Temperature Data Research Articles

A framework for multivariate analysis of compound extremes based on correlated hydrologic time series

While hydrologic design has primarily relied on use of annual maxima values, many events of hydrologic interest such as active and break spells in monsoonal rains, heat waves, flash flooding from snowmelt, etc. manifest at time scales of days to weeks, and require daily (or finer resolution) data for proper characterization. Often combinations of several hydrologic variables herald highly impactful events and are labelled as compound extremes. Using a time-varying interval-censored estimation method of copula models, a novel multivariate approach for dealing with compound extremes under temporal dependence amongst variables and when data contain significant ties is developed here to enable the determination of design magnitudes and associated risk. The efficacy of this method is demonstrated over the Godavari River Basin, India, using daily precipitation and temperature data from a recent period (1977 to 2020) during the monsoon season. A conservative approach is proposed for estimating the design magnitudes of hydrologic variables in multivariate settings. The significance of ties and temporal dependence amongst precipitation and temperature data in estimation of design magnitudes of cold-wet compound extremes at specified probabilities of exceedance is explored at various spatial scales. Ties and temporal dependence are both shown to have a profound influence on design estimates. Since ties and temporal variation in dependence amongst hydrologic variables are ubiquitous features of most hydrologic data, this framework would be applicable for characterization of other compound extremes in hydrology.

Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods

Groundwater (GW) plays a crucial role in coastal aquifers and arid regions, serving as a lifeline for communities by providing a reliable and resilient water source, making its monitoring essential for sustainable water management. This study aimed at modeling GW via regionalization of the Gravity Recovery and Climate Experiment (GRACE) data based on two methods. The first method directly regionalized the GRACE data for modeling GW via in situ measurements, including the lake level, precipitation, temperature, observed GW and Penman-Monteith-Leuning (PML) evapotranspiration data. The second method included two stages, in the first stage, the GRACE data were downscaled via the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) data which contains satellite based precipitation, temperature, soil moisture, and snow water equivalent data. In the second stage, the downscaled GRACE was bias corrected to provide regionalized data. Artificial intelligence models consist of shallow networks (Feed Forward Neural Network (FFNN), Adaptive neuro fuzzy (ANFIS), Support Vector Machine (SVR)), the ensemble of shallow networks and Long-Short Term Memory (LSTM) deep learning method were employed in the modeling process and the observed GW level data were targeted for the regionalization. The Link CluE clustering ensemble method was implemented to cluster the piezometers of the aquifer to separate different GW patterns in the area. The proposed methodology was examined over the Miandoab plain, one of the sub-basins of the Lake Urmia, located in Northwest Iran. The modeling results demonstrated that the first method could exhibit superior performance with the Nash-Sutcliffe Efficiency (NSE) of up to 17% higher than the second method. Thus, using in situ observed data for downscaling proved to be more accurate than relying on the data based on the satellite imagery. The results indicated that the ensemble of shallow networks could lead to more precise results than using the deep and shallow learning models, individually, where the NSE for the ensemble of shallow networks was up to 50% higher compared to the LSTM model.

Optimizing gula apong production with an IoT-based temperature monitoring system

<p>Determining the quality of <em>gula apong</em> is crucial to optimizing its production, with cooking temperature being a key factor affecting both taste and shelf life. The <em>gula apong</em> industry faced challenges due to the lack of reliable real-time temperature monitoring methods during the cooking process. Traditional approaches were inefficient and inaccurate, leading to difficulties in maintaining consistent product quality and meeting market demands. This highlights the necessity of monitoring the temperature throughout each cooking process. This research aims to develop an internet of things (IoT)- based cooking temperature monitoring system to enhance quality control measures in the production of <em>gula apong</em>. The IoT prototype collects temperature data from the thermocouple sensor, then transmits it to cloud storage through a Wi-Fi communication network, utilizing the Node-RED platform for data processing and analysis. Data obtained from the on-site measurement shows that the optimal temperature for producing standard-quality <em>gula apong</em> is approximately around 165 °C. The recommended boiling temperature for Nipah sap is 140 °C. This IoT system can reduce the cooking time of <em>gula apong</em> to 3 hours from the traditional 4 to 6 hours. Utilizing the data acquired from this study helps the producers not only maintaining the quality of <em>gula apong</em> but also reduce the cooking time and cost.</p>

Improving defect detection capability of pulse and pulse phase thermography method for CFRP plates by enhancing rear surface heat transfer coefficient

