WASTE LOADINGS FROM A FRESHWATER ATLANTIC SALMON FARM IN SCOTLAND1

ABSTRACT: Recent studies suggest that waste generation from the freshwater phase of Atlantic salmon (Salmo salar L.) production varies considerably on an annual basis. A fish farm on the West Coast of Scotland was visited regularly during a two-year period to determine inflow and outflow water quality. Waste output budgets of suspended solids (SS), biochemical oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP), total ammonia nitrogen (TAN = NH₃+NH₄⁺), dissolved reactive phosphorus (DRP) and total phosphorus (TP) were produced. The annual waste loadings obtained were 71 kg TN t fish^?1 yr^?1 (one year of data only), 10.9–11.1 kg TP t fish^?1 yr^?1, 1.2–2.1 kg DRP t fish^?1 yr^?1, 422–485 kg BOD₅ t fish^?1 yr^?1, 327–337 kg SS t fish^?1 yr^?1, and 30–35 kg TAN-N t fish^?1 yr^?1. Simple linear regression models relating waste parameter production to water temperature and feeding regime were developed. When compared to existing data for other salmonid production systems, greater ranges of daily waste loadings were observed. Wide variations in concentrations of these parameters during a daily cycle were also observed, suggesting that mass balance estimates of waste production will provide more robust estimates of waste output than frequent monitoring of outflow water quality.     

Spatiotemporal Variability in Water Sources Controls Chemical and Physical Properties of a Semi‐arid Urban River System

We conducted synoptic surveys over three seasons in one year to evaluate the variability in water sources and geochemistry of an urban river with complex water infrastructure in the state of Utah. Using stable isotopes of river water (δ¹⁸O and δ²H) within a Bayesian mixing model framework and a separate hydrologic mass balance approach, we quantified both the proportional inputs and magnitude of discharge associated with “natural” (lake, groundwater, and tributary inputs) and “engineered” (effluent and canal inflows) sources. The relative importance of these major contributors to streamflow varied both spatially and seasonally. Spatiotemporal patterns of dissolved oxygen, temperature, pH, calcium, chloride, nitrate, and orthophosphate indicated seasonal shifts in dominant sources of river water played an important role in determining water quality. We show although urban rivers are clearly influenced by novel water sources created by water infrastructure, they continue to reflect the imprint of “natural” water sources, including diffuse groundwater. Resource managers thus may need to account for the quantity of both surface waters and also historically overlooked groundwater inputs to address water quality concerns in urban rivers.     

A remote sensing tool for near real-time monitoring of harmful algal blooms and turbidity in reservoirs

Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real-time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll-a concentrations (chl-a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real-time monitoring of HABs. Landsat-8 and Sentinel-2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl-a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R² values of 0.75, 0.82, and 0.79 for chl-a, turbidity, and phycocyanin, while multiple regression had R² values of 0.29, 0.51, and 0.46 and SVR had R² values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open-source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality.     

Investigation of long-term river water quality variations using different urbanization indices and assessment of common scientific perspectives of urbanization on water quality

This study investigated the water quality variation spanning 30 years (1986–2017) in 16 catchments of Hong Kong against different urbanization indices, namely, built area fraction; population; and product of population and built area fraction. Pearson correlations of three different periods of time (1988–1990, 1998–2000, and 2015–2017) indicated that water quality trends were dependent on the urbanization index. Total solids, nitrite-nitrogen, total phosphorus, electrical conductivity, dissolved oxygen, and flow rate had significant deteriorative trends (Pearson r > 0.5 and p < 0.05) with population and product of built area and population. Results also interpreted that built area fraction and product of built area and population were the worst and best indices that represented urbanization and/or its impacts, respectively. Mann-Kendall test for the entire 30 year period showed that water quality had improved with time with respect to certain water quality parameters (e.g., dissolved oxygen, ammoniacal nitrogen and total suspended solids). The results portrayed that although the urbanization of catchments had increased with time, the river water quality with respect to many parameters showed signs of improvement and the legislative measures implemented seemed to be effective in controlling pollution.     

