Calibration of watershed models using cloud computing

Abstract

Understanding hydrologic systems at the scale of large watersheds and river basins is critically important to society when faced with extreme events, such as floods and droughts, or with concerns about water quality. A critical requirement of watershed modeling is model calibration, in which the computational model's parameters are varied during a search algorithm in order to find the best match against physically-observed phenomena such as streamflow. Because it is generally performed on a laptop computer, this calibration phase can be very time-consuming, significantly limiting the ability of a hydrologist to experiment with different models. In this paper, we describe our system for watershed model calibration using cloud computing, specifically Microsoft Windows Azure. With a representative watershed model whose calibration takes 11.4 hours on a commodity laptop, our cloud-based system calibrates the watershed model in 43.32 minutes using 16 cloud cores (15.78x speedup), 11.76 minutes using 64 cloud cores (58.13x speedup), and 5.03 minutes using 256 cloud cores (135.89x speedup). We believe that such speed-ups offer the potential toward real-time interactive model creation with continuous calibration, ushering in a new paradigm for watershed modeling.