| The first: That only a few time points belong to weak variation of soil moisture of bare land(CK), new gravel-mulched field(NGM), middle gravel-mulched field(MGM), old gravel-mulched field(OGM), others belong to medium variation. The average relative(-14.81%-12.84%) and the standard deviation(0.78%-18.99%) vary in a narrow range, we can choose the representative measuring point 39, point 42, point 10 and point 4 to represent the estimation average soil moisture of bare land, NGM, MGM, OGM. Spearman rank correlation coefficient and significance level were high. Statistical regression analysis of represent points soil moisture and average soil moisture with least square method showed that the correlation coefficient lay between 0.8640-0.9973, standard error and average deviation were smaller. To provide a theoretical basis for monitoring points of soil moisture in the area reasonable layout, implement accurately forecast of soil moisture.The second: There was apparent difference of different layers soil moisture in gravel-sand mulched field. The variability of surface soil moisture was largest and soil moisture distribution was in small patch-like pattern. The variability of soil moisture decreased with the increase of soil depth. Variation trend of mean soil moisture and coefficient of variation kept consistent over time in different soil layers. Mean soil moisture was 11.70% in 0-10 cm, 14.99% in 10-20 cm, 13.26% in 20-30 cm and 12.49% in 30-50 cm, respectively, indicating a upward trend with soil depth. However, the coefficient of variation was 0.2068 in 0-10 cm, 0.1821 in 10-20 cm, 0.1483 in 20-30 cm and 0.1241 in 30-50 cm, respectively, showing a downward trend with soil depth. The variation of soil moisture was largest in 0-10 cm. In addition, the study found that fragmentation degree of soil moisture distribution under dry condition was higher than wet condition, and temporal stability was high with the increase of soil depth. The relative deviation of soil moisture lied between-26.94% and 21.89% in 0-10 cm, between-23.40% and 17.26% in 10-20 cm, between-18.24% and 13.33% in 20-30 cm, between-15.73% and 11.60% in 30-50 cm. The standard deviation of the average relative deviation lied between 11.71% and 23.15% in 0-10 cm, between 10.85% and 22.18% in 10-20 cm, between 8.77% and 21.27% in 20-30 cm, between 6.96% and 17.35% in 30-50 cm. Spatial-temporal stability of soil moisture increased with the increase of soil depth. Based on information above, the representative measuring points of point 1, point 1, point 44, point 36 were selected for soil moisture estimation for 0-10, 10-20, 20-30 and 30-50 cm. The correlation coefficient between the estimated by representative measuring points and measured values was 0.877-0.968, and standard error and average deviation were small. Linear regression models were established based on the representative measuring points and validated by data obtained from 2014. The relative error between measured value and predicted value lied between 5.18% and 0.59% in 0-10 cm, between 15.42% and 0.55% in 10-20 cm, between 1.07% and 10.33% in 20-30 cm, between-12.87% and 0.07% in 30-50 cm. It suggested that the prediction accuracy of models was high and the representative measuring points could be used for soil moisture estimation in the study area.The third: Gravel-sand mulched field had different spatial variability to different precipitation pulses. The average soil moisture of NGM, MGM and OGM were lower than CK when precipitation pulses to 7.1mm on the first day. But the average soil moisture of NGM was increased 5.5344% than CK, MGM was increased 4.7829% than CK and OGM was increased 4.0235% than CK when precipitation pulses to 7.1mm on the third day. The large rainfall event had an impact on small rainfall event, the average soil moisture of gravel-sand mulched field were higher than precipitation pulses to 7.1mm except the third day of OGM when precipitation pulses to 1.9mm. But the average soil moisture of CK was obviously lower when precipitation pulses to 1.9mm than precipitation pulses to 7.1 mm. The average soil moisture of gravel-sand mulched field were NGM>MGM>OGM and third day>second day>first day, soil moisture belonged to the weak variation and medium variation. The best theoretical model was Gaussian model of NGM, spherical and exponential of MGM and OGM to different precipitation pulses, CK was simulated by Gaussian, exponential and spherical. Semi-variance function curve of soil moisture changed relatively steadily and the correlation coefficient of model fitting were higher. The three dimensional space distributions of soil moisture were unsmooth because of the topography and geomorphology influence. Provide theoretical basis for soil water utilization.The fourth: Soil water infiltration capacity: NGM > MGM > OGM > CK, at the beginning of infiltration capacity of NGM was significantly higher than MGM, OGM, CK. Infiltration rate of new gravel-mulched field was highest, middle gravel-mulched field was higher than old gravel-mulched field. The process of bare land soil water infiltration presented index negative correlation and gravel-mulched field presented power function negative correlation. Soil cumulative infiltration volume had power function positive correlation as time went on, the correlation coefficient between 0.9764 and 0.9960. Both Horton infiltration model and Kostiakov infiltration model can describe soil water infiltration rule, can represent the model of soil water infiltration rule. Based on grey relational grade, soil water infiltration capacity was: NGM(1.000) > MGM(0.756) > OGM(0.729) > CK(0.622). |
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