EFFECTS OF CLIMATE VARIABILITY ON RIVERS: CONSEQUENCES FOR LONG TERM WATER QUALITY ANALYSIS1

ABSTRACT: Associations between the El Nino Southern Oscillation (ENSO) climate pattern and temporal variability in flow and 12 water quality variables were assessed at 77 river sites throughout New Zealand over a 13‐year period (1989 through 2001). Trends in water quality were determined for the same period. All 13 variables showed statistically significant linear regression relationships with values of the Southern Oscillation Index (SOI). The strongest relationships were for water temperature (mean R²= 0.20), dissolved reactive phosphorus (0.18), and oxidized nitrogen (0.17). The association with SOI varied by climate region. The observed patterns were generally consistent with known ENSO effects on New Zealand rainfall and air temperature. Trends in water quality variables for the periods 1989 through 1993, 1994 through 1998, and 1989 through 1998 were reasonably consistent with trends in SOI, even when the influence of river flow was removed from the data. This suggests that SOI effects on water quality are not necessarily a direct consequence of changes in flow associated with rainfall variation. In addition, both Baseline (32 upstream) and Impact (45 downstream) sites showed similar trends, indicating that changes in management were not directly responsible. We conclude that interpretation of long term water quality datasets in rivers requires that climate variability be fully acknowledged and dealt with explicitly in trend analyses.     

Calibration and Validation of ADAPT and SWAT for Field‐Scale Runoff Prediction1

Abstract: The pollutant reduction possible with a given agricultural best‐management practice (BMP) is complex and site‐specific. Water‐quality models can evaluate BMPs, but model results are often limited by the lack of calibrated parameters for a given BMP. This study calibrated runoff prediction of two models (ADAPT and SWAT) for individual field plots having one till and two no‐till management practices. The factors used for runoff calibration were curve number II (CN_II) and saturated hydraulic conductivity (Ksat) for ADAPT, and CN_II, Ksat, and available water capacity for SWAT. Results were evaluated using coefficient of determination (R²), Nash‐Sutcliffe efficiency (E_f), root‐mean square error, median‐based E_f, and sign tests. Results indicated that for ADAPT, the best‐fit CN_II was 66 for the NT/SB (no‐till plot with surface‐broadcast fertilizer) treatment, 68 for the NT/DB (no‐till with deep‐banded fertilizer) treatment, and 70 for the tilled plot, whereas for SWAT the best‐fit CN_II was much higher, 86, for all treatments. Neither agreed with the textbook CN_II, 78, for sorghum in silty clay loam soil. The best‐fit model parameters for both runoff calibration phases had excellent correlation to monthly totals and moderate correlation to individual events.     

NONLINEAR PROGRAMMING IN RIVER BASIN MODELING1

ABSTRACT: This paper explores the use of nonlinear programming in river basin water quality modelling. Applications recently reported in the literature, along with the author's experience with nonlinear programming, are reviewed. Results obtained using nonlinear programming are compared with the results obtained by other researchers using linear and dynamic programming to solve river basin water quality optimization problems. These water quality models have objective functions with continuous first partial derivatives, several inequality and variable bound constraints, and are of the form: minizie Σ^j=n_j=1Y_j(X_j) subject to Σ^j=n_j=1a_ijX_jb_i, i=1,2, …, m c_jX_jd_j, j= 1,2, …, n The variable X_i is the maximum allowable ratio of the BOD (biochemical oxygen demand) of the effluent outflow to the BOD of the wastewater inflow for treatment plant j, in the range c_j to d_j. The a_ijd and b_i are constants in the DO (dissolved oxygen) and BOD constraints. The resuks show, given certain assumptions about the data, that nonlinear programming is a better solution method for these problems than is either linear programming or dynamic programming.     

WATER QUALITY MODELING IN THE UPPER REACHES OF THE CHOWAN RIVER1

ABSTRACT: The Chowan River system consists of three rivers in southeast Virginia that form two confluences before flowing into Albermarle Sound in North Carolina. A computer program was written to simulate flows through the river system to determine flow rates, velocities, and depths. The output of the flow program was input into a second program that calculated the concentrations of BOD₅, COD, DO, and four nitrogen parameters (organic, ammonia, nitrite-nitrate and algal-N). Measured field data were used to calibrate the model. The effect of reducing the concentration of nutrients from overland runoff on algal concentrations at the mouth of the river was studied. The program was also run to simulate the water quality of the watershed in a primitive condition, in which the watershed was assumed to consist only of forests. The results of the computer program indicate that the major changes in the water quality of the river are simulated satisfactorily. The program can be used to assess the impact of any management scheme to improve water quality.     

