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Coordinating transboundary aquifer management is becoming increasingly important worldwide to minimize adverse transboundary impacts. Moreover, the global trend of groundwater consumption is increasing, and groundwater abstraction exceeds sustainable limits in many parts of the world. To avoid future international disputes and maximize the rational and equitable use of common transboundary aquifers, it is imperative to accurately and comprehensively assess groundwater resources' development potential in these aquifers.As part of this study, a transient hydrogeological model using MODFLOW-NWT was developed to assess the changes in groundwater balance and groundwater flow in the transboundary area between Estonia and Latvia in northeast Europe. The model consists of eleven layers that discretize four main aquifers (Quaternary aquifer, Upper-Devonian aquifer, Upper-Middle-Devonian aquifer, and Lower-Middle-Devonian-Silurian aquifer) and represents an area of 45 000 km2. The cell size of the model varies from 0.25 to 1.00 km. The model was used to simulate three scenarios: (1) the base case scenario, which involved no abstraction, (2) the current abstraction, and (3) the maximum abstraction allowed. A detailed water balance for eight transboundary groundwater bodies for all three scenarios was calculated to assess water balance changes and groundwater flow along the national border.Model results indicate that the groundwater balance between groundwater bodies remained the same in Simulations 1 (base case scenario) and 2 (current water extraction), which indicates that the existing water extraction in the territory does not significantly affect the transboundary groundwater flow. Some minor changes were observed in Simulation 3 (maximum water abstraction rates), mostly in the Upper-Devonian groundwater bodies. However, significant changes in cross-border groundwater flow patterns were not expected even with the maximum possible water abstraction.The study has been founded by Iceland, Liechtenstein and Norway through the EEA and Norway Grants Fund for Regional Cooperation project No.2018-1-0137 “EU-WATERRES: EU-integrated management system of cross-border groundwater resources and anthropogenic hazards”.

Groundwater level data are often spatially and temporary unevenly distributed, and the heterogeneity of hydrogeological systems limit simple extrapolation to data-scarce locations. Here, similarity of hydrogeological systems can be used to transfer dynamics of groundwater levels from monitored to unmonitored locations. Therefore, the major controls of hydrogeological systems need to be investigated. This study aims to uncover dominant patterns of the groundwater levels and to connect them with possible controls or driving features (such as the catchment and topography characteristics, aquifer properties, climate and land use) of hydrogeological systems in the Baltic states. Part of the spatial features were calculated for three different buffer sizes (300m, 3km and 5km). Gapless daily groundwater hydrographs (prepared by Bikše et al., 2023) were used to create groundwater level patterns by clustering approach. Then, random forest classification was performed using a backward variable elimination approach to find the most significant site-descriptive features for each cluster. The results show that the most significant tend to be geological features in the well cross-section, followed by topographic features within the vicinity of the well. We also found that anthropogenic impacts can play a significant role and should be considered when analyzing groundwater level patterns.References:Bikše, J., Retike, I., Haaf, E., Kalvāns, A., 2023. Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns. EarthArXiv. https://doi.org/10.31223/x5n94x "The study has been funded by Iceland, Liechtenstein and Norway through the EEA and Norway Grants Fund for Regional Cooperation project No.2018-1-0137 “EU-WATERRES: EU-integrated management system of cross-border groundwater resources and anthropogenic hazards" and by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165.

