River Water Temperature Research Articles

River water temperature quantiles as thermal stress indicators: Case study in Switzerland

The variability of the river water thermal regime has important consequences on the environment and aquatic habitat. In 25 independent and identically distributed stations in Switzerland, local frequency analysis is used to quantify extreme river temperatures. Probability distributions are fitted to the data to estimate maximum water temperatures corresponding to different return periods. The goodness of fit of statistical distributions are evaluated using the Akaike and Bayesian Information Criteria. L-moment ratio diagrams are also used to validate the choices of appropriate candidate distributions. Results show that for high altitude stations the two-parameter Weibull (W2) distribution is the most adequate distribution to represent extreme river water temperatures while for low altitude stations the most commonly selected distributions are the Normal (N) and Inverse Gamma (IG). The L-moment ratio diagrams confirm the results of the local frequency analysis. These results point to the presence of a regional homogeneity in the thermal regime of the study area. River temperature quantiles are compared to know thresholds above which thermal stress occurs for a relatively ubiquitous salmonid species in Europe (Brown trout).

Integrating regional and local monitoring data and assessment tools to evaluate habitat conditions and inform river restoration

Restoring degraded rivers requires initial assessment of the fluvial landscape to identify stressors and riverine features that can be enhanced. We associated local-scale river habitat data collected using standardized national monitoring tools with modeled regional water temperature and flow data on mid-sized northwest U.S. rivers (30–60 m wide). We grouped these rivers according to quartiles of their modeled mean August water temperature and examined their physical habitat structure and flow. We then used principal components analysis to summarize the variation in several dimensions of physical habitat. We also compared local conditions in the Priest River, a river targeted for restoration of native salmonid habitat in northern Idaho, with those in other rivers of the region to infer potential drivers controlling water temperature. The warmest rivers had physical structure and fluvial characteristics typical of thermally degraded rivers, whereas the coldest rivers had higher mean summer flows and greater channel planform complexity. The Priest River sites had approximately twice as many deep residual pools (>50, >75, and >100 cm) and incision that averaged approximately twice that in the coldest rivers. Percentage fines and natural cover in the Priest were also more typical of the higher-temperature river groups. We found generally low instream cover and low levels of large wood both across the region and within the Priest River. Our approach enabled us to consider the local habitat conditions of a river in the context of other similarly sized rivers in the surrounding region. Understanding this context is important for identifying potential influences on river water temperature within the focal basin and for defining attainable goals for management and restoration of thermal and habitat conditions.

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

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

Joint temporal trends in river thermal and hydrological conditions can threaten the downstream migration of the critically endangered European eel

Climate change is modifying the hydrological and thermal regimes of rivers worldwide, threatening the triggering of organisms’ key life-cycle processes. European eel (Anguilla anguilla) is a critically endangered fish species that migrates over several thousand kilometres between its rearing habitats in continental waters of Europe and North Africa and its spawning area in the Sargasso Sea. Downstream migration of adult eels occurs during periods of decreasing river water temperature associated with high discharge but changes in these environmental cues may affected eel migratory conditions. An innovative multivariate method was developed to analyse long-term datasets of daily water temperature, discharge and eel passage in two European rivers. Over the past 50 years, water temperature and discharge increased in both rivers during the downstream migration period from August to November. Silver eels preferentially migrated at temperatures between 10 and 20 °C combined with high discharge. Environmental changes have resulted in the migration of silver eels under warmer water temperatures. This example illustrates how the changes in environmental cues have led to a growing mismatch between the migratory conditions preferentially selected and those actually used, which may threaten the completion of the eel’s life cycle and ultimately the persistence of this already critically endangered species.

