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1. Teleseismic Converted Waves Image of the Indo-Burma Subduction System Across the Bangladesh-India-Myanmar (BIMA) Array

   Ajala, R. ; Persaud, P. ; Steckler, M. ; Sandvol, E. ; Islam, M. ; Gaherty, J. ; Than, O. ; Oo, K. ; Akhter, S. ; Ni, J. ( January 2020 , American Geophysical Union, Fall Meeting 2020)

   null (Ed.)

   Recent GPS studies show that the Indo-Burma subduction system is locked with the implication of a potential large-magnitude earthquake. To inform better seismic hazard models in the region, we need an improved understanding of the crustal structure and the dynamics of the Indo-Burma subduction system. The Bangladesh-India-Myanmar (BIMA) tripartite project deployed 60 broadband seismometers across the subduction system and have been continuously recording data for ~2 years. In this study, we computed receiver functions from 30 high-quality earthquakes (M≥5.9) with epicentral distances between 30º and 90º recorded by the array. The algorithm utilized ensures the uniqueness of the seismic model and provides an uncertainty estimate of every converted wave amplitude. We stacked all the receiver functions produced at each station along the entire transect to generate a cross-sectional model of the average crustal structure. The level of detail in the image is improved by computing higher frequency receiver functions up to 4 Hz. The results represent some of the strongest constraints on crustal structure across the subduction system. Beneath the Neogene accretionary prism's outer belt, we observe a primary conversion associated with the Ganges Brahmaputra Delta that ranges in depth from ~10 km near the deformation front up to ~12 km at the eastern boundary. From the eastern end of the Neogene accretionary prism to the Sagaing Fault, we image the Indian subducting slab and the Central Myanmar basin. The depth-extent of seismicity associated with the Wadati-Benioff zone is consistent with the locations of primary conversions from the subducting plate. We further verify the converted phases of the slab by analyzing azimuthal moveout variations. The Central Myanmar basin is roughly bowl-shaped in cross-section with a maximum thickness of ~15 km about halfway between the Kabaw and Sagaing faults. The average crustal thickness beneath the Ganges-Brahmaputra delta is ~20 km, most likely representing a transitional crust formed from thinning of the continental crust intruded and underplated by igneous rocks. In contrast, the average thickness of the continental crust beneath the Central Myanmar basin is ~40 km. Our results provide a baseline model for future geophysical investigations of the Indo-Burma subduction zone. 

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2. Assessment of the Catastrophic Asia Floods and Potentially Affected Population in Summer 2020 Using VIIRS Flood Products

   https://doi.org/10.3390/rs12193176  

   Li, Sanmei ; Goldberg, Mitchell D. ; Sjoberg, William ; Zhou, Lihang ; Nandi, Sreela ; Chowdhury, Nazmi ; Straka, William ; Yang, Tianshu ; Sun, Donglian ( October 2020 , Remote Sensing)

   null (Ed.)

   Since 2 June 2020, unusual heavy and continuous rainfall from the Asian summer monsoon rainy season caused widespread catastrophic floods in many Asian countries, including primarily the two most populated countries, China and India. To detect and monitor the floods and estimate the potentially affected population, data from sensors aboard the operational polar-orbiting satellites Suomi National Polar-Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA)-20 were used. The Visible Infrared Imaging Radiometer Suite (VIIRS) with a spatial resolution of 375 m available twice per day aboard these two satellites can observe floodwaters over large spatial regions. The flood maps derived from the VIIRS imagery provide a big picture over the entire flooding regions, and demonstrate that, in July, in China, floods mainly occurred across the Yangtze River, Hui River and their tributaries. The VIIRS 5-day composite flood maps, along with a population density dataset, were combined to estimate the population potentially exposed (PPE) to flooding. We report here on the procedure to combine such data using the Zonal Statistic Function from the ArcGIS Spatial Analyst toolbox. Based on the flood extend for July 2020 along with the population density dataset, the Jiangxi and Anhui provinces were the most affected regions with more than 10 million people in Jingdezhen and Shangrao in Jiangxi province, and Fuyang and Luan in Anhui province, and it is estimated that about 55 million people in China might have been affected by the floodwaters. In addition to China, several other countries, including India, Bangladesh, and Myanmar, were also severely impacted. In India, the worst inundated states include Utter Pradesh, Bihar, Assam, and West Bengal, and it is estimated that about 40 million people might have been affected by severe floods, mainly in the northern states of Bihar, Assam, and West Bengal. The most affected country was Bangladesh, where one third of the country was underwater, and the estimated population potentially exposed to floods is about 30 million in Bangladesh. 

