A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

Regime shifts in the thermal dynamics of offshore China due to accelerated global warming

Thermal dynamics play a pivotal role in offshore ecosystems, influencing a multitude of ecological and biogeochemical processes. Assessing how water temperature (WT) responds to climate change is vital for the sustainable development of marine ecosystems. Despite the scarcity of long-term sea surface temperature (SST) data, this study reconstructs SSTs from 1973 to 2020 in China's coastal zones using the data-driven Air2water model. A probabilistic approach was applied to investigate the joint dependency structures between air temperature (AT) and WT at offshore oceanic stations in China, focusing on variations during periods of decelerated and accelerated warming. The results indicate that the Air2water model performs well in reconstructing SSTs of the coastal zone of China. Furthermore, the joint probability of AT-WT events, characterized by bimodal distributions, tends to increase during accelerated warming. This suggests intensified extreme SST events in the coastal zone of China due to global warming, with the significant warming primarily related with regional oscillations, atmospheric dynamics, and the complex temperature trends in the regional marine environment. These findings highlight the escalating impact of global warming on marine ecosystems in China's coastal regions, underscoring the urgency of developing adaptive strategies to mitigate these effects.

Development of a basin-scale total nitrogen prediction model by integrating clustering and regression methods

Nutrient runoff into rivers caused by human activity has led to global eutrophication issues. The Nakdong River in South Korea is currently facing significant challenges related to eutrophication and harmful algal blooms, underscoring the critical importance of managing total nitrogen (T-N) levels. However, traditional methods of indoor analysis, which depend on sampling, are labor-intensive and face limitations in collecting high-frequency data. Despite advancements in sensor allowing for the measurement of various parameters, sensors still cannot directly measure T-N, necessitating surrogate regression methods. Therefore, we conducted T-N predictions using a water quality dataset collected from 2018 to 2022 at 157 observatories within the Nakdong River basin. To account for the water quality characteristics of each location, we employed a clustering technique to divide the basin and compared a Gaussian mixture model with K-means clustering. Moreover, optimal regressor for each cluster was selected by comparing multiple linear regression (MLR), random forest, and XGBoost. The results showed that forming four clusters via K-means clustering was the most suitable approach and MLR was reasonably accurate for all clusters. Subsequently, recursive feature elimination cross-validation was used to identify suitable parameters for T-N prediction, thus leading to the construction of high-accuracy T-N prediction models. Clustering was useful not only for improving the regressors but also for spatially analyzing the water quality characteristics of the Nakdong River. The MLR model can reveal causal relationships and thus is useful for decision-making. The results of this study revealed that the combination of a simple linear regression model and clustering method can be applied to a wide watershed. The clustering-based regression model showed potential for accurately predicting T-N at the basin level and is expected to contribute to nationwide water quality management through future applications in various fields.

Diurnal patterns of spatial stream temperature variations reveal the need for integrating thermal heterogeneity in riverscape habitat restoration

Longer durations of warmer weather, altered precipitation, and modified streamflow patterns driven by climate change are expected to impair ecosystem resilience, exposing freshwater ecosystems and their biota to a severe threat worldwide. Understanding the spatio-temporal temperature variations and the processes governing thermal heterogeneity within the riverscape are essential to inform water management and climate adaptation strategies. We combined UAS-based imagery data of aquatic habitats with meteorological, hydraulic, river morphology and water quality data to investigate how key factors influence spatio-temporal stream heterogeneity on a diurnal basis within different thermal regions of a large recently restored Danube floodplain. Diurnal temperature ranges of aquatic habitats were larger than expected and ranged between 14.2 and 28.0 °C (mean = 20.7 °C), with peak median temperatures (26.1 °C) around 16:00 h. The observed temperature differences in timing and amplitude among thermal regions were unexpectedly high and created a mosaic pattern of temperature heterogeneity. For example, cooler groundwater-influenced thermal regions provided several cold water patches (CWP, below 19.0 °C) and potential cold water refuges (CWRs) around 12:00 h, at the time when other habitats were warmer than 21.0 °C, exceeding the ecological threshold (20.0 °C) for key aquatic species. Within the morphological complexity of the restored floodplain, we identified groundwater influence, shading and river morphology as the key processes driving thermal riverscape heterogeneity. Promoting stream thermal refuges will become increasingly relevant under climate change scenarios, and river restoration should consider both measures to physically prevent habitat from excessive warming and measures to improve connectivity that meet the temperature requirements of target species for conservation. This requires restoring mosaics of complex and dynamic temperature riverscapes.

