shanon connelly. in situ measurements examine the phenomenon exactly in place where it occurs. the...

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


Within -5mm and 5mm

Overestimated more than 5mm


²0 90 18045 Miles


Oklahoma, U.S.A.

March 5, 2003




Within -5mm and 5mm



²0 80 16040 Miles


Oklahoma, U.S.A.

August 26, 2000




Within -5mm and 5mm



²0 90 18045 Miles


Oklahoma, U.S.A.

March 5, 2003




Within -5mm and 5mm



²0 80 16040 Miles


Oklahoma, U.S.A.

October 19, 2000




Within -5mm and 5mm



²0 90 18045 Miles


Oklahoma, U.S.A.

March 5, 2003




Within -5mm and 5mm



²0 80 16040 Miles


Oklahoma, U.S.A.

October 20, 2000




Within -5mm and 5mm



²0 90 18045 Miles


Oklahoma, U.S.A.

March 5, 2003




Within -5mm and 5mm



²0 80 16040 Miles


Oklahoma, U.S.A.

March 5, 2003




Within -5mm and 5mm



²0 80 16040 Miles


Oklahoma, U.S.A.

March 5, 2003




Within -5mm and 5mm



²0 80 16040 Miles


Oklahoma, U.S.A.

March 19, 2003




Within -5mm and 5mm



²0 80 16040 Miles


Oklahoma, U.S.A.

March 5, 2003




Within -5mm and 5mm



²0 80 16040 Miles


Oklahoma, U.S.A.

April 16, 2003




Within -5mm and 5mm



²0 75 15037.5 Miles


Oklahoma, U.S.A.

March 5, 2003




Within -5mm and 5mm



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.

shanon connelly. in situ measurements examine the phenomenon exactly in place where it occurs. the...

Documents

soil measure soil temperature

soil water balance equation

soilwater balance equation

soil moisture estimates

soil moisture13 atmospheric

hour precipitation

critical moisture point

saturation point