CAREER: Unveiling the role of catchment physiography in the hydrologic response of headwater streams

水质和可获得性对社会和野生动物的可持续性至关重要。然而，我们仍然无法预测水在河流中从降水到溪流的路径，也无法预测这个过程需要多长时间。更深入的了解对于预测总体水质和供应至关重要。对于源头溪流尤其如此，因为它们的输入控制着较大淡水系统的水量和水质。虽然人们普遍认为，水可以采用的流动路径取决于可用的集水储存，即地下“水桶”的大小，但目前还不可能衡量这个储存有多大。该项目将调查地质、地貌和地形之间的关系，以及根据水稳定同位素等水文示踪剂得出的储存可用性。这项调查将提高社会适应和确保在改变的水文条件下可持续供应清洁水的能力，这种改变很可能是人类活动(森林采伐、城市化)和气候变化的后果。通过这一项目，将与俄勒冈州科学和工业博物馆建立关系，开展实践活动，并向公众传授气候变化背景下的水资源供应情况。本研究的目的是利用LiDAR的蓄水潜力和示踪数据，了解水源流域降雨径流产生量的时空变异性。这项研究将在大范围(0.1-62平方公里)集水区的不同地貌和地质条件下的源头溪流中进行。该方法将结合水稳定同位素和电导率作为示踪剂，由NSF LTER站点收集的水文数据，以及流域规模模型中的LiDAR，以预测事件通过时间和未监测地点的储存。这项研究有可能改变目前估算蓄水量的方法，并将其纳入多尺度水文模型。这项研究将形成一个教育项目的结构，让公众参与到提高他们对气候变化背景下水运动的理解的活动中。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。

Water quality and availability are vital to society and wildlife sustainability. However, we are still not able to predict the paths that water follows from precipitation to streamflow in a river, nor how long this process takes. A deeper understanding is crucial to anticipate overall water quality and supply. This is particularly true for headwater streams because their input controls the water quantity and quality of larger freshwater systems. While it is widely recognized that the flow paths water can take depend on the available catchment storage, i.e., the size of the underground “bucket”, it is not yet possible to measure how large this storage is. This project will investigate the relationship between geology, geomorphology, and topography and storage availability, as derived from hydrologic tracers such as water stable isotopes. This investigation will enhance societal ability to adapt and ensure sustainable supply of clean water under modified hydrologic conditions, likely to occur as consequences of human activities (forest harvesting, urbanization) and climate change. Through this project a relationship with the Oregon Museum of Science and Industry will be established to develop hands-on activities and to teach the general public about water availability in the context of climate change. The goal of this research is to understand the temporal-spatial variability of rainfall-runoff generation in headwater streams using LiDAR derived metrics of storage potential and tracer data. This research will be conducted in headwater streams over variable geomorphology and geology over a wide range (0.1–62 km2) of catchment areas. The approach will integrate water stable isotopes and electrical conductivity as tracers, hydrometric data collected by an NSF LTER site, and LiDAR in catchment-scale models to predict event transit times and storage in unmonitored locations. This study has the potential to transform the current approach to estimate water storage and to incorporate it in multiscale hydrologic modeling. This research will form the structure for an educational program to engage the public in activities that enhance their understanding of the water movement in the context of climate change.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.