Spatio-temporal Variation And Acquisition Of Raster Dataset Of Precipitation In The Qinling Mountains

The increase of atmospheric temperature caused by global warming may lead to the redistribution of global precipitation.As one of the most important meteorological elements,precipitation is one of the important links in the water cycle,and plays an important role in the global water cycle and the exchange of material and energy.Meanwhile,as the main source of fresh water,precipitation also plays a vital role in human survival and social and economic development.As the geographical boundary between north and south of China,it is of great significance for quantitative research and scientific response to the impact of climate change on mountain ecosystem to study the change trend and abrupt change characteristics of precipitation and temperature in the Qinling Mountains under the background of climate change,and the temporal and spatial distribution law of extreme precipitation events,especially how to scientifically obtain high-resolution precipitation grid data sets in mountainous areas with complex topography.Taking the hinterland of the Qinling Mountains in Shaanxi Province as the study area,this paper studied the changing trend,abrupt change and periodic change characteristics of precipitation and temperature in the Qinling Mountains,revealed the temporal and spatial variation law of extreme precipitation events,the climate attribution and terrain effect of precipitation in the Qinling Mountains,and probed into the acquisition method of precipitation raster dataset in the Qinling Mountains.It provided theoretical support for revealing the response mechanism of the Qinling Mountains ecosystem to climate change,disaster prevention and mitigation of the Qinling Mountains and protection and restoration of lakes and grasses in landscape forest fields.The main achievements and conclusions of this study were as follows:(1)From 1959 to 2018,the annual precipitation decreased in the Qinling Mountains and the average annual temperature increased.Precipitation in spring and autumn showed a downward trend,while precipitation in summer and winter showed an upward trend.Seasonal temperatures were on the rise.From 1959 to 2018,the annual precipitation in the Qinling Mountains showed a downward trend.On the seasonal scale,precipitation in spring and autumn showed a downward trend,while precipitation in summer and winter showed an upward trend.The decline of precipitation in spring and autumn is the main reason for the decline of annual precipitation.On the whole,the annual precipitation showed a downward trend.Annual and seasonal precipitation showed abrupt changes in the 1970 s and the 1980 s.Annual precipitation in the Qinling Mountains has the characteristics of short-period change in the 1970 s,the 1980 s and early 21 st century.In the past 60 years,the average annual temperature in the Qinling Mountains showed an upward trend,and a sudden change point appeared in a significant range in 2001,showing a fluctuating downward trend before 2001,and the temperature showed a fluctuating upward trend after 2001.Seasonal air temperature showed an upward trend in different degrees,and abrupt changes appeared around 1990 s.In recent 60 years,the annual average temperature in the Qinling Mountains has a change cycle of 3 ? 4 years,2 ? 4 years and 3 ? 4 years in 1968,the 1990 s to the beginning of the 21 century and 2010,respectively.(2)There were obvious spatial differences in the distribution of extreme precipitation in the Qinling Mountains from 1960 to 2015.The persistence of extreme precipitation in the Qinling Mountains was decreasing,while the intensity was increasing.There were obvious spatial differences in extreme precipitation distribution in the Qinling Mountains.Baoji area in the western section of the northern slope of the Qinling Mountains is a high-value area of annual continuous dry days(CDD),while the western section of the Qinling Mountains is a high-value area of continuous wet days(CWD).The indexes of heavy precipitation days(R10),heavy precipitation(R95p),5-day maximum precipitation(RX5day)and precipitation intensity(SDII)showed a distribution pattern of "high in the south and low in the north",and Ziyang County,located at the southernmost of the Qinling Mountains,is the maximum area of each extreme precipitation index.In the past 56 years,the persistence of extreme precipitation in the Qinling Mountains showed a decreasing trend.The intensity showed an increasing trend.The precipitation time in the Qinling Mountains is short and the intensity is high,especially in the southern slope of the Qinling Mountains,so we should strengthen our preparedness to avoid causing great damage caused by flood disasters.(3)From 1959 to 2018,the changes of temperature and precipitation in the Qinling Mountains have obvious slope effect.The five atmospheric indexes that have the greatest influence on annual and seasonal precipitation in the Qinling Mountains are EASMI,SOI,SWACI,SASMI and SCSMI.In the past 60 years,the annual precipitation showed an upward trend with the elevation,and the average annual temperature on the southern,northern slope and western section of southern slope,eastern section of southern slopes showed a downward trend with the elevation.The precipitation showed an upward trend with the increase of slope,but it was not significant.The temperature showed a downward trend with the increase of slope,except for the western section of the southern slope of the Qinling Mountains.On the annual scale,the precipitation on the southern,northern slope and western section of southern slope,eastern section of southern slopes has decreased significantly in the past 60 years,and the temperature on the southern,northern slope and western section of southern slope,eastern section of southern slopes of