Pub Date : 2024-05-24DOI: 10.5194/essd-16-2483-2024
Hui Shen, Robert N. Spengler, Xinying Zhou, Alison Betts, Peter Weiming Jia, Keliang Zhao, Xiaoqiang Li
Abstract. Due largely to demographic growth, agricultural populations during the Holocene became increasingly more impactful ecosystem engineers. Multidisciplinary research has revealed a deep history of human–environmental dynamics; however, these pre-modern anthropogenic ecosystem transformations and cultural adaptions are still poorly understood. Here, we synthesis anthracological data to explore the complex array of human–environmental interactions in the regions of the prehistoric Silk Road. Our results suggest that these ancient humans were not passively impacted by environmental change; rather, they culturally adapted to, and in turn altered, arid ecosystems. Underpinned by the establishment of complex agricultural systems on the western Loess Plateau, people may have started to manage chestnut trees, likely through conservation of economically significant species, as early as 4600 BP. Since ca. 3500 BP, with the appearance of high-yielding wheat and barley farming in Xinjiang and the Hexi Corridor, people appear to have been cultivating Prunus and Morus trees. We also argue that people were transporting preferred coniferous woods over long distances to meet the need for fuel and timber. After 2500 BP, people in our study area were making conscious selections between wood types for craft production and were also clearly cultivating a wide range of long-generation perennials, showing a remarkable traditional knowledge tied into the arid environment. At the same time, the data suggest that there was significant deforestation throughout the chronology of occupation, including a rapid decline of slow-growing spruce forests and riparian woodlands across northwestern China. The wood charcoal dataset is publicly available at https://doi.org/10.5281/zenodo.8158277 (Shen et al., 2023).
{"title":"Seeing the wood for the trees: active human–environmental interactions in arid northwestern China","authors":"Hui Shen, Robert N. Spengler, Xinying Zhou, Alison Betts, Peter Weiming Jia, Keliang Zhao, Xiaoqiang Li","doi":"10.5194/essd-16-2483-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2483-2024","url":null,"abstract":"Abstract. Due largely to demographic growth, agricultural populations during the Holocene became increasingly more impactful ecosystem engineers. Multidisciplinary research has revealed a deep history of human–environmental dynamics; however, these pre-modern anthropogenic ecosystem transformations and cultural adaptions are still poorly understood. Here, we synthesis anthracological data to explore the complex array of human–environmental interactions in the regions of the prehistoric Silk Road. Our results suggest that these ancient humans were not passively impacted by environmental change; rather, they culturally adapted to, and in turn altered, arid ecosystems. Underpinned by the establishment of complex agricultural systems on the western Loess Plateau, people may have started to manage chestnut trees, likely through conservation of economically significant species, as early as 4600 BP. Since ca. 3500 BP, with the appearance of high-yielding wheat and barley farming in Xinjiang and the Hexi Corridor, people appear to have been cultivating Prunus and Morus trees. We also argue that people were transporting preferred coniferous woods over long distances to meet the need for fuel and timber. After 2500 BP, people in our study area were making conscious selections between wood types for craft production and were also clearly cultivating a wide range of long-generation perennials, showing a remarkable traditional knowledge tied into the arid environment. At the same time, the data suggest that there was significant deforestation throughout the chronology of occupation, including a rapid decline of slow-growing spruce forests and riparian woodlands across northwestern China. The wood charcoal dataset is publicly available at https://doi.org/10.5281/zenodo.8158277 (Shen et al., 2023).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"9 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141092069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-24DOI: 10.5194/essd-16-2465-2024
Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, Wenping Yuan
Abstract. Forest harvesting is one of the anthropogenic activities that most significantly affect the carbon budget of forests. However, the absence of explicit spatial information on harvested carbon poses a huge challenge in assessing forest-harvesting impacts, as well as the forest carbon budget. This study utilized provincial-level statistical data on wood harvest, the tree cover loss (TCL) dataset, and a satellite-based vegetation index to develop a Long-term harvEst and Allocation of Forest Biomass (LEAF) dataset. The aim was to provide the spatial location of forest harvesting with a spatial resolution of 30 m and to quantify the post-harvest carbon dynamics. The validations against the surveyed forest harvesting in 133 cities and counties indicated a good performance of the LEAF dataset in capturing the spatial variation of harvested carbon, with a coefficient of determination (R2) of 0.83 between the identified and surveyed harvested carbon. The linear regression slope was up to 0.99. Averaged from 2003 to 2018, forest harvesting removed 68.3 ± 9.3 Mt C yr−1, of which more than 80 % was from selective logging. Of the harvested carbon, 19.6 ± 4.0 %, 2.1 ± 1.1 %, 35.5 ± 12.6 % 6.2 ± 0.3 %, 17.5 ± 0.9 %, and 19.1 ± 9.8 % entered the fuelwood, paper and paperboard, wood-based panels, solid wooden furniture, structural constructions, and residue pools, respectively. Direct combustion of fuelwood was the primary source of carbon emissions after wood harvest. However, carbon can be stored in wood products for a long time, and by 2100, almost 40 % of the carbon harvested during the study period will still be retained. This dataset is expected to provide a foundation and reference for estimating the forestry and national carbon budgets. The 30 m × 30 m harvested-carbon dataset from forests in China can be downloaded at https://doi.org/10.6084/m9.figshare.23641164.v2 (Wang et al., 2023).
