The impact of meteorological forcing uncertainty on hydrological modeling: a gobal analysis of cryosphere basins

Publikationstyp
Journal Article
Date Issued
2023-06-01
Sprache
English
Author(s)
Tang, Guoqiang  
Knoben, Wouter J M.
Clark, Martyn P.  
Liu, Hongli
Beck, Hylke E.
Arnal, Louise  
Papalexiou, Simon Michael  
Wood, Andrew W.  
Gharari, Shervan  
Newman Andrew J.  
TORE-URI
https://hdl.handle.net/11420/57668
Journal
Water resources research  
Volume
59
Issue
6
Article Number
e2022WR033767
Citation
Water Resources Research 59 (6): e2022WR033767 (2023)
Publisher DOI
10.1029/2022WR033767
Scopus ID
2-s2.0-85162168591
Publisher
Wiley
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.
Subjects
air temperature | cryosphere | hydrological model | precipitation | uncertainty
DDC Class
600: Technology