Center for Environmental Sciences & Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

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Proceedings of Indian National Science Academy

Role of humidity on sporulation pattern and infection in Anthracnose of sorghum caused by Colletotrichum graminicola

The BRAHMATWINN project was funded by the European Community (EC contract no. 036952) and was carried out between June 2006 and December 2009. Its overall objective was to provide a comprehensive impact assessment of climate change to the hydrological dynamics and integrated land and water resources management (ILWRM) in alpine mountain massifs. © Springer India 2015.

Applied Geoinformatics for Sustainable Integrated Land and Water Resources Management (Ilwrm) in the Brahmaputra River Basin: Results From the Ec-Project Brahmatwinn

The Ec-Project Brahmatwinn

Prediction of the sediment generated within a catchment basin is a crucial input in the management and design of water resources projects. Due to the unavailability and complexity of the precipitation and hydrological process, reliable sediment concentration is hardly predicted by applying linear and nonlinear regression methods. In the present study, an attempt has been made to explore the use of Tropical Rainfall Measuring Mission (TRMM-3B42) dataset for modeling suspended sediment using neural networks (NNs) with different training functions, i.e., Levenberg-Marquardt (LM), scaled conjugated gradient (SCG), and Bayesian regulation (BR) for the Kopili River basin, India. The input vector to the various models using different algorithms were derived considering the statistical properties such as autocorrelation function, partial autocorrelation function, and cross-correlation function of the time series. The daily rainfall data from 2000 to 2010 (4,018 days) were considered for the training (70%) and validation (30%) of the models. The model ANNLM6 performed better than other models during calibration (ENS = 99.49%, CORR = 0.997, RSR = 0.049) and validation (ENS = 98.11%, CORR = 0.990, RSR = 0.085), and the optimum structure of the artificial neural network (ANN) model was found to be six neurons in the hidden layer. Moreover, the analysis also concludes that ANN model using the LM algorithm is able to predict suspended sediment at the outlet fairly well. The overall analysis reveals that TRMM rainfall is promising for suspended sediment prediction in the absence of ground-based measurements of rainfall. It is inferred that, the TRMM-3B42 rainfall estimates can be used for the conceptual rainfall-runoff-sediment modeling where the gauges are sparsely distributed and the radar measurements are rarely available. © 2014 American Society of Civil Engineers.

Journal of Hydrologic Engineering

Modeling suspended sediment using artificial neural networks and TRMM-3B42 version 7 rainfall dataset

The comprehensive integrated system analysis (ISA), modelling studies, spatial analysis of water balance components, and the integrated socioeconomic analysis and scenario evaluation discussed in the previous chapters must be made available to local actors, decision makers, and planners as otherwise they will be stand-alone information with little value for sustainable integrated land and water resources management (ILWRM). This research challenge was addressed by the development of an ILWRMS based on the sophisticated integrated land management system and the ILMS info data information system applied as one of the ILMS components. This component was enhanced by complement software developments and modelling toward an ILWRMS. It should be noted, however, that the system presented in brief herein is under a continuous development by integrating demands and requirements identified by users of the system in various applied research projects. © Springer India 2015.

Integrated land and water resources management system (ILWRMS)

Many of the major rivers in India originate from the Himalayas. These rivers have significant contribution from snow and ice which makes these rivers perennial. More than 3 billion people are benefitted by the food and energy produced in these river basins. This study has been done to Modeling of Snowmelt Runoff in the part of Sharda (Kali) River basin upto NHPC’s Tanakpur Barrage by integrating MODIS snow cover data and SRTM digital elevation model (DEM) with Geographic Information System (GIS) and finally into the windows version of snowmelt runoff model (WinSRM or SRM). The temporal SCA for the period Oct’2006–Sep’2010 was derived from MODIS Snow Cover data and SRTM DEM was used along with these data to get the snow cover in different elevation zones. SRM is a temperature index-based snowmelt runoff simulation model, and has been widely used in almost all the part of the world for simulating snowmelt runoff. The daily hydro-meteorological data (rainfall, max. & min. temperature and discharge) procured from NHPC Ltd. was used for simulation of snowmelt runoff. Overall accuracy of SRM for part of the Sharda River basin in terms of coefficient of correlation (R2) is 0.72–0.89 for the period Oct’2006–Sep’2010. Average Snowmelt contribution to the total streamflow computed at Tanakpur barrage in Sharda River basin is about 16.5%. © 2015, Central Board of Irrigation and Power. All rights reserved.

