A multi-method validation framework for seasonal environmental-indicator forecasting: Demonstrated with the Keetch–Byram Drought Index

Environmental indicators that capture seasonal dynamics are widely used in wildfire management, vector control, and natural resource monitoring, but operational forecasting requires more than a fitted seasonal model: the candidate structure must be estimable, its seasonal period sufficiently resolved, and its forecasts demonstrably useful out of sample. We develop a forecast-readiness framework for seasonal environmental indicators and demonstrate it with the Keetch–Byram Drought Index (KBDI), a meteorologically derived hazard indicator. Harmonic regression models, with and without preliminary linear detrending, were evaluated using complementary Leave-One-Year-Out (LOYO), Rolling Origin (RO), and Rolling Window (RW) validation. RO and RW forecasts used only observations available before each held-out calendar year and covered the entire following year without updating. Seasonal climatology, lagged persistence, and exponential smoothing provided operational benchmarks. Recurring seasonal structure carried most predictive signal, whereas preliminary detrending did not consistently improve forecasts. The harmonics-only formulation was generally more robust under deployment-oriented validation, and exponential smoothing independently favored a no-trend seasonal structure. For the Lee County record, two-year training windows were consistently inadequate; three years was the shortest history showing partial recovery, but longer records did not guarantee positive skill relative to climatology. A seven-county exploratory holdout under the same two-year constraint likewise showed predominantly negative skill. Seasonal climatology remained a strong benchmark, while peak underprediction and phase errors of weeks constrained point-forecast reliability. These results motivate a six-step forecast-readiness protocol that separates algebraic identifiability, period resolution, and demonstrated out-of-sample value before deployment.