Reducing uncertainty in selecting climate models for hydrological impact assessments

A. J. Pitman, S. E. Perkins

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

5 Citations (Scopus)

Abstract

Deciding which climate models to use to assess the impact of climate change on water resources is particularly difficult in environments where precipitation dominates resource vulnerability. We show that assessing climate models based on their simulation of mean precipitation provides little guide to a model's ability to simulate the more extreme events that affect hydrological systems. In contrast, a probability density function based assessment using daily climate model data provides a good basis for confidence in a model's ability to simulate the 95th rainfall percentile. We demonstrate that climate models have useful skill in simulating observed probability density functions over two regions of Australia, although the well-known bias of excess rainfall at low rates remains common. We conclude by identifying those climate models that produce the best basis for hydrological impacts assessment over two regions of Australia.

Original languageEnglish
Title of host publicationIAHS-AISH Publication - Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management
Pages3-15
Number of pages13
Edition313
Publication statusPublished - 2007
EventInternational Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management - 24th General Assembly of the International Union of Geodesy and Geophysics (IUGG) - Perugia, Italy
Duration: 2 Jul 200713 Jul 2007

Other

OtherInternational Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management - 24th General Assembly of the International Union of Geodesy and Geophysics (IUGG)
CountryItaly
CityPerugia
Period2/07/0713/07/07

Keywords

  • Climate models
  • Probability density function
  • Skill-score

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