An Implicit Scheme for Shallow Water Flow with Wet Dry Interface

A finite difference implicit scheme is presented in this paper for solution of the shallow water equations in one dimensional (1D) form. The present model has many advantages like, handling of discontinuous and complex bed topography, satisfying C-property (preservation of motionless water surface over a wet or dry bed) and capability of handling large value of temporal step etc. Another very important feature of the present model is that, no special treatment of the source vector of the governing equations is required here to deal with very less water depth. To investigate the performance of the present model in diverse situations, it is used to replicate four different problems of known analytical solution, and the model is found to be quite capable for varied situations.     

Numerical and experimental investigations on inelastic cyclic performance of mid‐span gusset plate connections

The design and detailing of gusset plate connections greatly influence the seismic performance of a special concentrically braced frame (SCBF). Recently, a balanced design approach has been proposed in order to develop significant inelastic deformation from multiple yield mechanisms and to delay the failure of connections of SCBF system. Although extensive studies have been conducted on the corner gusset plate connections of SCBFs, research on the detailing of mid‐span beam gusset plates is rather limited. This study aims at investigating the required free length for the detailing of the mid‐span gusset plates with different brace slenderness ratios. A nonlinear finite element analysis has been conducted for a braced frame with 4 different values of linear clearance in the mid‐span gusset plates and 2 values of brace slenderness ratios. In all simulation models, the corner gusset plates have been designed using balanced design approach and detailed using an elliptical clearance of 8 times the gusset plate thickness. An experimental study has also been conducted on 2 gusset plate sub‐assemblages having similar brace slenderness ratio but with 2 different values of linear clearance in the middle gusset plates. The lateral drift capacity corresponding to the brace fracture and the level of damage are found to be dependent on the detailing of the gusset plates. Based on the results of numerical and experimental studies, the required free length has been recommended for the detailing of middle gusset plates of SCBFs of different brace slenderness ratios.     

Climate change impact assessment of a river basin using CMIP5 climate models and the VIC hydrological model

Climate change has significant impacts on water availability in larger river basins. The present study evaluates the possible impacts of projected future daily rainfall (2011–2099) on the hydrology of a major river basin in peninsular India, the Godavari River Basin, (GRB), under RCP4.5 and RCP8.5 scenarios. The study highlights a criteria-based approach for selecting the CMIP5 GCMs, based on their fidelity in simulating the Indian Summer Monsoon rainfall. The nonparametric kernel regression based statistical downscaling model is employed to project future daily rainfall and the variable infiltration capacity (VIC) macroscale hydrological model is used for hydrological simulations. The results indicate an increase in future rainfall without significant change in the spatial pattern of hydrological variables in the GRB. The climate-change-induced projected hydrological changes provide a crucial input to define water resource policies in the GRB. This methodology can be adopted for the climate change impacts assessment of larger river basins worldwide.     

Changes in daily maximum temperature extremes across India over 1951–2014 and their relation with cereal crop productivity

This study used gridded daily maximum temperature data (1°?×?1°) for 1951–2014 period to analyze the trend in monthly extreme warm days (ExWD) and changes in its probability distribution in each grid. It also analyzed the trend in spatial spread of annual ExWD over the study period at four exceedance levels and further related the number of ExWDs with cereal crop productivity of India. Extreme warm days have increased throughout India but were statistically significant in 42% grids. The increase was consistent over all the months in north-eastern region, southern plateau and both the coastal plains. It also increased significantly over north-western and central India during April to June summer period. The probability distribution of ExWD also changed significantly in many grids, especially in southern plateau and both the coastal plains. The changes indicated increased frequency in the existing levels of extremes and new occurrences of higher frequency of extremes. The analysis of land area affected by different levels of extremes indicated significant increase, with the rate being highest for higher extremes. In terms of extreme warm day temperatures, the study identified southern plateau, east and west coast plains, and north-eastern India as highly vulnerable. Using copula probability model, study showed that increase in ExWD from 20 to 60% may increase the probability of 5% or more yield loss from 17 to 53% for Kharif cereals, 11 to 43% for Rabi cereals and 19 to 63% for wheat crop. The results may be used for devising zone specific adaptation strategies.     

