Bloom of Hemidiscus hardmannianus (Bacillariophyceae) and Its Impact on Water Quality and Plankton Community Structure in a Mangrove Wetland

An intense monospecies bloom of the centric diatom Hemidiscus hardmannianus (Greville) Mann. (Bacillariophyceae) occurred in the coastal waters of Sundarban mangrove wetland, India on 22 July, 2010. The species was abruptly flourished (mean density 8.86 × 10⁶ cells/L) resulting intense green coloration of water. The greatest impoverishment of diversity and density of phytoplankton, micro‐ and mesozooplankton community was observed during the bloom. The phytoplankton community showed an extreme reduction in diversity (12 species to one) and sharp decrease in standing stock (12.84 × 10³ to 8.86 × 10⁶ cells/L) during this period. The late bloom condition coincided with sudden and huge arrival of a clupoid fish Hilsa ilisha which forms the most potential fisheries in Sundarban deltaic region. An attempt has been made to correlate the satellite remote sensing‐derived information to the bloom conditions. The MODIS‐Aqua derived chlorophyll and TMI satellite derived sea surface temperature and wind speed maps have been interpreted.     

Urban base flow with low impact development

A novel form of urbanization, low impact development (LID), aims to engineer systems that replicate natural hydrologic functioning, in part by infiltrating stormwater close to the impervious surfaces that generate it. We sought to statistically evaluate changes in a base flow regime because of urbanization with LID, specifically changes in base flow magnitude, seasonality, and rate of change. We used a case study watershed in Clarksburg, Maryland, in which streamflow was monitored during whole‐watershed urbanization from forest and agricultural to suburban residential development using LID. The 1.11‐km² watershed contains 73 infiltration‐focused stormwater facilities, including bioretention facilities, dry wells, and dry swales. We examined annual and monthly flow during and after urbanization (2004–2014) and compared alterations to nearby forested and urban control watersheds. We show that total streamflow and base flow increased in the LID watershed during urbanization as compared with control watersheds. The LID watershed had more gradual storm recessions after urbanization and attenuated seasonality in base flow. These flow regime changes may be because of a reduction in evapotranspiration because of the overall decrease in vegetative cover with urbanization and the increase in point sources of recharge. Precipitation that may once have infiltrated soil, been stored in soil moisture to be eventually transpired in a forested landscape, may now be recharged and become base flow. The transfer of evapotranspiration to base flow is an unintended consequence to the water balance of LID. © 2016 The Authors Hydrological Processes Published by John Wiley & Sons Ltd.     

Modeling of particle breakage in a smooth double roll crusher

A model has been developed to describe the breakage behavior of particle breakage in a smooth double roll crusher. In addition to this, an empirical relationship has been obtained between Hardgrove Grindability Index (HGI) of material and breakage parameters. The model uses matrix method of breakage process analysis. Experiments were carried out in a laboratory scale smooth double roll crusher using different materials. The model has been verified experimentally and gives product size distribution after breakage which matches with the experimental distribution well within the satisfactory range.     

Sedimentological analysis of a Late Quaternary coastal dune system: An example from Gopnath,south‐east Saurashtra,Western India

Coastal dune systems consisting of allochemical grains are important sedimentary archives of Pleistocene age in both of the hemispheres between the latitudes of 20° to 40°. The south Saurashtra coast in western India exhibits a large section of Middle Pleistocene aeolianites in the form of coastal cliffs, which is famous as ‘Miliolite’. Miliolites of Gopnath in south‐east Saurashtra are the oldest known coastal aeolianite deposits (age >156 ka which corresponds to Marine Isotope Stage 6) in western India. Aeolian deposits of similar ages have also been reported from the Thar Desert in north‐west India and from Southern Arabia which were largely controlled by the south‐west monsoon wind system that affects the entire belt corresponding to Sahara–Sahel, the Arabian Peninsula and north‐western India. Miliolite deposits in Gopnath are characterized by grainfall, grainflow and wind ripple laminations. At least three types of aeolian bounding surfaces have been identified. Five major facies have been identified which represent the dune and interdune relationship within the coastal aeolian system. The major dune bodies are identified as transverse dune types. The Gopnath aeolianites were deposited under dominantly dry aeolian conditions. Facies association reveals two different phases of aeolian accumulation, namely initiation of aeolian sedimentation after a prolonged hiatus and the establishment of a regularized aeolian sedimentation system. While initiation of aeolian sedimentation is marked by vast stretches of sheet sand with occasional dune bodies, the overlying thick, tabular, laterally extensive cross‐stratified units manifest regular aeolian sedimentation. However, the dune building events in Gopnath were interrupted by development of laterally extensive palaeosol horizons. Eustasy and climate exerted the major allogenic controls on the aeolian sedimentation by affecting the sediment budget as well as influencing the sedimentation pattern.     

Analysis of cyclogenesis parameter for developing and nondeveloping low-pressure systems over the Indian Sea

A cyclone genesis parameter, termed the genesis potential parameter (GPP), for the Indian Sea is proposed. The parameter is defined as the product of four variables, namely vorticity at 850 hPa, middle tropospheric relative humidity, middle tropospheric instability, and the inverse of vertical wind shear. The variables are calculated using the National Centers for Environmental Prediction (NCEP), USA, reanalysis data, averaged within a circle of 2.5° radius around the centre of cyclonic system. The parameter is tested with a sample dataset of 35 nondeveloping and developing low-pressure systems that formed over the Indian Sea during the period 1995–2005. The result shows that there is a distinction between GPP values for nondeveloping and developing systems in more than 85% cases. The composite GPP value is found to be around three to five times greater for developing systems than for nondeveloping systems. The analysis of the parameter at early development stage of a cyclonic storm appears to provide a useful predictive signal for intensification of the system.     

SST and ENSO variability and change simulated in historical experiments of CMIP5 models

This work documents the diversity in Coupled Model Inter-comparison Project Phase 5 (CMIP5) models in simulating different aspects of sea surface temperature (SST) variability, particularly those associated with the El Niño–Southern Oscillation (ENSO), as well as the impact of low-frequency variations on the ENSO variability and its global teleconnection. The historical simulations (1870–2005) include 10 models with ensemble member ranging from 3 to 10 that are forced with observed atmospheric composition changes reflecting both natural and anthropogenic forcings. It is shown that the majority of the CMIP5 models capture the relative large SST anomaly variance in the tropical central and eastern Pacific, as well as in North Pacific and North Atlantic. The frequency of ENSO is not well captured by almost all models, particularly for the period of 5–6 years. The low-frequency variations in SST caused by external forcings affect the SST variability and also modify the global teleconnection of ENSO. The models reproduce the global averaged SST low-frequency variations, particularly since 1970s. However, majority of the models are unable to correctly simulate the spatial pattern of the observed SST trends. These results suggest that it is still a challenge to reproduce the features of global historical SST variations with the state-of-the-art coupled general circulation model.     

Flood susceptibility modelling using advanced ensemble machine learning models

Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities.It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods.Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters.In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace(RS) coupled with Artificial Neural Network(ANN), Random Forest(RF), and Support Vector Machine(SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh.The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment.The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors.For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve(ROC) were employed.The value of the Area Under the Curve(AUC) of ROC was above 0.80 for all models.For flood susceptibility modelling, the Dagging model performs superior, followed by RF,the ANN, the SVM, and the RS, then the several benchmark models.The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.