1. Shanon Connelly

2.  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).

3.  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.

4.  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

5.  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

6.  Data obtained from the Oklahoma Mesonet encompasses 4months of observations.  August and October,2000 - dry period  March – April 2003 - wet period  For this project, several key dates were chosen based upon the climate trends occurring at that time.

7. 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”)

8.  The soil-water balance equation will be used to quantify soil moisture: Δw Δ t = P – E – S w i = w i-1 + P – E – S or

9.  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.

10. Equation ComponentData 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.

11.  For each layer, the data points were divided into two sets:  Training set with 85% of sites was used for developing a geospatial model  Testing set with the remaining 15% of sites was used to test the performance of the model

12.  Point data was then interpolated into continuous surface layers and validated.  Various interpolation techniques were tested to find the most appropriate geostatistical method.  Geostatistical (Kriging)  Deterministic (Inverse Distance Weighted)

13.  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

14. Date8/7/20008/26/200010/19/200010/20/20003/5/20033/19/20034/16/2003 Count86908583828890 Minimum-165.8022-174.4563-210.4428-203.9146-197.6153-214.0744-217.3216 Maximum50.140435.104208.1363199.6216156.2219179.883948.9228 Average-10.43402-12.765687-20.071792-18.102-45.082996-55.54878-52.40135 Underestimate > 20mm26224039486459 Underestimate > 5mm1123121112610 Within -5mm and 5mm1720108433 Overestimate > 5mm202187637 Overestimate > 20mm124151812 11

22. Date8/7/20008/26/200010/19/200010/20/20003/5/20033/19/20034/16/2003 Count90 8583828890 Minimum-243.378-262.7851-292.0266-292.8517-314.8727-322.8947-398.8954 Maximum59.89547.551742.134246.039742.673525.334371.2037 Average-11.172103-14.035441-23.681219-22.160365-53.026518-61.47354-56.99088 Underestimated > 20mm11133631577054 Underestimated > 5mm435826221179 Within -5mm and 5mm2515 194816 Overestimated > 5mm8245817 Overestimated > 20mm3246224

23.  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

24. STID Kriging 03052003 Kriging 03192003 Kriging 04162003 IDW 03052003 IDW 03192003 IDW 04162003 ACME-20.3127-13.08145.7378-64.8383-66.6643-19.2345 ADAX14.2958-13.1394-5.6912-27.6352-45.6900-15.4939 ALTU-69.0035-89.9628-106.8401-21.7217-16.4008-27.1364 ALV2-84.1125-98.8147-140.4594-38.0241-31.6019-88.9651 ANTL80.443140.896825.14867.8222-4.2107-0.7215 ARDM-53.2295-110.6949-111.5389-85.3641-88.9974-124.9146 ARNE--57.8628-49.4104--59.8418-38.1378 BEAV--90.0245-119.5648--80.0803-148.6082 BESS-12.6944-27.8246-46.77403.56080.1717-32.4433 BIXB61.955826.718812.931815.5142-4.58888.0525 BLAC-61.0129-65.4505-81.9027-55.5567-54.5864-58.3882 BOWL-15.5319-30.95288.6032-29.3473-46.62558.7024 BREC--46.906814.8144--44.517229.7529 BRIS26.150420.433637.119742.673525.334343.8859

25.  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

26.  Earls, Julie, and Barnali Dixon. 2007. "Spatial Interpolation of Rainfall Data Using ArcGIS: A Comparitive Study." Accessed from http://gis.esri.com/library/userconf/proc07/papers/papers/pap_1451.pdf on January 6, 2009. http://gis.esri.com/library/userconf/proc07/papers/papers/pap_1451.pdf  Oklahoma Climatological Survey. Estimates of soil moisture from the Oklahoma Mesonet. [Available online at http://www.mesonet.org/instruments/SoilMoisture.pdf.] http://www.mesonet.org/instruments/SoilMoisture.pdf  Schmugge, T.J., Jackson, T.J., and McKim, H.L. 1980. Survey of Methods for Soil Moisture Determination. Water Resources Research 16 (6): 961-979.  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. 2000. ASCE’s standardized reference evapotranspiration equation. In Proc. of the 4th National Irrigation Symposium, ASAE, Nov. 14-16, Phoenix, AZ.