1. Spatial Variability of Near-Surface and Profile Water Content: Sensors Impact the Result ABSTRACT Spatial variability of soil water content has been shown to depend on time since wetting, plant vigor and variability, and sample size. Four electromagnetic sensors, the neutron moisture meter and gravimetric sampling were used to assess spatial and temporal variability of near-surface (top 10 cm) and profile (0 to 100 cm) water content over periods of natural rainfall, deficit irrigation and full irrigation. Sensor type and measurement method had a large effect on spatial statistics. Sensors based on capacitance methods reported much more variability than actually existed as determined by gravimetric measurements. Capacitance sensors also indicated trends in spatial distribution of water when none existed or when variation was minor. A sensor based on quasi-TDR principles was somewhat better than capacitance sensors but not as reliable as the neutron probe, which produced results nearly identical to those from gravimetric measurements and with the smallest variability. 184-3 Figure 2. Season-long mean standard deviation (SD) of soil water content by depth for six methods of soil water determination used in the field in 2003 at Bushland, TX. Standard deviations were computed for all ten access tubes, for the five access tubes in the Wet (irrigated) side of the field, and for the five access tubes in the Dry (non-irrigated) side of the field. Figure 4. Ranking of mean relative difference of storage in ten access tubes for eight measurement dates after irrigation of winter wheat began in 2003. Data are shown for gravimetric measurements, the neutron moisture meter (NMM), the Trime T3 probe, the EnviroSCAN and Diviner 2000 capacitance probes, and the Delta-T PR1/6 capacitance probe. The bars indicate the minimum and maximum values of the relative difference at each of the ten access tube locations. Steve Evett 1, Naem Th. Mazahrih 2, Judy A. Tolk 1, Terry A. Howell 1, and Nedal Katbeh Bader 3. (1) USDA-ARS, Bushland, Texas, (2) National Centre for Agricultural Research and Technology Transfer, Amman, Jordan, (3) Deputy Director General, Environmental Resources, Ramallah, Palestine Figure 1. Profile water contents for ten transect locations for each of five sensor systems, in a winter wheat field on 5 November, 2003, compared with gravimetric measurements. Half of the field (five transect locations) was irrigated. Sensing methods were frequency domain (EnviroSCAN, Diviner 2000, and PR1/6), quasi-TDR (Trime T3), and the neutron moisture meter (NMM). Figure 3. Deviations from the difference in soil water storage (DS), between irrigated and dryland sides of a field measured gravimetrically, and the differences in storage as sensed by each of five methods. Data are for eight measurement dates in 2003 in a winter wheat field. INTRODUCTION Studies of root water uptake, crop water use and use efficiency, irrigation methods and efficiency and soil hydrology all require accurate determination of soil water content. Most of these studies require knowledge of the change in water content, ΔS, stored in a soil prism, known as the control volume, to which the soil water balance equation is applied: E ET + ΔS + R – P – I – F = 0[1] where E ET is crop water use, P is precipitation, I is irrigation, R is the sum of runoff and runon, and F is flux across the lower boundary of the soil profile (control volume) (Evett, 2002). Increasingly, studies focus on the spatial and temporal variability of the water balance components. Theory of spatial variability analysis assumes that values of the studied property are representative of a volume known as the support volume, which raises the question of the volume of sensitivity of different soil water sensing systems. The neutron moisture meter (NMM) has been used successfully in studies of spatial and temporal variability, but cannot be left unattended and so is not appropriate for many studies of temporal variability or dynamics. In the event that spatial variability is important, the number of samples must be increased such that an adequate number of samples is available for each spatially different area or plot (Vauclin et al., 1984). This is essential to reduce variance in the support volume. If no other information were available about soil water variability, sampling a field for profile water content would typically require many profiles to be sampled, either directly or using water content sensor(s) in access tubes. However, distribution of profile water content tends to be temporally stable in some fields (Vachaud et al., 1985; Villagra et al., 1995). The relative difference in soil water content, δ ij, for location i and time j was defined by Vachaud et al. (1985) as: δ ij = (S ij - E[S ij ])/E[S ij ] [2] tube probe, which is a cylindrical probe with two waveguides oriented vertically on opposite sides of a cylindrical plastic body (IMKO model Trime-T3 Tube Access Probe). The neutron probe was used in steel access tubes and the other sensors were used in plastic access tubes sold by the manufacturers. Gravimetric samples were taken to 2-m depth using a hydraulic-push machine and two pushes of a 1.5-m long sampling tube with a 3.1-cm inside diameter bit. Cores were extruded into a plastic tray and sectioned into 10-cm lengths that were put into individual soil cans for weighing, drying for 24-h, and re-weighing. Each core section had a volume of 75.5 cm 3. specific calibrations (Table 1), although the Trime was almost as accurate over the season. In 2004 and again in 2005, the same order of variability was observed (Table 2). As in 2003, in the drier soil the NMM showed much less variability than did other sensors. Also, the NMM determined soil profile change in water storage more accurately than did the other sensors (Fig. 3, Table 2). The NMM also represented the spatial variation of water storage the most accurately, as shown by the ranking of mean relative difference in storage for 2003 (Fig. 4). Other sensors showed variation in profile water