A long-term context (931–2005 C.E.) for rapid warming over Central Asia

Warming over Mongolia and Central Asia has been unusually rapid over the past few decades, particularly in the summer, with surface temperature anomalies higher than for much of the globe. With few temperature station records available in this remote region prior to the 1950s, paleoclimatic data must be used to understand annual-to-centennial scale climate variability, local response to large-scale forcing mechanisms, and the significance of major features of the past millennium such as the Medieval Climate Anomaly (MCA) and Little Ice Age (LIA) both of which can vary globally. Here we use an extensive collection of living and subfossil wood samples from temperature-sensitive trees to produce a millennial-length, validated reconstruction of summer temperatures for Mongolia and Central Asia from 931 to 2005 CE. This tree-ring reconstruction shows general agreement with the MCA (warming) and LIA (cooling) trends, a significant volcanic signature, and warming in the 20th and 21st Century. Recent warming (2000–2005) exceeds that from any other time and is concurrent with, and likely exacerbated, the impact of extreme drought (1999–2002) that resulted in massive livestock loss across Mongolia

Six Centuries of Upper Indus Basin Streamflow Variability and Its Climatic Drivers

Our understanding of the full range of natural variability in streamflow, including how modern flow compares to the past, is poorly understood for the Upper Indus Basin because of short instrumental gauge records. To help address this challenge, we use Hierarchical Bayesian Regression with partial pooling to develop six centuries long (1394–2008 CE) streamflow reconstructions at three Upper Indus Basin gauges (Doyian, Gilgit, and Kachora), concurrently demonstrating that Hierarchical Bayesian Regression can be used to reconstruct short records with interspersed missing data. At one gauge (Partab Bridge), with a longer instrumental record (47 years), we develop reconstructions using both Bayesian regression and the more conventionally used principal components regression. The reconstructions produced by principal components regression and Bayesian regression at Partab Bridge are nearly identical and yield comparable reconstruction skill statistics, highlighting that the resulting tree ring reconstruction of streamflow is not dependent on the choice of statistical method. Reconstructions at all four reconstructions indicate that flow levels in the 1990s were higher than mean flow for the past six centuries. While streamflow appears most sensitive to accumulated winter (January–March) precipitation and summer (May–September) temperature, with warm summers contributing to high flow through increased melt of snow and glaciers, shifts in winter precipitation and summer temperatures cannot explain the anomalously high flow during the 1990s. Regardless, the sensitivity of streamflow to summer temperatures suggests that projected warming may increase streamflow in coming decades, though long-term water risk will additionally depend on changes in snowfall and glacial mass balance

Seven centuries of reconstructed Brahmaputra River discharge demonstrate underestimated high discharge and flood hazard frequency

The lower Brahmaputra River in Bangladesh and Northeast India often floods during the monsoon season, with catastrophic consequences for people throughout the region. While most climate models predict an intensified monsoon and increase in flood risk with warming, robust baseline estimates of natural climate variability in the basin are limited by the short observational record. Here we use a new seven-century (1309–2004 C.E) tree-ring reconstruction of monsoon season Brahmaputra discharge to demonstrate that the early instrumental period (1956–1986 C.E.) ranks amongst the driest of the past seven centuries (13th percentile). Further, flood hazard inferred from the recurrence frequency of high discharge years is severely underestimated by 24–38% in the instrumental record compared to previous centuries and climate model projections. A focus on only recent observations will therefore be insufficient to accurately characterise flood hazard risk in the region, both in the context of natural variability and climate change

Secure positioning in a UAV Swarm using on-board stereo cameras

The widespread use of quadcopters as unmanned aerial ve-\ud hicles (UAVs) provides a number of application possibilities,\ud mostly using a collaborative swarm of small UAVs to op-\ud timise critical missions. The common application domains\ud for small UAV swarms are surveillance, path planning, air-\ud bone and as relay networks. Cooperative applications make\ud use of the UAVs' locations to make decisions. However, se-\ud curity vulnerabilities must be considered when the system\ud infers rights based on the UAV's location. An attacker can\ud cheat the system by declaring a false or inaccurate location\ud to gain access to resources that are restricted or to undertake\ud malicious activities without detection. In this paper, we pro-\ud pose the use of a UAV payload stereo camera to measure the\ud distance between a UAV, called a prover node, and the set\ud of closest veri er nodes. Using a multilateration algorithm\ud to estimate the prover's position, we performed simulations\ud for two cameras with di erent capabilities. The simulations\ud showed that the proposed technique provides 98% validation\ud accuracy for distances up to 50 m for 1280x720-resolution\ud cameras and more than 99% validation accuracy for distances\ud up to 100 m for 1920x1080-resolution cameras. For distances\ud greater than 100 m between the prover and the veri ers, the\ud accuracy depends exclusively on the stereo camera with the\ud highest capacity.CNPq-INCT-SEC (Processo no. 573963/2008-8)CAPESFAPESP (processos nos. 2008/57870-9 e 2009/17720-0

Correction: Health-related quality of life and its socio-economic and cultural predictors among advanced cancer patients: Evidence from the APPROACH cross-sectional survey in Hyderabad-India

10.1186/s12904-020-0519-1BMC Palliative Care1911