Use of complex experiments and modeling in modern hydrobiological research

The integration of complex experimental methodologies and advanced computational modeling has revolutionized contemporary hydrobiological research. These integrative approaches enable a mechanistic understanding of aquatic ecosystem dynamics and provide predictive capabilities essential for adaptive management in the face of global environmental change. Controlled experimental systems - such as microcosms and mesocosms - allow for the manipulation of key abiotic and biotic variables, thereby generating high-resolution empirical data. In parallel, mechanistic, statistical, and machine learning-based models synthesize this information to simulate nutrient cycling, trophic interactions, and ecosystem responses under various stress scenarios including eutrophication, climate perturbation, and pollutant influx. Recent advancements in sensor technologies, remote monitoring, and genomic tools have further enhanced data granularity, facilitating model calibration, validation, and real-time ecological forecasting. This paper critically reviews the methodologies and applications of complex experimentation and modeling in hydrobiology, highlighting case studies that demonstrate their synergistic potential in ecosystem assessment, scenario development, and evidence-based policy formulation. By bridging empirical observation with computational simulation, these tools collectively offer a robust framework for the sustainnable management and conservation of aquatic environments in an era of rapid environmental transformation.