Underlying trends confound estimates of fish population responses to river discharge

Abstract Conservation management of freshwater ecosystems often focuses on mitigating or reversing the negative effects of altered patterns of river discharge. Assessments of management interventions frequently focus on direct, short‐term responses to discharge without consideration of underlying population trends that span multiple years. We sought to determine the effects on fish populations of annual variation in river discharge and water temperature after accounting for underlying population trends. We used data on five native and one non‐native fish species, collected over 7–20 years in seven rivers in the Murray‐Darling Basin, south‐eastern Australia. Population trends explained 3.4%–24.6% of the total variation in abundance and biomass of our six study species, while discharge and water temperature explained a further 1.2%–11.4% of this variation. However, population trends confounded the effects of discharge and water temperature, which suggests that the effects of annual discharge conditions may be contingent on past conditions and factors intrinsic to populations (e.g. age structure). Failing to account for population trends led to a combination of plausible and implausible associations with discharge and water temperature. Plausible associations included positive associations with the magnitude of spring discharge and negative associations with the number of days where discharge was below the long‐term 10th percentile. Determining whether estimated associations are real or artefactual requires a greater focus on the processes that underpin multiyear population trends. Our results highlight the potential for underlying trends in populations to confound the short‐term effects of discharge. Potential confounding of short‐ and long‐term changes in populations underscores the need to assess responses to river discharge in the context of overarching environmental conditions, including factors other than discharge.

Serpentine (Floating) Ice Channels and their Interaction with Riverbed Permafrost in the Lena River Delta, Russia

Arctic deltas and their river channels are characterized by three components of the cryosphere: snow, river ice, and permafrost, making them especially sensitive to ongoing climate change. Thinning river ice and rising river water temperatures may affect the thermal state of permafrost beneath the riverbed, with consequences for delta hydrology, erosion, and sediment transport. In this study, we use optical and radar remote sensing to map ice frozen to the riverbed (bedfast ice) vs. ice, resting on top of the unfrozen water layer (floating or so-called serpentine ice) within the Arctic’s largest delta, the Lena River Delta. The optical data is used to differentiate elevated floating ice from bedfast ice, which is flooded ice during the spring melt, while radar data is used to differentiate floating from bedfast ice during the winter months. We use numerical modeling and geophysical field surveys to investigate the temperature field and sediment properties beneath the riverbed. Our results show that the serpentine ice identified with both types of remote sensing spatially coincides with the location of thawed riverbed sediment observed with in situ geoelectrical measurements and as simulated with the thermal model. Besides insight into sub-river thermal properties, our study shows the potential of remote sensing for identifying river channels with active sub-ice flow during winter vs. channels, presumably disconnected for winter water flow. Furthermore, our results provide viable information for the summer navigation for shallow-draught vessels.

Influence of the choice of stream temperature model on the projections of water temperature in rivers

In the majority of studies aiming at stream temperature warming due to climate change just a single water temperature model is used. Choosing a single model may highly impact the conclusions from the study. In this paper four relatively different empirical or semi-empirical models: perceptron neural networks, product unit networks, extended logistic regression and air2stream were applied to project the impact of climate change on water temperature in rivers located in temperate climatic zones of the USA and Poland. The models were driven by daily air temperature and streamflow projected by the rainfall-runoff model. In the first step, the models were calibrated and validated. Then the projections of water temperature were derived for the historical periods and two future periods taking into account: (a) climate simulations from the CORDEX initiative (NA-CORDEX and EURO-CORDEX), (b) the GR4J rainfall-runoff model and (c) different water temperature models. The obtained results indicate that due to global warming, the stream temperatures are expected to increase by about 1–2 °C for 2021–2050 and by 2–3 °C for 2071–2100 periods. These changes are not uniformly distributed throughout the year. The largest warming in the USA is found in the summertime, in Poland – in spring and autumn. For some months the discrepancies in the projected stream temperature between various stream temperature models are large. Product unit neural network, logistic regression-based model or air2stream occasionally led to projections that differ from those obtained by the majority of models even by 2 °C. We strongly recommend using at least a few stream temperature models for analysing the impact of climate change on water temperatures or the fate of the aquatic ecosystem.

