Temperature Data Research Articles

Occurrence of Wetness on the Fruit Surface Modeled Using Spatio-Temporal Temperature Data from Sweet Cherry Tree Canopies

Occurrence of Wetness on the Fruit Surface Modeled Using Spatio-Temporal Temperature Data from Sweet Cherry Tree Canopies

Effects of Climate Variability on Bushfire Regimes in the Bagoué Region, Côte d'Ivoire

Bushfires are an important factor in the dynamics of savannah landscapes. This study is carried out in the Bagoué region to evaluate the relationships between climatic variability and bushfire regimes. To achieve this objective, rainfall and temperature data were analyzed. The information’s were collected with 300 heads of household surveyed, 100 in Boundiali, Kouto and Tengrela were selected, respectively. A purposive sampling was defined according to the following criteria: any person who uses fires in agricultural activity; the respondent must be at least 30 years old and have been in the survey for at least 20 years. The results recorded from 1990 to 2002, showed that the rainfall was increased at 14.8%followed by a 10.7% and was decreased between 2003 and 2020 in Boundiali. Average annual rainfall was 1,408.99 mm, with a 13.2% increase between 1990 and 2002 and a 20.1% deficit between 2003 and 2020. Average annual rainfall in Tengrela also showed h1,515.9 mm, with a surplus of 171.5 mm, i.e. an increase of 12.8% from 1990 to 2002 and a rainfall deficit of 9.2% from 2003 to 2020. Temperatures rise to 0.7°C, 0.74°C and 0.74°C in Boundiali, Kouto and Tengrela between 1990 and 2020, respectively. Three fire periods were identified with lowest intensities and frequencies. The combination of rainfall declining, the temperatures increasing, the highest dry biomass, and the agricultural activities contributes to the emergence of bushfires in the Bagoué region. This study highlights the importance of addressing these factors to mitigate the impact of bushfires. This research highlights the importance of considering climatic factors in understanding bushfire dynamics in savannah landscapes.

Assessing the Volatility of Daily Maximum Temperature across Germany between 1990 and 2022

Climate change causes a global rise in mean air temperature and an increased frequency of temperature extremes. Recent studies link sharp temperature changes between consecutive days to increased mortality, reduced economic growth, and negative effects on ecosystems. This study assesses the variability of the daily maximum air temperature between two consecutive days (i.e., temperature volatility) across Germany from 1990 to 2022. Using observation-based raster data of maximum daily temperature at 0.1° × 0.1° spatial resolution, we assess temperature volatility regarding: (1) magnitude, (2) seasonality, (3) directionality (day-to-day warming vs. cooling), and (4) trends. Further, we analyse land cover changes during the period and examine their correlation to extreme temperature volatility (Tve). The results show that Tve mostly occurred during spring and summer. The magnitude of Tve increased with distance to the coast north-west to south during all seasons and was highest during spring and summer (>10 °C). Overall, Tve was particularly associated with day-to-day cooling (in spring, summer, autumn), while in winter, >60% of days were associated with warming in north and central Germany. Less than 12% of Germany showed significant trends in median Tve associated with warming over the period. Significant trends included increases (>0.26 °C/year) and decreases (−0.09 °C/year) in extreme day-to-day warming during autumn and winter in northeastern Germany. In spring, the majority (>60%) of southern areas showed significant positive trends (up to 0.16 °C/year) in Tve associated with day-to-day cooling. During winter, summer, and autumn, trends in extreme day-to-day cooling were insignificant in over 80% of Germany. Within all land cover types, Tve predominantly varied between 6 °C and 9 °C. Changes in land cover, especially transforming coniferous forests for agricultural purposes, were accompanied by increasing Tve up to 0.49 °C. Understanding rapid temperature changes is crucial for climate change mitigation strategies and limiting the impacts on human health and on the environment.

