Transboundary hazard and downstream impact of glacial lakes in Hindu-Kush Karakoram Himalayas.

Glacial Lake Outburst Floods (GLOFs) can generate catastrophic flash floods when the damming structure is breached or overtopped. Some of these glacial lakes are located in transboundary regions where floods originating from the lake in one country could inundate a neighboring country, devastating the population and infrastructure of both nations and influencing socio-political relationships. Therefore, assessing the lakes' hazard is crucial. This study investigates transboundary glacial lakes, considering their GLOF hazard, including potential mass movement intrusion, moraine's stability, upstream and downstream process cascades, downstream flood extents, and the exposure and vulnerability of the downstream infrastructure and affected population. GLOF exposure assessments were carried out to identify exposed buildings, bridges, and hydropower systems in transboundary regions. China currently has the highest number of transboundary lakes, with most of them potentially impacting India and Nepal. Most of the transboundary lakes in China, and many in India and Nepal, are susceptible to mass movements. Among the 230 transboundary glacial lakes in the Hindu Kush Karakoram Himalaya, 55 lakes can potentially impact other glacial lakes along their flow path, creating a cascade of events. Five transboundary lakes could potentially impact over 1000 buildings, and 16 lakes could impact over 500 buildings. A total of 35 lakes can impact at least one hydropower station along their flow path, and 4 lakes can impact two hydropower stations. This research emphasizes the critical importance of conducting comprehensive risk analyses of GLOFs in transboundary regions to inform policy-makers. It calls for investing in broad-scale assessments and data-driven decision-making for mitigating and adapting to GLOF risks effectively. Finally, by raising awareness among policy-makers, the study aims to drive actions that safeguard communities and infrastructure vulnerable to GLOF.

Characterizing the spatio-temporal distribution, detection, and prediction of aerosol atmospheric rivers on a global scale.

Aerosol Atmospheric Rivers (AARs) are elongated and narrow regions that carry high concentrations of aerosols (tiny particles suspended in the atmosphere) across large distances, exerting effects on both air quality and human health (Chakraborty et al., 2021, 2022). Monitoring and modeling these aerosols present distinct challenges due to their dynamic nature and complex interactions within the atmosphere. In this context, the present study detects and predicts the AARs using MERRA-2 reanalysis datasets with their seasonal climatology of key aerosol species, including Black Carbon (BC), Dust (DU), Organic Carbon (OC), Sea Salt (SS), and Sulphates (SU). The study employs an innovative Integrated Aerosol Transport (IAT) based AAR algorithm from 2015 to 2022. A total count of 44,020 BC AARs, 13,280 DU AARs, 21,599 OC AARs, 17,925 SS AARs, and 31,437 SU AARs were detected globally. The seasonal climatology of BC and OC AARs intensifies in areas such as the Amazon rainforest and Congo during AMJJAS (April-September) due to forest fires. Similarly, DU AARs are more frequent in regions near the Saharan desert, primarily around the equator during AMJJAS. SS AARs tend to predominate over the oceans, while SU AARs are predominantly found in the northern hemisphere, primarily due to higher anthropogenic emissions. Furthermore, convolutional autoencoder-based models were developed for key aerosol species, strengthening predictive accuracy by effectively capturing complex data relationships and delivering precise predictions for the last 5-time frames. During validation, the model evaluation parameters for image prediction such as the Structural Similarity Index ranged from 0.86 to 0.94, Peak Signal-to-Noise Ratio fluctuated between 1.14 and 42.25 dB, Root Mean Square Error varied from 2.39 to 296.4 mg/(m-sec), and Mean Square Error fell within the range of 1.55-17.22 mg/(m-sec). These collectively reflect image closeness, quality, dissimilarity, and accuracy in AAR prediction. This study demonstrates the effectiveness of advanced machine and deep learning models in predicting AARs, offering the potential for advanced forecasting and enhancing resilience in high-aerosol concentration regions.

Spatiotemporal-based automated inundation mapping of Ramsar wetlands using Google Earth Engine.

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.

Heat waves characteristics intensification across Indian smart cities.

