| The grasslands in arid areas of China are rich in mineral resources,and how to maintain the balance of soil and water resources during the mining process is a key issue for current ecological sustainable development.The continuous mining of mineral resources damages the vegetation,surface soil,and water resources in the area,which can exacerbate the degradation of the ecological environment system.In order to repair and maintain the ecological balance,artificial tree species are often transplanted,so it is important to explore the temporal and spatial dynamic changes of soil moisture and the transpiration rule of transplanted trees in the region.This article selects a typical grassland mining area in Xilinhot,Inner Mongolia as the research area.From April to September 2021,experiments were conducted in different vegetation coverage areas to explore the spatiotemporal variability of soil moisture.The principle of heat balance was used to monitor the stem sap flow of Populus tomentosa and Pinus tabulaeformis in the artificial transplant site,and the soil moisture sensor was used to synchronously monitor the root soil moisture of the corresponding trees,explore the changes in the stem sap flow rate of Populus tomentosa and Pinus tabulaeformis at different time scales,and analyze the main factors affecting the stem sap flow rate of Populus tomentosa and Pinus tabulaeformis with meteorological data,at the same time,obtain the transpiration and soil evaporation of Populus tomentosa and Pinus tabulaeformis stands,Compare the difference of transpiration evaporation of different vegetation(Leymus chinensis,Populus tomentosa,Pinus tabulaeformis).Three regression models were applied to simulate and predict the transpiration of Populus tomentosa and Pinus tabulaeformis stands.The main research findings are as follows:(1)During the research period,the vegetation coverage in the mixed transplant area was greater than that in the native vegetation area,with the largest difference of 12% on May 2nd and the smallest difference of 1% on August 2nd.As the seasons change,the differences in leaf growth among different vegetation types lead to differences in vegetation coverage.(2)The coefficient of variation of soil moisture content from 0-20 cm in the original landform area from May to July(5.68%～9.69%)belongs to weak variability,while the soil moisture content of each layer in other months(11.16%～21.81%)belongs to moderate variability.The coefficient of variation of soil moisture content in the mixed transplant area was weakly variable at 60-80 cm(9.19%)and 80-100 cm(8.95%)in September,while the soil moisture content of each layer in the remaining months(12.04%～40.26%)was moderately variable.The overall degree of soil moisture variation in different layers of the mixed transplant area at different stages is higher than that in the original landform area.(3)The spatial variation of soil moisture in the original landform area and the mixed transplantation area is well fitted,with the soil moisture block coefficient(1.82% to18.92%)of each layer in the original landform area being less than 25% in each month.The spatial autocorrelation of soil moisture in the area is strong;In the mixed transplantation area,except for the block gold coefficient of 60-80 cm(26.62%～32.05%)from August to September and 80-100cm(26.19%～37.04%)from July to September,which is greater than 25% but less than 75%,and soil moisture shows moderate spatial autocorrelation.In other months,the block gold coefficient of each layer(0.110%～21.026%)is less than 25%,and soil moisture shows strong spatial autocorrelation.This indicates that the original geomorphic area is more affected by structural factors and has stronger autocorrelation compared to the mixed transplant area.Using Matlab software Kriging model tool for interpolation analysis,the results showed that compared with the mixed transplant area,the soil moisture distribution in the original landform area is different,and the spatial interpolation changes have diversity in shape;The soil moisture content is higher in areas with deeper vegetation roots in the mixed transplantation area,and the spatial correlation is higher with the area under planting coverage.(4)During the research period,on an hourly scale,the regularity of the instantaneous velocity of the trunk sap flow of Populus tomentosa in April and May was poor,and the daily variation from June to September showed a "few" shaped or single peak shape,with the highest correlation with average temperature,with a correlation coefficient of 0.562;The instantaneous velocity rate of the trunk sap flow of Pinus tabulaeformis has a relatively stable fluctuation range from April to September,with small differences in peak values.Most dates show a single peak shape,while some dates show a double peak shape,with the highest correlation with average temperature,with a correlation coefficient of 0.546.On a daily scale,the daily rate of trunk sap flow of Populus tomentosa is similar to that of Pinus tabulaeformis in April and early May.From late May to September,there is a significant fluctuation in the daily rate of trunk sap flow of Populus tomentosa,while the fluctuation in the daily rate of trunk sap flow of Pinus tabulaeformis is relatively small;The highest correlation with the daily rate of trunk sap flow of Populus tomentosa is the average temperature,with a correlation coefficient of0.597.The highest correlation with the daily rate of trunk sap flow of Pinus tabulaeformis is the saturated water vapor pressure difference,with a correlation coefficient of 0.447;Within the range of 0-100 cm of soil depth,the correlation between soil moisture in each layer and the daily rate of stem sap flow of Populus tomentosa is similar.However,as the soil depth increases,the correlation between soil moisture and the daily rate of stem sap flow of Pinus tabulaeformis gradually increases.On a monthly scale,the instantaneous trunk sap flow rate of Populus tomentosa is more affected by various meteorological factors in July,while the instantaneous trunk sap flow rate of Pinus tabulaeformis changes with the month,and the meteorological factor with the highest correlation also changes.As the time scale changes,the meteorological factors that affect the changes in trunk sap flow rate of Populus tomentosa and Pinus tabulaeformis also change,and the composition of trunk sap flow rate of Populus tomentosa is more complex under the influence of meteorological factors.(5)During the study period,the monthly cumulative transpiration evaporation of Populus tomentosa was higher than that of Pinus tabulaeformis evaporation,and the transpiration water consumption of the two forests accounted for a large proportion of the transpiration evaporation.Although the transpiration of the two forests increased after the rainfall,the rainfall was relatively large,which restricted the production of tree transpiration,so that the cumulative transpiration evaporation in July was lower than that in June and August.The monthly cumulative evaporation of Populus tomentosa and Leymus chinensis changed significantly,while the monthly cumulative evaporation of Pinus tabulaeformis did not differ significantly,indicating that the changes of evaporation of Pinus tabulaeformis transpiration were more stable under the condition of environmental water restrictions.(6)Three regression models were used for fitting,and the degree of determination of the three models was determined by the multiple linear stepwise regression model based on environmental factors,the univariate linear regression model based on soil meteorological coupling factors,and the univariate linear regression model based on environmental factors.The multiple stepwise linear regression model and the univariate linear regression model,which have the highest impact on the transpiration of Populus tomentosa stand and Pinus tabulaeformis stand,were used to predict the transpiration of Populus tomentosa stand and Pinus tabulaeformis stand.The fitting degree of the two models to the transpiration of Pinus tabulaeformis stand was higher than that to the transpiration of Populus tomentosa stand,and the prediction deviation of the multiple linear regression model was smaller than that of the univariate linear regression model with the coupling factor.From this,it can be seen that under natural conditions,fitting multiple linear regression models containing multiple environmental factors can to some extent explain the transpiration changes of artificial trees. |
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