Object-oriented hydrologic modeling with GIS

Kwangmin Kang, Purdue University

Abstract

A prototype geographic information system (GIS) based tightly coupled object oriented framework called GIS and Hydrologic Information System Modeling Object (GHISMO) is presented in this thesis. The proposed GHISMO framework is developed within ArcGIS environment such that geographic datasets can be treated as hydrologic objects that have properties and methods to simulate a hydrologic system. The overall GHISMO framework consists of HydroShed as a super class which is composed of six sub classes, namely, HydroGrid (for grid based data such as digital elevation model), ParameterGrid (for grid based parameters such as land use type), HydroArea (for polygon features such as lakes and reservoirs), HydroCatchment (for polygon features representing catchments and watersheds), HydroLine (for polyline features such as rivers) and HydroTable (for input and output tabular data). The GHISMO framework is applied to develop a modular hydrologic modeling system called the Storage Release based Distributed Hydrologic Model (STORE DHM). The storage–release concept uses the travel time within each grid cell to compute how much water is stored or discharged to the watershed outlet at each time step. The STORE DHM is tested by simulating multiple hydrologic events in three watersheds in Indiana. In addition, the GHISMO framework is tested for its flexibility to adopt additional modules by implementing three rainfall bias correction methods to provide accurate input for the STORE DHM. Application of STORE DHM to multiple hydrologic events in three different watersheds in Indiana show that the model is able to predict runoff hydrographs for different types of events in terms of storm duration, peak flow magnitude and time–to– peak. In addition, STORE DHM output is compared with outputs from two hydrologic models including Hydrologic Engineering Center’s Hydrologic Modeling System (HEC– HMS) and time variant Spatially Distributed Direct Hydrograph travel time method (SDDH). Results from these comparisons show that the STORE DHM outperforms both HEC–HMS and SDDH in terms of overall hydrograph shape and flow magnitude. The flexibility of GHISMO framework is tested by extending it to include a rainfall bias correction module. The rainfall bias correction module is then used to correct NEXRAD radar rainfall by implanting two non–uniform bias correction techniques. Results from STORE DHM simulations using the original NEXRAD rainfall and bias-corrected rainfall created in this study shows that the model response is dictated by rainfall variations in the study area. The performance of STORE DHM output is relatively better in a larger watershed with high variable rainfall compared to a smaller watershed with uniform rainfall pattern. The findings from this study are limited by the number of watersheds used, and the quality of the data. More testing of the GHISMO framework and its modules is needed to make the proposed framework applicable for different watersheds with varying scales.

Degree

Ph.D.

Advisors

Merwade, Purdue University.

Subject Area

Geographic information science|Hydrologic sciences|Geological|Civil engineering

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