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Abstract One of the most conspicuous signals of climate change in high‐latitude tundra is the expansion of ice wedge thermokarst pools. These small but abundant water features form rapidly in depressions caused by the melting of ice wedges (i.e., meter‐scale bodies of ice embedded within the top of the permafrost). Pool expansion impacts subsequent thaw rates through a series of complex positive and negative feedbacks which play out over timescales of decades and may accelerate carbon release from the underlying sediments. Although many local observations of ice wedge thermokarst pool expansion have been documented, analyses at continental to pan‐Arctic scales have been rare, hindering efforts to project how strongly this process may impact the global carbon cycle. Here we present one of the most geographically extensive and temporally dense records yet compiled of recent pool expansion, in which changes to pool area from 2008 to 2020 were quantified through satellite‐image analysis at 27 survey areas (measuring 10–35 km²each, or 400 km²in total) dispersed throughout the circumpolar tundra. The results revealed instances of rapid expansion at 44% (15%) of survey areas. Considered alone, the extent of departures from historical mean air temperatures did not account for between site variation in rates of change to pool area. Pool growth was most clearly associated with upland (i.e., hilly) terrain and elevated silt content at soil depths greater than one meter. These findings suggest that, at short time scales, pedologic and geomorphologic conditions may exert greater control on pool dynamics in the warming Arctic than spatial variability in the rate of air temperature increases. 

Identifying the subset of events that influence events of interest from continuous time datasets is of great interest in various applications. Existing methods however often fail to produce accurate and interpretable results in a time-efficient manner. In this paper, we propose a neural model – Influence-Aware Attention for Multivariate Temporal Point Processes (IAA-MTPPs) – which leverages the powerful attention mechanism in transformers to capture temporal dynamics between event types, which is different from existing instance-to-instance attentions, using variational inference while maintaining interpretability. Given event sequences and a prior influence matrix, IAA-MTPP efficiently learns an approximate posterior by an Attention-to-Influence mechanism, and subsequently models the conditional likelihood of the sequences given a sampled influence through an Influence-to-Attention formulation. Both steps are completed efficiently inside a Bblock multi-head self-attention layer, thus our end-to-end training with parallelizable transformer architecture enables faster training compared to sequential models such as RNNs. We demonstrate strong empirical performance compared to existing baselines on multiple synthetic and real benchmarks, including qualitative analysis for an application in decentralized finance. 

Abstract Around the world, water rights systems govern the allocation of water to a multitude of users. Such systems primarily come into play during times of drought, when some users have to be shorted. Yet their management during times of excess can have implications for subsequent drought impacts. This is evident in the State of Colorado, where under “free river conditions” in which there is sufficient water to satisfy all water rights, anyone—including individuals lacking water rights—can divert as much as they want, unconstrained by the limit of their water right. Here, we estimate the amount of excess water used under such conditions within Division five of the Upper Colorado River Basin in the State of Colorado. Comparing the daily water withdrawals of diversion structures along the Colorado River and its tributaries with their (daily) water rights, we find that in 2017, 339 structures report days with excess withdrawals, amounting to 108 million cubic meters (87,577 acer feet). While such excess withdrawal is legal in Colorado, we argue that the free river condition is an antiquated rule that will make much needed reform of water allocation within the water‐stressed Colorado River Basin more difficult. We offer policy suggestions to address it.