Assessing uncertainty in bioclimatic modelling: a comparison of two high-resolution climate datasets in northern Patagonia

Climate change is reshaping forest ecosystems, presenting urgent and complex challenges that demand attention. In this context, research that quantifies interactions between climate and forests is substantial. However, modelling at a spatial resolution relevant for ecological processes presents a significant challenge, especially given the diverse geographical contexts in which it is applied. In our study, we aimed to assess the effects of applying CHELSA v.2.1 and WorldClim v2.1 data on bioclimatic analysis within the Río Puelo catchment area in northern Patagonia. To achieve this, we inter-compared and evaluated present and future bioclimates, drawing on data from both climate datasets. Our findings underscore substantial consistency between both datasets for temperature variables, confirming the reliability of both for temperature analysis. However, a strong contrast emerges in precipitation predictions, with significant discrepancies highlighted by minimal overlap in bioclimatic classes, particularly in steep and elevated terrains. Thus, while CHELSA and WorldClim provide valuable temperature data for northern Patagonia, their use for precipitation analysis requires careful consideration of their limitations and potential inaccuracies. Nevertheless, our bioclimatic analyses of both datasets under different scenarios reveal a uniform decline in mountain climates currently occupied by N. pumilio, with projections suggesting a sharp decrease in their coverage under future climate scenarios.

Drivers of Daily Water Level Fluctuation of Shallow Groundwater in the Inner Delta of the River Danube

Groundwater flow systems are influenced by the changes in surface waters as well as climatic factors. These teleconnections significantly increase in cases of extreme weather conditions. To prepare and mitigate the effect of such phenomena, the background factors that create and influence natural processes must be recognized. In the present study, 94 shallow groundwater (SGW) wells’ water level time series were analyzed in the inner delta of the River Danube (Europe) the Szigetköz region to explore which factors contribute to the development of diurnal periodicity of SGW and what its drivers are. The relationship between surface meteorological processes and SGW dynamics in the Szigetköz region was investigated using hourly data from monitoring wells. Hourly water temperature data exhibited weak correlations with meteorological parameters. However, daily averaged data revealed stronger correlations, particularly between SGW levels and air temperature and potential evapotranspiration. Diurnal periodicity in SGW fluctuations correlated strongly with potential evapotranspiration. The study also demonstrated the role of capillary fringe dynamics in linking surface evapotranspiration with SGW fluctuations. Changes in groundwater levels, even small, can significantly affect soil moisture, vegetation, and ecosystem functioning, highlighting the sensitivity of the unsaturated zone to SGW fluctuations driven by surface processes.

Integrated monitoring and prediction of thermal discharge from nuclear power plants using satellite, UAV, and numerical simulation.

Global nuclear power is surging ahead in its quest for global carbon neutrality, eyeing an anticipated installed capacity of 436 GW for coastal nuclear power plants by 2040. As these plants operate, they emit substantial amounts of warm water into the ocean, known as thermal discharge, to regulate the temperature of their nuclear reactors. This discharge has the potential to elevate the temperature of the surrounding seawater, potentially influencing the marine ecosystem in the discharge vicinity. Therefore, our study area is on the Qinshan and Jinqimen Nuclear Power Plants in China, employing a blend of Landsat 8/9, and unmanned aerial vehicle (UAV) imagery to gather sea surface temperature (SST) data. In situ measurements validate the temperature data procured through remote sensing. Leveraging these SST observations alongside hydrodynamic and meteorological data from field measurements, we input them into the MIKE 3 model to prognosticate the three-dimensional (3D) spatial distribution and temperature elevation resulting from thermal discharge. The findings reveal that (1) satellite remote sensing can instantly acquire the horizontal distribution of thermal discharge, but with a spatial resolution much lower than that of UAV. The spatial resolution of UAV is higher, but the imaging efficiency of UAV is only 1/40,000 of that of satellite remote sensing. (2) Numerical simulation models can predict the 3D spatial distribution of thermal discharge. Although UAV and satellite remote sensing cannot directly obtain the 3D spatial distribution of thermal discharge, using remotely sensed SST as the temperature field input for the MIKE 3 model can reduce the quantity of measured temperature data and lower the cost of numerical simulation. (3) In the process of monitoring and predicting the thermal discharge of nuclear power plants, achieving an effective balance between monitoring accuracy and cost can be realized by comprehensively considering the advantages and costs of satellite, UAV, and numerical simulation technologies.

