Identification of the influencing factors on groundwater drought and depletion in north-western Bangladesh

Groundwater drought is a specific type of hydrological drought that concerns groundwater bodies. It may have a significant adverse effect on the socio-economic, agricultural, and environmental conditions. Investigating the effect of different climatic and anthropogenic factors on groundwater drought provides essential information for sustainable planning and management of (ground) water resources. The aim of this study is to identify the influencing factors on groundwater drought in north-western Bangladesh, to understand the forcing mechanisms. A multi-step methodology is proposed to achieve this objective. The standardised precipitation index (SPI) and reconnaissance drought index (RDI) have been used to quantify the aggregated deficit between precipitation and the evaporative demand of the atmosphere, i.e. meteorological drought. The influence of land-cover patterns on the groundwater drought has been identified by calculating spatially distributed groundwater recharge as a function of land cover. Groundwater drought is defined by a threshold method. The results show that the evapotranspiration and rainfall deficits are determining meteorological drought, which shows a direct relation with groundwater recharge deficits. Land-cover change has a small effect on groundwater recharge but does not seem to be the main cause of groundwater-level decline (depletion) in the study area. The groundwater depth and groundwater-level deficit (drought) is continuously increasing with little correlation to meteorological drought or recharge anomalies. Overexploitation of groundwater for irrigation seems to be the main cause of groundwater-level decline in the study area. Efficient irrigation management is essential to reduce the growing pressure on groundwater resources and ensure sustainable water management.     

Identification of pre-seismic anomalies of soil radon-222 signal using Hilbert–Huang transform

Concentration of Rn-222 in soil has been monitored continuously at Ravangla in the Sikkim Himalayan Region of eastern India for about 7 months from October 2015 to May 2016 to detect earthquake-induced anomalies. The recorded data clearly show that various physical and meteorological parameters influence the soil radon concentration, leading to very complex soil Rn-222 time series. The components due to such external influences have been removed from the present time series, and Hilbert–Huang transform (HHT) applied for analysis of the data. Two radon anomalies caused due to earthquakes of magnitude M _b = 5.0 that occurred on 19 November 2015 and 5 April 2016 within an epicentral distance of 500 km from the monitoring station have been identified on the soil Rn-222 time series. These two precursory anomalies occurred 9 and 10 days, respectively, before the occurrence of the earthquakes. The absence of spurious signals or missing anomalies demonstrates that HHT is advantageous for analysis of nonlinear non-stationary data, and hence, it is a promising technique to analyse soil radon behaviour for predicting the possibility of occurrence of earthquakes.     

Spectroscopic characterization of fulvic acids extracted from the rock exudate Shilajit

The present work describes the extraction of fulvic acids (FA) from Shilajit and its spectroscopic and mass spectrometric characterization. The spectral features obtained from FT-IR and ¹HNMR were similar to those reported for humic substances from other sources. The molecular elemental composition analysis by Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) in electrospray negative ion mode resulted in extraordinary high average O/C ratios (0.55) which might be caused by a significant contribution of carbohydrates in Shilajit. A very high average H/C ratio of 1.27 also points to dominant aliphatic or alicyclic structures and relatively low aromaticity. The average molecular formula of the nitrogen free elemental compositions measured by FT-ICR mass spectrometry is C_18.2H_23.0O_10.0.     

重庆老龙洞地下河流域水体有机氯农药污染及来源初步研究

为查明重庆老龙洞地下河流域有机氯农药的污染情况及其特征和来源,通过气相色谱-微池电子捕获检测器(GC-μECD)检测水体OCPs含量。结果表明研究区水样中受到了有机氯农药不同程度的污染。α-HCH是研究区水体HCHs主要成分,HCHs的残留主要来自于大气长距离传输。DDTs的主要成分各采样点不尽相同。DDTs的输入为历史上工业DDTs的使用且在水体中其主要以好氧转化为主。与国内外河流相比较,老龙洞地下河流域水体有机氯农药含量处于较高水平。     

Assessing the long-term planform dynamics of Ganges–Jamuna confluence with the aid of remote sensing and GIS

Natural Hazards - The confluence of the Ganges, Jamuna, and Padma rivers is one of the most dynamic in the world, an internationally important research area because of the confluence of two of the...     

Spatial modeling of the highest daily maximum temperature in Korea via max-stable processes

This paper examines the annual highest daily maximum temperature （DMT） in Korea by using data from 56 weather stations and employing spatial extreme modeling. Our approach is based on max-stable processes （MSP） with Schlather＇s characterization. We divide the country into four regions for a better model fit and identify the best model for each region. We show that regional MSP modeling is more suitable than MSP modeling for the entire region and the pointwise generalized extreme value distribution approach. The advantage of spatial extreme modeling is that more precise and robust return levels and some indices of the highest temperatures can be obtained for observation stations and for locations with no observed data, and so help to determine the effects and assessment of vulnerability as well as to downscale extreme events.     

Sorption Behaviour of Phenols on Natural Sandy Aquifer Material during Flow‐through Column Experiments: The Effect of pH

Flow‐through column experiments were carried out to investigate the influence of pH on the sorption of three phenols (2‐methyl‐4, 6‐dinitrophenol, 2, 4, 6‐trichlorophenol, pentachlorophenol) onto a natural sandy aquifer material collected from a bank filtration site of River Elbe, Germany. For the phenols investigated, an increase in sorption (retardation) with decreasing pH is observed indicating a stronger sorption of the neutral species in comparison to that of the anions formed by dissociation. The anions of 2‐methyl‐4, 6‐dinitrophenol and 2, 4, 6‐trichlorophenol do not show significant sorption. On the contrary, pentachlorophenol showed sorption not only in neutral form but also in ionic form significantly which should be taken into account while assessing the fate and transport of such compound. A linear model based on the degree of protonation (calculated from pH and pK_a) can be used to resolve the apparent (observed) sorption coefficient (K_{d, app}) into its neutral (K_{d, n}) and ionised (K_{d, i}) components. Knowing pK_a, K_{d, n}, and K_{d, i} the apparent sorption coefficient for pH values other than experimentally investigated can be predicted.     

Discretization of Complex Geometric Domain Through Computer-Based Stair-Step Representation Method for Estimating Water Levels Associated with A Storm

In this paper, an improvement has been made to the approximation technique of a complex domain through the stairstep approach to have a considerable accuracy, minimize computational cost, and avoid the hardship of manual work. A novel stair-step representation algorithm is used in this regard, where the entire procedure is carried out through our developed MATLAB routine. Arakawa C-grid is used in our approximation with (1/120)° grid resolution. As a test case, the method is applied to approximate the domain covering the area between 15°–23°N latitudes and 85°–95° E longitudes in the Bay of Bengal. Along with the approximation of the land-sea interface, coastal stations are also identified. Approximated land-sea interfaces and coastal stations are found to be in good agreement with the actual ones based on the similarity index, overlap fraction, and extra fraction criteria. The method can be used for approximating an irregular geometric domain to employ the finite difference method in solving problems related to long waves. As a test case, shallow water equations in Cartesian coordinates are solved on the domain of interest for simulating water levels due to the nonlinear tide-surge interaction associated with the storms April 1991 and AILA, 2009 along the coast of Bangladesh. The same input except for the discretized domain and bathymetry as that of Paul et al. (2016) is used in our simulation. The results are found to be in reasonable agreement with the observed data procured from Bangladesh Inland Water Transport Authority.

     

A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data

The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.