{"title":"National forest carbon harvesting and allocation dataset for the period 2003 to 2018","authors":"Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, Wenping Yuan","doi":"10.5194/essd-16-2465-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2465-2024","url":null,"abstract":"Abstract. Forest harvesting is one of the anthropogenic activities that most significantly affect the carbon budget of forests. However, the absence of explicit spatial information on harvested carbon poses a huge challenge in assessing forest-harvesting impacts, as well as the forest carbon budget. This study utilized provincial-level statistical data on wood harvest, the tree cover loss (TCL) dataset, and a satellite-based vegetation index to develop a Long-term harvEst and Allocation of Forest Biomass (LEAF) dataset. The aim was to provide the spatial location of forest harvesting with a spatial resolution of 30 m and to quantify the post-harvest carbon dynamics. The validations against the surveyed forest harvesting in 133 cities and counties indicated a good performance of the LEAF dataset in capturing the spatial variation of harvested carbon, with a coefficient of determination (R2) of 0.83 between the identified and surveyed harvested carbon. The linear regression slope was up to 0.99. Averaged from 2003 to 2018, forest harvesting removed 68.3 ± 9.3 Mt C yr−1, of which more than 80 % was from selective logging. Of the harvested carbon, 19.6 ± 4.0 %, 2.1 ± 1.1 %, 35.5 ± 12.6 % 6.2 ± 0.3 %, 17.5 ± 0.9 %, and 19.1 ± 9.8 % entered the fuelwood, paper and paperboard, wood-based panels, solid wooden furniture, structural constructions, and residue pools, respectively. Direct combustion of fuelwood was the primary source of carbon emissions after wood harvest. However, carbon can be stored in wood products for a long time, and by 2100, almost 40 % of the carbon harvested during the study period will still be retained. This dataset is expected to provide a foundation and reference for estimating the forestry and national carbon budgets. The 30 m × 30 m harvested-carbon dataset from forests in China can be downloaded at https://doi.org/10.6084/m9.figshare.23641164.v2 (Wang et al., 2023).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"43 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141092029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. High-quality gridded data on industrial water use are vital for research and water resource management. However, such data in China usually have low accuracy. In this study, we developed a gridded dataset of monthly industrial water withdrawal (IWW) for China, which is called the China Industrial Water Withdrawal (CIWW) dataset; this dataset spans a 56-year period from 1965 to 2020 at spatial resolutions of 0.1 and 0.25°. We utilized > 400 000 records of industrial enterprises, monthly industrial product output data, and continuous statistical IWW records from 1965 to 2020 to facilitate spatial scaling, seasonal allocation, and long-term temporal coverage in developing the dataset. Our CIWW dataset is a significant improvement in comparison to previous data for the characterization of the spatial and seasonal patterns of the IWW dynamics in China and achieves better consistency with statistical records at the local scale. The CIWW dataset, together with its methodology and auxiliary data, will be useful for water resource management and hydrological models. This new dataset is now available at https://doi.org/10.6084/m9.figshare.21901074 (Hou and Li, 2023).
