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Integrated analysis of atmospheric and ionospheric precursors using SARIMAX, NARX, and LSTM approaches for the 2024 Mw 7.4 Taiwan earthquake
(2026-08-01)
Tahreem, Azka
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Shah, Munawar
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Jamjareegulgarn, Punyawi
Earthquakes are among the most damaging natural hazards, highlighting the need for improved monitoring frameworks and rigorous analysis of potential precursory signals. The Mw 7.4 Taiwan earthquake provides a relevant case for evaluating methodologies to identify and interpret atmospheric and ionospheric anomalies in seismically vulnerable regions. In this study, satellite-based Remote Sensing (RS) products and Global Navigation Satellite System (GNSS) observations are integrated to examine candidate precursors, including Outgoing Longwave Radiation (OLR), Relative Humidity (RH), Air Temperature (AT), Air Pressure (AP), and Total Electron Content (TEC). Using statistical approaches, including the standard deviation (STDEV) method and the Seasonal AutoRegressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, together with machine-learning frameworks such as the Nonlinear AutoRegressive model with eXogenous inputs (NARX) and Long Short-Term Memory (LSTM) networks, this study identified synchronized anomalies approximately 5–6 days prior to the event. In addition, geomagnetic perturbations were observed approximately nine days before the event, coinciding with a pronounced geomagnetic storm (Kp > 8; Dst < −120 nT; ap > 225 nT). To limit the influence of background variability and potential false alarms, a historical comparative analysis was performed using atmospheric parameters from the same region and comparable time window across the preceding five years, which further supported the robustness of the observed anomalies. By integrating statistical detection, spatial screening, and time-series forecasting models, this work contributes to a more detailed understanding of atmospheric–ionospheric signals associated with seismic activity and highlights the value of multi-parameter monitoring for seismic hazard assessment and risk-reduction planning.
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Understanding green house gases emission dynamics from forest fires in Thailand using predictive models
(2026-02-01)
Shahzad, Fahad
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Mehmood, Kaleem
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Anees, Shoaib Ahmad
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Adnan, Muhammad
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Hussain, Khadim
Forest fires are a major driver of carbon emissions, particularly in tropical regions where climate variability and land use practices intensify their frequency and impact. This study investigates the spatiotemporal trends and emission dynamics of forest fires across Thailand's three dominant vegetation types- Evergreen Broadleaf Forest (EBF), Deciduous Broadleaf Forest (DBF), and Grassland over three climatic seasons (Dry, Hot, and Wet) in the period 2001–2023. Using the Mann-Kendall trend test and Sen's Slope estimator, we observed significant declines in burnt area during the Dry season in EBF and Grasslands, with no consistent trend in DBF. Fire–vegetation interactions revealed seasonally specific effects: positive correlations between fire count and Net Primary Productivity (NPP) were detected in the Wet and the hot seasons in the case of DBF and Grasslands, respectively. Emission analysis showed that CO₂ was the dominant greenhouse gas released, with the Dry season contributing to most emissions, although Hot season emissions have increased over time. Machine learning models Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) explained over 78 % of the variance in CO₂ emissions on test data (R2 = 0.79 for RF, 0.78 for XGBoost), despite higher Root Mean Square Error (RMSE) values (∼550) on unseen data. The Shapley Additive Explanations (SHAP) analysis identified wind components and solar radiation as key predictive variables. Central, Northeastern, and Northern Thailand emerged as emission hotspots. These findings improve our understanding of emission dynamics from tropical fires and underscore the need for region-specific mitigation strategies to inform carbon inventories and climate policy.
