Lake 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.

This study investigated the spatial distribution, contamination, potential ecological risks and quantities of pollutant sources of six heavy metals (HMs) in sediments of 47 rivers. The catchments of the investigated rivers are situated in Poland, but some of them are located in Slovakia, the Czech Republic, and Germany. Cluster analysis was applied to analyze the spatial distribution of Cd, Cr, Cu, Ni, Pb, and Zn in river sediments. Moran I and Getis-Ord $G_i^{*}$ statistics were calculated to reveal the distribution pattern and hotspot values. Principal component analysis (PCA) and positive matrix factorization (PMF) were used to identify pollution sources. Furthermore, geochemical indices and sediment quality guidelines allowed us to assess sediment contamination and potential toxic effects on aquatic biota. The results showed that in 1/3rd of the rivers, the HM pattern and concentrations indicate sediment contamination. The EF, PLI, and MPI indices indicate that concentrations were at a rather low level in 2/3rd of the analyzed rivers. Only in individual rivers may the HMs have toxic effects on aquatic biota. Spatial autocorrelation analysis using the Moran I statistic revealed a random and dispersed pattern of HMs in river sediments. PCA analysis identified two sources of HMs’ delivery to the aquatic environment. Cr, Cu, Ni, Pb, and Zn originate from point and non-point sources, while Cd concentrations have a dominant natural origin. The PMF identified three sources of pollution. Among them, urban pollution sources are responsible for Cu delivery, agricultural pollution for Zn, and industrial pollution for Ni and Cr. Moreover, the analysis showed no relationship between catchment land-use patterns and HM content in river sediments.

The paper presents the results of a study of heavy metals (HMs) concentrations in six retention reservoirs located in the lowland area of western Poland. The objectives of this study were to analyze the Cd, Cr, Cu, Ni, Pb and Zn concentrations, assess contamination and ecological risk, analyze the spatial variability of HM concentrations and identify potential sources and factors determining the concentration and spatial distribution. The bottom sediment pollution by HMs was assessed on the basis of the index of geo-accumulation (I_geo), enrichment factor (EF), pollution load index (PLI) and metal pollution index (MPI). To assess the ecological risk associated with multiple HMs, the mean probable effect concentration (PEC) quotient (Q_m-PEC) and the toxic risk index (TRI) were used. In order to determine the similarities and differences between sampling sites in regard to the HM concentration, cluster analysis (CA) was applied. Principal component analysis (PCA) was performed to assess the impact of grain size, total organic matter (TOM) content and sampling site location on HM spatial distribution. Additionally, PCA was used to assess the impact of catchment, reservoir characteristics and hydrological conditions. The values of I_geo, EF, MPI and PLI show that Cd, Cr, Cu, Ni and Pb mainly originate from geogenic sources. In contrast, Zn concentrations come from point sources related to agriculture. The mean PEC quotient (Q_m-PEC) and TRI value show that the greatest ecological risk occurred at the inlet to the reservoir and near the dam. The analysis showed that the HMs concentration depends on silt and sand content. However, the Pb, Cu, Cd and Zn concentrations are associated with TOM as well. The relationship between individual HMs and silt was stronger than with TOM. The PCA results indicate that HMs with the exception of Zn originate from geogenic sources—weathering of rock material. However, the Ni concentration may additionally depend on road traffic. The results show that a reservoir with more frequent water exchange has higher HMs concentrations, whereas the Zn concentration in bottom sediments is associated with agricultural point sources.

The study evaluated the effect of environmental conditions and morphometric parameters on lake water temperature changes. The analysis was carried out on the basis of 14 lakes located in northern Poland. The assessment was based on the daily water and air temperatures from 1972 to 2016. It took into account the location of lakes (latitude, longitude, altitude) morphometric parameters (surface area, maximum and mean depth, volume), hydrological processes (rate of water exchange, course of ice phenomena), and trophic status (water transparency) as factors that can modify lake water temperature changes. Direction and rate of air and water temperature changes were analysed by means of Mann–Kendall’s and Sen’s tests. Cluster analysis (CA) was applied to group lakes characterised by similar water temperature changes. The effect of climatic and non-climatic parameters on a lake’s water temperature was assessed on the basis of principal component analysis (PCA). Water temperatures in the lakes in the years 1972–2016 were characterised by a higher rate of increase of 0.43 °C·dec⁻¹ than the air temperature decrease of 0.34 °C·dec⁻¹. The analysis showed a faster rate of heating of waters in western Poland. This can be explained by shorter duration of ice cover. Moreover, the changes of water temperature were affected by other factors, including the location of the lakes, their morphometric parameters, wind speed, water transparency and water exchange time.

Pollution of river bottom sediments with heavy metals (HMs) has emerged as a main environmental issue related to intensive anthropopressure on the water environment. In this context, the risk of harmful effects of the HMs presence in the bottom sediments of the Warta River, the third longest river in Poland, has been assessed. The concentrations of Cr, Ni, Cu, Zn, Cd, and Pb in the river bottom sediments collected at 24 sample collection stations along the whole river length have been measured and analyzed. Moreover, in the GIS environment, a method predicting variation of HMs concentrations along the whole river length, not at particular sites, has been proposed. Analysis of the Warta River bottom sediment pollution with heavy metals in terms of the indices: the Geoaccumulation Index (Igeo), Enrichment Factor (EF), Pollution Load Index (PLI), and Metal Pollution Index (MPI), has proved that, in 2016, the pollution was heavier than in 2017. Assessment of the potential toxic effects of HMs accumulated in bottom sediments, made on the basis of Threshold Effect Concentration (TEC), Midpoint Effect Concentration (MEC), and Probable Effect Concentration (PEC) values, and the Toxic Risk Index (TRI), has shown that the ecological hazard in 2017 was much lower. Cluster analysis revealed two main groups of sample collection stations at which bottom sediments showed similar chemical properties. Changes in classification of particular sample collection stations into the two groups analyzed over a period of two subsequent years indicated that the main impact on the concentrations of HMs could have their point sources in urbanized areas and river fluvial process.