Effects of seawater irrigation on soil microbial community structure and physiological function

Irrigation with diluted seawater would be an alternative water resource which can play an important role under scarce resources of freshwater for promoting agricultural production in coastal areas. Salvadora persica Linn. was irrigated with different concentrations of seawater (0, 10, 20, 40, 60, 80 and 100 % seawater), and their effect on plant growth, nutrient contents in soil and plants, shift in soil microbial community structure (phospholipid fatty acid; PLFA) and community-level physiological profiling (CLPP, Biolog ECO MicroPlate) were studied. Plant dry matter was significantly increased with all seawater treatments, and highest increase was at 20 % seawater treatment. Sodium and chloride contents were significantly increased, whereas ratios of K/Na and Ca/Na were significantly decreased in plants with seawater irrigation. Soil electrical conductivity (EC), available K and Na were significantly increased with increasing the concentration of seawater. Total PLFA concentration and PLFA profile of soils were used as indices of total microbial biomass and community composition, respectively. The concentrations of total PLFA, gram-positive, gram-negative and actinomycetes biomarker PLFAs were significantly reduced at 20, 40, 80 and 40 % concentrations of seawater, respectively. The application of different concentrations of seawater induced a clear shift in the soil microbial community structure toward the bacterial abundance. The microbial community structure and community-level physiological profiling in seawater irrigation treatments had significantly differentiated. It can be concluded that irrigation with different concentrations of seawater had significant impact on soil chemical and microbial properties which is attributed due to the salinity stress.     

A global,high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km² of inland water globally – nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal ‘inland water’ biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high, with commission errors <4% and omission errors <14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds – effects which were compounded by differences in image acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset is available for open access at the GLCF website (http://www.landcover.org).     

A framework for risk-based assessment of urban floods in coastal cities

Natural Hazards - Many coastal cities in developing countries are at the risk of flooding due to a progressive increase in the built-up areas and poor management of storm water. The flooding...     

Study of agricultural impacts through fish base variables in different rivers

Dominant agricultural society of Nepal demonstrates the sign of compromises on its rural and virgin landscape. The current study investigates the fish base indices on some of its watershed. It is a proven fact that fish base variables such as species diversity and the abundance indicate the extent of impact by various disturbances on the ecological integrity of the river system. Three rivers, namely Jhikhukhola, Rapti and Tinau were studied to see the agricultural impact by taking fish species number and their abundance as the indicators in this work. The study was done for an academic purpose from 2003 to 2006 in which sampling was done using electro-fishing gear by standard wading method. Four replicates of sample taken in four major seasons were obtained in the field. Altogether, 10542 fishes of 24 genera and 34 species were captured during the entire sampling. The comparison of their distribution and abundance showed a clear sign of impact mainly on the downstream of the rivers indicating the rapid increase in unaccounted and indiscriminate use of agricultural inputs such as synthetic fertilizers and pesticides. This was further verified by nonparametric Kruskal-Wallis test which showed the significant variations in the impact on the downstream in major agricultural season.     

Delineation of groundwater recharge zones and identification of artificial recharge sites in West Medinipur district,West Bengal,using RS,GIS and MCDM techniques

Artificial recharge plays a pivotal role in the sustainable management of groundwater resources. This study proposes a methodology to delineate artificial recharge zones as well as to identify favorable artificial recharge sites using integrated remote sensing (RS), geographical information system (GIS) and multi-criteria decision making (MCDM) techniques for augmenting groundwater resources in the West Medinipur district of West Bengal, India, which has been facing water shortage problems for the past few years. The thematic layers considered in this study are: geomorphology, geology, drainage density, slope and aquifer transmissivity, which were prepared using IRS-1D imagery and conventional data. Different themes and their corresponding features were assigned proper weights based on their relative contribution to groundwater recharge in the area, and normalized weights were computed using the Saaty’s analytic hierarchy process (AHP). These thematic layers were then integrated in the GIS environment to delineate artificial recharge zones in the study area. The artificial recharge map thus obtained divided the study area into three zones, viz., ‘suitable,’ ‘moderately suitable’ and ‘unsuitable’ according to their suitability for artificial groundwater recharge. It was found that about 46% of the study area falls under ‘suitable’ zone, whereas 43% falls under the ‘moderately suitable’ zone. The western portion of the study area was found to be unsuitable for artificial recharge. The artificial recharge zone map of the study area was found to be in agreement with the map of mean groundwater depths over the area. Furthermore, forty possible sites for artificial recharge were also identified using RS and GIS techniques. Based on the available field information, check dams are suggested as promising artificial recharge structures. The results of this study could be used to formulate an efficient groundwater management plan for the study area so as to ensure sustainable utilization of scarce groundwater resources.     

Some studies in the resistivity of Indian sandstones

Summary In this paper some measurements of the resistivity of Talchir and Barakar sandstones of the Jharia Coal field under dry and saturated conditions have been made and the results are discussed in the light of known theories. Techniques of measurement have been briefly described.     

Seismic vulnerability assessment of earthquake-prone mega-city Shillong,India using geophysical mapping and remote sensing

ABSTRACT

The concept of seismic vulnerability is a yard-stick of damage estimation from a probable earthquake considering physical cum social dimension and enables a basis for decision-makers to develop preparedness and mitigation strategies. We aim at vulnerability assessment of the typical urban system of capital city Shillong situated on hilly terrain. High-resolution satellite imagery of Shillong facilitates analysis of building footprints, communication network, and open ground. Different building typologies are identified taking into account the building’s structural configuration assessed through a rapid visual survey of more than 15% of total residential households. Slope map demarcates the landslide-prone area through discrete elevation modelling. A methodology incorporating several parameters e.g. building typology, slope angle, shear wave velocity characteristics, geomorphology, and the number of occupants in correlation with a physical measurement of vulnerability is presented and is applied to estimate the dimension of vulnerability. Additionally, MASW survey indicates lithology up to 30?m deep along with the existence of stiff soil and rocks at different depths whereas resonant frequency is identified to be in the range of 6–8?Hz through H/V ratio. Integrating all, it is observed that more than 60% of Shillong city falls under moderate to higher vulnerability and the rest is less vulnerable.     

Length-weight relationship of sucker head, Garra Gotyla Gotyla (Gray, 1830) in different rivers of Nepal and the influence of monsoon

Size structure data particularly that of length and weight is very important fisheries management tool, which is also applied to assess the health, habitat and integrity of the rivers. Nepal is tremendously rich in both fisheries and water resource and thus, this analysis is very important. However, this kind of analysis is not common in fisheries studies and hence the size structure information of all the fish in Nepal is wanting. This study simply tries to open the door for such studies with the study of the sucker head Garra gotyla gotyla, a very common fish in Nepal. The field observation was done in nine rivers of the country in all the seasons of the 2003 by using electrofishing gear. The length-weight relationship was found to vary both spatially and temporally among rivers and seasons respectively indicating different conditions in different rivers. The length weight relationship also showed some interesting facts about the seasonal cycle of the species indicating the period of growth and stress in different rivers. The monsoon event was found to be highly influential in this relationship.     

Forecasting monthly precipitation using sequential modelling

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. “All-India” monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes.