Water Temperature Prediction Research Articles

Abnormal high water temperature prediction in nearshore waters around the Korean Peninsula using ECMWF ERA5 data and a deep learning model

The abnormally high-water temperature (AHWT) phenomena have caused the mass stranding of farmed fish in the Korean coastal waters, leading to a substantial monetary loss in recent decades. It is most important to predict the HWT occurrence and take responsive measures before the HWT arrival to prevent such loss, we proposed a methodology to predict HWT occurrences using a deep-learning technology. Firstly, we trained a long short-term memory (LSTM) deep-learning model using the sea surface temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 product to estimate future water temperature in advance. Secondly, we used the estimated water temperature data to predict HWT occurrences from 1 day to 7 days later. We computed root mean square error (RMSE), mean absolute percentage error (MAPE) metrics, and F1-scores to evaluate the accuracy of the proposed LSTM model. In the cases of 1-day and 7-day water temperature predictions, RMSE and MAPE values between the estimated data and the sea-truth data were 0.293 degrees Celsius with 1.313 % and 0.854 degrees Celsius with 4.175 %, respectively. The F1-scores of the classification algorithm of 1- and 7-day HWT predictions were 0.96 and 0.74, respectively. This study contributes to developing measures to reduce the monetary loss of HWT damage on fish farms.

Integration of the WRF model and IoT sensors to develop an early cold snap warning system for inland fishponds

The cold weather-related economic losses in the aquaculture and fisheries industries are enormous and will only increase due to future climate change. Advancements in weather forecasting have increased the accuracy of predicting environmental factors like air temperature, solar radiation, and wind speed. However, the water temperature of fishponds, which affects the lives of fish, cannot be accurately predicted. As a result, fishermen are unable to implement early disaster mitigation and avoidance measures effectively. In this study, we developed an early warning system for extreme temperature events in fishponds by using a weather forecasting model in combination with local observations from a customized sensor placed in a pond. This system could provide water temperature forecasts with up to 120 h of lead time. A fishpond and multiple events were selected to assess the performance. Compared to the actual observations, the predicted water temperature difference had a root mean square error of <2 °C for up to 72 h of lead time. Furthermore, due to limited computational resources for weather forecasting models, the water temperature and depth data collected by the sensor improved the accuracy of temperature prediction specific to each pond. The results have confirmed that the integrated method can effectively predict the water temperature of farmed fishponds and assist fishermen in implementing precautionary measures in time.

Utilizing Multi-Source Datasets for the Reconstruction and Prediction of Water Temperature in Lake Miedwie (Poland)

Water temperature is a fundamental parameter of aquatic ecosystems. It directly influences most processes occurring within them. Hence, knowledge of this parameter’s behavior, based on long-term (reliable) observations, is crucial. Gaps in these observations can be filled using contemporary methodological solutions. Difficulties in reconstructing water temperature arise from the selection of an appropriate methodology, and overcoming them involves the proper selection of input data and choosing the optimal modeling approach. This study employed the air2water model and Landsat satellite imagery to reconstruct the water temperature of Lake Miedwie (the fifth largest in Poland), for which field observations conducted by the Institute of Meteorology and Water Management—National Research Institute ended in the late 1980s. The approach based on satellite images in this case yielded less accurate results than model analyses. However, it is important to emphasize the advantage of satellite images over point measurements in the spatial interpretation of lake thermal conditions. In the studied case, due to the lake’s shape, the surface water layer showed no significant thermal contrasts. Based on the model data, long-term changes in water temperature were determined, which historically (1972–2023) amounted to 0.20 °C per decade. According to the adopted climate change scenarios by the end of the 21st century (SSP245 and SSP585), the average annual water temperature will be higher by 1.8 °C and 3.2 °C, respectively. It should be emphasized that the current and simulated changes are unfavorable, especially considering the impact of temperature on water quality. From an economic perspective, Lake Miedwie serves as a reservoir of drinking water, and changes in the thermal regime should be considered in the management of this ecosystem.

