Abstract

This research was carried out along the Bhadra River, which is one of the tributaries of the TungaBhadra and originates near Gangamula in Karnataka’s the Western Ghats. The study stretch is around 27 km divided into three reaches with elements of 1 km as 3, 4, and 20 for each reach, respectively. The objectives of the study are to assess the effects of wastewater discharges on the water quality of the Bhadra River stretch, to simulate the Dissolved oxygen (DO) by varying the Biochemical oxygen demand (BOD) loads coming from different pollutant sources within the study stretch using the QUAL2K river water quality model. The study period considered is about 24 months (April 2014-March 2016) for the simulation of temperature, pH, DO, and Nitrate parameters with the help of observed data at three monitoring stations. The investigation period was separated into two parts: the model was calibrated using monthly average data from April 2014 to March 2015 (12 months), and the model was validated using monthly average data from April 2015 to March 2016 (12 months). The R-square (R2 ) value for temperature and pH ranges from 0.87-0.56 and 0.64-0.62, respectively. The Root Mean Square Error (RMSE) for Nitrate and DO ranges from 0.06-0.02 and 0.47-0.34, respectively. The results revealed that the Bhadra river stretch was highly polluted due to effluents coming from the industries present in the study stretch. It was also discovered that river flow rate, point source discharge, fast Carbonaceous Biochemical oxygen demand (CBOD) oxidation rate, and nitrification rate as the highly sensitive quality parameters defining the Bhadra river water quality. There must be a reduction of 25% of BOD effluent to reach the minimum standards set by the Central Pollution Control Board (CPCB). It is noted that a 75% reduction of BOD effluent from point sources will lead to an increase of 15% average DO throughout the study stretch. Keyword Qual2k model, Water quality, Bhadra River, Dissolved oxygen, BOD.

Abstract

Regional water resource modelling is important for evaluating system performance by analyzing the reliability, resilience and vulnerability criteria of the system. In water resource systems modelling, several uncertainties abound, including data inadequacy and errors, modeling inaccuracy, lack of knowledge, imprecision, inexactness, randomness of natural phenomena, and operational variability, in addition to challenges such as growing population, increasing water demands, diminishing water sources and climate change. Recent advances in modelling techniques along with high computational capabilities have facilitated rapid progress in this area. In India, several studies have been carried out to understand and quantify uncertainties in various basins, enumerate large temporal and regional mismatches between water availability and demands, and project likely changes due to warming. A comprehensive review of uncertainties in water resource modelling from an Indian perspective is yet to be done. In this work, we aim to appraise the quantification of uncertainties in systems modelling in India and discuss various water resource management and operation models. Basic formulation of models for probabilistic, fuzzy and grey/inexact simulation, optimization, and multi-objective analyses to water resource design, planning and operations are presented. We further discuss challenges in modelling uncertainties, missing links in integrated systems approach, along with directions for future.

Abstract

The Standardized Precipitation and Evapotranspiration Index (SPEI) became one of the popular drought indices due to the consideration of difference between precipitation (P) and potential evapotranspiration (PET), which represents the energy-based climatic water balance. Implementation of actual evapotranspiration (AET), which accounts for both water and energy-based climatic evaporative demand in drought characterization studies, is limited. This study proposes a meteorological drought index with the structure of the SPEI and actual evapotranspiration modeled with empirical formulations and remote sensing data integrated with surface energy models at annual scale. The proposed drought index imposes the effect of precipitation, PET, and AET using operational meteorological data sets of precipitation and temperatures. The present study aimed to test how a drought index based on PET and P can outperform with the inclusion of AET at a river basin scale at 12-month scale. The proposed hypothesis was tested considering Krishna River basin, India, as a case study for which most of the basin is in arid climate. The performance of drought indices was compared using historical droughts in terms of severity, areal extent, frequency, and duration based on empirical AET models along with satellite-based land surface ET data-based drought indices. The proposed AET-based drought indices have effectively captured the historical drought years over the Krishna River basin. The empirical AET formulation-based drought index was identified as a more reliable measure in the estimation of drought characteristics by comparing with satellite-based land surface AET-based drought index. The AET-based drought indices were able to drive the areas into moderate, which or otherwise categorized under severe drought regions with PET-based drought indices. Inclusion of AET in the drought characterization along with precipitation and PET can drive the highly intensified drought events determined by SPEI into moderate and less frequent droughts with short durations over a large river basin with arid climate.

