中国西北、西藏和周边地区1961—2020年每十年1 km季节冻土最大冻结深度数据集

Decadal Dataset of the Seasonal Maximum Freezing Depth with 1 km Resolution from 1961 to 2020 in Northwest China,Tibet and Surrounding Area

最大冻结深度是季节冻土热状态的重要指标,其变化对区域水循环、生态过程、气候以及工程稳定性都有重要影响。发布了我国西北五省和西藏及周边地区1961—2020年间每十年的季节冻土最大冻结深度数据集,空间分辨率为1 km。该数据集是利用支持向量机模型集合模拟产生的,以冻结指数、融化指数、降雪、降雨、太阳辐射、高程以及土壤容重为预测因子,利用基准时期(2001—2010年)气象站最大冻结深度的实测数据进行模型训练。10折交叉验证表明,模型具有良好的精度[(R^(2)=0.70±0.29,RMSE=(23.63±10.30)cm,bias=(-0.77±6.01)cm)]。基于实测数据的直接验证表明,1980s、1990s、2000s和2010s 4个时期模拟结果的R^(2)分别为0.77、0.83、0.73和0.71,RMSE分别为27.14、22.42、21.63和23.58 cm。运用预测集合的百分位数区间评估了模拟的不确定性,结果表明模拟结果具有良好的稳定性。基于该数据集,发现1961—2020年我国西北五省和西藏地区季节冻土最大冻结深度总体呈显著下降趋势,平均每十年下降3.02 cm。1961—2010年新疆、西藏、青海、甘肃、宁夏和陕西的最大冻结深度平均每十年分别下降2.66、3.04、3.16、2.09、1.98和1.45 cm。该数据集可通过国家青藏高原科学数据中心下载(DOI:10.11888/Geocry.tpdc.271774)。

The maximum freezing depth is an important indicator for the thermal state of seasonally frozen ground,and its changes have an important impact on the regional water cycle,ecological processes and engineering stability.This paper released a soil maximum freezing depth grid dataset for 10-year period from1961 to 2020 in Northwest China and Tibet,with a spatial resolution of 1 km.The dataset was produced by integrating downscaled and bias corrected weather data,elevation and soil properties using a support vector machine model with 200 ensemble simulations.The 10-fold cross-validation shows that the accuracy of the support vector machine model is acceptable[R^(2)=0.70±0.29,RMSE=(23.63±10.30)cm,bias=(-0.77±6.01)cm].Validation using in-situ data shows that the R^(2) for the four periods 1980 s,1990 s,2000 s and 2010 s are 0.77,0.83,0.73 and 0.71 respectively,and the RMSE are 27.14 cm,22.42 cm,21.63 cm and 23.58 cm respectively.The uncertainty of the simulation results is stable throughout the simulation period.Based on this dataset,we found that the soil maximum freezing depth in the Northwest China and Tibet decreased significantly between 1960 s and 2020 s,with an average rate of 3.02 cm per decade.The dataset can be downloaded via the National Tibetan Plateau/Third Pole Environment Data Center(DOI:10.11888/Geocry.tpdc.271774).