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Woodward S.J.R.,Lincoln Agritech Ltd. | Stenger R.,Lincoln Agritech Ltd. | Hill R.B.,Waikato Regional Council
Transactions of the ASABE | Year: 2016

While analysis of river water quality time series data alone allows observation of means, variances, trends, and seasonality, it cannot elucidate the catchment mechanisms responsible for these observations. Incorporating river flow data into the analysis allows additional insight to be gained into the mechanisms driving water quality change. Twenty-year series of monthly water quality samples were analyzed alongside high-resolution flow records in 26 catchments across the agriculturally dominated Waikato region of New Zealand. Concentration-discharge relationships indicated the importance of near-surface flow paths in transporting nitrogen and non-dissolved phosphorus species from the land into rivers. Dissolved phosphorus, on the other hand, appears to be discharged primarily in deeper groundwater carrying higher concentrations of geogenic origin. Subsequent data stratification was able to explain the origin of nitrate or phosphorus trends in some catchments as being due to either historical or recent land use changes. These results highlight the value of combined analysis of water quality data with river flow records. © 2016 American Society of Agricultural and Biological Engineers.

Platt I.,Lincoln Agritech Ltd. | Woodhead I.,Lincoln Agritech Ltd. | Harrington P.,Opus International Consultants Ltd. | Tan A.E.-C.,Lincoln Agritech Ltd.
IEEE Sensors Journal | Year: 2016

This paper presents the experimental results of using a non-invasive time domain reflectometry (TDR) sensor to image and parameterize the moisture content of basecourse materials used in road construction. Imaging, resulting from the amplitude losses of a pulsed signal travelling down TDR transmission lines, is found to produce excellent information on the relative changes in basecourse moisture content to a spatial resolution <5 mm in the plane of the road surface. A simple model relating the volumetric moisture content to the TDR measured propagation time is shown to provide a consistent transform between the two. The experimental data also indicate that the smallest transform error of \sim 0.25 % in volumetric moisture content is achieved when model coefficients are separately fitted for each of the two basecourse aggregate types tested. © 2001-2012 IEEE.

PubMed | University of Auckland, Lincoln Agritech Ltd and Max Planck Institute of Biochemistry
Type: | Journal: Nature communications | Year: 2016

Cryo-EM of large, macromolecular assemblies has seen a significant increase in the numbers of high-resolution structures since the arrival of direct electron detectors. However, sub-nanometre resolution cryo-EM structures are rare compared with crystal structure depositions, particularly for relatively small particles (<400 kDa). Here we demonstrate the benefits of Volta phase plates for single-particle analysis by time-efficient cryo-EM structure determination of 257 kDa human peroxiredoxin-3 dodecamers at 4.4 resolution. The Volta phase plate improves the applicability of cryo-EM for small molecules and accelerates structure determination.

Wohling T.,University of Tübingen | Wohling T.,Lincoln Agritech Ltd. | Schoniger A.,University of Tübingen | Gayler S.,University of Tübingen | Nowak W.,University of Stuttgart
Water Resources Research | Year: 2015

A Bayesian model averaging (BMA) framework is presented to evaluate the worth of different observation types and experimental design options for (1) more confidence in model selection and (2) for increased predictive reliability. These two modeling tasks are handled separately because model selection aims at identifying the most appropriate model with respect to a given calibration data set, while predictive reliability aims at reducing uncertainty in model predictions through constraining the plausible range of both models and model parameters. For that purpose, we pursue an optimal design of measurement framework that is based on BMA and that considers uncertainty in parameters, measurements, and model structures. We apply this framework to select between four crop models (the vegetation components of CERES, SUCROS, GECROS, and SPASS), which are coupled to identical routines for simulating soil carbon and nitrogen turnover, soil heat and nitrogen transport, and soil water movement. An ensemble of parameter realizations was generated for each model using Monte-Carlo simulation. We assess each model's plausibility by determining its posterior weight, which signifies the probability to have generated a given experimental data set. Several BMA analyses were conducted for different data packages with measurements of soil moisture, evapotranspiration (ETa), and leaf area index (LAI). The posterior weights resulting from the different BMA runs were compared to the weight distribution of a reference run with all data types to investigate the utility of different data packages and monitoring design options in identifying the most appropriate model in the ensemble. We found that different (combinations of) data types support different models and none of the four crop models outperforms all others under all data scenarios. The best model discrimination was observed for those data where the competing models disagree the most. The data worth for reducing prediction uncertainty depends on the prediction to be made. LAI data have the highest utility for predicting ETa, while soil moisture data are better for predicting soil water drainage. Our study illustrates, that BMA provides an objective framework for data worth analysis with respect to both model discrimination and model calibration for a wide range of applications. Key Points: BMA provides a data worth analysis framework for model selection and calibration BMA does not converge to the "true" model Different data types support different models and none outperforms all others © 2015. American Geophysical Union. All Rights Reserved.

