Earth System Science Data Discussions最新文献

{"title":"Global Covenant of Mayors, a dataset of GHG emissions for 6,200 cities in Europe and the Southern Mediterranean","authors":"A. Kona, P. Bertoldi, F. Monforti-Ferrario, M. Baldi, E. L. Vullo, G. Kakoulaki, N. Vetters, C. Thiel, G. Melica, A. Sgobbi, Christofer Ahlgren, Brieuc Posnic","doi":"10.5194/ESSD-2021-67","DOIUrl":"https://doi.org/10.5194/ESSD-2021-67","url":null,"abstract":"Abstract. The Paris Agreement has underlined the role of cities in combating climate change. The Global Covenant of Mayors for Climate and Energy (GCoM) is the largest dedicated international initiative to promote climate action at city level, covering globally over 10,000 cities and almost half the population of the European Union (EU) by end of March 2020. The latest Intergovernmental Panel on Climate Change (IPCC) report denotes that there is a lack of comprehensive, consistent datasets of cities' Greenhouse Gas (GHG) emissions inventories. To address part of this gap, we present here a harmonised, complete and verified dataset of GHG inventories for 6,200 cities in European and Southern Mediterranean countries, signatories of the GCoM initiative. To complement the emission data reported, a set of ancillary data that have a direct or indirect potential impact on cities' climate action plans were collected from other databases, supporting further research on local climate action and monitoring the EU's progress on Sustainable Development Goal 13 on Climate Action. The dataset is archived and publicly available with the DOI number https://doi.org/10.2905/57A615EB-CFBC-435A-A8C5-553BD40F76C9.\u0000","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126152091","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}

{"title":"Evaluation of the global glacier inventories and assessment of glacier elevation changes\u0000over north-western Himalaya","authors":"S. Romshoo, Tariq Abdullah, Mustafa Hameed Bhat","doi":"10.5194/ESSD-2021-28","DOIUrl":"https://doi.org/10.5194/ESSD-2021-28","url":null,"abstract":"Abstract. The study evaluates the global glacier inventories available for the study area viz., RGI, GAMDAM and ICIMOD, with the newly generated Kashmir University Glacier Inventory (KUGI) for three Himalaya basins; Jhelum, Suru and Chenab in the north-western Himalaya, comprising of 2096 glaciers spread over an area of 3300 km2. The KUGI was prepared from the Landsat data supplemented by Digital Elevation Model, Google Earth images and limited field surveys. The KUGI comprises of 154 glaciers in the Jhelum, 328 in the Suru and 1614 in the Chenab basin, corresponding to the glacier area of 85.9 ± 11.4 km2, 487 ± 16.2 km2 and 2727 ± 90.2 km2 respectively. The investigation revealed that most of the glaciers in the study area are http://doi.org/10.5281/zenodo.4461799 (Romshoo et al., 2021).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130676146","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}

{"title":"An Integrated Marine Data Collection for the German Bight –\u0000Part I: Subaqueous Geomorphology and Surface Sedimentology","authors":"J. Sievers, P. Milbradt, R. Ihde, Jennifer Valerius, R. Hagen, A. Plüß","doi":"10.5194/ESSD-2021-47","DOIUrl":"https://doi.org/10.5194/ESSD-2021-47","url":null,"abstract":"Abstract. The German Bight located within the central North Sea is a hydro- and morphodynamically highly complex system of estuaries, barrier islands and part of the world’s largest coherent tidal flats, the Wadden Sea. To identify and understand challenges faced by coastal stakeholders, such as harbor operators or governmental agencies, to maintain waterways and employ numerical models for further analyses, it is imperative to have a consistent data base for both bathymetry and surface sedimentology. Current commercial and public data products are insufficient in spatial and temporal 15 resolution and coverage for recent analyses methods. Thus, this first part of a two-part publication series of the German joint project EasyGSH-DB describes annual bathymetric digital terrain models in a 10 m gridded resolution for the German North Sea coast and German Bight from 1996 to 2016 (Sievers et al., 2020a, https://doi.org/10.48437/02.2020.K2.7000.0001 ), as well as surface sedimentological models of discretized cumulative grain size distribution functions for 1996, 2006 and 2016 on 100 m grids (Sievers et al., 2020b, https://doi.org/10.48437/02.2020.K2.7000.0005 ). Furthermore, basic morphodynamic and sedimentological 20 processing analyses, such as the estimation of e.g. bathymetric stability or surface maps of sedimentological parameters, are provided (Sievers et al., 2020a, 2020b, see respective download links).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116230821","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}

