BikeScout Encyclopedia | TAEL Model: Terrain-Aware Evaporation Lag
Description: Discover the TAEL Model: BikeScout's proprietary algorithm for predicting soil moisture and mud risk using photonic energy and drainage coefficients.
Keywords: TAEL Model, Mud Prediction, Soil Moisture Algorithm, Cycling Tech, Evaporation Lag, hifly81, BikeScout

# TAEL
Model

Terrain-Aware Evaporation Lag

The TAEL (Terrain-Aware Evaporation Lag) Model is BikeScout’s proprietary algorithm designed to solve the "Post-Rain Paradox": why do trails remain saturated even after hours of direct sunlight? Unlike basic weather apps, TAEL treats the soil as a dynamic capacitor that stores and releases moisture at different rates.

## The Core Logic

TAEL operates on the principle that evaporation is not instantaneous. It calculates a "Lag Factor" based on three variables: Cumulative Precipitation (72h), Soil Drainage Class (Clay, Loam, Sand), and Photonic Energy flux.

// TAEL MOISTURE INDEX FORMULA
Index = (Precip_72h * Drainage_Coeff) / (Temp_Efficiency + Solar_Persistence)

## Drainage Coefficients

Different surfaces have different "memories". TAEL assigns specific weights to soil types detected via MCP surface sensing:

- Clay/Compact: High retention, slow lag (Mud risk remains high for 48h+).
- Gravel/Fine Gravel: Low retention, fast lag (Quick recovery).
- Forest Loam: Variable lag based on organic saturation.

## Tactical Advantage

The TAEL Model provides a `mud_risk_score` from 1 to 100. This is used by the AI to suggest tire tread patterns and pressure. If TAEL predicts a "Saturation Lock", the briefing will recommend higher-profile knobs and lower PSI, even if the current sky is clear.

"Don't look at the sky to see if the trail is dry. Look at the TAEL."

#Soil-Science #Predictive-Modeling #TAEL