Abstract
AbstractHydraulicproperties of soils could play an important role in affecting the partitioning of precipitation in the critical zone. In addition to traditional approaches, in the last two decades, many geophysical methods have been used to aid the hydrologic characterization and measurement of geological materials. In particular, the self‐potential (SP) method shows great potential in these hydrogeophysical applications. The objective of this study is to evaluate whether the addition of SP data can improve the estimation of hydraulic properties of soils in an outflow experiment. A stochastic, coupled hydrogeophysical inversion was developed, in which the governing equations were solved using the finite volume method and the parameter estimation was conducted using a Bayesian approach associated with the Markov chain Monte Carlo technique. The results show that the addition of SP data in the inversion could reduce the uncertainty related to the estimated hydraulic parameters of soils and the length of the associated 95% confidence interval can be shortened by ∼1/3. It is also shown that the electrical properties of soils at saturated and unsaturated conditions may also be estimated from the outflow experiment when SP data are available. Compared with hydraulic parameters, the accuracy of the estimated electrical properties is slightly lower. Among them, the saturated streaming potential coupling coefficientCsathas the highest accuracy and lowest uncertainty sinceCsatdirectly influences the magnitude of SP signals. The accuracy of other electrical parameters is lower than that ofCsat(and hydraulic parameters), and the associated uncertainty can be one order of magnitude larger.
Highlights
The spatial variability of hydraulic properties in the subsurface could significantly affect the partitioning of precipitation in the critical zone (Takagi & Lin, 2011), an open system extending from the top of the canopy to the base of active
Due to the analytically intractable nature of many forward modeling problems, the implementation of the Bayesian method for parameter estimations is usually aided by the Markov chain Monte Carlo (MCMC) techniques, which use random walk approaches to generate samples that follow the posterior distributions of the model parameters (Vrugt et al, 2003)
Note: Q, cumulative outflow; h, pressure head; Δφ, streaming potential difference; θr, residual water content; α, fitting parameter interpreted as the inverse of the air-entry pressure; n, the parameter characterizing the shape of the soil water retention curve; Ks, saturated hydraulic conductivity; na, the Archie saturation exponent; Csat, the coupling coefficient at saturation; m, the porosity exponent
Summary
The spatial variability of hydraulic properties in the subsurface could significantly affect the partitioning of precipitation in the critical zone (Takagi & Lin, 2011), an open system extending from the top of the canopy to the base of active. The objective of this study is to conduct such an evaluation using hydraulic and SP monitoring data during outflow experiments These data will be inverted in the Bayesian framework with the coupled hydrogeophysical inversion approach (Hinnell et al, 2010) to estimate the hydraulic and electrical properties of soils. We first introduce the coupled forward modeling of water flow and streaming potential generation in saturated and unsaturated soils, followed by the stochastic, coupled inversion, which adopts the adaptive Metropolis (AM) algorithm to estimate model parameters as well as their uncertainties. Synthetic outflow experiments are conducted on sand and loam samples to produce time-series cumulative flow, water pressure, and SP data These datasets are inverted to obtain the electrical and hydraulic properties of soils, which are compared with the true values to evaluate the benefits of including SP data. Discussions and major conclusions are presented at the end of the paper
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