本数据集采用 WRF v4.2 区域天气模式，结合改进的土壤水热、积雪参数化方案，和Spectral Nudging方案，开展了黑河流域未来（2025-2040年）不同气候情景（SSP126、SSP245和SSP585）下9km气候动力降尺度模拟。所用驱动数据源于中国科学院大气物理研究所开发的全球偏差校正CMIP6数据集（BC-CMIP6; Xu et al., 2021；https://www.nature.com/articles/s41597-021-01079-3#Sec6），\n提供动力降尺度模拟的边界和初始条件。该数据集基于18个CMIP6模式数据，采用ERA5再分析数据进行均值与方差偏差校正，同时引入多模式集合（MME）的非线性趋势。校正后的数据具有ERA5基准期的气候统计特征，又有效保留未来气候变化趋势，空间分辨率为1.25°×1.25°，时间步长为6小时。WRF输出的高分辨率水文气象强迫场进一步经过时间插值、日尺度统计与空间插值处理，最终形成0.1°×0.1°、日尺度的网格化数据产品。数据以NetCDF（.nc）格式发布，文件命名为{变量名}_{年份}_BJT_interp.nc（其中{变量名}为变量名称，{年份}为对应年份），时间系统采用北京时间（BJT，UTC+8）。数据集包含日累计降水、日最高/最低气温、日累计向下短波辐射、日均 10 m 风速和日均 2 m 相对湿度等要素，可为流域尺度水文模拟、生态过程研究及气候变化影响评估提供高分辨率气象强迫数据。", "ds_source": "
数值模式模拟数据： 大气驱动与边界条件，不同气候情景（SSP126、SSP245和SSP585）所用驱动数据源于中国科学院大气物理研究所开发的全球偏差校正CMIP6数据集（BC-CMIP6; Xu et al., 2021；https://www.nature.com/articles/s41597-021-01079-3#Sec6）， 该数据集基于18个CMIP6模式数据，采用ERA5再分析数据进行均值与方差偏差校正，同时引入多模式集合（MME）的非线性趋势。校正后的数据具有ERA5基准期的气候统计特征，又有效保留未来气候变化趋势，空间分辨率为1.25°×1.25°，时间步长为6小时； 下垫面数据来源:WRF 官方静态地理数据（地形高度、土地利用类型、植被类型等） 是否来源于观测/调查/试验:否（本数据集为模式模拟产生）", "ds_process_way": "
本数据集基于WRFv4.2区域天气模式动力降尺度结果加工生成。首先采用不同气候情景（SSP126、SSP245和SSP585）的CMIP6资料驱动WRF，在9km分辨率下对黑河流域开展2025-2040年逐小时模拟；模式中对CLM陆面过程的积雪密度参数化和砾石方案进行了改进，并优化了YSU边界层方案与动力学粗糙度参数，同时引入SpectralNudging约束大尺度环流。随后对逐小时输出数据进行时间转换（统一为北京时间UTC+8）和日尺度统计，并使用CDO工具中的remapbil（双线性插值）方法将各变量插值到规则经纬度0.1°×0.1°网格。最终数据以NetCDF格式发布，文件命名为{VAR}_{YYYY}_BJT_interp.nc。变量计算与单位换算遵循统一算法：降水由 RAINC+RAINNC 逐小时差分得到逐小时降水并汇总为日累计降水（mm/day）；气温由 T2 从 K 转为 °C（K−273.15）并取日最大/最小得到 TMAX/TMIN；短波辐射由 SWDOWN 逐小时积分并换算为日累计（W/m² → MJ/m²/day，乘 0.0864）；风速由 U10、V10 合成 sqrt(U10²+V10²) 并取日均；相对湿度由逐小时数据计算日均。", "ds_quality": "
本数据集来源于 WRF 动力降尺度输出，并通过 CDO 统一插值至 0.1° × 0.1° 规则网格，插值前后严格保持时间轴与单位一致，逐小时数据统一转换为北京时间（UTC+8）并汇总为日值。数据生产过程中建立了规范的数据质量控制流程：首先开展文件完整性检查，使用 ncdump 核验 NetCDF 文件头信息与变量清单，检查文件大小与基本结构，验证关键变量是否齐全及数据是否存在异常截断，并对损坏文件进行自动剔除；其次实施数据验证与质量控制方法，包括时间连续性检查（确保时间序列无跳时、重复或缺口）、空间范围验证（经纬度范围与目标区域一致）、数值合理性检查（对降水、温度、辐射、风速、相对湿度等施加物理值域约束），以及缺失值处理（保留 _FillValue/missing_value 标识，读取时可统一识别为 NaN）。同时，所有变量统一到同一目标网格以保证网格一致性。", "ds_acq_start_time": "2025-01-01 00:00:00", "ds_acq_end_time": "2025-01-01 00:00:00", "ds_acq_place": "黑河流域", "ds_acq_lon_east": 105.2, "ds_acq_lat_south": 35.7, "ds_acq_lon_west": 93.9, "ds_acq_lat_north": 44.28333333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 2958073578, "ds_files_count": 289, "ds_format": "netcdf", "ds_space_res": "0.1°", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "e8a8a8d2-37a2-466d-83e7-97ed13fff833.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-12-16 17:02:10", "last_updated": "2025-12-16 17:02:10", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7030.2025", "i18n": { "en": { "title": "High resolution climate dynamic downscaling dataset for future SSP126, SSP245, and SSP585 scenarios in inland river basins (Heihe River Basin) in arid regions (2025-2040)", "ds_format": "", "ds_source": "
