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    Item type:Publication, Access status: without files ,
    Which Precipitation Dataset to Choose for Hydrological Studies of the Terrestrial Water Cycle?
    (2025-09-01)
    Vargas Godoy, Mijael Rodrigo
    ;
    Markonis, Yannis
    ;
    Thomson, Johanna Ruth
    ;
    Ballarin, André Simões
    ;
    Perri, Saverio
    Precipitation is a critical component of the terrestrial hydrological cycle. It plays a crucial role in shaping climate patterns and ecosystem dynamics. The quest for accurate measurements of global precipitation started over 150 years ago. Comprehensive evaluations of the estimates were rare until the 1970s, mainly due to the challenges of acquiring and maintaining up-to-date datasets. Nowadays, data availability is no longer an issue. However, in the face of the seemingly ever-growing number of available datasets, determining which one to rely upon poses a new obstacle, especially in the absence of global ground truth, further complicated by the interdependencies among datasets. Here, we identify the genealogy of multiple precipitation datasets and define multiplicity artifacts. Then, we compute an evaluation reference benchmark free of multiplicity artifacts to identify the dataset that best represents the artifact-free ensemble over different terrestrial spatial domains. These include countries, IPCC assessment report reference regions, major world river basins, land-cover types, elevation zones, biome categories, and Köppen–Geiger climate classes. It should be noted that the datasets assessed herein had a monthly temporal scale, and our findings might not apply to the study of climate extreme events regardless of the terrestrial spatial domain. We repeatedly found GPM IMERG Final v07 to emerge as the most representative dataset over multiple domains. Furthermore, we found that the dataset’s representativeness is largely influenced by how spatial domains are defined rather than their scale. SIGNIFICANCE STATEMENT: Over the past decades, we have amassed a vast array of precipitation datasets. While offering significant opportunities, this abundance of data creates a new challenge: which precipitation dataset should we use? In this work, we address this challenge by developing a method to identify and mitigate the impact of overlapping or dependent data sources, ensuring more reliable comparisons across diverse spatial domains. This is important because our results can help researchers select more reliable datasets and emphasize the need for a deeper understanding of the data used, thus avoiding misleading conclusions. Such efforts contribute to advancing hydrological sciences and the broader nexus fields, where reliable precipitation data serve as a cornerstone for modeling and projecting climate change impacts.
    Publicationtype: Journal Article
      10
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    Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings
    (2025)
    Tang, Guoqiang
    ;
    Clark, Martyn P.
    ;
    Knoben, Wouter J. M.
    ;
    Liu, Hongli
    ;
    Gharari, Shervan
    Quantifying hydrologic modeling uncertainties originating from meteorological uncertainties is crucial yet unexplored on the global scale. This study integrates a novel ensemble meteorological dataset with process-based hydrologic models, assessing the impact of precipitation and temperature uncertainties across approximately three million subbasins globally. We introduce two metrics to identify uncertainty hotspots: one tracing the uncertainty propagation from inputs to model outputs and the other measuring the uncertainty magnitude relative to hydrologic climatology (i.e., ratio between uncertainty and climate average). Our findings reveal different uncertainty responses across hydrologic variables to the combined precipitation and temperature uncertainties. For routed river streamflow, uncertainty propagation is strong in tropical rain forests and Europe (except the Scandinavian Peninsula) but weak in the deserts, which is partly attributed to the regional differences in baseflow ratios. In contrast, both uncertainty metrics indicate low streamflow uncertainties in cryosphere areas and downstream areas of major rivers. The substantial modeling uncertainties observed, particularly in the Southern Hemisphere and less developed regions, underscore the need to improve global spatial meteorological datasets.
    Publicationtype: Journal Article
      20
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    EM-earth: the ensemble meteorological dataset for planet earth
    (American Meteorological Society (AMS), 2022-04-01)
    Tang, Guoqiang
    ;
    Clark, Martyn P.
    ;
    Papalexiou, Simon Michael
    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 dewpoint 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. To produce EM-Earth, we first developed a station-based Serially Complete Earth (SC-Earth) dataset, which removes the temporal discontinuities in raw station observations. Then, we optimally merged SC-Earth station data and ERA5 estimates to generate EM-Earth deterministic estimates and their uncertainties. The EM-Earth ensemble members are produced by sampling from parametric probability distributions using spatiotemporally correlated random fields. The EM-Earth dataset is evaluated by leave-one-out validation, using independent evaluation stations, and comparing it with many widely used datasets. The results show that EM-Earth is better in Europe, North America, and Oceania than in Africa, Asia, and South America, mainly due to differences in the available stations and differences in climate conditions. Probabilistic spatial meteorological datasets are particularly valuable in regions with large meteorological uncertainties, where almost all existing deterministic datasets face great challenges in obtaining accurate estimates.
    Publicationtype: Journal Article
      4