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A novel optimized model based on NARX networks for predicting thermal anomalies in Polish lakes during heatwaves, with special reference to the 2018 heatwave
Utilizing Multi-Source Datasets for the Reconstruction and Prediction of Water Temperature in Lake Miedwie (Poland)
Water temperature is a fundamental parameter of aquatic ecosystems. It directly influences most processes occurring within them. Hence, knowledge of this parameter’s behavior, based on long-term (reliable) observations, is crucial. Gaps in these observations can be filled using contemporary methodological solutions. Difficulties in reconstructing water temperature arise from the selection of an appropriate methodology, and overcoming them involves the proper selection of input data and choosing the optimal modeling approach. This study employed the air2water model and Landsat satellite imagery to reconstruct the water temperature of Lake Miedwie (the fifth largest in Poland), for which field observations conducted by the Institute of Meteorology and Water Management—National Research Institute ended in the late 1980s. The approach based on satellite images in this case yielded less accurate results than model analyses. However, it is important to emphasize the advantage of satellite images over point measurements in the spatial interpretation of lake thermal conditions. In the studied case, due to the lake’s shape, the surface water layer showed no significant thermal contrasts. Based on the model data, long-term changes in water temperature were determined, which historically (1972–2023) amounted to 0.20 °C per decade. According to the adopted climate change scenarios by the end of the 21st century (SSP245 and SSP585), the average annual water temperature will be higher by 1.8 °C and 3.2 °C, respectively. It should be emphasized that the current and simulated changes are unfavorable, especially considering the impact of temperature on water quality. From an economic perspective, Lake Miedwie serves as a reservoir of drinking water, and changes in the thermal regime should be considered in the management of this ecosystem.
Are Agroecosystem Services Under Threat? Examining the Influence of Climate Externalities on Ecosystem Stability
This study examines the impacts of climate-induced externalities on the stability of agroecosystems and the ecosystem services they provide. Using the PRISMA approach, we review literature published from 2015 to 2024. The study identifies how extreme weather events such as droughts, floods, heatwaves, and altered precipitation patterns disrupt the provisioning, regulating, and supporting services critical to food security, soil fertility, water purification, and biodiversity. Our findings show a continued increase in climate extremes, raising concerns about food security, environmental resilience, and socio-economic stability. It also reveals that regions dependent on rain-fed agriculture, such as parts of Africa, Asia, and the Mediterranean, are particularly vulnerable to these stressors. Adaptation strategies, including conservation agriculture, crop diversification, agroforestry, and improved water management, are identified as crucial for mitigating these impacts. This study emphasises the importance of proactive, policy-driven approaches to foster climate resilience, support agroecosystem productivity, and secure ecosystem services critical to human well-being and environmental health.
River water temperature prediction using hybrid machine learning coupled signal decomposition: EWT versus MODWT
Analysis of the impact of road noise on urban green spaces: a case study of the dendrological garden in Poznań, Poland
Abstract Green parks and gardens in urban areas are an essential part of ecosystem services for inhabitants, especially in cities where people are affected by road noise. These problems have been the subject of many studies worldwide. Such works have been carried out at many levels—both in the contexts of spatial planning and ecological analyses. In relation to this, the design of noise maps and related spatial modelling is significant. The paper presents the results of a survey of the acoustic environment in the Dendrological Garden in Poznań—the fifth largest city in Poland—which is a vital part of the city’s green zone. The analysis of the noise distribution across the park’s zones was carried out at frequent intervals using sound-level surveys during 2014 and 2020. Moreover, acoustic maps developed for Poznań in 2012 and 2017 were compared with these recent studies. The changes (gain or loss) in those areas with a particular noise level are based on the area variability index.
Less and thinner ice: seven decades of change in the ice cover of temperate lakes (Central Europe, Poland)
Challenges and Prospects for Modeling Lake Water Temperature in a Changing Climate
Climate change is having a significant impact on the temperature dynamics of lakes worldwide, affirming the need for accurate modeling to inform management and conservation strategies.
150-year daily data (1870–2021) in lakes and rivers reveals intensifying surface water warming and heatwaves in the Pannonian Ecoregion (Hungary)
Diversification of macrophytes within aquatic nature-based solutions (NBS) developing under urban environmental conditions across European cities
Long-term trends in water level fluctuations in lowland lakes in central Europe (Northern Poland)
Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects
AbstractLake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.
Seven Decades of Surface Temperature Changes in Central European Lakes: What Is Next?