An assessment of marine, estuarine, and riverine habitat vulnerability to climate change in the Northeast U.S

Climate change is impacting the function and distribution of habitats used by marine, coastal, and diadromous species. These impacts often exacerbate the anthropogenic stressors that habitats face, particularly in the coastal environment. We conducted a climate vulnerability assessment of 52 marine, estuarine, and riverine habitats in the Northeast U.S. to develop an ecosystem-scale understanding of the impact of climate change on these habitats. The trait-based assessment considers the overall vulnerability of a habitat to climate change to be a function of two main components, sensitivity and exposure, and relies on a process of expert elicitation. The climate vulnerability ranks ranged from low to very high, with living habitats identified as the most vulnerable. Over half of the habitats examined in this study are expected to be impacted negatively by climate change, while four habitats are expected to have positive effects. Coastal habitats were also identified as highly vulnerable, in part due to the influence of non-climate anthropogenic stressors. The results of this assessment provide regional managers and scientists with a tool to inform habitat conservation, restoration, and research priorities, fisheries and protected species management, and coastal and ocean planning.

Daily River Water Temperature Prediction: A Comparison between Neural Network and Stochastic Techniques

The temperature of river water (TRW) is an important factor in river ecosystem predictions. This study aims to compare two different types of numerical model for predicting daily TRW in the Warta River basin in Poland. The implemented models were of the stochastic type—Autoregressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA)—and the artificial intelligence (AI) type—Adaptive Neuro Fuzzy Inference System (ANFIS), Radial Basis Function (RBF) and Group Method of Data Handling (GMDH). The ANFIS and RBF models had the most fitted outputs and the AR, ARMA and ARIMA patterns were the most accurate ones. The results showed that both of the model types can significantly present suitable predictions. The stochastic models have somewhat less error with respect to both the highest and lowest TRW deciles than the AIs and were found to be better for prediction studies, with the GMDH complex model in some cases reaching Root Mean Square Error (RMSE) = 0.619 °C and Nash-Sutcliff coefficient (NS) = 0.992, while the AR(2) simple linear model with just two inputs was partially able to achieve better results (RMSE = 0.606 °C and NS = 0.994). Due to these promising outcomes, it is suggested that this work be extended to other catchment areas to extend and generalize the results.

Continental-scale analysis of shallow and deep groundwater contributions to streams

Groundwater discharge generates streamflow and influences stream thermal regimes. However, the water quality and thermal buffering capacity of groundwater depends on the aquifer source-depth. Here, we pair multi-year air and stream temperature signals to categorize 1729 sites across the continental United States as having major dam influence, shallow or deep groundwater signatures, or lack of pronounced groundwater (atmospheric) signatures. Approximately 40% of non-dam stream sites have substantial groundwater contributions as indicated by characteristic paired air and stream temperature signal metrics. Streams with shallow groundwater signatures account for half of all groundwater signature sites and show reduced baseflow and a higher proportion of warming trends compared to sites with deep groundwater signatures. These findings align with theory that shallow groundwater is more vulnerable to temperature increase and depletion. Streams with atmospheric signatures tend to drain watersheds with low slope and greater human disturbance, indicating reduced stream-groundwater connectivity in populated valley settings.

An Interactive Data Visualization Framework for Exploring Geospatial Environmental Datasets and Model Predictions

With the rise of large-scale environmental models comes new challenges for how we best utilize this information in research, management and decision making. Interactive data visualizations can make large and complex datasets easier to access and explore, which can lead to knowledge discovery, hypothesis formation and improved understanding. Here, we present a web-based interactive data visualization framework, the Interactive Catchment Explorer (ICE), for exploring environmental datasets and model outputs. Using a client-based architecture, the ICE framework provides a highly interactive user experience for discovering spatial patterns, evaluating relationships between variables and identifying specific locations using multivariate criteria. Through a series of case studies, we demonstrate the application of the ICE framework to datasets and models associated with three separate research projects covering different regions in North America. From these case studies, we provide specific examples of the broader impacts that tools like these can have, including fostering discussion and collaboration among stakeholders and playing a central role in the iterative process of data collection, analysis and decision making. Overall, the ICE framework demonstrates the potential benefits and impacts of using web-based interactive data visualization tools to place environmental datasets and model outputs directly into the hands of stakeholders, managers, decision makers and other researchers.

