Spatial-Temporal Variability Of Soil Moisture In Relation To Land Use In Hilly-sloppy Lands Of Red Soil Region

Hilly red soil region, which is located in the southeast of China , is one of regions with the highest Agricultural Productivity . Affected by uneven precipitation, the region have very serious seasonal drought. So soil water is of critical importance to maintain agroecosystem in the region . Studying temporal and spatial correlation of soil moisture in hilly red soil region, is not only useful for us to further understand the rule of temporal and spatial distribution of soil moisture, but also can provide some effective ways to make use of and simulate soil moisture change of closer actual situation.To know the distribution rule of soil moisture under different season , during Sep of 2006 to Jul of 2007, we used grid sample with 20mx20m to measure the surface(0-30cm) and subsurface(30-60cm) soil moisture in hilly-sloppy land, which is composed of tea land ,upland and forest land in the hilly red soil region, and used grid sample with 40mx40m to measure soil bulk density and saturated hydraulic conductivity in Jun 2006. Spatial structures of its soil moisture was analyzed by the geostatistics method and the classical statistics method. The main findings are as follows:1. Spatial variability of soil bulk density and saturated hydraulic conductivity was analyzed and found that spatial variability of soil bulk density was very weak; spatial variability of saturated hydraulic conductivity was very strong. Spatial variability intensity of saturated hydraulic conductivity was far higher than that of soil bulk density , and both of them present negative correlation in the same region .2. Soil moisture of forest land was more abundant than soil moisture of tea land and upland, the coefficient of variation of soil moisture in the entire research region was much higher than that of the single land use. According to autocorrelation analysis, soil moisture in the entire region had markedly different variation characteristics because of different land use. Based on phenomena from seim-variogram functions, we found that soil moisture in the area of tea -upland boundary had no spatial correlation at all, so it can't be simulated effectively . With respect to influence factors, such complex distribution of soil moisture was explained by different land use and microtopographty in winter and autumn; However, land use was the dominant factor to the variation of soil moisture in spring and summer.3. Besides tea-upland boundary, soil moisture in the whole area had obvious spatial continuity. So it was advisable to apply geostatistics method to study the spatial variability of soil moisture in red hilly region. In a word, geostatistics method is an effective tool to analyze spatial variability of soil moisture in hilly red soil region, but when analyze variable form the region with complex situation, we must consider feasibility of the method .4. Study on scale seasonal effects of soil moisture shows the nugget and range increased as sampling space increased , but the sill always maintain stability. The change in sampling extent can also affect semi-variogram's parameters of soil moisture. The sill had the same change rule under different season, namely increased along with the sampling extent increased. The range varies complicatedly, it increased obviously with sampling extent in spring , but no evident rule in other seasons . These results show that the change in sampling space and sampling extent affected spatial structure of soil moisture . However, according to the analysis result to all semi-variogram's parameters, seasonal change can not caused the same change rule.5. Kriging method and condition simulation was used to simulated soil moisture and soil bulk density ,we found their simulation precision was more higher than other interpolation methods. Inverse distance weight was more suitable for soil saturated hydraulic conductivity ; condition simulation can simulate the fluctuation rule of soil moisture , but kriging method was more close to practice value . In a word , each method has its own advantages and disadvantages. When research different space variable, we must use appropriate interpolation method in view of different variable characteristics.