River Water Temperature Research Articles

Characteristics of Oder river water temperature for heat pump

Currently there is increasing interest in using surface water bodies as renewable energy sources for year around or seasonal heating and cooling applications e.g. heat pumps. This paper presents the statistical analysis of average water temperature and the flow rate of the Oder River. Linear regression analysis was carried out on the base of ten years (2005–2014) diurnal water temperatures measurements data. The results determine the relation between average air temperature in Wroclaw and Oder river water average temperature for two analysed locations. The Pearson's r correlation coefficients were determined. Additionally, the variability of water flow rates from 2008–2010 in the annual cycle were illustrated. The article proposes the conceptual framework for surface water bodies technical aspects. This proposition is the first step for wider analysis in order to identify the thermal potential of the Oder river water as heat source for heat pumps.

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

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

Meltwater temperature in streams draining Alpine glaciers

Climate change is significantly impacting lotic environments, through changes to hydrology, biodiversity and species distribution. Effects of climate change are greatest at high elevation and biota in and around glacier-fed rivers is likely, therefore, to be at great risk. How climate change influences hydrology will have great impact on river water temperature as glacier-fed rivers in Alpine environments are extremely sensitive to climatic change. This paper assesses five rivers: Four glacier-fed rivers (36.9–83.7% basin glacierisation) located in the Swiss Alps, and one located in an ice-free catchment in the Bernese Oberland, Switzerland. The aim was to assess the impact of basin characteristics on river water temperature. A distinct paradoxical relationship was identified whereby water temperature in some glacier-fed rivers was reduced during the time of highest incoming shortwave radiation receipts and high air temperature. Whether a summer cooling effect presented itself in all glacier-fed rivers within this study was researched. The key findings were that the identified summer cooling effect was not present in all rivers, despite percentage glacierisation. Percentage glacier cover has often been reported as they key determiner of water temperature in such rivers. More important was the stream dimensions, notably stream surface area. Understanding the controlling factors that influence water temperature of glacier-fed rivers will help river managers and planners in understanding how climate change will affect fisheries downstream of glaciers over the coming decades. This may allow plans to be introduced to try and mitigate warmer water temperature that will result, in some glacier-fed rivers, as the climate warms.

Influence of river discharge on grass carp occupancy dynamics in south‐eastern Iowa rivers

AbstractDespite the longstanding presence of grass carp Ctenopharyngodon idella in the Upper Mississippi River (UMR) watershed, information regarding their populations remains largely unknown, in part because capture is difficult. Occupancy models are a popular wildlife assessment tool to account for imperfect detections but have been slow to be adopted in fisheries. Herein, we used occupancy modelling to evaluate the influence of two environmental covariates (river discharge and water temperature) on grass carp occupancy, extinction, colonization, and detection at nine sites within south‐eastern Iowa rivers from April to October 2014 and 2015. Grass carp were detected at least once at all but one site. The most parsimonious model indicated that grass carp colonization probability increased from 0.15 to 0.67 with increases in river discharge. In contrast, occupancy (0.20), extinction (0.29), and detection (0.50) probabilities were temporally constant. Models indicated that water temperatures did not influence grass carp extinction or colonization probabilities relative to river discharge. Cumulative grass carp detection probability approached 1.0, whereas conditional occupancy estimates were less than 0.1 when using five or more sampling transects. The use of a robust design occupancy model allowed us to estimate site occupancy rates of grass carp corrected for imperfect detections, while demonstrating the importance of river discharge for site colonization. These results can be used to assess the distribution of a cryptic fish while helping to guide grass carp sampling and removal efforts.