Breaking the limits of ambient air conditions in evaporative cooling with vacuum-assisted hollow fiber membrane technology

Conventional direct evaporative cooling systems suffer from persistent issues like air–water cross-contamination, mold growth, and scaling of paddings. Moreover, their widespread application in humid climates is hindered by ambient air conditions. This research presents a promising solution in the form of a vacuum-assisted hollow fiber membrane-based evaporative water cooler (VMEWC). It shifts from relying on air state to vacuum pressure for cold water production. A numerical model was established to reflect the coupled heat and mass transfer characteristics, employing Knudsen diffusion to describe water vapor migration within membrane pores. Experimental tests were performed on the constructed VMEWC system, revealing strong alignment between simulation and measured data, with a 2.2 % error in outlet water temperature predictions. Numerical studies explored the effects of six parameters (inlet water temperature, water velocity, shell-side pressure, fiber length, fiber inner diameter, and overall membrane structure parameter) on VMEWC’s outlet water temperature (Tw2), cooling capacity per unit volume (Qv), and coefficient of performance (COP). The results emphasized that elevated inlet water temperature, reduced shell-side pressure, and a large overall membrane structure parameter effectively enhanced the overall performance of VMEWC. Under specific conditions, Qv and COP reached impressive values of 3753.1 kW/m3 and 13.6, respectively. This work provides a novel and practical perspective, offering references for advancing efficient and sustainable refrigeration technologies.

Multi-step ahead prediction of lake water temperature using neural network and physically-based model

ABSTRACT Deep learning (DL) is a powerful tool that has proven highly effective in many applications, but creating new deep learning models is becoming increasingly challenging. However, in some fields, such as fluid dynamics, theoretical models can help design powerful DL models. Based on the existing air2water (A2W) model, this paper proposes a hybrid neural network model, DL-A2W, which combines the long short-term memory (LSTM) neural network with the A2W model to predict lake water temperature. The DL-A2W model was established using the datasets of the UK Centre for Ecology & Hydrology, and the performance of the model was evaluated through three experiments. Compared with other models, the DL-A2W model has the lowest mean absolute error (MAE), mean absolute percent error (MAPE), root mean squared error (RMSE), and the highest Nash-Sutcliffe efficiency coefficient (NSC) at any given prediction step. The values of MAE, MAPE, RMSE and NSC of the DL-A2W model on the test set were 0.223–0.388, 1.946–3.296%, 0.375–0.647 and 0.985–0.995, respectively. The results show that the DL-A2W model has good generalization ability and portability, and can accurately perform multi-step ahead prediction of lake water temperature.

Experimental and simulated evaluation of inverse model for shallow underground thermal storage

This study employs an inverse grey-box (IGB) modeling approach, which combines measured data and the physics of systems to predict the performance of shallow underground thermal energy storage (UTES) with top and side insulation. A simplified IGB model of the shallow UTES was developed using thermal network analysis. Experimental studies were conducted for shallow vertical and horizontal UTES configurations. Detailed models representing the field experiments were developed using the TRNSYS simulation tool and calibrated with the measured data from the experimental setup. The 4 Resistance 2 Capacitance (4R2C) IGB model was trained and tested in MATLAB using field experimental data and the data from the calibrated TRNSYS model. In comparing experimental data with the TRNSYS model for UTES, the prediction of outlet water temperature showed good agreement, with root mean square error (RMSE) and coefficient of variation of RMSE (CVRMSE) values of 0.94 °C and 3.16 % for vertical, and 0.99 °C and 2.97 % for horizontal configurations. The IGB model also aligned well with the experimental data, showing CVRMSE of 7.91 % and 3.17 % for vertical and horizontal systems, respectively. Sensitivity analysis revealed that model performance improves with longer training durations and closer testing times to training periods. However, a convergence point of 20 weeks of training data was achieved for making long-term performance predictions.

