A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change

Long-term patterns and changes of unglaciated High Arctic stream thermal regime

Although water temperature is one of the most important factors influencing hydrochemistry and river ecology, long-term monitoring and modelling of stream thermal temporal variability are uncommon. There is sparse research regarding the thermal regimes of Arctic rivers, especially in Svalbard, a geographical hotspot affected by extreme climate change and Arctic amplification. There is a need for improvement and better understanding of the factors influencing the stream water temperature regime. To address this research gap, we present a study of the non-glaciated arctic catchment, Fuglebekken (Spitsbergen, Svalbard). We propose methods for reconstructing the thermal regime of the Arctic stream based on available in-situ data. This study evaluates different sets of input variables with hourly time steps required to explain the variability in water temperature. The study comprises two modelling approaches, a stochastic transfer function Multiple Input Single Output and a supervised machine learning technique, Gaussian Process Regression, to simulate the water temperature in the years 2005-2022. The ground temperature at a depth of 20 cm and total solar radiation were found to be the main forcings that explain most of the water temperature variability. The outputs of both models showed similar tendencies and patterns. A diurnal warming trend of 0.5-3.5 °C per decade has been detected in stream water temperature throughout the summer season. The highest increase of 6.0 °C in the water temperature in 2005-2022 was found to be present in the second part of June. The outcomes prove that the thermal regime of the Fuglebekken stream is sensitive to ongoing climatic changes. This variability is an important factor with many environmental implications.

Spatial and Temporal Variability in Stream Thermal Regime Drivers for Three River Networks During the Summer Growing Season

Many cold-water dependent aquatic organisms are experiencing habitat and population declines from increasing water temperatures. Identifying mechanisms which drive local and regional stream thermal regimes facilitates restoration at ecologically relevant scales. Stream temperatures vary spatially and temporally both within and among river basins. We developed a modeling process to identify statistical relationships between drivers of stream temperature and covariates representing landscape, climate, and management-related processes. The modeling process was tested in 3 study areas of the Pacific Northwest USA during the growing season (May [start], August [warmest], September [end]). Across all months and study systems, covariates with the highest relative importance represented the physical landscape (elevation [1st], catchment area [3rd], main channel slope [5th]) and climate covariates (mean monthly air temperature [2nd] and discharge [4th]). Two management covariates (ground water use [6th] and riparian shade [7th]) also had high relative importance. Across the growing season (for all basins) local reach slope had high relative importance in May, but transitioned to a regional main channel slope covariate in August and September. This modeling process identified regionally similar and locally unique relationships among drivers of stream temperature. High relative importance of management-related covariates suggested potential restoration actions for each system.

High summer temperatures are associated with poorer performance of underyearling Atlantic salmon (Salmo salar) in upland streams

Future warming scenarios are predicted to result in an increased frequency of high, and potentially stressful, temperatures in aquatic ecosystems. Here we examined whether the performance of wild underyearling Atlantic salmon (Salmo salar) in Scottish streams stocked with identical egg densities was influenced by thermal stress. Biomass and density declined with degree hours exceeding 23°C, indicating apparent mortality or emigration as a possible result of exposure to high temperatures. These results strengthen the need for further action such as riparian tree planting to reduce stream summer temperatures.

Intelligent Simulation of Water Temperature Stratification in the Reservoir

In order to fully make use of limited water resources, humans have built many water conservancy projects. The projects produce many economic benefits, but they also change the natural environment. For example, the phenomenon of water temperature stratification often occurs in deep reservoirs. Thus, effective ways are needed to predict the water temperature stratification in a reservoir to control its discharge water temperature. Empirical formula methods have low computational accuracy if few factors are considered. Mathematical model methods rely on large amounts of accurate hydrological data and cost long calculation times. The purpose of the research was to simulate water temperature stratification in a reservoir by constructing an intelligent simulation model (ISM-RWTS) with five inputs and one output, determined on the basis of artificial neural networks (ANN). A 3D numerical model (3DNM) was also constructed to provide training samples for the ISM-RWTS and be used to test its simulation effect. The ISM-RWTS was applied to the Tankeng Reservoir, located in the Zhejiang province of China, and performed well, with an average error of 0.72 °C. Additionally, the Intelligent Computation Model of Reservoir Water Temperature Stratification (ICM-RWTS) was also discussed in this paper. The results indicated that the intelligent method was a powerful tool to estimate the water temperature stratification in a deep reservoir. Finally, it was concluded that the advantages of the intelligent method lay in its simplicity of use, its lower demand for hydrological data, its well generalized performance, and its flexibility for considering different input and output parameters.

