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Flood is always a source of social lamentation, huge infrastructural losses and disruption to economic activities in Bhagirathi Sub-basin in India. Climate variability and increasing flood incidents have created a dilemma for social, economic and environmental conditions of the affected communities. These implications necessitate assessing overall flood vulnerability to minimize their short and long-term impacts. This study presents a comprehensive analysis of composite vulnerability among the flood affected communities in Bhagirathi Sub-basin. Data for analyzing composite flood vulnerability were derived from an in-depth survey of 432 households selected through stratified random sampling method in the Sub-basin. Domains of vulnerability such as quality of life, social & economic status, health impacts, ecological implications, losses and adaptation were examined. A total of 95 indicators of these domains were considered to prepare composite vulnerability index of the selected villages. Relationship between vulnerability and households' characteristics was ascertained using cross tabulation and multinomial logistic regression. Analysis of composite vulnerability index (CVI) revealed very high vulnerability in Nutanhat, Bakkhali, Jhara, Gopalpur, Jayarampur, Titiha, Uchildaha and Mohanpur villages. High vulnerability was observed in Banagram, Mayapur, Amravati, Gobindapur, Raichak Boltala, Talim Nagar Minakhan and Majhirmana villages while Kalna Municipality was found under moderate vulnerability. High losses, ecological & health implications and low socioeconomic conditions of the households aggravated very high to moderate vulnerability in these villages. Gender, income and land possession were found strongly associated with high vulnerability while flood insurance, farming purposes and changes in rainfall pattern were identified inducing moderate vulnerability. CVI analysis assisted in identifying the priority villages for effective policy implications. The study calls for policy implications for lessening the impact of flood in the Sub-basin. [ABSTRACT FROM AUTHOR]

Ecosystem services provided by wetlands are essential for communities living near wetlands, especially in an underdeveloped semi-arid landscape. The land use land cover changes and ecosystem degradation and water quality change over the past few decades have had immense effects on declining wetland ecosystem services. With the degradation, it is exerting superfluous effects on wetland communities including loss of livelihood, and decline in other wetland services like fishing, aquaculture, fuelwood, fodder, and many more. The present study attempts to assess the changing nature of wetland health, water quality, and declining ecosystem services of Mount Abu wetlands in Rajasthan, India. For assessing the change of wetland extent, we have used the remote sensing–based data for preparation of land use land cover change from 1992 to 2020. The water samples have been collected from the wetland, and different biophysical parameters of the water have been tested in the laboratory. A questionnaire-based household survey has been conducted to understand the perception of the wetland communities on the loss of ecosystem services over three decades. Further, a correlation and cluster assessment has been conducted to understand the degradation of wetland health in the selected wetlands. The study results indicated deteriorating conditions of wetland health and declining ecosystem services in the study area over the time periods. The land use land cover change analysis indicated a decrease in the spatial extent of the wetlands in the study area. Wetland communities are being affected due to the degradation of wetland health. The study recommended executing a wetland management plan for long-term conservation and livelihood management for the Mount Abu wetlands and communities. [ABSTRACT FROM AUTHOR]

