本数据集包含1984-2020年新疆区域1：50万洪水致灾类型专题图及其对应的制图数据。专题图为jpg格式，数据为栅格数据（tif），地理坐标系为WGS_1984，投影为等积圆锥投影 Asia North Albers Equal Area Conic，空间分辨率为500m。\n
其栅格value值代表洪水风险等级，可用于定量描述春季高温致使冰雪快速融化引发的融雪型洪水和夏秋季强降雨造成的暴雨洪涝,这两类不同成因所致洪水风险等级及其空间特征。此专题图为区域防洪减灾宏观管理提供融雪型洪水、暴雨洪涝及其混合的洪水灾害风险水平空间参考数据。采用完全开放共享。", "ds_source": "
新疆区域多年日降水量≥25mm日数极值；新疆区域多年日降水量极值；新疆区域多年最大月降水量等，详细描述见附件。", "ds_process_way": "
本专题图由“新疆区域1：50万融雪型洪水灾害风险水平专题图”和“新疆区域1：50万暴雨洪涝灾害风险水平专题图”经GIS栅格叠加制作而成。\n
其中，新疆区域1：50万融雪型洪水灾害风险水平专题图基于新疆多年（1984-2020）融雪型洪水灾害及其关联自然、社会和风险基础信息，通过GIS和随机森林（RF）方法构建模型并经GIS制图完成。栅格value值为1该像元为融雪型洪水灾害易发区，属高风险区，0为无风险区域。新疆区域1：50万暴雨洪涝灾害风险水平专题图基于新疆多年（1984-2020）暴雨洪涝灾害及其关联自然、社会和风险基础信息，通过GIS和随机森林（RF）方法构建模型并经GIS制图完成，栅格value值为1该像元为暴雨洪涝无风险区，2为低风险区，3为一般风险区，4为重点风险区。\n
以新疆区域暴雨洪涝灾害为例，RF基本原理为假设有N个暴雨洪涝风险等级、M个特征因子，首先通过自助法（Bootstrap Method）从样本集中有放回地随机抽取样本来构建新的训练样本集，每次重抽样未被抽到的样本组成袋外数据（out-of-bag，OOB），作为测试样本集。\n
其次，从M个特征因子中随机抽取特征因子，每株分类树都是从这Mtry个特征因子中选择一个最具有分类能力的特征因子进行分支且每株树都最大限度地生长，不需要任何剪枝。上述过程重复多次，生成Ntree株分类树。\n
最后，将所生成的Ntree株分类树聚合得到随机森林，分类结果的众数作为随机森林的分类结果，即暴雨洪涝风险等级。这里使用R语言randomForest包来构建新疆暴雨洪涝灾害RF评估模型，技术核心是确定最优Ntree和Mtry。", "ds_quality": "
新疆区域不同成因所致洪水风险等级专题图通过加工处理得到。具体加工处理包括投影转换、极差标准化、GIS和RF计算模拟及其精度验证和GIS制图等。", "ds_acq_start_time": "1984-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "新疆区域", "ds_acq_lon_east": 49.163888888888884, "ds_acq_lat_south": 34.34444444444445, "ds_acq_lon_west": 73.44722222222222, "ds_acq_lat_north": 96.37777777777777, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 5890202, "ds_files_count": 2, "ds_format": "tif.jpg", "ds_space_res": "500m", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "等积圆锥", "ds_thumbnail": "9de1ee13-4ccd-4c30-b9a5-9007cce6fe94.jpg", "ds_thumb_from": 2, "ds_ref_way": "刘艳，1984-2020年新疆区域1:50万融雪型洪水致灾数据，国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2023，doi：10.12072/ncdc.isnow.db2716.2023", "paper_ref_way": "", "ds_ref_instruction": "刘艳,中国气象局乌鲁木齐沙漠气象研究所,1984-2020年新疆区域1:50万融雪型洪水致灾类型专题图.2021", "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": "10.12072/ncdc.isnow.db2716.2023", "subject_codes": [ "170.15", "170.45", "170.55" ], "quality_level": 3, "publish_time": "2023-01-25 16:50:16", "last_updated": "2023-02-16 08:50:43", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2716.2023", "i18n": { "en": { "title": "Disaster data of 1:500000 snowmelt flood in Xinjiang from 1984 to 2020", "ds_format": "", "ds_source": "
\nThe annual daily precipitation in Xinjiang is ≥ 25mm, the extreme number of days; The extreme value of annual daily precipitation in Xinjiang; The maximum monthly precipitation for many years in Xinjiang is described in the annex.", "ds_quality": "
\nThe thematic map of flood risk level caused by different causes in Xinjiang region is obtained through processing. The specific processing includes projection conversion, range standardization, GIS and RF calculation simulation, accuracy verification and GIS mapping.", "ds_ref_way": "", "ds_abstract": "
\nThis data set contains the thematic map of 1:500000 flood disaster types in Xinjiang from 1984 to 2020 and its corresponding mapping data. The thematic map is in jpg format, the data is raster data (tif), and the geographic coordinate system is WGS_ In 1984, the projection was the isoproduct conic projection of Asia North Alberts Equal Area Conic, with a spatial resolution of 500m.\n
