Willem Bouten

TL;DR: Three case studies demonstrate that the adaptive capability of the SCEM‐UA algorithm significantly reduces the number of model simulations needed to infer the posterior distribution of the parameters when compared with the traditional Metropolis‐Hastings samplers.

TL;DR: The Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) as discussed by the authors is an improvement over the SCEM-UA global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution.

TL;DR: In this paper, the authors present a simultaneous optimization and data assimilation (SODA) method, which improves the treatment of uncertainty in hydrologic modeling by treating the uncertainty in the input-output relationship as being primarily attributable to uncertainty in model parameters.

TL;DR: In this paper, the authors used ground-penetrating radar (GPR) to measure soil water content at an intermediate scale in between point and remote sensing measurements, using the velocity of the ground wave, which is the signal traveling directly from source to receiving antenna through the upper centimeters of the soil.

TL;DR: In this article, the authors used neural networks to model the drying water retention curve (WRC) of 204 sandy soil samples from particle-size distribution (PSD), soil organic matter content (SOM), and bulk density (BD).