Prediction of river temperature surges is dependent on precipitation method

AbstractUrban river systems are particularly sensitive to precipitation‐driven water temperature surges and fluctuations. These result from rapid heat transfer from low‐specific heat capacity surfaces to precipitation, which can cause thermally polluted surface run‐off to enter urban streams. This can lead to additional ecological stress on these already precarious ecosystems. Although precipitation is a first‐order driver of hydrological response, water temperature studies rarely characterize rain event dynamics and typically rely on single gauge data that yield only partial estimates of catchment precipitation. This paper examines three precipitation measuring methods (a statutory automatic weather station, citizen science gauges, and radar estimates) and investigates relationships between estimated rainfall inputs and subhourly surges and diurnal fluctuations in urban river water temperature. Water temperatures were monitored at 12 sites in summer 2016 in the River Rea, in Birmingham, UK. Generalized additive models were used to model the relationship between subhourly water temperature surges and precipitation intensity and subsequently the relationship between daily precipitation totals and standardized mean water temperature. The different precipitation measurement sources give highly variable precipitation estimates that relate differently to water temperature fluctuations. The radar catchment‐averaged method produced the best model fit (generalized cross‐validation score [GCV] = 0.30) and was the only model to show a significant relationship between water temperature surges and precipitation intensity (P < 0.001, R2 = 0.69). With respect to daily metrics, catchment‐averaged precipitation estimates from citizen science data yielded the best model fit (GCV score = 0.20). All precipitation measurement and calculation methods successfully modelled the relationship between standardized mean water temperature and daily precipitation (P < 0.001). This research highlights the potential for the use of alternative precipitation datasets to enhance understanding of event‐based variability in water quality studies. We conclude by recommending the use of spatially distributed precipitation data operating at high spatial (<1 km2) and temporal (<15 min) resolutions to improve the analysis of event‐based water temperature and water quality studies.

Joint Dependence Between River Water Temperature, Air Temperature, and Discharge in the Yangtze River: The Role of the Three Gorges Dam

AbstractThe water temperature of rivers is of considerable significance to freshwater ecosystems and human water use. In contrast to traditional regression models or other deterministic statistical models, we present a probabilistic approach to examine the joint dependence structures of water‐air temperature and water temperature‐discharge in the Yangtze River. Such dependence structures were built based on bivariate probabilistic models with well‐fitted marginal distributions of each variable. To investigate the stability of the dependence structures and the effects of the Three Gorges Dam (TGD), three stations (i.e., Cuntan, Yichang, and Datong) located in different sections of the river were involved in comparison between two periods, that is, pre‐TGD (1975–1987) and post‐TGD (2003–2014) periods (the dam began operating in 2003). Our results indicate that at the three stations the joint occurrence probabilities of the air‐water temperature events (e.g., the compound extreme events) tend to be lower for the second period. The dependence structures of water temperature and river discharge were stable between the two periods at the upstream station. For the downstream station relatively close to the TGD (Yichang station), a clear disturbance was observed in the dependence structures of water temperature‐discharge. In addition, relying on the established bivariate and trivariate dependence models, we further assessed the risk of different water temperature events under individual and coupled extreme conditions of air temperature and discharge. The trivariate model also revealed substantial changes in the probabilistic difference between the pre‐TGD and post‐TGD periods at Yichang station, indicating the impact from the TGD.

Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models.

River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature (Ta), river flow discharge (Q), and the components of the Gregorian calendar (CGC) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors (Ta, Q, and the CGC) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only Ta is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation.

The Impact of River Discharge and Water Temperature on Manganese Release from the Riverbed during Riverbank Filtration: A Case Study from Dresden, Germany

The climate-related variables, river discharge, and water temperature, are the main factors controlling the quality of the bank filtrate by affecting infiltration rates, travel times, and redox conditions. The impact of temperature and discharge on manganese release from a riverbed were assessed by water quality data from a monitoring transect at a riverbank filtration site in Dresden-Tolkewitz. Column experiments with riverbed material were used to assess the Mn release for four temperature and three discharge conditions, represented by varying infiltration rates. The observed Mn release was modeled as kinetic reactions via Monod-type rate formulations in PHREEQC. The temperature had a bigger impact than the infiltration rates on the Mn release. Infiltration rates of <0.3 m3/(m2·d) required temperatures >20 °C to trigger the Mn release. With increasing temperatures, the infiltration rates became less important. The modeled consumption rates of dissolved oxygen are in agreement with results from other bank filtration sites and are potentially suited for the further application of the given conditions. The determined Mn reduction rate constants were appropriate to simulate Mn release from the riverbed sediments but seemed not to be suited for simulations in which Mn reduction is likely to occur within the aquifer. Sequential extractions revealed a decrease of easily reducible Mn up to 25%, which was found to reflect the natural stratification within the riverbed, rather than a depletion of the Mn reservoir.

