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  1. Abstract

    Air temperature (Ta), snow depth (Sd), and soil temperature (Tg) are crucial variables for studying the above- and below-ground thermal conditions, especially in high latitudes. However,in-situobservations are frequently sparse and inconsistent across various datasets, with a significant amount of missing data. This study has assembled a comprehensive dataset ofin-situobservations of Ta, Sd, and Tg for the Northern Hemisphere (higher than 30°N latitude), spanning 1960–2021. This dataset encompasses metadata and daily data time series for 27,768, 32,417, and 659 gages for Ta, Sd, and Tg, respectively. Using the ERA5-Land reanalysis data product, we applied deep learning methodology to reconstruct the missing data that account for 54.5%, 59.3%, and 74.3% of Ta, Sd, and Tg daily time series, respectively. The obtained high temporal resolution dataset can be used to better understand physical phenomena and relevant mechanisms, such as the dynamics of land-surface-atmosphere energy exchange, snowpack, and permafrost.

     
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  2. Abstract

    Applications of process‐based models (PBM) for predictions are confounded by multiple uncertainties and computational burdens, resulting in appreciable errors. A novel modeling framework combining a high‐fidelity PBM with surrogate and machine learning (ML) models is developed to tackle these challenges and applied for streamflow prediction. A surrogate model permits high computational efficiency of a PBM solution at a minimum loss of its accuracy. A novel probabilistic ML model partitions the PBM‐surrogate prediction errors into reducible and irreducible types, quantifying their distributions that arise due to both explicitly perceived uncertainties (such as parametric) or those that are entirely hidden to the modeler (not included or unexpected). Using this approach, we demonstrate a substantial improvement of streamflow predictive accuracy for a case study urbanized watershed. Such a framework provides an efficient solution combining the strengths of high‐fidelity and physics‐agnostic models for a wide range of prediction problems in geosciences.

     
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  3. Abstract

    The land surface hydrology of the North American Great Lakes region regulates ecosystem water availability, lake levels, vegetation dynamics, and agricultural practices. In this study, we analyze the Great Lakes terrestrial water budget using the Noah‐MP land surface model to characterize the catchment hydrological regimes and identify the dominant quantities contributing to the variability in the land surface hydrology. We show that the Great Lakes domain is not hydrologically uniform and strong spatiotemporal differences exist in the regulators of the hydrological budget at daily, monthly, and annual timescales. Subseasonally, precipitation and soil moisture explain nearly all the terrestrial water budget variability in the southern basins, while the northern latitudes are snow‐dominated regimes. Seasonal assessments reveal greater differences among the basins. Precipitation, evaporation, and runoff are the dominant sources of variability at lower latitudes, while at higher latitudes, terrestrial water storage in the form of ground snowpack and soil moisture has the leading role. Differences in land cover categorizations, for example, croplands, forests, or urban zones, further induce spatial differences in the hydrological characteristics. This quantification of variability in the terrestrial water cycle embedded at different temporal scales is important to assess the impacts of changes in climate and land cover on catchment sensitivities across the diverse hydroclimate of the Great Lakes region.

     
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