•Nonstationary COVID 19 risk analysis combining climatic and socioeconomic factors.•Strong climate influence on COVID 19 cases was observed in 474 (76.08%) out of 623 districts.•The total population ...in 50% of districts in 19 out of 35 states were lying under high risk.
This study investigates the influence of climate variables (pressure, relative humidity, temperature and wind speed) in inducing risk due to COVID 19 at rural, urban and total (rural and urban) population scale in 623 pandemic affected districts of India incorporating the socioeconomic vulnerability factors. We employed nonstationary extreme value analysis to model the different quantiles of cumulative COVID 19 cases in the districts by using climatic factors as covariates. Wind speed was the most dominating climatic factor followed by relative humidity, pressure, and temperature in the evolution of the cases. The results reveal that stationarity, i.e., the COVID 19 cases which are independent of pressure, relative humidity, temperature and wind speed, existed only in 148 (23.7%) out of 623 districts. Whereas, strong nonstationarity, i.e., climate dependence, was detected in the cases of 474 (76.08%) districts. 334 (53.6%), 200 (32.1%) and 336 (53.9%) districts out of 623 districts were at high risk (or above) at rural, urban and total population scales respectively. 19 out of 35 states were observed to be under high (or above) Kerala, Maharashtra, Goa and Delhi being the most risked ones. The study provides high-risk maps of COVID 19 pandemic at the district level and is aimed at supporting the decision-makers to identify climatic and socioeconomic factors in augmenting the risks.
Climate change significantly impacts the global hydrological cycle, leading to pronounced shifts in hydroclimatic extremes such as increased duration, occurrence, and intensity. Despite these ...significant changes, our understanding of hydroclimatic risks and hydrological resilience remains limited, particularly at the catchment scale in peninsular India. This study aims to address this gap by examining hydroclimatic extremes and resilience in 54 peninsular catchments from 1988 to 2011. We initially assess extreme precipitation and discharge indices and estimate design return levels using non-stationary Generalized Extreme Value (GEV) models that use global climate modes (ENSO, IOD, and AMO) as covariates. Further, hydrological resilience is evaluated using a convex model that inputs simulated discharge from the best hydrological model among SVM, RVM, random forest, and a conceptual model (abcd). Our analysis shows that the spatial patterns of mean extreme precipitation indices (R1 and R5) mostly resemble with extreme discharge indices (Q1 and Q5). Additionally, all extreme indices, including R1, Q1, R5, and Q5, demonstrate non-stationary behavior, indicating the substantial influence of global climate modes on extreme precipitation and flooding across the catchments. Our results indicate that the random forest model outperforms the others. Furthermore, we find that 68.52% of the catchments exhibit low to moderate hydrological resilience. Our findings emphasize the importance of understanding hydroclimatic risks and catchment resilience for accurate climate change impact predictions and effective adaptation strategies.
•The RI approach was used to understand the characteristics of flash droughts.•The study was carried out over 25 major river basins of India.•Investigates how the regional terrestrial carbon dynamics ...respond to flash droughts.•Additionally, the response of WUE to flash droughts was also investigated.
Rapid onset droughts, termed as “flash droughts”, cause short-term but serious threats to terrestrial ecosystems and influence carbon dynamics due to insufficient warning. To date, how the regional terrestrial carbon dynamics respond to flash droughts in India remains unknown. Since, India is highly dependent on its cropland and vegetation, identifying the influence of flash droughts on terrestrial ecosystem is important. Here we use MODIS remote sensing satellite sensor based gross primary productivity (GPP) and remote sensing-based soil moisture data to compute the response of ecosystems to flash droughts in India. From the investigation, it was observed that GPP responds to more than 95% of the flash droughts across India, with the highest response frequency occurring over Ganga basin and southern India while the lowest response across northeastern India. The discrepancies in the response frequencies are mainly attributed to different vegetation resilience conditions across different parts of the country. Moreover, the mean response time is about 10 to 19 days averaged over India, with the lowest and highest response time over Indus-Ganga basins and northeastern Indian river basins (including the Brahmaputra, Minor rivers draining into Myanmar basin (MRMB), and Barak basins), respectively. Severe reduction in water use efficiency (WUE) was observed for the Ganga river basin and some parts of southern India, which highlighted the non-resilient nature of ecosystem towards rapid soil moisture variations. The study facilitates the identification of flash drought hotspots in the country including the Indus basin, Southern river basins (Cauveri, EFRPCP, and EFRSCB basins), some parts of the Ganga basin, and the ability of an ecosystem to withstand such drastic conditions. These findings highlight the need to adopt essential drought mitigation measures to safeguard the sustainability of ecosystems.
Sarcopenia and Cardiovascular Diseases Damluji, Abdulla A; Alfaraidhy, Maha; AlHajri, Noora ...
Circulation (New York, N.Y.),
05/2023, Volume:
147, Issue:
20
Journal Article
Peer reviewed
Open access
Sarcopenia is the loss of muscle strength, mass, and function, which is often exacerbated by chronic comorbidities including cardiovascular diseases, chronic kidney disease, and cancer. Sarcopenia is ...associated with faster progression of cardiovascular diseases and higher risk of mortality, falls, and reduced quality of life, particularly among older adults. Although the pathophysiologic mechanisms are complex, the broad underlying cause of sarcopenia includes an imbalance between anabolic and catabolic muscle homeostasis with or without neuronal degeneration. The intrinsic molecular mechanisms of aging, chronic illness, malnutrition, and immobility are associated with the development of sarcopenia. Screening and testing for sarcopenia may be particularly important among those with chronic disease states. Early recognition of sarcopenia is important because it can provide an opportunity for interventions to reverse or delay the progression of muscle disorder, which may ultimately impact cardiovascular outcomes. Relying on body mass index is not useful for screening because many patients will have sarcopenic obesity, a particularly important phenotype among older cardiac patients. In this review, we aimed to: (1) provide a definition of sarcopenia within the context of muscle wasting disorders; (2) summarize the associations between sarcopenia and different cardiovascular diseases; (3) highlight an approach for a diagnostic evaluation; (4) discuss management strategies for sarcopenia; and (5) outline key gaps in knowledge with implications for the future of the field.