A Simple Artificial Neural Model to Predict Dambovita River Temperature Affected by Urban Heat Islands in Bucharest City

Water bodies can offer local microclimates that have the potential to attenuate the effects of urban heat islands by reducing local temperature. This capability is shaded when the river is channelized. In such cases, the river temperature rises during hot periods, leading to negative impacts on the water quality. The main aim of this paper is to develop a local simple model to predict the temperature of the Dâmbovița River at its exit from Bucharest City, the capital of Romania. The location is chosen based on the historical critical impacts, in terms of extreme heatwaves that took place during hot summers, as well as future possible risks due to climate change. The water temperature prediction model is based on an artificial neural network that uses the Levenberg–Marquardt algorithm, due to its stability and rapid convergence capabilities. The model forecasts, with an accuracy of ±1 °C, the water temperature in an ungauged, downstream location, as a function of measured air and upstream water temperatures. The proposed model represents a first attempt to provide water managers in Bucharest City with a useful tool that will allow them to take timely measures to counteract the unwanted effects that can be generated by high water temperatures.

Prediction of daily river water temperatures using an optimized model based on NARX networks

Water temperature is an important physical indicator of rivers because it impacts many other physical and biogeochemical processes and controls the metabolism of aquatic species in rivers. Having a good knowledge of river thermal dynamics is of great importance. In this study, an advanced machine learning based model that is fast, accurate and easy to use, namely the nonlinear autoregressive network with exogenous inputs (NARX) neural network, was coupled with Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training, to forecast daily river water temperatures (RWT). Long-term observed data from 18 rivers of the Vistula River Basin, one of the largest rivers in Europe, were used for model testing, and model performance was compared with the air2stream model. The results showed that the NARX-based model performs significantly better than the air2stream model in the calibration and validation stages, and can better capture the seasonal pattern and peak values of RWT. Input combinations impact the performance of the NARX-based model in RWT modeling, and air temperature and the day of the year (DOY) are the major inputs, while streamflow and rainfall play a minor role on modeling RWT at the Vistula River Basin. Considering that future times series of air temperatures are easily accessible from climate models and DOY is easy to be considered in the model, the NARX-based model can serve as a promising tool to investigate the impact of climate change on river thermal dynamics.

Fair Graph Learning Using Constraint-Aware Priority Adjustment and Graph Masking in River Networks

Accurate prediction of water quality and quantity is crucial for sustainable development and human well-being. However, existing data-driven methods often suffer from spatial biases in model performance due to heterogeneous data, limited observations, and noisy sensor data. To overcome these challenges, we propose Fair-Graph, a novel graph-based recurrent neural network that leverages interrelated knowledge from multiple rivers to predict water flow and temperature within large-scale stream networks. Additionally, we introduce node-specific graph masks for information aggregation and adaptation to enhance prediction over heterogeneous river segments. To reduce performance disparities across river segments, we introduce a centralized coordination strategy that adjusts training priorities for segments. We evaluate the prediction of water temperature within the Delaware River Basin, and the prediction of streamflow using simulated data from U.S. National Water Model in the Houston River network. The results showcase improvements in predictive performance and highlight the proposed model's ability to maintain spatial fairness over different river segments.

Application of real-time water temperature prediction system in winter for long-distance water diversion projects

ABSTRACT Water diversion projects in high-latitude areas often reduce the risk of ice jams in winter by reducing the water transfer flow, which might cause the waste of water transfer benefits. This paper establishes a real-time prediction system of water temperature in winter, which can predict the change in water temperature by inputting the air temperature forecast data and the current hydraulic data. Taking the middle route of the south-to-north water diversion project as the background, the model parameters calibration and system application testing at different time periods are carried out. The results show that the prediction errors of water temperature for the 1 and 7 days are relatively small, and the prediction errors of water temperature at four observation stations can be controlled within ±0.3 and ±0.6 °C, with the root mean square error (RMSE) ranging from 0.07 to 0.25 and 0.12 to 0.36, respectively. The 15-day water temperature prediction results are greatly affected by air temperature input conditions. The prediction errors for the first 7 days are relatively small, ranging from −0.59 to 0.36 °C, and the errors for the last 8 days increase as the accuracy of the air temperature forecast decreases, ranging from −2.42 to 0.22 °C.

