the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Predictive Understanding of Socioeconomic Flood Impact in Data-Scarce Regions Based on Channel Properties and Storm Characteristics: Application in High Mountain Asia (HMA)
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.
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Status: final response (author comments only)
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RC1: 'Comment on nhess-2023-120', Jakob F. Steiner, 26 Sep 2023
- AC1: 'Reply on RC1', Mariam Khanam, 24 Nov 2023
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CC1: 'Comment on nhess-2023-120', Donghui Shangguan, 01 Feb 2024
The manuscript “Predictive understanding of socioeconomic flood impact in data scarce regions based on channel properties and storm characteristics: Application in High Mountain Asia(HMA)” by Khanam et al. used LYI and ML methods to evaluate and predict the flood impacts and risk due to precipitation in HMA. This work is first time to evaluate socioeconomic impacts of flood hazards in data scarce region. However, it is not good writing. The structure is not reasonable. And the XGboosting tools is not clear to solve what? Thus, I would suggest it should be major revision.
General comments
- In HMA there are also GLOF which risk the human being and infrastructure. If possible, please include evaluating the socioeconomic flood impact.
- Data-Scarce regions should be clear (which data or which type of data). In HMA, population is scarce. And Socio activity is also low.
Specific comments
1.Section 2.2 methods. This title is not reasonable. Is section 2.3(Machine learning model) methods? In addition, the dataset and methods in this section should be divided, for example, 2.2.4 exposure(population) is datasets.
2.Line 135 Why is it classified by LYI values(<2,2-3, and >3).
3.Line 217 While XGBoosting is …,this sentence is incomplete.
- Line 217 this section (machine learning model) is a little difficult to understand the role that is plays.
Citation: https://doi.org/10.5194/nhess-2023-120-CC1 - AC2: 'Reply on CC1', Mariam Khanam, 07 Feb 2024
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RC2: 'Comment on nhess-2023-120', Anonymous Referee #2, 18 Feb 2024
1. Clarity and Structure: The abstract is well-structured, presenting the problem, the proposed solution, and a few findings. However, some sentences are complex (starting from the title), and more concise wording could enhance clarity,
2. Methodology: The use of the Lifeyears Index (LYI) as a measure for socioeconomic flood impact is well explained. It would be beneficial to provide a brief explanation of how the geomorphologically guided machine learning approach works, even if it is in a bit summary.
3. Data: The abstract mentions training the model with over 6000 flood events from 1980 to 2020, but It is mentioned that the model shows variability from 1980 to 2022 as temporal Coverage. So what's the correct timeline?
4. Conclusion: A brief conclusion summarizing the main contributions and implications of the study would be beneficial.
Citation: https://doi.org/10.5194/nhess-2023-120-RC2 - AC3: 'Reply on RC2', Mariam Khanam, 06 Mar 2024
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