Scholarlink

<p>Employing timely short-term rainfall time series can alleviate the limitations of outdated meteorological information embedded in long-term datasets when assessing the performance of rainwater harvesting systems (RWHs) under climate change. However, indicators derived from short-term series often lack statistical stability across large samples, leading to uncertainty in projected outcomes. This study examines the influence of varying lengths of short-term series on RWH performance across 14 cities in Japan under climate change scenarios. A Bayesian network is then developed to capture the probabilistic features of these impacts, thereby identifying optimal rainfall conditions for RWH design under climate change. Results reveal that rainfall series longer than 16 years are unsuitable for RWH designing in cool temperate regions. In general, rainfall series with higher wet-period frequencies should be prioritized in inland cities, whereas the opposite trend is evident in coastal areas. Moreover, in northern regions the optimal series are characterized by longer dry periods, greater annual rainfall, and higher seasonal indices, while southern regions exhibit the reverse pattern. Validation confirms that the proposed Bayesian network reliably infers optimal short-term rainfall time series from statistical indictors, providing a robust framework for climate-adaptive RWH planning across diverse regional climates.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimal Short-Time Rainfall Time Series for Adapting Rainwater Harvesting System to Climate Change in Urban Runoff Management

  • 期刊论文

  • 发表日期：

    2026年1月24日

查看全文
  • Weilun Chen,
  • Zhonghui Liu,
  • Wei Ou,
  • Pengxuan Wang,
  • Xindong Wei,
  • Weijun Gao

摘要

Employing timely short-term rainfall time series can alleviate the limitations of outdated meteorological information embedded in long-term datasets when assessing the performance of rainwater harvesting systems (RWHs) under climate change. However, indicators derived from short-term series often lack statistical stability across large samples, leading to uncertainty in projected outcomes. This study examines the influence of varying lengths of short-term series on RWH performance across 14 cities in Japan under climate change scenarios. A Bayesian network is then developed to capture the probabilistic features of these impacts, thereby identifying optimal rainfall conditions for RWH design under climate change. Results reveal that rainfall series longer than 16 years are unsuitable for RWH designing in cool temperate regions. In general, rainfall series with higher wet-period frequencies should be prioritized in inland cities, whereas the opposite trend is evident in coastal areas. Moreover, in northern regions the optimal series are characterized by longer dry periods, greater annual rainfall, and higher seasonal indices, while southern regions exhibit the reverse pattern. Validation confirms that the proposed Bayesian network reliably infers optimal short-term rainfall time series from statistical indictors, providing a robust framework for climate-adaptive RWH planning across diverse regional climates.