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We present algorithms and tools we developed to automatically link an overland flow model to a hydrodynamic water quality model with different spatial and temporal discretizations. These tools run the linked models which provide a stochastic simulation frame. We also briefly present the tools and algorithms we developed to facilitate and analyze stochastic simulations of the linked models. We demonstrate the algorithms by linking the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model for overland flow with the CE-QUAL-W2 model for water quality and reservoir hydrodynamics. GSSHA uses a two-dimensional horizontal grid while CE-QUAL-W2 uses a two-dimensional vertical grid. We implemented the algorithms and tools in the Watershed Modeling System (WMS) which allows modelers to easily create and use models. The algorithms are general and could be used for other models. Our tools create and analyze stochastic simulations to help understand uncertainty in the model application. While a number of examples of linked models exist, the ability to perform automatic, unassisted linking is a step forward and provides the framework to easily implement stochastic modeling studies.

This study discusses research that develops a general framework and presents a specific implementation of a stochastic modelling system, using linked overland flow and routing simulation models. The specific implementation uses the Gridded Surface Sub-surface Hydrological Analysis (GSSHA) overland flow model, and the reservoir routing and quality model CE-QUAL-W2, to develop the stochastic modelling system. For stochastic simulations, modellers can define up to six GSSHA parameters for stochastic treatment, select the appropriate probability density functions and range, and determine the number of runs in the simulation. The tools described herein then create the correct input and output files, run the linked simulation models using the defined stochastic parameters, and aggregate the voluminous results. Interactive tools were developed to compute credible intervals from the results, and create reports that present the variability in a manner that is easily understood and communicated. Model set-up and development, stochastic and statistical parameter input, stochastic simulation execution and results analysis were implemented using the Watershed Modeling System, model pre-and postprocessing system. This study presents the tools, algorithms and user interfaces developed to implement the linked stochastic modelling system, as well as a simple example demonstrating the tools and the type of analysis supported by this system.

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