Research on Temperature Distribution and Gradient Prediction of U-Shaped Girder Bridge under Solar Radiation Effect

With the development of bridge engineering, U-shaped girder bridges have been applied in numerous bridge designs due to their structural characteristics. However, the U-shaped girder bridge is sensitive to solar radiation effects, leading to uneven temperature distributions that can affect the service performance of the structure. Thus, this study proposes an analysis method for the temperature distribution of U-shaped girder bridges and develops a prediction model to estimate temperature gradients. First, an improved ASHRAE clear sky model is proposed to calculate the structural shadow areas under sunlight, which provides a basis for the numerical simulation of U-shaped girder bridges under solar radiation effect. Then, a three-dimensional finite element model of the U-shaped girder bridge is established, and its correctness is verified by comparing with the actual temperature data. The temperature distribution of the U-shaped girder bridge under solar radiation is simulated using the verified model to obtain the maximum temperature difference and temperature variation characteristics. Finally, a prediction model for the temperature gradient is developed using nonlinear fitting approaches, and its accuracy is confirmed through comparison with actual data. The results indicate that the temperature distribution of the U-shaped girder bridge has minor changes along the longitudinal direction, while there are significant changes in the transverse distribution; the temperature distribution exhibits nonlinear changes in the height direction of the two side webs and the lateral direction of the bottom slab, with the maximum temperature difference reaching 17 °C; the fitting effect of the prediction model is very good, the correlation coefficients of the fitting curve and the actual data are all greater than 88%, providing a basis for the analysis of the temperature effects on U-shaped girder bridges and its application in design specifications.

ConvTrans-CL: Ocean time series temperature data anomaly detection based context contrast learning

Ocean temperature data anomaly detection is instrumental in monitoring environmental changes and implementing measures to alleviate adverse consequences. This holds immense importance for marine environmental observation and scientific inquiry. However, existing anomaly detection methods encounter significant challenges in extracting features from data, which severely affects the performance of anomaly detection. Existing models have limitations in capturing the local contextual distribution features and high stochastic distribution trends of ocean temperature data. Therefore, this paper introduces the ConvTrans-CL model, which integrates the Transformer encoder with causal convolution and employs a contrastive learning approach. Causal convolution extracts the distributional features of short-time subsequences within a sliding window and integrates them into the self-attention mechanism, enabling the model to focus on the localized features of the data. Contrastive learning efficiently captures the long-range dependencies of time-series data by distinguishing between pairs of subsequences with adjacent time intervals and pairs of non-adjacent subsequences. This enables the model to capture the trend of high stochasticity in the distribution of the temperature data. Finally, we select temperature data from two sea areas that are susceptible to multiple environmental factors for experiments and compare the feature extraction and anomaly detection capabilities of ConvTrans-CL with other methods, confirming the superior performance of our method.

Intelligent inversion of mesoscale eddy temperature anomaly profiles based on multi-source remote sensing data

Inversion of the underwater temperature anomaly of mesoscale eddies from sea surface remote sensing data plays an important role in understanding the three-dimensional structure of eddies. In this article, a neural network structure, named the eddy temperature anomaly inversion network (EddyTAINet), is proposed and established to invert the vertical temperature anomalies of eddies using 20-year (2002–2022) Argo profiles and multi-source remote sensing data in the northwestern Pacific Ocean. Vertical temperature anomaly profiles with 45 depth levels ranging from 10 to 1000 m are derived as the outputs of the inversion networks. The input feature sequences are extracted from the mesoscale eddy product based on sea level anomaly (SLA) data, and sea surface temperature (SST) data are also introduced into the proposed network structure to improve its accuracy. For the problem of inconsistent sizes of the 2D matrix SST data corresponding to mesoscale eddies, the spatial pyramid pooling (SPP) network module is introduced, which allows different sizes of SST data to be simply convolved and then subjected to hierarchical feature extraction to obtain fixed-size feature vectors. Validation and comparison of the network structure are performed and show the improved performance of EddyTAINet. The average root mean square error (RMSE) and mean absolute error (MAE) are 0.63 °C/0.66 °C and 0.48 °C/0.51 °C for cyclonic and anticyclonic eddies, respectively, which are more than 20% lower than the results of the network using the SLA-based data.

