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Projected change in precipitation forms in the Chinese Tianshan Mountains based on the back propagation neural network model

引用

Journal of Mountain Science 2022年第3期19卷 689-703页

作者： REN Rui LI Xue-mei LI Zhen LI Lan-hai HUANG Yi-yu Faculty of Geomatics Lanzhou Jiaotong UniversityLanzhou 730070China Key Laboratory of Regional Climate-Environment in Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Xinjiang Institute of Ecology and Geography Chinese Academy of SciencesUrumqi 830011China National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring Lanzhou 730070China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou 730070China

In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced *** change in precipitation forms will inevitably affect the processes of regional runoff generation and confluence as well as the annual distribution of *** researchers used precipitation data from the CMIP5 model directly to study future precipitation trends without distinguishing between snowfall and ***5 models have been proven to have better performance in simulating temperature but poorer performance in simulating *** overcome the above limitations,this paper used a back propagation neural network(BNN)to predict the rainfall-to-precipitation ratio(RPR)in months experiencing freezing-thawing transitions(FTTs).We utilized the meteorological(air pressure,air temperature,evaporation,relative humidity,wind speed,sunshine hours,surface temperature),topographic(altitude,slope,aspect)and geographic(longitude,latitude)data from 28 meteorological stations in the Chinese Tianshan Mountains region(CTMR)from 1961 to 2018 to calculate the RPR and constructed an index system of impact *** on the BNN,decision-making trial and evaluation laboratory method(BP-DEMATEL),the key factors driving the transformation of the RPR in the CTMR were *** found that temperature was the only key factor affecting the transformation of the RPR in the BP-DEMATEL *** the relationship between temperature and the RPR,the future temperature under different representative concentration pathways(RCPs)(RCP2.6/RCP4.5/RCP8.5)provided by 21 CMIP5 models and the meteorological factors from meteorological stations were input into the BNN model to acquire the future RPR from 2011 to *** results showed that under the three scenarios,the RPR in the number of months experiencing FTTs during 2011-2100 will be higher than that in the historical period(1981-2010)in the ***,in terms of spatial variation,

关键词： Global warming Tianshan Mountains region Precipitation forms CMIP5 models back propagation neural network model

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