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Soil Moisture Measurement for Ecological and Hydrological Watershed-Scale Observatories: A Review

^a Dep. of Geophysics, 397 Panama Mall, Stanford Univ., Stanford, CA 94305-2215
^b Dep. of Crop and Soil Sciences, Washington State Univ., Pullman, WA 99164
^c Dep. of Land, Air and Water Resources, Univ. of California, Davis, CA 95616
^d Dep. of Agronomy, Iowa State Univ. of Science and Technology, Ames, IA 50011-1010
^e Dep. of Plants, Soils and Climate, Utah State Univ., Logan, UT 84322-4820
^f Dep. of Civil & Architectural Engineering, Univ. of Wyoming, Laramie, WY 82071
^g Dep. of Biological and Ecological Engineering, Oregon State Univ., Corvallis, OR 97331-3906
^h Dep. of Plant and Soil Sciences, Univ. of Kentucky, Lexington, KY 40546-0091
ⁱ current address: Dep. of Food Production, Univ. of the West Indies, Trinidad and Tobago

View larger version (15K): [in this window] [in a new window]   FIG. 1. (a) Soil water content control on soil physical properties affecting plant growth. (b) Soil water content control of soil biogeochemical reactions and processes including pH, acidity alkalinity, and Eh, the electrical potential of the system relative to the potential of a standard hydrogen electrode.

View larger version (25K): [in this window] [in a new window]   FIG. 2. (a) Schematic altered from Skoop et al. (1990) indicating conceptually how microbial activity is affected by soil water content. (b) Data from Schjonning et al. (2003) for a sand (L1), sandy loam (L2), and clay (L3) showing nitrification and CO2 production as a function of water content.

View larger version (34K): [in this window] [in a new window]   FIG. 3. Conceptual diagram showing the estimated extent (m) of measurements and the spacing in time. The extent of the watersheds is determined from the major axis of a 2:1 ellipse and surface areas according to Zielinski (2002). B, basin (2500–25,000 km2); SB, subbasin (250–2,500 km2); W watershed (80–250 km2); SW, subwatershed (1–80 km2); C, catchment (0.1–1 km2). Current technology is constrained to measuring processes with space and time scales consistent with boxes having solid lines. The new technologies and methods (dashed lines) form a bridge between current sensor and remote sensing capabilities. They improve our ability to monitor rapid soil moisture change at small watershed scales.

View larger version (30K): [in this window] [in a new window]   FIG. 4. Adapted from Western et al. (1999), soil moisture in the 10-ha Tarrawarra watershed in Australia when dry (left) and wet (right). Each pixel is one time domain reflectometry point measurement to 30 cm depth. The 75th (left) and 90th (right) percentile indicator plots are shown under each respective figure. During dry periods, the pattern is random, but an organized patter emerges as the soil becomes wet.

View larger version (28K): [in this window] [in a new window]   FIG. 5. Thermogravimetric analysis of common soil minerals and clay minerals. 105°C is the standard drying temperature; 160°C is the temperature at which most clays have undergone primary dehydration (adapted from Gardner, 1986).

View larger version (55K): [in this window] [in a new window]   FIG. 6. Dielectric data for quartz sand with no dielectric dispersion and moist clays showing dielectric dispersion; the real (Eq. [7a]) and imaginary (Eq. [7b]) permittivity for water without ionic conductivity are indicated for reference. The upper-left figure shows six common soil water sensors: (A) time domain reflectometer, (B) ECH2O EC-20 probe, (C) Hydra probe, (D) Acclima time domain transmission sensor, (E) ThetaProbe, (F) CS-616. The center figure shows a cart-mounted ground penetrating radar (GPR) (courtesy, Sensors & Software, Inc., Mississauga, ON); the top-right figure is a passive microwave remote sensing radiometer mounted on a crane.

View larger version (20K): [in this window] [in a new window]   FIG. 7. (a) Water content measurements in a silt loam soil during two irrigation events for the seven sensors shown in Fig. 6. Diurnal fluctuations in sensor response occur as a result of temperature changing the bulk soil electrical conductivity to which certain instruments are sensitive. (b) The calibration indicating the Acclima is the only sensor to achieve accurate water content determination in this trial. Deviations from absolute values are considered to occur because of small sensor sampling volume.

View larger version (28K): [in this window] [in a new window]   FIG. 8. A conceptual wireless mesh network or "multihop" system in a watershed. The sensors can all communicate to determine the most efficient wireless data transfer path to the gateway from where the data is transferred to the computer. The remote sensor sends data to a relay node, which passes it on to the gateway. Also shown are examples of the scale triplet (Blöschl and Sivapalan, 1995): support (Supp), spacing, and extent.

View larger version (50K): [in this window] [in a new window]   FIG. 9. (a) Picture of "blade-" and "sled-configured" prototype open-ended time domain reflectometry probes. (b) Waveforms measured with the blade probe in the field at uniform (i.e., Millville silt loam [coarse-silty, carbonatic, mesic Typic Haploxeroll]) along a 50-m transect. (c) interpreted from the blade open-ended probe reading in a nonuniformly wetted transect in the same field.

View larger version (27K): [in this window] [in a new window]   FIG. 10. Maps of bulk soil electrical conductivity measured with electromagnetic induction across a 12-ha field, rendered with 10- by 10-m pixels. (a) Dry soil before a major rainfall event, (b) soil after 75 mm of rain, and (c) the difference between the two. The field slopes from right to left, with the bottom-left corner the lowest point. The red areas in (a) and (b) indicate what appears to be the emergence of subsurface flow paths after rainfall. The red areas in (c) indicate zones of increased electrical conductivity after rainfall.

View larger version (43K): [in this window] [in a new window]   FIG. 11. Characteristic soil moisture under a corn crop before and after soil wetting. is determined from DC resistivity measurements; figure altered from Michot et al. (2003).

View larger version (35K): [in this window] [in a new window]   FIG. 12. Original soil water content (SWC) data (a) at the 0 to 10 cm depth and elevation, and point and aggregated measurements for (b) 25-m, and (c) 55-m spacing, respectively.

View larger version (22K): [in this window] [in a new window]   FIG. 13. Soil water content (SWC) at 0–10 cm, measured at different spacings and aggregated over different distances, and their respective standard deviations.

View larger version (32K): [in this window] [in a new window]   FIG. 14. Spearman rank correlation coefficients for various pairs of variables as a function of spacing for point and aggregated measurements. SWC = soil water content.

View larger version (22K): [in this window] [in a new window]   FIG. 15. Semivariograms obtained from soil water content at 0 to 10 cm depth for point and aggregated measurements at different spacings as a function of lag distance. The lines are semivariogram models fitted to the data.

View larger version (36K): [in this window] [in a new window]   FIG. 16. Soil water content at 0 to 10 cm [SWC(10)] estimated with sand content at 0 to 10 cm [Sand(10)] for three scenarios based on different data spacing of point or aggregated measurements.

View larger version (82K): [in this window] [in a new window]   FIG. 17. An example of a nested calibration site; the blue disks represent sensor arrays, and the yellow vehicle has a crane-mounted passive remote sensing device (as in Fig. 6). The airborne methods include drone-mounted, aircraft-mounted, and satellite remote sensing devices. All the data are telemetered back to the office via the Internet so that the site can be continuously monitored.