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Climatic Change

, Volume 133, Issue 4, pp 681–693 | Cite as

Examining why trends in very heavy precipitation should not be mistaken for trends in very high river discharge

  • Timothy J. IvancicEmail author
  • Stephen B. Shaw
Article

Abstract

It is firmly established in the hydrologic literature that flooding depends on both antecedent watershed wetness and precipitation. One could phrase this relationship as “heavy precipitation does not necessarily lead to high stream discharge”, but rarely do studies directly affirm this statement. We have observed several non-hydrologists mistake trends in heavy precipitation as a proxy for trends in riverine flooding. If the relationship between heavy precipitation and high discharge was more often explicitly presented, heavy precipitation may less often be misinterpreted as a proxy for discharge. In this paper, we undertake such an analysis for 390 watersheds across the contiguous U.S. We found that 99th percentile precipitation only results in 99th percentage discharge 36 % of the time. However, when conditioned on soil moisture from the Variable Infiltration Capacity model, 62 % of 99th percentile precipitation results in 99th percentile discharge during wet periods and only 13 % during dry periods. When relating trends in heavy precipitation to hydrologic response, precipitation data should, therefore, be segregated based on concurrent soil moisture. Taking this approach for climate predictions, we found that CMIP-5 atmosphere–ocean global circulation model (AOGCM) simulations for a RCP 6.0 forcing project increases in concurrence of greater than median soil wetness and extreme precipitation in the northern United States and a decrease in the south, suggesting northern regions could see an increase in very high discharges while southern regions could see decreases despite both regions having an increase in extreme precipitation. While the actual outcome is speculative given the uncertainties of the AOGCM’s, such an analysis provides a more sophisticated framework from which to evaluate the output as well as historic climate data.

Keywords

Soil Moisture High Discharge Extreme Precipitation Heavy Precipitation Heavy Precipitation Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP 5, and we thank the climate modeling groups for producing and making available their model output at http://pcmdi9.llnl.gov/esgf-web-fe. We also acknowledge NOAA’s National Weather Services’ Hydrologic Modeling division which is responsible for the MOPEX project. These data are available at ftp://hydrology.nws.noaa.gov/pub/gcip/mopex/US_Data/Us_438_Daily/. Finally we acknowledge the University of Washington VIC group who provided the soil moisture data available at http://jisao.washington.edu/data/vic/.

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Environmental Resources EngineeringSUNY College of Environmental Science and ForestrySyracuseUSA

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