The present study led to setting up a grid-based soil fertility map along with the best fit model in the coastal regions based on soil physical (coarse, sand, silt, clay, bulk density), chemical (CEC, pH, and soil organic carbon), topographic (elevation), and nutrient elements (P2O5, K2O, Na, Zn, B) in the active Ganga deltaic region of Sundarban Biosphere Reserve, India. Soil samples have been collected from 30 soil grids, and 0–15 cm soil depth was preferred for fertility analysis because most essential soil chemical and nutrient elements affecting soil fertility are concentrated in this depth range. We have used the fuzzy-AHP-Delphi (FAHP) and fuzzy logic-Delphi (FL) methods to determine the soil fertility zone. The rules are generated on the MATLAB interface in the text form; the words "IF," "THEN," "IS," "AND," etc., are used to complete the mode-building process. The weights and the desirable limits for each criterion were set based on the expert opinions and existing literature. The kriging interpolation method and natural break classification were used to represent the soil fertility maps into five classes, namely very high fertility (0.80–1.0), high fertility (0.60–0.80), moderate fertility (0.40–0.60), low fertility (0.20–0.40), and very low fertility (0.00–0.20) respectively. Both the models show that soil fertility is respectively higher near the Hooghly River bank. In many cases, the results obtained from FAHP and FL are quite similar but huge dissimilarity has been noticed in grid numbers G2, G3, G4, F1, and F2. Since the FAHP method has been used for the weight of each criterion, therefore, it only prefers those more important parameters over others. The overall accuracy of the soil fertility map was 82.16% for the fuzzy logic model, and 79.62% for the FAHP model and the kappa coefficient value was determined as 0.82 for the fuzzy logic model and 0.79 for the FAHP model. The soil fertility map was validated using the success rate curve under the ROC technique, and the area under curve (AUC) was calculated as 84.02% for the fuzzy logic model and 81.60% for the FAHP model. Since the standard limits for each criterion were known, therefore, fuzzy logic was found to best fit the model for analyzing soil fertility for each grid. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad. [ABSTRACT FROM AUTHOR]

The present study attempts to model the spatiotemporal urban growth of the Kolkata metropolitan area (KMA), India, in a comparative modeling framework using three (remote sensing and geographic information system) RS-GIS integrated techniques, namely stochastic-choice Markov-chain (STCHOICE), cellular automataMarkov (CA-MARKOV), and multi-layer perceptron neural network (MLPNN) coupled with Markov-chain approaches intending to monitor land-use efficiency defined by United Nations for sustainable urban development in KMA. In order to find out the best modeling approach, each of the three techniques is engaged in modeling KMA’s growth, and the model thus obtained is employed to predict future growth. The MLPNN (Kappa = 0.9025) appears to be a considerably better approach as compared to the other two approaches: CA-MARKOV (Kappa = 0.6941) and STCHOICE (Kappa = 0.5392). The MLPNN simulated urban growth for 2036 reveals that the urban built-up cover is expected to be about 55% from that of 31% in 2016 due to the significant conversion of other land covers. The study reveals that urban and peri-urban areas do not have a similar pattern of land consumption and land-use efficiency in KMA. The central KMA reflects better land-use efficiency due to the compact built-up growth compared to the periphery reflecting leapfrog growth as a result of rapid urban sprawling. [ABSTRACT FROM AUTHOR]

Mangrove forests being the abode of diverse fauna and flora are vital for healthy coastal ecosystems. These forests act as a carbon sequester and protection shield against floods, storms, and cyclones. The mangroves of the Sundarban Biosphere Reserve (SBR), being one of the most dynamic and productive ecosystems in the world are in constant degradation. Hence, habitat suitability assessment of mangrove species is of paramount significance for its restoration and ecological benefits. The study aims to assess and prioritize restoration targets for 18 true mangrove species using 10 machine-learning algorithm-based habitat suitability models in the SBR. We identified the degraded mangrove areas between 1975 and 2020 by using Landsat images and field verification. The reserve was divided into 5609 grids using 1 km gird size for understanding the nature of mangrove degradation and collection of species occurrence data. A total of 36 parameters covering physical, environmental, soil, water, bio-climatic and disturbance aspects were chosen for habitat suitability assessment. Niche overlay function and grid-based habitat suitability classes were used to identify the species-based restoration prioritize grids. Habitat suitability analysis revealed that nearly half of the grids are highly suitable for mangrove habitat in the Reserve. Restoration within highly suitable mangrove grids could be achieved in the areas covered with less than 75 percent mangroves and lesser anthropogenic disturbance. The study calls for devising effective management strategies for monitoring and conserving the degraded mangrove cover. Monitoring and effective management strategies can help in maintaining and conserving the degraded mangrove cover. The model proves to be useful for assessing site suitability for restoring mangroves. The other geographical regions interested in assessing habitat suitability and prioritizing the restoration of mangroves may find the methodology adopted in this study effective. [ABSTRACT FROM AUTHOR]

