Accurate assessments of glacier velocity are essential for understanding ice flow mechanics, monitoring natural hazards, and projecting future sea-level rise. However, the most commonly used method for deriving glacier velocity maps, known as feature tracking, relies on empirical parameter choices that rarely account for glacier physics or uncertainty. The GLAcier Feature Tracking testkit (GLAFT) aims to assess velocity maps using two statistically and physically based metrics. Velocity maps with metrics falling within our recommended ranges contain fewer erroneous measurements and more spatially correlated noise than velocity maps with metrics that deviate from those ranges. Consequently, these metric ranges are suitable for refining feature-tracking workflows and evaluating the resulting velocity products. GLAFT provides modulized workflows for calculating these metrics and the associated visualization, facilitating the velocity map assessments. To ensure the package is available, reusable, and redistributable to the maximum extent, GLAFT adopts several open science practices including the narrative documentation and demos using Jupyter Book and cloud access using Ghub. By providing the benchmarking framework for evaluating the quality of glacier velocity maps procedure, GLAFT enables the cryospheric sciences and natural hazards communities to leverage the rich glacier velocity data now available, whether they are sourced from public archives or made through custom feature-tracking processes.

Supplemental material (SM; also known as supplementary information) comes with its associated research article and provides study details such as metadata, additional figures and text, multimedia, and code. Well-designed SM helps readers fully understand the underlying scientific analysis, reproduce the work, and even reuse the workflows for exploratory ideas. Thus, the concept of FAIR (Findable, Accessible, Interoperable, and Reusable), which is originally designed for data sharing guidelines, also matches these core qualities for SM.We evaluate different SM-preparation practices that are commonly found in Earth Science journal articles. These practices are classified into five tiers based on the FAIR principles and the narrative structure. We show that Jupyter Book-based SM belongs to the top tier and outperforms the other practices, despite being not as popular as the other SM-preparation practices as of 2022.We identify the advantages of the Jupyter Book-based SM as follows. Jupyter Book uses a narrative structure to combine different elements of SM into a single scholarly object, increasing readability. Jupyter Book's direct support of HTML publishing allows users to web host the SM using services such as Github Pages, improving the web indexing ranks and resulting in higher exposure of both the research article and the SM. The entire SM is also eligible to be archived in a data repository and receive a Digital Object Identifier (DOI) that can be used for citations. In addition, Jupyter Book-based SM lowers the threshold of reproducing and reusing the work by accessing an interactive cloud computing service (e.g., MyBinder.org) with all data and code imported if the content is available on a code-hosting platform (e.g., Github).These features summarize the core values of SM from the perspective of open science. We encourage researchers to use these good practices and urge journal publishers to be open to receiving such supplements for maximum effectiveness.

Glaciers and ice sheets lose their mass by ablation (the output term of their surface mass balance) and discharging into a water body (dynamic loss). The latter is associated with multiple physical characteristics such as bed geometry, inland thinning, terminus stability, and basal conditions. Better assessing the dynamic loss, especially its spatiotemporal variability within a drainage basin, will help improve our understanding of the underlying processes and quantify the future contribution of sea level rise. We propose a new inverse model to decompose glacier elevation change and optimize the dynamic mass loss components for each pixel of the elevation data grid. The model unmixes the observed elevation change from remote sensing data using the modeled surface mass balance and the ice flux as constraints. We use two approaches to design the ice flux term; one is based on glacier surface velocity and the conservation of mass, and the other builds on the flow law and the Shallow Ice Approximation. We test the model for selected marine-terminating glacier outlets in the Greenland ice sheet. If the surface velocity can be decomposed into short-term (seasonal) and multi-year signals, our model may be able to further resolve the dynamic loss components of different physical processes.