TUHH Open Research
Help
  • Log In
    New user? Click here to register.Have you forgotten your password?
  • English
  • Deutsch
  • Communities & Collections
  • Publications
  • Research Data
  • People
  • Institutions
  • Projects
  • Statistics
  1. Home
  2. TUHH
  3. Publications
  4. A global analysis of the influence of shallow and deep groundwater tables on relationships between environmental parameters and heatwaves
 
Options

A global analysis of the influence of shallow and deep groundwater tables on relationships between environmental parameters and heatwaves

Citation Link: https://doi.org/10.15480/882.16277
Publikationstyp
Journal Article
Date Issued
2025-11-19
Sprache
English
Author(s)
Vogelbacher, Anastasia  
Geohydroinformatik B-9  
Afshar, Mehdi H.  
Geohydroinformatik B-9  
Aminzadeh, Milad  
Geohydroinformatik B-9  
Madani, Kaveh  
AghaKouchak, Amir  
Shokri, Nima  
Geohydroinformatik B-9  
TORE-DOI
10.15480/882.16277
TORE-URI
https://hdl.handle.net/11420/59470
Lizenz
https://creativecommons.org/licenses/by/4.0/
Journal
Environmental research  
Volume
289
Article Number
123354
Citation
Environmental Research 289: 123354 (2026)
Publisher DOI
10.1016/j.envres.2025.123354
Scopus ID
2-s2.0-105022825113
Publisher
Elsevier
Heatwaves increasingly impact ecosystems, human health, and economic activities worldwide. As their frequency and intensity rise, understanding the mechanisms driving heatwave dynamics and interactions with land surface processes becomes crucial. While numerous studies have examined atmospheric and land surface variables, the role of groundwater, through its effects on soil moisture and surface evaporative fluxes, remains less understood. Although modeling approaches at various scales have enhanced our understanding of groundwater-atmosphere coupling, machine learning (ML) enables capturing complex, nonlinear interactions and evaluating the relative importance of key drivers globally. We developed pixel-based ML models to estimate global summer heatwave frequency over the past 21 years. For each pixel, we considered data within a 1.5° radius (149 neighboring pixels), identified as the optimal scale through a saturation radius analysis. We used feature importance metrics to identify the dominant drivers among surface fluxes, land characteristics, atmospheric and hydrological variables, and interpreted these results in relation to contrasting groundwater depths (<10 m and >100 m). We ensured robustness using 10-fold cross-validation and confirmed that results were not driven by randomness with two additional validation runs on a subset of the data, with shuffled targets and randomized covariates. Our findings suggest that geopotential height showed the highest relative importance among predictors in regions with deep groundwater tables, while in areas with shallow groundwater, surface fluxes emerge as the key contributor. Incorporating groundwater-related processes may therefore improve understanding of land-atmosphere interactions and support more robust assessments of future heatwave risks.
Subjects
Groundwater
Heatwave
Land-atmosphere interaction
Soil moisture
DDC Class
551: Geology, Hydrology Meteorology
Funding(s)
Klima, Klimawandel und Gesellschaft  
Publication version
publishedVersion
Loading...
Thumbnail Image
Name

1-s2.0-S0013935125026076-main.pdf

Type

Main Article

Size

8.95 MB

Format

Adobe PDF

TUHH
Weiterführende Links
  • Contact
  • Send Feedback
  • Cookie settings
  • Privacy policy
  • Impress
DSpace Software

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science
Design by effective webwork GmbH

  • Deutsche NationalbibliothekDeutsche Nationalbibliothek
  • ORCiD Member OrganizationORCiD Member Organization
  • DataCiteDataCite
  • Re3DataRe3Data
  • OpenDOAROpenDOAR
  • OpenAireOpenAire
  • BASE Bielefeld Academic Search EngineBASE Bielefeld Academic Search Engine
Feedback