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Article

Projections of Climate Change Impact on Stream Temperature: A National-Scale Assessment for Poland

by
Paweł Marcinkowski
Department of Hydrology, Meteorology and Water Management, Institute of Environmental Engineering, Warsaw University of Life Sciences, Nowoursynowska 166, 02-787 Warsaw, Poland
Appl. Sci. 2024, 14(23), 10900; https://doi.org/10.3390/app142310900
Submission received: 25 October 2024 / Revised: 18 November 2024 / Accepted: 22 November 2024 / Published: 25 November 2024
(This article belongs to the Special Issue 5th Anniversary of Environment Section—Climate Change and Natural Disaster)
Browse Figures
Figure 1
<p>Study location with SWAT sub-basins (<b>A</b>), stream temperature gauging stations (<b>B</b>), and mean seasonal air temperatures in summer (<b>C</b>) and winter (<b>D</b>).</p> "> Figure 2
<p>Country-averaged monthly changes in mean daily air temperature under RCP4.5 (blue) and RCP8.5 (orange). The intensity of each colour represents different horizons: light (baseline—ACT), medium (near future—NF), and dark (far future—FF).</p> "> Figure 3
<p>Goodness-of-fit measures derived upon validation: Kling–Gupta efficiency (KGE) (<b>A</b>), percent bias (PBIAS) (<b>B</b>), and coefficient of determination (R<sup>2</sup>) (<b>C</b>).</p> "> Figure 4
<p>Box plots showing the model performance expressed by Kling–Gupta efficiency (KGE), coefficient of determination (R2) (<b>A</b>), and percent bias (PBIAS) (<b>B</b>) values in 369 water quality monitoring points.</p> "> Figure 5
<p>Spatial distribution of multi-annual summer season mean stream temperature at the reach level for Representative Concentration Pathways (RCPs) 4.5 and 8.5 at baseline (ACT), in the near future (NF), and in the far future (FF).</p> "> Figure 6
<p>Spatial distribution of multi-annual winter season mean stream temperature at the reach level for Representative Concentration Pathways (RCPs) 4.5 and 8.5 at baseline (ACT), in the near future (NF), and in the far future (FF).</p> "> Figure 7
<p>Projections of the average daily air temperature during the summer period over multiple years for RCP4.5 (<b>A</b>) and RCP8.5 (<b>B</b>). The bands indicate extreme values (min and max) from nine climate models; the solid line represents the median. The green colour represents the historical period. The intensity of the blue and orange colours denotes the time horizon, where lighter shades indicate the near future, while darker shades indicate the far future.</p> "> Figure 8
<p>Projections of the average daily air temperature during the winter period over multiple years for RCP4.5 (<b>A</b>) and RCP8.5 (<b>B</b>). The bands indicate extreme values (min and max) from nine climate models; the solid line represents the median. The green colour represents the historical period. The intensity of the blue and orange colours denotes the time horizon, where lighter shades indicate the near future, while darker shades indicate the far future.</p> ">
Versions Notes

Abstract

:
This national-scale assessment explores the anticipated impact of climate change on stream temperature in Poland. Utilizing an ensemble of six EURO-CORDEX projections (2006 to 2100) under Representative Concentration Pathways (RCPs) 4.5 and 8.5, the study employs the Soil and Water Assessment Tool (SWAT) to simulate stream temperature regimes. Validation against observed stream temperatures at 369 monitoring points demonstrates the reliability and accuracy of the SWAT model performance. Projected changes in air temperature reveal distinct seasonal variations and emission scenario dependencies. The validated stream temperature model indicates a uniform warming tendency across Poland, emphasizing the widespread nature of climate change impacts on aquatic ecosystems. Results show an increase in country-averaged stream temperature from the baseline (16.1 °C), with a rise of 0.5 °C in the near future (NF) and a further increase by 1 °C in the far future (FF) under RCP4.5. Under RCP8.5, the increase is more pronounced, reaching 1 °C in the NF and a substantial 2.6 °C in the FF. These findings offer essential insights for environmental management, emphasizing the need for adaptive strategies to mitigate adverse effects on freshwater ecosystems. However, as a preliminary study, this work uses a simplified temperature model that does not account for detailed hydrological processes and spatial variability, making it a good starting point for more detailed future research.

