Water Temperature Model Research Articles

Towards a better understanding of the evaporative cooling of rivers: case study for the Little Southwest Miramichi River (New Brunswick, Canada)

Stream temperature plays an important role in many biotic and abiotic processes, as it influences many physical, chemical, and biological properties in rivers. As such, a good understanding of the thermal regime of rivers is essential for effective fisheries management and the protection of aquatic habitats. Moreover, a thorough understanding of underlying physical processes and river heat fluxes is essential in developing better and more adaptive water temperature models. Very few studies have quantified river evaporation and rivers’ corresponding evaporative cooling component. The present study investigated the evaporative cooling of the Little Southwest Miramichi River in Eastern Canada by calculating the evaporative heat flux and overall heat fluxes using in-situ data. Results showed that the evaporative heat flux reached −300 W m−2 mid-day when high water temperatures were observed. The daily evaporative heat flux can thus account for close to 50% of the total heat losses, followed by longwave radiation (25%), streambed heat fluxes (20%), and sensible heat (5%). Our results show that the evaporative heat flux can be a critical cooling mechanism for wide and shallow rivers during high summer temperatures.

Quantifying the evolution of ensemble water temperature forecasts as a function of weather forecast lead-time: case study on the Nechako River watershed

Producing and improving hydrological and hydrodynamic forecasts while accounting for uncertainty through a probabilistic approach is useful in various applications, such as for water temperature forecasting. To produce such ensembles, probabilistic meteorological forecasts can be fed into hydrological and water temperature models for different lead-times. This study aims to gauge the impact of the meteorological forecast quality on the accuracy and reliability of water temperature forecast ensembles generated through the HEC-RAS process-based hydrothermal model. The Nechako River, a managed river system in British Columbia, Canada, is used in this case study. The thermal forecasts were generated and evaluated from 2017 to 2020. The tested hypothesis is that improvements in the meteorological forecasts would result in reducing the uncertainty and improving the accuracy of the water temperature forecast ensembles at various lead-times. The results of this study show that the thermal forecasts were indeed improved in terms of their sharpness and their individual accuracy, but no significant impacts were noted over the accuracy of the ensembles. Reliability was also investigated, and it was revealed that water temperature forecast ensembles were initially under-dispersed over the Nechako River, and that this issue was exacerbated when the HEC-RAS model was forced with better quality ensemble weather forecasts. The presence of lakes along the river and the meteorological forecast’s reliability are considered and discussed as causes for this issue. Overall, it was concluded that a reduction of the meteorological inputs’ uncertainty did not improve the uncertainty representation of water temperature forecasts for this system.

Water Temperature Modeling for Water Tank in Recirculating Aquaculture System for Nile Tilapia Raising

Water Temperature Modeling for Water Tank in Recirculating Aquaculture System for Nile Tilapia Raising

Factors affecting urban climate at different times of the day in China: A case study in Yibin, a riverside mountain city

Urban heat islands and global warming degrade urban climate substantially and negatively affect residents’ lifestyle. Experts have explored strategies to mitigate their effects. They found that vegetation and water surfaces had a significant cooling effect, which varied with the time of the day. Thermal performances of the two elements echoed the strategies of nature-based solutions in cooling. Meteorology stations in different counties of one city are sources of big databases providing regionally long-term meteorological data, available for climate studies. The present study used this data to investigate the variation in cooling effects in Yibin, a riverside mountain city located in China. The cooling effects of vegetation and water surface (indicated by river width) were significantly affected by the time of the day. Increasing vegetation cover reduced air temperature by a maximum of 2.2 °C (R² = 0.615, at 4:00 h). A similar cooling effect was observed with river (R² = 0.5149, at 14:00 h). Nevertheless, the cooling effects fluctuated during the day. Vegetation had significant thermal effects in the late afternoon and early evening (4:00–7:00), which is the coolest time during the day. Conversely, waterbodies showed limited effect on weather conditions in the evening. They were thermally adjustable in the afternoon (13:00–16:00, daily warmest time). This phenomenon is attributed to hourly weather changes. Overall, R² values of vegetation-temperature models decreased, while those of water-temperature models increased with temperature. These findings represent an advance in our understanding of urban climate. Also, it is important to find that the two key cooling factors (vegetation and river width) behave differently at different times of the day. To achieve full day cooling, it is imperative that future planning should incorporate proper landscaping in their design works. This finding is helpful for future generations in terms of climate change adaptation.