In this study, we have investigated rainfall induced landslide susceptibility of the Uttarkashi district of India through the developmentof different novel GIS based soft computing approaches namely Bagging-MLPC, Dagging-MLPC, Decorate-MLPC which are a combination Multi-layer Perceptron Neural Network Classifier (MLPC) and Bagging, Dagging, and Decorate ensemble methods, respectively. The proposed models were trained and validated with the help of 103 historical landslide events (divided into 2 samples: training (70%) and validation (30%)) and 12 landslide conditioning factors. The accuracy of the models was evaluated using different statistical methods including Area Under Curve (AUC) of Receiver Operating Characteristic (ROC). The results show that though performance of all the studied models is good (AUC > 0.80) but of the hybrid Bagging-MLPC model is the best (AUC:0.965). Therefore, this newly hybrid model (Bagging-MLPC) can be used for the accurate landslide susceptibility mapping and assessment of landslide prone areas for landslide prevention and management. [ABSTRACT FROM AUTHOR]

Climate variability analysis is essential for predicting the behavior of various extreme weather events and making communities resilient. Notwithstanding the profound concerns, climate variability assessment faces numerous challenges due to inadequate and sometimes unavailability of data at spatiotemporal scales. This study makes an attempt to analyse climate variability in the Bhagirathi Sub-basin of India. Six meteorological variables were analysed from fourteen weather stations located in the Sub-basin during 1968–2017. Modified Mann–Kendall test was employed to ascertain the trends in meteorological variables. One-way ANOVA was used to assess the relationship between and within the variables. A total of 432 households were selected for reaffirming climate variability and impact on landscape. Significant trends were detected in highest maximum, mean maximum (Mmax) and mean minimum (Mmin) temperatures, relative humidity (Rh), rainfall and vapour pressure (Vp) at annual and seasonal scales. Stations located in eastern and deltaic Sub-basins registered varying trends in these meteorological variables due to anthropogenic activities-induced land use changes. ANOVA revealed a robust relation among rainfall, Vp, Mmin and Mmax. Perceptions of the sampled households revealed that climate variability has considerably affected flood intensity, vegetation, soil, water resources and agricultural pattern. We find modified Mann–Kendall method effective in analysing climate variability in the Sub-basin. Thus, this method can be utilized for effective analysis of climate variability at spatial scales in geographical regions. [ABSTRACT FROM AUTHOR]

Coastal districts of India being diverse in nature are under constant vulnerability to hazards. The article analyzes vulnerability as a function of exposure, sensitivity and adaptation capacity. We selected site-specific factors of exposure, sensitivity and adaptation for constructing the composite vulnerability index. Findings revealed very high and high vulnerability in the eastern coastal districts while western coastal districts experienced moderate and low vulnerability. High frequency of cyclone and storm surge, low socio-economic status and lack of basic amenities and facilities were found significant stressors for increasing vulnerability in coastal districts. Composite vulnerability index not only helped in analyzing causative factors of vulnerability but also proved to be an effective tool in identifying priority districts where efforts could be made to lessen vulnerability. The study calls for policy measures to reduce vulnerability and safeguard the coastal communities from the risk of disasters. [ABSTRACT FROM AUTHOR]

In this paper, we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D), and Random Subspace (RSS) ensemble learning techniques for spatial prediction of rainfall-induced landslides in the Uttarkashi district, located in the Himalayan range, India. To do so, a total of 103 historical landslide events were linked to twelve conditioning factors for generating training and validation datasets. Root Mean Square Error (RMSE) and Area Under the receiver operating characteristic Curve (AUC) were used to evaluate the training and validation performances of the models. The results showed that the single REPT model and its derived ensembles provided a satisfactory accuracy for the prediction of landslides. The D-REPT model with RMSE = 0.351 and AUC = 0.907 was identified as the most accurate model, followed by RSS-REPT (RMSE = 0.353 and AUC = 0.898), B-REPT (RMSE = 0.396 and AUC = 0.876), and the single REPT model (RMSE = 0.398 and AUC = 0.836), respectively. The prominent ensemble models proposed and verified in this study provide engineers and modelers with insights for development of more advanced predictive models for different landslide-susceptible areas around the world. [ABSTRACT FROM AUTHOR]

Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high intensity and kinetic energy over short time periods. So, keeping this scenario in view, the main objective of this work is to identify areas vulnerable to soil erosion and propose the most suitable model for soil erosion susceptibility in subtropical environment. The implementation of machine learning and artificial intelligence techniques with a GIS environment for determining erosion susceptibility is highly acceptable in terms of optimal accuracy. The point-specific values of different elements from random sampling were considered for this study. Sensitivity analysis of the predicted models (i.e., analytical neural network, geographically weighted regression and GWR–ANN ensemble) was performed using the maximum causative factors and related primary field observations. The area under curve of receiver operating system reveals precision with 87.13, 89.57 and 91.64 for GWR, ANN and ensemble GWR–ANN, respectively. The ensemble GWR–ANN is more optimal than the GWR, ANN for determining water-induced soil erosion susceptibility. The process of soil erosion is not a unidirectional process, so the multidimensional impacts from the conditioning factors have to be determined precisely by considering the maximum possible factors as well as selecting optimal models for specific regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • High Conservation Value Areas (HCVAs) have been assessed for rare, endemic and threatened (RET) species. • Species distribution patterns have been modelled and predicted for RET flora, fauna and avifauna species. • Ecological niche overlap assessment has been performed with 95% confidence level. • 62.9 % of the total geographical area has been identified as HCVAs for RET species. The Forest Stewardship Council developed the concept of High Conservation Values (HCVs) as a criteria in the forest certification process in order to promote sustainable forest management. It has six major components or values and component one and two of HCVs deal with the habitat for viable populations of "rare, endemic and threatened (RET) species" using the IUCN Red List category and other national / regional / local lists. But a consistent robust methodology for identification of these areas, does not exist. The present study tried to develop for the first time, a straight forward inclusive methodology for identification of HCVAs for the RET species on a spatio-temporal scale. A total of 50 RET and other significant species (32 flora, 10 fauna and 8 avifauna) were identified after a thorough literature review, field surveys and consultations with experts. Occurrence data of the selected species was collected from different secondary sources, field surveys, institutes and scientists who have worked on them. A 10 km grid-based approach and stratified random sampling was used for the primary GPS field surveys conducted during 2018–2019. MaxEnt species distribution model (SDM) software was used based on the occurrence data and environmental variables for identification of potential suitable habitats for the selected species. Linear support vector machine (LSVM) model was used for assessing the performance of the SDMs. The performance of each SDM has been validated through Cohen's Kappa (KAPPA), true skill statistic (TSS) and receiver operating characteristics (ROC) models. The proposed methodology addresses the urgent need for a holistic and robust set of techniques to apply the HCV toolkit. This is key to identify and map HCVAs for RET species at the landscape level and can be easily adapted to and adopted at the national, regional, state or local level in India. The methods offer an efficient, reliable approach for the application of the HCV concept, elsewhere in the world. [ABSTRACT FROM AUTHOR]

Rudraprayag in Garhwal Himalayan division is one of the most vulnerable districts to landslides in India. Heavy rainfall, steep slope and developmental activities are important factors for the occurrence of landslides in the district. Therefore, specific assessment of landslide susceptibility and its accuracy at regional level is essential for disaster management and proper land use planning. The article evaluates effectiveness of frequency ratio, fuzzy logic and logistic regression models for assessing landslide susceptibility in Rudraprayag district of Uttarakhand state, India. A landslide inventory map was prepared and verified by field data. Fourteen landslide parameters and generated inventory map were utilized to prepare landslide susceptibility maps through frequency ratio, fuzzy logic and logistic regression models. Landslide susceptibility maps generated through these models were classified into very high, high, medium, low and very low categories using natural breaks classification. Receiver operating characteristics (ROC) curve, spatially agreed area approach and seed cell area index (SCAI) method were used to validate the landslide models. Validation results revealed that fuzzy logic model was found to be more effective in assessing landslide susceptibility in the study area. The landslide susceptibility map generated through fuzzy logic model can be best utilized for landslide disaster management and effective land use planning. [ABSTRACT FROM AUTHOR]

