Abstract

Water quality modelling has proved to be effective method for managing river water quality. But the most effective and comprehensive approach involving integration of river water quality simulation and pollution visualization with the objective of pollution reduction and maintenance of environmental flow strategies has gained less attention. Thus, the objective of this study was to employ an integrated approach for mapping and analysing river water quality under various hydro-climatic and pollution scenarios. Specifically, this approach involved the integration of a river water quality simulation model, QUAL2K, Global Environmental Flow Calculator (GEFC), and Geographical Information System (GIS) to develop water quality index (WQI) based map charts of water quality. The calibrated QUAL2K model was utilized to simulate WQI parameters including water temperature, pH, electrical conductivity, dissolved oxygen (DO), biological oxygen demand (BOD), nitrates (NO3), ammonia (NH4), and alkalinity. To analyse the WQI, the Weighted Arithmetic-Water Quality Index (WA-WQI) method was employed for various individual and combined pollution scenarios, environmental flow (Eflow), and climate change scenarios. The developed integrated approach was applied to the Bhadravati segment of Bhadra River, India. The findings revealed that the prevailing WQI status of the study stretch ranged from poor to unsuitable for drinking purposes. This deterioration can be attributed to the impact of both industrial and municipal effluents. By maintaining the effective Environmental Management Class (EMC) flow rates (class C flowrate of EMC (40.32 m3/s)) in conjunction with appropriate Pollution Reduction (PR) level (10% PR) at headwater and incoming drains, the stream self-purification capacity was enhanced resulting in the Bhadravati River stretch water quality transitioning to favourable water quality condition.

Abstract

This study presents an Internet of Things (IoT)- based system that utilises machine learning (ML) techniques to estimate water flow through pipes based on pressure. The system incorporates an ESP-32 microcontroller, a Danfoss MBS 3000 pressure sensor, and a flow meter deployed at three locations to collect data for three months. To model the relationship between pressure and flow rate, ML algorithms such as linear regression (LR), support vector regression (SVR), and convolutional neural network (CNN) were trained, analysed, and compared. By establishing a model to estimate the flow rate based on pressure, the need for a flow meter in the setup can be eliminated. The system’s low-cost, easy-to-implement, and non-invasive nature makes it suitable for widespread adoption in residential areas offering a promising solution for optimising water distribution and reducing water wastage.

Abstract

Drought is a natural hazard due to the lack of water availability in terms of precipitation and the consequent shortage of streamflow and soil moisture affecting the socioeconomics of a country. Although drought impacts on water quantity in terms of precipitation, streamflow, and soil moisture are widely recognized, the impacts on water quality are less known. Specifically, hydrological drought is observed to be impacted by climate change, which in turn probably increased the frequency and intensity of low river flows, affecting the water quality. Hydrological drought in combination with high water temperatures may deteriorate river water quality. This study analyzes hydrological drought impacts on river water quality for a peninsular river system, the Tunga-Bhadra River in India. A drought assessment has been done over the Tunga-Bhadra River Basin with a keen focus on studying the impact of hydrological drought on the water quality by observing the behavior of water quality parameters between the drought period and the reference years. The impact of hydrological drought on water quality during a drought period was assessed by calculating the Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) for the studied period from June 1, 2005, to May 31, 2017. A statistical analysis has been done to study the significance of the impact on the water quality over the river basin. Time series of water quality parameters were investigated at three monitoring stations during the common drought period, occurring in the year 2013. A total amount of five water quality parameters were involved in the analysis: water temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), and nitrates. The behavioral changes of the parameters were studied, analyzed, and justified. An increase in river water temperature (3.4°C) with a decrease in discharge (25%) have resulted in lower DO (0.3 mg/l) values during the drought year of 2013 compared to the 2012 reference period along with the Bhadravathi station along the Bhadra River. A decrease in discharge during drought events along with an increase in river water temperature under an increase of air temperature resulted in the lowering of oxygen saturation concentrations and decrease in DO levels. The results indicate deterioration of river water quality of the Tunga-Bhadra River during drought, with respect to high water temperatures, reduction in dilution

Abstract

Drought complexity may not be accurately characterized by univariate meteorological or hydrological drought indices under the intensification of hydrological cycle due to climate change and human activities. In particular, such drought indices require long series of hydro-meteorological data, which are unavailable over ungauged and data-scarce catchments. In this study, a multivariate drought index, Standardized Precipitation Actual Evapotranspiration Index-Agro (SPAEI-Agro), is proposed, which com- bines meteorological and hydrological variables as precipitation (P), actual evapotranspiration (AET), runoff and groundwater (GW) of ungauged catchments and sub-catchment scales with diverse climatol- ogy. SPAEI-Agro was able to characterize severe drought events more accurately in humid and dry sub- humid sub-catchments compared to Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Streamflow Drought Index (SSDI) for Tunga-Bhadra River, India. SPEI (based upon potential evapotranspiration) and SSDI (based on streamflow) showed intensified drought characteristics com- pared to the new P, AET and GW-based drought indicator SPAEI-Agro in semi-arid and dry sub-humid climates.

Abstract

In Himalayas, the high-elevational mountains have warmed more rapidly in recent decades than other areas of the globe. Himalayan timberline is climate dependent and sensitive to changes, thus can provide biological proof of global warming. The present analysis changes in timberline elevations over four decades and corresponding climatic parameters (temperature and precipitation) in the Western Himalayan region (Himachal Pradesh). Landsat-2 Multispectral Scanner (MSS) and Landsat-8 were used to evaluate the long-term (1976–2015) timberline dynamics. The climate data APHRODITE was used to calculate the annual mean temperature and annual precipitation along the spatially distinct timberline locations. The mean elevation of timberline position has shifted vertically 145 m with a rate of ~37 m per decade (@ 3.7 m per year) over the past four decades, however majority of timberline remained

Abstract

Understanding the riverine thermal regimes is challenging due to sparse spatiotemporal data of river water temperatures (RWTs). The development of systematic models combined with machine learning models under data‐limited context has not been intensively studied for the prediction of RWT. The present study developed hybrid models using long short‐term memory (LSTM), integrated with (a) k‐nearest neighbor (k‐NN) bootstrap resampling algorithms (kNN‐LSTM) to address the data limitations of RWT prediction and (b) discrete wavelet transform (WT) approach (WT‐LSTM) to address the time–frequency localized features of RWT prediction. The study assessed the climate change impacts on RWT using an ensemble of National Aeronautics Space Administration Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway scenarios 4.5 and 8.5 for

Abstract

The management and regulation of the waste generated from coal-based power plants have been a key issue throughout the world. There is a need for the voluminous utilization of coal ash in India. The bulk utilization of the industrial waste materials like coal ash can control the exploitation of the natural resources like soil in diferent aspects. Here, it has been attempted to evaluate the potential use of coal ash as a foundation/fll material. In addition, physical, geotechnical, morphological, and chemical characterization of the pond ash has been done. Then, fnite element modeling of the vibratory roller compaction was simulated using PLAXIS3D software for the recommendation of the pond ash as a construction material in place of soil. Also, the reduction in CO2 emission with pond ash was compared with that of the soil. The reduction in CO2 emission was found to be about 15% considering several factors like distance from the source station to the test site, types of coal ash used, types of construction equipment employed, etc. The utilization of pond ash as fll material was found to be economical, and its utilization saves around INR 32 to INR 399 per cubic meter. Keywords Coal ash · Foundation/fll material · Finite element modeling · Vibratory roller compaction · CO2 emission

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