Projected change in precipitation forms in the Chinese Tianshan Mountains based on the Back Propagation Neural Network Model  

作　　者：REN Rui LI Xue-mei LI Zhen LI Lan-hai HUANG Yi-yu 

机构地区：[1]Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China [2]Key Laboratory of Regional Climate-Environment in Temperate East Asia,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China [3]Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,China [4]National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China [5]Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China

出　　处：《Journal of Mountain Science》2022年第3期689-703,共15页山地科学学报（英文）

基　　金：financially supported by the National Natural Science Foundation of China(41761014,42161025,42101096);the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA20020201);the Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University,and the Excellent Platform of Lanzhou Jiaotong University。

摘　　要：In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced trend.The change in precipitation forms will inevitably affect the processes of regional runoff generation and confluence as well as the annual distribution of runoff.Most researchers used precipitation data from the CMIP5 model directly to study future precipitation trends without distinguishing between snowfall and rainfall.CMIP5 models have been proven to have better performance in simulating temperature but poorer performance in simulating precipitation.To 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 factors.Based 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 identified.We found that temperature was the only key factor affecting the transformation of the RPR in the BP-DEMATEL model.Considering 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 2100.The 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 CTMR.Furthermore,in terms of spatial variation,the RPR values on the south slope will be larger

关 键 词：Global warming Tianshan Mountains region Precipitation forms CMIP5 models Back Propagation Neural Network Model 

分 类 号：P426.614[天文地球—大气科学及气象学]