The study aims to assess the landscape vulnerability to forest fire susceptibility of Rudraprayag district, India, using frequency ratio model. Firstly, forest-fire-affected pixels were identified by using normalized difference burning ratio and ground survey. A total of 19,834 forest fire pixels were identified; out of these, 14,876 (70%) pixels were used to generate forest fire susceptibility map and the remaining 4958 affected pixels (30%) were used to validate the susceptibility model. Twelve forest fire conditioning indicators were selected: slope angle, slope aspect, curvature, elevation, topographic wetness index, soil texture, land use/land cover, normalized difference moisture index, annual average rainfall, road buffer, distance from settlement and distance from drainage to build the forest fire susceptibility model. Receiver operating characteristic curve was used to validate the forest fire susceptibility map, and 85% prediction accuracy was found. Final landscape vulnerability to forest fire susceptibility was assessed by using overlay function in GIS environment. The result shows that 73% area of Rudraprayag district falls into low and moderate susceptibility classes and approximately 16% area falls into high and very high susceptibility classes. Landscape vulnerability analysis revealed that moderate and very high forest fire susceptibility occupies the inaccessible parts of the core forest area of the district. [ABSTRACT FROM AUTHOR]

The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon's entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years. [ABSTRACT FROM AUTHOR]

• Effectiveness of trajectory models was examined for storm surge susceptibility. • Historical storm surge inventory map was prepared for using it as a training dataset. • Causative parameters were integrated into frequency ratio models to prepare maps. • Susceptibility maps were validated through support vector machine model. • Support vector machine and modified frequency ratio were found to be effective models. The Sundarban Biosphere Reserve (SBR), which is one of the important coastal regions of India, is vulnerable to storm surge hazards. It experiences storm surge flood of varying magnitudes every year causing immense loss to life and property. Thus, an accurate storm surge flood susceptibility assessment in the Reserve is essential for safeguarding the coastal communities. The present study attempts to explore the effectiveness of the conventional frequency ratio, modified frequency ratio and support vector machine (SVM) models in storm surge flood susceptibility analysis. Core areas of the SBR and some areas along rivers experience very high and high susceptibility. Moderate susceptibility was prevalent in the north, north-western and some areas in the south-western parts of the Reserve while low susceptibility was observed in the western part of the Reserve (located away from the coast). The maps were validated using the receiver operator characteristic (ROC), the seed cell area index (SCAI), and the spatially agreed area approach. The area under the success rate (0.8347) and prediction rate (0.8221) curves was highest for the SVM model. Frequency ratio and modified frequency ratio models showed 71.2% spatially agreed area of susceptibility. The spatially agreed area of susceptibility was lower (65.1%) in case of modified frequency ratio and the SVM model while it was highest (83.2%) between the modified frequency ratio and SVM models. Thus, the SVM model was found to be the best fit model for analyzing storm surge flood susceptibility in SBR. [ABSTRACT FROM AUTHOR]

Rising sea levels and the increasing intensity of storm surges and tropical cyclones due to climate change and the resulting dynamic shifts in shoreline positions have dramatically increased the exposure risk and vulnerability of local communities inhabiting the ecologically sensitive deltaic tracts of the Sunderbans in India. The impacts arising from such hazard events on this fragile ecosystem need to be gauged to ameliorate the lives and livelihoods of these residents. This article examines the spatial distribution of vulnerability to coastal hazards within the Sundarban Biosphere Reserve (SBR) in India. For this, we have utilized several structural and process variables, which were integrated to construct a coastal vulnerability index (CVI), using the square root equation. The coastlines of the islands located within the SBR were overlain by 543 grids, each of 2 × 2 km dimension, to assign the risk rank for each considered variable. This revealed that of the total shoreline length (754 km), nearly one-fourth was very highly vulnerable, followed by highly vulnerable (27.8%), moderately vulnerable (27.9%) and low vulnerability (18.8%). Of the total islands located in these grids (27), the coastline of eleven islands was found to have very high vulnerability, five experienced high vulnerability, eight recorded moderate vulnerability while only three had low vulnerability status. The ambient geomorphological characteristics, coastal area slope, the rate of shoreline change and sea level rise were significant variables that accorded high and very high vulnerability to the islands. The CVI helped in identifying islands that require immediate attention for lessening the impact of climate change induced hazards in the SBR and also aided the assessment of the physical and coastal vulnerability conditions of these islands. This approach can be effectively utilized for assessing coastal vulnerability and for creating a holistic approach towards coastal conservation and management. [ABSTRACT FROM AUTHOR]

