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.

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Basic Parameters of the Thermal Regime of Rivers in the Vistula River Catchment

Miscellanea Geographica ◽

10.2478/mgrsd-2010-0017 ◽

2010 ◽

Vol 14 (1) ◽

pp. 185-192 ◽

Cited By ~ 1

Author(s):

Monika Oksiuta

Keyword(s):

Water Temperature ◽

River Water ◽

Thermal Regime ◽

Measurement Data ◽

Natural Variability ◽

Stream Network ◽

River Catchment ◽

Vistula River ◽

Basic Parameters ◽

River Water Temperature

Abstract Seasonal and multi-annual variability of river water temperature has been analysed based on data from 24 gauge stations of the IMGW network. It has been characterised by means of several values of the thermal regime parameters: mean annual, semi-annual (November-April, May-October) and amplitude. The variability of water temperature in the catchment and in the stream network has been estimated. Measurement data included seven stations at the Vistula river. On the background of natural variability, rivers or their segments have been distinguished where water temperature is impacted by anthropopressure.

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Genetic-Algorithm-Optimized Sequential Model for Water Temperature Prediction

Sustainability ◽

10.3390/su12135374 ◽

2020 ◽

Vol 12 (13) ◽

pp. 5374 ◽

Cited By ~ 2

Author(s):

Stephen Stajkowski ◽

Deepak Kumar ◽

Pijush Samui ◽

Hossein Bonakdari ◽

Bahram Gharabaghi

Keyword(s):

Water Quality ◽

Quality Management ◽

Water Temperature ◽

Real Time ◽

River Water ◽

Time Window ◽

Water Quality Management ◽

Water Quality Monitoring ◽

Lstm Network ◽

River Water Temperature

Advances in establishing real-time river water quality monitoring networks combined with novel artificial intelligence techniques for more accurate forecasting is at the forefront of urban water management. The preservation and improvement of the quality of our impaired urban streams are at the core of the global challenge of ensuring water sustainability. This work adopted a genetic-algorithm (GA)-optimized long short-term memory (LSTM) technique to predict river water temperature (WT) as a key indicator of the health state of the aquatic habitat, where its modeling is crucial for effective urban water quality management. To our knowledge, this is the first attempt to adopt a GA-LSTM to predict the WT in urban rivers. In recent research trends, large volumes of real-time water quality data, including water temperature, conductivity, pH, and turbidity, are constantly being collected. Specifically, in the field of water quality management, this provides countless opportunities for understanding water quality impairment and forecasting, and to develop models for aquatic habitat assessment purposes. The main objective of this research was to develop a reliable and simple urban river water temperature forecasting tool using advanced machine learning methods that can be used in conjunction with a real-time network of water quality monitoring stations for proactive water quality management. We proposed a hybrid time series regression model for WT forecasting. This hybrid approach was applied to solve problems regarding the time window size and architectural factors (number of units) of the LSTM network. We have chosen an hourly water temperature record collected over 5 years as the input. Furthermore, to check its robustness, a recurrent neural network (RNN) was also tested as a benchmark model and the performances were compared. The experimental results revealed that the hybrid model of the GA-LSTM network outperformed the RNN and the basic problem of determining the optimal time window and number of units of the memory cell was solved. This research concluded that the GA-LSTM can be used as an advanced deep learning technique for time series analysis.

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Anthropogenic influence on the Rhine water temperatures

Hydrology and Earth System Sciences ◽

10.5194/hess-24-5027-2020 ◽

2020 ◽

Vol 24 (10) ◽

pp. 5027-5041

Author(s):

Alex Zavarsky ◽

Lars Duester

Keyword(s):

