Hydrogen gas exhibits potential as a sustainable fuel for the future. Therefore, many attempts have been made with the aim of producing high yields of hydrogen gas through renewable biological ...routes. Engineering of strains to enhance the production of hydrogen gas has been an active area of research for the past 2 decades. This includes overexpression of hydrogen-producing genes (native and heterologous), knockout of competitive pathways, creation of a new productive pathway, and creation of dual systems. Interestingly, genetic mutations in 2 different strains of the same species may not yield similar results. Similarly, 2 different studies on hydrogen productivities may differ largely for the same mutation and on the same species. Consequently, here we analyzed the effect of various genetic modifications on several species, considering a wide range of published data on hydrogen biosynthesis. This article includes a comprehensive metabolic engineering analysis of hydrogen-producing organisms, namely Escherichia coli, Clostridium, and Enterobacter species, and in addition, a short discussion on thermophilic and halophilic organisms. Also, apart from single-culture utilization, dual systems of various organisms and associated developments have been discussed, which are considered potential future targets for economical hydrogen production. Additionally, an indirect contribution towards hydrogen production has been reviewed for associated species.
Himalaya is experiencing frequent catastrophic mass movement events such as avalanches and landslides, causing loss of human lives and infrastructure. Millions of people reside in critical zones ...potentially exposed to such catastrophes. Despite this, a comprehensive assessment of mass movement exposure is lacking at a regional scale. Here, we developed a novel method of determining mass movement trajectories and applied it to the Himalayan Mountain ranges for the first time to quantify the exposure of infrastructure, waterways, roadways, and population in six mountain ranges, including Hindu Kush, Karakoram, western Himalaya, eastern Himalaya, central Himalaya, and Hengduan Shan. Our results reveal that the exposure of buildings and roadways to mass movements is highest in Karakoram, whereas central Himalaya has the highest exposed waterways. The hotspots of exposed roadways are concentrated in Nepal, the North Indian states of Uttarakhand, Himachal Pradesh, the Union Territory of Ladakh, and China's Sichuan Province. Our analysis shows that the population in the central Himalaya is currently at the highest exposure to mass movement impacts. Projected future populations based on Shared Socio‐economic and Representative Concentration Pathways suggest that changing settlement patterns and emission scenarios will significantly influence the potential impact of these events on the human population. Assessment of anticipated secondary hazards (glacial lake outburst floods) shows an increase in probable headward impacts of mass movements on glacial lakes in the future. Our findings will support researchers, policymakers, stakeholders, and local governments in identifying critical areas that require detailed investigation for risk reduction and mitigation.
Plain Language Summary
Avalanches and landslides are common in mountainous terrain like the Himalaya. Failure of the steep slopes can threaten infrastructure and population in the downstream regions. Also, such failures in glaciated terrain can potentially impact high‐altitude lakes, thereby leading to downstream flooding. The assessment carried out in this study covering the mountain ranges in the Hindukush Karakoram Himalaya identifies the exposed elements, including buildings, roadways, waterways, population, and glacial lakes to potential mass movements. Further, downstream impact from glacial lake outburst depends on the location of the lake with respect to the mass movement impact direction, that is, striking direction. Our findings show that the exposure depends on the mass movement magnitude and is concentrated in the eastern and central Himalayas, where population and lake densities are high. Also, exposure of buildings and roadways to mass movements is highest in Karakoram. Future population exposure to slope failures shows significant changes based on the varied socio‐economic conditions.
Key Points
First‐order assessment shows that central Himalaya has the highest exposed waterways to potential mass movements
Central Himalaya is identified as the hotspot with the highest exposed population to mass movements in the present as well as in the future
Headward impacts on glacial lakes are highest in Eastern Himalaya, which shifts to the Karakoram and Western Himalaya in the future
The hydro-climatic variables are greatly influenced by the large-scale phenomena at global and regional scales. The present study attempts to characterise the influence of large-scale climatic ...oscillations on the monthly precipitation over meteorologically homogeneous regions in India. To accomplish the study, the monthly precipitation over selected six different regions are obtained during 1951–2015 and correlations with the eight large-scale climatic oscillations namely, Indian Ocean Dipole (IOD), Sea Surface Temperature (SST), Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and Indian Summer Monsoon Index (ISMI) are examined using wavelet and global coherence. The outcomes from the analysis suggest that though other climatic indices have noticeable effects on the monthly precipitation over India, the ISMI is the most effective climatic teleconnection. The predominant and effective period of ISMI is at intra-annual scale influencing Central Northeast India (CNI), Peninsular India (PI), and West Central India (WCI), while the major effective period of IOD is in between 8 and 16 months. For the El Niño–Southern Oscillation (ENSO) indices like SST, SOI, and MEI the most prominent period is noticed during 20 to 54 months time scale over different parts of India. The phase difference is not uniform between the studied climatic oscillations and monthly precipitation across the country. For long terms of ISMI, an in-phase situation is observed over all the meteorologically homogenous regions in India. The present study advocates that the wavelet and global coherence approaches are very powerful tools to analysing the relationship between multiple time-series in a time-frequency space and its application in hydrology enables the water resources managers in developing better understanding of meteorological connections with the large-scale low frequency climatic oscillations.