Groundwater accounts for nearly 99% of all liquid freshwater on Earth. Not only does groundwater provide almost half of the global drinking water supply and ensures the well-being of humans and sustain dependent ecosystems (rivers, lakes, and wetlands), it is an essential aspect in climate change mitigation too. Despite the exceptional importance of groundwater in social, economic, and environmental processes, it remains poorly understood, mismanaged, and often neglected. In order to increase global awareness of groundwater protection, the theme for World Water Day 2022 was “Groundwater: Making the invisible visible” with the culminating event - the first UN-Water Summit on Groundwater held in Paris on 7-8 December. Data collection and sharing on groundwater were among the key actions highlighted during the Summit because “we cannot manage what we do not measure”.One of the ways to make groundwater visible is to demystify the underground resources beneath our feet through citizen science. Many volunteer water monitoring programs have generated valuable data sets around the world. Data collection by volunteers is cost- and time effective, supports local communities by raising awareness of groundwater protection, and boosts outdoor tourism. Moreover, such campaigns provide information on water bodies that would otherwise remain unmonitored and, if carefully designed, can be used by national water managers and even support decision-making.Here we present a joint Estonian-Latvian web-based application for the volunteer monitoring of springs launched in February 2021 (accessible by allikad.info and avoti.info). The web-based map application aims to collect new information on already known springs and locate new ones by providing clear guidelines on how to carry out proper spring monitoring. More than 200 users have already joined the application and approximately 600 new springs have been added to the database. Together, 1132 new observations have been made and 2930 images of springs have been added. This valuable information source has been recently recognized by national water managing authorities and used to improve the transboundary groundwater monitoring network between Estonia and Latvia. We will present how citizen science can improve groundwater management, as well as our success stories and lessons learned. The study benefits from Iceland, Liechtenstein and Norway through the EEA and Norway Grants Fund for Regional Cooperation project No.2018-1-0137 “EU-WATERRES: EU-integrated management system of cross-border groundwater resources and anthropogenic hazards” and Interreg Estonia-Latvia cooperation program project “WaterAct”.

Uneven measurement frequencies and continuous gaps in hydrographs are among the major challenges when dealing with regional-scale groundwater level data sets, especially if compiled from different countries. A variety of automated gap imputation methods can be applied to infill a large number of missing values, yet the assessment of modeling performance remains a difficult task often performed by randomly introduced missing values. However, large groundwater level data sets rarely have random gaps and more complex gap patterns can be observed. Here we present a new artificial gap introduction technique (TGP - typical gap patterns) mimicking realistic gap patterns characteristic to regional scale groundwater level data sets thus improving the assessment of gap imputation methods. Imputation performance of machine learning algorithm missForest and imputePCA were compared with routinely used linear interpolation to create gapless groundwater hydrographs for the Baltic states (Estonia, Latvia, Lithuania). Our results showed that infilling performance varies among different gap patterns (TGP). Overall, the missForest algorithm significantly outperformed imputePCA and linear interpolation even when infilling up to 2.5 years long gaps, while linear interpolation produced similarly good results to missForest when infilling relatively short (random-like) gaps. It was observed that imputation performance substantially decreased when infilling previously unseen extremes (such as severe drought episodes in 2018) or groundwater hydrographs likely affected by water abstraction (located near major agglomerations).The study has been founded by Iceland, Liechtenstein and Norway through the EEA and Norway Grants Fund for Regional Cooperation project No.2018-1-0137 “EU-WATERRES: EU-integrated management system of cross-border groundwater resources and anthropogenic hazards”. The research further contributes to the grant TRV2019/45670 awarded by the Swedish Transport Administration (Trafikverket).

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 TGP approach can be of use to assess the complexity of missing observation patterns in a data set and its value lies in assessing the performance of gap filling methods in a more realistic way. Thus the approach aids the appropriate selection of imputation methods, a task not limited to groundwater level time series alone. The study further provides insights into region-specific data peculiarities that can assist groundwater analysis and modelling.

