This dissertation explores the delicate balance between complexity and conceptual clarity in hydrological modeling, with an emphasis on principles that support flexible modular frameworks; these frameworks not only allow for a range of process formulations, but also facilitate the integration of diverse models across different watersheds and the use of spatial data most relevant to watershed characteristics and model structure. Recognizing the challenges of capturing the complex interactions between climate and land-surface hydrology—especially in environments altered by urbanization and other anthropogenic factors—the study investigates how different model configurations impact predictive accuracy and process representation across varied watershed environments. This journey is guided by two core assumptions; First, the research posits that integrating distributed Land Surface Models (LSMs) with simpler hydrologic models can enhance explanatory power. Second, the usefulness the spatial model inputs (static watershed characteristics), such as land cover and topography, has varying effects depending on modeling decisions related to spatial representations (e.g., resampling algorithm, resolution). These insights aim to advance both academic understanding and practical strategies in hydrology.The first study (Chapter 2) addresses the integration of TOPMODEL, a conceptual hydrologic model, with Noah-MP, the land surface model of the National Water Model (NWM), through a one-way coupling approach. This chapter demonstrates that simplifying the complex subsurface representation of NWM can enhance streamflow predictions, especially in headwater catchments. By testing six different coupling scenarios, the study reveals that preserving the internal states of both models yields the best results, outperforming either model used independently. However, the research also highlights that the design of the coupling interface can introduce structural uncertainty, significantly impacting model performance and parameter sensitivity. These findings emphasize the need for a cautious approach in model coupling to maintain consistency and accuracy. The second study (Chapter 3) focuses on the influence of land cover representation and resampling methods within the WRF-Hydro/NWM framework. Through a controlled sensitivity analysis, the research uncovers that the areal proportion of land cover classes significantly affects vertical hydrologic fluxes and streamflow characteristics at the catchment scale. In contrast, the spatial arrangement of land cover has a minimal impact on vertical hydrological fluxes, though it can slightly alter streamflow through routing processes. These results challenge the necessity of detailed representation spatial pattern and allocation of land cover in large-scale hydrologic modeling, suggesting that a flexible, modular approach to spatial configuration may be more effective. The chapter advocates for land surface modeling strategies that balance the need for detailed process representation with the simplicity required for broader applicability. The third study (Chapter 4) investigates the effects of urbanization on hydrologic processes within a landscape-oriented model, focusing on the incorporation of spatially variable effective impervious area (EIA). The EIA is estimated using the latest techniques involving series of statistical regressions and publicly available soil and landcover dataset. By modeling two watersheds with contrasting climates and urbanization patterns, the research demonstrates that integrating urban impact significantly enhances model performance, far surpassing the original model configuration. Notably, a simpler EIA-only adjustment outperformed a more complex configuration that also included additional subsurface urban impacts. Furthermore, the study finds that models with coarser spatial resolutions often outperform those with finer resolutions, despite the loss of detailed spatial information. This outcome challenges the assumption that detailed spatial representation is necessary for accurate urban hydrologic modeling. Instead, it suggests that capturing the most relevant spatial aspect for hydrological flux calculation is more crucial. The chapter underscores the importance of balancing model complexity with practical applicability, particularly in urban hydrology, where both oversimplification and overcomplication present significant challenges. Overall, this dissertation stressed the challenges of developing scalable hydrologic modeling approaches that are both effective and adaptable. The findings confirm that increased complexity and spatial detail do not necessarily improve model performance. Instead, a balanced approach that considers both complexity and conceptual clarity proves to be more effective. The principles reaffirmed and new insights gained in this study can be applied to modular modeling approaches, contributing to the development of more scalable frameworks that enhance model suitability testing and explanation of hydrologic processes across diverse watershed environments. The dissertation advocates for more robust, adaptable, and transparent modeling practices, which are essential for expanding the hydrologic knowledge base, addressing future environmental changes, and enhancing water resource management amidst evolving hydrologic demands.