ABSTRACT In hydrological research, the development of accurate prediction models necessitates the establishment of intricate rain gauge networks. This prerequisite is especially pronounced in geographically challenging regions, such as the Northwestern Himalayas (NWH). In such topographically complex areas, the configuration and spatial density of rain gauges play a pivotal role in shaping the reliability and accuracy of the collected data. Over the past few decades, this region has witnessed an unprecedented surge in extreme localised weather events, like cloudburst, resulting in catastrophic loss of lives and livelihoods. We designed an optimal rain gauge network that would provide significantly better sampling of rainfall patterns in the study region. In this work, we utilise advanced techniques and methodologies, such as dynamic time warping (DTW) and neural networks, to extract complex underlying patterns from the historical rainfall data. Leveraging historical meteorological data spanning the past four decades, we employed data‐driven analyses to unveil discernible rainfall patterns. These patterns were instrumental in identifying geographical regions characterised by analogous rainfall trends. Subsequently, we engineered tailored rain gauge networks in these identified areas to acquire rainfall data of the highest precision. For Himachal Pradesh and adjoining areas, we designed a network consisting of 50 strategically identified rain gauge locations. This network was carefully evaluated to ensure the optimal number of rain gauges, placement, and overall quality of the rain gauges. This innovative approach holds significant promise in laying the groundwork for advanced and reliable rainfall monitoring systems. In doing so, it has the potential to yield substantial benefits for a multitude of stakeholders, including local communities, agriculture, infrastructure development, and the overarching goals of environmental sustainability.
Suri et al. (2025) studied this question.