1. Introduction

The temperature of streams plays a crucial role in shaping the health and vitality of freshwater ecosystems [1]. It profoundly influences the metabolic rates, growth, and reproductive behaviours of aquatic organisms, ranging from fish to invertebrates. It directly affects the solubility of oxygen in water, with warmer temperatures reducing the amount of dissolved oxygen available for aquatic life [2]. Furthermore, stream temperature influences the rates of biological and chemical processes, such as nutrient cycling and microbial activity [3]. Many species of fish and macroinvertebrates have specific temperature requirements for optimal development and survival, and deviations from these thresholds can lead to adverse effects on their populations [4,5]. Elevated temperatures can exacerbate stress on aquatic organisms, potentially triggering shifts in species composition and disrupting the delicate balance of the ecosystem [6]. Monitoring and managing stream temperature are thus essential for the preservation and sustainable management of freshwater ecosystems, ensuring their resilience in the face of environmental changes.
As climate change progresses, the consequences for our environment are becoming increasingly pronounced, reshaping the balance of Earth’s natural systems and raising urgent concerns for the future. The anticipated effects of climate change on the environment, biodiversity, and freshwater ecosystems are poised to escalate, creating challenges for the balance of Earth’s interconnected ecosystems [7]. The acceleration of rising global temperatures, coupled with altered precipitation patterns and an increased frequency of extreme weather events, is expected to exacerbate these challenges [8]. This shift in temperature dynamics is expected to exert considerable stress on biodiversity, as many species may struggle to adapt or face habitat loss [9]. In freshwater ecosystems, the consequences could be particularly severe, with shifts in water availability, heightened incidences of droughts and floods, and the potential for widespread disruptions to aquatic habitats [10].
The scientific literature has demonstrated a notable emphasis on studying the impact of climate change on water discharge compared to the exploration of its effects on stream temperature [11,12,13,14]. The heightened attention to water discharge is primarily attributed to its direct relevance in water resource management, ecosystem functioning, and human activities such as agriculture and water supplies [15]. While the importance of stream temperature in understanding aquatic ecosystems and water quality is acknowledged, it has historically received relatively less attention compared to water discharge [16]. Nevertheless, the projections of climate change impact on stream temperatures indicate a range of significant and far-reaching consequences for freshwater ecosystems. As global temperatures continue to rise, streams are expected to experience warmer water temperatures, which can disrupt the thermal balance critical for aquatic life [17,18]. Increased air temperatures contribute to elevated stream temperatures, affecting the distribution and abundance of cold-water species, such as trout and salmon, as they may face reduced suitable habitats [19]. Moreover, higher temperatures can intensify thermal stratification, leading to decreased dissolved oxygen levels in deeper waters, adversely affecting fish and invertebrate populations [20]. These shifts in stream temperatures pose a threat to biodiversity, potentially triggering species migrations, declines, or even local extinctions [21]. Addressing the projected impacts of climate change on stream temperatures is imperative for the conservation and adaptive management of freshwater ecosystems in the face of this global environmental challenge.
The projections of climate change impact on stream temperatures underscore the urgency of advancing our understanding and modelling capabilities to address challenges for freshwater ecosystems. The consequences of rising global temperatures on streams are multifaceted, influencing both thermal balance and the distribution of aquatic species. By synergizing modelling methodologies, researchers and resource managers gain tools for pinpointing vulnerable areas, devising effective conservation strategies, and proactively addressing the ecological disruptions precipitated by the warming of stream environments [22]. Mathematical models play a valuable role in the modelling of stream temperature and assessing the impact of climate change. Over the years, numerous stream temperature models have emerged, falling into two main categories, namely mechanistic and statistical models. A mechanistic model operates on the principles of the energy balance of heat fluxes and water mass balance [23]. Statistical models, in particular, have been extensively employed for these purposes, as evidenced by their widespread use [24,25,26]. These models rely on empirical regression relationships, establishing connections between stream temperatures and meteorological parameters. More sophisticated approaches, exemplified by the heat source model, consider the interplay of atmospheric, terrestrial, and aquatic heat exchange processes [27]. Several studies have also undertaken the modelling of stream temperature and the evaluation of climate change effects through the utilization of diverse hydrological models. These models take into account the influences of both meteorological and hydrological conditions [22,28]. Piotrowski et al. [29] underscored the significance of employing multiple models for a comprehensive analysis of climate change impacts on water temperatures and aquatic ecosystems. Their emphasis on using diverse models stems from observed discrepancies among results, recognizing that employing a range of modelling approaches enhances the robustness and reliability of assessments related to the effects of climate change on freshwater environments. In general, the modelling approach not only fortifies our capacity to adaptively manage freshwater ecosystems but also underscores the important role of interdisciplinary collaboration in navigating the complex terrain of climate change impacts.
Against this background, the study aims to evaluate the anticipated impact of climate change on stream temperature and its potential ramifications for freshwater ecosystems in Poland. This investigation utilizes an ensemble of six EURO-CORDEX projections, spanning the period from 2006 to 2100, across two Representative Concentration Pathways, namely RCP4.5 and 8.5. Additionally, the Soil and Water Assessment Tool (SWAT), a hydrological model customized for the Polish region, is employed to address this issue. This study represents a first-of-a-kind macro-scale analysis for Poland, providing valuable insights into the projected changes in stream temperature and their implications for the freshwater ecosystems in Poland, thereby contributing to a more profound understanding of climate change impacts in the region. This national-scale assessment fills a gap in the literature, as no prior studies have comprehensively examined the impacts of climate change on stream temperatures across the entire country.

2. Materials and Methods

2.1. Study Area

The research area encompasses the entire territory of Poland (312,683 km2; Figure 1) featuring a temperate climate influenced by its topography and geographical location. Regional climatic conditions are significantly shaped by diverse air masses, including maritime, cold polar, and subtropical influences. The southern mountainous region receives the highest annual precipitation, exceeding 1000 mm, while the central lowland areas receive the lowest, ranging from 400 to 500 mm [30,31]. This variation results in an average annual precipitation total of 600 mm. Poland experiences a diverse range of air temperature characteristics influenced by its geographical location and continental climate [32]. With its location in Central Europe, Poland undergoes distinct seasonal variations. Winters, typically from December to February, can be cold, with temperatures often dropping below freezing, accompanied by snowfall. The coldest temperatures are usually recorded in the eastern regions. Summers, spanning from June to August, bring warmer weather with average temperatures ranging from 20 to 30 °C [33]. However, occasional heatwaves may lead to higher temperatures. Spring and autumn serve as transitional seasons, marked by moderate temperatures and changing weather patterns. Coastal areas, especially along the Baltic Sea, tend to experience milder temperatures due to the maritime [30,31] influence.