Infrared pulse thermography (PT) is a noncontact and convenient nondestructive inspection method, and its application to various objects has been reported in many papers. However, conventional PT is not suitable for detecting deep- or near-rear-surface defects. This study focuses on PT inspections of CFRP plates and improvements in their defect detection capabilities. To improve the capability of detecting near-rear-surface defects, a method for enhancing the heat transfer coefficient (HTC) of the rear surface during PT inspection was examined in this study; this would increase the temperature difference in the defective areas observed in thermal images. To investigate the effectiveness of the proposed method, experiments were conducted on CFRP specimens with artificial defects. The CFRP specimens were inspected using the PT method, during which water was brought into contact with the rear surfaces of the specimens to enhance the HTC. The experimental results showed that the capability of detecting near-rear-surface defects improved when the rear surfaces were in contact with water with forced convection. In addition, obtaining phase images by applying a Fourier transform to the experimentally obtained temperature data (that is, pulse phase thermography: PPT) was examined. Consequently, the combination of the enhancement of the rear-surface HTC and the use of phase images was significantly effective in improving the defect detection capability.

A hierarchical spline model for correcting and hindcasting temperature data

A hierarchical spline model for correcting and hindcasting temperature data

Experimental and numerical analysis of an asphalt solar collector with a conductive asphalt mixture

Asphalt solar collectors (ASCs) have emerged as a promising renewable energy source. They offer a practical solution for generating renewable energy and thereby reduce the reliance on fossil fuels. In this study, a computational analysis and experimental validation of an ASC with a conductive asphalt mixture were conducted. The ASC that was investigated had 3000 mm long serpentine copper tubes positioned at the center of a 1000 mm ×60 mm ×8 mm sample of hot-mix asphalt. Ten 250 W infrared bulbs installed in two rows served as the simulated heat source of the ASC. The ASC had a water flow in the range of 14–97 L⁄h and solar intensities of 600, 800, and 1000W/m2. The thermal response of the ASC was modeled using the COMSOL Multiphysics 5.6.0.280 heat-transfer module. The experimental results indicted that the efficiency of the ASC was 53.56% or 64.1% with either reference or conductive mixtures, respectively. When a conductive mixture was used in the ASC, its surface temperature decreased by 11.37% when compared to that obtained using the reference mixture. The numerical simulation revealed that the average deviation between the numerical and experimental temperature data of the water that flowed within the ASC tubes was between 6% and 14%. The use of conductive asphalt mix would enhance the thermal performance of ASCs.

Validating ANSYS Heat Transfer Models Using Experimental Data Analysis of Two Phase Change Materials with Differing Melting Temperatures

Phase change materials are becoming more and more popular as viable options for improving the thermal performance and energy efficiency of a variety of applications, especially in innovative building envelope designs. In this paper, experimental data from two different phase change materials with differing melting temperatures (21°C and 28°C) are used to validate numerical system heat transfer models. The experimental setup included a heat flux apparatus, which was utilized to ascertain average temperature and heat flow changes over time during both heating and cooling phases for both phase change materials. The data obtained from the experiments were utilized to generate ANSYS Fluent simulation models replicating the experimental setup. The parameters and boundary conditions for the models can be assigned in several ways within ANSYS Fluent software. Consequently, two simulation models were created: one integrated the thermal and physical properties of the experimental setup's system components, while the other utilized the measured heat-flux values over time from the experiments as an input source for calculating average temperatures within the phase change material. The average temperature data from both simulation and experimental results were compared to validate both ANSYS models. By aligning the simulated results with the experimental data, the accuracy and reliability of the numerical models have been established in predicting the thermal behaviour of the two phase change materials. The two numerical system heat transfer models developed in this study serve as valuable tools for conducting further analysis and optimization of systems based on phase change materials. This research highlights the significance of phase change materials in enhancing the thermal performance of building envelopes, particularly in solar energy applications.

Kontrol Temperatur dan Kelembapan pada Ingkubator Bayi Menggunakan Platform Antares

The condition of premature birth is a condition where a baby needs an incubator to maintain the baby's body temperature, because premature babies do not have many weaknesses to regulate body temperature so that they are prone to hypothermia. Setting the temperature and humidity in the baby incubator is an important aspect of caring for premature babies. The use of baby incubators in hospitals requires a high cost of up to Rp. 500,000 per night, even though premature babies need care in an incubator for up to one month, therefore a tool is needed to control humidity and temperature based on the Internet of Things. Control the humidity and temperature of the air used in the patient's home using a microcontroller where the microcontroller will detect the humidity and temperature inside the baby and the temperature measurement results can be viewed via the internet of things which can display temperature and humidity reading data remotely. The results of the comparison between LYNX32 and the platform between the displayed temperature and humidity values have an error of 0%. The Antares platform display displays temperature and humidity data in real time and has historical data that can be downloaded via the Antares platform, making it easier for nurses to monitor conditions.