Wildfire Smoke Cools Summer River and Stream Water Temperatures

AbstractTo test the hypothesis that wildfire smoke can cool summer river and stream water temperatures by attenuating solar radiation and air temperature, we analyzed data on summer wildfire smoke, solar radiation, air temperatures, precipitation, river discharge, and water temperatures in the lower Klamath River Basin in Northern California. Previous studies have focused on the effect of combustion heat on water temperatures during fires and the effect of riparian vegetation losses on postfire water temperatures, but we know of no studies of the effects of wildfire smoke on river or stream water temperatures. Wildfire smoke is difficult to quantify, but we successfully used a newly available daily high‐resolution (1 km) data set of aerosol optical thickness (AOT) derived from satellite imagery to represent smoke density during 6 years with extensive wildfire activity (2006, 2008, and 2012–2015). Smoke reduced solar radiation by 121 W m−2 per 1.0 AOT relative to clear‐sky conditions. Linear mixed‐effects models showed that on average, smoke cooled daily maximum and mean air temperatures by 0.98 °C and 0.47 °C per 1.0 AOT, respectively, across 19 remote automated weather stations. Smoke had a cooling effect on water temperatures at all 12 river and stream locations analyzed. On average, smoke cooled daily maximum and mean water temperatures by 1.32 °C and 0.74 °C per 1.0 AOT, respectively. This smoke‐induced cooling has the potential to benefit cold‐water adapted species, particularly because wildfires are more likely to occur during the warmest and driest years and seasons.

Assessing impacts of climate change on habitat suitability of Coregonus ussuriensis and other coldwater fishes in northern China.

Climate change seriously affects habitat suitability for coldwater fish. Amur whitefish (Coregonus ussuriensis) is a commercially important coldwater fish species found in northeast China that is particularly sensitive to water temperature. We use water temperature models for Songhua (Harbin to Yilan) and Mayi rivers to predict habitat suitability for Amur whitefish and other coldwater fish species during the months of July and August in 2014 and 2100-2months during which river temperatures can reach extremes. The predict-results showed that fish habitat was to deteriorate over time, and to be less suitable for survival of coldwater fish species in 2100 than that in 2014. Under scenario RCP4.5 (Representative Concentration Pathway 4.5), the time period during which habitat for Amur whitefish was unsuitable increased continually; under scenario RCP8.5 (Representative Concentration Pathway 8.5), Amur whitefish might even disappear from the river basin. A significant increase in river water temperature will compromise coldwater fish survival and possibly lead to their disappearance. Practical measures to control greenhouse gas emissions and reduce the impact of global warming on aquatic ecosystems must be taken.

Exploring and Quantifying River Thermal Response to Heatwaves

Most of the existing literature on river water temperature focuseds on river thermal sensitivity to long-term trends of climate variables, whereas how river water temperature responds to extreme weather events, such as heatwaves, still requires in-depth analysis. Research in this direction is particularly relevant in that heatwaves are expected to increase in intensity, frequency, and duration in the coming decades, with likely consequences on river thermal regimes and ecology. In this study we analyzed the long-term temperature and streamflow series of 19 Swiss rivers with different hydrological regime (regulated, low-land, and snow-fed), and characterized how concurrent changes in air temperature and streamflow concurred to affect their thermal dynamics. We focused on quantifying the thermal response to the three most significant heatwave events that occurred in Central Europe since 1950 (July–August 2003, July 2006, and July 2015). We found that the thermal response of the analyzed rivers contrasted strongly depending on the river hydrological regime, confirming the behavior observed under typical weather conditions. Low-land rivers were extremely sensitive to heatwaves. In sharp contrast, high-altitude snow-fed rivers and regulated rivers receiving cold water from higher altitude hydropower reservoirs or diversions showed a damped thermal response. The results presented in this study suggest that water resource managers should be aware of the multiple consequences of heatwave events on river water temperature and incorporate expected thermal responses in adaptive management policy. In this respect, additional efforts and dedicated studies are required to deepen our knowledge on how extreme heatwave events can affect river ecosystems.

KUALITAS AIR SUNGAI DI DESA TANIPAH (GAMBUT PANTAI), KALIMANTAN SELATAN

Tanipah village is a coastal village in Kecamatan Aluh-Aluh, Kabupaten Banjar, Kalimantan Selatan and a tidal area of sea water. Tanipah villagers utilize river water for daily needs, so the quality of river water is a major concern. The results of this research are expected to be informations for the community about quality of river water in Tanipah. This research is a survey research and the analysis was conducted in-situ and ex-situ. The analysis was done by comparing the test result against the predefined quality standard. River water temperature in Tanipah ranges from 280C – 33,60C and has no taste and no smell. Turbidity of 54,8 NTU, indicating river water is not feasible for consumption. Dissolved oxygen is 5,9 mg/L and is in the mild contaminated category. The COD and BOD value respectively 17,03 mg/L and 6,70 mg/L, exceeds the limit of the class I water quality standard. The pH value is at 7,33, indicating that the pH is normal. The iron concentration is 1,71 mg/L, exceeds the maximum permissible level for drinking water quality. Other metals likes Mn, Pb, Cu and Cd, are within thresholds for the waters. River water in Tanipah village based on the value of dissolved oxygen, is in category of light contaminated. Thus, in general, river water in Tanipah village can only be used for class III water, as irrigation.