the Qinling Mountains has not increased significantly.The dry and wet grades on the southern,northern slope and western section of southern slope,eastern section of southern slopes in the Qinling Mountains belonged to normal grades,and the dry and wet conditions in the northern slope and the western section of the southern slope were consistent.The average annual SPEI in 60 years was 0.07,and the eastern section of the southern slope was warm and humid,with SPEI of 0.08.The south slope was warm and dry,with SPEI of 0.05.On the seasonal scale,the spring precipitation on the southern,northern slope and western section of southern slope,eastern section of southern slopes showed a significant downward trend,while the precipitation in the other three seasons showed no significant change trend on the southern,northern slope and western section of southern slope,eastern section of southern slopes.The spring temperature in the eastern section of the southern slope,the summer,autumn and winter temperature in the western section of the southern slope and the northern slope of the Qinling Mountains showed a significant downward trend,while the temperature in different directions in other seasons showed an insignificant upward trend.The changes of dry and wet seasons on the southern,northern slope and western section of southern slope,eastern section of southern slopes in the Qinling Mountains belonged to normal grades.The northern slope of the Qinling Mountains showed the trend of "warm and dry" in spring.The southern slope was warmer and wetter in autumn.The winter in the eastern and western sections of the southern slope was characterized by "warm and humid".The western section of the southern slope was characterized by "warm and dry" in summer.Among the fifteen large-scale climate indexes,the annual precipitation in the Qinling Mountains has the strongest correlation with five large-scale climate factors-EASMI,SOI,SASMI,SCSMI and SWACI in the past 60 years.While the relationship with NAO and WASMI was not significant.Seasonally,EASMI had a significant negative correlation with precipitation in the Qinling Mountains in four seasons.The effect of SOI on spring and autumn in the Qinling Mountains was sensitive to SWACI,while SASMI had a strong positive correlation with winter precipitation in the Qinling Mountains,and SCSMI had a negative correlation with autumn and winter precipitation in the Qinling Mountains.(4)Three Long-term precipitation raster data sets of the Qinling Mountains were obtained by using Anusplin spatial interpolation method,Ordinary Kriging method and Inverse Distance Weighting method.After verification,it is found that Anusplin method is more suitable for precipitation interpolation in the Qinling Mountains than the other two spatial interpolation methods.According to the spatial distribution and self-test,the measured sample test results of the precipitation grid data set in the Qinling Mountains obtained by three precipitation spatial interpolation methods showed that Anusplin method is more suitable for spatial interpolation of precipitation in the Qinling Mountains,and the standard deviation of middle and low altitude stations was within 20 mm,while that of high altitude stations is within 30 mm.The obtained precipitation raster data of the Qinling Mountains showed that the average annual precipitation varied from 545.4 mm to 1 155.5 mm,and the average precipitation was 824.8 mm.The average precipitation on the southern slope of the Qinling Mountains was 847.4 mm,and the average precipitation on the northern slope was 737.3mm,and the average precipitation difference between the northern and southern slopes was110.1 mm.The average precipitation in four seasons was: summer(403.8 mm)> autumn(237.3 mm)> spring(169.1 mm)> winter(25.6 mm),and the precipitation in the southern slope was greater than that in the northern slope.No matter on the annual scale or the seasonal scale,the change rate of precipitation in the Qinling Mountains failed the significance test.Precipitation reduction areas were mainly concentrated in Taibai Mountain,the main peak of the Qinling Mountains,and Ankang Station on the southern slope of the Qinling Mountains,with an average altitude of 1 177 m.However,precipitation increased mainly in Lueyang Station,Shangnan Station and Shiquan Station on the southern slope of the Qinling Mountains,with an average altitude of 811 m.(5)The downscaling method of Geographically Weighted Regression method could improve the accuracy of TRMM annual scale data of the Qinling Mountains,and obtain the precipitation grid data set of the Qinling Mountains from 2002 to 2015.The more climate factors and terrain factors were considered in the input parameters of downscaling process,the higher the accuracy of downscaling results.There was a certain error between TRMM data and measured precipitation data in the Qinling Mountains,which can be reduced by statistical downscaling method of Geographical Weighed Regression.After analysis,TRMM data only showed high accuracy in the western part of the southern slope of the Qinling Mountains,while the results of downscaling by geographically weighted regression method showed high accuracy in the whole southern slope of the Qinling Mountains.Downscaling improved the accuracy of TRMM annual scale data.The correlation coefficient increased from 0.71 to 0.86,the relative error BIAS reduced from-3.60% to-2.77%,and the root mean square error reduced from 99.2 mm to 93.2 mm.Six downscaled models designed by Geographically Weighted Regression method showed that the more climate factors and terrain factors were considered,the higher the accuracy of the downscaled raster dataset.That is to say,not only altitude,temperature,but also wind speed,humidity,slope and aspect should be considered in the process of mountain precipitation downscaling research.