Climate Dynamics over Kerala, India: Insight from a Century-long Temperature and Rainfall Data Analysis

Historical climate data analysis is of great significance in climate change adaptation and mitigation planning at global as well as regional levels. This article attempted to study the long-term trends of temperature and rainfall across the districts in Kerala, India. CRU monthly time series data of rainfall and temperature data spanning from 1901-2022 were used for the analysis. Mann-Kendall test and Sen’s Slop estimator were applied to detect the presence and magnitude of the trend, and Pettitt’s homogeneity test was used to find the climate change point in temperature time series data. The analysis found a significant and positive temperature trend across all districts in Kerala, with temperature increases ranging from 0.0086°C/year to 0.0102°C/year. In terms of rainfall trends, June and January experienced a significant decrease, while July and September saw a significant increase over the years. The year 1976 was identified as the point of climate change. It was observed that there was an increase in the southwest and Northeast monsoons in the post-period, with higher variability in the latter. Winter rainfall notably decreased during the post-period. The variability in climate parameters identified in this study could impact crop cycles and agricultural productivity, requiring further investigation at a micro-level for effective adaptation and mitigation strategies for the state.

Rice Husk-Based Insulators: Manufacturing Process and Thermal Potential Assessment

The development of bio-insultation materials has attracted increasing attention in building energy-saving fields. In tropical and hot–humid climates, building envelope insulation is important for an energy efficient and comfortable indoor environment. In this study, several experiments were carried out on a bio-insulation material, which was prepared by using rice husk as a raw material. Square rice husk-based insultation panels were developed, considering the ASTM C-177 dimensions, to perform thermal conductivity coefficient tests. The thermal conductivity coefficient obtained was 0.073 W/(m K), which is in the range of conventional thermal insulators. In a second phase of this study, two experimental enclosures (chambers) were constructed, one with rice husk-based insulation panels and the second one without this insulation. The measures of the temperatures and thermal flows through the chambers were obtained with an electronic module based on the ARDUINO platform. This module consisted of three DS18B20 temperature sensors and four Peltier plates. Daily temperature and heat flux data were collected for the two chambers during the dry season in Panama, specifically between April and May. In the experimental chamber that did not have rice husk panel insulation on the roof, a flow of up to 28.18 W/m2 was observed, while in the chamber that did have rice husk panels, the presence of a flow toward the interior was rarely observed. The rice husk-based insulation panels showed comparable performance with conventional insulators, as a sustainable solution that takes advantage of a local resource to improve thermal comfort and the reduction of the environmental impact.

Implementation of inverse method for identification of process parameters in a laser heating process

Changes in process parameters can alter the temperature data of a solid body during the laser heating process. This paper proposes an inverse method to determine the laser heating parameters, namely, sheet width, sheet thickness, laser power, and scan speed of the work specimen for the given temperature response which is assigned by the user. The method uses an analytical tool on moving heat source as a forward model capable of real-time temperature prediction of the sheet under the laser heating process. The effectiveness of the present forward model that incorporated the temperature-dependent material properties is tested by experimental findings performed on Al 6061-T6 sheets. Next, an iterative search process based on heuristic method is carried out for satisfying a prescribed temperature response by the optimization of unknown parameters. To illustrate the implementation and assess the practicality of the inverse method, two examples based on laser heating applications are used. The method proposed herein recover the unknowns for the prescribed temperature response after a few iterations, provides an efficient way to optimize the laser heating process. Additionally, the effect of measurement error on the findings of the inverse problem is addressed.