{"title":"High-resolution mapping of monthly industrial water withdrawal in China from 1965 to 2020","authors":"Chengcheng Hou, Yan Li, Shan Sang, Xu Zhao, Yanxu Liu, Yinglu Liu, Fang Zhao","doi":"10.5194/essd-16-2449-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2449-2024","url":null,"abstract":"Abstract. High-quality gridded data on industrial water use are vital for research and water resource management. However, such data in China usually have low accuracy. In this study, we developed a gridded dataset of monthly industrial water withdrawal (IWW) for China, which is called the China Industrial Water Withdrawal (CIWW) dataset; this dataset spans a 56-year period from 1965 to 2020 at spatial resolutions of 0.1 and 0.25°. We utilized > 400 000 records of industrial enterprises, monthly industrial product output data, and continuous statistical IWW records from 1965 to 2020 to facilitate spatial scaling, seasonal allocation, and long-term temporal coverage in developing the dataset. Our CIWW dataset is a significant improvement in comparison to previous data for the characterization of the spatial and seasonal patterns of the IWW dynamics in China and achieves better consistency with statistical records at the local scale. The CIWW dataset, together with its methodology and auxiliary data, will be useful for water resource management and hydrological models. This new dataset is now available at https://doi.org/10.6084/m9.figshare.21901074 (Hou and Li, 2023).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"44 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141079312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Climate change has precipitated recurrent extreme events and emerged as an imposing global challenge, exerting profound and far-reaching impacts on both the environment and human existence. The Universal Thermal Climate Index (UTCI), serving as an important approach to human comfort assessment, plays a pivotal role in gauging how humans adapt to meteorological conditions and copes with thermal and cold stress. However, the existing UTCI datasets still grapple with limitations in terms of data availability, hindering their effective application across diverse domains. We have produced GloUTCI-M, a monthly UTCI dataset boasting global coverage and an extensive time series spanning March 2000 to October 2022, with a high spatial resolution of 1 km. This dataset is the product of a comprehensive approach leveraging multiple data sources and advanced machine learning models. Our findings underscored the superior predictive capabilities of CatBoost in forecasting the UTCI (mean absolute error, MAE = 0.747 °C; root mean square error, RMSE = 0.943 °C; and coefficient of determination, R2=0.994) when compared to machine learning models such as XGBoost and LightGBM. Utilizing GloUTCI-M, the geographical boundaries of cold stress and thermal stress areas at global scale were effectively delineated. Spanning 2001–2021, the mean annual global UTCI was recorded at 17.24 °C, with a pronounced upward trend. Countries like Russia and Brazil emerged as key contributors to the mean annual global UTCI increasing, while countries like China and India exerted a more inhibitory influence on this trend. Furthermore, in contrast to existing UTCI datasets, GloUTCI-M excelled at portraying UTCI distribution at finer spatial resolutions, augmenting data accuracy. This dataset can enhance our capacity to evaluate thermal stress experienced by humans, offering substantial prospects across a wide array of applications. GloUTCI-M is publicly available at https://doi.org/10.5281/zenodo.8310513 (Yang et al., 2023).
{"title":"GloUTCI-M: a global monthly 1 km Universal Thermal Climate Index dataset from 2000 to 2022","authors":"Zhiwei Yang, Jian Peng, Yanxu Liu, Song Jiang, Xueyan Cheng, Xuebang Liu, Jianquan Dong, Tiantian Hua, Xiaoyu Yu","doi":"10.5194/essd-16-2407-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2407-2024","url":null,"abstract":"Abstract. Climate change has precipitated recurrent extreme events and emerged as an imposing global challenge, exerting profound and far-reaching impacts on both the environment and human existence. The Universal Thermal Climate Index (UTCI), serving as an important approach to human comfort assessment, plays a pivotal role in gauging how humans adapt to meteorological conditions and copes with thermal and cold stress. However, the existing UTCI datasets still grapple with limitations in terms of data availability, hindering their effective application across diverse domains. We have produced GloUTCI-M, a monthly UTCI dataset boasting global coverage and an extensive time series spanning March 2000 to October 2022, with a high spatial resolution of 1 km. This dataset is the product of a comprehensive approach leveraging multiple data sources and advanced machine learning models. Our findings underscored the superior predictive capabilities of CatBoost in forecasting the UTCI (mean absolute error, MAE = 0.747 °C; root mean square error, RMSE = 0.943 °C; and coefficient of determination, R2=0.994) when compared to machine learning models such as XGBoost and LightGBM. Utilizing GloUTCI-M, the geographical boundaries of cold stress and thermal stress areas at global scale were effectively delineated. Spanning 2001–2021, the mean annual global UTCI was recorded at 17.24 °C, with a pronounced upward trend. Countries like Russia and Brazil emerged as key contributors to the mean annual global UTCI increasing, while countries like China and India exerted a more inhibitory influence on this trend. Furthermore, in contrast to existing UTCI datasets, GloUTCI-M excelled at portraying UTCI distribution at finer spatial resolutions, augmenting data accuracy. This dataset can enhance our capacity to evaluate thermal stress experienced by humans, offering substantial prospects across a wide array of applications. GloUTCI-M is publicly available at https://doi.org/10.5281/zenodo.8310513 (Yang et al., 2023).","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"56 