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

该项目寻求有关景观和河流之间联系的基本问题的答案，对如何测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，利用美国东部的大陆尺度数据预测水质的空间和时间模式。将利用与管理机构的强有力的现有伙伴关系来传播研究结果，并将其综合为有效和高效设计水质监测方案的原则。将与全球伙伴协作，反复制定这些方法，并通过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受众的科学传播培训。最后，将努力从代表性不足的群体中招募和留住学生和博士后参与者。流溶质信号编码上游源，景观过滤和网络转换的信息，但一个全面的预测理解仍然是难以捉摸的。这项工作旨在了解水质模式的时空变异的起源，回答以下具体问题：什么是流溶质的空间与时间变异的相对重要性？河流溶质时空变化的驱动因素是什么？主要重点是三个对比溶质组（地质，生物，人为）与地理，人为，水文和气候驱动因素的不同联系。将利用横跨美国东部的大陆尺度数据清单，评估一系列关于控制时空变化模式的预测，这些数据涵盖纬度和水文气候区、岩性、湿地和森林覆盖以及人类土地利用强度的大梯度。该项目旨在解释所有三个溶质组观察到的平均浓度的实质性空间变化，根据耦合的溶质特定的生态区，网络位置和溶质源区的影响。预计平均浓度会因总体景观特性（如湿地覆盖率或农业土地使用密度）和平均气候条件（如干旱和温度）而异。据推测，时间上的变化周围的平均值可以理解和解释的关键驱动因素，如流量变化，季节性和网络的位置，虽然溶质特定的敏感性。这一空间与时间的变化框架为总结、理解和预测水质变化提供了一个新的模板。该奖项反映了NSF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。

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.