| The global climate change nowadays is significant. As an important parameter in the terrestrial ecosystem, soil moisture content could be influenced by climate change. Meanwhile the thermodynamic properties of soil and hydrological process could be affected by climate change, which would change various parameters of the surface earth. Moreover, the changes mentioned above would impact climate change as well,In this paper, annual precipitation data from 1960 to 2010 of Changshu, a typical region of Tai Lake basin, were analyzed. The T-FACE system platform was built to study the changing condition of the soil moisture at different stages of the wheat season under simulated climate change scenarios. The soil moisture data from October 2011 to June 2012 in 0-14 cm soil layer were collected by the TDR(Time-Domain Reflectometry) in the T-FACE system platform. The daily meteorological data of meteorological station in the experimental platform were collected. The path analysis method was used to determine the order of the meteorological parameters, which affect soil moisture of the typical farmland in Tai Lake Region. The multiple regression equation between soil moisture and meteorological factors were established using the method of stepwise regression, and the changes of annual precipitation in the study area were predicted using the weighted markov chain method. The results were as follows:(1) The precipitation of the Tai Lake region was inhomogeneous greatly within the year. It mainly concentrated in April to September. About 62.0% of the annual precipitation occurs in the six months period. The annual average precipitation of the study area was below average precipitation in the 60s、70s and 80s of the 20th century and annual average precipitation was more than average precipitation in 1990-1999 and 2000-2009.(2) By analyzing the nearly 50 years’ annual precipitation of Tai Lake region, the classification of annual precipitation numerical interval which suits the study area has been established, (low flow year:x<857.5 mm, partial low flow year:857.5 mm<x<969.3 mm, normal flow year:969.3 mm≤x<1192.9 mm, partial flow year: 1192.9 mm≤x<1304.7 mm, wet year:x≥1304.7 mm). Based on the correspond of the Markov chain characteristics of precipitation, the weighted Markov chain model was used to predict the annual precipitation state and annual rainfall between 2011 and 2012. The relative errors were 6.59% and -10.74%, respectively. The relative errors of weighted Markov chain precipitation simulation from 2002 to 2011 showed that the relative errors of wet year and partial low flow year were big and the normal flow year was small. The annual rainfall from 2013 to 2020 was predicted.(3) Analysis on Markov chain of various step size showed that the appearance probability of normal flow year, partial low flow year, partial flow year, low flow year and wet year were 0.2685、0.2067、0.1845、0.1661 and 0.1742, respectively. The appearance probability of normal flow year, partial low flow year and partial flow year were larger. The annual precipitation in Tai Lake region had been predicted based on the weighted Markov chain, which showed significance of reasonable farmland water management in this area.(4) The Soil moisture content in the plough layer (0-14 cm) in T circles (Temperature elevated) is much less than that in CK circle. The soil moisture content in CT circles (elevated [CO2] and temperature) is slightly reduced compared with CK.The variation of soil moisture content in C circles (elevated [CO2]) was not obvious, which compared with CK. The soil moisture content of the three treatments (C, T, CT) in the plow pan (14-33 cm) all reduced, which compared with CK. The level of reducing was CT>T>C; The soil moisture content of three treatment in the storage layer(33-59 cm) was quite similar, which compared with CK in the seeding period, overwintering period and mature period. While the order of soil moisture content was CK>C>CT>T in the tillering stage; Compared with CK, the soil moisture content of three treatments in the gley lawyer (59-80 cm) was not obvious in the seeding time, overwintering period and tillering stage. While the order of soil moisture content was CK>C>CT>T in the mature period.(5) The soil moisture of experimental sits was positive correlated with daily precipitation and daily maximum air humidity, which was extremely negatively correlated with daily evaporation, sunshine duration and average wind speed (The correlation coefficients were 0.648,0.371,-0.566,-0.454 and -0.331, respectively). But no significant correlation was found between the soil moisture and the daily maximum air temperature.(6) The path analysis showed that the decreasing influence sequence of meteorological factors on soil moisture was as follows:daily precipitation, average wind speed, sunshine duration, daily evaporation, daily mean air humidity, daily minimum air temperature, daily minimum air humidity, daily maximum air temperature, daily maximum air humidity and daily mean temperature. The daily precipitation had a comprehensive influencing ability on soil moisture. (7) The multiple regression function between soil moisture and meteorological factors was Y=10.17+0.386X4+1.095X7-0.509X8-0.766X9-0.345X10, (R2=0.912, P<0.01) The stepwise regression of daily mean temperature (Xi), daily maximum air temperature (X2), daily minimum air temperature (X3), daily mean air humidity (X4), daily maximum air humidity (X5), daily minimum air humidity (X6), daily precipitation (X7), sunshine duration (Xg), daily evaporation (X9), average wind speed (X10) with soil moisture data (Y) in 0-14cm was achieved. The soil moisture and daily mean air humidity (X4), daily precipitation (X7), sunshine duration (X8), daily evaporation (K8), average wind speed (X10) had a significant linear relationship among the ten factors, which showed these five factors had bigger influence on the soil moisture. |
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