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141079124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The Long-term Gap-free High-resolution Air Pollutants (LGHAP) concentration dataset generated in our previous study has provided spatially contiguous daily aerosol optical depth (AOD) and fine particulate matter (PM2.5) concentrations at a 1 km grid resolution in China since 2000. This advancement empowered unprecedented assessments of regional aerosol variations and their influence on the environment, health, and climate over the past 20 years. However, there is a need to enhance such a high-quality AOD and PM2.5 concentration dataset with new robust features and extended spatial coverage. In this study, we present version 2 of a global-scale LGHAP dataset (LGHAP v2), which was generated using improved big Earth data analytics via a seamless integration of versatile data science, pattern recognition, and machine learning methods. Specifically, multimodal AODs and air quality measurements acquired from relevant satellites, ground monitoring stations, and numerical models were harmonized by harnessing the capability of random-forest-based data-driven models. Subsequently, an improved tensor-flow-based AOD reconstruction algorithm was developed to weave the harmonized multisource AOD products together for filling data gaps in Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals from Terra. The results of the ablation experiments demonstrated better performance of the improved tensor-flow-based gap-filling method in terms of both convergence speed and data accuracy. Ground-based validation results indicated good data accuracy of this global gap-free AOD dataset, with a correlation coefficient (R) of 0.85 and a root mean square error (RMSE) of 0.14 compared to the worldwide AOD observations from the AErosol RObotic NETwork (AERONET), outperforming the purely reconstructed AODs (R = 0.83, RMSE = 0.15), but they were slightly worse than raw MAIAC AOD retrievals (R = 0.88, RMSE = 0.11). For PM2.5 concentration mapping, a novel deep-learning approach, termed the SCene-Aware ensemble learning Graph ATtention network (SCAGAT), was hereby applied. While accounting for the scene representativeness of data-driven models across regions, the SCAGAT algorithm performed better during spatial extrapolation, largely reducing modeling biases over regions with limited and/or even absent in situ PM2.5 concentration measurements. The validation results indicated that the gap-free PM2.5 concentration estimates exhibit higher prediction accuracies, with an R of 0.95 and an RMSE of 5.7 µg m−3, compared to PM2.5 concentration measurements obtained from former holdout sites worldwide. Overall, while leveraging state-of-the-art methods in data science and artificial intelligence, a quality-enhanced LGHAP v2 dataset was generated through big Earth data analytics by cohesively weaving together multimodal AODs and air quality measurements from diverse sources. The gap-free, high-resolution, and global coverage merits render the LGHAP v2 dataset
{"title":"LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics","authors":"Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, Jianping Guo","doi":"10.5194/essd-16-2425-2024","DOIUrl":"https://doi.org/10.5194/essd-16-2425-2024","url":null,"abstract":"Abstract. The Long-term Gap-free High-resolution Air Pollutants (LGHAP) concentration dataset generated in our previous study has provided spatially contiguous daily aerosol optical depth (AOD) and fine particulate matter (PM2.5) concentrations at a 1 km grid resolution in China since 2000. This advancement empowered unprecedented assessments of regional aerosol variations and their influence on the environment, health, and climate over the past 20 years. However, there is a need to enhance such a high-quality AOD and PM2.5 concentration dataset with new robust features and extended spatial coverage. In this study, we present version 2 of a global-scale LGHAP dataset (LGHAP v2), which was generated using improved big Earth data analytics via a seamless integration of versatile data science, pattern recognition, and machine learning methods. Specifically, multimodal AODs and air quality measurements acquired from relevant satellites, ground monitoring stations, and numerical models were harmonized by harnessing the capability of random-forest-based data-driven models. Subsequently, an improved tensor-flow-based AOD reconstruction algorithm was developed to weave the harmonized multisource AOD products together for filling data gaps in Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals from Terra. The results of the ablation experiments demonstrated better performance of the improved tensor-flow-based gap-filling method in terms of both convergence speed and data accuracy. Ground-based validation results indicated good data accuracy of this global gap-free AOD dataset, with a correlation coefficient (R) of 0.85 and a root mean square error (RMSE) of 0.14 compared to the worldwide AOD observations from the AErosol RObotic NETwork (AERONET), outperforming the purely reconstructed AODs (R = 0.83, RMSE = 0.15), but they were slightly worse than raw MAIAC AOD retrievals (R = 0.88, RMSE = 0.11). For PM2.5 concentration mapping, a novel deep-learning approach, termed the SCene-Aware ensemble learning Graph ATtention network (SCAGAT), was hereby applied. While accounting for the scene representativeness of data-driven models across regions, the SCAGAT algorithm performed better during spatial extrapolation, largely reducing modeling biases over regions with limited and/or even absent in situ PM2.5 concentration measurements. The validation results indicated that the gap-free PM2.5 concentration estimates exhibit higher prediction accuracies, with an R of 0.95 and an RMSE of 5.7 µg m−3, compared to PM2.5 concentration measurements obtained from former holdout sites worldwide. Overall, while leveraging state-of-the-art methods in data science and artificial intelligence, a quality-enhanced LGHAP v2 dataset was generated through big Earth data analytics by cohesively weaving together multimodal AODs and air quality measurements from diverse sources. The gap-free, high-resolution, and global coverage merits render the LGHAP v2 dataset ","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"17 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141079199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Can Li, Nickolay A. Krotkov, Joanna Joiner, Vitali Fioletov, Chris McLinden, Debora Griffin, Peter J. T. Leonard, Simon Carn, Colin Seftor, Alexander Vasilkov
