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Towards improved identification of spatially-distributed rainfall runoff models.

机译:旨在更好地识别空间分布的降雨径流模型。

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摘要

Distributed rainfall runoff hydrologic models can be highly effective in improving flood forecasting capabilities at ungauged, interior locations of the watershed. However, their implementation in operational decision-making is hindered by the high dimensionality of the state-parameter space and by lack of methods/understanding on how to properly exploit and incorporate available spatio-temporal information about the system. This dissertation is composed of a sequence of five studies, whose overall goal is to improve understanding on problems relating to parameter identifiability in distributed models and to develop methodologies for their calibration.;The first study proposes and investigates an approach for calibrating catchment scale distributed rainfall-runoff models using conventionally available data. The process, called regularization, uses spatial information about soils and land-use that is embedded in prior parameter estimates (Koren et al. 2000) and knowledge of watershed characteristics, to constrain and reduce the dimensionality of the feasible parameter space.;The methodology is further extended in the second and third studies to improve extraction of 'hydrologically relevant' information from the observed streamflow hydrograph. Hydrological relevance is provided by using signature measures (Yilmaz et al. 2008) that correspond to major watershed functions. While the second study applies a manual selection procedure to constrain parameter sets from the subset of post calibrated solutions, the third develops an automatic procedure based on a penalty function optimization approach.;The fourth paper investigates the relative impact of using the commonly used multiplier approach to distributed model calibration, in comparison with other spatial regularization strategies and also includes investigations on whether calibration to data at the catchment outlet can provide improved performance at interior locations. The model calibration study conducted for three mid sized catchments in the US led to the important finding that basin outlet hydrographs might not generally contain information regarding spatial variability of the parameters, and that calibration of the overall mean of the spatially distributed parameter fields may be sufficient for flow forecasting at the outlet. This then was the motivation for the fifth paper which investigates to what degree the spatial characteristics of parameter and rainfall fields can be observable in catchment outlet hydrographs.
机译:分布式降雨径流水文模型可以有效提高流域内部未定位的洪水预报能力。但是,它们在操作决策中的实现受到状态参数空间的高维性以及缺乏有关如何正确利用和合并有关系统的时空信息的方法/知识的阻碍。本论文由五项研究组成,其总体目标是增进对分布式模型中参数可识别性问题的认识,并为它们的标定开发方法。第一项研究提出并研究了一种对流域尺度分布式降雨进行标定的方法。使用常规可用数据的径流模型。该过程称为正则化,它使用嵌入在先前参数估计中的有关土壤和土地利用的空间信息(Koren等,2000)和流域特征的知识,来约束和减少可行参数空间的维数。在第二和第三项研究中进一步扩展了该方法,以改进从观察到的水流水文图中提取“与水文相关”的信息。通过使用与主要流域功能相对应的特征性测量(Yilmaz等,2008)来提供水文相关性。第二项研究应用手动选择程序来约束后校准解子集中的参数集,而第三项研究则基于惩罚函数优化方法开发了一种自动程序。第四篇论文研究了使用常用乘数法的相对影响与其他空间正则化策略相比,该模型还涉及分布式模型校准,并且还包括对流域出口处的数据进行校准是否可以改善内部位置性能的调查。在美国针对三个中型集水区进行的模型校准研究得出了重要发现,即流域出口水位图通常可能不包含有关参数空间变异性的信息,并且对空间分布参数场的整体平均值进行校准可能就足够了用于出口处的流量预测。这就是第五篇论文的动机,该论文探讨了在集水区出口水位图中可以观测到参数和降雨场的空间特征的程度。

著录项

  • 作者

    Pokhrel, Prafulla.;

  • 作者单位

    The University of Arizona.;

  • 授予单位 The University of Arizona.;
  • 学科 Hydrology.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 295 p.
  • 总页数 295
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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