when a tragic avalanche accident occurred in the vicinity of the station. All new data sets are provided to the scientific community according to the Creative Commons Attribution License by means of the PANGAEA repository (https://www.pangaea.de/?q=%40ref104365).\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125923887","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}
Pub Date : 2021-04-12DOI: 10.5194/ESSD-13-1519-2021
O. Shumilova, A. Sukhodolov, G. Constantinescu, B. MacVicar
Abstract. Natural dynamics of river floodplains are driven by the interaction of flow and patchy riparian vegetation, which has implications for riverbed morphology and diversity of riparian habitats. Fundamental mechanisms affecting the dynamics of flow in such systems are still not fully understood due to a lack of experimental data collected in natural environments that are free of scaling effects present in laboratory studies. Here we present a detailed dataset on hydrodynamics of shallow wake flows that develop behind solid and porous obstructions. The dataset was collected during a field experimental campaign carried out in a side branch of the gravel-bed Tagliamento River in Northeast Italy. The dataset consists of thirty experimental runs in which we varied the diameter of the surface-mounted obstruction, its solid volume fraction, and the porosity at the leading edge, the object's submergence, and the approach velocity. Each run included: (1) measurements of mean velocity and turbulence in the longitudinal transect through the centreline of the flow with up to 25–30 sampling locations, and from 8 to 10 lateral profiles measured at 14 locations; (2) detailed surveys of the free surface topography; and (3) flow visualizations and video-recordings of the wakes patterns using a drone. The field scale of the experimental setup, the precise control of the approaching velocity, configuration of models, and the natural gravel-bed context for this experiment makes this data set unique. Besides enabling the examination of scaling effects, these data also allow the verification of numerical models and provide insight into the effects of driftwood accumulations on the dynamics of wakes. Data are made available as open access via the Zenodo portal (Shumilova et al. 2020) with DOI https://doi.org/10.5281/zenodo.3968748 .
摘要河流洪泛平原的自然动态是由水流和斑块状河岸植被的相互作用驱动的，这对河床形态和河岸生境的多样性具有重要影响。由于缺乏在实验室研究中没有标度效应的自然环境中收集的实验数据，影响此类系统中流动动力学的基本机制仍未完全了解。在这里，我们提出了一个关于固体和多孔障碍物后面发展的浅尾流的流体动力学的详细数据集。该数据集是在意大利东北部塔利亚门托河砾石河床侧分支进行的现场实验活动中收集的。该数据集由30个实验组成，其中我们改变了表面安装障碍物的直径，其固体体积分数，前缘孔隙率，物体的淹没度和接近速度。每次运行包括:(1)测量25-30个采样点的流动中心线纵向样带的平均速度和湍流，以及在14个地点测量8 - 10个横向剖面;(2)自由表面地形的详细调查;(3)流动可视化和视频记录尾迹模式使用无人机。实验设置的现场规模、接近速度的精确控制、模型的配置以及该实验的天然砾石床环境使该数据集独一无二。除了能够检查尺度效应，这些数据还允许验证数值模型，并深入了解浮木积累对尾流动力学的影响。数据通过Zenodo门户网站(Shumilova et al. 2020)开放获取，DOI: https://doi.org/10.5281/zenodo.3968748。
{"title":"Dynamics of shallow wakes on gravel-bed floodplains: dataset from field experiments","authors":"O. Shumilova, A. Sukhodolov, G. Constantinescu, B. MacVicar","doi":"10.5194/ESSD-13-1519-2021","DOIUrl":"https://doi.org/10.5194/ESSD-13-1519-2021","url":null,"abstract":"Abstract. Natural dynamics of river floodplains are driven by the interaction of flow and patchy riparian vegetation, which has implications for riverbed morphology and diversity of riparian habitats. Fundamental mechanisms affecting the dynamics of flow in such systems are still not fully understood due to a lack of experimental data