Numerical model simulation data: atmospheric driving and boundary conditions, driving data for different climate scenarios (SSP126, SSP245 and SSP585) are derived from the global bias correction CMIP6 dataset developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (BC-CMIP6; Xu et al., 2021; https://www.nature.com/articles/s41597-021-01079-3#Sec6 ）This dataset is based on 18 CMIP6 pattern data, and ERA5 reanalysis data is used for mean and variance bias correction, while introducing non-linear trends from a multimodal set (MME). The corrected data exhibits climate statistical characteristics of the ERA5 baseline period while effectively preserving future climate change trends, with a spatial resolution of 1.25 °× 1.25 ° and a time step of 6 hours; Source of underlying surface data: WRF official static geographic data (terrain height, land use type, vegetation type, etc.) Whether it comes from observation/investigation/experiment: No (this dataset is generated by model simulation)", "ds_quality": "
This dataset is derived from WRF power downscaling output and interpolated uniformly to a 0.1 °× 0.1 ° regular grid through CDO. The time axis and units are strictly kept consistent before and after interpolation, and hourly data is uniformly converted to Beijing time (UTC+8) and summarized as daily values. A standardized data quality control process has been established in the data production process: firstly, file integrity checks are carried out, using ncdump to verify the NetCDF file header information and variable list, checking the file size and basic structure, verifying whether key variables are complete and whether there are abnormal data cuts, and automatically removing damaged files; Secondly, data validation and quality control methods will be implemented, including time continuity checks (to ensure that the time series has no jumps, repetitions, or gaps), spatial range validation (to ensure that the latitude and longitude ranges are consistent with the target area), numerical rationality checks (to impose physical value domain constraints on precipitation, temperature, radiation, wind speed, relative humidity, etc.), and missing value handling (to retain the tFillValue/missing_ralue identifier, which can be uniformly identified as NaN when read). Meanwhile, all variables are unified to the same target grid to ensure grid consistency.", "ds_ref_way": "", "ds_abstract": "
This dataset adopts the WRF v4.2 regional weather model, combined with improved soil water and heat, snow parameterization schemes, and Spectral Nudging schemes, to carry out downscaling simulations of climate dynamics at 9km under different climate scenarios (SSP126, SSP245, and SSP585) in the Heihe River Basin in the future (2025-2040). The driving data used is from the global bias correction CMIP6 dataset developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (BC-CMIP6; Xu et al., 2021; https://www.nature.com/articles/s41597-021-01079-3#Sec6 ），\nProvide boundary and initial conditions for dynamic downscaling simulation. This dataset is based on 18 CMIP6 pattern data, and ERA5 reanalysis data is used for mean and variance bias correction, while introducing non-linear trends from the Multi Pattern Set (MME). The corrected data exhibits climate statistical characteristics of the ERA5 baseline period while effectively preserving future climate change trends, with a spatial resolution of 1.25 °× 1.25 ° and a time step of 6 hours. The high-resolution hydro meteorological forcing field output by WRF is further processed through time interpolation, daily scale statistics, and spatial interpolation, ultimately forming a 0.1 °× 0.1 °, daily scale gridded data