Multi-Sensor Satellite-Based Retrospective Early-Warning Assessment of the 26 August 2026 Flood Event in the Langtang Region of Nepal
Abstract
Rapid-onset floods in high-mountain environments can be difficult to anticipate because their triggering mechanisms may involve interactions among precipitation, glacierized terrain, surface-water dynamics, and sudden geomorphological disturbances. This study presents a retrospective multi-sensor satellite assessment of the flood event reported in the Langtang region of Nepal on 26 August 2026. The objective was to determine whether remotely sensed observations could identify potentially relevant environmental changes several days before the event. A Google Earth Engine-based workflow was developed using ERA5-Land precipitation, Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral imagery, MODIS snow-cover observations, the GLIMS glacier inventory, and the JRC Global Surface Water dataset. The analysis followed a sequential workflow consisting of precipitation anomaly assessment, persistent-water screening, glacier inventory screening, candidate source identification, SAR disturbance analysis, optical validation, temporal precursor analysis, and retrospective early-warning scoring. Cumulative precipitation during 1–21 August 2026 was 322.14 mm, compared with a 2020–2025 mean of 336.44 mm and a standard deviation of 44.10 mm, corresponding to an anomaly of −14.31 mm and a standardized anomaly of −0.24. Thus, cumulative pre-event precipitation was not anomalously high relative to the selected baseline. In contrast, a glacierized source region approximately 1.96 km from the independently selected source point exhibited substantial multi-sensor change. The leading GLIMS glacier candidate had an inventory area of 2.23 km², while approximately 2.16 km² of its surface exhibited the defined SAR-change signal. The resulting affected fraction was approximately 97.08%. Direct source-area analysis also indicated changes in NDSI, NDVI, and MODIS-derived snow-cover metrics. A retrospective early-warning index increased substantially during the days preceding the event, reaching a maximum score of 44.51 during the T−5 to T−3 day interval. However, the predefined strong-precursor threshold of 60 was not reached. The results therefore indicate that detectable multi-sensor precursor signals existed several days before the event, but the available observations and heuristic index were insufficient to demonstrate a reliable operational early-warning capability. The study demonstrates the potential value of integrating SAR, optical, thermal/snow-cover, precipitation, glacier inventory, and surface-water information within a common geospatial framework. More importantly, it provides a reproducible workflow for retrospective investigation of rapid-onset mountain flood events in data-sparse regions.
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BibTeX
@misc{asadollahi2026multisensor,
title = {{Multi-Sensor Satellite-Based Retrospective Early-Warning Assessment of the 26 August 2026 Flood Event in the Langtang Region of Nepal}},
author = {Avideh Asadollahi},
howpublished = {EarthArXiv},
year = {2026},
month = sep,
doi = {10.31223/x5kb85},
url = {https://eartharxiv.org/repository/view/14773/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Multi-Sensor Satellite-Based Retrospective Early-Warning Assessment of the 26 August 2026 Flood Event in the Langtang Region of Nepal
AU - Avideh Asadollahi
PY - 2026
DA - 2026/09/03
PB - EarthArXiv
DO - 10.31223/x5kb85
UR - https://eartharxiv.org/repository/view/14773/
AB - Rapid-onset floods in high-mountain environments can be difficult to anticipate because their triggering mechanisms may involve interactions among precipitation, glacierized terrain, surface-water dynamics, and sudden geomorphological disturbances. This study presents a retrospective multi-sensor satellite assessment of the flood event reported in the Langtang region of Nepal on 26 August 2026. The objective was to determine whether remotely sensed observations could identify potentially relevant environmental changes several days before the event. A Google Earth Engine-based workflow was developed using ERA5-Land precipitation, Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral imagery, MODIS snow-cover observations, the GLIMS glacier inventory, and the JRC Global Surface Water dataset. The analysis followed a sequential workflow consisting of precipitation anomaly assessment, persistent-water screening, glacier inventory screening, candidate source identification, SAR disturbance analysis, optical validation, temporal precursor analysis, and retrospective early-warning scoring. Cumulative precipitation during 1–21 August 2026 was 322.14 mm, compared with a 2020–2025 mean of 336.44 mm and a standard deviation of 44.10 mm, corresponding to an anomaly of −14.31 mm and a standardized anomaly of −0.24. Thus, cumulative pre-event precipitation was not anomalously high relative to the selected baseline. In contrast, a glacierized source region approximately 1.96 km from the independently selected source point exhibited substantial multi-sensor change. The leading GLIMS glacier candidate had an inventory area of 2.23 km², while approximately 2.16 km² of its surface exhibited the defined SAR-change signal. The resulting affected fraction was approximately 97.08%. Direct source-area analysis also indicated changes in NDSI, NDVI, and MODIS-derived snow-cover metrics. A retrospective early-warning index increased substantially during the days preceding the event, reaching a maximum score of 44.51 during the T−5 to T−3 day interval. However, the predefined strong-precursor threshold of 60 was not reached. The results therefore indicate that detectable multi-sensor precursor signals existed several days before the event, but the available observations and heuristic index were insufficient to demonstrate a reliable operational early-warning capability. The study demonstrates the potential value of integrating SAR, optical, thermal/snow-cover, precipitation, glacier inventory, and surface-water information within a common geospatial framework. More importantly, it provides a reproducible workflow for retrospective investigation of rapid-onset mountain flood events in data-sparse regions.
ER -