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.

Using CDOM optical properties for estimating DOC concentrations and pCO2 in the Lower Amazon River.

Coloured dissolved organic matter (CDOM) is one of the major contributors to the absorption budget of most freshwaters and can be used as a proxy to assess non-optical carbon fractions such as dissolved organic carbon (DOC) and the partial pressure of carbon dioxide (pCO2). Nevertheless, riverine studies that explore the former relationships are still relatively scarce, especially within tropical regions. Here we document the spatial-seasonal variability of CDOM, DOC and pCO2, and assess the potential of CDOM absorption coefficient (aCDOM(412)) for estimating DOC concentration and pCO2 along the Lower Amazon River. Our results revealed differences in the dissolved organic matter (DOM) quality between clearwater (CW) tributaries and the Amazon River mainstream. A linear relationship between DOC and CDOM was observed when tributaries and mainstream are evaluated separately (Amazon River: N = 42, R2 = 0.74, p<0.05; CW: N = 13, R2 = 0.57, p<0.05). However, this linear relationship was not observed during periods of higher rainfall and river discharge, requiring a specific model for these time periods to be developed (N = 25, R2 = 0.58, p<0.05). A strong linear positive relation was found between aCDOM(412) and pCO2(N = 69, R2 = 0.65, p<0.05) along the lower river. pCO2 was less affected by the optical difference between tributaries and mainstream waters or by the discharge conditions when compared to CDOM to DOC relationships. Including the river water temperature in the model improves our ability to estimate pCO2 (N = 69; R2 = 0.80, p<0.05). The ability to assess both DOC and pCO2 from CDOM optical properties opens further perspectives on the use of ocean colour remote sensing data for monitoring carbon dynamics in large running water systems worldwide.

Quantifying the impact of the Three Gorges Dam on the thermal dynamics of the Yangtze River

This study examines the impact of the world’s largest dam, the Three Gorges Dam (TGD), on the thermal dynamics of the Yangtze River (China). The analysis uses long-term observations of river water temperature (RWT) in four stations and reconstructs the RWT that would have occurred in absence of the TGD. Relative to pre-TGD conditions, RWT consistently warmed in the region due to air temperature (AT) increase. In addition, the analysis demonstrates that the TGD significantly affected RWT in the downstream reach. At the closest downstream station (Yichang) to the TGD, the annual cycle of RWT experienced a damped response to AT and a marked seasonal alteration: warming during all seasons except for spring and early summer which were characterized by cooling. Both effects were a direct consequence of the larger thermal inertia of the massive water volume stored in the TGD reservoir, causing the downstream reach to be more thermally resilient. The approach used here to quantify the separate contributions of climate and human interventions on RWT can be used to set scientific guidelines for river management and conservation planning strategies.

Effects of air temperature and discharge on Upper Mississippi River summer water temperatures

AbstractRecent interest in the potential effects of climate change has prompted studies of air temperature and precipitation associations with water temperatures in rivers and streams. We examined associations between summer surface water temperatures and both air temperature and discharge for 5 reaches of the Upper Mississippi River during 1994–2011. Water–air temperature associations at a given reach approximated 1:1 when estimated under an assumption of reach independence but declined to approximately 1:2 when water temperatures were permitted to covary among reaches and were also adjusted for upstream air temperatures. Estimated water temperature–discharge associations were weak. An apparently novel feature of this study is that of addressing changes in associations between water and air temperatures when both are correlated among reaches.

