URN-SAM: Underwater radiated noise semi-analytical model. Applied in the Spanish Levantine-Balearic marine demarcation.

Abstract

This paper presents the underwater radiated noise semi-analytical model (URN-SAM), a SAM kernel-based Python framework for mapping regional URN from maritime traffic using open and standardized datasets. The model combines European Marine Observation and Data Network vessel density layers, General Bathymetric Chart of the Oceans bathymetry, World Ocean Atlas climatologies, class-dependent source levels derived from the JOMOPANS-ECHO formulation, and propagation kernels precomputed from Bellhop simulations to estimate received levels in the 63 and 125 Hz Marine Strategy Framework Directive indicator bands. Applied to the Spanish Levantine-Balearic Marine Demarcation, URN-SAM reproduced coherent large-scale spatial patterns dominated by major shipping corridors, marked seasonal variability linked to monthly traffic inputs, and clear differences between the two frequency bands. Sensitivity tests showed that vertical aggregation is a nontrivial modeling choice: changing the shallowest evaluation depth materially altered annual maps constructed from the maximum across depths and their exceedance statistics. A numerical benchmark against Bellhop showed close agreement for the selected kernel implementation (root mean square error 1.68 dB; Pearson correlation 0.990). Comparisons with hydrophone data and official Spanish Levantine-Balearic Marine Demarcation products indicate that URN-SAM is more robust at regional and offshore scales than in shallow coastal environments. URN-SAM is proposed as a transparent and computationally efficient screening tool for regional diagnosis, hotspot identification, and category-resolved contribution analysis based on aggregated vessel density inputs.