collected in natural environments that are free of scaling effects present in laboratory studies. Here we present a detailed dataset on hydrodynamics of shallow wake flows that develop behind solid and porous obstructions. The dataset was collected during a field experimental campaign carried out in a side branch of the gravel-bed Tagliamento River in Northeast Italy. The dataset consists of thirty experimental runs in which we varied the diameter of the surface-mounted obstruction, its solid volume fraction, and the porosity at the leading edge, the object's submergence, and the approach velocity. Each run included: (1) measurements of mean velocity and turbulence in the longitudinal transect through the centreline of the flow with up to 25–30 sampling locations, and from 8 to 10 lateral profiles measured at 14 locations; (2) detailed surveys of the free surface topography; and (3) flow visualizations and video-recordings of the wakes patterns using a drone. The field scale of the experimental setup, the precise control of the approaching velocity, configuration of models, and the natural gravel-bed context for this experiment makes this data set unique. Besides enabling the examination of scaling effects, these data also allow the verification of numerical models and provide insight into the effects of driftwood accumulations on the dynamics of wakes. Data are made available as open access via the Zenodo portal (Shumilova et al. 2020) with DOI https://doi.org/10.5281/zenodo.3968748 .","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131526180","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. The Arctic regions experience intense transformations, such that efficient methods are needed to monitor and understand Arcticlandscape changes in response to climate warming and low-frequency high-magnitude events. One example of such events,capable of causing serious landscape changes, is glacier lake outburst floods. On 6 August 2017, a flood event related to glacial lake outburst affected the Zackenberg River (NE Greenland). Here, we provided a very high-resolution dataset representingunique time-series of data captured immediately before (5 August 2017), during (6 August 2017), and after (8 August 2017)the flood. Our dataset covers a 2.1-km-long distal section of the Zackenberg River. The available files comprise: (1)unprocessed images captured using an unmanned aerial vehicle (UAV): https://doi.org/10.5281/zenodo.4495282 (Tomczykand Ewertowski, 2021a); and (2) results of structure-from-motion (SfM) processing (orthomosaics, digital elevation models, and hillshade models in a raster format), uncertainty assessments (precision maps) and effects of geomorphological mappingin vector formats: https://doi.org/10.5281/zenodo.4498296 (Tomczyk and Ewertowski, 2021b). Potential applications of thepresented dataset include: (1) assessment and quantification of landscape changes as an immediate result of glacier lakeoutburst flood; (2) long-term monitoring of high-Arctic river valley development (in conjunction with other datasets); (3)establishing a baseline for quantification of geomorphological impacts of future glacier lake outburst floods; (4) assessment of geohazards related to bank erosion and debris flow development (hazards for research station infrastructure – station buildingsand bridge); (5) monitoring of permafrost degradation; and (6) modelling flood impacts on river ecosystem, transport capacity,and channel stability.
