Collaborative Research: SGER--Dynamical Origins of Statistical Scaling in Floods on Real Networks-An Exploratory Diagnostic Analysis

合作研究:SGER--真实网络洪水统计缩放的动态起源-探索性诊断分析

基本信息

  • 批准号:
    0713809
  • 负责人:
  • 金额:
    --
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2007
  • 资助国家:
    美国
  • 起止时间:
    2007-04-01 至 2008-09-30
  • 项目状态:
    已结题

项目摘要

A new nonlinear geophysical theory of floods, henceforth called the scaling theory, has been developing for nearly a decade. It has the explicit goal to predict spatial statistical power laws in floods from conservations equations and related physical processes at the scale of hillslope-channel links that partition a natural terrain. In many empirical studies of regional flood frequencies, power law relationships have been observed between annual peak discharge statistics and drainage areas, and recently in individual rainfall-runoff events. Preliminary analyses show that event-based scaling exponents and the annual flood quantile scaling exponents are closely related. A close relationship between these two sets of exponents suggests a very important hypothesis, namely that it is possible to predict flood-scaling parameters from physical processes not only for individual RF-RO events, but also for annual floods by considering multiple events in a year.Intellectual Merit: Research in idealized deterministic self-similar networks has shown that statistical power laws emerge asymptotically as drainage area goes to infinity. They are not built into the physical equations governing floods. It has led to a key scientific hypothesis that power laws in floods have their physical origins in the self-similarity (self-affinity) of channel networks, which is also the basis for the widely observed fractal structure of networks and their Horton relations. The current challenge is to generalize the theory to real networks. To achieve this important goal, we propose to test a diagnostic framework using existing data from two Agricultural Research Service basins, Walnut Gulch, Arizona and Goodwin Creek, Mississippi. The key idea is to predict power laws from physical processes under a set of distributed parametric assumptions. Then compare the predictions with observed power laws for diagnosing the validity of our physical assumptions, and proposing a new set of assumptions. For predicting scaling laws in floods, a mass conservation equation, which parameterizes physical processes at the scale of hillslope-channel links in a natural terrain, is solved and flow hydrographs are computed for every link in a network. For solving the mass conservation equation, we will use a GIS-based digital watershed-modeling framework that our research group has developed in last five years. The objectives are exploratory in nature and are designed to establish a "proof of concept".Broader Impacts: Scientific consensus has grown that global warming is real and in substantial part is caused by human activities. This anthropogenic perturbation to the planetary hydro-climate is causing a non-stationary change, which precludes making statistical flood predictions from long-term historic rainfall and stream flow data. Historical hydrologic data are routinely used in statistical hydrologic models for the purposes of water resources management in the United States and other countries of the World. Therefore, hydrologic predictions in a non-stationary climate present an enormous problem for future management of water resources. The scaling theory of floods is developing new scientific foundations that would be particularly suited to make flood predictions in the context of the emerging problem of non-stationary hydro-climate change due to global warming. The scientific foundations of the scaling theory of floods can be generalized to include ecological and bio-geo-chemical processes that are coupled to water, for example, riparian evapotranspiration, and to make predictions in a changing hydro-climate.
近十年来,一种新的洪水非线性地球物理理论(以下称为标度理论)得到了发展。它有明确的目标,预测空间统计幂律洪水保护方程和相关的物理过程中的山坡通道连接的规模,划分自然地形。在区域洪水频率的许多经验研究中,已经观察到年洪峰流量统计数据和流域面积之间的幂律关系,最近在单个的洪水径流事件中也是如此。初步分析表明,基于事件的标度指数与年洪水分位数标度指数密切相关。这两组指数之间的密切关系表明了一个非常重要的假设,即它是可能的预测洪水尺度参数的物理过程,不仅为个人的RF-RO事件,但也为每年的洪水考虑多个事件在一年中。智力优点:在理想化的确定性自相似网络的研究表明,统计幂律出现渐近流域面积趋于无穷大。它们并不存在于控制洪水的物理方程中。它导致了一个关键的科学假设,即洪水中的幂律在通道网络的自相似性(自仿射性)中有其物理起源,这也是广泛观察到的网络分形结构及其霍顿关系的基础。目前的挑战是将该理论推广到真实的网络。为了实现这一重要目标,我们建议使用现有的数据从两个农业研究服务流域,核桃峡谷,亚利桑那州和古德温溪,密西西比测试诊断框架。其核心思想是在一组分布参数假设下从物理过程预测幂律。然后将预测与观察到的幂律进行比较,以诊断我们的物理假设的有效性,并提出一组新的假设。为了预测洪水的标度律,质量守恒方程,它参数化的物理过程在自然地形的山坡渠道链接的规模,求解和流量过程线计算网络中的每个环节。为了求解质量守恒方程,我们将使用我们的研究小组在过去五年中开发的基于GIS的数字流域建模框架。更广泛的影响:科学界越来越多地达成共识,认为全球变暖是真实的,而且在很大程度上是由人类活动造成的。这种对地球水文气候的人为扰动正在引起非平稳变化,这使得无法根据长期历史降雨和水流数据进行洪水统计预测。在美国和世界其他国家,历史水文数据通常用于统计水文模型,用于水资源管理。因此,在一个非平稳气候水文预测提出了一个巨大的问题,为未来的水资源管理。洪水的定标理论正在发展新的科学基础,特别适合在全球变暖引起的非平稳水文气候变化的新问题的背景下进行洪水预测。洪水定标理论的科学基础可以概括为包括与水相耦合的生态和生物地球化学过程,例如河岸蒸散,并在不断变化的水文气候中进行预测。

项目成果

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Peter Furey其他文献

Peter Furey的其他文献

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{{ truncateString('Peter Furey', 18)}}的其他基金

Collaborative Research: Investigating the Physical Origins of Spatial Statistical Scaling in Peak Streamflows from Event to Annual Time Scales
合作研究:调查从事件到年度时间尺度的峰值水流空间统计尺度的物理起源
  • 批准号:
    1007324
  • 财政年份:
    2010
  • 资助金额:
    --
  • 项目类别:
    Continuing Grant

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Research on the Rapid Growth Mechanism of KDP Crystal
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