Advancing Depth-Storage-Discharge Modeling in Regional Hydrology

Abstract

This dissertation presents the development of an innovative approach to populating rating characteristics and supporting hydrologic modeling, designed to simplify complex real-world hydrological systems and accurately estimate their responses to rainfall, runoff, baseflow and evaporation stresses. The core of this research addresses the challenges inherent in characterizing hydrography elements in hydrologic modeling, particularly in regions lacking comprehensive stream reach survey data, flow and stage. This issue is pronounced in areas with extensive wetland hydrography, where traditional modeling requires intensive manual calibration, and course rating data that are often unavailable. To overcome these challenges, this study introduces a novel procedure that leverages Geographic Information System (GIS) coverages and limited streamflow gauging station data to characterize and define rating conditions for hydrologic models. This methodology employs non-dimensionalizing techniques to enable effective (and not overparameterized) modeling of large, poorly gauged areas. Key to this approach is the reduced calibration required, significantly reducing the labor-intensive aspect of traditional methods. Results from applying this procedure on a large regional basin in W-C Florida demonstrate efficacy and accuracy for modeling reach water levels, and discharge behavior in large modestly gauged stream systems. This area exhibits sizeable fluxes of both event runoff and sustained groundwater contributions. The findings suggest substantial advancements in capability, alongside time savings and effort, in construction and calibration of large regional hydrologic models especially where significant wetland areas are present, presenting what is believed to be a significant contribution to the field of hydrologic modeling. This research not only provides a practical solution to a longstanding modeling problem but also suggests future research needs and possible advancements for the future.