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: 1005311
• 负责人: Vijay Gupta
• 金额: $ 15.02万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1005311&HistoricalAwards=false
• 关键词: Collaborative; Research; Investigating; Physical; Origins

For decades, hydrologic studies in homogeneous regions and river basins have shown that quantiles of the annual peak streamflow distribution (e.g. the mean annual peak flow, the 100-year peak flow) have a power-law dependence on upstream basin area with an exponent that usually varies between 0.5 and 1.0. A new geophysical theory has been developing to understand this non-linear dependence (scaling) in peak flows (floods) in terms of space-time rainfall, runoff generation processes and water transport dynamics in channel networks. The central hypothesis of the theory is that scaling in peak flows for rainfall-runoff events arises from solutions of mass and momentum conservation equations in self-similar network topologies and geometries in the limit of large drainage areas.The research being pursued is built on diagnosis, in contrast to the widely used practice of fitting a model to data to minimize errors. The purpose of diagnosis is to understand the relationships between data, theory, and computer simulations without fitting. Based on diagnostic results, new hypotheses can be introduced, assumptions can be modified and diagnosis repeated. The researchers have prior experience in diagnosing the role of rainfall, infiltration, and runoff generation on the slopes and intercepts of spatial scaling relations for floods at the event time scale in the Goodwin Creek Experimental Watershed (GCEW), Mississippi. This project is building on their published results and extending them to an annual time scale. They are diagnosing peak streamflow scaling relations in GCEW using a probabilistic (ensemble) framework. An ensemble is defined as a collection of different hydrographs that are produced from the same rainfall field but from a different set of initial hillslope infiltration and runoff generation conditions. This definition is made because published research indicates that hillslope runoff conditions substantially impact the timing and scaling features of streamflows in small basins like GCEW. Two key questions being addressed are, "How sensitive is the spatial scaling of peak flows to spatial variability in hillslope infiltration and runoff generation?" and "How is the scaling of annual maximum peak flows connected to the scaling of peak flows in rainfall-runoff events."A recent article in Science (319, 2008) stated that, "In view of the magnitude and ubiquity of the hydro-climatic change apparently now under way, however, we assert that stationarity is dead and should no longer serve as a central default assumption in water-resource risk assessment and planning. Finding a suitable successor is crucial for human adaptation to changing climate". Self-similarity in river networks changes little over the decadal and centennial time scales of climate change. Consequently, the emerging scaling theory of peak streamflows, which is based on network self-similarity, applies whether or not climatic stationarity holds. If we can better understand how basins operate physically and how physical processes and conditions can be used to predict observed spatial scaling in peak streamflows, then the theory can be used to predict floods under a non stationary climate change. Results from this research are also making fundamental contributions to Prediction in Ungauged Basins (PUB), the decade-long research initiative (2003-2013) of the International Association of Hydrologic Sciences.

几十年来，均匀区域和河流流域的水文研究表明，年峰值流量分布的分位数（如年平均峰值流量，100年峰值流量）与上游流域面积呈幂律关系，指数通常在0.5和1.0之间变化。一种新的地球物理理论已经发展起来，从时空降雨、径流生成过程和渠道网络中的水运动力学的角度来理解峰值流量（洪水）的非线性依赖（缩放）。该理论的中心假设是，降雨径流事件的峰值流量缩放源于自相似网络拓扑和大流域极限几何中的质量和动量守恒方程的解。正在进行的研究是建立在诊断的基础上的，而不是广泛使用的将模型拟合到数据以尽量减少误差的做法。诊断的目的是在没有拟合的情况下理解数据、理论和计算机模拟之间的关系。根据诊断结果，可以引入新的假设，修改假设，重复诊断。在密西西比州古德温河实验流域（GCEW）的事件时间尺度上，研究人员在诊断降雨、入渗和径流产生对斜坡和截流的空间尺度关系方面具有先前的经验。这个项目是建立在他们发表的结果的基础上，并将它们扩展到每年的时间尺度。他们正在使用概率（集成）框架诊断GCEW中的峰值流量缩放关系。集合被定义为不同水文曲线的集合，这些水文曲线产生于相同的降雨场，但产生于不同的初始山坡入渗和产流条件。之所以做出这样的定义，是因为已发表的研究表明，在GCEW这样的小流域中，山坡径流条件对水流的时序和尺度特征有很大的影响。要解决的两个关键问题是：“峰值流量的空间尺度对山坡入渗和径流生成的空间变异性有多敏感？”以及“年最大峰值流量的尺度如何与降雨径流事件中峰值流量的尺度联系起来？”《科学》杂志（2008年第319期）最近的一篇文章指出，“鉴于目前正在发生的水文气候变化的规模和普遍性，我们断言，平稳性已经死亡，不应再作为水资源风险评估和规划的核心默认假设。”寻找合适的继任者对于人类适应气候变化至关重要。”在气候变化的年代际和百年时间尺度上，河网的自相似性变化不大。因此，无论气候平稳性是否成立，基于网络自相似性的峰值流量标度理论都适用。如果我们能够更好地理解流域的物理运作方式，以及如何利用物理过程和条件来预测观测到的峰值流量的空间尺度，那么该理论就可以用于预测非平稳气候变化下的洪水。这项研究的结果也为国际水文科学协会长达十年的研究计划（2003-2013）“未测量盆地预测”（PUB）做出了基础性贡献。