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<p>As a result of climate change, precipitation extremes are becoming more intense, leading to more frequent and more severe flooding, even in small watercourses. This report presents a method that aims to derive the W-Q relationships for arbitrary watercourse sections quickly and with reduced measurement effort. An artificial neural network (ANN), uses the correlation between water level data, radar precipitation and water level measurements to theoretically derive the W-Q relationship. This reduces the metrological effort, as direct discharge measurements are supplemented by model results. The method shows potential for accelerating the hydraulic engineering planning process and reducing remaining uncertainties.</p>

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Abflussschätzung mittels regionalisierter W-Q-Beziehungen

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Benjamin Mewes,
  • Peter Ghaly,
  • Felix Schmid,
  • Benjamin Freudenberg,
  • Daniel Bittner,
  • Tilo Keller

摘要

As a result of climate change, precipitation extremes are becoming more intense, leading to more frequent and more severe flooding, even in small watercourses. This report presents a method that aims to derive the W-Q relationships for arbitrary watercourse sections quickly and with reduced measurement effort. An artificial neural network (ANN), uses the correlation between water level data, radar precipitation and water level measurements to theoretically derive the W-Q relationship. This reduces the metrological effort, as direct discharge measurements are supplemented by model results. The method shows potential for accelerating the hydraulic engineering planning process and reducing remaining uncertainties.