Jānis Bikše

As climate change accelerates, extreme events like droughts and floods are becoming more frequent and severe across Europe, and pollution adds further strain on groundwater resources. Groundwater sustains ecosystems and provides most of the drinking water in Estonia and Latvia, making its sustainable and proactive management more important than ever. In practice, however, that kind of management is difficult to achieve. Current monitoring systems in Estonia and Latvia still operate on slow cycles of data collection and manual analysis, leaving little room for early intervention. Because of this, municipalities often find out about issues with groundwater quality and quantity when they have already escalated – when wells have run dry or contaminants have reached drinking water supplies. Even when data is available, it often requires expert interpretation, making it difficult to act quickly and prevent problems in time.The cross-border 'HydroScope' project addresses these challenges by developing a groundwater early warning system for two pilot municipalities: Saaremaa (Estonia) and Dienvidkurzeme (Latvia). In these municipalities, telemetry systems tailored to local groundwater conditions are installed in monitoring wells, introducing real-time groundwater monitoring in Estonia for the first time and expanding the network in Latvia. Real-time digital spring systems complement the well monitoring network. Machine learning models are developed to automatically detect patterns in real-time groundwater quantity and quality data and to generate short-term predictions. This information feeds into two municipality-specific early warning platforms. These platforms visualize insights from near real-time data in an easily interpretable way, along with recommendations for what actions to take under different scenarios – e.g. reducing water use if a groundwater drought is likely, or identifying potential contamination sources when thresholds are approached.The ‘HydroScope’ project is a first step toward establishing a real-time groundwater monitoring paradigm in Estonia and Latvia, and also toward making that real-time data directly usable for local decision-making, thus supporting sustainable and proactive groundwater management practices.The project HydroScope (EE-LV00250) is funded by the European Union through the European Regional Development Fund (ERDF) within the Interreg VI-A Estonia–Latvia Programme 2021–2027.

Peatlands play a crucial role in carbon sequestration, covering approximately 10-15 % of Latvia’s territory, with a large proportion drained for forestry or peat extraction. Peat is currently extracted on ~0.4 % of Latvia’s territory and largely exported. Despite their relevance for national greenhouse gas (GHG) balances, continuous ecosystem-scale CO2 flux measurements are absent in Latvia. Current GHG accounting relies on chamber campaigns and emission factors, limiting monitoring, reporting, and verification for restoration and climate mitigation.To address this gap, we initiated the first deployment of autonomous eddy covariance (EC) Carbon Node systems (LI-COR) in Latvian peatlands to measure CO2 exchange together with ancillary environmental variables. The first unit was installed in August 2025 in an active peat extraction site (cutover bog), while a second unit is devoted to a natural raised bog. The distance between sites is < 5 km, ensuring similar regional meteorological conditions and enabling better comparative assessment of land-use impacts.We present first operational results, focusing on data continuity and system performance, including the feasibility of solar-powered operation at northern mid-latitudes, which proved challenging during the winter of 2025/2026. Preliminary CO2 flux dynamics provide early insights into emission magnitude and temporal variability at the active extraction site. This initiative represents a foundational step toward long-term EC observations in Latvia with implications for national GHG accounting and evidence-based peatland restoration policy.The study is supported by a donation from the "Mikrotīkls", which is administered by the University of Latvia Foundation and by the project PeatTransform (No. 6.1.1.2/1/25/A/001).

Extracted peatlands experience strong hydrological fluctuations due to drainage, vegetation succession, and climatic variability, yet long-term, high-frequency groundwater data remain scarce in Northern Europe. Our dataset presents two years (June 2023–May 2025) of 30-min groundwater table depth (WTD) measurements from six wells installed across contrasting Greenhouse Gass Emission Site Types (GEST 5, 6, 15, 20) in the Kaigu peatlands, central Latvia. Each well was equipped with an automatic pressure transducer (TD-Diver, van Essen Instruments) recording absolute pressure (m H₂O). The dataset also includes metadata on coordinates, installation elevation, well construction, and manual control measurements. All values are unprocessed, i.e., they represent original logger outputs without atmospheric or elevation correction, enabling users to apply their own calibration or referencing methods. This is the first openly available high-frequency extracted peatland groundwater pressure dataset from the Baltic region and provides a foundation for hydrological modelling and rewetting designs.

Lakes play a significant role in the global carbon cycle, acting as sources and sinks of carbon dioxide (CO ₂ ). In situ measurements of CO ₂ flux (FCO ₂ ) from lakes have generally been collected during daylight, despite indications of significant diel variability. This introduces bias when scaling up to whole‐lake annual aquatic carbon budgets. We conducted an international sampling program to ascertain the extent of diel variation in FCO ₂ across lakes. We sampled 21 lakes over 41 campaigns and measured FCO ₂ at 4‐h intervals over a full diel cycle. Rates of FCO ₂ ranged from −3.16 to 4.39 mmol m ⁻² h ⁻¹ . Integrated over a day, FCO ₂ ranged from −381.68 to 878.49 mg C m ⁻² d ⁻¹ (mean = 76.54) across campaigns. We identified three characteristic diel patterns in FCO ₂ related to trophic status and show that for half of the campaigns, daily flux estimates were biased by > 50% if based on a single (daytime) measurement.

