Stream Temperature Model Research Articles

Hybridizing evolutionary algorithms and multiple non-linear regression technique for stream temperature modeling

Abstract The present study hybridizes the new-generation evolutionary algorithms and the nonlinear regression technique for stream temperature modeling and compares this approach with conventional gray and black box approaches under natural flow conditions, providing a comprehensive assessment. The nonlinear equation for water temperature modeling was optimized using biogeography-based optimization (BBO) and invasive weed optimization (IWO), simulated annealing algorithm (SA) and particle swarm optimization (PSO). Two black box approaches, a feedforward neural network (FNN) and a long short-term memory (LSTM) network, were also employed for comparison. Additionally, an adaptive neuro-fuzzy inference system (ANFIS) served as a gray box model for river thermal regimes. The models were evaluated based on accuracy, complexity, generality and interpretability. Performance metrics, such as the Nash–Sutcliffe efficiency (NSE), showed that the LSTM model achieved the highest accuracy (NSE = 0.96) but required significant computational resources. In contrast, evolutionary algorithm-based models offered acceptable performance while reducing the computational complexities of LSTM, with all models achieving NSE values above 0.5. Considering interpretability, accuracy and complexity, evolutionary-based nonlinear models are recommended for general applications, such as assessing thermal river habitats. For tasks requiring very high accuracy, the LSTM model is preferred, while ANFIS provides a balanced trade-off between accuracy and interpretability, making it suitable for engineers and ecologists. While all models demonstrate similar generality, this model is developed for a specific location. For other locations, independent models with a similar architecture would need to be developed. Ultimately, the choice of model depends on specific objectives and available resources.

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

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.

Quantifying Hydraulic Geometry and Whitewater Coverage for Steep Proglacial Streams to Support Process-Based Stream Temperature Modelling.

At-a-station hydraulic geometry (AASHG) relationships describe the dependence of a river's width, mean depth and mean velocity on discharge at a given location, and are typically modelled as power-law functions. They are often used when modelling stream temperature under unsteady flow conditions. Deriving AASHG relationships is challenging for steep proglacial streams due to the combination of complex morphology and velocity distributions, and rapidly varying flow. The objective of this study was to combine tracer injections with drone-based photogrammetry to derive AASHG relationships for a steep proglacial channel and to quantify whitewater coverage and its relationship with discharge to support process-based stream temperature modelling. Velocity-discharge and width-discharge relationships were reasonably well characterised using power-law functions, but varied amongst sub-reaches. Whitewater coverage as a fraction of total stream surface area generally exceeded 50% for the range of flows sampled, and exhibited a statistically significant positive relationship with discharge, which varied amongst sub-reaches. For the range of flows captured during drone flights, the relationship could be represented by a linear function. However, an asymptotic model would be required to extend the relationship to higher flows. The magnitude of whitewater coverage indicates that the albedo of the stream should be substantially higher than values typically used in stream temperature models, and the relationship with discharge means that ongoing glacier retreat, and the associated reduction in summer discharge, should result in lower albedo and higher downstream warming rates, reinforcing the effects of decreasing velocity and mean depth as flows decline.

Scenario Planning Management Actions to Restore Cold Water Stream Habitat: Comparing Mechanistic and Statistical Modeling Approaches

ABSTRACTUnder the United States Clean Water Act, states are required to periodically assess state waters to determine compliance with water quality criteria (including temperature) and then to develop total maximum daily loads (TMDLs) for impaired waters as necessary to bring them into compliance. We compared the performance of mechanistic stream temperature models (HeatSource, QUAL2K, and QUAL2Kw) applied to the mainstem of three TMDL watersheds (Middle Fork John Day, OR; Wind River, WA; South Fork Nooksack, WA) with that of spatial stream network (SSN) models applied to the full watersheds and used these to evaluate the potential effectiveness of restoration strategies. SSN models performed well with slightly lesser accuracy (RMSE = 0.47–0.87) for mainstem predictions than mechanistic models (RMSE = 0.4) but provided additional benefits to inform management, including information on spatial and temporal heterogeneity of restoration effectiveness throughout the watershed. Of the four scenarios considered (restoration of riparian zones to potential natural vegetation, channel narrowing, increasing flow by restricting irrigation withdrawals, and combined applications), riparian zone restoration was consistently the most effective in reducing temperatures at the outlet, mainstem, and throughout the watersheds. Predicted restoration effectiveness for thermal regimes varied significantly both within and among watersheds. A focus on water quality criteria exceedance only at the watershed outlet or along the mainstem reach can obscure knowledge of restoration potential for fish habitat in tributaries and headwaters, potential for creation of thermal refuge areas along the mainstem critical for maintaining migration corridors, and thermal regime heterogeneity across space and time.

