Type of Document Master's Thesis Author McCormick, Brian Christopher Author's Email Address email@example.com URN etd-04042003-113102 Title Distributed Hydrologic Modeling of the Upper Roanoke River Watershed using GIS and NEXRAD Degree Master of Science Department Civil Engineering Advisory Committee
Advisor Name Title Dymond, Randel L. Committee Co-Chair Kibler, David F. Committee Co-Chair Heatwole, Conrad D. Committee Member Keywords
- Distributed Hydrologic Modeling
Date of Defense 2003-03-27 Availability unrestricted AbstractPrecipitation and surface runoff producing mechanisms are inherently spatially variable. Many hydrologic runoff models do not account for this spatial variability and instead use "lumped" or spatially averaged parameters. Lumped model parameters often must be developed empirically or through optimization rather than be calculated from field measurements or existing data. Recent advances in geographic information systems (GIS) remote sensing (RS), radar measurement of precipitation, and desktop computing have made it easier for the hydrologist to account for the spatial variability of the hydrologic cycle using distributed models, theoretically improving hydrologic model accuracy.
Grid based distributed models assume homogeneity of model parameters within each grid cell, raising the question of optimum grid scale to adequately and efficiently model the process in question. For a grid or raster based hydrologic model, as grid cell size decreases, modeling accuracy typically increases, but data and computational requirements increase as well. There is great interest in determining the optimal grid resolution for hydrologic models as well as the sensitivity of hydrologic model outputs to grid resolution.
This research involves the application of a grid based hydrologic runoff model to the Upper Roanoke River watershed (1480km2) to investigate the effects of precipitation resolution and grid cell size on modeled peak flow, time to peak and runoff volume. The gridded NRCS curve number (CN) rainfall excess determination and ModClark runoff transformation of HEC-HMS is used in this modeling study. Model results are evaluated against observed streamflow at seven USGS stream gage locations throughout the watershed.
Runoff model inputs and parameters are developed from public domain digital datasets using commonly available GIS tools and public domain modeling software. Watersheds and stream networks are delineated from a USGS DEM using GIS tools. Topographic parameters describing these watersheds and stream channel networks are also derived from the GIS. A gridded representation of the NRCS CN is calculated from the soil survey geographic database of the NRCS and national land cover dataset of the USGS. Spatially distributed precipitation depths derived from WSR-88D next generation radar (NEXRAD) products are used as precipitation inputs. Archives of NEXRAD Stage III data are decoded, spatially and temporally registered, and verified against archived IFLOWS rain gage data. Stage III data are systematically degraded to coarser resolutions to examine model sensitivity to gridded rainfall resolution.
The effects of precipitation resolution and grid cell size on model outputs are examined. The performance of the grid based distributed model is compared to a similarly specified and parameterized lumped watershed model. The applicability of public domain digital datasets to hydrologic modeling is also investigated.
The HEC-HMS gridded SCS CN rainfall excess calculation and ModClark runoff transformation, as applied to the Upper Roanoke watershed and for the storm events chosen in this study, does not exhibit significant sensitivity to precipitation resolution, grid scale, or spatial distribution of parameters and inputs. Expected trends in peak flow, time to peak and overall runoff volume are observed with changes in precipitation resolution, however the changes in these outputs are small compared with their magnitudes and compared to the discrepancies between modeled and observed values. Significant sensitivity of runoff volume and consequently peak flow, to CN choices and antecedent moisture condition (AMC) was observed. The changes in model outputs between the distributed and lumped versions of the model were also small compared to the magnitudes of model outputs.
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