Landscape controls on hydrologic responses to long-term climate oscillations

景观对长期气候振荡水文响应的控制

• 批准号: 1558675
• 负责人: Ryan Emanuel
• 金额: $ 21.5万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1558675&HistoricalAwards=false
• 关键词: Landscape; controls; hydrologic; responses; long

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.

该项目旨在了解和量化水文响应长期气候振荡，如厄尔尼诺南方涛动（ENSO），为2733个个别流域横跨美国。 这些流域的地形、土地覆被、土地利用和气候条件各不相同，其中包括不同程度的人类干扰。 有了这样一个广泛的数据集，该项目将提供关于流域如何以及为什么对ENSO有不同反应（或根本没有反应）的见解。 这项研究将增进关于气候对陆地水循环影响的科学知识和理解，不仅涉及多年气候波动，而且涉及长期气候变化，这对管理供水、规划民用基础设施和防备自然灾害都至关重要。 该项目还将提供面向地理空间的工具，以改善中小学和大学生的水文学教育，同时为一名新兴的水文学家提供博士后培训。初步分析表明，流域对厄尔尼诺/南方涛动信号的过滤方式不同，即使在考虑到全国各地对厄尔尼诺/南方涛动的降水反应的变化之后。与内部流域组织的空间特征的假设解释了大量的观测到的变化流域响应ENSO。 这一假设是植根于地貌瞬时单位线（GIUH）理论，该理论将流域的空间组织与水文响应联系起来。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，使用统计学习方法沿着水文建模来测试假设。总体而言，这项工作旨在通过弥合水文响应的流域尺度机制研究与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，提高对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得到充分利用的统计学习方法应用于大型公开数据集。