Effects of energy sharing and electricity tariffs on optimal sizing of PV-battery systems for grid-connected houses

This paper investigates the optimal sizing of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected houses based on mutually-agreed energy sharing prices by considering the flat and time-of-use (TOU) tariffs. The grid-tied house with PV-BES, referred to house 1 (H1) in the paper shares electricity with house 2 (H2) whenever needed with mutually-agreed electricity tariffs. The main objective function of the study is to minimize the cost of electricity (COE) for the H1 while decreasing COE for H2. Eight different schemes are investigated with the combination of flat and TOU tariffs for buying, selling, and mutually-agreed rates, respectively. For each scheme, optimal sizing of components and COE for both houses are evaluated. Realistic hourly-arranged annual data of temperature, solar irradiation, and load consumption of two houses are used as the input data. For each scheme, the results are compared with the situation when H1 does not have PV-BES and there is no electricity sharing. Sensitivity, operational, and uncertainty analyses are provided for the scheme with the lowest COE.

Quantitative assessment and driving factors analysis of surface urban heat island of urban agglomerations in China based on GEE.

The urban heat island (UHI) effect generated by the development of high-speed urbanization has become one of the major problems affecting the urban ecological environment. As the main body of urbanization in China, China's urban agglomerations are the core areas of urban heat island effect. The purpose of this study is to study the spatial-temporal characteristics and driving factors of surface urban heat island in 19 urban agglomerations in China, with a view to providing theoretical references for the prevention of urban thermal environmental risks. Based on Google Earth Engine (GEE), this paper estimated the surface urban heat island intensity (SUHII) of 19 urban agglomerations in China from 2003 to 2019 using MODIS land surface temperature (LST) data. Correlation analysis and regression analysis were used to explore the correlation between the change of SUHII and driving factors. Finally, the driving factors of SUHII were detected by the geo-detector model. Results showed that (1) the SUHII of 19 urban agglomerations in arid and semi-arid areas of northwestern China is higher than that in humid areas of eastern and southeastern China. (2) The SUHII of 19 urban agglomerations in China generally shows a decreasing trend, and the spatial variation of the change trend is significant. (3) There are positive correlations between SUHII and reference evapotranspiration (ET0), population density (POP), gross domestic product (GDP), nitrogen dioxide (NO2), ozone (O3), and ultraviolet aerosol index (UVAI); negative correlations with normalized difference vegetation index (NDVI), DEM, sulfur dioxide (SO2), carbon monoxide (CO), and formaldehyde (HCHO); the correlations all pass the significance test of P < 0.05 and are statistically significant. (4) The factor detection results showed that NDVI, land cover type (LC), and UVAI were the main driving factors of SUHII. The interaction detection results showed that the interaction between O3 and UVAI had the most significant impact on SUHII.

Thermal Management of Lithium-Ion Battery Pack Using Equivalent Circuit Model

The design of an efficient thermal management system for a lithium-ion battery pack hinges on a deep understanding of the cells’ thermal behavior. This understanding can be gained through theoretical or experimental methods. While the theoretical study of the cells using electrochemical and numerical methods requires expensive computing facilities and time, the Equivalent Circuit Model (ECM) offers a more direct approach. However, upfront experimental cell characterization is needed to determine the ECM parameters. In this study, the behavior of a cell is characterized experimentally, and the results are used to build a second-order equivalent electrical circuit model of the cell. This model is then integrated with the cooling system of the battery pack for effective thermal management. The Equivalent Circuit Model estimates the internal heat generation inside the cell using instantaneous load current, terminal voltage, and temperature data. By extrapolating the heat generation data of a single cell, we can determine the heat generation of the cells in the pack. With the implementation of the ECM in the cooling system, the coolant flow rate can be adjusted to ensure the attainment of a safe operating cell temperature. Our study confirms that 14% of pumping power can be reduced when compared to the conventional constant flow rate cooling system, while still maintaining the temperature of the cells within safe limits.

