Hydrogeology and groundwater quality in the Nordic and Baltic countries

Kitterød, Nils‐Otto; Kværner, Jens; Aagaard, Per; Arustienė, Jurga; Bikš, Jānis; Dagestad, Atle; Gundersen, Pål; Hansen, Birgitte; Hjartarson, Árni; Karro, Enn; Kļaviņš, Māris; Marandi, Andres; Radienė, Rasa; Retiķe, Inga; Rossi, Pekka M.; Thorling, Lærke

doi:10.2166/nh.2022.018

Cited by 15 publications

(9 citation statements)

References 58 publications

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“…The evaluation of abstraction effects on time series is often limited by the absence of actual water usage data (Wendt et al, 2020), including unregistered abstractions (Arriagada et al 2021). Our study shows that poor imputation performance can be spatially linked to some documented cases of anthropogenically altered groundwater levels in Baltic states (Kitterød et al, 2022;Klimas et al, 2018) (Figure 10). Most negative NSE values are found near northeastern Estonia, where extensive oil shale mining has significantly changed the groundwater levels (Terasmaa et al, 2020).…”

Section: Discussionmentioning

confidence: 80%

“…Most negative NSE values are found near northeastern Estonia, where extensive oil shale mining has significantly changed the groundwater levels (Terasmaa et al, 2020). Poor model performance can be observed for hydrographs near all three capital cities of the Baltic states (Tallinn, Riga and Vilnius) where groundwater is used as water supply (Kitterød et al 2022;Klimas et al 2018;Marandi and Karro 2008;Vallner and Porman 2016).…”

Section: Discussionmentioning

confidence: 99%

“…The aquifers are mostly confined, while unconfined aquifers are found in Quaternary sediments. A comprehensive overview of the hydrogeological setting and groundwater quality in the study region can be found in Kitterød et al (2022).…”

Section: Study Regionmentioning

confidence: 99%

“…Assessment of groundwater resources at the regional scale is essential to sustainably manage transboundary aquifers and secure water supply (Kitterød et al, 2022;Wunsch et al, 2021). The EU Water Framework Directive requires member states to ensure good quantitative status of groundwater bodies by timely detection of negative trends posing a risk for resource depletion and deterioration of groundwater dependent ecosystems (WFD, 2000).…”

Section: Introductionmentioning

confidence: 99%

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Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Bikše¹,

Retiķe²,

Haaf³

et al. 2023

Preprint

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Large groundwater level (GWL) data sets are often patchy with hydrographs containing continuous gaps and irregular measurement frequencies. However, most statistical time series analyses require regular observations, thus hydrographs with larger gaps are routinely excluded from further analysis despite the loss of coverage and representativity of an initially large data set. Missing values can be filled in with different imputation methods, yet the challenge is to assess the imputation performance of automated methods. Assessment of such methods tends to be carried out on randomly introduced missing values. However, large GWL data sets are commonly dominated by more complex patterns of missing values with longer contiguous gaps. This study presents a new artificial gap introduction approach (TGP- typical gap patterns) that improves our understanding of automated imputation performance by mimicking typical gap patterns found in regional scale groundwater hydrographs. Imputation performance of machine learning algorithm missForest and imputePCA is then compared with commonly applied linear interpolation to prepare a gapless daily GWL data set for the Baltic states (Estonia, Latvia, Lithuania). We observed that imputation performance varies among different gap patterns, and performance for all imputation algorithms declined when infilling previously unseen extremes and hydrographs influenced by groundwater abstraction. Further, missForest algorithm substantially outperformed other methods when infilling contiguous gaps (up to 2.5 years), while linear interpolation performs similarly for short random gaps. The value of the developed gap introduction approach lies in aiding the selection of imputation methods, a task not limited to groundwater level time series alone. The results further provide insights into region-specific data peculiarities that can assist groundwater analysis and modelling.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Study Regionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Bikše¹,

Retiķe²,

Haaf³

et al. 2023

Preprint

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“…The quality of groundwater directly affects the living standard of residents and agricultural development [ 3 ]. Many scholars have evaluated the groundwater quality in Nordic and Baltic countries, Korea, and Beijing [ 4 , 5 , 6 ]. Scholars assessed the quality, groundwater chemical characteristics, and quantity of groundwater by different methods.…”

Section: Introductionmentioning

confidence: 99%

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Liu

et al. 2022

IJERPH

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Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.

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Strategies to remove hydrological effects in continuous gravity time series

Mouyen

2023

J Geod

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Multi-annual gravity time series offer a unique, noninvasive way to monitor mass redistributions within the Earth. However, for non-hydrological purposes, gravity time series must be corrected from hydrological effects to properly quantify mass redistribution involved in other geodynamic processes, such as volcanic activities or sedimentation processes. Such a hydrological correction remains challenging. The objective of this work is to investigate various ways to remove hydrological effects from a 11-years long gravity time series acquired by a superconducting gravimeter (SG) located at the Onsala Space Observatory (Sweden). For that we use hydrological loading corrections distributed by the EOST loading service, and local in situ groundwater level measurements. The most efficient approaches correct up to 89% of the seasonal component of the hydrological signal in the gravity series. On the other hand, for that specific site, we observe local water redistributions at shorter time scales, hours to few days, have a negligible influence on the gravity time series.

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Hydrogeology and groundwater quality in the Nordic and Baltic countries

Cited by 15 publications

References 58 publications

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Strategies to remove hydrological effects in continuous gravity time series

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