22 Apr 2020

PUBLICATION NOTICE: Nested Physics-Based Watershed Modeling at Seven Mile Creek: Minnesota River Integrated Watershed Study

ABSTRACT: The Minnesota River Basin (MRB) Integrated Study Team (IST) was tasked with assessing the condition of the MRB and recommending management options to reduce suspended sediments and improve the water quality in the basin. The Gridded Surface Subsurface Hydrologic Analysis (GSSHA) was chosen by the IST as the fine scale model for the Seven Mile Creek Watershed to help quantify the physical effects from best management practices within the MRB. The predominately agricultural Seven Mile Creek Watershed produces high total suspended solids and nutrients loads, contributing roughly 10% of the total load to the Minnesota River. GSSHA models were developed for a small experimental field research site called Red Top Farms, a Hydrologic Unit Code (HUC)-12 model for the entire Seven Mile Creek Watershed, a sub-basin of the Seven Mile Creek Watershed. After calibration, the resulting models were able to simulate measured tile drain flows, stream flow, suspended sediments, and to a lesser extent, nutrients. A selected suite of alternative land-use scenarios was simulated with the models to determine the watershed response to land-use changes at the small and medium scale and to test whether the type, size, and spatial distribution of land uses will influence the effectiveness of land management options.

22 Apr 2020

PUBLICATION NOTICE: A Practical Two-Phase Approach to Improve the Reliability and Efficiency of Markov Chain Monte Carlo Directed Hydrologic Model Calibration

ABSTRACT: Markov chain Monte Carlo (MCMC) methods are widely used in hydrology and other fields for posterior inference in a Bayesian framework. A properly constructed MCMC sampler is guaranteed to converge to the correct limiting distribution, but convergence can be very slow. While most research is focused on improving the proposal distribution used to generate trial moves in the Markov chain, this work instead focuses on efficiently finding an initial population for population-based MCMC samplers that will expedite convergence. Four case studies, including two hydrological models, are used to demonstrate that using multi-level single linkage implicit filtering stochastic global optimization to initialize the population both reduces the overall computational cost and significantly increases the chance of finding the correct limiting distribution within the constraint of a fixed computational budget.