The influence of treated wastewaterfrom the Lodz city agglomeration on the ice regime and water temperature of the Ner river

AbstractThe study attempts to answer the following question: Does human impact contribute to changes recorded in the Ner river ice regime? In replying to this question, data on water consumption in Lodz (Łódź) (a city in central Poland) in 1951–2017 were used, as well as observations of ice cover and all ice phenomena for the same period. The ice regime and water temperature of the river have changed over the past 70 years. The changes result not only from changes caused by global warming but also from additional fluctuations in this temperature as determined by changes in the quantity and quality of wastewater discharged into the river from the Lodz city agglomeration. The frequency of ice phenomena in the river decreased, and their duration dropped by almost half. This tendency was compounded by a decrease in number of days with ice phenomena, which in turn was caused by a rapid increase in the amount of waste and thermally polluted waters supplied from Lodz. The river water temperature has now stopped increasing. The course of the river ice regime now resembles that of a natural watercourse again.

Near-daily monitoring of surface temperature and channel width of the six largest Arctic rivers from space using GCOM-C/SGLI

Spatio-temporal changes in water temperature and discharge of the Arctic rivers are important variables to be observed not only for estimating runoff contributions from snow melt, rainfall, and thawing of permafrost over the continents but also for assessing their impacts on the Arctic sea-ice and marine ecosystem. Nevertheless, the number of ground water gauging stations and the frequency of in-situ measurements for temperature and discharge has been decreasing since the 20 Century due to the shrinkage of budgets for maintaining the in-situ stations. In this study, we explored the possibility to perform near-daily monitoring of river surface temperature (RST) and river channel width (RCW) from space using a Japanese satellite-borne optical sensor named SGLI which can observe radiances at the spectral bands from near-ultraviolet to thermal infrared regions at the same spatial resolution of 250-m on a global scale. River surface brightness temperature (RSBT) measured with SGLI TIR band was used as RST without atmospheric correction. RCW was derived as the widths of water pixels identified along river channels using SGLI reflectances at visible to shortwave infrared bands. Analysis results of two-year SGLI data acquired in 2018 and 2019 show that RSBT and RCW can be retrieved successfully from the SGLI observations on a daily basis after applying a spatio-temporal interpolation. The accuracies of the retrieved RSBT evaluated with in-situ water temperatures are about 2.57 K with the interpolation (and 1.84 K without the interpolation but with lower observation frequencies). Retrieved RCWs are correlated well with in-situ river water discharges indicating potentials to assess variations of not only snow melt water but also precipitation within the river basin. Thus, SGLI data can be used to reconstruct not only the river water temperature but also the river water discharges along the continental river channels for assessing heat flux flowing into the Arctic Ocean.

Machine-learning methods for stream water temperature prediction

Abstract. Water temperature in rivers is a crucial environmental factor with the ability to alter hydro-ecological as well as socio-economic conditions within a catchment. The development of modelling concepts for predicting river water temperature is and will be essential for effective integrated water management and the development of adaptation strategies to future global changes (e.g. climate change). This study tests the performance of six different machine-learning models: step-wise linear regression, random forest, eXtreme Gradient Boosting (XGBoost), feed-forward neural networks (FNNs), and two types of recurrent neural networks (RNNs). All models are applied using different data inputs for daily water temperature prediction in 10 Austrian catchments ranging from 200 to 96 000 km2 and exhibiting a wide range of physiographic characteristics. The evaluated input data sets include combinations of daily means of air temperature, runoff, precipitation and global radiation. Bayesian optimization is applied to optimize the hyperparameters of all applied machine-learning models. To make the results comparable to previous studies, two widely used benchmark models are applied additionally: linear regression and air2stream. With a mean root mean squared error (RMSE) of 0.55 ∘C, the tested models could significantly improve water temperature prediction compared to linear regression (1.55 ∘C) and air2stream (0.98 ∘C). In general, the results show a very similar performance of the tested machine-learning models, with a median RMSE difference of 0.08 ∘C between the models. From the six tested machine-learning models both FNNs and XGBoost performed best in 4 of the 10 catchments. RNNs are the best-performing models in the largest catchment, indicating that RNNs mainly perform well when processes with long-term dependencies are important. Furthermore, a wide range of performance was observed for different hyperparameter sets for the tested models, showing the importance of hyperparameter optimization. Especially the FNN model results showed an extremely large RMSE standard deviation of 1.60 ∘C due to the chosen hyperparameters. This study evaluates different sets of input variables, machine-learning models and training characteristics for daily stream water temperature prediction, acting as a basis for future development of regional multi-catchment water temperature prediction models. All preprocessing steps and models are implemented in the open-source R package wateRtemp to provide easy access to these modelling approaches and facilitate further research.