Review of Statistical Water Temperature Models for a Peruvian Andean River

Review of Statistical Water Temperature Models for a Peruvian Andean River

Bat algorithm optimised extreme learning machine (Bat‐ELM): A novel approach for daily river water temperature modelling

AbstractHere, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat‐ELM) is demonstrated for river water temperature (Tw) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented for comparison. The models were developed according to two scenarios: (1) using air temperature (Ta) as input for predicting Tw, and (2) using Ta and the periodicity (i.e., day, month and year number). River Tw calibration and validation results derived from air temperature and the periodicity show its potential application. The Bat‐ELM accurately predicts the Tw and surpassed all other models with coefficient of correlation (R) values ranging within the limits of 0.973 to 0.981, and the Nash‐Sutcliffe efficiency (NSE) values will fall within the interval of 0.947 to 0.963. Findings from this research also highlight the robustness of the Bat‐ELM using the periodicity by enhancing its ability to estimate river Tw.

A coupled streamflow and water temperature (VIC-RBM-CE-QUAL-W2) model for the Nechako Reservoir

Study regionNechako Reservoir, British Columbia, Canada. Study focusHydrological regulation affect both hydrological and thermal conditions in the reservoir and downstream reach, subsequently disrupting fish habitats. This paper aims at developing an integrated model simulating physical processes that govern the quantity and quality of inflow, reservoir, and outflow water of the Nechako Reservoir. Such a model would help stakeholders understand the response of in-reservoir water temperature stratification and downstream water temperature to changes in inflow and reservoir operation under future climate change. New hydrological insights for the regionThe model was calibrated against historical reservoir levels and in-reservoir and outlet water temperature field data. The integrated model simulated accurately the wide variation of reservoir levels as well as the in-reservoir water temperature at Kenney Dam and the outlet temperature. Sensitivity analysis shows that reservoir water temperature particularly the epilimnion is sensitive to changes in both meteorological and hydrological forcing. Forcing the model with different outflow scenarios shows the weak sensitivity of temperature of water released to outflow rates. Given epilimnion water releases at the spillway, the Summer Temperature Management Program could be inefficient to provide cool water in the Nechako River during the critical period of salmon migration in a warming climate. However, colder water remains available at depth at Kenney Dam to potentially mitigate and better control downstream water temperature.

Tables, Figures and data on Review of statistical water temperature models for a Peruvian Andean river

Tables, Figures and data on Review of statistical water temperature models for a Peruvian Andean river

Comparative Study on Water Temperature Stratified Flow under Different Vertical Coordinate Systems in Delft3D

Reservoirs often suffer from water blooms, which are likely related to the hydrodynamic and water temperature characteristics of the tributary bays. To obtain the detailed changing process of hydrodynamics and water temperature stratification, it is necessary to choose a suitable vertical coordinate system in order to achieve the required precision. Based on a physical model experiment of cold water flowing into the Generalized Reservoir Hydraulics (GRH) flume, both the σ-coordinate system model and the z-coordinate system model are built for comparison. For the z-coordinate system model, the influences of different grid resolutions and different bottom slopes on the simulation accuracy are also analyzed. The results show that the σ-coordinate system model can simulate cold-water underflow in a reservoir better than the z-coordinate system model, and the numerical errors of the z-coordinate system model can be reduced but not eliminated by increasing the horizontal grid resolution. When the bottom slope of the reservoir is less than 18‰, the z-coordinate system model can also be used to simulate cold-water underflow in a reservoir. The conclusions about vertical coordinate systems can be applied to the development of a three-dimensional hydrodynamic and water temperature model of reservoirs.

