The phenomena of drought is common in the world, particularly in Pakistan.Drought in Pakistan has been studied in terms of its spatial and temporal variability, as well as its impact on the El Niño-Southern Oscillation (ENSO) cycle.The objectives of this study are to identify homogeneous rainfall regions and their trend regions, as well as the impact of ENSO cycle. For the analysis, 44 meteorological sites during 1980-2019 are used for monthly rainfall data. The descriptive and exploratory statistics tests (e.g., Pettitt and Mann-Kendall-MK), Sen method, and cluster analysis (CA) are implemented with the annual Standardized Precipitation Index (SPI). The ENSO occurrences were classified based on the Oceanic Nio Index (ONI) for region 3.4. Using the Cophenetic Correlation Coefficient (CCC) and a significance level of 5%, seven approaches were applied to the pluviometric dataset. The CCC > 0.9082 indicates that the Complete approach is the best. According to the CA method, Pakistan has four homogenous rainfall groups (G1, G2, G3, and G4). Descriptive and exploratory statistics yielded lower values for G1 than for the other groups. Pettitt's technique identified the most extreme El Niño years in terms of the drought's spatial and temporal variability. According to the Pettitt test, the wettest months were March, August, September, June, and December. Non-significant increases in Pakistan's annual rainfall were found in the MK test, with exceptions in the southern and northern regions, respectively. No significant rise in Pakistan's rainfall was found using Sen's Sen's approach, especially in the G2, G3, and G4 regions. The severity of the drought in Pakistan is made worse by months, homogeneous groups of rainfall, and El Niño events, all of which require public officials' attention while managing water resources, agriculture, and the country's economy.
The remote sensing-based Earth satellites has become a beneficial instrument for the monitoring of natural hazards. This study includes a multi-sensors analysis to estimate the spatial-temporal variations of atmospheric parameters as precursory signals to the Mw 7.2 Haiti Earthquake (EQ). We studied EQ anomalies in Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP), and Outgoing Longwave Radiation (OLR). Moreover, we found EQ-associated atmospheric abnormalities in a time window of 3–10 days before the main shock by different methods (e.g., statistical, wavelet transformation, deep learning, and Machine Learning (ML)-based neural networks). We observed a sharp decrease in the RH and AP before the main shock, followed by an immense enhancement in AT. Similarly, we also observed enhancement in LST and OLR around the seismic preparation region within 3–10 days before the EQ, which validates the precursory behavior of all the atmospheric parameters. These multiple-parameter irregularities can contribute with the physical understanding of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) in the future in order to forecast EQs.
We analyze vertical total electron content (vTEC) variations from the Global Navigation Satellite System (GNSS) at different latitudes in different continents of the world during the geomagnetic storms of June 2015, August 2018, and November 2021. The resulting ionospheric perturbations at the low and mid-latitudes are investigated in terms of the prompt penetration electric field (PPEF), the equatorial electrojet (EEJ), and the magnetic H component from INTERMAGNET stations near the equator. East and Southeast Asia, Russia, and Oceania exhibited positive vTEC disturbances, while South American stations showed negative vTEC disturbances during all the storms. We also analyzed the vTEC from the Swarm satellites and found similar results to the retrieved vTEC data during the June 2015 and August 2018 storms. Moreover, we observed that ionospheric plasma tended to increase rapidly during the local afternoon in the main phase of the storms and has the opposite behavior at nighttime. The equatorial ionization anomaly (EIA) crest expansion to higher latitudes is driven by PPEF during daytime at the main and recovery phases of the storms. The magnetic H component exhibits longitudinal behavior along with the EEJ enhancement near the magnetic equator.
Global Navigation Satellite System (GNSS)- and Remote Sensing (RS)-based Earth observations have a significant approach on the monitoring of natural disasters. Since the evolution and appearance of earthquake precursors exhibit complex behavior, the need for different methods on multiple satellite data for earthquake precursors is vital for prior and after the impending main shock. This study provided a new approach of deep machine learning (ML)-based detection of ionosphere and atmosphere precursors. In this study, we investigate multi-parameter precursors of different physical nature defining the states of ionosphere and atmosphere associated with the event in Japan on 13 February 2021 (Mw 7.1). We analyzed possible precursors from surface to ionosphere, including Sea Surface Temperature (SST), Air Temperature (AT), Relative Humidity (RH), Outgoing Longwave Radiation (OLR), and Total Electron Content (TEC). Furthermore, the aim is to find a possible pre-and post-seismic anomaly by implementing standard deviation (STDEV), wavelet transformation, the Nonlinear Autoregressive Network with Exogenous Inputs (NARX) model, and the Long Short-Term Memory Inputs (LSTM) network. Interestingly, every method shows anomalous variations in both atmospheric and ionospheric precursors before and after the earthquake. Moreover, the geomagnetic irregularities are also observed seven days after the main shock during active storm days (Kp > 3.7; Dst < −30 nT). This study demonstrates the significance of ML techniques for detecting earthquake anomalies to support the Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) mechanism for future studies.
Global Navigation Satellite System (GNSS)-based ionospheric anomalies are nowadays used to identify a possible earthquake (EQ) precursor and hence a new research topic in seismic studies. The current study also aims to provide an investigation of ionospheric anomalies associated to EQs. In order to study possible pre-and post-seismic perturbations during the preparation phase of large-magnitude EQs, statistical and machine learning algorithms are applied to Total Electron Content (TEC) from the Global Positioning System (GPS) and Global Ionosphere Maps (GIMs). We observed TEC perturbation from the Sukkur (27.8° N, 68.9° E) GNSS station near the epicenter of Mw 5.4 Mirpur EQ within 5–10 days before the main shock day by implementing machine learning and statistical analysis. However, no TEC anomaly occurred in GIM-TEC over the Mirpur EQ epicenter. Furthermore, machine learning and statistical techniques are also implemented on GIM TEC data before and after the Mw 7.7 Awaran, where TEC anomalies can be clearly seen within 5–10 days before the seismic day and the subsequent rise in TEC during the 2 days after the main shock. These variations are also evident in GIM maps over the Awaran EQ epicenter. The findings point towards a large emission of EQ energy before and after the main shock during quiet storm days, which aid in the development of lithosphere ionosphere coupling. However, the entire analysis can be expanded to more satellite and ground-based measurements in Pakistan and other countries to reveal the pattern of air ionization from the epicenter through the atmosphere to the ionosphere.
Abstract. Scintillations of transionospheric satellite signals during geomagnetic storms can severely threaten navigation accuracy and the integrity of space assets. We analyze vertical Total Electron Content (vTEC) variations from the Global Navigation Satellite System (GNSS) at different latitudes around the world during the geomagnetic storms of June 2015 and August 2018. The resulting ionospheric perturbations at the low-and mid-latitudes are investigated in terms of the prompt penetration electric field (PPEF), the equatorial electrojet (EEJ), and the magnetic H component from INTERMAGNET stations near the equator. East and South-East Asia, Russia, and Oceania exhibited positive vTEC disturbances, while South American stations showed negative vTEC disturbances during both storms. We also analyzed the vTEC from the Swarm satellites and found similar results to the GNSS retrieved vTEC during different phases of both geomagnetic storms. Moreover, we observed that ionospheric plasma tended to increase rapidly during the afternoon in the main phase of the storms. At nighttime, the ionosphere depicted an opposite behavior under similar conditions. The equatorial ionization anomaly (EIA) crest expansion to mid and high latitudes is driven by PPEF during daytime at the main and recovery phases of the storms. The magnetic H component exhibits a longitudinal behavior along with the EEJ enhancement near the magnetic equator.
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