Changes In Atmospheric Conditions Research Articles

Nitrogen Storage in Rice: Analysis of Physical Quality by Respiration, Weight, and Storage According to Nitrogen Ratio.

Various studies have been conducted to minimize the damage and loss of stored grain. For safe storage, the moisture content must be reduced, or respiration must be suppressed. In this study, grain respiration rates were analyzed under various nitrogen atmospheric conditions, and the quality of stored rice was evaluated. As the nitrogen content of the storage space increases, the respiration rate of the grain decreases accordingly. In this study, the effect of the modified atmospheric nitrogen concentration on reducing respiration was determined. When predicting weight loss due to respiration, low moisture content, and high nitrogen concentration could reduce loss. Quality analysis was performed to compare different respiration rate conditions and showed that lower oxygen concentration and moisture content were associated with safer storage. Our results indicate that changes in atmospheric conditions depending on climate and storage conditions can be considered for the safe storage of harvested rice.

Unsupervised multi-target domain adaptation for deforestation detection in tropical rainforest

Abstract. Geographic variability of the classes of interest, differences in sensor characteristics and changes in atmospheric conditions during image acquisition, among other factors, make it challenging to use a pre-trained deep learning classifier on new remote sensing data without a substantial drop in classification accuracy. This phenomenon occurs due to the so-called domain shift problem. Deep domain adaptation techniques have been used to mitigate the problem and thus avoid the time-consuming and costly collection of new labeled samples. Most recent domain adaptation approaches rely on single-source and single-target domains, refraining from exploiting other data distributions that are usually available. This work introduces a new unsupervised multi-target domain adaptation in the context of a change detection application, namely deforestation detection. The proposed approach addresses the substantial class imbalance typical of such application by applying unsupervised algorithms for selecting pseudo-labels in the target domain that will later serve as additional training references. We report results of experiments to evaluate the proposed method in four distinct sites of two Brazilian biomes using Sentinel-2 images. The results indicate that the proposed unsupervised domain adaptation method is a promising solution to reduce the effects of domain shift and to deal with the scarcity of labeled training data.

Dependence of cyanobacterium growth and Mars-specific photobioreactor mass on total pressure, pN2 and pCO2

In situ resource utilization systems based on cyanobacteria could support the sustainability of crewed missions to Mars. However, their resource-efficiency will depend on the extent to which gases from the Martian atmosphere must be processed to support cyanobacterial growth. The main purpose of the present work is to help assess this extent. We therefore start with investigating the impact of changes in atmospheric conditions on the photoautotrophic, diazotrophic growth of the cyanobacterium Anabaena sp. PCC 7938. We show that lowering atmospheric pressure from 1 bar down to 80 hPa, without changing the partial pressures of metabolizable gases, does not reduce growth rates. We also provide equations, analogous to Monod’s, that describe the dependence of growth rates on the partial pressures of CO2 and N2. We then outline the relationships between atmospheric pressure and composition, the minimal mass of a photobioreactor’s outer walls (which is dependent on the inner-outer pressure difference), and growth rates. Relying on these relationships, we demonstrate that the structural mass of a photobioreactor can be decreased – without affecting cyanobacterial productivity – by reducing the inner gas pressure. We argue, however, that this reduction would be small next to the equivalent system mass of the cultivation system. A greater impact on resource-efficiency could come from the selection of atmospheric conditions which minimize gas processing requirements while adequately supporting cyanobacterial growth. The data and equations we provide can help identify these conditions.

Impact of Global Warming on Cancer Development: A Review of Environmental Carcinogens and Human Immunogenetics

This paper examines the impact of global warming on cancer development, specifically focusing on the intensified effects of environmental carcinogens such as ultraviolet (UV) radiation and air pollutants. Our review elucidates the intricate interplay between global warming, ecological carcinogens, human immunogenetics, and cancer susceptibility. The analysis highlights the exacerbating effects of rising temperatures and changes in atmospheric conditions on exposure to UV radiation and air pollutants, including particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), nitrogen dioxide (NO2), nitrogen oxides (NOx), and ground-level ozone (O3). Furthermore, the study explores the pivotal role of human immunogenetics in modulating individual responses to carcinogen exposure and shaping cancer susceptibility and progression. Genetic variations in key immune-related genes and their influence on the interplay between environmental carcinogens and cancer development are discussed. The paper underscores the importance of longitudinal cohort studies, integrative approaches, and interdisciplinary collaborations to advance our understanding of the complex interactions between global warming, environmental carcinogens, human immunogenetics, and cancer biology. Additionally, evidence-based public health interventions targeting environmental carcinogens and personalized prevention strategies based on genetic susceptibility profiles and environmental exposure assessments are proposed to address the growing challenges of environmentally induced cancers.

