High-magnitude outburst floods in mountainous terrains can exert significant impacts on Earth’s surface due to their immense hydraulic force. However, the mechanisms involved in bedrock erosion during extreme floods remain incompletely understood, primarily due to the scarcity of such events and challenges in conducting high-resolution measurements during flood events. Using a 2D HEC-RAS dam-breach hydrodynamic model, we investigate two outburst floods of varying magnitudes - the Gega Megaflood and the Yigong Superflood - along the Tsangpo Gorge in the eastern Himalayas. Our study reveals distinct flow patterns and erosion mechanisms associated with each flood event. The Gega Megaflood (~106 m3/s) exhibits a high potential for focused erosion, characterized by elevated shear stress levels (10-20 kPa) and flood power (~105 kW/m2), resulting in the formation of a persistent vortex for up to two days. In contrast, the Yigong Superflood (~105 m3/s) displays intense spindle-shaped flow dynamics lasting several hours. Changes in flood magnitudes yield variations in inundation extent, flow structures, and erosion mechanisms, with the Superflood erosion primarily driven by abrasion and lateral scour, leading to slope failures and valley widening. While the erosion process of the Megaflood involves a dynamic vortex with effective “plucking” sustained by alternating rotational forces, high shear stress, and significant water depths. Our findings underscore the critical role of hydraulic thresholds, defined by water depth and velocity, in shaping distinct flow structures and erosion mechanisms observed in outburst floods of varying magnitudes in the rugged mountains of the eastern Himalayas.

Eyewall replacements occur in 70% of major tropical cyclones (TCs), and are associated with rapid changes in storm intensity and rapid broadening of strong winds. In this study, we present to use the radial gradient of absolute angular momentum with Holland’s wind profile as a simple diagnostic for eyewall replacement as internal variability of the storm. The diagnostic is solely dependent on the maximum wind speed, the radius of maximum wind, and the latitude and found to coincident with 70% of satellite observed concentric eyewalls for 1991-2020. The diagnostic can be expressed as a latitude-irrelevant Rossby Number of primary eyewall, varying with the peakedness of wind. It highlights the importance of pressure-wind relationship in eyewall replacement and provides a valuable tool to improve the understanding, modeling and risk assessment of storms with eyewall replacements.

Accurate estimation of precipitation in both space and time is essential for hydrological research. We compared multi-source weighted ensemble precipitation (MSWEP) with multi-source fused satellite precipitation (CHIRPS) based on high-density rain gauge precipitation observations in the Taihu Lake basin. We proposed a new merge precipitation algorithm GWRMP based on the geographically weighted regression (GWR) method. GWRMP corrects the bias of MSWEP by using high-density rain gauge precipitation to address the common problem of daily precipitation underestimation in MSWEP. The large-scale spatial coverage of the water surface in this region leads to the uneven distribution of rain gauges on the lake. There are differences in the descriptive ability of the three spatial precipitation types, MSWEP, GWRMP, and IDW, for spatial and temporal precipitation information in the Taihu Lake basin. A comparison shows that GWRMP has a significant advantage in obtaining the spatial and temporal variability of precipitation in areas with complex topographic conditions. GWRMP compensates the problem of underestimation of precipitation by MSWEP (10% to 25%), and avoids the risk of the high dependence of IDW on rain gauges, and improves the accuracy of spatial and temporal precipitation in large lake areas with sparse distribution of rain gauges (Pbias limited to 10%). GWRMP improved the estimation for different rainfall intensities in the Taihu Lake basin, especially in the mid-level rainfall and above precipitation frequencies. Compared with IDW and MSWEP, GWRMP is more suitable for intense precipitation monitoring and storm flood frequency study in the basin. Therefore, GWRMP is a better choice for spatial and temporal estimation of precipitation in the Taihu Lake basin. The GWRMP algorithm can be applied to other regions with unevenly spaced high-density rain gauges.