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    Item type:Publication, Access status: without files ,
    Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings
    (2025)
    Tang, Guoqiang
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    Clark, Martyn P.
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    Knoben, Wouter J. M.
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    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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    Item type:Publication, Access status: without files ,
    GPEP v1.0: the geospatial probabilistic estimation package to support earth science applications
    (Copernicus Publications, 2024-02-12)
    Tang, Guoqiang
    ;
    Wood, Andrew W.
    ;
    Newman Andrew J.
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    Clark, Martyn P.
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    Papalexiou, Simon Michael
    Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.
    Publicationtype: Journal Article
      6
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    The impact of meteorological forcing uncertainty on hydrological modeling: a gobal analysis of cryosphere basins
    (Wiley, 2023-06-01)
    Tang, Guoqiang
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    Knoben, Wouter J M.
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    Clark, Martyn P.
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    Liu, Hongli
    ;
    Beck, Hylke E.
    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.
    Publicationtype: Journal Article
      10
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    Emdna: an ensemble meteorological dataset for north america
    (Copernicus Publications, 2021-07-13)
    Tang, Guoqiang
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    Clark, Martyn P.
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    Papalexiou, Simon Michael
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    Newman, Andrew J.
    ;
    Wood, Andrew W.
    Probabilistic methods are useful to estimate the uncertainty in spatial meteorological fields (e.g., the uncertainty in spatial patterns of precipitation and temperature across large domains). In ensemble probabilistic methods, "equally plausible"ensemble members are used to approximate the probability distribution, hence the uncertainty, of a spatially distributed meteorological variable conditioned to the available information. The ensemble members can be used to evaluate the impact of uncertainties in spatial meteorological fields for a myriad of applications. This study develops the Ensemble Meteorological Dataset for North America (EMDNA). EMDNA has 100 ensemble members with daily precipitation amount, mean daily temperature, and daily temperature range at 0.1g spatial resolution (approx. 10g€¯km grids) from 1979 to 2018, derived from a fusion of station observations and reanalysis model outputs. The station data used in EMDNA are from a serially complete dataset for North America (SCDNA) that fills gaps in precipitation and temperature measurements using multiple strategies. Outputs from three reanalysis products are regridded, corrected, and merged using Bayesian model averaging. Optimal interpolation (OI) is used to merge station- and reanalysis-based estimates. EMDNA estimates are generated using spatiotemporally correlated random fields to sample from the OI estimates. Evaluation results show that (1) the merged reanalysis estimates outperform raw reanalysis estimates, particularly in high latitudes and mountainous regions; (2) the OI estimates are more accurate than the reanalysis and station-based regression estimates, with the most notable improvements for precipitation evident in sparsely gauged regions; and (3) EMDNA estimates exhibit good performance according to the diagrams and metrics used for probabilistic evaluation. We discuss the limitations of the current framework and highlight that further research is needed to improve ensemble meteorological datasets. Overall, EMDNA is expected to be useful for hydrological and meteorological applications in North America. The entire dataset and a teaser dataset (a small subset of EMDNA for easy download and preview) are available at 10.20383/101.0275 (Tang et al., 2020a).
    Publicationtype: Journal Article
      2
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    Item type:Publication, Access status: without files ,
    SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018
    (2020-10-02)
    Tang, Guoqiang
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    Clark, Martyn P.
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    Newman, Andrew J.
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    Wood, Andrew W.
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    Papalexiou, Simon Michael
    Station-based serially complete datasets (SCDs) of precipitation and temperature observations are important for hydrometeorological studies. Motivated by the lack of serially complete station observations for North America, this study seeks to develop an SCD from 1979 to 2018 from station data. The new SCD for North America (SCDNA) includes daily precipitation, minimum temperature (Tmin), and maximum temperature (Tmax) data for 27 276 stations. Raw meteorological station data were obtained from the Global Historical Climate Network Daily (GHCN-D), the Global Surface Summary of the Day (GSOD), Environment and Climate Change Canada (ECCC), and a compiled station database in Mexico. Stations with at least 8-year-long records were selected, which underwent location correction and were subjected to strict quality control. Outputs from three reanalysis products (ERA5, JRA-55, and MERRA-2) provided auxiliary information to estimate station records. Infilling during the observation period and reconstruction beyond the observation period were accomplished by combining estimates from 16 strategies (variants of quantile mapping, spatial interpolation, and machine learning). A sensitivity experiment was conducted by assuming that 30 % of observations from stations were missing - this enabled independent validation and provided a reference for reconstruction. Quantile mapping and mean value corrections were applied to the final estimates. The median Kling-Gupta efficiency (KGE´) values of the final SCDNA for all stations are 0.90, 0.98, and 0.99 for precipitation, Tmin, and Tmax, respectively. The SCDNA is closer to station observations than the four benchmark gridded products and can be used in applications that require either quality-controlled meteorological station observations or reconstructed long-term estimates for analysis and modeling. The dataset is available at https://doi.org/10.5281/zenodo.3735533 (Tang et al., 2020).
    Publicationtype: Review Article
      9