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This review outlines major directions of simpler model development in environmental modeling, metamodeling, statistical-regression and machine-learning-based empirical models, and mechanistic models with reduced structures. Simpler models may be favored due to limited observational data, uncertainty in the complex model predictions, and intent of using a model as a component of a multimedia or multicompartmental model. Decision-making often relies on simple models. Model simplification can be useful in understanding the behavior of complex models. Understanding the role of models of different complexity as affected by intended uses and problem statements is an important part of the modern ontology of environmental science and technology.