Annual pattern of zooplankton communities and their environmental response in a subtropical maritime channel system in the northern Bay of Bengal, Bangladesh

Zooplankton plays an important role in aquatic food webs by fluxing of energy from primary producer to subsequent trophic levels in the food chain. The annual pattern of zooplankton communities and potential environmental drivers were studied in the Kohelia channel, Bangladesh from summer 2014 to spring 2015. Samples were collected using net at a depth of 1 m. A total of 32 species belonged to 18 orders, 27 families and 15 taxonomic groups were identified. Of these species, 22 distributed in all four seasons of which 8 were dominant and highly contributing to the total communities. Species number peaked in summer next to winter and fall in spring while maximum abundance was in summer and minimum in spring. Multivariate analyses showed that there was a clear annual pattern in the zooplankton communities. Species diversity and evenness peaked in spring but fall in autumn while the high value of species richness was found in winter. Biological-environmental best matching (BIO-ENV) analyses conformed that community pattern of zooplankton was mainly driven by transparency salinity, and temperature individually or combined with water nutrients. These results demonstrate that annual pattern of the zooplankton community shaped by channel environmental factors in subtropical channel ecosystems, thus might be used for community-based subtropical coastal water bioassessment.     

Spatial modeling of Dengue prevalence and kriging prediction of Dengue outbreak in Khyber Pakhtunkhwa (Pakistan) using presence only data

Stochastic Environmental Research and Risk Assessment - During the span of August–October, 2017 a major outbreak of Dengue fever happened in Khyber Pakhtunkhwa province of Pakistan. Cases...     

Investigating effect of climate change on drought propagation from meteorological to hydrological drought using multi-model ensemble projections

Stochastic Environmental Research and Risk Assessment - Climate change is a main driving force that affects the hydrological cycle, leading to an increase in natural hazards. Among these natural...     

Characteristics and Health Risk Assessment of Potentially Toxic Metals in Urban Topsoil in Shenyang City,Northeast China

Soil samples are collected from 74 sites in Shenyang city, northeast China, and the concentrations, spatial distribution, enrichment factors (EF), and potential ecological and human health risk (E_r) of potentially toxic metals (Sr, Zn, Cr, Pb, Cu, Ni, and As) are investigated. The mean concentrations of potentially toxic metals in topsoil follow the order Sr > Zn > Cr > Pb > Cu > Ni > As. Compared with 2011, the contamination magnitude has improved in 2017, but the pollution status still exists, especially for As and Cr. EF and the mean E_r of potentially toxic metals decrease following the order As > Pb > Cu > Ni > Zn > Cr > Sr and As > Cu > Zn > Pb > Sr > Ni > Cr, suggesting that the potentially toxic metals in the topsoil of Shenyang city mainly come from natural sources, and the potential ecological risk for potentially toxic metals is low. There are potentially non‐carcinogenic and carcinogenic health risks, but they are not considered significant. For non‐carcinogenic health risks, oral ingestion is the main exposure pathway, and Cr and Pb are the major pollutants. Finally, As is found to be the main carcinogenic contamination metal.     

Disintegration of uncertainties associated with real-time multi-satellite precipitation products in diverse topographic and climatic area in Pakistan

Satellite-based Precipitation Estimates(SPEs)have gained importance due to enhanced spatial and temporal resolution,particularly in Indus basin,where raingauge network has fewer observation stations and drainage area is laying in many countries.Formulation of SPEs is based on indirect mechanism,therefore,assessment and correction of associated uncertainties is required.In the present study,disintegration of uncertainties associated with four prominent real time SPEs,IMERG,TMPA,CMORPH and PERSIANN has been conducted at grid level,regional scale,and summarized in terms of regions as well as whole study area basis.The bias has been disintegrated into hit,missed,false biases,and Root Mean Square Error(RMSE)into systematic and random errors.A comparison among gauge-and satellite-based precipitation estimates at annual scale,showed promising result,encouraging use of real time SPEs in the study area.On grid basis,at daily scale,from box plots,the median values of total bias(-0.5 to 0.5 mm)of the used SPEs were also encouraging although some under/over estimations were noted in terms of hit bias(-0.15 to 0.05 mm/day).Relatively higher values of missed(0.3 to 0.5 mm/day)and false(0.5 to 0.7 mm/day)biases were observed.The detected average daily RMSE,systematic errors,and random errors were also comparatively higher.Regional-scale spatial distribution of uncertainties revealed lower values of uncertainties in plain areas,depicting the better performance of satellite-based products in these areas.However,in areas of high altitude(>4000 m),due to complex topography and climatic conditions(orographic precipitation and glaciated peaks)higher values of biases and errors were observed.Topographic barriers and point scale gauge data could also be a cause of poor performance of SPEs in these areas,where precipitation is more on ridges and less in valleys where gauge stations are usually located.Precipitation system’s size and intensity can also be a reason of higher biases,because Microwave Imager underestimate precipitation in small systems(<200 km²)and overestimate in large systems(>2000 km²).At present,use of bias correction techniques at daily time scale is compulsory to utilize real time SPEs in estimation of floods in the study area.Inter comparison of satellite products indicated that IMERG gave better results than the others with the lowest values of systematic errors,missed and false biases.     