香港吐露港地区河流水质管理效果研究

为探讨香港水质管理的措施及效果,本文对香港吐露港地区5条河流10个监测点1985—2014年的地表水质数据进行分析,观察水体整治管理前后污染物浓度的变化。结果表明:10个监测点的氨氮、BOD_5、COD和TP的浓度均逐年下降,上下游共同努力,使各项污染物得到很大程度的削减,并且治理前后的浓度存在明显统计学差异(p0.05),且治理之后各项污染物年均浓度趋于平稳,达到标准以内。由此可以看出,香港地区水质管理效果的显著与其所实施的《水污染管制条例》、禽畜饲养禁制区设立与吐露港及牛尾海污水收集整体计划密不可分。随着国务院颁布的《水污染防治行动计划》的施行,香港吐露港地区治理的措施可为我国现阶段黑臭水体整治工作所借鉴。     

WATER QUALITY FROM TWO SMALL FORESTED WATERSHEDS IN SOUTHERN ILLINOIS1

Two intermittent streams on oak-hickory watersheds in southern Illinois were gaged with a V-notched weir and sampled with an automatic water sampler. For three years data were collected on flow volume and water quality. Flow volumes show large variations between years and watersheds. Samples were analyzed for Na⁺, K⁺, Ca⁺⁺, Mg⁺⁺, P, and NO^-₃. Water quality was consistently high, although there were significant differences between watersheds. A baseline for water quality has been established for comparison after one of the watersheds is clearcut at a later date.     

Hydrochemistry of drinking water sources and water purifiers in relation to epidemiological study in Hisar,North-West India

The concern related to the drinking of reverse osmosis (RO) water containing low levels of minerals is growing day by day. This study involves the analysis of water samples from various drinking water sources in a rural site, Mirchpur village, an Indus Valley civilization site (grid location: 29° 18′ 42.3″ N, 76° 10′ 33.0″ E) of Hisar, India, along with the health survey of human subjects. The hydrochemistry of water collected from hand pumps, river canals, tube wells, submersibles, and the RO systems installed in various homes was explored for pH, EC, TH, TDS, turbidity, cations (Na⁺, Ca²⁺, Mg²⁺), anions (CO₃²⁻, HCO₃⁻, Cl⁻, SO₄²⁻, NO₃⁻, F⁻), and elements (Fe, Pb, Se) employing the ion chromatography, flame photometry, and ICP-AES techniques. Lead (Pb) and Selenium (Se) were detected in trace amounts (0.30–2.6 μg L⁻¹; 0.10–4.1 μg L⁻¹, respectively) in all the samples, including the samples collected from RO purifiers, but Iron (Fe) was not detected in RO samples even in trace amounts. The F-levels in hand pump water (HPW) and submersible water (SW) (1.9  and 1.7 mg L⁻¹, respectively) and TDS levels in SW (3048 mg L⁻¹) were found to be above WHO and BIS safe limits. TDS levels in the river canal (900 mg L⁻¹), tube well (1104 mg L⁻¹), hand pump (1170 mg L⁻¹), and submersible samples (3048 mg L⁻¹) were found significantly higher as compared to the RO personal water (ROPW; 216 mg L⁻¹) and RO supply water (ROSW; 90 mg L⁻¹). The collected epidemiological data reveals that 21%, 19%, 13%, and 12% of natives reported skin, kidney, hair fall, liver, and stomach issues, respectively, suspecting the crucial role of high TDS and fluoride levels in the area. This study also provides a comparison between the quality of RO and the direct supply water, along with correlation matrices for different parameters, which gives a rationale for the limitations of drinking direct supply water without any purification and RO water containing low mineral content.     

