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Hydrologic and water quality models are increasingly used to determine the environmental impacts of climate variability and land management. Due to differing model objectives and differences in monitored data, there are currently no universally accepted procedures for model calibration and validation in the literature. In an effort to develop accepted model calibration and validation procedures or guidelines, a special collection of 22 research articles that present and discuss calibration strategies for 25 hydrologic and water quality models was previously assembled. The models vary in scale temporally as well as spatially from point source to the watershed level. One suggestion for future work was to synthesize relevant information from this special collection and to identify significant calibration and validation topics. The objective of this article is to discuss the importance of accurate representation of model processes and its impact on calibration and scenario analysis using the information from these 22 research articles and other relevant literature. Models are divided into three categories: (1) flow, heat, and solute transport, (2) field scale, and (3) watershed scale. Processes simulated by models in each category are reviewed and discussed. In this article, model case studies are used to illustrate situations in which a model can show excellent statistical agreement with measured stream gauge data, while misrepresented processes (water balance, nutrient balance, sediment source/sinks) within a field or watershed can cause errors when running management scenarios. These errors may be amplified at the watershed scale where additional sources and transport processes are simulated. To account for processes in calibration, a diagnostic approach is recommended using both hard and soft data. The diagnostic approach looks at signature patterns of behavior of model outputs to determine which processes, and thus parameters representing them, need further adjustment during calibration. This overcomes the weaknesses of traditional regression-based calibration by discriminating between multiple processes within a budget. Hard data are defined as long-term, measured time series, typically at a point within a watershed. Soft data are defined as information on individual processes within a budget that may not be directly measured within the study area, may be just an average annual estimate, and may entail considerable uncertainty. The advantage of developing soft data sets for calibration is that they require a basic understanding of processes (water, sediment, nutrient, and carbon budgets) within the spatial area being modeled and constrain the calibration. [ABSTRACT FROM AUTHOR]