Groundwater is a critical resource that supports ecosystems, agriculture, and drinking water supply, yet its sustainable management faces challenges, particularly in the transboundary regions. Collaborative approaches are needed to protect this shared resource, as pollution or overexploitation in one country can have significant consequences across borders. The Estonian-Latvian transboundary area is a good example of these challenges, with its reliance on both unconfined Quaternary aquifers, essential for ecosystems and rural communities, and confined first bedrock aquifers, critical for centralized water systems.This study develops a transboundary aquifer vulnerability assessment framework, integrating harmonized methodologies to evaluate natural vulnerability and pollution risk. The analysis uses the DRASTIC and modified DRASTIC index-based methods, combined with the DRASTIC-L approach, which incorporates land use data for a comprehensive evaluation of both natural and anthropogenic pressures. To improve the reliability of the assessment, pollutant travel time calculations were used to validate the findings.The results show high variability in aquifer vulnerability across the study area. The unconfined Quaternary aquifer is most vulnerable in regions with sandy sediments, shallow groundwater tables, and high recharge rates. The confined bedrock aquifers are covered with protective sediment layers, but their vulnerability varies depending on sediment thickness and hydraulic conductivity. Notably, discrepancies between Estonian and Latvian geological data were uncovered, as large polygons of well-protected areas often terminate abruptly at the border, highlighting inconsistencies in geological data between Estonia and Latvia.This framework emphasizes the importance of integrating natural vulnerability maps, pollution risk assessments, and harmonized methodologies developed for regional geological conditions. The study offers a scalable and adaptable solution for transboundary aquifer management by addressing data inconsistencies and fostering international cooperation.Integrating these insights into transboundary water management strategies can greatly improve decision-making by providing a more comprehensive understanding of groundwater vulnerability and pollution risks. Additionally, it helps to make groundwater systems more resilient to future challenges like climate change, land use changes, and increasing water demand. Ultimately, this approach supports the sustainable use and protection of shared groundwater resources, ensuring their availability and quality for current and future generations.

Despite growing research on microplastic contamination in beach environments, the factors influencing pollution distribution remain poorly understood. This study aims to bridge this knowledge gap by investigating microplastic pollution across 11 Latvian marine beaches (northeastern Europe). The study area experiences a four-season climate and is influenced by the Gulf of Riga and the Baltic Sea. Beaches were selected based on prior research (Dimante-Deimantovica et al., 2023), and data collection took place from autumn 2022 to summer 2023.Microplastic samples were collected seasonally - autumn, winter, spring, and summer - across three distinct 100 m transects at each beach: the waterline (closest to the sea), the mid-section (between the waterline and vegetation), and the area in front of vegetation or bluffs (farthest from the sea). The results revealed seasonal variations in microplastic abundance, with higher pollution levels observed in autumn and winter compared to spring and summer. Furthermore, plastic particle distribution was uneven across the transects, with vegetation occasionally acting as a barrier for microplastic accumulation. Rounded particles are wind-transported and gather near vegetation, while longer particles accumulate already in the first transect near the sea. This study emphasizes the importance of year-round sampling to ensure accurate pollution assessments in environments with pronounced seasonality. Considering seasonal variability is also crucial when interpreting and comparing existing monitoring results.The research is supported by GRANDE-U project “Groundwater Resilience Assessment through iNtegrated Data Exploration for Ukraine” (NSF Awards No. 2409395/2409396) and Latvian Environmental Protection Fund project No. 1-08/37/2022.Reference: Dimante-Deimantovica, Inta et al. (2023) The baseline for micro- and mesoplastic pollution in open Baltic Sea and Gulf of Riga beach. Frontiers in Marine Science. https://doi.org/10.3389/fmars.2023.1251068

