Reducing hydrograph uncertainty through subsurface characterization

Steven B. Meyerhoff, Reed M. Maxwell

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

Abstract

Subsurface heterogeneity in saturated hydraulic conductivity is one of the largest sources of uncertainty in hydrology and hydrogeology. However, recent work has demonstrated that uncertainty in hydraulic conductivity can also impart significant uncertainty in runoff processes. Here, the role of site characterization in reducing hydrograph uncertainty and bias is demonstrated numerically. A fully integrated hydrologic model is used in a hypothetical experiment where a control hillslope is generated using correlated, Gaussian random fields. Direct measurements of hydraulic conductivity at varying density are obtained from this control simulation and assimilated into stochastic transient simulations. The hydrographs, resulting from integrated flow simulations for each realization, are shown to much more accurately match the control. Data assimilation resolves large-scale features in surface ponding and saturation. This implies that substantial reduction in hydrograph uncertainty may be reached through site characterization.

Original languageEnglish (US)
Title of host publicationModels - Repositories of Knowledge
Pages78-84
Number of pages7
StatePublished - 2012
Externally publishedYes
Event8th International Conference on Calibration and Reliability in Groundwater Modeling - Repositories of Knowledge, ModelCARE 2011 - Leipzig, Germany
Duration: Sep 18 2011Sep 22 2011

Publication series

NameIAHS-AISH Publication
Volume355
ISSN (Print)0144-7815

Conference

Conference8th International Conference on Calibration and Reliability in Groundwater Modeling - Repositories of Knowledge, ModelCARE 2011
CountryGermany
CityLeipzig
Period9/18/119/22/11

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

Keywords

  • Conditioning
  • Groundwater surface water interactions
  • Hillslope
  • Runoff generation

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