Landscape controls on hydrologic responses to long-term climate oscillations

该项目旨在了解和量化美国2733个独立流域对长期气候振荡的水文响应，如厄尔Niño-Southern振荡（ENSO）。这些流域代表了广泛的地形、土地覆盖、土地利用和气候条件，它们包括不同程度的人为干扰。有了如此庞大的数据集，该项目将深入了解流域如何以及为什么对ENSO做出不同（或根本没有）的反应。这项研究将提高关于气候对陆地水循环影响的科学知识和理解，不仅针对多年气候振荡，而且针对长期气候变化，这对于管理供水、规划民用基础设施和为自然灾害做准备都至关重要。该项目还将提供地理空间导向的工具，以改善K12和大学生的水文学教育，同时为新兴的水文学家提供博士后培训。初步分析表明，流域对ENSO信号的过滤方式不同，即使在考虑了全国降水对ENSO响应的变化之后也是如此。假设与流域内部组织相关的空间特征可以解释观测到的流域对ENSO响应的大量变异性。这一假设植根于地貌瞬时单位水线（GIUH）理论，该理论将流域的空间组织与其水文响应联系起来。该项目建立在GIUH框架的基础上，并将其应用于长期气候现象，如ENSO，使用统计学习方法和水文建模来测试假设。总的来说，这项工作旨在通过弥合水文反应的机制、流域尺度研究和主要是统计性质的气候-流相互作用的大规模研究之间的差距，提高关于气候对陆地水循环影响的科学知识和理解。这项工作承认水文科学中许多地理空间研究问题的所谓“大数据”性质，并将目前在该领域未充分利用的统计学习方法应用于大型公开数据集。

This project seeks to understand and quantify hydrologic responses to long-term climate oscillations, such as the El Niño-Southern Oscillation (ENSO), for 2733 individual watersheds across the conterminous United States. These watersheds represent a wide range of terrain, land cover, land use, and climate conditions, and they include varying degrees of human disturbance. With such an expansive dataset, the project will provide insight as to how and why watersheds respond differently (or not at all) to ENSO. The study will improve scientific knowledge and understanding about climate impacts on the terrestrial water cycle, not only for multi-year climate oscillations but also for long-term climate change, which are both critical for managing water supplies, planning civil infrastructure, and preparing for natural disasters. The project will also provide geospatially-oriented tools to improve hydrology education for K12 and university students while providing postdoctoral training for an emerging hydrologist.Preliminary analyses suggest that watersheds filter ENSO signals differently, even after accounting for variations in precipitation responses to ENSO across the country. Spatial characteristics associated with internal watershed organization are hypothesized to explain a significant amount of the observed variability in watershed responses to ENSO. This hypothesis is rooted in geomorphological instantaneous unit hydrograph (GIUH) theory, which links the spatial organization of watersheds to their hydrologic responses. This project builds on the GIUH framework and applies it to long-term climate phenomena such as ENSO, using statistical learning methods along with hydrological modeling to test hypotheses. Overall, the work seeks to improve scientific knowledge and understanding about climate impacts on the terrestrial water cycle by bridging the gap between mechanistic, watershed-scale studies of hydrological responses and large-scale studies of climate-streamflow interactions that are predominantly statistical in nature. This work acknowledges the so-called "big data" nature of many geospatial research problems in the hydrologic sciences and brings statistical learning methods, currently under-utilized in the field, to bear on large, publicly available datasets.