A Review of Advances in Probable Maximum Precipitation and Flood Estimation in the Kaligandaki Basin, Nepal

The Kaligandaki River Basin in Nepal, a region characterized by extreme precipitation variability and frequent flooding, has been the focus of recent hydrological studies to estimate Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF). This review synthesizes the methodologies, findings, and challenges of a pivotal study that employed the HEC-HMS model to estimate PMF from PMP in the Kaligandaki Basin. The study’s approach, which integrates hydro-meteorological data, statistical methods, and hydrological modeling, is evaluated in the context of global PMP/PMF estimation practices. Key strengths include robust data utilization and sub-basin-specific analysis, while limitations, such as sparse data networks and the omission of climate change impacts, are highlighted. Recommendations for advancing PMP/PMF research in data-scarce, topographically complex regions, such as Nepal, are provided, emphasizing the need for enhanced data collection, multi-model approaches, and climate-resilient methodologies.The Kaligandaki River Basin study represents a significant advancement in hydrological research for Nepal’s complex terrain. By employing the HEC-HMS model to estimate Probable Maximum Flood (PMF) from Probable Maximum Precipitation (PMP), the research provides crucial insights into extreme flood scenarios in a region prone to hydrological extremes. The methodology’s strength lies in its comprehensive approach, integrating diverse hydro-meteorological data and statistical methods to produce sub-basin-specific analyses. This granular  approach allows for a more nuanced understanding of flood risks across the basin’s varied topography. However, the study faces notable challenges inherent to its geographical context. The sparse data networks in Nepal limit the accuracy and spatial resolution of the analysis, potentially affecting the reliability of PMP and PMF estimates. Additionally, the omission of climate change impacts in the modeling process represents a significant gap, given the increasing influence of global warming on precipitation patterns and flood frequencies. To address these limitations and enhance future research, there is a pressing need for improved data collection infrastructure, the adoption of multi-model approaches to reduce uncertainty, and the integration of climate change projections into PMP/PMF estimations. These advancements would not only improve the accuracy of flood risk assessments but also contribute to more effective flood management and climate adaptation strategies in the Kaligandaki Basin and similar data-scarce, topographically complex regions worldwide.