Using the ERA5 and ERA5-Land reanalysis datasets for river water temperature modelling in a data-scarce region

It has become apparent in recent decades that river water temperature can have immediate and lasting impacts on aquatic organisms and their lotic habitat. In rivers that are dammed, there is an opportunity and a responsibility to regulate flows in order to control these temperatures to ensure the survival of the fish and other aquatic life. This paper uses a physically based hydraulic model (HEC-RAS) to run a water temperature component, allowing the thermal model to simulate water temperatures at the same hourly time step as the hydraulic model in a data-sparse region using two meteorological reanalysis datasets (ERA5 and ERA5-Land) as inputs allowing for a full representation of the diurnal cycle. This was achieved by making use of the HEC-RAS controller to automate the calibration and subsequent simulation processes. Results show that these products are able to provide high-quality thermal simulations on a 200 km river system in British Columbia, Canada, obtaining mean absolute errors in validation of 0.66 °C and a root mean square error of 0.84 °C. Some of the boundary conditions seemed to have little effect on downstream water temperatures. This is due to the measured point of interest being far enough downstream of the dam that a thermal equilibrium is reached well before. Simulations using shorter river reaches confirm that long lakes in the study region contribute to the thermal equilibrium being attained. There also seems to be a limit to the advantage conveyed by increased spatial density of the data, as results indicate a form of skill plateau after a certain input data density is attained.

Evaluation of the WRF-Lake Model in the Large Dimictic Reservoir: Comparisons with Field Data and Another Water Temperature Model

Abstract The WRF-lake, as a one-dimensional (1D) lake model popularly used for coupling with the Weather Research and Forecasting (WRF) system and modeling lake–atmosphere interactions, does not consider the heat exchange caused by inflow–outflow, which is an important characteristic of large reservoirs and can affect the energy budget and reservoir–atmosphere interactions. We evaluated the WRF-lake model by applying it at a large dimictic reservoir, Miyun Reservoir, in northern China. The results show that the WRF-lake model, though ignoring inflow–outflow, yields good surface water temperature simulation through reasonable parameterization. The Minlake model, as a better physically based model in reservoirs, was used to test the effect of inflow–outflow, including heat carried by inflow–outflow water exchange and water level change on the 1D model’s performance. The effect of heat carried by inflow–outflow is mainly in summer, negatively correlated with hydraulic residence time and positively correlated with temperature difference between inflow and outflow. For a reservoir with hydraulic residence time of 3 years and temperature difference between inflow and outflow about 10°C in summer, the heat carried by inflow–outflow is far less than the heat exchange through the surface (<2%) and therefore has little influence on total energy balance. The effect of water level change is mainly on latent heat and sensible heat in unit area, rather than outgoing longwave radiation. Though influencing the temperature in deep layers, the water level change does not have a significant impact on the surface temperature. Significance Statement The purpose of this study is to evaluate the applicability of WRF-lake, an important submodule of the Weather Research and Forecasting (WRF) system, in the large dimictic reservoir. This is important because WRF-lake does not consider the effect of inflow–outflow and water level change, which are important characteristics of large reservoirs and can affect the heat budget and reservoir–atmosphere interactions. The applicability of WRF-lake in large reservoirs with frequent inflow–outflow and water level change is widely concerned but has never been discussed in previous studies. Our research explored the applicability of WRF-lake in the large dimictic reservoir and discussed the effect of inflow–outflow and water level change quantitively.

