RII Track-4: Canary in the Watershed: Concentration-Discharge Relationships as a Sentinel of Change

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The goal of this project is to improve understanding of how climate and watershed characteristics interact to control solute and sediment export and ultimately water quality. The export of material from watersheds to river networks and downstream receiving ecosystems is influenced by a myriad of factors including climate, lithology, hydrology, soil type and porosity and vegetation, among others. These factors and their complex interactions make developing predictive models of watershed solute and sediment export difficult and synthesizing the required data for continental-scale analyses is wrought with computational challenges. The overarching vision of this EPSCoR Track 4 project is to synthesize and analyze spatially and temporally rich data sets to develop a more holistic understanding of how material export (expressed as concentration-discharge relationships: C-Q) changes across broad environmental gradients including climatic variability. Understanding these processes has direct implications for water quality and determining how watersheds and ecosystems will respond to climate-based perturbations. Results will be shared through peer-reviewed manuscripts, international conferences, and a series of departmental seminars. The proposed work offers unique training opportunities for an early-career faculty member and a female post-doctoral scholar.The analysis of concentration-discharge (C-Q) relationships provides a holistic and systems-level approach to assess where material is stored within a watershed and the internal and external controls that regulate the mobilization of this material from the landscape to river networks. Putting C-Q behavior into a predictive framework is extremely challenging however, as factors vary with space and time. Traditional approaches to data synthesis and analytics may be inadequate to fully take advantage of novel data resources and to quantify, predict and describe complex dynamics at the earth?s surface. This project will address this challenge by taking a big data and data synthesis approach to 1) build predictive continental-scale models of C-Q behavior and 2) to understanding how C-Q behavior responds to climate variability. This project will advance the field by examining the efficacy of multiple predictive algorithms including machine learning based approaches which may be able to handle the unique challenges associated with big environmental data. The outputs of this project will help to stimulate competitive research at the University of New Hampshire including the future use of high-frequency environmental sensor technology.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.