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Multi-temporal scale of urban rainfall in the Eastern Northeast based on observed data and gridded products: physiographic factors and changes in land use and occupation
(2026-02-01)
da Silva, Luis Felipe Francisco Ferreira
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de Oliveira Romão, William Max
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Lyra, Gustavo Bastos
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da Silva, Elania Barros
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da Silva Costa, Micejane
Rainfall is one of the most important meteorological variables in the daily lives of urban populations. The city of Maceió, the capital of Alagoas, located in the eastern part of the Northeast of Brazil (ENEB), has 50 neighborhoods and a population of approximately one million people, with few studies on the subject. The objectives were: (i) to validate the CHIRPS product; (ii) to identify the preferential rainfall periods in Maceió via GIS; (iii) to map areas for the installation of in situ stations in the city with the aim of supporting the prevention of hydrometeorological disasters; and (iv) creation of a theoretical-conceptual rainfall model. The statistical indicators (R², ρ, BIAS, MAPE and RMSE) were used to validate the gridded precipitation product CHIRPS from 11 CEMADEN rain gauge stations. Monthly rain occurrence maps via Spline tension were developed by QGIS (Quantum GIS) software. The HAND model was applied at neighborhood level for the assessment of urban floods. Waterborne disease data were obtained from SINAN, Natural Disaster data via S2iD from the period 2000 to 2023, and the NDVI and EVI indices in the years 2015 and 2022 were evaluated in the study. All stations were monotonically positive (ρ > 0.65) and significant (p-value < 0.001), indicating that CHIRPS is able to capture rainfall variability despite the influences of the coast, Lagoa Mundaú, and topography. Most stations showed underestimation (negative BIAS) and lower errors (MAE and RMSE). Spatially, the increase in rainfall on the coastal plateau is due to the interaction of the wind regime with the relief, driven by the circulation of breezes and the influence of trade winds. The preferential rainfall period occurs between 04:00 am and 07:00 am. The HAND model identified very high and high susceptibility, mainly on the coast, in areas adjacent to Lagoa Mundaú, and in neighborhoods crossed by rivers and urban canals, and low susceptibility in densely populated neighborhoods. Waterborne diseases together with transformations via NDVI and EVI indicated that rainfall amplifies risk scenarios for the most vulnerable and densely populated populations. In light of this, it is perceived that the rainfall patterns in Maceió are due to the interaction of physiographic and/or anthropogenic factors and meteorological systems – theoretical-conceptual model – which requires improvements in infrastructure and an active monitoring system.
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Exploring vegetation health in Southern Thailand under climate stress from temperature and water impacts between 2000 and 2023
(2025-12-01)
Mehmood, Kaleem
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Anees, Shoaib Ahmad
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Shahzad, Fahad
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Muhammad, Sultan
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Liu, Qijing
This study provides a detailed spatiotemporal analysis of vegetation health in Southern Thailand from 2000 to 2023, focusing on the impacts of temperature and water stress on vegetation degradation. Using high-resolution Landsat-derived kernel Normalized Difference Vegetation Index (kNDVI) and Land Surface Temperature (LST), alongside precipitation (PPT), soil moisture (SM), vapor pressure deficit (VPD), and solar radiation (SR), several key indices were derived such as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). The study offers a robust framework to monitor vegetation health under climate stress by integrating satellite-based indices with detailed climate datasets. Our findings reveal significant temperature-induced stress during critical years like 2005 and 2016, with over 60% of the region experiencing vegetation degradation. Long-term trend analysis indicates that while 22.5% of forested areas show signs of recovery, 3.6% continue to degrade, primarily due to persistent temperature extremes and water stress. Soil moisture emerged as a critical driver during the dry season, positively influencing 11.16% of the region, while solar radiation exhibited mixed effects depending on moisture availability. These insights highlight the complex interplay of climatic drivers on vegetation dynamics, particularly in tropical ecosystems. The study underscores the need for adaptive management strategies to enhance resilience against climate extremes, providing valuable guidance for sustainable land management in Southern Thailand.
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Hemispheric responses of ionosphere-thermosphere to intense geomagnetic storms over the East Asian-Australian sector
(2025-11-15)
Tahir, Afnan
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Wu, Falin
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Shah, Munawar
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Jamjareegulgarn, Punyawi
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Ameen, Muhammad Ayyaz
The irregularities of huge geomagnetic storms impact the satellite communication by enforcing large delays in ray path. This work investigates the hemispheric asymmetries in ionosphere-thermosphere responses to three large geomagnetic storms (Dst ∼−170 nT), occurring in different seasons and storm phases beginning in different local times, over the middle and low latitudes of East Asian-Australian longitude sector. The variations have been studied by critical frequency of F2 layer (foF2) and multi-satellites observables at ±60° conjugate latitudes along ∼115° E. The enhanced equatorial ionization anomaly (EIA) is associated to the strong eastward electric fields, trigger significant part in main phases of the December 2015 and August 2018 storms during dayside. The main phase covering the night side during March 2023 storm, exhibited no significant total electron content (TEC)/foF2 variations except small-scale ionospheric irregularities. The unexpected ionospheric-thermospheric asymmetry on 26 August 2018 is mainly influenced by the northward neutral winds as opposed to the seasonal winds. Moreover, the recovery phases of the December 2015 and March 2023 commenced around morning hours activate a classic seasonal response at middle latitudes along with disturbance dynamo effect at low latitudes. On contrary, the ionospheric responses at the recovery phase during August 2018 storm is not dominated by expected seasonal flow and disturbance dynamo. At different phases of the three storms, E region electric fields, thermospheric composition and neutral winds played a major role for ionospheric disturbances.
