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As discussions swirl over the nation鈥檚 water usage from AI data centers to agriculture and everything in between, researchers at UNH are working to ensure the data about our ever-changing aquatic resources are easier to access without a supercomputer or a Ph.D. in the water cycle.

UNH is leading a $600,000听Cyberinfrastructure for Sustained Scientific Innovation grant from the National Science Foundation (NSF) that supports the development an open computational system, called the OpenGHM platform. OpenGHM aims to accelerate innovation in water resources research, says UNH research assistant professor in the 听and lead PI on the project. Grogan is leading a team of five scientists at UNH and two researchers at Purdue University over the next three years to build and test the new platform.

鈥淭o better protect water supplies, food and energy production, and infrastructure, it is in the national interest to provide reliable information so stakeholders can make informed decisions regarding the role of people in the water cycle,鈥� Grogan says.

Global Hydrologic Models (GHMs) are computer models frequently used by those in the water resources research community that compile huge amounts of data 鈥� things like daily weather, soil type, vegetation, impervious surfaces, human populations, reservoirs and canals, crops and irrigation, and so forth, Grogan explains. Researchers use GHMs, designed to simulate both natural water flows along with a wide range of human interactions on water bodies, to study large-scale changes and impacts to water resources.

However, to operate GHMs, scientists must currently download incredibly large datasets, run the model simulations on a local high-powered computer, and then upload their results to public repositories. This can take many hours 鈥� or even days 鈥� to accomplish, even on the most capable computers. OpenGHM will remove this time bottleneck by leveraging existing NSF-supported听computing resources to enable scientists to directly access data, models, and surrounding workflows on efficient, cloud-based storage systems, Grogan says.

The grant will provide more training and collaboration opportunities so that scientists, resource managers, and other decision makers can more rapidly develop and test hydrologic computer models that run simulations of changes to water sources and uses. In addition, it supports scientific transparency by allowing anyone to more easily and quickly reproduce the data, demonstrate the workflow, and ensure its accuracy. Ultimately, it鈥檚 all about enabling collaborative science to more effectively study the changes in hydrology.