Global characterization factors for quantifying the impacts of increasing water temperature on freshwater fish

Water temperature is an abiotic master variable for the survival of aquatic organisms. Global warming alters the thermal regimes of rivers and, thus, poses a threat to freshwater biodiversity. To address the impacts of water temperature changes related to global warming on freshwater fish species in life cycle assessment (LCA), we developed spatially explicit characterization factors (CFs) for 207 greenhouse gases under four representative concentration pathways. We calculated fate factors by using the output of a global hydrological model fully coupled with a dynamic water temperature model. We developed six species sensitivity distribution curves for two thermal effects (i.e., lethal and sub-lethal) to derive effect factors, which take the differences in sensitivity between climate regions into account. The regional CFs for CO2 ranged from 2.91 × 10−22 to 6.53 × 10−18 PAF·yr/kg for sub-lethal effects and from 1.98 × 10−22 to 4.58 × 10−18 PDF·yr/kg for lethal effects, depending on the river watersheds and future climate scenarios. To identify the contribution of regional impacts on freshwater fish to their potential global extinction, the regional CFs were converted into global CFs. The largest CFs always occur in the tropical watersheds. The regional impacts in the Amazon watershed contribute the most to the global freshwater fish species extinction. This study contributes to assessing the potential impacts on freshwater biodiversity from global warming from a new cause-effect pathway in LCA.

Modelling water temperature in the lower Olifants River and the implications for climate change

Freshwater systems in southern Africa are under threat of climate change, not only from altered flow regimes as rainfall patterns change, but also from biologically significant increases in water temperature. Statistical models can predict water temperatures from air temperatures, and air temperatures may rise by up to 7 °C by 2100. Statistical water temperature models require less data input than physical models, which is particularly useful in data deficient regions. We validated a statistical water temperature model in the lower Olifants River, South Africa, and verified its spatial applicability in the upper Klaserie River. Monthly and daily temporal scale calibrations and validations were conducted. The results show that simulated water temperatures in all cases closely mimicked those of the observed data for both temporal resolutions and across sites (NSE>0.75 for the Olifants River and NSE>0.8 for the Klaserie). Overall, the model performed better at a monthly than a daily scale, while generally underestimating from the observed (indicated by negative percentage bias values). The statistical models can be used to predict water temperature variance using air temperature and this use can have implications for future climate projections and the effects climate change will have on aquatic species. Significance: Statistical modelling can be used to simulate water temperature variance from observed air temperature, which has implications for future projections and climate change scenarios. While there are many other factors affecting water temperature, air temperature accounts for up to 95% of water temperature variance. The model used can successfully simulate water temperature variance for different rivers.

FutureStreams, a global dataset of future streamflow and water temperature

There is growing evidence that climate change impacts ecosystems and socio-economic activities in freshwater environments. Consistent global data of projected streamflow and water temperature are key to global impact assessments, but such a dataset is currently lacking. Here we present FutureStreams, the first global dataset of projected future streamflow and water temperature for multiple climate scenarios (up to 2099) gridded at a 5 arcminute spatial resolution (~10 km at the equator), including recent past data (1976–2005) for comparison. We generated the data using global hydrological and water temperature models (PCR-GLOBWB, DynWat) forced with climate data from five general circulation models. We included four representative concentration pathways to cover multiple future greenhouse gas emission trajectories and associated changes in climate. Our dataset includes weekly streamflow and water temperature for each year as well as a set of derived indicators that are particularly relevant from an ecological perspective. FutureStreams provides a crucial starting point for large-scale assessments of the implications of changes in streamflow and water temperature for society and freshwater ecosystems.

Least square support vector machine-based variational mode decomposition: a new hybrid model for daily river water temperature modeling.

