Near surface lithology and spatial variation of arsenic in the shallow groundwater: southeastern Bangladesh

This study investigated the relationship between near-surface lithology and the spatial variability of As concentrations using sediment grain-size analysis and electromagnetic induction survey in the southeast Bangladesh. It has been observed that the aquifers overlain by finer sediments have higher concentrations of As in groundwater, whereas As concentrations are remarkably low in aquifers having permeable sandy materials or thinner silt/clay layer at the surface. The near-surface lithology acts as a controlling factor for spatial distributions of groundwater As within the very shallow depths (<15 m). Shallow alluvial aquifers can provide low-As drinking water in many areas of the country when tube wells are properly installed after investigation of the overlying near-surface sediment attributes and hydraulic properties.     

Upscaling of the permeability by multiscale wavelet transformations and simulation of multiphase flows in heterogeneous porous media

We describe a new approach for simulation of multiphase flows through heterogeneous porous media, such as oil reservoirs. The method, which is based on the wavelet transformation of the spatial distribution of the single-phase permeabilities, incorporates in the upscaled computational grid all the relevant data on the permeability, porosity, and other important properties of a porous medium at all the length scales. The upscaling method generates a nonuniform computational grid which preserves the resolved structure of the geological model in the near-well zones as well as in the high-permeability sectors and upscales the rest of the geological model. As such, the method is a multiscale one that preserves all the important information across all the relevant length scales. Using a robust front-detection method which eliminates the numerical dispersion by a high-order total variation diminishing method (suitable for the type of nonuniform upscaled grid that we generate), we obtain highly accurate results with a greatly reduced computational cost. The speed-up in the computations is up to over three orders of magnitude, depending on the degree of heterogeneity of the model. To demonstrate the accuracy and efficiency of our methods, five distinct models (including one with fractures) of heterogeneous porous media are considered, and two-phase flows in the models are studied, with and without the capillary pressure.     

Flooding and its relationship with land cover change,population growth,and road density

Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve-receiver operating char-acteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC＞90％)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the per-iod between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72％between 2000 and 2017,while the PG rate increased by 51.68％over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.     

Study of the global and regional climatic impacts of ENSO magnitude using SPEEDY AGCM

ENSO is considered as a strong atmospheric teleconnection that has pronounced global and regional circulation effects. It modifies global monsoon system, especially, Asian and African monsoons. Previous studies suggest that both the frequency and magnitude of ENSO events have increased over the last few decades resulting in a need to study climatic impacts of ENSO magnitude both at global and regional scales. Hence, to better understand the impact of ENSO amplitude over the tropical and extratropical regions focussing on the Asian and African domains, ENSO sensitivity experiments are conducted using ICTPAGCM (‘SPEEDY’). It is anticipated that the tropical Pacific SST forcing will be enough to produce ENSO-induced teleconnection patterns; therefore, the model is forced using NINO3.4 regressed SST anomalies over the tropical Pacific only. SPEEDY reproduces the impact of ENSO over the Pacific, North and South America and African regions very well. However, it underestimates ENSO teleconnection patterns and associated changes over South Asia, particularly in the Indian region, which suggests that the tropical Pacific SST forcing is not sufficient to represent ENSO-induced teleconnection patterns over South Asia. Therefore, SST forcing over the tropical Indian Ocean together with air–sea coupling is also required for better representation of ENSO-induced changes in these regions. Moreover, results obtained by this pacemaker experiment show that ENSO impacts are relatively stronger over the Inter-Tropical Convergence Zone (ITCZ) compared to extratropics and high latitude regions. The positive phase of ENSO causes weakening in rainfall activity over African tropical rain belt, parts of South and Southeast Asia, whereas, the La Niña phase produces more rain over these regions during the summer season. Model results further reveal that ENSO magnitude has a stronger impact over African Sahel and South Asia, especially over the Indian region because of its significant impact over the tropical Atlantic and the Indian Ocean through Walker circulation. ENSO-induced negative (positive) NAO-like response and associated changes over Southern Europe and North Africa get significantly strong following increased intensity of El Niño (La Niña) in the northern (southern) hemisphere in the boreal winter (summer) season. We further find that ENSO magnitude significantly impacts Hadley and Walker circulations. The positive phase of ENSO (El Niño) overall strengthens Hadley cell and a reverse is true for the La Niña phase. ENSO-induced strengthening and weakening of Hadley cell induces significant impact over South Asian and African ITCZ convective regions through modification of ITCZ/monsoon circulation system.     

Detection of water infiltration and deformation of unsaturated soils by elastic wave velocity

Intense rainfall is the most important landslide trigger. In many mountainous environments of the world, heavy rainfall has caused many landslides and slope failures in a matter of seconds without warning. Therefore, an early warning system can be an effective measure to reduce the damage caused by landslides and slope failures by facilitating the timely evacuation of people from landslide-prone areas. In this study, we propose an idea to correlate soil moisture changes and deformations in slope surface by means of elastic wave propagation in soil. Constant shear stress drained triaxial tests where water was infiltrated from the bottom of specimen until failure, and slope model tests under artificial rainfall were performed to investigate the response of elastic wave velocities during pre-failure phases of rainwater infiltration and deformation. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation, and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Possible mechanisms were interpreted based on the test results. It is proposed that a warning be issued at switch of wave velocity decrease rate. This approach can thus serve as the basis of an early warning system for landslides and slope failure considering both moisture content and deformation.     