2.2. SWAT Model

The SWAT model, a widely employed semi-distributed hydrological model [28], was utilized in this study. It is a widely recognized hydrological tool and has been employed due to its computational efficiency and established performance in large-scale studies. The SWAT model employs a linear equation established by Stefan and Preudhomme [34] to compute the mean daily temperature for a uniformly mixed stream.
T w = 5.0 + 0.75 · T a i r
where Tw represents the stream temperature for the day (°C) and Tair denotes the average air temperature for the day (°C). This equation operates under the assumption that the time delay between air and stream temperature is under 1 day.
In this study, the SWAT2012 model setup (revision 664) was used, covering the entire territory of Poland [35,36]. While a detailed processing and preparation of input datasets are available in Marcinkowski and Piniewski’s work [36], a concise summary of input data for the model setup is presented here. Watershed delineation utilized a 50 m resolution digital terrain model (DTM). Hydrologic response units (HRUs) were defined using an agricultural soil map (1:100,000 scale) and the refined Corine Land Cover 2018 (CLC 2018), in which an actual crop distribution acquired from the agricultural census (2010, Statistics Poland) for 2477 communes in Poland within arable lands was included. The methodology was for the distribution-preserving random scatter of eight crops (spring barley, winter wheat, corn for grain and silage, potato, sugar beet, spring canola, and vegetables) within the arable [35,36] class of the land cover map [37]. The automatic delineation resulted in 4381 sub-basins (enclosing river reaches) covering the entire area, with an average area of 79.8 km2. By overlaying soil, land cover, and slope maps, 47,725 HRUs were defined, with an average area of 7.33 km2.
Daily climate data (precipitation, temperatures, relative humidity, wind speed, and solar radiation) were obtained from a 2 km gridded daily climate dataset (G2DC-PL+) [38] and ~9 km resolution ERA5-Land [39]. In this study, the emphasis lies on temperature variables due to their paramount importance in the context of the research objectives. Specifically, air temperature variables in Piniewski et al.’s work [38] were interpolated using the kriging method, capitalizing on data obtained from a robust network of 200 gauging stations with verified observations. The interpolation process underwent rigorous validation through a leave-one-out cross-validation approach conducted on a daily time step across all stations. To gauge the accuracy of the interpolated values, the root mean squared error (RMSE), normalized to the standard deviation of the observed data, was employed. The resulting normalized RMSE values were determined to be 0.52 for the minimum temperature and 0.40 for the maximum temperature. These metrics provide a quantitative measure of the interpolation errors, offering insights into the reliability and precision of the kriging method specifically in capturing daily variations in temperature across the study area.

2.3. Stream Temperature Validation

In the implementation of the SWAT model for simulating stream temperature in this study, a conscious decision was made not to engage in a hard calibration process, owing to the inherent simplicity of the equation employed, which is Equation (1). Its clarity and transparency provide a straightforward and easily interpretable method for estimating stream temperature. Unlike more complex models requiring extensive parameterization and calibration, the equation used here involves a limited set of parameters, eliminating the need for fine-tuning. This decision is supported by the equation’s alignment with established methodologies found in the scientific literature, ensuring consistency with proven approaches [40,41]. Additionally, the high-quality input temperature data utilized in this study, representative of the study area, contribute to the reliability of the equation. Instead of calibration, a thorough validation process was conducted to assess the equation’s performance. The validation process (2000–2019) of daily temperature time series were carried out at 369 water quality monitoring points (Figure 1B) obtained from the Chief Inspectorate for Environmental Protection. The data collected for this study originate from a network of monitoring stations distributed across the entire study area. The temporal resolution and frequency of data measurements varied depending on the specific monitoring point. Measurements were sampled irregularly within the monitoring network, reflecting operational schedules and monitoring objectives unique to each site. Specifically, the frequency of data collection ranges from once a month at some locations to several times per month at others. During the validation period, each point was assessed, and the total number of observations ranged from 100 to 690 (148 on average). The assessment of the SWAT model’s performance involved three statistical measures, KGE (Kling–Gupta Efficiency) [42], percent bias (PBIAS) measuring the average tendency of the simulated values to be larger or smaller than their observed ones, and the coefficient of determination (R2). To assess the accuracy of SWAT model temperature simulations, a comparison was conducted between the daily mean simulated outcomes and the observed stream temperatures (continuously throughout the entire validation period) at 369 water quality monitoring points spanning the entire country.

