Toxic organic contaminants may enter the subsurface as slightly soluble and volatile
nonaqueous phase liquids (NAPLs) or as dissolved solutes resulting in contaminant plumes …

We define model-data interaction (MDI) as a two way process between models and data, in
which on one hand data can serve the modeling purpose by supporting model …

Geostatistical optimal design optimizes subsurface exploration for maximum information
toward task‐specific prediction goals. Until recently, most geostatistical design studies have …

Linearized representations of the stochastic groundwater flow and transport equations have
been heavily used in hydrogeology, eg, for geostatistical inversion or generating conditional …

The evolution of groundwater quality in natural and contaminated aquifers is affected by
complex interactions between physical transport and biogeochemical reactions. Identifying …

Incorporating hydro (geo) logical data, such as head and tracer data, into stochastic models
of (subsurface) flow and transport helps to reduce prediction uncertainty. Because of …

Choosing between competing models lies at the heart of scientific work, and is a frequent
motivation for experimentation. Optimal experimental design (OD) methods maximize the …

Most field campaigns aim at helping in specified scientific or practical tasks, such as
modeling, prediction, optimization, or management. Often these tasks involve binary …

In this study, we assess probability density functions of hydraulic heads and specific
discharges in three‐dimensional bounded heterogeneous porous media by Monte Carlo …

The initial width of contaminant plumes is known to have a key influence on expected plume
development, dispersion and travel time statistics. In past studies, initial plume width has …