Influence of large-scale climate modes (ENSO, IOD and PJ) on marine heatwave characteristics in the Indian ocean region
by S. Swetha, K. V. Ramesh & V. Rakesh
Marine heatwaves (MHWs), defined as anomalously warm sea surface temperature (SST) events, are analyzed over the Indian Ocean using NOAA OISST V2 (0.25° resolution) from 1981 to 2022, focusing on frequency, heatdays, maximum intensity, and mean intensity. Spatial analysis reveals MHW hotspots in the central-southwestern Indian Ocean, Bay of Bengal, and northern Arabian Sea, with 1–3 events per year lasting 3–15 days. Basin-wide mean maximum and mean intensities are 1.1 °C and 0.9 °C, respectively. Significant upward trends are found in all MHW metrics, including frequency (0.73 events/decade), heatdays (1.7 days/decade), mean intensity (0.11 °C/decade), and maximum intensity (0.14 °C/decade), with the Arabian Sea exhibiting the largest rise in heatdays (17.79 days/decade). A comparison of the periods 1981–2000 and 2001–2022 shows increased frequency and duration in the latter, though with marginally lower intensities. Empirical Orthogonal Function (EOF) analysis explains 28% and 17% of the spatial variance in frequency and duration, respectively. Partial correlation and kernel-based methods identify the El Niño–Southern Oscillation (ENSO), particularly positive Niño3.4 anomalies, as the dominant driver of MHW variability, strongly correlated with frequency (r = 0.59) and intensity (r = 0.5). Granger causality and Convergent Cross Mapping confirm ENSO’s particularly Niño3.4’s causal influence and reveal bidirectional feedback with MHWs heatdays, linked to Walker circulation driven changes in zonal winds and thermocline depth. The Pacific–Japan (PJ) pattern exerts secondary influence, while the Indian Ocean Dipole (IOD) plays a limited role. This underscores the importance of incorporating such drivers in predictive models and regional adaptation strategies.
Improved learning by using a modified activation function of a Convolutional Neural Network in multi-spectral image classification
by Ramesh Kalidhasan Vasanthakumari, Rakesh Vasudevan Nair and Venkatesh Gowda Krishnappa
The Convolutional Neural Network (CNN) algorithm is used to classify multispectral images of labelled EuroSAT data from Sentinel-2 satellite. The main objective of this study to examine the role of newly proposed activation function, Modified Rectified Linear Unit (MReLU) in improving multi-spectral image classification accuracy. The experimental design deployed a 5-layer CNN with activation function Rectified Linear Unit (ReLU) and the MReLU having 27 learning rates ranged from 0.0001 to 0.01 with 10 % increment and 8 optimizers. ReLU has a diminishing gradient problem since it does not consider the significant information present in the negative values of the convolution layer for different spectral bands. In order to overcome this short coming, we have designed the MReLU which allows the absolute values of the small negative gradient and thereby introducing higher nonlinearity in the output. The experimental results show that the image classification accuracy of the CNN with ReLU and MReLU activation functions is definitely higher than the pre-trained ResNet-50 model which is a complex and computationally expensive architecture. The MReLU has advantage over ReLU in land use classification, particularly in the Industrial, Herbaceous Vegetation, and Permanent Crop classes where accuracies were typically low. In summary, this paper highlights the importance of deploying a suitable activation function and hyperparameter tuning in the CNN model for improving the accuracy of multispectral image classification.
Implications of seismic and GNSS strain rates in Himachal, Kashmir and Ladakh
by T. S. Shrungeshwara, Bhavani Narukula, Sridevi Jade, Sapna Ghavri, Chiranjeevi G. Vivek & I. A. Parvez
We report the present day GNSS velocities (2015–2021) and strain rates in Himachal, Kashmir and Ladakh Himalaya covering the rupture zones of the 2005 Muzaffarabad earthquake and the 1905 Kangra earthquake. Geodetic strain rates estimated from GPS velocities of about 58 sites spanning last two decades of measurements indicate a mean compression rate of − 32.5 ns/year (nanostrain/year) and dilatation of − 37.3 ns/year. Seismic strain rates are estimated using both the instrumental period (1964–2021) and historical earthquakes since 1500 AD in this region. Seismic strain rates during the instrumental period of the past 50 + years indicate a mean compression rate of − 28.1 ns/year and it slightly decreases to − 21.7 ns/year after including the historical earthquakes of the past 520 years. The Azimuth of the seismic strain tensor for the instrumental and historic periods and geodetic strain tensor is broadly consistent with orientation of major faulting in this region suggesting uniform compression over a long-time interval justifying combined analysis of the strain rate field to determine the seismic potential of the region. Composite analysis of geodetic and seismic strain rates and the associated moments estimate the accumulated strain budget of ~ 1E + 21 Nm in the past 520 years which has a potential of generating future earthquake of Mw > 8 in this segment of Northwest Himalaya.
Source: https://doi.org/10.1038/s41598-023-48997-3
Impact of Urbanization on Heavy Rainfall Events: A Case Study over the Megacity of Bengaluru, India
by S. K. Sahoo, S. Himesh & K. C. Gouda
This study is about the simulation and observational analysis of a heavy rainfall event (HRE) and its sensitivity to land-use changes. The impact of urbanization on processes and mechanisms of rainfall including cloud processes is discussed. This study is based on high-resolution (2 km), time-ensemble simulation of one of the HREs that occurred on 27 May 2017 over the city of Bengaluru in the southern part of India. The simulations are carried out using the Weather Research and Forecasting (WRF) model, which is coupled with a single-layer urban canopy model (UCM). The high-resolution (30 s) land-use data derived from the Indian Space Research Organization (ISRO) for the year 2016–2017 is shown to be realistic in representing the current land-use scenario with a threefold increase in urbanization when compared to USGS land-use data of 1991–1992. Simulation and analysis of large-scale circulation patterns revealed that the event was triggered and sustained by the low-pressure system and cyclonic circulation over the Bay of Bengal. Simulated rainfall was found to be remarkably sensitive to land-use changes as shown by control (USGS) and test (ISRO) simulations. The rainfall intensity and spatial distribution are close to observation in test simulations with relatively less error at station scale with a correlation of 0.49 (95% significance) when compared to control simulations, indicating the importance of realistic representation of land use in the model and its impact on heavy rainfall processes. The test simulation which represents the current urbanization scenario has shown a significant increase in rainfall by over 100–200%. The surface energy fluxes and thermodynamic indices as shown by test simulations are favorable to HREs, and also consistent with the current land-use scenario with increased urbanization. This study demonstrated how a realistic representation of land-use data in the model can help to improve model skill. The main limitation of this research work is that it is based on the generic parameterization of urbanization using single-layer UCM. An in-depth study based on multi-layer UCM and city-specific parameterization of urbanization using sub-kilometer-scale land-use data including buildings would further enhance our understanding on this subject.
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