Dynamic stress concentration in pre-stressed poroelastic media due to moving punch influenced by shear wave

During the occurrence of earthquake, the shear wave propagates in the rocks present inside/at the Earth’s crust. The propagation of shear wave may lead to the progression of punch present inside the rock medium. As a result of this, substantial stress accumulated at the vicinity of propagating punch inside rock medium which significantly affects the stability of various geological and human-made structure and, hence, may cause failure of structure. Therefore, the analysis of stress concentration at the vicinity of punch moving due to shear wave propagation has become prominent in the area of seismology. In the present paper, an analytical perspective has been employed to discuss the influence of velocity of moving punch associated with the propagation of shear wave on developed dynamic stress concentration (DSC) in three types of pre-stressed vertical transversely isotropic (VTI) poroelastic media viz. granite (an igneous rock); sandstone (a sedimentary rock); and marble (a metamorphic rock). The closed form expression of DSC for the force of constant intensity has been derived with the aid of Weiner-Hopf technique along with Galilean and two-sided Fourier integral transformations. The noticeable influence of different affecting parameters (viz. velocity of moving punch associated with the shear wave propagation, horizontal compressive/tensile initial stresses, vertical compressive/tensile initial stress, porosity, and anisotropy parameter) on dynamic stress concentration has also been reported. Numerical computation and graphical illustrations have been carried out for the aforementioned three different types of porous rocks to investigate the profound impact of affecting parameters on DSC. Moreover, some noteworthy peculiarities have also been derived from the obtained expression of dynamic stress concentration.     

A new fluid factor and its application using a deep learning approach

Amplitude interpretation for hydrocarbon prediction is an important task in the oil and gas industry. Seismic amplitude is dominated by porosity, the volume of clay, pore-filled fluid type and lithology. A few seismic attributes are proposed to predict the existence of hydrocarbon. This paper proposes a new fluid factor by adding a correct item based on the J attribute. The algorithm is verified through stochastic Monte Carlo modelling that contains various rock physical properties of sand and shale. Both gas and oil responses are separated by the new fluid factor. Furthermore, an approach based on the neural network model is trained using the deep learning method to predict the new fluid factor. The confusion matrix shows that this model performs well. This model allows the application of the new fluid factor in the seismic data. In this study, the Marmousi II data set is used to examine the performance of the new fluid factor, and the result is good. Most hydrocarbon reservoirs are identified in the shale–sandstone sequences. The combination of deep learning and the new fluid factor provides a more accurate way for hydrocarbon prediction.     

Assessment of future water provisioning and sediment load under climate and LULC change scenarios in a peninsular river basin,India

Assessment of the impact of changes in climate and land use and land cover (LULC) on ecosystem services (ES) is important for planning regional-scale strategies for sustainability and restoration of ES. The Upper Narmada River Basin (UNRB) in peninsular India has undergone rapid LULC change due to recent agricultural expansion. The impact of future climate and LULC change on ES in the UNRB is quantified and mapped using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST 3.3.0) tool. Our results show that water yield is projected to increase under climate change (about 43% for representative concentration pathway 4.5 for 2031–2040), whereas it is projected to decrease under the LULC change scenario. Sediment export is projected to increase (by 54.53%) under LULC change for 2031–2040. Under the combined effect of climate and LULC change, both water yield and sediment export are expected to increase. Climate change has a greater impact on projected water yield than LULC change, whereas LULC has greater impact on sediment export. Spatial analysis suggests a similar trend of variation in relative difference (RD) of ES in adjacent sub-basins. The quantified changes in ES provisioning will benefit future land management, particularly for operation of the Rani Avanti Bai Sagar Reservoir downstream of the UNRB.     

Forecasting monthly precipitation using sequential modelling

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. “All-India” monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes.     

Switch in chemical weathering caused by the mass balance variability in a Himalayan glacierized basin: a case of Chhota Shigri Glacier

Long-term data (2003–2015) on meltwater chemistry, mass balance and discharge of a benchmark glacier (Chhota Shigri Glacier, India) were studied to determine any association between these variables. To infer the factors governing the alteration of chemical weathering processes in glacierized basins, multi-annual records of the hydrochemical indices (Ca²⁺+Mg²⁺/Na⁺+K⁺) and the C-ratio were also examined. A succession of negative mass balance years has resulted in a decline in solute concentrations in the runoff, as discharge has increased. The (Ca²⁺+Mg²⁺/Na⁺+K⁺) and C-ratio are highest during periods of negative annual mass balance, when the spatial extent of the channelized drainage system increases. Conversely, these ratios are lowest in positive mass balance years, when the spatial extent of the channelized drainage system decreases, and chemical weathering in the distributed drainage system becomes more dominant. This paper is the first to show the inter-annual linkages between meltwater chemistry, mass balance and discharge for a valley glacier.