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Integrated landslide risk assessment utilizing open source satellite and ground data in the Himalayas of Northern Pakistan
(2025-07-01)
Ahmad, Naseem
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Shafique, Muhammad
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Hussain, Mian Luqman
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Shah, Munawar
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Jamjareegulgarn, Punyawi
Landslides have devastating effect on communities, infrastructure, and the environment, making them one of the most recurring and harmful natural hazards globally. This study propose an integrated approach of freely available geospatial data and semi-quantitative techniques to evaluate landslide hazard, vulnerability, and risk in one of the most landslide-prone valleys i.e., Kaghan Valley, northern Pakistan. The Google Earth Pro, high-resolution DEM and satellite images are used to develop a landslide inventory, derived causative factors and assess the landslide hazard and risk assessment in Kaghan Valley. The landslide susceptibility map is then integrated with landslide-triggering factors to derive a landslide hazardindex map. A geospatial database of element-at-risk data of 66,282 building footprints, typological data, road network, population, and land cover are obtained through remote sensing and extensive field surveys. The quantitative analysis revealed that 8.43 km2 (0.66%) of the total area falls under the very high-risk category, while 271.19 km2 (21.11%) is classified as high risk, and 80.91 km2 (6.30%) as moderate risk, establishing a strong basis for risk assessment.
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Multi-satellite based possible precursory signals detection linked to the 2024 Mw 7.5 Noto Peninsula Japan earthquake
(2025-06-15)
Shahzad, Rasim
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Shah, Munawar
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Nabi, Imtiaz
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Jamjareegulgarn, Punyawi
Satellite-based anomaly detection can provide substantial precursory information linked to impending earthquakes (EQ). The strong EQs are followed by some complex precursory signals both before and after the main shock. For this, different methods and datasets are employed to monitor these disastrous events. In our study, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to evaluate land surface temperature (LST), the Global Navigation Satellite System (GNSS) to observe total electron content (TEC) variations, Swarm satellites to monitor spatial variations in electron density, and Cosmic satellites were used to measure variations in the vertical profile of electron density to look for the complex precursors of Noto Peninsula Japan EQ of 7.5 Mw (occurred on 1st January 2024). Our objective was to observe both the pre- and post-EQ induced anomalies within 25 days and 10 days of the main shock by integrating the statistical, nonlinear autoregressive network with exogenous inputs (NARX) and continuous wavelet transformation (CWT) methods. We found synchronized and co-located pre-seismic anomalies on December 25 in LST, TEC and electron density. Which was further confirmed using NARX and CWT as well. Additionally, we found some potential post-seismic anomalies. There was an anomalous enhancement in daytime LST, TEC, and electron density on January 2nd with the exception of nighttime LST which showed abrupt increments on the night of the main shock (i.e., January 1st). These findings point towards the strong EQ-induced energy into the atmosphere and ionosphere for more prominent proof of lithosphere-atmosphere–ionosphere coupling (LAIC).
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Rainfall variability, land use and land cover dynamics, and implications for climate risk through a theoretical-conceptual model for the Eastern Northeast of Brazil
(2025-06-01)
da Silva Costa, Micejane
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Francisco de Oliveira Júnior, José
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Godoy de Barros, Bárbara
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Francisco Ferreira da Silva, Luís Felipe
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Alencar Cardoso, Kelvy Rosalvo
The study assessed the variability of rainfall, land use and land cover (LULC), the influence of meteorological systems and their interactions, followed by the increase in climate risks in Maceió – Alagoas – ENEB. Monthly rainfall data were obtained from the 64-year historical series (1958–2022) of the TerraClimate platform. The TerraClimate precipitation data were validated ANA and SEMARH data, resulting in R2 = 0.72 and r = 0.85. Spatially, the highest rainfall accumulations occurred from April to July (rainy season), with May and June being the wettest months (>800 mm). In contrast, the months from October to February (dry season) and March, August, and September (transition season) recorded lower rainfall, with November and December being the driest months (150–201 mm). The seasonal occurrence of rainfall and the relief in Maceió were associated with areas of climate risk. Maceió is a region vulnerable to extreme rainfall events due to its geographical location, the influence of the Atlantic Ocean and the Mundaú/Manguaba Lagoons, and the interaction with multi-scale meteorological systems. With LULC, there was an expansion of forest areas in rural areas and a reduction of agricultural areas, followed by increased urbanization in neighborhoods in the western part of the city (coastal plateau). The theoretical-conceptual model of climate risk provides essential information for planning and supports decision-making for public policies. The ability to assess the impacts of extreme rainfall underscores the importance of adaptive responses by local communities through the management of public policies and strategies for adapting to climate risk.