Indian cities have frequently observed intense and severe heat waves for the last few years. It will be primarily due to a significant increase in the variation in heat wave characteristics like duration, frequency, and intensity across the urban regions of India. This study will determine the impact of future climate scenarios like SSP 245 and 585 over the heat wave characteristics. It will present the comparison between heat waves characteristics in the historical time (1981 to 2020) with future projections, i.e., D1 (2021-2046), D2 (2047-2072), and D3 (2073-2098) for different climate scenarios across Indian smart cities. It is observed that the Coastal, Interior Peninsular, and North-Central regions will observe intense and frequent heat waves in the future under SSP 245 and 585 scenarios. A nearly two-fold increase in heat wave' mean duration will be observed in the smart cities of the Interior Peninsular, Coastal, and North Central zones. Thiruvananthapuram city on the west coast has the maximum hazard associated with heat waves among all the smart cities of India under both SSPs. This study assists smart city policymakers in improving the planning and implementation of heat wave adaptation and mitigation plans based on the proposed framework for heat action plans and heat wave characteristics for improving urban health well-being under hot weather extremes in different homogeneous temperature zones.

Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India.

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.

A study of thyroid profile in patients suffering from the first episode of mania: A cross-sectional study.

BACKGROUND: Thyroid function is commonly considered in the assessment of mood disorders. Reports of thyroid dysregulation in patients with mania are associated with several confounding factors. To eliminate confounding factors, studies of first-episode mania are desirable. This study tried to find out any relationship between thyroid disorders and mania. AIM: The aim of this study is to assess and compare the thyroid profile between first-episode mania and healthy controls and to ascertain the correlation between severity and duration of the manic episode with FT3, FT4, and thyroid-stimulating hormone (TSH) levels. MATERIALS AND METHODS: This was a cross-sectional study conducted in the psychiatry department of a tertiary care hospital. Forty consecutive drug-naïve patients with first-episode mania, diagnosed according to the International Classification of Disease-10 (study group), were matched with 40 healthy controls (control group). Both the groups were compared on the basis of thyroid profile and thyroid levels were correlated with duration and severity of illness in the study group. RESULTS: Nearly 7.5% of cases in the study group had hyperthyroidism, whereas 5% had subclinical hyperthyroidism. In contrast, normal controls showed 5% and 10% prevalence of hypothyroidism and subclinical hypothyroidism, respectively. A statistically significant lower level of TSH was observed in the study group (P < 0.001), whereas the mean serum levels of FT3 and FT4 were higher in the study group, but the difference was statistically nonsignificant. No significant correlation of thyroid hormones level with duration and severity of illness was noted. CONCLUSION: Our findings highlight a higher prevalence of hyperthyroidism in patients with mania and suggest the role of thyroid hormones in mania.

A novel analysis of COVID 19 risk in India incorporating climatic and socioeconomic Factors.

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.

Assessment of Risk and Resilience of Terrestrial Ecosystem Productivity under the Influence of Extreme Climatic Conditions over India.

Analysing the link between terrestrial ecosystem productivity (i.e., Net Primary Productivity: NPP) and extreme climate conditions is vital in the context of increasing threats due to climate change. To reveal the impact of changing extreme conditions on NPP, a copula-based probabilistic model was developed, and the study was carried out over 25 river basins and 10 vegetation types of India. Further, the resiliency of the terrestrial ecosystems to sustain the extreme disturbances was evaluated at annual scale, monsoon, and non-monsoon seasons. The results showed, 15 out of 25 river basins were at high risks, and terrestrial ecosystems in only 5 river basins were resilient to extreme climatic conditions. Moreover, at least 50% area under 4 out of 10 vegetation cover types was found to be facing high chances of a drastic reduction in NPP, and 8 out of 10 vegetation cover types were non-resilient with the changing extreme climate conditions.

Assessment of the impacts of climatic variability and anthropogenic stress on hydrologic resilience to warming shifts in Peninsular India.