Optimizing electrical and thermal performance in AlGaN/GaN HEMT devices using dual‐metal gate technology

AbstractThe investigation of aluminum gallium nitride/gallium nitride high electron mobility transistor (AlGaN/GaN HEMT) devices with a dual‐metal gate (DMG) structure encompasses both electrical and thermal characteristics. As efforts to enhance heat dissipation progress, there is a concurrent exploration of novel semiconductor materials boasting high thermal conductivity, like boron arsenide and phosphide. Combining these materials into a model and measuring their interface achieves efficient energy transport. Minimizing the self‐heating impact in AlGaN/GaN HEMTs is essential for enhancing device efficiency. This research exposes the heterogenous combination of boron arsenide and phosphide cooling substrates with metals, GaN semiconductors and HEMT. In this research, the autoencoder deep neural network techniques in GaN HEMT for self‐heat reduction is driven by the ability to effectively analyze and model the thermal behavior of the device. Autoencoders learn complex relationships within temperature data and identify patterns associated with self‐heating. By leveraging these learned representations, the deep neural network optimizes control strategies to mitigate self‐heating effects in GaN HEMT devices, ultimately contributing to improved thermal management and enhanced overall performance. In this research, the use of genetic algorithms in GaN HEMT aims to optimize device parameters systematically, to minimize self‐heating effects and enhance overall thermal performance. The structure also enhances electron mobility within the channel. Results show DMG structures, exhibiting higher saturation output currents and transconductance despite self‐heating. The DMG exhibits a maximum gm value of 0.164 S/mm, which is 10% higher significantly enhancing GaN‐based HEMTs for improved reliability and efficiency in various applications.

LOSS MODEL OF CLIMATE INSURANCE BASED ON EFFECT OF GROWING DEGREE DAYS INDEX

Climate change is a threat to agriculture, especially food crops such as rice. Climate index insurance is an alternative to cover the risk of agricultural losses due to crop failure due to climate change factors. The observed climate index is the effect of growing degree days which measures the impact of temperature on plant growth and development. The data used in this study is daily temperature data at Climatology Station Class 1 Darmaga, Bogor and Meteorological Station Class 3 Citeko, West Java, during the gadu (rice that is planted in the Gadu/Dry season) planting period. In determining the amount of loss, the average daily temperature on growing degree days is calculated using a combination of a time series model and a deterministic model. The deterministic model describes the trend and seasonality of the time series at each station. The parameters contained in the model will be estimated using least-square. To see the dependence of temperature at different stations using a normal bivariate distribution. The result show that the amount of loss based on the index of growing degree days per unit rupiah per degree Celsius (℃) for Meteorological Station Class 3 Citeko only occurs for certain percentages, namely 80%, 90%, and 95%, while for Climatology Station Class 1 Darmaga Bogor it can occur for each percentage. This indicates that the amount of losses obtained will depend on determining the strike level by using the mean and standard deviation of the growing degree days index distribution. Furthermore, these findings suggest that Climatology Station Class 1 Darmaga Bogor have higher risk of crop failure due to climate change than Meteorological Station Class 3 Citeko.

A Predictive Model for Wellbore Temperature in High-Sulfur Gas Wells Incorporating Sulfur Deposition

HSG (high-sulfur gas) reservoirs are prevalent globally, yet their exploitation is hindered by elevated levels of hydrogen sulfide. A decrease in temperature and pressure may result in the formation of sulfur deposits, thereby exerting a notable influence on gas production. Test instruments are susceptible to significant corrosion due to the presence of hydrogen sulfide, resulting in challenges in obtaining bottom hole temperature and pressure test data. Consequently, a WTD (wellbore temperature distribution) model incorporating sulfur precipitation was developed based on PPP (physical property parameter), heat transfer, and GSTP (gas–solid two-phase) flow models. The comparison of a 2.53% temperature error and a 4.80% pressure error with actual field test data indicates that the established model exhibits high accuracy. An analysis is conducted on the impact of various factors, such as production, sulfur layer thickness, reservoir temperature, and reservoir pressure, on the distribution of the wellbore temperature field and pressure field. Increased gas production leads to higher wellhead temperatures. The presence of sulfur deposits reduces the flow area and wellhead pressure. A 40% concentration of hydrogen sulfide results in a 2 MPa pressure drop compared to a 20% concentration. Decreased reservoir pressure and temperature facilitate the formation of sulfur deposits at the wellhead.

Analyzing inflow to Faye reservoir sensitivity to climate change using CMIP6 and random forest algorithm

