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Water quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate‐based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling‐Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non‐optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions. Plain Language Summary: Accurate data on the volume and temperature of river water are essential for understanding how changing river conditions affect water quality and freshwater ecosystems. However, direct measurements of river parameters are often lacking, leading researchers to rely on models for estimation. In this study, we utilized advanced models and techniques to compute daily water volume and temperature in 138 basins across Alaska, including the entirety of the Yukon River basin, spanning from 1990 to 2021. Our findings indicated that rivers in northern Alaska exhibited higher maximum summer water temperatures and more significant temperature fluctuations compared to those in the central and southern regions. Our analysis highlighted that adjusting air temperature and the model's internal variables were crucial in minimizing errors in river temperature prediction. We improved the accuracy of the river temperature model by applying a technique to refine the model output based on the limited available river measurements. Comparing our enhanced model to a simpler statistical approach, we observed superior performance once the necessary adjustments were implemented. Key Points: Our model system produced an accurate, high‐resolution, daily hindcast of Alaskan river temperature and discharge from 1990 to 2021We increased model performance by employing a new optimization of the River Basin Model and forcing it with a climate‐land surface modelA sensitivity analysis highlights important drivers of river temperatures in each region and the need for optimization [ABSTRACT FROM AUTHOR]

The Arctic is rapidly warming, and tundra vegetation community composition is changing from small, prostrate shrubs to taller, erect shrubs in some locations. Across much of the Arctic, the sensitivity of shrub secondary growth, as measured by growth ring width, to climate has changed with increased warming, but it is not fully understood how shrub age contributes to shifts in climate sensitivity.We studied Siberian alder, Alnus viridis ssp. fruticosa, a large nitrogen‐fixing shrub that has responded to climate warming with northward range expansion over the last 50 years. We used serial sectioning of 26 individual shrubs and 94 cross‐sections to generate a 98‐year growth ring chronology, including one 142‐year‐old, Siberian alder in Northern Alaska. We tested how secondary growth sensitivity to climate has changed over the past century (1920–2017) and how shrub age affects climate sensitivity of alder growth through time.We found that over time, alder growth as expressed by the stand chronology became more sensitive to July mean monthly air temperature. Older shrubs displayed higher sensitivity to June and July temperature than younger alders. However, during the first 30 years of growth of any shrub, temperature sensitivity did not differ among individuals. In addition, the June temperature sensitivity of growth series from individual cross‐sections depended on the age of the attached shrub.Our results suggest that age contributes to climate sensitivity, likely through modifying internal shrub carbon budgets by changing size and reducing alder's dependence on N‐fixation over time. Older, more sensitive alder may enhance C and N‐cycling while having greater recruitment potential. Linking alder age to climate sensitivity, recruitment and total N‐inputs will enable us to better predict ecosystem carbon and nitrogen cycling in a warmer Arctic. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Warming temperatures and increasing disturbance by wildfire and extreme weather events is driving permafrost change across northern latitudes. The state of permafrost varies widely in space and time, depending on landscape, climate, hydrologic, and ecological factors. Despite its importance, few approaches commonly measure and monitor the changes in deep (>1 m) permafrost conditions with high spatial resolution. Here, we use electrical resistivity tomography surveys along two transects in interior Alaska previously disturbed by wildfire and more recently by warming temperatures and extreme precipitation. Long‐term point observations of permafrost depth, temperature, and water content inform geophysical measurements which, in turn, are used to extrapolate interpretations over larger areas and with high spatial fidelity. We contrast gradual loss of recently formed permafrost driven by warmer temperatures and increased snowfall, with rapid permafrost loss driven by changes in air temperature, snow depth, and extreme summer precipitation in 2014. Plain Language Summary: Permafrost, or ground that remains perennially frozen, is thawing across Arctic and sub‐Arctic regions, driven in part by climate warming, wildfire, and changes in the amount and timing of rain and snow. When permafrost thaws, previously frozen carbon can be released to the atmosphere as greenhouse gases, water flow and storage is altered, and community land use and access are impacted. Permafrost landscapes can be mapped over large areas from satellites or airborne remote sensing methods, but only at the surface with limited information obtained at depth. Measurements of deeper permafrost properties are often limited to point observations. We use geophysical measurements to indirectly map and monitor high spatial resolution changes in permafrost characteristics within the ground. Combined with point observations of soil temperature and moisture content, and thaw depths, geophysical measurements help to extend our ability to measure changing permafrost conditions over time. At two nearby transects in interior Alaska, we document the long‐term trajectory of permafrost thaw following wildfire. Our results show similar yet distinct responses in shallow permafrost recovery and more recent degradation, both gradual and rapid, caused by warming temperatures, increased snowfall, and extreme summer rainfall events. Key Points: Changes in the state of permafrost can be highly variable in both space and timeDisturbance or changes in climate and precipitation influence the long‐and short‐term trajectory of permafrost and seasonally frozen groundGeophysical measurements and monitoring complement direct permafrost observations by providing high resolution information at depth [ABSTRACT FROM AUTHOR]

Tundra dominates two‐thirds of the unglaciated, terrestrial Arctic. Although this region has experienced rapid and widespread changes in vegetation phenology and productivity over the last several decades, the specific climatic drivers responsible for this change remain poorly understood. Here we quantified the effect of winter snowpack and early spring temperature conditions on growing season vegetation phenology (timing of the start, peak, and end of the growing season) and productivity of the dominant tundra vegetation communities of Arctic Alaska. We used daily remotely sensed normalized difference vegetation index (NDVI), and daily snowpack and temperature variables produced by SnowModel and MicroMet, coupled physically based snow and meteorological modeling tools, to (1) determine the most important snowpack and thermal controls on tundra vegetation phenology and productivity and (2) describe the direction of these relationships within each vegetation community. Our results show that soil temperature under the snowpack, snowmelt timing, and air temperature following snowmelt are the most important drivers of growing season timing and productivity among Arctic vegetation communities. Air temperature after snowmelt was the most important control on timing of season start and end, with warmer conditions contributing to earlier phenology in all vegetation communities. In contrast, the controls on the timing of peak season and productivity also included snowmelt timing and soil temperature under the snowpack, dictated in part by the snow insulating capacity. The results of this novel analysis suggest that while future warming effects on phenology may be consistent across communities of the tundra biome, warming may result in divergent, community‐specific productivity responses if coupled with reduced snow insulating capacity lowers winter soil temperature and potential nutrient cycling in the soil. [ABSTRACT FROM AUTHOR]