2.4. Climate Change Scenarios

Future climate scenarios were formulated using data derived from EURO-CORDEX projections, as outlined by Jacob et al. [43] following the guidelines of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). The EURO-CORDEX framework provides high-resolution climate projections that are particularly suitable for regional-scale studies in Europe. The simulations, obtained through the downscaling of global climate projections CMIP5, covered the European domain at a resolution of 12.5 km on a regular grid (0.11°). Recognizing the inherent uncertainty in future climate changes, the analysis focused on two emission scenarios, namely RCP4.5 and RCP8.5, illustrating potential increases in greenhouse gas concentrations in the atmosphere. Descriptions and underlying assumptions of both RCP scenarios were detailed in Moss et al.’s work [44]. Although not directly aligning with the latest Shared Socioeconomic Pathways (SSPs) presented in the IPCC’s 2022 report, these RCP scenarios offered a comparable range of possible emission trajectories. Specifically, the RCP4.5 scenario corresponds to the medium–low emissions trajectory SSP2 (Middle of the Road), while the RCP8.5 scenario aligns with the high-emissions trajectory SSP5 (Fossil-fueled Development) within the SSP framework.
The preparation of climatic data, encompassing variables such as temperature, precipitation, wind speed, relative humidity, and solar radiation, has been extensively detailed in the work of Marcinkowski et al. [45]. For the purposes of this study, a concise overview will be provided with a specific focus on temperature due to its pivotal role in the current research context. In the designated study area, we chose six EURO-CORDEX regional climate models (Table 1) to construct a comprehensive ensemble of climate projections for each emission scenario and every relevant climate variable. The analytical framework was executed at a daily temporal resolution, spanning a significant 95-year period from 2006 to 2100. The selected climate variables for examination comprised the minimum and maximum 2 m temperature (expressed in Kelvin). To enhance the reliability of each model, we implemented a systematic bias reduction through a statistical correction method. Specifically, we employed a non-parametric quantile mapping approach, known as robust empirical quantiles (RQUANTs), recognized for its effectiveness in addressing non-continuous climate parameters like precipitation, as evidenced in studies such as that by Enayati et al. [46]. This technique utilizes a local linear square regression function to estimate the quantile–quantile relation values between observed and modelled time series, specifically for regularly spaced quantiles. The quantile mapping procedure was applied to temperature grid cells independently within the analyzed domain.
To facilitate statistical correction, observation data matching the spatial and temporal resolution of EURO-CORDEX products were utilized. Ground monitoring stations operated by the Polish National Hydrological and Meteorological Service, Institute of Meteorology and Water Management—Polish Research Institute (IMGW-PIB), were used. The observation sets, crucial for the training period of RQUANT data fitting, spanned a duration of 10 years, from 2006 to 2015. Ground station data were interpolated to the Polish territory, established as part of Action COST VALUE, utilizing a linear radial basis function, resulting in a spatial resolution of 0.11°. A preview of this subdomain is accessible on the COST VALUE official webpage (http://www.value-cost.eu/WG2_griddedobs (accessed on 21 July 2022)). In the final step, temperature values were averaged over SWAT sub-basins before being incorporated into the model. For the purposes of this research, the entire projection period was stratified into three distinct time slices as follows: 2006–2025 (baseline—ACT), 2041–2060 (near future—NF), and 2081–2100 (far future—FF), resulting in the analysis of a total of 36 scenarios (6 climate models × 2 emission scenarios × 3 temporal horizons).

2.5. Analysis of Results

In this study, the potential impacts of climate change on stream temperature were assessed through a comparative analysis of baseline period simulations (2006–2025) against projections derived from GCM-RCMs for the mid- and late 21st century (2041–2060 and 2081–2100). Daily stream temperature values served as the primary focus for the analysis. When characterizing the average values for specific time periods, the study relies on the median of the six CMIP5 GCM-RCMs associated with each period. To facilitate temporal analysis, months were aggregated into distinct seasons, namely the summer season (May–October) and the winter season (November–April). A spatial representation of the results is presented at the river reach level (10 km on average), demarcated by sub-basins. This approach allows for an exploration of the potential changes in stream temperature across both temporal and spatial dimensions. In addition to analyzing the mean stream temperature values across seasons, this study also examined the lower and upper temperature ranges, represented by the 20th and 80th percentiles of seasonal stream temperature values averaged at the country level. Furthermore, the temporal (yearly) variability of country-averaged mean stream temperatures for summer and winter was assessed across the defined time horizons. To evaluate the significance of these yearly trends, the Mann–Kendall test [47,48] was employed at the significance level of 5%. For the time periods analyzed (ACT, NF, and FF), the statistical significance of changes in the yearly seasonal mean and high (80th percentile) and low (20th percentile) stream temperatures was evaluated using a two-sample t-test at a significance level of 5%.

3. Results

3.1. Projected Changes in Air Temperature in Poland

The temperature projections for climate change in Poland (described in this paragraph as country-averaged values) demonstrate discernible differences across seasons and emission scenarios. Under the RCP4.5 scenario, the summer season exhibits a progressive increase, with temperatures rising by 0.8 °C from the baseline to the NF and further increasing to 1.3 °C in the FF. In contrast, the winter season experiences a more substantial warming trend, with temperature differences of 1.0 °C and 1.8 °C in the NF and FF, respectively. The RCP8.5 scenario depicts even more pronounced changes, especially in the summer season, where temperatures increase by 1.2 °C from the baseline to the NF and show a significant rise of 3.2 °C in the FF. Similarly, the winter season under RCP8.5 experiences notable temperature differences of 1.6 °C and 3.8 °C in the NF and FF, respectively (Figure 2). These differential projections highlight the varied impacts of emission scenarios on seasonal temperature shifts, providing essential insights for climate change assessments specific to Poland.