{"title":"High-resolution dataset of thermokarst lakes on the\u0000Qinghai-Tibetan Plateau","authors":"Xu Chen, C. Mu, Lin Jia, Zhi-Long Li, Chengyan Fan, Mei Mu, X. Peng, Xiaodong Wu","doi":"10.5194/ESSD-2020-378","DOIUrl":"https://doi.org/10.5194/ESSD-2020-378","url":null,"abstract":"Abstract. The Qinghai-Tibetan Plateau (QTP), the largest high-altitude and low-latitude permafrost zone in the world, has experienced rapid permafrost degradation in recent decades, and one of the most remarkable resulting characteristics is the formation of thermokarst lakes. Such lakes have attracted significant attention because of their ability to regulate carbon cycle, water, and energy fluxes. However, the distribution of thermokarst lakes in this area remains largely unknown, hindering our understanding of the response of permafrost and its carbon feedback to climate change. Here, based on the Google Earth Engine platform, we examined the modern distribution (2018) of thermokarst lakes on the QTP using Sentinel-2A data; for the first time providing the true spatial distribution by using a resolution of 10 m with a relative error of 0–0.5. Results show that the total thermokarst lake area on the QTP is 1730.34 m2 km2, accounting for approximately 4 % of the total water area of lakes and ponds, and that overall thermokarst lake density is 12/100 m2 km2. More specifically, the densities of thermokarst lakes in the land types of alpine desert steppe (16/100 km2) and barren land (17/100 km2) were larger than those of alpine meadows (13/100 km2), alpine steppe (11/100 km2), and wet meadow (11/100 km2). These findings provide a scientific foundation for future investigations into the effects of climate change on the permafrost environment and carbon emissions from rapidly developing thermokarst landscapes. Data are made available as open access via the National Tibetan Plateau Data Center (Chen et al., 2021) with DOI: 10.11888/Geocry.tpdc.271205 ( https://data.tpdc.ac.cn/en/data/c0c05207-568d-41db-ab94- 8610bdcdbbe5/ ).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128065311","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}

{"title":"A Harmonized Global Land Evaporation Dataset from Reanalysis Products Covering 1980-2017","authors":"Lu Jiao, Wang Guojie, Chen Tiexi, Li Shijie, Hagan Fiifi T. Daniel, Kattel Giri, Peng Jian, Jiang Tong, S. Buda","doi":"10.5281/ZENODO.4595941","DOIUrl":"https://doi.org/10.5281/ZENODO.4595941","url":null,"abstract":"Abstract. Land evaporation (ET) plays a crucial role in hydrological and energy cycle. However, the widely used numerical products are still subject to great uncertainties due to imperfect model parameterizations and forcing data. Lack of available observed data has further complicated the estimation. Hence, there is an urgency to define the global benchmark land ET for climate-induced hydrology and energy change. In this study, we have used the coefficient of variation (CV) and carefully selected merging regions with high consistency of multiple data sets. Reliability Ensemble Averaging (REA) method has been used to generate a long-term (1980–2017) daily ET product with a spatial resolution of 0.25 degree by merging the selected three data sets, ERA5, GLDAS2 and MERRA2. GLEAM3.2a and flux tower observation data have been selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well under a variety of vegetation cover conditions as the weights were distributed across the east-west direction banding manner, with greater differences near the equator. The merged product also captured well the trend of land evaporation over different areas, showing the significant decreasing trend in Amazon plain in South America and Congo Basin in central Africa, and the increasing trend in the east of North America, west of Europe, south of Asia and north of Oceania. In addition to model performance, REA method also successfully worked for the model convergence showing as an outstanding reference for data merging of other variables. Data can be accessed at https://doi.org/10.5281/zenodo.4595941 (Lu et al., 2021).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115694105","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}

