Landscape controls on hydrologic responses to long-term climate oscillations

该项目旨在了解和量化美国毗邻的2733个独立分水岭对长期气候振荡，如厄尔尼诺-南方涛动(ENSO)的水文反应。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，其中包括不同程度的人类干扰。有了这样一个庞大的数据集，该项目将提供关于流域如何以及为什么对ENSO做出不同(或根本不)反应的见解。这项研究将提高关于气候对陆地水循环的影响的科学知识和理解，不仅对多年气候振荡，而且对长期气候变化，这对管理供水、规划民用基础设施和应对自然灾害都是至关重要的。该项目还将提供面向地球空间的工具，以改善K12和大学生的水文学教育，同时为新出现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，即使在考虑到全国各地对ENSO的降水响应的变化后也是如此。与内部分水岭组织相关的空间特征被假设为解释分水岭对ENSO响应的大量观测变异性。这一假设源于地貌瞬时单位线(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ENSO等长期气候现象，使用统计学习方法和水文模型来检验假设。总体而言，这项工作力求通过弥合对水文反应的分水岭规模的机械性研究和主要是统计性质的气候-径流相互作用的大规模研究之间的差距，来增进关于气候对陆地水循环的影响的科学知识和理解。这项工作确认了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得到充分利用的统计学习方法应用于可公开获得的大型数据集。

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.