For research in the atmospheric boundary layer and in the vicinity of wind turbines, the turbulent 3D wind vector can be measured from fixed-wing unmanned aerial systems (UAS) with a five-hole probe and an inertial navigation system. Since non-zero vertical wind and varying horizontal wind causes variations in the airspeed of the UAS, and since it is desirable to sample with a flexible cruising airspeed to match a broad range of operational requirements, the influence of airspeed variations on mean values and turbulence statistics is investigated. Three calibrations of the five-hole probe at three different airspeeds are applied to the data of three flight experiments. Mean values and statistical moments of second order, calculated from horizontal straight level flights are compared between flights in a stably stratified polar boundary layer and flights over complex terrain in high turbulence. Mean values are robust against airspeed variations, but the turbulent kinetic energy, variances and especially covariances, and the integral length scale are strongly influenced. Furthermore, a transect through the wake of a wind turbine and a tip vortex is analyzed, showing the instantaneous influence of the intense variations of the airspeed on the measurement of the turbulent 3D wind vector. For turbulence statistics, flux calculations, and quantitative analysis of turbine wake characteristics, an independent measurement of the true airspeed with a pitot tube and the interpolation of calibration polynomials at different Reynolds numbers of the probe's tip onto the Reynolds number during the measurement, reducing the uncertainty significantly.Atmosphere 2019, 10, 124 2 of 33 attitude, position, and velocity of the vehicle, measured by an inertial navigation system (INS), multiple coordinate transformations finally yield the wind vector. This method is widely used in manned aircraft [1,10] and on fixed-wing UAS [4][5][6][7]. The accuracy of the wind vector measurement is crucial, and the propagation of errors have many influencing factors, originating in the attitude and ground speed measurement of the aircraft, the flow angles and flow magnitude (true airspeed vector) measurement with the multi-hole probe, and also in the measurement of the thermodynamic state of the air. Extensive studies for various systems and subsystems of the wind vector measurement with manned research aircraft [11][12][13][14][15][16], including in-flight calibration procedures and uncertainty analysis [10,17], and with UAS (e.g., for the M 2 AV [7]) were performed.So far, for UAS, calibration maneuvers during flight and the influence of airspeed variations on the wind vector measurement were not addressed in terms of calibration and uncertainty analysis for the 3D wind vector measurement with multi-hole probes. Since a misalignment between the multi-hole probe's orientation and the aircraft cannot be avoided, an in-flight calibration must be applied [16]. Calibration maneuvers during flight such as the "acceleration-deceleration maneuver", the "ya...

Surface-water divides can be delineated by analyzing digital elevation models. They might, however, significantly differ from groundwater divides because the groundwater surface does not necessarily follow the surface topography. Thus, in order to delineate a groundwater divide, hydraulic-head measurements are needed. Because installing piezometers is cost- and labor-intensive, it is vital to optimize their placement. In this work, we introduce an optimal design analysis that can identify the best spatial configuration of piezometers. The method is based on formal minimization of the expected posterior uncertainty in localizing the groundwater divide. It is based on the preposterior data impact assessor, a Bayesian framework that uses a random sample of models (here: steady-state groundwater flow models) in a fully non-linear analysis. For each realization, we compute virtual hydraulic-head measurements at all potential well installation points and delineate the groundwater divide by particle tracking. Then, for each set of virtual measurements and their possible measurement values, we assess the uncertainty of the groundwater-divide location after Bayesian updating, and finally marginalize over all possible measurement values. We test the method mimicking an aquifer in South-West Germany. Previous works in this aquifer indicated a groundwater divide that substantially differs from the surface-water divide. Our analysis shows that the uncertainty in the localization of the groundwater divide can be reduced with each additional monitoring well. In our case study, the optimal configuration of three monitoring points involves the first well being close to the topographic surface water divide, the second one on the hillslope toward the valley, and the third one in between.

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Process-based numerical modeling of subsurface flow is an important tool in hydrogeological research and groundwater-resources management. The computational power (in terms of hardware and software) has improved drastically over the last decades. At the same time, models have increased in complexity, as new numerical methods have become feasible, more processes can been considered and evermore detail can be represented in the models (e.g., Venkataraman & Haftka, 2004;Y. Zhou & Li, 2011;Jakob, 2014). A side effect of this development is that modern models tend to have many adjustable parameters. The process of estimating values of model parameters so that the model output reasonably agrees with measured data is known as model calibration, inverse modeling, or parameter inference (e.g.,

The widening and narrowing of river‐valley aquifers can cause valley‐scale lateral hyporheic exchange even if the river is straight and its slope is uniform. For the aforementioned system, we derive a semi‐analytical solution describing steady‐state groundwater flow for a simplified two‐dimensional geometry of the aquifer and uniform lateral influx from hillslopes. We use this solution to evaluate the geometry‐driven lateral hyporheic exchange flux between the aquifer and the river. By systematically varying the model parameters, we decipher how this flux and the area of the exchange zone depend on geometric (e.g., minimum and maximum domain width) and hydrogeological parameters (e.g., hydraulic conductivity, ambient hydraulic gradient and lateral influxes). The results suggest pronounced hyporheic flow for cases with distinct widening behavior and small cross‐sectional widths at the floodplain inlet and outlet. Furthermore, we analyze the travel‐time distribution of water flowing through the exchange zone, which approximately follows a beta distribution. We express our findings in terms of simple proxy‐equations that can be used to easily estimate the exchange flux, the area of the exchange zone, and the associated travel‐time distribution for a given geographic/landscape setting.