Understanding the dynamics of 2024 extreme heat event in India: Spatial variability, hydrometeorological impacts, and model evaluation
(2025) In Atmospheric Research 322.- Abstract
- Heatwaves are becoming more intense, frequent, and prolonged due to global warming, posing significant risks to ecosystems and human societies. Despite their profound impact, detailed regional assessments of extreme heat events remain limited, particularly in India. This study addresses the gap by systematically investigating the 2024 extreme heat event in India. We evaluated the performance of various land surface schemes in simulating heat extremes using the Weather Research and Forecasting model and also assessed the accuracy of Global Forecast System (GFS) forecasts. Our analysis reveals a strong co-occurrence of drought and heat stress during the extreme heat event. This combination results in increased fire risk and negative impacts... (More)
- Heatwaves are becoming more intense, frequent, and prolonged due to global warming, posing significant risks to ecosystems and human societies. Despite their profound impact, detailed regional assessments of extreme heat events remain limited, particularly in India. This study addresses the gap by systematically investigating the 2024 extreme heat event in India. We evaluated the performance of various land surface schemes in simulating heat extremes using the Weather Research and Forecasting model and also assessed the accuracy of Global Forecast System (GFS) forecasts. Our analysis reveals a strong co-occurrence of drought and heat stress during the extreme heat event. This combination results in increased fire risk and negative impacts on vegetation productivity in regions affected by both drought and heat stress highlighting the severe consequences of this compound event. We compare different land surface models (RUC, Noah, Noah-MP, Noah-MP with dynamic vegetation, CLM) against India Meteorological Department (IMD) observations. We observe that Noah is optimal for reducing bias and RMSE, while Noah-MP with dynamic vegetation is most accurate for simulating extreme heat, with the highest hit rate and threat score for the 90th percentile threshold. Additionally, GFS maximum temperature forecasts for 1–3 day lead times perform well at short lead times, especially in Southern India but overestimate temperatures in heatwave-prone regions like the Indo-Gangetic Plains. Our findings highlight the importance of enhancing land surface models and forecasting systems to better predict extreme heat events, which is crucial for localized hazard and risk assessments and improving disaster management efficiency. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3e88e129-ab8e-4281-8551-050b4ad6ee18
- author
- Verma, Akash ; Khadke, Leena and Budakoti, Sachin LU
- organization
- publishing date
- 2025-04-14
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Atmospheric Research
- volume
- 322
- article number
- 108154
- publisher
- Elsevier
- external identifiers
-
- scopus:105002763410
- ISSN
- 0169-8095
- DOI
- 10.1016/j.atmosres.2025.108154
- language
- English
- LU publication?
- yes
- id
- 3e88e129-ab8e-4281-8551-050b4ad6ee18
- date added to LUP
- 2025-05-21 11:48:25
- date last changed
- 2025-05-22 13:12:54
@article{3e88e129-ab8e-4281-8551-050b4ad6ee18,
abstract = {{Heatwaves are becoming more intense, frequent, and prolonged due to global warming, posing significant risks to ecosystems and human societies. Despite their profound impact, detailed regional assessments of extreme heat events remain limited, particularly in India. This study addresses the gap by systematically investigating the 2024 extreme heat event in India. We evaluated the performance of various land surface schemes in simulating heat extremes using the Weather Research and Forecasting model and also assessed the accuracy of Global Forecast System (GFS) forecasts. Our analysis reveals a strong co-occurrence of drought and heat stress during the extreme heat event. This combination results in increased fire risk and negative impacts on vegetation productivity in regions affected by both drought and heat stress highlighting the severe consequences of this compound event. We compare different land surface models (RUC, Noah, Noah-MP, Noah-MP with dynamic vegetation, CLM) against India Meteorological Department (IMD) observations. We observe that Noah is optimal for reducing bias and RMSE, while Noah-MP with dynamic vegetation is most accurate for simulating extreme heat, with the highest hit rate and threat score for the 90th percentile threshold. Additionally, GFS maximum temperature forecasts for 1–3 day lead times perform well at short lead times, especially in Southern India but overestimate temperatures in heatwave-prone regions like the Indo-Gangetic Plains. Our findings highlight the importance of enhancing land surface models and forecasting systems to better predict extreme heat events, which is crucial for localized hazard and risk assessments and improving disaster management efficiency.}},
author = {{Verma, Akash and Khadke, Leena and Budakoti, Sachin}},
issn = {{0169-8095}},
language = {{eng}},
month = {{04}},
publisher = {{Elsevier}},
series = {{Atmospheric Research}},
title = {{Understanding the dynamics of 2024 extreme heat event in India: Spatial variability, hydrometeorological impacts, and model evaluation}},
url = {{http://dx.doi.org/10.1016/j.atmosres.2025.108154}},
doi = {{10.1016/j.atmosres.2025.108154}},
volume = {{322}},
year = {{2025}},
}