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Shanon Connelly
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.
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 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.
Date Reason chosen
August 7, 2000 high 24-hour precipitation (1.88”)
August 26, 2000 maximum daily temperature (111ºF)
October 19, 2000
last dry day of drought period
October 20, 2000
abundant precipitation after drought period
October 23, 2000
high 24-hour precipitation (9.15”)
March 5, 2003 minimum daily temperature (11ºF)
March 19, 2003 high 24-hour precipitation (3.34”)
April 16, 2003 high 24-hour precipitation (3.21”)
The soil-water balance equation will be used to quantify soil moisture:
ΔwΔ t
= P – E – S
wi = wi-1 + P – E – S
or
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.
Equation Component
Data Parameter(s) Notes
Precipitation Rainfall 24 hour cumulative rainfall
Evaporation Station 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.
Surplus Soil physical properties (Percent Sand/Silt/Clay, Saturation point, Field Capacity, Wilting Point, Critical Moisture Point, andBulk Density)
Derived using the soil texture triangle
Δ soil water content Volumetric Water Content Neutron probes in soil measure Δ soil temperature and soil water content is derived.
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
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)
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
Date 8/7/20008/26/200
010/19/20
0010/20/20
00 3/5/20033/19/200
34/16/200
3Count 86 90 85 83 82 88 90Minimum -165.8022 -174.4563 -210.4428 -203.9146 -197.6153 -214.0744 -217.3216Maximum 50.1404 35.104 208.1363 199.6216 156.2219 179.8839 48.9228
Average -10.43402-
12.765687-
20.071792 -18.102-
45.082996 -55.54878 -52.40135 Underestimate > 20mm 26 22 40 39 48 64 59Underestimate > 5mm 11 23 12 11 12 6 10Within -5mm and 5mm 17 20 10 8 4 3 3Overestimate > 5mm 20 21 8 7 6 3 7Overestimate > 20mm 12 4 15 18 12 12 11
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
August 7, 2000
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 90 18045 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
March 5, 2003
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 80 16040 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
August 26, 2000
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 5mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 90 18045 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
March 5, 2003
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 80 16040 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
October 19, 2000
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 90 18045 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
March 5, 2003
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 80 16040 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
October 20, 2000
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 90 18045 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
March 5, 2003
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 80 16040 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
March 5, 2003
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 80 16040 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
March 5, 2003
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 80 16040 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
March 19, 2003
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 80 16040 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
March 5, 2003
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 80 16040 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
April 16, 2003
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 75 15037.5 Miles
Variance between actual and derived Soil Moisture Content
Oklahoma, U.S.A.
March 5, 2003
Variance betweenDerived and ActualSoil Moisture Content
Underestimated more than 20mm
Underestimated more than 5mm
Within -5mm and 5mm
Overestimated more than 5mm
Overestimated more than 20mm
²0 80 16040 Miles
Date 8/7/20008/26/200
010/19/20
0010/20/20
00 3/5/20033/19/200
34/16/200
3Count 90 90 85 83 82 88 90Minimum -243.378 -262.7851 -292.0266 -292.8517 -314.8727 -322.8947 -398.8954Maximum 59.895 47.5517 42.1342 46.0397 42.6735 25.3343 71.2037
Average-
11.172103-
14.035441-
23.681219-
22.160365-
53.026518 -61.47354 -56.99088 Underestimated > 20mm 11 13 36 31 57 70 54Underestimated> 5mm 43 58 26 22 11 7 9Within-5mm and 5mm 25 15 15 19 4 8 16Overestimated> 5mm 8 2 4 5 8 1 7Overestimated> 20mm 3 2 4 6 2 2 4
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
STIDKriging
03052003Kriging
03192003Kriging
04162003IDW
03052003IDW
03192003IDW
04162003
ACME -20.3127 -13.0814 5.7378 -64.8383 -66.6643 -19.2345
ADAX 14.2958 -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
ANTL 80.4431 40.8968 25.1486 7.8222 -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 -46.7740 3.5608 0.1717 -32.4433
BIXB 61.9558 26.7188 12.9318 15.5142 -4.5888 8.0525
BLAC -61.0129 -65.4505 -81.9027 -55.5567 -54.5864 -58.3882
BOWL -15.5319 -30.9528 8.6032 -29.3473 -46.6255 8.7024
BREC - -46.9068 14.8144 - -44.5172 29.7529
BRIS 26.1504 20.4336 37.1197 42.6735 25.3343 43.8859
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
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.
Oklahoma Climatological Survey. Estimates of soil moisture from the Oklahoma Mesonet. [Available online at 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.
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