Journal Paper Digests

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Journal Paper Digests 2019 #1

  • Estimating Field Capacity from Volumetric Soil Water Content Time Series Using Automated Processing Algorithms
  • Effect of Air- and Water-Filled Voids on Neutron Moisture Meter Measurements of Clay Soil

Estimating Field Capacity from Volumetric Soil Water Content Time Series Using Automated Processing Algorithms

Authors: Bean, EZ; Huffaker, RG; Migliaccio, KW

Source: VADOSE ZONE JOURNAL, 17 (1):80073-80073; DEC 13 2018

Abstract: Vadose zone measurements of volumetric soil water content (theta) using soil moisture sensors (SMSs) have become more common due to advances in technology and reduction of costs. Soil moisture sensor data exhibit a characteristic cyclical pattern reflecting water flux dynamics into and out of the observed soil volume. Expert review of SMS datasets to distinguish valid from corrupt or incomplete soil water cycles is arguably the most precise method for determining field capacity (theta(FC)) but is impractically cumbersome and time consuming for increasingly large SMS datasets. We evaluated competing approaches for automated soil water cycles analysis that use widely available R packages based on pattern recognition and machine learning (findpeaks [R-FP], symbolic aggregate approximation [R-SAX], and density histogram [R-DH]), and a MATLAB code based on soil water dynamic principles (SWDP). These approaches were applied to three SMS datasets. Our empirical results showed superi ority of R-SAX for identifying valid soil water cycles, probably due to benefiting from training sets to calibrate to correct cycles. Two other approaches (SWDP and R-FP) provided similar results without need of training sets or preprocessing data. Three approaches for estimating field capacity were applied to valid cycles, R-FP, regression of exponential decay (SWDP-R), and estimated “knee” of curve (SWDP-K). Each performed similarly to the expert defined values, with R-FP and SWDP-R generally performing best across analyses. Results of this study also show temporal dynamics of theta(FC) within datasets used here. There is potential for optimizing theta(FC) and a need for automated, objective analysis to leverage dynamics in irrigation management and modeling.

Effect of Air- and Water-Filled Voids on Neutron Moisture Meter Measurements of Clay Soil

Authors: Bagnall, DK; Gutierrez, PMC; Yimam, YT; Morgan, CLS; Neely, HL; Ackerson, JP

Source: VADOSE ZONE JOURNAL, 17 (1):80137-80137; DEC 13 2018

Abstract: Air- and water-filled voids around neutron moisture meter (NMM) access tubes have been cited as sources of volumetric water content (theta(v)) measurement error in cracking clay soils. The objectives of this study were to experimentally quantify this potential error stemming from (i) uncertainty in bulk density (rho(b)) sampling and (ii) the impact of air- and water-filled voids. Air- and water-filled voids were simulated using similar to 0.6-cm (small) and similar to 1.9-cm (large) annuli around access tubes. After NMM measurements were taken in a tightly installed access tube, either a small or large annulus was installed in the same borehole. Additional NMM measurements were taken with the annulus filled with air, and then water and rho(b) and theta(v) were measured. The RMSE of the NMM calibration using all 11 installations was 0.02 m(3) m(-3). However, if two cores were used for calibration, the ratio of NMM-measured theta(v) to in situ theta(v) was significantly differe nt (p < 0.05) from measured theta(v) half the time (RMSE, 0.012-0.05 m(3) m(-3)). Small air-filled voids created drier estimates of theta(v) (bias, -0.039 m(3) m(-3); p < 0.001), wherease small water-filled voids were not significantly different from the calibration. Air- and water-filled voids from larger annuli were significantly lower and higher (p < 0.001) than core-measured theta(v), with biases of -0.068 and 0.080 m(3) m(-3), respectively. Although this work does not correct NMM-predicted theta(v) to matrix theta(v), it does bound NMM error under field conditions in a cracking clay soil.

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