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<p>High-resolution spatial and temporal rainfall data is crucial for water resource planning and management, agriculture, hydropower operations, and flood forecasting. However, resource-constrained countries like Nepal face challenges in maintaining adequate ground-based rainfall monitoring networks. Citizen science—utilizing cost-effective rain gauges and community engagement—offers a promising solution to address this data gap. This study evaluates the performance of citizen scientists in rainfall monitoring and visualization of its importance across the Kathmandu Valley, Nepal, by analyzing monsoonal rainfall data (May–September) collected from 2018 to 2023. The study also examines the influence of citizen scientist recruitment methods and rainfall intensity on citizen scientist participation. We assess the reliability of citizen science-based data by comparing it with standard ground-based measurements from the Department of Hydrology and Meteorology (DHM), Nepal, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), a satellite-based dataset. Our results reveal that recruitment methods and rainfall events significantly influenced measurement consistency, with sustained participation observed among citizen scientists recruited through personal connections and social media. A strong correlation (<i>r</i> &gt; 0.8) and a slight underestimation (relative bias = -3.04%) were found between citizen scientist data and DHM measurements, whereas CHIRPS showed lower accuracy. These findings demonstrate that citizen science can outperform satellite-based rainfall estimates and serve as a reliable and cost-effective complement to conventional ground-based monitoring methods, particularly in data-scarce and mountainous regions. They also highlight the importance of strategic recruitment and engagement approaches in sustaining the long-term participation of citizen scientists.</p> Graphical Abstract <p></p> <p><b>Graphical abstract description: </b>This graphical abstract provides a comprehensive summary of citizen science-based rainfall monitoring, citizen scientists' performance, and validation. Citizen scientists are recruited through outreach campaigns, personal connections, social media, random visits, and past citizen scientists. They collect daily rainfall data using cost-effective rain gauges and a smartphone application called Open Data Kit (ODK) Collect. Submitted records pass through a quality control process to confirm measurement accuracy. The citizen scientists' performance was evaluated based on data regularity and submission quality. The regular data is then processed, filtered, and compared with standard Department of Hydrology and Meteorology (DHM) rainfall datasets and satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Statistical analysis, including correlation (<i>r</i>), BIAS, and RMSE, is conducted to assess the reliability of citizen science data. This citizen science approach provides reliable rainfall data with a standard DHM dataset in comparison to the CHIRPS dataset. For further insights and applications, we encourage readers to explore the full article.</p>

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Leveraging a Citizen Science Approach for Rainfall Monitoring: Evaluating Performance and Reliability To Complement Standard Datasets

  • 期刊论文

  • 发表日期：

    2025年11月7日

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  • Sudeep Duwal,
  • Rajaram Prajapati,
  • Surabhi Upadhyay,
  • Sanjiv Neupane,
  • Hanik Lakhe,
  • Bhesh Raj Thapa,
  • Jeffrey C. Davids,
  • Rocky Talchabhadel

摘要

High-resolution spatial and temporal rainfall data is crucial for water resource planning and management, agriculture, hydropower operations, and flood forecasting. However, resource-constrained countries like Nepal face challenges in maintaining adequate ground-based rainfall monitoring networks. Citizen science—utilizing cost-effective rain gauges and community engagement—offers a promising solution to address this data gap. This study evaluates the performance of citizen scientists in rainfall monitoring and visualization of its importance across the Kathmandu Valley, Nepal, by analyzing monsoonal rainfall data (May–September) collected from 2018 to 2023. The study also examines the influence of citizen scientist recruitment methods and rainfall intensity on citizen scientist participation. We assess the reliability of citizen science-based data by comparing it with standard ground-based measurements from the Department of Hydrology and Meteorology (DHM), Nepal, and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), a satellite-based dataset. Our results reveal that recruitment methods and rainfall events significantly influenced measurement consistency, with sustained participation observed among citizen scientists recruited through personal connections and social media. A strong correlation (r > 0.8) and a slight underestimation (relative bias = -3.04%) were found between citizen scientist data and DHM measurements, whereas CHIRPS showed lower accuracy. These findings demonstrate that citizen science can outperform satellite-based rainfall estimates and serve as a reliable and cost-effective complement to conventional ground-based monitoring methods, particularly in data-scarce and mountainous regions. They also highlight the importance of strategic recruitment and engagement approaches in sustaining the long-term participation of citizen scientists.

Graphical Abstract

Graphical abstract description: This graphical abstract provides a comprehensive summary of citizen science-based rainfall monitoring, citizen scientists' performance, and validation. Citizen scientists are recruited through outreach campaigns, personal connections, social media, random visits, and past citizen scientists. They collect daily rainfall data using cost-effective rain gauges and a smartphone application called Open Data Kit (ODK) Collect. Submitted records pass through a quality control process to confirm measurement accuracy. The citizen scientists' performance was evaluated based on data regularity and submission quality. The regular data is then processed, filtered, and compared with standard Department of Hydrology and Meteorology (DHM) rainfall datasets and satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Statistical analysis, including correlation (r), BIAS, and RMSE, is conducted to assess the reliability of citizen science data. This citizen science approach provides reliable rainfall data with a standard DHM dataset in comparison to the CHIRPS dataset. For further insights and applications, we encourage readers to explore the full article.