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

2021 ◽  
Author(s):  
Andreas Wunsch ◽  
Tanja Liesch ◽  
Stefan Broda
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Neural Networks ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Anthropogenic Factors ◽  
Climate Projections ◽  
Climate Signal ◽  
Groundwater Levels ◽  
Groundwater Level Prediction

Abstract 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.

scholarly journals Deep Learning based assessment of groundwater level development in Germany until 2100

2021 ◽  
Author(s):  
Andreas Wunsch ◽  
Tanja Liesch ◽  
Stefan Broda
Keyword(s):  
Climate Change ◽  
Groundwater Level ◽  
Climate Models ◽  
Groundwater Resources ◽  
Groundwater Abstraction ◽  
Water Shortages ◽  
Entire Area ◽  
Groundwater Levels ◽  
The Past ◽  
Skill Scores

<p>Clear signs of climate stress on groundwater resources have been observed in recent years even in generally water-rich regions such as Germany. Severe droughts, resulting in decreased groundwater recharge, led to declining groundwater levels in many regions and even local drinking water shortages have occurred in past summers. We investigate how climate change will directly influence the groundwater resources in Germany until the year 2100. For this purpose, we use a machine learning groundwater level forecasting framework, based on Convolutional Neural Networks, which has already proven its suitability in modelling groundwater levels. We predict groundwater levels on more than 120 wells distributed over the entire area of Germany that showed strong reactions to meteorological signals in the past. The inputs are derived from the RCP8.5 scenario of six climate models, pre-selected and pre-processed by the German Meteorological Service, thus representing large parts of the range of the expected change in the next 80 years. Our models are based on precipitation and temperature and are carefully evaluated in the past and only wells with models reaching high forecasting skill scores are included in our study. We only consider natural climate change effects based on meteorological changes, while highly uncertain human factors, such as increased groundwater abstraction or irrigation effects, remain unconsidered due to a lack of reliable input data. We can show significant (p<0.05) declining groundwater levels for a large majority of the considered wells, however, at the same time we interestingly observe the opposite behaviour for a small portion of the considered locations. Further, we show mostly strong increasing variability, thus an increasing number of extreme groundwater events. The spatial patterns of all observed changes reveal stronger decreasing groundwater levels especially in the northern and eastern part of Germany, emphasizing the already existing decreasing trends in these regions</p>

scholarly journals Development of groundwater levels as a consequense of climate change

2012 ◽  
Vol 20 (1) ◽  
pp. 29-34
Author(s):  
M. Pásztorová ◽  
J. Skalová ◽  
J. Vitková ◽  
M. Juráková
Keyword(s):  
Climate Change ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Winter Season ◽  
Water Levels ◽  
Rainfall Regime ◽  
Wetland Ecosystems ◽  
Groundwater Levels ◽  
Run Off ◽  
The Impact

Development of groundwater levels as a consequense of climate changeClimate change poses a significant threat to many wetland ecosystems. Wetlands exist in a transition zone between aquatic and terrestrial environments and can be affected by slight alterations in regional hydrology, which can influence climate change through air temperature changes, regional changes in a rainfall regime, surface run-off, snow, duration of the winter season, groundwater resources and evapotranspiration.Climate change in wetland areas is most significantly reflected in water levels and adjacent groundwater levels, and it can significantly change the hydroecological proportions of wetland ecosystems and endanger rare wetland fauna and flora communities. The focus of this paper is the impact of climate change on the groundwater level in the Záhorie Protected Landscape area in the Zelienka national nature reservation. The impact of the climate change was solved through the meteorological characteristic changes adapted by the GISS98 and CCCM2000 climatic scenarios. The groundwater level was determined by the HYDRUS-ET model for the time frames 2010, 2030 and 2075 in 20-year time intervals and consequently compared to the reference period of 1971-1990.

scholarly journals Seismically induced changes in groundwater levels and temperatures following the ML5.8 (ML5.1) Gyeongju earthquake in South Korea

2021 ◽  
Author(s):  
Soo-Hyoung Lee ◽  
Jae Min Lee ◽  
Sang-Ho Moon ◽  
Kyoochul Ha ◽  
Yongcheol Kim ◽  
...  
Keyword(s):  
South Korea ◽  
Groundwater Level ◽  
Seismic Waves ◽  
Groundwater Resources ◽  
Fluid Pressure ◽  
Water Levels ◽  
Aquifer System ◽  
Groundwater Levels ◽  
Monitoring Wells ◽  
Gyeongju Earthquake

