EAR-Climate: Continental-Scale Determinants of Stream Solute Spatial and Temporal Patterns

该项目寻求关于景观和河流之间联系的基本问题的答案，这些问题对如何衡量和管理水质具有广泛的社会影响，因此与环境管理和恢复有关。将开发统计模型，利用美国东部大陆规模的数据预测水质的时空模式。将利用与管理机构现有的强大伙伴关系来传播调查结果，并将其综合为有效和高效地设计水质监测计划的原则。这些方法将与全球合作伙伴合作反复开发，并由研究讲习班促进交流。从事这项工作的学生将接受面向普通观众的科学传播方面的培训。最后，将努力从代表性不足的群体中招聘和留住学生和博士后参与者。河流溶质信号编码了关于上游来源、景观过滤和网络改造的信息，但全面的预测性理解仍然难以捉摸。这项工作试图理解水质模式中时空变异性的起源，回答以下具体问题：河流溶质空间变异性与时间变异性的相对重要性是什么？水系溶质时空变异性的驱动因素是什么？主要集中在三个不同的溶质组(地源、生物、人为)上，它们与地理、人为、水文和气候驱动因素有不同的联系。将使用美国东部大陆规模的数据清单来评估一系列关于时空变化模式控制的预测，这些数据清单涵盖了纬度和水文气候带、岩性、湿地和森林覆盖以及人为土地利用强度的大梯度。该项目试图解释在生态区、网络位置和溶质源区的耦合溶质特定影响的基础上，观察到的所有三种溶质组的平均浓度的显著空间差异。平均浓度预计会随着湿地覆盖或农业土地利用强度等综合景观属性以及平均气候条件(如干旱和温度)的不同而变化。假设平均值附近的时间变异性可以用流量变化、季节性和网络位置等关键驱动因素来理解和解释，尽管具有特定于溶质的敏感性。这一时空变化框架为总结、理解和预测水质变化提供了一个新的模板。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project seeks answers to foundational questions about links between landscapes and rivers, with broad societal implications for how water quality is measured and managed, and thus is relevant to environmental stewardship and restoration. Statistical models will be developed to predict spatial and temporal patterns in water quality using continental-scale data across the eastern United States. Strong existing partnerships with management agencies will be leveraged to disseminate findings, synthesized as principles for effective and efficient design of water quality monitoring programs. The approaches will be iteratively developed in collaboration with global partners, with the exchange facilitated by research workshops. Students engaged in this work will be trained in science communication for general audiences. Finally, efforts will be made to recruit and retain student and post-doc participants from underrepresented groups. Stream solute signals encode information about upstream sources, landscape filtering, and network transformations, yet a comprehensive predictive understanding remains elusive. This work seeks to understand the origins of spatial vs temporal variability in water quality patterns, answering the following specific questions: What is the relative importance of stream solute spatial vs. temporal variability? What are the drivers of stream solute spatial and temporal variability? The primary focus is on three contrasting solute groups (geogenic, biogenic, anthropogenic) with varying links to geographical, anthropogenic, hydrological, and climatological drivers. A series of predictions about controls on patterns of space-time variation will be evaluated, using a continental-scale data inventory across the eastern United States, spanning large gradients in latitude and hydroclimate zones, lithology, wetland and forest cover, and anthropogenic land use intensity. The project seeks to explain the substantial spatial variation in mean concentrations observed for all three solute groups, based on the coupled solute-specific influences of ecoregion, network position, and solute source area. Mean concentrations are expected to vary with aggregated landscape properties, such as wetland coverage or agricultural land use intensity, and mean climate conditions, such as aridity and temperature. It is hypothesized that temporal variability around the mean can be understood and explained in terms of key drivers such as flow variation, seasonality, and network position, albeit with solute-specific sensitivities. This spatial vs. temporal variance framework provides a novel template for summarizing, understanding, and predicting variation in water quality.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.