Lakes are vital components of the hydrosphere, holding both environmental and economic significance. In recent times, they have undergone transformations in one of their key characteristics—water temperature. Assessing the scale and pace of these changes depends on the length and accuracy of the available data. This study focuses on the two lakes in Poland (Białe Augustowskie and Studzieniczne) with the longest continuous water temperature records, ranging from 1954 to 2023. The results reveal a relatively stable thermal regime until the late 1980s (with changes that were statistically insignificant) and a significant shift over the past three decades, during which the water temperature increased at a rate of 0.5 °C per decade. Importantly, simulations indicate further warming of the water by the end of the 21st century. Depending on the chosen climate change scenario, the warming of both lakes is expected to continue, with the Shared Socioeconomic Pathways (SSP585) scenario projecting a steady increase of 0.5 °C per decade. Given the fundamental importance of water temperature in determining factors such as water quality, these future changes present a significant challenge for water management authorities in terms of maintaining and managing these ecosystems.
Long-Term Changes in the Thermal and Ice Regime of the Biebrza River (Northeastern Poland) in the Era of Global Warming
In the context of ongoing environmental changes, particularly against the backdrop of global warming, significant attention is being given to areas of exceptional natural value that, in many aspects, retain a pristine character. One such area is the Biebrza River in northeastern Poland, which, together with the wetlands in its basin, forms one of the most valuable ecosystems of its kind in Europe. This study analyses the changes in the thermal and ice regime for two hydrological stations, Sztabin and Burzyn, in the period from 1959 to 2023. It was found that the average annual water temperature in this period for the Biebrza River increased by 0.28 °C/decade, and in the case of ice phenomena, statistically significant changes for both stations showed a decline, with an acceleration of the ice cover disappearance by an average of 3 days/decade. These recorded changes should be considered unfavourable, as they will affect the transformation of both the biotic and abiotic characteristics of the river itself, as well as the natural elements associated with it.
Assessment of the Impact of Meteorological Variables on Lake Water Temperature Using the SHapley Additive exPlanations Method
The water temperature of lakes is one of their fundamental characteristics, upon which numerous processes in lake ecosystems depend. Therefore, it is crucial to have detailed knowledge about its changes and the factors driving those changes. In this article, a neural network model was developed to examine the impact of meteorological variables on lake water temperature by integrating daily meteorological data with data on interday variations. Neural networks were selected for their ability to model complex, non-linear relationships between variables, often found in environmental data. Among various architectures, the Artificial Neural Network (ANN) was chosen due to its superior performance, achieving an R2 of 0.999, MSE of 0.0352, and MAE of 0.1511 in validation tests. These results significantly outperformed other models such as Multi-Layer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM). Two lakes (Lake Mikołajskie and Sławskie) differing in morphometric parameters and located in different physico-geographical regions of Poland were analyzed. Performance metrics for both lakes show that the model is capable of providing accurate water temperature forecasts, effectively capturing the primary patterns in the data, and generalizing well to new datasets. Key variables in both cases turned out to be air temperature, while the response to wind and cloud cover exhibited diverse characteristics, which is a result of the morphometric features and locations of the measurement sites.
The Relationship between Mortality from Cardiovascular Diseases and Total Drinking Water Hardness: Systematic Review with Meta-Analysis
Background: Interest in water chemical activity, its content, and its impact on human health has greatly increased throughout the last decade. Some studies suggest that drinking water with high hardness may have preventative effects on cardiovascular diseases. This study aims to investigate the association between drinking water hardness and cardiovascular disease (CVD) mortality. Methods: The study selection process was designed to find the association between drinking water hardness and CVDs mortality. The search included both qualitative and quantitative research and was performed in three databases: Web of Science (Clarivate Analytics, Ann Arbor, MI, USA), PubMed (National Institute of Health, Bethesda, MA, USA), and Scopus (Elsevier, RELX Group plc, London, UK). The project was registered in the International Prospective Register of Systematic Reviews (PROSPERO), registration number: CRD42020213102. Results: Seventeen studies out of a total of twenty-five studies qualitatively analyzed indicated a significant relation between total water hardness and protection from CVD mortality. The quantitative analysis concluded that high drinking water hardness has a significantly lowering effect on mortality from CVDs, however, the heterogeneity was high. Conclusions: This systematic literature review shows that total water hardness could affect CVD prevention and mortality. Due to the many confounding factors in the studies, more research is needed.