To assess groundwater response to climate change scenarios in the context of groundwater drought it is fundamental to understand controlling periods of groundwater recharge as well response time to meteorological forcing (Hughes et al 2021, Jasechko et al 2014). The study aims at assessing the spatial and temporal distribution of groundwater drought events in the Baltic States over the period 1989 – 2018 as well as the meteorological and hydrological indicators associated with periods of below-normal groundwater level. A set of meteorological, hydrological and groundwater drought indices were used for the identification of significant drought events in the region. Used groundwater level time series were treated and checked for errors according to the Retike et al. (2021).Four major drought events affecting all monitoring sites under study are identified (1992–1994, 1996–1997, 2002–2004 and 2005–2007), although, the fluctuations in groundwater level display distinct patterns which might be associated with the impact of local meteorological conditions and geological setting of particular monitoring sites such as seasonally derived recharge. It was found that meteorological drought indices (SPI, SPEI, DRI) showed the highest correlation with groundwater drought conditions rather than hydrological indices (SRI and SSRI) calculated from ERA-5 reanalysis data. FHowever, for most indices and most monitoring sites there is a one month time lag between the signal of the hydrometeorological index and the response of groundwater drought. Time series analysis of drought indices at groundwater monitoring sites demonstrated varying characteristics of the onset, duration, and propagation of drought events through different scales thus highlighting the complex nature of groundwater drought events in the Baltic State area.This research is funded by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165. ReferencesJasechko, Scott & Birks, Sandra & Gleeson, Tom & Wada, Yoshihide & Fawcett, Peter & Sharp, Zachary & McDonnell, Jeffrey & Welker, Jeff. (2014). The pronounced seasonality of global groundwater recharge. Water Resources Research. 50. 10.1002/2014WR015809.Hughes, A. & Mansour, Majdi & Ward, Rob & Kieboom, Natalie & Allen, S. & Seccombe, David & Charlton, Matthew & Prudhomme, C. (2021). The impact of climate change on groundwater recharge: National-scale assessment for the British mainland. Journal of Hydrology. 598. 126336. 10.1016/j.jhydrol.2021.126336.Retike, I., Bikše, J., Kalvāns, A., Dēliņa, A, Avotniece, Z., Zaadnoordijk, W.J., Jemeljanova, M., Popovs,K., Babre, A., Zelenkevičs, A., Baikovs, A. (2022) Rescue of groundwater level time series: How tovisually identify and treat errors. Journal of Hydrology, 605, 127294.https://doi.org/10.1016/j.jhydrol.2021.127294

Regular and gapless observations are necessary to perform a range of statistical analysis on the parameter of interest. Groundwater level (GWL) hydrographs are often recorded at irregular frequencies and have time periods without any observations. As a result, groundwater level hydrographs have missing values. Typically groundwater hydrographs are removed from further analysis if large gaps are present, while each groundwater observation point is valuable and methods exist that can impute (fill in) the missing observations. This study aims to evaluate performance of machine learning methods to prepare gapless daily groundwater level hydrographs and to assess the imputation error according to various approaches. Filled groundwater level hydrographs will further be used to identify typical groundwater level patterns in the Baltic region.The performance of two machine learning imputation methods - missForest and missMDA - along with conventional approaches (linear interpolation, mean imputation) - were tested. A subset of the GWL observation data from Lithuania, Latvia and Estonia were used for the time period from 2011 to mid-2019 comprising 283 groundwater monitoring wells. Cluster analysis of the temporal distribution of actual missing values in the GWL time series provided 13 different gap patterns. Next a corresponding number of artificially generated gap distribution scenarios were defined. The performance of various gap-filing approaches were then evaluated by imputing each artificially generated gap pattern in each hydrograph. Results indicated that imputation performance varies among different clusters of missing value patterns, while generally the best performance was achieved by the missForest algorithm.This research is funded by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165.

The aim of this study is to identify salient patterns of groundwater level dynamics in the Baltic region. The study investigates correspondence between (grouped) groundwater level dynamics and catchment, well and physiographic site characteristics. The analysis was carried out in five consecutive steps. Firstly, 1691 groundwater hydrographs were collected from Baltic surveys responsible for national groundwater level monitoring (Latvain Environment, Geology and Meteorology Centre; Estonian Environment Agency; Geological Survey of Lithuania). Observation wells represent both unconfined and confined aquifers. Groundwater level time series were checked for errors and treated according to an approach proposed by Retike et al., 2021. Secondly, the dataset was limited to the time period when daily groundwater level measurements were available in all three countries. The resulting time period covers 8.5 years at 689 wells. Then, the acceptable amount of missing values was defined as the balance between most complete hydrographs, the number of retrained hydrographs and spatial coverage of the wells. Gaps (if present) in the remaining 283 wells were filled with missForest (Stekhoven and Bühlmann, 2012). The results were visually inspected to identify groundwater hydrographs with suspicious modeling patterns and five wells were removed from further analysis based on expert judgment. Finally, 278 groundwater hydrographs (136 from Latvia, 58 from Lithuania and 86 from Estonia) were retained and clustered using Hierarchical Cluster Analysis. The identified clusters of groundwater level times series were then explained using descriptive geological (like aquifer lithology, thickness), hydrological (distance to the nearest stream), climatic (precipitation, seasonality) and anthropogenic (land use) characteristics. This research is funded by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165.References:Retike, I., Bikše, J., Kalvāns, A., Dēliņa, A, Avotniece, Z., Zaadnoordijk, W.J., Jemeljanova, M., Popovs, K., Babre, A., Zelenkevičs, A., Baikovs, A. (2022) Rescue of groundwater level time series: How to visually identify and treat errors. Journal of Hydrology, 605, 127294. https://doi.org/10.1016/j.jhydrol.2021.127294Stekhoven, D.J., Bühlmann, P. (2012) Missforest-Non-parametric missing value imputation for mixed-type data. Bioinformatics, 28, 112–118. https://doi.org/10.1093/bioinformatics/btr597

