Date of Award
Doctor of Philosophy (PhD)
Civil & Environmental Engineering
A. Osman Akan
Correctly and efficiently estimating hydrological responses corresponding to a specific storm event at the streams in a watershed is the main goal of any sound water resource management strategy. Methods for calculating a stream flow hydrograph at the selected streams typically require a great deal of spatial and temporal watershed data such as geomorphological data, soil survey, landcover, precipitation data, and stream network information to name a few. However, extracting and preprocessing such data for estimation and analysis is a hugely time-consuming task, especially for a watershed with hundreds of streams and lakes and complicated landcover and soil characteristics. To deal with the complexity, traditional models have to simplify the watershed and the streams network, use average values for each subcatchment, and then indirectly validate the model by adjusting the parameters through calibration and verification.
To obviate such difficulties, and to better utilize the new, high precision spatial/temporal data, a new massive spatiotemporal watershed hydrological storm event response model (MHSERM) was developed and implemented on ESRI ArcMap platform. Different from other hydrological modeling systems, the MHSERM calculated the rainfall run off at a resolution of finer grids that reflects high precision spatial/temporal data characteristics of the watershed, not at conventional catchment or subcatchment scales, and that can simulate the variations of terrain, vegetation and soil far more accurately. The MHSERM provides a framework to utilize the USGS DEM and Landcover data, NRCS SSURGO and STATSGO soil data and National Hydrology Dataset (NHD) by handling millions of elements (grids) and thousands of streams in a real watershed and utilizing the Spatiotemporal NEXRAD precipitation data for each grid in pseudo real-time. Specifically, the MHSERM model has the following new functionalities: (1) Grid the watershed on the basis of high precision data like USGS DEM and Landcover data, NRCS SSURGO and STATSGO soil data, e.g., at a 30 meter by 30 meter resolution; (2) Delineate catchments based on the USGS National Digital Elevation Model (DEM) and the stream network data of the National Hydrography Dataset (NHD); (3) Establish the stream network and routing sequence for a watershed with hundreds of streams and lakes extracted from the National Hydrography Dataset (NHD) either in a supervised or unsupervised manner; (4) Utilize the NCDC NEXRAD precipitation data as spatial and temporal input, and extract the precipitation data for each grid; (5) Calculate the overland runoff volume, flow path and slope to the stream for each grid; (6) Dynamically estimates time of concentration to the stream for each interval, and only for the grids with rainfall excess, not for the whole catchment; (7) Deal with different hydrologic conditions (Good, Fair, Poor) for landcover data and different Antecedent Moisture Condition (AMC); (8) Process single or a series of storm events automatically; thus, the MHSERM model is capable of simulating both discrete and continuous storm events; (9) Calculate the temporal flow rate (i.e., hydrograph) for all the streams in the stream network within the watershed, save them to a database for further analysis and evaluation of various what-if scenarios and BMP designs.
In MHSERM model, the SCS Curve number method is used for calculating overland flow runoff volume, and the Muskingum-Cunge method is used for flow routing of the stream network.
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"Massive Spatiotemporal Watershed Hydrological Storm Event Response Model (MHSERM) with Time-Lapsed NEXRAD Radar Feed"
(2008). Doctor of Philosophy (PhD), Dissertation, Civil & Environmental Engineering, Old Dominion University, DOI: 10.25777/0k3b-h287