Abstract

Warm and dry climate extremes have large impact on water resources, agriculture, ecosystems, and human health. The present study used high-resolution rainfall and temperature data at 0.25-degree resolution from 1951 to 2014 to investigate the occurrence of compound warm and dry extreme events across India. The simultaneous occurrence of temperature exceeding the 75th percentile and a Standardized Precipitation Evapotranspiration Index (SPEI) of less than –1 is considered to represent the compound extreme event of warm and dry. The study reveals a strong negative correlation between temperature and SPEI across most of the country except south-west of India. Analysis reveals that about 3.1% of increase in the spatial areal extent of Warm/Dry compound extreme events allover India. The insights derived from this research could significantly contribute to shaping climate change mitigation and adaptation strategies related to compound extreme events.

Abstract

Water quality monitoring of reservoirs is currently a signifcant challenge in the tropical regions of the world due to limited monitoring stations and hydrological data. Remote sensing techniques have proven to be a powerful tool for continuous real-time monitoring and assessment of tropical reservoirs water quality. Although many studies have detected chlorophyll-a (Chl-a) concentrations as a proxy to represent nutrient contamination, using Sentinel 2 for eutrophic or hypereutrophic inland water bodies, mainly reservoirs, minimal eforts have been made for oligotrophic and mesotrophic reservoirs. The present study aimed to develop a modeling framework to map and estimate spatio-temporal variability of Chl-a levels and associated water spread using the Modifed Normalized Diference Water Index (MNDWI) and Maximum Chlorophyll Index (MCI). Moreover, the impact of land use/land cover type of the contributing watershed in the oligo-mesotrophic reservoir, Bhadra (tropical reservoir), for 2018 and 2019 using Sentinel 2 satellite data was analyzed. The results show that the water spread area was higher in the post-monsoon months and lower in the summer months. This was further validated by the correlation with reservoir storage, which showed a strong relationship (R2 = 0.97, 2018; R2 = 0.93, 2019). The estimated Chl-a spread was higher in the winter season, because the reservoir catchment was dominated by deciduous forest, producing a large amount of leaf litter in tropical regions, which leads to an increase in the level of Chl-a. It was found that Chl-a spread in the reservoir, specifcally at the inlet sources and near agricultural land practices (western parts of the Bhadra reservoir). Based on the fndings of this study, the MCI spectral index derived from Sentinel 2 data can be used to accurately map the spread of Chl-a in diverse water bodies, thereby ofering a robust scientifc basis for efective reservoir management.

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 and 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 to 0.64–0.62, respectively. The Root Mean Square Error (RMSE) for nitrate and DO ranges from 0.06–0.02 to 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.

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