机器学习与统计模型在石羊河流域气候降尺度研究中的适用性对比

高分辨率气候数据是研究气候变化对农业、生态、水文影响的驱动数据,动力和统计降尺度模型是两类常用的生成高分辨率气候数据的方法,近年来机器学习模型也被用到气候变化的研究中,但针对不同站点(下垫面)的多种统计降尺度模型的对比研究较少.石羊河流域土地利用类型多样,海拔变化显著,适合研究降尺度模型的适用性.本研究选择2种传统统计...

A comparative study of machine learning and statistical models in climate downscaling in the Shiyang River Basin

High-resolution climate data are an important data source for studying the impacts of climate change on agriculture， ecology and hydrology. The Shiyang River Basin has diverse land use types and significant elevation changes， which is suitable for studying the applicability of downscaling models. In this study， two traditional statistical downscaling models and four machine learning models were selected and combined with two standardized methods to compare the downscaling of temperature and precipitation at four sites in the Shiyang River Basin to explore the optimal downscaling model for the region. The results show that the machine learning models have better downscaling capability than the traditional statistical models. The ensemble of multi-model gives stable downscaling results with high accuracy. The correlation coefficients of 0.98 （exceeding the 99% confidence level） for temperature results and 0.74 （exceeding the 99% confidence level） for precipitation results at each site indicate that the models screened in this study can achieve better downscaling and can be used to generate future climate scenario data for the region， providing reliable climate data for climate change related studies.