Water and Energy International

Snowmelt runoff modeling in an Indian Himalayan river basin using WinSRM, RS &amp; GIS

Physics-based models for simulation of runoff and sediment yield from watersheds are relatively composite model based on learning algorithm. Physics-based is complex model due to involvement of tremendous spatial variability of watershed characteristics and precipitation patterns. Recently, pattern-learning algorithms such as the artificial neural networks (ANNs) have gained recognition in simulating the rainfall–runoff–sediment yield processes producing a comparable accuracy. We have simulated daily runoff and sediment yield from a Nepal watershed, Kankaimai (area = 1180 km2), with data from 1995 to 1999 for runoff prediction and 2001–2003 of the wet season for sediment yield prediction, using support vector machines (SVMs), a statistical learning theory based pattern-learning algorithm. The performance of the model was evaluated using the root mean square error, correlation coefficient and coefficient of efficiency. The results of SVM were compared to those of ANN and simple regression. ANN being a computationally intensive method, SVM could be used as an efficient alternative for runoff and sediment yield predictions under comparable accuracy in predictions. © 2015, Springer International Publishing Switzerland.

Fulltext

Modeling Earth Systems and Environment

Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed

This paper presents fundamental outcomes from an experimental study on the hydrodynamic performance near inlet key of Piano Key Weir (PKW). Hydrodynamic performance was tested in a circulated open channel that comprised of PKW and sand bed (d50 = 0.25 mm). Instantaneous velocities were measured at 20 cross sections using Laser Doppler Velocimeter (LDV) with constant discharge and depth. Average velocity and turbulence intensities in both directions were investigated. Average longitudinal velocities are found very much consistent at every point and maximum around the midway of inlet key. In transverse direction, flow is bifurcating in two directions which are also confirmed by average transverse velocity estimation. Variation of turbulence intensity presents average 10 times higher transverse turbulence than longitudinal turbulence near inlet key of PKW. © 2015, Indian Academy of Sciences.

Sadhana - Academy Proceedings in Engineering Sciences

Flow hydrodynamics near inlet key of Piano Key Weir (PKW)

The central theme of this book is focused on the analyses and the results which emerged from the international research project BRAHMATWINN sponsored by European Commission (EC) and conducted during 2006 - 2009. The book highlights the achievements of BRAHMATWINN to carry out a harmonised integrated water resources management (IWRM) approach as addressed by the European Water Initiative (EWI) in headwater river systems of alpine mountain massifs. The latter are already impacted from climate change, and the BRAHMATWINN project established transfer of professional IWRM expertise, approaches and tools based on case studies carried out in twinning European and Asian river basins. The project addresses all important IWRM issues in a balanced way, including conflict resolution in the trans- boundary Danube and Brahmaputra River Basins in Europe and South Asia respectively. This book will be useful to researchers, professionals, managers and decision makers associated with study and application of sustainable integrated land and water resources management (ILWRM) in the backdrop of climate change. © Springer India 2015.

Applied Geoinformatics for Sustainable Integrated Land and Water Resources Management (ILWRM) in the Brahmaputra River Basin: Results from the Ec-Project Brahmatwinn

Applied geoinformatics for sustainable integrated land and water resources management (ILWRM) in the Brahmaputra River Basin: Results from the ec-project Brahmatwinn

Tropical forest is one of the great biodiversity repositories of the world ecosystem. Biodiversity is depleting very fast due to conversion of forest region into agricultural or other land use. Here comes the role of biodiversity assessment and evaluation of spatial data of species to prioritize the conservation purposes. Traditionally, ground-based plots were used to assess different biodiversity. Later on, remote sensing approaches were also incorporated along with field-based studies to quantify the results accurately. Assessment of biodiversity constitutes estimation of various indices that were obtained using ground-based plot or survey. With the advancement of the remote sensing technology, spatial information about tree species was collected using field sample and satellite data and field sample plots within the Sariska Tiger Reserve. Different diversity indices were calculated like α, β, diversity, and others, i.e., Pilot's index (J), Shannon-Wiener index (SR), Margalef index (Ew), and Whittaker's index (H′). The multistage statistical techniques, which integrate high spatial resolution and spectral characteristics of satellite data (LISS IV), will help in providing precise information about tree species. Regression analysis provides better results to identify forest species among different bands. A positive correlation has been found in the infrared band even negative correlation has been found in other bands. This paper incorporates field-based surveys along with remote sensing technologies using a regression model (r2 = 0.636) to estimate and recognize different species diversity in Sariska Tiger Reserve. © 2001-2012 IEEE.