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Exploring Electromagnetic Wave Propagation Through the Ionosphere Over Seismic Active Zones
(2025-03-01)
Eshkuvatov, Husan
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Ahmedov, Bobomurat
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Shah, Munawar
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Begmatova, Dilfuza
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Jamjareegulgarn, Punyawi
This study presents an analytical solution for the electric current formation in the lower ionosphere as a result of charged aerosols being ejected from the ground before the earthquakes. The impact of ionosphere-related processes on radio wave propagation through the atmosphere is explored by investigating the resulting energy losses of electromagnetic waves traversing this ionospheric layer. Theoretical considerations suggest that these processes may generate detectable electromagnetic signals, offering insights into seismic precursors. The effects of electron density inhomogeneities in the upper ionospheric layers on electromagnetic wave properties such as group delay, Faraday rotation, and Doppler frequency shift are examined. Understanding these effects aims to improve ionospheric monitoring techniques to detect pre-earthquake disturbances. To validate the theoretical findings, a comparison is made with the empirical data from various sources, including VLF transmitters and GPS-TEC measurements. This comparative analysis underscores the potential of electromagnetic phenomena as credible indicators of impending seismic events.
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Spatiotemporal analysis of surface Urban Heat Island intensity and the role of vegetation in six major Pakistani cities
(2025-03-01)
Anees, Shoaib Ahmad
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Mehmood, Kaleem
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Raza, Syed Imran Haider
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Pfautsch, Sebastian
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Shah, Munawar
The Urban Heat Island (UHI) phenomenon exacerbates thermal discomfort in urban areas and significantly contributes to urban overheating when combined with climate change. This study investigates the spatiotemporal patterns of Surface Urban Heat Island Intensity (SUHII) in six major cities of Pakistan, focusing on the interplay between urban expansion, vegetation cover, and SUHII. To quantify SUHII dynamics, the impact of urban sprawl and vegetation changes was analyzed. The study offers critical insights into the implications for urban planning and policymaking in Pakistan. Using remote sensing data from Landsat satellites, analyzed with Geographic Information Systems (GIS) techniques, estimates of SUHII, urban expansion, and vegetation cover were derived. Specifically, imagery from Landsat-5 (2010−2013) and Landsat-8 (2014–2022), obtained from the US Geological Survey (USGS), was employed. Statistical analyses, including Pearson's correlation and linear regression, were conducted to assess relationships between these variables from 2010 to 2022. SUHII was found to increase annually by 0.18 °C in Islamabad and 0.19 °C in Peshawar, with corresponding urban expansion rates of 8.07 km2 (8967.75 pixels) and 1.67 km2 (1860.42 pixels) per year, respectively. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC) were inversely correlated with SUHII, explaining up to 50 % of the variance in Peshawar. However, weaker correlations in Lahore suggest the presence of additional factors influencing SUHII. A distinct spatial relationship between increased vegetation and cooler areas was observed. For instance, Islamabad has greater vegetation cover and cool zones over 41.5 km2. In contrast, Lahore's hot spots spanned 127.1 km2, compared to Abbottabad's 10.4 km2, underscoring the thermal impact of reduced vegetation. The findings emphasize the effectiveness of urban greening, particularly in Islamabad's neutral thermal regions, in mitigating SUHII. This study offers important insights for urban planners in developing sustainable, climate-resilient cities within similar urban contexts. While the results are specific to Pakistani cities, the role of vegetation in mitigating SUHII may hold broader relevance for urban planning strategies in comparable settings.
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