Extreme Heatwave Scenarios with Impact on Thermal Regime of Dâmboviţa River in Bucharest, Romania

The study analyses the qualitative influence of different parameters on Dambovita River water temperature along the 17.5km length reach of concrete canal passing through Bucharest City, Romania. Air temperature has been increasing during the investigated last 4 decades with detrimental consequences on the river ecosystem. The simplified physical processes taken into account, equations and numerical methods used to develop a deterministic water temperature model are explained. Different scenarios of possible extreme summer heatwaves with influence on river water temperature are defined analysing the available data. A sensitivity analysis is performed for meteorological parameters such as: air temperature, humidity, short wave solar radiation, cloudiness and wind speed to determine their influence on river temperature.

Prediction of river water temperature using machine learning algorithms: a tropical river system of India

Abstract Machine learning (ML) has been increasingly adopted due to its ability to model complex and non-linearities between river water temperature (RWT) and its predictors (e.g., Air Temperature, AT). Most of these ML approaches have been applied using average AT without any detailed sensitivity analysis of other forms of AT (e.g., maximum and minimum). The present study demonstrates how new ML approaches, such as ridge regression (RR), K-nearest neighbors (KNN) regressor, random forest (RF) regressor, and support vector regression (SVR), can be coupled with Sobol’ global sensitivity analysis (GSA) to predict accurate RWT estimates with the most appropriate form of AT. Furthermore, the proposed ML approaches have been combined with the Ensemble Kalman Filter (EnKF), a data assimilation (DA) technique to improve the predicted values based on the measured data. The proposed modelling framework's effectiveness is demonstrated with a tropical river system of India, Tunga-Bhadra River, as a case study. The SVR has been noted as the most robust ML model to predict RWT at a monthly time scale compared with daily and seasonal. The study demonstrates how ML methods can be coupled with a global sensitivity algorithm and DA techniques to generate accurate RWT predictions in river water quality modelling.

How does summer environmental degradation influence native mussels in Corbicula invaded rivers?

Abstract Droughts and heat waves are becoming more frequent, persistent, and intense in a global warming context. Although previous studies reported Corbicula mass mortality events associated with the occurrence of extreme summer conditions, knowledge of their effects lags behind ecosystem functioning. The goal of this study was to determine whether summer environmental degradation (droughts, extreme temperatures, and spikes in ammonium concentration) influences the survival and behaviour of native mussels in rivers invaded by Corbicula and, in consequence, their functioning in ecosystems. Unio delphinus mussels were exposed to increasing summer environmental degradation. Water column and pore water samples were collected at six sampling time‐points for nutrient analysis. Environmental conditions, survival and behaviour were monitored at 12‐hour intervals. Mortality risk was greatest under no‐flow conditions, reaching 100% of mortality after 84 ± 22 hours of exposure to anoxic (<2 mg O2 L−1) conditions. At the sub‐lethal level, quiescent conditions impaired filtration activity. In addition, when facing rising temperatures, native mussels buried deeply and vertical movements were depleted. There were significant differences between the water column and the pore water in physicochemical characteristics. Oxygen and nitrate concentration were generally higher in the water column. In contrast, ammonium and phosphate maximum concentrations were measured in the pore water. The results suggest that environmental degradation affects the role of mussels in ecosystem functioning. For instance, prolonged valve closure under poor water quality conditions may have impacts on particle removal from the water column. Furthermore, impairment of vertical movements may reduce nutrient release from sediments. The present study has key implications for water management and for the conservation of mussel populations. For instance, a responsible approach for maintaining ecosystem integrity should include the water needs of freshwater mussels in environmental flow recommendations and the conservation of riparian vegetation corridors to mitigate the effects of increased water temperature in rivers.