Projecting exceedance of juvenile salmonid thermal maxima in streams under climate change: A crosswalk from lab experiments to riparian restoration

Abstract Concern over rising water temperatures for freshwater ectotherms has led to application of experimentally derived thermal thresholds to stream temperature models for estimation of streams at high risk of exceeding thermal thresholds under current and future climate conditions. We optimised an approach that links field‐relevant thermal maxima experiments to corresponding stream temperature models and identifies opportunities to reduce stream temperatures through riparian tree growth. We conducted a thermal maxima experiment on cold‐water adapted juvenile Chinook (Oncorhynchus tshawytscha) and coho salmon (Oncorhynchus kisutch) that reflected natural temperatures by using incremental temperature ramping with diel fluctuations (IT‐Dmax) and refit a regionally specific stream temperature model for British Columbia, Canada to directly relate to lab‐derived thresholds. Salmon‐bearing streams across British Columbia were categorised by threshold exceedance risk (i.e., low, moderate, high, severe) based on risk tolerance scenarios for management decision making (i.e., considering a range of prediction intervals [PIs] and stream thermal sensitivities to air temperature). We linked these results directly to riparian management and restoration actions by estimating the potential for riparian tree growth to reduce threshold exceedance risk. Lab‐derived IT‐Dmax endpoints were consistently 24°C (based on the median value between the 7‐day average of the daily maximum and the mean weekly average temperature) across acclimation treatments for both species. Under current conditions, most stream reaches (99.6% using an intermediate risk tolerance scenario) were below the thermal threshold; streams with moderate to high risk of threshold exceedance were projected to increase from 0.4% to 1.5% (total linear stream length of 6,929 km) by end of century. The risk‐averse (high sensitivity, 75% PI) and risk‐tolerant (low sensitivity, 95% PI) scenarios differed by 1,107 km of streams predicted to have a moderate to severe risk of exceeding thresholds by end of century. Maximal riparian tree growth was predicted to shift 670 km of streams from moderate to low exceedance risk under end‐of‐century conditions and intermediate risk tolerance, showing the potential for mitigation from thermal impacts. Our integrative approach tackled several key considerations in identifying streams at high temperature risk for freshwater ectotherms that to date have not been addressed. Furthermore, we showcased the delineation of spatially comprehensive estimates that identify where management mitigation and a specific restoration activity may be most needed to reduce impacts of rising temperatures on rearing salmonids across an extensive region.

Effects of multicollinearity and data granularity on regression models of stream temperature

Water temperature is a key factor influencing biota of stream ecosystems. Hence, it is important to comprehend the environmental drivers of stream temperature for robust prediction of conditions and effective management of stream communities. Linear regression models are commonly used for predictive purposes, but their predictive capacity and interpretability can be significantly affected by their complexity and the structure of input data. In some cases, researchers may be obligated to favor prediction power or interpretability while compromising the other. Therefore, insight into relationships between model fit, correlation among predictor variables (i.e., multicollinearity), and level of temporal aggregation of data (i.e., data granularity) may be helpful to reduce such trade-offs. In this paper, we investigated these relationships within a hierarchical set of multiple linear regression (MLR) models examining environmental factors influencing stream temperature dynamics. Our findings showed that as the number of predictor variables (i.e., model complexity) increased, the magnitude of multicollinearity in MLR models increased, but model fit also increased. The results also revealed that using data averaged over longer time frames (i.e., coarser data granularity) yielded high multicollinearity, as indexed by variance inflation factor values (VIF) for all model predictors. This led to higher variance in parameter estimates (i.e., parameter instability) and potential challenges in model interpretation as the sign of parameter estimates changed in many streams examined. Multicollinearity was not the only reason for these changes in the sign of parameter estimates as they were also observed in simple linear regression models across varying levels of data granularity. Based on our findings, we conclude that the selection of data granularity is an important consideration in multiple regression modeling, with profound implications for model interpretability.