The grid value value represents the flood risk level, which can be used to quantitatively describe the snowmelt flood caused by the rapid melting of ice and snow caused by the high temperature in spring and the rainstorm flood caused by the heavy rainfall in summer and autumn, and the flood risk level and its spatial characteristics caused by these two different causes. This thematic map provides the spatial reference data of snowmelt flood, rainstorm flood and its mixed flood disaster risk level for regional flood control and disaster reduction macro-management. Adopt fully open sharing.
", "ds_time_res": "", "ds_acq_place": "Xinjiang Region", "ds_space_res": "500m", "ds_projection": "", "ds_process_way": "\nThis thematic map is composed of the \"thematic map of 1:500000 snowmelt flood disaster risk level in Xinjiang region\" and the \"thematic map of 1:500000 storm flood disaster risk level in Xinjiang region\", which are superimposed by GIS grid.\n
Among them, the 1:500000 snowmelt flood risk level thematic map in Xinjiang region is based on the snowmelt flood disaster and its associated natural, social and risk basic information for many years (1984-2020) in Xinjiang, and the model is built by GIS and random forest (RF) methods and completed by GIS mapping. The grid value value is 1. This pixel is the snowmelt flood disaster prone area, which is a high-risk area, and 0 is a risk-free area. The thematic map of risk level of 1:500000 rainstorm and flood disaster in Xinjiang region is based on the basic information of rainstorm and flood disaster and its associated nature, society and risk in Xinjiang for many years (1984-2020). The model is built by GIS and random forest (RF) methods and completed by GIS mapping. The grid value is 1. This pixel is the rainstorm and flood risk-free area, 2 is the low-risk area, 3 is the general risk area, and 4 is the key risk area.\n
Taking the regional rainstorm and flood disaster in Xinjiang as an example, the basic principle of RF is to assume that there are N rainstorm and flood risk levels and M characteristic factors. First, the bootstrap method is used to randomly select samples from the sample set to build a new training sample set. The samples that are not sampled each time constitute out-of-bag (OOB) data as the test sample set.\n
Secondly, feature factors are randomly selected from M feature factors. Each classification tree branches from one of the Mtry feature factors with the most classification ability, and each tree grows to the maximum without any pruning. The above process was repeated several times to generate Ntree classification tree.\n
Finally, the generated Ntree classification tree is aggregated to obtain a random forest, and the mode of the classification result is used as the classification result of the random forest, that is, the risk level of rainstorm and flood. Here, the R language randomForest package is used to build the RF assessment model of rainstorm and flood disaster in Xinjiang. The technical core is to determine the optimal Ntree and Mtry.", "ds_ref_instruction": " Liu Yan, Urumqi Desert Meteorological Research Institute, China Meteorological Administration, thematic map of disaster types caused by 1:500000 snowmelt flood in Xinjiang from 1984 to 2020. 2021" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "融雪型洪水" ], "ds_subject_tags": [ "大气科学", "地理学", "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆区域" ], "ds_time_tags": [ 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, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "刘艳", "email": "liuyan@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘艳", "email": "liuyan@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "刘艳", "email": "liuyan@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" } ], "category": "积雪" }