Avian influenza (AI) viruses pose a continual threat to human and animal health worldwide. As wild waterfowl serve as their natural reservoir, AI surveillance in these populations is essential for outbreak prevention and control although it is challenging to detect in animals. Active monitoring of waterfowl habitats has been proposed as a less-biased, alternative strategy that may better capture the genetic diversity – and spread - of AI viruses to inform preparedness and prevention. However, molecular analysis of viral RNA in the natural environment is fraught with challenges, such as high levels of PCR inhibitors. Here, we adapted our previous wastewater work to environmental monitoring methods for the genomic characterisation of AI in wetland samples. During the 2024 highly pathogenic avian influenza (HPAI) season, we detected and sequenced AI genomes from sediments from an urban wetland. Half of specimens tested positive for IAV by RT-qPCR, with levels changing over time. Whole genome sequencing by Oxford Nanopore Technologies sequencing revealed both full-length and deletion-containing genomic segments with diversity observed both between and within samples. Phylogenetic analysis revealed an NS gene (segment 8) from sediments was closely related to gull-adapted H13 viruses and clustered with sequences from contemporaneous Belfast wastewater and local H5N1 wildlife. Our findings suggest that environmental monitoring may complement traditional AI surveillance in wild birds. Ongoing work involving longitudinal sampling at more sites across Ireland aim to better understand the ecology and environmental dynamics of IAV, enhancing preparedness for future incursions for the protection of human, livestock and wildlife health.