{"title":"Baseline data for monitoring geomorphological effects of glacier lake outburst flood: A very high-resolution image and GIS datasets of the distal part of the Zackenberg River, northeast Greenland","authors":"A. Tomczyk, M. Ewertowski","doi":"10.5194/ESSD-2021-48","DOIUrl":"https://doi.org/10.5194/ESSD-2021-48","url":null,"abstract":"Abstract. The Arctic regions experience intense transformations, such that efficient methods are needed to monitor and understand Arcticlandscape changes in response to climate warming and low-frequency high-magnitude events. One example of such events,capable of causing serious landscape changes, is glacier lake outburst floods. On 6 August 2017, a flood event related to glacial lake outburst affected the Zackenberg River (NE Greenland). Here, we provided a very high-resolution dataset representingunique time-series of data captured immediately before (5 August 2017), during (6 August 2017), and after (8 August 2017)the flood. Our dataset covers a 2.1-km-long distal section of the Zackenberg River. The available files comprise: (1)unprocessed images captured using an unmanned aerial vehicle (UAV): https://doi.org/10.5281/zenodo.4495282 (Tomczykand Ewertowski, 2021a); and (2) results of structure-from-motion (SfM) processing (orthomosaics, digital elevation models, and hillshade models in a raster format), uncertainty assessments (precision maps) and effects of geomorphological mappingin vector formats: https://doi.org/10.5281/zenodo.4498296 (Tomczyk and Ewertowski, 2021b). Potential applications of thepresented dataset include: (1) assessment and quantification of landscape changes as an immediate result of glacier lakeoutburst flood; (2) long-term monitoring of high-Arctic river valley development (in conjunction with other datasets); (3)establishing a baseline for quantification of geomorphological impacts of future glacier lake outburst floods; (4) assessment of geohazards related to bank erosion and debris flow development (hazards for research station infrastructure – station buildingsand bridge); (5) monitoring of permafrost degradation; and (6) modelling flood impacts on river ecosystem, transport capacity,and channel stability. \u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124957574","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}
C. Diekmann, M. Schneider, B. Ertl, F. Hase, O. García, F. Khosrawi, E. Sepúlveda, P. Knippertz, P. Braesicke
Abstract. We present a global and multi-annual space-borne dataset of tropospheric {H2O, δD} pairs that is based on radiance measurements from the nadir thermal infrared sensor IASI (Infrared Atmospheric Sounding Interferometer) onboard the Metop satellites of EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). This dataset is an a posteriori processed extension of the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) IASI full product dataset as presented in Schneider et al. (2021b). From the independently retrieved H2O and δD proxy states, their a priori settings and constraints, and their error covariances provided by the IASI full product dataset we generate an optimal estimation product for pairs of H2O and δD. Here, this standard MUSICA method for deriving {H2O, δD} pairs is extended using an a posteriori reduction of the constraints for improving the retrieval sensitivity at dry conditions. By applying this improved water isotopologue post-processing for all cloud-free MUSICA IASI retrievals, this yields a {H2O, δD} pair dataset for the whole period from October 2014 to June 2019 with a global coverage twice per day (local morning and evening overpass times). In total, the dataset covers more than 1200 million individually processed observations. The retrievals are most sensitivity to variations of {H2O, δD} pairs within the free troposphere, with up to 30 % of all retrievals containing vertical profile information in the {H2O, δD} pair product. After applying appropriate quality filters, the largest number of reliable pair data arises for tropical and subtropical summer regions, but also for higher latitudes there is a considerable amount of reliable data. Exemplary time-series over the Tropical Atlantic and West Africa are chosen to illustrates the potential of the MUSICA IASI {H2O, δD} pair data for atmospheric moisture pathway studiess. Finally, the dataset is referenced with the DOI 10.35097/415 (Diekmann et al., 2021).