Water Temperature ◽

River Water ◽

Air Temperature ◽

Nuclear Power ◽

Important Variable ◽

Anthropogenic Heat ◽

River Rhine ◽

River Temperature ◽

Time Lagged ◽

River Water Temperature

Abstract. River temperature is an important parameter for water quality and an important variable for physical, chemical and biological processes. River water is also used by production facilities as cooling agent. We introduced a new way of calculating a catchment-wide air temperature using a time-lagged and weighed average. Regressing the new air temperature vs. river water temperature, the meteorological influence and the anthropogenic heat input could be studied separately. The new method was tested at four monitoring stations (Basel, Worms, Koblenz and Cologne) along the river Rhine and lowered the root mean square error of the regression from 2.37 ∘C (simple average) to 1.02 ∘C. The analysis also showed that the long-term trend (1979–2018) of river water temperature was, next to the increasing air temperature, mostly influenced by decreasing nuclear power production. Short-term changes in timescales < 5 years were connected with changes in industrial production. We found significant positive correlations for the relationship.

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River water temperature in the United Kingdom

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133314550669 ◽

2015 ◽

Vol 39 (1) ◽

pp. 68-92 ◽

Cited By ~ 62

Author(s):

David M. Hannah ◽

Grace Garner

Keyword(s):

Water Temperature ◽

River Water ◽

Temperature Change ◽

20Th Century ◽

21St Century ◽

Anthropogenic Impacts ◽

Time Variability ◽

River Temperature ◽

The Uk ◽

River Water Temperature

Change in river water temperature has important consequences for the environment and people. This review provides a new perspective on the topic by evaluating changes in river water temperature for the UK over the 20th century and possible changes over the 21st century. There is limited knowledge of space-time variability in, and controls on, river temperature at the region scale and beyond over the 20th century. There is historical evidence that UK river temperature has increased in the latter part of the 20th century, but low agreement on the attribution of changes to climatic warming because river temperature is a complex, dynamic response to climate and hydrological patterns moderated by basin properties and anthropogenic impacts. Literature is scarce to evaluate changes to UK river temperature in the 21st century, but it appears as likely as not that UK river temperature will increase in the future. However, there are a number of interlinked sources of uncertainty (related to observations, scenarios, process interactions and feedback) that make estimating direction and rate of temperature change for rivers across the UK with confidence very challenging. Priority knowledge gaps are identified that must be addressed to improve understanding of past, contemporary and future river temperature change.

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Efficient modelling of water temperature patterns in river systems – benchmarking a set of machine learning approaches

10.5194/egusphere-egu2020-19090 ◽

2020 ◽

Author(s):

Mathew Herrnegger ◽

Moritz Feigl ◽

Katharina Lebiedzinski ◽

Karsten Schulz

Keyword(s):

Machine Learning ◽

Time Series ◽

Water Temperature ◽

River Water ◽

Large Scale ◽

Data Availability ◽

Learning Approaches ◽

River Temperature ◽

River Water Temperature ◽

Process Based Models

Many approaches for modelling river water temperature are available, but not one exist that can be applied without restrictions. The applied method depends on data availability, dominant processes, scales and transferability. Process-based models are currently the best way to evaluate detailed management scenarios on reach scale and to understand underlying processes. &#160;Due to limitations of data availability, however, more simplified approaches are frequently applied, where different meteorological or hydrological time series are statistically related to water temperature (or in the simplest case only using air temperature). Here, machine learning methods could help bridging a gap by allowing for more complex relationships without setting prior assumptions. They are thus integrating reasonable processes and dynamics within the catchment by learning from given data. However, up-to-date machine learning approaches have rarely been used in this field until now.This contribution analyses a set of machine learning approaches for large-scale river temperature modelling. Deep learning methods, random forests and boosting methods are compared with the performance of commonly used simple and multiple regression models. These approaches are tested on 10 catchments with different characteristics, human impacts (e.g. hydropower, river regulation) and time series lengths (10 to 39 years). They are situated in the Austrian Alps or flatlands with areas ranging from 200 to 96.000 km&#178;. Observed data including daily means of river water temperature, air temperature, discharge, precipitation and global radiation are grouped to simple and advanced sets of input variables to analyse possible data dependencies.&#160;In summary, we compare up-to-date machine learning approaches for their applicability in river water temperature prediction. By implementing necessary data preprocessing steps and machine learning routines in a R package, we aim to make these findings easily accessible and reproducible for the community. This tool provides an attractive approach for large-scale river temperature modelling, where the requirements for using process-based models are not able to be met. Future applications can include e.g. short and long term forecasting of river water temperature to find management options for balancing environmental requirements.&#160;

