This project advances hydrologic science and water resource management by uniquely leveraging cloud computing for modeling and managing large watershed systems. The first project task creates a cloud-enabled hydrologic model, the second project task creates generic cloud-based data processing workflows needed by hydrologic models and other domains, and the third project task applies the hydrologic model and data processing workflows to model a large watershed system at detail and scale to address research questions related to quantifying impacts of climate change on water resources. This research project addresses fundamental issues with regard to understanding how a cloud computing paradigm can and should be used to model hydrologic systems and other scientific applications.
The work has the potential to produce broad impacts because of the importance of clean water to society, the need for interdisciplinary education between the hydrologic and computer sciences, and through the creation of open source software that will extend existing software provided through the NSF-funded CUAHSI Hydrologic Information System (HIS) project. This research provides important and timely interdisciplinary training for a graduate student and a postdoctoral scholar so that they are able to address complex water resource problems using cutting edge computer science techniques.
Understanding hydrologic systems at the scale of large watersheds is of critical importance to society when faced with extreme events such as floods and droughts, or with minimizing human impacts on water quality. Climate change and increasing population are further complicating watershed-scale prediction by placing additional stress and uncertainty on future hydrologic system conditions. New data collection and management approaches are allowing models to capture water flow through built and natural environments at an increasing level of detail. A significant barrier to advancing hydrologic science and water resource management is insufficient computational infrastructure to leverage these existing and future data resources within simulation models. This project advanced hydrologic science and water resource management by uniquely leveraging cloud computing for modeling and managing large watershed systems. The main computational capability we created involved watershed model calibration. This process presents computational challenges because it requires running the hydrologic hundreds or thousands of times with different parameter values in order to estimate reasonable parameter values for a particular watershed system. We viewed model calibration as an ideal first task for our work, given these computational challenges faced by hydrologists. To overcome this challenge, we designed and implemented software to perform the task of watershed model calibration. The software is able to complete approximately 1000 iterations and in each iteration a specific set of model parameters is evaluated by comparing the model estimation of streamflow against known physical measurements of streamflow at the watershed outlet. We are currently investigating ways by which to make this capability more widely available beyond the life of this research project. We also prototyped two additional capabilities: large-scale model simulation on cloud resources and cloud workflows for data preparation for watershed modeling. The major findings and outcomes of this project were research papers and a scientific portal for running watershed model calibration on the cloud. This project was also used as the basis for discussions and projects in the undergraduate Computer Science curriculum at the University of Virginia.
