# Supraglacial Lake Fate Is Knowable Long Before the Season Ends

arXiv: https://arxiv.org/abs/2608.30113 · HTML: https://mlsanity.com/paper/2608.30113/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 63 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [Bake off redux: a review and experimental evaluation of recent time series classification algorithms](https://mlsanity.com/paper/2304.13029/) (2023)
- [Approaches and Applications of Early Classification of Time Series: A Review](https://mlsanity.com/paper/2005.02595/) (2020)
- [Learning to Stop While Learning to Predict](https://mlsanity.com/paper/2006.05082/) (2020)
- [TEASER: Early and Accurate Time Series Classification](https://mlsanity.com/paper/1908.03405/) (2019)
- [Stop&Hop: Early Classification of Irregular Time Series](https://mlsanity.com/paper/2208.09795/) (2022)
- [Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction](https://mlsanity.com/paper/2503.02093/) (2025)
- [Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift](https://mlsanity.com/paper/2510.15265/) (2025)

## Abstract (arXiv)

A supraglacial lake on the Greenland Ice Sheet ends its melt season in one of four ways: it drains rapidly through a hydrofracture, drains slowly across the surface, refreezes in place, or is buried by late-season snowfall. Which one occurs decides whether the meltwater reaches the ice bed. Satellite classifiers recover the outcome accurately but only after the season closes, and how much of a season each outcome actually requires has never been measured. We measure it directly: holding the representation and the classifier fixed, we truncate the input at 14 cutoffs from May 1 to December 31, retrain at each, and record the earliest cutoff at which each outcome's per-class F1 reaches a fixed target. The outcomes resolve in a consistent order, two of them months early: rapid drainage by July 15 and slow drainage by August 1, respectively 92 and 75 days ahead of the earliest date a full-season pipeline can be computed at all, with buried and refreeze following at 44 and 30 days. Five further learners, from a majority-class floor and 54 summary statistics to a trigger-based early classifier, leave the ordering largely intact: the three that produce a per-class trajectory reproduce it in five of six cases despite end-of-season accuracies differing by up to 18 percentage points, and it survives leave-one-basin-out evaluation, though not a move to machine-labeled lakes in an unseen season. Every feature we compute at day t reads only days up to t, at a cost of at most 1.3 percentage points. A monitoring system should therefore not have one release date: rapid drainage can be flagged on July 15, three months before a full-season pipeline can be computed at all.