Abstract

This study aimed to estimate water quality–based minimum environmental flow (Eflow) of a river under the impact of different plausible scenarios. The water quality model QUAL2K was deployed to simulate levels of dissolved oxygen (DO) and biochemical oxygen demand (BOD) in a river during the dry season. Hypothetical scenarios of pollution were generated by varying BOD in contributing drains, and climate change scenarios were generated by altering air temperature over the study region. DO and BOD levels were simulated under the …

Abstract

Parametric models of actual evapotranspiration (AET) based on precipitation (P) and potential evapotranspiration (PET) are region-specific and purely climate-induced and limited to represent the hydrological water balances. Basin-averaged model parameters considering P, AET, and runoff (R) using a machine learning algorithm, ensemble regression model, is proposed. Hydrologically calibrated model parameters allowed the study of AET under alterations of water use for current and for future scenarios under climate change. The effect of climate, water, and land use changes on AET was studied for the post-change period of 2004–2014 compared to pre-change period of 1965–2003 over Krishna river basin (KRB), India. The AET has increased under climate and water use changes while there is both increase and decreases of AET under land use changes for post-change period compared to pre-change period over the basin. Severe water shortages were estimated under pronounced increase of temperature (1.29
C) compared to precipitation increase (2.19%) based on Coordinated Regional Downscaling Experiment (CORDEX) projections for the period 2021–2060. Hydrologically induced AET changes were more pronounced than climate for current climate; whereas climate-induced AET changes were found to be more prominent for projected climate signals over the basin

Abstract

This study aimed to estimate water quality–based minimum environmental flow (Eflow) of a river under the impact of different plausible scenarios. The water quality model QUAL2K was deployed to simulate levels of dissolved oxygen (DO) and biochemical oxygen demand (BOD) in a river during the dry season. Hypothetical scenarios of pollution were generated by varying BOD in contributing drains, and climate change scenarios were generated by altering air temperature over the study region. DO and BOD levels were simulated under the impact of various scenarios. Corresponding to each scenario, minimum Eflow throughout the river stretch—that is, the headwaters flow (quantity and quality) meeting desirable river water quality standards (as proposed by the Indian Central Pollution Control Board)— was explored. Water quality charts were developed for direct and user-friendly estimation of Eflows. The proposed approach was applied to the Yamuna and Bhadra rivers in India. The results reveal the inefficiency of existing river flow conditions in maintaining permissible water quality standards. Adverse effects of pollution load, upstream diversions, and climate change are highlighted. DOI: 10.1061/(ASCE) HE.1943-5584.0001780. © 2019 American Society of Civil Engineer

Abstract

n this study, a 3D seismic response of soil deposit, soil-ash deposit and soil-ash-foundation system was investigated. Homogeneous sand deposit of 80m × 9m × 20m was initially analyzed. A pond ash layer is on top of the sand deposit with varying thicknesses and the efficiency of the pond ash layer on the sand deposit was evaluated for its best suitability. The optimum sand-ash deposit overlain by a shallow foundation has been analysed under the excitation of the Nepal (Mw:7.8) and North East India earthquake (Mw:7.5). A seismic response analysis was performed using finite element software PLAXIS3D. The finite element model adopted for the present study has been validated using 1D nonlinear ground response analysis programs. e.g. DEEPSOIL and Cyclic1D. Results of the response analysis have been determined in terms of acceleration, displacement, excess pore pressure, and excess pore pressure ratio. It was observed that, the sand-pond ash-foundation system experienced liquefaction when excited under the Nepal earthquake motion whereas it is safe against the North East India earthquake.

Abstract

We thank the respected discussers for appreciating the relevance of the present work, critically analyzing it, and bringing forth the possible constraints in its applicability. This discussion provides us an opportunity to elaborate the point related to the scope and applicability of original work, especially with focus on the hydrological community that otherwise would possibly remain unclarified. Because there are two discussions of our paper, we have addressed them in two different sections.

Abstract

In the present study, it was attempted to investigate the cyclic resistance of equi-proportionate silt–sand range pond ash (with 50% fines) at relatively high shear strains using the strain-controlled cyclic triaxial test. The cyclic triaxial tests have been performed considering the effect of relative compaction (97–99%), cyclic shear strain (0.6–1.35%), frequency of loading (0.3–1 Hz) and effective confining pressure (50–100 kPa) on cyclic resistance of pond ash. Dynamic characteristics such as dynamic shear modulus and damping ratio of equi-proportionate silt–sand range pond ash was evaluated for all the parameters considered at high shear strain. The maximum value of the dynamic shear modulus and damping ratio of pond ash observed in this study was 6534.8 kPa and 23.64%, respectively. The dynamic shear modulus and damping ratio of pond ash was decreased from 6534.8 to 5023.87 kPa and 23.64 to 14.17 …

Abstract

Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector …