冰川储量和面积是开展冰川变化对河流水文过程影响研究的重要基础数据。受历史时期卫星遥感影像匮乏的影响，传统基于遥感影像的历史时期冰川编目编制困难。长江黄河源区冰川的快速变化对流域中上游水文过程具有重要影响，但多时期冰川编目和储量数据的缺乏阻碍了这两个流域冰川变化影响的研究。1960-2020年长江黄河源冰川厚度与面积IGM模型模拟数据集是为弥补这一数据空缺而制作。所采用的IGM冰川模型是国际冰川学界近期推出的基于深度学习方法模拟冰川演化过程的模型，能够基于冰川动力学原理和物质平衡过程模拟百年尺度的冰川演化历史，模型结果对气候变化影响下的冰川演化过程具有较好的反映。对长江黄河源区冰川的模拟结果包括1960-2020年期间以5年为间隔输出的冰川厚度以及由其推算的冰川面积，可在一定程度上弥补长江黄河源区早期数据缺失的问题。", "ds_source": "
IGM（即Instructed Glacier Model)由瑞士苏黎世大学Guillaume Jouvet等人开发，2021年正式发布，主要用于模拟三维冰川演化过程。IGM模型利用Python编制，充分利用了OGGM等现有工具，采用模块化架构，并使用水平规则网格进行数值离散化，实现冰川质量守恒、高阶三维冰流力学、冰热状态焓模型、融化/积累表面质量平衡模型和其他冰川过程的模拟。IGM采用TensorFlow库，基于GPU运算，以矢量化方式对冰流物理进行建模，计算效率优异，主要用于大区域/高分辨率建模，同时也可用于单条冰川演化过程的模拟。在模型输入为冰川范围（冰川边界）时，IGM将自动收集冰川所在区域的DEM和再分析气象数据，对冰川在指定时段内的物质平衡和动力过程进行模拟，默认输出为空间分辨率为100-1000m、时间分辨率为逐月至逐年的冰川厚度，并以厚度这一冰川状态根本性指征的变化来表示冰川的演化过程。", "ds_process_way": "
(1)以长江黄河源地区1970年冰川边界为IGM模型的初始条件，以遥感获取的1990、2000、2010和2020年前后冰川边界作为控制条件，逐条模拟1960-2020年期间长江黄河源区冰川的物质平衡和动力过程，并输出1960-2020期间每5年一期、分辨率为100m的冰川厚度；\n
(2)采用双线性插值方法，将原100m分辨率冰川厚度插值为25m；\n
(3)提取重采样模拟厚度中的有效（非零值）模拟范围并裁剪形成逐条冰川的冰川厚度模拟结果；\n
(4)以2m为判断冰川与非冰川的厚度阈值，从裁剪后冰川厚度中生成编码唯一的逐冰川掩膜，并进行拼接和栅矢转换，形成冰川边界。", "ds_quality": "
由于模拟的冰川边界是从冰川厚度模拟结果中生成，驱动模型的再分析气候资料分辨率较差、数据不确定性较大，且所用DEM及模型本身也均存在诸多不确定性，因此无论是模拟的冰川厚度和冰川边界均存在一定模糊性和不确定性，难以与真实冰川厚度和边界一一对应。在模拟过程中，为控制模型模拟过程，除了以1970s冰川边界为初始约束条件外，还加入1990、2000、2010和2020年前后冰川边界作为控制条件，最大限度保证模拟边界和遥感提取的真实边界相契合。通过对比发现，模拟生成的1970、1990、2000、2010和2020年冰川面积与真实面积间的平均差异为2.8%。", "ds_acq_start_time": "1960-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "长江黄河源区", "ds_acq_lon_east": 101.13, "ds_acq_lat_south": 31.683333333333334, "ds_acq_lon_west": 90.5, "ds_acq_lat_north": 36.01, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 258726079, "ds_files_count": 3, "ds_format": "*.tif, *.shp", "ds_space_res": "25 m", "ds_time_res": "5年", "ds_coordinate": "WGS84", "ds_projection": "冰川厚度采用通用横轴墨卡托（UTM）投影坐标系统；冰川边界采用Albers等面积割圆锥投影坐标系统", "ds_thumbnail": "b1d24e82-3154-46c5-b29f-a58af6766857.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "1）模拟冰川厚度单位为米，文件格式为GeoTIFF，文件名称为\"RGI6冰川编码_年份.tif\"，投影方式为UTM投影，每个年份所有冰川的厚度tif文件位于同一个以年份为名称的文件夹中；\r\n2）模拟冰川边界以ESRI Shapefile格式存储，投影方式为Albers等面积投影，同一年份的所有冰川边界存储于同一个以年份命名的shape文件中。", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-08-27 19:14:50", "last_updated": "2025-09-08 16:41:56", "protected": false, "protected_to": "2027-09-08 00:00:00", "lang": "zh", "cstr": "11738.11.NCDC.GLACIER.DB6957.2025", "i18n": { "en": { "title": "Simulated thickness and area of glaciers in the source region of Yangtze and Yellow Rivers using IGM model", "ds_format": "*.tif, *.shp", "ds_source": "
IGM (Instructed Glacier Model) was developed by Guillaume Jouvet et al. of University of Zuich, Switzerland, and was formally published in 2021, aimed to simulate 3D glacier evolution. The IGM model was developed using Python with module-wise organization, takes large benefit of existing tools such as OGGM, and uses a horizontal regular grid for numerical discretization. IGM implements mass conservation, high-order 3D ice flow mechanics, an Enthalpy model for the thermic regime of ice, melt/accumulation surface mass balance model, and other glaciological processes. It based on TensorFlow library which facilitate GPU to realize fully vectorized simulation on the ice flow physics, therefore has very high calculation effeciency. The IGM can be used to simulate large domain / high resolution glacier evolution, but can also simulate individual glaciers. When take glacier extent as the input, IGM will automatically collect the DEM and reanalyzed climate data, and simulate the glacier mass balance and dynamic process in specified time periods. The default of IGM output is glacier depth with spatial resolution of 100-1000m and temporaral resolution of month to year, and can describe the glacier evolution with time series of glacier depths.", "ds_quality": "
