Water balance models are important for the characterization of hydrologic systems, to help understand regional scale dynamics, and to identify hydro-climatic trends and systematic biases in data. Because existing models have, to-date, only been tested on data sets of limited spatial representativeness and extent, it has not yet been established that they are capable of reproducing the range of dynamics observed in nature. This dissertation develops systematic strategies to guide selection of water balance models, establish data requirements, estimate parameters, and evaluate performance. Through a series of three papers, these challenges are investigated in the context of monthly water balance modeling across the conterminous United States. The first paper reports on an initial diagnostic iteration to evaluate relevant components of model error, and to examine details of its spatial variability. We find that to conduct a robust model evaluation it is not sufficient to rely upon conventional NSE and/or r^2aggregate statistics of performance; to have reasonable confidence that the model can provide hydrologically consistent simulations, it is also necessary to examine measures of water balance and hydrologic variability. The second paper builds upon the results of the first, and evaluates the suitability of several candidate model structures, focusing specifically snow-free catchments. A diagnostic Maximum-Likelihood model evaluation procedure is developed to incorporate the notion of `Hydrological Consistency' and controls for structural complexity. The results confirm that the evaluation of hydrologic consistency, based on benchmark comparisons and on stringent analysis of residuals, provides a robust basis for guiding model selection. The results reveal strong spatial persistence of certain model structures that needs to be understood in future studies. The third paper focuses on understanding and improving the procedure for constraining model parameters to provide hydrologically consistent results. In particular, it develops a penalty-function based modification of the Mean Squared Error estimation to help ensure proper reproduction of system behaviors by minimizing interaction of error components and by facilitating inclusion of relevant information. The analysis and results provide insight into the identifiability of model parameters, and further our understanding of how performance criteria should be applied during model identification.