सीएसआईआर-राष्ट्रीय डेटा विज्ञान एवं कृत्रिम बुद्धिमत्ता संस्थान

by Sumana Sarkar and S.Himesh

Abstract
The livelihoods and biodiversity in the Kodagu district of Karnataka are greatly altered by the growing urbanization through huge deforestation. The changes in the Land Use and Land Cover (LULC) patterns have important implications on the heavy rainfall activity due to significant alterations in the spatial distribution of existing land-surface properties. Hence, in order to make region (like Kodagu) more climate resilient; it is utmost important to construct a novel coupled framework for hydro-meteorological prediction to assess the impact of changing LULC (degradation of forest cover owing to urban expansion) on extreme rainfall events. Only a well-calibrated, validated, and optimized coupled high-resolution hydrological-atmospheric modelling system can be one of the best tools to study such complex problems. Thus in the present study, we have enhanced the forecasting capability of the state-of-the-art Weather Research and Forecasting (WRF) model by coupling with a physically based, fully distributed, WRF-Hydro. The evaluation of the proposed model configurations (stand-alone and coupled mode) is done with respect to two LULC datasets (USGS, 1992: less urbanized scenario and ISRO, 2017: more urbanized scenario) to explore the underlying causes and hydrological responses behind the 2018 Kodagu flood event. The simulations have been contended with both the stand-alone and coupled WRF/WRF-hydro models with different LULC datasets and a comparative evaluation have been carried out. The simulated variables (rainfall, runoff, surface fluxes, and peak stream discharge) are evaluated and compared with observation to assess the impact of changing LULC pattern during the flood event.

Source: https://www.sciencedirect.com/science/article/abs/pii/S1364682622001341

by Randhir Singh, V Rakesh & A K Varma 

by Sapna Ghavri, Rajeev Kumar Yadav and Sridevi Jade

The Kachchh paleo-rift basin of western India which recently experienced Mw 7.6, 26th January 2001 Bhuj earthquake, is one of the most active and vulnerable intra-cratonic zones in the world categorized as seismic zone V, i.e. having potential to produce earthquakes of intensity IX or higher. We analysed the geodetic and seismic principal strain rates to reveal the seismic hazard potential of the Kachchh paleo-rift basin. The geodetic strain rates are calculated from the reported surface motion in the region using a modified least-square inversion scheme that utilizes distance and spatial coverage dependent weight factor on the uncertainty in the plate motion to get a reliable estimate of principal strain rates and their orientations at regular grid nodes. An optimal weighting factor yields a mean rate of compression of ~ − 16 ± 6 nano strain/yr towards north-northeast in the region. The seismic strain rates are estimated based on fault plane solutions of the significant past earthquakes (Mw ≥ 2.5) of the region using the Kostrov formulations. The seismic strain rate and stress inversion estimate indicate that the region released elastic strain at an average rate of ~ − 85 nano strain/yr towards north-south in the past 350 years. A composite analysis of the geodetic and seismic strain rates indicates 900–3000 years of recurrence interval of major (Mw 7.8–8) earthquakes in the region. Our analysis indicates a high rate of strain build-up on the reactivated major faults in the region that have the potential to generate devastating (Mw ≥6) earthquakes.

Source: https://doi.org/10.1016/j.tecto.2022.229458 

by Rameshan Kallummal

Low-frequency changes in the tropical Indian Ocean surface temperature have previously been investigated in the context of the Indian Ocean basin-wide (IOBM) and dipole (IOD) modes. The IOBM and IOD are the leading eigenmodes estimated from a traditional anomaly of SST. This approach ignores the possibility of multiple seasonal cycles (SCs) having different geographic patterns and interannually modulating amplitudes. The analyses presented here are anchored on the four sets of multivariate seasonal cycles independently extracted from the monthly observations of sea surface temperature (SST), surface wind, and surface pressure variations. We show that the secular warming, encapsulated by the monotonic variations of the first SC of SST (SST–SC1), differs from the previous linear trend patterns and has the most significant variance in the Indian Ocean Warm Pool (IOWP). Hence, these warming tendencies quantify the monotonic expansion rates of IOWP. The most significant interannual responses of Indian Ocean SST to remote forces (such as El Niño and La Niña) are also captured by SST–SC1. Unlike the traditional IOBM but similar to SST–SC1’s secular warming, these remotely forced interannual signals also have considerable variances in IOWP. The interannual variations in SST’s third seasonal cycle (i.e., SST–SC3) inherit SST–SC3’s dipole pattern but diverge from classical IOD in many aspects and are predominantly controlled by local processes. However, they are insufficient to account for the total interannual signals on their own. The collective interannual variations of four seasonal cycles—with significant variances off Africa’s eastern shores—demonstrate basin-wide unipolar patterns. Hence, SST interannual signals in the north-western Indian Ocean and the constantly growing warming in the IOWP influence climate and weather over countries surrounding the Indian Ocean. Thus, this study offers a simple way to separate three types of climate signals: secular, internal, and remotely induced climate fluctuations.

Source: https://doi.org/10.1007/s00382-022-06347-5