Abstract

This study was undertaken to describe characteristics of climate (air temperature and precipitation) at timberline in relation to topography, and also bring out the changes in climate that have taken place over the past 37 years (1977–2015) in the Sikkim Himalaya. The rate of increase in mean air temperature was more at timberline elevations of the outer Himalayan ranges than the inner Himalayan ranges (0.21 °C decade−1 for island type timberline [ITL] vs 0.23 °C decade−1 for continuous type timberline [CTL]), and same was true for rainfall (more at elevations of the outer Himalayan timberline [ITL, 192 mm decade−1] than the elevations of the inner Himalayan timberline [CTL, 96 mm decade−1]). Elevational bands of similar elevation (2800–3500 m asl) in a timberline zone that were away from permanent snowline were 3.3–4.5 °C warmer than the same elevation close to permanent snowline (inner ranges), which received less rainfall (438–650 mm) than the outer ranges. Annual mean air temperature was higher (~1 °C) in the locations where timberline moved upwards in 37 years. It was observed that the outer Himalayan ranges became warmer and wetter than the inner Himalayan timberline in the studied period. The warming rate was more at island type timberline (ITL) sites than continuous type timberline (CTL) sites indicating that inner regions in Sikkim are able to hold their own climatic features compared to CTL areas that are more vulnerable or responsive to climate change.

Abstract

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model perfor- mances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. How- ever, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reser- voir, Bhadra reservoir Tunga-Bhadra River, located in India.

Abstract

Rivers are a significant and low-cost supply of irrigation, industrial, and residential water across the world. Due to urbanization, population growth, and climate change, many rivers are now confronting an irreparable water crisis. According to a study carried out in 2018 by the Central Pollution Control Board (CPCB) of India, there are a total of 351 polluted river stretches in India. Accessing the pollution level in these rivers is a challenge and requires the measurement of several water quality indices including turbidity, coliform levels, total phosphate, etc. These indices are then used to calculate the water quality index which gives us a fair estimate of the level of pollution in water. However, the measurement of all these indices is challenging and requires the use of complex measuring tools. In this paper, we try to solve this problem by building machine learning models that can predict the water quality index just by using a subset of the indices that are required for the calculation of the water quality index. The models trained take turbidity, pH, Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), total coliform, hardness as CaCo3, fluoride, ammonia, Chemical Oxygen Demand (COD) and total suspended solids as input and make predictions for water quality using regression techniques. The models are trained specifically for Bhadra river data, but the technique can be extended for other river stretches and water bodies. We use mean squared error, mean absolute error and R2 score to evaluate our models. An artificial neural network trained with 7 dense layers outperforms other regressors in terms of the evaluation metrics used. With the availability of better datasets, the technique of using regressors for the prediction of water quality could help in the better and faster assessment of water quality which can eventually lead to quicker remedial measures

Abstract

PERFORMANCE ANALYSIS OF 339 MLD SEWAGE TREATMENT PLANT AT AMBERPET – HYDERABAD. Veerannapet Santhosh Vishal1, Shaik Rehana2, N. Munilakshmi3, L Partha Praveen4 1- Research Scholar, International Institute of Information Technology, Gachibowli, Hyderbad, Telangana-500032. 2- 2- Assistant Professor, International Institute of Information Technology, Gachibowli, Hyderbad, Telangana - 500032. 3- 3- Assistant Professor, Department of Civil Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh – 517502. 4- 4- Research Scholar, Department of Civil Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh – 517502 * Corresponding author Email: santhoshvishal.v@research.iiit.ac.in Abstract Environmental and water resources have deteriorated due to rapid population growth, chemical industry effluents, agricultural practices, and climate changes. The primary cause of water contamination is due to sewage water pollution. Hence understanding and implementing an effective sewage treatment is necessary. Many cutting-edge techniques have been developed in recent years to increase the effectiveness of the removal of organic matter and nutrients by wastewater treatment plants (WWTP). To comprehend the effectiveness of wastewater treatment, the current study considered a sewage treatment plant (STP) located in Amberpet, Hyderabad. The capacity of the STP at Amberpet is 339 MLD (Million Litres per Day) and is evaluated by collecting 156 samples for 12 months (January 2018 – December 2018). An STP aims to minimize or remove organic debris, sediments, disease-causing organisms, and other pollutants in sewage water before disposing into the streams. The current study observed the removal efficiencies of the constituents Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD). This investigation assesses whether the effluents emitted into the river body are within the NRCD- set limitations (National River Conservative Directorate) as the treated sewage water is discharged into the Musi River (a tributary of Krishna Basin). The MSE values of different parameters like TSS, BOD, COD are 23.85, 28.54, 72.59. The RMSE values of TSS, BOD, COD are 4.88, 5.32, 8.52. The R2 values of TSS, BOD, COD are 0.95, 0.79, 0.90. Keywords: Wastewater Treatment Plant (WTTP), Biochemical Oxygen Demand, Chemical Oxygen Demand, Total suspended solids