SchoNiger A.,University of Tübingen | Wohling T.,University of Tübingen | Wohling T.,Lincoln Agritech Ltd | Nowak W.,University of Stuttgart
Water Resources Research | Year: 2015

Bayesian model averaging (BMA) ranks the plausibility of alternative conceptual models according to Bayes' theorem. A prior belief about each model's adequacy is updated to a posterior model probability based on the skill to reproduce observed data and on the principle of parsimony. The posterior model probabilities are then used as model weights for model ranking, selection, or averaging. Despite the statistically rigorous BMA procedure, model weights can become uncertain quantities due to measurement noise in the calibration data set or due to uncertainty in model input. Uncertain weights may in turn compromise the reliability of BMA results. We present a new statistical concept to investigate this weighting uncertainty, and thus, to assess the significance of model weights and the confidence in model ranking. Our concept is to resample the uncertain input or output data and then to analyze the induced variability in model weights. In the special case of weighting uncertainty due to measurement noise in the calibration data set, we interpret statistics of Bayesian model evidence to assess the distance of a model's performance from the theoretical upper limit. To illustrate our suggested approach, we investigate the reliability of soil-plant model selection following up on a study by Wöhling et al. (2015). Results show that the BMA routine should be equipped with our suggested upgrade to (1) reveal the significant but otherwise undetected impact of measurement noise on model ranking results and (2) to decide whether the considered set of models should be extended with better performing alternatives. © 2015. American Geophysical Union. All Rights Reserved.

PubMed | Landcare Research, Institute of Environmental Science and Research and Lincoln Agritech Ltd.
Type: | Journal: Journal of contaminant hydrology | Year: 2016

Reducing conditions are necessary for denitrification, thus the groundwater redox status can be used to identify subsurface zones where potentially significant nitrate reduction can occur. Groundwater chemistry in two contrasting regions of New Zealand was classified with respect to redox status and related to mappable factors, such as geology, topography and soil characteristics using discriminant analysis. Redox assignment was carried out for water sampled from 568 and 2223 wells in the Waikato and Canterbury regions, respectively. For the Waikato region 64% of wells sampled indicated oxic conditions in the water; 18% indicated reduced conditions and 18% had attributes indicating both reducing and oxic conditions termed mixed. In Canterbury 84% of wells indicated oxic conditions; 10% were mixed; and only 5% indicated reduced conditions. The analysis was performed over three different well depths, <25m, 25 to 100 and >100m. For both regions, the percentage of oxidised groundwater decreased with increasing well depth. Linear discriminant analysis was used to develop models to differentiate between the three redox states. Models were derived for each depth and region using 67% of the data, and then subsequently validated on the remaining 33%. The average agreement between predicted and measured redox status was 63% and 70% for the Waikato and Canterbury regions, respectively. The models were incorporated into GIS and the prediction of redox status was extended over the whole region, excluding mountainous land. This knowledge improves spatial prediction of reduced groundwater zones, and therefore, when combined with groundwater flow paths, improves estimates of denitrification.

Fourie J.,Lincoln Agritech Ltd. | Irie K.,Lincoln Agritech Ltd. | Roberts J.,Lincoln Agritech Ltd.
ACM International Conference Proceeding Series | Year: 2014

Maintaining appropriate exposure times is challenging in applications that involve continuous capturing and processing of images in an environment of dynamically changing light. This problem is typically solved by post processing the captured images and adapting the exposure time for the next image based on some brightness metric derived from a previous image. However, this results in a delay between the light changing and the exposure time being properly updated, resulting in under- or overexposed images. In this article we propose using a light sensor to actively measure the changing light and use this to calculate the appropriate exposure time prior to images being captured. We show that by using this method we can capture images that are appropriately exposed for maximum contrast even when the ambient light changes dramatically.

Eccleston K.W.,Lincoln Agritech Ltd
2016 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization, NEMO 2016 | Year: 2016

Substrate-integrated waveguide (SIW) distributed amplifiers require complicated waveguide components which can make comprehensive full-wave simulation difficult. To better assess the amplifier, much of the waveguide componentry can be accurately represented by equivalent half-mode rectangular waveguide. In this work, spurious coupling between close proximity waveguide open-edges are shown to adversely affect performance and the challenge is to mitigate this affect. © 2016 IEEE.

