edu.ucar.opensky::articles:26420 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T19:31:32.519491 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d71v5jz4 https://doi.org/10.20383/102.0547 https Related Dataset #1 EM-Earth: The Ensemble Meteorological Dataset for Planet Earth https://doi.org/10.5067/MODIS/MOD10A1.061 https Related Dataset #2 MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 61 https://doi.org/10.5281/zenodo.7545956 https Related Dataset #3 SUMMA/mizuRoute model configurations, parameters, and ensemble statistics for representative cryosphere basins https://doi.org/10.5285/3f034f4a08854eb59d58e1fa92d207b6 https Related Dataset #4 ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2018), version 2.0 The impact of meteorological forcing uncertainty on hydrological modeling: A global analysis of cryosphere basins 2023-06-01T00:00:00Z publication https://n2t.org/ark:/85065/d71v5jz4 https://doi.org/10.1029/2022WR033767 Tang, Guoqiang author Clark, M. P. author Knoben, W. J. M. author Liu, H. author Gharari, S. author Arnal, L. author Beck, H. E. author Wood, Andrew author Newman, Andrew J. author Papalexiou, S. M. author UCAR/NCAR - Library publisher 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. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. None eng geoscientificInformation