Zhang Mingyi, Wang Jiwei, Lai Yuanming
State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Sci Total Environ. 2019 Jun 20;670:1190-1203. doi: 10.1016/j.scitotenv.2019.03.090. Epub 2019 Mar 7.
Hydro-thermal properties of permafrost and its distribution are sensitive to climate changes and human activities. Accurate and reasonable prediction on aforementioned information is important for eco-environment construction and vital infrastructures development. To model the current and future states of permafrost, it is a key challenge to effectively determine the upper hydro-thermal boundary conditions for permafrost models under changing climate and different underlying surfaces at proper spatial and temporal scales. An approach, combined regional climate downscaling method with model output statistics method, was developed to produce a time series of air temperature, surface temperatures, and surface unfrozen water contents for different underlying surfaces. It provided various climate and surface parameters at a spatial scale on the order of 10 m for engineering designs, which was used to predict boundary conditions under possible climate scenarios. The predicted and simulated models were calibrated and validated by the monitored data at an experimental site in Chumar, China, close to the Qinghai-Tibet Railway and the Qinghai-Tibet Highway. Results show that the multiple linear regression model (MLRM) can predict the current states and future changes of upper hydro-thermal boundary conditions for permafrost while the original states of natural surface are modified by natural or human factors on the condition of complicated climatic and complex topography regions. The statistical regression model (SRM) based on the outputs of regional climate model (RCM) and MLRM provides a simple method for the convenience of numerical calculation. These results also indicate the possible applications to other areas and situations.
多年冻土的水热特性及其分布对气候变化和人类活动敏感。对上述信息进行准确合理的预测对于生态环境建设和重要基础设施发展至关重要。为了模拟多年冻土的当前和未来状态，在适当的时空尺度下，有效确定变化气候和不同下垫面条件下多年冻土模型的上部水热边界条件是一个关键挑战。开发了一种将区域气候降尺度方法与模型输出统计方法相结合的方法，以生成不同下垫面的气温、地表温度和地表未冻水含量的时间序列。它在10米量级的空间尺度上提供了各种气候和地表参数用于工程设计，这些参数被用于预测可能气候情景下的边界条件。在中国靠近青藏铁路和青藏公路的楚玛尔河一个实验场地的监测数据对预测和模拟模型进行了校准和验证。结果表明，在复杂气候和复杂地形区域，当自然表面的原始状态受到自然或人为因素影响时，多元线性回归模型（MLRM）能够预测多年冻土上部水热边界条件的当前状态和未来变化。基于区域气候模型（RCM）输出和MLRM的统计回归模型（SRM）为数值计算提供了一种简便的方法。这些结果也表明了其在其他区域和情形下的可能应用。
