BIO2 = 平均昼夜温差，月内最高温度与最低温度之差的平均值，℃。\nBIO4 = 温度季节性，每个月平均温度的标准差，并将其放大100倍。\nBIO5 = 最暖月的最高温度，一年中最暖月份的最高温度，℃。\nBIO8 = 最湿季度平均温度，一年中降水量最多的三个月（即最湿润季度）的平均温度，℃。\nBIO9 = 最干季度平均气温，一年中降水量最少的三个月（即最干燥季度）的平均气温，℃。\nBIO14 = 最干月降水量，mm。\nBIO15 = 降水季节性。\nBIO18 = 最暖季降水量，mm。\n海拔（elev）、坡度（slope）、坡向（aspect）\n未来数据选自第六次国际耦合模式比较计划（CMIP6）中的中等分辨率气候模式（BCC-CSM2-MR）下的 4 种共享社会经济路径（SSP），即SSP1-2.6、SSP2-4.5、SSP3-7.0 和 SSP5-8.5。
", "ds_source": "数据来自WorldClim version 2.1，该数据提供历史（1970-2000）和未来（2021-2040，2041-2060，2061-2080，2081-2100）四种分辨率（30″、2.5′、5′、10′）数据集，数据指标有最低温度、平均温度和最高温度、降水量、太阳辐射、风速、水气压和总降水量的月度气候数据，还有 19 个“生物气候”变量，高程数据源自STRM数据产品。未来月度气候数据来自CMIP6 降尺度，包括9个GCM模式和4种SSP。
", "ds_process_way": "利用SPSS 25.0 软件对 19 个气候因子和 3 个地形因子进行皮尔逊相关系数检验，当两个因子相关性较高时(∣r∣>0.80），结合 MaxEnt 模型建模结果中的各因子贡献率，去除贡献率较小的变量。最终选取平均昼夜温差（bio2）、温度季节性变化（bio4）、最暖月最高温度（bio5）、最湿季度平均温度（bio8）、最干季度平均温度（bio9）、最干月份降水量（bio14）、降水季节性（bio15）、最暖季度降水量（bio18）、海拔（elev）、坡度（slope）、坡向（aspect）11 个环境因子用于预测软紫草适生区模型的构建。
", "ds_quality": "", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "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": 1144709010, "ds_files_count": 400, "ds_format": "", "ds_space_res": "2.5‘", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "9b56ad5d-7f2f-4733-892e-4fc4d69a4415.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2026-06-15 10:16:34", "first_publish_time": null, "last_updated": "2026-06-15 16:51:32", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "license": null, "extra": null, "files_shape": [ { "name": "当前环境变量数据1970-2000", "size": null, "is_dir": true }, { "name": "未来时期环境变量数据", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "China's current and future environmental variable data WorldClim v2.1", "ds_abstract": "BIO2 = Average day and night temperature difference, average value of the difference between the highest and lowest temperatures in the month, ℃.\nBIO4 = Temperature seasonality, the standard deviation of the average temperature for each month, amplified by a factor of 100.\nBIO5 = highest temperature in the warmest month, highest temperature in the warmest month of the year, ℃.\nBIO8 = Average temperature in the wettest season, the average temperature in the three months of the year with the most precipitation (i.e., the wettest season), ℃.\nBIO9 = Average temperature in the driest quarter, average temperature in the three months of the year with the least precipitation (i.e., the driest quarter), ℃.\nBIO14 = Driest monthly precipitation, mm.\nBIO15 = Precipitation seasonal.\nBIO18 = warmest season precipitation, mm.\nElevation (elev), slope (slope), aspect (aspect)\nFuture data are selected from four shared socio-economic paths (SSPs) under the Medium Resolution Climate Model (BCC-CSM2-MR) in the Sixth International Coupled Model Comparison Project (CMIP6), namely SSP1 -2.6, SSP2 -4.5, SSP3 -7.0 and SSP5 -8.5.
", "ds_source": "Data from WorldClim version 2.1, which provides history (1970-2000) and the future (2021-2040, 2041-2060, 2061-2080, 2081-2100) Data sets with four resolutions (30 \", 2.5', 5', 10'). Data indicators include monthly climate data of minimum temperature, average temperature and maximum temperature, precipitation, solar radiation, wind speed, water pressure and total precipitation, as well as 19\" bioclimate \"variables. Elevation data is derived from the STRM data product. Future monthly climate data comes from CMIP6 downscaling, including 9 GCM models and 4 SSPs.
", "ds_process_way": "Using SPSS25.0 software, the Pearson correlation coefficient test was carried out on 19 climate factors and 3 terrain factors. When the correlation between the two factors is high (r 0.80), the contribution rate of each factor in the MaxEnt model modeling results was combined to remove variables with small contribution rates. Finally, 11 environmental factors were selected, including average day and night temperature difference (bio2), seasonal change in temperature (bio4), highest temperature in the warmest month (bio5), average temperature in the wettest season (bio8), average temperature in the driest season (bio9), precipitation in the driest month (bio14), precipitation seasonality (bio15), precipitation in the warmest season (bio18), altitude (elev), slope (slope), and aspect (aspect). The construction of a model for predicting the suitable area of soft lithospermum.
", "ds_space_res": "2.5‘" } }, "license_type": null, "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": [ "气温", "降水", "模拟" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [ 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060 ], "ds_contributors": [ "商淑静" ], "ds_meta_authors": [ "商淑静" ], "ds_managers": [ "刘丹辉" ], "category": "生态环境" }