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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.

Stable isotopes are used to decipher hydrological processes in watershed research. A two-year monthly monitoring of hydrogen and oxygen stable isotope ratios (δ2H and δ18O) in a temperate catchment in Norther Europa, Latvia was undertaken. Isotope ratios in common water types – raised bog, confined groundwater, unconfined groundwater and surface water – were measured. We found characteristic signatures of isotope ratios for each of these four water types. The average isotope ratios of different water types ranged from −80.8 to −68.3‰ for δ2H and −11.46 to −8.76‰ for δ18O, with standard deviations from 18 to 25‰ and 0.10 to 1.59‰, respectively. The isotope ratios of the stream base flow were consistent with the groundwater isoscape and seasonally enriched by evaporation. Most enriched water are found in raised bogs and large lakes. The most depleted water is found in a spring discharging phreatic groundwater in a forest site with sandy soil. The most enriched water was associated with significant short-lived precipitation events, resulting in impoundment of water on the land surface and its enrichment due to evaporation. The novelty of this study is that this enriched isotope signal is propagated throughout the hydrological system, temporarily albeit significantly shifting isotope ratios of phreatic groundwater and surface runoff. Further case studies are needed to affirm if this is a regionally significant mechanism controlling isotope ratios of surface and subsurface water. The observed difference between the average δ18O of phreatic groundwater at two locations was 0.9‰. We suggest that the differences are due to different land use and soil conditions.

A new data set of δ(2)H and δ(18)O in the groundwater from the central part of the Baltic Artesian Basin is presented. The hydrogeological section is subdivided into stagnation, slow exchange and active exchange zones. Na-Ca-Cl brine found at the deepest part - the stagnation zone - is characterized by δ(18)O values above -5 ‰ and δ(2)H values approaching -40 ‰ with respect to Vienna Standard Mean Ocean Water. The slow exchange zone where waters of mostly intermediate salinity reside is characterized by δ(18)O values around -11.7 ‰ and δ(2)H values around -85.3 ‰. Mean δ(18)O and δ(2)H values of the fresh groundwater in the active water exchange zone are -11.1 and -79.9 ‰, respectively. Characteristically, the groundwater in the active and slow exchange zone is isotopically more depleted compared with the precipitation values observed, and the depletion increases with depth down to the level where strongly enriched brines are encountered.

The paper is about the largest landfill in Latvia Getlini. Waste from Riga city and surroundings are disposed here. The waste landfilling once was started in the old sand-gravel quarry, next to the Getlini bog, and no environment protection measures were taken. Currently total area of the Getlini landfill is 87 ha and 36 ha of it occupy the old dumpsite (waste hill). Contaminants from the waste with storm water leached to the over groundwater and under groundwater and contaminated it. In the paper contamination level and spreading of leachate, surface water and groundwater are described.