Woodward S.J.R.,Lincoln Agritech Ltd | Stenger R.,Lincoln Agritech Ltd | Bidwell V.J.,Lincoln Agritech Ltd
Journal of Hydrology | Year: 2013

The use of process-based, dynamic and spatially-explicit models to describe water and nitrogen fluxes at the catchment-scale is often hampered by a shortage of detailed land use, hydrological and biogeochemical information. Accordingly, such complex models tend to be restricted to a small number of well investigated catchments, often associated with research projects. On the other hand, stream flow and stream water chemistry time series data are available for a much larger number of catchments, e.g. for many catchments that are routinely monitored by government agencies for state-of-the-environment reporting. It was the main aim of this study to provide a spatially lumped model that allows meaningful analysis of catchment-scale water and nitrate fluxes based on such data sets.Based on stream flow time series data, catchment hydrodynamics are often analysed using approaches derived from the linearised Boussinesq equation, which has analytical solutions for dynamic groundwater discharge expressed in terms of eigenvalues and eigenfunctions (eigenmodel approach). Calibrated Boussinesq models generally yield a good reproduction of stream flow dynamics, and stable estimates for aquifer parameters such as hydraulic conductivity and mean aquifer depth. By linking a soil water balance model with two Boussinesq groundwater eigenmodels linked in series, and assuming constant solute concentrations discharging from each source, a dynamic catchment model predicting stream flow and water chemistry at the catchment outlet ("StreamGEM") was developed. Compared with previous approaches, inclusion of water chemistry in this model both aided hydrological understanding, and allowed assessment of catchmentscale nitrate fluxes.Simultaneous calibration of the model to stream flow and nitrate concentration data from a small lowland dairying catchment yielded good predictions to both variables (Nash-Sutcliffe Model Efficiency of 0.90 and 0.84), and the fitted parameters were able to be used to estimate annual flow and nitrate fluxes through near-surface, shallow groundwater, and deeper groundwater reservoirs conceptually present in the catchment. The calibration was cross-validated using an independent time series from the same catchment.The results support the hypothesis, based on groundwater observations, that stream flow in the catchment is the result of mixed discharge from a shallower, rapidly draining zone of oxidised groundwater carrying relatively high loads of agricultural nitrate, with a relatively deeper and slower draining zone of reduced groundwater that is essentially nitrate free. The proportions of stream flow discharging from the near-surface, shallow groundwater, and deeper groundwater reservoirs were estimated to be 5%, 80% and 15%, respectively. In spite of its small contribution to total stream flow, the deeper groundwater reservoir sustained stream flow during summer and dominated stream water chemistry 61% of the time.By combining the flow and nitrate concentration estimates derived from model calibration, it was estimated that discharge of shallow groundwater was responsible for 91% of the nitrate load entering the stream. However, the predicted nitrate concentration in this reservoir was significantly lower than the predicted nitrate concentration of near-surface flow and root zone leachate concentrations estimated using a nutrient budgeting model. This indicates that denitrification occurs within this reservoir. On the basis of the calibrated model, it was estimated that 36% of the nitrate recharged from the vadose zone gets denitrified within the shallow groundwater reservoir, and up to 9% in the deeper groundwater reservoir. © 2013 Elsevier B.V.

Woodward S.J.R.,Lincoln Agritech Ltd | Wohling T.,Lincoln Agritech Ltd | Wohling T.,TU Dresden | Stenger R.,Lincoln Agritech Ltd
Journal of Hydrology | Year: 2016

Understanding the hydrological and hydrogeochemical responses of hillslopes and other small scale groundwater systems requires mapping the velocity and direction of groundwater flow relative to the controlling subsurface material features. Since point observations of subsurface materials and groundwater head are often the basis for modelling these complex, dynamic, three-dimensional systems, considerable uncertainties are inevitable, but are rarely assessed. This study explored whether piezometric head data measured at high spatial and temporal resolution over six years at a hillslope research site provided sufficient information to determine the flow paths that transfer nitrate leached from the soil zone through the shallow saturated zone into a nearby wetland and stream. Transient groundwater flow paths were modelled using MODFLOW and MODPATH, with spatial patterns of hydraulic conductivity in the three material layers at the site being estimated by regularised pilot point calibration using PEST, constrained by slug test estimates of saturated hydraulic conductivity at several locations. Subsequent Null Space Monte Carlo uncertainty analysis showed that this data was not sufficient to definitively determine the spatial pattern of hydraulic conductivity at the site, although modelled water table dynamics matched the measured heads with acceptable accuracy in space and time. Particle tracking analysis predicted that the saturated flow direction was similar throughout the year as the water table rose and fell, but was not aligned with either the ground surface or subsurface material contours; indeed the subsurface material layers, having relatively similar hydraulic properties, appeared to have little effect on saturated water flow at the site. Flow path uncertainty analysis showed that, while accurate flow path direction or velocity could not be determined on the basis of the available head and slug test data alone, the origin of well water samples relative to the material layers and site contour could still be broadly deduced. This study highlights both the challenge of collecting suitably informative field data with which to characterise subsurface hydrology, and the power of modern calibration and uncertainty modelling techniques to assess flow path uncertainty in hillslopes and other small scale systems. © 2016 Elsevier B.V.

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