ABSTRACT
With increasing focus on large-scale planning and allocation of resources for protection against future flood risk, it is necessary to analyze and improve the deficiencies in the existing flood modeling approach through a better understanding of the interactions among overland flow, subsurface flow, and river hydrodynamics. Recent studies have shown that it is possible to improve flood inundation modeling and mapping using physically based distributed models that incorporate the observable data through assimilation and simulate hydrologic fluxes using the fundamental laws of conservation of mass at multiple spatiotemporal scales. However, despite the significance of distributed modeling in hydrology, it has received relatively less attention within the context of flood hazards. While significant strides have been taken by the research community in estimating streamflow, surface-subsurface volumes, and soil moisture; predicting the flood depths and extents accurately across large scales remains a challenge for distributed models. This chapter addresses the challenges that exist within the flood modeling approach while advocating for a large-scale holistic approach that goes beyond streamflow prediction to provide flood inundation extents and depths. As the way forward, this chapter presents an overview of physically based distributed modeling by providing valuable insights into the essential characteristics of distributed models and highlighting the important factors that need to be considered for large-scale flood simulation. Finally, the chapter provides a prototype modeling framework for large-scale distributed flood modeling using the Interconnected Channel and Pond Routing (ICPR) model for the Wabash River Basin in the United States to illustrate how distributed models can be applied for flood inundation mapping for a large watershed.