Impact of climate change on river water temperature and dissolved oxygen: Indian riverine thermal regimes

The impact of climate change on the oxygen saturation content of the world’s surface waters is a significant topic for future water quality in a warming environment. While increasing river water temperatures (RWTs) with climate change signals have been the subject of several recent research, how climate change affects Dissolved Oxygen (DO) saturation levels have not been intensively studied. This study examined the direct effect of rising RWTs on saturated DO concentrations. For this, a hybrid deep learning model using Long Short-Term Memory integrated with k-nearest neighbor bootstrap resampling algorithm is developed for RWT prediction addressing sparse spatiotemporal RWT data for seven major polluted river catchments of India at a monthly scale. The summer RWT increase for Tunga-Bhadra, Sabarmati, Musi, Ganga, and Narmada basins are predicted as 3.1, 3.8, 5.8, 7.3, 7.8 °C, respectively, for 2071–2100 with ensemble of NASA Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway 8.5 scenario. The RWT increases up to7 °C for summer, reaching close to 35 °C, and decreases DO saturation capacity by 2–12% for 2071–2100. Overall, for every 1 °C RWT increase, there will be about 2.3% decrease in DO saturation level concentrations over Indian catchments under climate signals.

Quantitative Assessment of Climatic and Reservoir-Induced Effects on River Water Temperature Using Bayesian Network-Based Approach

River flow regulations and thermal regimes have been altered by human-induced interventions (such as dam construction) or climate change (such as air temperature variations). It is of great significance to adopt a well-performed data-driven model to accurately quantify the impact of human-induced interventions or climate change over river water temperature (WT), which can help understand the underlying evolution mechanism of the river thermal regimes by dam operation or climate change. This research applied the Bayesian network-based model (BNM), which can easily identify inherently stronger associated variables with a target variable from multiple influencing variables to predict the daily WT and make a quantitative assessment of the effect produced by Three Gorges Reservoir (TGR) construction in the Yangtze River, China. A comparative study between the proposed model and two other models was implemented to verify the predicted accuracy of the BNM. With the help of the BNM model, the impact of reservoir impoundment over water temperature was quantitatively analyzed by calculating the difference between reconstructed water temperature series and observed series during the post-TGR period. The construction of the TGR posed more impact on variations in WT than the impact induced by the climate change according to results. The effect of TGR on WT can be concluded as follows: WT from October to January in post-TGR showed a remarkable warming tendency and an increase in released warmer water volumes than before, while WT showed a cooling tendency during March to June because of the hysteretic effect of WT response to increasing air temperature. The proposed BNM model shows great potential for WT prediction and ecological risk management of rivers.

Manganese Pollution in Mining-Influenced Rivers and Lakes: Current State and Forecast under Climate Change in the Russian Arctic

Mining regions in different parts of the world have been associated with the significant pollution of water, sediments, and soils by manganese and other chemical elements. This study assessed the degree of geochemical transformation caused by open-pit extraction and processing of mineral resources in the Kovdorsky District of Murmansk Oblast, 20 km from the Russia–Finland border. A second objective was to predict further changes co-driven by industrial pressure and high climatic instability in the polar region. The field study involved sampling water and sediments from virgin background streams and from the tailings storage facility, settling ponds, rivers, and lakes affected by ore mining and disintegration. Laboratory analyses included the study of elemental composition, redox potential, alkalinity and acidity, organic matter content, and other geochemical characteristics for a better understanding of pollutant migration patterns. We revealed elevated levels of potentially toxic elements in surface waters and bottom sediments which pose a risk to the human health via the household and drinking water supply. Pollution with manganese (Mn) was found to be the major environmental issue. Its natural presence in the river water was overridden a hundredfold by anthropogenic enrichment. This is problematic as Mn is easily bioaccumulated, which can lead to unwanted ecotoxicological effects, and—in the case of prolonged exposure to high doses of Mn and its compounds—to detrimental human health impacts. We believe that the changing climate may raise the water flow and thus expand the area of the hydrochemical anomaly. On the other hand, the activation of self-purification and dilution processes could lead to decreasing environmental Mn concentrations.

