The intensive study of an individual watershed is required to develop effective and efficient watershed management plans. Identification of critical erosion-prone areas of the watershed and implementation of best management practices (BMPs) is necessary to control the watershed degradation by reducing the sediment and nutrient losses. The present study evaluates and recommends the BMPs in an agriculture-based Marol watershed (5092 km 2 ) of India, using a hydrologic model, Soil and Water Assessment Tool (SWAT). After successful calibration and validation, the model simulated daily/monthly discharge and sediment were found satisfactory throughout the simulation period. The model was then applied with a calibrated set of parameters for evaluating the effectiveness of various management practices for sediment and nutrient loss control. Keeping in mind the existing agricultural practices, socio-economic aspects and geography of the study area, the management practices were focused on four crops (Maize, Rice, Soybeans and Ground nut), three fertilization levels (high, medium and low), four tillage treatments (Field cultivator, Conservation tillage, Zero tillage and Mould board plough), and two conservation operations (Contour farming and Filter strips). The simulated annual average sediment yield from the watershed was found to be 12.2 t.ha -1 yr -1 . The water balance analysis revealed that, the evapotranspiration is predominant over the watershed (approximately 46.3% of the annual average rainfall). Reduction in sediment yield and nutrient loss was observed with alternate cropping treatments of Groundnut and Soybean, as compared to Paddy and Maize cultivation. Overall, based on simulated results, the field cultivator tillage practice and conservation practices viz., contour

The focus of this study was to investigate fate and transport of toluene, a light non-aqueous phase liquids (LNAPL), in subsurface under dynamic groundwater table conditions. A series of experiments were conducted using two-dimensional (2-D) sand tank setup having the dimension of 125cm L×90cm H×10cm W and integrated with an auxiliary column of inner diameter 14 cm and height 120 cm. In the beginning a steady state flow and LNAPL transport experiment was conducted under stable groundwater table condition. Thereafter, three groundwater table fluctuation experiments were conducted by rising and falling groundwater table in 2, 4 and 8 hours to maintain a rapid, general, and slow fluctuation conditions respectively. Pure phase of toluene was injected at the rate of 1mL/minute for a total duration of 5 minutes. The soil-water and soil vapor samples were periodically collected and analyzed for toluene concentrations. Later, the representation of 2-D sand tank setup was numerically simulated to get the response of flow and the LNAPL transport under varying groundwater table conditions. The analysis of the results show that a large LNAPL pool area (250 cm 2) gets developed under rapid fluctuating groundwater condition which significantly enhances the dissolution rate and contributes for a high concentration of

India is facing the worst water crisis in its history, and major Indian cities which accommodates about 50% of its population will be among highly groundwater stressed cities by 2020. In past few decades, the urban groundwater resources declined significantly due to over exploitation, urbanization, population growth and climate change. To understand the role of these variables on groundwater level fluctuation, we developed a machine learning based modelling approach considering singular spectrum analysis (SSA), mutual information (MI), genetic algorithm (GA), artificial neural network (ANN), and support vector machine (SVM). The developed approach was used to predict the groundwater levels in Bengaluru, a densely populated city with declining groundwater water resources. The input data consist of groundwater levels, rainfall, temperature, NOI, SOI, NIÑO3 and monthly population growth rate, and were pre-processed using mutual information, genetic algorithm and lag analysis. Later, the optimized input sets were used in ANN and SVM to predict monthly groundwater level fluctuations. The results suggest that the machine learning based approach with data pre

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Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.