摘要我们提出了一个全球和多年的对流层{H2O， δD}对的星载数据集，该数据集基于EUMETSAT(欧洲气象卫星利用组织)Metop卫星上的最底热红外传感器IASI(红外大气探测干涉仪)的辐射测量。该数据集是Schneider等人(2021b)中提出的MUSICA (MUlti-platform remote Sensing of Isotopologues for investigation the Cycle of Atmospheric water) IASI完整产品数据集的后测扩展。从独立检索的H2O和δD代理状态、它们的先验设置和约束以及IASI完整产品数据集提供的误差协方差中，我们生成了H2O和δD对的最优估计产品。为了提高干燥条件下的检索灵敏度，对导出{H2O， δD}对的标准MUSICA方法进行了后验化简。通过将这种改进的水同位素后处理应用于所有无云的MUSICA IASI检索，得到了2014年10月至2019年6月整个期间的{H2O， δD}对数据集，每天两次覆盖全球(当地早晚立交时间)。总的来说，该数据集涵盖了超过12亿个单独处理的观测数据。反演结果对自由对流层内{H2O， δD}对的变化最为敏感，高达30%的反演结果包含{H2O， δD}对乘积的垂直剖面信息。在应用适当的质量过滤器后，热带和亚热带夏季地区出现了数量最多的可靠对数据，但对于高纬度地区也有相当数量的可靠数据。本文选择了热带大西洋和西非的典型时间序列，以说明MUSICA IASI {H2O， δD}对数据在大气水分途径研究中的潜力。最后，数据集引用DOI 10.35097/415 (Diekmann et al.， 2021)。
{"title":"The MUSICA IASI {H2O, δD} pair product","authors":"C. Diekmann, M. Schneider, B. Ertl, F. Hase, O. García, F. Khosrawi, E. Sepúlveda, P. Knippertz, P. Braesicke","doi":"10.5194/ESSD-2021-87","DOIUrl":"https://doi.org/10.5194/ESSD-2021-87","url":null,"abstract":"Abstract. We present a global and multi-annual space-borne dataset of tropospheric {H2O, δD} pairs that is based on radiance measurements from the nadir thermal infrared sensor IASI (Infrared Atmospheric Sounding Interferometer) onboard the Metop satellites of EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). This dataset is an a posteriori processed extension of the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) IASI full product dataset as presented in Schneider et al. (2021b). From the independently retrieved H2O and δD proxy states, their a priori settings and constraints, and their error covariances provided by the IASI full product dataset we generate an optimal estimation product for pairs of H2O and δD. Here, this standard MUSICA method for deriving {H2O, δD} pairs is extended using an a posteriori reduction of the constraints for improving the retrieval sensitivity at dry conditions. By applying this improved water isotopologue post-processing for all cloud-free MUSICA IASI retrievals, this yields a {H2O, δD} pair dataset for the whole period from October 2014 to June 2019 with a global coverage twice per day (local morning and evening overpass times). In total, the dataset covers more than 1200 million individually processed observations. The retrievals are most sensitivity to variations of {H2O, δD} pairs within the free troposphere, with up to 30 % of all retrievals containing vertical profile information in the {H2O, δD} pair product. After applying appropriate quality filters, the largest number of reliable pair data arises for tropical and subtropical summer regions, but also for higher latitudes there is a considerable amount of reliable data. Exemplary time-series over the Tropical Atlantic and West Africa are chosen to illustrates the potential of the MUSICA IASI {H2O, δD} pair data for atmospheric moisture pathway studiess. Finally, the dataset is referenced with the DOI 10.35097/415 (Diekmann et al., 2021).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114606294","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}
A. Fay, L. Gregor, P. Landschützer, G. McKinley, N. Gruber, M. Gehlen, Y. Iida, G. Laruelle, C. Rödenbeck, J. Zeng
Abstract. Air-sea flux of carbon dioxide (CO2) is a critical component of the global carbon cycle and the climate system with the ocean removing about a quarter of the CO2 emitted into the atmosphere by human activities over the last decade. A common approach to estimate this net flux of CO2 across the air-sea interface is the use of surface ocean CO2 observations and the computation of the flux through a bulk parameterization approach. Yet, the details for how this is done in order to arrive at a global ocean CO2 uptake estimate varies greatly, unnecessarily enhancing the uncertainties. Here we reduce some of these uncertainties by harmonizing an ensemble of products that interpolate surface ocean CO2 observations to near global coverage. We propose a common methodology to fill in missing areas in the products and to calculate fluxes and present a new estimate of the net flux. The ensemble data product, SeaFlux (Fay et al. (2021), doi.org/10.5281/zenodo.4133802 , https://github.com/luke-gregor/SeaFlux ), accounts for the diversity of the underlying mapping methodologies. Utilizing six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN), the SeaFlux ensemble approach adjusts for methodological inconsistencies in flux calculations that can result in an average error of 15 % in global mean flux estimates. We address differences in spatial coverage of the surface ocean CO2 between the mapping products which ultimately yields an increase in CO2 uptake of up to 19 % for some products. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55). Application of an appropriately scaled gas exchange coefficient has a greater impact on the resulting flux than solely the choice of wind product. With these adjustments, we derive an improved ensemble of surface ocean pCO2 and air-sea carbon flux estimates. The SeaFlux ensemble suggests a global mean uptake of CO2 from the atmosphere of 1.92 +/- 0.35 PgC yr-1. This work aims to support the community effort to perform model-data intercomparisons which will help to identify missing fluxes as we strive to close the global carbon budget.