ABSTRACT In the era of Climate Change and Climate Variability (CC and CV), renewable energy sources such as Hydropower (HP) have a significant role to play in mitigation. However, inflow to reservoir which is the key fuel for HP generation is vulnerable to CC and CV. Thus, there is a need to investigate the potential impacts of CC and CV on HP systems in the future. This study attempts to assess the potential impacts of CC and CV on the Faye reservoir inflow using the Random Forest (RF) algorithm. For this purpose, bias-adjusted precipitation and temperature data of thirteen climate model outputs and their ensemble mean from Coupled Model Inter-comparison Project Phase 6 (CMIP6) under three Shared Socioeconomic Pathways scenarios (SSP1-2.6; SSP2-4.5 and SSP5-8.5) were used as predictors. The potential changes in reservoir inflows were evaluated in the near (2025–2049), mid (2050–2074) and far (2075–2099) futures relative to the reference period (1990–2014). The results show the good performance of the RF algorithm in simulating reservoir inflows with Cor > 0.6 for all models. The annual inflows to the Faye reservoir are noted to increase in the future compared to the reference period despite the potential decrease in future precipitation probably due to land use/cover change. For the ensemble mean of models, this projected increase is estimated to around 16%, 23% and 10%, respectively under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios for all projection periods. The largest annual increase is noted under the SSP2-4.5 scenario while the lowest increase is noted under the SSP5-8.5 scenario for all projection periods. This study could help the small dam managers better consider the implications of CC and CV on inflow management.

Integrated Underground Analyses as a Key for Seasonal Heat Storage and Smart Urban Areas

The design and performance of a shallow geothermal system is influenced by the geological and hydrogeological context, environmental conditions and thermal demand loads. In order to preserve the natural thermal resource, it is crucial to have a balance between the supply and the demand for the renewable energy. In this context, this article presents a case study where an innovative system is created for the storage of seasonal solar thermal energy underground, exploiting geotechnical micropiles technology. The new geoprobes system (energy micropile; EmP) consists of the installation of coaxial geothermal probes within existing micropiles realized for the seismic requalification of buildings. The underground geothermal system has been realized, starting from the basement of an existing holiday home Condominium, and was installed in dry subsoil, 20 m-deep below the parking floor. The building consists of 140 apartments, with a total area of 5553 m2, and is located at an altitude of about 1490 m above sea level. Within the framework of a circular economy, energy saving and the use of renewable sources, the design of the geothermal system was based on geological, hydrogeological and thermophysical analytical studies, in situ measurements (e.g., Lefranc and Lugeon test during drilling; Rock Quality Designation index; thermal response tests; acquisition of temperature data along the borehole), numerical modelling and long-term simulations. Due to the strong energy imbalance of the demand from the building (heating only), and in order to optimize the underground annual balance, both solar thermal storage and geothermal heat extraction/injection to/from a field of 380 EmPs, with a relative distance varying from 1 to 2 m, were adopted. The integrated solution, resulting from this investigation, allowed us to overcome the standard barriers of similar geological settings, such as the lack of groundwater for shallow geothermal energy exploitation, the lack of space for borehole heat exchanger drilling, the waste of solar heat during the warm season, etc., and it can pave the way for similar renewable and low carbon emission hybrid applications as well as contribute to the creation of smart buildings/urban areas.

Preterm and Early-Term Delivery After Heat Waves in 50 US Metropolitan Areas

Heat waves are increasing in frequency, intensity, and duration and may be acutely associated with pregnancy outcomes. To examine changes in daily rates of preterm and early-term birth after heat waves in a 25-year nationwide study. This cohort study of singleton births used birth records from 1993 to 2017 from the 50 most populous US metropolitan statistical areas (MSAs). The study included 53 million births, covering 52.8% of US births over the period. Data were analyzed between October 2022 and March 2023 at the National Center for Health Statistics. Daily temperature data from Daymet at 1-km2 resolution were averaged over each MSA using population weighting. Heat waves were defined in the 4 days (lag, 0-3 days) or 7 days (lag, 0-6 days) preceding birth. Daily counts of preterm birth (28 to <37 weeks), early-term birth (37 to <39 weeks), and ongoing pregnancies in each gestational week on each day were enumerated in each MSA. Rate ratios for heat wave metrics were obtained from time-series models restricted to the warm season (May to September) adjusting for MSA, year, day of season, and day of week, and offset by pregnancies at risk. There were 53 154 816 eligible births in the 50 MSAs from 1993 to 2017; 2 153 609 preterm births and 5 795 313 early-term births occurring in the warm season were analyzed. A total of 30.0% of mothers were younger than 25 years, 53.8% were 25 to 34 years, and 16.3% were 35 years or older. Heat waves were positively associated with daily rates of preterm and early-term births, showing a dose-response association with heat wave duration and temperatures and stronger associations in the more acute 4-day window. After 4 consecutive days of mean temperatures exceeding the local 97.5th percentile, the rate ratio for preterm birth was 1.02 (95% CI, 1.00-1.03), and the rate ratio for early-term birth was 1.01 (95% CI, 1.01-1.02). For the same exposure, among those who were 29 years of age or younger, had a high school education or less, and belonged to a racial or ethnic minority group, the rate ratios were 1.04 (95% CI, 1.02-1.06) for preterm birth and 1.03 (95% CI, 1.02-1.05) for early-term birth. Results were robust to alternative heat wave definitions, excluding medically induced deliveries, and alternative statistical model specifications. In this cohort study, preterm and early-term birth rates increased after heat waves, particularly among socioeconomically disadvantaged subgroups. Extreme heat events have implications for perinatal health.