Water temperature forecasting based on modified artificial neural network methods: Two cases of the Yangtze River

Water temperature is a controlling indicator of river habitat since many physical, chemical and biological processes in rivers are temperature dependent. Highly precise and reliable predictions of water temperature are important for river ecological management. In this study, a hybrid model named BP_PSO3, based on the BPNN (back propagation neural network) optimized by the PSO (particle swarm optimization) algorithm, is proposed for water temperature prediction using air temperature (Ta), discharge (Q) and day of year (DOY) as input variables. The performance of the BP_PSO3 model was compared with that of the BP_PSO1 (with Ta as the input) and BP_PSO2 (with Ta and Q as the inputs) models to evaluate the importance of the inputs. In addition, a comparison among the BPNN, RBFNN (radial basis function neural network), WNN (wavelet neural network), GRNN (general regression neural network), ELMNN (Elman neural network), and BP_PSO-based models was carried out based on the MAE, RMSE, NSE and R². The eight artificial intelligence models were examined to predict the water temperature at the Cuntan and Datong stations in the Yangtze River. The results indicated that the hybrid BPNN-PSO3 model had a stronger ability to forecast water temperature under both normal and extreme drought conditions. Optimization by the PSO algorithm and the inclusion of Q and DOY could help capture river thermal dynamics more accurately. The findings of this study could provide scientific references for river water temperature forecasting and river ecosystem protection.

Use of Multiple Temperature Logger Models Can Alter Conclusions

Remote temperature loggers are often used to measure water temperatures for ecological studies and by regulatory agencies to determine whether water quality standards are being maintained. Equipment specifications are often given a cursory review in the methods; however, the effect of temperature logger model is rarely addressed in the discussion. In a laboratory environment, we compared measurements from three models of temperature loggers at 5 to 40 °C to better understand the utility of these devices. Mean water temperatures recorded by logger models differed statistically even for those with similar accuracy specifications, but were still within manufacturer accuracy specifications. Maximum mean temperature difference between models was 0.4 °C which could have regulatory and ecological implications, such as when a 0.3 °C temperature change triggers a water quality violation or increases species mortality rates. Additionally, precision should be reported as the overall precision (including a consideration of significant digits) for combined model types which in our experiment was 0.7 °C, not the ≤0.4 °C for individual models. Our results affirm that analyzing data collected by different logger models can result in potentially erroneous conclusions when <1 °C difference has regulatory compliance or ecological implications and that combining data from multiple logger models can reduce the overall precision of results.

How repeatable is CTmax within individual brook trout over short- and long-time intervals?

As stream temperatures increase due to factors such as heated runoff from impervious surfaces, deforestation, and climate change, fish species adapted to cold water streams are forced to move to more suitable habitat, acclimate or adapt to increased thermal regimes, or die. To estimate the potential for adaptation, a (within individual) repeatable metric of thermal tolerance is imperative. Critical thermal maximum (CT_max) is a dynamic test that is widely used to measure thermal tolerance across many taxa and has been used in fishes for decades, but its repeatability in most species is unknown. CT_max tests increase water temperature steadily over time until loss of equilibrium (LOE) is achieved. To determine if CT_max is a consistent metric within individual fish, we measured CT_max on the same lab-held individually-marked adult brook trout Salvelinus fontinalis at three different times (August & September 2016, September 2017). We found that CT_max is a repeatable trait (Repeatability ± S.E.: 0.48 ± 0.14). CT_max of individuals males was consistent over time, but the CT_max of females increased slightly over time. This result indicates that CT_max is a robust, repeatable estimate of thermal tolerance in a cold-water adapted fish.

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.