Physiological and Biochemical Responses of Maize to Elevated CO2 Concentrations: Implications for Growth and Metabolism

Rising atmospheric CO2 levels, a significant consequence of anthropogenic activities, profoundly impact global agriculture and food security by altering plant physiological processes. Despite extensive research, a comprehensive understanding of the specific effects of elevated CO2 on maize (Zea mays L.)’s primary and secondary metabolism remains elusive. This study investigated the responses of maize seedlings cultivated in open-top chambers (OTCs) under three CO2 concentrations: ambient (380 ppm), elevated (600 ppm), and high (1800 ppm). Key growth parameters, including plant height, leaf area, and aboveground biomass (leaf and stem), were assessed alongside metabolic profiles encompassing nonstructural and structural carbohydrates, syringyl (S) and guaiacyl lignin, the syringyl-to-guaiacyl (S/G)-lignin ratio, photosynthetic pigments, total soluble protein, and malondialdehyde (MDA) levels. The results demonstrated that exposure to 600 ppm CO2 significantly enhanced plant height, leaf area, and aboveground biomass compared to ambient conditions. Concurrently, there were notable increases in the concentrations of primary metabolites. In contrast, exposure to 1800 ppm CO2 severely inhibited these growth parameters and induced reductions in secondary metabolites, such as chlorophyll and soluble proteins, throughout the growth stages. The findings underscore the intricate responses of maize metabolism to varying CO2 levels, highlighting adaptive strategies in primary and secondary metabolism under changing atmospheric conditions. This research contributes to a nuanced understanding of maize’s physiological adaptations to future climate scenarios characterized by elevated CO2, with implications for sustainable agriculture and food security.

A global comparative study on the impact of COVID-19 policy on atmospheric nitrogen dioxide (NO2): Evidence from remote sensing data in 2019–2022

A significant body of research has documented the profound changes in global atmospheric conditions during the COVID-19 pandemic. However, there is still an inadequate comprehensive comparison and assessment of countries before, during, and after the pandemic. Variations in restriction policies, human behaviors, and national traits lead to significant differences in how restriction policies affect atmospheric pollution. This study focuses on NO2, a pollutant with high temporal sensitivity, and utilizes the Oxford COVID-19 policy stringency index along with demographic information. Through spatial-temporal mapping, we analyzed NO2 emission fluctuations and calculated the emission changes in each country. Drawing from this analysis, we explored the relationships among these factors and found that over the span of 2019–2022, across 193 countries, global NO2 emissions displayed a distinct trajectory: initially decreasing, subsequently rebounding, and eventually fluctuating. Most countries exhibited seasonal variations in NO2 emissions. Additionally, the study uncovered a correlation between the stringency of COVID-19 policies and the reduction in NO2 emissions: as policies became stricter, emissions significantly decreased in most countries. In contrast, in countries with lower population densities, stricter policies paradoxically led to an increase in emissions. These findings underscore the importance of considering demographic factors and geographical context in the formulation and implementation of environmental policies.

Future Changes of Extreme Precipitation and Related Atmospheric Conditions in East Asia under Global Warming Projected in Large Ensemble Climate Prediction Data

Abstract Extreme precipitation is expected to pose a more severe threat to human society in the future. This work assessed the historical performance and future changes of extreme precipitation and related atmospheric conditions in a large ensemble climate prediction dataset, the database for Policy Decision-making for Future climate change (d4PDF), over East Asia. Compared with the TRMM and ERA5 datasets, the historical climate in d4PDF represents favorably the precipitation characteristics and the atmospheric conditions, although some differences are notable in the moisture, vertical motion, and cloud water fields. The future climate projection indicates that both the frequency and intensity of heavy precipitation events over East Asia increase compared with those in the present climate. However, when comparing the atmospheric conditions in the historical and future climates for the same precipitation intensity range, the future climate indicates smaller relatively humidity, weaker ascent, less cloud water content, and smaller temperature lapse rate, which negatively affect generating extreme precipitation events. The comparison of the precipitation intensity at the same amount of precipitable water between the historical and future climates indicates that extreme precipitation is weaker in the future, because of the more stabilized troposphere in the future. The general increase in extreme precipitation under future climate is primarily due to the enhanced increase in precipitable water in the higher temperature ranges, which counteracts the negative conditions of the stabilized troposphere.

