H53C-1670
A Bayesian inversion of hydrological and thermal parameters in the hyporheic zone

Friday, 18 December 2015
Poster Hall (Moscone South)
Karina Cucchi1, Nicolas Flipo2, Agnès Rivière3 and Yoram Rubin1, (1)University of California Berkeley, Berkeley, CA, United States, (2)MINES ParisTech, Fontainebleau, France, (3)Mines ParisTech, Paris, France
Abstract:
Reliable estimates of hydrological properties at the stream-aquifer interface are necessary for quantifying surface-subsurface exchanges but are challenging to get due to spatial variability and field data scarcity. Our study introduces a novel approach for inferring uncertainty-quantified hydrological and thermal parameters from a combination of pressure and temperature measurements in the hyporheic zone (HZ).

We use a stochastic approach to infer thermal and hydrological parameters in a HZ vertical profile. The column is forced by pressure and temperature time series at the boundaries and conditioned on temperature at multiple depths. The inversion process is based on a Bayesian algorithm called Method of Anchored Distribution (MAD) and on the associated open-source program MAD++, with extensions in the post-processing toolbox. This approach has several benefits. First, the hierarchical framework built-in in MAD allows the specification of a non-parametric and assumption-free likelihood function. Moreover, the Bayesian approach yields data-driven and naturally uncertainty-quantified parameter estimates.

We present two outcomes of the inversion approach. (1) Repeating the analysis at multiple locations yields spatially-distributed snapshots of uncertainty-quantified HZ parameters. (2) The unconditional posterior distribution of hydrological parameters is integrated in a Monte-Carlo error estimation framework to provide statistical distributions of surface-subsurface exchanges time series.

We present the methodology and demonstrate its application using field measurements from the Avenelles basin, France.