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This study investigates and compares soil moisture and hydrology projections of broadly used land models with permafrost processes and highlights the causes and impacts of permafrost zone soil moisture projections. Climate models project warmer temperatures and increases in precipitation (P) which will intensify evapotranspiration (ET) and runoff in land models. However, this study shows that most models project a long-term drying of the surface soil (0-20 cm) for the permafrost region despite increases in the net air-surface water flux (P-ET). Drying is generally explained by infiltration of moisture to deeper soil layers as the active layer deepens or permafrost thaws completely. Although most models agree on drying, the projections vary strongly in magnitude and spatial pattern. Land models tend to agree with decadal runoff trends but underestimate runoff volume when compared to gauge data across the major Arctic river basins, potentially indicating model structural limitations. Coordinated efforts to address the ongoing challenges presented in this study will help reduce uncertainty in our capability to predict the future Arctic hydrological state and associated land-atmosphere biogeochemical processes across spatial and temporal scales.

Ponds and lakes are abundant in Arctic permafrost lowlands. They play an important role in Arctic wetland ecosystems by regulating carbon, water, and energy fluxes and providing freshwater habitats. However, ponds, i.e., waterbodies with surface areas smaller than 1. 0 × 104ĝ€m2, have not been inventoried on global and regional scales. The Permafrost Region Pond and Lake (PeRL) database presents the results of a circum-Arctic effort to map ponds and lakes from modern (2002-2013) high-resolution aerial and satellite imagery with a resolution of 5ĝ€m or better. The database also includes historical imagery from 1948 to 1965 with a resolution of 6ĝ€m or better. PeRL includes 69 maps covering a wide range of environmental conditions from tundra to boreal regions and from continuous to discontinuous permafrost zones. Waterbody maps are linked to regional permafrost landscape maps which provide information on permafrost extent, ground ice volume, geology, and lithology. This paper describes waterbody classification and accuracy, and presents statistics of waterbody distribution for each site. Maps of permafrost landscapes in Alaska, Canada, and Russia are used to extrapolate waterbody statistics from the site level to regional landscape units. PeRL presents pond and lake estimates for a total area of 1. 4 × 106ĝ€km2 across the Arctic, about 17ĝ€% of the Arctic lowland ( < ĝ€300ĝ€mĝ€a.s.l.) land surface area. PeRL waterbodies with sizes of 1. 0 × 106ĝ€m2 down to 1. 0 × 102ĝ€m2 contributed up to 21ĝ€% to the total water fraction. Waterbody density ranged from 1. 0 × 10 to 9. 4 × 101ĝ€kmĝ'2. Ponds are the dominant waterbody type by number in all landscapes representing 45-99ĝ€% of the total waterbody number. The implementation of PeRL size distributions in land surface models will greatly improve the investigation and projection of surface inundation and carbon fluxes in permafrost lowlands. Waterbody maps, study area boundaries, and maps of regional permafrost landscapes including detailed metadata are available at https://doi.pangaea.de/10.1594/PANGAEA.868349.

Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels to train a multi-layer deep neural network that learns abstract semantic features of the input data. This research introduces a novel weakly supervised approach for pixel-wise flood mapping by leveraging multi-temporal remote sensing imagery and image processing techniques (e.g., Normalized Difference Water Index and edge detection) to create weakly labeled data. Using these weakly labeled data, a bi-temporal U-Net model is then proposed and trained for flood detection without the need for time-consuming and labor-intensive human annotations. Using floods from Hurricanes Florence and Harvey as case studies, we evaluated the performance of the proposed bi-temporal U-Net model and baseline models, such as decision tree, random forest, gradient boost, and adaptive boosting classifiers. To assess the effectiveness of our approach, we conducted a comprehensive assessment that (1) covered multiple test sites with varying degrees of urbanization, and (2) utilized both bi-temporal (i.e., pre- and post-flood) and uni-temporal (i.e., only post-flood) input. The experimental results showed that the proposed framework of weakly labeled data generation and the bi-temporal U-Net could produce near real-time urban flood maps with consistently high precision, recall, f1 score, IoU score, and overall accuracy compared with baseline machine learning algorithms. [ABSTRACT FROM AUTHOR]

The active and passive contractile performance of skeletal muscle fibers largely depends on the myosin heavy chain (MHC) isoform and the stiffness of the titin spring, respectively. Open questions concern the relationship between titin-based stiffness and active contractile parameters, and titin's importance for total passive muscle stiffness. Here, a large set of adult rabbit muscles (n = 37) was studied for titin size diversity, passive mechanical properties, and possible correlations with the fiber/MHC composition. Titin isoform analyses showed sizes between ~3300 and 3700 kD; 31 muscles contained a single isoform, six muscles coexpressed two isoforms, including the psoas, where individual fibers expressed similar isoform ratios of 30:70 (3.4:3.3 MD). Gel electrophoresis and Western blotting of two other giant muscle proteins, nebulin and obscurin, demonstrated muscle type--dependent size differences of [less than or equal to]70 kD. Single fiber and single myofibril mechanics performed on a subset of muscles showed inverse relationships between titin size and titin-borne tension. Force measurements on muscle strips suggested that titin-based stiffness is not correlated with total passive stiffness, which is largely determined also by extramyo-fibrillar structures, particularly collagen. Some muscles have low titin-based stiffness but high total passive stiffness, whereas the opposite is true for other muscles. Plots of titin size versus percentage of fiber type or MHC isoform (I-IIB-IIA-IID) determined by myofibrillar ATPase staining and gel electrophoresis revealed modest correlations with the type I fiber and MHC-I proportions. No relationships were found with the proportions of the different type II fiber/MHC-II subtypes. Titin-based stiffness decreased with the slow fiber/MHC percentage, whereas neither extramyofibrillar nor total passive stiffness depended on the fiber/MHC composition. In conclusion, a low correlation exists between the active and passive mechanical properties of skeletal muscle fibers. Slow muscles usually express long titin(s), predominantly fast muscles can express either short or long titin (s), giving rise to low titin-based stiffness in slow muscles and highly variable stiffness in fast muscles. Titin contributes substantially to total passive stiffness, but this contribution varies greatly among muscles.