Flow and erosion at a bend in the braided Jamuna River

The braided Jamuna River frequently changes its courses.Sometimes the secondary channel in a braided river acts as a single thread meandering channel.In the present study an attempt has been made to investigate the flow patterns and to estimate the rate of bank erosion in a bend along the Jamuna River.The three dimensional(3D) flow velocities were measured using Acoustic Doppler Current Profiler(ADCP).It is found that the near bank velocity is amplified by 1.1 to 1.3 times as compared with the section averaged velocity.A dominant secondary current is found in the upstream bend.The evolution as well as decay of the secondary current is not as clear as it is found in the laboratory experiments.It is revealed from the analysis of the flow process that the causes of higher rate of erosion at the study bend are the oblique flow near bankline,six times amplified shear velocity than critical shear velocity near bankline and the secondary current which acts as a sediment transporting agent from the outer bank towards the inner bank or the sand bar.Based on the flow processes,a simplified erosion prediction model is developed and applied to estimate the rate of erosion at a selected bend.Finally the predicted results have been compared with the observed data at the bend and all the available data at other bends along the Jamuna River.     

Drought adaptation measures and their effectiveness at Barind Tract in northwest Bangladesh: a perception study

Natural Hazards - Drought is a natural calamity frequently occurs at Barind Tract in northwest Bangladesh and affects both the human and natural life. An initiative has been taken...     

Geochemistry of subsurface Late Quaternary ironstones in Rajshahi and Bogra Districts,Bangladesh: implications for genetic and depositional conditions

The present study deals with the geochemistry of Late Quaternary ironstones in the subsurface in Rajshahi and Bogra districts, Bangladesh with the lithological study of the boreholes sediments. Major lithofacies of the studied boreholes are clay, silty clay, sandy clay, fine to coarse grained sand, gravels and sands with (fragmentary) ironstones. The ironstones contain major oxides, Fe₂O₃* (* total Fe) (avg. 66.6 wt%), SiO₂ (avg. 15.3 wt%), Al₂O₃ (avg. 4.0 wt%), MnO (avg. 7.7 wt%), and CaO (avg. 3.4 wt%). These geochemical data imply that the higher percentage of Fe₂O₃* along with Al₂O₃ and MnO indicate the ironstone as goethite and siderite, which is also validated by XRD data. A comparatively higher percentage of SiO₂ indicates the presence of relative amounts of clastic quartz and manganese-rich silicate or clay in these rocks. These ironstones also have significant amounts of MnO (avg. 7.7 wt%) suggesting their depositional environments under oxygenated condition. Chemical data of these ironstones suggest that the source rock suffered deep chemical weathering and iron was mostly carried in association with the clay fraction and organic matter. Iron concretion was mostly formed by bacterial build up in swamps and marshes, and was subsequently embedded in clayey mud. Within the coastal environments, the water table fluctuates and goethite and siderite with mud and quartz became dry and compacted to form ironstone.

     

Malachite Green Adsorption onto Chitosan Coated Bentonite Beads: Isotherms,Kinetics and Mechanism

The removal of Malachite green (MG) from aqueous solutions by cross‐linked chitosan coated bentonite (CCB) beads was investigated and the CCB beads were characterized by Fourier Transform Infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS) and X‐ray diffraction (XRD) analysis. Solubility and swelling tests were performed in order to determine the stability of the CCB beads in acidic solution, basic solution and distilled water. The amount of MG adsorbed was shown to be influenced by the initial pH of the solution, contact time and the initial MG concentration. A kinetic study indicated that a pseudo‐second‐order model agreed well with the experimental data. From the Langmuir isotherm model, the maximum adsorption capacity of MG was found to be 435.0 mg g^–1. Desorption tests were carried out at different concentrations of EDTA, H₂SO₄ and NaOH. However, all desorbing solutions showed zero recovery of MG at all concentrations.     

