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输出并最终控制水质的理解。物质从集水区向河流网络和下游接收生态系统的输出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流域溶质和沉积物输出的预测模型的开发变得困难，并且合成大陆尺度分析所需的数据具有计算挑战性。这个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，以更全面地了解材料出口（表示为浓度-排放关系：C-Q）如何在包括气候变化在内的广泛环境梯度中变化。了解这些过程对水质和确定流域和生态系统如何应对气候扰动具有直接影响。结果将通过同行评审的手稿，国际会议和一系列部门研讨会共享。拟议的工作提供了独特的培训机会，为早期的职业教师和女博士后scholer.The浓度-排放（C-Q）关系的分析提供了一个整体和系统级的方法来评估材料存储在一个流域和内部和外部的控制，规范动员这种材料从景观到河流网络。然而，将C-Q行为纳入预测框架是极具挑战性的，因为因素会随空间和时间而变化。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，并量化，预测和描述地球上复杂的动态？s表面。该项目将通过采用大数据和数据合成方法来应对这一挑战，以1）建立C-Q行为的预测性大陆尺度模型，2）了解C-Q行为如何对气候变化做出反应。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促进新罕布什尔州大学的竞争性研究，包括未来使用高频环境传感器技术。该奖项反映了NSF的法定使命，并被认为值得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来支持。

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.