The impact of meteorological forcing uncertainty on hydrological modeling: A global analysis of cryosphere basins

Guoqiang Tang, Martyn P. Clark, Wouter J. M. Knoben, Hongli Liu, Shervan Gharari, Louise Arnal, Hylke E. Beck, Andrew W. Wood, Andrew J. Newman, Simon Michael Papalexiou

Research output: Contribution to journalArticlepeer-review


Meteorological 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.
Original languageEnglish (US)
JournalWater Resources Research
StatePublished - Jun 12 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-06-15
Acknowledgements: The study is funded by the Global Water Futures project. SMP acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN-2019-06,894). The GRDC is thanked for providing part of the observed streamflow data. The authors would like to acknowledge that collectively they reside on Traditional territories of the Cree, Haudenosaunee, Ktunaxa, Mohawk, Niitsitapi (Blackfoot), Stoney, and Tsuut'ina (including Treaties 6 and 7), and homelands of the Métis. The authors thank these nations for their care and stewardship over this land and water and pay their respect to the ancestors of these places.

ASJC Scopus subject areas

  • Water Science and Technology


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