The Role of Climate Changes in the Spread of Freshwater Fishes: Implications for Alien Cool and Warm-Water Species in a Mediterranean Basin

In running waters, under climate change conditions, the combined effect of water warming and decreasing flow rates may encourage colonisation by invasive cool and warm-water fish species. The aim of the study was to analyze the potential climate change effects on the spread of four invasive alien fishes in the Tiber River basin, taking into account the effects of river fragmentation. Fish and environmental data collected in 91 sites over the years 1998–2018, were used to analyze temporal changes in their habitat requirements. A multivariate analysis was conducted, and the hypothesis of a range expansion towards the upstream reaches has been tested. For Barbus barbus, Gobio gobio, Padogobius bonelli and Pseudorasbora parva population abundances and body condition were analyzed. Detectability, occupancy, local extinction and colonization probabilities were estimated. We showed that B. barbus and P. bonelli have significantly extended their range toward upstream. P. parva did not move toward higher altitudes significantly, suggesting that, at this stage, the species has probably reached an equilibrium. River fragmentation, elevation, water temperature and average current speed seem to be major determinants in colonization processes, affecting the dispersal ability of the species. Not surprisingly for species introduced in relatively recent times, the colonization probabilities were much higher than extinction probabilities. Our results provided evidence for some synergistic effects between climate changes and alien fish species invasions, in terms of species range shifts mediated by rising water temperatures, although they should be interpreted cautiously, taking into account that these species most likely were not yet stabilized.

River water temperature forecasting using a deep learning method

Accurate water temperature forecasting is essential for understanding thermal regimes of rivers in the context of climate change and anthropogenic disturbances, such as dam construction. Machine-learning models proffer an empirically based approach to predicting water temperatures with a high degree of accuracy. This study explores the potential of long short-term neural network (LSTM), a type of deep learning method, to forecast daily river water temperatures and quantify temporal variations in thermal regime induced by changes in climate and by dam construction. The performance of LSTM is compared with that of several other models using daily water-temperature data for nine river gauges around the world. In a detailed analysis, the models are evaluated for the Yichang gauge on the Yangtze River to reconstruct the natural thermal conditions and help to assess daily water temperature variations induced by operation of the Three Gorges Reservoir (TGR). The collective results show that LSTM outperforms other methods for predicting mean daily water temperature in rivers, capturing accurately mean daily variations in thermal regime. The construction of the TGR strongly influenced water temperature variations at Yichang, producing the strongest cooling effect from mid-April to mid-May and the greatest warming effect in late December and early January. These marked effects are most prominent at the highest water levels in the TGR. The enhanced predictive capabilities of the LSTM model provide a powerful tool for water temperature forecasting and ecological management of rivers in the Anthropocene.

Hot moments and hotspots of cyanobacteria hyperblooms in the Curonian Lagoon (SE Baltic Sea) revealed via remote sensing-based retrospective analysis

A temporally and spatially detailed historical (1985–2018) analysis of cyanobacteria blooms was performed in the Curonian Lagoon (Lithuania, Russia), the largest coastal lagoon in the Baltic Sea. Satellite data allowed the mapping of cyanobacteria surface accumulations, so-called “scums”, and of chlorophyll-a concentration. The 34-year time series shows a tendency towards later occurrence (October–November) of the cyanobacteria scum presence, whereas the period of its onset (June–July) remains relatively constant. The periods when scums are present, “hot moments”, have been consistently increasing in duration since 2008. The differences in the starting, ending and annual duration of cyanobacteria blooms have been significantly altered by hydro-meteorological conditions (river discharge, water temperature, and wind conditions) and their year-round patterns. The most important environmental factors that determined the temporal changes of the scum presence and area were the standing stock of cyanobacteria and the ambient wind conditions. The “hotspots”, the areas where the blooms most likely occur, were distributed in the south-southwestern and central parts of the lagoon. The least affected areas were the northern part, which is connected to the coastal waters of the Baltic Sea, and the Nemunas River delta region. The longstanding, well-established spatial patterns of cyanobacteria blooms were linked to hydrodynamic features, namely water renewal time and current patterns, and to potential nutrient sources that included muddy sediments and the locations of colonies of piscivorous birds. Our findings confirmed that the annual and seasonal variations of cyanobacteria blooms and their regulation are a complex issue due to interactions between multiple factors over spatially and temporally broad scales. Despite great progress in the prevention and control of eutrophication and cyanobacteria blooms, the lagoon is still considered to be in a poor ecological status. This work provides a new and missing understanding on the spatial and temporal extent of cyanobacteria blooms and the factors that govern them. Such an understanding can help in planning management strategies, forecasting the magnitude and severity of blooms under changing nutrient loads and potential climate scenarios.

