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.
GNSS and its impact on position estimates
by Chiranjeevi G. Vivek, T. S. Shrungeshwara and Sridevi Jade
This study evaluates the impact of multi-GNSS (Global Navigation Satellite System) signals on the estimation of precise position with millimetre accuracy. Compared to standalone satellite systems like the Global Positioning System (GPS), multi-GNSS improves start-up time, performance, satellite visibility, accuracy, spatial geometry and reliability. However, on the flip side it increases the noise, signal interference, hardware complexity of the receiver, intersystem interference and computation complexity which may degrade its performance. Though GNSS is similar at fundamental levels, differences exist in signal structures, reference frames and timing standards. Compatibility and interoperability between the different constellations of the highest order is required to achieve the best results. At present, GPS and Glonass navigation systems are fully functional with global coverage and comparable precision. Glonass satellite constellation, signal structure is slightly different when compared to GPS, whereas major differences exist in the reference frame and epoch time. Combined GPS–Glonass solution significantly improves the accuracy in navigation applications with increased satellite signal observations and spatial distribution of visible satellites. For precise geodetic studies using static post-processing, combined solution may degrade the accuracy, if these differences are not handled carefully. Currently for geodetic studies, only GPS observations are majorly used worldwide. For the first time, daily precise position is estimated for continuous GNSS stations located in India using static postprocessing with standalone GPS, Glonass as well as combined GPS–Glonass to study the impact of multiGNSS signals for geodetic studies.
Source: https://www.currentscience.ac.in/Volumes/119/09/1503.pdf
Crustal deformation rates in Kashmir valley and adjoining regions from continuous GPS measurements from 2008 to 2019
by Sridevi Jade, Ramees R. Mir, Chiranjeevi G. Vivek, T. S. Shrungeshwara, I. A. Parvez, Rakesh Chandra, D. Suri Babu, S. Vishal Gupta, Ankit, Siva Sai Kumar Rajana & V. K. Gaur
We present GPS velocities in Kashmir valley and adjoining regions from continuous Global Positioning System (cGPS) network during 2008 to 2019. Results indicate total arc normal shortening rates of ~ 14 mm/year across this transect of Himalaya that is comparable to the rates of ~ 10 to 20 mm/year reported else-where in the 2500 km Himalaya Arc. For the first time in Himalayas, arc-parallel extension rate of ~ 7 mm/year was recorded in the Kashmir valley, pointing to oblique deformation. Inverse modeling of the contemporary deformation rates in Kashmir valley indicate oblique slip of ~ 16 mm/year along the decollement with locking depth of ~ 15 km and width of ~ 145 km. This result is consistent with the recorded micro-seismicity and low velocity layer at a depth of 12 to 16 km beneath the Kashmir valley obtained from collocated broadband seismic network. Geodetic strain rates are consistent with the dislocation model and micro-seismic activity, with high strain accumulation (~ 7e−08 maximum compression) to the north of Kashmir valley and south of Zanskar ranges. Assuming the stored energy was fully released during 1555 earthquake, high geodetic strain rate since then and observed micro-seismicity point to probable future large earthquakes of Mw ~ 7.7 in Kashmir seismic gap.
Site Effects Investigation in Srinagar City of Kashmir Basin Using Microtremor and Its Inversion
by S Vishal Gupta, Imtiyaz A Parvez, Ankit, Prosanta K Khan & Rakesh Chandra
The Srinagar region of Kashmir Valley in North West Himalayas, covers more than 2 million inhabitants and is exposed to high seismic risk. In order to gain insight on potential site effects and subsurface structure of the region, we carried out an extensive high-resolution microtremor ambient noise survey at 429 locations. The acquired dataset was processed using the Horizontal to Vertical Spectral Ratio (HVSR) technique to map the resonance frequency, the thickness of sedimentary cover, and to identify areas prone to seismic amplification. We provide a spatial classification of the obtained HVSR curves in four types: (1) clear peak H/V curves relating the strong impedance contrast in the subsurface; (2) multiple peaks (or Broad) H/V curves corresponding to sloping internal stratification of sediments; (3) two peaks H/V curves related to two different impedance contrast existing in the subsurface; (4) flat H/V curves around and over hard rock outcroppings. The HVSR curves show the peaks in the range of 0.22 Hz to 9.96 Hz indicating heterogeneous and complex sedimentary cover in the region. Inversion of the HVSR curves gives the shear waves velocity distribution which highlights two distinct reflective surfaces in most of the areas. In addition, we also used the estimated fundamental frequency of various types of houses/buildings located in Srinagar city to assess the possibility of resonance in case of occurrence of any earthquake. This study adds a value to the region in earthquake engineering, seismic hazard and risk evaluation purpose for Srinagar and its suburbs.
Source: https://www.tandfonline.com/doi/abs/10.1080/13632469.2020.1816232?journalCode=ueqe20
Future changes in rice yield over Kerala using climate change scenario from high resolution global climate model projection
by Stella Jes Varghese, Sajani Surendran, B Ajithkumar, Kavirajan Rajendran & Akio Kitoh
The impact of climate change on agricultural yield is one amongst the major concerns the world is witnessing. Our study focusses on rice yield prediction for an agricultural research station in Kerala with the help of climate change scenario input from the Meteorological Research Institute (MRI) Global Climate Model (GCM) projection under Representative Concentration Pathway 8.5 (RCP8.5). We have used Cropping System Model (CSM) Crop Estimation through Resource and Environment Synthesis (CERES) Rice within Decision Support System for Agrotechnology Transfer (DSSAT) package for predicting the yield. Our study has the novelty of using very high-resolution climate data from a model which is highly skilful in capturing the present-day climate features and climatic trends over India (in particular, over the Western Ghats), as input for simulating the future crop yield. From this study, we find that the rice yield decreases due to rise in temperature and reduction in rainfall, thereby reducing the crops maturity time in the future. Based on our results, the adaptation measures suggested to achieve better yield under future warming conditions are: (i) to opt for alternative rice varieties which have tolerance to high temperatures and consume less water, and (ii) shifting of planting date to the most appropriate window.
- A note on stress rotations due to the 2004 Mw 9.2 Sumatra-Andaman megathrust earthquake
- Identification of vegetation responses to soil moisture, rainfall, and LULC over different meteorological subdivisions in India using remote sensing data
- Self-Organized Fractal Seismicity and b-Value of Aftershocks of the 2015 Gorkha Earthquake, Nepal
- Detecting ionospheric disturbances using GPS without aliasing caused by non-uniform spatial sampling: Algorithm, validation and illustration
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