Statistical stream temperature modelling with SSN and INLA: an introduction for conservation practitioners

Statistical stream temperature models predicting the fine-scale spatial distribution of water temperatures (i.e., “thermalscape”) can guide aquatic species recovery and habitat restoration efforts. However, stream temperature modelling is complicated by spatial autocorrelation arising from non-independence of sampling sites within dendritic networks. We used August mean temperature data from miniature sensors deployed in Canadian Rocky Mountain streams to demonstrate two statistical stream temperature modelling techniques that account for spatial autocorrelation. The first was a spatial steam network (SSN) model specifically developed to account for spatial autocorrelation in dendritic stream networks. The second was an integrated nested Laplace approximation (INLA) model that accounts for spatial autocorrelation but was not designed to address anisotropic stream network data. We evaluated the best-fitting SSN and INLA models using leave-one-out cross-validation. Relative to INLA, SSN models had lower RMSE (1.23 vs. 1.45 C) and higher r2 (0.71 vs. 0.61); however, the SSN models required more preprocessing steps before incorporating spatially correlated random errors. We provide practical advice, an open-access r-script, and data to help non-experts develop statistical stream temperature models.

Integrating spatial stream network models and environmental DNA to estimate current and future distributions of nonnative Smallmouth Bass

Abstract Objective Climate change is fueling the rapid range expansion of invasive species in freshwater ecosystems. This has led to mounting calls from natural resource managers for more robust predictions of invasive species distributions to anticipate threats to species of concern and implement proactive conservation and restoration actions. Here, we applied recent advances in fish sampling and statistical modeling in river networks to estimate the current and future watershed-scale spatial distribution of nonnative Smallmouth Bass Micropterus dolomieu. Methods We integrated a spatial stream network (SSN) model of stream temperature, landscape environmental covariates, and Smallmouth Bass occurrence data based on environmental DNA (eDNA) detections to develop an SSN species distribution model (SDM) representing current Smallmouth Bass distributions in the Chehalis River, Washington State, a large coastal river basin of ongoing watershed-scale restoration. The SDM was informed by spatially intensive eDNA sampling from 135 locations in the main stem and major tributaries. We then applied downscaled climate change projections to the SSN SDM to predict Smallmouth Bass range expansion in the basin by late century. Result We identified high levels of spatial autocorrelation at hydrological distances of ≤10 km in our eDNA data set, underscoring the importance of applying an SSN modeling framework. Stream temperature was identified as the most important environmental covariate explaining variability in Smallmouth Bass occurrence. Model predictions estimated that current suitable summer habitat for Smallmouth Bass habitat spans 681 km and is projected to nearly double by late century (1333 km) under a moderate climate change scenario. Current and future suitable habitat for Smallmouth Bass is prevalent in important tributaries for spring Chinook Salmon Oncorhynchus tshawytscha, a species of major conservation concern in the Chehalis River and more broadly along the Pacific coast. In both the main stem and tributaries, the SSN SDM predictions of the upstream leading edges of Smallmouth Bass closely align with (within 4.8 km) edges identified by spatially intensive eDNA sampling. Conclusion Our study highlights the value of integrating SSN models with rapidly growing eDNA data sets for accurate and precise riverine fish distribution estimation. Our application provides crucial insights for anticipating the impacts of shifting invasive species on Pacific salmon Oncorhynchus spp. in a warming world.