Seasonal quality variation and environmental risks associated with the consumption of surface water: implication from the Landzun Stream, Bida Nigeria

Water constitutes a major environmental and public health concerns worldwide. A large proportion of global water consumption is sourced from surface water. The dependency level on surface water is higher in developing countries, especially in rural-to-semi-urban areas, where subsurface water is not accessible. Presented in this paper is a spatiotemporal and hydrochemical quality assessment of the spring-originated Landzun Stream in Bida, Nigeria; which is usually consumed in its untreated state. Water samples were systematically collected in eighteen locations along the stream channel in both rainy and dry seasons at an equidistance interval of 500m. On-site and laboratory measurement of important physical and hydrochemical parameters were carried out using standard procedures. Water temperature in the rainy season (34–37 °C) slightly exceeds measured values in the dry season (29–33 °C). 72.22% (rainy) and 83.33% (dry) of collected samples did not meet the odourless requirement for drinking water. Similarly, estimated percentages of 66.67 and 94.44 of collected samples in rainy and dry seasons respectively have a taste. Contrary to data in the rainy season, 89%, 11%, 67% and 56% of the dry season's samples were enriched in magnesium (Mg), lead (Pb), potassium (K) and iron (Fe) respectively above the 2018 World Health Organisation guidelines for drinking water. This study further established that seasonal variation plays a major role in altering the aesthetic surface water quality. The intake of untreated surface water is a vehicle for potential water-borne diseases and allergies, hence alternative sources of drinking water for the populace dependent on the Landzun Stream is recommended to reduce risks and possible dangers of consuming the stream water.

Estimating thermal performance curves from repeated field observations

Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation. We fit the model in a Bayesian framework using MCMC sampling, which allowed for estimation of unobserved latent growth while propagating uncertainty. Fitting the model to simulated data varying in sampling design and parameter values demonstrated that the parameter estimates were accurate, precise, and unbiased. Fitting the model to individual growth data from wild trout revealed high out-of-sample predictive ability relative to laboratory-derived models, which produced more biased predictions for field performance. The field-based estimates of thermal maxima were lower than those based on laboratory studies. Under warming temperature scenarios, field-derived performance models predicted stronger declines in body size than laboratory-derived models, suggesting that laboratory-based models may underestimate climate change effects. The presented model estimates true, realized field performance, avoiding assumptions required for applying laboratory-based models to field performance, which should improve estimates of performance under climate change and advance thermal ecology.

Modelling daily water temperature from air temperature for the Missouri River

The bio-chemical and physical characteristics of a river are directly affected by water temperature, which thereby affects the overall health of aquatic ecosystems. It is a complex problem to accurately estimate water temperature. Modelling of river water temperature is usually based on a suitable mathematical model and field measurements of various atmospheric factors. In this article, the air–water temperature relationship of the Missouri River is investigated by developing three different machine learning models (Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Bootstrap Aggregated Decision Trees (BA-DT)). Standard models (linear regression, non-linear regression, and stochastic models) are also developed and compared to machine learning models. Analyzing the three standard models, the stochastic model clearly outperforms the standard linear model and nonlinear model. All the three machine learning models have comparable results and outperform the stochastic model, with GPR having slightly better results for stations No. 2 and 3, while BA-DT has slightly better results for station No. 1. The machine learning models are very effective tools which can be used for the prediction of daily river temperature.

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

The thermal suitability of riverine habitats for cold water adapted species may be reduced under climate change. Riparian tree planting is a practical climate change mitigation measure, but it is often unclear where to focus effort for maximum benefit. Recent developments in data collection, monitoring and statistical methods have facilitated the development of increasingly sophisticated river temperature models capable of predicting spatial variability at large scales appropriate to management. In parallel, improvements in temporal river temperature models have increased the accuracy of temperature predictions at individual sites. This study developed a novel large scale spatio-temporal model of maximum daily river temperature (Tw_max) for Scotland that predicts variability in both river temperature and climate sensitivity. Tw_max was modelled as a linear function of maximum daily air temperature (Ta_max), with the slope and intercept allowed to vary as a smooth function of day of the year (DoY) and further modified by landscape covariates including elevation, channel orientation and riparian woodland. Spatial correlation in Tw_max was modelled at two scales; (1) river network (2) regional. Temporal correlation was addressed through an autoregressive (AR1) error structure for observations within sites. Additional site level variability was modelled with random effects. The resulting model was used to map (1) spatial variability in predicted Tw_max under current (but extreme) climate conditions (2) the sensitivity of rivers to climate variability and (3) the effects of riparian tree planting. These visualisations provide innovative tools for informing fisheries and land-use management under current and future climate.

