Filter by collection

Current filters:


Current filters:


Refine By:

Search Results

Results 21-23 of 23 (Search time: 0.013 seconds).
Item hits:
  • BB

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

    • Authors: Bean, E.Z.;  Advisor: -;  Participants: Huffaker, R.G.; Migliaccio, K.W. (2018)

  • 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 superiority 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 estimate...
  • BB

  • Specific Conductance as a Tracer of Preferential Flow in a Subsurface‐Drained Field

    • Authors: Smith, E.A.;  Advisor: -;  Participants: Capel, P.D. (2018)

  • A specific conductance end‐member mixing analysis (SC‐EMMA) was used to determine the volume of water that infiltrated through preferential flow pathways. The SC‐EMMA was used for 20 of the 25 paired rainfall–SC events; of the 20 classified events, the maximum preferential flow ranged from 11 to 75% of the total subsurface drain flow, with a mean maximum preferential flow of 31%. Overall, SC‐EMMA illustrated that a significant portion of the subsurface drain discharge can be attributed to preferential flow, mainly through macropores or other largely open preferential flow pathways. The other primary mechanism, antecedent moisture conditions shifts, could only be shown for four of the 25 classified events. Specific conductance as a tracer of preferential flow was shown to be an effec...
  • BB

  • Drivers of Potential Recharge from Irrigated Agroecosystems in the Wisconsin Central Sands

    • Authors: Nocco, M.A.;  Advisor: -;  Participants: Kraft, G.J.; Loheide II, S.P.; Kucharik, C.J. (2018)

  • We found that interannual climate variability, subtle differences in soil texture, and cropping system type drove potential recharge to varying degrees during the summer and fall seasons. Relatively finer soil texture was positively correlated to point estimates of potential recharge. This correlation was the strongest following large precipitation events. June to November cumulative potential recharge for 2013 to 2016 averaged 71 ± 235 mm across all lysimeters. Our findings suggest that aquifer depletion will be an episodic process that leaves surface waters most vulnerable to pumping and recharge impacts during and following drier years in the WCS. Differences among cropping systems were most pronounced under average precipitation conditions, which facilitated potential groundwate...