3.2. Stream Temperature Validation Results

The outcomes of the SWAT model employed in this investigation exhibit a commendable concordance with the observed data at the majority of monitoring stations. Notably, the SWAT stream temperature model demonstrated median KGE, R2, and PBIAS values of 0.76, 0.86, and −10.9%, respectively, during the validation phase, as illustrated in Figure 3 and Figure 4. Although no distinct spatial patterns of performance emerged, a marginally diminished performance was discerned in the northeastern region of the country. These validation metrics collectively underscore the reliability and accuracy of the SWAT temperature model.

3.3. Projected Stream Temperature Changes

The validated stream temperature model was applied to simulate climate change scenarios, aiming to evaluate the prospective impact of climate change on stream temperature regimes in Poland. The study focused on seasonal average stream temperatures at the reach scale, considering two future periods across six distinct climate models under RCP4.5 and 8.5 scenarios. Comparative analyses with the baseline period were conducted to discern the climatic alterations. Figure 5 visually represents the outcomes, revealing that all reaches in Poland are anticipated to exhibit elevated stream temperatures during the summer season compared to the baseline conditions. In the baseline scenario, the country-averaged stream temperature, initially at 16.1 °C, is projected to rise by 0.5 °C in the NF and further increase by 1 °C in the FF under the RCP4.5 scenario. The impact is notably more pronounced under the RCP8.5 scenario, with increases reaching 1 °C in the NF and a substantial 2.6 °C increase in the FF. In the winter season, the projected increases in stream temperatures are also illustrated in Figure 6. Under the RCP4.5 scenario, the anticipated rise is 0.6 °C in the NF and a further elevation of 1.3 °C in the FF. Comparatively, the RCP8.5 scenario exhibits more substantial increases, reaching 1.1 °C in the NF and a notable 2.8 °C increase in the FF. The analysis revealed that climate-induced changes in stream temperatures across Poland do not exhibit distinct spatial patterns. Instead, the projections suggest a uniform warming tendency, encompassing the entire territory of the country. These projections emphasize the persistent tendency of escalating stream temperatures during winter across Poland, underscoring the differentiated impacts foreseen under the distinct RCP scenarios. The temporal variability of mean seasonal stream temperature is presented in Figure 7 and Figure 8. The Mann–Kendall test revealed no statistically significant trends in the data within the multi-year periods considered (ACT, NF, FF). However, when comparing the analyzed time frames (ACT-NF and ACT-FF), all changes in stream temperature were found to be statistically significant based on the t-test, indicating notable shifts in temperature between the baseline and future projections under the given climate scenarios.
The analysis of stream temperature projections across seasons and RCP scenarios reveals distinct changes in the low (20th percentile) and high (80th percentile) temperature values. For the summer season under the RCP4.5 scenario, the 20th percentile temperature is projected to increase from 13.2 °C in the baseline period (ACT) to 13.7 °C in the near future (NF) and 14.2 °C in the far future (FF), resulting in an absolute change of 0.6 °C by the NF and 1.0 °C by the FF. Under the more extreme RCP8.5 scenario, summer low temperatures exhibit larger increases, reaching 14.2 °C in the NF and 15.8 °C in the FF, corresponding to absolute changes of 1.0 °C and 2.6 °C, respectively. The high summer temperatures (80th percentile) follow a similar trend, with RCP4.5 projections showing an increase from 19.0 °C in ACT to 19.4 °C in the NF and 19.9 °C in the FF, while RCP8.5 projects a rise to 19.8 °C in the NF and 21.4 °C in the FF, with absolute changes of up to 2.4 °C by the FF. In the winter season, low temperatures (20th percentile) also display marked increases, particularly under RCP8.5, where the baseline value of 3.9 °C rises to 5.2 °C in the NF and 7.0 °C in the FF, reflecting absolute changes of 1.3 °C and 3.1 °C, respectively. Under RCP4.5, the winter low temperatures increase to 4.5 °C in the NF and 5.6 °C in the FF, with corresponding absolute changes of 0.7 °C and 1.7 °C. For high winter temperatures (80th percentile), RCP4.5 projections indicate a moderate increase from 10.0 °C in ACT to 10.4 °C in the NF and 10.9 °C in the FF, with absolute changes of 0.5 °C and 1.0 °C, respectively. The RCP8.5 scenario shows a more pronounced increase, reaching 10.8 °C in the NF and 12.5 °C in the FF, resulting in absolute changes of 0.9 °C by the NF and 2.5 °C by the FF (Table 2). For both low (20th percentile) and high (80th percentile) temperatures, the t-test revealed that the changes between the analyzed time periods (ACT-NF and ACT-FF) were statistically significant.

