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This paper deals with the application of universal kriging to interpolate water table elevations from their measurements at random locations. Geographic information system tools were used to generate the continuous surface of water table elevations for the Carlsbad area alluvial aquifer located to the southeast of New Mexico, USA. Water table elevations in the 38 monitoring wells that are common to 1996 and 2003 irrigation years follows normal distribution. A generalized MATLAB code was developed to generate omni-directional and directional semi-variograms (at 22.5 • intervals). Low-order polynomials were used to model the trend as the water table profile exhibits a south-east gradient. Different theoretical semivariogram models were tried to select the base semi-variogram for performing geostatistical interpolation. The contour maps of water table elevations exhibit significant decrease in the water table from 1996 to 2003. Statistical analysis performed on the estimated contours revealed that the decrease in water table is between 0.6 and 4.5 m at 90% confidence. The estimation variance contours show that the error in estimation was more than 8 m 2 in the west and south-west portions of the aquifer due to the absence of monitoring wells.

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We present a novel pilot-point-based hydraulic tomography (HT) inversion procedure to delineate preferential flow paths and estimate hydraulic properties in a fractured aquifer. Our procedure considers a binary prior model developed using a randomized algorithm. The randomized algorithm involves discretizing the domain into grid cells, assigning a binary label to each cell, traversing the grid randomly, and choosing the optimal grid configuration cell-by-cell. This binary prior model is used to guide the placement of pilot points and to constrain aquifer parameters during pilot-point-based HT inversion. A two-dimensional fractured granite rock block was considered to test our methodology under controlled laboratory conditions. Multiple pumping tests were conducted at selected ports and the pressure responses were monitored. The pumping datasets thus obtained were preprocessed using median filters to remove random noise, and then analyzed using the proposed procedure. The proposed binary prior algorithm was implemented in C++ by supplying the forward groundwater model, HydroGeoSphere (HGS). Pilot-point-assisted HT inversion was performed using the parameter-estimation tool, coupled to HGS. The resulting parameter distributions were assessed by: (1) a visual comparison of the K-and S s -tomograms with the known topology of the fractures and (2) comparing model predictions with measurements made at two validation ports that were not used in calibration. The performance assessment revealed that HT with the proposed randomized binary prior could be used to recover fracture-connectivity and to predict drawdowns in fractured aquifers with reasonable accuracy, when compared to a conventional pilot-point inversion scheme.

Flux footprint models simulate the source area of scalar fluxes from a measurement site but are hindered by the assumptions that are rarely satisfied in real field conditions. We conducted artificial tracer (CO2) experiments in unstable conditions over an open field, to evaluate three analytical footprint models (Kormann and Meixner‐KM, Hsieh‐HS, and Schuepp‐SP) under variable source‐receptor settings. Experimental configurations include (i) source length (point to semi‐infinite), (ii) emission height (below and above zero‐plane displacement), (iii) measurement height (1.5 and 2.8 m above ground level), and (iv) offset from the principal wind direction (±60°). Eddy covariance (EC) fluxes were measured using one source‐multiple sampling system approach. Results indicate that KM model is in good agreement with the EC measurements under ideal conditions (R2 = 0.81, root‐mean‐square error (RMSE) = 0.008 m−1) compared to HS (R2 = 0.60, RMSE = 0.014 m−1) and SP (R2 = 0.72, RMSE = 0.009 m−1) models. The KM model captured both footprint maximum and its location. Uncertainties in the model estimates were ascertained by considering random ensembles of wind speed, friction velocity, and Obukhov length. The KM model has resulted in the least uncertainty band containing all the flux observations. Error propagation into model simulations resulting from a progressive deviation of the underlying assumptions was assessed. Model‐to‐measurement discrepancies were positive (flux overestimation) for offset deviations up to ±30° and negative beyond. Modeled fluxes were sensitive to source length and emission height in comparison to the offset deviation. Overall, the KM model showed the least error (9.6 ± 8.12%) for different source‐receptor deviations. Study findings can help in evaluating and improving the analytical models for use under nonideal source‐receptor configurations and scaling measurements for different applications.