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Lakes and reservoirs support rich biodiversity and are essential for the natural production of over 12% of Germany's drinking water. Their biodiversity is crucial for maintaining water quality but is increasingly threatened by climate change, pollution and the spread of invasive species. In the context of the IQ Water project, a multifaceted approach is employed to assess water quality, encompassing conventional parameters such as physical, chemical, and hygienic metrics, as well as assessments of the planktonic community, in conjunction with innovative molecular methodologies (e.g., eDNA analyses). This integrated strategy enables the exploration of reservoir behavior across multiple dimensions, including physicochemical aspects, in addition to the often-overlooked domain of microbial biodiversity, encompassing bacteria, viruses, protozoa, and fungi. The focus of this research is on water quality parameters, including, but not limited to blooms of potentially toxic cyanobacteria or hygienic relevant bacteria, antibiotic resistance genes (ARG), viruses, and invasive species. The overarching objective of the project is to develop biodiversity models for drinking water reservoirs by integrating complex biological, chemical, and physical data with machine learning (ML) technologies. These ML models aim to enable the prediction of important hygienic challenges like cyanobacterial blooms and the distribution of pathogens and ARGs, thereby contributing to the advancement of understanding of aquatic freshwater ecosystem dynamics.In this contribution, we present preliminary findings regarding the efficacy of molecular methodologies in analyzing reservoir biodiversity. We also offer insights from a data-centric perspective, including the necessity of a unified data schema for the collection of highly heterogeneous data and the support of machine learning (ML)-based modeling. We showcase ML-based cross-reservoir assessments for ecosystem monitoring based on the proposed framework by modeling biochemical and physicochemical fingerprints. Furthermore, we propose a neural network–based multi‑resolution modelling approach that explicitly accounts for strongly variable sampling intervals (hours to months) within a single model. The architecture treats meteorological and physicochemical time series as primary sequential inputs and uses sparsely sampled molecular profiles as contextual information via learned embeddings and cross‑attention.