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

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 than those on the north slope under the three emission scenarios.Moreover,the RPR values exhibited different variation characteristics under different emission scenarios.Under the low-emission scenario(RCP2.6),as time passed,the RPR values changed slightly at more stations.Under the mediumemission scenario(RCP4.5),the RPR increased in the whole CTMR and stabilized on the north slope by the end of this century.Under the high-emission scenario(RCP8.5),the RPR values increased significantly through the 21 st century in the whole CTMR.This study may help to provide a scientific management basis for agricultural production and hydrology.

Spatial variability of glacial changes and their effects on water resources in the Chinese Tianshan Mountains during the last five decades

Changes in glaciers in the Chinese Tianshan Mountains have been analyzed previously. However, most previous studies focused on individual glaciers and/or decentralized glacial basins. Moreover, a majority of these studies were published only in Chinese, which limited their usefulness at the international level. With this in mind, the authors reviewed the previous studies to create an overview of glacial changes in the Chinese Tianshan Mountains over the last five decades and discussed the effects of glacial changes on water resources. In response to climate change, glaciers in the Tianshan Mountains are shrinking rapidly and are ca. 20% smaller on average in the past five decades. Overall, the area reduction of glacial basins in the central part of the Chinese Tianshan Mountains is larger than that in the eastern and western parts. The spatial differentiation in glacial changes are caused by both differences in regional climate and in glacial factors. The effects of glacial changes on water resources vary in different river basins due to the differences in glacier distribution, characteristics of glacial change and proportion of the glacier meltwater in river runoff.

Alpine vegetation responses to snow phenology in the Chinese Tianshan mountainous region

Investigating the interrelation between snow and vegetation is essential to explain the response of alpine ecosystems to climate change.Based on the MOD10 A1 daily cloud-free snow product and MOD13 A1 NDVI(normalized difference vegetation index)data,this study analysed the spatial and temporal patterns of snow phenology including snow onset date,snow end date,snow cover days,and vegetation phenology including the start of growing season,the end of growing season and the length of growing season in the Chinese Tianshan Mountainous Region(CTMR)from 2002 to 2018,and then investigated the snow phenological effects on the vegetation phenology among different ecogeographic zones and diverse vegetation types.The results indicated that snow onset date was earlier at higher elevations and later at lower elevations,while snow end date showed opposite spatial distribution characteristics.The end of growing season occurred later on the northwest slope of the CTMR and the Yili Valley.The earliest end of growing season was in the middle part of the CTMR.A long growing season was mainly distributed along the northern slope and the Yili Valley,while a short growing season was concentrated in the middle part of the CTMR.The response of vegetation phenology to changes in snow phenology varied among vegetation types and ecogeographic zones.The effect of snow phenology on vegetation phenology was more significant in IID5(Yili Valley)than in the other ecogeographic zones.A negative correlation was observed between the start of growing season and snow end date in nearly 54.78%of the study area,while a positive correlation was observed between the start of growing season and the snow end date in 66.85%of the study area.The sensitivity of vegetation phenology to changes in snow cover varied among different vegetation types.Snow onset date had the greatest effect on the start of growing season in the four vegetation cover types(alpine meadows,alpine steppes,shrubs,and alpine sparse vegetation),whereas the snow cover days had the least impact.Snow end date had the greatest impact on the end of growing season in the alpine steppes and shrub areas.The study results are helpful for understanding the vegetation dynamics under ongoing climate change,and can benefit vegetation management and pasture development in the CTMR.