SGER: Evaluation of the Rating Curve Hysteresis Due to Unsteady Channel Flows Using Non-Intrusive Measurements Acquired During the Iowa 2008 Flood

SGER：使用 2008 年爱荷华州洪水期间获得的非侵入式测量结果评估因不稳定河道流量造成的额定曲线滞后

• 批准号: 0843798
• 负责人: Marian Muste
• 金额: --
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0843798&HistoricalAwards=false
• 关键词: SGER; Evaluation; Rating; Curve; Hysteresis

0843798MusteThe PI proposes to better understand and refine river discharge estimates using improved measures of rating curves. Traditional river discharge estimates are conducted at times when flow is relatively normal and steady, allowing safe and convenient measurement. These measurements serve as the basis for calculating rating curves. The curves, in turn, are used to estimate flow and discharge during flood events ? possibly leading to significant uncertainty due to hysteresis. Understanding the discharge-flow hysteresis relationship requires measurements during high flow events. The 2008 Midwestern flood offers a unique opportunity to couple flow and discharge measurements in order to better understand and model rating curve hysteresis during extreme events. With the goal of coupling river stage and discharge, measurements will be made in a non-contact manner developed at IIHR-Hydroscience & Engineering which uses Large-Scale Particle Image Velocimetry (LSPIV). This technique was initiated on June 10, 2008. Funds are requested to 1) finalize the on-going measurements; 2) perform topographic and bathymetric surveys; 3) evaluate deployed instruments by conducting baseline flow measurements; 4) analyze data and report findings; and 5) produce conceptual process models and define future research needs. The unique aspect of the proposed research is the use of non-intrusive measurement techniques in a natural extreme event provided by the 2008 Midwestern flood. The series of measurements will allow the PI to assess the technique and its ability to provide improved rating curve estimates under rare unsteady flow situations in natural channels. The expectation is that LSPIV will provide a simple, compact, cost- and effort-effective package for providing these critical measurements. Unsteady flows will be investigated in detail and conceptual frameworks will be developed in order to determine what ancillary data are needed to answer discharge hypotheses. The transformative, exploratory nature of this project will provide field testing for technical aspects of the LSPIV technique to include specular reflection for free-surface tracing, optimal velocity distribution laws in the water column testing as a function of stream characteristics, flood wave raising and falling data capture, and uncertainty analysis. Rating curves are the basis of stream stage measurements, yet their uncertainty during high-flow events obviates the ability to fully understand unsteady fluvial processes. River processes hypotheses regarding extreme and unusual flow regimes can be tested and the results of this study will enable river stage and discharge estimation in regions without gauging stations. Integrated hydrologic-geomorphic-biological studies will be possible through a better understanding of key temporal and spatial processes in flood propagation. Uncertainty in rating curves calculated during stable river flow regimes will hinder flood stage forecasting whereas this project will provide data useable by national mission agencies for more accurate flood estimates. With billions of dollars lost each year, more accurate flood data are timely and of broad interest.***

0843798 pi建议使用改进的评级曲线度量来更好地理解和完善河流排放估计。传统的河流排放估计是在流动相对正常且稳定的情况下进行的，从而可以安全方便地进行测量。这些测量值是计算额定曲线的基础。反过来，曲线被用来估计洪水事件期间的流量和排放？可能导致由于滞后而导致明显的不确定性。了解排放流磁滞关系需要在高流量事件中进行测量。 2008年中西部的洪水为搭配流程和排放测量提供了独特的机会，以便在极端事件中更好地理解和建模额定曲线磁滞。以耦合河阶段和排放量的目标，将以非接触方式进行测量，该方式在IIHR-Hydroscience＆Engineering中开发，该方式使用大规模粒子图像速度计（LSPIV）。该技术于2008年6月10日启动。要求资金1）最终确定正在进行的测量； 2）进行地形和测深调查； 3）通过进行基线流量测量来评估部署的工具； 4）分析数据并报告结果； 5）产生概念过程模型并定义未来的研究需求。拟议的研究的独特方面是在2008年中西部洪水提供的自然极端事件中使用非侵入性测量技术。这一系列的测量将使PI能够评估在自然通道中罕见的不稳定流动情况下提供改进的额定速率估计值的技术及其能力。期望LSPIV将提供一个简单，紧凑，成本和努力有效的软件包，以提供这些关键测量。将对不稳定的流进行详细研究，并将开发概念框架，以确定需要哪些辅助数据来回答排放假设。该项目的变革性，探索性质将为LSPIV技术的技术方面提供现场测试，包括自由表面跟踪的镜面反射，水柱测试中的最佳速度分布法，作为流特征的函数，洪水波升高和下降数据捕获以及不确定性分析。评分曲线是流阶段测量的基础，但是它们在高流量事件中的不确定性避免了完全理解不稳定的河流过程的能力。河流过程可以测试有关极端和异常流动方案的假设，这项研究的结果将使河流阶段和无衡量站的区域的排出估计能够进行。通过更好地理解洪水繁殖中的关键时间和空间过程，可以进行综合的水文 - 地球形态生物学研究。在稳定的河流流程中计算出的评级曲线的不确定性将阻碍洪水阶段的预测，而该项目将为国家任务机构提供可用的数据，以进行更准确的洪水估算。每年损失数十亿美元，更准确的洪水数据是及时且广泛的。***