Pumping tests are performed during aquifer characterization to gain conceptual understanding about the system through diagnostic plots and to estimate hydraulic properties. Recovery tests consist of measuring head response in observation and/or pumping wells after pumping termination. They are especially useful when the pumping rate cannot be accurately controlled. They have been traditionally interpreted using Theis' recovery method, which yields robust estimates of effective transmissivity but does not provide information about the conceptual model. Agarwal proposed a method that has become standard in the oil industry, to obtain both early and late time reservoir responses to pumping from recovery data. However, the validity of the method has only been tested to a limited extent. In this work, we analyze Agarwal's method in terms of both drawdowns and log derivatives for non‐ideal conditions: leaky aquifer, presence of boundaries, and one‐dimensional flow. Our results show that Agarwal's method provides excellent recovery plots (i.e., the drawdown curve that would be obtained during pumping) and parameter estimates for nearly all aquifer conditions, provided that a constant pumping rate is used and the log derivative at the end of pumping is constant, which is too limiting for groundwater hydrology practice, where observation wells are usually monitored. We generalize Agarwal's method by (1) deriving an improved equivalent time for time‐dependent pumping rate and (2) proposing to recover drawdown curves by extrapolating the pumping phase drawdowns. These yield excellent diagnostic plots, thus facilitating the conceptual model analysis for a broad range of conditions.

Understanding groundwater flow involves characterizing the spatial variability of aquifer attributes and possible hydraulic connectivity structures. The latter are of crucial importance because high permeability channels may control groundwater flow and contaminant transport. In evaporitic aquifer systems, these preferential channels can consist of karst conduits, developed at different scales, as well as fault zones. These features condition the economic development of salt flats at the Central Andes. Hydraulic connectivity may affect exploitation efficiency by enhancing the inflow of less evaporated brine thus diminishing its mineral content (i.e., concentration of Li and K). In this context, we investigate if it is possible to use (tomographic) stochastic inversion (regularized pilot point method) in order to characterize the presence of connectivity structures in an evaporitic aquifer of great extension (some 1,500 km²) from head response measured at numerous observation points during a year long sequence of three hydraulic tests. Results show that, even though the solution is nonunique, the main preferential flow zones are identified. Numerous inversions yield similar fits to observed drawdowns with maximum errors of few centimeters. Preferential flow is identified not only by elongated high permeability regions, but also by a marked scale effect (model transmissivities are some 30 times larger than their local tests counterparts). The main high conductivity zones are consistent with independent information based on geophysics, isotopes, mixing ratios, piezometric data, and the expected dissolution processes.

The drawdown response to a hydraulic stress contains crucial information to characterize an aquifer. Modeling drawdowns is far easier than modeling heads because they are subject to homogeneous (zero) internal sink/sources, and boundary and initial conditions. The problem lies on the fact that drawdowns are not measured directly but derived from measurements of head fluctuations. Resulting drawdowns may suffer persistent inaccuracies in complex systems with uncertain long‐acting external stresses, so that they are affected not only by errors in head measurements, but also in estimates of the natural head evolution. This hinders the use of drawdowns in groundwater models, and forces modelers to employ absolute heads and soft information. In this context, we present a method to filter systematic errors in drawdown data during the calibration of a groundwater model. To do this, we introduce a bias correction term in a composite inverse problem that combines a natural head model with a drawdown model. Since these two models share the same parameters, a two‐stage iterative optimization algorithm is developed to jointly estimate the bias, natural trends, and parameters. The method is illustrated by a synthetic example in a heterogeneous aquifer. The example shows that the method converges to the best conditional estimate even when absolute head data is strongly biased. In the same example, we demonstrate that the use of biased absolute head data in the traditional inverse problem can also provide good fittings but, in this case, the bias leads to an incorrect estimation of the transmissivity field.

Several microtechnology devices quantify the external load of team sports using Global Positioning Systems sampling at 5, 10, or 15 Hz. However, for short, explosive actions, such as collisions, these sample rates may be limiting. It is known that very high-frequency sampling is capable of capturing changes in actions over a short period of time. Therefore, the aim of this study was to compare the mean acceleration and entropy values obtained from 100 Hz and 1000 Hz tri-axial accelerometers in tackling actions performed by rugby players. A total of 11 elite adolescent male rugby league players (mean ± SD; age: 18.5 ± 0.5 years; height: 179.5 ± 5.0 cm; body mass: 88.3 ± 13.0 kg) participate in this study. Participants performed tackles (n = 200), which were recorded using two triaxial accelerometers sampling at 100 Hz and 1000 Hz, respectively. The devices were placed together inside the Lycra vests on the players’ backs. The mean acceleration, sample entropy (SampEn), and approximate entropy (ApEn) were analyzed. In mean acceleration, the 1000 Hz accelerometer obtained greater values (p < 0.05). However, SampEn and ApEn were greater with the 100 Hz accelerometer (p < 0.05). A large relationship was observed between the two devices in all the parameters analyzed (R² > 0.5; p < 0.0001). Sampling frequency can affect the quality of the data collected, and a higher sampling frequency potentially allows for the collection of more accurate motion data. A frequency of 1000 Hz may be suitable for recording short and explosive actions.

Adaptability to all types of terrain changes, slopes, and course settings is a key aspect related to the coordinative ability that elite skiers possess. In recent years, several studies have analyzed coordinative aspects of different motor actions via the assessment of movement variability (MV), an indicator of the motor control that assesses movement regularity. The aims of this study were (a) to evaluate the influence of different slopes and slalom (SL) gate offsets on MV and performance and (b) to assess the relationship between MV and performance. Four SL courses were set: a flat-turned (FT), a steep-turned (ST), a flat-straighter (FS), and a steep-straighter (SS). Five elite alpine skiers (21.2 ± 3.3 years, 180.2 ± 5.6 cm, 72.8 ± 6.6 kg) completed several runs at maximum speed for each SL course. A total of 77 runs were obtained. The use of an IMU accelerometer attached to the lower back of skiers measured MV through entropy. The skiers’ performance was evaluated with the total time of each run. The one-way repeated measures analysis revealed that the steepness of the slope significantly increases skiers’ MV, concretely between FS and ST courses (p = 0.004). Differences at the 10% level have been found between FS and SS and FT and ST courses (p= 0.055 and p = 0.078, respectively). For a given slope, turned courses (FT and ST) tend to produce a higher MV. In addition, faster times correlate with lower MV (r = 0.587, p = 0.01). It has been observed that both steeper and turned courses produce greater MV and that the best performing skiers have lower MV. Determining MV through entropy can be used to assess skiers’ expertise regarding different types of slopes and gate offsets.