Abstract

River water temperature signifies the health of the river body and regulates many physical and chemical parameters related to river water quality parameters, which speculatively depend on many factors. So far, most river water temperature models are either physical or data-driven models requiring large amounts of hydrological and meteorological observations. Many climate change studies have been conducted in relation to increasing stream water temperatures, but how it affects saturated dissolved oxygen levels have not been addressed. To address these, the present work aims to work with a hybrid model - Air2Stream as a function of air temperature and discharge and also demonstrates how the Air2Stream method can be used to generate accurate RWT predictions and subsequent DO concentrations in river water quality modeling. The Air2Stream model can be used to forecast river water temperature which combines the concepts of the heat budget equation that generalizes the physical processes and infers links between input and output data. The proposed modeling framework's effectiveness is demonstrated with three river gauging stations of Mantralayam, Shimoga and Keesara in the Krishna River basin. Model performance was evaluated by comparing simulated river water temperature time series with corresponding observations using the Nash-Sutcliffe efficiency (NSE) and Root Mean Square Error (RMSE) coefficients. Model results show that for all study sites, NSE values range from 0.76 to 0.97 for

Abstract

Maintaining the river water quality has become more prominent these days due to excess pollutants from various sources like industries and domestic sewage discharge. The assessment of river water quality is a significant task for the management and sustainability of the riverine health system for future generations. This study evaluates the water quality of the Bhadra river stretch in India using the water quality index and qual2k model. The water quality index (WQI) is estimated using Weighted Average Water Quality Index (WAWQI) method and water quality indicators like Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO), Electrical Conductivity (Ec), Nitrate (NO3), pH, and Temperature are simulated using Qual2k model. The Qual2k model was calibrated and validated for a period of 91-months (April 2006 to October 2013). The estimated WQI values range from 92.35 to 112, and the quality classification ranges from very poor to unfit for consumption. It was observed that the quality status of the river water was very poor in upstream and downstream segments, while unfit for consumption in the middle segment indicating access to industrial and anthropogenic activities. To conserve river water from pollution, strict laws and regulations must be implemented to improve health and preserve water resources for future generations.

Abstract

Environmental flow (Eflow) allocations of reservoir-river systems do not adequately address the connections between reservoir inflows, releases, and corresponding downstream river water quality rendering water resources management difficult. The study integrated the Soil and Water Assessment Tool (SWAT) for reservoir inflows estimation and corresponding releases with Global Environmental Flow Calculator (GEFC) for Eflows allocations to simulate the resulting Dissolved Oxygen (DO) of the downstream river stretch using QUAL2K. The study considered various plausible scenarios of inflows (10 to 20% reduction), pollution scenarios of Biological Oxygen Demand (BOD) (0 to 100% treatment) for arriving drains by altering headwaters DO (4 to 8 mg/l). Eflow water quality charts were developed for the regulation of reservoir downstream river water quality for Bhadra river in India as case study. The study revealed that by maintaining the headwater DO in conjunction with BOD treatment of drains and with sufficient Eflow allocation, downstream river water quality can be improved. By maintaining a headwater DO of 7 mg/l with BOD treatment of drains of 25% and by providing Eflow allocation of 40.12 m3/s with class D, the Bhadravathi river stretch has shown an improved river water quality with about 6.65 mg/l of average DO.

Abstract

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model performances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. However, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reservoir, Bhadra reservoir Tunga-Bhadra River, located in India

Abstract

mpact Assessment of Environmental Flows Using CORDEX Regional Climate Models: Case Study of Nagarjuna Sagar Dam, Krishna River, India

Abstract

Utilization of coal ash under a foundation of structure deals with huge quantity of its usage that solves the issue of its disposal, which is trivial in most parts of the world. Adaptability of coal ash under foundation needs to be examined on a large scale. In this study, it is attempted to investigate static and seismic response of shallow foundation resting on pond ash deposit. Two-dimensional (2D) response analysis is carried out for the ash-foundation system by employing finite element software PLAXIS 2D. It has been recognized since decades that soil-foundation interaction or ash-foundation interaction is prime factor in the seismic response of structure or portions of structure. Ash-foundation system is excited under the influence of Nepal earthquake motion (Mw: 7.8) and North East India earthquake motion (Mw: 7.5). The displacement was observed more in case of North East India earthquake, i.e. 6.028 m. However, acceleration and excess pore pressure response were high in case of Nepal earthquake, i.e. 0.152 g and 129.16 kPa, respectively. These responses can be utilized to assess the performance of ash-foundation system.