Distributed computation of large scale SWAT models on the Grid

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Abstract

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.

Section snippets

Software availability

The codes and tools developed in this research are freely available through the GNU general public license (http://www.gnu.org/copyleft/gpl.html) for the SWAT user community as well as for the general public. They can be accessed through the following ftp link: ftp://ftp.ihe.nl/SWAT-GRID/ with user name and password combinations of ftpguest and fi3tsb3l, respectively.

The Soil and Water Assessment Tool (SWAT)

The Soil and Water Assessment Tool (SWAT)(Arnold et al., 1998; Neitsch et al., 2005) is an increasingly popular watershed modeling software. It is a physically-based model that can simulate water quality and quantity at a watershed scale. The spatial scales of the problems analyzed with this modeling software have increased to a remarkably large degree over time. The whole of continental USA (Arnold et al., 1999) and the whole of Africa (Schuol et al., 2008) were modeled on separate occasions

Test models: setup and job submission

Three separate SWAT models were used for demonstration of the parallelization process: the Nzoia catchment model in Kenya, the Lake Balaton catchment model in Central Europe (Hungary) and the trans-boundary Danube catchment model in the Black Sea Basin (Fig. 5). The Nzoia catchment model, presented in Fig. 1, includes 7 sub-basins and contains data to simulate 43 years. The Lake Balaton catchment model contains 204 sub-basins with a simulation period of 16 years whereas the Danube River Basin

Results and discussion

First, each of the models were setup with single HRU per sub-basin and tested computation time both with Approach I (where all sub-basins are computed in parallel followed by all reaches in series) and with Approach II (where all sub-basins and only upstream reaches are computed in parallel, followed by downstream reaches). As shown in Table 1, the Nzoia catchment shows a speedup ratio of 2.96 in computation (which means that this execution setup is 2.96 faster than the original model if it was

Conclusion

The ‘gridification’ of the Soil and Water Assessment Tool demonstrates the use of emerging Grid technologies and infrastructures for hydrological and environmental modeling purposes. Experimental results show a remarkable improvement of performance in computation time. Large scale and time intensive hydrological/environmental models with higher resolution (e.g. multiple HRUs per sub-basin) and longer simulation periods make the most of the Grid computing infrastructure. In addition to reducing

Acknowledgments

The authors would like to acknowledge the European Commission “Seventh Framework Programme” that funded the enviroGRIDS project (Grant Agreement n° 227640). In addition, heartily thank you goes to the editor and the anonymous reviewers of this manuscript for their meticulous analysis and feedback during the peer-review process.

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