Prediction of Water Temperature Based on Graph Neural Network in a Small-Scale Observation via Coastal Acoustic Tomography

Coastal acoustic tomography (CAT) is a remote sensing technique that utilizes acoustic methodologies to measure the dynamic characteristics of the ocean in expansive marine domains. This approach leverages the speed of sound propagation to derive vital ocean parameters such as temperature and salinity by inversely estimating the acoustic ray speed during its traversal through the aquatic medium. Concurrently, analyzing the speed of different acoustic waves in their round-trip propagation enables the inverse estimation of dynamic hydrographic features, including flow velocity and directional attributes. An accurate forecasting of inversion answers in CAT rapidly contributes to a comprehensive analysis of the evolving ocean environment and its inherent characteristics. Graph neural network (GNN) is a new network architecture with strong spatial modeling and extraordinary generalization. We proposed a novel method: employing GraphSAGE to predict inversion answers in OAT, using experimental datasets collected at the Huangcai Reservoir for prediction. The results show an average error 0.01% for sound speed prediction and 0.29% for temperature predictions along each station pairwise. This adequately fulfills the real-time and exigent requirements for practical deployment.

Simulation and prediction of water temperature in a water transfer channel during winter periods using a new approach based on the wavelet noise reduction-deep learning method

Abstract In winter, the water transfer channel of the Middle Route of South-to-North Water Transfer Project (MR-StNWTP) in China always encounters ice problems. The preciously simulation and prediction of water temperature is essential for analyzing the ice condition, which is important for the safety control of the water transfer channel in winter. Due to the difference of specific heat between water and air, when the air temperature rises and falls dramatically, the range of change of water temperature is relatively small and has a lag, which often affects the accuracy of simulation and prediction of water temperature based on air temperature. In the present study, a new approach for simulating and predicting water temperature in water transfer channels in winter has been proposed. By coupling the neural network theory to equations describing water temperature, a model has been developed for predicting water temperature. The temperature data of prototype observations in winter are preprocessed through the wavelet decomposition and noise reduction. Then, the wavelet soft threshold denoising method is used to eliminate the fluctuation of certain temperature data of prototype observations, and the corresponding water temperature is calculated afterward. Compared to calculation results using both general neural network and multiple regression approaches, the calculation results using the proposed model agree well with those of prototype measurements and can effectively improve the accuracy of prediction of water temperature.

Prediction of water temperature of Sebou estuary (Morocco) using ANN and LR

Water temperature is an important component for river water quality. Good knowledge of river thermal regime is critical for aquatic resources management and environmental impact studies. This study aims to predicting surface water temperature at a given site of Sebou estuary (Morocco) from air temperature, using artificial intelligence applied to neural networks (NN) and linear regression (LR). The models used were applied to an hourly temperature database obtained by simulating hydraulic and thermal regime of the estuary using HEC-RAS model. Temperature data (1560) was divided into training and validation series. The results showed that coefficient of determination for training was 86.26%(NN) and 79.94%(LR). For validation it was 91.23%(NN) and 80.3%(LR). Moreover the residuals generally vary between −1.5°C and +1.5°C, no trend was noted as function of estimated temperature. Hence, it is possible to predict water temperature of estuary using air temperature. Additionally, NN model gave slightly results compared to LR.

