Advances in the Measurement, Utility and Evaluation of Precipitation Observations

This Reprint presents a collection of recent advances in the observation, measurement, and application of precipitation data in hydrological research. Precipitation is a central driver of the hydrological cycle, influencing processes such as runoff generation, groundwater recharge, flood forecasting, and water resource management. Accurate and reliable precipitation information remains a cornerstone for understanding hydrologic variability and for developing effective mitigation and adaptation strategies in the face of climate change.

The studies in this Reprint showcase a wide range of innovative approaches, from the integration of satellite and ground-based observations to the use of machine learning, interpolation, and regional calibration methods for improving rainfall estimates. Several contributions explore long-term precipitation trends, mathematical models to describe precipitation characteristics, storm dynamics in complex terrains, and the assessment of rainfall-related hazards. Others propose new techniques for downscaling, uncertainty quantification, and multi-source data fusion to enhance the spatial and temporal resolution of precipitation datasets. By highlighting methodological developments and practical applications across diverse climatic and geographic contexts, this Reprint provides valuable insights into how modern technologies and analytical frameworks are advancing the measurement and understanding of precipitation and its role in hydrologic systems.