Investigation on River Thermal Regime under Dam Influence by Integrating Remote Sensing and Water Temperature Model

River water temperature (RWT), a primary parameter for hydrological and ecological processes, is influenced by both climate change and anthropogenic intervention. Studies on such influences have been severely restricted due to the scarcity of river temperature data. This paper proposed a three-stage method to obtain long-term daily water temperature for rivers and river-type reservoirs by integrating remote sensing technique and river water temperature modelling. The proposed three-stage method was applied to the Three Gorges Reservoir (TGR) and validated against in situ measured RWTs in the two study sites, Cuntan and Huanglingmiao. The result showed improvements in the method: the quadrate window selection and RWT correction jointly reduce RMSE from 1.8 to 0.9 °C in Cuntan and from 2.1 to 1.2 °C in Huanglingmiao. As a whole, the estimated daily RWT has a consistent RMSE of 1.1–1.9 °C. Meanwhile, by analysing the Landsat-derived daily RWT, we demonstrated that the TGR had a significant impact on the outflow’s thermal regime. At the downstream reach of TGR, an apparent increase in RWT in the cold season and interannual thermal regime delay compared to inflow were found with the increasing water level after the dam construction. All the results and analyses indicate that the proposed three-stage method could be applied to obtain long time series of daily RWT and provide a promising approach to qualitatively analyse RWT variation in the poorly gauged catchment for river water quality monitoring and management.

Features of the application of the method of quantile regression in problems of hydroecology

Such perspective method of the analysis of the natural time series is considered in article as method of quantile regression. They are discussed imperfection existing methods of the analysis of the natural processes (least square method and the method of linear robust regression). It is shown that transition towards quantile regression allows greatly to raise efficiency of the investigations of the natural time series. Main gоal of the work consists in practical applications of the quantile regression method for decision of the different problemsof the hydroecology. Hydrochemical time series were considered in article. By means of quantile regressin method was investigated behavior of the dissolved oxygen in water depending on temperature of river water and depending on discharge of river water (for quantiles of order 0.05; 0,50 und 0,95). When performing the investigations more perfect method of quantile regression was designed such as method piecewisf quantile regression (with uce polynomial degree 1; 2; 3). Numenical experiments when use the natural time series have shown greater advantage of the designed method of piecewise quantile regression in contrast with classical method of quantile regression.Such perspective method of the analysis of the natural time series is considered in article as method of quantile regression. They are discussed imperfection existing methods of the analysis of the natural processes (least square method and the method of linear robust regression). It is shown that transition towards quantile regression allows greatly to raise efficiency of the investigations of the natural time series. Main gоal of the work consists in practical applications of the quantile regression method for decision of the different problemsof the hydroecology. Hydrochemical time series were considered in article. By means of quantile regressin method was investigated behavior of the dissolved oxygen in water depending on temperature of river water and depending on discharge of river water (for quantiles of order 0.05; 0,50 und 0,95). When performing the investigations more perfect method of quantile regression was designed such as method piecewisf quantile regression (with uce polynomial degree 1; 2; 3). Numenical experiments when use the natural time series have shown greater advantage of the designed method of piecewise quantile regression in contrast with classical method of quantile regression.

