Soft computing assessment of current and future groundwater resources under CMIP6 scenarios in northwestern Iran
Kayhomayoon, Zahra ; Jamnani, Mostafa Rahimi ; Rashidi, Sajjad ; Ghordoyee Milan, Sami ; Arya Azar, Naser and Berndtsson, Ronny LU
(2023)
In Agricultural Water Management
285.
- Abstract
Excessive use of water resources in combination with climate change threaten to significantly reduce groundwater in arid and semiarid regions. We studied the effects of climate change on the groundwater level for the important Dehgolan Aquifer in northwestern Iran. The water level in this aquifer has dropped by about 35 m during the last 30 years. Soft computing techniques were used together with climate projections in three methodological steps to estimate the groundwater level drop by 2045. Firstly, MODFLOW was used to simulate groundwater flow and movement. Secondly, simulation results, support vector regression (SVR), and least-squares SVR (LSSVR) machine learning models were used to predict groundwater levels for the future 20-year... (More)
Excessive use of water resources in combination with climate change threaten to significantly reduce groundwater in arid and semiarid regions. We studied the effects of climate change on the groundwater level for the important Dehgolan Aquifer in northwestern Iran. The water level in this aquifer has dropped by about 35 m during the last 30 years. Soft computing techniques were used together with climate projections in three methodological steps to estimate the groundwater level drop by 2045. Firstly, MODFLOW was used to simulate groundwater flow and movement. Secondly, simulation results, support vector regression (SVR), and least-squares SVR (LSSVR) machine learning models were used to predict groundwater levels for the future 20-year period (2026–2045). The whale optimization algorithm (WOA) was used to improve the prediction results by optimizing the SVR parameters. Thirdly, three climate models of CMIP6 (ACCESS-CM2, BCC-CSM2-MR, and CMCC-ESM2) were used to predict the changes in precipitation for the future period (2026–2045) using SSP 2.6 and SSP 8.5 scenarios. The results showed that the MODFLOW-LSSVR model predicted the groundwater level more accurately than MODFLOW-SVR and MODFLOW-SVR-WOA. The calculation scenario containing previous month groundwater level, monthly aquifer withdrawal, and monthly precipitation had the highest performance in predicting groundwater level with root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash Sutcliffe efficiency (NSE) equal to 0.305 m, 0.014 m, and 0.998, respectively. The results indicate that precipitation may decrease in the future period for the SSP 8.5 scenario (about 6% compared to the reference period 1987–2005). This decrease, along with the continuation of the current aquifer withdrawal, will cause a drop of about 36 m (during 28 years) of the groundwater level (1.3 m per year). The results reveal that the drop could be reduced to 12 m by adopting a 25% reduction in the current aquifer withdrawal. The findings show the necessity of providing a suitable management approach to prevent future aquifer exhaustion due to the continuation of the current withdrawal situation in the region.
(Less)
- author
- Kayhomayoon, Zahra
; Jamnani, Mostafa Rahimi
; Rashidi, Sajjad
; Ghordoyee Milan, Sami
; Arya Azar, Naser
and Berndtsson, Ronny
LU
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Aquifer, Climate change, Groundwater level prediction, Groundwater management, Machine learning
- in
- Agricultural Water Management
- volume
- 285
- article number
- 108369
- publisher
- Elsevier
- external identifiers
-
- scopus:85160654410
- ISSN
- 0378-3774
- DOI
- 10.1016/j.agwat.2023.108369
- language
- English
- LU publication?
- yes
- id
- 759639a2-ca48-434e-af2b-cfad7346ad0c
- date added to LUP
- 2023-08-17 10:46:17
- date last changed
- 2023-10-10 14:40:43
@article{759639a2-ca48-434e-af2b-cfad7346ad0c,
abstract = {{<p>Excessive use of water resources in combination with climate change threaten to significantly reduce groundwater in arid and semiarid regions. We studied the effects of climate change on the groundwater level for the important Dehgolan Aquifer in northwestern Iran. The water level in this aquifer has dropped by about 35 m during the last 30 years. Soft computing techniques were used together with climate projections in three methodological steps to estimate the groundwater level drop by 2045. Firstly, MODFLOW was used to simulate groundwater flow and movement. Secondly, simulation results, support vector regression (SVR), and least-squares SVR (LSSVR) machine learning models were used to predict groundwater levels for the future 20-year period (2026–2045). The whale optimization algorithm (WOA) was used to improve the prediction results by optimizing the SVR parameters. Thirdly, three climate models of CMIP6 (ACCESS-CM2, BCC-CSM2-MR, and CMCC-ESM2) were used to predict the changes in precipitation for the future period (2026–2045) using SSP 2.6 and SSP 8.5 scenarios. The results showed that the MODFLOW-LSSVR model predicted the groundwater level more accurately than MODFLOW-SVR and MODFLOW-SVR-WOA. The calculation scenario containing previous month groundwater level, monthly aquifer withdrawal, and monthly precipitation had the highest performance in predicting groundwater level with root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash Sutcliffe efficiency (NSE) equal to 0.305 m, 0.014 m, and 0.998, respectively. The results indicate that precipitation may decrease in the future period for the SSP 8.5 scenario (about 6% compared to the reference period 1987–2005). This decrease, along with the continuation of the current aquifer withdrawal, will cause a drop of about 36 m (during 28 years) of the groundwater level (1.3 m per year). The results reveal that the drop could be reduced to 12 m by adopting a 25% reduction in the current aquifer withdrawal. The findings show the necessity of providing a suitable management approach to prevent future aquifer exhaustion due to the continuation of the current withdrawal situation in the region.</p>}},
author = {{Kayhomayoon, Zahra and Jamnani, Mostafa Rahimi and Rashidi, Sajjad and Ghordoyee Milan, Sami and Arya Azar, Naser and Berndtsson, Ronny}},
issn = {{0378-3774}},
keywords = {{Aquifer; Climate change; Groundwater level prediction; Groundwater management; Machine learning}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Agricultural Water Management}},
title = {{Soft computing assessment of current and future groundwater resources under CMIP6 scenarios in northwestern Iran}},
url = {{http://dx.doi.org/10.1016/j.agwat.2023.108369}},
doi = {{10.1016/j.agwat.2023.108369}},
volume = {{285}},
year = {{2023}},
}