The Influence of Atmospheric Conditions on a Glider’s Lift Forces

The article presents the methodology and results of a simulation study on the air flow around a glider, focusing on the issue of changing the lift force and drag force in changing atmospheric conditions. The experiment was based on simulations in the SolidWorks Flow Simulation 2022 software suite. The model for simulation research was developed at the Faculty of Technical Sciences of the University of Warmia and Mazury in Olsztyn (Poland).

InSAR-DEM Block Adjustment Model for Upcoming BIOMASS Mission: Considering Atmospheric Effects

The unique P-band synthetic aperture radar (SAR) instrument, BIOMASS, is scheduled for launch in 2024. This satellite will enhance the estimation of subcanopy topography, owing to its strong penetration and fully polarimetric observation capability. In order to conduct global-scale mapping of the subcanopy topography, it is crucial to calibrate systematic errors of different strips through interferometric SAR (InSAR) DEM (digital elevation model) block adjustment. Furthermore, the BIOMASS mission will operate in repeat-pass interferometric mode, facing the atmospheric delay errors introduced by changes in atmospheric conditions. However, the existing block adjustment methods aim to calibrate systematic errors in bistatic mode, which can avoid possible errors from atmospheric effects through interferometry. Therefore, there is still a lack of systematic error calibration methods under the interference of atmospheric effects. To address this issue, we propose a block adjustment model considering atmospheric effects. Our model begins by employing the sub-aperture decomposition technique to form forward-looking and backward-looking interferograms, then multi-resolution weighted correlation analysis based on sub-aperture interferograms (SA-MRWCA) is utilized to detect atmospheric delay errors. Subsequently, the block adjustment model considering atmospheric effects can be established based on the SA-MRWCA. Finally, we use robust Helmert variance component estimation (RHVCE) to build the posterior stochastic model to improve parameter estimation accuracy. Due to the lack of spaceborne P-band data, this paper utilized L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR data, which is also long-wavelength, to emulate systematic error calibration of the BIOMASS mission. We chose climatically diverse inland regions of Asia and the coastal regions of South America to assess the model’s effectiveness. The results show that the proposed block adjustment model considering atmospheric effects improved accuracy by 72.2% in the inland test site, with root mean square error (RMSE) decreasing from 10.85 m to 3.02 m. Moreover, the accuracy in the coastal test site improved by 80.2%, with RMSE decreasing from 16.19 m to 3.22 m.

Computing bias and variance for Pacific sardine (Sardinops sagax) biomass estimated from aerial surveys in California nearshore waters

Pacific sardine (Sardinops sagax) stocks off the U.S. Pacific coast undergo boom and bust cycles modulated by decadal changes in oceanic and atmospheric conditions. During periods of low abundance, Pacific sardine may contract into nearshore waters, making it more challenging to be surveyed by research vessels that cannot operate in the shallowest habitats, 0 to 40.00 m. In response, a new transect-based design and methods were developed to compute bias and variance of biomass estimated from a collaborative aerial survey implemented in nearshore waters. From experimental aerial surveys conducted in 2010 and from 2018 to 2020, two professional aerial fish spotters underestimated school biomass by 10.5% and 12.6%, whereas most of the variability of aerial biomass was due to within-transect variance, with no significant contribution from the among-transect variance. Implementation of the new transect-based design to aerial surveys from 2020 to 2023 in southern and northern California showed that a substantial biomass of Pacific sardine occurred in nearshore waters, representing 69.9% of total biomass estimated from acoustic surveys in spring 2021, and 36.5% in summer 2021. Seasonal biomass estimates were variable within each region, affected by school dynamics during daytime in nearshore waters and the timing of aerial surveys as determined by weather and environmental conditions. Given that nearshore aerial biomass comprised a measurable proportion of total biomass estimates for Pacific sardine during the recent period of low abundance, it has been used to inform acoustic survey catchability, which is no longer assumed to be 1.0 in recent Pacific sardine stock assessments.