4. Discussion

4.1. Modelling of Climate Change Impact on Stream Temperature

The modelling of stream temperature under climate change exhibits a wide range of spatial scales, each tailored to specific research objectives and contextual intricacies [49]. Most commonly, studies have focused on the watershed scale, where the integration of climate, land use, and hydrological factors within a distinct drainage area allows for a holistic understanding of stream temperature dynamics [22,28,50,51]. Some investigations extend their purview to a regional scale, encompassing multiple watersheds to capture larger climate patterns and diverse landscapes [52]. Although less common, certain studies adopt a national or global perspective to assess large-scale patterns, relying on coarse-resolution climate models. For instance, Punzet et al. [53] explored the influence of anticipated global temperature rise on the temperatures of freshwater resources on a global-continental scale across various climatic zones using a simple regression model based on the relationship between stream water temperature and air temperature. They asserted that the forecasts for future alterations in stream temperature, based on the ECHAM5 A2 and B1 scenarios, suggest a potential increase ranging from 1 °C to 5 °C by the 2050s, aligning with the outcomes of the current investigation for the territory of Poland.
To date, there has been no national-scale assessment of the influence of climate change on stream temperature covering the entire territory of Poland. Earlier studies, such as those conducted by Piotrowski et al. [29], focused on individual sub-catchments and utilized EURO-CORDEX projections under RCP8.5, employing intricate stream temperature models. According to their findings, the anticipated rise in stream temperatures across these specific sub-catchments is projected to be approximately 1–2 °C for the period of 2021–2050 and 2–3 °C for 2071–2100, aligning with the outcomes of the current investigation. Despite relying on a simplified stream temperature model, this study can be regarded as the first macro-scale analysis, offering valuable insights into the predicted alterations in stream temperature and their potential repercussions for freshwater ecosystems in Poland.
Generally, in the assessment of climate change impact on stream temperatures, an increase in the spatial scale of analysis often prompts a careful consideration of methodological choices, including the complexity of modelling approaches [41]. As the analysis expands to encompass larger geographic areas, there is a pragmatic need to streamline methodologies to enhance computational efficiency and facilitate broader insights. This may involve opting for modelling frameworks that strike a balance between accuracy and computational feasibility. While high-resolution detailed models might be suitable for smaller scales, the computational demands become prohibitive when applied over extensive regions [40]. Ultimately, the challenge lies in finding methodologies that are both robust and scalable, allowing researchers to navigate the trade-off between analytical complexity and the broader spatial insights required for comprehensive climate change impact assessments.

4.2. Potential Consequences of Stream Temperature Increase for Freshwater Ecosystems

The observed elevations in stream temperatures during the summer and winter seasons underline the urgency of addressing climate change-induced alterations in aquatic environments. The disparities between RCP4.5 and RCP8.5 scenarios emphasize the significance of emission trajectories in shaping the magnitude of these changes. The potential consequences of an increase in stream temperatures for freshwater ecosystems are multifaceted and can have far-reaching impacts on aquatic life and overall ecosystem health [7]. These consequences manifest across various dimensions, highlighting the interplay between temperature changes and ecosystem dynamics.
Altered habitat suitability stands out as a primary concern, as the link between thermal conditions and stream temperatures can lead to shifts in the suitability of freshwater habitats [54]. These changes could significantly impact reproductive success, growth, and the survival of aquatic species [55]. Some species may face decline, while those adapted to warmer conditions might thrive, leading to substantial alterations in the overall composition of freshwater communities. For example, within the freshwater ecosystems of Poland, changes in stream temperatures could present considerable risks to various fish species. Coldwater inhabitants, including brown trout and Atlantic salmon, are especially sensitive to fluctuations in river temperatures [56]. The spread of invasive species adds yet another dimension to the challenges posed by changing stream temperatures. The creation of conditions conducive to invasive species, adapted to warmer environments, introduces the risk of increased competition for resources and potential disruptions to the ecological balance of native ecosystems [57].
Beyond individual species, stream temperature plays a crucial role in broader ecosystem processes, such as nutrient cycling [58]. Changes in temperature can influence microbial activity and nutrient availability, potentially disrupting the balance of essential elements in freshwater ecosystems. Moreover, the reduction in dissolved oxygen levels due to elevated water temperatures poses a direct threat to aquatic organisms that rely on well-oxygenated water for respiration [59]. This holds particular significance within the Polish context. In the summer of 2022, the River Odra in Poland and Germany witnessed one of the most significant environmental disasters ever documented in Europe [60]. This event led to substantial fish mortality, attributed to a toxic algal bloom. Contributing factors to this disaster likely included the release of saline water into the river, low dissolved oxygen, and various forms of water pollution (such as industrial and municipal wastewater from specific sources and the runoff of nutrients and chemicals from agricultural zones), along with hydrometeorological conditions like elevated water temperatures and reduced river stages and flows. The following urgent query arises: could the frequency of such disasters increase in the future, given the findings presented in this study?
The multifaceted consequences of increased stream temperatures necessitate a holistic and adaptive approach to freshwater ecosystem management. Understanding these complexities is crucial for developing strategies that not only mitigate the immediate impacts but also enhance the resilience of aquatic ecosystems in the face of ongoing climate change.

