World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

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

By Franz, K. J.

Click here to view

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


APA MLA Chicago

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

Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, 2006.; Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, doi:10.1029/2005WR004745, 2007.; Bartholmes, J. C., Thielen, J., Ramos, M. H., and Gentilini, S.: The european flood alert system EFAS – Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts, Hydrol. Earth Syst. Sci., 13, 141–153, doi:10.5194/hess-13-141-2009, 2009.; Beven, K. and Binley, A.: Future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992.; Beven, K. and Freer, J.: Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology, J. Hydrol., 249, 11–29, doi:10.1016/S0022-1694(01)00421-8, 2001.; Box, G. E. P. and Tiao, G. C.: Bayesian Inference in Statistical Analysis, Addison-Wesley, 1973.; Bradley, A. A. and Schwartz, S. S.: Summary verification measures and their interpretation for ensemble forecasts, Mon. Weather Rev., 3075–3089, 2011.; Bradley, A. A., Hashino, T., and Schwartz, S. S.: Distributions-oriented verification of probability forecast for small data samples, Weather Forecast., 18, 903–917, 2003.; Bradley, A. A., Schwartz, S. S., and Hashino, T.: Distributions-oriented verification of ensemble streamflow predictions, J. Hydrometeorol., 5, 532–545, 2004.; Brazil, L. E. and Hudlow, M. D.: Calibration procedures used with the National Weather Service Forecast System, in: Water and Realted Land Resource Systems, edited by: Haimes, Y. Y. and Kindler, J., Pergamon, Tarrytown, N.Y., 457–466, 1981.; Brier, G. W.: Verification of forecasts expressed in terms of probabilities, Mon. Weather Rev., 78, 1–3, 1950.; Brown, J., Demargne, J., Seo, D.-J., and Liu, Y.: The Ensemble Verification System (EVS): a software tool for verifying ensemble forecasts of hydrometeorological and hydrologic variables at discrete locations, Environ. Modell. Softw., 25, 854–872, doi:10/1016/j.envsoft.2010.01.009, 2010.; Burnash, R. J., Ferral, R. L., and McGuire, R. A.: A Generalized Streamflow Simulation System Conceptual: Modeling for Digital Computers, Joint Federal-State River Forecast Center, Sacramento, CA, 1973.; Clark, M. P. and Kavetski, D.: Ancient numerical daemons of conceptual hydrological modeling: 1. Fidelity and efficiency of time stepping schemes, Water Resour. Res., 46, W10510, doi:10.1029/2009WR008894, 2010.; Coccia, G. and Todini, E.: Recent developments in predictive uncertainty assessment based on the model conditional processor approach, Hydrol. Earth Syst. Sci., 15, 3253–3274, doi:10.5194/hess-15-3253-2011, 2011.; Cooke, W. E.: Forecasts and verifications in Western Australia, Mon. Weather Rev., 34, 23–24, 1906.; Christoffersen, P. F.: Evaluating interval forecasts, Int. Econ. Rev., 39, 841–862, 1998.; Day, G. N.: Extended streamflow forecasting using NWSRFS, J. Water Resour. Plann. Manage., 111, 157–170, 1985.; De Finetti, B.: Foresight: its logical laws, its subjective sources, in: Studies in Subjective Probability, edited y: Kyburg Jr., H. E. and Smokler, H. E., Wiley, New York, 1964, 94–158, 1937.; De Lannoy, G. J. M., Houser, P. R., Pauwels, V. R. N., and Verhoest, N. E. C.: Assessment of model uncertainty for soil moisture through ensemble verification, J. Geophys. Res., 111, D10101, doi:10.1029/2005JD006367, 2006


Click To View

Additional Books

  • Using 14C and 3H to Understand Groundwat... (by )
  • Recovery from Episodic Acidification Del... (by )
  • Aspects of Seasonality and Flood Generat... (by )
  • A Space-time Hybrid Hourly Rainfall Mode... (by )
  • Evaluating the Effect of Partial Contrib... (by )
  • A Conceptual Dynamic Vegetation-soil Mod... (by )
  • Analysis of Intra-country Virtual Water ... (by )
  • Uncertainties Associated with Digital El... (by )
  • Comparison of Monsoon Variations Over Gr... (by )
  • Iterative Approach to Modeling Subsurfac... (by )
  • Joint Impact of Rainfall and Tidal Level... (by )
  • A Distributed Continuous Simulation Mode... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from World eBook Fair are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.