Spatio-Temporal Variability in a Turbid and Dynamic Tidal Estuarine Environment (Tasmania, Australia): An Assessment of MODIS Band 1 Reflectance

Water quality assessment in the ecologically stressed lower and estuarine stretches of river Ganga using multivariate statistical tool

Water quality of the Ganga River system is changing day by day due to multifold increase in population, especially near the banks of river Ganga, and associated exponential amplification of anthropogenic activities also played a remarkable role in it. The ecologically important lower and estuarine stretch of river Ganga comprising 7 different sampling stations, i.e., Jangipur, Berhampore, Balagarh, Tribeni, Godakhali, Diamond Harbour and Fraserganj, were selected for the study as the stretch is enriched with the vast number of floral and faunal diversity. The study was conducted for a period of 5 years, i.e., from 2016 to 2020. In the study, various analytical tools and techniques were used for the assessment of riverine water quality, i.e., for calculation of water quality index (WQI); The National Sanitation Foundation Water Quality Index (NSF-WQI) and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) were used for the assessment. Along with WQI various statistical univariate as well as multivariate analytical tools like principal component analysis, correlation, ANOVA, and cluster analysis were also used to achieve the desired outputs. In the study, it has been observed that NSF-WQI varied from 61 to 2552, in which the higher value of NSF-WQI denoted the unsuitability of the water quality concerning the drinking water standards and vice versa. The CCME-WQI represented a similar trend as that of NSF-WQI, as it varied from 18 to 92 in which the lower value denoted degradation in the drinking water quality and vice versa. The study revealed that the Diamond Harbour-Fraserganj stretch is having an undesired level of water quality which were analyzed based on the drinking water guideline values of the Bureau of Indian Standards and that of NSF-WQI and CCME-WQI.

Monitoring the Water Quality of Small Water Bodies Using High-Resolution Remote Sensing Data

Remotely sensed data can reinforce the abilities of water resources researchers and decision-makers to monitor water quality more effectively. In the past few decades, remote sensing techniques have been widely used to measure qualitative water quality parameters. However, the use of moderate resolution sensors may not meet the requirements for monitoring small water bodies. Water quality in a small dam was assessed using high-resolution satellite data from RapidEye and in situ measurements collected a few days apart. The satellite carries a five-band multispectral optical imager with a ground sampling distance of 5 m at its nadir and a swath width of 80 km. Several different algorithms were evaluated using Pearson correlation coefficients for electrical conductivity (EC), total dissolved soils (TDS), water transparency, water turbidity, depth, suspended particular matter (SPM), and chlorophyll-a. The results indicate strong correlation between the investigated parameters and RapidEye reflectance, especially in the red and red-edge portion with highest correlation between red-edge band and water turbidity (r2 = 0.92). Two of the investigated indices showed good correlation in almost all of the water quality parameters with correlation higher than 0.80. The findings of this study emphasize the use of both high-resolution remote sensing imagery and red-edge portion of the electromagnetic spectrum for monitoring several water quality parameters in small water areas.

Long-Term High-Resolution Sediment and Sea Surface Temperature Spatial Patterns in Arctic Nearshore Waters Retrieved Using 30-Year Landsat Archive Imagery

The Arctic is directly impacted by climate change. The increase in air temperature drives the thawing of permafrost and an increase in coastal erosion and river discharge. This leads to a greater input of sediment and organic matter into coastal waters, which substantially impacts the ecosystems, the subsistence economy of the local population, and the climate because of the transformation of organic matter into greenhouse gases. Yet, the patterns of sediment dispersal in the nearshore zone are not well known, because ships do not often reach shallow waters and satellite remote sensing is traditionally focused on less dynamic environments. The goal of this study is to use the extensive Landsat archive to investigate sediment dispersal patterns specifically on an exemplary Arctic nearshore environment, where field measurements are often scarce. Multiple Landsat scenes were combined to calculate means of sediment dispersal and sea surface temperature under changing seasonal wind conditions in the nearshore zone of Herschel Island Qikiqtaruk in the western Canadian Arctic since 1982. We use observations in the Landsat red and thermal wavebands, as well as a recently published water turbidity algorithm to relate archive wind data to turbidity and sea surface temperature. We map the spatial patterns of turbidity and water temperature at high spatial resolution in order to resolve transport pathways of water and sediment at the water surface. Our results show that these pathways are clearly related to the prevailing wind conditions, being ESE and NW. During easterly wind conditions, both turbidity and water temperature are significantly higher in the nearshore area. The extent of the Mackenzie River plume and coastal erosion are the main explanatory variables for sediment dispersal and sea surface temperature distributions in the study area. During northwesterly wind conditions, the influence of the Mackenzie River plume is negligible. Our results highlight the potential of high spatial resolution Landsat imagery to detect small-scale hydrodynamic processes, but also show the need to specifically tune optical models for Arctic nearshore environments.