Multi-scale remote sensing retrieval model for land surface emissivity and its impact on land surface temperature estimation

ABSTRACT As a fundamental parameter of the surface radiation process, land surface emissivity (LSE) is the most direct factor affecting the land surface temperature (LST) retrieval accuracy. Based on the measured data from 2017 to 2023, the characteristics of LSE changes were analysed, different timescale LSE retrieval models were developed, and the impact of LSE on pixel scale observational LST acquisition was evaluated by using unmanned aerial vehicle (UAV) remote sensing images. The results show that with changes in atmospheric environmental conditions, the LSE changes are relatively less pronounced at 3.99 µm and 4.02 µm in the mid-infrared band and at 10.8 µm and 11.8 µm in the thermal infrared band than in the other bands. At the timescale above day, the LSE is significant correlation with the normalized differential vegetation index (NDVI). Vegetation is the main parameter that dominates the LSE change under heterogeneous underlying surfaces. On a daily timescale, LSE exhibits typical diurnal variation characteristics, and the amplitude of diurnal variation is influenced mainly by shallow soil water and heat conditions. The developed LSE model based on Fengyun-4A (FY-4A) satellite remote-sensing data can effectively reproduce the daily variation characteristics of LST and avoid the ‘jumping’ phenomenon that may occur in the NDVI threshold method. The root-mean-square error (RMSE) between the retrieved LST and in situ data is reduced to 3.78°C, and the retrieval accuracy is better than that of the published LST product. When using UAVs to obtain pixel-scale LSTs, dry bare soil is more sensitive to LSE. When the LSE increases from 0.95 to 1.0, the average LST obtained by the UAV deviates by approximately 1.0°C. This study preliminarily provides some spatiotemporal variation patterns of LSE and lays a foundation for the later-modified LST retrieval algorithms.

Shape-Changing Wood Joints in Crafts and Industry and Their Potential for Building Construction and Wood Culture

Abstract Timber has regained popularity in construction in recent years due to its ecological benefits. The connection methods used in this study play a vital role in the sustainability of structures and materials. Monomaterial timber connections are sustainable alternatives to metal fasteners and adhesives commonly used in construction. Wood is an anisotropic material with dimensional changes resulting from changes in atmospheric conditions. Understanding and accounting for this property are crucial for the longevity and functionality of wooden structures. The cumulative knowledge of wood´s material characteristics and its use in design, construction, and human culture can be defined as wood culture developed through artists’ and craftsmen’s experiences, science, and industry. The development of various techniques by artisans to leverage the dimensional change in wood to join timber elements is a major contribution to wood culture. In contrast, until now, the timber industry has mainly focused on limiting or controlling these changes in standardized production and has neglected their use for joining timber elements. However, technological advances have changed dramatically. The digital manufacturing and analysis of wood structures have the potential to guide machine tools and may allow the integration of dimensional changes, especially in the design and construction of timber joints. This study explores the state-of-the-art utilization of dimensional changes in timber to join elements in craft, material science, and industrial production. The potential of techniques utilizing this behavior for innovation in modern design and construction and their implications for wood culture were examined. Research gaps and avenues for further research are identified.

Himalayas as a global hot spot of springtime stratospheric intrusions: Insight from isotopic signatures in sulfate aerosols

Downward transport of stratospheric air into the troposphere (identified as stratospheric intrusions) could potentially modify the radiation budget and chemical of the Earth's surface atmosphere. As the highest and largest plateau on earth, the Tibetan Plateau including the Himalayas couples to global climate, and has attracted widespread attention due to rapid warming and cryospheric shrinking. Previous studies recognized strong stratospheric intrusions in the Himalayas but are poorly understood due to limited direct evidences and the complexity of the meteorological dynamics of the third pole. Cosmogenic 35S is a radioactive isotope predominately produced in the lower stratosphere and has been demonstrated as a sensitive chemical tracer to detect stratospherically sourced air mass in the planetary boundary layer. Here, we report 6-month (April–September 2018) observation of 35S in atmospheric sulfate aerosols (35SO42−) collected from a remote site in the Himalayas to reveal the stratospheric intrusion phenomenon as well as its potential impacts in this region. Throughout the sampling campaign, the 35SO42− concentrations show an average of 1,070 ± 980 atoms/m3. In springtime, the average is 1,620 ± 730 atoms/m3, significantly higher than the global existing data measured so far. The significant enrichments of 35SO42− measured in this study verified the hypothesis that the Himalayas is a global hot spot of stratospheric intrusions, especially during the springtime as a consequence of its unique geology and atmospheric couplings. In combined with the ancillary evidences, e.g., oxygen-17 anomaly in sulfate and modeling results, we found that the stratospheric intrusions have a profound impact on the surface ozone concentrations over the study region, and potentially have the ability to constrain how the mechanisms of sulfate oxidation are affected by a change in plateau atmospheric properties and conditions. This study provides new observational constraints on stratospheric intrusions in the Himalayas, which would further provide additional information for a deeper understanding on the environment and climatic changes over the Tibetan Plateau.

