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

Wünsch, Andreas; Liesch, Tanja; Broda, Stefan

doi:10.21203/rs.3.rs-420056/v1

Cited by 5 publications

(10 citation statements)

References 27 publications

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“…This finding is also in accordance with other studies with different modelling approaches, which showed that direct climatic influences on groundwater levels are rather low and account for only a small part of the falling groundwater levels (e.g. Alfaro et al 2017;Wunsch et al 2021).…”

Section: Discussionsupporting

confidence: 93%

Quantification and possible causes of declining groundwater resources in the Euro-Mediterranean region from 2003 to 2020

Xanke

Liesch

2022

Hydrogeol J

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Groundwater resources in Euro-Mediterranean countries provide a large part of the population’s water supply and are affected to varying degrees by anthropogenic use and climatic impacts. In many places, significant groundwater-level declines have already been observed, indicating an imbalance between natural groundwater recharge and groundwater abstraction. The extent of changes in groundwater storage (GWS) in the period 2003–2020 is quantified for the Euro-Mediterranean region using the latest data from the Gravity Recovery and Climate Experiment (GRACE/GRACE-FO) satellite mission and recently reanalyzed ERA5-Land climate data from the European Centre for Medium-Range Weather Forecasts. The results are set in relation to the prevailing climate, the regional hydrogeological setting, and annual groundwater recharge and abstractions on country level. Analysis of the mean annual trends over the study period shows significant decreases in GWS in many countries of Europe, Northern Africa and the entire Arabian Peninsula. Overall, there are significantly negative trends in about 70% of the study region. The mean of the trends across the Euro-Mediterranean region is –2.1 mm/year. The strongest negative trends in GWS per country are observed in Iraq and Syria (–8.8 and –6.0 mm/year, respectively), but also countries in central and eastern Europe are affected by depleting aquifers. The results are a clear indicator of the already medium-term groundwater stress in the Euro-Mediterranean region, which is expected to increase in the future, and demonstrate the need for adapted strategies for sustainable groundwater management on a transregional scale in the context of climate change and population growth.

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Section: Discussionsupporting

confidence: 93%

Quantification and possible causes of declining groundwater resources in the Euro-Mediterranean region from 2003 to 2020

Xanke

Liesch

2022

Hydrogeol J

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“…Therefore, the decrease in the GWL in the future period can be related to the increase in temperature and evaporation. Our result is consistent with the results of (Chang et al, 2015;Arkoç, 2022;Wunsch et al, 2022).…”

Section: -2064supporting

confidence: 93%

“…Their results showed the average annual temperature will increase and the amount of rainfall will decrease. The deep learning method was used to project GWL in Germany until 2100 under climate change (Wunsch, Liesch and Broda, 2022). The results of using ANN neural network and NARX model indicated decreased in GWL.…”

Section: Introductionmentioning

confidence: 99%

Projection of Groundwater Level Fluctuations Using Different Machine Learning Algorithms under Climate Change in the Mashhad Aquifer, Iran

Eskafi

Faridhosseini

Khodashenas

et al. 2022

Preprint

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Due to population growth in recent years and climate change in arid and semi-arid regions, the lack of rainfall and the reduction of surface water flows required in various sectors, monitoring and projection of the climate change impact on the Groundwater Level (GWL) in the future is vital in the management and control of these resources. The purpose of this study is the projection of climate change impact on the GWL fluctuations in the Mashhad aquifer during the future period (2022-2064). In the first step, the climatic variables using ACCESS-CM2 under the Shared Socio-economic Pathways (SSPs) 5-8.5 scenario from the CMIP6 model were extracted. We used the CMhyd model to downscale the climatic data from the GCMs model. In the second step, different machine learning algorithms, including Multilayer Perceptron Neural Network (MLP), Adaptive Neuro-fuzzy Inference System Neutral Network (ANFIS), Radial Basis Function Neural Network (RBF), and Support Vector Machine (SVM) were used to predict the GWL fluctuations under climate change in the future period. Our results point out that temperatures and evaporation will increase in the autumn season, and precipitation will decrease by 26% in the future in the Mashhad aquifer. The results showed that the RBF model was an excellent performance in predicting GWL compared to other models. Based on the result of the RBF model, the GWL will decrease by 6.60 meters under the SSP5-8.5 scenario in the future. The findings of this research have a practical role in making helpful groundwater resources management decisions.

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“…The code necessary to reproduce our results is available on GitHub. 60 Received: 13 April 2021; Accepted: 11 February 2022;…”

Section: Data Availabilitymentioning

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

Cited by 5 publications

References 27 publications

Quantification and possible causes of declining groundwater resources in the Euro-Mediterranean region from 2003 to 2020

Quantification and possible causes of declining groundwater resources in the Euro-Mediterranean region from 2003 to 2020

Projection of Groundwater Level Fluctuations Using Different Machine Learning Algorithms under Climate Change in the Mashhad Aquifer, Iran

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

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