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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-328
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-328
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 Jul 2019

Research article | 03 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Identifying uncertainties in simulated streamflow from hydrologic model components for climate change impact assessments

Dongmei Feng1 and Edward Beighley2,3 Dongmei Feng and Edward Beighley
  • 1Civil and Environmental Engineering, University of Massachusetts, Amherst, MA, USA
  • 2Civil and Environmental Engineering, Northeastern University, MA, USA
  • 3Marine and Environmental Sciences, Northeastern University, MA, USA

Abstract. Assessing the impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control and ecosystem conservation practices. Such assessments are commonly accomplished using outputs from a hydrologic model forced with future precipitation and temperature projections. The algorithms used in the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties in the simulated hydrologic variables, yet the identification and quantification of such uncertainties is rarely studied. Here, a modeling framework is developed that integrates multiple runoff generation algorithms with a routing model and associated parameter optimizations. This framework is able to identify uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Three fundamentally different runoff generation approaches: runoff coefficient method (RCM, conceptual), variable infiltration capacity (VIC, physically-based, infiltration excess) and simple-TOPMODEL (STP, physically-based, saturation excess), are coupled with Hillslope River Routing model to simulate streamflow. A case study conducted in Santa Barbara County, California, reveals that the median changes are 1–10 % increases in mean annual discharge (Qm) and 10–40 % increases in annual maximum daily discharge (Qp) and 100-yr flood discharge (Q100). The Bayesian Model Averaging analysis indicates that the probability of increase in streamflow can be up to 85 %. However, the simulated discharge uncertainties are large (i.e., 230 % for Qm and 330 % for Qp and Q100) with general circulation models (GCMs) and emission scenarios accounting for more than half of the total uncertainty. Hydrologic process models contribute 10–30 % of the total uncertainty, while uncertainty due to hydrologic model parameterization is almost negligible (< 1 %), limiting the impacts of hydrologic model parameter equifinality in climate change impact analysis. This study also provides insights on how to optimize the selection of hydrologic models for projecting future streamflow conditions.

Dongmei Feng and Edward Beighley
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Status: open (until 28 Aug 2019)
Status: open (until 28 Aug 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Dongmei Feng and Edward Beighley
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Latest update: 19 Jul 2019
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Short summary
A modeling framework is developed and used to investigate uncertainty sources for simulated future streamflow. Results show that climate projections (precipitation and temperature) and conceptualized process models used for runoff generation are the dominant sources of uncertainty in forecasted discharge. Uncertainty due to hydrologic model parameterization is almost negligible, limiting the impacts of hydrologic model parameter equifinality in climate change impact analyses.
A modeling framework is developed and used to investigate uncertainty sources for simulated...
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