The Kosi megafan region of eastern Bihar, India, comprising of eight districts, is regularly afflicted by large floods that cause extensive damage. Mapping the possible inundation susceptible zones in the region accurately is, therefore, paramount for land resource conservation and livelihood preservation. This paper compares the relative efficiency of two flood zonation methods, the frequency ratio and fuzzy logic models, for flood susceptibility evaluation and delineation. Flooded extents from two past events were combined to create the training dataset, which was then compared individually with respective maps of 12 well-documented causal factors of inundation to gauge their relative influence on the flood spread. These were merged using the two models to generate the respective flood susceptibility maps, which were subsequently validated using the Receiver Operating Characteristics Curve and the Seed Cell Area Index methods. Results revealed that an enhanced fuzzy logic model (prepared by assigning relative weights to the respective causal factors as obtained from their analysis in the frequency ratio model) was more accurate and robust in demarcating flood susceptibility zones, as was further ascertained by comparison with a recent flood event, thereby providing a novel way to merge these methods. The western and southern tracts of the region were found to be more inundation prone, with the greatest risk posed along the narrow interfluve between the Kosi and Ganga rivers prior to their confluence further east. The districts most likely to be inundated were identified by computing their respective proportionate areas under the very highly susceptible flood class. [ABSTRACT FROM AUTHOR]

Enhancing and conserving carbon pools in vegetation is a major climate change mitigation strategy. The dynamics of carbon in terrestrial ecosystems depend on processes like photosynthesis, respiration, decomposition and combustion, anthropogenic activities like emission of greenhouse gases and climatic variations in rainfall and temperature, and is stored as biomass in different forms. The quantification of biomass is necessary to understand the productivity of forests, carbon sink and source dynamics and recommend ways to aid sequestration through supply side interventions like afforestation and demand side measures that check wood consumption. In this study, carbon sequestration based on the change in total carbon in two different scenarios using Markov chain and InVEST model is evaluated for the years 2000, 2018 and predicted for 2035. The results show that 1.351 Tg carbon has already been lost from 2000 to 2018 in the forest area of Sariska Tiger Reserve and another 0.107 Tg of carbon is expected to be lost in the predicted future, when we compare the land use land cover of the year 2035 with the present scenario. The local communities extract fuelwood by cutting down tree branches illegally, which leads to carbon loss into the atmosphere. The lower release of carbon in 2035 as compared to 2018 is because of forest management practices e.g., plantations done in the range Tehla-Talabberi, Sariska- Ganeshpura, Alwar buffer-Shoydanpura, Talvriksh-Manavaas, Akbarpur-Kalikhol, Ajabgarh-Jatwana. The study shows that the economic value of carbon lost from 2000 to 2018 is million $ 214.57 and if we see the economic value of the predicted year 2035, carbon sequestration is worth million $ 17.19. Outputs demonstrate the potential for restoration/reforestation and afforestation as the most practical carbon loss mitigation strategy and calls for improved access to cleaner energy sources where the renewable energy industry and government may pitch in. Image 1 • Markov chain and InVEST model were used to predict carbon sequestration. • Prediction maps revealed that open forest will be decreased in future. • Total carbon sequestration decreased 1.351 Tg during 2000–18. • It is predicted that −0.107 (Tg) carbon to be sequestered from 2018 to 35. [ABSTRACT FROM AUTHOR]

