Joint Methodology for the Identification and Assessment of Groundwater Dependent Terrestrial Ecosystems in Estonia and Latvia

Terasmaa, Jaanus; Retiķe, Inga; Vainu, Marko; Priede, Agnese; Lode, Elve; Pajula, Raimo; Koit, Oliver; Tarros, Siim; Bikše, Jānis; Popovs, Konrāds

doi:10.1007/978-3-030-39701-2_12

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…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). 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%

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: 99%

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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“…Groundwater does not seem to follow country borders; thus, unsustainable usage of shared groundwater sources can lead to groundwater contamination and depletion across borders [1,19]. Currently, there are 468 identified transboundary aquifers [20] worldwide, and this number has been increasing since 2009, indicating the need for and interest in identifying and addressing these aquifers.…”

Section: Introductionmentioning

confidence: 99%

Water Balance Calculation for a Transboundary Aquifer System between Estonia and Latvia

Hunt,

Marandi,

Retike

2023

Water

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Groundwater management in transboundary aquifers is complex due to the hidden nature of groundwater and its intricate interactions with surface water. The overarching issue lies in the need for improved tools and strategies to manage transboundary aquifers effectively, given their critical importance to regional water resources and ecosystems. In addressing these challenges, this study focuses on a specific transboundary aquifer system between Estonia and Latvia, situated within the sedimentary rocks of North-East Europe. Here, we propose a conceptual model approach as a valuable tool for comprehending the hydrogeological characteristics of transboundary aquifers and supporting more efficient management strategies. Utilizing open-source software MODFLOW-6, our developed conceptual model integrates the aquifer system’s geological and hydrogeological framework and simulates groundwater flow under transient conditions. Our findings highlight a profound hydrological connection between groundwater and surface water, particularly within the first 200 m of the aquifer. Despite varying cross-border groundwater flows, collaboration remains vital for sustainable transboundary aquifer management, aligning with international agreements such as the Water Convention and the EU Water Framework Directive, benefiting similar systems.

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Contribution of local factors to the status of a groundwater dependent terrestrial ecosystem in the transboundary Gauja-Koiva River basin, North-Eastern Europe

Koit

Tarros

Pärn

et al. 2021

Journal of Hydrology

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Joint Methodology for the Identification and Assessment of Groundwater Dependent Terrestrial Ecosystems in Estonia and Latvia

Cited by 3 publications

References 13 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

Water Balance Calculation for a Transboundary Aquifer System between Estonia and Latvia

Contribution of local factors to the status of a groundwater dependent terrestrial ecosystem in the transboundary Gauja-Koiva River basin, North-Eastern Europe

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