Air temperature data source affects inference from statistical stream temperature models in mountainous terrain

Instream temperatures control numerous biophysical processes and are frequently the subject of modeling efforts to understand and predict responses to watershed conditions, habitat alterations, and climate change. Air temperature (AT) is regularly used in statistical temperature models as a covariate proxy for physical processes and because it correlates strongly with spatiotemporal variability in water temperatures (Tw). Air temperature data are broadly available and sourced from sensors paired with Tw sites, remote weather stations, and gridded climate data sets—often with limited recognition of the tradeoffs these sources present and how microclimatic variation in topographically complex mountain environments could affect model inference. To address these issues, we collected daily Tw records at 13 sites throughout a mountain river network, linked the records to AT data from 11 sources available across much of North America, and fit linear regression models to assess predictive performance and the consistency of parameter estimation. Although the predictive accuracy of these models was generally high, estimates of the AT slope parameter, which is commonly interpreted as thermal sensitivity, varied substantially depending on the AT data source. These results have implications for the comparability of estimates among Tw studies and highlight the challenges that modeling stream temperatures in mountain landscapes presents. Although no AT data source is ideal, some are more advantageous than others for specific use cases and we provide general recommendations on this topic.

Scales of Connectivity within Stream Temperature Networks of the Clackamas River Basin, Oregon

Water quality varies along the stream network; thus, considering the directional, dendritic nature of stream networks with surrounding landscape variables is essential in explaining spatial variations of water quality. Using a spatially extensive stream temperature monitoring effort in the Clackamas River Basin in the United States, we first compare spatial scales of analysis of atmospheric, landscape, and in-stream explanatory variables through their correlation with summer stream temperatures. We then derive a predictive stream temperature model with factors representing the spatial variation of local climate, recent wildfire effects, and discharge. Finally, we compare nonspatial multiple linear regression to a spatial stream network (SSN) model to assess the combined importance of the spatial scale of analysis and flow-connected stream distance in explaining total variation in stream temperatures. Most explanatory variables show the most highly significant relationships to stream temperature when derived as a percentage of the total upstream area above observation sites. Elevation and vegetation cover, however, were most significantly correlated to stream temperature at the riparian buffer area scale and the local reach contributing area scale, respectively. Multiple regression analysis using total upstream burned area, total upstream area with underlying High Cascades geology, and the elevation within the 100-m-wide riparian area explained 81 percent of variation in stream temperature. SSN outperformed this nonspatial statistical model, however, in explaining the total variation in stream temperature. These comparisons of scaled data sets demonstrate both the local and cumulative upstream effects on stream temperature, providing a spatial network-informed framework to those prioritizing watershed restoration and wildfire recovery activities.

Train, Inform, Borrow, or Combine? Approaches to Process‐Guided Deep Learning for Groundwater‐Influenced Stream Temperature Prediction

AbstractAlthough groundwater discharge is a critical stream temperature control process, it is not explicitly represented in many stream temperature models, an omission that may reduce predictive accuracy, hinder management of aquatic habitat, and decrease user confidence. We assessed the performance of a previously‐described process‐guided deep learning model of stream temperature in the Delaware River Basin (USA). We found lower accuracy (root mean square error [RMSE] of 1.71 versus 1.35°C) and stronger seasonal bias (absolute mean monthly bias of 1.06 vs. 0.68°C) for reaches primarily influenced by deep groundwater as compared to atmospheric conditions. We then tested four approaches for improving groundwater process representation: (a) a custom loss function leveraging the unique patterns of air and water temperature coupling characteristic of different temperature drivers, (b) inclusion of additional groundwater‐relevant catchment attributes, (c) incorporation of additional process model outputs, and (d) a composite model. The custom loss function and the additional attributes significantly improved the predictive accuracy in groundwater‐dominated reaches (RMSE of 1.37 and 1.26°C) and reduced the seasonal bias (absolute mean monthly bias of 0.44 and 0.48°C), but neither approach could identify holdout groundwater reaches. Variable importance analysis indicates the custom loss function nudges the model to use the existing inputs more efficiently, whereas with the added features the model relies on a broader suite of inputs. This analysis is a substantial step toward more accurately representing groundwater discharge processes in stream temperature models and will improve predictive accuracy and inform habitat management.

