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<p>An accurate and reliable runoff estimate would help to predict better streamflow forecasts. The present study aims to select key rain gauges from randomly installed rain gauges in large basins with wide ungauged areas. Middle Tapi Basin (MTB) has 26 rain gauges which have been modelled in the present study. Hall’s method, K-mean clustering, hierarchical clustering (HC), and self-organizing maps (SOM) have been explored to identify the key rain gauges of the basin. The 15 stations were selected using Hall’s method, and nine stations were selected as key rain gauges using other clustering approaches. The rain gauge network designed by each approach has been evaluated based on the performance of the modelled runoff with the lumped hydrologic model. The runoff simulated by Hall’s network outperformed other clustering approaches. It is proposed that 15 key rain gauges identified by Hall’s method be used for the runoff prediction for MTB. The transferability of the present study offers a universal framework for optimizing gauge networks across diverse geographical locations, aiding researchers in efficiently designing monitoring systems for accurate runoff estimation.</p>

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Precision in rainfall monitoring: Comparative assessment of rain gauge network design techniques and key rain gauge identification

  • 期刊论文

  • 发表日期：

    2025年2月24日

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  • Ayushi Panchal,
  • Sanjaykumar Yadav

摘要

An accurate and reliable runoff estimate would help to predict better streamflow forecasts. The present study aims to select key rain gauges from randomly installed rain gauges in large basins with wide ungauged areas. Middle Tapi Basin (MTB) has 26 rain gauges which have been modelled in the present study. Hall’s method, K-mean clustering, hierarchical clustering (HC), and self-organizing maps (SOM) have been explored to identify the key rain gauges of the basin. The 15 stations were selected using Hall’s method, and nine stations were selected as key rain gauges using other clustering approaches. The rain gauge network designed by each approach has been evaluated based on the performance of the modelled runoff with the lumped hydrologic model. The runoff simulated by Hall’s network outperformed other clustering approaches. It is proposed that 15 key rain gauges identified by Hall’s method be used for the runoff prediction for MTB. The transferability of the present study offers a universal framework for optimizing gauge networks across diverse geographical locations, aiding researchers in efficiently designing monitoring systems for accurate runoff estimation.