The effects of floods on the temperature of riparian groundwater

AbstractThe transition area between rivers and their adjacent riparian aquifers, which may comprise the hyporheic zone, hosts important biochemical reactions, which control water quality. The rates of these reactions and metabolic processes are temperature dependent. Yet the thermal dynamics of riparian aquifers, especially during flooding and dynamic groundwater flow conditions, has seldom been studied. Thus, we investigated heat transport in riparian aquifers during 3 flood events of different magnitudes at 2 sites along the same river. River and riparian aquifer temperature and water‐level data along the Lower Colorado River in Central Texas, USA, were monitored across 2‐dimensional vertical sections perpendicular to the bank. At the downstream site, preflood temperature penetration distance into the bank suggested that advective heat transport from lateral hyporheic exchange of river water into the riparian aquifer was occurring during relatively steady low‐flow river conditions. Although a small (20‐cm stage increase) dam‐controlled flood pulse had no observable influence on groundwater temperature, larger floods (40‐cm and &gt;3‐m stage increases) caused lateral movement of distinct heat plumes away from the river during flood stage, which then retreated back towards the river after flood recession. These plumes result from advective heat transport caused by flood waters being forced into the riparian aquifer. These flood‐induced temperature responses were controlled by the size of the flood, river water temperature during the flood, and local factors at the study sites, such as topography and local ambient water table configuration. For the intermediate and large floods, the thermal disturbance in the riparian aquifer lasted days after flood waters receded. Large floods therefore have impacts on the temperature regime of riparian aquifers lasting long beyond the flood's timescale. These persistent thermal disturbances may have a significant impact on biochemical reaction rates, nutrient cycling, and ecological niches in the river corridor.

Early development and diets of non‐native juvenile Chinook Salmon (Oncorhynchus tshawytscha) in an invaded river of Patagonia, southern South America

AbstractChinook salmon (Oncorhynchus tshawytscha) have established populations in both the Atlantic and Pacific basins of southern South America. Yet, basic biological information about these salmon population is absent. Here, we documented relative densities, body size, condition and diet composition of juvenile Chinook Salmon from the Futaleufú River, Argentina. We sampled Chinook Salmon juvenile and environmental variables including daily river discharge, water temperature, electrical conductance, dissolved oxygen and total dissolved solids. We assessed individual variation in the diet of juvenile salmon over time. We estimated the incubation time before fry emergence to be around 101 days under a mean water temperature of 7.5°C. Salmon fry emerged during the Austral Winter (July‐August) in off‐channel habitats near the redds. Juvenile salmon were between 3.8 and 12.1 cm in length (TL) and their highest body condition occurred early during the Austral summer (December). Juvenile salmon abundances were positively associated with water temperature, but not flow. Diet analyses revealed a specialist feeding behaviour based on food pulses that included both aquatic and terrestrial insects; piscivory was not detected. Juvenile salmon co‐occurred with native fishes suggesting a potential for interspecific interference. Our findings provide baseline information for the future management of non‐native Chinook Salmon populations in the Southern Hemisphere and elsewhere.

Combined impacts of future land-use and climate stressors on water resources and quality in groundwater and surface waterbodies of the upper Thames river basin, UK

It is widely acknowledged that waterbodies are becoming increasingly affected by a wide range of drivers of change arising from human activity. To illustrate how this can be quantified a linked modelling approach was applied in the Thames river basin in southern UK. Changes to river flows, water temperature, river and reservoir quality were predicted under three contrasting future “storylines”; one an extension of present day rates of economic development, the others representing more extreme and less sustainable visions. Modelling revealed that lower baseflow conditions will arise under all storylines. For the less extreme storyline river water quality is likely to deteriorate but reservoir quality will improve slightly. The two more extreme futures could not be supported by current management strategies to meet water demand. To satisfy these scenarios, transfer of river water from outside the Thames river basin would be necessary. Consequently, some improvement over present day water quality in the river may be seen, and for most indicators conditions would be better than in the less extreme storyline. However, because phosphorus concentrations will rise, the invoked changes in water demand management would not be of a form suitable to prevent a marked deterioration in reservoir water quality.

Modeling of extreme risk in river water quality under climate change

Abstract A river water quality management model under average climatic conditions may not be able to account for the extreme risk of low water quality which is more prominent under an increase in river water temperature and altered river flows. A modeling framework is developed to assess the risk of river low water quality extremes by integrating a statistical downscaling model based on Canonical Correlation Analysis, risk quantification model based on Frank Archimedean Copula function and multiple logistic regression model integrated with a river water quality simulation model, QUAL2 K. The results reveal that the combination of predicted decrease in low flows of approximately 57% and increase in maximum river water temperatures of approximately 1.2°C has shown an increase of about 46% in risk of low water quality conditions for the future scenarios along Tunga-Bhadra River, India. The extreme risk of low water quality is observed to increase by 50.6% for the period 2020–2040 when compared with the current extreme conditions of 4.5% and average risk conditions of about 3% for the period 1988–2005. The study captured the occurrence of extremes of low water quality with evidence of a strong link between climate and water quality impairment events.