Geostatistical inference using crosshole ground-penetrating radar

High-resolution tomographic images obtained from crosshole geophysical measurements have the potential to provide valuable information about the geostatistical properties of unsaturated-zone hydrologic-state va riables such as moisture content. Under drained or quasi-steady-state conditions, the moisture content will reflect the variation of the physical properties of the subsurface, which determine the flow patterns in the unsaturated zone. Deterministic least-squares inversion of crosshole groundpenetrating-radar GPR traveltimes result in smooth, minimumvariance estimates of the subsurface radar wave velocity structure, which may diminish the utility of these images for geostatistical inference. We have used a linearized stochastic inversion technique to infer the geostatistical properties of the subsurface radar wave velocity distribution using crosshole GPR traveltimes directly. Expanding on a previous study, we have determined that it is possible to obtain estimates of global variance and mean velocity values of the subsurface as well as the correlation lengths describing the subsurface velocity structures. Accurate estimation of the global variance is crucial if stochastic realizations of the subsurface are used to evaluate the uncertainty of the inversion estimate. We have explored the full potential of the geostatistical inference method using several synthetic models of varying correlation structures and have tested the influence of different assumptions concerning the choice of covariance function and data noise level. In addition, we have tested the methodology on traveltime data collected at a field site in Denmark. There, inferred correlation structures indicate that structural differences exist between two areas located approximately 10 m apart, an observation confirmed by a GPR reflection profile. Furthermore, the inferred values of the subsurface global variance and the mean velocity have been corroborated with moisturecontent measurements, obtained gravimetrically from samples collected at the field site

The Impact of Out-of-School IT and Media Use on ICT in Education

Mr. Peter Looms Senior Consultant, DR Interactive, Danish Broadcasting Corporation. Visiting lecturer of the MSc(ECom-IComp) Programme, Department of Computer Science & Information Systems, The University of Hong Kong.As educators we have a tendency to assume that the use of information and communication technologies in school plays a dominant part in the acquisition of effective knowledge and skills in this domain. We tend to overlook what our pupils and students do in their own time and the impact this also has on their cognitive and affective development. Using examples from Europe, US and Asia this seminar the speaker explore current trends in media use by children and adolescents and the implications of this out-of-school learning for what we do in education.published_or_final_versionHosted by: The MSc(ECom-IComp) Programme Office; Co-organized by: The Centre for Information Technology in School & Teacher Education, School of Professional & Continuing Education, The University of Hong Kon

Bayesian Markov-chain-Monte-Carlo inversion of time-lapse cross hole ground-penetrating radar data to characterize the vadose zone at the Arrenaes field site, Denmark

The ground-penetrating radar (GPR) geophysical method has the potential to provide valuable information on the hydraulic properties of the vadose zone because of its strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR traveltime data can allow for a significant reduction in uncertainty regarding subsurface van Genuchten-Mualem (VGM) parameters. Much of the previous work on the stochastic estimation of VGM parameters from crosshole GPR data has considered the case of steady-state infiltration conditions, which represent only a small fraction of practically relevant scenarios. We explored in detail the dynamic infiltration case, specifically examining to what extent time-lapse crosshole GPR traveltimes, measured during a forced infiltration experiment at the Arreneas field site in Denmark, could help to quantify VGM parameters and their uncertainties in a layered medium, as well as the corresponding soil hydraulic properties. We used a Bayesian Markov-chain-Monte-Carlo inversion approach. We first explored the advantages and limitations of this approach with regard to a realistic synthetic example before applying it to field measurements. In our analysis, we also considered different degrees of prior information. Our findings indicate that the stochastic inversion of the time-lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions compared with the corresponding priors, which in turn significantly improves knowledge of soil hydraulic properties. Overall, the results obtained clearly demonstrate the value of the information contained in time-lapse GPR data for characterizing vadose zone dynamics

Comparison of evapotranspiration estimates using the water balance and the eddy covariance methods

Abstract The eddy covariance method estimates the energy flux of latent heat for evapotranspiration. However, imbalance between the land surface energy output and input is a well‐known fact. Energy balance closure is most commonly not achieved, and therefore the eddy covariance method potentially underestimates actual evapotranspiration. Notwithstanding, the method is one of the most established measurement techniques for estimating evapotranspiration. Here, evapotranspiration from eddy covariance (ETEC) is cross‐checked with evapotranspiration calculated as the residual of the water balance (ETwb). The water balance closure using ETEC is simultaneously validated. Over a 6‐yr period, all major terms of the water balance are measured including precipitation, recharge from percolation lysimeters, and soil moisture content from a cosmic‐ray neutron sensor, a capacitance sensor network, and time domain reflectometry (TDR), respectively. In addition, we estimate their respective uncertainties. The study demonstrates that both monthly and yearly ETEC and ETwb compare well and that the water balance is closed when ETEC is used. Concurrently, incoming available energy (net radiation minus ground heat flux) on average exceeds the turbulent energy fluxes (latent heat flux and sensible heat flux) by 31%, exposing the energy–surface imbalance. Consequently, the imbalance in the energy balance using the eddy covariance method must, to a lesser degree, be caused by errors in the latent heat estimates but can mainly be attributed to errors in the other energy flux components