Dynamism of river bed morphology is continuous and an integrated process of mountain's natural outflow in Himalayan Foothill Rivers. In-stream seasonal sediment flux, bridge establishments, bridge scouring and railway-road construction have detrimental influence over the natural flow regime of the river thereby causing channel degradation in the piedmont zone of eastern Himalaya of India, specifically near the urban areas where river management strategies are very weak. Such extreme anthropogenic events have a distinctive influence in changing river bed morphology in the middle-lower part of river Chel in Darjeeling Himalayan piedmont zone of India. Huge sediment aggradation and transportation networks historically have affected River Chel and have caused channel avulsion which poses a serious threat to the physical and ecological environment of Chel. Aggradation and lifting of sediments from the river bed are fatal for the geomorphologic character of the river, though no studies have been undertaken yet, to analyse the situation quantitatively in this regard. This paper presents evidence that suggests the changing river bed morphology, the dynamic nature of the river, the poor health of the river which is principally the impact of the seasonal sediment flux and bridge construction and thus some methods of river restoration in the middle-lower part of the river have been explained. Agglomeration of sediment, flow bifurcation, the volume of seasonal water mass, bed elevation changes and scouring are a major dynamic disposition for change of river bed morphology. Point bar, sidebar, braided active channel, ripples, and rise in the bed elevation are rapid changes in upper to lower part of bridges that are examined in the present study. In the dry season (December to February), a huge amount of sediment lifting makes incommodious man-made holes and a number of emergent channels which principally control the formation of a new active channel in the next monsoon season. The avulsion of major intra-active channels and morphological changes of the river bed for the last 85 years have also been observed in this study. Unlabelled Image • This paper presents evidence that suggests that the changing river bed morphology, the dynamic nature of the river, the poor health of the river is principally the impact of the huge sediment aggradation and bridge construction and thus some methods of river restoration in the middle-lower part of river Chel. • Suspended sediment, aggradation and degradation, Scouring calculation for boulder agglomeration, Compare of satellite images and Topographical map for braided channel shifting are major methods for understanding of outcome results. • Agglomeration of sediment, flow bifurcation, volume of seasonal water mass, bed elevation change and scouring are major dynamic disposition for change of river bed morphology by bridge construction scouring. [ABSTRACT FROM AUTHOR]

In this study, we trace the COVID-19 pandemic's footprint across India's districts. We identify its primary epicentres and the outbreak's imprint in India's hinterlands in four separate time-steps, signifying the different lockdown stages. We also identify hotspots and predict areas where the pandemic may spread next. Significant clusters in the country's western and northern parts pose risk, along with the threat of rising numbers in the east. We also perform epidemiological and socioeconomic susceptibility and vulnerability analyses, identifying resident populations that may be physiologically weaker, leading to a high incidence of cases and pinpoint regions that may report high fatalities due to ambient poor demographic and health-related factors. Districts with a high share of urban population and high population density face elevated COVID-19 risks. Aspirational districts have a higher magnitude of transmission and fatality. Discerning such locations can allow targeted resource allocation to combat the pandemic's next phase in India., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020. Published by Elsevier Ltd.)

This study aimed to model deforestation susceptibility in forest ecosystem of Rudraprayag district, India. For this purpose, site-specific physical (slope angle, slope aspect, altitude, annual average rainfall, soil texture, soil depth), and anthropogenic (population distribution, distance from road, distance from settlement, proximity to agricultural land) deforestation conditioning factors were chosen. Landsat TM and OLI images for 1990 and 2015 were utilized to evaluate the changes in forest cover. The frequency ratio model was used for deforestation susceptibility mapping. The extent of deforestation was examined by overlaying forest fragmentation map and deforestation susceptibility map. The results showed that about 112.5km 2 forest area has been deforested over the last 25years. Of the total existing forest, nearly 10% area falls under very high, 17% under high and 30% under moderate deforestation susceptibility categories. Patch, edge and perforated have influenced high (64%) and very high (81%) deforestation susceptibility zones. The integrated methodology involving frequency ratio model, fragmentation approach and remote sensing and GIS techniques has proved useful in analyzing deforestation susceptibility and identifying its causative factors. Thus, the methodology adopted in this study can best be utilized for effective planning and management of forest ecosystem., (Copyright © 2018 Elsevier B.V. All rights reserved.)