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Problem <p>Mountain regions exhibit rapid climatic variability; small shifts in temperature, snowfall, or wind direction can push off-grid micro–hydro across nonlinear operating thresholds, risking outages and inequitable impacts.</p> Methods <p>Using open hourly climate records from the Kalam Valley (Pakistan), we implement a </p> <p>climate-informed AI pipeline that (i) estimates early warning thresholds via change-point detection and segmented regression with block-bootstrap 95% CIs; (ii) discovers latent climate regimes using PCA + K-means, with full diagnostics and robustness checks against GMM/HDBSCAN; and (iii) predicts snowfall with a regression ensemble. To prevent leakage, we use rolling-origin time-series cross-validation with expanding windows and a final chronological hold-out. The single primary endpoint is 24-h early warning skill, assessed by PR-AUC and Brier score with calibration diagnostics.</p> Findings <p>We identify statistically significant snow–temperature thresholds linked to observed stress events. Snowpack persistence depends strongly on wind direction, with easterly/northeasterly flows retaining more snow. Four stable climate regimes structure reliability patterns. On the hold-out period, the warning model shows high precision-recall performance and good calibration (low Brier), outperforming seasonal-persistence baselines. Descriptively—without causal claims—milder regimes co-vary with higher renewable adoption and lower inequality indicators.</p>

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Climate-informed AI for 24-h early warning in off-grid micro–hydro: thresholds, wind–snow dynamics, and regime diagnostics in the Kalam Valley (Pakistan)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Hector F Gomez A,
  • Roberto Carlos Jimenez Martinez,
  • Geoconda Rocio Garcia Sanchez,
  • Milton Rodrigo Aldas,
  • Juan Gilberto Paredes Salinas,
  • Susana A Arias T

摘要

Problem

Mountain regions exhibit rapid climatic variability; small shifts in temperature, snowfall, or wind direction can push off-grid micro–hydro across nonlinear operating thresholds, risking outages and inequitable impacts.

Methods

Using open hourly climate records from the Kalam Valley (Pakistan), we implement a

climate-informed AI pipeline that (i) estimates early warning thresholds via change-point detection and segmented regression with block-bootstrap 95% CIs; (ii) discovers latent climate regimes using PCA + K-means, with full diagnostics and robustness checks against GMM/HDBSCAN; and (iii) predicts snowfall with a regression ensemble. To prevent leakage, we use rolling-origin time-series cross-validation with expanding windows and a final chronological hold-out. The single primary endpoint is 24-h early warning skill, assessed by PR-AUC and Brier score with calibration diagnostics.

Findings

We identify statistically significant snow–temperature thresholds linked to observed stress events. Snowpack persistence depends strongly on wind direction, with easterly/northeasterly flows retaining more snow. Four stable climate regimes structure reliability patterns. On the hold-out period, the warning model shows high precision-recall performance and good calibration (low Brier), outperforming seasonal-persistence baselines. Descriptively—without causal claims—milder regimes co-vary with higher renewable adoption and lower inequality indicators.