Machines learning models have recently been proposed for predicting rivers water temperature (Tw) using only air temperature (Ta). The proposed models relied on a nonlinear relationship between the Tw and Ta and they have proven to be robust modelling tools. The main motivation for this study was to evaluate how the variational mode decomposition (VMD) contributed to the improvement of machines learning performances for river Tw modelling. Measured data collected at five stations located in Poland from 1987 to 2014 were acquired and used for the analysis. Six machines learning models were used and compared namely, K-nearest neighbor's regression (KNNR), least square support vector machine (LSSVM), generalized regression neural network (GRNN), cascade correlation artificial neural networks (CCNN), relevance vector machine (RVM), and locally weighted polynomials regression (LWPR). The six models were developed according to three scenarios. First, the models were calibrated using only the Ta as input and obtained results show that the models were able to predict consistently water temperature, showing a high determination coefficient (R2) and Nash-Sutcliffe efficiency (NSE) with values near or above 0.910 and 0.915, respectively, and in overall the six models worked equally without clear superiority of one above another. Second, the air temperature was combined with the periodicity (i.e., day, month and year number) as input variable and a significant improvement was achieved. Both models show their ability to accurately predict river Tw with an overall accuracy of 0.956 for R2 and 0.955 for NSE values, but the LSSVM2 have some advantages such as a small errors metrics, and high fitting capabilities and it slightly surpasses the others models. Thirdly, air temperature was decomposed into several intrinsic mode functions (IMF) using the VMD method and the performances of the models were evaluated. The VMD parameters appeared to cause much influence on the prediction accuracy, exhibiting an improvement of about 40.50% and 39.12% in terms of RMSE and MAE between the first and the third scenarios, however, some models, i.e., GRNN and KNNR have not benefited from the VMD. This research has demonstrated the high capability of the VMD algorithm as a preprocessing approach in improving the accuracies of the machine learning models for river water temperature prediction.

Assessing climate change impacts on Pacific salmon and trout using bioenergetics and spatiotemporal explicit river temperature predictions under varying riparian conditions.

Pacific salmon and trout populations are affected by timber harvest, the removal and alteration of riparian vegetation, and the resulting physical changes to water quality, temperature, and associated delivery of high-quality terrestrial prey. Juvenile salmon and trout growth, a key predictor of survival, is poorly understood in the context of current and future (climate-change mediated) conditions, with resource managers needing information on how land use will impact future river conditions for these commercially and culturally important species. We used the Heat Source water temperature modeling framework to develop a spatiotemporal model to assess how riparian canopy and vegetation preservation and addition could influence river temperatures under future climate predictions in a coastal river fed by a moraine-dammed lake: the Quinault River in Washington State. The model predicted higher water temperatures under future carbon emission projections, representative concentration pathway (RCP) 4.5 and 8.5, with varying magnitude based on different riparian vegetation scenarios. We used the daily average temperature output from these scenarios to predict potential juvenile fish growth using the Wisconsin bioenergetics model. A combination of riparian vegetation removal and continued high carbon emissions resulted in a predicted seven-day average daily maximum temperature (7DADM) increase of 1.7°C in the lower river by 2080; increases in riparian shading mitigate this 7DADM increase to only 0.9°C. Under the current thermal regime, bioenergetics modeling predicts juvenile fish lose weight in the lower river; this loss of potential growth worsens by an average of 20–83% in the lower river by 2080, increasing with the loss of riparian shading. This study assess the impact of riparian vegetation management on future thermal habitat for Pacific salmon and trout under warming climates and provide a useful spatially explicit modeling framework that managers can use to make decisions regarding riparian vegetation management and its mechanistic impact to water temperature and rearing juvenile fish.

An Artificial Neural Network Mechanism for Optimizing the Water Treatment Process and Desalination Process

An artificial neural network (ANN) was created to predict the operation of a reverse osmosis desalination process, and then it was utilized to supply water temperature modeling. The techniques used for producing the new water by utilizing the water treatment process and desalination of seawater process are growing rapidly. Desalination produces water for residential use, the processing industry, water supply, and other reasons. Membrane procedures (Reverse Osmosis RO) or thermal desalination are always the most common desalination techniques. The optimization process consists of some units such as coagulation, sedimentation, disinfection, settling, and filtration. The process of desalination has various parameters such as vacuum pressure, feed salt concentration, temperature feed into the inlet, and rate of feed flow. For the desalination process, reverse osmosis is the most helpful process for desalination that can be coupled with more variable renewable energy sources are wind and solar. Using a couple of renewable energy to produce freshwater from saltwater can able to reduce the impact on the environment that desalination can create due to the power consumption of energy. The optimization of water treatment and desalination experimental results was developed by the model of an artificial neural network (ANN).

