Introduction  In situ measurements examine the phenomenon exactly in place where it occurs.  The most accurate of soil moisture measurements are in situ, but these methods can be labor intensive, expensive, and destructive to the soil, and are only accurate at the point of measurement (Schmugge et al., 1980).  The first portion of this project is to find the best interpolation methods for spatial prediction of continuous surface layers from in-situ point measurements of weather data.  Then, using the raster calculator function in the Spatial Analyst extension, the interpolated surface layers will serve as inputs into the soil water balance equation to derive soil moisture estimates

Study Area  The Oklahoma Mesonet  Operated by the Oklahoma Climatological Survey  Network of over 110 automated stations covering Oklahoma  At least one Mesonet station in each of the 77 counties  Approximately 100 sites monitor soil moisture

 13 atmospheric and subsurface variables recorded every 5 minutes, producing 288 observations of each parameter per station per day.  Air temperature  Humidity  Barometric pressure  Wind speed  Wind direction  Rainfall  Solar radiation  Soil temperature

Data Availability  Data obtained from the Oklahoma Mesonet encompasses 4months of observations.  August and October, dry period  March – April wet period  For this project, several key dates were chosen based upon the climate trends occurring at that time. DateReason chosen August 7, 2000high 24-hour precipitation (1.88”) August 26, 2000maximum daily temperature (111ºF) October 19, 2000last dry day of drought period October 20, 2000abundant precipitation after drought period October 23, 2000high 24-hour precipitation (9.15”) March 5, 2003minimum daily temperature (11ºF) March 19, 2003high 24-hour precipitation (3.34”) April 16, 2003high 24-hour precipitation (3.21”)

Methodology  The soil-water balance equation will be used to quantify soil moisture:  A database was compiled containing the point data from each of the study areas  Precipitation measurements  Net radiation  Soil physical properties  Etc.  Some variables were used directly, while others were used to derive inputs to be used in the soil water balance equation. Δw Δ t = P – E – S w i = w i-1 + P – E – S OR

Equation Component Data Parameter(s)Notes PrecipitationRainfall24 hour cumulative rainfall EvaporationStation Pressure (avg) Solar Radiation (total) Relative Humidity (min, max, avg) Temperature (min, max, avg) Average Wind Speed (avg) Derived from the ACSE ‘s standardized reference evapotranspiration equation and multiplied by an evapotranspiration coefficient. SurplusSoil physical properties (Percent Sand/Silt/Clay, Saturation point, Field Capacity, Wilting Point, Critical Moisture Point, and Bulk Density) Derived using the soil texture triangle Δ soil water contentVolumetric Water ContentNeutron probes in soil measure Δ soil temperature and soil water content is derived.

Results  Kriging is more accurate than IDW  Simple and Ordinary kriging methods are most suitable  No data transformation was used  Gaussian, Spherical and Exponential variogram models are most appropriate  Slight modification to variogram models  Lag size, number of lags and searching neighborhoods were slightly modified on each run to yield the best predictions

Results: Kriging Date8/7/20008/26/ /19/ /20/200 03/5/20033/19/20034/16/2003 Count Minimum Maximum Average Underestimate > 20mm Underestimate > 5mm Within -5mm and 5mm Overestimate > 5mm Overestimate > 20mm

Results – August 7, 2000

Results – October 19, 2000

Results – March 5, 2003

Results – April 16, 2003

Results: IDW Date 8/7/200 08/26/200010/19/200010/20/20003/5/20033/19/20034/16/2003 Count Minimum Maximum Average Underestimated > 20mm Underestimated > 5mm Within -5mm and 5mm Overestimated > 5mm Overestimated > 20mm

Future Research  Spatial join the validation layer with the master data layer  Determine which stations have greatest amount of error, and if it is a single occurrence or recurring error  Pinpoint cause of error  Calibrate or eliminate

STID Kriging Kriging Kriging IDW IDW IDW ACME ADAX ALTU ALV ANTL ARDM ARNE BEAV BESS BIXB BLAC BOWL BREC BRIS

Conclusion  Kriging is more accurate method than IDW  Simple and Ordinary kriging methods  Gaussian, Spherical and Exponential variogram models  Soil moisture content estimates tend to be greatly underestimated  Future research to pinpoint stations with high errors  Investigate further – calibrate or eliminate  Wet season yields less accurate SMC estimates using this methodology

References  Earls, Julie, and Barnali Dixon "Spatial Interpolation of Rainfall Data Using ArcGIS: A Comparitive Study." Accessed from on January 6,  Oklahoma Climatological Survey. Estimates of soil moisture from the Oklahoma Mesonet. [Available online at  Schmugge, T.J., Jackson, T.J., and McKim, H.L Survey of Methods for Soil Moisture Determination. Water Resources Research 16 (6):  Walter, I.A., R.G. Allen, R. Elliott, M.E. Jensen, D. Itenfisu, B. Mecham, T.A. Howell, R. Snyder, P. Brown, S. Echings, T. Spofford, M. Hattendorf, R.H. Cuenca, J.L. Wright, and D. Martin ASCE’s standardized reference evapotranspiration equation. In Proc. of the 4th National Irrigation Symposium, ASAE, Nov , Phoenix, AZ.