{"title":"UV-Indien Network ground-based measurements: comparisons\u0000with satellite and model estimates of UV radiation over the Western\u0000Indian Ocean","authors":"K. Lamy, T. Portafaix, C. Brogniez, K. Lakkala, M. Pitkänen, A. Arola, Jean-Baptiste Forestier, V. Amelie, Mohamed Abdoulwahab Toihir, S. Rakotoniaina","doi":"10.5194/ESSD-2021-55","DOIUrl":"https://doi.org/10.5194/ESSD-2021-55","url":null,"abstract":"Abstract. As part of the UV-Indien Network, 9 ground-based stations have been equipped with one spectroradiometer, radiometers and all-sky cameras. These stations are homogeneously distributed in 5 countries of the Western Indian Ocean region (Comoros, France, Madagascar, Mauritius and Seychelles), a part of the world where almost no measurements have been made so far. The main scientific objectives of this network are to study the annual and inter-annual variability of the ultraviolet (UV) radiation in this area, to validate the output of numerical models and satellite estimates of ground-based UV measurements, and to monitor UV radiation in the context of climate change and projected ozone depletion in this region. The first results are presented here for the oldest stations (Antananarivo, Anse Quitor, Mahe and Saint-Denis). Ground-based measurements of UV index (UVI) are compared against satellite estimates (Ozone Monitoring Instrument (OMI), the TROPOspheric Monitoring Instrument (TROPOMI), the Global Ozone Monitoring Experiment (GOME) and model forecasts of UVI (Tropospheric Emission Monitoring Internet Service (TEMIS) and Copernicus Atmospheric Monitoring Service (CAMS). The median relative differences between satellite or model estimates and ground-based measurements of clear-sky UVI range between −34.5 % and 15.8 %. Under clear skies, the smallest UVI median difference between the satellites or model estimates and the measurements of ground-based instruments is found to be 0.02 (TROPOMI), 0.04 (OMI), −0.1 (CAMS) and −0.4 (CAMS) at St-Denis, Antananarivo, Anse Quitor and Mahe respectively. The cloud fraction and UVI diurnal profile are calculated for these four stations. The mean UVI values at local solar noon range between 10 (Antananarivo, Anse Quitor and Saint-Denis) and 14 at Mahe. The mean UVIs in clear-sky conditions are higher than mean UVI in all-sky conditions, although it can still be noted that UVI maxima are higher for all-sky conditions than for clear sky conditions. This is the result of UVI enhancement induced by clouds, observed at these four stations. The greatest increase in UV radiation under cloudy conditions was observed at the Mahe station, with increases of more than 4. The data used in this study is available at https://doi.org/10.5281/zenodo.4572026 (Lamy and Portafaix, 2021).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133881828","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}

{"title":"The 3D groundwater salinity distribution and fresh groundwater\u0000volumes in the Mekong Delta, Vietnam, inferred from geostatistical\u0000analyses","authors":"J. Gunnink, H. Pham, G. O. Oude Essink, M. Bierkens","doi":"10.5194/ESSD-2021-15","DOIUrl":"https://doi.org/10.5194/ESSD-2021-15","url":null,"abstract":"Abstract. Over the last decades, economic developments in the Vietnamese Mekong delta have led to a sharp increase in groundwater pumping for domestic, agricultural and industrial use. This has resulted in alarming rates of land subsidence and groundwater salinization. Effective groundwater management, including strategies to work towards sustainable groundwater use, requires knowledge about the current groundwater salinity distribution, in particular the available volumes of fresh groundwater. At the moment, no comprehensive dataset of the spatial distribution of fresh groundwater is available. To create a 3D model of Total Dissolved Solids (TDS), an existing geological model of the spatial distribution and thickness of the aquifers and aquitards is updated. Next, based on the sedimentological description of the borehole data, maps of drainable porosity for each aquifer are interpolated. Measured TDS in groundwater, inferred TDS from resistivity measurements in boreholes and soft incomplete data from domestic wells are combined in an indicator kriging routine to obtain the full probability distribution of TDS for each (x,y,z) location. This statistical distribution of TDS combined with drainable porosity yields estimates of the volume of fresh groundwater (TDS  https://doi.org/10.5281/zenodo.4441776 (Gunnink et al, 2021).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127586556","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}

{"title":"Rainfall erosivity mapping over mainland China based on high\u0000density hourly rainfall records","authors":"Tianyu Yue, S. Yin, Yun Xie, Bofu Yu, Baoyuan Liu","doi":"10.5194/ESSD-2020-370","DOIUrl":"https://doi.org/10.5194/ESSD-2020-370","url":null,"abstract":"Abstract. Rainfall erosivity represents the effect of rainfall and runoff on the average rate of soil erosion. Maps of rainfall erosivity are indispensable for soil erosion assessment using the Universal Soil Loss Equation (USLE) and its successors. To improve current erosivity maps based on daily rainfall data for mainland China, hourly rainfall data from 2381 stations for the period 1951–2018 were collected to generate the R factor and the 1-in-10-year EI30 maps (available at https://dx.doi.org/10.12275/bnu.clicia.rainfallerosivity.CN.001; Yue et al., 2020). Rainfall data at 1-min intervals from 62 stations (18 stations) were collected to calculate rainfall erosivities as true values to evaluate the improvement of the new R factor map (1-in-10-year EI30 map) from the current maps. Both the R factor and 1-in-10-year EI30 decreased from the southeastern to the northwestern, ranging from 0 to 25300 MJ mm ha−1 h−1 a−1 for the R factor and 0 to 11246 MJ mm ha−1 h−1 for the 1-in-10-year EI30. New maps indicated current maps existed an underestimation for most of the southeastern areas and an overestimation for most of the middle and western areas. Comparing with the current maps, the R factor map generated in this study improved the accuracy from 19.4 % to 15.9 % in the mid-western and eastern regions, from 45.2 % to 21.6 % in the western region, and the 1-in-10-year EI30 map in the mid-western and eastern regions improved the accuracy from 21.7 % to 13.0 %. The improvement of the new R factor map can be mainly contributed to the increase of data resolution from daily data to hourly data, whereas that of new 1-in-10-year EI30 map to the increase of the number of stations from 744 to 2381. The effect of increasing the number of stations to improve the interpolation seems to be not very obvious when the station density was denser than about 10 · 103 km2 1 station.","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115158145","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}

