Abstract

In the present study an attempt has been made to study the 1D (One Dimensional), 2D (Two Dimensional) and 3D (Three Dimensional) ground response of pond ash collected from Odisha under different earthquake motions. 1D ground response analysis of pond ash deposit has been carried out by using equivalent linear and nonlinear analysis method using SHAKE2000 and Cyclic1D software respectively. 2D and 3D ground response analysis of pond ash deposit has been carried out by using finite element software Plaxis2D and Plaxis3D respectively. The response analyses of the pond ash deposit have been performed under the excitation of North East India earthquake (Mw-7.5) and Nepal earthquake (Mw-7.8). The results of the response analyses have been presented in terms of acceleration, displacement, excess pore pressure and excess pore pressure ratio. A comparative study between 1D, 2D and 3D ground response analysis has been performed to predict seismic risk of the pond ash site in Odisha.

Abstract

Among the extreme events, droughts are the most widespread and slowly developing hydro-meteorological events remain for a long duration affecting natural resources, environment and the people. Few studies included reference evapotranspiration to account for climatic water availability in drought characterization such as Standardized Precipitation–Evapotranspiration Index (SPEI). However, a drought prediction model should consider the actual evapotranspiration to include the physical water availability and land surface changes of a region. The Budyko curve is used to estimate the actual evapotranspiration as a function of the aridity index. The drought index is based on the probability distribution of the difference between precipitation and Actual Evapotranspiration (AET), which represents the measure of the water surplus or deficit for a particular month. The gridded daily precipitation data from India Meteorological Department (IMD) available for the period of 1901 to 2015 at 0.25o X 0.25o resolution and the gridded daily average temperature at a resolution of 1o X 1o resolution was used in the study as temperature observational data sets. The regional drought prediction model developed in the study is applied on Krishna River basin in India. The monthly PET was estimated with the Thornthwaite equation using mean temperatures over the entire Krishna river basin. The AET is estimated at 12 month scale using Budyko equation, which combines the precipitation, AET and PET estimated from Thornthwaite equation. The performance of drought index is evaluated using historical droughts and projected variability under climate change. The results of the study reveal that inclusion of AET in the drought characterization along with PET and precipitation into account can drive the areas into moderate drought that would experience extreme drought under if only PET is considered.

Abstract

A river water quality management model under average climatic conditions may not be able to account for the extreme risk of low water quality which is more prominent under an increase in river water temperature and altered river flows. A modeling framework is developed to assess the risk of river low water quality extremes by integrating a statistical downscaling model based on Canonical Correlation Analysis, risk quantification model based on Frank Archimedean Copula function and multiple logistic regression model integrated with a river water quality simulation model, QUAL2 K. The results reveal that the combination of predicted decrease in low flows of approximately 57% and increase in maximum river water temperatures of approximately 1.2
C has shown an increase of about 46% in risk of low water quality conditions for the future scenarios along Tunga-Bhadra River, India. The extreme risk of low water quality is observed to increase by 50.6% for the period 2020– 2040 when compared with the current extreme conditions of 4.5% and average risk conditions of about 3% for the period 1988–2005. The study captured the occurrence of extremes of low water quality with evidence of a strong link between climate and water quality impairment events.

Abstract

Predictability of river water temperature is a big argument for many environmental applications, hydrology and ecology research. River water temperature mainly depends on several parameters of water bodies such as streamflow, Dissolved Oxygen (DO), oxidation reduction potential, pollutant temperature, thermal effluent discharges, ground water interactions and its surrounding atmosphere. Commonly, the river water temperature always has been correlated with air temperature as a substitute due to the ease of applicability for rivers with some limitations over detailed meteorological and thermal data. In addition to air temperature the most influencing component of water body is streamflow which is having strong response towards the water temperature. An evaluation of integrated river water temperature and streamflow fluctuations is proposed to evidence biological activity, chemical specimen, oxygen solubility, self-purification capacity of a river system and variation of flows due to hydro-climatic changes. Prediction of river water temperature at various locations over a river basin is vital for the water quality management. The present study aims to estimate the river water temperature (RWT) under air temperature changes and local to regional anthropogenic activities of streamflow over Krishna river basin, India. A standardized multi-linear regression model is developed for predicting river water temperature over various discharge locations of the Krishna river basin. The performance of the model was tested mainly for 8 stations using historical daily data of river temperature, air temperature and river discharges from 1991 to 2005. The attainment of a model is carried out for these stations with air temperature, stream flows are predictors whereas water temperature as predictability component through training, testing. In this study the correlation coefficients between air minimum, maximum and average temperature with river water temperature are determined for selecting the possible air temperature variable to be considered in the analysis. The correlation coefficients are obtained with air minimum temperature (r = 0.541-0.772), air maximum temperature (r= 0.267-0.613) and air average temperature (r = 0.463-0.715) for all stations and it is observed that air minimum has the highest correlation coefficient value and it could be suitable for modeling. The performance of the multi-linear regression model for training and testing was obtained in terms of Root Mean Square Error (RMSE = 0.934 to 4.556), Correlation of determination (R = 0.984 and 0.986).

Abstract

Climate change could aggravate periodic and chronic shortfalls of water, particularly in arid and semi-arid areas of the world (IPCC, 2001). Climate change is likely to accelerate the global hydrological cycle, with increase in temperature, changes in precipitation patterns, and evapotranspiration affecting the water quantity and quality, water availability and demands. The various components of a surface water resources system affected by climate change may include the water availability, irrigation demands, water quality, hydropower generation, ground water recharge, soil moisture etc. It is prudent to examine the anticipated impacts of climate change on these different components individually or combinedly with a view to developing responses to minimize the climate change induced risk in water resources systems. Assessment of climate change impacts on water resources essentially involves downscaling the projections of climatic variable