本图集针对东北农田地区，包括黑龙江省、吉林省、辽宁省及内蒙古东四盟市地区，利用“中国积雪分布与特性调查”项目第二课题提供的25km×25km分辨率逐日雪水当量产品，计算了2017-2020年冬季（12月至翌年3月）东北农田地区的雪水潜在供应量。在不考虑蒸散发的作用下，雪水潜在供应量可以认为是整个降雪季的雪水当量的总和。由2017-2020年的气象站观测雪深数据可知，每年冬季的地面最大积雪量在3月份之前，而3月份后积雪逐渐消融，但期间仍存在降雪，因此可认为雪水潜在供应量为3月份之前的最大雪水当量和3月份后增加的雪水当量的总和。本数据集共包含2017-2020年3个积雪季专题图图件和对应的制图数据，采用完全开放共享。", "ds_source": "
原始数据的主要来源为“中国积雪分布与特性调查”项目第二课题提供的2017-2020年的25km×25km分辨率逐日雪水当量产品。", "ds_process_way": "
本图集主要利用第二课题每日雪水当量产品计算每年冬季（12月至翌年3月）东北农田地区的雪水潜在供应量。在不考虑蒸散发作用的情况下，东北农田区雪水潜在供应量可认为是积累期和稳定期期间累积的最大雪水当量（3月1日前）与消融期（3月1日之后）期间新增降雪量的雪水当量的总和。因此将每年3月1日作为时间节点，基于第二课题每日雪水当量产品，通过MATLAB软件编程计算得到在3月份前东北农田区的日最大雪水当量值，然后再计算3月份后由于降雪增加的雪水当量，最终得到每年冬季的东北农田地区的雪水潜在供应量的数据集，具体算法如等式（1）-（3）所示。\n
其中，SWE为第二课题每日雪水当量产品，t1为每年12月1日，t2为翌年3月1日, t3为翌年3月31日, 为新增雪水当量。", "ds_quality": "
数据主要利用第二课题每日雪水当量产品，进而得到2017-2020年25km×25km分辨率的东北农田区雪水潜在供应量专题图，因此其精度依赖于这些数据产品的时空分辨率和精度。第二课题利用2011-2019年八个积雪季的站点雪深数据对发展的雪水当量反演算法进行验证，结果显示雪深的unRMSE为5.09cm，相当于雪水当量unRMSE为10mm左右（雪密度假设为200kg/m3）。", "ds_acq_start_time": "2017-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "东北", "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": "apply-access", "ds_total_size": 3707925, "ds_files_count": 2, "ds_format": "geotif.jpg", "ds_space_res": "25km*25km", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "5494d08b-c849-4061-938f-34ebb27157bb.png", "ds_thumb_from": 0, "ds_ref_way": "李晓峰，2017-2020年东北农田雪水潜在供应量数据，国家冰川冻土沙漠科学数据中心(http://www.ncdc.ac.cn/)，2023，doi：10.12072/ncdc.isnow.db2720.2023", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2023-01-25 16:52:23", "last_updated": "2023-02-17 02:10:06", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2720.2023", "i18n": { "en": { "title": "2017-2020 potential supply data of farmland snow water in Northeast China", "ds_format": "", "ds_source": "
\nThe main source of the original data is the 25km from 2017-2020 provided by the second subject of the \"Survey of Snow Distribution and Characteristics in China\" project × Daily snow water equivalent products with 25km resolution.", "ds_quality": "
\nThe data is mainly based on the daily snow-water equivalent products of the second subject, and then 25 km from 2017 to 2020 is obtained × The thematic map of the potential supply of snow water in the northeast farmland area with 25km resolution, so its accuracy depends on the spatial and temporal resolution and accuracy of these data products. The second topic uses the station snow depth data of eight snow seasons from 2011 to 2019 to verify the developed snow water equivalent inversion algorithm. The results show that the unRMSE of snow depth is 5.09cm, which is equivalent to the unRMSE of snow water equivalent is about 10mm (the snow density is assumed to be 200kg/m3).", "ds_ref_way": "", "ds_abstract": "
\nThis atlas is aimed at the farmland areas in Northeast China, including Heilongjiang Province, Jilin Province, Liaoning Province and the East Four League cities in Inner Mongolia, and uses the 25km provided by the second subject of the \"Investigation on the Distribution and Characteristics of Snow Cover in China\" project × The potential supply of snow water in the northeast farmland area in the winter of 2017-2020 (December to March of the next year) is calculated by using the daily snow water equivalent product with 25km resolution. Without considering the effect of evapotranspiration, the potential supply of snow water can be considered as the sum of snow water equivalent in the whole snowfall season. According to the observed snow depth data of meteorological stations in 2017-2020, the maximum snow cover on the ground in each winter is before March, and the snow gradually melts after March, but there is still snow during the period. Therefore, the potential supply of snow water can be considered as the sum of the maximum snow equivalent before March and the increased snow equivalent after March. This data set contains thematic maps and corresponding mapping data of three snow seasons from 2017 to 2020, which are fully open and shared.
", "ds_time_res": "", "ds_acq_place": "northeast", "ds_space_res": "25km*25km", "ds_projection": "", "ds_process_way": "\nThis atlas mainly uses the daily snow-water equivalent products of the second subject to calculate the potential supply of snow-water in the northeast farmland area every winter (December to March of the next year). Without considering the role of evapotranspiration, the potential supply of snow water in the northeast farmland can be considered as the sum of the maximum accumulated snow water equivalent (before March 1) during the accumulation period and the stable period and the snow water equivalent of the new snowfall during the melting period (after March 1). Therefore, taking March 1 of each year as the time node, based on the daily snow water equivalent product of the second subject, the daily maximum snow water equivalent value of the northeast farmland area before March is calculated through MATLAB software programming, and then the snow water equivalent increased by snowfall after March is calculated, and finally the data set of the potential snow water supply of the northeast farmland area in each winter is obtained. The specific algorithm is shown in equations (1) - (3).\n
Among them, SWE is the daily snow water equivalent product of the second subject, t1 is December 1 of each year, t2 is March 1 of the next year, and t3 is March 31 of the next year, which is the new snow water equivalent.", "ds_ref_instruction": " " } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "积雪量" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "东北农田地区" ], "ds_time_tags": [ 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" } ], "category": "积雪" }