4.3. Modelling Limitations

This study uses a simplified default equation [34] for stream temperature modelling. This equation provided a practical and efficient approach, particularly suited for large-scale assessments and national-level studies. While the default equation does not account for detailed hydrological processes, such as streamflow, snowmelt, and groundwater flow, its simplicity allows for a straightforward implementation and interpretation. In the pursuit of more accurate simulations, more advancements have extended beyond the basic SWAT framework. Various researchers have contributed to advancing and modifying the SWAT model for improved simulations of stream temperatures. These enhancements aimed to capture the complex interplay of hydro-climatological factors influencing stream temperature dynamics. Key modifications include the incorporation of simplified terms to represent the mass transfer process, allowing for a more comprehensive modelling of inflows and outflows into and out of rivers. This involves considering various SWAT model outcomes, such as snowmelt flow, surface runoff, lateral flow, and groundwater flow, each associated with their respective temperatures [61]. Additionally, Noa-Yarasca [62] have introduced explicit energy balance models that consider additional factors, such as the shade factor of riparian vegetation. These modifications contribute to a more nuanced understanding of stream temperature dynamics by incorporating the ecological context and hydrological conditions into the simulation process. While modified versions of the SWAT model incorporating additional complexities for stream temperature simulations exist, it is crucial to acknowledge that their widespread validation across various case studies might be limited. The default stream temperature equation, as employed in this study, benefits from a more extensive validation history and broader application in diverse geographical settings. The modified versions, although potentially offering more detailed representations of specific local conditions, may lack the comprehensive validation required for robust and generalized use.
The use of a simplified default equation for stream temperature modelling in this study holds significant value and merits consideration. The model assumes a linear relationship between air and stream temperatures, which may not fully capture thermal dynamics in cold regions. Specifically, in scenarios where air temperatures fall below approximately −6.7 °C, the model may inaccurately predict stream temperatures below 0 °C. This limitation highlights the need for enhanced modelling approaches in future studies, particularly those involving ice formation, groundwater influences, and other hydrological processes specific to cold-region rivers. Despite its simplicity, the default equation provides a practical and efficient approach, especially when dealing with large-scale assessments and national-level studies. The streamlined nature of the default equation allows for a straightforward implementation and interpretation, making it accessible to a broad range of users, including researchers, policymakers, and resource managers. Additionally, the default equation can serve as a baseline or reference point, offering a clear comparison against more complex models. In cases where the primary focus is on broader trends and large-scale patterns rather than fine-scale nuances, the simplified equation remains a valuable tool for gaining insights into the potential impacts of climate change on stream temperature regimes. Its ease of use and interpretability make it a practical choice for studies seeking to provide a comprehensive overview of climate change effects on freshwater ecosystems at a national scale. Given the paucity of case studies validating the modified SWAT versions for stream temperature, the default equation stands out as a well-established and validated option. Its simplicity facilitates broad-scale applicability, ensuring preliminary insights into large-scale climate change impacts on stream temperatures. Until further validation studies accumulate for the modified versions, the default equation remains a valuable and pragmatic choice for studies aiming to assess climate change impacts at a national level. This study, as the first national assessment of potential future stream temperatures in Poland, provides a foundational understanding that can be built upon in future research. While recognizing the limitations of our simplified approach, it is well suited to the scale of this initial assessment and offers high potential for future studies. The preliminary results presented here are insightful for readers and serve as a starting point for more detailed and nuanced analyses in the future.

5. Conclusions

In conclusion, this national-scale assessment provides preliminary but valuable insights into the anticipated impact of climate change on stream temperature in Poland. Leveraging an ensemble of EURO-CORDEX projections and the SWAT model, the study offers a preliminary understanding of the dynamics shaping freshwater ecosystems in the face of environmental change. The validated stream temperature model demonstrates its reliability across diverse geographical locations, instilling confidence in the projected outcomes. The results reveal a uniform warming tendency across the country, with discernible increases in stream temperatures during both summer and winter seasons. The choice of emission scenarios (RCP4.5 and RCP8.5) introduces variations in the magnitude of temperature changes, emphasizing the critical role of mitigation strategies in influencing future climate outcomes. As global temperatures continue to rise, the findings of this study underscore the urgency of implementing adaptive strategies to safeguard the ecological integrity of Poland’s freshwater systems. The integrated approach, combining advanced climate models and a hydrological model, positions this study as a valuable resource for policymakers, researchers, and environmental managers striving to develop targeted conservation strategies. In summary, this research contributes to the growing body of knowledge on climate change impacts, emphasizing the consequences on stream temperature and, by extension, freshwater ecosystems. While this study provides essential insights, future work could focus on refining the model to capture more complex hydrological and seasonal processes, particularly those relevant to cold-region dynamics. Incorporating elements such as freeze–thaw cycles, groundwater influences, and riparian shading could increase model accuracy.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The author declares no conflict of interest.

