陆地蒸发（ET）在水文和能量循环中起着至关重要的作用。然而，由于模型参数和强迫数据不完善，广泛使用的基于模型的产品尽管很有帮助，但仍存在很大的不确定性。现有观测数据的缺乏使估算工作更加复杂。因此，迫切需要为气候引起的水文和能源变化确定不确定性较低的全球替代土地蒸散发。本研究将现有的三个基于模式的产品--第五代 ECMWF 再分析（ERA5）、全球陆地数据同化系统版本 2（GLDAS2）和第二个现代-年代研究与应用回顾分析（MERRA-2）--结合起来，获得了一个空间分辨率为 0.25°的长期（1980-2017）日蒸散发产品的单一框架。在此，我们使用可靠性集合平均（REA）方法（该方法利用参考数据将误差降至最低），利用变异系数（CV）将三种产品在产品之间一致性较高的区域进行组合。分别选择全球陆地蒸发阿姆斯特丹模式 3.2a 版（GLEAM3.2a）和通量塔观测数据作为参考和评估数据。结果表明，合并后的产品在各种植被覆盖情况下均表现良好。合并产品还很好地捕捉了不同地区的陆地蒸发趋势，在南美洲的亚马逊平原和非洲中部的刚果盆地显示出明显的下降趋势，而在北美洲东部、欧洲西部、亚洲南部和大洋洲北部则显示出上升趋势。
", "ds_source": "选择了三个广泛使用的陆地蒸散发数据集进行合并，包括第五代 ECMWF 再分析（ERA5；Hersbach 等，2020 年）、第二次现代-年代研究与应用回顾分析（MERRA-2；Gelaro 等，2017 年）和全球陆地数据同化系统第 2 版蒸散发（GLDAS2；Sheffield 和 Wood，2007 年）。这些蒸散发产品的空间和时间分辨率差异被调整为日时间尺度和 0.25°，时间跨度为 1980 年至 2017 年。
", "ds_process_way": "在这项研究中，我们探讨了是否可以通过加权组合ERA5、GLDAS和MERRA-2的估算结果来获得更准确的蒸散发结果。理想情况下，分配给每个产品的权重应基于对合并过程中不确定性的准确描述。因此，根据 GLEAM 对三个数据集进行了加权，并在选定地点对合并后的蒸散发产品的性能进行了研究。需要为三个产品的加权组合定义一组权重，通常是基于每个产品的单独不确定性。以往研究中使用的最简单策略是假设所有三个产品具有相同的不确定性，因此合并产品是每个产品的简单平均值。本研究中使用的一种更详细的策略是根据产品的不确定性对其进行权衡。我们的预期目标是开发出最小均方根偏差 (RMSD) 的产品，在我们的合并策略中，我们称之为最优产品。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2017-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 25210552112, "ds_files_count": 77, "ds_format": "NetCDF", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "e811ae35-3453-4bac-b0c3-6630c25165f6.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-09-27 09:24:05", "last_updated": "2025-06-30 16:18:32", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6664.2024", "i18n": { "en": { "title": "A Harmonized Global Land Evaporation Dataset from Model-based Products Covering 1980-2017", "ds_format": "NetCDF", "ds_source": "Three widely used land ET datasets were selected for merging, including the fifth-generation ECMWF reanalysis (ERA5; Hersbach et al., 2020), the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2; Gelaro et al., 2017), and Global Land Data Assimilation System Version 2 ET (GLDAS2; Sheffield and Wood, 2007). The differences in spatial and temporal resolution among the ET products were rescaled to a daily timescale and 0.25°, with the time span from 1980 to 2017.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Land evaporation (ET) plays a crucial role in the hydrological and energy cycle. However, the widely used model-based products, even though helpful, are still subject to great uncertainties due to imperfect model parameterizations and forcing data. The lack of available observed data has further complicated estimation. Hence, there is an urgency to define the global proxy land ET with lower uncertainties for climate-induced hydrology and energy change. This study has combined three existing model-based products – the fifth-generation ECMWF reanalysis (ERA5), Global Land Data Assimilation System Version 2 (GLDAS2), and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) – to obtain a single framework of a long-term (1980–2017) daily ET product at a spatial resolution of 0.25°. Here, we use the reliability ensemble averaging (REA) method, which minimizes errors using reference data, to combine the three products over regions with high consistencies between the products using the coefficient of variation (CV). The Global Land Evaporation Amsterdam Model Version 3.2a (GLEAM3.2a) and flux tower observation data were selected as the data for reference and evaluation, respectively. The results showed that the merged product performed well over a range of vegetation cover scenarios. The merged product also captured the trend of land evaporation over different areas well, showing the significant decreasing trend in the 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.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "In this study, we investigated whether more accurate ET results could be obtained through the weighted combination of ERA5, GLDAS and MERRA-2 estimation. Ideally, the weight assigned to each product should be based on an accurate description of the uncertainty during the merging process. Therefore, the three datasets have been weighted with respect to GLEAM, and the performance of the merged ET products has been studied at the selected sites. A set of weights need to be defined for the weighted combination of three products, usually based on the individual uncertainty in each product. The simplest strategy used in previous studies has been to assume that all three products have the same uncertainty; thus the merged product is a simple average of each product. A more detailed strategy used in this study is to weigh the products based on their uncertainties. The expected goal is to develop a product that minimizes root mean square deviation (RMSD), which we call optimal in the context of our merging strategy.
", "ds_ref_instruction": "When using data, users should clearly declare the source of the data in the main text and cite the citation method provided by this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "土地蒸发", "ERA5", "GLDAS2", "MERRA-2", "GLEAM3.2a" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017 ], "ds_contributors": [ { "true_name": "王国杰", "email": "gwang@nuist.edu.cn", "work_for": "南京信息工程大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "王国杰", "email": "gwang@nuist.edu.cn", "work_for": "南京信息工程大学", "country": "中国" } ], "ds_managers": [ { "true_name": "王国杰", "email": "gwang@nuist.edu.cn", "work_for": "南京信息工程大学", "country": "中国" } ], "category": "生态" }