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

该项目的目标是提高对气候和流域特征如何相互作用以控制溶质和沉积物出口并最终控制水质的理解。从流域向河网和下游接收生态系统输出的材料受到无数因素的影响，包括气候、岩性、水文、土壤类型和孔隙度以及植被等。这些因素及其复杂的相互作用使得开发流域溶质和沉积物输出的预测模型变得困难，并且合成大陆尺度分析所需的数据充满了计算挑战。EPSCoR轨道4项目的总体愿景是综合和分析空间和时间上丰富的数据集，以更全面地了解包括气候变率在内的广泛环境梯度下物质出口（以浓度-排放关系表示：C-Q）的变化。了解这些过程对水质和确定流域和生态系统如何应对基于气候的扰动具有直接意义。研究结果将通过同行评议的手稿、国际会议和一系列部门研讨会进行分享。这项工作为一名早期教师和一名女性博士后学者提供了独特的培训机会。浓度-排放（C-Q）关系的分析提供了一种整体和系统级的方法来评估材料在流域内的储存位置，以及调节这些材料从景观到河流网络的动员的内部和外部控制。然而，将C-Q行为放入预测框架是极具挑战性的，因为因素随时间和空间而变化。传统的数据综合和分析方法可能不足以充分利用新的数据资源，也不足以量化、预测和描述地球上的复杂动态。年代的表面。该项目将通过采用大数据和数据综合方法来解决这一挑战：1)建立碳q行为的预测大陆尺度模型；2)了解碳q行为如何响应气候变化。该项目将通过检查多种预测算法的有效性来推进该领域，包括基于机器学习的方法，这些方法可能能够处理与大环境数据相关的独特挑战。该项目的成果将有助于刺激新罕布什尔大学的竞争性研究，包括未来使用高频环境传感器技术。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.