Thermal Regime of River Water as a Response to Climatic Processes in the Upper Don Drainage Basin

The modern thermal regime of the Upper Don rivers formed in the conditions of regional climate change in response to the challenge of global climate. The increase in the annual mean temperature of the atmospheric surface layer in the Upper Don in the first decade of the current century nearly by 1°С on average changes the thermal regime of the river water, which, in its turn, reflects the constant interaction between two spheres of the geographic envelope, namely, the atmosphere and hydrosphere. The water mass is concurrently in contact with the streambed, which affects thermal state of the water. Under the conditions of constant interaction and the interference of various environments, the thermal state of the atmosphere does not always remain the leading factor in behavior of the river-water temperature. In light of the unidirectional trend in the behavior of the regional air temperature and the increase in the latter over the entire Don River drainage area, the character of the water temperature response proves to be ambiguous and even contradictory. The maximum water temperature consistently declines in the right-bank Don River tributaries, such as the Krasnaya Mecha, Devitsa, and Chernaya Kalitva rivers, etc., which feed into the main stem from the Central Russian Uplands. The temperature rises in the left-bank Don River tributaries, e.g., Voronezh and Bityug, which feed into the Don River from the Oka–Don Lowlands. The Podgornaya River, which is also a left-bank tributary that feeds into the Don River from the Kalachskaya Uplands, exhibits a drop in temperature. A slight temperature increase is recorded in the streambed of the Don River itself. The ambiguous temperature response of the upper water layer can be attributed to the intra-annual redistribution of drainage, reduced snowmelt, and increase in the proportion and role of groundwater recharge during the formation of the maximum water temperature.

GÜNLÜK ÇÖZÜNMÜŞ OKSİJEN KONSANTRASYONUNUN ÇOK DEĞİŞKENLİ UYARLANABİLİR REGRESYON EĞRİLERİ İLE TAHMİN EDİLMESİ

Bu çalışmada su sıcaklığı (T), özgül iletkenlik (Öİ) verilerinden hesaplanmış elektriksel iletkenlik (Eİ), pH ve debi (Q) verileri kullanılarak çok değişkenli uyarlanabilir regresyon eğrileri (MARS) ve regresyon analizi (RA) yöntemleri ile ÇO konsnatrasyonunun tahmin edilmesi amaçlanmıştır. MARS yönteminde en iyi tahmin değerlerini üreten temel fonksiyonlar ve denklemler belirlenmiş, RA yöntemi doğrusal, üs, üstel ve kuadratik olmak üzere dört farklı fonksiyona uygulanmış ve bu fonksiyonlara ait katsayılar hesaplanmıştır. Modelleme çalışmalarında Amerika Birleşik Devletleri’nin Oregon eyaletinin kuzey batısında yer alan Willamette Nehri’nin yan kollarından biri olan ve yaklaşık 2435 km2’lik bir havza alanına sahip Clackamas Nehri’ne ait Eylül 2016 − Ağustos 2017 dönemi günlük ortalama verileri kullanılmıştır. Her bir su kalitesi değişkeninin ÇO konsantrasyonu tahmin performansına etkisini belirlemek amacıyla sekiz farklı model oluşturulmuştur. ÇO konsantrasyonu tahmininde kurulan modellerin ve kullanılan yöntemlerin performanslarının değerlendirilebilmesi için çeşitli istatistikler (ortalama karesel hatanın karekökü, ortalama mutlak hata, saçılım indeksi ve Nash Sutcliffe verimlilik katsayısı) kullanılmıştır. Modelleme çalışmalarından elde edilen sonuçlar irdelendiğinde, MARS yönteminin RA yönteminden daha iyi sonuçlar verdiği anlaşılmıştır. Regresyon fonksiyonları içerisinden ise en başarılı tahmin sonuçlarının kuadratik fonksiyondan elde edildikleri ve MARS yöntemi ile elde edilen değerlere de oldukça yakın oldukları görülmüştür. ÇO konsantrasyonu tahmininde en etkili değişkenlerin T ve Q oldukları dolayısıyla en etkisiz değişkenlerin ise Eİ ve pH oldukları anlaşılmıştır. Model 3, Model 5, Model 7 ve Model 8’den elde edilen sonuçların birbirine çok yakın olması sebebiyle daha az değişken ile güçlü tahminler yapması ve daha sade bir model olması bakımından ÇO tahmininde Model 3’ün kullanılmasının daha avantajlı olacağı sonucuna varılmıştır.