Preprints
https://doi.org/10.5194/hess-2024-41
https://doi.org/10.5194/hess-2024-41
14 Mar 2024
 | 14 Mar 2024
Status: this preprint is currently under review for the journal HESS.

Support system for heat pump planning in response to drought conditions

Justyna Kubicz and Maciej Karczewski

Abstract. Access to extensive data enables advanced research on the impacts of climate change, specifically drought, on groundwater resources and their consequential effects on water quality. While scientific studies offer insights into predicting groundwater shortages at a high level, the data often remain inaccessible and incomprehensible to potential users. To bridge this gap, this study proposes a relatively straightforward method for assessing the risk of groundwater availability for heat pump usage. The method involves creating informative color-coded charts illustrating periods of potential excessive groundwater level decline near utilized wells. Additionally, it provides the ability to monitor changes in the risk of groundwater level reduction within a predefined observational period.

During droughts, groundwater levels can significantly drop, impacting groundwater availability and potentially reducing heat pump efficiency. This, in turn, may lead to system overheating, decreasing effectiveness, and causing damage. Exceeding critical groundwater levels may result in well infrastructure damage, affecting water quality and energy extraction efficiency. Excessive well exploitation often leads to chemical and mechanical clogging, further influencing well performance.

In contrast to commonly used hydrogeological drought indicators, this method focuses on a probabilistic model, simplifying calculations as it only requires historical groundwater level data. By applying statistical tests and distribution functions, the study evaluates the risk of extreme groundwater level reduction. The proposed method categorizes risk into very high, high, moderate, and low levels, providing a practical tool for users and groundwater management.

The study area, located in the northwest Eurasian continent, encompasses diverse geological and hydrogeological settings. Utilizing data from 27 groundwater observation points, spanning from 1980 to 2020, the research identifies periods and regions at risk of groundwater depletion. The findings highlight specific points vulnerable to high or very high risks, emphasizing the importance of groundwater management strategies.

By analysing monthly, quarterly, and seasonal risk variations and comparing results between the decades 2001–2010 and 2011–2020, the study unveils critical insights into groundwater dynamics. Points such as 15 exhibit pronounced risk increases, indicating potential overexploitation or insufficient replenishment. Notably, certain points display decreasing risks, showcasing positive trends that align with effective groundwater management practices.

This comprehensive probabilistic approach provides valuable information for stakeholders, empowering them to make informed decisions in selecting a sustainable energy source.

Justyna Kubicz and Maciej Karczewski

Status: open (until 11 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-41', Anonymous Referee #1, 21 Apr 2024 reply
Justyna Kubicz and Maciej Karczewski
Justyna Kubicz and Maciej Karczewski

Viewed

Total article views: 177 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
141 23 13 177 11 10
  • HTML: 141
  • PDF: 23
  • XML: 13
  • Total: 177
  • BibTeX: 11
  • EndNote: 10
Views and downloads (calculated since 14 Mar 2024)
Cumulative views and downloads (calculated since 14 Mar 2024)

Viewed (geographical distribution)

Total article views: 147 (including HTML, PDF, and XML) Thereof 147 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Apr 2024
Download
Short summary
In contrast to commonly used hydrogeological drought indicators, this method focuses on a probabilistic model, simplifying calculations as it only requires historical groundwater level data. By applying statistical tests and distribution functions, the study evaluates the risk of extreme groundwater level reduction. The proposed method categorizes risk into very high, high, moderate, and low levels, providing a practical tool for users and groundwater management.