Recent years have seen several major groundwater drought instances in Europe, increasing the interest in their research and future predictions. Modeling methods can be used to explore groundwater drought risk in near and medium term future using climate projection data sets as an input. Therefore an optimal modeling approach has to be chosen according to the available data and modeling capacity of the historic series. We explore two approaches for modeling groundwater level time series: Transfer function-noise models with Impulse response functions (TFN-IRF) and machine learning (ML). In both approaches, daily meteorological variables are used as an input and models are calibrated against historical groundwater level observations. TFN-IRF input parameters are daily precipitation and potential evapotranspiration. Other time series data, such as groundwater abstraction, can be added to potentially increase the fit. Machine learning models can, in addition to the aforementioned, benefit from a wider variety of site parameters, including derived parameters (e.g., meteorological indices), however at the expense of increased data collection effort. In addition, the obscure input data interpretation in ML methods can erode the trust in these models. In this study, only the most basic meteorological parameters - precipitation, temperature - and derived parameters such as potential evapotranspiration were used. We draw from an extensive groundwater level monitoring database of more than one thousand monitoring wells from three Baltic countries. The study provides an insight into differences between the two modelling approaches, keeping in mind the limitations of future projection data.This research is funded by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165.

As groundwater does not follow human-drawn boundaries such as country borders, groundwater pollution in one country can adversely affect groundwater quality and availability in a neighboring country. It is vital to develop a conceptual understanding of shared groundwater resources not only to ensure their protection, but also to avoid future conflicts, especially in a changing climate. Both the Water Convention and EU Water Framework Directive emphasize the need for joint assessment and management of transboundary groundwater resources (commonly referred to as “transboundary aquifers” or “groundwater bodies''), and it is crucial to establish a representative transboundary groundwater monitoring network to gather the necessary data. Often, the coverage of monitoring points in the existing groundwater monitoring networks is scarce in the peripheral areas and the installation of new wells would be economically unreasonable. Springs are natural groundwater outflows that can fill gaps in monitoring networks. Monitoring springs can be cost-effective, make water sampling easier, moreover, their water can provide information on a significantly larger catchment area than monitoring wells. The spring site cannot be selected like monitoring wells, so selecting the best springs requires a thorough preliminary assessment. A good conceptual understanding of the recharge area is a prerequisite for the selection of suitable monitoring springs. In this study, 46 springs in the transboundary area of Estonia and Latvia (NE Europe) were screened in 2021-2022 for a variety of geochemical parameters (field parameters, major ions, biogenic and trace elements, water stable isotopes). Some springs were sampled more than once to assess seasonal variability. Then springs were clustered based on their geochemical characteristics using multivariate statistics. This study is the first step in the procedure established on how to select representative springs for transboundary aquifer monitoring.This study is financed by the Interreg Estonia-Latvia cooperation program project “WaterAct”, the EEA and Norway Grants Fund for Regional Cooperation project “EU-WATERRES”, and by performance-based funding of University of Latvia Nr.AAP2016/B041 within the “Climate change and sustainable use of natural resources” program.