Satellite-based rainfall estimation and discharge measurement of Middle Indus River,Pakistan

The impacts of floods and droughts are intensified by climate change, lack of preparedness, and coordination. The average rainfall in study area is ranging from 200 to 400 mm per year. Rain gauge generally provides very accurate measurement of point rain rates and the amounts of rainfall but due to scarcity of the gauge locations provides very general information of the area on regional scale. Recognizing these practical limitations, it is essential to use remote sensing techniques for measuring the quantity of rainfall in the Middle Indus. In this research, Tropical Rainfall Measuring Mission (TRMM) estimation can be used as a proxy for the magnitude of rainfall estimates from classical methods (rain gauge), quantity, and its spatial distribution for Middle Indus river basin. In order to use TRMM satellite data for discharge measurement, its accuracy is determined by statistically comparing it with in situ gauged data on daily and monthly bases. The daily R ² value (0.42) is significantly lower than monthly R ² value (0.82), probably due to the time of summation of TRMM 3-hourly precipitation data into daily estimates. Daily TRMM data from 2003 to 2012 was used as input forcing in Soil and Water Assessment Tool (SWAT) hydrological model along with other input parameters. The calibration and validation results of SWAT model give R ² = 0.72 and 0.73 and Nash-Sutcliffe coefficient of efficiency = 0.69 and 0.65, respectively. Daily and monthly comparison graphs are generated on the basis of model discharge output and observed data.     

Evaluating the impact of classification algorithms and spatial resolution on the accuracy of land cover mapping in a mountain environment in Pakistan

Satellite images of various spatial resolutions and different image classification techniques have been utilized for land cover (LC) mapping at local and regional scale studies. Mapping capabilities and achievable accuracies of LC classification in a mountain environment are, however, influenced by the spatial resolution of the utilized images and applied classification techniques. Hence, developing and characterizing regionally optimized methods are essential for the planning and monitoring of natural resources. In this study, the potential of four non-parametric image classification techniques, i.e., k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and neural network (NN) on the accuracy of LC classification was evaluated in the Hindu Kush mountains ranges of northern Pakistan. Moreover, we have assessed the impact of the spatial resolution of the utilized satellite imagery, i.e., SPOT-5 with 2.5 m and Landsat-8 with 30 m on the accuracy of the derived LC classification. For the classification of LC based on SPOT-5, we have achieved the highest overall classification accuracy (OCA) = 89% with kappa coefficient (KC = 0.86) using SVM followed by k-NN, RF, and NN. However, for LC classification derived from Landsat-8 imagery, we achieved the highest OCA = 71% with KC = 0.59 using RF and SVM followed by k-NN and NN. The higher accuracy derived from SPOT-5 versus Landsat-8 indicated that the results of LC classification based on SPOT-5 are more accurate and reliable than Landsat-8. The findings of the present study will be useful for the classification and mapping task of LC in a mountain environment using SPOT-5 and Landsat-8 at local and regional scale studies.     

Temporal changes in trace elements in brown soil and soybean after long-term fertilization

In agricultural production, specific elements in soil and plants are very important for the soil quality and plant productivity. Trace elements and rare earth elements enter in agri-environment by the application of fertilizers and, through anthropogenic activities, pose important health impacts even at low concentration due to non-biodegradable nature with long half-life. The micro nutrients are essential for human body in a quantity most of less than 100 mg/day. The nutritional element uptake by plants is regulated by availability of the required elements and element accumulation ability of the plants. Therefore, this study was aimed to investigate the accumulation of trace and rare earth elements after long-term application of different fertilizers in soil and soybean uptake. The inorganic fertilizers (NKP) and pig manure were applied to maize-maize-soybean rotation in Alfisols (brown) soil since 1979. Atomic emission spectrometry along with inductively coupled plasma technique was applied to determine trace and rare earth elements. The accumulation pattern of trace elements (TEs) observed in soil was Ni > Co > Se > Mo and rare earth elements (REEs) was found as La > Nd > Tb > Y > Pr > Gd > Er > Yb > Lu. In soybean stem, the TE and REE concentrations were Se > Ni > Co > Mo and Pr > Gd > Er > Yb > Tb > Nd > Lu > La > Y, respectively. Elemental concentrations in the seed samples were Ni > Se > Mo > Co and Pr > Gd > Er > Yb > Tb > Nd > Lu > Y > La. The seed of soybean accumulated Co, Mo, Ni, Gd, Pr, Er, Lu, and Tb more than the stem. Higher concentrations of Se, Nd, Y, and Yb were found in soil. These elements were higher in soybean stem followed by the seed. However, these elements are within safe toxic level and light pollution level.     

Continuous wavelet transforms of spectral decomposition analyses for fluvial reservoir characterization of Miano Gas Field,Indus Platform,Pakistan

Fluvial depositional systems constitute diverse host for the hydrocarbons all over the globe. Nevertheless, predicting the hydrocarbon sands within these assorted sand systems is a challenge for the prospect generation. We apply the continuous wavelet transform of spectral decomposition technique to a 3D seismic data of the Miano Gas Field in the Indus Platform, Pakistan. Full spectrum seismic attributes like the amplitude and root mean square (RMS) detect the reservoir sands down to a restricted reservoir compartment. Nonetheless, the continuous wavelet transforms can be advantageous in imaging and enactment of these clastic reservoirs within the whole reservoir zone. Rock physics reveal high-quality reservoir with an average porosity ～25% together with the discrimination of lithology/fluids, signifying the bright chances for ascertaining the potential stratigraphic reservoirs.