•CMIP5 decadal predictions analysed for five basins of Brahmaputra.•Daily precipitation data of five GCMs used for assessment.•Functional relationship between rainfall spell and flood characteristics ...formulated.•Flood behaviour in 2010–2020 predicted based on changes in rainfall spell properties.•Floods with larger peak stage and flood volume predicted in future.
Climate change has the potential to intensify the hydrological cycle, leading to more intense precipitation with associated changes in the intensity, frequency and severity of floods. Climate variability and change beyond a few years to a few decades ahead have significant social, economic, and environmental implications. It is believed that some aspects of this decadal variability could be predictable for a decade or longer in advance. Keeping this in mind, phase five of the Coupled Model Intercomparison Project (CMIP5), for the first time, provides 10–30years predictions obtained from the General Circulation Models (GCMs).
This study aims to analyse the CMIP5 decadal predictions for precipitation over five sub-basins of river Brahmaputra. Daily precipitation data of five GCMs, namely F-GOALS-g2, BCC-CSM1-1, IPSL-CM5A, CanCM4 and MRI-CGCM3 are used for this assessment. Empirical relationships between the basin averaged rainfall wet spell (storm) properties and the characteristics of the floods are formulated for storms which lead to significant short-term flood response. Following this, the changes in the flood behaviour in the future are derived on the basis of changes in the characteristics of wet rainfall spells in 2010–2020. The results suggest an increase in the number of spells with higher rainfall and longer duration which can lead to increase in peak flood and the total flood volume.
Abstract
The Narmada basin is one of the major river basins of Central India. The basin frequently experiences droughts and floods due to its geography and uneven topography. Therefore, it is ...important to understand the spatiotemporal variability of hydroclimatic extremes over the basin. Large-scale climate oscillations (LSCOs) have been observed significantly affecting the patterns of hydroclimatic extremes at the basin and continental scale. In this study, we have analysed the relative influence of LSCOs (EL Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Atlantic multidecadal oscillation (AMO)) over hydroclimatic extremes of the Narmada basin. Precipitation, temperature, and streamflow extremes were analysed in stationary and nonstationary frameworks of generalized extreme value distribution. The precipitation extremes, PRCPTOT and R95p were observed significantly influenced by ENSO, IOD, and AMO individually whereas extreme Rx5day was relatively more influenced by ENSO and AMO individually and collectively. Temperature extremes, TXx was significantly more influenced by ENSO alone (26.47% of the region), while TNx was observed to be substantially more influenced by ENSO and AMO. The upper Narmada basin was found vulnerable to flooding and whereas the basin was projected to experience more frequent and intense heatwave-associated disasters in long term.
Abstract
Wetlands are one of the most critical components of an ecosystem, supporting many ecological niches and a rich diversity of flora and fauna. The ecological significance of these sites makes ...it imperative to study the changes in their inundation extent and propose necessary measures for their conservation. This study analyzes all 64 Ramsar sites in China based on their inundation patterns using Landsat imagery from 1991 to 2020. Annual composites were generated using the short-wave infrared thresholding technique from June to September to create inundation maps. The analysis was carried out on each Ramsar site individually to account for its typical behavior due to regional geographical and climatic conditions. The results of the inundation analysis for each site were subjected to the Mann–Kendall test to determine their trends. The analysis showed that 8 sites exhibited a significantly decreasing trend, while 14 sites displayed a significantly increasing trend. The accuracy of the analysis ranged from a minimum of 72.0% for Hubei Wang Lake to a maximum of 98.0% for Zhangye Heihe Wetland National Nature Reserve. The average overall accuracy of the sites was found to be 90.0%. The findings emphasize the necessity for conservation strategies and policies for Ramsar sites.
Indian Himalayas are home to numerous glacial lakes, which can pose serious threat to downstream communities and lead to catastrophic socioeconomic disasters in case of a glacial lake outburst flood ...(GLOF). This study first identified 329 glacial lakes of size greater than 0.05 km2 in the Indian Himalayas, and then a remote sensing‐based hazard and risk assessment was performed on these lakes. Different factors such as avalanche, rockfall, upstream GLOF, lake expansion, identification of the presence of ice cores, and assessment of the stability of moraine were considered for the hazard modeling. Further, a stochastic inundation model was applied to quantify the potential number of buildings, bridges, and hydropower systems that could be inundated by GLOF in each lake. Finally, the hazard parameters and downstream impact were collectively considered to determine the risk linked with each lake. A total of 23 lakes were identified as very high risk lakes and 50 as high‐risk lakes. The potential flood volumes associated with various triggering mechanisms were also measured and were used to identify the lakes with the most considerable risk, such as Shakho Cho and Khangchung Tso. This study is anticipated to support stakeholders and decision‐makers in identifying critical glacial lakes and make well‐informed decisions related to future modeling efforts, field studies, and risk mitigation measures.
Key Points
Avalanche trajectories suggest that 36 out of 329 glacial lakes are susceptible to dynamic failure via an avalanche entering the lake
Application of stochastic flood model reveals that 67 glacial lakes contain at least one hydropower system along their flow path
Indian Himalayas contain 23 critical glacial lakes, 17 of which are located in the state of Sikkim
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