Abstract. For nearly two decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO2 column densities from both anthropogenic and volcanic activities. Here, we describe the version 1 NOAA-20 (N20)/OMPS SO2 product, aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO2 products, to N20/OMPS. For volcanic SO2 retrievals, the algorithm is identical between N20 and SNPP/OMPS and produces consistent retrievals for eruptions such as the 2018 Kilauea and 2019 Raikoke. For anthropogenic SO2 retrievals, the algorithm has been customized for N20/OMPS, considering its greater spatial resolution and reduced signal-to-noise ratio as compared with SNPP/OMPS. Over background areas, N20/OMPS SO2 slant column densities (SCD) show relatively small biases, comparable retrieval noise with SNPP/OMPS (after aggregation to the same spatial resolution), and remarkable stability with essentially no drift during 2018–2023. Over major anthropogenic source areas, the two OMPS retrievals are generally well-correlated but N20/OMPS SO2 is biased low especially for India and the Middle East, where the differences reach ~20 % on average. The reasons for these differences are not fully understood but are partly due to algorithmic differences. Better agreement (typical differences of ~10–15 %) is found over degassing volcanoes. SO2 emissions from large point sources, inferred from N20/OMPS retrievals, agree well with those based on OMI, SNPP/OMPS, and TROPOspheric Monitoring Instrument (TROPOMI), with correlation coefficients > 0.98 and overall differences < 10 %. The ratios between the estimated emissions and their uncertainties offer insights into the ability of different satellite instruments to detect and quantify SO2 sources. While TROPOMI has the highest ratios among all four sensors, ratios from N20/OMPS are slightly greater than OMI and substantially greater than SNPP/OMPS. Overall, our results suggest that the version 1 N20/OMPS SO2 product will successfully continue the long-term OMI and SNPP/OMPS SO2 data records. Efforts currently underway will further enhance the consistency of retrievals between different instruments, facilitating the development of multi-decade, coherent global SO2 datasets across multiple satellites.
{"title":"Version 1 NOAA-20/OMPS Nadir Mapper Total Column SO2 Product: Continuation of NASA Long-term Global Data Record","authors":"Can Li, Nickolay A. Krotkov, Joanna Joiner, Vitali Fioletov, Chris McLinden, Debora Griffin, Peter J. T. Leonard, Simon Carn, Colin Seftor, Alexander Vasilkov","doi":"10.5194/essd-2024-168","DOIUrl":"https://doi.org/10.5194/essd-2024-168","url":null,"abstract":"<strong>Abstract.</strong> For nearly two decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO<sub>2</sub> column densities from both anthropogenic and volcanic activities. Here, we describe the version 1 NOAA-20 (N20)/OMPS SO<sub>2</sub> product, aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO<sub>2</sub> products, to N20/OMPS. For volcanic SO<sub>2</sub> retrievals, the algorithm is identical between N20 and SNPP/OMPS and produces consistent retrievals for eruptions such as the 2018 Kilauea and 2019 Raikoke. For anthropogenic SO<sub>2</sub> retrievals, the algorithm has been customized for N20/OMPS, considering its greater spatial resolution and reduced signal-to-noise ratio as compared with SNPP/OMPS. Over background areas, N20/OMPS SO<sub>2</sub> slant column densities (SCD) show relatively small biases, comparable retrieval noise with SNPP/OMPS (after aggregation to the same spatial resolution), and remarkable stability with essentially no drift during 2018–2023. Over major anthropogenic source areas, the two OMPS retrievals are generally well-correlated but N20/OMPS SO<sub>2</sub> is biased low especially for India and the Middle East, where the differences reach ~20 % on average. The reasons for these differences are not fully understood but are partly due to algorithmic differences. Better agreement (typical differences of ~10–15 %) is found over degassing volcanoes. SO<sub>2</sub> emissions from large point sources, inferred from N20/OMPS retrievals, agree well with those based on OMI, SNPP/OMPS, and TROPOspheric Monitoring Instrument (TROPOMI), with correlation coefficients > 0.98 and overall differences < 10 %. The ratios between the estimated emissions and their uncertainties offer insights into the ability of different satellite instruments to detect and quantify SO<sub>2</sub> sources. While TROPOMI has the highest ratios among all four sensors, ratios from N20/OMPS are slightly greater than OMI and substantially greater than SNPP/OMPS. Overall, our results suggest that the version 1 N20/OMPS SO<sub>2</sub> product will successfully continue the long-term OMI and SNPP/OMPS SO<sub>2</sub> data records. Efforts currently underway will further enhance the consistency of retrievals between different instruments, facilitating the development of multi-decade, coherent global SO<sub>2</sub> datasets across multiple satellites.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"34 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141073897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher J. Cox, Janet M. Intrieri, Brian Butterworth, Gijs de Boer, Michael R. Gallagher, Jonathan Hamilton, Erik Hulm, Tilden Meyers, Sara M. Morris, Jackson Osborn, P. Ola G. Persson, Benjamin Schmatz, Matthew D. Shupe, James M. Wilczak