Historical and Future Changes in Water Temperature in the Pilica River (Central Europe) in Response to Global Warming
This study analyzes changes in the water temperature in the Pilica River (Poland), encompassing both historical data (1958–2023) and projections extending to the year 2100. We use multi-model ensembles (MMEs) with Bayesian Model Averaging (BMA) to integrate various Global Climate Model (GCM) datasets for current and projected climate data. Additionally, a Random Forest (RF) machine learning method is applied to project future water temperatures in the Pilica River. It has been demonstrated that over a period of more than sixty years, the average annual water temperature has increased by nearly 2 °C. Further changes are expected to continue in a similar direction with a gradual rise in this parameter, reaching a temperature increase of 3 °C by the end of the 21st century (SSP585). In the distant future, with average monthly water temperature changes at the Przedbórz station ranging from 0.27 °C to 0.87 °C·decade−1 and at the Białobrzegi station from 0.22 °C to 1.06 °C·decade−1. The results of these changes are concerning, especially considering the crucial role of water temperature in shaping seasonality and the dynamics of processes occurring within the river. In the context of the sustainability of the river itself, but also of the entire catchment area, strategies developed by relevant public administration bodies are needed to mitigate the impacts of global warming observed in the thermal regime of the Pilica River.
Reconstruction of Surface Water Temperature in Lakes as a Source for Long-Term Analysis of Its Changes
One of the key parameters of lakes is water temperature, which influences many physical and biochemical processes. In Poland, in situ temperature measurements are or have been conducted in only about 30 lakes, whereas there are over 3000 lakes with an area larger than 10 hectares. In many cases, the length of existing observation series is not always sufficient for long-term analysis. Using artificial neural networks of the multilayer perceptron network (MLP) type, the reconstruction of average monthly water temperatures was carried out for nine lakes located in northern Poland. During the validation stage of the reconstruction results, BIAS values were obtained in the range of −0.33 to 0.44 °C, the mean absolute error was 0.46 °C, and the root mean square error was 0.61 °C. The high quality of the reconstructed data allowed for an assessment of water temperature changes in the analyzed lakes from 1993 to 2022 using the Mann–Kendall and Sen tests. It was found that, on an annual basis, the water temperature increased by an average of 0.50 °C per decade, ranging from 0.36 °C per decade to 0.64 °C per decade for individual lakes. For specific months, the largest increase was observed in November, about 0.99 °C per decade, and the smallest in May, 0.07 °C per decade. The obtained results confirm previous studies in this field while adding new data from lakes, which are particularly significant for the western part of Poland—a region with a previously limited number of monitored lakes. According to the findings, the analyzed lakes have undergone significant warming over the past three decades, which is important information for water management authorities.
Ecological and Health Risk Assessments of Heavy Metals Contained in Sediments of Polish Dam Reservoirs
This study aimed at investigating the distribution of heavy metals (HMs: Zn, Pb, Cd, Ni, Cr, and Cu) in the bottom sediments of 28 reservoirs covered area of Poland. The paper evaluates the pollution of sediments with HMs and their potential toxic effects on aquatic organisms and human health on the basis of results provided by the Chief Inspectorate of Environmental Protection in Poland. The average concentrations of HMs in the bottom sediments of the reservoirs were as follows: Cd < Ni < Cr < Cu < Pb < Zn. (0.187, 7.30, 7.74, 10.62, 12.47, and 52.67 mg∙dm−3). The pollution load index values were from 0.05 to 2.45. They indicate contamination of the bottom sediments in seven reservoirs. The contamination-factor values suggest pollution with individual HMs in 19 reservoirs, primarily Cr, Ni, Cu, and Pb. The analysis showed that only two reservoirs had the potential for toxic effects on aquatic organisms due to high concentrations of Cd and Pb. The hazard index values for all the analyzed HMs were less than one. Therefore, there was no non-carcinogenic risk for dredging workers. The reservoirs were divided into two groups in terms of composition and concentration values. Reservoirs with higher concentrations of HMs in bottom sediments are dispersed, suggesting local pollution sources. For the second group of reservoirs, HMs’ concentrations may be determined by regional pollution sources. The analysis showed that Pb, Zn, and Cd concentrations are higher in older reservoirs and those with higher proportions of artificial areas in their catchments. Concentrations of Ni, Cu, and Cr are higher in reservoirs in south Poland and those with higher Schindler’s ratios.