Most parts of the world are witnessing climatic warming and the trend is expected to increase in the future. It is important to assess the response of watershed hydrology to this warming. Moreover, human interactions and climatic variability influence the water balance of a catchment. We perform contribution analysis along with resilience study using Budyko framework and two parameters (dynamic deviation and modified elasticity), in-order to comprehend the involvement of anthropogenic stress and climatic variance on partitioning of precipitation and their relation with hydrologic resilience to warming shifts across 55 catchments in peninsular India. Here, 23 catchments have displayed hydrologic resilience (low departure and high elasticity) to climatic warming shifts. Only 37.14% of anthropogenic dominated catchments (higher contribution from human activities in runoff changes) were found to be resilient whereas 58.82% of climate dominated catchments had resilience attributes. Most of the catchments on western and extreme southern part of India were not hydrologic resilient. Extensive human interactions tend to depart the catchment from expected hydrological functioning under critical climatic conditions (Warming in our study) that lead to declining of hydrological resilience.

Diagnosing climate change impacts and identifying adaptation strategies by involving key stakeholder organisations and farmers in Sikkim, India: Challenges and opportunities.

Narrowing the gap between research, policy making and implementing adaptation remains a challenge in many parts of the world where climate change is likely to severely impact water security. This research aims to narrow this gap by matching the adaptation strategies being framed by policy makers to that of the perspectives of development agencies, researchers and farmers in the Himalayan state of Sikkim in India. Our case study examined the perspectives of various stakeholders for climate change impacts, current adaptation strategies, knowledge gaps and adaptation barriers, particularly in the context of implementing the Sikkim State Action Plan on Climate Change through semi-structured interviews carried out with decision makers in the Sikkim State Government, researchers, consultants, local academia, development agencies and farmers. Using Stakeholders Network Analysis tools, this research unravels the complexities of perceiving climate change impacts, identifying strategies, and implementing adaptation. While farmers are less aware about the global phenomenon of climate change impacts for water security, their knowledge of the local conditions and their close interaction with the State Government Agriculture Department provides them opportunities. Although important steps are being initiated through the Sikkim State Action Plan on Climate Change it is yet to deliver effective means of adaptation implementation and hence, strengthening the networks of close coordination between the various implementing agencies will pay dividends. Knowledge gaps and the need for capacity building identified in this research, based on the understandings of key stakeholders are highly relevant to both the research community and for informing policy.

Assessment of ecosystem resilience to hydroclimatic disturbances in India.

Recent studies have shown an increasing trend in hydroclimatic disturbances like droughts, which are anticipated to become more frequent and intense under global warming and climate change. Droughts adversely affect the vegetation growth and crop yield, which enhances the risks to food security for a country like India with over 1.2 billion people to feed. Here, we compared the response of terrestrial net primary productivity (NPP) to hydroclimatic disturbances in India at different scales (i.e., at river basins, land covers, and climate types) to examine the ecosystems' resilience to such adverse conditions. The ecosystem water use efficiency (WUEe : NPP/Evapotranspiration) is an effective indicator of ecosystem productivity, linking carbon (C) and water cycles. We found a significant difference (p < .05) in WUEe across India at different scales. The ecosystem resilience analysis indicated that most of the river basins were not resilient enough to hydroclimatic disturbances. Drastic reduction in WUEe under dry conditions was observed for some basins, which highlighted the cross-biome incapability to withstand such conditions. The ecosystem resilience at land cover and climate type scale did not completely relate to the basin-scale ecosystem resilience, which indicated that ecosystem resilience at basin scale is controlled by some other ecohydrological processes. Our results facilitate the identification of the most sensitive regions in the country for ecosystem management and climate policy making, and highlight the need for taking sufficient adaptation measures to ensure sustainability of ecosystems.

Plexiform Angiomyxoid Myofibroblastic Tumor (PAMT) of Stomach with Synchronous Bilateral Cystic Ovarian Neoplasms, a Rare Case Presentation.

