首页> 外文OA文献 >Extension du potentiel de la modélisation hydrologique. : inversions heuristiques de modèles pluie-débit pour l'identification des paramètres simultanément aux pluies ou à la courbe de tarage.
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Extension du potentiel de la modélisation hydrologique. : inversions heuristiques de modèles pluie-débit pour l'identification des paramètres simultanément aux pluies ou à la courbe de tarage.

机译:扩展了水文模拟的潜力。 :雨流模型的启发式反演,用于与雨或等级曲线同时识别参数。

摘要

Rainfall-runoff models are used for flood forecasting by warning authorities. However their implementation on a particular territory is still a challenge. Indeed, they generally need to be calibrated by using observed rainfall and discharge time series. These data may be subject to errors and uncertainties. They are not always available. Then, the model calibration relevancy may be affected and the forecasts may also be subject to significant uncertainties. This research would like to address such issues related to the rainfall-runoff models calibration, by proposing original methods which may set up a model by using less data than the ``classical'' calibration. The unused data might be either subject to uncertainties or not available. Moreover, these unused data may be estimated by the methods. Two model independant approach were suggested. Both are an heuristic inversion algorithm of rainfall-runoff models. The first method estimates simultaneously hourly rainfall time series and models parameters, by using only observed hourly discharge time series and total areal rainfall of flood events. A specific application of this method to set up models (with fixed parameters), generalises to models which are not invertible analyticaly, the ``hydrology backward'' approach proposed by Kirchner (2009).The second method estimates simultaneously models parameters and a rating-curve, by using only observed hourly rainfall and stage time series. Original analysis may be performed on the rainfall time series and the rating-curve estimated by the methods. Also, they extend the applicability rainfall-runoff models to hydrological context with restricted available data and offer promising operational applications. Yet, this research lead us to build a conceptual framework, denoted knowledge space. This framework unifies not only the original approaches which were proposed, but also some more ``classical'' approaches to hydrology as the calibration and the simulation.
机译:降雨径流模型被预警机构用于洪水预报。但是,它们在特定地区的实施仍然是一个挑战。实际上,通常需要通过使用观测到的降雨和排放时间序列来对它们进行校准。这些数据可能会有错误和不确定性。它们并不总是可用。然后,模型校准相关性可能会受到影响,并且预测也可能会受到重大不确定性的影响。这项研究希望通过提出原始方法来解决与降雨径流模型校准有关的问题,该方法可以使用比``经典''校准少的数据来建立模型。未使用的数据可能会不确定或无法获取。此外,可以通过这些方法来估计这些未使用的数据。提出了两种与模型无关的方法。两者都是降雨径流模型的启发式反演算法。第一种方法通过仅使用观察到的每小时排放时间序列和洪水事件的总面降雨量来同时估算每小时降雨时间序列并建模参数。该方法用于建立模型(具有固定参数)的特定应用,推广到不可逆分析的模型,即Kirchner(2009)提出的``水文学向后''方法。第二种方法同时估算模型参数和等级-曲线,仅使用观察到的每小时降雨量和阶段时间序列。可以对降雨时间序列和通过这些方法估算的等级曲线进行原始分析。而且,他们将适用的降雨径流模型扩展到水文环境中,可获得的数据有限,并提供了有希望的运行应用。然而,这项研究引导我们建立了一个概念框架,称为知识空间。该框架不仅统一了提出的原始方法,还统一了水文学的一些``经典''方法,作为标定和模拟。

著录项

  • 作者

    Michon Timothée;

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  • 年度 2015
  • 总页数
  • 原文格式 PDF
  • 正文语种 fr
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