Bayesian parameter estimation and evaluation of the K-ω shear stress transport model for plane impinging jets

Numerical simulations with semi-empirical turbulence models are commonly used to model impinging jets, often used for cooling solid surfaces. In this work, the constants in the k-ω shear stress transport model in ANSYS FLUENT are calibrated to experimental velocity and heat transfer data for a plane turbulent impinging air jet to determine if Kennedy-O’Hagan calibration (Kennedy and O’Hagan 2001 J. R. Stat. Soc. B 63 425–64) can improve predictions of near-surface velocities and surface Nusselt numbers for similar flows. Impinging jets have been proposed to cool the target plates of the divertor in future magnetic fusion energy reactors, where simulations are used to estimate divertor performance. The flat-plate divertor (Wang et al 2009 Fusion Sci. Technol. 56 1023–7) uses a plane jet of helium issuing from a B = 0.5 mm slot to cool a surface with radius of curvature of 44B at a distance 4B from the slot. Predictions from the calibrated numerical model are compared with independent experimental data at different flow conditions, as well as surface temperature data for a flat plate divertor test section. The contribution of this work is evaluation of the accuracy of a calibrated turbulence model for modest extrapolations in flow geometry and flow conditions for a plane impinging jet.

Site-specific apparent optimum air temperature for vegetation photosynthesis across the globe

The apparent optimum air temperature for vegetation photosynthesis (Topt) is a key temperature parameter in terrestrial ecosystem models estimating daily photosynthesis or gross primary production (GPP, g C/m2/day). To date, most models use biome-specific Topt (Topt-biome) parameter values. Given vegetation acclimation and adaptation to local climate, site-specific Topt (Topt-site) is needed to reduce uncertainties in estimating daily GPP across the scales from site to region and the globe. Previous studies have demonstrated using the Enhanced Vegetation Index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images and daytime air temperature data to estimate the Topt-site at the eddy covariance tower sites. This study used MODIS-derived EVI and ERA5 climate data to estimate and generate global Topt-site data products from 2000 to 2019. The Topt-site of individual pixels within a biome has large variation, which clearly cannot be represented accurately by the widely used Topt-biome. Therefore, using this global dataset of Topt-site estimates might significantly affect GPP simulation in current ecosystem models.

Periodic Energy Optimization Using IOT and ML

The rapid expansion of Internet of Things (IoT) applications across various sectors generates an enormous volume of continuous time-series data. However, transmitting this massive amount of sensor data from energy constrained IoT nodes poses a significant challenge. The continuous transmission of such data consumes vast amounts of energy.In this work, we present a solution to this problem by predicting the periodic behavior of sensor data through a higher-level view of continuous transmission data from nodes in IoT at server side. Our system is composed of an IoT sensor network and a data processing unit. The local sensor network: temperature and humidity data is collected using 4 different nodes, as well, which afterward this info is transferred into a data processing unit built on the Raspberry Pi device. We use the machine learning model Autoregressive Integrated Moving Average (ARIMA) on the processing unit. This model is then applied individually to the data from each of the four nodes, predicting processed sensor values in the future accurately. In short, after getting highly accurate prediction, then we settle down proper energy saving pattern which reduces the data transmission requirements hence results in energy saving pattern.By utilizing the predictive capabilities of the ARIMA model, we minimize the need for constant transmission of raw sensor data. Instead, we transmit only essential updates or deviations from the predicted values. This approach substantially reduces energy consumption by eliminating the transmission of redundant information. In summary, our project aims to overcome the energy limitations of IoT sensor nodes by leveraging predictive modelling techniques, specifically the ARIMA model. By accurately predicting periodic patterns in sensor data, we can optimize energy usage by transmitting only the necessary information, while still ensuring effective monitoring of temperature and humidity in the IoT network.

