Estimation of spatial variability of aquifer parameters from geophysical methods: a case study of Sindhudurg district,Maharashtra, India

Accurate estimation of aquifer parameters, especially from crystalline hard rock area, assumes a special significance for management of groundwater resources. The aquifer parameters are usually estimated through pumping tests carried out on water wells. While it may be costly and time consuming for carrying out pumping tests at a number of sites, the application of geophysical methods in combination with hydro-geochemical information proves to be potential and cost effective to estimate aquifer parameters. Here a method to estimate aquifer parameters such as hydraulic conductivity, formation factor, porosity and transmissivity is presented by utilizing electrical conductivity values analysed via hydro-geochemical analysis of existing wells and the respective vertical electrical sounding (VES) points of Sindhudurg district, western Maharashtra, India. Further, prior to interpolating the distribution of aquifer parameters of the study area, variogram modelling was carried out using data driven techniques of kriging, automatic relevance determination based Bayesian neural networks (ARD-BNN) and adaptive neuro-fuzzy neural networks (ANFIS). In total, four variogram model fitting techniques such as spherical, exponential, ARD-BNN and ANFIS were compared. According to the obtained results, the spherical variogram model in interpolating transmissivity, ARD-BNN variogram model in interpolating porosity, exponential variogram model in interpolating aquifer thickness and ANFIS variogram model in interpolating hydraulic conductivity outperformed rest of the variogram models. Accordingly, the accurate aquifer parameters maps of the study area were produced by using the best variogram model. The present results suggest that there are relatively high value of hydraulic conductivity, porosity and transmissivity at Parule, Mogarne, Kudal, and Zarap, which would be useful to characterize the aquifer system over western Maharashtra.     

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.     

Surface wave dynamics off Mumbai coast,north-eastern Arabian Sea

Ocean Dynamics - Directional wave data collected in the coastal area at 10-m water depth for a period of 1 year is used to describe the surface wave dynamics off Mumbai coast. Partitioning...     

Influence of rooftop telecommunication tower on set back-step back building resting on different ground slopes

In the hilly region due to scarcity of the plain area, buildings like set back-step back are more often used and also as a big surge in the telecommunication industries, rooftop tower adaptation is very common story nowadays. In the present study an analogy has been drawn to find out the influence of the rooftop telecommunication tower on the setback-step back building resting on ground at 20° and 30° slopes. A dynamic analysis has been performed and compared on the 4 legged angled section telecommunication tower which is located on the roof top of set back-step back building by varying positions of tower with the existing host structure built up on ground slope of 20° and 30° in both directions(X and Y).     

Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments

Rainfall data in continuous space provide an essential input for most hydrological and water resources planning studies. Spatial distribution of rainfall is usually estimated using ground‐based point rainfall data from sparsely positioned rain‐gauge stations in a rain‐gauge network. Kriging has become a widely used interpolation method to estimate the spatial distribution of climate variables including rainfall. The objective of this study is to evaluate three geostatistical (ordinary kriging [OK], ordinary cokriging [OCK], kriging with an external drift [KED]), and two deterministic (inverse distance weighting, radial basis function) interpolation methods for enhanced spatial interpolation of monthly rainfall in the Middle Yarra River catchment and the Ovens River catchment in Victoria, Australia. Historical rainfall records from existing rain‐gauge stations of the catchments during 1980–2012 period are used for the analysis. A digital elevation model of each catchment is used as the supplementary information in addition to rainfall for the OCK and kriging with an external drift methods. The prediction performance of the adopted interpolation methods is assessed through cross‐validation. Results indicate that the geostatistical methods outperform the deterministic methods for spatial interpolation of rainfall. Results also indicate that among the geostatistical methods, the OCK method is found to be the best interpolator for estimating spatial rainfall distribution in both the catchments with the lowest prediction error between the observed and estimated monthly rainfall. Thus, this study demonstrates that the use of elevation as an auxiliary variable in addition to rainfall data in the geostatistical framework can significantly enhance the estimation of rainfall over a catchment.     

Assessment of glacier stored water in Karakoram Himalaya using satellite remote sensing and field investigation

Karakoram Himalaya(KH) has continental climatic conditions and possesses largest concentration of glaciers outside the polar regions. The melt water from these glaciers is a major contributor to the Indus river. In this study, various methods have been used to estimate the ice volume in the Karakoram Range of glaciers such as Coregistration of Optically Sensed Images and Correlation(COSI-Corr) method and Area-Volume relations. Landsat 8 satellite data has been used to generate the ice displacement, velocity and thickness map. Our study for 558 Karakoram glaciers revealed that the average ice thickness in Karakoram is 90 m. Ground Penetrating Radar(GPR) survey has been conducted in one of the KH glacier i.e. Saser La glacier and the collected GPR data is used for the validation of satellite derived thickness map. GPR measured glacier thickness values are found comparable with satellite estimated values with RMSE of 4.3 m. The total ice volume of the Karakoram glaciers is estimated to be 1607±19 km3(1473±17 Gt), which is equivalent to 1473±17 km3 of water equivalent. Present study also covers the analysis of glacier surface displacement, velocity and ice thickness values with reference to glacier mean slope.     

Modelling Chorabari Lake outburst flood,Kedarnath, India

In this study, the Glacier Lake Outburst Flood(GLOF) that occurred over Kedarnath in June 2013 was modeled using integrated observations from the field and Remote Sensing(RS). The lake breach parameters such as area, depth, breach, and height have been estimated from the field observations and Remote Sensing(RS) data. A number of modelling approaches, including Snow Melt Runoff Model(SRM), Modified Single Flow model(MSF), Watershed Management System(WMS), Simplified Dam Breach Model(SMPDBK) and BREACH were used to model the GLOF. Estimations from SRM produced a runoff of about 22.7 m3 during 16–17, June 2013 over Chorabari Lake. Bathymetry data reported that the lake got filled to its maximum capacity(3822.7 m3) due to excess discharge. Hydrograph obtained from the BREACH model revealed a peak discharge of about 1699 m3/s during an intense water flow episode that lasted for 10–15 minutes on 17 th June 2013. Excess discharge from heavy rainfall and snowmelt into the lake increased its hydrostatic pressure and the lake breached cataclysmically.