Stream thermalscape scenarios for British Columbia, Canada

Water temperature is a key feature of freshwater ecosystems but comprehensive datasets are severely lacking, a limiting factor in research and management of freshwater species and habitats. An existing statistical stream temperature model developed for British Columbia (BC), Canada, was refit to predict August mean stream temperatures, a common index of stream thermal regime also used in thermalscapes developed for the western United States (US). Thermalscapes of predicted August mean stream temperature were produced for 680,000 km of stream network at approximately 400 m intervals. Temperature predictions were averaged for 20-year periods from 1981-2100 to produce 86 scenarios: one for each historical period (i.e. 1981-2000, 2001-2020), and 21 for each future period (i.e. six global climate models and an ensemble average under three representative concentration pathways). The final model performance was consistent with other published regional-scale statistical models (R2 = 0.79, RMSE = 1.53 °C, MAE = 1.18 °C), performing well given the relative paucity of data, large geographic extent, and range of climatic and physiographic conditions. Model results suggested an average increase of August mean stream temperature of 2.9 ± 1.0 °C (RCP 4.5 ensemble mean ± SD) by end of century, with significant heterogeneity in predicted temperatures and warming rates across the province. Compared to stream temperature predictions from the western US, the predictions for BC showed good agreement at cross-border streams (Pearson’s r = 0.91), suggesting the possible integration of both products for a thermalscape covering much of western North America. These stream thermalscapes for BC address a major data deficiency in freshwater ecosystems and have potential applications to stream ecology, species distribution modelling, and evaluation of climate change impacts.

Daily stream temperature predictions for free-flowing streams in the Pacific Northwest, USA

Supporting sustainable lotic ecosystems and thermal habitats requires estimates of stream temperature that are high in scope and resolution across space and time. We combined and enhanced elements of existing stream temperature models to produce a new statistical model to address this need. Contrasting with previous models that estimated coarser metrics such as monthly or seasonal stream temperature or focused on individual watersheds, we modeled daily stream temperature across the entire calendar year for a broad geographic region. This model reflects mechanistic processes using publicly available climate and landscape covariates in a Generalized Additive Model framework. We allowed covariates to interact while accounting for nonlinear relationships between temporal and spatial covariates to better capture seasonal patterns. To represent variation in sensitivity to climate, we used a moving average of antecedent air temperatures over a variable duration linked to area-standardized streamflow. The moving average window size was longer for reaches having snow-dominated hydrology, especially at higher flows, whereas window size was relatively constant and low for reaches having rain-dominated hydrology. Our model’s ability to capture the temporally-variable impact of snowmelt improved its capacity to predict stream temperature across diverse geography for multiple years. We fit the model to stream temperatures from 1993–2013 and predicted daily stream temperatures for ~261,200 free-flowing stream reaches across the Pacific Northwest USA from 1990–2021. Our daily model fit well (RMSE = 1.76; MAE = 1.32°C). Cross-validation suggested that the model produced useful predictions at unsampled locations across diverse landscapes and climate conditions. These stream temperature predictions will be useful to natural resource practitioners for effective conservation planning in lotic ecosystems and for managing species such as Pacific salmon. Our approach is straightforward and can be adapted to new spatial regions, time periods, or scenarios such as the anticipated decline in snowmelt with climate change.

An improved model of shade-affected stream temperature in Soil & Water Assessment Tool

Abstract. Stream temperatures have been increasing worldwide, in some cases reaching unsustainable levels for aquatic life. Riparian revegetation has been identified as a strategy for managing stream temperatures by blocking direct solar radiation. In this study, the effects of riparian vegetation on stream temperatures were included within the Soil & Water Assessment Tool (SWAT) model through a shade factor parameter. An equilibrium temperature approach was used to integrate the shade factor in an energy balance context. The stream temperature sub-model was improved using the new energy balance equation and integrated into SWAT. Unlike existing models, the modified SWAT model enables improved representation of two processes – mass and heat transfer – that influence stream temperature change and enables simulation of shading and its effects on stream temperatures at sub-basin scales. The updated SWAT model was tested in Dairy McKay Watershed, OR, USA, for four scenarios: current conditions of riparian vegetation, full restoration, efficient restoration, and no vegetation. The model calibration under current riparian vegetation showed good performance (Nash–Sutcliffe efficiency NSE > 0.74). Stream temperature reduction and number of days with stream temperatures above survival limits (NDSTASL) for aquatic species were also evaluated as measures of riparian shade performance. Findings showed average temperature reductions of 0.91 ∘C (SD = 0.69 ∘C) and reductions in NDSTASL of 17.1 d over a year for full riparian restoration and average reductions of 0.86 ∘C (SD = 0.67 ∘C) and 16.2 d for efficient restoration. Notwithstanding the similar benefits, efficient restoration was 14.4 % cheaper than full riparian vegetation restoration.

