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Geodetic networks enable us to investigate interseismic crustal deformation along the northwest Himalaya. Using 144 GNSS surface velocities and a Bayesian inversion model, we estimate the slip rate and fault geometry of the Main Himalayan Thrust (MHT) along six arc-normal transects in the northwest Himalaya. We consider that the fault plane consists of three sections along the décollement, namely the locking zone (0−12 km), the transition zone (10−22 km) and the creeping zone (≥22 km). The MHT is found to be completely locked from the surface down to an average depth of 6 ± 2 km. The locking-to-creeping transition zone along the décollement extends from the edge of the fully locked area to a deeper depth (14 ± 3 km) to the tip of the creeping zone of the MHT (17 ± 2 km) with a slip rate of 1.6 ± 0.9 to 3.7 ± 1.1 mm yr−1. Considering the range of uncertainties between 1−2 mm yr−1 for the GNSS velocities, the inverted slip rate along the transition zone of MHT turns out to be insignificant. Thus, the locking zone along the northwest Himalaya extends from the MFT to ∼111 ± 6 km in the north with a locking depth of ∼17 ± 2 km. The deeper part of the MHT is inferred to be creeping with an average slip rate of ∼19.1 ± 1.9 mm yr−1 along the northwest Himalaya. In addition, we have also illustrated a splay-fault model to account for the fault kinematics along the splay faults and the main décollement. The splay-fault model indicates a distributed slip rate at the locking-to-creeping transition zone and about ∼15 per cent smaller slip rate of the MHT than that of the single-fault model. Further, the checkerboard test and the uniform slip model exhibit the reliability of the current GNSS network and the inversion model (single- and splay-fault models). Overall, the updated fault kinematics inevitably contribute to the improvement of seismic hazard evaluation along the northwest Himalaya. [ABSTRACT FROM AUTHOR]

Accurate forecasting of renewable energy resources has a deep societal and environmental impact. In this work, we investigate the efficacy and applicability of the Window-Sliding ARIMA (WS-ARIMA) method for daily and weekly forecasting of wind speed. The WS-ARIMA technique with a fixed or variable window length belongs to the class of adaptive models. Particularly, the sliding windows of fixed length are popular in the areas of finance, energy, and traffic management, where the dataset of necessity exhibits a seasonal pattern. To carry out the proposed analysis, the following processes were done: (1) we first perform a stationarity test on the wind speed data and observe weak stationarity; (2) we then apply a grid search method to obtain the optimal parameters of the ARIMA model; (3) we implement the WS-ARIMA method for both daily and weekly wind speed data and compare the results with the conventional ARIMA model, and (4) finally, we perform a residual analysis as a post processing step to examine any systematic bias in the implemented models. The experimental results based on 15 years (2000–2014) of daily and weekly wind speed data collected at four different locations in India reveal that the WS-ARIMA method consistently outperforms the conventional ARIMA method. The inclusion of window sliding in ARIMA has resulted in the overall RMSE reduction up to 75% in daily wind speed data and 50% in the weekly data. Therefore, we recommend the WS-ARIMA model as one of the potential techniques in wind speed forecasting at daily and weekly time horizons. [ABSTRACT FROM AUTHOR]

This study presents a geodetic deformation monitoring approach and its application on two severely affected and uninvestigated study sites in India, namely the Bhurkunda coal mines in Jharkhand, and the Sirobagarh landslide in Rudraprayag, Uttarakhand. For the Bhurkunda site (characterized by underground coal mining related subsidence), a network of 56 GNSS points, densified by Total Station (TS) ground points, was set up during three field visits. GNSS derived deformation estimates reveal that the southern part of the investigated area underwent subsidence (maximum displacement 1.813 m). Furthermore, DEM differences of the three epochs generated using TS points detect a subsidence of 1.358±0.057 m with similar pattern and magnitude of movement as detected by GNSS stations. For the Sirobagarh landslide (witnessing recurring slides due to local geological conditions and rainfall), a GNSS network of six control points in an area of 500×600 m2 was established. Deformation estimates from GNSS show subsidence in the area with a horizontal trend of movement towards the Alaknanda river situated below the landslide (maximum horizontal and vertical displacement values are 0.1306 m and 2.1315 m respectively). Further, for the Sirobagarh landslide, multi-temporal interferometry using Sentinel-1 SAR images detect displacement vectors showing downward movement of the landslide, with displacement rates ranging from 0.008 to −0.115 m yr−1. It is evident that the landslide scarp encountered high magnitude subsidence, whereas the area containing and surrounding the landslide (~30 km2) underwent a moderate rate of subsidence. A set of surveying guidelines for monitoring deformation events of similar kind are discussed. [ABSTRACT FROM AUTHOR]

In view of the growing importance of stochastic earthquake modeling in disaster preparation, the present study introduces a new family of exponentiated Weibull distribution and examines its performance in earthquake interevent time analysis in a stationary point process. This three-parameter (one scale and two shapes) distribution not only covers the Weibull distribution, exponentiated exponential distribution, Burr-type X distribution, Rayleigh distribution, and exponential distribution as special sub-families, but also offers monotone and non-monotone hazard shapes. Here we first describe some of the exponentiated Weibull distribution properties, such as the survival rate, mode, median, and hazard rate. We then provide statistical inference and goodness-of-fit measures to examine the suitability of exponentiated Weibull model in comparison with other popular models, like exponential, gamma, lognormal, Weibull, and exponentiated exponential. Finally, we conduct real data analysis to assess the usefulness and flexibility of exponentiated Weibull distribution in the context of seismic interevent time modeling and associated applications. Results suggest that the exponentiated Weibull distribution has a comparable performance with other popular distributions of its nature. However, further investigations are necessary to confirm the importance and flexibility of exponentiated Weibull distribution in statistical seismology. [ABSTRACT FROM AUTHOR]

This work concentrates on the distribution of earthquake interevent times in northwest Himalaya and its adjacent regions. We consider 12 time-dependent probability distributions for analysis. The maximum likelihood estimation and Fisher information matrix-based methods are used to estimate model parameters and their respective uncertainties. Results from three model selection criteria suggest that the best fit arises from exponentiated Weibull, exponentiated exponential, Weibull and gamma distributions. An intermediate fit comes from exponentiated Rayleigh, and lognormal distributions, whereas the remaining distributions exhibit a poor fit to the seismic interoccurrence times of present catalogue. Using exponentiated Weibull model, it is observed that the estimated cumulative probability of magnitude 6.0 or higher event in the northwest Himalaya reaches 0.92-0.95 after about 20-23 (2019-2022) years since the last event in 1999. The conditional probability, for an elapsed time of 19 years (i.e. 2018), reaches 0.90-0.95 after about 20-25 (2038-2043) years from now. A series of conditional probability curves is also presented to understand the recent and future earthquake hazard in the study region. This temporal evolution of seismic interevent times may provide important clues to the underlying physical mechanism of earthquake genesis in the northwest Himalaya region. [ABSTRACT FROM AUTHOR]