Abstract. From autumn 2021 through summer 2023, scientists from the National Oceanic and Atmospheric Administration (NOAA) and partners conducted the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign in the East River Watershed of Colorado. One objective of SPLASH was to observe the transfer of energy between the atmosphere and the surface, which was done at several locations. Two remote sites were chosen that did not have access to power utilities. These were along the valley floor near the East River in the vicinity of the unincorporated town of Gothic, Colorado. Energy balance measurements were made at these locations using autonomous, single-level flux towers referred to as Atmospheric Surface Flux Stations (ASFS). The ASFS were deployed on 28 September 2021 at the “Kettle Ponds Annex” site and on 12 October 2021 at the “Avery Picnic” site and operated until 19 July and 21 June 2023, respectively. Measurements included basic meteorology; upward and downward longwave and shortwave radiative fluxes, and subsurface conductive flux, each at 1-minute resolution; 3-d winds from a sonic anemometer and H2O/CO2 from an open-path gas analyser, both at 20 Hz from which sensible, latent heat, and CO2 fluxes were derived; and profiles of soil properties in the upper 0.5 m (both sites) and temperature profiles through the snow (at Avery Picnic), each reported between 10 min and 6 hours. For most measurements, uptime was 96 % (Kettle Ponds) and 89 % (Avery Picnic), and collectively 1,184 days of data were obtained between the stations. The purpose of this manuscript is to document the ASFS deployment at SPLASH, the data acquisition and post-processing of measurements, and to serve as a guide for interested users of the data sets, which are archived under the Creative Commons 4.0 Public Domain licensing at Zenodo.
{"title":"Observations of surface energy fluxes and meteorology in the seasonally snow-covered high-elevation East River Watershed during SPLASH, 2021–2023","authors":"Christopher J. Cox, Janet M. Intrieri, Brian Butterworth, Gijs de Boer, Michael R. Gallagher, Jonathan Hamilton, Erik Hulm, Tilden Meyers, Sara M. Morris, Jackson Osborn, P. Ola G. Persson, Benjamin Schmatz, Matthew D. Shupe, James M. Wilczak","doi":"10.5194/essd-2024-158","DOIUrl":"https://doi.org/10.5194/essd-2024-158","url":null,"abstract":"<strong>Abstract.</strong> From autumn 2021 through summer 2023, scientists from the National Oceanic and Atmospheric Administration (NOAA) and partners conducted the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign in the East River Watershed of Colorado. One objective of SPLASH was to observe the transfer of energy between the atmosphere and the surface, which was done at several locations. Two remote sites were chosen that did not have access to power utilities. These were along the valley floor near the East River in the vicinity of the unincorporated town of Gothic, Colorado. Energy balance measurements were made at these locations using autonomous, single-level flux towers referred to as Atmospheric Surface Flux Stations (ASFS). The ASFS were deployed on 28 September 2021 at the “Kettle Ponds Annex” site and on 12 October 2021 at the “Avery Picnic” site and operated until 19 July and 21 June 2023, respectively. Measurements included basic meteorology; upward and downward longwave and shortwave radiative fluxes, and subsurface conductive flux, each at 1-minute resolution; 3-d winds from a sonic anemometer and H<sub>2</sub>O/CO<sub>2</sub> from an open-path gas analyser, both at 20 Hz from which sensible, latent heat, and CO<sub>2</sub> fluxes were derived; and profiles of soil properties in the upper 0.5 m (both sites) and temperature profiles through the snow (at Avery Picnic), each reported between 10 min and 6 hours. For most measurements, uptime was 96 % (Kettle Ponds) and 89 % (Avery Picnic), and collectively 1,184 days of data were obtained between the stations. The purpose of this manuscript is to document the ASFS deployment at SPLASH, the data acquisition and post-processing of measurements, and to serve as a guide for interested users of the data sets, which are archived under the Creative Commons 4.0 Public Domain licensing at Zenodo.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"38 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Drew C. Pendergrass, Daniel J. Jacob, Yujin J. Oak, Jeewoo Lee, Minseok Kim, Jhoon Kim, Seoyoung Lee, Shixian Zhai, Hitoshi Irie, Hong Liao
Abstract. We construct a continuous 24-h daily fine particulate matter (PM2.5)record with 2×2 km2 resolution over eastern China, South Korea, and Japan for 2011–2022 by applying a random forest (RF) algorithm to aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) I and II satellite instruments. The RF uses PM2.5 observations from the national surface networks as training data. PM2.5 network data starting in 2015 in South Korea are extended to pre-2015 with a RF trained on other air quality data available from the network including PM10. PM2.5 network data starting in 2014 in China are supplemented by pre-2014 data from the US embassy and consulates. Missing AODs in the GOCI data are gap-filled by a separate RF fit. We show that the resulting GOCI PM2.5 dataset is successful in reproducing the surface network observations including extreme events, and that the network data in the different countries are representative of population-weighted exposure. We find that PM2.5 peaked in 2014 (China) and 2013 (South Korea, Japan), and has been decreasing steadily since with no region left behind. We quantify the population in each country exposed to annual PM2.5 in excess of national ambient air quality standards and how this exposure evolves with time. The long record for the Seoul Metropolitan Area (SMA) shows a steady decrease from 2013 to 2022 that was not present in the first five years of AirKorea network PM2.5 measurements. Mapping of an extreme pollution event in Seoul with GOCI PM2.5 shows a predicted distribution indistinguishable from the dense urban network observations, while our previous 6×6 km2 product smoothed local features. Our product should be useful for public health studies where long-term spatial continuity of PM2.5 information is essential.
