The vision of this research aims to integrate and implement, in a real world watershed, the concepts of real-time distributed sensor networks, high performance computing and cyberinfrastructure, and advanced simulation models within a connected framework. This framework needs to recognize the inherent uncertainty and imprecision in our ability, even with advanced tools, to completely understand complex systems and predict future implications of natural drivers and human decisions. This uncertainty increases with both spatial and temporal distance (ie, space/time scales) from the given decision.
Imagine the following scenario for a watershed. Based on real time sensor array input (essentially measuring the "pulse" of the watershed); combined with advanced surface-groundwater based models that are executed within a high performance computing environment (supported by cyberinfrastructure enabled grid computing); and linked to a visualization system that integrates information at various levels from local to regional to national; such that a decision maker can manage a group of irrigation wells pumping within a given region of the watershed. At the same time, consideration is given to the community-water cycle interface in which irrigation impacts on community water supply, and community pumping rate is tied to arsenic concentration levels. Thus, the natural system is tightly linked to the human system, and decisions result in a feedback to the natural system. In the near term, these decisions, which are based on multiple inputs and projections, will have more certainty associated with them, than in the longer term due to uncertainty in our ability to predict the impacts at future times. Moreover, the decision to increase irrigation well pumping will have a more defined impact at the field scale in which the irrigation pumping takes place, with increasing levels of uncertainty as the scale is increased to watershed level. As these translations occur (ie, space/time scale), the uncertainty will increase, and the ability of the information system to adapt to new information will be paramount in its ultimate usefulness. Thus, our hydrologic information system will need to be adaptive in form and function.This research project will seek to implement this concept on a pilot scale in a catchment of the Republican Basin. The project will lead to a research-based “proof-of-concept” for integrated water resources management that includes distributed, real-time sensor networks, simulation modeling and visualization in a high performance computing (HPC) environment, and cyberinfrastructure to integrate the key components. The distributed, real-time sensor network will focus on surface water discharge, groundwater level, precipitation data, irrigation water application rate, community pumping rate, and arsenic concentration in the community water supply. The modeling and visualization within a HPC setting will focus on simulation of watershed hydrology with consideration for 1-D streamflow, 2-D overland flow and 3-D groundwater. The cyberinfrastructure research component will focus on bringing these key system elements together into an integrated system.