Integration of numerical model and cloud computing

With the significant advancements in Information and Communications Technology (ICT), cloud based applications provide a novel approach to access applications which are not installed on the local computers. The integration of cloud computing and Internet of Things (IoT) indicated a bright future of the Internet. In this paper, a new architecture of cloud computing-Model as a Service (MaaS) is proposed. The feasibility of the proposed architecture is proved by implementing a groundwater model on cloud as a case study. The groundwater model is established using MODFLOW for the middle reach of the Heihe River Basin (HRB). The model is calibrated using in situ observation to ensure capability of simulating the groundwater process with Root Mean Square Error (RMSE) of 1.70 m and coefficient of determination (R2) of 0.64. The parameter uncertainties of the groundwater model are analyzed by sequential data assimilation algorithms (PF, Particle Filter; EnKF, Ensemble Kalman Filter) in a synthetic case. The results show that the parameter uncertainties are effectively reduced by incorporating observed information recursively. A comparison between PF and EnKF indicate that the results from PF are slightly better than those from EnKF. The integration shows a bright future for simulating the groundwater system in real-time. This study provides a flexible and effective approach for analyzing the uncertainties and time variant properties of the parameters and the proposed architecture of cloud computing provides a novel approach for the researchers and decision-makers to construct numerical models and follow-up researches.