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Evaluating Uncertainty Estimates in Hydrologic Models: Borrowing Measures from the Forecast Verification Community : Volume 15, Issue 11 (15/11/2011)

By Franz, K. J.

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Book Id: WPLBN0003991155
Format Type: PDF Article :
File Size: Pages 16
Reproduction Date: 2015

Title: Evaluating Uncertainty Estimates in Hydrologic Models: Borrowing Measures from the Forecast Verification Community : Volume 15, Issue 11 (15/11/2011)  
Author: Franz, K. J.
Volume: Vol. 15, Issue 11
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Franz, K. J., & Hogue, T. S. (2011). Evaluating Uncertainty Estimates in Hydrologic Models: Borrowing Measures from the Forecast Verification Community : Volume 15, Issue 11 (15/11/2011). Retrieved from

Description: Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA. The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP) systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE), and the Shuffle Complex Evolution Metropolis (SCEM). Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA) model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.

Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community

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