Effects of thermopeaking on the thermal response of alpine river systems to heatwaves

Within the past 30years there have been two major heatwave events (in 2003 and 2006) that broke 500-year-old temperature records in Europe. Owing to the growing concern of rising temperatures, we analyzed the potential response in a number of river sections that are subject to hydropeaking and thermopeaking through the intermittent release of water from hydropower stations. Thermopeaking in alpine streams is known to intermittently cool down the river water in summer and to warm it up in winter. We analyzed the response of river water temperature to air temperature during heatwaves at 19 gauging stations across Switzerland, using a 30-yr dataset at a 10-min resolution. Stations were either classified into "unpeaked" or "peaked" groups according to four statistical indicators related to hydropeaking and thermopeaking pressure. Peaked stations were exposed to reduced temporal variability in river water temperature, and it was determined that correlations between river water and air temperature were weaker for peaked stations compared with unpeaked stations. Similarly, peaked stations showed a much weaker response to heatwaves compared with unpeaked stations. It is important to note that this "cooling effect" created by hydro-thermopeaking was most pronounced during the two major heatwave events that took place in 2003 and 2006. Furthermore, results from thermal stress events on the growth of a typical cold eurythermic fish species (brown trout) increased continuously in rivers subject to peaked station water release during heatwaves. While hydropower operations that take place high up on mountains releasing hypolimnetic water may mitigate the adverse effects of heatwaves on downstream alpine river ecosystems locally, our results show the complexity of an artificial physical template associated with flow regime regulation in alpine streams.

Projections of the future occurrence, distribution, and seasonality of three Vibrio species in the Chesapeake Bay under a high-emission climate change scenario

Illness caused by pathogenic strains of Vibrio bacteria incurs significant economic and health care costs in many areas around the world. In the Chesapeake Bay, the two most problematic species are V. vulnificus and V. parahaemolyticus, which cause infection both from exposure to contaminated water and consumption of contaminated seafood. We used existing Vibrio habitat models, four global climate models, and a recently developed statistical downscaling framework to project the spatiotemporal probability of occurrence of V. vulnificus and V. cholerae in the estuarine environment, and the mean concentration of V. parahaemolyticus in oysters in the Chesapeake Bay by the end of the 21st century. Results showed substantial future increases in season length and spatial habitat for V. vulnificus and V. parahaemolyticus, while projected increase in V. cholerae habitat was less marked and more spatially heterogeneous. Our findings underscore the need for spatially variable inputs into models of climate impacts on Vibrios in estuarine environments. Overall, economic costs associated with Vibrios in the Chesapeake Bay, such as incidence of illness and management measures on the shellfish industry, may increase under climate change, with implications for recreational and commercial uses of the ecosystem.

Keeping things local: Subpopulation Nb and Ne in a stream network with partial barriers to fish migration

For organisms with overlapping generations that occur in metapopulations, uncertainty remains regarding the spatiotemporal scale of inference of estimates of the effective number of breeders (N^b) and whether these estimates can be used to predict generational N_e. We conducted a series of tests of the spatiotemporal scale of inference of estimates of N_b in nine consecutive cohorts within a long‐term study of brook trout (Salvelinus fontinalis). We also tested a recently developed approach to estimate generational N_e from N^b and compared this to an alternative approach for estimating N^e that also accounts for age structure. Multiple lines of evidence were consistent with N^b corresponding to the local (subpopulation) spatial scale and the cohort‐specific temporal scale. We found that at least four consecutive cohort‐specific estimates of N^b were necessary to obtain reliable estimates of harmonic mean N^b for a subpopulation. Generational N^e derived from cohort‐specific N^b was within 7%–50% of an alternative approach to obtain N^e, suggesting some population specificity for concordance between approaches. Our results regarding the spatiotemporal scale of inference for N_b should apply broadly to many taxa that exhibit overlapping generations and metapopulation structure and point to promising avenues for using cohort‐specific N^b for local‐scale genetic monitoring.