Statistical Soil Moisture Analysis Kelly Wilhelm and David Smith Statistical Methods 105 Project Report May 5 2008 Introduction Soil the weathered remains of rocks and organic matter provides both faunal and floral organisms a medium for ecological growth A specific characteristic of soil is the pore space between nodules of clay silts and clastic rock grains that allow for percolation of water from precipitation events This critical attribute allows for the aqueous migration of nutrients to the roots of plants which are processed as inputs into the biological cycle Thus soil moisture is defined as the amount of water one can evaporate by weight percent of an original soil sample Interestingly soil moisture is highly variable and is influenced by many surface characteristics included topography changes slope declination vegetation cover and potentially the amount of solar radiation received creating evaporative surface conditions The focus of this project is to find and statistically represent the variability of soil moisture content within the grade of surface topography Our goal is to analyze the changes in soil moisture content on three sections of a hill summit slope and foot see figure 1 Then repeat the sampling system and compare the three sections between an east facing and west facing slopes for statistical differences see figure 2 Figure 1 Summit East Facing Hill Sample Field Slope Base Figure 2 Transect Line West Facing Slope East Facing Slope Methods Initially two hills were selected for having similar characteristics of slope vegetation cover area and height but varying in the overall facing direction of East and West Figure 2 In order to complete a survey transect methods of stratified random sampling were implemented For each hill a transect line from summit to base was selected for have traits of smooth surface transitions and lack of visible disturbance in surface flow down a slope White line in figure 1 Samples collected were measured in a fifty foot by fifty foot square the three locations in figure 1 area of sampling For the three sampling locations found on a hill fifteen samples were chosen to be collected For each sample the program Microsoft Excel was used to randomly select the length down the transect line whether to sample on the left or right side of the transect and the overall width away from the transect An example or our stratified random sample output can be seen in table 1 At each selected location a soil auger was used to extract the sample The soil samples were subsequently labeled and stored in individual sandwich bags for further processing and to prevent evaporation of moisture Since a direct measure of moisture in the soil is not possible using a precise scale we measured the original sample weight then evaporate all water and re measure the dried sample Evaporation was accomplished by cooking the samples at 450 degrees for 8 hours The potential weight difference will indicate how much moisture was evaporated from each sample as found in table 2 appendix Table 1 West Facing Slope Lengt Sample h Right Left 1 10 R 2 5 L 3 27 R 4 40 R 5 50 R 6 35 L 7 11 R 8 4 R 9 30 L 10 21 L 11 2 L 12 33 L 13 2 R 14 49 R 15 3 R Widt h 11 1 24 6 8 17 22 4 2 2 24 11 2 7 13 East Facing Slope Lengt Sample h Right Left 1 1 R 2 22 R 3 10 R 4 6 L 5 20 R 6 2 R 7 19 L 8 20 L 9 21 L 10 14 R 11 25 R 12 8 L 13 17 R 14 4 L 15 23 L Width 0 5 4 7 9 3 1 0 3 3 19 11 5 15 20 Statistical Analysis The soil sampling analysis was conducted under the hypothesis of finding differences in variability of soil moisture content within the grade of surface topography Our initial hypothesis was to find variation within the three sections of a hill This occurs as precipitation follows topography from high to low regions High gradation of slopes significantly decreases the moisture holding capacity of the soil therefore yielding potential differences Therefore the mean soil moisture of samples collected from the summit slope and base of a hill will potentially have statistically significant differences This is represented by our Hypothesis Ho top slope base Ha top slope base Another interesting statistical analysis would be the variability of soil moisture content within topography regions between two similar hills Thus by measuring the aforementioned regions in two hills a statistical comparison of variation could be made to find a potential statistical difference between facing hills This is stated by our second hypothesis Ho top1 top2 slope1 slope2 base1 base2 Ha top1 top2 slope1 slope2 base1 base2 The analyses will be entered through SAS statistical software Initially means procedure and box plot will be utilized for an overall statistical analysis To find statistical differences between east and west facing locations and compiled means individual t tests will be conducted For the comparison of multiple individual location means ANOVA statistical analysis will be used to find the variance for more than two means Tests of regression will be utilized as evidence for potential sampling biasness Table 3 The MEANS Procedure Analysis Variable pwet Lower 95 Upper 95 hill Obs N Mean Median Std Dev CL for Mean CL for Mean Variance EastBase 15 15 0 0026350 0 0025980 0 000721298 0 0022355 0 0030344 5 2027049E 7 EastSlop 15 15 0 0026373 0 0025305 0 000677979 0 0022619 0 0030128 4 5965512E 7 EastTop 15 15 0 0028267 0 0029243 0 000727137 0 0024240 0 0032294 5 2872833E 7 WestBase 15 15 0 0021408 0 0022641 0 000434937 0 0018999 0 0023816 1 8917028E 7 WestSlop 15 15 0 0023937 0 0022049 0 000683253 0 0020154 0 0027721 4 6683402E 7 WestTop 15 15 0 0025823 0 0025526 0 000539567 0 0022835 0 0028811 2 9113221E 7 Statistical Results Means Procedure To begin the analysis from the proc means procedure yielded data that supports the use of two independent t tests and ANOVA The data collected is represented by a simple random sample all sample locations have the same amount of sample individuals as well The standard deviations do not display a high level of variance which ranges from 00043 to 00072 Thus the largest standard deviation is smaller than twice the smallest sample standard deviation yielding a ration of 1 67 which is less than 2 This is a great indication for the data being in compliance with the rules that satisfy ANOVA The overall dataset does not appear to have significant outliers as the mean and median of all samples are approximately equal with little variance This data indicates the