Two-pathway spatiotemporal representation learning for extreme water temperature prediction

Accurate predictions of extreme water temperatures are criticalto understanding the variability of the marine environment and reducing marine disasters maximized by global warming. In this study, we propose a two-pathway framework with separated spatial and temporal encoders for accurate prediction of water temperature, especially extremely high water temperature, through effective spatiotemporal representation learning. The spatial and temporal encoder networks based on the Transformer’s self-attention mechanism performs the task of predicting the water temperature time series at the 16 coastal locations around the Korean Peninsula for the seven consecutive days ahead at daily intervals with various combinations of patch embedding methods, positional embedding for spatial features. Comparative experiments with conventional deep convolutional and recurrent networks are also conducted for comparison. By comparing and assessing these results, the proposed two-pathway framework can improve the predictability of extremely high coastal water temperature by better capturing spatiotemporal interrelationships and long-range dependencies from open ocean and regional sea, and further determines the optimal architectural details of self-attention-based spatial and temporal encoders. Furthermore, to examine the explainability of the proposed model and its consistency with domain knowledge, spatial and temporal attention maps are visualized and analyzed that represents weights for spatiotemporal input sequences that are more relevant to predict for future predictions.

TemproNet: A transformer-based deep learning model for seawater temperature prediction

Accurate prediction of seawater temperature is crucial for meteorological model understanding and climate change assessment. This study proposes TempreNet, a deep learning model based on a transformer and convolutional neural network, to accurately predict subsurface seawater temperature using satellite observations in the South China Sea. TemproNet uses multivariate sea surface observations such as sea level anomaly (SLA), sea surface temperature (SST), and sea surface wind (SSW) as model inputs, which employs a hierarchical transformer encoder to extract the multi-scale feature, uses a lightweight convolutional decoder to predict seawater temperature. We train and validate the model using the CMEMS temperature dataset and compare its accuracy with Attention-Unet, LightGMB, and ANN. Experimental results show that TemproNet has significantly outperformed other models with RMSE and R2 of 0.52 °C and 0.83 in a 32-layer temperature profile prediction task over 200 m in the South China Sea. In addition, we fully demonstrate the error of our model in space, in time, and at different depths, showing the efficiency and stability of our model. The input sensitivity analysis showed that SST contributed more to predicting shallow water temperature, while SLA significantly impacted the prediction of mid-deep water temperature. The results of this study provide an innovative and reliable solution for seawater temperature prediction and have important implications for meteorological model understanding and climate change assessment.

Prediction of Surface Water Temperature and Its Spatial-Temporal Variation Characteristics of 11 Main Lakes in Yunnan–Guizhou Plateau

Prediction of Surface Water Temperature and Its Spatial-Temporal Variation Characteristics of 11 Main Lakes in Yunnan–Guizhou Plateau

Modeling the Water Temperature Profile of Lakes: A Physics Informed Neural Network (PINN) Approach

Water temperature plays an important role in our environment and is applicable to nearly all limnology research, as the temperature of a body of water affects biological activity and growth of organisms such as algae and bacteria. Certain organisms have a preferred temperature range within which they can survive, while others become dormant or die when the water reaches extreme temperatures. The temperature of water also governs the maximum dissolved oxygen concentration of water. Dissolved oxygen in water is important for aquatic life because of its vital role in cellular respiration. Predicting water temperature is also an important factor in determining whether a body of water is acceptable for human use. Warm bodies of water may contain pathogens that can be dangerous to humans.&#x0D; &#x0D; Our research presents a computational method of determining lake water temperature through a novel technique known as physics-informed neural networks (PINNs). PINNs can be used to model and forecast the temperature of water over a specific time period by training a neural network using the data points derived from the discrete form of a partial differential equation and taking into account the boundary conditions. Several factors such as wind, precipitation, and solar energy effects on water temperature were investigated. By using a computer simulation in place of an analytical mathematical model, a tremendous increase in run time speed can be achieved. The results can be used to determine the patterns in water temperature throughout a year, demonstrating the advantages of a PINN over an analytical model.