Learning from mistakes-Assessing the performance and uncertainty in process-based models.

Typical applications of process‐ or physically‐based models aim to gain a better process understanding or provide the basis for a decision‐making process. To adequately represent the physical system, models should include all essential processes. However, model errors can still occur. Other than large systematic observation errors, simplified, misrepresented, inadequately parametrised or missing processes are potential sources of errors. This study presents a set of methods and a proposed workflow for analysing errors of process‐based models as a basis for relating them to process representations. The evaluated approach consists of three steps: (1) training a machine‐learning (ML) error model using the input data of the process‐based model and other available variables, (2) estimation of local explanations (i.e., contributions of each variable to an individual prediction) for each predicted model error using SHapley Additive exPlanations (SHAP) in combination with principal component analysis, (3) clustering of SHAP values of all predicted errors to derive groups with similar error generation characteristics. By analysing these groups of different error‐variable association, hypotheses on error generation and corresponding processes can be formulated. That can ultimately lead to improvements in process understanding and prediction. The approach is applied to a process‐based stream water temperature model HFLUX in a case study for modelling an alpine stream in the Canadian Rocky Mountains. By using available meteorological and hydrological variables as inputs, the applied ML model is able to predict model residuals. Clustering of SHAP values results in three distinct error groups that are mainly related to shading and vegetation‐emitted long wave radiation. Model errors are rarely random and often contain valuable information. Assessing model error associations is ultimately a way of enhancing trust in implemented processes and of providing information on potential areas of improvement to the model.

Optimisation of a recirculating domestic hot water system to minimise wait time and heat loss

Many houses suffer from a delay in hot water arrival, after opening the hot tap, due to the length of pipe work from the boiler or thermal store. Measurements in three properties showed delays of up to 46 s. A recirculation system could maintain the water in the pipe at a suitable temperature and for a 15 m pipe is expected to use 30 W less electrical power than a local thermal store at the delivery point. Time-steady eigenvector solutions to the linked temperature equations allow optimisation of the recirculation flow rate, pipe diameters and insulation thickness. The pumping power is <1 mW and a pumpless thermo-syphon system should be possible in some installations. Heat loss coefficients have been calculated for a pair of pipes in a common insulation sleeve to minimise the losses. A plug flow transient advection model for water and pipe temperature based on a sliding 1-D grid has been developed and validated for a single pipe, then extended to also model recirculating systems. Heat losses in a pumped system may be reduced using a timer so the system can cool overnight. The transient model predicts the relationship between flow and warm-up rate to optimise pump size and timing.

A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters

Demand-response techniques are crucial for providing a proper quality of service under the paradigm of smart electricity grids. However, control strategies may perturb and cause discomfort to clients. This article proposes a methodology for defining an index to estimate the discomfort associated with an active demand management consisting of the interruption of domestic electric water heaters. Methods are applied to build the index include pattern detection for estimating the water utilization using an Extra Trees ensemble learning method and a linear model for water temperature, both based on analysis of real data. In turn, Monte Carlo simulations are applied to calculate the defined index. The proposed approach is evaluated over one real scenario and two simulated scenarios to validate that the thermal discomfort index correctly models the impact on temperature. The simulated scenarios consider a number of households using water heaters to analyze and compare the thermal discomfort index for different interruptions and the effect of using different penalty terms for deviations of the comfort temperature. The obtained results allow designing a proper management strategy to fairly decide which water heaters should be interrupted to guarantee the lower discomfort of users.