Simulation of groundwater level fluctuations in response to main climate parameters using a wavelet–ANN hybrid technique for the Shabestar Plain, Iran

Jeihouni, Esmaeil; Eslamian, Saeid; Mohammadi, Mirali; Zareian, Mohammad Javad

doi:10.1007/s12665-019-8283-3

Cited by 41 publications

(14 citation statements)

References 41 publications

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“…These results fit with the results of similar studies in Iran which emphasize the impact of climate change on the decrease in precipitation (Zarghami et al, 2011;Jeihouni et al, 2019a;Jeihouni et al, 2019b;Vaghefi et al, 2019;Baghanam et al, 2020).…”

Section: Discussion Of Resultssupporting

confidence: 91%

“…Also, the superiority of GP and GEP models to other data-driven models in the prediction of groundwater level variations has been demonstrated by several researchers (Fallah-Mehdipour et al, 2013;Fallah-Mehdipour et al, 2014;Kasiviswanathan et al, 2016;Bahmani et al, 2020). Some scholars have studied the impact of climate change on water resources (Abbaspour et al, 2009;Tapoglou et al, 2014;Chang et al, 2015;Salem et al, 2018;Jeihouni et al, 2019a;Razack et al, 2019;Idrizovic et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

et al. 2021

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The main objective of this research was to evaluate the possible impact of climate change on groundwater levels in the Tasuj Plain, Iran. To accomplish this, the values of precipitation for a near future period was projected through four General Circulation Models (GCM). Then, the groundwater level variations through the Genetic Expression Programming (GEP) model were evaluated. The projection results indicated that the average annual precipitation with 33.55 mm in the base period would decline to 20.51 mm, 20.11 mm, and 19.14 mm under three Representative Concentration Pathway (RCP) scenarios, namely RCP2.6, RCP4.5, RCP8.5, respectively. The values of the determination coefficient range from 0.92-0.99; the root mean square error between 0.12 and 0.61, and mean absolute error from 0.08 to 0.54 showed that in the forecasting of groundwater level through the GEP model, all models have acceptable results. The evaluation of groundwater level simulation demonstrated that the developed model through the precipitation and previous groundwater level performs better. Prediction of groundwater level for a future period based on climate change scenarios indicated that the average groundwater level in Tasuj Plain at the beginning of the period would experience a gradual reduction and would then increase very slightly to the end of the period. Overall, it was found that the declining precipitation under climate change has no significant impacts on the groundwater level in the Tasuj Plain, and other parameters like anthropogenic activities could be the primary reason for groundwater depletion.

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Section: Discussion Of Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

et al. 2021

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“…The application of ANNs to study groundwater level development in the long-term and in the context of climate change for a larger area like Germany has not been performed yet. Related studies with applications of ANNs either used a very small number of wells 27 – 29 and limited time horizons 27 , 28 or use ANNs without directly presenting future climate signals to the ANN 29 . In the case of streamflow runoff simulation, however, ANNs have been successfully applied to analyze the future development under climate change influences in several catchments all over California 30 as well as in two catchments in China 31 , 32 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

2022

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In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21st century. We apply a machine learning groundwater level prediction approach based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under different RCP scenarios (2.6, 4.5, 8.5). We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. While less pronounced and fewer significant trends can be found under RCP2.6 and RCP4.5, we detect significantly declining trends of groundwater levels for most of the sites under RCP8.5, revealing a spatial pattern of stronger decreases, especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century.

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“…Furthermore, the application of ANNs to study groundwater level development in the long-term and in the context of climate change for a larger area like Germany has not been performed yet. Related studies with applications of ANNs either used a very small number of wells [23][24][25] and limited time horizons 23,24 or use ANNs without directly presenting future climate signals to the ANN 25 . In case of stream ow runoff simulation, however, ANNs have been successfully applied to analyze the future development under climate change in uences in several catchments all over California 26 as well as two catchments in China 27,28 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

Wünsch

Liesch

Broda

2021

Preprint

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In this study we investigate how climate change will directly influence the groundwater resources in Germany during the 21st century. We apply a machine learning groundwater level prediction framework, based on convolutional neural networks to 118 sites well distributed over Germany to assess the groundwater level development under the RCP8.5 scenario, based on six selected climate projections, which represent 80% of the bandwidth of the possible future climate signal for Germany. We consider only direct meteorological inputs, while highly uncertain anthropogenic factors such as groundwater extractions are excluded. We detected significant declining trends of groundwater levels for most of the sites, revealing a spatial pattern of stronger decreases especially in the northern and eastern part of Germany, emphasizing already existing decreasing trends in these regions. We can further show an increased variability and longer periods of low groundwater levels during the annual cycle towards the end of the century.

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Simulation of groundwater level fluctuations in response to main climate parameters using a wavelet–ANN hybrid technique for the Shabestar Plain, Iran

Cited by 41 publications

References 41 publications

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model

Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

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