The increasing interest in larger spatial and temporal scale models and high resolution input data processing comes at a price of higher computational demand. This price is evidently even higher when common modeling routines such as calibration and uncertainty analysis are involved. Likewise, methods and techniques for reducing computation time in large scale socio-environmental modeling software is growing. Recent advancements in distributed computing such as Grid infrastructure have provided further opportunity to this effort. In the interest of gaining computational efficiency, we developed generic tools and techniques for enabling the Soil and Water Assessment Tool (SWAT) model application to run on the EGEE (Enabling Grids for E-science projects in Europe) Grid. Various program components/scripts were written to split a large scale hydrological model of the Soil and Water Assessment Tool (SWAT), to submit the split models to the Grid, and to collect and merge results into single output format. A three-step procedure was applied to take advantage of the Grid. Firstly, a python script was run in order to split the SWAT model into several sub-models. Then, individual sub-models were submitted in parallel for execution on the Grid. Finally, the outputs of the sub-basins were collected and the reach routing process was performed with another script executing a modified SWAT program. We conducted experimental simulations with multiple temporal and spatial scale hydrological models on the Grid infrastructure. Results showed that, in spite of computing overheads, parallel computation of socio-environmental models on the Grid is beneficial for model applications especially with large spatial and temporal scales. In the end, we conclude by recommending methods for further reducing computational overheads while running large scale model applications on the Grid.

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The Soil and Water Assessment Tool (SWAT) has been used widely for large scale applications, reaching entire continents. Within the EU funded EnviroGrids project, a detailed application of SWAT on the Black Sea Basin is envisaged using high resolution data. In order to support the computation, the model is run on a computer grid. The use of the SWAT allowed for such computations with little adaptations to the source. A 3-step procedure is needed. In the first step, a program is run in order to split the model into several sub-models. Afterwards, the sub-models are run in parallel. In a last step, the outputs of the sub-basins are collected at a central computer and the routing is performed. High computations are also needed when simulations have to be repeated, such as for sensitivity, calibration and uncertainty analysis. In these cases, the simulations are repeated for different parameter sets. In this paper, we discuss the gridification of the algorithm "LH-OAT" that performs sensitivity analysis and has been linked to the SWAT model. The results show a clear improvement in calculation time. Nevertheless, it is concluded that the parallel computing of a distributed model is mainly beneficial for large scale applications with high resolution, while running the sensitivity analysis algorithm has more general and obvious benefit. In a next step, the gridification will be optimised depending on the application and the overheads that are due to submission and receiving of files, as well as potential waiting times for executions on the grid.@en