Preprints
https://doi.org/10.5194/nhess-2023-120
https://doi.org/10.5194/nhess-2023-120
08 Aug 2023
 | 08 Aug 2023
Status: this preprint is currently under review for the journal NHESS.

Predictive Understanding of Socioeconomic Flood Impact in Data-Scarce Regions Based on Channel Properties and Storm Characteristics: Application in High Mountain Asia (HMA)

Mariam Khanam, Giulia Sofia, Wilmalis Rodriguez, Efthymios I. Nikolopoulos, Binghao Lu, Dongjin Song, and Emmanouil N. Anagnostou

Abstract. The exposure of High Mountain Asia (HMA) to disaster risks is heightened by extreme weather conditions and the impacts of climate change. Obtaining knowledge about the long-term response of the landscape to hydroclimatic variations in HMA is paramount, as millions of people are affected by these changes every year. During monsoons, substantial human suffering, and damage to crops and infrastructure in populated communities result from the flooding and debris flow caused by the increase in precipitation extremes each year. Although a few initiatives have undertaken the estimation of flood risk locally, the use of traditional techniques in ungauged basins is, unfortunately, not always possible because of the lack of extensive data required. To address this problem, we present in this study a geomorphologically guided machine learning (ML) approach for mapping flood impacts across HMA. We defined socioeconomic flood impact using the Lifeyears Index (LYI), a systematic index that measures the economic cost and loss of life caused by flooding. This index quantifies the importance of the destruction to infrastructure, capital, and housing in an overall assessment. We trained the proposed model with over 6000 flood events, from 1980 to 2020, and their computed five-year and ten-year LYIs. We used as predictors, (1) the five-year rainfall concentrations (which correlate the magnitude of precipitation events with the time of occurrence) of events retrieved from ERA5 daily data; (2) a geomorphic classifier (flood geomorphic potential) based on hydraulic scaling functions automatically derived from an 8 and 30-meter digital elevation model (DEM) for the region and (3) population. This model proved capable of identifying the hotspots of flood susceptibility on a national scale and showing its variability from 1980 to 2022. The study also highlights the severity of the impacts of hydroclimatic extremes in the entire HMA region. The framework is generic and can be used to derive a wide variety of flood vulnerability and subsequent risk maps in data-scarce regions.

Mariam Khanam, Giulia Sofia, Wilmalis Rodriguez, Efthymios I. Nikolopoulos, Binghao Lu, Dongjin Song, and Emmanouil N. Anagnostou

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-120', Jakob F. Steiner, 26 Sep 2023
    • AC1: 'Reply on RC1', Mariam Khanam, 24 Nov 2023
  • CC1: 'Comment on nhess-2023-120', Donghui Shangguan, 01 Feb 2024
    • AC2: 'Reply on CC1', Mariam Khanam, 07 Feb 2024
  • RC2: 'Comment on nhess-2023-120', Anonymous Referee #2, 18 Feb 2024
    • AC3: 'Reply on RC2', Mariam Khanam, 06 Mar 2024
Mariam Khanam, Giulia Sofia, Wilmalis Rodriguez, Efthymios I. Nikolopoulos, Binghao Lu, Dongjin Song, and Emmanouil N. Anagnostou
Mariam Khanam, Giulia Sofia, Wilmalis Rodriguez, Efthymios I. Nikolopoulos, Binghao Lu, Dongjin Song, and Emmanouil N. Anagnostou

Viewed

Total article views: 583 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
427 137 19 583 14 14
  • HTML: 427
  • PDF: 137
  • XML: 19
  • Total: 583
  • BibTeX: 14
  • EndNote: 14
Views and downloads (calculated since 08 Aug 2023)
Cumulative views and downloads (calculated since 08 Aug 2023)

Viewed (geographical distribution)

Total article views: 547 (including HTML, PDF, and XML) Thereof 547 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 22 Mar 2024
Download
Short summary
This study comprehends and predicts the socioeconomic effects of floods in the High Mountain Asia (HMA) region. We proposed a machine-learning strategy for mapping flood damages. We predicted the Lifeyears Index (LYI), which quantifies the financial cost and loss of life caused by floods, using variables including climate, geomorphology, and population. The study's overall goal is to offer useful information on flood susceptibility and subsequent risk mapping in the HMA region.
Altmetrics