Detecting the Dynamic Linkage between Landscape Characteristics and Water Quality in a Subtropical Coastal Watershed, Southeast China

Geospatial analysis and statistical analysis are coupled in this study to determine the dynamic linkage between landscape characteristics and water quality for the years 1996, 2002, and 2007 in a subtropical coastal watershed of Southeast China. The landscape characteristics include Percent of Built (%BL), Percent of Agriculture, Percent of Natural, Patch Density and Shannon’s Diversity Index (SHDI), with water quality expressed in terms of COD_Mn and NH₄ ⁺–N. The %BL was consistently positively correlated with NH₄ ⁺–N and COD_Mn at time three points. SHDI is significantly positively correlated with COD_Mn in 2002. The relationship between NH₄ ⁺–N, COD_Mn and landscape variables in the wet precipitation year 2007 is stronger, with R² = 0.892, than that in the dry precipitation years 1996 and 2002, which had R² values of 0.712 and 0.455, respectively. Two empirical regression models constructed in this study proved more suitable for predicting COD_Mn than for predicting NH₄ ⁺–N concentration in the unmonitored watersheds that do not have wastewater treatment plants. The calibrated regression equations have a better predictive ability over space within the wet precipitation year of 2007 than over time during the dry precipitation years from 1996 to 2002. Results show clearly that climatic variability influences the linkage of water quality-landscape characteristics and the fit of empirical regression models.     

Water quality indices appraisal and health risk assessment of nitrate,mercury and lead in water distribution network: A case study of Robat Karim in Tehran,Iran

Groundwater is a basic source of drinking water supply for urban and rural areas. This is especially the case for communities located in arid and semi-arid regions that rely on groundwater for drinking purposes. The present study set out to assess the potential health impacts of water impurities and to investigate the qualitative status of drinking water in Robat Karim rural areas, located in southwest Tehran, Iran. A total of 66 samples were collected from the water distribution network of 11 villages (33 sampling points, on two occasions) during September 2020 and were tested in terms of the most common quality parameters such as pH, mercury (Hg), lead (Pb), copper (Cu), zinc (Zn), chloride (Cl^–), chlorate (ClO₃^–), nitrite (NO₂^–), nitrate (NO₃^–), and flouride (F^–). Multiple methods and indexes including water quality index (WQI), hazard quotient (HQ), and hazard index (HI), were worked out to assess the quality of water and health risk assessment of NO₃^– Pb²⁺ and Hg²⁺. The results revealed that 33% and 90% of sampling sites have significantly high nitrate and total hardness (TH) concentrations, exceeding the maximum permissible limits set by World Health Organization (WHO; 50 and 200 mg/L, respectively). Furthermore, five sampling points exhibited poor WQIs mainly related to NO₃^– and TH. HQ values higher than 1 for nitrate were noticed in most sampling locations. Except for one sampling point, the HQ obtained for Pb²⁺ and Hg²⁺ were below 1 indicating no obvious health hazard. This study represents that children and infants are at higher risk of chronic toxicity by excess NO₃^– intake. The health hazard that is yet imposed on the community by NO₃^– necessitates regular monitoring of drinking water, the use of advanced technologies to purify water or otherwise alternative resources should be proposed.     

SPATIAL DELINEATION OF BIOTIC AND ABIOTIC GRADIENTS IN A RURAL NEW YORK RESERVOIR1

ABSTRACT: The spatial changes in abiotic and biotic variables from riverine to lacustrine areas characterized by the river-lake concept of reservoir function was applied to the Tomhannock Reservoir, Rensselaer County, New York. To identify these longitudinal gradients, a two-year investigation (May 1991 to October 1992) was conducted to measure primary productivity, nutrient concentrations, chlorophyll α and phytoplankton biomass at three locations in the 705-ha water supply reservoir. Emphasis was placed on the measurement of primary production using the carbon-14 artificial incubator (photosynthetron) technique. The average annual production in 1992 was 247.3 gm^?2 245 d^?1, ranging from 52 to 2677 mg C m^?2. Mean alpha^B (assimilation efficiency), P^Bm (assimilation number), and I_k (saturation irradiance) were 4.40 mg C mgChl^?1 E^?1 m^?2, 3.82 mg C mgChl^?1 h^?1, and 236.5 μE m^?2 s^?1, respectively. Neither seasonal nor spatial variability of these photosynethetic parameters were observed. Except for Secchi depth, distinct longitudinal zones from river inflow to darn were not statistically demonstrated in the Tomhannock Reservoir. Mean extinction coefficient, chlorophyll α and total phosphorus concentrations decreased; Secchi transparency and phytoplankton biomass increased; while primary productivity and dissolved inorganic nitrogen concentration remained the same from headwater to darn. These baseline data will be used to assess the future effectiveness of best management practices (BMPs) recently instituted on selected watershed farmland in an attempt to reduce the detrimental impact of agricultural activities on drinking water quality.     

