Less Is More: Short‐Term Window Calibration Improves Seasonal Shoreline Prediction in Modeling

Abstract Coastal communities worldwide rely on shoreline models for risk assessment and management, yet these models often struggle to capture observed variability across different temporal scales. We analyzed 30 years of shoreline observations at Hasaki Beach, Japan, using Discrete Wavelet Transform to separate variation by timescale. Spectral analysis revealed wave‐driven annual and semi‐annual cycles, while long‐term trends contributed significantly to total variance. The ShoreFor model, when calibrated using the full 30‐year data set, severely underestimated seasonal variability. In contrast, 2‐year calibration windows successfully reproduced seasonal variations both within calibration periods and, after DWT‐based detrending, across the entire 30‐year validation period. Our findings demonstrate that short‐window calibration substantially enhances model capability for capturing wave‐driven seasonal shoreline changes, offering a practical solution for coastal risk assessment using limited observational data. This approach is particularly valuable given increasing availability of satellite‐derived shoreline data and the need for accurate seasonal predictions under changing climate conditions.