{"title":"Harmonization of global surface ocean pCO2 mapped products and their flux calculations; an improved estimate of the ocean carbon sink","authors":"A. Fay, L. Gregor, P. Landschützer, G. McKinley, N. Gruber, M. Gehlen, Y. Iida, G. Laruelle, C. Rödenbeck, J. Zeng","doi":"10.5194/ESSD-2021-16","DOIUrl":"https://doi.org/10.5194/ESSD-2021-16","url":null,"abstract":"Abstract. Air-sea flux of carbon dioxide (CO2) is a critical component of the global carbon cycle and the climate system with the ocean removing about a quarter of the CO2 emitted into the atmosphere by human activities over the last decade. A common approach to estimate this net flux of CO2 across the air-sea interface is the use of surface ocean CO2 observations and the computation of the flux through a bulk parameterization approach. Yet, the details for how this is done in order to arrive at a global ocean CO2 uptake estimate varies greatly, unnecessarily enhancing the uncertainties. Here we reduce some of these uncertainties by harmonizing an ensemble of products that interpolate surface ocean CO2 observations to near global coverage. We propose a common methodology to fill in missing areas in the products and to calculate fluxes and present a new estimate of the net flux. The ensemble data product, SeaFlux (Fay et al. (2021), doi.org/10.5281/zenodo.4133802 , https://github.com/luke-gregor/SeaFlux ), accounts for the diversity of the underlying mapping methodologies. Utilizing six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN), the SeaFlux ensemble approach adjusts for methodological inconsistencies in flux calculations that can result in an average error of 15 % in global mean flux estimates. We address differences in spatial coverage of the surface ocean CO2 between the mapping products which ultimately yields an increase in CO2 uptake of up to 19 % for some products. Fluxes are calculated using three wind products (CCMPv2, ERA5, and JRA55). Application of an appropriately scaled gas exchange coefficient has a greater impact on the resulting flux than solely the choice of wind product. With these adjustments, we derive an improved ensemble of surface ocean pCO2 and air-sea carbon flux estimates. The SeaFlux ensemble suggests a global mean uptake of CO2 from the atmosphere of 1.92 +/- 0.35 PgC yr-1. This work aims to support the community effort to perform model-data intercomparisons which will help to identify missing fluxes as we strive to close the global carbon budget.","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125029937","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}
Jida Wang, B. Walter, Fangfang Yao, Chunqiao Song, Meng Ding, A. S. Maroof, Jingying Zhu, Chenyu Fan, Aote Xin, J. Mcalister, S. Sikder, Y. Sheng, G. Allen, J. Crétaux, Y. Wada
Abstract. Dams and reservoirs are among the most widespread human-made infrastructure on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GOODD) or detailed attributes for limited dam quantity or regions (e.g., GRanD and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD) maintained by the International Commission on Large Dams (ICOLD), documents nearly 60,000 dams with an extensive suite of attributes. Unfortunately, WRD records are not georeferenced, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dam And Reservoir (GeoDAR) dataset, created by utilizing online geocoding API and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at https://doi.org/10.6084/m9.figshare.13670527. GeoDAR v1.0 holds 21,051 dam points georeferenced from WRD, whereas v1.1 consists of a) 23,680 dam points after a careful harmonization between GeoDAR v1.0 and GRanD and b) 20,214 reservoir polygons retrieved from high-resolution water masks. Due to geocoding challenges, GeoDAR spatially resolved 40 % of the records in WRD which, however, comprise over 90 % of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we can assist in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. With a dam quantity triple that of GRanD, GeoDAR significantly enhances the spatial details of smaller but more widespread dams and reservoirs, and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modelling, water resource management, ecosystem health, and energy planning.