Groundwater pollution by agrochemicals such as nitrogen fertilizers can cause complex biogeochemical transformations to take place in groundwater-dependent ecosystems. To explore the interaction between nitrogen load and groundwater-dependent, spring-fed ecosystems, a study was conducted in Latvia in an area of suspected high nitrate (NO3−) vulnerability due to its geological settings. A map of NO3− vulnerability along the margins of the carbonate aquifer in Latvia is presented. The map is based on a conceptual model that was developed during an extensive case study involving hydrological, hydrochemical, and habitat investigation of springs discharging from a karst aquifer and spring-fed ecosystems. Areas that should be prime targets for restricting fertilizer application are highlighted on the map. Although the case study revealed increased nitrogen pollution (up to 51 mg L−1, standard deviation of 9 mg L−1, in the springs discharging from the karst aquifer), no clear evidence of adverse effects due to NO3− pollution on the groundwater-dependent ecosystems using biotic indicators was found, highlighting the resilience of spring-fed ecosystems against high nitrogen inputs. In the case study, downstream groundwater-dependent ecosystems retained 70% of the reactive nitrogen during the vegetation season, but only a small proportion during the cold season. Thus, NO3− pollution can be partly mitigated by restoring wetlands along valley slopes where natural groundwater discharge takes place. The conceptual model developed for groundwater NO3− vulnerability is applicable to other areas in the Baltic region and other places with similar climatic and geological conditions.

Groundwater level time series are the basis for various groundwater-related studies. The most valuable are long term, gapless and evenly spatially distributed datasets. However, most historical datasets have been acquired during a long-term period by various operators and database maintainers, using different data collection methods (manual measurements or automatic data loggers) and usually contain gaps and errors, that can originate both from measurement process and data processing. The easiest way is to eliminate the time series with obvious errors from further analysis, but then most of the valuable dataset may be lost, decreasing spatial and time coverage. Some gaps can be easily replaced by traditional methods (e.g. by mean values), but filling longer observation gaps (missing months, years) is complicated and often leads to false results. Thus, an effort should be made to retain as much as possible actual observation data.In this study we present (1) most typical data errors found in long-term groundwater level monitoring datasets, (2) provide techniques to visually identify such errors and finally, (3) propose best ways of how to treat such errors. The approach also includes confidence levels for identification and decision-making process. The aim of the study was to pre-treat groundwater level time series obtained from the national monitoring network in Latvia for further use in groundwater drought modelling studies.This research is funded by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165.

More severe and frequent drought events are one of the main challenges faced worldwide in the context of climate change. Now droughts can be observed in the areas that are typically not classified as drought prone regions and more often groundwater vulnerability to prolonged drought events is reported. Groundwater drought is relatively new drought type defined as lower than normal groundwater level.Most recent drought event in Europe in 2018 significantly affected shallow groundwater aquifers in the Baltic states. That year, groundwater droughts in Latvia caused large financial losses to many farmers, and rural households frequently reported dry dug wells. Even though main groundwater drought consequences are depleted aquifers and/or reduced base flows to rivers, drought may have an influence on groundwater quality as well (e.g. reduced denitrification rates due to lower groundwater levels and shorter travel times in anoxic zone).This study presents groundwater chemical composition changes with respect to groundwater level variations between six sampling campaigns carried out during the groundwater drought event in 2017-2018 in central part of Latvia. Groundwater samples were taken from specifically established monitoring network with seven stations, each having two to four shallow groundwater wells with the maximum depth of four meters. In total more than 100 groundwater, surface water and spring water samples were collected every two months for a one-year period. Major ions, water stable isotopes, biogenic and trace elements were analyzed in laboratory. Patterns were analyzed by multivariate statistical analysis (Principal Component Analysis, Cluster Analysis and Discriminant Analysis). The study is supported by fundamental and applied science research programme, project Nr.lzp-2019/1-0165 “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”.

