Continuous Hydrologic Modeling Improved by Intensive Event Data

Abstract

Continuous hydrologic modeling for a watershed involves characterization of a series of hydrologic processes affected by numerous factors over a long time period, which consists of both wet and dry conditions with/without rainfall. The complexity of those processes and their cumulative effects, and the lack of intensive long-term monitoring data, often prevent a modeler from effectively identifying the underlying processes and mechanisms, as well as accurately estimating the relevant hydrologic parameters. To circumvent this difficulty, a modeling strategy that combined event and continuous hydrologic modeling was implemented in this study. The fine-scale event hydrologic modeling, supported by intensive storm data, was used to improve the coarse-scale continuous modeling by providing more accurate and well-calibrated parameters. Two different rainfall-runoff models: the soil moisture accounting (SMA) and the SCS curve number (SCS-CN) models in HEC-HMS were utilized and discussed. In an application to the Mona Lake watershed in west Michigan, the Watershed Modeling System (WMS) and HEC-HMS were coupled for processing spatially-distributed GIS data, delineating the watershed, computing geometric and hydrologic parameters, and implementing hydrologic modeling. Several rainfall events were selected for calibrating/verifying the event model and identifying the related parameters, which were further used in the continuous hydrologic model. Two methods: normalized objective function (NOF) and modeling efficiency (EF) were utilized to quantify the goodness-of-fit between simulated and observed hydrographs and evaluate the model performance. Results from this study reveal that the joint event and continuous hydrologic modeling can be effective, as it takes full advantage of different hydrologic approaches and available data, and improves the overall quality of watershed hydrologic modeling.