Since the simulated glacier boundaries are generated from glacier thickness simulation results, and the reanalysis climate data driving the model has coarse resolution and high uncertainty, coupled with inherent uncertainties in the DEM used and the model itself, both the simulated glacier thickness and boundaries possess a certain degree of ambiguity and uncertainty, which both have limited comparability with the actual glacier thickness and boundaries. However, during the simulation process, in addition to using the 1970s glacier boundary as the initial constraint, glacier boundaries around 1990, 2000, 2010, and 2020 were also incorporated as control conditions. This approach maximizes the consistency between the simulated boundaries and the real boundaries extracted from remote sensing. Comparative analysis shows that the mean difference between the simulated and actual glacier areas for 1970, 1990, 2000, 2010, and 2020 is approximately 2.8%.", "ds_ref_way": "", "ds_abstract": "
The glacier volume and area are two fundamental data sources on simulating the impacts of glacier change on hrdrologic processes at drainage basin scale. However, the lack of historical satellite imageries significantly limited the compilation of early stage glacier inventories. The rapid changes of glaciers in the source region of Yangtze and Yellow Rivers have large influences on the basin scale hydrological processes, but the shortage of multi-temporaral glacier inventories has largely impeded the studies on the hydrological impacts of glacier change in both river basins. The glacier thickness and area dataset during 1960-2020 in the source region of Yangtze and Yellow Rivers were aimed to fill this data gap. They were produced by the latest glacier model named IGM, which is a deep learnig based glacier model drived by glacier dynamic model and mass balance model, can simulate the century scale glacier evolution and give acceptable illustration on glacier change processe under climate change. The dataset includes the glacier depth with 5-year interval between 1960-2020 of glaciers in the source regions of Yangtze and Yellow Rivers, and the glacier area deduced from the glacier depth, which can fill up the glacier data gaps in both river basins to a certain extent.
", "ds_time_res": "5年", "ds_acq_place": "Source Regions of Yangtze and Yellow Rivers", "ds_space_res": "25 m", "ds_projection": "", "ds_process_way": "(1) Using the 1970 glacier boundary in the source regions of the Yangtze and Yellow Rivers as the initial condition and remote sensing-derived glacier boundaries around 1990, 2000, 2010, and 2020 as control conditions for the IGM model, simulate the glacier mass balance and dynamic processes from 1960 to 2020, and output glacier thickness at five-year intervals with a resolution of 100 meters; (2) Employ bilinear interpolation to resample the original 100-meter resolution glacier thickness into 25-meter resolution; (3) Extract the valid (non-zero) simulated area from the resampled glacier thickness, then clip to form the glacier thickness simulation results for each individual glacier; (4) Using a 2-meter thickness threshold to identify glacier from non-glacier areas from the clipped glacier thickness data to generate coded glacier masks, then perform mosaicking and raster to vector conversion to delineate glacier boundaries.", "ds_ref_instruction": "1) The simulated glacier thickness is in meters, stored in GeoTIFF format. The filename is \"RGI6_(GlacierCode)_Year.tif\" in UTM projection. All glacier thickness TIFF files for each year locate in same folder named with the year. \r\n2) The simulated glacier boundaries are stored in ESRI Shapefile format in Albers equal area projection. All glacier outlines for the same year are stored within a single shapefile named with the year." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "冰川厚度", "冰川面积", "冰川动力模拟", "冰川物质平衡模拟", "IGM模型" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "长江源地区", "黄河源地区" ], "ds_time_tags": [ 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020 ], "ds_contributors": [ { "true_name": "郭万钦", "email": "guowq@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王振峰", "email": "zhenfeng905@gmail.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "吴晓东", "email": "wxd565@163.com", "work_for": "中国科学院西北生态环境与资源研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "郭万钦", "email": "guowq@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "吴晓东", "email": "wxd565@163.com", "work_for": "中国科学院西北生态环境与资源研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "郭万钦", "email": "guowq@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "冰川" }