{"title":"GeoDAR: Georeferenced global dam and reservoir dataset for\u0000bridging attributes and geolocations","authors":"Jida Wang, B. Walter, Fangfang Yao, Chunqiao Song, Meng Ding, A. S. Maroof, Jingying Zhu, Chenyu Fan, Aote Xin, J. Mcalister, S. Sikder, Y. Sheng, G. Allen, J. Crétaux, Y. Wada","doi":"10.5194/ESSD-2021-58","DOIUrl":"https://doi.org/10.5194/ESSD-2021-58","url":null,"abstract":"Abstract. Dams and reservoirs are among the most widespread human-made infrastructure on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GOODD) or detailed attributes for limited dam quantity or regions (e.g., GRanD and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD) maintained by the International Commission on Large Dams (ICOLD), documents nearly 60,000 dams with an extensive suite of attributes. Unfortunately, WRD records are not georeferenced, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dam And Reservoir (GeoDAR) dataset, created by utilizing online geocoding API and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at https://doi.org/10.6084/m9.figshare.13670527. GeoDAR v1.0 holds 21,051 dam points georeferenced from WRD, whereas v1.1 consists of a) 23,680 dam points after a careful harmonization between GeoDAR v1.0 and GRanD and b) 20,214 reservoir polygons retrieved from high-resolution water masks. Due to geocoding challenges, GeoDAR spatially resolved 40 % of the records in WRD which, however, comprise over 90 % of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we can assist in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. With a dam quantity triple that of GRanD, GeoDAR significantly enhances the spatial details of smaller but more widespread dams and reservoirs, and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modelling, water resource management, ecosystem health, and energy planning.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124375312","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}
R. Fausto, D. van As, K. Mankoff, B. Vandecrux, M. Citterio, A. Ahlstrøm, S. Andersen, Wiliiam Colgan, N. Karlsson, K. Kjeldsen, N. Korsgaard, S. H. Larsen, S. Nielsen, Allan Ø. Pedersen, Christopher L. Shields, A. Solgaard, J. Box
Abstract. The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has been measuring climate and ice sheet properties since 2007. Currently the PROMICE automatic weather station network includes 25 instrumented sites in Greenland. Accurate measurements of the surface and near-surface atmospheric conditions in a changing climate is important for reliable present and future assessment of changes to the Greenland ice sheet. Here we present the PROMICE vision, methodology, and each link in the production chain for obtaining and sharing quality-checked data. In this paper we mainly focus on the critical components for calculating the surface energy balance and surface mass balance. A user-contributable dynamic webbased database of known data quality issues is associated with the data products at ( https://github.com/GEUS-PROMICE/ PROMICE-AWS-data-issues/ ). As part of the living data option, the datasets presented and described here are available at DOI: 10.22008/promice/data/aws, https://doi.org/10.22008/promice/data/aws (Fausto and van As, 2019).