•Influence of large-scale climatic oscillations is analysed over the precipitation homogeneous regions in India.•Wavelet and global coherence approaches are used to perform the analysis.•At annual scale, the Indian Summer Monsoon Index (ISMI) has significant correlation/effects over all the regions in India.•The present study will enable water resources managers in developing sustainable water resources management practices.
The increasing demand for energy, especially from renewable and sustainable resources, encourages the development of small hydropower plants (SHPs). Earlier hydropower studies were time consuming and ...less effective due to maximum involvement of the ground based and handheld surveys. This study aimed to present a new multi-criteria approach for harnessing hydropower through establishing small hydropower projects (SHPs) (≤25 MW) instead of large hydropower projects. The multi-criteria approach is based on an integration of advance raster/grid based preparation of geospatial data layers, hydrological modeling and weighted sum overlay analysis. The hydrological data simulation and parameterization were done in SWAT (soil and water assessment tool) model by utilizing 17 years long duration real time hydro-meteorological data sets. For this reason, we selected an Inland based Hamp river catchment, which is a part of Mahanadi river basin of India. The outcomes of this study allow spotting identification of four hydropower potential zones and 10 suitable sites location for SHPs along the stream network by characterizing whole catchment into different sub-catchments. Slope, soil, landuse/landcover (LULC), ET (evapotranspiration), water yield, and rainfall were identified as most important variables for hydropower assessment.
Atmospheric rivers (ARs) are filamentary regions of high moisture content in mid-latitude regions through which most of the poleward moisture is being transported. These ARs carry a huge amount of ...water in the form of vapor and thus landfalling of these ARs may bring either a beneficial supply of water or may create hazardous flood situations and thus cause damage to life and property. These regions have been statistically characterized as intense integrated water vapor transport (IVT) regions in the troposphere based on various thresholds of magnitude, direction, and geometry. To enhance the knowledge of data-driven methods for modelling nonlinear atmospheric dynamics associated with ARs, a first ever study with data-driven methodology incorporating a Deep Learning architecture, Autoencoder has been proposed. While training the proposed model, the Adam optimizer was used to reduce the mean squared error loss and was optimized using the Rectified Linear Unit (ReLU) and Sigmoid activation functions. The prediction results of the availability of ARs at next frames by the Autoencoder were assessed by popularly used performance evaluation metrics structural similarity index metrics (SSMI), mean squared error (MSE), root mean squared error (RMSE), and peak signal to noise ratio (PSNR). We have got comparatively higher scores (average) of SSIM (0.739) and PSNR (64.422) and lower scores (average) of RMSE (0.155) and MSE (0.0247) for AR prediction from our model which signifies the accuracy of the proposed Autoencoder in capturing AR dynamics. The findings of the study could be useful in giving important insights to incorporate Deep Learning models for forecasting ARs at significant lead time and consequently reducing the risk and increasing the resilience of AR flood prone regions.
•85th percentile of IVT with fixed threshold of 100 kg-m−1 s−1 was taken as reference to define AR.•Data driven method, Autoencoder was applied to learn the atmospheric dynamics and predict ARs.•AR detected maps from testing dataset, were taken as ground truth for validation.•We got on average higher PSNR (64.42) and SSIM (0.15) while evaluating the model predictions.
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.
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•A case study involving key stakeholders in understanding climate change impacts and identifying adaptation strategies•Demonstrates how the gaps between research, policy and adaptation implementation needs to be narrowed•Farmers with limited climate change awareness benefits from social networks and adaptation trainings by government•Highly relevant for both policymakers and researchers because it identifies research needs from stakeholders’ perspectives
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.
