Laura Condon - US grants

This award supports the design and implementation of a software framework to simulate the movement of water at various scales. Understanding the movement and availability of water locally and across the country is of paramount importance to economic productivity and human health of our nation. Hydrologic scientists, are actively tackling these challenges using increasingly complex computational methods. However, modeling advances have not been easily translated to the broader community of scientists and professionals due to technical barriers to entry. This software platform draws from computer models and employs supercomputers capable of analyzing big data to provide unprecedented simulations of water movement over the continental US. Combining hydrologists and computer scientists the team behind the project envision a broad community of users who will have multiple ways to interact with the software framework. For the hydrologic scientist who is interested in generating their own scenarios the framework will facilitate direct interaction with the hydrologic models and the ability to generate simulations on the fly. Conversely, the framework will also provide a set of static output and a range of tools for a broader set of users who would like to evaluate hydrologic projections locally or extract model data for use in other analyses.

Continental scale simulation of water flow through rivers, streams and groundwater is an identified grand challenge in hydrology. Decades of model development, combined with advances in solver technology and software engineering have enabled large-scale, high-resolution simulations of the hydrologic cycle over the US, yet substantial technical and communication challenges remain. With support from this award, an interdisciplinary team of computer scientists and hydrologists is developing a framework to leverage advances in computer science transforming simulation and data-driven discovery in the Hydrologic Sciences and beyond. This project is advancing the science behind these national scale hydrologic models, accelerating their capabilities and building novel interfaces for user interaction. The framework brings computational and domain science (hydrology) communities together to move more quickly from tools (models, big data, high-performance computing) to discoveries. It facilitates decadal, national scale simulations, which are an unprecedented resource for both the hydrologic community and the much broader community of people working in water dependent systems (e.g., biological system, energy and food production). These simulations will enable the community to address scientific questions about water availability and dynamics from the watershed to the national scale. Additionally, this framework is designed to facilitate multiple modes of interaction and engage a broad spectrum of users outside the hydrologic community. We will provide easy-to-access pre-processed datasets that can be visualized and plotted using built-in tools that will require no computer science or hydrology background. Recognizing that most hydrology training does not generally include High Performance Computing and data analytics or software engineering, this framework will provide a gateway for computationally enhanced hydrologic discovery. Additionally, for educators we will develop packaged videos and educational modules on different hydrologic systems geared towards K-12 classrooms.

This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Cross-Cutting Activities Program of the Division of Earth Sciences within the NSF Directorate for Geosciences.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Water is critical for growing food and generating power. This study deals with two globally important agricultural systems, the Heihe River Basin in China and the Central Valley of California, USA, that exemplify the complexities of large scale water-energy systems. The Heihe and the Central Valley represent billions of dollars in economic productivity and produce billions of kilowatt hours of electricity every year. While the two basins are in many ways similar (water flows from high in the mountains to nourish crops below), there are key differences in their history and management that provides many important information. This project brings together researchers from the US and China to better understand tradeoffs between water and energy supply in these complex agricultural systems. Advantage is taken of computer simulations, datasets and research from US and Chinese teams in their local basins and collaborate to advance our shared understanding of these basins. The state of the art computer simulation platforms developed and applied here are designed to capture connections between humans and natural systems not possible with previous modeling approaches. This project also seeks to educate the next generation of water users, planners and scientists on groundwater sustainability by developing K-12 education materials for both the US and China that will be piloted in real classrooms in both countries. This project will help us better understand weaknesses in managed food-water-energy systems like the Heihe and Central Valley to strengthen them moving forward.

Water connects food production, energy demand and energy production in irrigated agricultural systems. Intensively managed basins routinely have surface water irrigation, groundwater irrigation and hydropower production operating in tandem. While there have been many operational studies of large scale irrigated systems, the majority of tools applied to these problems focus on the human systems and simplify the natural hydrology. This study bridges this gap developing novel tools that can simulate FEW interactions in complex human and natural systems. In this project leverage of international advances in physically based integrated numerical modeling is accomplished by bringing together two teams of modelers from the US and China. The goal is to explore the tradeoffs between agricultural water supply, hydropower production and environmental degradation in two globally important agricultural systems: the Central Valley of California (USA) and the Heihe River basin in China. Specifically, exploring (1) how the vulnerabilities of food and energy systems differ, (2) where conflicting interests can lead to system inefficiency and environmental degradation, and (3) the advantages of applying integrated hydrologic models to these human systems. The project also seeks to educate the next generation of water users, planners and scientists on groundwater sustainability. Project outputs will be used to develop K-12 education materials for both the US and China that will be piloted in real classrooms.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to utilize artificial intelligence methods such as machine learning (ML) to achieve better water management outcomes that directly benefit society by developing the ability to better plan for and manage extreme events through improved hydrologic forecasting. HydroFrame-ML is motivated by, and structured around, applied solutions for water management planning and decision making. Extreme events like drought and floods have far-reaching societal impacts. They are common, costly and likely to get worse in the future. The project team is partnered with the Bureau of Reclamation, which is the largest wholesale water provider in the country, providing water to more than 31 million people and 10 million acres of farmland. The Bureau of Reclamation will drive use case design and the metrics used to evaluate success in Phase 1, as well as partner in the expansion of the project team for Phase 2. Additionally, the project team will develop hands-on activities and challenges designed to give undergraduates experience in machine learning and data science, in the context of pressing real-world challenges. Aided by the planned addition of a STEM mentorship program partner in Phase 2, the team will build content with the vision of helping to broaden participation of underrepresented students well beyond the timeframe of this project.

The proposed project brings together the most physically rigorous national scale groundwater simulations developed through HydroFrame with national leaders in Earth Systems Modeling and water management. By providing end-to-end workflows combining state of groundwater science with operational management tools, HydroFrame-ML will advance both large-scale water management as well as our understanding of how human operations and groundwater interact in extreme events. Their products will provide innovative ways to improve forecasts and in the process will expand our knowledge about the (1) contributions of groundwater to extreme events in managed systems; (2) biases in our current risk-assessment approaches which do not consider groundwater; and (3) potential to improve long-term sustainability by more actively managing groundwater and accounting for groundwater surface water interactions in projections.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Water is the driving force behind extreme events like floods, droughts and wildfires. These events have cost the US $234.3B in damages just in the past three years, and this figure is projected to increase. Recent events like the record setting wildfires in California and the mega drought on the Colorado river are merely the latest illustrations. Historical data are no longer a reliable guide for the risks we will face in the future. This project addresses the uncertainty that poses a huge challenge for decision makers.

HydroGEN is a web-based machine learning (ML) platform that generates custom hydrologic scenarios on demand. It combines powerful physics-based simulations with ML and observations to provide customizable scenarios from the bedrock through the treetops. Without any prior modeling experience, water managers and planners can directly manipulate state-of-the-art tools to explore scenarios that matter to them.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.