Riverscape approaches in practice: perspectives and applications

Landscape perspectives in riverine ecology have been undertaken increasingly in the last 30 years, leading aquatic ecologists to develop a diverse set of approaches for conceptualizing, mapping and understanding 'riverscapes'. Spatiotemporally explicit perspectives of rivers and their biota nested within the socio-ecological landscape now provide guiding principles and approaches in inland fisheries and watershed management. During the last two decades, scientific literature on riverscapes has increased rapidly, indicating that the term and associated approaches are serving an important purpose in freshwater science and management. We trace the origins and theoretical foundations of riverscape perspectives and approaches and examine trends in the published literature to assess the state of the science and demonstrate how they are being applied to address recent challenges in the management of riverine ecosystems. We focus on approaches for studying and visualizing rivers and streams with remote sensing, modelling and sampling designs that enable pattern detection as seen from above (e.g. river channel, floodplain, and riparian areas) but also into the water itself (e.g. aquatic organisms and the aqueous environment). Key concepts from landscape ecology that are central to riverscape approaches are heterogeneity, scale (resolution, extent and scope) and connectivity (structural and functional), which underpin spatial and temporal aspects of study design, data collection and analysis. Mapping of physical and biological characteristics of rivers and floodplains with high-resolution, spatially intensive techniques improves understanding of the causes and ecological consequences of spatial patterns at multiple scales. This information is crucial for managing river ecosystems, especially for the successful implementation of conservation, restoration and monitoring programs. Recent advances in remote sensing, field-sampling approaches and geospatial technology are making it increasingly feasible to collect high-resolution data over larger scales in space and time. We highlight challenges and opportunities and discuss future avenues of research with emerging tools that can potentially help to overcome obstacles to collecting, analysing and displaying these data. This synthesis is intended to help researchers and resource managers understand and apply these concepts and approaches to address real-world problems in freshwater management.

Monthly river temperature trends across the US confound annual changes

Climate variations and human modifications of the water cycle continue to alter the Earth's surface water and energy exchanges. It is therefore critical to ascertain how these changes impact water quality and aquatic ecosystem habitat metrics such as river temperatures. Though river temperature trend analyses exist in the literature, studies on seasonal trends in river temperatures across large spatial extents, e.g. the contiguous United States (US), are limited. As we show through both annual and monthly trend analyses for 20 year (n = 138 sites) and 40 year (n = 40 sites) periods, annual temperature trends across the US mask extensive monthly variability. While most sites exhibited annual warming trends, these annual trends obscured sub-annual cooling trends at many sites. Monthly trend anomalies were spatially organized, with persistent regional patterns at both reference and human-impacted sites. The largest warming and cooling anomalies happened at human impacted sites and during summer months. Though our analysis points to coherence in trends as well as the overall impact of human activity in driving these patterns, we did not investigate the impact of river temperature observation accuracy on reported trends, an area needed for future work. Overall, these patterns emphasize the need to consider sub-annual behavior when managing the ecological impacts of river temperature throughout lotic networks.

Thermal renaturation of rivers in the post-industrial age - An example of the Przemsza River basin (Poland)

This article concerns temperature, which is one of the most important physical properties of surface waters. During the period of climate warming, tendencies of increasing river temperature have been repeatedly identified in the literature. This article discusses the lowering of the river temperature, a phenomenon rarely considered, which is occurring in a heavily industrialised area in the southern part of Poland (Upper Silesia Region), undergoing deep restructuring. The main aim of the study is to analyse and evaluate the unique Przemsza River basin, which differs from other river basins in terms of the thermal regime of its rivers and its tendencies for change. These changes are presented through the long period of 1961-2015, and additionally in two sub-periods, 1961-1994 and 1995-2015, differing with respect to the degree of the organisation of wastewater management. This was possible to demonstrate thanks to the high density of hydrological stations within a small area, which is rare, especially over such a long period. This work, therefore, fills the research gap on changes in the thermal regime of rivers caused by water management. Its results differ from those presented so far in the literature and expand the knowledge of this subject. In the years under consideration (1961-2015), the air temperature increased by 0.03 °C·per year^-1 on average. It was found that the temperature of rivers (or their sections) decreased by as much as -0.8 °C·year^-1. The greatest drops in temperature occurred in the winter season and amounted to a maximum of -0.12 °C·season^-1. In the first part of the analysed period (1961-1994), unnaturally high river temperatures caused by water management factors were recorded. In turn, in the years 1995-2015 there was a decrease in temperature caused by various technical operations carried out in the catchments, including the construction of modern sewage treatment plants, as well as limitation of the introduction of water into rivers from closed hard coal mines. This phenomenon is referred to as thermal renaturation. Maintaining positive changes in the thermal regime requires, inter alia, a further reduction in the amount of pollutants discharged into rivers by water discharges from mine drainage.