More severe and frequent drought events are one of the challenges faced worldwide in the context of climate change. There are multiple anecdotal evidence of dug wells and small streams running dry during drought events in years 2015 and 2018 in Latvia. However, no comprehensive research has been made to assess groundwater drought and its ecological and socioeconomic impacts in Latvia and wider Baltic region. More intensive irrigation can further exaggerate the groundwater drought problem in the future. We aim to analyse past drought events from meteorological and groundwater drought perspective. Groundwater drought development and propagation is complex, however, we try to find the best simple predictors that can be used for evaluating purposes. We examine groundwater level data set from “Dricani” monitoring station with 14 groundwater wells uncovering unconfined heterogenous quaternary aquifer with well depths ranging from 2.5 to 15 m and monthly data records starting from 1970.-ies. Such a high number of wells in a single monitoring station permit detailed groundwater level analysis with a focus on local scale disturbances and groundwater drought propagation that could be caused by heterogeneous sediments in the aquifer, terrain and other drivers. We us “Dricani” groundwater level data series to calculate Standardized groundwater level index (SGI) (Bloomfield, Marchant 2013) revealing several major groundwater drought events during the last 50 years. Although largest groundwater drought events shows similar pattern within all the wells, minor changes in SGI can be identified that can be attributed to different depths of groundwater wells. The study is supported by fundamental and applied science research programme, project No. lzp-2019/1-0165 “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”.ReferencesBloomfield JP, Marchant BP. 2013. Analysis of groundwater drought building on the standardised precipitation index approach. Hydrology and Earth System Sciences 17 (12): 4769–4787 DOI: 10.5194/hess-17-4769-2013

Groundwater level time series are of great value for a variety of groundwater studies, particularly for those dealing with the impacts of anthropogenic and climate change. Quality control of groundwater level observations is an essential step prior to any further application, e.g., trend analysis. Often the quality control of data is limited to the removal of outliers or elimination of entire time series from a dataset, while such approaches drastically reduce the spatial coverage of initially huge datasets. Frequently studies tend to present already qualitycontrolleddata, but neglect to demonstrate how the data were selected, judged, and modified. We present a data rescue approach developed for correcting the Latvian national groundwater level database, containing 1.68 million groundwater level observations since 1959, including 0.69 million manual measurements. A web-based R-Shiny interface was developed and used for visual identification and manual correction of erroneous measurements in groundwater level time series. All data manipulations were performed programmatically. Reproducibilityand traceability were ensured by deploying separate data tables for raw observations, data repair actions and the final dataset. As a result of applied actions, 34.3% of all automatic measurements were either deleted or corrected, while only 6.5% of manual measurements were edited. Commonly found errors ingroundwater level time series were grouped into: errors in measurement and data recording; technical problems at the observation site; local anthropogenic impact and other unclassified problems. The improvement from the rescue approach was assessed by comparing the Akaike information criterion derived from fitted ARMA and ARIMA models to both original and repaired time series. The results showed that models fitted using repaired time series were better than those fitted on the original time series for the same time series sections. The presented rescue approach and results can be of great value for all studies using groundwater level time series as an input.

Dynamic interactions between ground- and surface water are widely known, but the role of groundwater in terrestrial and aquatic ecosystems is often poorly understood and documented due to the spatiotemporal complexity. Many countries have not yet completed the assessment of groundwater dependent ecosystems (GDEs). GDEs are valuable ecosystems that depend on groundwater input and can not be considered and assessed separately. Changes in the quantity and chemical composition of groundwater recharge may result in significant and permanent damage on GDE flora and fauna. Aquifers are dynamic systems which are not subject to administrative boundaries and borders, therefore should be managed in close cooperation between neighbouring countries. According to the European Union’s Water Framework Directive 2000/60/EC, a groundwater body is considered to be in “poor status” if environmentally negative pressure on groundwater causes significant damage to related GDEs. The identification of GDEs in Estonia is currently underway. A theoretical approach on how to identify, assess, and monitor the groundwater dependent terrestrial ecosystems (GDTEs) has been developed. Similar climatic and hydrogeological conditions allow to adapt the methodology to Latvia and develop it jointly further. The first step in this joint methodology is to (i) find indicators and (ii) define criteria for (i) the evaluation of quantitative and qualitative effects of groundwater bodies on GDTEs and (ii) assessment of ecosystems. Subsequently, the quantitative and qualitative effects on GDTEs using assessment schemes must be identified. In this chapter, we are presenting a methodology for GDTE identification and assessment which could be used in similar situation in other countries.