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Background <p>In the hyper-arid environment of the United Arab Emirates (UAE), understanding rainfall patterns is essential for effective water resource management, agricultural planning, and ecological conservation. This study investigates rainfall variability from 2003 to 2021, emphasizing the integration of machine learning to enhance the accuracy of Quantitative Precipitation Estimation (QPE). </p> Methods <p>This research utilizes extensive rainfall records from gauges and ERA5 climate data, processed at high temporal resolution across 19&#xa0;years. Traditional spatial interpolation methods such as Classic Inverse Distance Weighting (C-IDW), Elevation-Adjusted IDW (EA-IDW), and Rainfall Similarity IDW (RS-IDW) were initially applied and analyzed in conjunction with the GTOPO30 Digital Elevation Model (DEM). Building on this, we developed and refined a neural network model tailored to capture the intricate patterns of rainfall distribution that conventional methods might overlook, particularly in complex terrains. </p> Results <p>The neural network model outperformed traditional interpolation techniques, achieving a 47% reduction in Root Mean Square Error (RMSE), and a 0.56 increase in R<sup>2</sup> compared to the best-performing IDW variant (RS-IDW). These improvements highlight the model’s ability to assimilate diverse climatic and topographical inputs for more accurate rainfall prediction, particularly in areas where conventional methods fall short due to sparse or irregular precipitation. </p> Conclusion <p>By effectively merging advanced neural network algorithms with conventional rainfall estimation methods, this study provides a robust framework for understanding and estimating precipitation in arid regions like the UAE. The superior performance of the neural network approach suggests significant potential for improving water resource management practices, optimizing cloud seeding interventions, and informing policy decisions. Future studies are encouraged to expand the use of machine learning models to unravel the complexities of arid climate hydrology further.</p>

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Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月17日

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  • Bayan Banimfreg,
  • Ernesto Damiani,
  • Vesta Afzali Gorooh,
  • Duncan Axisa,
  • Luca Delle Monache,
  • Youssef Wehbe

摘要

Background

In the hyper-arid environment of the United Arab Emirates (UAE), understanding rainfall patterns is essential for effective water resource management, agricultural planning, and ecological conservation. This study investigates rainfall variability from 2003 to 2021, emphasizing the integration of machine learning to enhance the accuracy of Quantitative Precipitation Estimation (QPE).

Methods

This research utilizes extensive rainfall records from gauges and ERA5 climate data, processed at high temporal resolution across 19 years. Traditional spatial interpolation methods such as Classic Inverse Distance Weighting (C-IDW), Elevation-Adjusted IDW (EA-IDW), and Rainfall Similarity IDW (RS-IDW) were initially applied and analyzed in conjunction with the GTOPO30 Digital Elevation Model (DEM). Building on this, we developed and refined a neural network model tailored to capture the intricate patterns of rainfall distribution that conventional methods might overlook, particularly in complex terrains.

Results

The neural network model outperformed traditional interpolation techniques, achieving a 47% reduction in Root Mean Square Error (RMSE), and a 0.56 increase in R2 compared to the best-performing IDW variant (RS-IDW). These improvements highlight the model’s ability to assimilate diverse climatic and topographical inputs for more accurate rainfall prediction, particularly in areas where conventional methods fall short due to sparse or irregular precipitation.

Conclusion

By effectively merging advanced neural network algorithms with conventional rainfall estimation methods, this study provides a robust framework for understanding and estimating precipitation in arid regions like the UAE. The superior performance of the neural network approach suggests significant potential for improving water resource management practices, optimizing cloud seeding interventions, and informing policy decisions. Future studies are encouraged to expand the use of machine learning models to unravel the complexities of arid climate hydrology further.