content where little or none existed as determined by gravimetric measurements. Results from 2004 were similar (Fig. 5). The spatial variation represented by NMM data was four times smaller than that from other sensors. Spatial variation in the wetter and drier plots roughly followed the where E[] is the expected value operator and S ij is the profile water content at location i and time j. They calculated δ ij for all times and locations and found the mean relative difference over time for each location. Plotting mean δ ij versus rank, with error bars for the maximum and minimum relative difference at each location, allowed easy identification both of locations which represented the mean and extreme values and of locations that maintained their relative rank with the most precision. The objectives of this study were to i) compare the accuracy and variability of point and profile water contents determined by six methods [gravimetric sampling, the NMM, and four electromagnetic (EM) sensors] in the field using soil-specific calibrations, and ii) determine the relative usefulness of the six methods for spatial and temporal variability studies, including the number of access tubes or gravimetric sampling points needed to determine profile water content to within a given precision. METHODS Three field experiments were conducted at Bushland, Texas in 2003, 2004 and early 2005. Water treatments were intentionally varied in time and space (2003) and in space (2004-2005). Three capacitance type EM soil water sensing systems were used (Delta-T model PR1/6 Profile Probe; and Sentek models EnviroSCAN and Diviner 2000). A NMM (Campbell Pacific Nuclear Int. model 503DR1.5) was used with a depth control stand (Evett et al., 2003) for measurements centered at the 10-cm depth and in 20-cm increments below to 230 cm. Also, we used a quasi-TDR Figure 6. Plots of normalized semivariance of profile water content (cm) in the top 100 cm of soil, γ (cm 2 ), versus lag distance, |h| (m), for days before irrigation began in 2003 for gravimetric measurements and five sensors. VZJ http://www.cprl.ars.usda.gov Figure 5. Ranked mean relative differences in storage for the NMM, Trime, EnviroSCAN and Diviner sensors during the 2004 irrigation season. Table 1. For five sensors and gravimetric measurements, mean values and mean standard deviations (SD) across all measurement locations and dates in 2003 of water content (cm) in the soil profile from the surface to 100-cm depth; and mean SD before irrigation began, for all ten access tubes (All), the five access tubes in the irrigated half of the field (Irr.), and the five access tubes in the un- irrigated half of the field (Dry). Also shown are numbers of access tubes, N, required to determine field mean profile water content to specified precision, d, and probability level, α, using Eq. [1]. Table 2. Mean standard deviations (SD, cm) of profile water contents to 100-cm depth for four soil water sensing systems over four dates in August-November, 2004. And, mean differences (cm) between profile water contents measured by each method and by gravimetric sampling, and mean SD values for profile water contents for four dates in early January-April, 2005. Also shown for are the mean differences (cm) between the change in storage (storage in the 100% irrigation treatment minus storage in the 33% treatment) as measured by gravimetric sampling and by each system for three intervals in 2005 (day 20 to 62, day 62 to 83, and day 83 to 102). direction of runoff, with wetter soil occurring down slope from drier soil within a plot. The NMM showed a clear difference between the wetter and drier plots, while differences were less clear for the Trime and unclear for the capacitance sensors. The normalized semivariance showed a clear spatial structure for the NMM (Fig. 6). Oddly, reproducible spatial structure was also present for the PR1/6, the sensor with the least accuracy and most variation. However, the sill was ~40 times larger than for the NMM and ~6 times larger than for the other two capacitance sensors. The other methods did not exhibit consistent spatial structure. We conjecture that the EM sensor fields do not evenly permeate the soil surrounding the access tubes and so the sensors do not respond to the representative elemental soil volumetric water content. RESULTS Profile water contents reported by the six methods using soil-specific calibrations differed considerably (Fig. 1), particularly in the degree of water content variability and the shape of the profile, including over/under estimation of water content at different depths. While all of the EM sensors exhibited more variability than the NMM, the three capacitance sensors exhibited the most variability as well as a tendency to severely underestimate water content above 50-cm depth. Mean SD values for the five tubes in the irrigated side and for the five tubes in the non-irrigated side were reflective of the soil water variability at the scale of measurement of each device (Table 1). The NMM was more accurate than the other sensors, even using soil- CONCLUSIONS The four EM sensors required too many access tubes to produce statistically significant differences. The NMM and gravimetric sampling compared well, although fewer NMM access tubes than gravimetric profiles were needed to get significant differences. Although all sensors were calibrated for the three soil horizons present, the NMM produced by far the most accurate change in storage results. The NMM was the only sensor that consistently reproduced field water content spatial structure. New EM sensor designs are needed that overcome the lack of representativeness of the ones studied, produce soil water storage data with small SD values comparable to the NMM or at least gravimetric sampling, and accurately reproduce field water content spatial structure. Depth (cm) Relative rank Mean relative difference in storage Normalized semivariance (cm 2 ) Lag distance (m) SD of water content (m 3 m -3 ) Water content (m 3 m -3 ) Mean relative difference in storage