Research on Runoff Prediction Based on Time2Vec-TCN-Transformer Driven by Multi-Source Data

Due to the frequent occurrence of extreme weather in recent years, accurate runoff prediction is crucial for the rational planning and management of water resources. Addressing the high uncertainty and multiple influencing factors in runoff prediction, this paper proposes a runoff prediction method driven by multi-source data. Based on multivariate observed data of runoff, water level, temperature, and precipitation, a Time2Vec-TCN-Transformer model is proposed for runoff prediction research and compared with LSTM, TCN, and TCN-Transformer models. The results show that the Time2Vec-TCN-Transformer model outperforms other models in metrics including MAE, RRMSE, MAPE, and NSE, demonstrating higher prediction accuracy and reliability. By effectively combining Time2Vec, TCN, and Transformer, the proposed model improves the MAPE for forecasting 1–4 days in the future by approximately 7% compared to the traditional LSTM model and 4% compared to the standalone TCN model, while maintaining NSE consistently between 0.9 and 1. This model can better capture the periodicity, long-term scale information, and relationships among multiple variables of runoff data, providing reliable predictive support for flood forecasting and water resources management.

Little Evidence of Hysteresis in Regional Precipitation, When Indexed by Global Temperature Rise and Fall in an Overshoot Climate Simulation

Abstract Society has set the aim of stabilising climate at key temperature thresholds, such as global warming at or below 1.5°C or 2.0°C above preindustrial levels. However, greenhouse gas emissions are failing to decline, and if they continue on their current trajectory it is likely that such thresholds will be crossed in the decades ahead. Because of this risk, there is an emerging focus on overshoot, where, for a temporary period, global warming is allowed to cross critical thresholds to reach a peak value before decreasing to the desired limit. A key question about overshoots is whether there are hysteresis effects—that is, whether global or regional climate has properties that differ between the phase of global warming increase and the phase of decreasing. Here, we analyse temperature and precipitation data from five Earth System Models (ESMs) forced by the SSP5-3.4-OS CMIP6 overshoot scenario. We look at the level of precipitation during two periods of near-identical global warming: one whilst temperatures are rising, and the other when they are falling. For global means, we find a statistically significant difference between precipitation values during the two periods. This is an example of hysteresis, as the reversion to an earlier global warming state results in a level of global rainfall which is different from that observed when warming was increasing. Spatial disaggregation of rainfall differences between the two near-identical warming levels shows the largest differences in the tropical region, which are statistically significant for four of the five ESMs. When considering much smaller regions, including parts of the tropics, there remains some evidence of hysteresis. However, the differences are no longer statistically significant against a background of substantial interannual rainfall variability. We discuss the implications of our findings for climate impacts assesments.&amp;#xD;

Multiple serial correlations in global air temperature anomaly time series.

Serial correlations within temperature time series serve as indicators of the temporal consistency of climate events. This study delves into the serial correlations embedded in global surface air temperature (SAT) data. Initially, we preprocess the SAT time series to eradicate seasonal patterns and linear trends, resulting in the SAT anomaly time series, which encapsulates the inherent variability of Earth's climate system. Employing diverse statistical techniques, we identify three distinct types of serial correlations: short-term, long-term, and nonlinear. To identify short-term correlations, we utilize the first-order autoregressive model, AR(1), revealing a global pattern that can be partially attributed to atmospheric Rossby waves in extratropical regions and the Eastern Pacific warm pool. For long-term correlations, we adopt the standard detrended fluctuation analysis, finding that the global pattern aligns with long-term climate variability, such as the El Niño-Southern Oscillation (ENSO) over the Eastern Pacific. Furthermore, we apply the horizontal visibility graph (HVG) algorithm to transform the SAT anomaly time series into complex networks. The topological parameters of these networks aptly capture the long-term correlations present in the data. Additionally, we introduce a novel topological parameter, Δσ, to detect nonlinear correlations. The statistical significance of this parameter is rigorously tested using the Monte Carlo method, simulating fractional Brownian motion and fractional Gaussian noise processes with a predefined DFA exponent to estimate confidence intervals. In conclusion, serial correlations are universal in global SAT time series and the presence of these serial correlations should be considered carefully in climate sciences.

