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The University of Massachusetts Amherst

A hierarchical Bayesian model for regionalized seasonal forecasts: Application to low flows in the northeastern United States

Authors:

Kuk-Hyun Ahn

Richard Palmer

Scott Steinschneider

Publication Type:
Journal Article
Year of Publication:
2017
Secondary Title:
Water Resources Research
DOI:
10.1002/2016WR019605
Pages:
503-521
Volume:
53
Year:
2017
Date:
Jan-01-2017
URL:
http://doi.wiley.com/10.1002/2016WR019605

Abstract

This study presents a regional, probabilistic framework for seasonal forecasts of extreme low summer flows in the northeastern United States conditioned on antecedent climate and hydrologic conditions. The model is developed to explore three innovations in hierarchical modeling for seasonal forecasting at ungaged sites: (1) predictive climate teleconnections are inferred directly from ocean fields instead of predefined climate indices, (2) a parsimonious modeling structure is introduced to allow climate teleconnections to vary spatially across streamflow gages, and (3) climate teleconnections and antecedent hydrologic conditions are considered jointly for regional forecast development. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with five simpler nested formulations to test specific hypotheses embedded in the full model structure. Results indicate that each of the three innovations improve out-of-sample summer low-flow forecasts, with the greatest benefits derived from the spatially heterogeneous effect of climate teleconnections. We conclude with a discussion of possible model improvements from a better representation of antecedent hydrologic conditions at ungaged sites.