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首页> 外文学位 >Stepwise nonparametric disaggregation for daily streamflow generation conditional on hydrologic and large-scale climatic signals.
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Stepwise nonparametric disaggregation for daily streamflow generation conditional on hydrologic and large-scale climatic signals.

机译：以水文和大规模气候信号为条件的每日流量生成的逐步非参数分解。

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摘要

A stepwise nonparametric stochastic disaggregation framework to produce synthetic scenarios of daily streamflow conditional on volumes of spring runoff and large-scale ocean-atmosphere oscillations is presented. This thesis examines statistical links (i.e., teleconnections) between decadal/interannual climatic variations in the Pacific Ocean and hydrologic variability in US northwest region, and includes a spectral analysis of climate signals to detect coherences of their behavior in the frequency domain. We explore the use of such teleconnections of selected signals (e.g., north Pacific gyre oscillation, southern oscillation, and Pacific decadal oscillation indices) in the proposed data-driven framework by means of a cross-validation-based combinatorial approach with the aim of simulating improved streamflow sequences when compared with disaggregated series generated from flows alone. A nearest neighbor time series bootstrapping approach is integrated with principal component analysis to resample from the empirical multivariate distribution. A volume-dependent scaling transformation is implemented to guarantee the summability condition. The downscaling process includes a two-level cascade scheme: seasonal-to-monthly disaggregation first followed by monthly-to-daily disaggregation. Although the stepwise procedure may lead to a lack of preservation of the historical correlation between flows of the last day of a month and flows of the first day of the following month, we present a new and simple algorithm, based on nonparametric resampling, that overcomes this limitation. The downscaling framework presented here is parsimonious in parameters and model assumptions, does not generate negative values, and preserves very well the statistical characteristics, temporal dependences, and distributional properties of historical flows. We also show that both including conditional information of climatic teleconnection signals and developing the downscaling in cascades decrease significantly the mean error between synthetic and observed flow traces. The downscaling framework is tested with data from the Payette River Basin in Idaho.
机译：提出了一个逐步的非参数随机分解框架，该框架以春季径流量和大规模海洋-大气振荡为条件，产生日流量的综合情景。本文研究了太平洋年代际/年际气候变化与美国西北地区水文变异之间的统计联系（即遥相关），并包括了气候信号的频谱分析，以检测其在频域中行为的连贯性。我们通过基于交叉验证的组合方法，在拟议的数据驱动框架中探索了所选信号的这种遥相关（例如北太平洋回旋振荡，南部振荡和太平洋年代际振荡指数）的使用，目的是模拟与仅从流中生成的分解序列相比，改进了流序列。最近邻时间序列自举方法与主成分分析相集成，以从经验多元分布中重新采样。实现与体积有关的缩放变换，以确保可加和条件。降级过程包括两个级别的层叠方案：首先按季节对每月进行分类，然后按月对每日进行分类。尽管分步过程可能会导致无法保留一个月最后一天的流量与下个月第一天的流量之间的历史相关性，但我们提出了一种基于非参数重采样的新的简单算法，该算法克服了这个限制。此处介绍的降级框架在参数和模型假设方面是简约的，不会产生负值，并且很好地保留了历史流量的统计特征，时间依赖性和分布特性。我们还表明，既包括气候遥相关信号的条件信息，又包括级联中的按比例缩小，这都显着降低了合成流迹和观测流迹之间的平均误差。使用爱达荷州Payette流域的数据测试了缩减规模的框架。

著录项

  • 作者

    Molina Tabares, Jose Manuel.;

  • 作者单位

    Colorado State University.;

  • 授予单位 Colorado State University.;
  • 学科 Hydrology.;Water Resource Management.;Statistics.
  • 学位 M.S.
  • 年度 2010
  • 页码 106 p.
  • 总页数 106
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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