Modeled Summer Background Concentration of Nutrients and Suspended Sediment in the Mid‐Continent (USA) Great Rivers1

Angradi, Ted R., David W. Bolgrien, Matt A. Starry, and Brian H. Hill, 2012. Modeled Summer Background Concentration of Nutrients and Suspended Sediment in the Mid‐Continent (USA) Great Rivers. Journal of the American Water Resources Association (JAWRA) 48(5): 1054‐1070. DOI: 10.1111/j.1752‐1688.2012.00669.x Abstract: We used regression models to predict summer background concentration of total nitrogen (N), total phosphorus (P), and total suspended solids (TSS), in the mid‐continent great rivers: the Upper Mississippi, the Lower Missouri, and the Ohio. From multiple linear regressions of water quality indicators with land use and other stressor variables, we determined the concentration of the indicators when the predictor variables were all set to zero — the y‐intercept. Except for total P on the Upper Mississippi River, we could predict background concentration using regression models. Predicted background concentration of total N was about the same on the Upper Mississippi and Lower Missouri Rivers (430 μg l^?1), which was lower than percentile‐based values, but was similar to concentrations derived from the response of sestonic chlorophyll a to great river total N concentration. Background concentration of total P on the Lower Missouri (65 μg l^?1) was also lower than published and percentile‐based concentrations. Background TSS concentration was higher on the Lower Missouri (40 mg l^?1) than the other rivers. Background TSS concentration on the Upper Mississippi (16 mg l^?1) was below a threshold (30 mg l^?1) designed to protect aquatic vegetation. Our model‐predicted concentrations for the great rivers are an attempt to estimate background concentrations for water quality indicators independent from thresholds based on percentiles or derived from stressor‐response relationships.     

GENERATED MONTHLY STREAMFLOWS FOR THE MIDWESTERN AND SOUTHWESTERN UNITED STATES1

ABSTRACT A critical examination of single gage site, monthly streamflow statistical characteristics for two southern Illinois rivers, an Oklahoma river and a Texas river was made using a digital computer at Northwestern University. High flow variability for the rivers was evident in that, for the rivers tested, 8 to 11 months had coefficients of variation in excess of unity. The gamma distribution was not as efficient as the normal distribution for fitting power or logarithmic transforms of the historical monthly flow data (i.e., F^1-0, F^0-5, F^0-25, Fa¹²⁵, F^0.085, and log F). No single transform to a normal distribution was adequate for all twelve monthly flows, since definite seasonal grouping patterns were found for the four rivers examined. The highly variable flow in the low-flow season(s) indicated much more skewness than was typical of the remainder of the year. For the low-flow seasons, the higher-root (smaller exponent) transforms were particularly useful. Flows were generated from a linear regression model of lag one utilizing two or more transforms for the twelve periods. The definite seasonal patterns found historically were reproduced quite well in the generated streamflows. The effect of a change in data transform from one season to the next was insignificant after one month. Thus the use of different transforms within the year did not bias the results from the linear regression model appreciably, but did help in reproducing the seasonal distribution pattern. The technique seems especially well suited for rivers with highly variable flows.     

ALTERNATIVE FUTURES USING THE WOLLMAN-BONEM MODELS1

Alternative futures are defined to be mutually consistent alternative combinations of assumed sets of future conditions. Alternative futures are employed in conjunction with the Wollman-Bonem model to project future water use. The model solutions under different alternative futures provide an indication of the direction and relative magnitude of changes in water use, both in quantity and quality, that may result from changes in policy-influenced variables, technology, and other data. The Wollman-Bonem model is employed in this paper as a tool to illustrate the alternative futures concept. The model can best be cast as an economic model. The model solutions are not given as a set of formal projections, but as various possible water-use-over-time curves. This should aid in disspelling the erroneous idea that it is possible to make distant projections of water use as a single curve. Multiple curves suggest that water use is really a function of many variables.     

