Integrated water resources management has been used for decades in various formats. The limited resources and the ever growing population keep imposing pressure on decision makers to better-, and reliably, manage the available waters. On the other hand, the continuous development in computing and modeling power has helped modelers and decision makers considerably. To use these models, assumptions have to be made to fill in the gaps of missing data and to approximate the current conditions. The type and amount of information available can also be used to help select the best model from the currently available models. Advances in data collection have not kept up to the pace of advances in model development and the need for more and reliable input parameter values. Hence, uncertainty in model input parameters also needs to be quantified and addressed. This research effort develops a spatially-based modeling framework to model watersheds from both water quantity and quality standpoints. In this research, Gridded Surface Sub-Surface Hydrologic Analysis (GSSHA) and CE-QUAL-W2 models are linked within the Watershed Modeling System (WMS); a GIS interface for hydrologic and hydraulic models, to better handle both models pre and post processing. In addition, stochastic analysis routines are developed and used to examine and address the uncertainty inherent in the modeling process of the interface between land and water in the designated watershed. The linkage routines are developed in WMS using C++. The two models are linked spatially and temporally with the general direction of data flow from GSSHA to CE-QUAL-W2. Pre-processing of the CE-QUAL-W2 model is performed first. Then stochastic parameters and their associated distributions are defined for stochastic analysis in GSSHA before a batch run is performed. GSSHA output is then aggregated by CE-QUAL-W2 segments to generate multiple CE-QUAL-W2 runs. WMS then reads the stochastic CE-QUAL-W2 runs upon successful completion for data analysis. Modelers need to generate a WMS Gage for each location where they want to examine the stochastic output. A Gage is defined by a segment and a layer in the CE-QUAl-W2 model. Once defined, modelers are able to view a computed credible interval with lower, upper bounds in addition to the mean time series of a pre-selected constituent. Decision makers can utilize this output to better manage watersheds by understanding and incorporating the spatio-temporal uncertainty for the land-water interface.
College and Department
Ira A. Fulton College of Engineering and Technology; Civil and Environmental Engineering
BYU ScholarsArchive Citation
Salah, Ahmad Mohamad, "Stochastic Spatio-Temporal Uncertainty in GIS-Based Water Quality Modeling of the Land Water Interface" (2009). Theses and Dissertations. 1837.
water resources, water quality, GIS, uncertainty, modeling, stochastics, Monte Carlo Simulation, model linkage, WMS, GSSHA, CE-QUAL-W2