AbstractHydrogeological responses to earthquakes such as changes in groundwater level, temperature, and chemistry, have been observed for several decades. This study examines behavior associated with ML 5.8 and ML 5.1 earthquakes that occurred on 12 September 2016 near Gyeongju, a city located on the southeast coast of the Korean peninsula. The ML 5.8 event stands as the largest recorded earthquake in South Korea since the advent of modern recording systems. There was considerable damage associated with the earthquakes and many aftershocks. Records from monitoring wells located about 135 km west of the epicenter displayed various patterns of change in both water level and temperature. There were transient-type, step-like-type (up and down), and persistent-type (rise and fall) changes in water levels. The water temperature changes were of transient, shift-change, and tendency-change types. Transient changes in the groundwater level and temperature were particularly well developed in monitoring wells installed along a major boundary fault that bisected the study area. These changes were interpreted as representing an aquifer system deformed by seismic waves. The various patterns in groundwater level and temperature, therefore, suggested that seismic waves impacted the fractured units through the reactivation of fractures, joints, and microcracks, which resulted from a pulse in fluid pressure. This study points to the value of long-term monitoring efforts, which in this case were able to provide detailed information needed to manage the groundwater resources in areas potentially affected by further earthquakes.

Machine-learning-based regional-scale groundwater level prediction using GRACE

2021 ◽  
Vol 29 (3) ◽  
pp. 1027-1042 ◽  
Author(s):  
Pragnaditya Malakar ◽  
Abhijit Mukherjee ◽  
Soumendra N. Bhanja ◽  
Ranjan Kumar Ray ◽  
Sudeshna Sarkar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Groundwater Level ◽  
Regional Scale ◽  
Groundwater Level Prediction

scholarly journals A Review of Case Studies on Climate Change Impact on Hydrologic Cycle: An Indian Perspective

2021 ◽  
pp. 249-266
Author(s):  
P K Bhunya ◽  
Sanjay Kumar ◽  
Sunil Gurrapu ◽  
M K Bhuyan
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Groundwater Resources ◽  
Rainfall Events ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Change Impact ◽  
Heavy Rainfall Events ◽  
Flood Characteristics

In recent times, several studies focused on the global warming that may affect the hydrological cycle due to intensification of temporal and spatial variations in precipitation. Such climatic change is likely to impact significantly upon freshwater resources availability. In India, demand for water has already increased manifold over the years due to urbanization, agriculture expansion, increasing population, rapid industrialization and economic development. Numerous scientific studies also report increases in the intensity, duration, and spatial extents of floods, higher atmospheric temperatures, warmer sea, changes in precipitation patterns, and changing groundwater levels. This work briefly discusses about the present scenario regarding impact of climate change on water resources in India. Due to the insufficient resolution of climate models and their generally crude representation of sub-grid scale and convective processes, little confidence can be placed in any definite predictions of such effects, although a tendency for more heavy rainfall events seems likely, and a modest increase in frequency in floods. Thus to analyses this effect, this work considers real problems about the changing flood characteristics pattern in two river regions, and the effect of spatial and temporal pattern in rainfall. In addition to these, the work also examines the trend of groundwater level fluctuations in few blocks of Ganga–Yamuna and Sutlej-Yamuna Link interfluves region. As a whole, it examines the potential for sustainable development of surface water and groundwater resources within the constraints imposed by climate change.

scholarly journals Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

2017 ◽  
Vol 32 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Basant Yadav ◽  
Sudheer Ch ◽  
Shashi Mathur ◽  
Jan Adamowski
Keyword(s):  
Soft Computing ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Support Vector ◽  
Complex Nature ◽  
Data Set ◽  
Groundwater Levels ◽  
Soft Computing Techniques ◽  
Learning Machine ◽  
The Comparative Study

Abstract Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

scholarly journals Groundwater Modeling with Machine Learning Techniques: Ljubljana polje Aquifer

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 697 ◽  
Author(s):  
Klemen Kenda ◽  
Matej Čerin ◽  
Mark Bogataj ◽  
Matej Senožetnik ◽  
Kristina Klemen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Groundwater Level ◽  
Groundwater Modeling ◽  
Machine Learning Techniques ◽  
Physical Parameters ◽  
Strongly Correlated ◽  
Groundwater Levels ◽  
Groundwater Level Fluctuations ◽  
Decision Making Processes ◽  
Input Variables

In this study a thorough analysis is conducted concerning the prediction of groundwater levels of Ljubljana polje aquifer. Machine learning methodologies are implemented using strongly correlated physical parameters as input variables. The results show that data-driven modelling approaches can perform sufficiently well in predicting groundwater level changes. Different evaluation metrics confirm and highlight the capability of these models to catch the trend of groundwater level fluctuations. Despite the overall adequate performance, further investigation is needed towards improving their accuracy in order to be comprised in decision making processes.

