Lucy Marshall

TL;DR: In this article, the authors quantified water table connectivity based on 84 recording wells distributed across 24 HRS transects within the Tenderfoot Creek Experimental Forest (U.S. Forest Service), northern Rocky Mountains, Montana.

TL;DR: In this paper, Markov chain Monte Carlo (MCMCMC) sampling of the posterior distribution has been used to estimate parameter uncertainty in hydrological models, where prior knowledge about the parameter is combined with information from the available data to produce a probability distribution (the posterior distribution) that describes uncertainty about the parameters and serves as a basis for selecting appropriate values for use in modeling applications.

TL;DR: In this paper, the authors characterized the relationship between air and stream temperature via linear and nonlinear regression for 57 sites across Pennsylvania at daily and weekly timescales, and investigated the mechanisms controlling stream temperature sensitivity to environmental change, defined as the sensitivity of stream temperature of a given site to change in air temperature.

TL;DR: Comparisons between three recently introduced MCMC approaches, the adaptive Metropolis, the delayed rejection adaptiveMetropolis, and the differential evolution Markov chain algorithms are presented via two case studies of a real‐world hydrologic modeling scenario where each algorithm will serve as the uncertainty and parameter estimation framework for a conceptual precipitation‐runoff model.

TL;DR: A residual error model (likelihood function) specification framework that aims to provide guidance for the application of more appropriate residual error models through a nested approach that is both flexible and extendible is outlined.