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.     

Cohesive strength and seismogenic stress pattern along the active basement faults of the Precordillera-Sierras Pampeanas ranges, western Argentina: An experimental analysis by means of numerical model

A two-dimensional finite element method （FEM） model that incorporates faults, elastic rock physical properties, topographical load due to gravity and far-field plate velocity boundary conditions was used to recognize the seismogenic stress state along the fold-and-thrust belt of the Precordillera-Sierras Pampeanas ranges of western Argentina. A plane strain model with nine experiments was presented here to examine the fault strength with two major rock phyical properties： cohesion and angle of internal friction. Mohr-Coulomb failure criterion with bulk rock properties were applied to analyse faults. The stress field at any point of the model was assumed to be comprised of gravitational and tectonic components. The analysis was focused to recognize the seismogenic shear strain concentrated in the internal-cristaline domain of the orogene shown by the modeling. Modeling results are presented in terms of four parameters, i. e., （i） distributions, orientations, and magnitudes of principal stresses （σ1 and σ3）, （ii） displacement vector1 （iii） strain distribution, and （iv） maximum shear stress （τmax） contour line within the model. The simulation results show that the compressive stress is distributed in and around the fault systems. The overall orientation of of σ1 is in horizontal directions, although reorientations do occur within some stress weaker parts, especially subsequent to the faults. A large-scale shear stress is accumulating along the active faults of Tapias-Villicum Fault （TVF）, Salinas-Berros Fault （SBF）, Ampacama-Niquizanga Fault （ANF） and Las Charas Fault （CF）, which could act as local stress and strain modulators to localize the earthquakesoccurrence.     

Analyzing the occurrence of floods and droughts in connection with climate change in Punjab province,Pakistan

Natural Hazards - Frequency and intensity of extreme weather events are immensely changing throughout the world. This study aims to give insight into the changing climatic patterns leading to...     

Impact of variational assimilation technique on simulation of a heavy rainfall event over Pune,India

Prediction of heavy rainfall events due to severe convective storms in terms of their spatial and temporal scales is a challenging task for an operational forecaster. The present study is about a record-breaking heavy rainfall event observed in Pune (18°31′N, 73°55′E) on October 4, 2010. The day witnessed highest 24-h accumulated precipitation of 181.3 mm and caused flash floods in the city. The WRF model-based real-time weather system, operating daily at Centre for Development of Advanced Computing using PARAM Yuva supercomputer showed the signature of this convective event 4-h before, but failed to capture the actual peak rainfall and its location with reference to the city’s observational network. To investigate further, five numerical experiments were conducted to check the impact of assimilation of observations in the WRF model forecast. First, a control experiment was conducted with initialization using National Centre for Environmental Prediction (NCEP)’s Global Forecast System 0.5° data, while surface observational data from NCEP Prepbufr system were assimilated in the second experiment (VARSFC). In the third experiment (VARAMV), NCEP Prepbufr atmospheric motion vectors were assimilated. Fourth experiment (VARPRO) was assimilated with conventional soundings data, and all the available NCEP Prepbufr observations were assimilated in the fifth experiment (VARALL). Model runs were compared with observations from automated weather stations (AWS), synoptic charts of Indian Meteorological Department (IMD). Comparison of 24-h accumulated rainfall with IMD AWS 24-h gridded data showed that the fifth experiment (VARALL) produced better picture of heavy rainfall, maximum up to 251 mm/day toward the southern side, 31 km away from Pune’s IMD observatory. It was noticed that the effect of soundings observations experiment (VARPRO) caused heavy precipitation of 210 mm toward the southern side 49 km away from Pune. The wind analysis at 850 and 200 hPa indicated that the surface and atmospheric motion vector observations (VARAMV) helped in shifting its peak rainfall toward Pune, IMD observatory by 18 km, though VARALL over-predicted rainfall by 60 mm than the observed.     

Macrophytes’ Performance in Floating Treatment Wetlands for the Remediation of Ciprofloxacin-Contaminated Water

The presence of trace antibiotics in domestic and industrial effluents poses a risk of toxicity to fauna and flora. The application of floating treatment wetlands (FTWs) is a low-cost and sustainable approach to remediate wastewater. In this study, the performance of different macrophytes vegetated in FTWs for the remediation of ciprofloxacin (CIP)-contaminated water is investigated. Six macrophytes, Brachia mutica, Typha domingensis, Phragmites australis, Canna indica, Cyperus laevigatus, and Leptochloa fusca, are vegetated in FTWs for the removal of CIP (50 mg L⁻¹) from water. The FTWs show the potential to remove 30–43.58 mg L⁻¹ CIP from water in 28 days. They also reduce the chemical oxygen demand (118–138 mg L⁻¹) and biochemical oxygen demand (35–45 mg L⁻¹) of water. Among the macrophytes, C. indica removes maximum (43.58 mg L⁻¹) and T. domingensis minimum (30 mg L⁻¹) CIP. Canna indica and T. domingensis exhibit the maximum and minimum increase (30% and 12% of dry biomass) in growth, respectively. This study reveals that the FTWs vegetated with different plant species exhibit varying performances in removing CIP from water. This investigation is a step forward toward sustainable bioremediation of water contaminated with antibiotics.     

Seed supply for coastal brackishwater shrimp farming: environmental impacts and sustainability

Globally, shrimp farming has been a significant agro-based economic activity since the early 1970s. Because it offered a huge immediate economic return, shrimp farming showed a booming expansion and soon became a multimillion dollar industry. However, it has been under extreme criticism because of its devastating ecological and socio-economic impacts. Because seed is the primary input, the impact from farming has started from the source of seed supply, so that not only are natural stocks of shrimp seeds now overexploited worldwide but seed collection activities also significantly reduce stocks of other living resources. Although hatcheries were developed as potential alternative and have replaced the natural seed source to a great extent, large-scale hatchery productions provide a potential source of coastal pollution. However, this area is still poorly studied. The present paper provides a review of the environmental impacts of the wild shrimp seed fishery as well as the possibility of environmental degradation from artificial shrimp seed production in hatcheries.     

Future change in the frequency of warm and cold spells over Pakistan simulated by the PRECIS regional climate model

Climate change caused by anthropogenic activities has generated a variety of research focusing on investigating the past climate, predicting the future climate and quantifying the change in climate extreme events by using different climate models. Climate extreme events are valuable to evaluate the potential impact of climate change on human activities, agriculture and economy and are also useful to monitor the climate change on global scale. Here, a Regional Climate Model (RCM) simulation is used to study the future variations in the temperature extreme indices, particularly change in frequency of warm and cold spells duration over Pakistan. The analyses are done on the basis of simulating two 30 years simulations with the Hadley Center’s RCM PRECIS, at a horizontal resolution of 50 km. Simulation for the period 1961–1990 represents the recent climate and simulation for the period 2071–2100 represents the future climate. These simulations are driven by lateral boundary conditions from HadAM3P GCM of Hadley centre UK. For the validation of model, observed mean, maximum and minimum temperatures for the period 1961–1990 at all the available stations in Pakistan are first averaged and are then compared with the PRECIS averaged grid-box data. Also the observed monthly gridded data set of Climate Research Unit (UK) data is used to validate the model. Temperature indices in the base period as well as in future are then calculated and the corresponding change is observed. Percentile based spatial change of temperature shows that in summer, increase in daily minimum temperature is more as compared to the increase of daily maximum temperature whereas in winter, the change in maximum temperature is high. The occurrence of annual cold spells shows significantly decreasing trend while for warm spells there is slight increasing trend over Pakistan.