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3. Eikonal Tomography of the Southern California Plate Boundary Region

   https://doi.org/10.1029/2019JB017806  

   Qiu, Hongrui ; Lin, Fan‐Chi ; Ben‐Zion, Yehuda ( September 2019 , Journal of Geophysical Research: Solid Earth)

   We use Eikonal tomography to derive phase and group velocities of surface waves for the plate boundary region in Southern California. Seismic noise data in the period range 2 and 20 s recorded in year 2014 by 346 stations with ~1‐ to 30‐km station spacing are analyzed. Rayleigh and Love wave phase travel times are measured using vertical‐vertical and transverse‐transverse noise cross correlations, and group travel times are derived from the phase measurements. Using the Eikonal equation for each location and period, isotropic phase and group velocities and 2‐psi azimuthal anisotropy are determined statistically with measurements from different virtual sources. Starting with the SCEC Community Velocity Model, the observed 2.5‐ to 16‐s isotropic phase and group dispersion curves are jointly inverted on a 0.05° × 0.05° grid to obtain local 1‐D piecewise shear wave velocity (Vs) models. Compared to the starting model, the final results have generally lowerVsin the shallow crust (top 3–10 km), particularly in areas such as basins and fault zones. The results also show clear velocity contrasts across the San Andreas, San Jacinto, Elsinore, and Garlock Faults and suggest that the San Andreas Fault southeast of San Gorgonio Pass is dipping to the northeast. Investigation of the nonuniqueness of the 1‐DVsinversion suggests that imaging the top 3‐kmVsstructure requires either shorter period (≤2 s) surface wave dispersion measurements or other types of data set such as Rayleigh wave ellipticity.

    

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4. High‐Resolution 3‐D Shear Wave Velocity Model of Northern Taiwan via Bayesian Joint Inversion of Rayleigh Wave Ellipticity and Phase Velocity With Formosa Array

   https://doi.org/10.1029/2020JB021610  

   Liu, Cheng‐Nan ; Lin, Fan‐Chi ; Huang, Hsin‐Hua ; Wang, Yu ; Berg, Elizabeth M. ; Lin, Cheng‐Horng ( May 2021 , Journal of Geophysical Research: Solid Earth)

   The Formosa array, with 137 broadband seismometers and ∼5 km station spacing, was deployed recently in Northern Taiwan. Here by using eight months of continuous ambient noise records, we construct the first high‐resolution three‐dimensional (3‐D) shear wave velocity model of the crust in the area. We first calculate multi‐component cross‐correlations to extract robust Rayleigh wave signals. We then determine phase velocity maps between 3 and 10 s periods using Eikonal tomography and measure Rayleigh wave ellipticity at each station location between 2 and 13 s periods. For each location, we jointly invert the two types of Rayleigh wave measurements with a Bayesian‐based inversion method for a one‐dimensional shear wave velocity model. All piecewise continuous one‐dimensional models are then used to construct the final 3‐D model. Our 3‐D model reveals upper crustal structures that correlate well with surface geological features. Near the surface, the model delineates the low‐velocity Taipei and Ilan Basins from the adjacent fast‐velocity mountainous areas, with basin geometries consistent with the results of previous geophysical exploration and geological studies. At a greater depth, low velocity anomalies are observed associated with the Linkou Tableland, Tatun Volcano Group, and a possible dyke intrusion beneath the Southern Ilan Basin. The model also provides new geometrical constraints on the major active fault systems in the area, which are important to understand the basin formation, orogeny dynamics, and regional seismic hazard. The new 3‐D shear wave velocity model allows a comprehensive investigation of shallow geologic structures in the Northern Taiwan.

    

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5. Best practices for estimating near‐surface air temperature lapse rates

   https://doi.org/10.1002/joc.6668  

   Lute, A. C. ; Abatzoglou, John T. ( June 2020 , International Journal of Climatology)

   The near‐surface air temperature lapse rate is the predominant source of spatial temperature variability in mountains and controls snowfall and snowmelt regimes, glacier mass balance, and species distributions. Lapse rates are often estimated from observational data, however there is little guidance on best practices for estimating lapse rates. We use observational and synthetic datasets to evaluate the error and uncertainty in lapse rate estimates stemming from sample size, dataset noise, covariate collinearity, domain selection, and estimation methods. We find that lapse rates estimated from small sample sizes (<5) or datasets with high noise or collinearity can have errors of several °C km⁻¹. Uncertainty in lapse rates due to non‐elevation related large‐scale temperature variability was reduced by correcting for spatial temperature gradients and restricting domains based on spatial clusters of stations. We generally found simple linear regression to be more robust than multiple linear regression for lapse rate estimation. Finally, lapse rates had lower error and uncertainty when estimated from a sample of topoclimatically self‐similar stations. Motivated by these results, we outline a set of best practices for lapse rate estimation that include using quality controlled temperature observations from as many locations as possible within the study domain, accounting for and minimizing non‐elevational sources of climatic gradients, and calculating lapse rates using simple linear regression across topoclimatically self‐similar samples of stations which are roughly 80% of the station population size.

    

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