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SUMMA/mizuRoute model configurations and parameters for global ensemble modeling
Zenodo
Tang, Guoqiang; Clark, Martyn — 2024-07-30 Meteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large-domain meteorological datasets enables convenient uncertainty characterization, which however is rarely explored in large-domain research.   We analyze how uncertainties in meteorological forcing data affect hydrological modeling on the global scale by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Dataset for Planet Earth (EM-Earth). EM-Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The global land area is divided into ~3 million sub-basins using the MERIT-Basins dataset.   This dataset contains the SUMMA and mizuRoute configuration files (e.g., diverse land attributes, model parameters, and model physics decisions) to reproduce the global simulation results. The global land is divided into different continents, including Africa, Arctic, Europe, North America, North Asia, Oceania, SouthAmerica, and SouthAsia. Greenland is not included because of its complexity and low quality of available data. Antarctic is not included in MERIT-Basins. https://creativecommons.org/licenses/by/4.0/legalcode
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EMDNA: Ensemble Meteorological Dataset for North America
Federated Research Data Repository / dépôt fédéré de données de recherche
Tang, Guoqiang; Clark, Martyn P.; Papalexiou, Simon Michael; Newman, Andrew J.; Wood, Andrew W.; Brunet, Dominique; Whitfield, Paul H. — 2024-04-25 The Ensemble Meteorological Dataset for North America (EMDNA) contains daily precipitation, mean daily temperature, and daily temperature range at the 0.1-degree resolution from 1979 to 2018. Minimum and maximum temperature can be calculated from mean temperature and temperature range. EMDNA merges station observations and reanalysis model outputs to improve the quality of estimates. The dataset is expected to be useful for hydrological and meteorological applications in North America. Two types of datasets are provided by EMDNA, including the probabilistic dataset and the deterministic dataset. The probabilistic dataset has 100 equally plausible ensemble members, which can be used to evaluate the impact of the uncertainties in a myriad of applications. The deterministic dataset is generated during the production of ensemble members and can be applied in studies that do not need uncertainty estimation. https://creativecommons.org/licenses/by/4.0/
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EM-Earth: The Ensemble Meteorological Dataset for Planet Earth
Federated Research Data Repository / dépôt fédéré de données de recherche
Tang, Guoqiang; Clark, Martyn; Papalexiou, Simon Michael — 2024-03-27 Gridded meteorological estimates are essential for many applications. Most existing meteorological datasets are deterministic and have limitations in representing the inherent uncertainties from both the data and methodology used to create gridded products. We develop the Ensemble Meteorological Dataset for Planet Earth (EM-Earth) for precipitation, mean daily temperature, daily temperature range, and dew-point temperature at 0.1° spatial resolution over global land areas from 1950 to 2019. EM-Earth provides hourly/daily deterministic estimates, and daily probabilistic estimates (25 ensemble members), to meet the diverse requirements of hydrometeorological applications. The deterministic estimates can be used like most meteorological datasets such as ERA5, MERRA2, and GPM IMERG. The probabilistic estimates can enable ensemble hydrological simulation and support easy uncertainty analysis. Please read the README.txt before downloading. The document introduces the dataset structure, including the meaning of different folders and their total sizes, which can help you decide the best downloading option. You can also contact the authors (guoqiang.tang@usask.ca) if you have problems downloading the dataset. Reference: Guoqiang Tang, Martyn P. Clark, Simon Michael Papalexiou. EM-Earth: The Ensemble Meteorological Dataset for Planet Earth. Bulletin of the American Meteorological Society. 2022 https://creativecommons.org/licenses/by/4.0/

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(Author: Tang, Guoqiang)

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