Plexiform Angiomyxoid Myofibroblastic Tumor (PAMT) is a recently identified mesenchymal tumor of the stomach, which was first described in the year 2007 and was added in the 2010 WHO classification of tumors of the digestive system World J Gastroenterol 16(6): 2835-2840, 2010. It closely resembles with other gastric tumors but distinctly varies in clinical management as well as the histopathology. We had a 51 year, female patient, laborer by profession with low socio economic status, who had abdominal pain with vomiting since 6 months. She had similar complaints 3 years ago for which she was evaluated and presumed to have Carcinoma Stomach and underwent laparotomy which ended up only with Gastro- Jejunal anastomosis. She was admitted at our institution. Endoscopy revealed antral bulge with central area ulceration and biopsy was taken which was not confirmatory for malignancy. CT images showed heterogeneous mass with necrotic changes arising from the duodenum favored the diagnosis of perigastric neoplasm. PET CT was done, 8.4 × 5 × 6.1 cm exophytic mass in the pyloric region of stomach with solid and cystic components causing significant gastric outlet obstruction. She underwent exploratory laparotomy and complete excision of mass with achievement of R0 clearance. Histopathology was reported as Plexiform angiomyxoid myofibroblastic tumor (PAMT).

Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India.

The prediction of streamflow is required in many activities associated with the planning and operation of the components of a water resources system. Soft computing techniques have proven to be an efficient alternative to traditional methods for modelling qualitative and quantitative water resource variables such as streamflow, etc. The focus of this paper is to present the development of models using multiple linear regression (MLR), artificial neural network (ANN), fuzzy logic and decision tree algorithms such as M5 and REPTree for predicting the streamflow at Kasol located at the upstream of Bhakra reservoir in Sutlej basin in northern India. The input vector to the various models using different algorithms was derived considering statistical properties such as auto-correlation function, partial auto-correlation and cross-correlation function of the time series. It was found that REPtree model performed well compared to other soft computing techniques such as MLR, ANN, fuzzy logic, and M5P investigated in this study and the results of the REPTree model indicate that the entire range of streamflow values were simulated fairly well. The performance of the naïve persistence model was compared with other models and the requirement of the development of the naïve persistence model was also analysed by persistence index.

Fate of pharmaceutical compounds in hydroponic mesocosms planted with Scirpus validus.

A systematic approach to assess the fate of selected pharmaceuticals (carbamazepine, naproxen, diclofenac, clofibric acid and caffeine) in hydroponic mesocosms is described. The overall objective was to determine the kinetics of depletion (from solution) and plant uptake for these compounds in mesocosms planted with S. validus growing hydroponically. The potential for translocation of these pharmaceuticals from the roots to the shoots was also assessed. After 21 days of incubation, nearly all of the caffeine, naproxen and diclofenac were eliminated from solution, whereas carbamazepine and clofibric acid were recalcitrant to both photodegradation and biodegradation. The fact that the BAFs for roots for carbamazepine and clofibric acid were greater than 5, while the BAFs for naproxen, diclofenac and caffeine were less than 5, implied that the latter two compounds although recalcitrant to biodegradation, still had relatively high potential for plant uptake. Naproxen was sensitive to both photodegradation (30-42%) and biodegradation (>50%), while diclofenac was particularly sensitive (>70%) to photodegradation alone. No significant correlations (p > 0.05) were found between the rate constants of depletion or plant tissue levels of the pharmaceuticals and either log Kow or log Dow.

Applying Fuzzy logic and the point count system to select landfill sites.

The treatment of solid waste is currently one of the major environmental problems facing municipalities. Thousands of tonnes of waste are generated each day, requiring a large area for disposal purposes. It is difficult to find suitable areas for the construction of such sanitary landfills as numerous criteria must be met, and landfill sites vary considerably in terms of their sophistication. The selection criteria for landfill sites should be as simple as possible, and with this in mind, we have evaluated a large number of random cases for the suitability of the site for landfill purposes using the recently advocated fuzzy approach. Using the fuzzy classification, we have attempted to develop a simple classification which uses only certain point values for available attributes. A normalized average of such attributes based on the proposed classifier is further evaluated using additionally generated random data sets. The results appear to be encouraging and indicate that the present classifier can be used as a substitute for the fuzzy-based ranking of landfill sites.