Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Water resources research, 2023-06, Vol.59 (6), p.n/a

Ort / Verlag

Washington: John Wiley & Sons, Inc

Erscheinungsjahr

2023

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• 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. Plain Language Summary The uncertainty in meteorological data has a notable impact on hydrological modeling. However, most studies focus on small domains or simple models/processes and have difficulty representing real‐world meteorological uncertainties. This study utilizes a probabilistic meteorological data set to drive a physically based hydrological model for uncertainty analysis in 289 cryosphere basins around the world. We use a 25‐member ensemble simulation to investigate the magnitude, spatial distribution, and scale effect of the uncertainties of meteorological, snow, runoff, soil water, and energy variables. The uncertainty of model outputs stemming from precipitation and air temperature input is substantial in many cases. The uncertainty shows a notable scale effect due to spatial averaging or temporal averaging. Precipitation uncertainties have a larger impact on hydrological modeling than air temperature uncertainties in most cases, while the impact of air temperature uncertainties is nonnegligible. Using snow‐related variables can indicate the impact of air temperature uncertainties for some hydrological variables. This study can advance our understanding of how real‐world precipitation and air temperature uncertainties affect hydrological modeling in cryosphere basins. Key Points We combine physically based models and a global ensemble data set to assess hydrological modeling uncertainties in 289 cryosphere basins Precipitation and air temperature uncertainties lead to substantial modeling uncertainties, exceeding 100% for some basins/variables Precipitation uncertainties generally dominate for most basins/variables, but air temperature uncertainties dominate in certain