Regional and local scale modeling of stream temperatures and spatio-temporal variation in thermal sensitivities

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

Managers need to know how to mitigate rising stream water temperature (WT) due to climate change. This requires identifying the environmental drivers that influence thermal regime and determining the spatial area where interventions are most effective. We hypothesized that (i) extreme thermal events can be influenced by a set of environmental factors that reduce thermal sensitivity and (ii) the role played by those factors varies spatially. To test these hypotheses, we (i) determined which of the environmental variables reported to be the most influential affected WT and (ii)identified the spatial scales over which those environmental variables influenced WT. To this end, the influence of multi-scale environmental variables, namely land cover, topography (channel slope, elevation), hydromorphology (channel sinuosity, water level, watershed area, baseflow index) and shade conditions, was analyzed on the three model variables (day thermal sensitivity, night thermal sensitivity, and non-convective thermal flux) in the model developed by Georges et al. (2021) of the temporal thermal dynamics of daily maximum WT during extreme events. Values were calculated on six spatial scales (the entire upstream catchment and the associated 1 km and 2 km circular buffer, and 50 m wide corridors on each side of the stream with the associated 1 km and 2 km circular buffer). The period considered was 17 extreme days during the summer identified by Georges et al. (2021) based on WT data measured every 10 min for 7 years (2012-2018) at 92 measurement sites. Sites were located evenly throughout the Wallonia (southern Belgium) hydrological network. Results showed that shade, baseflow index (a proxy of the influence of groundwater), water level and watershed area were the most significant variables influencing thermal sensitivity. Since managers with finite financial and human resources can act on only a few environmental variables, we advocate restoring and preserving the vegetation cover that limits solar radiation on the watercourse as a cost-effective solution to reduce thermal sensitivity. Moreover, management at small spatial scale (50 m riparian buffer) should be strategically promoted (for finance and staffing) as our results show that a larger management scale is not more effective in reducing thermal sensitivity to extreme events.

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.

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.

Relationship between Water Temperature of Polish Rivers and Large-Scale Atmospheric Circulation

The objective of the paper consisted in determining the effect of macroscale types of NAO, AO, EA, EAWR, SCAND, and AMO atmospheric circulation on changes in water temperature in Polish rivers. The study has made use of a broad body of hydrometeorological materials covering daily water temperature values for 96 water gauge stations located on 53 rivers and air temperature values for 43 meteorological stations. Percentage shares of positive and negative coefficients of correlation of annual, seasonal, and monthly circulation type indices with air and river water temperature were determined, demonstrating the character of teleconnection. Determinations were made of water temperature deviations in positive and negative phases of the analyzed indices from average values from the years 1971–2015, and their statistical significance ascertained. Research has shown that relations between the temperature of river waters in Poland and macroscale circulation types are not strong, however they are noticeable, sometimes even statistically significant, and both temporally and spatially diverse. NAO, AO, EA, and AMO indices are characterized by a generally positive correlation with temperature, whereas SCAND and EWAR indices are characterized by a negative correlation. Research showed a varying impact of types of atmospheric circulation, with their effectiveness increasing in the winter season. The strongest impact on temperature was observed for the positive and negative NAO and AO phases, when deviations of water temperature from average values are correspondingly higher (up to 1.0 °C) and lower (by a maximum of 1.5 °C), and also for the positive and negative SCAND phases, when water temperature are correspondingly lower (by a maximum of 0.8 °C) and higher (by 1.2 °C) than average values. The strongest impact on water temperature in summer, mainly in July, was observed for AMO. The results point to the complexity of processes determining the thermal regime of rivers and to the possibility of additional factors—both regional and local—exerting an influence on their temporal and spatial variability.

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.

Can brook trout survive climate change in large rivers? If it rains

We provide an assessment of thermal characteristics and climate change vulnerability for brook trout (Salvelinus fontinalis) habitats in the upper Shavers Fork sub-watershed, West Virginia. Spatial and temporal (2001-2015) variability in observed summer (6/1-8/31) stream temperatures was quantified in 23 (9 tributary, 14 main-stem) reaches. We developed a mixed effects model to predict site-specific mean daily stream temperature from air temperature and discharge and coupled this model with a hydrologic model to predict future (2016-2100) changes in stream temperature under low (RCP 4.5) and high (RCP 8.5) emissions scenarios. Observed mean daily stream temperature exceeded the 21°C brook trout physiological threshold in all but one main-stem site, and 3 sites exceeded proposed thermal limits for either 63- and 7-day mean stream temperature. We modeled mean daily stream temperature with a high degree of certainty (R²=0.93; RMSE=0.76°C). Predicted increases in mean daily stream temperature in main-stem and tributary reaches ranged from 0.2°C (RCP 4.5) to 1.2°C (RCP 8.5). Between 2091 and 2100, the average number of days with mean daily stream temperature>21°C increased within main-stem sites under the RCP 4.5 (0-1.2days) and 8.5 (0-13) scenarios; however, no site is expected to exceed 63- or 7-day thermal limits. During the warmest 10years, ≥5 main-stem sites exceeded the 63- or 7-day thermal tolerance limits under both climate emissions scenarios. Years with the greatest increases in stream temperature were characterized by low mean daily discharge. Main-stem reaches below major tributaries never exceed thermal limits, despite neighboring reaches having among the highest observed and predicted stream temperatures. Persistence of thermal refugia within upper Shavers Fork would enable persistence of metapopulation structure and life history processes. However, this will only be possible if projected increases in discharge are realized and offset expected increases in air temperature.

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

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade(-1)) and a widening of the synchronized period (29 d decade(-1)). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.

Accounting for groundwater in stream fish thermal habitat responses to climate change

Forecasting climate change effects on aquatic fauna and their habitat requires an understanding of how water temperature responds to changing air temperature (i.e., thermal sensitivity). Previous efforts to forecast climate effects on brook trout (Salvelinus fontinalis) habitat have generally assumed uniform air-water temperature relationships over large areas that cannot account for groundwater inputs and other processes that operate at finer spatial scales. We developed regression models that accounted for groundwater influences on thermal sensitivity from measured air-water temperature relationships within forested watersheds in eastern North America (Shenandoah National Park, Virginia, USA, 78 sites in nine watersheds). We used these reach-scale models to forecast climate change effects on stream temperature and brook trout thermal habitat, and compared our results to previous forecasts based upon large-scale models. Observed stream temperatures were generally less sensitive to air temperature than previously assumed, and we attribute this to the moderating effect of shallow groundwater inputs. Predicted groundwater temperatures from air-water regression models corresponded well to observed groundwater temperatures elsewhere in the study area. Predictions of brook trout future habitat loss derived from our fine-grained models. were far less pessimistic than those from prior models developed at coarser spatial resolutions. However, our models also revealed spatial variation in thermal sensitivity within and among catchments resulting in a patchy distribution of thermally suitable habitat. Habitat fragmentation due to thermal barriers therefore may have an increasingly important role for trout population viability in headwater streams. Our results demonstrate that simple adjustments to air-water temperature regression models can provide a powerful and cost-effective approach for predicting future stream temperatures while accounting for effects of groundwater.