Elevation-dependent warming of streams in mountainous regions: implications for temperature modeling and headwater climate refugia

Climate change is warming stream temperatures with significant implications for species that require cold temperatures to persist. These species often rely on headwater habitats in mountainous regions where elevation gradients in hydroclimatic conditions may induce differential patterns of long-term warming that affect the resistance of refugia. Forecasts from mechanistic and statistical stream temperature models diverge regarding whether this elevation dependence will cause above- or below-average warming in headwaters during warm summer periods, so we examined monitoring records for stream temperature (n = 271), air temperature (n = 690), and stream discharge (n = 131) across broad elevation gradients in a mountainous region of western North America to better understand potential future trends. Over a 40-year period characterized by rapid climate change from 1976–2015, air temperature stations exhibited below-average warming rates at high elevations while stream discharge declined at above average rates. Between climatically extreme years that involved summer air temperature increases >5 °C and discharge declines >70%, temperatures in high-elevation streams exhibited below average increases but otherwise showed negligible elevation dependence during intermediate climate years. In a subsequent example, it was demonstrated that elevation dependent stream warming has a minor effect on the amount of thermal habitat loss relative to the average water temperature increase within a mountain river network. We conclude that predictions of above average warming effects on headwater organisms for this region may be overly pessimistic and discuss reasons why different types of temperature models make divergent forecasts. Several research areas warrant greater attention, including descriptions of elevation-dependent patterns in other regions for comparative purposes, examination of long-term stream temperature records to understand how sensitivity to climate forcing may be evolving, use of new data sources to better represent key processes in temperature models across broad areas, and development of hybrid models that integrate the best attributes of mechanistic and statistical approaches.

Spatial and temporal variability of the solar radiation heat flux in streams of a forested catchment

Solar radiation is generally the largest contributing flux to the heat budget of streams and its estimation is crucial to predict stream water temperature with process-based models. The objective of this research is to quantify the spatial (between-site comparison of different stream sizes, within-site comparison at the reach scale) and temporal (seasonal, daily and hourly scales) variability in the transmission coefficient, which represents the proportion of incoming solar radiation reaching streams. We measured solar radiation at an open site with a meteorological station and at microclimate sites located in three streams of various sizes in the Miramichi River basin (Canada). During the summer, the percentage of incoming daily solar radiation reaching a stream varied from 8% in a small headwater stream (Trib) to 43% in a medium-sized stream (CatBk) and was close to 100% in a wide river (LSWM). We observed the largest variability between transmission coefficients for different stream sizes (range of variation = 92%) due to very different canopy closures, followed by variability at the reach scale between lateral positions (range = 21% between left and right banks) and between longitudinal positions (range = 11% between upstream and downstream sites), as measured at the medium-sized stream. Temporal variability was greatest at the seasonal scale where the transmission coefficient varied by 23% between May and September at the small headwater stream. The hourly variability of the transmission coefficient (i.e. associated with different solar angles) surpassed daily variability (i.e. associated with different cloud cover conditions), with coefficients of variation computed at the hourly time scale three to five times greater than at the daily time scale. Overall, this research offers insight regarding the handling of spatial and temporal variability of solar radiation which should provide further insight to improve process-based stream temperature models.

