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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2019-52
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-52
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Feb 2019

18 Feb 2019

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This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Contribution of model parameter uncertainty to future hydrological projections

Qinghuan Zhang1, Qiuhong Tang1,2, John F. Knowles3,4, and Ben Livneh5 Qinghuan Zhang et al.
  • 1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • 2University of Chinese Academy of Sciences, Beijing, China
  • 3USDA ARS Southwest Watershed Research Center, Tucson, AZ, USA
  • 4Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USA
  • 5Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, Boulder, CO, USA

Abstract. Hydrologic models have been applied to predict land surface water and energy budgets in mountainous watersheds that are characterized by complex geological features and climatic variability. A common practice is to calibrate the models and achieve the best performing parameter set according to historical observations, and then the calibrated model was used to do future projections. One drawback is that the influence of parameter uncertainty on model projections is not well discussed. In this study, we applied multiple objective functions to choose a group of best performing parameter sets to the Boulder Creek Watershed, USA to investigate how parameter uncertainties can propagate to future projections. We used 16 parameter sets that have similar performance in simulating streamflow amount and regime historically, and applied the same parameter sets to predict hydrologic variables including streamflow, evapotranspiration, and soil moisture in two future phases (Phase 1 is 2040-2069 and Phase 2 is 2070-2099). The results show that variability due to parameter uncertainty was up to 10 % annually and 26 % monthly under future climate change scenarios, and the uncertainties are especially prominent during May to September. The different parameter sets can result to annual streamflow changes in opposite directions. The results indicate that a single parameter set may yield biased representation of hydrologic variability. It is necessary to consider multiple optimal parameter sets in applying hydrologic models for hydrological projections and water resources decision making.

Qinghuan Zhang et al.

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Qinghuan Zhang et al.

Qinghuan Zhang et al.

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Short summary
The uncertainty from model parameters is not well understood compared to that from climate data in hydrologic modeling. This study quantifies the projection uncertainty in three hydrologic variables using a group of best performing parameter sets. It shows that model parameter uncertainty takes an important role in hydrologic modeling, especially for seasonal projections. Thus it is necessary to consider multiple optimal parameter sets in hydrologic projection and water resources management.
The uncertainty from model parameters is not well understood compared to that from climate data...
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