Understanding the physical and hydraulic properties of the vadose zone is important for modeling land use effects on groundwater quality. This study used a variety of characterization methods to derive conceptual understanding and estimates of hydraulic properties of a coarse alluvial gravel vadose zone in New Zealand. Sandy gravel (SG) material constituted approximately 90% of the vadose zone, with the remainder comprising sand lenses and open‐framework gravels. The gravel content of the SG material was approximately 70% (v/v) (range 68–73%). The water content of the bulk SG material (43 samples) ranged from 3.5 to 13.9%. The average bulk density of the SG material was 2.20 g/cm3 (range 2.00–2.33 g/cm3) giving an average calculated porosity of 17%. The average porosity of the open‐framework gravels was 34% and these gravels were often coated with 2‐ to 3‐mm‐thick deposits of amorphous Fe and Mn oxides. Neutron probe (NP) depth profiles indicated unsteady conditions, with variable water contents with depth and time reflecting the vertical heterogeneity and the variably saturated state of the vadose zone. Time series NP data to 3 m indicated water content in the alluvial gravels responded quickly to soil drainage events, and saturation variability was greater in the sand lenses and the SG material immediately underlying. When compared with derived water retention curves, variability in the water content equated to significant fluctuation in unsaturated hydraulic conductivity (Kunsat). Tension infiltrometer measurements were variable but were within the range of the Kunsat estimates from site‐average particle size distribution data. The gravel‐transformed, texture‐based models used to estimate saturated water content values in this study appeared to underestimate the measured values.
Intensification of dairying on irrigated pastures has led to concern over the microbial quality of shallow groundwater used for drinking purposes. The effects of intensive dairying and border-strip irrigation on the leaching of E. coli and Campylobacter to shallow groundwater were assessed over a three-year period in the Waikakahi catchment, Canterbury, New Zealand. Well selection excluded other sources of contamination so that the effect of dairying with border-strip irrigation could be assessed. Groundwater samples (135) were collected, mostly during the irrigation season, with E. coli being detected in 75% of samples. Campylobacter was identified in 16 samples (12%). A risk assessment of drinking water with these levels of Campylobacter was undertaken. A probability distribution was fitted to the observed Campylobacter data and the @RISK modeling software was used, assuming a dose response relationship for Campylobacter and consumption of 1 L/day of water. The probability of infection on any given day in the study area was estimated at 0.50% to 0.76%, giving an estimated probability of infection during the irrigation season of 60% to 75%. An epidemiological assessment of the Canterbury region comparing areas encompassing dairy within major irrigation schemes (,55% border-strip irrigation) to two control groups was undertaken. Control group 1 (CG1) encompasses areas of dairying without major irrigation schemes, and a second larger control group (CG2) comprises the rest of the Canterbury region. Comparisons of the subject group to control groups indicated that there was a statistically significant increase in age-standardised rates of campylobacteriosis (CG1 Relative Risk (RR) ¼ 1.51 (95% CI ¼ 1.31-1.75); CG2 RR ¼ 1.51 (1.33-1.72)); cryptosporidiosis (CG1 RR ¼ 2.08 (1.55-2.79); CG2 RR ¼ 5.33 (4.12-6.90)); and salmonellosis (CG2 RR ¼ 2.05 (1.55-2.71)).
Accurate input data for leaching models are expensive and difficult to obtain which may lead to the use of "general" non-site-specific input data. This study investigated the effect of using different quality data on model outputs. Three models of varying complexity, GLEAMS, LEACHM, and HYDRUS-2D, were used to simulate pesticide leaching at a field trial near Hamilton, New Zealand, on an allophanic silt loam using input data of varying quality. Each model was run for four different pesticides (hexazinone, procymidone, picloram and triclopyr); three different sets of pesticide sorption and degradation parameters (i.e., site optimized, laboratory derived, and sourced from the USDA Pesticide Properties Database); and three different sets of soil physical data of varying quality (i.e., site specific, regional database, and particle size distribution data). We found that the selection of site-optimized pesticide sorption (Koc) and degradation parameters (half-life), compared to the use of more general database derived values, had significantly more impact than the quality of the soil input data used, but interestingly also more impact than the choice of the models. Models run with pesticide sorption and degradation parameters derived from observed solute concentrations data provided simulation outputs with goodness-of-fit values closest to optimum, followed by laboratory-derived parameters, with the USDA parameters providing the least accurate simulations. In general, when using pesticide sorption and degradation parameters optimized from site solute concentrations, the more complex models (LEACHM and HYDRUS-2D) were more accurate. However, when using USDA database derived parameters, all models performed about equally.
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