product. The data is published in NetCDF (. nc) format, with the file named {Variable Name} _ {Year} _SJT_interp.nc (where {Variable Name} is the variable name and {Year} is the corresponding year), and the time system uses Beijing Time (BJT, UTC+8). The dataset includes daily cumulative precipitation, daily maximum/minimum temperature, daily cumulative shortwave radiation, daily average wind speed of 10 meters, and daily average relative humidity of 2 meters. It can provide high-resolution meteorological forcing data for watershed scale hydrological simulation, ecological process research, and climate change impact assessment.
", "ds_time_res": "日", "ds_acq_place": "Heihe River Basin", "ds_space_res": "0.1°", "ds_projection": "", "ds_process_way": "This dataset is generated based on the dynamic downscaling results of the WRFv4.2 regional weather model. Firstly, using CMIP6 data from different climate scenarios (SSP126, SSP245, and SSP585) to drive WRF, hourly simulations of the Heihe River Basin from 2025 to 2040 were conducted at a resolution of 9km; The parameterization of snow density and gravel scheme for CLM land surface processes were improved in the model, and the YSU boundary layer scheme and dynamic roughness parameters were optimized. At the same time, SpectralNudging was introduced to constrain large-scale circulation. Subsequently, the hourly output data was subjected to time conversion (unified as Beijing time UTC+8) and daily scale statistics, and the variables were interpolated to a regular latitude and longitude grid of 0.1 °× 0.1 ° using the reparbil (bilinear interpolation) method in the CDO tool. The final data will be published in NetCDF format, with the file named {VAR}_{YYYY}_BJT_interp.nc The calculation of variables and unit conversion follow a unified algorithm: precipitation is divided into hourly precipitation by RAINC+AINNC and summarized into daily cumulative precipitation (mm/day); The temperature changes from T2 from K to ° C (K − 273.15) and the daily maximum/minimum values are taken to obtain TMAX/TMIN; Shortwave radiation is integrated hourly by SWDOWN and converted to daily accumulation (W/m ² → MJ/m ²/day, multiplied by 0.0864); The wind speed is synthesized into sqrt (U10 ²+V10 ²) from U10 and V10, and the daily average is taken; Relative humidity is calculated daily based on hourly data.", "ds_ref_instruction": "" } }, "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": [ "水文模型驱动数据", "动力降尺度" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黑河流域" ], "ds_time_tags": [ 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040 ], "ds_contributors": [ { "true_name": "赵林", "email": "zhaolin_110@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": "方功焕", "email": "fanggh@ms.xjb.ac.cn", "work_for": "中国科学院新疆生态与地理研究所", "country": "中国" }, { "true_name": "陈昊", "email": "chenhao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "赵林", "email": "zhaolin_110@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": "陈昊", "email": "chenhao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "赵林", "email": "zhaolin_110@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": "陈昊", "email": "chenhao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，兰州大学", "country": "中国" } ], "category": "气象" }