Abstract

Mountainous terrain poses a challenge in estimating rainfall, because of high variability, both in space and time, associated with the mesoscale process. Accurate rainfall data are essential for various hydrological analyses and studies. A well-designed network with optimal rain gauge density provides critical input for designing, managing, and operating projects focused on water resources management. A dense rain gauge network can help capture the spatiotemporal variations in precipitation. Traditionally, rain gauge networks are designed based on accessibility and economic constraints. Studies have found that networks designed using traditional techniques are sub-optimal at monitoring extreme weather events like flash floods, especially in mountainous terrain. In comparison, satellite and radar-based rainfall estimates have higher spatiotemporal accuracies, making them very effective for monitoring local weather events. However, these alternate datasets require bias correction through ground observations due to sensor and modeling errors. This review evaluates the various techniques for designing optimal rain gauge networks and their effectiveness in capturing hyperlocal extreme weather events in mountainous regions. Our goal is to analyze existing networks in Arizona (USA), Switzerland, Peru, Iran, Taiwan, England, and a sparse network in the Himalayan region, as well as suggest improvements to current approaches employed in these terrains. This will aid in the development of more robust methodologies and the improvement of extreme weather prediction skills. Finally, we conclude this review with the future direction of rain gauge network design for sparse data regions.