A lidar‐based assessment of riparian shade and large wood potential in the Skagit River watershed, WA

AbstractWild salmon stocks in the Pacific Northwest are imperiled by a variety of declining habitat factors, including riparian shade and in‐channel large wood. In this paper, a relatively simple lidar model of the riparian canopy was used along anadromous streams in the Skagit River watershed in western Washington State, United States, to delineate where riparian trees were most lacking, and where restoration efforts would have the greatest benefit in terms of shade and large wood recruitment potential. Within a 45‐m riparian buffer, 61% of riparian zones were currently incapable of delivering large wood to the stream. Current potential for large wood recruitment is greatest adjacent to stream edges and falls off rapidly with distance from the channel. Approximately 99% of large wood recruitment potential lies within 45 m of the channel edge, and 50% of the wood potential is within 9 m. A hypothetical canopy model in which all trees mature to a 100‐year height would provide 18% more shade distributed over the entire watershed, and 90% more shade in the tributaries. Most of the potential gains in improved shade and large wood contributions are in agricultural areas, as opposed to forestry or urban land uses. The shade and large wood models were constructed from widely available geographic information system tools and are readily transferable to other watersheds with similar characteristics. Model outputs are intended for use in planning restoration projects, as an input to stream temperature models, and to inform policy on restoration priorities and regulatory buffer widths.

Accounting for snowpack and time-varying lags in statistical models of stream temperature

Water temperature plays a primary role in driving ecological processes in streams due to its direct impact on biogeochemical cycles and the physiological processes of stream fauna, such as growth, development, and the timing of life history events. Streams influenced by snowpack melt are generally cooler in the summer and demonstrate less sensitivity to climate variability in what is commonly referred to as “climate buffering”. Despite the substantial influence of snowpack on stream temperature and expected changes in snowpack accumulation and melt timing with climate change, methods for representing snowpack in statistical models for stream temperature have not been well explored. In this investigation, we quantified the extent of stream temperature buffering in free-flowing streams across a geographically diverse region in the Pacific Northwest USA. We demonstrated that statistical models of daily mean stream temperature can be improved by explicitly accounting for temporal variability in a small number of climate covariates believed to be mechanistically related to stream temperature. Our novel statistical approach included as predictors combinations and interactions between the following variables: (1) air temperature, (2) lagged air temperature (where the lag duration varied according to its relationship with flow on a given day at that site), (3) flow, (4) snowpack in the upstream catchment, and (5) day of year. We found that sites with substantial snow influence were associated with increased air temperature buffering during the warm season and longer air temperature lags (>30 days during spring high flows and ∼ 10 days during late summer low flows) compared to sites where precipitation predominantly fell as rain (<6 days year-round). By accounting for snowpack and temporal variation in lagged heat transfer processes, our models were able to accurately predict seasonal patterns and interannual variability in stream temperature in validation data from years not used in model fits using publicly available data sources (average RMPSE ∼ 0.80).

Do headwater lakes moderate downstream temperature response to forest harvesting? Illustrating opportunities and obstacles associated with virtual experiments

AbstractThere are concerns that environmental changes, such as climate variability and forest harvesting, are altering stream thermal regimes and impacting aquatic ecosystems. Previous studies have suggested that the abundant headwater lakes found in northern landscapes may moderate downstream temperature response to forest harvesting. We investigated this hypothesis using a virtual experimental approach based on detailed field measurements made at boreal catchments in northern Sweden coupled with a process‐based stream temperature model. We simulated streamside harvesting for stream reaches with and without a headwater lake. Mean daily summer stream temperature response to harvesting was generally between 0.5 and 1.5°C higher for the stream without a lake than for the stream with a lake. However, during rain events the stream with the lake showed a greater stream temperature response than the stream without a lake. Headwater lakes typically store and delay runoff from rain events, augment baseflow, and have elevated outflow temperatures. These differences in upstream boundary conditions, in terms of flow and water temperature, were the key drivers for the contrasting harvest responses between streams with and without headwater lakes. These findings were generally consistent across different harvesting scenarios; however, uncertainty in the hyporheic term and post‐harvest microclimate conditions influenced the simulated magnitude of post‐harvest stream temperature response. Our study highlights the utility of virtual experiments for gaining insight on systems understanding but caution is needed when using models for predictions outside the conditions for which models are calibrated.

Data on An Improved Model of Shade-affected Stream Temperature in Soil & Water Assessment Tool

Data on An Improved Model of Shade-affected Stream Temperature in Soil & Water Assessment Tool