{"title":"Soil organic carbon distribution for 0–3 m soils at 1 km2 scale of the frozen ground in the Third Pole Regions","authors":"Dong Wang, Tonghua Wu, Xiaodong Wu, Xianhua Wei, C. Mu, Ren Li, G. Hu, D. Zou, Xiaofan Zhu, Jie Chen, Junming Hao, J. Ni, Xiangfei Li, Wensi Ma, Amin Wen, Chenpeng Shang, Yune La, Xin Ma","doi":"10.5194/ESSD-2020-368","DOIUrl":"https://doi.org/10.5194/ESSD-2020-368","url":null,"abstract":"Abstract. Soil organic carbon (SOC) is very important in the vulnerable ecological environment of the Third Pole; however, data regarding the spatial distribution of SOC are still scarce and uncertain. Based on multiple environmental variables and soil profile data from 458 pits (depth of 0–1 m) and 114 cores (depth of 0–3 m), this study uses a machine-learning approach to evaluate the SOC storage and spatial distribution at a depth interval of 0–3 m in the frozen ground area of the Third Pole region. Our results showed that SOC stocks (SOCS) exhibited a decreasing spatial pattern from the southeast towards the northwest. The estimated SOC storage in the upper 3 m of the soil profile was 46.18 Pg for an area of 3.27 × 106 km2, which included 21.69 Pg and 24.49 Pg for areas of permafrost and seasonally frozen ground, respectively. The mean SOCS under different vegetation types showed a decreasing pattern as follows: forest > shrub > cropland > grassland > desert. Among all soil orders, histosols and gleisoil had the largest SOCSs, while gypsisols and salt flats had the smallest SOCS. Our results provide information on the storage and patterns of SOCS at a 1 km2 scale for areas of frozen ground in the Third Pole region, thus providing a scientific basis for future studies pertaining to Earth system models. The dataset is open-access and available at https://doi.org/10.5281/zenodo.4293454 (Wang et al., 2020).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132759006","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}

{"title":"Total column ozone measurements by the Dobson spectrophotometer\u0000at Belsk (Poland) for the period 1963–2019: homogenization and\u0000adjustment to the Brewer spectrophotometer","authors":"J. Krzyścin, B. Rajewska-Więch, J. Jarosławski","doi":"10.5194/ESSD-2021-39","DOIUrl":"https://doi.org/10.5194/ESSD-2021-39","url":null,"abstract":"Abstract. The total column ozone (TCO3) measurements by the Dobson spectrophotometer #84 have been carried out at Belsk (51°50', 20°47'), Poland, since March 23, 1963. In total, ~115,000 intra-day manual observations have been taken up to December 31, 2019. These observations were made for various combinations of double wavelength pairs in UV range (AD, CD) and the observation category, i.e., direct Sun, zenith blue, and zenith cloudy depending on the weather conditions. The long-term stability of the instrument was supported by frequent (~almost every 4 yr.) intercomparisons with the world standard spectrophotometers. Trend analyses, based on the monthly and yearly averaged TCO3, can be carried out without any additional corrections to the intraday values. To adjust this data to the Brewer spectrophotometer observations also performed at Belsk, a procedure is proposed to account for: less accurate Dobson observations under low solar elevation, presence of clouds, and sensitivity of the ozone absorption on temperature. The adjusted time series shows that the Brewer-Dobson monthly averaged differences are in the range of about ±0.5 %. The intra-day TCO3 data base, divided into three periods (1963–1979, 1980–1999, and 2000–2019), is freely available at https://doi.pangaea.de/10.1594/PANGAEA.919378 (Rajewska-Wiech et al., 2020).","PeriodicalId":326085,"journal":{"name":"Earth System Science Data Discussions","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128743920","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}