{"title":"A continuous 2011–2022 record of fine particulate matter (PM2.5) in East Asia at daily 2-km resolution from geostationary satellite observations: population exposure and long-term trends","authors":"Drew C. Pendergrass, Daniel J. Jacob, Yujin J. Oak, Jeewoo Lee, Minseok Kim, Jhoon Kim, Seoyoung Lee, Shixian Zhai, Hitoshi Irie, Hong Liao","doi":"10.5194/essd-2024-172","DOIUrl":"https://doi.org/10.5194/essd-2024-172","url":null,"abstract":"<strong>Abstract.</strong> We construct a continuous 24-h daily fine particulate matter (PM<sub>2.5</sub>)<sup> </sup>record with 2×2 km<sup>2</sup> resolution over eastern China, South Korea, and Japan for 2011–2022 by applying a random forest (RF) algorithm to aerosol optical depth (AOD) observations from the Geostationary Ocean Color Imager (GOCI) I and II satellite instruments. The RF uses PM<sub>2.5</sub> observations from the national surface networks as training data. PM<sub>2.5</sub> network data starting in 2015 in South Korea are extended to pre-2015 with a RF trained on other air quality data available from the network including PM<sub>10</sub>. PM<sub>2.5</sub> network data starting in 2014 in China are supplemented by pre-2014 data from the US embassy and consulates. Missing AODs in the GOCI data are gap-filled by a separate RF fit. We show that the resulting GOCI PM<sub>2.5</sub> dataset is successful in reproducing the surface network observations including extreme events, and that the network data in the different countries are representative of population-weighted exposure. We find that PM<sub>2.5</sub> peaked in 2014 (China) and 2013 (South Korea, Japan), and has been decreasing steadily since with no region left behind. We quantify the population in each country exposed to annual PM<sub>2.5</sub> in excess of national ambient air quality standards and how this exposure evolves with time. The long record for the Seoul Metropolitan Area (SMA) shows a steady decrease from 2013 to 2022 that was not present in the first five years of AirKorea network PM<sub>2.5</sub> measurements. Mapping of an extreme pollution event in Seoul with GOCI PM<sub>2.5</sub> shows a predicted distribution indistinguishable from the dense urban network observations, while our previous 6×6 km<sup>2</sup> product smoothed local features. Our product should be useful for public health studies where long-term spatial continuity of PM<sub>2.5</sub> information is essential.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"2012 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paola Emilia Souto-Ceccon, Juan Montes-Perez, Enrico Duo, Paolo Ciavola, Tomas Fernandez Montblanc, Clara Armaroli
Abstract. Detailed information on coastal storm impacts is crucial to evaluate the degree of damages caused by floods, implementing effective recovery actions for risk prevention and preparedness, and to design appropriate coastal zone management plans. This article presents a new database containing information on extreme storm events that generated damage and flooding along European coastlines between 2010 and 2020. The storm events, associated with specific locations, define the test cases which are then used to retrieve information from different extreme coastal storms that hit the same area. The database is a workbook that collects items organised in worksheets and constitutes an inventory of resources defined as a collection of different types of information used to characterize the event (i.e., hydrodynamics, weather information) and its consequences (impacts, flood extent, etc.). The guidelines and polygons in GeoJSON format that define the domain of the sites are also provided together with the workbook. The database contains 11 coastal storm events, 26 sites, 28 test cases, and 232 resources and is designed to allow the addition of new events and resources. Descriptive statistical analyses were performed to define the types and topics addressed by the resources and the distribution of types of resources per country. Lastly, an example of application of the database to European-scale flood modelling is provided.