Utilizing Data-Driven Approaches to Forecast Fluctuations in Groundwater Table

Accurate forecasting of fluctuations in groundwater table is crucial for the effective management of regional water resources. This study explores the potential of utilizing remotely sensed satellite data to predict and forecast water table variations. Specifically, two Artificial Neural Network (ANN) models were developed to simulate water table fluctuations at two distinct well sites, namely BA Ea 18 and FR Df 35 in Maryland. One model leveraged the relationship between variations in brightness temperature and water table depth, while the other model was founded on the association between changes in soil moisture and water table depth. These models were trained and validated using recorded water table depths from the aforementioned wells, brightness temperature data acquired from the Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E), and soil moisture information generated using the Land Data Assimilation System (LDAS). All models exhibited strong performance in predicting and forecasting water table fluctuations, with root mean square errors ranging from 0.043 m to 0.047 m for a 12-month forecasting horizon. Sensitivity tests revealed that the models displayed greater sensitivity to uncertainties in water table depth compared to uncertainties in both brightness temperature and soil moisture content. This underscores the feasibility of constructing an ANN-based water table prediction model, even in cases where high-resolution remotely sensed data is unavailable. In such situations, the model’s efficacy is contingent on the compatibility of the time series trends in data, such as brightness temperature or soil moisture, with those observed at the study site.

MSAIL: Milligram-Scale Multi-Modal Sensor Platform for Monarch Butterfly Migration Tracking

Each fall, millions of monarch butterflies across the northern US and Canada migrate up to 4,000km to overwinter in specific mountain peaks in central Mexico. To track monarchs precisely and study their navigation, a monarch tracker must obtain daily localization of the butterfly as it progresses on its three-month journey. And, the tracker must perform this task while having a weight in the tens of milligrams (mg) and measuring a few millimeters (mm) in size to avoid interfering with the monarch's flight. This paper proposes mSAIL, 8 × 8 × 2.6mm and 62mg embedded system for monarch migration tracking, constructed using 8 prior custom-designed ICs providing solar energy harvesting, an ultra-low power processor, light/temperature sensors, power management, and a wireless transceiver, all integrated and 3D stacked on a micro PCB with an 8 × 8mm printed antenna. The proposed system is designed to record and compress light and temperature data during the migration path while harvesting solar energy for energy autonomy, and wirelessly transmit the data at the overwintering site in Mexico, from which the daily location of the butterfly can be estimated using a deep learning-based localization algorithm. A two-day trial experiment of mSAIL attached to a live butterfly in an outdoor botanical garden demonstrates the feasibility of individual butterfly localization and tracking.

The spatiotemporal characteristics and driving mechanisms of subsurface marine heatwaves in the Xisha Region

Under the background of global climate change, Subsurface Marine Heatwaves (SSMHWs) have become a ‘hot-spot’ research due to their significant impacts on marine ecosystems. Temperature data from the ECCO2 for the years 1992 to 2021 is used to research the spatiotemporal characteristics of SSMHWs in the upper 500 m over the Xisha region (110°E to 113°E, 15°N to 18°N). This study indicates that SSMHWs of high intensity occur in the Xisha area, with the vertical maximum intensity at approximately 100 m. These events exhibit significant seasonal variations, with the highest intensity occurring in May. The intensity of SSMHW in the analysis region varies interannually. SSMHWs have intensified at a rate of 1.53°C·Days/Year and tend to shift to deeper water over the past three decades. A block-based method for SSMHW identification is proposed, in order to take the vertical extent of the event in consideration. By analyzing the eight most intense events based on spatial cumulative intensity, it suggested that warm mesoscale eddies may play an important role on the spatial distribution of the SSMHWs. Statistical analysis shows that the intensity and coverage of the warm mesoscale eddy in the Xisha zone may influence the intensity and evolution of the SSMHWs. Additionally, only 18.27% of SSMHW events occur with apparent surface marine heatwave signals. These findings are vital for uncovering SSMHW dynamics in the Xisha area, and are important for future monitoring, early warnings and marine conservation.