The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins

Author:

Tang Guoqiang12ORCID,Clark Martyn P.13ORCID,Knoben Wouter J. M.1ORCID,Liu Hongli1ORCID,Gharari Shervan4ORCID,Arnal Louise1ORCID,Beck Hylke E.5ORCID,Wood Andrew W.2,Newman Andrew J.6ORCID,Papalexiou Simon Michael7ORCID

Affiliation:

1. Centre for Hydrology University of Saskatchewan Canmore AB Canada

2. Climate and Global Dynamics National Center for Atmospheric Research Boulder CO USA

3. Department of Geography and Planning University of Saskatchewan Saskatoon SK Canada

4. Centre for Hydrology University of Saskatchewan Saskatoon SK Canada

5. King Abdullah University of Science and Technology Thuwal Saudi Arabia

6. Research Applications Laboratory National Center for Atmospheric Research Boulder CO USA

7. Department of Civil Engineering University of Calgary Calgary AB Canada

Abstract

AbstractMeteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large‐domain meteorological data sets enables convenient uncertainty characterization, which however is rarely explored in large‐domain research. This study analyzes how uncertainties in meteorological forcing data affect hydrological modeling in 289 representative cryosphere basins by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Data set for Planet Earth (EM‐Earth). EM‐Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The results reveal the magnitude, spatial distribution, and scale effect of uncertainties in meteorological, snow, runoff, soil water, and energy variables. There are three main findings. (a) The uncertainties in precipitation and temperature lead to substantial uncertainties in hydrological model outputs, some of which exceed 100% of the magnitude of the output variables themselves. (b) The uncertainties of different variables show distinct scale effects caused by spatial averaging or temporal averaging. (c) Precipitation uncertainties have the dominant impact for most basins and variables, while air temperature uncertainties are also nonnegligible, sometimes contributing more to modeling uncertainties than precipitation uncertainties. We find that three snow‐related variables (snow water equivalent, snowfall amount, and snowfall fraction) can be used to estimate the impact of air temperature uncertainties for different model output variables. In summary, this study provides insight into the impact of probabilistic data sets on hydrological modeling and quantifies the uncertainties in cryosphere basin modeling that stem from the meteorological forcing data.

Funder

Global Water Futures

Natural Sciences and Engineering Research Council of Canada

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

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