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Detecting Patterns of Changes in River Water Temperature in Poland

Water ◽

10.3390/w12051327 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1327 ◽

Cited By ~ 2

Author(s):

Renata Graf ◽

Dariusz Wrzesiński

Keyword(s):

Water Temperature ◽

River Water ◽

Temperature Change ◽

Air Temperature ◽

Thermal Regime ◽

Thermal Characteristics ◽

Temperature Trends ◽

Annual Water ◽

River Water Temperature ◽

Annual Water Temperature

The study determined water temperature trends of rivers in Poland in the period 1971–2015, and also their spatial and temporal patterns. The analysis covered daily water temperature of 53 rivers recorded at 94 water gauge stations and air temperature at 43 meteorological stations. Average monthly, annual, seasonal and maximum annual tendencies of temperature change were calculated using the Mann–Kendall (M–K) test. Regional patterns of water temperature change were determined on the basis of Ward’s hierarchical grouping for 16 correlation coefficients of average annual water temperature in successive 30-year sub-periods of the multi-annual period of 1971–2015. Moreover, regularities in monthly temperature trends in the annual cycle were identified using 12 monthly values obtained from the M–K Z test. The majority of average annual air and water temperature series demonstrate statistically significant positive trends. In three seasons: spring, summer and autumn, upward tendencies of temperature were detected at 70%–90% of the investigated water gauges. In 82% of the analysed rivers, similarity to the tendencies of change of monthly air temperature was concluded, with the climatic factor being recognised as of decisive importance for the changes in water thermal characteristics of the majority of rivers in Poland. In the winter months, positive trends of temperature were considerably weaker and in general statistically insignificant. On a regional scale, rivers with a quasi-natural thermal regime experienced temperature increases from April to November. In the other cases, different directions of change in river water temperature (RWT) were attributed to various forms of human impact. It was also found that for the majority of rivers the average annual water temperature in the analysed 30-year sub-periods displayed upward trends, statistically significant or close to the significance threshold. Stronger trends were observed in the periods after 1980, while a different nature of water temperature change was detected only in a couple of mountainous rivers or rivers transformed by human impact. In the beginning of the analysed period (1971–2015), the average annual water temperature of these rivers displayed positive and statistically significant trends, while after 1980 the trends were negative. The detected regularities and spatial patterns of water temperature change in rivers with a quasi-natural regime revealed a strong influence of climate on the modification of their thermal regime features. Rivers characterised by a clearly different nature of temperature change, both in terms of the direction of the tendencies observed and their statistical significance, were distinguished by alterations of water thermal characteristics caused by human activity. The results obtained may be useful in optimising the management of aquatic ecosystems, for which water temperature is a significant indicator of the ongoing environmental changes.

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STUDY ON CHANGING IN RIVER WATER TEMPERATURE IN KYUSYU REGION

Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering) ◽

10.2208/jscejhe.74.5_i_79 ◽

2018 ◽

Vol 74 (5) ◽

pp. I_79-I_84

Author(s):

Yoji NODA ◽

Tomoko MINAGAWA ◽

Hidetaka ICHIYANAGI ◽

Akihiko KOYAMA

Keyword(s):

Water Temperature ◽

River Water ◽

River Water Temperature

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Exploring and Quantifying River Thermal Response to Heatwaves

Water ◽

10.3390/w10081098 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1098 ◽

Cited By ~ 7

Author(s):

Sebastiano Piccolroaz ◽

Marco Toffolon ◽

Christopher Robinson ◽

Annunziato Siviglia

Keyword(s):

Water Temperature ◽

River Water ◽

Thermal Response ◽

Hydrological Regime ◽

Management Policy ◽

Extreme Weather Events ◽

Thermal Dynamics ◽

Depth Analysis ◽

River Water Temperature

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.

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