|De Mello, C|
Submitted to: Scientia Agricola
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/7/2010
Publication Date: 5/31/2011
Citation: De Mello, C.R., Avila, L.F., Norton, L.D., Mello, J.M., Silva, A.M., Beskow, S. 2011. Spatial distribution of top soil water content in an experimental catchment of the Southeast Brazil. Scientia Agricola. 68(3):285-294. Interpretive Summary: Water holding properties of soils within a watershed are very important for the ability of farmers to utilize land for production of food and fiber and the sustainability of doing so. Changing the land-use such as to increase runoff and erosion or increase losing moisture to the atmosphere may cause changes in downstream flow. We conducted a study to measure soil moisture through time and space in a watershed that provided flow for an important downstream hydro-electric generating plant to determine which land-uses were detrimental to retaining soil moisture and promoting aquifer recharge. Measurements were taken every two weeks for one year starting in the fall. We used a statistical procedure to extrapolate these measurements across the entire watershed and relate the predicted measurements to land-uses. During the rainy season soil moisture and recharge was controlled by the amount of rainfall. During the dry season grassland sites lost more soil moisture than the native forest areas and therefore were determined to reduce the amount of potential recharge and downstream flow. Converting land from the native forest to grassland in these important headwater source areas should be avoided to prevent loss of flow for these hydro-electric facilities. The significance of this research is that it provides decision makers with information to allow better management of critically important head-water areas.
Technical Abstract: Soil moisture is an important hydrological variable that characterizes the soil water dynamics influencing surface runoff generation and consequently sediment transport. This work aimed to analyze the spatial patterns of surface soil moisture, identify the elements that exert the most influence in the context of its spatial distribution and to characterize the spatial mean and standard deviation of surface soil moisture through time in an experimental catchment located in Mantiqueira Range region, southeast Brazil. The measurements of surface soil moisture was carried out between May/2007 and May/2008, every 15 days, in the top 20 cm of soil profile by TDR equipment, at 69 points. Geostatistical procedures were applied in all steps of work. Firstly, the spatial continuity of each data set for surface soil moisture was evaluated, modeling the experimental semi-variogram by testing of Exponential, Spherical and Gaussian models which were also fitted by applying the Weighted Minimum Square (WMS), Ordinary Minimum Square (OMS) and Maximum Likelihood (ML) methods. We verified good performance for all models, and especially, those fitted by WMS. However, the exponential model was found to be the best. For development of surface soil moisture mapping, two approaches were applied: ordinary kriging, and co-kriging using the land slope as a secondary variable. Important influence of slope, and consequently, topography characteristics, was verified as important during the dry season. Rainfall regime controls the surface soil moisture in the wet season due to the high moisture contents (greater than the soil field capacity). Land-use was another local fundamental factor identified as important, especially under dry conditions. In the Atlantic Forest, the surface soil moisture present, over the period, decreased slower than the grassland sites which showed a much faster reduction due to the greater evapotranspiration from the shallow root system. The spatial standard deviation had low values under dry and high values under wet conditions. Thus, we can conclude that more variability occurs under wet conditions. These results can be applied to help devise sampling schemes for ground measurements of surface soil moisture with the purpose of validating remote sensing signals and their interpretation for mapping soil moisture directly from satellite images.