River water temperature prediction using hybrid machine learning coupled signal decomposition: EWT versus MODWT

Accurate prediction of water temperature (Tw) will greatly help in improving our understanding of the overall thermal regime fluctuation, and it can help in making sound decisions. While great efforts have been devoted to the development of Tw models, further improvement in the prediction accuracy is challenging. Here, we propose a new hybrid machine learning (ML) models for predicting Tw from air temperature (Ta). First, two signal decomposition algorithms, i.e., the empirical wavelet transform (EWT) and maximum overlap discrete wavelet transform (MODWT) are used for decomposing the Ta into several subsequences. Second, the obtained subsequences are used as input variables for four ML models, i.e., the feedforward neural network (FFNN), the optimally pruned extreme learning machine (OPELM), the adaptive boosting (AdaBoost), and the bootstrap aggregating (Bagging). The development of the hybrid models is based on measured data form five measurement stations distributed over Poland. The experimental results show that, the new hybrid ML models achieved high predictive accuracies with Pearson correlation coefficient (R), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE) and mean absolute error (MAE) of approximately ≈0.99, ≈0.98, ≈0.718 °C, and ≈0.599 °C, respectively. Finally, the experimental results have demonstrated that, the MAE and RMSE of the single models were reduced by 66.12% and 65.30%, while the R and NSE values were improved by 4.85% and 10.02%, respectively, showing a very promising prediction performance. Our new modelling approach proposed in the present study clearly highlight the high contribution of the EWT and MODWT in improving the Tw estimation that is most strongly influenced by Ta. The significant difference between single and hybrid models can be explained by the ability of the EWT and MODWT to capture the high nonlinearity between Ta and Tw. Our new approach is of interest for water resources management and for assessing the variability of Tw over time and space. Finally yet importantly, this is the first study in the literature for which EWT and MODWT were used for modelling Tw, through which we have demonstrated that, an important amount of information is available and cannot be captured using single ML models, and signal decomposition have helped to overcome this challenge.

Incorporating physically-based water temperature predictions into the National water model framework

While river water temperatures are a strong control on instream processes and aquatic ecosystems, monitoring networks for river water temperatures are often sparse. Despite recent advancements in water temperature modeling strategies, current models struggle to provide real-time and reach-specific predictions across broad spatial domains. We developed a physically-based water temperature model coupled to the National Water Model (NWM) to assess the potential for water temperature prediction to be incorporated into the NWM at the continental scale. Using model forcings and outputs from the NWM v2.1 retrospective, we evaluated the ability of four model configurations of increasing complexity to simulate hourly water temperatures in the forested headwaters of H.J. Andrews Experimental Forest, Oregon, USA during a six-week summer period. Our modeling framework, representing a first effort at pairing water temperature simulation with predictions from the NWM, suggests that the NWM can be leveraged to give insight into other water quality variables.

Predicting stream water temperature with artificial neural networks based on open‐access data

AbstractPredictions of stream water temperature are an important tool for assessing potential impacts of climate warming on aquatic ecosystems and for prioritizing targeted adaptation and mitigation measures. Since predictions require reliable baseline data, we assessed whether open‐access data can serve as a suitable resource for accurate and reliable water temperature prediction using artificial neural networks (ANNs). For this purpose, we trained and tested ANNs in 16 small () headwater streams of major types located in Bavaria, Germany. Between four and eight different combinations of input parameters were trained and tested for each stream ANN, based on data availability. These were air temperature (mean, minimum and maximum), day of the year, discharge, water level and sunshine duration per day. We found that the input combination with the highest accuracy (lowest RMSE) was stream‐specific, suggesting that the optimal input combination cannot be generalized across streams. Using a reasonable, but random, input combination resulted in an increase in error (RMSE) of up to &gt;100% compared to the stream‐specific optimal combination. Hence, we conclude that the accuracy of water temperature prediction strongly depends on the availability of open‐access input data. We also found that environmental parameters such as hydrological characteristics and the proportion of land use in the 5 m riparian strip and the entire catchment are important drivers, affecting the accuracy and reliability of ANNs. ANNs' prediction accuracy was strongly negatively related to river length, total catchment area and water level. High proportions of semi‐natural and forested land cover correlated with a higher accuracy, while open‐canopy land use types such as grassland were negatively associated with ANN accuracy. In conclusion, open‐access data were found to be suitable for accurate and reliable predictions of water temperature using ANNs. However, we recommend incorporating stream‐specific environmental information and tailor the combination of input parameters to individual streams in order to obtain optimal results.