{"title":"PROMICE automatic weather station data","authors":"R. Fausto, D. van As, K. Mankoff, B. Vandecrux, M. Citterio, A. Ahlstrøm, S. Andersen, Wiliiam Colgan, N. Karlsson, K. Kjeldsen, N. Korsgaard, S. H. Larsen, S. Nielsen, Allan Ø. Pedersen, Christopher L. Shields, A. Solgaard, J. Box","doi":"10.5194/ESSD-2021-80","DOIUrl":"https://doi.org/10.5194/ESSD-2021-80","url":null,"abstract":"Abstract. The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has been measuring climate and ice sheet properties since 2007. Currently the PROMICE automatic weather station network includes 25 instrumented sites in Greenland. Accurate measurements of the surface and near-surface atmospheric conditions in a changing climate is important for reliable present and future assessment of changes to the Greenland ice sheet. Here we present the PROMICE vision, methodology, and each link in the production chain for obtaining and sharing quality-checked data. In this paper we mainly focus on the critical components for calculating the surface energy balance and surface mass balance. A user-contributable dynamic webbased database of known data quality issues is associated with the data products at ( https://github.com/GEUS-PROMICE/ PROMICE-AWS-data-issues/ ). As part of the living data option, the datasets presented and described here are available at DOI: 10.22008/promice/data/aws, https://doi.org/10.22008/promice/data/aws (Fausto and van As, 2019).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"380 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126899615","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. Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH (Large-Sample Data for Hydrology) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border Austria/Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of 170 000 km2 in 9 different countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 observed catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with daily and also hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses, also with a high temporal resolution. The runoff time series are classified by over 20 different attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS datasets. In contrast, however, LamaH does not only consider headwater basins. Intermediate catchments are also covered, allowing, for the first time within a hydrological large sample dataset, to consider the hydrological network and river topology in applications. We discuss not only the data basis and the methodology of data preparation, but also focus on possible limitations and uncertainties. Potential applications of LamaH are also outlined, since it is intended to serve as a uniform basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).
摘要在水文学领域，越来越多地使用非常庞大和全面的数据集。大样本研究提供了对水文循环的深入了解，这可能是小规模研究无法提供的。LamaH (Large-Sample Data for Hydrology)是一个用于中欧大样本研究和比较水文的新数据集。它涵盖了整个多瑙河上游至奥地利/斯洛伐克国家边界，以及所有其他奥地利集水区，包括其国外上游地区。LamaH覆盖9个不同国家的17万平方公里面积，从以大陆性气候为特征的低地地区到以冰雪为主的高山地区。因此，在各个集水区存在着各种各样的属性。我们用859个观测到的集水区的60多个集水区属性来表示这种变异，包括地形、气候、水文、土地覆盖、植被、土壤和地质属性。LamaH还包含径流时间序列和气象时间序列的集合。这些时间序列提供每日和每小时的分辨率。所有气象和大部分径流时间序列的跨度超过35年，因此能够进行长期分析，也具有较高的时间分辨率。径流时间序列根据20多种不同的属性进行分类，包括人类影响信息和数据质量和完整性指标。LamaH的结构基于著名的camel数据集。然而，相比之下，LamaH并不仅仅考虑水源盆地。中间流域也被覆盖，允许，第一次在水文大样本数据集中，考虑应用中的水文网络和河流拓扑。我们不仅讨论了数据基础和数据准备的方法，而且还关注了可能的局限性和不确定性。本文还概述了LamaH的潜在应用，因为它的目的是作为进一步研究的统一基础。LamaH可在https://doi.org/10.5281/zenodo.4525244获得(Klingler et al.， 2021)。
{"title":"LamaH | Large-Sample Data for Hydrology and Environmental Sciences for Central Europe","authors":"Christoph Klingler, K. Schulz, M. Herrnegger","doi":"10.5194/ESSD-2021-72","DOIUrl":"https://doi.org/10.5194/ESSD-2021-72","url":null,"abstract":"Abstract. Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH (Large-Sample Data for Hydrology) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border Austria/Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of 170 000 km2 in 9 different countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 observed catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with daily and also hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses, also with a high temporal resolution. The runoff time series are classified by over 20 different attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS datasets. In contrast, however, LamaH does not only consider headwater basins. Intermediate catchments are also covered, allowing, for the first time within a hydrological large sample dataset, to consider the hydrological network and river topology in applications. We discuss not only the data basis and the methodology of data preparation, but also focus on possible limitations and uncertainties. Potential applications of LamaH are also outlined, since it is intended to serve as a uniform basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130131302","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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