Year of Award


Document Type


Degree Type

Master of Science (MS)

Degree Name

Computer Science

Department or School/College

Computer Science

Committee Chair

Jesse Johnson

Commitee Members

Marco Maneta, Douglas Brinkerhoff


Kalman Filter, HIERARCHICAL, Machine Learning, Computer, Hydrology


University of Montana

Subject Categories

Artificial Intelligence and Robotics | Hydrology | Other Mathematics


Dynamic models that simulate processes across large geographic locations, such as hydrologic models, are often informed by empirical parameters that are distributed across a geographical area and segmented by geological features such as watersheds. These parameters may be referred to as spatially distributed parameters. Spatially distributed parameters are frequently spatially correlated and any techniques utilized in their calibration ideally incorporate existing spatial hierarchical relationships into their structure. In this paper, a parameter estimation method based on the Dual State Ensemble Kalman Filter called the Dual State Hierarchical Ensemble Kalman Filter (DSHEnKF) is presented. This modified filter is innovative in that it allows parameters to be placed into a set of groups that are smoothed using hierarchical modeling techniques. The usability and effectiveness of this new technique is demonstrated by applying it to a rainfall-runoff model that simulates subcatchment-scale hydrologic processes and contains high dimensional spatially distributed empirical parameters.



© Copyright 2019 William J. Cook, Jesse Johnson, Marko Maneta, and Doug Brinkerhoff