Substructure design optimization and nonlinear responses control analysis of the mega-sub controlled structural system(MSCSS) under earthquake action

The newly proposed mega sub-controlled structure system(MSCSS) and related studies have drawn the attention of civil engineers for practice in improving the performance and enhancing the structural effectiveness of mega frame structures. However, there is still a need for improvement to its basic structural arrangement. In this project, an advanced, reasonable arrangement of mega sub-controlled structure models, composed of three mega stories with different numbers and arrangements of substructures, are designed to investigate the control performance of the models and obtain the optimal model configuration(model with minimum acceleration and displacement responses) under strong earthquake excitation. In addition, the dynamic parameters that affect the performance effectiveness of the optimal model of MSCSS are studied and discussed. The area of the relative stiffness ratio RD, with different mass ratio MR, within which the acceleration and displacement of the optimal model of MSCSS reaches its optimum(minimum) value is considered as an optimum region. It serves as a useful tool in practical engineering design. The study demonstrates that the proposed MSCSS configuration can efficiently control the displacement and acceleration of high rise buildings. In addition, some analytical guidelines are provided for selecting the control parameters of the structure.     

EFFECTS OF SPATIAL VARIABILITY ON THE SCALING OF LAND SURFACE PARAMETERIZATIONS

Understanding and modelling physical and dynamical processes over heterogeneous land surfaces have becomea central focus of many recent studies. There is aconsiderable debate, however, over how to representthe effects of spatial heterogeneity in mesoscale andglobal scale models. Here, a computationally efficientanalytical approach is presented to evaluate scalingproperties of land surface representations. It isshown that the effects of spatial variability may not benegligible for commonly encountered land surfaces andassociated parameterizations. Second-order correction termsinvolving variances of the parameters and covariancesof each pair of land surface parameters are developedto account for the effects ofheterogeneity. Using this analytical approach, weshow that the detail of spatial heterogeneity may not beimportant for the infrared radiation and reflectedsolar radiation from the surface, while sensible andlatent heat fluxes are shown to be sensitive toheterogeneity. Assumptions related todifferent parameterizations for the same physicalprocess could potentially lead to different inferencesregarding the influence of spatial heterogeneity. Theproposed approach, however, is capable of identifying therole of different parameterizations in estimating theinfluence of spatial heterogeneity. These analyticalresults are consistent with the results of severalrecent numerical and field experiments that deal withthe effects of small-scale heterogeneity in landsurface characteristics.     

Modeling Local Tidal Constituents Using TOPEX/POSEIDON SSH for the Malaysian Marine Region

A new Local Ocean Tide Model, has been produced for the Exclusive Economic Zone (EEZ) of Malaysia, which incorporates some of the latest TOPEX/POSEIDON data for the years 1992 to 1998. Local tide gauge data are used as a comparison, along with another leading Global Ocean Tide Model, Ori96. The leading diurnal and semidiurnal constituents M₂, S₂, N₂, K₁, O₁, P₁ and Q₁ are reproduced using TOPEX/POSEIDON Sea Surface Heights (SSH) in a response analysis type least squares derivation following Munk and Cartwright (1966).     

Population pressure, agricultural intensification and changes in rural systems in Bangladesh

Sustainable agricultural growth is the key to rural system changes that include changes in rural bio-physical environment, economic infrastructure and social conditions. The present study has examined the temporal changes in 18 selected indicators of rural systems in Bangladesh during the period 1975-2000, and explored the influences of demographic, market forces, environmental, institutional and technological factors inducing and mediating such changes. An analysis of 64 district level published census data showed significant increase in agricultural intensity, cropping patterns, land productivity and farm income; decline in labor and technological productivities; and major improvement in rural housing, economic and social conditions during this period. Spatially, major agricultural growth and rural development were observed in districts with high population density, less constrained environments, and better access to markets, irrigation canals, and capital loans.     

Exploring socio-hydrological determinants of crop yield in under-performing irrigation schemes: pathways for sustainable intensification

ABSTRACT

Understanding the human–water–food–climate nexus is central to achieving sustainable intensification (SI) in agriculture. This research uses a socio-hydrological approach to understand the underpinning for implementing SI in the Gezira Irrigation Scheme, Sudan, by integrating vegetation indices derived from remote sensing, ancillary, gridded soil and precipitation data, supplemented by interviews with 393 farmers. The productivity gap was estimated as the difference between the potential and actual productivities. Based upon data on farmers’ socio-economic status and field practices, a regression tree model was built to determine the factors that control the sorghum yield. The model revealed that the financial status of farmers and access to water are the most influential factors on sorghum yield. A conceptual framework that elucidates SI and its bi-directional feedback to the environment, society and the economy is proposed. Implementing SI in the scheme has implications on water and food security in Sudan and beyond its borders.