The variability in the extreme rainfall events is of growing concern in the context of climate change. Several high rainfall events have occurred in India in recent years and simulations from the ...Intergovernmental Panel on Climate Change suggest a rise in extremes. The low‐frequency global‐scale modes/oscillations are widely considered as the significant drivers of inter‐annual variability of the Indian rainfall pattern and extreme rainfall events. To account for climate external forcings, we assessed the influence of El Nino Southern Oscillation, Indian Ocean Dipole and Atlantic Multidecadal Oscillation on extreme precipitation over 24 major river basins of India using the nonstationary extreme value analysis. Moreover, the uncertainty in the parameters of the fitted nonstationary extreme value distribution is assessed using Bayesian inference. It was found that extreme precipitation events in the country are dominated by these oscillations, especially in central India. Moreover, the return levels of high rainfall were found to be intensifying with increasing return period. We also observed that uncertainty in return levels was significant in almost every river basin. The results presented here contribute to a better understanding of the large‐scale climate variability and its impact on high rainfall pattern, which would provide an essential understanding of the rainfall‐induced hazard prevention and enhance the risk management strategy.
Nonstationary extreme value theory and Bayesian analysis was used to detect the influence of El Nino Southern Oscillation, Indian Ocean Dipole and Atlantic Multidecadal Oscillation in governing extreme precipitation. Analysis was carried out for calculating the return levels of extreme precipitation over 24 major river basins of India. Results show that the association of the oscillations makes extreme precipitation more prolong and intense.
•Study reveals high-risk coastal wetlands for extreme rainfall events in India.•Inundation maps highlight the susceptibility of coastal wetlands to flooding.•Urbanization and sedimentation contribute ...to flood risk in Thane Creek.•The study emphasizes the need for managing and protecting infrastructure in wetland.
Wetlands are often found in areas that undergo periodic flooding, such as coastal seas, lakes, and rivers. Coastal wetlands are particularly vulnerable to climate change effects, such as changes in precipitation patterns, risk of extreme rainfall, and cyclones/storms. This study assessed the uncertainties associated with extreme rainfalls in terms of return levels (RLs; 20 and 50 years) and quantified the potential risk level of these events in the future for coastal wetlands in India. The extreme precipitation indices (EPIs) were evaluated using a non-stationary approach, and the results showed that Thane Creek had the highest RLs, followed by Kolleru Lake. The risk level for each wetland was assessed using the fuzzy logic approach, which considered parameters such as exposure, vulnerability, and threat. The overall risk assessment showed that Thane Creek, Kolleru Lake, Pallikaranai Marsh Reserve Forest, and Tampara Lake are at a “High” risk level for both RLs of EPIs. Furthermore, the automated Shortwave Infrared (SWIR) thresholding technique was employed in Google Earth Engine to create inundation maps of wetlands. This study also indicated that Thane Creek is at risk of flooding based on the analysis of spatiotemporal changes. The impact evaluation of Thane Creek showed that rapid urbanization has encroached upon the creek's boundaries. Therefore, the variability of EPIs may be affected by climatic oscillations, leading to an upsurge in extreme rainfalls, causing the coastal wetlands to flood. Policymakers can use these findings to develop effective strategies for the proper management of coastal wetlands.
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•Present models performed well in estimating watershed parameter (ω) in India.•Machine learning techniques assessed ω better than regression methods.•Regional Bio-geographical ...attributes explained precipitation partitioning better.•Vegetation explained higher variance in ω in the low vegetation months.
Evaluation of the engrossment of watershed surface characteristics on partitioning of precipitation to runoff and evapotranspiration is key to inspect the availability of water at watershed scale. It is more evident in the cases of ungauged watersheds. The present study develops models using multiple linear regression method and machine learning techniques (ANN: Artificial Neural Network and RVM: Relevance Vector Machine) over 793 (25 major river basins and 768 watersheds across India) to estimate the watershed parameter ‘ω’ (in Fu’s Budyko based equation) that represents intrinsic watershed attributes. In addition, seasonality factor is incorporated in the model due to intra-annual variability in vegetation across India. The models attempt to explain the intricate relationship between vegetation alterations and regional water balance. It is seen that the ANN and RVM models have performed better in estimating ω, than the MLR (Multiple Linear Regression) models. In addition, NDVI has shown more engagement in explaining the partitioning process of water in intra-annual low NDVI period compared to high NDVI period. We have also found the present models to be more accurate than the previously developed Budyko based methods in predicting ω. The newly improved models have closely imitated the intrinsic basin attributes and enhanced the functionality of Budyko framework in estimation of water availability, which would play a crucial role in assessment of hydrology of ungauged watersheds of India.
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