Interactive Ground Water Modeling Using Grid Computing Case Study – India (Garuda)

Interactive Ground Water Modeling Using Grid Computing Case Study – India (Garuda)

Landage Pravin S., Landage Jeetkumar S.

Landage Pravin S., Landage Jeetkumar S. "Interactive Ground Water Modeling Using Grid Computing Case Study – India (Garuda)" Published in International Journal of Trend in Research and Development (IJTRD), ISSN: 2394-9333, Volume-2 | Issue-3 , June 2015, URL:

Developments in numerical groundwater modelling have shown that models become more and more ambitious with increasing computer capacity to meet the need for accurate instruments to support decision-making, both scale and resolution of models have grown enormously during last 10 years. Country need to development of a methodology for interactive planning for water management.The challenge to develop a high-resolution numerical groundwater model for the whole country. This model contain data of whole country from last 10 years of daily groundwater fluctuations had to be simulated both running and calibrating much a large model require innovations in model building, model processing and data handling. data- compression techniques were required to store all input and output data. to run the model, both up scaling and model-decomposition techniques were developed. for a transient run (over 4500 time steps) on the highest resolution, the model was decomposed into several overlapping sub models. transient boundary conditions of the sub models were taken from a lower-resolution model. each sub model could be run individually, so the process was perfectly suited for parallel processing. Therefore, we developed a computational grid using the 200 computers available in our office. the moment employees logged off, their computer came available for the grid. Obviously the weekends appeared to be the most productive days! the grid was also crucial for model calibration. We used the represented method for calibrating model parameters in a stationary mode. The represented node method requires a forward run and an adjoint run each iteration to calculate the so-called represented of each observation. in total more than situated groundwater observation locations were available and hence several stations runs had to be carried out. each represented node run was distributed over the grid using pvm (parallel virtual machine).Grid computing revealed itself as the only way to complete the whole project within reasonable time. Total CPU time of model calibration and running (ca. 50 runs during model-construction process) was estimated at more than 20 years using grid computing, the calculation time was reduced to several months.In addition to model calibration and model running, grid computing is also helpful in data-assimilation applications. in a preliminary study, ensemble kalman filtering techniques were applied for now casting and forecasting of groundwater fluctuations using assimilated groundwater. Model states were estimated by calculating umber of ensembles distributed over the grid. Subsequently, 10-day forecasts of groundwater levels were calculated by processing 50 ensembles of the ensemble prediction system can be calculated by the IITM centres across country for medium-range weather forecasts. as the intention is to produce forecasts on daily basis, a computational grid is necessary to run all ensembles within one day.

Water Modeling, Grid Computing

Volume-2 | Issue-3 , June 2015


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