Evaluating the effects of climate change for groundwater quantitative status in Denmark

2020 ◽  
Author(s):  
Annesofie Jakosben ◽  
Hans Jørgen Henriksen ◽  
Ernesto Pasten-Zapata ◽  
Torben Sonnenborg ◽  
Lars Troldborg
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Balance ◽  
Overland Flow ◽  
Future Climate Change ◽  
Historical Period ◽  
Climate Projections ◽  
Monitoring Networks ◽  
Groundwater Levels ◽  
Ecological Flow

<p>By use of transient and distributed groundwater-surface water flow models, simulated time series of stream discharge and groundwater level for monitoring networks, groundwater bodies and river reaches have been analysed for a historical period and four different future scenarios toward 2100 in two large-scale catchments in Denmark. The purpose of the climate scenarios has been to qualify the existing knowledge on how future climate change most likely will impact hydrology, groundwater status and Ecological Quality Elements (EQR- Ecological flow in rivers). Another purpose has been to identify whether foreseen climate changes will be detected by the surface water and groundwater monitoring networks, and to which degree the River Basin Management Plan measures for supporting the goal of good quantitative status are robust to the projected changes in water balance and ecological flow. The developed hydrological models were run with climate inputs based on selected RCP4.5 and RCP8.5 climate model runs (RCP8.5 wet, median, dry and RCP4.5 median). Changes in groundwater quantitative status and ecological flow metrics were calculated based on 30-year model runs driven by RCP8.5 for 2071-2100 (RCP4.5 for 2041-70) and compared to 1981-2010.</p><p>Overall the four scenarios results in very significant water balance changes with increased precipitation: 3% to 27%, evapotranspiration: 6% to 17%, groundwater recharge: 0% to 49%, drainage flow: 0% to 71%, baseflow: 0% to 31% and overland flow: 16% to 281%. For one catchment an increase in abstraction of 23% to 171% due to an increase in irrigation demand by 36% to 113% is foreseen. The results have wide implications for groundwater flooding risks, quantitative status and ecological flow metrics. Most sensitive is changes in ecological flow conditions in rivers for fish, showing a relative high probability for decreased state for 10-20% of the reaches for the RCP8.5 wet and dry scenarios due to more extreme hydrological regimes toward 2071-2100. Maximum monthly runoff is increased for winter months by 100% for RCP8.5 wet and median scenarios and around 10% for RCP8.5 dry scenario. Annual maximum daily flows is simulated to increase by up to a factor of five, and late summer low flows decreased.</p><p>Impacts on groundwater levels and water balances of groundwater bodies will be significant, with increased seasonal fluctuations and also increased maximum and decreased minimum groundwater levels for 30 year periods for 2071-2100 compared to 1981-2010.</p><p>More rain, both when we look back on historical data and when we look forward with latest climate projections will result in more frequent flooding from groundwater and streams in the future. At the same time, the temperature and thus evapotranspiration rises. This means that in the long term we will have increased challenges with drought and increased irrigation demands on sandy soils while evapotranspiration will also increase on the clayey soils. This will result in greater fluctuation in the flow and groundwater levels between winters and summers, and between wet and dry years, challenging sustainable groundwater abstraction and maintaining good quantitative status of groundwater bodies.</p>

Identifying anthropogenic effects into Doñana aquifer (SW Spain) through hydrogram clustering of piezometric database

2020 ◽  
Author(s):  
Carolina Guardiola-Albert ◽  
Nuria Naranjo-Fernández ◽  
Héctor Aguilera ◽  
Esperanza Montero-González
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Groundwater Resources ◽  
Data Series ◽  
Time Series Clustering ◽  
Missing Data Imputation ◽  
Groundwater Levels ◽  
Aquifer Systems ◽  
Visual Clustering ◽  
Selection Of

<p>Nowadays, the application of time series clustering is increasing in hydrogeology works. Groundwater level long data series provides a useful record to identify different hydrological behaviors and to validate the conceptual model of groundwater flow in aquifer systems. Piezometers also register the response to any changes that directly affect the amount of available groundwater resources (recharge or exploitation).</p><p>What are the expected variations of groundwater levels in an aquifer under high exploitation pressure? In this work, groundwater level time series from 160 piezometers in the hydrological years from 1975 to 2016 were analyzed. Especially, 24 piezometers are deeply studied. Data were preprocessed and transformed: selection of points, missing data imputation and data standardization. Visual clustering, k-means clustering and time series clustering were applied to classify groundwater level hydrographs using the available database. Six and seven groups of piezometers were identified to be associated with the different hydrofacies and extraction rates. Time series clustering was found to be the best method to analyze the studied piezometric database. Moreover, it was possible to characterize actual hydrodynamics, which will be useful for groundwater managers to make sustainable decisions.</p>

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