Potential of support vector regression for prediction of monthly streamflow using endogenous property

In the recent past, a variety of statistical and other modelling approaches have been developed to capture the properties of hydrological time series for their reliable prediction. However, the extent of complexity hinders the applicability of such traditional models in many cases. Kernel‐based machine learning approaches have been found to be more popular due to their inherent advantages over traditional modelling techniques including artificial neural networks(ANNs ). In this paper, a kernel‐based learning approach is investigated for its suitability to capture the monthly variation of streamflow time series. Its performance is compared with that of the traditional approaches. Support vector machines (SVMs) are one such kernel‐based algorithm that has given promising results in hydrology and associated areas. In this paper, the application of SVMs to regression problems, known as support vector regression (SVR), is presented to predict the monthly streamflow of the Mahanadi River in the state of Orissa, India. The results obtained are compared against the results derived from the traditional Box–Jenkins approach. While the correlation coefficient between the observed and predicted streamflows was found to be 0·77 in case of SVR, the same for different auto‐regressive integrated moving average (ARIMA) models ranges between 0·67 and 0·69. The superiority of SVR as compared to traditional Box‐Jenkins approach is also explained through the feature space representation. Copyright © 2009 John Wiley & Sons, Ltd.     

Integrating an artificial intelligence approach with k-means clustering to model groundwater salinity: the case of Gaza coastal aquifer (Palestine)

Artificial intelligence (AI) techniques have increasingly become efficient alternative modeling tools in the water resources field, particularly when the modeled process is influenced by complex and interrelated variables. In this study, two AI techniques—artificial neural networks (ANNs) and support vector machine (SVM)—were employed to achieve deeper understanding of the salinization process (represented by chloride concentration) in complex coastal aquifers influenced by various salinity sources. Both models were trained using 11 years of groundwater quality data from 22 municipal wells in Khan Younis Governorate, Gaza, Palestine. Both techniques showed satisfactory prediction performance, where the mean absolute percentage error (MAPE) and correlation coefficient (R) for the test data set were, respectively, about 4.5 and 99.8% for the ANNs model, and 4.6 and 99.7% for SVM model. The performances of the developed models were further noticeably improved through preprocessing the wells data set using a k-means clustering method, then conducting AI techniques separately for each cluster. The developed models with clustered data were associated with higher performance, easiness and simplicity. They can be employed as an analytical tool to investigate the influence of input variables on coastal aquifer salinity, which is of great importance for understanding salinization processes, leading to more effective water-resources-related planning and decision making.     

Evaluating transport externalities of urban growth: a critical review of scenario-based planning methods

Urban growth is an important phenomenon, which is taking place on an unprecedented scale, and its impacts on society and the environment are evident. In theory, an evaluation of such urban growth through scenario-based planning helps planners to better assess the future impacts of growth and develop better policies and plans. Within this context, the assessment of transport impacts is particularly important as transport plays an important role in shaping urban growth. Additionally, transport sector alone is responsible for about one-third of the greenhouse gas emissions of cities, which has detrimental effects on the environment, economy, community health, and quality of life. In practice, however, scarce evidence exists outlining the challenges of scenario-based evaluation and how to best address these while modelling the transport impacts of various urban growth scenarios. This research addresses these gaps in the literature and assesses the effectiveness of scenario-based planning methods that are used for modelling the transport impacts of alternative urban growth scenarios. The methodological approach of the study consists of a critical review of the key literature and relevant methods that are commonly used to assess transport impacts. The results of this analysis highlight limitations of existing methods for effectively evaluating transport externalities of urban growth scenarios. The findings suggest that among many reviewed models, the ILUTE, URBANSIM and TRANUS simulation models are identified as significant ones. However, due to various limitations of the former two, TRANUS is noted as the most suitable one for evaluating the transport impacts of urban growth scenarios.     

Drought analysis and its implication in sustainable water resource management in Barind area,Bangladesh

The study analyzes drought using Standardized Precipitation Index (SPI) and Mann-Kendall (MK) Trend Test in the context of the impacts of drought on groundwater table (GWT) during the period 1971-2011 in the Barind area, Bangladesh. The area experienced twelve moderate to extreme agricultural droughts in the years 1972, 1975, 1979, 1982, 1986, 1989, 1992, 1994, 2003, 2005, 2009 and 2010. Some of them coincide with El Niño events. Hydrological drought also occurred almost in the same years. However, relationship between all drought events and El Niño is not clear. Southern and central parts of the area frequently suffer from hydrological drought, northern part is affected by agricultural drought. Trends in SPI values indicate that the area has an insignificant trend towards drought, and numbers of mild and moderate drought are increasing. GWT depth shows strong correlation with rainy season SPI values such that GWT regaining corresponds with rising SPI values and vice versa. However, 2000 onwards, GWT depth is continuously increasing even with positive SPI values. This is due to over-exploitation of groundwater and changes in cropping patterns. Agricultural practice in Barind area based on groundwater irrigation is vulnerable to drought. Hence, adaptation measures to minimize effects of drought on groundwater ought to be taken.