Different wildfire types promoted two-step terrestrial plant community change across the Triassic-Jurassic transition

Frequent wildfires associated with emplacement of the Central Atlantic Magmatic Province (CAMP) are thought to have been important drivers of two significant changes in terrestrial plant communities and diversity during the Triassic-Jurassic Mass Extinction (TJME, ca. 201.51 Ma). ​However, it remains to be investigated whether these two changes are potentially related to different wildfire types. To better understand this relationship, we used a new method to reanalyze fossil pollen and spores across the Triassic-Jurassic transition in the Jiyuan Basin from the North China Plate. Results show that two peaks in wildfire frequency experienced different types of wildfires, with each linked to significant changes in plant communities and diversity losses. In the first wildfire peak, canopy fires dominated and are accompanied by significant losses of canopy forming plants, while in the second wildfire peak, ground cover fires dominated accompanied by significant losses of ground cover plants. ​Changes in atmospheric humidity conditions were an important control on the two different wildfire peaks. Relatively humid climatic conditions corresponded to the prevalence of canopy fires and hindered the spread and development of ground cover fires in wet surface conditions. Conversely, relatively arid climatic conditions corresponded with the prevalence of ground cover fires in dry surface environments. Our results provide a potential relationship between terrestrial plant communities and wildfire types, which is important to further understanding of terrestrial environmental and floral changes driven by Large Igneous Provinces.

Grid Secondary Frequency Control by Fuzzy PI–PD Controller

This paper develops a new approach for controlling the frequency in a two-area power system using a fuzzy PI PD controller. Frequency stabilization is one of the important factors for any power system to operate efficiently and reliably. The fluctuating characteristics of RES, like solar PV and wind energy, due to changing atmospheric conditions, require additional reserves to compensate for fluctuations in generated power. With growing application of EVs and their charging requirement, charting huge loads may add to the power grid and lead to stability problems if not properly regulated. This paper coordinates EV charging into the secondary frequency regulation, aided by an enhanced fuzzy controller designed for dealing with fluctuating imbalances between electricity demand and generation. In this context, a control technique is suggested that incorporates the flexibility of fuzzy logic with the accurate PI and PD methods for control. The controller continuously monitors changes in frequency at each node in the power system network. The fuzzy logic works on uncertain input data in terms of prescribed linguistic variables and rules that allow it to achieve a reasoned output. The difference between the desired and actual frequencies alters the control variable through the PI component of the controller, while the PD component considers the rate of change of this error to make the system's response time better. The effectiveness of the fuzzy PI PD controller is proven through detailed simulation experiments under different operational conditions and disturbances. It can be rundown that the controller efficiently reduces these frequency deviations, which guarantees smooth and reliable operation of the two-area power system network. In addition, it can be seen that the controller has robust performance but is rather reactive to dynamical changes in system parameters. Case studies are included in the research to test the performance of the proposed control approach in reducing frequency deviations; simulation findings always prove its efficacy. This paper therefore presents a complex method of frequency regulation in power systems using fuzzy logic and combined PI PD control to deal with problems caused by RES integration and EV charging. The proposed technique enhances the frequency stability in order to enhance both the reliability and efficiency of power grid operations due to the ever-changing energy demands and conditions.

A Novel Dynamic and Self-Adaptive InCre Technique Based on PI Control and EO Optimization for Hybrid PV-TEG Conversion Systems

This work presents an improvement of the incremental conductance technique by adding PI controllers optimized by the recent equilibrium optimization algorithm. It addresses the main weaknesses of the incremental conductance algorithm consisting of the choice of step-size and the elimination of oscillations around the MPP. The proposed approach makes the MPPT control more dynamic and self-adaptive against the atmospheric conditions change. It has been applied for a photovoltaic generator, the thermoelectric generator and for the hybridization of the two generators. The simulation results, and the comparative study with the optimized step-size P&O and Incremental conductance methods, prove the efficiency and superiority of the proposed technique in terms of energy extraction and speed. The introduced work is developed in the MATLAB/Simulink environment.

The Online Identification of the Behaviour of Pollutants inside the Tunnel Tube

A tunnel tube is a relatively small space that allows for the accumulation of gaseous and liquid substances containing harmful substances. Given this fact, a ventilation system is the most critical component of a tunnel’s technological equipment, greatly influencing its reliability and safe operation. The dynamic behaviour of pollutants in the tunnel tube is characterized by a significant stochastic component and changing parameters over time due to pressure, airflow, and atmospheric condition changes. This work addresses the issue of modelling individual parts of the tunnel tube for optimal tunnel ventilation control. It is necessary to create a model of a controlled system that is used for predicting process variables to calculate optimal control action. By using recursive identification methods in conjunction with a predictive controller, the proposed concept can be applied to numerous similar applications.

Analysis and Design of Modified Incremental Conductance-Based MPPT Algorithm for Photovoltaic System

This study discusses the design of the Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems employing a modified incremental conductance (IncCond) algorithm to extract maximum power from a PV module. A PV module, a DC-DC converter, and a resistive load constitute the PV system. In the scientific literature, it is well-documented that typical MPPT algorithms have significant drawbacks, such as fluctuations around the MPP and poor tracking during a sudden change in atmospheric conditions. To solve the deficiencies of conventional methodology, a novel modified IncCond method is proposed in this study. The simulation results demonstrate that the updated IncCond algorithm presented allows for less oscillation around the maximum power point (MPP), a rapid dynamic response, and superior performance.

Assessment of the global applicability of three leading indices that characterize the spectral distribution of solar irradiance impacting various photovoltaic technologies

At each instant, the actual solar irradiance has a specific spectral distribution that most generally differs substantially from the standardized reference conditions under which photovoltaic (PV) cells are rated. To help reduce the dimensionality of the calculations needed to quantify the spectral effects on PV cells, various spectral indices have been introduced in the literature to simply characterize the incident spectral distribution with a single number. In previous studies, diverging conclusions have been reached about whether or not a single number can effectively represent a specific spectral distribution. This disagreement needs to be resolved before the usage of spectral indices can be considered best practice. To that effect, this study proposes a novel modeling approach. Here, three spectral indices (Average Photon Energy, APE; Blue Fraction, BF; and Useful Fraction, UF) are critically evaluated and compared using the SMARTS radiative transfer model and a large database of atmospheric inputs that are representative of the vast diversity of possible conditions worldwide, thus avoiding the rarity and limitations of costly experimental measurements. A total of 28,260 pairs of direct normal and global horizontal spectra are obtained, from which the three indices are calculated over six different wavebands that characterize different PV technologies. The key variables that have a significant impact on the indices are found to be: air mass, aerosol optical depth, Ångström exponent, and precipitable water. Using a variety of tests to evaluate the behavior of the three indices relatively to the reference direct and global spectra, results show that UF is prone to anomalous variations under a variety of circumstances. APE and BF behave similarly and reliably, but APE is found more responsive to even small changes in atmospheric conditions, and is thus preferable. A critical finding is that none of the indices can be considered bijective because compensations between red-shifting and blue-shifting atmospheric processes often result in different spectral distributions having the same index value.

Effect of Land Cover Change and Deforestation Around Maninjau Lake on Rainfall and Surface Temperature

Fish deaths in Maninjau Lake have been frequently reported. Apart from fishery activities, this can also be caused by changes in atmospheric conditions around Lake Maninjau due to changes in land cover around Lake Maninjau. Therefore, this study analyzes land cover changes and deforestation in 2001 and 2020 and their relationships with surface temperature and rainfall. Rainfall data and extreme temperatures were obtained from the Integrated Multi-satellite Retrievals for GPM (IMERG) and ECMWF v5 (ERA5), respectively. Land cover data based on the Landsat satellite is used to see land changes. The results showed a decrease in the amount of vegetated land every year and an increase in residential and building land and agricultural land, which correlates with an increase in the surface temperature in the Maninjau area. Furthermore, the results also show a coherent relationship between rainfall trends and areas of deforestation.