Climate change and anadromous fish: How does thermal acclimation affect the mechanics of the myotomal muscle of the Atlantic salmon, Salmo salar?

In response to accelerated temperature shifts due to climate change, the survival of many species will require forms of thermal acclimation to their changing environment. We were interested in how climate change will impact a commercially and recreationally important species of fish, Atlantic salmon (Salmo salar). As climate change alters the thermal environment of their natal streams, we asked how their muscle function will be altered by extended exposure to both warm and cold temperatures. We performed a thermal acclimation study of S. salar muscle mechanics of both fast-twitch, or white, and slow-twitch, or red, myotomal muscle bundles to investigate how temperature acclimated Atlantic salmon would respond across a range of different temperatures. Isometric contraction properties, maximum shortening velocity, and oscillatory power output were measured and compared amongst three groups of salmon-warm acclimated (20°C), cold-acclimated (2°C), and those at their rearing temperature (12°C). The Atlantic salmon showed limited thermal acclimation in their contraction kinetics, and some of the shifts in contractile properties that were observed would not be predicted to mitigate the impact of a warming environment. For instance, the maximum shortening velocity at a common test temperature was higher in the warm acclimated group and lower in the cold-acclimated group. In addition, critical swimming speed did not vary with temperature of acclimation when tested at a common temperature (12°C). Our results suggest that Atlantic salmon populations will continue to struggle in response to a warming environment.

River temperature research and practice: Recent challenges and emerging opportunities for managing thermal habitat conditions in stream ecosystems

There is growing evidence that river temperatures are increasing under climate change, which is expected to be exacerbated by increased abstractions to satisfy human water demands. Water temperature research has experienced crucial advances, both in terms of developing new monitoring and modelling tools, as well as understanding the mechanisms of temperature feedbacks with biogeochemical and ecological processes. However, water practitioners and regulators are challenged with translating the widespread and complex technological, modelling and conceptual advances made in river temperature research into improvements in management practice. This critical review provides a comprehensive overview of recent advances in the state-of-the-art monitoring and modelling tools available to inform ecological research and practice. In so doing, we identify pressing research gaps and suggest paths forward to address practical research and management challenges. The proposed research directions aim to provide new insights into spatio-temporal stream temperature dynamics and unravel drivers and controls of thermal river regimes, including the impacts of changing temperature on metabolism and aquatic biogeochemistry, as well as aquatic organisms. The findings of this review inform future research into ecosystem resilience in the face of thermal degradation and support the development of new management strategies cutting across spatial and temporal scales.

Accurate spatiotemporal predictions of daily stream temperature from statistical models accounting for interactions between climate and landscape

Spatial and temporal patterns in stream temperature are primary factors determining species composition, diversity and productivity in stream ecosystems. The availability of spatially and temporally continuous estimates of stream temperature would improve the ability of biologists to fully explore the effects of stream temperature on biota. Most statistical stream temperature modeling techniques are limited in their ability to account for the influence of variables changing across spatial and temporal gradients. We identified and described important interactions between climate and spatial variables that approximate mechanistic controls on spatiotemporal patterns in stream temperature. With identified relationships we formed models to generate reach-scale basin-wide spatially and temporally continuous predictions of daily mean stream temperature in four Columbia River tributaries watersheds of the Pacific Northwest, USA. Models were validated with a testing dataset composed of completely distinct sites and measurements from different years. While some patterns in residuals remained, testing dataset predictions of selected models demonstrated high accuracy and precision (averaged RMSE for each watershed ranged from 0.85–1.54 °C) and was only 17% higher on average than training dataset prediction error. Aggregating daily predictions to monthly predictions of mean stream temperature reduced prediction error by an average of 23%. The accuracy of predictions was largely consistent across diverse climate years, demonstrating the ability of the models to capture the influences of interannual climatic variability and extend predictions to timeframes with limited temperature logger data. Results suggest that the inclusion of a range of interactions between spatial and climatic variables can approximate dynamic mechanistic controls on stream temperatures.

Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature models

Climatic warming will increase river temperature globally, with consequences for cold water-adapted organisms. In regions with low forest cover, elevated river temperature is often associated with a lack of bankside shading. Consequently, river managers have advocated riparian tree planting as a strategy to reduce temperature extremes. However, the effect of riparian shading on river temperature varies substantially between locations. Process-based models can elucidate the relative importance of woodland and other factors driving river temperature and thus improve understanding of spatial variability of the effect of shading, but characterising the spatial distribution and height of riparian tree cover necessary to parameterise these models remains a significant challenge. Here, we document a novel approach that combines Structure-from-Motion (SfM) photogrammetry acquired from a drone to characterise the riparian canopy with a process based temperature model (Heat Source) to simulate the effects of tree shading on river temperature. Our approach was applied in the Girnock Burn, a tributary of the Aberdeenshire Dee, Scotland. Results show that SfM approximates true canopy elevation with a good degree of accuracy (R² = 0.96) and reveals notable spatial heterogeneity in shading. When these data were incorporated into a process-based temperature model, it was possible to simulate river temperatures with a similarly-high level of accuracy (RMSE <0.7 °C) to a model parameterised using 'conventional' LiDAR tree height data. We subsequently demonstrate the utility of our approach for quantifying the magnitude of shading effects on stream temperature by comparing simulated temperatures against another model from which all riparian woodland has been removed. Our findings highlight drone-based SfM as an effective tool for characterising riparian shading and improving river temperature models. This research provides valuable insights into the effects of riparian woodland on river temperature and the potential of bankside tree planting for climate change adaptation.

Assessing the performance of a suite of machine learning models for daily river water temperature prediction

In this study, different versions of feedforward neural network (FFNN), Gaussian process regression (GPR), and decision tree (DT) models were developed to estimate daily river water temperature using air temperature (T_a), flow discharge (Q), and the day of year (DOY) as predictors. The proposed models were assessed using observed data from eight river stations, and modelling results were compared with the air2stream model. Model performances were evaluated using four indicators in this study: the coefficient of correlation (R), the Willmott index of agreement (d), the root mean squared error (RMSE), and the mean absolute error (MAE). Results indicated that the three machine learning models had similar performance when only T_a was used as the predictor. When the day of year was included as model input, the performances of the three machine learning models dramatically improved. Including flow discharge instead of day of year, as an additional predictor, provided a lower gain in model accuracy, thereby showing the relatively minor role of flow discharge in river water temperature prediction. However, an increase in the relative importance of flow discharge was noticed for stations with high altitude catchments (Rhône, Dischmabach and Cedar) which are influenced by cold water releases from hydropower or snow melting, suggesting the dependence of the role of flow discharge on the hydrological characteristics of such rivers. The air2stream model outperformed the three machine learning models for most of the studied rivers except for the cases where including flow discharge as a predictor provided the highest benefits. The DT model outperformed the FFNN and GPR models in the calibration phase, however in the validation phase, its performance slightly decreased. In general, the FFNN model performed slightly better than GPR model. In summary, the overall modelling results showed that the three machine learning models performed well for river water temperature modelling.

Two hybrid data-driven models for modeling water-air temperature relationship in rivers

River water temperature (RWT) forecasting is important for the management of stream ecology. In this paper, a new method based on coupling of wavelet transformation (WT) and artificial intelligence (AI) techniques, including multilayer perceptron neural network (MLPNN) and adaptive neural-fuzzy inference system (ANFIS) for RWT prediction is proposed. The performances of the hybrid models are compared with regular MLPNN and ANFIS models and multiple linear regression (MLR) models for RWT forecasting in two river stations in the Drava River, Croatia. Model performance was evaluated using the coefficient of correlation (R), the Willmott index of agreement (d), the root mean squared error (RMSE), and the mean absolute error (MAE). Results indicate that the combination of WT and AI models (WTMLPNN and WTANFIS) yield better models than the conventional forecasting models for RWT simulation for both regular periods and heatwave events. The MLPNN and ANFIS models outperform the MLR models for RWT simulation for the studied river stations. RMSE values of WTMLPNN2 and WTANFIS2 models range from 1.127 to 1.286 °C, and 1.216 to 1.491 °C for the Botovo and Donji Miholjac stations respectively. Additionally, modeling results further confirm the importance of the day of year (DOY) on the thermal dynamics of the river. The results of this study indicate the potential of coupling of WT and MLPNN, ANFIS models in forecasting RWT.

