52 research outputs found
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
Point-scale multi-objective calibration of the Community Land Model (version 5.0) using in situ observations of water and energy fluxes and variables
This study evaluates water and energy fluxes and variables in combination with parameter optimization of version 5 of the state-of-the-art Community Land Model (CLM5) land surface model, using 6 years of hourly
observations of latent heat flux, sensible heat flux, groundwater recharge,
soil moisture and soil temperature from an agricultural observatory in
Denmark. The results show that multi-objective calibration in combination
with truncated singular value decomposition and Tikhonov regularization is a powerful method to improve the current practice of using lookup tables to define parameter values in land surface models. Using measurements of
turbulent fluxes as the target variable, parameter optimization is capable
of matching simulations and observations of latent heat, especially during
the summer period, whereas simulated sensible heat is clearly biased. Of the
30 parameters considered, the soil texture, monthly leaf area index (LAI) in summer, stomatal
conductance and root distribution have the highest influence on the
local-scale simulation results. The results from this study contribute to
improvements of the model characterization of water and energy fluxes. This work highlights the importance of performing parameter calibration using
observations of hydrologic and energy fluxes and variables to obtain the optimal parameter values for a land surface model.</p
Comparison of time-lapse GPR data collected under natural and forced infiltration conditions to estimate vadose zone hydraulic parameters
Time-lapse crosshole ground-penetrating radar (GPR) data, collected
while infiltration occurs, can provide valuable information regarding
the hydraulic properties of the unsaturated zone. In particular,
the stochastic inversion of such data provides estimates of parameter
uncertainties, which are necessary for hydrological prediction and
decision making. Here, we investigate the effect of different infiltration
conditions on the stochastic inversion of time-lapse, zero-offset-profile,
GPR data. Inversions are performed using a Bayesian Markov-chain-Monte-Carlo
methodology. Our results clearly indicate that considering data collected
during a forced infiltration test helps to better refine soil hydraulic
properties compared to data collected under natural infiltration
condition
Bayesian-MCMC inversion of time-lapse geophysical data to characterize vadose zone hydraulic behaviour
Geophysical methods have the potential to provide valuable information
on hydrological properties in the unsaturated zone. In particular,
time-lapse geophysical data, when coupled with a hydrological model
and inverted stochastically, may allow for the effective estimation
of subsurface hydraulic parameters and their corresponding uncertainties.
In this study, we use a Bayesian Markov-chain-Monte-Carlo (MCMC)
inversion approach to investigate how much information regarding
vadose zone hydraulic properties can be retrieved from time-lapse
crosshole GPR data collected at the Arrenaes field site in Denmark
during a forced infiltration experiment
Examining the information content of time-lapse crosshole GPR data collected under different infiltration conditions to estimate unsaturated soil hydraulic properties
Time-lapse geophysical data acquired during transient hydrological
experiments are being increasingly employed to estimate subsurface
hydraulic properties at the field scale. In particular, crosshole
ground-penetrating radar (GPR) data, collected while water infiltrates
into the subsurface either by natural or artificial means, have been
demonstrated in a number of studies to contain valuable information
concerning the hydraulic properties of the unsaturated zone. Previous
work in this domain has considered a variety of infiltration conditions
and different amounts of time-lapse GPR data in the estimation procedure.
However, the particular benefits and drawbacks of these different
strategies as well as the impact of a variety of key and common assumptions
remain unclear. Using a Bayesian Markov-chain-Monte-Carlo stochastic
inversion methodology, we examine in this paper the information content
of time-lapse zero-offset-profile (ZOP) GPR traveltime data, collected
under three different infiltration conditions, for the estimation
of van Genuchten-Mualem (VGM) parameters in a layered subsurface
medium. Specifically, we systematically analyze synthetic and field
GPR data acquired under natural loading and two rates of forced infiltration,
and we consider the value of incorporating different amounts of time-lapse
measurements into the estimation procedure. Our results confirm that,
for all infiltration scenarios considered, the ZOP GPR traveltime
data contain important information about subsurface hydraulic properties
as a function of depth, with forced infiltration offering the greatest
potential for VGM parameter refinement because of the higher stressing
of the hydrological system. Considering greater amounts of time-lapse
data in the inversion procedure is also found to help refine VGM
parameter estimates. Quite importantly, however, inconsistencies
observed in the field results point to the strong possibility that
posterior uncertainties are being influenced by model structural
errors, which in turn underlines the fundamental importance of a
systematic analysis of such errors in future related studies
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