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Applsci 14 10900 g001
Figure 1. Study location with SWAT sub-basins (A), stream temperature gauging stations (B), and mean seasonal air temperatures in summer (C) and winter (D).
Figure 1. Study location with SWAT sub-basins (A), stream temperature gauging stations (B), and mean seasonal air temperatures in summer (C) and winter (D).
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Figure 2. Country-averaged monthly changes in mean daily air temperature under RCP4.5 (blue) and RCP8.5 (orange). The intensity of each colour represents different horizons: light (baseline—ACT), medium (near future—NF), and dark (far future—FF).
Figure 2. Country-averaged monthly changes in mean daily air temperature under RCP4.5 (blue) and RCP8.5 (orange). The intensity of each colour represents different horizons: light (baseline—ACT), medium (near future—NF), and dark (far future—FF).
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Figure 3. Goodness-of-fit measures derived upon validation: Kling–Gupta efficiency (KGE) (A), percent bias (PBIAS) (B), and coefficient of determination (R2) (C).
Figure 3. Goodness-of-fit measures derived upon validation: Kling–Gupta efficiency (KGE) (A), percent bias (PBIAS) (B), and coefficient of determination (R2) (C).
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Figure 4. Box plots showing the model performance expressed by Kling–Gupta efficiency (KGE), coefficient of determination (R2) (A), and percent bias (PBIAS) (B) values in 369 water quality monitoring points.
Figure 4. Box plots showing the model performance expressed by Kling–Gupta efficiency (KGE), coefficient of determination (R2) (A), and percent bias (PBIAS) (B) values in 369 water quality monitoring points.
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Figure 5. Spatial distribution of multi-annual summer season mean stream temperature at the reach level for Representative Concentration Pathways (RCPs) 4.5 and 8.5 at baseline (ACT), in the near future (NF), and in the far future (FF).
Figure 5. Spatial distribution of multi-annual summer season mean stream temperature at the reach level for Representative Concentration Pathways (RCPs) 4.5 and 8.5 at baseline (ACT), in the near future (NF), and in the far future (FF).
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Figure 6. Spatial distribution of multi-annual winter season mean stream temperature at the reach level for Representative Concentration Pathways (RCPs) 4.5 and 8.5 at baseline (ACT), in the near future (NF), and in the far future (FF).
Figure 6. Spatial distribution of multi-annual winter season mean stream temperature at the reach level for Representative Concentration Pathways (RCPs) 4.5 and 8.5 at baseline (ACT), in the near future (NF), and in the far future (FF).
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Figure 7. Projections of the average daily air temperature during the summer period over multiple years for RCP4.5 (A) and RCP8.5 (B). The bands indicate extreme values (min and max) from nine climate models; the solid line represents the median. The green colour represents the historical period. The intensity of the blue and orange colours denotes the time horizon, where lighter shades indicate the near future, while darker shades indicate the far future.
Figure 7. Projections of the average daily air temperature during the summer period over multiple years for RCP4.5 (A) and RCP8.5 (B). The bands indicate extreme values (min and max) from nine climate models; the solid line represents the median. The green colour represents the historical period. The intensity of the blue and orange colours denotes the time horizon, where lighter shades indicate the near future, while darker shades indicate the far future.
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Figure 8. Projections of the average daily air temperature during the winter period over multiple years for RCP4.5 (A) and RCP8.5 (B). The bands indicate extreme values (min and max) from nine climate models; the solid line represents the median. The green colour represents the historical period. The intensity of the blue and orange colours denotes the time horizon, where lighter shades indicate the near future, while darker shades indicate the far future.
Figure 8. Projections of the average daily air temperature during the winter period over multiple years for RCP4.5 (A) and RCP8.5 (B). The bands indicate extreme values (min and max) from nine climate models; the solid line represents the median. The green colour represents the historical period. The intensity of the blue and orange colours denotes the time horizon, where lighter shades indicate the near future, while darker shades indicate the far future.
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Table 1. GCM/RCM simulations.
Table 1. GCM/RCM simulations.
Model NumberInstitutionsGlobal ModelRegional ModelRCM VersionModel Run Scenario
CM1CNRM, CERFACSCNRM-CM5CNRM-ALADIN63 v2r1i1p1
CM2DMIICHEC-EC-EARTHDMI-HIRHAM5 v2r3i1p1
CM3KNMIICHEC-EC-EARTHKNMI-RACMO22E v1r12i1p1
CM4KNMIICHEC-EC-EARTHKNMI-RACMO22E v1r1i1p1
CM5SMHIICHEC-EC-EARTHSMHI-RCA4 v1r12i1p1
CM6SMHIMPI-M-MPI-ESM-LRSMHI- RCA4 v1ar1i1p1
Table 2. Changes in temperature characteristics for Representative Concentration Pathways (RCPs) 4.5 and 8.5 at baseline (ACT), in the near future (NF), and in the far future (FF).
Table 2. Changes in temperature characteristics for Representative Concentration Pathways (RCPs) 4.5 and 8.5 at baseline (ACT), in the near future (NF), and in the far future (FF).
Temperature CharacteristicSeasonRCPACTNFFFFF-ACT Absolute ChangeNF-ACT Absolute Change
20th percentile summer 4.513.213.714.21.00.6
20th percentile summer 8.513.214.215.82.61.0
80th percentile summer 4.519.019.419.90.90.4
80th percentile summer 8.519.019.821.42.40.9
20th percentile winter 4.53.94.55.61.70.7
20th percentile winter 8.53.95.27.03.11.3
80th percentile winter 4.510.010.410.91.00.5
80th percentile winter 8.510.010.812.52.50.9
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Marcinkowski, P. Projections of Climate Change Impact on Stream Temperature: A National-Scale Assessment for Poland. Appl. Sci. 2024, 14, 10900. https://doi.org/10.3390/app142310900

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Marcinkowski, P. (2024). Projections of Climate Change Impact on Stream Temperature: A National-Scale Assessment for Poland. Applied Sciences, 14(23), 10900. https://doi.org/10.3390/app142310900

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