{"title":"A European database of resources on coastal storm impacts","authors":"Paola Emilia Souto-Ceccon, Juan Montes-Perez, Enrico Duo, Paolo Ciavola, Tomas Fernandez Montblanc, Clara Armaroli","doi":"10.5194/essd-2024-183","DOIUrl":"https://doi.org/10.5194/essd-2024-183","url":null,"abstract":"<strong>Abstract.</strong> Detailed information on coastal storm impacts is crucial to evaluate the degree of damages caused by floods, implementing effective recovery actions for risk prevention and preparedness, and to design appropriate coastal zone management plans. This article presents a new database containing information on extreme storm events that generated damage and flooding along European coastlines between 2010 and 2020. The storm events, associated with specific locations, define the test cases which are then used to retrieve information from different extreme coastal storms that hit the same area. The database is a workbook that collects items organised in worksheets and constitutes an inventory of resources defined as a collection of different types of information used to characterize the event (i.e., hydrodynamics, weather information) and its consequences (impacts, flood extent, etc.). The guidelines and polygons in GeoJSON format that define the domain of the sites are also provided together with the workbook. The database contains 11 coastal storm events, 26 sites, 28 test cases, and 232 resources and is designed to allow the addition of new events and resources. Descriptive statistical analyses were performed to define the types and topics addressed by the resources and the distribution of types of resources per country. Lastly, an example of application of the database to European-scale flood modelling is provided.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"50 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Internal waves (IWs) are an important ocean process in transmitting energy between multiscale ocean dynamics, making them a crucial oceanic phenomenon. The South China Sea (SCS) is renowned for its frequent large-amplitude IW activities, emphasizing the importance of collecting and analyzing extensive observational data. In this study, we present a comprehensive IW dataset covering the northern SCS covering 112.40–121.32° E and 18.32–23.19° N, spanning from 2000 to 2022 with a 250 m spatial resolution. The IW dataset comprises 3085 high-resolution MODIS true-color IW images paired with precise IW position information extracted from 15830 MODIS images using advanced deep learning techniques. IWs in the northern SCS are divided into four regions based on extracted IW spatial distributions, facilitating detailed analyses of IW characteristics, including spatial and temporal distributions across both the entire northern SCS and its sub-regions. Notably, we uncover typical "double-peak" distributions corresponding to the lunar day, underscoring IWs' close relationship with tides. Furthermore, we identify two IW-free silence regions attributed to underwater topography influences, indicating varied IW characteristics across regions and suggesting underlying mechanisms warrant further investigation. The constructed dataset holds significant potential for applications in studying IW-environment interactions, developing monitoring and prediction models, validating and enhancing numerical simulations, and serving as an educational resource to foster awareness and interest in IW research.
{"title":"Deep Learning-Derived Long-Term Dataset of Internal Waves in the Northern South China Sea from Satellite Imagery","authors":"Xudong Zhang, Xiaofeng Li","doi":"10.5194/essd-2024-124","DOIUrl":"https://doi.org/10.5194/essd-2024-124","url":null,"abstract":"<strong>Abstract.</strong> Internal waves (IWs) are an important ocean process in transmitting energy between multiscale ocean dynamics, making them a crucial oceanic phenomenon. The South China Sea (SCS) is renowned for its frequent large-amplitude IW activities, emphasizing the importance of collecting and analyzing extensive observational data. In this study, we present a comprehensive IW dataset covering the northern SCS covering 112.40–121.32° E and 18.32–23.19° N, spanning from 2000 to 2022 with a 250 m spatial resolution. The IW dataset comprises 3085 high-resolution MODIS true-color IW images paired with precise IW position information extracted from 15830 MODIS images using advanced deep learning techniques. IWs in the northern SCS are divided into four regions based on extracted IW spatial distributions, facilitating detailed analyses of IW characteristics, including spatial and temporal distributions across both the entire northern SCS and its sub-regions. Notably, we uncover typical \"double-peak\" distributions corresponding to the lunar day, underscoring IWs' close relationship with tides. Furthermore, we identify two IW-free silence regions attributed to underwater topography influences, indicating varied IW characteristics across regions and suggesting underlying mechanisms warrant further investigation. The constructed dataset holds significant potential for applications in studying IW-environment interactions, developing monitoring and prediction models, validating and enhancing numerical simulations, and serving as an educational resource to foster awareness and interest in IW research.","PeriodicalId":48747,"journal":{"name":"Earth System Science Data","volume":"15 1","pages":""},"PeriodicalIF":11.4,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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