Using Regression Tree Analysis to Improve Predictions of Low-Flow Nitrate and Chloride in Willamette River Basin Watersheds

The use of regression tree analysis is examined as a tool to evaluate hydrologic and land use factors that affect nitrate and chloride stream concentrations during low-flow conditions. Although this data mining technique has been used to assess a range of ecological parameters, it has not previously been used for stream water quality analysis. Regression tree analysis was conducted on nitrate and chloride data from 71 watersheds in the Willamette River Basin to determine whether this method provides a greater predictive ability compared to standard multiple linear regression, and to elucidate the potential roles of controlling mechanisms. Metrics used in the models included a variety of watershed-scale landscape indices and land use classifications. Regression tree analysis significantly enhanced model accuracy over multiple linear regression, increasing nitrate R ² values from 0.38 to 0.75 and chloride R ² values from 0.64 to 0.85 and as indicated by the ΔAIC value. These improvements are primarily attributed to the ability for regression trees to more effectively handle interactions and manage non-linear functions associated with watershed heterogeneity within the basin. Whereas hydrologic factors governed the conservative chloride tracer in the model, land use dominated control of nitrate concentrations. Watersheds containing higher agricultural activity did not necessarily yield high nitrate concentrations, but agricultural areas combined with either small proportions of forested land or greater urbanization generated nitrate levels far exceeding water quality standards. Although further refinements are recommended, we conclude that regression tree analysis presents water resource managers a promising tool that improves on the predictive ability of standard statistical methods, provides insight into controlling mechanisms, and helps identify catchment characteristics associated with water quality impairment.     

CORRELATION OF SELECTED WELL WATER QUALITY PARAMETERS WITH SOIL AND AQUIFER HYDROLOGIC PROPERTIES1

ABSTRACT: The geographical distribution of well water specific electrical conductivity and nitrate levels in a 932 km² ground water quality study area in the Fresno-Clovis, California, indicated that frequently areas of lower ground water salinity were also areas of relatively greater soil and aquifer permeability. From these observations and certain assumptions we hypothesized that the quality of the well water should be better in areas with permeable soils and geological formations. Correlation and multiple linear regression analysis supported this hypothesis for well water salinity. However, well water nitrate levels were significantly negatively correlated with only the estimated equivalent specific yield of the aquifer system. The multiple R² values of the most significant multiple linear regression models showed that only a fourth to a third of the variability in well water specific electric conductivity and nitrate levels could be ascribed to the effects of the hydrogeological parameters considered with more than 90 percent confidence. This indicates that three-fourths to two-thirds of the variability in ground water salinity and nitrate levels may be related to land use. Thus, there is considerable room for land use management techniques to improve ground water quality and reduce its variability.     

Air‐Water Temperature Relationships in the Trout Streams of Southeastern Minnesota's Carbonate‐Sandstone Landscape

Carbonate‐sandstone geology in southeastern Minnesota creates a heterogeneous landscape of springs, seeps, and sinkholes that supply groundwater into streams. Air temperatures are effective predictors of water temperature in surface‐water dominated streams. However, no published work investigates the relationship between air and water temperatures in groundwater‐fed streams (GWFS) across watersheds. We used simple linear regressions to examine weekly air‐water temperature relationships for 40 GWFS in southeastern Minnesota. A 40‐stream, composite linear regression model has a slope of 0.38, an intercept of 6.63, and R² of 0.83. The regression models for GWFS have lower slopes and higher intercepts in comparison to surface‐water dominated streams. Regression models for streams with high R² values offer promise for use as predictive tools for future climate conditions. Climate change is expected to alter the thermal regime of groundwater‐fed systems, but will do so at a slower rate than surface‐water dominated systems. A regression model of intercept vs. slope can be used to identify streams for which water temperatures are more meteorologically than groundwater controlled, and thus more vulnerable to climate change. Such relationships can be used to guide restoration vs. management strategies to protect trout streams.