A framework to predict hydrologic processes at continental scales
预测大陆尺度水文过程的框架
基本信息
- 批准号:2124923
- 负责人:
- 金额:$ 29.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-09-01 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Streamflow predictions are essential for forecasting floods and managing water resources under intensifying pressures on water use. To make reliable streamflow predictions for all rivers, including those with no flow gauges, we need computer models that accurately simulate watershed processes and how they vary across the U.S. landscape. For example, how do surface flows, recharge, groundwater storage and flow patterns change from watershed to watershed? The latest hydrologic models are flexible enough to simulate spatially variable processes, but we currently lack the knowledge of how those processes vary by watershed. This project will fill this knowledge gap by developing a new framework to predict how watershed processes vary across the U.S.. The approach is novel in leveraging small-scale field hydrology knowledge within a continental-scale, machine learning application. The research will discover new relationships between landscape features, streamflow dynamics and watershed processes. Project scientists will work with NOAA’s National Water Center to apply the results in the design of the Next-Generation National Water Model that provides streamflow predictions for every river in the U.S.. The project will provide research experiences for under-represented minority students, and will develop online learning materials. The goals of the project are to (1) Identify a suite of landscape metrics that quantify landscape characteristics most likely to activate specific runoff generation processes. (2) Identify dominant hydrologic processes across a large database of gauged U.S. watersheds, by relating streamflow dynamics to the upstream processes that drive them. (3) Develop a data-driven model that predicts dominant hydrologic processes based on landscape metrics. (4) Evaluate the data-driven model by testing it for a range of locations and case studies. The framework developed in this project will improve on previous methods of identifying and predicting landscape and hydrologic metrics, by redesigning the metrics to target specific hydrologic processes. Further, the project will apply new machine learning developments to identify and interpret predictive relationships between landscapes and processes. Deliverables will include GIS (geographic information system) maps of hydrologic processes across the contiguous U.S., and open-source code to estimate hydrologic processes from landscape characteristics. Overall, the project aspires to transform how continental-scale hydrology models represent water fluxes in diverse climates and landscapes.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.
在用水压力日益加大的情况下,径流预测对于预报洪水和管理水资源至关重要。为了对所有河流(包括那些没有流量计的河流)做出可靠的径流预测,我们需要准确模拟分水岭过程的计算机模型,以及它们在美国各地的变化情况。例如,从一个流域到另一个流域,地表径流、补给、地下水储存和流态是如何变化的?最新的水文模型足够灵活,可以模拟空间变化的过程,但我们目前缺乏关于这些过程如何随流域变化的知识。该项目将通过开发一个新的框架来预测分水岭过程在美国各地的变化,从而填补这一知识空白。这种方法在利用大陆尺度的机器学习应用程序中利用小规模的野外水文学知识方面是新颖的。这项研究将发现景观特征、径流动力学和流域过程之间的新关系。项目科学家将与NOAA的国家水中心合作,将研究结果应用于下一代国家水模型的设计中,该模型为美国每条河流提供径流预测。该项目将为代表不足的少数民族学生提供研究经验,并将开发在线学习材料。该项目的目标是(1)确定一套景观指标,以量化最有可能激活特定径流产生过程的景观特征。(2)通过将径流动力学与驱动这些过程的上游过程联系起来,确定整个美国测量流域的大型数据库中的主导水文过程。(3)开发一个数据驱动的模型,根据景观指标预测主导的水文过程。(4)通过在一系列地点和案例研究中对数据驱动模型进行测试来对其进行评估。该项目制定的框架将改进以前确定和预测景观和水文指标的方法,重新设计针对具体水文过程的指标。此外,该项目将应用新的机器学习发展来确定和解释景观和过程之间的预测关系。交付成果将包括整个毗邻的美国的水文过程的地理信息系统(GIS)地图,以及根据景观特征估计水文过程的开放源代码。总体而言,该项目致力于改变大陆尺度水文模型在不同气候和景观中表示水通量的方式。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。
项目成果
期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework
使用机器学习通过编码器-解码器框架识别水文特征
- DOI:10.1029/2022wr033091
- 发表时间:2023
- 期刊:
- 影响因子:5.4
- 作者:Botterill, Tom E.;McMillan, Hilary K.
- 通讯作者:McMillan, Hilary K.
How do hydrologists perceive watersheds? A survey and analysis of perceptual model figures for experimental watersheds
- DOI:10.1002/hyp.14845
- 发表时间:2023-02
- 期刊:
- 影响因子:3.2
- 作者:H. McMillan;R. Araki;S. Gnann;R. Woods;Thorsten Wagener
- 通讯作者:H. McMillan;R. Araki;S. Gnann;R. Woods;Thorsten Wagener
A taxonomy of hydrological processes and watershed function
水文过程和流域功能的分类
- DOI:10.1002/hyp.14537
- 发表时间:2022
- 期刊:
- 影响因子:3.2
- 作者:McMillan, Hilary
- 通讯作者:McMillan, Hilary
Large Scale Evaluation of Relationships Between Hydrologic Signatures and Processes
水文特征与过程之间关系的大规模评估
- DOI:10.1029/2021wr031751
- 发表时间:2022
- 期刊:
- 影响因子:5.4
- 作者:McMillan, Hilary K.;Gnann, Sebastian J.;Araki, Ryoko
- 通讯作者:Araki, Ryoko
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Hilary McMillan其他文献
Hilary McMillan的其他文献
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{{ truncateString('Hilary McMillan', 18)}}的其他基金
Synthesizing hydrologic process knowledge to determine global drivers of dominant processes
综合水文过程知识以确定主导过程的全球驱动因素
- 批准号:
2322510 - 财政年份:2023
- 资助金额:
$ 29.5万 - 项目类别:
Standard Grant
GP-UP: Collaborative Research: Developing a diverse hydrology workforce through an undergraduate hydrological research experience in a coastal California watershed
GP-UP:合作研究:通过加州沿海流域的本科生水文学研究经验培养多元化的水文学队伍
- 批准号:
2119296 - 财政年份:2022
- 资助金额:
$ 29.5万 - 项目类别:
Standard Grant
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