Assessing the accuracy of two steady‐state temperature models for onboard passenger vehicle photovoltaics applications

AbstractWe assess the accuracy of two steady‐state temperature models, namely, Ross and Faiman, in the context of photovoltaics (PV) systems integrated in vehicles. Therefore, we present an analysis of irradiance and temperature data monitored on a PV system on top of a vehicle. Next, we have modeled PV cell temperatures in this PV system, representing onboard vehicle PV systems using the Ross and Faiman model. These models could predict temperatures with a coefficient of determination (R2) in the range of 0.61–0.88 for the Ross model and 0.63–0.93 for the Faiman model. It was observed that the Ross and Faiman model have high errors when instantaneous data are used but become more accurate when averaged to timesteps of greater than 1000–1500 s. The Faiman model's instantaneous response was independent of the variations in the weather conditions, especially wind speed, due to a lack of thermal capacitance term in the model. This study found that the power and energy yield calculations were minimally affected by the errors in temperature predictions. However, a transient model, which includes the thermal mass of the vehicle and PV modules, is necessary for an accurate instantaneous temperature prediction of PV modules in vehicle‐integrated (VIPV) applications.

Network Graph Laplacian-Based Sensor Projection in System-Level Modeling of Liquid-Metal Loop

Accurate modeling of nuclear reactor transients is important for improving operation and maintenance planning strategies in advanced reactors. An adequate understanding of the connectivity between various components and sensor signals can provide the means to project plant information even where sensors are not operational. This work presents a method for projecting temperature signals using network graph Laplacian and support vector regression (SVR). A system model of a scaled liquid-metal-cooled reactor experiment facility is used to generate thermal-hydraulic data simulating cold-shock transients into the reactor plenum. Two different flow rates are investigated, 0.03 kg/s and 0.06 kg/s, describing the different degrees of nonlinear disturbances in two cases. The temperatures at 72 locations within the system are used to test the temperature prediction model. The inter-connectivity of these points is described using the network graph Laplacian, which describes a location in the loop as a node and then comprises a weighted matrix of connections between each of these nodes. Additional nodes, called ghost nodes, are used to model the heat transfer of the loop with the surroundings. The graph Laplacian and corresponding nodal sensing data are then used to train a kernel, which can inform the impact of various phenomena at one location in the plant on phenomena at other locations in the plant. The sensing data and graph network of these nodes are used to construct a surrogate model of the liquid-metal loop. This model is then used to predict the behavior at certain nodes where the training data are not provided to the model. These model predictions are compared against the test data for the two different inlet flow rates. When optimized, the average error of the simulated temperature data remained below 5% when 3 of the 72 nodes are predicted using the graph-based SVR model. As the number of unknown sensors is increased, the root-mean-square error increases slightly but still remains below 2% when 24 of the 72 nodes are unknown or tested. The test or unknown sensor location plays a larger role than the number of unknowns, with sensor locations near ghost nodes and near the outlet pipes in the plenum having the largest error, with a maximum recorded error of 7%. The objective of the graph-based SVR model is to not only capture the temperature or field variable accurately but also to capture the relative connectivity between the simulated sensors. The correlation coefficient matrix provides a scaled reference for the correlation between temperatures at two different node locations. The calculated correlation coefficient for the simulated temperature data and actual temperature data is within 5% for most of the system, with a maximum relative error of 15%.