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Accurate measurement of net radiation in the high-latitude Arctic regions is challenging since rain and snow events often introduce substantial measurement errors. To reduce the precipitation-induced measurement errors of downward radiation, customized data-driven methods are developed to reconstruct downward radiative fluxes from the biased radiation measurements. This study uses four years of field data across ten plots covered with forest, trees, and tundra in the Polar Urals from July 2018 to July 2022. Rain and snow on the radiometers absorb and block shortwave radiation and emit longwave radiation, leading to underestimation of downward shortwave and overestimation of downward longwave radiation. Snow causes more errors than rain. Seasonal variation of reconstructed net radiation for three dominant vegetation types indicates that their differences are most pronounced in April and least in September. Furthermore, forest and tree plots consistently exhibit higher magnitudes of net radiation and longer seasons of positive net radiation than tundra plots. This study advances methodologies for reconstructing corrupted net radiation data in the Arctic and offers insights into the variability of net radiation patterns within the forest-tundra ecotone. 

Abstract Flooding is one of the most impactful weather‐related natural hazards. Numerical models that solve the two dimensional (2D) shallow water equations (SWE) represent the first‐principles approach to simulate all types of spatial flooding, such as pluvial, fluvial, and coastal flooding, and their compound dynamics. High spatial resolution (e.g., () m) is needed in 2D SWE simulations to capture flood dynamics accurately, resulting in formidable computational challenges. Thus, relatively coarser spatial resolutions are used for large‐scale simulations of flooding, which introduce uncertainties in the results. It is unclear how the uncertainty associated with the model resolution compares to the uncertainties in precipitation data sets and assumptions regarding boundary conditions when channelized flows interact with other water bodies. In this study, we compare these three sources of uncertainties in 2D SWE simulations for the 2017 Houston flooding event. Our results show that precipitation uncertainty and mesh resolution have more significant impacts on the simulated streamflow and inundation dynamics than the choice of the downstream boundary condition at the watershed outlet. We point out the viability to confine the uncertainty of coarsening mesh resolution by using the variable resolution mesh (VRM) which refines critical topographic features with far fewer grid cells. Specifically, in simulations with VRM, the simulated inundation depths over the refined region are comparable to that use the finest uniform mesh. This study contributes to understanding the challenges and pathways for applying 2D SWE models to improve the realism of flood simulations over large scales. 

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. 

Abstract Climate change can alter wetland extent and function, but such impacts are perplexing. Here, changes in wetland characteristics over North America from 25° to 53° North are projected under two climate scenarios using a state-of-the-science Earth system model. At the continental scale, annual wetland area decreases by ~10% (6%-14%) under the high emission scenario, but spatiotemporal changes vary, reaching up to ±50%. As the dominant driver of these changes shifts from precipitation to temperature in the higher emission scenario, wetlands undergo substantial drying during summer season when biotic processes peak. The projected disruptions to wetland seasonality cycles imply further impacts on biodiversity in major wetland habitats of upper Mississippi, Southeast Canada, and the Everglades. Furthermore, wetlands are projected to significantly shrink in cold regions due to the increased infiltration as warmer temperature reduces soil ice. The large dependence of the projections on climate change scenarios underscores the importance of emission mitigation to sustaining wetland ecosystems in the future. 

Abstract Nenets reindeer pastoralists of Yamal in the Russian Arctic, successfully deal with rapidly changing climate and natural gas industrialization. We present results from our long-term ethnographic study (2001–present) on the adaptive strategies that Nenets nomadic households have employed over time, their tradeoffs, inherent risks, and social implications of these strategies. While some strategies limit the adaptive flexibility of herding, they simultaneously enable agency that keeps Nenets households on the land—critical for maintaining their nomadism. Rapid climate change in the Arctic, which could lead to increased icing of pastures, makes reindeer herding more vulnerable. We examine meteorological data from Yamal to better understand the climatic trends challenging reindeer nomadism. Our analysis is relevant for policymakers through understanding Nenets adaptation and interactions with ecological processes and institutions. 

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. 

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.