Groundwater assessment is critical for addressing global and regional water security challenges, particularly in transboundary areas and conflict zones such as Ukraine. These regions often experience shifting water balance patterns due to excessive groundwater abstraction, damage to water infrastructure, and large-scale population displacement to safer border areas. Modeling transboundary groundwater storage and flows in such contexts is challenging due to uneven data availability across borders, inconsistent hydrogeological descriptions, restricted fieldwork, and limited local capacity to maintain data collection infrastructure. Effective water management in these areas requires pooling global expertise and resources, increasingly leveraging satellite observations, and fostering close collaboration among partner countries engaged in transboundary aquifer modeling.The Groundwater Resilience Assessment through Integrated Data Exploration for Ukraine (GRANDE-U) project addresses these challenges through an organizational and technological framework uniting researchers from six countries—the U.S., Ukraine, Poland, Latvia, Lithuania, and Estonia. The project integrates physics-based and machine learning models for transboundary aquifers with downscaled satellite remote sensing data. Building on the foundations of the NSF-funded AccelNet Transboundary Groundwater Resilience project and the European EU-WATERRES project, GRANDE-U employs the following methodology:- Developing a spatial database of water-related indicators for transboundary areas, including geology, water resources, land cover, monthly precipitation, evapotranspiration, runoff, soil moisture, and other characteristics at observation points and a 0.1"-0.25" grids covering the aquifer;- Creating algorithms to downscale GRACE/GRACE-FO-based terrestrial water storage (TWS) and groundwater storage (GWS) data to resolutions of 0.25" and finer for specific regions with the best available hydrogeologic data sufficient for GRACE/GRACE-FO adjustment; and- Developing machine learning models to describe GWS dynamics, utilizing as predictors the monthly averages organized in the spatial database.Initial results highlight the application of various machine learning models for accurate TWS-GRACE prediction, emphasizing hyperparameter tuning and encoding spatial dependencies. As the database and algorithms evolve, these models aim to improve transboundary groundwater monitoring and management.An additional novel component of the GRANDE-U collaboration involves analyzing global expertise in transboundary groundwater research. Using a co-authorship network analysis, the project identifies key contributors, emerging topics, knowledge gaps, and collaboration patterns across hydrogeological subdomains and related disciplines. The analysis tracks the formation and evolution of expertise clusters and explores subsets of the network based on environmental, socio-economic, and data-related issues mentioned in publication titles and abstracts. This network analysis is implemented on the SuAVE (Survey Analysis via Visual Exploration, suave.sdsc.edu) visual analytics platform, using OpenAlex, an open-access bibliographic database, to extract and tag relevant publications with keywords and aquifer names. The system provides interactive visualizations of the academic landscape and computes fragmentation and centrality measures for individual researchers and network subsets, offering valuable insights for enhancing international collaboration on transboundary groundwater issues.GRANDE-U funding under the “International Multilateral Partnerships for Resilient Education and Science System in Ukraine” (IMPRESS-U) initiative, led by the U.S. National Science Foundation, is gratefully acknowledged.

Understanding groundwater dynamics is crucial for assessing groundwater resilience and supporting water management, particularly in transboundary areas where shared aquifers are often evaluated only at a national level, overlooking the broader aquifer system and data from neighboring countries. Groundwater resilience—the ability of groundwater systems to recover from disturbances such as droughts—is a spatially variable trait influenced by a range of spatial-temporal factors that do not obey borders. This study investigates the spatial and hydrodynamic controls on groundwater dynamics within the Baltic region, focusing on the challenges posed by data discrepancies and monitoring network inconsistencies across Latvia, Lithuania, and Estonia. We utilized groundwater timeseries indices and the groundwater memory effect to investigate dominant patterns and their correlations with physiographic and climatic controls. Machine learning algorithms were used to explore spatial patterns with similar hydrodynamic characteristics. The analysis of national groundwater level data revealed monitoring gaps, particularly in transboundary aquifers, along with different national approaches in groundwater monitoring networks. These challenges complicate the comprehensive assessment of groundwater dynamics at a regional scale.The results reveal that topographic, climatic and hydrological features are the most significant drivers of groundwater dynamics, followed by geological features. Groundwater indices and trends highlighted not only natural variability but also anthropogenic impacts on aquifer systems near large cities (e.g. Riga) and mining sites (e.g. Kohtla-Järve). We identify regions lacking monitoring wells and propose potential locations for new groundwater wells based on physiographic and hydrodynamic characteristics. This research is supported by the GRANDE-U “Groundwater Resilience Assessment through Integrated Data Exploration for Ukraine” (No. 2409395) project.

Rivers are considered key pathways for microplastics, transporting these pollutants from inland sources to marine environments. In this study, we investigated the seasonal fluctuations of microplastics in the surface waters of four major Latvian rivers (Daugava, Lielupe, Gauja, and Salaca) that flow into the Gulf of Riga. Sampling was conducted from spring 2022 to summer 2023 to represent distinct seasonal and environmental conditions. Using a Manta net (100 µm mesh size) samples were collected in triplicate and analysed for microplastic concentration, shape, size, colour, and polymer composition. Microplastic concentrations ranged from 0.63 to 132.88 particles/m³, with the highest levels observed in the River Salaca. Fibres and fragments were the predominant microplastic shapes, with polyethylene, polypropylene, and ethylene propylene diene monomer being the most abundant polymers. We found significant spatial and seasonal variations in microplastic concentrations in some rivers, with the River Salaca showing the most extreme seasonal fluctuations. However, overall, no significant correlation was observed between the suspended material and river discharge variables. Replicate sampling revealed variability between samples, highlighting the need to include replicas. These findings highlight the complexity of microplastic pollution dynamics and the need for careful consideration of seasonal factors when assessing environmental contamination. This article is the first to contribute data from Latvian rivers on the growing pool of information on microplastics contamination in waterways.