The Library
A precipitation downscaling framework for regional warning of debris flow in mountainous areas
Tools
Qiu, Chenchen, Su, Lijun and Geng, Xueyu (2024) A precipitation downscaling framework for regional warning of debris flow in mountainous areas. Natural Hazards, 120 . pp. 1979-2004. doi:10.1007/s11069-023-06279-1 ISSN 0921-030X.
|
PDF
WRAP-precipitation-downscaling-framework-regional-warning-debris-flow-mountainous-areas-Qiu-2023.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (7Mb) | Preview |
|
PDF
WRAP-precipitation-downscaling-framework-regional-warning-debris-flow-mountainous-areas-2023.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (5Mb) |
Official URL: https://doi.org/10.1007/s11069-023-06279-1
Abstract
A timely warning system for debris-flow mitigation in mountainous areas is vital to decrease casualties. However, the lack of rainfall monitoring stations and coarse resolution of satellite-based observations pose challenges for developing such a debris-flow warning model in data-scarce areas. To offer an effective method for the generation of precipitation with fine resolution, a machine learning (ML) based approach is proposed to establish the relationship between precipitation and regional environmental factors (REVs), including normalized difference vegetation index (NDVI), digital elevation model (DEM), geolocations (longitude and latitude) and land surface temperature (LST). This approach enables the downscaling of 3B42 TRMM precipitation data, providing fine temporal and spatial resolution precipitation data. We use PERSIANN-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) data to calibrate the downscaled results using geographical differential analysis (GDA) before applying them in a case study in the Gyirong Zangbo Basin. After that, we calculate the rainfall thresholds of effective antecedent rainfall (Pe) - intraday rainfall (Po) based on the calibrated precipitation and integrate them into a susceptibility map to develop a debris-flow warning model. The results show that: (1) this ML-based approach can effectively achieve the downscaling of TRMM data; (2) calibrated TRMM data outperforms the original TRMM and downscaled TRMM data, reducing deviations by 55% and 57%; (3) the integrated model, incorporating rainfall thresholds, outperforms a single susceptibility map in providing debris-flow warnings. The developed warning model can offer dynamic warnings for debris flows that may have been missed by the original warning system at a regional scale.
Item Type: | Journal Article | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QE Geology | |||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Debris avalanches, Debris avalanches -- Forecasting, Debris avalanches -- Computer simulation, Natural disaster warning systems | |||||||||||||||
Journal or Publication Title: | Natural Hazards | |||||||||||||||
Publisher: | Springer | |||||||||||||||
ISSN: | 0921-030X | |||||||||||||||
Official Date: | January 2024 | |||||||||||||||
Dates: |
|
|||||||||||||||
Volume: | 120 | |||||||||||||||
Page Range: | pp. 1979-2004 | |||||||||||||||
DOI: | 10.1007/s11069-023-06279-1 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Re-use Statement: | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11069-023-06279-1. | |||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
Date of first compliant deposit: | 6 November 2023 | |||||||||||||||
Date of first compliant Open Access: | 23 November 2023 | |||||||||||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year