Citizen science evidence from the past century shows that Scottish rivers are warming

Salmonid species are highly sensitive to river water temperature. Although long-term river temperature monitoring is essential for assessing drivers of change in ecological systems, these data are rarely available from statutory monitoring. We utilized a 105-year citizen science data set of river water temperature from the River Spey, North-East Scotland, gathered during the fishing season (April-October) between 1912 and 2016. As there were gaps in the records we applied generalised additive models to reconstruct long-term daily river temperature in the fishing season from air temperature, cumulative air temperature, day length and runoff. For that, continuous hydrometeorological data have been obtained from statutory monitoring and process-based models. Long-term warming trends of river temperature, namely an increase of 0.2 K per decade after 1961, have been mostly related to increasing air temperature of the same magnitude. Indirect impacts of rising air temperatures include less snow accumulation and snow melt as well as earlier snow melt. The snow free period starts around 2 days earlier per decade throughout the study period and 7 days earlier per decade after 1965. Consequently, the contribution of snow melt and its cooling properties to river temperature in spring are declining. Citizen science delivered a data set that filled a vital knowledge gap in the long-term historical assessment of river temperatures. Such information provides a robust basis for future assessments of global change and can help inform decision-makers about the potential importance of enhancing the resilience of rivers and aquatic ecology to climate change.

Integration of juvenile habitat quality and river connectivity models to understand and prioritise the management of barriers for Atlantic salmon populations across spatial scales

Diadromous fish populations are strongly affected by in-stream barriers that cause river network fragmentation, constraining productivity or preventing completion of their lifecycle. Removal or reduction of barrier impacts is a restoration measure associated with unambiguous benefits. Management of barriers is therefore often prioritised above other restoration actions. Barrier management is prioritised at local and national scales depending on funding. However, barrier prioritisation is potentially sub-optimal because existing tools do not consider habitat quality. Furthermore, effects of partial barriers (those passable under certain conditions) are uncertain, depending on location and potential cumulative effects. A framework is presented for assessing effects of impassable manmade barriers (IMBs) on longitudinal river network connectivity (percentage of upstream habitat accessible from the river mouth) for Atlantic salmon across spatial scales, using Scotland as an example. The framework integrates juvenile habitat quality and network connectivity models to (1) provide information necessary for local and national prioritisation of barriers, and (2) assess potential effects of passable manmade barriers (PMBs) within a sensitivity framework. If only IMBs are considered, high levels of longitudinal connectivity are observed across most of Scotland's rivers. Barrier prioritisation is sensitive to habitat weighting: not accounting for habitat quality can lead to over- or underestimating the importance of IMBs. Prioritisation is also highly sensitive to the passability of PMBs: if passability drops to <97% (combined up- and downstream passability), the mean effect of PMBs becomes greater than IMBs at the national level. Moreover, impacts on catchment connectivity, and thus production (number of juvenile salmon produced by the river), could be severe, suggesting a better understanding of the passability of PMBs is important for future management of migration barriers. The presented framework can be transferred to other catchments, regions, or countries where necessary data are available, making it a valuable tool to the broader restoration community.

Effects of water turbidity and different temperatures on oxidative stress in caddisfly (Stenopsyche marmorata) larvae

Anthropogenic water turbidity derived from suspended solids (SS) is caused by reservoir sediment management practices such as drawdown flushing. Turbid water induces stress in many aquatic organisms, but the effects of turbidity on oxidative stress responses in aquatic insects have not yet been demonstrated. Here, we examined antioxidant responses, oxidative damage, and energy reserves in caddisfly (Stenopsyche marmorata) larvae exposed to turbid water (0 mg SS L^-1, 500 mg SS L^-1, and 2000 mg SS L^-1) at different temperatures. We evaluated the combined effects of turbid water and temperature by measuring oxidative stress and using metabolic biomarkers. No turbidity level was significantly lethal to S. marmorata larvae. Moreover, there were no significant differences in antioxidant response or oxidative damage between the control and turbid water treatments at a low temperature (10 °C). However, at a high temperature (25 °C), turbid water modulated the activity of the antioxidant enzymes superoxide dismutase and catalase and the oxygen radical absorbance capacity as an indicator of the redox state of the insect larvae. Antioxidant defenses require energy, and high temperature was associated with low energy reserves, which might limit the capability of organisms to counteract reactive oxygen species. Moreover, co-exposure to turbid water and high temperature caused fluctuation of antioxidant defenses and increased the oxidative damage caused by the production of reactive oxygen species. Furthermore, the combined effect of high temperature and turbid water on antioxidant defenses and oxidative damage was larger than the individual effects. Therefore, our results demonstrate that exposure to both turbid water and high temperature generates additive and synergistic interactions causing oxidative stress in this aquatic insect species.