CRYO·Q
operator's guide

CRYO-Q Documentation

The console shows the state of a chosen Himalayan basin as an ensemble: what the ice is doing now, where it is likely to be, what is driving the change, and which interventions the optimizer thinks buy the most slowdown per rupee.

Global controls

Basin
Selects the drainage basin under study (Gangotri, Khumbu, Baltoro, Zemu, or all HMA basins). Every panel — KPIs, forecast fan, driver attribution, portfolio — re-derives from the basin's baseline mass balance, area-loss rate, ELA and velocity.
Horizon
Seasonal (a few years), Decadal (2005–2045) or To 2100. Sets the x-range of the forecast fan and how wide the uncertainty spread grows with time.
Scenario
SSP1-2.6 (aggressive mitigation), SSP2-4.5 (middle-of-the-road) or SSP5-8.5 (fossil-fueled). Chooses the post-2025 drift of the median projection, widens temperature-driven attribution, and rescales the intervention planner and hazard leads.

Provenance badges

Every panel is labelled with which engine produced it, so a reader can tell what is measured vs. modeled vs. quantum-sampled.

Classical
GPU/CPU compute — observations, harmonized reanalysis, deterministic emulators. What you'd get from a normal cluster.
Quantum-inspired
Tensor-network / hybrid solvers running on classical hardware but with quantum-style structure — fusion of heterogeneous fields, early-warning.
Quantum
Runs on a QPU: qPCA for pattern discovery, quantum kernels for driver attribution, quantum sampling for forecast fans, QAOA / annealing for the intervention planner, amplitude estimation for tail-risk.

Panels

Cryosphere map · elevation change & velocity

Each stroke is a synthetic glacier; colour encodes Δh/Δt (rose = strong loss, teal = stable). Small arrows show ice velocity direction. Violet nodes are glacial-lake hazard sites — hover for a mini-readout. Scenario intensifies the loss colouring.

Headline indicators

Four KPIs — net mass balance (m w.e.), area lost YoY (%), ELA shift (m) and mean velocity (m·a⁻¹). All four re-compute from a deterministic per-basin baseline and the scenario multiplier.

Mass-balance forecast · probability fan

Grey line: observed cumulative mass balance up to now (2025). Blue line: quantum-sampled median projection. Two shaded bands: 50% and 90% intervals whose width grows linearly with horizon. Change scenario to bend the median; change horizon to shorten or extend the projection window.

Driver attribution

Estimated contribution of each driver (air temperature, black carbon, monsoon, ENSO/IOD, debris cover) to recent mass change. Under SSP5-8.5 the temperature bar swells (attrShift = 1.18); under SSP1-2.6 it shrinks (0.85). Total is capped at 100% per row.

Scenario comparator

Three cards, one per SSP. Click one to make it the active scenario across the whole console. The card also shows 90% CI on 2100 mass loss and expected area loss.

Pattern explorer · driver modes

Force-graph of coupled ocean–atmosphere–cryosphere modes. Node radius encodes variance captured; edge width encodes coupling strength. Under a hotter scenario, temperature / black-carbon / ablation nodes and their edges swell.

Intervention planner

A ranked portfolio the optimizer returns under the selected scenario and a fixed budget. Each row: action, target site, expected melt slowdown, cost tier, confidence. Sorting and the bar widths recompute when you switch scenario or basin. Quantum result is only surfaced because it beat the classical MILP baseline on the instance (A/B gate).

Alerts & hazard watch

Hybrid early-warning surfaces active GLOF precursors, ablation events and free-air warming anomalies. Lead times and confidence tighten under hotter scenarios.

Prototype limits